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Academic literature on the topic 'Auto keyword detection'
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Journal articles on the topic "Auto keyword detection"
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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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<strong>Purpose:</strong> <em>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. </em> <strong>Design/Methodology/Approach:</strong> <em>Developing a theoretical and experimental approach for parallelizing the lexical scanning process on a multi-core system.</em> <strong>Findings/Result:</strong><em> 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.</em> <strong>Originality/Value:</strong> <em>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.</em> <strong>Paper Type:</strong> <em>Experimental Research.</em>
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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.
Conference papers on the topic "Auto keyword detection"
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Wedasingha, Nushara, Pradeep Samarasinghe, Lasantha Seneviratne, Michela Papandrea, and Alessandro Puiatti. "Auto encorder Based Data Clustering for Typical & Atypical Repetitive Child Hand Movement Pattern Identification." In 3rd SLIIT International Conference on Engineering and Technology. SLIIT, 2024. http://dx.doi.org/10.54389/mvgk5982.
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This study is dedicated to the important task of identifying unique repetitive hand movement patterns in children, with the aim of facilitating early anomaly detection. The current body of literature lacks a comprehensive model capable of effectively discerning distinctive patterns in child repetitive hand movements. To address this gap, our innovative approach employs autoencoders to efficiently compress intricate data and extract latent features from a dataset with inherent limitations. By utilizing clustering techniques, we analyze these features to reveal distinct behaviors associated with child hand movements. Despite the challenges posed by binary annotated datasets, our model demonstrates outstanding performance in categorizing movements into four distinct types, thereby providing valuable insights into the intricate landscape of child hand movement patterns. Statistical assessments further underscore the superiority of our autoencoder, achieving a mean Bayesian value of 0.112, outperforming state-of-the-art algorithms in this domain. Subsequent in-depth analysis exposes notable inter-cluster patterns, elucidating transitions from typical to atypical behavior in child hand movements. This research constitutes a significant advancement in the field of child hand movement pattern analysis, offering a powerful and sophisticated tool for healthcare professionals and researchers alike. The automation capabilities embedded in our model empower these professionals to address childhood behavioral disorders more effectively and efficiently. In essence, our research not only contributes to the enhancement of early anomaly detection systems but also serves as a valuable resource for professionals engaged in child healthcare and behavioral research, facilitating a deeper understanding of these nuanced patterns. Keywords— Autoencoders, K-means, Child Repetitive Behavior Analysis, Child Hand Movement Pattern Analysis, Dimension Reduction, Unsupervised Learning