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Journal articles on the topic 'Auto keyword detection'

Author: Grafiati

Published: 5 June 2025

Last updated: 15 July 2025

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Vaikunta, Pai T., P. S. Nethravathi, and S. Aithal P. "Improved Parallel Scanner for the Concurrent Execution of Lexical Analysis Tasks on Multi-Core Systems." International Journal of Applied Engineering and Management Letters (IJAEML) 6, no. 1 (2022): 184–97. https://doi.org/10.5281/zenodo.6375532.

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Purpose: The processing power of machines will continue to accelerate massively. Modern eras of computing are driven by elevated parallel processing by the revolution of multi-core processors. This continuing trend toward parallel architectural paradigms facilitates parallel processing on a single machine and necessitates parallel programming in order to utilize the machine's enormous processing power. As a consequence, scanner generator applications will eventually need to be parallelized in order to fully leverage the throughput benefits of multi-core processors. This article discusses the way of processing the tasks in parallel during the scanning stage of lexical analysis. This is done by recognizing tokens in different lines of the source program in parallel along with auto detection of keyword in a character stream. Tasks are allocated line-by-line to the multiple instance of the lexical analyzer program. Then, each of the instances is run in parallel to detect tokens on different cores that are not yet engaged. Design/Methodology/Approach: Developing a theoretical and experimental approach for parallelizing the lexical scanning process on a multi-core system. Findings/Result: Based on the developed model, the theoretical and practical results indicate that the suggested methodology outperforms the sequential strategy in terms of tokenization consistently. It significantly decreases the amount of time spent on lexical analysis during the compilation process. It is clearly observed that the speedup should increase at or close to the same rate as the number of cores and keywords in the source program increases. This enhancement would improve the overall compilation time even more. Originality/Value: A hybrid model is developed for the concurrent execution of a lexical analyzer on multi-core systems using a dynamic task allocation algorithm and an auto-keyword detection method. Paper Type: Experimental Research.

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Zhang, Bo, and Jiao Zhuo Yu. "Auto Detection of Top Part for Research Paper with a Mixed Method." Applied Mechanics and Materials 380-384 (August 2013): 2691–94. http://dx.doi.org/10.4028/www.scientific.net/amm.380-384.2691.

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The detection of elements in top part of research paper is very important, because these elements are often used as the search items by user. This paper provides a mixed method for auto detection of top part from research paper. The papers feature of keyword, layout and content similarity are mixed to accurately find the area of top part and recognize the elements in top part. Experiments show the advantage of our method over existing methods, and future work is also described in the paper.

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Balla, H. J., H. I. Inabo, and S. O. Olonitola. "Detection of Urinary schistosomiasis, the associated risk factors, and its impact on blood parameters among Almajiris in two selected rural communities of Kaduna State." UMYU Journal of Microbiology Research (UJMR) 7, no. 1 (2022): 82–88. http://dx.doi.org/10.47430/ujmr.2271.013.

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This study aimed to detect the presence of urinary schistosomiasis, the associated risk factors and its impact on blood parameters among Almajiris in two selected rural communities of Kaduna State. Urine samples were collected from 193 Almajiri subjects and processed by sedimentation method and examined under the microscope. Blood samples were also collected from the subjects and processed using SWELAB auto analyser for full blood count. A well-structured knowledge, attitude and practice (KAP) questionnaire was administered to the subjects and used to obtain demographic and other associated risk factors. The overall prevalence of urinary schistosomiasis in the 2 study areas was 16.1%. Bomo recorded 17.5% while Rafin Guza recorded 22.9% prevalence respectively. Subjects in the age-group 11-16 years had a higher prevalence of 33% (p<0.05). Among the risk’s factors assessed, subjects that visit the stream for swimming and used well water recorded a higher prevalence of (33.7%) and (17.2%) respectively (p<0.05). Awareness about the disease revealed higher prevalence (p<0.05). Prevalence of the infection among the subjects was also found to be significantly associated with White blood cell (WBC) count, Lymphocyte and monocyte count (p<0.05). The present study identified the study areas to represent moderate–risk community for urinary schistosomiasis. The study advocates the use of mass treatment with Praziquantel to help in reducing the infection level and help to control transmission of the disease. Keyword: Urinary schistosomiasis, risk factors, haematological parameters

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Sahu, Subhrajyoti Ranjan, and S. Swetha. "Improving the Predicting Rate of Alzheimer's disease through Neuro imaging Data using Deep Learning Approaches." Transaction on Biomedical Engineering Applications and Healthcare 1, no. 1 (2020): 18–25. http://dx.doi.org/10.36647/tbeah/01.01.a003.

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Recently deep learning has shown a improved performance than machine learning in many of the areas like pattern recognition, image classification computer vision, video segmentation and many more. But out of all these areas, disease classification is one of the major area in which deep learning has shown a remarkable performance than the traditional machine learning algorithms especially in the area of image recognition. Machine learning algorithms are not enough capable to handle the image so in this work we will apply the deep learning approach on the Alzheimer's disease dataset for performing the early detection and classification of the disease and this has done through using neuroimaging data. Previous work done in this area was based on traditional machine learning algorithm and they have used stacked auto encoder (SAC) for dimensionality reduction and they have achieved a classification accuracy of 83.7% during the prediction from initial symptom to final development of Alzheimer's disease. The deep learning algorithm ResNet which is implemented in this paper has shown a classification accuracy of 93% and this is also achieved without applying any dimensionality reduction approach and this has been considered as the best predictive rate on the neuroimaging data till now. The applied ResNet is the improved ResNet and the comparison of both the Resnet models are shown in this work. This deep learning application will also be useful for other types of disease classification like cancer, diabetics, etc. Keyword : ResNet, mild cognitive impairments (MCI), ADNI, ReLU, Residual Block, Convolutions.

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Reddy, Mr M. Sreenivasulu, G. Siva Shankar Reddy, B. Akhileswari, L. Navitha, and Krishnaveni Krishnaveni. "Arduino Radar Model with Auto Detection and Target Neutralization." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 04 (2025): 1–9. https://doi.org/10.55041/ijsrem43825.

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Arduino Radar Model With Auto Detection and Target Neutralization develops a real-time obstacle detection system using an Arduino, ultrasonic sensors, a servo motor, and a laser module. The system scans a 180-degree area like a radar, detecting obstacles and activating a laser when they are too close. Sensor data is sent to a Processing-based platform, which displays a live radar-like interface showing detected objects. The dual-sensor setup improves accuracy and coverage, making the system reliable for applications in security, robotics, and automation. By combining low-cost hardware with smart software, this project creates an efficient and responsive obstacle detection and engagement system. KEYWORDS- Arduino, Ultrasonic Sensor, Servo Motor, Laser-Based Neutralization.

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SAKPAL, KRISH. "Anomaly Detection in Healthcare : Brain Tumor." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 01 (2025): 1–9. https://doi.org/10.55041/ijsrem41228.

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Anomaly detection plays a critical role in healthcare by enabling the early identification and diagnosis of medical conditions, such as brain tumors, which significantly impact patient outcomes. This study focuses on developing and evaluating advanced techniques for detecting brain tumor anomalies using medical imaging modalities like MRI and CT scans. Leveraging deep learning, machine learning, and statistical methods, the proposed approach seeks to enhance sensitivity and specificity in tumor detection. We explore various algorithms, including con- volutional neural networks (CNNs) and auto encoders, to identify subtle anomalies and differentiate between malignant and benign tumors. Our results demonstrate improved diagnostic accuracy, reduced false positives, and greater robustness in diverse clinical scenarios. This research highlights the transformative potential of anomaly detection systems in healthcare, offering a pathway toward more precise, timely, and cost-effective brain tumor diagnostics. Keywords— Anomaly detection, brain tumors, healthcare, medical imaging, MRI, CT scans, deep learning, machine learning, CNNs, auto encoders, diagnostic accuracy, malignant, benign, early detection, precision medicine.

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Rao, Dr K. Madhusudhana. "LPG CYLINDER AUTO FILLING AND GAS LEAKAGE DETECTION." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 04 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem30127.

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The project "LPG Cylinder Auto Filling and Gas Leakage Detection" integrates IoT technology for automating the LPG cylinder filling process and detecting gas leakages. The system utilizes a Load Cell connected to a Node MCU to measure the weight of the cylinder. Concurrently, a gas sensor interfaced with an Arduino board detects any gas leakages. Through serial communication between the Node MCU and Arduino, the weight data from the Load Cell is transmitted to the Arduino, which then displays both the weight information and gas leakage status on an LCD display. Additionally, a Python installed PC is employed to facilitate email notifications. Upon detecting a gas leak or a decrease in weight, the Arduino sends this information to the Python PC via serial communication. Subsequently, email alerts are generated and sent to the designated recipients, notifying them of the detected gas leakage and the need for refilling the cylinder. Moreover, in case of a gas leak, the system activates a pump to initiate the refilling process. Through this integrated approach, the project aims to enhance safety and efficiency in LPG cylinder management. Keywords : Node MCU, Arduino UNO, Gas sensor, Load cell, Python installed PC, Buzzer, Relay, DC Motor.

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Kazi,, Ayaan. "Auto-Adjusting Rear View Mirror: Enhancing Safety and Driving Experience." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 02 (2025): 1–9. https://doi.org/10.55041/ijsrem41230.

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This paper explores the development of an auto-adjusting rear-view mirror system designed to enhance driving safety by automating mirror adjustments based on real-time data from various sensors. The system leverages face detection and environmental data to provide optimal visibility, reduce blind spots, and improve driver convenience. Future expansions include integration with advanced driver-assistance systems (ADAS) and personalization features. Keywords--- Auto-adjusting mirror, face detection, servo motor control, Raspberry Pi, driving safety, ADAS.

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Deva, Dinesh. "Review on Methodologies Used for Knocking Detection and Intensity Evaluation in Internal Combustion Engines." International Journal for Research in Applied Science and Engineering Technology 10, no. 3 (2022): 2223–33. http://dx.doi.org/10.22214/ijraset.2022.41079.

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Abstract: Extracting the knocks characteristics is the key of ignition control in auto-ignition compression engines, especially those severely suffered from abnormal consumption in the engine chamber. Most of the advanced detecting methods of knocking are generally related to the two main variables, including engine cylinder pressure signals and the engine cylinder block vibration signals, commonly measured using piezoelectric accelerometer sensors. Being familiar with different approaches capable of determining the engine knocking occurrence probability as well as providing suitable methods for knock intensity evaluation is significantly helpful to propose a comprehensive predicting model for measuring the level of oscillation pressure waves frequencies originated by the knocks. Following by predicting the knock intensity and extracting the knock feature, optimizing the engine operation in terms of fuel consumption, thermal efficiencies, and lifetime duration would be possible. For this aim, the current paper is addressed with overviewing upon the researches performed in the regard of developing high accurate models and optimized knock detection approaches. Keywords: auto-ignition, Internal combustion engine, Knocking detection approach, Knock intensity evaluation.

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Parthiban, Mr S., R. .Buvaneswaran, K. SriSanjay, and R. Vajravelmaran ,. "Smart LPG Leak Detection and Auto Shut-Off System." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 03 (2025): 1–9. https://doi.org/10.55041/ijsrem42760.

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The Smart LPG Leak Detection and Auto Shut-off System is designed to enhance safety in residential, commercial, and industrial environments where LPG (Liquefied Petroleum Gas) is commonly used. Our system utilizes highly sensitive gas sensors to detect even minor leaks and immediately activates an automatic shut-off valve to prevent the continued flow of gas, thereby minimizing the risk of fire, explosions, and health hazards. With potential integration into IoT ecosystems, the system offers remote monitoring and control, ensuring ease of use and increased reliability. Keywords— Smart LPG Leak Detection, Auto shut-off system, IoT integration, Remote monitoring.

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Tripathi, Dr Diwakar Ramanuj. "Understanding Deep Learning Architecture to Various Problems of Cyber Security." International Journal for Research in Applied Science and Engineering Technology 9, no. 12 (2021): 740–46. http://dx.doi.org/10.22214/ijraset.2021.39349.

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Abstract: Traditional machine learning has evolved into deep learning. It's capable of extracting the best feature representation from raw input samples. Intrusion detection, malware classification, Android malware detection, spam and phishing detection, and binary analysis are just a few examples of how this has been used in cyber security. Deep auto encoders, limited Boltzmann machines, recurrent neural networks, generative adversarial networks, and other DL methods are all described in this study in a brief tutorial-style method. After that, we'll go over how each of the DL methods is employed in security applications. Keywords: Machine, Cyber, Security, Architecture, Technology.

