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4th International Conference on Sentiment Analysis and deep learning, ICSAdL 2025

作者： Naveen, S. dhamotharan, r. Sahebzathi, S. Krishnan, r. Santhana Kumar, V. Vinoth Sundaravadivel, P. Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Mechanical Engineering Chennai India Kalasalingam Academy of Research and Education Department of Computer Science and Information Technology Krishnankoil India Velammal College of Engineering and Technology Department of Computer Science and Engineering Madurai India Scad College of Engineering and Technology Department of Electronics and Communication Engineering Cheranmahadevi India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Electronics and Communication Engineering Chennai India Saveetha Engineering College Department of Artificial Intelligence and Machine Learning Chennai India

ISBN: (纸本)9798331523923

The ElephantGuardNet system is an advanced technological framework designed to monitor and protect elephants while minimizing human-elephant conflict. The system leverages a combination of real-time data collection, AI-driven analysis, and IoT sensors to track elephant movements across vast territories. By integrating wildlife camera traps, drones, satellite imagery, and IoT sensors such as infrared motion detectors and seismic sensors, the system provides continuous monitoring of elephant activity, enabling proactive management of potential risks. A custom dataset, ElephantMovementTracker, compiled from diverse data sources, forms the foundation for the model's training. Preprocessing techniques such as data normalization, augmentation, and noise filtering enhance the dataset's quality and ensure its suitability for deep learning applications. EfficientNet B7 and YOLO are employed for image and real-time detection tasks, respectively, while 3d CNNs are used for video analytics, enabling accurate tracking of dynamic movement patterns. GrU-based models predict future elephant movements, helping assess the likelihood of conflict. XGBoost is utilized for classification tasks, evaluating the risk of human-elephant conflict. The system is deployed on AWS and incorporates NVIdIA Jetson Nano for real-time detection and automated alerting via SMS and email. IoT sensors and automated deterrent systems, including sound emitters and drones, further mitigate conflict risk. The system's effectiveness in reducing human-elephant conflicts and providing a scalable solution for elephant conservation demonstrates the potential of AI and IoT integration in wildlife protection efforts, offering a comprehensive approach to both conservation and human-elephant coexistence. © 2025 IEEE.

关键词： drones

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Evaluation of machine learning Models for Kidney Stone Classification Using HOG Features on CT Images

Evaluation of Machine Learning Models for Kidney Stone Class...

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Intelligent & Innovative Practices in Engineering & Management (IIPEM), International Conference on

作者： ravikumar S Chandralekha E Vijay K Koushik Sundar Department of Computer Science & Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai Department of Artificial Intelligence and Machine Learning Rajalakshmi Engineering College Chennai India Citibank NA Jersey City NJ USA

ISBN: (数字)9798350390049

ISBN: (纸本)9798350390056

Kidney stones, or renal calculi, represent hard mineral and salt deposits which are formed in the kidneys and may cause intense pain, infections of the urinary tracts, and other complications. machine learning techniques are commonly applied to the problem of kidney stone classification, as this task is critical in medical imaging and contributes to correct diagnosis and treatment. This work aims to evaluate the performance of Gradient Boost, XGBoost, AdaBoost, and CatBoost models when processing a set of kidney CT images organized in Cyst, Normal, Stone, and Tumor classes. The initial dataset, available on Kaggle, is placed in the public domain and includes grayscale images resized to 128×128 pixels: these images are further processed using Histograms of Oriented Gradients. It was found that although AdaBoost achieved high precision for Cysts, lower overall accuracy (0.71) and variable performance, particularly in Stone classification, were observed. Conversely, XGBoost, Gradient Boosting, and CatBoost demonstrated near-perfect accuracy (0.99) and rOC values of 1.00 across all categories, with CatBoost showing especially consistent performance. The superior classification capabilities of XGBoost, Gradient Boosting, and CatBoost for kidney stone identification were highlighted, emphasizing the importance of selecting ML models based on detailed performance metrics. Future research may focus on optimizing these models or enhancing feature extraction methods.

关键词： Measurement Histograms Salt Accuracy Pain Computed tomography Gray-scale Boosting Feature extraction Tumors

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Automated Personalized Health Analytics using IoT and machine learning Algorithms 2

Automated Personalized Health Analytics using IoT and Machin...

