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Journal articles on the topic 'ML TECHNIQUES'

Author: Grafiati

Published: 19 January 2024

Last updated: 25 July 2025

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1

Jeevana, P., T. Nandini, D. Srilekha, G. Dinesh, and Mrs Archana. "Diabetic Prediction using ML Techniques." YMER Digital 21, no. 04 (2022): 585–93. http://dx.doi.org/10.37896/ymer21.04/59.

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In today's world, diabetes is a huge problem. Diabetes can cause blood sugar levels to rise, which can contribute to strokes and heart attacks. One of the most rapidly spreading diseases is this one. After speaking with a doctor and receiving a diagnosis, patients are normally required to receive their reports. Because this procedure is time-consuming and costly, we were able to fix the problem utilizing machine learning techniques. In medical organizations, many machine learning applications are both exciting and important. Machine learning is being more widely used in the medical field. Our

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Adigun, Taiwo O., Akinola Oshinubi, and Ibukun Eweoya. "Cryptojacking Detection Using ML Techniques." IUP Journal of Computer Sciences 19, no. 1 (2025): 7–17. https://doi.org/10.71329/iupjcs/2025.19.1.7-17.

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Venkata Vara Prasad, D., P. Senthil Kumar, Lokeswari Y. Venkataramana, et al. "Automating water quality analysis using ML and auto ML techniques." Environmental Research 202 (November 2021): 111720. http://dx.doi.org/10.1016/j.envres.2021.111720.

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Mukobara, Yuta, Satoshi Chiba, Kazuki Fujio, Tatsuya Katabuchi, and Chikako Ishizuka. "Fission trajectory analysis using ML techniques." EPJ Web of Conferences 306 (2024): 01042. http://dx.doi.org/10.1051/epjconf/202430601042.

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This research analyzed trajectories of nuclear fission leading to symmetric or assymmetric mass division, obtained by a four-dimensional Langevin-model, using machine learning models. A hybrid neural network, combining Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU), both of which were types of Recurrent Neural Networks (RNN), was utilized to classify whether each Langevin trajectory led to symmetric or asymmetric mass division. It was found that the current model could classify fate of these trajectories before reaching to the final destination (symmetric or assymmetric mode) wit

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Kosinska, Joanna, and Maciej Tobiasz. "Detection of Cluster Anomalies With ML Techniques." IEEE Access 10 (2022): 110742–53. http://dx.doi.org/10.1109/access.2022.3216080.

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Derangula, Sirisha. "Identification of phishing websites using ML techniques." International Journal of Communication and Information Technology 1, no. 2 (2020): 28–32. http://dx.doi.org/10.33545/2707661x.2020.v1.i2a.16.

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Nanajkar, Jyotsna, Mayuresh Warang, Pratik Suthar, Shivam Shinde, and Atharv Pawar. "DDoS Attack Detection Using ML/DL Techniques." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 01 (2024): 1–10. http://dx.doi.org/10.55041/ijsrem27967.

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The increasing integration of IoT devices has heightened the vulnerability of networks to sophisticated and evolving cyber threats, particularly DDoS attacks, which can severely disrupt service availability. Leveraging machine learning algorithms, this research aims to enhance the proactive identification of anomalous patterns indicative of DDoS attacks within IoT environments. By employing a combination of feature extraction, classification, and ensemble learning methods, the proposed model demonstrates promising results in distinguishing between normal network behaviour and malicious activit

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BALAJI, MR B. ARJUN. "EFFICIENT BREAST CANCER PREDICTION USING ML TECHNIQUES." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 04 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem30845.

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During their life, among 8% of women are diagnosed with Breast cancer (BC), after lung cancer, BC is the second popular cause of death in both developed and undeveloped worlds. BC is characterized by the mutation of genes, constant pain, changes in the size, color(redness), skin texture of breasts. Classification of breast cancer leads pathologists to find a systematic and objective prognostic, generally the most frequent classification is binary (benign cancer/malign cancer). Today, Machine Learning (ML) techniques are being broadly used in the breast cancer classification problem. They provi

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P, Bhagya Sri, Sindhu Sri G, Jaya Sri K, Leela Poojitha V, and Sajida Sultana Sk. "Intelligent book recommendation system using ML techniques." ITM Web of Conferences 74 (2025): 03007. https://doi.org/10.1051/itmconf/20257403007.