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Biswas, ML Pespendu. "DROWSINESS DETECTION USING." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 05 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem32980.

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This review paper delves into the critical realm of drowsiness detection systems designed to mitigate the perilous consequences of driver fatigue. As instances of accidents caused by drowsy driving continue to pose significant risks on roadways, the proposed work adopts a comprehensive approach. Leveraging advanced technologies such as machine learning, computer vision, and sensor integration, the system focuses on early detection of drowsiness indicators. The algorithm, refined through iterative development, analyses facial expressions, eye movements, and physiological data for enhanced accuracy. Customizable alert mechanisms and autonomous responses, including auto-driver modes and forced parking, aim to provide timely interventions. Prospects involve multi-sensor fusion, adaptive machine learning, and integration with autonomous vehicles. As technology evolves, the goal is to create a user friendly, adaptive system that not only detects drowsiness effectively but also actively prevents potential accidents, contributing to a safer driving environment. Keywords: Drowsiness Detection, Adaptive Machine Learning, Forced Parking, Human- Machine Collaboration, Digital Object Identifier (DOI), Real- time Processing, - Friendly Design.

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R, Koteswararaonaik, and V. K. Sharma G. "Enhance Auto and Cross-Correlation Properties to Detect Multiple Moving Targets in Radar using the Booths Algorithm." Indian Journal of Science and Technology 16, no. 23 (2023): 1745–53. https://doi.org/10.17485/IJST/v16i23.686.

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Abstract Objective: To enhance multiple moving target detection of Radar by improving auto and cross-correlation properties of the digital signal received from the target. Methods: This study uses the Booths Algorithm to generate various groups of digital codes to test the auto and cross-correlation properties, and Doppler tolerance of the desired digital signal received from the target. Furthermore, Matlab is used to validate the investigated results. The side-noise of the presented digital codes is optimal which improves both static and moving target detection in multi-target environment. Standard length codes (i.e. 8, 16, 32, 64,128 and 256-bit) are designed. Findings: The designed group of digital codes discovers a broad range in smart 5th-generation battlefields to detect the target, in which multiple and fast-moving warfare including Unmanned Aerial vehicles will actively participate in the mission. To confirm the study, a comparison has been made between the proposed approach and the current art of work (vide figure A). Novelty: Simple novel and productive set of digital codes using the Booths Algorithm have been designed and tested in real-time applications; wherein, the side noise peaks have been reduced to a minimum when compared to the current art of work Keywords: Booths Algorithm; Autocorrelation; Digital Codes; Target Detection; Cross Correlation

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Prasad, R. Haarindra, U. Hashim, K. L. Foo, Tijjani Adam, and Mohd Shafiq. "Fabrication and Characterization of IDE Based Sensor through Conventional Lithography Method." Advanced Materials Research 832 (November 2013): 517–21. http://dx.doi.org/10.4028/www.scientific.net/amr.832.517.

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This paper mainly illustrate regarding the fabrication process of IDE based sensor for bio-molecular detection process. Material that is utilized in this process is zinc oxide due to bio-compability and elevated electrical characteristic. IDE mask is designed by using auto-cad software which tailors for detection of bio substance which is extremely small scale in size. Zinc Oxide material is also used due to presented of nanostructure that can be synthesized through hydrothermal route. Zinc oxide solution is prepared by series of sol-gel process and is coated on the SiO substrate which acts as insulator layer during the lithography process. IDE mask is patterned transfer on sample by using conventional lithography process which the parameters are critically adjusted to ensure that the pattern transfer process occur with minimal defects. The fabricated sensor will be further validated through electrical and morphological characteristic. Capacitance test and impedance test is taken with various pH solution to observe the response of the sensor with different pH values. Keywords: IDE sensor, bio-compability, Zinc Oxide, auto-cad software, sol-gel, SiO substrate, hydrothermal route

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W.A., Rushan Fernando, and Bandara W.P.A.R.D. "Effects of Automobile Fluids for the Detection of Gasoline in an Arson Investigation." Effects of Automobile Fluids for the Detection of Gasoline in an Arson Investigation 9, no. 1 (2024): 8. https://doi.org/10.5281/zenodo.10609131.

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In this paper, GC-MS analysis of gasoline traces on engine oil, brake oil, hydraulic oil, and power steering oil has been carried out using Alstoniascholaris wooden slips as the substrate. The GC-MS analysis was conducted with electron impact ionization at 70 eV and a flow rate of 1.5 ml/min. Only indane with 1,2,3,5- tetramethylbenzene and only dodecane were identified,respectively, as interferences with gasoline target compounds related to hydraulic oiland engine oil. After burning, indane and 1,2,3,5-tetramethylbenzene were disappeared. Keywords:- Gas Chromatography-Mass Spectrometry, Auto Mobile fluids, Substrate.

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Baybars, Sağlamtimur. "Advancements in Face Detection Technology: Artistic and Functional Implications in Photography, Journalism, and Media." International JOURNAL OF SOCIAL, HUMANITIES AND ADMINISTRATIVE SCIENCES 11, no. 2 (2025): 92–104. https://doi.org/10.5281/zenodo.15069114.

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Face detection technology identifies and locates the position of human or animal faces within digital images, often by analyzing specific facial features and patterns. It has revolutionized many fields, most significantly photography, journalism and media. It plays a pivotal role in tasks such as tagging and auto focusing. This technology, which includes not only human and animal face detection but also wink detection, enhances both technical accuracy and innovative expression in the visual arts. Face detection technology enables photographers to capture moments with greater precision and emotional depth, improving the realism of graphics and increasing creative possibilities. In journalism and media, this technology streamlines photo optimisation and sorting to improve overall organisational performance and content enrichment. Automated tagging and categorisation facilitate faster information production and richer, more authentic assessment, important for investigative journalism. This paper demonstrates that face detection has been useful in the creation of new technology and has opened up opportunities. In this study, advancements in human and animal face detection, as well as wink detection, are addressed. The artistic and functional implications of facial recognition technology in photography, journalism, and media are presented. Keywords: Face detection, photography, journalism, media, technology

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Liu, Ziyi, Yong He, Haiyan Cen, and Renfu Lu. "Deep Feature Representation with Stacked Sparse Auto-Encoder and Convolutional Neural Network for Hyperspectral Imaging-Based Detection of Cucumber Defects." Transactions of the ASABE 61, no. 2 (2018): 425–36. http://dx.doi.org/10.13031/trans.12214.

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Abstract. It is challenging to achieve rapid and accurate processing of large amounts of hyperspectral image data. This research was aimed to develop a novel classification method by employing deep feature representation with the stacked sparse auto-encoder (SSAE) and the SSAE combined with convolutional neural network (CNN-SSAE) learning for hyperspectral imaging-based defect detection of pickling cucumbers. Hyperspectral images of normal and defective pickling cucumbers were acquired using a hyperspectral imaging system running at two conveyor speeds of 85 and 165 mm s-1. An SSAE model was developed to learn the feature representation from the preprocessed data and to perform five-class (normal, watery, split/hollow, shrivel, and surface defect) classification. To deal with a more complicated task for different types of surface defects (i.e., dirt/sand and gouge/rot classes) in six-class classification, a CNN-SSAE system was developed. The results showed that the CNN-SSAE system improved the classification performance, compared with the SSAE, with overall accuracies of 91.1% and 88.3% for six-class classification at the two conveyor speeds. Additionally, the average running time of the CNN-SSAE system for each sample was less than 14 ms, showing considerable potential for application in an automated on-line inspection system for cucumber sorting and grading. Keywords: Convolutional neural network, Defect detection, Hyperspectral imaging, Pickling cucumber, Representation learning, Stacked sparse auto-encoder.

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Khan,, Shahadatt. "Real Time Attendance Monitoring System using Face Recognition." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 05 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem34800.

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The management of the attendance can a huge burden on the teachers if it's done by hand. To solve this problem, a smart and auto attendance management system is being used. However, authentication is a significant issue in this system. The smart attendance system is commonly executed with the assistance of biometrics. Facial recognition is one of biometric methods to enhance this system. By using this framework, the situation of proxies and students being marked present despite their physical absence can easily be resolved. The main implementation steps used in these types of systems are facial detections and recognizing the detected faces. This paper suggests a model for implement an automated attendance management system for students of a class. The suggesting system makes the usage of Haar classifiers, OpenCV and LBPH algorithm. Following facial recognition, attendance reports will be produced and store in excel formats. The system is experimented under various conditions like illumination, head moves, the changes of distance between the student and cameras. After intensive testing, overall complexities and accuracies are calculated. The Suggested system demonstrated to be an effective and powerful device for taking attending in a classroom without any time use and manual works. Keywords – Face Recognition, Face Detection, OpenCV, Haar Classifiers.

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panwar, Mukul. "Real Time Attendance Monitoring System using Face Recognition." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 06 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem35443.

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Asif, Aleena, Zunera Riaz Cheema, Farah Naz Tahir, Nazia Zaib, and Muhammad Umair Shoukat. "Diagnostic Accuracy of Serum Creatine Kinase Muscle Brain Fraction and Lactate Dehydrogenase for Detection of Perinatal Asphyxia in Term Neonates." Pakistan Journal of Medical and Health Sciences 16, no. 3 (2022): 734–36. http://dx.doi.org/10.53350/pjmhs22163734.

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Objective: To assess the diagnostic accuracy of serum creatine kinase muscle brain fraction and lactate dehydrogenase for detection of perinatal asphyxia in term neonates using clinical findings as Gold standard Design of the Study: Cross sectional study Study Settings: The study was conducted at Pediatric Department, Lahore General Hospital, Lahore from June 2021 to December 2021. Material and Methods: Demographic information (name, age (in hours), gender, birth weight, and gestational age) was gathered. Under aseptic conditions, blood was taken with a 3cc BD syringe. For CK-MB and LDH testing, samples were sent to the pathology laboratory at the hospital. Two millilitres of clotted blood were tested using reagent kits and auto analyzers. Patients were classified as either positive or negative (according to the operational definition) based on the results of the evaluations of their reports. Results of the Study: In our study, age distribution shows that 63.33%(n=152) were upto 6 hours and 36.67%(n=88) had >6 hours, mean+sd was calculated as 5.60+1.79 hours, 39.17%(n=94) were male and 60.83%(n=146) were females. The diagnostic accuracy of Serum Creatine kinase muscle brain fraction for detection of perinatal asphyxia in term neonates using clinical findings as gold standard was recorded as 93.59% sensitivity, 66.67% specificity, 99.10% positive predictive value, 21.05% negative predictive value and 92.92% accuracy rate. Conclusion: For detecting prenatal asphyxia in term infants, the diagnostic accuracy of serum creatine kinase muscle brain fraction and lactate dehydrogenase (LDH) is useful, however LDH is more accurate than serum creatine kinase muscle brain fraction Keywords: Perinatal asphyxia, detection, diagnostic accuracy

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Mohd Asri, Muhammad Firdaus, Norashikin Yahya, and Irraivan Elamvazuthi. "AUTOMATIC ARRHYTHMIA DETECTION ALGORITHM USING STATISTICAL AND AUTOREGRESSIVE MODEL FEATURES." Platform : A Journal of Engineering 4, no. 1 (2020): 41. http://dx.doi.org/10.61762/pajevol4iss1art5898.

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Human heart healthiness is one of the major components to determine a person’s overall healthiness. Automatic arrhythmia detection is important in a remote area where there is a lack of experienced cardiologists. In this work, an automatic arrhythmia detection algorithm is developed using statistical and autoregressive features to assist medical officers in the diagnosis of arrhythmia diseases. Basic statistical components namely mean, energy, standard deviation, mean absolute deviation, fractal, inter-quartile range and min/max value, were calculated. Alongside with statistical features, 10th order auto-regressive model parameters are used as input features to support vector machine (SVM). All features are calculated using an electrocardiogram (ECG) signals windowed into per beat manner. The proposed algorithm is able to classify normal ECG beat and five types of abnormal ECG beat; paced beat, right & left bundle branch block beat, premature ventricular contraction beat and aberrated atrial premature beat. By using SVM with quadratic and cubic kernel function, the proposed algorithm achieved the best accuracy of 95.8%. Keywords: ECG, cascade-SVM, AR model, statistical model, heart condition, computer-aided diagnosis

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R, Sathya, and Balamurugan P. "Glaucoma Identification in Digital Fundus Images using Deep Learning Enhanced Auto Encoder Networks (DL-EAEN) for Accurate Diagnosis." Indian Journal of Science and Technology 16, no. 44 (2023): 4026–37. https://doi.org/10.17485/IJST/v16i44.2325.