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2nd International Conference on Augmented Intelligence and Sustainable Systems, ICAISS 2023

作者： Sivanesan, T.M. Smitha, J.A. Arul, U. Balasubramanian, K. Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Sri Sairam College of Engineering Department of Computer Science and Engineering Karnataka Bengaluru India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Artificial Intelligence and Machine Learning Tamil Nadu Chennai India Kalasalingam Academy of Research and Education Kalasalingam Business School Department of Business Administration Tamilnadu Srivilliputhur India

ISBN: (纸本)9798350325799

This research work develops and implements an integrated system using IoT technologies and machine learning algorithm to measure Body Mass Index (BMI) and perform health analytics in hospitals. Obesity, overweight, and chronic diseases are major public health issues. Healthcare workers need accurate and quick BMI readings to monitor weight status and health issues. To overcome these limitations, the proposed system uses IoT sensors to automate BMI evaluations and collect real-time health data. Wireless IoT devices interface with hospital information systems. The Support Vector machine (SVM) algorithm analyzes BMI, physical activity, and vital sign data. It can identify and predict BMI values from data. SVM can predict BMI for customized weight loss recommendations. The proposed setup helps hospitals to automate BMI measuring, thus reducing medical staff burden and manual intervention. real-time monitoring and analysis enable early identification and individualized treatment of obesity-related health concerns. IoT devices with the SVM algorithm enable data-driven decision-making, improving patient outcomes and healthcare efficiency. © 2023 IEEE.

关键词： Support vector machines

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real-time Human Action recognition using deep Convolutional Neural Networks 15

Real-time Human Action Recognition using Deep Convolutional ...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Vignesh, r. Balaraju, J. Poongothai, P. rajput, Vaishali rajkumar, N. Pratheep, V.G. Karunya Institute of Technology and Sciences Division of Artificial Intelligence and Machine Learning Karunya Nagar Coimbatore India Anuraag University Department of Cse Hyderabad India Department of Computer Science Bangalore India Vishwakarma Institute o Technology Computer engineering Pune India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Cse Thiruvalluvar Tamil Nadu Avadi India Kongu Engineering College Department of Mechatronics Engineering Tamil Nadu Perundurai638060 India

ISBN: (纸本)9798350370249

current advances in deep gaining knowledge of have enabled automated systems to understand human moves from movies in actual-time. deep convolutional neural networks (dCNNs) had been used to allow accurate and efficient real-time movement reputation. with the aid of leveraging the statistics-driven illustration mastering capability of dCNNs, movement popularity has become a powerful device for video analysis applications including robotics, surveillance, entertainment, augmented truth, and healthcare. dCNNs use an aggregate of densely connected convolutional layers and in addition completely linked layers for function extraction from films. The proposed architectures are usually skilled using supervised learning. Convolutional layers are used to understand nearby abstractions in motion pictures, which in turn are used to research worldwide features. diverse techniques together with temporal segmentation, movement-based characteristic extraction, and feature switch gaining knowledge of are also hired by means of dCNNs for achieving higher performance. recent works have investigated exceptional techniques to enhance the accuracy and speed of action reputation the use of dCNNs. This consists of techniques which includes model compression, decoupled inference networks, and efficient convolutional networks. By using combining those strategies, it's far viable to acquire higher popularity accuracy at the same time as at the equal time preserving an excessive computational speed. © 2024 IEEE.

关键词： Computational Equal Excessive Gaining research Speed

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dynamic Patient Triage Optimization in Healthcare Settings Using rNNs for decision Support 10

Dynamic Patient Triage Optimization in Healthcare Settings U...

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10th International Conference on Smart Computing and Communication, ICSCC 2024

作者： ranganathan, Chitra Sabapathy Basavaraddi, Chethan Chandra S Saillaja, V. Pandey, Pramod Sundaramurthy, B. Murugan, S. Mphasis Corporation Tamil Nadu Chennai India Artificial Intelligence and Machine Learning R&D Don Bosco Institute of Technology VTU Karnataka Belagavi India SRM Institute of Science and Technology Faculty of Management Studies Tamil Nadu Chennai India Nagpur Campus Maharashtra Pune India Department of Computer Science and Engineering Tamil Nadu Salem India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Biomedical Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350363104

The article presents a sophisticated healthcare system that uses recurrent neural networks (rNNs) to optimize real-time patient triage. The developed model integrates patient data from the Internet of Things (IoT), and it performs dynamic assessments of vital indicators like heart rate, blood pressure, and temperature to prioritize pre-operation care based on the urgency and severity of diseases. The rNN architecture considers the temporal connections included in the data, which enables a more sophisticated comprehension of the changing patient states. The training and evaluation of the model make use of a large dataset. In comparison to more conventional triage methods, the model displays considerable gains in both accuracy and efficiency. The system not only reduces the amount of time needed to respond and allocate resources, but it also improves the flexibility to react to shifting patient conditions. This research represents a significant step toward the development of intelligent decision support systems in the healthcare industry. It demonstrates the potential of more sophisticated machine learning approaches to transform patient care processes in environments with high stakes and a dynamic nature. © 2024 IEEE.