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The current research focuses on a recommendation system based on Decision Tree, Naive Bayes, Ridge Classifier, and Random Forest, using a new hybrid method combining Singular Value Decomposition (SVD) and K-Nearest Neighbors (KNN). The Decision Tree model reaches a good trade-off for precision, recall, and F1 metrics, acting as a benchmark. On the other hand, the hybrid model greatly surpasses the remaining ones in such a way that precision is as high as 89.35%, recall is 59.01%, and F1 is up to 71.30%, thus reinforcing the notion that it finds user preferences for recommendations more effecti

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Tigga, Onima, Jaya Pal, and Debjani Mustafi. "A Novel Data Handling Technique for Wine Quality Analysis using ML Techniques." International Journal of Experimental Research and Review 45, Spl Vol (2024): 25–40. https://doi.org/10.52756/ijerr.2024.v45spl.003.

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In this era, wine is a regularly redeemed beverage, and industries are seeing increased sales due to product quality certification. This research aims to identify key wine characteristics that contribute to significant outcomes through the application of machine learning classification techniques, specifically Random Forest (RF), Decision Tree (DT) and Multi-Layer Perceptron (MLP), using white and red wine datasets sourced from the UCI Machine Learning repository. This research aims to develop a multiclass classification model using machine learning (ML) to accurately assess the quality of a b

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Kumar, Sachin, and Subhasree Bhattacharjee. "REVIEW OF AI/ML TECHNIQUES ON COVID-19." International Journal of Engineering Applied Sciences and Technology 7, no. 5 (2022): 116–18. http://dx.doi.org/10.33564/ijeast.2022.v07i05.020.

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COVID-19 has caused severe worldwide threat by taking away over 6 million lives. Artificial intelligence and machine learning methods are used significantly for fighting against this pandemic. For solving different problems of COVID-19, different AI/ML techniques are required. This article provides comprehensive review on different AI/ML applications on COVD-19. For classification, prediction, and diagnosis AI/ML methods have huge contribution. AI/ML methods for risk assessment of COVID-19 have also been narrated.

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Abuzaid, Nawal. "Image SPAM Detection Using ML and DL Techniques." International Journal of Advances in Soft Computing and its Applications 14, no. 1 (2022): 227–43. http://dx.doi.org/10.15849/ijasca.220328.15.

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Abstract Since e-mail is one of the most common places to send messages, spammers have, in recent years, targeted it as a preferred way of distributing undesired messages (spam) to several users to spread viruses, cause destruction, and obtain user's information. Spam images are considered one of the known spam types. The spammer processes images and changes their characteristics, especially background colour, font type, or adding artefacts to the images to spread spam. In this paper, we proposed a spam detection model using Several ML (Random-Forest (RF), Decision-Tree (DT), KNearest Neighbor

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T., Logeswari. "Performance Analysis of Ml Techniques for Spam Filtering." International Research Journal on Advanced Science Hub 2, Special Issue ICIES 9S (2020): 64–69. http://dx.doi.org/10.47392/irjash.2020.161.

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Kore, Rahul C., Prachi Ray, Priyanka Lade, and Amit Nerurkar. "Legal Document Summarization Using Nlp and Ml Techniques." International Journal of Engineering and Computer Science 9, no. 05 (2020): 25039–46. http://dx.doi.org/10.18535/ijecs/v9i05.4488.

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Reading legal documents are tedious and sometimes it requires domain knowledge related to that document. It is hard to read the full legal document without missing the key important sentences. With increasing number of legal documents it would be convenient to get the essential information from the document without having to go through the whole document. The purpose of this study is to understand a large legal document within a short duration of time. Summarization gives flexibility and convenience to the reader. Using vector representation of words, text ranking algorithms, similarity techni

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Sethuraman, Sriram, V. S. Nithya, and D. Venkata Narayanababu Laveti. "Noniterative Content-Adaptive Distributed Encoding Through ML Techniques." SMPTE Motion Imaging Journal 127, no. 9 (2018): 50–55. http://dx.doi.org/10.5594/jmi.2018.2862647.

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Akula, Roopesh. "Fraud identification of credit card using ML techniques." International Journal of Computing and Artificial Intelligence 1, no. 2 (2020): 31–33. http://dx.doi.org/10.33545/27076571.2020.v1.i2a.15.

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S, Sabarinath, Thirumalaivasan R, Shiam S, Mohamed Aashik M. S, K. Sudhakar, and Dr P. Rama. "Analysis of Stock Price Prediction Using ML Techniques." International Journal for Research in Applied Science and Engineering Technology 11, no. 4 (2023): 1533–37. http://dx.doi.org/10.22214/ijraset.2023.50378.