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Abstract Objectives: To propose a novel method to enhance glaucoma identification by leveraging Digital Fundus Images (DFI). The deep learning-based approach, along with feature detection techniques, is utilized to discover the built-in features of the DFI in an unsupervised manner to enable robust detection with high accuracy. Methods: The Enhanced Auto Encoder Networks (DL-EAEN) approach is used to evaluate the latent representations from DFI and identify the morphological changes associated with glaucoma for prompt identification and classification. The fundus images are utilized for optic disc localization and glaucoma detection, and the Scale-Invariant Feature Transform (SIFT) approach is used to identify the local features as well as significant spots in the images. The PAPILA retinal dataset is used for this study with a record of 244 patients, which includes 488 fundus images of the left and right eye of all the patients in the M & F category with clinical results of healthy, glaucoma, suspect, and eye with crystalline and with IOL. In order to construct pixel-level masks and define the outer edges of the optic cup, U-Net and Mask-R-CNN techniques are employed as an image segmentation process. To measure the performance of DL-EAEN, MATLAB software is used and compared against existing models such as SVM, Adaboost, and CNN-Softmax classifiers. Findings: The proposed deep learning based enhanced AEN method outperforms the prevailing methods of SVM, Adaboost, and CNN-Softmax classifiers with promising results of 95.6% accuracy, 0.8 dice-score, 96.2% sensitivity, 97.01% specificity, 97.08% F-score, 97.41% precision, 98.02% recall, and AUC-ROC with 0.89 TPR & 0.16 FPR. Novelty: The evident results of DL-EAEN shows accurate and consistent rate in glaucoma detection and classification, which helps ophthalmologists, make easy diagnosis. In terms of accuracy, dice score, AUC-ROC, sensitivity, specificity, precision and F-score, the DL-EAEN overcomes the limitations of existing models SVM, Adaboost, and CNN-Softmax classifiers. Keywords: Auto Encoder Networks, Deep Learning, Glaucoma Identification, Classification, Machine Learning, Data Mining, Image Segmentation

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Sai, Maniveer Adapa, Guptha Perla Sai, and Reddy. P. Adithya. "Enabling Non-Invasive Diagnosis Of Thyroid Nodules With High Specificity And Sensitivity." Enabling Non-Invasive Diagnosis Of Thyroid Nodules With High Specificity And Sensitivity 9, no. 1 (2024): 9. https://doi.org/10.5281/zenodo.10634167.

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Thyroid nodules can often be diagnosed with ultrasound imaging, although differentiating between benign and malignant nodules can be challenging for medical professionals. This work suggests a novel approach to increase the precision of thyroid nodule identification by combining machine learning and deep learning. The new approach first extracts information from the ultrasound pictures using a deep learning method known as a convolutional autoencoder. A support vector machine, a type of machine learning model, is then trained using these features. With an accuracy of 92.52%, the support vector machine can differentiate between benign and malignant nodules. This innovative technique may decrease the need for pointless biopsies and increase the accuracy of thyroid nodule detection. Keywords:- Thyroid Tumor Diagnosis, Ultrasound Images,  Deep Learning, Machine Learning, Convolutional Auto- Encoder, Support Vector Machine.

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Hehsan, Muhamad Rafiqi. "Acute abdomen: an atypical presentation of acquired hemophilia." Anaesthesia, Pain & Intensive Care 28, no. 5 (2024): 974–76. http://dx.doi.org/10.35975/apic.v28i5.2556.

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Acquired hemophilia is a rare but potentially life-threatening disorder characterized by the development of acquired hemophilia A, leading to bleeding complications. Spontaneous, mild-to-life-threatening bleeding in typically elderly individuals without a personal or family history of bleeding is the hallmark of acquired hemophilia A (AHA), a rare autoimmune bleeding illness caused by the formation of auto-antibodies against endogenous factor VIII (FVIII). This hallmark represents our present case, with no obvious history related to the bleeding issue. Prompt detection and treatment of bleeding are essential for the best outcomes. We present a case of a 66-year-old male who had acute-onset severe abdominal pain and with constipation for five days, associated with nausea and loss of appetite. On physical examination, the patient appeared pale, and abdominal examination revealed diffuse tenderness with guarding. Initial evaluation revealed tachycardia and hypotension. Initial abdomen X-rays revealed features suggestive of small bowel obstruction. The patient was stabilized with intravenous fluids, referred to a surgical team, and admitted to the acute bed in the general ward for further management. Keywords: Acquired Hemophilia; Auto-Antibodies; Factor VIII; Intraabdominal Bleeding Citation: Hehsan MR. Acute abdomen: an atypical presentation of acquired hemophilia. Anaesth. pain intensive care 2024;28(5):974−976; DOI: 10.35975/apic.v28i5.2556 Received: March 19, 2024; Reviewed & Accepted: September 08, 2024

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Belhiba, O., Z. Aadam, L. Jeddane, et al. "Research of anti-GAD and anti-IA2 autoantibodies by ELISA test in a series of Moroccan pediatric patients with diabetes type 1." African Health Sciences 20, no. 3 (2020): 1337–43. http://dx.doi.org/10.4314/ahs.v20i3.40.

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Introduction: Type I diabetes (T1D) is an autoimmune disease with a prediabetic, asymptomatic period characterized by the selective destruction of insulin-producing β cells. During the pre-clinical phase, various auto-antibodies are generated against several beta cell antigens such as anti glutamate acid decarboxylase (Anti-GAD), anti tyrosine phosphatase (Anti-IA2). Today, the coupled detection of Anti-IA2 with that of Anti-GAD proves its great importance in the diagnosis and prediction of type 1 diabetes. The combined positivity for both antibodies has a specificity and a positive predictive value of 100%. Objectives: In this work, we evaluate the diagnostic value of anti-GAD and anti-IA2 antibodies in a series based on 78 Moroccan subjects initially under 16, suspected T1D. Results and Discussion: Our series consists mainly of 74% of newly diagnosed patients for T1D and 26% of confirmed diagnostic patients, of whom 52% are females. The mean age of diagnosis is 7 ± 4 years, the mean of HbA1c at the time of diagnosis is 11.63 ± 2.16%, and the percentage of family history in our series is 69%. The proportion of positive results for anti-IA2 antibodies and anti-GAD antibodies are, respectively, 76.92% and 62.82%, and 52.56% of patients are positive for both auto-antibodies. This study confirms that anti-GAD and anti-IA2 auto-antibodies assays can detect patients early and the autoantibodies can persist several years after diagnosis of type 1 diabetes. Conclusion: This study confirmed the diagnosis and classification of T1D (type 1A) in 87.18% of patients, and we reported that the prevalence of anti-GAD and anti-IA2 is higher in girls than in boys. Keywords: Type 1 diabetes; autoimmunity; autoantibodies; anti-GAD; anti-IA2; ELISA; classification.

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JAGIRAPU, BANGARUTHALLI, U. Harini, M. Divya, and P. Sushma. "Simultaneous estimation of telmisartan and atorvastatin calcium in API and tablet dosage form." Journal of Drug Delivery and Therapeutics 9, no. 1 (2019): 175–79. http://dx.doi.org/10.22270/jddt.v9i1.2268.

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A new method has been established for the simultaneous estimation of Telmisartan and Atorvastatin calcium by RP-HPLC method. The chromatographic conditions were successfully developed for the separation of Telmisartan and Atorvastatin calcium by using boston ODS C18 column, flow rate was 1.0ml/min, mobile Phase consists of methanol:Acetonitrile:buffer in ratio of 35:25:40. Detection wave length was 235nm.The instrument used was SHIMADZU HPLC auto sampler. The retention time of Atorvastatin calcium and Telmisartan was found to be 2.350 and 3.490 minutes respectively. The analytical method was validated according to ICH guidelines (ICH Q2b). The correlation coefficient (r2) was found to be 0.997 and 0.999 for Telmisartan and Atorvastatin calcium respectively. % mean recovery was found to be 100.943% and 100.576% for Telmisartan and Atorvastatin calcium respectively. %RSD for precision on replicate injection was 0.46 and 0.70 for Telmisartan and Atorvastatin calcium respectively. The validation study was found to be precise, robust, and repeatable. Keywords: Telmisartan, Atorvastatin calcium, ICH guidelines, Validation.

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Pravinkumar Pawar, Dr Rupali. "Current Trends of Object Oriented Programming Paradigm in Software Development." International Scientific Journal of Engineering and Management 04, no. 05 (2025): 1–7. https://doi.org/10.55041/isjem03440.

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ABSTRACT: Object oriented programming is the most popular programming paradigm in the current Information technology era. This paper focuses on in-depth Object- Oriented Programming paradigm, OOP modelling and design, features of OOP, benefits and challenges of OOP, and languages which follow OOP in recent times. This paper analyzes some of the most important technological innovations in object-oriented software engineering in recent times. One of the most challenging jobs of software developers is to fit the development process in a specific architecture or framework or programming paradigms. This paper focus on advancements in Object technologies that include object cloning, class evolution, introspection and reflection, interfaces, global software development contexts, namespaces, query-able source codes, meta-model for generating design alternatives, magic methods, design pattern detection, auto-active functional verification. This paper discusses current challenges and issues of implementing object-oriented paradigm in software development. KEYWORDS: Objects, Class, Object Oriented Programming, Software Engineering, Software Development.

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Abbas, Muhammad, Nasima Iqbal, Rahman Ullah, Waheed Ur Rahman, and Habib Ullah. "Immunochromatographic Detection of SARS CoV-2 Antigen In Comparison With the Polymerase Chain Reaction for Laboratory Diagnosis of Covid-19 in Karachi, Pakistan." Journal of Rehman Medical Institute 9, no. 4 (2024): 03–07. http://dx.doi.org/10.52442/jrmi.v9i4.569.

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Introduction: The Covid-19 pandemic raised the question of which laboratory test was relevant for a rapid and accurate diagnosis of the disease. The current two methods for the detection of SARS CoV-2 are the ICT (Immunochromatographic Technique) and the PCR (Polymerase Chain Reaction) tests; therefore comparative analysis of test performance has to be done. Objective: To perform a comparative analysis of two different techniques (Immunochromatographic technique for Antigen and Polymerase Chain Reaction for RNA) which are implicated for the detection of SARS CoV-2. Materials & Methods: The Rapid Antigen Test (RAT) and Polymerase Chain Reaction (PCR) were performed for the detection of Covid-19 on individuals having the signs and symptoms of SARS Covid-19. Immunochromatographic technique (ICT) was performed by SD Biosensor kits, while for Polymerase Chain Reaction RNA extraction was performed by Liferiver® auto extraction analyzer, and amplification was performed by SLAN®-48P Real-Time PCR System. Results: Among of 100 samples tested, 62 (23.56%) were positive on ICT while PCR had 66 (22.44%) and 38 were negative on ICT while PCR had 34. Thus the sensitivity of ICT was 90.77% (95% CI, 80.98% - 96.54%) and specificity was 91.43% (95% CI 76.94%-98.20%). The Positive Predictive Value was 95.16% while the Negative Predictive Value was 84.21%. However the Cohen’s Kappa Index Value was recorded as 0.806. Conclusion: The clinical performance of SD Biosensor Roche kits was excellent. On the basis of sensitivity and specificity it was concluded that RAT have comparable results with RT PCR. Keywords: Antigen, COVID-19, diagnosis, immunoassay, PCR, SARS-CoV-2.

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Olaitan, Olawale Lukman, Adeniji Oluwashola David, and Odejayi Adeniyi Michael. "Deep Learning Approach for Classification of Tweets in Detecting Cyber Truculent." Advances in Research 25, no. 2 (2024): 113–22. http://dx.doi.org/10.9734/air/2024/v25i21038.

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Identification and Classification of truculent tweets is a serious problem. Studies have shown how truculent tweets on Twitter have caused anxiety and in extreme cases death of victims. Various researches have applied Machine Learning approaches while others Deep Learning in classifying toxic sentences. Impressive results have been obtained using Deep Learning approaches, the focus of the research is to develop an extended model for deep learning classification using cyber tweets. The Model developed in this research work used labelled dataset (twitter_parsed_dataset.csv), Maximum Entropy was used to extract keywords and entities. One-Dimensional Convolutional Neural Network (1d-CNN) was used to detect truculent in tweets. The experimental result from the developed model consider four preprocess datasets for classification, the Unigram, Bigram, Trigram and N-gram. The result that was obtained for Accuracy 96.1%, Precision 93.6%, Recall 73.7% and F1-Score 83.8% for different test during classification. The result obtained from the developed model for accuracy, 96.1% was compared with related work Banerjee’s accuracy of 93.97%. The developed model can be used for auto detection of truculent messages in cyberspace.