关键词： recurrent neural networks

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Streamlining Attendance with Fast Face recognition Model 8

Streamlining Attendance with Fast Face Recognition Model

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8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2024

作者： Sundar, Koushik ravikumar, S. Jeyalakshmi, J. Eugene Berna, I. Samuel, Prithi Citibank Na Jersey CityNJ United States Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Computer Science & Engineering Chennai India Amrita School of Computing Amrita Vishwa Vidhyapeetham Department of Computer Science and Engineering Chennai India Bannari Amman Institute of Technology Department of Artificial Intelligence and Machine Learning Erode India School of Computing Srm Institute of Science and Technology Department of Computational Intelligence Kattankulathur Campus Chennai India

ISBN: (纸本)9798350376425

Facial recognition technology has become increasingly prevalent in today's world, with applications ranging from mobile phones to banking and workplace security. This research proposes a facial recognition-based solution to replace the manual attendance tracking process, streamlining the process and reducing human error. The proposed system integrates facial recognition, registration forms, and a redis database to automate attendance tracking. By leveraging the ML search algorithm (similarity approach) and the Insight face Python module, the system can accurately identify individuals within a database. The system's implementation involves two phases: the initial phase focuses on integrating facial recognition, registration forms, and database access. The second phase involves utilizing software such as Streamlit and dashboard to enhance user experience and visualization. This innovative solution offers a more efficient and accurate alternative to traditional manual attendance tracking methods, improving overall efficiency and reducing administrative burdens. By automating the process, the system contributes to a more streamlined and secure environment. © 2024 IEEE.

关键词： database systems

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Bayer's in silico AdMET platform: a journey of machine learning over the past two decades

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drUG dISCOVErY TOdAY 2020年第9期25卷 1702-1709页

作者： Goeller, Andreas H. Kuhnke, Lara Montanari, Floriane Bonin, Anne Schneckener, Sebastian ter Laak, Antonius Wichard, Joerg Lobell, Mario Hillisch, Alexander Bayer AG Pharmaceut Computat Mol Design R&D D-42096 Wuppertal Germany Bayer AG R&D Pharmaceut Computat Mol Design D-13342 Berlin Germany Bayer AG R&D Pharmaceut Machine Learning Res D-13342 Berlin Germany Bayer AG Engn & Technol Appl Math D-51368 Leverkusen Germany Bayer AG Pharmaceut Genet Toxicol R&D D-13342 Berlin Germany

Over the past two decades, an in silico absorption, distribution, metabolism, and excretion (AdMET) platform has been created at Bayer Pharma with the goal to generate models for a variety of pharmacokinetic and physicochemical endpoints in early drug discovery. These tools are accessible to all scientists within the company and can be a useful in assisting with the selection and design of novel leads, as well as the process of lead optimization. Here. we discuss the development of machine-learning (ML) approaches with special emphasis on data, descriptors, and algorithms. We show that high company internal data quality and tailored descriptors, as well as a thorough understanding of the experimental endpoints, are essential to the utility of our models. We discuss the recent impact of deep neural networks and show selected application examples.

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Early diagnosis of diabetes and Prediction using machine learning Algorithm 2

Early Diagnosis of Diabetes and Prediction using Machine Lea...

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2nd International Conference on Intelligent Cyber Physical Systems and Internet of Things (ICoICI)

作者： Pavithra, S. ravikumar, S. Sreesubha, S. Babu, r. Kalaiselvi, M. Vellore Inst Technol Sch Comp Sci & Engn Chennai Tamil Nadu India Vel Tech Rangarajan Dr Sagunthala R&D Inst Sci & Dept Comp Sci & Engn Chennai Tamil Nadu India Rajalakshmi Engn Coll Dept Artificial Intelligence & Machine Learning Chennai Tamil Nadu India SRM Inst Sci & Technol Sch Comp Dept Computat Intelligence Kattankulathur Campus Chennai Tamil Nadu India Alagappa Chettiar Govt Coll Engn & Technol Dept Comp Sci & Engn Karaikkudi Tamil Nadu India

ISBN: (纸本)9798331540661;9798331540678

This study utilizes machine learning techniques to predict the presence of diabetes at an early stage. diabetic complications, including kidney and heart disease, can worsen over time if not detected at an earlier stage. Making the correct changes to one's lifestyle at the right time is crucial for preventing diabetes and its complications. So, there is an increasing need for a tool that can help doctors to detect this potentially deadly disease. This study has proposed machine learning based early stage diabetes detection model. The proposed model's performance is evaluated by analyzing how well the random forest classifier, a model known for its high accuracy, and resistance to overfitting performs. random forest is a great option for feature selection because they deal with feature-intensive datasets, even in high-dimensional settings, and give useful information regarding the importance of features.