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Abstract: Time series forecasting has been widely used to determine the future prices of stock, and the analysis and modelling of finance time series importantly guide investors’ decisions and trades. This work proposes an intelligent time series prediction system that uses sliding-window optimization for the purpose of predicting the stock prices using data science techniques. The system has a graphical user interface and functions as a stand-alone application. The proposed model is a promising predictive technique for highly non-linear time series, whose patterns are difficult to capture by

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Reddy, N. Narasimha, and G. M. Vema Reddy. "DDoS Attack Detection in SDN using ML Techniques." International Journal for Research in Applied Science and Engineering Technology 11, no. 10 (2023): 2035–38. http://dx.doi.org/10.22214/ijraset.2023.56350.

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Abstract: The increasing prevalence of DDoS attacks poses a serious threat to modern network infrastructures. SDN has been proposed as a promising solution for enhancing network security. However, detecting and mitigating DDoS attack in software definednetwork remains a challenging task. In this research paper, suggest an innovative approach in order to identify DDoS assaults in software-defined networks using (ML) techniques. Ourmethod entails gathering and analyzing network data. Traffic data using SDN controllers. We use variety of ML techniques analyze the traffic information to discover u

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Deshpande, Deepali. "Risk Prediction Models in Adolescents Using ML Techniques." International Journal for Research in Applied Science and Engineering Technology 13, no. 5 (2025): 5037–44. https://doi.org/10.22214/ijraset.2025.71171.

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Abstract: Adolescents are at high risk for mental health issues, including depression, due to factors such as social media exposure, peer pressure, and behavioral influences. Early detection of at-risk individuals enables timely intervention and support. This study presents a hybrid depression detection model that integrates Naïve Bayes, TF-IDF, and emoji sentiment analysis to enhance classification accuracy. The approach utilizes textual and emoji-based sentiment cues extracted from social media data to identify depressive tendencies. The model is evaluated against traditional classifiers, de

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Nalla, Venu, M. Anil Kumar, V. Samhith, and G. Padmavathi. "Exploring HPC and ML Techniques on Various Classical, Machine and Modern Ciphers." Indian Journal Of Science And Technology 18, Sp1 (2025): 19–27. https://doi.org/10.17485/ijst/v18si1.icamada16.

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Objectives: To investigate High Performance Computing (HPC) techniques for communication ciphers. To explore machine cipher classification using encrypted image data and ML for 4G & 5G cipher classification based on ciphertext analysis. Methods: Employed MPI and CUDA + MPI models to assess the strength of ciphers, classified machine ciphers with image data, and applied ML for identification communication cipher classification. Findings: Communication ciphers are resistant to exhaustive key search attacks using HPC. Applied various classification and clustering models on machine ciphers usi

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Khan, Sara, and Saurabh Pal. "User Interface Bug Classification Model Using ML and NLP Techniques: A Comparative Performance Analysis of ML Models." International Journal of Experimental Research and Review 45, Spl Vol (2024): 56–69. https://doi.org/10.52756/ijerr.2024.v45spl.005.

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Analyzing user interface (UI) bugs is an important step taken by testers and developers to assess the usability of the software product. UI bug classification helps in understanding the nature and cause of software failures. Manually classifying thousands of bugs is an inefficient and tedious job for both testers and developers. Objective of this research is to develop a classification model for the User Interface (UI) related bugs using supervised Machine Learning (ML) algorithms and Natural Language Processing (NLP) techniques. Also, to assess the effect of different sampling and feature vec

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Babhulkar, Vaishnavi, Apurva Salphale, Anagha Garkade, Kalyanee Pachghare, Tanvi Sagane, and Prof. Vanisha P. Vaidya. "ML-Driven Technique for Adaptive Email Filtering." International Journal of Ingenious Research, Invention and Development (IJIRID) 3, no. 1 (2024): 50–55. https://doi.org/10.5281/zenodo.10822621.

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<em>Email filtering technology must be developed quickly due to the increase of unsolicited emails, or spam mail. Computer security has struggled with spam emails consistently. They are incredibly expensive economically and exceedingly risky for networks and computers. Spam emails are found and filtered using machine learning techniques. This project mainly focuses on machine learning used to find and remove spam emails. Using the K-nearest neighbour algorithm for email spam detection is one of the simple supervised learning techniques. Initially, the relevant features for filtering the spam m

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Mulge, Manikrao. "CLOUD-BASED DATA INTRUSION DETECTION SYSTEM USING ML TECHNIQUES." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 05 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem34718.