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Hitesh, B. Patel, Brahmbhatt Keyur, and Joshi Mahasweta. "Data Augmentation Approach for Type-2 Diabetes Prediction and Classification." Indian Journal Of Science And Technology 18, no. 14 (2025): 1096–104. https://doi.org/10.17485/ijst/v18i14.3904.

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Objectives: The main objective of this study is to develop a generalized machine learning framework for Type 2 diabetes prediction that integrates robust preprocessing, feature analysis, and advanced data augmentation techniques to enhance prediction accuracy. Methods: The proposed model of Type-2 Diabetes Prediction and Classification uses publicly available PIMA Diabetes Dataset includes missing value analysis, outliers detection, standardization as a pre-processing task, and a diabetic and non-diabetic class-wise data augmentation module using Adversarial Variational Auto-Encoder (AVAE) with Random Forest Classifier. The experimental results are evaluated and compared with Logistic Regression, Decision Tree, AdaBoost, SVC, Random Forest, GradientBoosting, and KNeighbors with cross-validation scores using mean accuracy. Findings: The experimental findings using RandomForestClassifier suggest a Type 2 diabetes prediction based on class-wise data augmentation has an accuracy level of 93.09%. Novelty: For medical dataset, class-wise data augmentation using Adversarial Variational Autoencoder plays crucial role while developing an accurate model. Keywords: Type 2 Diabetes, Data Augmentation, AVAE, Random Forest Classifier, Cross Validation Scores

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B., Patel Hitesh, Keyur Brahmbhatt, and Mahasweta Joshi. "Data Augmentation Approach for Type-2 Diabetes Prediction and Classification." Indian Journal of Science and Technology 18, no. 14 (2025): 1096–104. https://doi.org/10.17485/IJST/v18i14.3904.

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Abstract Objectives: The main objective of this study is to develop a generalized machine learning framework for Type 2 diabetes prediction that integrates robust preprocessing, feature analysis, and advanced data augmentation techniques to enhance prediction accuracy. Methods: The proposed model of Type-2 Diabetes Prediction and Classification uses publicly available PIMA Diabetes Dataset includes missing value analysis, outliers detection, standardization as a pre-processing task, and a diabetic and non-diabetic class-wise data augmentation module using Adversarial Variational Auto-Encoder (AVAE) with Random Forest Classifier. The experimental results are evaluated and compared with Logistic Regression, Decision Tree, AdaBoost, SVC, Random Forest, GradientBoosting, and KNeighbors with cross-validation scores using mean accuracy. Findings: The experimental findings using RandomForestClassifier suggest a Type 2 diabetes prediction based on class-wise data augmentation has an accuracy level of 93.09%. Novelty: For medical dataset, class-wise data augmentation using Adversarial Variational Autoencoder plays crucial role while developing an accurate model. Keywords: Type 2 Diabetes, Data Augmentation, AVAE, Random Forest Classifier, Cross Validation Scores

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Prisinda, Diani, Yuti Malinda, Yurika Ambar Lita, and Sri Tjahajawati. "Diagnosis determination of chronic periapical abscess case using imaging radiography software and microbiological examination in female patients." Padjadjaran Journal of Dentistry 31, no. 1 (2019): 38. http://dx.doi.org/10.24198/pjd.vol31no1.15974.

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Introduction: Diagnosis determination of chronic periapical abscess can be achieved by subjective, objective, and other examination such as radiographic and microbiological examination based on clinical finding and differences intensity at the periapical area with healthy bone suspected to be chronic abscess periapical. A microbiological examination performed to obtain the predominant bacteria that involved to determine an adequate medicament to succeed endodontic treatment. This study was aimed to compare intensity abscess lesion segmentation image reconstruction and surrounding bone for determine the diagnosis of periapical abscess more accurately and to determine the characteristic of bacteria colony from a periapical abscess tooth. Methods: The study sampling method was consecutive sampling (6 female) consisted of inclusion criteria patient with a periapical lesion in anterior maxillary that diagnosed based on clinical signs and symptoms. CBCT 3D scan of the maxillofacial region taken from of patients was measured image intensity periapical lesion and surrounding bone using semi-auto segmentation with ITK-SNAP 3.6.0. Samples from the root canals taken using three sequential sterile paper points. The samples were plated, and microorganisms were then isolated and identiﬁed by the colony characteristics. Results: There was a signiﬁcant difference between the mean content of lesion intensity of (n = 6,256.710 ± 81.930 and 636.022 ± 79.981 lesion boundary. The two-tailed p-value = 0.0050, this difference was considered to be very statistically significant. The mean of group lesion minus group surrounding bone equals to 163.715. 95% confidence interval of this difference from 75.542 to 251.888. Conclusions: Demineralisation of periapical bone can be determined by voxel intensity value in CBCT 3D radiograph using semi-auto segmentation ITK SNAP, could be recommended as a quantitative interpretation for detection periapical lesion and chronic periapical abscesses is a poly-microbial disease. Keywords: Chronic periapical abscess, endodontic, microbiology, radiograph CBCT

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Chode, Balaji. "AI-Driven Risk-Adaptive Authorization for Multi-Tenant Cloud APIs - Microsoft Azure." International Journal of Computer Science and Data Engineering 2, no. 3 (2025): 1–11. https://doi.org/10.55124/csdb.v2i3.254.

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Application Programming Interfaces (APIs) have become the nervous system of modern financial-services platforms; yet fractured authorization logic remains a dominant breach vector, exposing organizations to data exfiltration, fraudulent transactions and regulatory fines. We introduce a cloud-native Contextual Authorization Framework (CAF) embedded in an enterprise Shared Services Platform (SSP) that supports more than 200 customers facing and internal micro-services worldwide. CAF sits behind Azure API Management, authenticates callers via OAuth 2.0 / OpenID Connect, and merges an ensemble machine learning risk score—Isolation Forest, GRU auto-encoder and XGBoost—with attribute-based rules expressed as policy-as-code in Open Policy Agent. A twelve-month evaluation covering 2.1 billion production requests demonstrates that CAF increases attack-detection recall by 42 % and precision by 18 % compared with a signature Web Application Firewall and static RBAC baseline, while adding only 8 ms to the p95 gateway latency—well inside the 15 ms service-level objective required for real-time quote, billing and claim APIs. Operational metrics show a 60 % reduction in security-integration effort and a net annual benefit of $7.8 million due to prevented fraud and lower SOC triage workload. We have proven datasets, Azure ML notebooks and policy templates, demonstrating that latency-bounded, AI-augmented authorization is both technically feasible and economically compelling for enterprises pursuing Zero-Trust maturity. Keywords: API security, Zero Trust Architecture, Contextual Authorization, Cloud-native access control, Policy-as-Code, Open Policy Agent (OPA), OAuth 2.0, OpenID Connect, Machine Learning for security, Risk-adaptive authorization, XGBoost, GRU auto encoder, Isolation Forest, Azure API Management, Real-time access control, Shared Services Platform, Financial services cyber security, AI-driven fraud prevention, Authorization latency optimization, Security integration automation

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N., Anjaneyulu* R. Naga Kishore M. Ravi Kumar and D. Anil Naik. "DEVELOPMENT AND VALIDATION OF A RP- HPLC METHOD FOR THE BIO-ANALYSIS OF OXCARBAZEPINE IN RAT PLASMA." INDO AMERICAN JOURNAL OF PHARMACEUTICAL SCIENCES 05, no. 12 (2018): 16786–96. https://doi.org/10.5281/zenodo.2525362.

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A new plan was fixed for estimation of Oxcarbazepine by RP-HPLC method. The chromatographic actions continue strongly grow for the break of Oxcarbazepine by applying Agilent C18 column (4.6×150mm) 5µ, flow rate was 1.0 ml/min, mobile phase ratio was Di- potassium hydrogen phosphate: ACN (70:30% v/v) detection wavelength was 593nm. The instrument used was WATERS HPLC Auto Sampler, Separation module 2695, photo diode array detector 996, Empower-software version-2. The confinements times continue begin to be 2.954mins. The % pureness containing Oxcarbazepine was begin to be 99.87%.The system fitness parameters for Oxcarbazepine such as theoretical plates also tracking component were begin to be 4600, 1.2. The analytic plan was approve in agreement with ICH protocol (ICH, Q2 (R1)). The linearity study of Oxcarbazepine act begin in absorption area of 20µg-100µg and correlation coefficient (r2) was begin to be 0.999, % recovery was begin to be 100.56%, %RSD for repeatability was 0.17, % RSD for transitional precision was 0.10. The precision study was precision, robustness also repeatability, LOD value was 0.303 and LOQ value was 1.011. Keywords: Agilent C18, Oxcarbazepine, RP-HPLC, and Rat plasma.

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Kolambkar, Bhuvan. "Precision Targeting Companion: Empowering Marksmen with Smart Rifle Scopes." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 05 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem35166.

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In the realm of modern warfare, the ability to swiftly identify and engage adversaries with precision is indispensable. This paper outlines our objective to contribute to this paradigm shift by undertaking a comprehensive exploration, and design, of accessories crucial for rifles, including smart scopes, auto- matic triggers, and intelligent magazines. Moreover, this paper introduces a groundbreaking innovation - the Precision Targeting Companion, an intuitive system integrated into rifle scopes, aimed at elevating the effectiveness and accuracy of marksmanship in dynamic combat scenarios. At its core, this system is a testament to the fusion of advanced technology with human ingenuity. By harnessing the power of cutting-edge image processing and predictive analytic, the Precision Targeting Companion offers marksmen a seamless and intuitive interface for detecting potential threats and predicting bullet trajectories in real-time. Yet, beyond its computational prowess, this system embodies a deeper understanding of the challenges faced by soldiers and law enforcement officers on the front-lines. It is a trusted companion, empowering users with the confidence to make split-second decisions in high-pressure situations. Key features of the Precision Targeting Companion include: 1. Swift identification of threats: Leveraging sophisticated image processing algorithms, the system swiftly identifies adver- saries within the user’s line of sight, providing crucial situational awareness. 2. Predictive guidance: Through intricate calculations and analysis, the system predicts bullet trajectories, enabling marksmen to make informed decisions and engage targets with precision. 3. Intuitive assistance: The system’s user-friendly interface overlays visual cues on detected targets and cross-hair alignment, offering intuitive guidance and facilitating seamless target tracking. In essence, the Precision Targeting Companion represents more than just a technological advancement; it is a testament to the enduring partnership between man and machine, working in harmony to safeguard lives and achieve mission success on the battlefield. Keywords: Precision Targeting Companion, Smart Rifle Scopes, Human-Centric Design, Tactical Engagement, Threat Detection, Predictive Guidance.

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Shan, Nisha, Muhammad Sharif, Muhammad Slaeem, Fatima Majeed, Hafiz Muhammad Arif, and Muhammad Azam. "Predictive Accuracy of Paediatric Trauma Score, our experience at Children Hospital, Lahore." Pakistan Journal of Medical and Health Sciences 16, no. 12 (2022): 90–92. http://dx.doi.org/10.53350/pjmhs2022161290.

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Aim: To find predictive accuracy of pediatric trauma score (PTS) in terms of mortality in the Children’s hospital and institute of child health Lahore. Study design: Cross sectional study. Setting: Paediatric Surgery department, CH & ICH, Lahore. Duration of study: 1st January 2019 to 30th June 2019. Methods: All patients presented with trauma, aged 1-15 years were included in study. All patients with major burn, associated cardiac injuries (e.g., stab heart), victims who required monitoring and hospitalization less than 24 hours, and those referred to other hospitals were excluded from the study. Initial assessment of all patients was done and paediatric trauma score was evaluated in the emergency room. Patients were managed and 24 hours follow-up was done to predict the mortality. Results: From 290 children, 207(71.38%) were males and 83 (28.6%) were females. Mean age of the children was 7.53 years. The most common mode of trauma was Car accident in children 67(23.1%), followed by Auto rickshaw vs. motorcycle n= 50 (17.24%), and auto rickshaw vs. pedestrian 40(13.79%). Mean Hospital Arrival Time in Minutes was 100.31 minutes. Mean paediatric trauma score amongst all children was 7.22. Overall mortality was reported in 27(9.3%) children. PTS < 8 turned out to be statistically significantly related to mortality (P value < 0.001). Patients with diagnostic test characteristics of PTS < 8 to predict mortality were having sensitivity 91.0%, specificity 56.1%, positive predictive value 38.4%, negative predictive value 95.4% and the diagnostic accuracy of 64.1%. Conclusion: By Paediatric trauma score we can predict about the necessity of critical interventions in patients and also, we can predict percentage of mortality in paediatric trauma patients. However, accuracy of paediatric trauma score seems to be moderate in detecting most severe paediatric trauma cases. Keywords: PTS, mortality, hospital arrival time, morbidity, major burn predictor.