关键词： diabetes Prediction System data Feeding random Forest

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real-Time GPS Tracking and Health Monitoring for Animals Using IoT and Mobile Application

Real-Time GPS Tracking and Health Monitoring for Animals Usi...

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machine learning and Autonomous Systems (ICMLAS), International Conference on

作者： V. Usha Sandeep Karagatla P. Sheela Gowr P.M.G Jegathambal A. Lizy V. Sathya Dept. of CSE Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Dept. of CSE (AI&DS) Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Vels Institute of Science Technology & Advanced Studies Chennai Dept. of Artificial Intelligence and Machine Learning Vel Tech Rangarajan Dr. Sagunthala (R&D) Institute of Science and Technology Chennai India Dept of Information Technology Velammal Engineering College Chennai India

ISBN: (数字)9798331505745

ISBN: (纸本)9798331505752

The Internet of Things (IoT) grounded solution of real Time Global Positioning System (GPS) tracking with Health Monitoring of Animals for Owners to track their animals like cows, buffaloes, dogs etc. This GPS module and a heartbeat sensor are attached to the animals in order to be able to follow them closely. This data is sent to a mobile app, and the owner can see their heart on the map as well as monitor its pulse. Based on analysis of such heart rate variations, the system determines when it is seeing an abnormal activity related to e.g. stress (increased heart rate) or potential health risks (no sign of a heartbeat), which could be interpreted as fear or death. Each animal has an initial heart rate level that is set, and when this rate exceeds it, the system sounds an alert to indicate danger. This method is especially helpful for keeping an eye on animals in urban areas, areas close to forests, and areas with high rates of theft and loss. The project demonstrates the successful execution of a reliable, real-time animal monitoring system with practical applications in both urban and rural environments.

关键词： Animals Tracking Heart beat Lora real-time systems Mobile applications Safety Internet of Things Monitoring Global Positioning System

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Grey wolf optimization technique with U-shaped and capsule networks-A novel framework for glaucoma diagnosis

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METHOdSX 2025年 14卷 103285页

作者： Govindharaj, I ramesh, T. Poongodai, A. Senthilkumar, K. P. Udayasankaran, P. ravichandran, S. Vel Tech Rangarajan Dr Sagunthala R&D Inst Sci & T Dept Comp Sci & Engn Morai 600062 Tamil Nadu India RMK Engn Coll Dept Comp Sci & Engn Thiruvallur 601206 Tamil Nadu India Madanapalle Inst Technol & Sci Dept Comp Sci & Engn Artificial Intelligence Madanapalle 517325 Andhra Pradesh India Kings Engn Coll Dept Artificial Intelligence & Data Sci Chennai 602117 Tamil Nadu India Kings Engn Coll Dept Artificial Intelligence & Machine Learning Chennai 602117 Tamil Nadu India

The worldwide prevalence of glaucoma makes it a major reason for blindness thus proper early diagnosis remains essential for preventing major vision deterioration. Current glaucoma screening methods that need expert handling prove to be time-intensive and complicated before yielding appropriate diagnosis and treatment. Our system addresses these difficulties through an automated glaucoma screening platform which combines advanced segmentation methods with classification approaches. A hybrid segmentation method combines Grey Wolf Optimization Algorithm with U-Shaped Networks to obtain precise extraction of the optic disc regions in retinal fundus images. Through GWOA the network achieves optimal segmentation by adopting wolf-inspired behaviors such as circular and jumping movements to identify diverse image textures. The glaucoma classification depends on CapsNet as a deep learning model that provides exceptional image detection to ensure precise diagnosis. The combination of our method delivers 96.01 % segmentation together with classification precision which outstrips traditional approaches while indicating strong capabilities for discovering glaucoma at early stages. This automated diagnosis system elevates clinical accuracy levels through an automated screening method that solves manual process limitations. The detection framework produces better accuracy to improve clinical results in a strong effort to minimize glaucoma-induced blindness worldwide and display its capabilities in real clinical environments. center dot Hybrid GWOA-UNet ++ for precise optic disc segmentation. center dot CapsNet-based classification for robust glaucoma detection. center dot Achieved 96.01 % accuracy, surpassing existing methods.

关键词： Glaucoma detection Optic disc segmentation Grey wolf optimization U-Net ++ Capsule network deep learning classification Vision loss

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