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Cloud computing (CC) stands as a groundbreaking technology, streamlining access to network and computer resources while offering a plethora of services, such as storage and data management, all with the aim of optimizing system functionality. Despite its array of advantages, cloud providers grapple with notable security challenges, particularly concerning the safeguarding of resources and services. Addressing these concerns and bolstering security measures necessitates vigilant monitoring of resources, services, and networks to promptly detect and respond to potential attacks. Central to this

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Sanapala, Anuradha, B. Jaya Lakshmi, K. Sandhya Rani Kundra, and K. B. Madhuri. "Air Pollution Detection and Control System Using ML Techniques." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 4 (2023): 219–25. http://dx.doi.org/10.17762/ijritcc.v11i4.6442.

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In present times, air pollution is increasing day by day, depriving the health of many people due to the various toxic components in air. So, it is necessary to monitor and detect the levels of pollution in various areas and try to control it by taking precautionary actions. Air pollution detection and control system is all about detecting the level of pollution in a particular area based on the amount of polluting components and proposing the measures to control the pollution. Analysis is made on the regions of Visakhapatnam city in Andhra Pradesh, India and grouped based on their pollution a

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Mahesh T R and Vinoth Kumar. "Early Detection of Cancer using Machine Learning (ML) Techniques." International Journal of Information Technology, Research and Applications 2, no. 1 (2023): 14–21. http://dx.doi.org/10.59461/ijitra.v2i1.24.

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Early detection of cancer sickness leads to rapid treatment, reducing the risk of morbidity and mortality. The diagnosis of oral cancer continues to be a challenge for dental careers, particularly in the location, evaluation, and review of early-stage oral disease. Due to the lack of optimal analysis using conventional methods, oral cancer is identified and grouped using AI at an early stage. AI techniques are used to show the movement and treatment of dangerous locations and may accurately predict future disease effects. AI techniques are used to show the movement and treatment of dangerous l

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Calabe P S, Prabha R, and Veena Potdar. "Cardiac ailment recognition using ML techniques in E-healthcare." World Journal of Advanced Research and Reviews 17, no. 1 (2023): 302–7. http://dx.doi.org/10.30574/wjarr.2023.17.1.0010.

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Heart ailments can take numerous forms, and they are frequently referred to as cardio vascular illnesses. These can range from heart rhythm problems to birth anomalies to blood vessel disorders. It has been the main cause of death worldwide for several decades. To recognize the illness early and properly manage, it is critical to discover a precise and trustworthy approach for automating the process. Processing massive amounts of data in the field of medical sciences necessitates the application of data science. Here we employ a range of machine learning approaches to examine enormous data set

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Bavarva, Sneh, Kalpesh Senva, and Priyank Bhojak. "SQLI Attack: An Approach using ML and Hybrid Techniques." International Journal for Research in Applied Science and Engineering Technology 11, no. 10 (2023): 1244–49. http://dx.doi.org/10.22214/ijraset.2023.56190.

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Abstract: Web-based systems are significantly at risk from SQL Injection Attacks (SQLIA), particularly in industries that handle sensitive data, like finance and healthcare. During these attacks, hostile actors insert false SQL queries into the database server of a web application in an effort to steal sensitive data. The use of classifiers and techniques like end-to-end deep learning and expanding the Aho-Corasick algorithm to detect SQLIA attacks have been covered in the literature. These researches have shed light on identifying and minimizing SQLIA, but the problem still exists. To detect

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Mahesh, T. R., and Kumar Vinoth. "Early Detection of Cancer using Machine Learning (ML) Techniques." International Journal of Information Technology, Research and Applications (IJITRA) ISSN: 2583-5343 2, no. 1 (2023): 14–21. https://doi.org/10.5281/zenodo.7780162.

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Early detection of cancer sickness leads to rapid treatment, reducing the risk of morbidity and mortality. The diagnosis of oral cancer continues to be a challenge for dental careers, particularly in the location, evaluation, and review of early-stage oral disease. Due to the lack of optimal analysis using conventional methods, oral cancer is identified and grouped using AI at an early stage. AI techniques are used to show the movement and treatment of dangerous locations and may accurately predict future disease effects. AI techniques are used to show the movement and treatment of dangerous l

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Calabe, P. S., R. Prabha, and Potdar Veena. "Cardiac ailment recognition using ML techniques in E-healthcare." World Journal of Advanced Research and Reviews 17, no. 1 (2023): 302–7. https://doi.org/10.5281/zenodo.8072547.