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Taiwo, Adigun, and Ebereonwu Einstein. "A Web-Based Document Summarizing and Topic Prediction System Using Natural Language Processing." International Journal of Recent Research in Mathematics Computer Science and Information Technology 11, no. 2 (2025): 74–83. https://doi.org/10.5281/zenodo.14863280.

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Abstract: The need to read through large volumes of information or other bodies of text and produce a summarized version of the documents is usually a daunting task for many. For instance, researchers often times find themselves reading through lines, paragraphs, pages or chapters of documents before finally coming across the key points which will help them with their research work. Most existing systems generate the summary only in text and a specific language. Therefore, this study provides a web-based document summarizing and topic prediction system with Text-To-Speech and auto-language detection features. The system provides two types of two types of summaries; Extractive and abstractive summaries. The abstractive summary feature makes use of the GPT-3 API while the extractive summary and topic prediction make use of sentence scoring. The language detection model is trained with language data found on Kaggle. The text-to-speech feature is implemented using a JavaScript library. The system is able to detect the topic of the body of text and displays it to the user, and identifies the language of the text entered while also providing an appropriate summary. Users are able to play audio summaries and download them as a word document. Experimental results of the system have found that both the extractive and abstractive summary features are able to capture the main points of any given body of text and present summaries that convey information about the entire body of text, leveraging on Artificial Intelligence (AI) with a focus on Natural Language Processing (NLP). Keywords: Artificial Intelligence, Natural Language Processing, Topic Prediction, Language Detection, Text-to-Speech. Title: A Web-Based Document Summarizing and Topic Prediction System Using Natural Language Processing Author: Taiwo Adigun, Einstein Ebereonwu International Journal of Recent Research in Mathematics Computer Science and Information Technology ISSN 2350-1022 Vol. 11, Issue 2, October 2024 - March 2025 Page No: 74-83 Paper Publications Website: www.paperpublications.org Published Date: 13-February-2025 DOI: https://doi.org/10.5281/zenodo.14863280 Paper Download Link (Source) https://www.paperpublications.org/upload/book/A%20Web-Based%20Document%20Summarizing-13022025-2.pdf

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Haroon, Azka. "Early diagnosis of oral cancer - A narrative review." Pakistan Journal of Pathology 35, no. 4 (2024): 217–24. https://doi.org/10.55629/pakjpathol.v35i4.854.

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Oral cancer (OC) is a common malignancy in Asian countries. Early diagnosis is crucial because of its high mortality rate. Patients and physicians both contribute to the delay in oral cancer diagnosis. Improved knowledge of the disease, its risk factors and symptoms would have a positive influence upon the early diagnosis. Majority of the risk factors such as alcohol consumption, tobacco, betel nut, viral infections, chronic irritation and inflammation if identified timely can mostly avoid the cancer. Prevention and anticipation of diagnosis begin with identification of potentially malignant lesions of the oral mucosa and local conditions promoting chronic inflammation. Each lesion needs to be identified quickly and given the proper care. The clinical recognition and evaluation of oral mucosal lesions can detect up to 99% of oral cancers/ pre-malignancies. The World Health Organization states that a biopsy is required for any suspicious lesion that does not go away after two weeks of local causes of irritation have been identified and removed. Surgical biopsy is the gold standard for diagnosis of oral cancer. However oral brush biopsy, toluidine blue vital staining, auto-fluorescence imaging, and other non-invasive techniques have been created and researched to aid physicians in the diagnostic process. Currently, artificial intelligence is a key component in the early detection of oral malignancies. Present review was written to highlight various factors resulting in delayed diagnosis of oral cancer as well as to discuss different non-invasive techniques that can easily be used in early categorization of oral pre-malignant and malignant lesions. Keywords: Oral cancer, Early diagnosis, Noninvasive techniques, Salivary biomarkers, Biopsy, Artificial intelligence.

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Rasheeda, Amreen *and Dr.Iffath Rizwana. "RP-HPLC METHOD FOR SIMULTANEOUS ESTIMATION OF FLOUXETINE, OLANZAPINE AND OLANZAPINE IN BULK AND PHARMACEUTICAL DOSAGE FORMS." INDO AMERICAN JOURNAL OF PHARMACEUTICAL SCIENCES 05, no. 09 (2018): 8756–70. https://doi.org/10.5281/zenodo.1418641.

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A new method was established for simultaneous estimation of Flouxatine and Olanzapine by RP-HPLC method. The chromatographic conditions were successfully developed for the separation of Flouxatine and Olanzapine by using Zodiac sil C18 column (4.6×150mm)5µ, flow rate was 1ml/min, mobile phase ratio was (70:30 v/v) methanol: phosphate buffer(KH2PO4and K2HPO4) phosphate pH 3 ( pH was adjusted with orthophosphoricacid),detection wavelength was 240nm. The instrument used was WATERS HPLC Auto Sampler, Separation module 2695, photo diode array detector 996, Empower-software version-2. The retention times were found to be 2.170 mins and 7.025 mins. The % purity of Flouxatine and Olanzapine was found to be 99.1% and 98.2% respectively. The system suitability parameters for Flouxatine and Olanzapine such as theoretical plates and tailing factor were found to be 12294, 1.27 and 10491 and 1.03, the resolution was found to be 8.67. The analytical method was validated according to ICH guidelines (ICH, Q2 (R1)). The linearity study of Flouxatine and Olanzapine was found in concentration range of 16µg-80µg and 25µg-125µg and correlation coefficient (r2 ) was found to be 0.999 and 0.998, % recovery was found to be 101.7% and 102.0%, %RSD for repeatability was 0.8and 0.5, % RSD for intermediate precision was 1.99 and 1.82 respectively. The precision study was precision, robustness and repeatabilty.LOD value was 2.17 and 0.0372 and LOQ value was 6.60 and 0.1125 respectively.Hence the suggested RP-HPLC method can be used for routine analysis of Flouxatine and Olanzapine in API and Pharmaceutical dosage form. Keywords: Zodiac sil C18 column, Flouxatine and Olanzapine, RP-HPLC

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Rafi, Kashif, Hassan Nadeem, Muhammad Omer Farooq, Niaz Muhammad, Sadia Jabbar, and Asmat Ullah. "Diagnostic Accuracy of Mean Platelet Volume for Spontaneous Bacterial Peritonitis (SBP) in Patients with Hepatitis C Related Decompensated Liver Cirrhosis Taking Ascitic Fluid Analysis as the Gold Standard." Pakistan Journal of Medical and Health Sciences 15, no. 12 (2021): 3959–61. http://dx.doi.org/10.53350/pjmhs2115123959.

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Objective: To determine the diagnostic accuracy of Mean Platelet Volume for Spontaneous Bacterial Peritonitis (SBP) in patients with Hepatitis C related Decompensated Liver Cirrhosis taking Ascitic Fluid Analysis as the gold standard. Study Setting: The study was conducted at Department of Gastroenterology, Shaikh Zayed Hospital, Lahore. Duration of Study: August 29, 2018 to March 1, 2019 Study Design: Cross-Sectional Study Subjects & Methods: Total 180 patients were counseled. Four ml of venous blood samples were taken from all patients using aseptic technique. CBC was done by the auto analyser and MPV was noted. Ultrasound guided 20ml of ascitic fluid was aspirated using aseptic technique. SBP on ascitic fluid analysis and MPV was diagnosed as per operational definition. All the collected data were entered and analysed into SPSS v25.0. A 2x2 table was generated to calculate sensitivity, specificity, positive predictive value, negative predictive value and diagnostic accuracy. Data will be stratified for age, gender and duration of liver cirrhosis. Post-stratification, Chi-Square test was applied taking p-value of ≤0.05 as statistically significant. Results: In this study, 180 patients who have Hepatitis C related Decompensated Liver Cirrhosis with intraperitoneal fluid on ultrasound were enrolled. Among these patients, 107(59.4%) were males, while 73(40.6%) were females. Age range in this study was from 20 years to 65 years with mean age of 42.1±12.9 years. Sensitivity (Se), specificity (Sp), positive predictive value (PPV), negative predictive value (NPV) and accuracy of MPV was 75.1%, 55.8%, 84.4%, 41.3% and 70.5% respectively. Conclusion: Mean Platelet Volume can be used for the screening and detection of Spontaneous Bacterial Peritonitis (SBP) in patients with Hepatitis C related Decompensated Liver Cirrhosis. Keywords: Ascitic Fluid Analysis, Mean Platelet Volume, Liver Cirrhosis.

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Safia, Begum and K.Manasa. "ANALYTICAL METHOD DEVELOPMENT AND VALIDATION FOR CLINDAMYCIN AND CLOTRIMAZOLE IN COMBINE DOSAGE FORM BY RP-HPLC METHOD." INDO AMERICAN JOURNAL OF PHARMACEUTICAL SCIENCES 05, no. 08 (2018): 8260–69. https://doi.org/10.5281/zenodo.1407706.

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New method was established for simultaneous estimation of Clindamycin and Clotrimazole by RP-HPLC method. The chromatographic conditions were successfully developed for the separation of Clindamycin and Clotrimazole by using ACE C18 column (4.6×150mm) 5µ, flow rate was 1.2 ml/min, mobile phase ratio was (70:30 v/v) methanol:Phosphate buffer pH 3 (pH was adjusted with orthophosphoricacid), detection wavelength was 240nm. The instrument used was WATERS HPLC Auto Sampler, Separation module 2690, photo diode array detector 996, Empower-software version-2. The retention times were found to be 2.344 mins and 3.284 mins. The % purity of Clindamycin and Clotrimazole was found to be 101.27% and 99.97% respectively. The system suitability parameters for Clindamycin and Clotrimazole such as theoretical plates and tailing factor were found to be 4668, 1.3 and 6089 and 1.2, the resolution was found to be 6.0. The analytical method was validated according to ICH guidelines (ICH, Q2 (R1)). The linearity study n Clindamycin and Clotrimazole was found in concentration range of 50µg-250µg and 5µg-50µg and correlation coefficient (r2) was found to be 0.999 and 0.999, % recovery was found to be 99.56% and 99.48%, %RSD for repeatability was 0.2 and 0.2, % RSD for intermediate precision was 0.2 and 0.1respectively. The precision study was precise, robust, and repeatable. LOD value was 3.17 and 5.68, and LOQ value was 0.0172 and 0.2125 respectively.Hence the suggested RP-HPLC method can be used for routine analysis of Clindamycin and Clotrimazole in API and Pharmaceutical dosage form. Keywords: ACE C18 column, Clindamycin and Clotrimazole, RP-HPLC

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Safia, Begum 1. *. T. Mahender 2. "ANALYTICAL METHOD DEVELOPMENT AND VALIDATION FOR THE SIMULTANEOUS ESTIMATION OF CEFUROXIME AND LINEZOLID BY RP-HPLC METHOD." Journal of Scientific Research in Pharmacy 6, no. 12 (2017): 185–89. https://doi.org/10.5281/zenodo.1432830.

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ABSTRACT A new method was established for simultaneous estimation of Cefuroxime and Linezolid by RP-HPLC method. The chromatographic conditions were successfully developed for the separation of Cefuroxime and Linezolid by using Agilent C18 5µm (4.6*250mm) column, flow rate was 1ml/min, mobile phase ratio was Phosphate buffer ph 4.0 : ACN (30:70%v/v), detection wave length was 254nm. The instrument used was WATERS HPLC Auto Sampler, Separation module 2695, PDA Detector 996, Empower-software version-2.The retention times were found to be 3.503 mins and 2.577 mins. The % purity of Cefuroxime and Linezolid was found to be 100.3% and 101.1% respectively. The system suitability parameters for Cefuroxime and Linezolid such as theoretical plates and tailing factor were found to be 1.3, 5824.4 and 1.2, 2936.0 the resolution was found to be 9.4. The analytical method was validated according to ICH guidelines (ICH, Q2 (R1)). The linearity study for Cefuroxime and Linezolid was found in concentration range of 20μg-100μg and 20μg-100μg   and correlation coefficient (r2) was found to be 0.999 and 0.999, % mean recovery was found to be 102.5% and 101.0%, %RSD for repeatability was 0.6 and 0.5, % RSD for intermediate precision was 0.7 and 0.6 respectively. The precision study was precise, robust, and repeatable. LOD value was 3.1 and 3.02, and LOQ value was 10.1 and 10 respectively. Hence the suggested RP-HPLC method can be used for routine analysis of Cefuroxime and Linezolid in API and Pharmaceutical dosage form. KEYWORDS: Agilent C18, Cefuroxime and Linezolid, RP-HPLC.

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S, Dhanasekar. "GEOSILENCE – LOCATION TRIGERRED DEVICE MUTING." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 03 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem29517.

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Mobile devices have become an integral part of human lifestyle. The convenience offered by mobile devices usage can’t be denied. However, there are growing concerns about the disturbances caused by usage of mobile devices. One of the concerns is the disturbances caused by ringing sound of mobile devices at places that require silence such as mosque, library, cinema, and meeting room. Mobile device owners often forget to turn on silence mode on their devices in these places. To overcome such problem, this paper proposes an auto-notification application for mobile device using geofencing technique. Geofencing technique is used to detect virtual boundary that have been preset around required places. When a user enters the particular place, the application will detect that the user’s mobile device has crossed the virtual boundary and will automatically notify the user to turn the silence mode on the device. The opposite process will happen when the user crosses the boundary to exit the place. The main advantage of the application is that it will remind mobile device users to mute their devices when they are entering places that require silence. Nearly everything around us holds significance, be it data, expensive items, land, people and/or animals. It is essential that we keep all that matters to us safe and secure. To employ the highest form of security, we want to keep track of all the significant things on our own. This gives rise to the need for the meantime tracking o anything which is highly valuable to us. Hence, GPS tracking is employed in this technological solution to keep track. Innovative technology, based on telematics and satellite positioning, is the subject of this study on geofencing. With the use of geofencing technology, users can create virtual boundaries in the context of the actual world. By automatically detecting when monitored mobile items enter or leave geographical areas that are encircled by a virtual fence, it aids in the surveillance of those areas. A geofence is a virtual barrier that is set up around a specific region of interest utilizing several technologies, including Wi-Fi, cellular mobile, RFID, and GPS. A specific radius area on the map can be specified by the user. If any entry or exit is made from that location, they will be updated in real time. This assures the asset's safety and security and provides a system for inspection to deter any unwanted activity. Keyword : Auto notification,Cellular mobile, Geofencing, GPS, Mobile device, RFID, Window phone.

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Nohario, Sultan Ahmed, Bakhat Ali Siyal, Muhammad Ali Memon, Abdul Razzaque Nohri, Sajan ., and Qurrat Ul Ain Shahid. "Frequency of Anemia and Iron Deficiency Anemia in Patients with Helicobacter Pylori Infection: A Cross-Sectional Study." Pakistan Journal of Medical and Health Sciences 17, no. 1 (2023): 482–84. http://dx.doi.org/10.53350/pjmhs2023171482.

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Objective: To determine the frequency of anemia and iron deficiency anemia in patients with Helicobacter pylori infection. Study design: Cross-sectional study Place and Duration: Department of Medicine, Liaquat University Hospital, Hyderabad from May to November 2019 Methodology: We included a total of 140 patients with anemia, either inpatients or outpatients at Liaquat University Hospital in Hyderabad's department of medicine. We acquired demographic information, such as name, gender, and age. The study involved everyone who meets the inclusion requirement. Anemia's brief history, its duration, the colour of the stools (melena black faeces), and the ferritin level were all noted. The cubital vein of either upper arm was used to collect 10 ml of blood, which was then sent to the lab for analysis of anaemia, ID, and H. Pylori detection. A multiparameter cell counter Sysmex k 1000 hematology analyzer was used to determine the amounts of haemoglobin (Hb) and hematocrit (Hct) in the CBC (Tao electronics, Japan). Serum iron levels were evaluated using a Hitachi 917 biochemistry automated analyzer, and ferritin concentrations were determined using a Cobas e411 hormones auto-analyzer from Roche Diagnostics GmbH in Mannheim, Germany (Roche Diagnostics GmbH, Mannheim, Germany). The researcher himself entered all the data on a proforma that had been previously created. Results: Patients' average ages ranged from 40.44± 14.268 years, and their haemoglobin levels were 8.77 ± 3.14. Patients' serum ferritin levels were 10.5 ±63.21 and their BMI was 24.5 ±3.21 respectively. Of the H. pylori patients, there were 70 (50%) anaemic patients and 70 (50%) with normal Hb levels. In this study, 102 individuals (72.9%) had normal serum profiles while 38 patients (27.1%) had iron deficiency anemia. Conclusion: According to our research, treating H. pylori infection may help IDA patients who are infected with the parasite especially those who have moderate to severe anaemia. Individuals with H. pylori infection had a significant prevalence of anaemia and IDA, and both conditions were linked to patients' ages and several behavioural traits Keywords: H. pylori infection, anemia, iron deficiency anemia

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LAHRACH, Rahhal, Basma MOUMNI, and Nadia TAMOUH. "The Impact of Entrepreneurship Teaching on Mohammed First University Students." African Scientific Journal Vol 3, N° 7 (2021): 023. https://doi.org/10.5281/zenodo.5651827.

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Résumé L’enseignement de l’entrepreneuriat est au c&oelig;ur de la tendance actuelle de la recherche académique du fait de ses nombreux enjeux, toutefois, il ne suffit pas de dispenser des cours d’entrepreneuriat pour atteindre les objectifs escomptés. Il est d’autant plus important de mesurer l’impact de ces formations afin d’évaluer l’ampleur des changements créés auprès des participants aux formations entrepreneuriales dans le but de réadapter les approches d’enseignement aux objectifs visés. De ce fait, nous avons opté pour une posture post-positiviste pour mener notre étude et nous avons évalué l’impact de l’enseignement d’entrepreneuriat auprès de 306 étudiants de l’université Mohammed 1er à travers un questionnaire se basant sur une autoévaluation des compétences acquises. En effet, notre étude se distingue des travaux similaires par le mode de mesure adopté. Au lieu d’opter pour une évaluation académique par l’enseignant des compétences avant et après la formation, nous avons préféré mesurer le sentiment d’auto-efficacité de l’étudiant, car ce dernier représente un élément clé à l’acte entrepreneurial. A l’issue de ce travail, nous avons remarqué que les étudiants présentent une intention entrepreneuriale mais à des horizons temporels différents. Tandis que pour les compétences, il apparait que la planification de l’activité et la créativité sont les compétences les plus présentes chez les questionnés, à contrario du calcul des besoins financiers, de la détection des opportunités et la gestion des risques qui sont les aptitudes les plus regrettés.    Mots clés : Entrepreneuriat, Enseignement, Compétences Entrepreneuriales, Impact, Etudiants.   Abstract Certainly, the teaching of Entrepreneurship is at the heart of the current trend in academic research. However, providing Entrepreneurship courses seems to be not enough to achieve the desired goals. It is of paramount importance to measure the impacts of these trainings as a step to verify the various changes created among the participants in the Entrepreneurial trainings in an attempt to readjust the teaching approaches with the targeted objectives. Therefore, we opted for a post positivist to conduct our study, and we evaluated the impact of entrepreneurship education among 306 students from Mohammed I University through a questionnaire based on a self-assessment of acquired skills. Indeed, our study differs from similar work by the method of measurement adopted. Instead of opting for an academic evaluation by the teacher of the skills before and after the training, we preferred to measure the feeling of self-efficacy of the student, because the latter represents a key element to the entrepreneurial act. At the end of this work, we noticed that the students present an Entrepreneurial intention, but with different time horizons. While for skills, it appears that activity planning and creativity are the most common skills, which are present among participants, in contrast to the calculation of financial needs, the detection of opportunities and risk management, which are most regretted. Keywords : Entrepreneurship, Education, Entrepreneurial skills, Impact, Students.

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Sousa, Maianna Macêdo de, Sabrina Barreto Figueredo, and Reinaldo Magalhães Fernandes. "Perfil clínico-epidemiológico de mulheres com neoplasia de mama atendidas no Hospital Regional de Referência no município de Araguaína-TO no período de 2000 a 2015." DESAFIOS: Revista Interdisciplinar da Universidade Federal do Tocantins 2, no. 2 (2016): 283. http://dx.doi.org/10.20873/uft.2359-3652.2016v2n2p283.

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O câncer de mama é uma neoplasia rara antes dos 35 anos de idade, acometendo em cerca de 99% as mulheres. Ainda possui etiologia desconhecida, no entanto apresenta alguns fatores de risco importantes, tais como: menarca precoce antes dos 12 anos, menopausa tardia acima de 50 anos, primeira gestação acima de 35 anos, nuliparidade, idade, fatores genéticos, além de fatores relacionados à estilo de vida como consumo de tabaco e histórico de consumo de bebidas alcoólicas. No Brasil, o câncer de mama também é o mais incidente (excluídos os tumores de pele não melanoma) em mulheres de todas as regiões, exceto na região Norte, onde o câncer do colo do útero ocupa a primeira posição. De acordo com a Organização Mundial de Saúde (OMS) como ação de prevenção secundária, ou seja, de detecção precoce do câncer de mama, são mencionadas três estratégias complementares entre si, que são: o auto-exame das mamas, o exame clínico e a mamografia, sendo que tais fatores podem ser influenciados pelo nível de escolaridade das mulheres. É incomum a associação com fatores hereditários, sendo de suma importância a detecção e tratamento precoces adequados de forma a contribuir para um prognóstico favorável. Objetivo: Descrever o perfil clínico-epidemiológico das Neoplasias de Mama em pacientes do sexo feminino atendidas no Hospital Regional de Referência de Araguaína-TO, no período de 2000-2015. Metodologia: Quanto à natureza o estudo pode ser classificado como quantitativo, pois se baseará na geração de dados e na análise crítica dos mesmos. Será de caráter descritivo, retrospectivo e documental realizado no Hospital Regional de Referência de Araguaína através de informações que serão coletadas por meio da análise de fichas do Registro Hospitalar do Câncer disponibilizadas pelo Instituto Nacional do Câncer (INCA).Palavras-chave: Câncer de Mama – perfil clínico-epidemiológico e fatores de risco ABSTRACTBreast cancer is a rare neoplasm before 35 years of age, affecting approximately 99% women. Still has unknown etiology, however presents some important risk factors, such as: early menarche before 12 years, late menopause above 50 years, first pregnancy above 35 years, nulliparity, age, genetic factors, in addition to factors related to lifestyle as the consumption of tobacco and history of consumption of alcoholic beverages. In Brazil, breast cancer is also the most incident (excluding non-melanoma skin tumors) in women of all regions, except in the northern region, where the cervical cancer occupies the first position. According to the World Health Organization (WHO) as secondary prevention action, i.e. for early detection of breast cancer, are mentioned three complementary strategies among themselves, which are: the breast self-exam, the clinical examination and mammography, being that such factors may be influenced by the level of education of women. It is uncommon to association with hereditary factors and highlighted the importance of early detection and treatment appropriate to contribute to a favorable prognosis. Objective: To describe the clinical-epidemiological profile of Breast Neoplasms in female patients answered the Regional Hospital of reference in the municipality of Araguaína- TO, in the period 2000-2015. Methodology: As to the nature of the study can be classified as quantitative, because it is based on data generation and critical analysis of the same. Will be a descriptive, retrospective and documentary checks carried out at the Regional Hospital of reference in the municipality of Araguaína- TO through the information that will be collected by means of the analysis of the connectors of the Cancer Hospital Registry made available by the National Cancer Institute. Keywords: Breast Cancer - clinical- epidemiological profile and risk factors

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Maldonado Zúñiga, Kirenia, Raquel Vera Velázquez, Alfredo González Vásquez, Pedro Roberto Valdés Tamayo, and Washington Narvaez Campana. "EL APRENDIZAJE DE LA GEOMETRÍA A TRAVÉS DE PROGRAMAS COMPUTACIONALES." UNESUM-Ciencias. Revista Científica Multidisciplinaria. ISSN 2602-8166 2, no. 3 (2019): 49–58. http://dx.doi.org/10.47230/unesum-ciencias.v2.n3.2018.102.