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Heart ailments can take numerous forms, and they are frequently referred to as cardio vascular illnesses. These can range from heart rhythm problems to birth anomalies to blood vessel disorders. It has been the main cause of death worldwide for several decades. To recognize the illness early and properly manage, it is critical to discover a precise and trustworthy approach for automating the process. Processing massive amounts of data in the field of medical sciences necessitates the application of data science. Here we employ a range of machine learning approaches to examine enormous data set

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sengaliappan, Dr M. "Optimizing Energy Consumption in Smart Homes Using Ml Techniques." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 03 (2025): 1–9. https://doi.org/10.55041/ijsrem41971.

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Because of growing demand and rising electricity prices, energy consumption in smart homes is a serious concern. Conventional energy management systems frequently lack flexibility, which results in wasteful energy use. In order to improve energy efficiency in smart homes, this study suggests a machine learning-based optimization strategy that combines Reinforcement Learning (RL), Support Vector Machine (SVM), and Genetic Algorithm (GA). Using past consumption trends and environmental variables, SVM is used to forecast energy demand. GA minimizes energy consumption while preserving user comfort

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Motiramani, Mamta, Priyanshi Solanki, Vidhi Patel, et al. "AI-ML techniques for green hydrogen: A comprehensive review." Next Energy 8 (July 2025): 100252. https://doi.org/10.1016/j.nxener.2025.100252.

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Bhaskara Rao B. "Renaissance for Alzheimer’s Disease Detection using ML DL Techniques." Power System Technology 49, no. 1 (2025): 982–98. https://doi.org/10.52783/pst.1646.

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The early diagnosis of Alzheimer’s disease (AD) is very important in ensuring proper intervention in a timely manner so that patients can be managed better and have better outcomes. Earlier, cognitivedata and numerical patient information were used with conventional machine learning (ML) methods for early detection. One of the past ensemble-based approaches trained up to seven ML classifiers that included Decision Tree, Random Forest, SVM, ANN, and AdaBoost, gaining 93.92% accuracy. However, deep learning integration and optimal feature extraction was not present in that study. This study prop

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Patidar, Govind, Aayushi bhardwaj, Vebhav Kumar Tiwari, and Ajay Maurya. "Predictive Modelling of Thermal Wave Propagation Using Ml Techniques." Journal of Neonatal Surgery 14, no. 30S (2025): 22–31. https://doi.org/10.63682/jns.v14i30s.6907.

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Corrosion is a major challenge in industrial applications, leading to material degradation, safety risks, and high maintenance costs. Traditional detection techniques, such as ultrasonic and radiographic testing, often require invasive procedures or specialized equipment, making large-scale monitoring difficult. This project aims to develop an automated, non-invasive corrosion detection framework using passive infrared thermography and machine learning-based image processing to enhance detection accuracy and efficiency. The core problem addressed in this study is the difficulty in identifying

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Owusu, Lawrence, Ahmad Patooghy, Masud R. Rashel, Marwan Bikdash, and Islam AKM Kamrul. "Speeding Up ML-Based IDSs Through Data Preprocessing Techniques." International Journal of Computer Applications 187, no. 12 (2025): 1–9. https://doi.org/10.5120/ijca2025925071.

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Kasture, Pradnya. "DDoS Attack Detection using ML." International Journal for Research in Applied Science and Engineering Technology 11, no. 5 (2023): 6421–24. http://dx.doi.org/10.22214/ijraset.2023.53133.

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Abstract: DDoS attacks are an attempt to prevent the service from being unavailable by overloading the server with malicious traffic. In the past few years, distributed denial of service attacks is becoming the most difficult and burdensome problem. The number and magnitude of attacks have increased from few megabytes of data to 100s of terabytes of data these days. As there are different attack patterns or new types of attacks, it is difficult to detect such attacks effectively. New techniques for generating and mitigating distributed denial of service attacks have been developed in the prese

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Naresh, Lokiny, and Reddy Pradip. "Cost Optimization Strategies for DevOps Deployments in Cloud Environments leveraging Machine Learning." European Journal of Advances in Engineering and Technology 8, no. 3 (2021): 69–72. https://doi.org/10.5281/zenodo.13325845.