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Este trabajo tiene como objetivo describir la construcción y validación de un cuestionario para medir la motivación de los estudiantes con respecto al empleo de programas computacionales como GeoGebra para la enseñanza de la geometría. Se realizó un análisis componencial a partir de su aplicación a 185 estudiantes de segundo semestre de Ingeniería Agropecuaria y Tecnologías de la Información de la Universidad Estatal del Sur de Manabí, teniendo en cuenta cuatro factores que se consideran importantes: utilidad del programa computacional GeoGebra en la enseñanza de la geometría, no utilidad del programa computacional GeoGebra, motivación de los estudiantes por el aprendizaje de la geometría con la utilización del programa computacional GeoGebra y confiabilidad de los estudiantes en el programa computacional Geogebra. Se realizó un análisis factorial exploratorio con el propósito de detectar factores comunes para formar categorías apropiadas que pudieran precisar de manera general los diferentes aspectos de las motivaciones de los estudiantes que se pretende medir. La solución factorial inicial se llevó a cabo mediante el método de componentes principales y se obtuvieron ocho factores con auto-valor mayor que uno que explicaron el 60.51% de la varianza total. Se determinó el grado de homogeneidad que existe entre los elementos de la escala en conjunto, calculando el Coeficiente Alfa de Cronbach para el total de los ítems, el cual se basó en elementos estandarizados, por lo que se considera que la escala tiene una buena consistencia interna permitiendo identificar que los cuatro factores del modelo, son relevantes. PALABRAS CLAVE: geométricos, análisis, figuras, gráficos, matemáticas THE LEARNING OF GEOMETRY THROUGH COMPUTATIONAL PROGRAMSABSTRACTThe objective of this work is to describe the construction and validation of a questionnaire to measure the motivation of students with respect to the use of GeoGebra computer programs for the teaching of geometry. A componential analysis was made from its application to 185 students of second semester of Agricultural Engineering and Information Technologies of the Southern State University of Manabí, taking into account four factors that are considered important: utility of the Geogebra computer program in the teaching of geometry, not usefulness of the computer program GeoGebra, motivation of students for the learning of geometry with the use of the computer program GeoGebra and reliability of students in the computer program GeoGebra. An exploratory factorial analysis was carried out with the purpose of detecting common factors to form appropriate categories that could specify in a general way the different aspects of the motivations of the students that it is intended to measure. The initial factorial solution was carried out by means of the principal components method and eight factors with self-value greater than one were obtained that explained 60.51% of the total variance. The degree of homogeneity that exists between the elements of the scale as a whole was determined, calculating the Cronbach's Alpha Coefficient for the total of the items, which was based on standardized elements, which is why the scale is considered to have a good consistency internal allowing to identify that the four factors of the model are relevant. KEYWORDS: geometric, analysis, figures, graphics, mathematics

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Trapero Fernández, Pedro, Francisco Javier Catalán González, and Enrique José Ruiz Pilares. "El río Guadalete en el tránsito de la Edad Media a la Edad Moderna. Una propuesta de restitución de su cauce y los condicionantes de navegabilidad a partir de los SIG." Vínculos de Historia Revista del Departamento de Historia de la Universidad de Castilla-La Mancha, no. 12 (June 28, 2023): 261–84. http://dx.doi.org/10.18239//vdh_2023.12.13.

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RESUMEN La bahía de Cádiz, donde desemboca el río Guadalete, en el suroeste de la península ibérica, es un espacio privilegiado para estudiar los cambios antrópicos y naturales a lo largo del tiempo, gracias a diversos procesos de transformación y una rica cartografía histórica. El Guadalete era navegable desde época romana, formando un amplio estuario que se fue colmatando paulatinamente. Para época medieval la documentación archivística testimonia la dificultad que tenían los barcos ligeros para remontar el río y alcanzar el puerto fluvial de El Portal, emplazamiento clave para la comercialización de vinos de Jerez de la Frontera. En este artículo modelamos el paisaje de la cuenca baja del Guadalete, analizando los procesos que influencian su cegamiento. Para ello, recurrimos a la creación de un modelo digital del terreno a partir de datos LiDAR (light detection and ranging). Proponemos un método para la restitución de la topografía en un momento histórico determinado, el uso de cartografía histórica para establecer la deriva del cauce del río y un análisis de los condicionantes naturales (geología, hidrología, mareas, eventos catastróficos) y antrópicos (molinos, salinas, lastre de los barcos) que condicionaron la navegabilidad. Palabras clave: navegación fluvial, SIG, LiDAR, modelado del paisajeTopónimos: río Guadalete, bahía de CádizPeriodo: siglos xv-xviii ABSTRACTThe Bay of Cadiz, where the Guadalete River flows into the southwest of the Iberian Peninsula, is a privileged area for studying anthropic and natural changes over time, thanks to various transformation processes and its rich historical cartography. The river was navigable from Roman times, forming a wide estuary that gradually silted up. In medieval times, archival documentation attests to the difficulty of crossing the river with light boats to reach El Portal Port, for the commercialization of Jerez de la Frontera wines. In this article, we model the landscape of the lower basin of the Guadalete, analysing the processes that influence its clogging. To do so, we resort to the creation of a Digital Terrain Model from LiDAR data. We propose a method for the restitution of the topography at a given historical moment, the use of historical cartography to establish the drift of the riverbed and an analysis of the natural (geology, hydrology, tides, catastrophic events) and anthropic (mills, salt works, ship ballast) factors that conditioned its navigability. Keywords: fluvial navigation, GIS, LiDAR, landscape modelling Place names: Guadalete River, Bay of CadizPeriod: 15th-18th centuries REFERENCIASAlonso Villalobos, C., Gracia Prieto, J., Rodríguez Polo, S., Martín Puertas, C. M. (2015): “El registro de eventos energéticos marinos en la bahía de Cádiz durante épocas históricas”. Cuaternario y geomorfología (2015), 29 (1-2), 95-117.Alonso, C., Gracia, F.J. y Rodríguez, S. (2014): “Modelo de evolución histórica de la flecha-barrera de Valdelagrana (bahía de Cádiz)”, XIII Reunión Nacional de Geomorfología, 584-587.Alonso, C., Gracia, F.J., y Benavente, J. (2009): “Evolución histórica de la línea de costa en el sector meridional de la bahía de Cádiz”, Revista Atlántica-Mediterránea de Prehistoria y Arqueología Social, 11, pp.13-37. Álvarez Rogel, Y., y Conesa García, C. (2018): “Georreferenciación de documentos cartográficos históricos para el análisis del trazado fluvial del bajo Segura, Vega Media (Murcia, España)”, Geofocus: revista internacional de ciencia y tecnología de la información geográfica, 21, 101-118.Arteaga O. y Schuldz, H.D. (2008): “Geoarqueología y proceso histórico en la bahía de Cádiz”. Revista de Prehistoria y Arqueología de la Universidad de Sevilla 10, 7-20.Astilleros, J.M. (2015): “Río Guadalete”, en J.M. Fernández-Palacios (dir.), Río Guadalete, Editado por la Consejería de Medioambiente y Ordenación del Territorio, Junta de Andalucía. Sevilla, 116-136.Aycart Luengo, J. (2015): “Hidrología de la cuenca del Guadalete”. en J.M. Fernández-Palacios (dir.), Río Guadalete, Editado por la Consejería de Medioambiente y Ordenación del Territorio, Junta de Andalucía, Sevilla.Borja F. y Díaz, F. (1994): “Paleogeografía posflandriense del litoral de Cádiz. Transformación protohistórica del paisaje de Doña Blanca”, en E. Roselló y A. Morales (Eds.), Castillo de Doña Blanca. Archaeo-environmental investigations in the Bay of Cádiz, Spain (750-500 B.C.), BAR International Series 593, Oxford, 185-200. Caballero Sánchez, M. (2008): “Las vistas de El Puerto de Santa María en 1567 de Antón Van den Wyngaerdepautas interpretativas y análisis de contenidos”, Revista de historia de El Puerto, 41, 109-147.Caporizzo, C, Gracia, F.J. y Aucelli, P.P.C., et alii (2021): “Late-Holocene evolution of the Northern Bay of Cádiz from geomorphological, stratigraphic and archaeological data”, Quaternary International, 602, pp. 92-109.Casarotto, A., Stek, T., Pelgrom, J., van Otterloo, R.H. y Sevink, J. (2018): “Assessing visibility and geomorphological biases in regional field surveys: The case of Roman Aesernia”, Geoarchaeology, 33(2), 177-192.Cascón Katchadourian, J. y Alberich Pascual, J. (2021): La geoerreferenciación de cartografía antigua en los sistemas de información geográficos (SIG): Revisión, análisis y estudio comparativo de softwares de georreferenciación. Revista General de Información y Documentación, 31.Catalán González, F. (2021): “El paisaje socio-natural de la bahía de Cádiz: análisis histórico de su formación”, Tesis Doctoral, Universidad de Cádiz, pp. 375.Cerrillo Cuenca, E. (2017): “An approach to the automatic surveying of prehistoric barrows through LiDAR”, Quaternary International, 435, 135-145.Chic, G. (2004): “La ordenación territorial en la bahía de Cádiz durante el Alto Imperio romano”, Revista de Historia de El Puerto, 33, 11-50. Chica, A. (2011): “Aproximación a los paisajes de la bahía de Cádiz”, El paisaje rural en Andalucía Occidental durante los siglos bajomedievales: actas de las I Jornadas Internacionales sobre Paisajes Rurales en Época Medieval, Cádiz, 1 y 2 de abril de 2009. Servicio de Publicaciones de la Universidad de Cádiz, pp. 17-30.Clemente Carrillo, M., (2015): Proceso de construcción de la carabela Niña, Proyecto fin de carrera Universidad Politécnica de Cartagena. 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K K A, Abdullah, Robert A B C, and Adeyemo A B. "August 2016 VOLUME 5, ISSUE 8, AUGUST 2016 5th Generation Wi-Fi Shatha Ghazal, Raina S Alkhlailah Abstract | PDF with Text | DOI: 10.17148/IJARCCE.2016.5801 ECG Arrhythmia Detection Using Choi-Williams Time-Frequency Distribution and Artificial Neural Network Sanjit K. Dash, G. Sasibhushana Rao Abstract | PDF with Text | DOI: 10.17148/IJARCCE.2016.5802 Data Security using RSA Algorithm in Cloud Computing Santosh Kumar Singh, Dr. P.K. Manjhi, Dr. R.K. Tiwari Abstract | PDF with Text | DOI: 10.17148/IJARCCE.2016.5803 Detection Algorithms in Medical Imaging Priyanka Pareek, Pankaj Dalal Abstract | PDF with Text | DOI: 10.17148/IJARCCE.2016.5804 A Review Study on the CPU Scheduling Algorithms Shweta Jain, Dr. Saurabh Jain Abstract | PDF with Text | DOI: 10.17148/IJARCCE.2016.5805 Healthcare Biosensors - A Paradigm Shift To Wireless Technology Taha Mukhtar Abstract | PDF with Text | DOI: 10.17148/IJARCCE.2016.5806 Congestion Control for Peer to Peer Application using Random Early Detection Algorithm Sonam Abstract | PDF with Text | DOI: 10.17148/IJARCCE.2016.5807 Quantitative and Qualitative Analysis of Milk Parameters using Arduino Controller Y.R. Bhamare, M.B. Matsagar, C.G. Dighavkar Abstract | PDF with Text | DOI: 10.17148/IJARCCE.2016.5808 Ardunio Based Security and Safety using GSM as Fault Alert System for BTS (Base Transceiver Station) Umeshwari Khot, Prof. Venkat N. Ghodke Abstract | PDF with Text | DOI: 10.17148/IJARCCE.2016.5809 Automatic Single and Multi Topic Summarization and Evolution to Generate Timeline Mrs. V. Meenakshi, Ms. S. Jeyanthi Abstract | PDF with Text | DOI: 10.17148/IJARCCE.2016.5810 Data Hiding in Encrypted HEVC/AVC Video Streams Saltanat Shaikh, Prof. Shahzia Sayyad Abstract | PDF with Text | DOI: 10.17148/IJARCCE.2016.5811 A Study of Imbalanced Classification Problem P. Rajeshwari, D. Maheshwari Abstract | PDF with Text | DOI: 10.17148/IJARCCE.2016.5812 Design of PTL based Area Efficient and Low Power 4-bit ALU Saraabu Narendra Achari, Mr. C. Pakkiraiah Abstract | PDF with Text | DOI: 10.17148/IJARCCE.2016.5813 The Design of Driver Safety Awareness and Assistance System through Sleep Activated and Auto Brake System for Vehicle Control D. Sivabalaselvamani, Dr. A. Tamilarasi, L. Rahunathan and A.S. Harishankher Abstract | PDF with Text | DOI: 10.17148/IJARCCE.2016.5814 Parameters Selection, Applications & Convergence Analysis of PSO Algorithms Sachin Kumar, Mr. N.K. Gupta Abstract | PDF with Text | DOI: 10.17148/IJARCCE.2016.5815 Effective Pattern Deploying Model for the Document Restructuring and Classification Niketa, Jharna Chopra Abstract | PDF with Text | DOI: 10.17148/IJARCCE.2016.5816 Cataloging Telugu Sentences by Hidden Morkov Techniques V. Suresh Abstract | PDF with Text | DOI: 10.17148/IJARCCE.2016.5817 Biometrics for Cell Phone Safety Jyoti Tiwari, Santosh Kumar Abstract | PDF with Text | DOI: 10.17148/IJARCCE.2016.5818 Digital Image Watermarking using Modified DWT&DCT Combination and Bi Linear Interpolation Yannam .Nagarjuna, K. Chaitanya Abstract | PDF with Text | DOI: 10.17148/IJARCCE.2016.5819 Comparative Study and Analysis on the Techniques of Web Mining Dipika Sahu, Yamini Chouhan Abstract | PDF with Text | DOI: 10.17148/IJARCCE.2016.5820 A Review of MIL-STD-1553 Bus Trends and Future K. Padmanabham, Prabhakar Kanugo, Dr. K. Nagabhushan Raju, M. Chandrashekar Abstract | PDF with Text | DOI: 10.17148/IJARCCE.2016.5821 Design of QPSK Digital Modulation Scheme Using Turbo Codes for an Air Borne System D. Sai Brunda, B. Geetha Rani Abstract | PDF with Text | DOI: 10.17148/IJARCCE.2016.5822 An Efficient Locally Weighted Spectral Cluster for Automatic Image Segmentation Vishnu Priya M, J Santhosh Abstract | PDF with Text | DOI: 10.17148/IJARCCE.2016.5823 An Efficient Sliding Window Based Micro Cluster Over Data Streams Nancy Mary, A. Venugopal Abstract | PDF with Text | DOI: 10.17148/IJARCCE.2016.5824 Comparative Analysis of Traditional Frequency Reuse Techniques in LTE Network Neelam Rani, Dr. Sanjeev Kumar Abstract | PDF with Text | DOI: 10.17148/IJARCCE.2016.5825 Score Level Integration of Fingerprint and Hand Geometry Biometrics Jyoti Tiwari, Santosh Kumar Abstract | PDF with Text | DOI: 10.17148/IJARCCE.2016.5826 CHARM: Intelligently Cost and Bandwidth Detection for FTP Servers using Heuristic Algorithm Shiva Urolagin Abstract | PDF with Text | DOI: 10.17148/IJARCCE.2016.5827 Image Enhancement Using Modified Exposure Based Histogram SK. Nasreen, N. Anupama Abstract | PDF with Text | DOI: 10.17148/IJARCCE.2016.5828 Human Gesture Based Recognition and Classification Using MATLAB Suman, Er. Kapil Sirohi Abstract | PDF with Text | DOI: 10.17148/IJARCCE.2016.5829 Image Denoising- A Novel Approach Dipali D. Sathe, Prof. K.N. Barbole Abstract | PDF with Text | DOI: 10.17148/IJARCCE.2016.5830 Design of Low Pass Digital FIR Filter Using Nature Inspired Technique Nisha Rani, Balraj Singh, Darshan Singh Sidhu Abstract | PDF with Text | DOI: 10.17148/IJARCCE.2016.5831 Issues and Challenges in Software Quality Assurance Himangi, Surender singh Abstract | PDF with Text | DOI: 10.17148/IJARCCE.2016.5832 Hybridization of GSA and AFSA to Detect Black Hole Attack in Wireless Sensor Network Soni Rani, Charanjit Singh Abstract | PDF with Text | DOI: 10.17148/IJARCCE.2016.5833 Reversible Watermarking Technique for Data Hiding, Accurate Tamper Detection in ROI and Exact Recovery of ROI Y. Usha Madhuri, K. Chaitanya Abstract | PDF with Text | DOI: 10.17148/IJARCCE.2016.5834 Fault Tolerance and Concurrency Control in Heterogeneous Distributed Database Systems Sagar Patel, Meghna Burli, Nidhi Shah, Prof. (Mrs.) Vinaya Sawant Abstract | PDF with Text | DOI: 10.17148/IJARCCE.2016.5835 Collection of Offline Tamil Handwriting Samples and Database Creation D. Rajalakshmi, Dr. S.K. Jayanthi Abstract | PDF with Text | DOI: 10.17148/IJARCCE.2016.5836 Overview of Renewable Energy in Maharashtra Mr. Sagar P. Thombare, Mr. Vishal Gunjal, Miss. Snehal Bhandarkar Abstract | PDF with Text | DOI: 10.17148/IJARCCE.2016.5837 Comparative Analysis of Efficient Image Steganographic Technique with the 2-bit LSB Algorithm for Color Images K. S. Sadasiva Rao, Dr A. Damodaram Abstract | PDF with Text | DOI: 10.17148/IJARCCE.2016.5838 An Efficient Reverse Converter Design for Five Moduli Set RNS Y. Ayyavaru Reddy, B. Sekhar Abstract | PDF with Text | DOI: 10.17148/IJARCCE.2016.5839 VLSI Design of Area Efficient High Performance SPMV Accelerator using VBW-CBQCSR Scheme N. Narasimharao, A. Mallaiah Abstract | PDF with Text | DOI: 10.17148/IJARCCE.2016.5840 Customer Retention of MCDR using 3SCDM Approaches Suban Ravichandran, Chandrasekaran Ramasamy Abstract | PDF with Text | DOI: 10.17148/IJARCCE.2016.5841 User Privacy and Data Trustworthiness in Mobile Crowd Sensing Ms. T. Sharadha, Dr. R. Vijaya Bhanu Abstract | PDF with Text | DOI: 10.17148/IJARCCE.2016.5842 A Safe Anti-Conspiracy Data Model For Changing Groups in Cloud G. Ajay Kumar, Devaraj Verma C Abstract | PDF with Text | DOI: 10.17148/IJARCCE.2016.5843 Scope and Adoption of M-Commerce in India Anurag Mishra, Sanjay Medhavi, Khan Shah Mohd, P.C. Mishra Abstract | PDF with Text | DOI: 10.17148/IJARCCE.2016.5844 A Secure Data Hiding Scheme For Color Image Mrs. S.A. Bhavani Abstract | PDF with Text | DOI: 10.17148/IJARCCE.2016.5845 A Study of Different Content Based Image Retrieval Techniques C. Gururaj, D. Jayadevappa, Satish Tunga Abstract | PDF with Text | DOI: 10.17148/IJARCCE.2016.5846 Cache Management for Big Data Applications: Survey Kiran Grover, Surender Singh Abstract | PDF with Text | DOI: 10.17148/IJARCCE.2016.5847 Survey on Energy Efficient Protocols and Challenges in IOT Syeda Butool Fatima, Sayyada Fahmeeda Sultana, Sadiya Ansari Abstract | PDF with Text | DOI: 10.17148/IJARCCE.2016.5848 Educational Data Mining For Evaluating Students Performance Sampreethi P.K, VR. Nagarajan Abstract | PDF with Text | DOI: 10.17148/IJARCCE.2016.5849 Iterative Pareto Principle for Software Test Case Prioritization Manas Kumar Yogi, G. Vijay Kumar, D. Uma Abstract | PDF with Text | DOI: 10.17148/IJARCCE.2016.5850 Localization Techniques in Wireless Sensor Networks: A Review Abhishek Kumar, Deepak Prashar Abstract | PDF with Text | DOI: 10.17148/IJARCCE.2016.5851 Ensemble Averaging Filter for Noise Reduction Tom Thomas Abstract | PDF with Text | DOI: 10.17148/IJARCCE.2016.5852 Survey Paper on Get My Route Application Shubham A. Purohit, Tushar R. Khandare, Prof. Swapnil V. Deshmukh Abstract | PDF with Text | DOI: 10.17148/IJARCCE.2016.5853 Design and Implementation of Smart Car with Self-Navigation and Self-Parking Systems using Sensors and RFID Technology Madhuri M. Bijamwar, Prof. S.G. Kole, Prof. S.S. Savkare Abstract | PDF with Text | DOI: 10.17148/IJARCCE.2016.5854 Comparison Study of Induction Motor Drives using Microcontroller and FPGA Sooraj M S, Sreerag K T V Abstract | PDF with Text | DOI: 10.17148/IJARCCE.2016.5855 A Survey on Text Categorization Senthil Kumar B, Bhavitha Varma E Abstract | PDF with Text | DOI: 10.17148/IJARCCE.2016.5856 Multirate Signal Reconstruction Using Two Channel Orthogonal Filter Bank Sijo Thomas, Darsana P Abstract | PDF with Text | DOI: 10.17148/IJARCCE.2016.5857 The Multi-keyword Synonym Search for Encrypted Cloud Data Using Clustering Method Monika Rani H G, Varshini Vidyadhar Abstract | PDF with Text | DOI: 10.17148/IJARCCE.2016.5858 A Review on Various Speech Enhancement Techniques Alugonda Rajani, Soundarya .S.V.S Abstract | PDF with Text | DOI: 10.17148/IJARCCE.2016.5859 A Survey on Various Spoofing Attacks and Image Fusion Techniques Pravallika .P, Dr. K. Satya Prasad Abstract | PDF with Text | DOI: 10.17148/IJARCCE.2016.5860 Non-Invasive Vein Detection using Infra-red Rays Aradhana Singh, Dr. S.C. Prasanna Kumar, Dr. B.G. Sudershan Abstract | PDF with Text | DOI: 10.17148/IJARCCE.2016.5861 Boundary-Polygons for Minutiae based Fingerprinst Recognition Kusha Maharshi, Prashant Sahai Saxena Abstract | PDF with Text | DOI: 10.17148/IJARCCE.2016.5862 Image Forgery Detection on Digital Images Nimi Susan Saji, Ranjitha Rajan Abstract | PDF with Text | DOI: 10.17148/IJARCCE.2016.5863 Enhancing Information Security in Big Data Renu Kesharwani Abstract | PDF with Text | DOI: 10.17148/IJARCCE.2016.5864 Secure Multi-Owner Data Sharing for Dynamic Groups in Cloud Ms. Nilophar M. Masuldar, Prof. V. P. Kshirsagar Abstract | PDF with Text | DOI: 10.17148/IJARCCE.2016.5865 Compact Microstrip Octagonal Slot Antenna for Wireless Communication Applications Thasneem .H, Midhun Joy Abstract | PDF with Text | DOI: 10.17148/IJARCCE.2016.5866 ‘Aquarius’- Smart IOT Technology for Water Level Monitoring System Prof. A. M. Jagtap, Bhaldar Saniya Sikandar, Shinde Sharmila Shivaji, Khalate Vrushali Pramod, Nirmal Kalyani Sarangdhar Abstract | PDF with Text | DOI: 10.17148/IJARCCE.2016.5867 Future of Results in Select Search Engine Peerzada Mohammad Iqbal, Dr. Abdul Majid Baba, Aasim Bashir Abstract | PDF with Text | DOI: 10.17148/IJARCCE.2016.5868 Semantic Indexing Techniques on Information Retrieval of Web Content." IJARCCE 5, no. 8 (2016): 347–52. http://dx.doi.org/10.17148/ijarcce.2016.5869.

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"Keyword Detection using Auto Associative Neural Network with Reference to Assamese Language." International Journal of Recent Technology and Engineering 8, no. 3 (2019): 3290–94. http://dx.doi.org/10.35940/ijrte.c5428.098319.

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Abstract:

Speech and speaker recognition has yet not achieved the state-of-the-art position. Keyword detection in audio clips is gaining importance as it contributes to the audio recognition and detection systems. In this area, very few works have been carried out. In this paper, we present our experiment on keyword detection within recorded news clips. It is based on Assamese language spoken by Assamese native speakers. For this experiment, the audio clips are collected from local TV news debates, whereas the keywords are recorded by random speakers. The keywords are selected for recording considering the fact that they appear somewhere within the audio clips for a finite number of times. Mel Frequency Cepstral Coefficient (MFCC) is considered as feature and Auto Associative Neural Network (AANN) is considered as the classifier tool. With this detection model an average accuracy of 87% is achieved.

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