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This paper explores cost optimization strategies for DevOps deployments in cloud environments by leveraging machine learning (ML) techniques. With the growing adoption of cloud services, managing costs while maintaining high performance and reliability has become critical. This study investigates various ML algorithms and their applications in predicting resource needs, automating scaling, and optimizing workloads. The findings demonstrate that integrating ML into DevOps practices can significantly reduce operational costs and improve efficiency.

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Drabeck, Lawrence, Buvana Ramanan, Thomas Woo, and Troy Cauble. "Automated Techniques for Creating Speech Corpora from Public Data Sources for ML Training." International Journal of Machine Learning and Computing 10, no. 1 (2020): 1–9. http://dx.doi.org/10.18178/ijmlc.2020.10.1.890.

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Satarkar, Bhagyashri, Dhavalsigh Vibhute, Vishal Wadgoankar, and Yasar Sayyad. "Phishguard: ML- based phishing detection system." International Scientific Journal of Engineering and Management 04, no. 04 (2025): 1–9. https://doi.org/10.55041/isjem02759.

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Spam messages in SMS and email systems pose significant security and productivity risks. Traditional detection methods, such as rule-based filters, struggle to adapt to evolving spam techniques. This paper explores machine learning techniques for spam detection, emphasizing algorithms like Naïve Bayes and Support Vector Machines (SVM), along with their applications, strengths, and limitations. Key challenges, including scalability, dataset diversity, and evolving spam patterns, are identified. The study highlights future directions such as real-time classification, deep learning models, and im

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Rathod, Tushar. "CSRF ML-SHIELD." International Journal for Research in Applied Science and Engineering Technology 12, no. 4 (2024): 2273–78. http://dx.doi.org/10.22214/ijraset.2024.60305.

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Abstract: Cross-Site Request Forgery (CSRF) remains a pervasive threat to web applications, compromising user data, session integrity, and overall system security. In response to this ongoing challenge, this research paper proposes a robust CSRF detection system aimed at identifying and mitigating CSRF attacks effectively. The system leverages state-of-the-art techniques in web security and machine learning to analyze user interactions, request patterns, and session attributes in real-time. The CSRF detection system operates by generating and validating unique tokens for each user session, eff

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Pranit, Adhangle, Kamankar Yogesh, Aher Darshan, Ahirrao Aaryan, and Mahesh P. Bhandakkar Prof. "YOUTUBE VIDEO OPTIMIZER USING ML." Journal of the Maharaja Sayajirao University of Baroda 59, no. 1 (I) (2025): 317–29. https://doi.org/10.5281/zenodo.15221518.

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ABSTRACTIn today's fast-paced world, people have limited time to consume content. This has led to the increasingpopularity of video summarization, which aims to provide users with a condensed and more digestibleversion of a longer video. In this project, we will develop a YouTube summarization system usingNatural Language Processing (NLP) techniques. The system will take a video as input and output ashorter, summarized version of the video. The summarized version will contain the most importantand relevant information from the original video, making it easier for users to understand andremembe

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詹木, 子豪. "Translation Techniques of Highway Engineering Price Foundation." Modern Linguistics 06, no. 03 (2018): 391–96. http://dx.doi.org/10.12677/ml.2018.63042.

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Naomi, Chukwurah, Idowu Afolabi Adeoye, and Anthony Abieba Olumese. "Harnessing Machine Learning Techniques for Driving Sustainable Economic Growth and Market Efficiency." Engineering and Technology Journal 10, no. 02 (2025): 3929–38. https://doi.org/10.5281/zenodo.14948701.

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Machine learning (ML) is revolutionizing industries by providing innovative solutions that drive sustainable economic growth and market efficiency. The integration of ML techniques in various sectors, including finance, healthcare, manufacturing, and energy, offers significant potential for enhancing productivity, optimizing resource allocation, and fostering innovation. This review explores the transformative impact of ML on economic growth and market efficiency, emphasizing its role in predictive analytics, decision-making, and automation. In finance, ML algorithms are employed to predict ma

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Anupama, Y. K., A. Monisha, Sutar Sahana, N. Kavya, and M. R. Mahananda. "Survey of Diagnosis Cardiovascular Disease using Ml (Machine Learning) Techniques." Journal of Image Processing and Artificial Intelligence 6, no. 1 (2020): 14–18. https://doi.org/10.5281/zenodo.3697362.

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<strong>Heart Disease is one of transience reason in the present age as demonstrated by a particular course of action to diminish the amount of passing’ due to coronary disease, for instance cardiovascular breakdown, hypertension, coronary ailment, Arrhythmia needs to foresee suitably through detection systems. In most recent examination AI strategies has been utilized to enable the wellbeing to mind industry and diagnosis of heart related disease. Numerous amounts of patient’s reports are retained and techniques of machine learning such like K-Nearest Neighbor (KNN), Decision tree

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Choubey, Shubham. "Diabetes Prediction Using ML." International Journal for Research in Applied Science and Engineering Technology 11, no. 6 (2023): 4209–12. http://dx.doi.org/10.22214/ijraset.2023.54415.

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Abstract: The goal of this research is to create a machine learning algorithm-based system that is effective in detecting diabetes with high accuracy. Machine learning approaches have the potential to develop into trustworthy tools for diabetes diagnosis by utilising data analytics and pattern identification. Utilising feature selection techniques, the most pertinent elements that significantly influence diabetes prediction are found. Implemented and assessed using performance metrics including accuracy, recall, precision, and F1 Score are various machine learning algorithms, such as K-Nearest

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Gupta, Diya. "AI-ML based HealthCare Chatbot." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 02 (2025): 1–9. https://doi.org/10.55041/ijsrem41352.

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In the realm of modern healthcare, the exigency for instantaneous, accurate, and personalized medical assistance remains a paramount concern, primarily due to infrastructural deficits and delayed medical interventions, especially in remote locales. The AI-ML-powered healthcare chatbot aspires to ameliorate this predicament by offering 24/7 medical support, ranging from symptom assessment to scheduling consultations and medication reminders, thereby fortifying healthcare accessibility. The focal point of this inquiry entails a meticulous exploration of the underlying factors contributing to del

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Devi, P. Rama, Srujitha Meesala, Ramya Reddy, Kushal Senapathi, and Udaya Kolala. "Anticipating Consumer Demand using ML." International Journal for Research in Applied Science and Engineering Technology 11, no. 4 (2023): 1053–58. http://dx.doi.org/10.22214/ijraset.2023.50283.

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Abstract: Demand forecasting is essential for every growing online business. Without efficient demand forecasting systems in place, it might be next to impossible to always have the right amount of stock on hand. Because a food delivery service deals with a high volume of perishable raw materials, it is critical for the company to accurately forecast daily and weekly demand. If a warehouse has too much inventory, there is a greater likelihood of wastage, and if it has too little, there may be shortages, which would encourage customers to turn to your competitors. Therefore, predicting demand i

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Padmanabha Reddy, Y. C. A., Sai Sathwik Kosuru, Nageswara Rao Sirisalla, G. N. Vivekananda, and Venkata Akanksha Perala. "Empirical techniques for effort estimation in designing effective ML models." International Journal of Computers and Applications 47, no. 2 (2025): 226–34. https://doi.org/10.1080/1206212x.2025.2454509.

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Scheider, DM, R. Alhalel, M. Bourke, A. Elfant, P. Kortan, and GB Haber. "Endoscopic retrieval techniques for cable fractures complicating mechanical lithotripsy (ML)." Gastrointestinal Endoscopy 41, no. 4 (1995): 413. http://dx.doi.org/10.1016/s0016-5107(05)80508-5.

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Chandra Mouli, K., B. Indupriya, D. Ushasree, Ch V. Raghavendran, Babita Rawat, and Bhukya Madhu. "Network Intrusion Detection using ML Techniques for Sustainable Information System." E3S Web of Conferences 430 (2023): 01064. http://dx.doi.org/10.1051/e3sconf/202343001064.

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Network intrusion detection is a vital element of cybersecurity, focusing on identification of malicious activities within computer networks. With the increasing complexity of cyber-attacks and the vast volume of network data being spawned, traditional intrusion detection methods are becoming less effective. In response, machine learning has emerged as a promising solution to enhance the accuracy and efficiency of intrusion detection. This abstract provides an overview of proper utilization of machine learning techniques in intrusion detection and its associated benefits. The base paper explor

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Laiq, Muhammad, Nauman bin Ali, Jürgen Börstler, and Emelie Engström. "A comparative analysis of ML techniques for bug report classification." Journal of Systems and Software 227 (September 2025): 112457. https://doi.org/10.1016/j.jss.2025.112457.

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