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A Novel Framework in Scheduling Packets for Energy-Efficient Bandwidth Allocation in Wireless Networks 2

A Novel Framework in Scheduling Packets for Energy-Efficient...

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2nd International Conference on Applied Artificial Intelligence and Computing, ICAAIC 2023

作者： Sivajothi, E. Jayaudhaya, J. Santhiya, S. Thapa, Naresh Kumar Kamatchi, S. Ganapathy, N. Bala Sundara Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu India R.M. D. Engineering College Department of Electronics and Communication Engineering Tamil Nadu India M. Kumarasamy College of Engineerin Department of Computer Science and Engineering Tamil Nadu India R.M. K. Engineering College Department of Electronics and Communication Engineering Tamil Nadu India Saveetha School of Engineering Simats Department of Electronics and Communication Engineering Tamil Nadu India Panimalar Engineering College Department of Information Technology Tamil Nadu India

ISBN: (纸本)9781665456302

The research work provides a brand-new technique for distributing bandwidth in wireless networks that is also energy-efficient. To begin, look into how channel allocations, which ultimately determine the transmission rate for mobile terminals, affect the fundamental relationship that exists between energy use and transmission rates. The Fatality Selection Algorithm (FSA) and the Receiver Selection Algorithm (RSA) are two methodologies that control connection admittance to reduce the amount of energy used by each individual terminal. Furthermore, provide a corrective strategy for statistically satisfying the quality of service (QoS) criteria throughout the resource allocation process. Throughput, call blockage probability, and the energy consumption rate of each successfully sent bit are used to evaluate the efficiency of the recommended solutions. An extensive investigation into analysis and simulation is carried out in the case of Poisson and self-similar. © 2023 IEEE.

关键词： Quality of service

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Enhancing Heart Disease Prediction: A Novel Deep Learning Approach with Artificial Neural Networks

Enhancing Heart Disease Prediction: A Novel Deep Learning Ap...

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Sustainable Computing and Smart Systems (ICSCSS), International Conference on

作者： Baby Shamini P Jemi Gold P K. mahalakshmi Shanker mC Sterlin Rani Devakadacham Sruthi S Department of Computer Science and Engineering R.M.K Engineering College Department of Computer Science and Engineering RMK College of Engineering and Technology Department of Computer Science and Engineering Vel Tech High Tech Dr.Rangarajan Dr.Sakunthala Engineering College Department of Electronics and Communication Engineering Saveetha school of Engineering Saveetha Institute of Medical And Technical Science (SIMATS) Chennai Department of Computer Science and Engineering R.M.D Engineering College

ISBN: (数字)9798350379990

ISBN: (纸本)9798350391558

The crucial role of the heart in all living beings necessitates a high degree of accuracy in diagnosing and predicting heart disease. Even minor errors can lead to serious consequences, highlighting the importance of developing efficient methods for heart disease prediction. Deep learning techniques offer a promising approach in this regard. Early identification of cardiac disease markers is vital for clinical decision-making and paves way for further diagnostic evaluation. However, traditional manual analysis is time-consuming and laborious, especially when dealing with large datasets. Here, this study proposes a novel model using deep learning techniques to achieve rapid and effective heart disease diagnosis based on various physiological markers. The proposed model demonstrates exceptional performance on both training and testing data. We employ Artificial Neural Networks (ANN) to assess the model's efficacy in accurately predicting the presence or absence of heart disease. The proposed approach offers several advantages, including high success rates, superior performance and accuracy compared to traditional methods like Random Forest and SVm, flexibility, and adaptability.

关键词： Heart Deep learning Adaptation models Accuracy Artificial neural networks Predictive models Prediction algorithms

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Big Data Analytics for Smart meter Data in Power Systems 9

Big Data Analytics for Smart Meter Data in Power Systems

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9th International Conference on science, technology, engineering and mathematics, ICONSTEm 2024

作者： Banu, E. Afreen Lavanya, R. Sandhiya, R. Vijayakumar, Bondu Kavitha, K. Vibhakar, Chirag Department of Computer Science And Engineering Chennai Tamil Nadu Thandalam602105 India Vel Tech High Tech Dr. Rangarajan Dr. Sakunthala Engineering College Department of Informtion Technology Chennai Tamil Nadu Avadi600062 India R M K College of Engineering And Technology Department of Computer Science And Engineering Tamil Nadu Puduvoyal601206 India Madanapalle Institute of Technology & Science Department of Electrical & Electronics Engineering Andhra Pradesh Madanapalle517325 India Annamalai University Department of Electrical & Electronics Engineering Tamil Nadu 608002 India Gujarat Power Engineering & Research Institute Gujarat Technological University Department of Electrical Engineering Gujarat Ahmedabad382710 India

ISBN: (纸本)9798350365092

The integration of big data analytics in power systems has surfaced as a promising approach to influence smart cadence data for perfecting functional effectiveness, enhancing grid trustability, and enabling demand- side operation. This paper explores the operation of big data analytics ways to reuse, dissect, and excerpt precious perceptivity from smart cadence data in power systems. By employing the vast quantum of data generated by smart measures, serviceability can gain a deeper understanding of client energy consumption patterns, identify anomalies, and optimize grid operations in real- time. The paper discusses colorful big data analytics methodologies, including machine literacy algorithms, data mining ways, and prophetic analytics models, applied to smart cadence data for cargo soothsaying, fault discovery, and energy optimization. Through a comprehensive review of being literature and case studies, this exploration elucidates the implicit benefits, challenges, and unborn directions of big data analytics for smart cadence data in power systems, offering precious perceptivity for experimenters, interpreters, and policymakers in the energy sector. © 2024 IEEE.

关键词： Big data

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LDCP: A Novel Approach to Predict Fake Reviews in Online Social Network by using Learning based Data Classification Principle

LDCP: A Novel Approach to Predict Fake Reviews in Online Soc...

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2024 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems, ICSES 2024

作者： mercy, S.Sudha Venkatesan, S. Bharaneedharan, m. Nayagan, S. Venkateswarlu, L. Padmaja, Nimmagadda R.M.K. Engineering College Department of Computer Science and Design Chennai India Qis College of Engineering and Technology Department of Electrical and Electronics Engineering AndhraPradesh Ongole India S.A. Engineering College Department of Ece Chennai India Vel Tech High Tech Dr. Rangarajan Dr. Sakunthala Engineering College Department of Cse Chennai India Qis College of Engineering and -Technology Department of Electrical and Electronics Engineering Andhra Pradesh Ongole India Department of Ece Tirupati India

ISBN: (纸本)9798331543617

The proliferation of online reviews has significantly impacted consumer decision-making processes, making the detection of fake reviews crucial for maintaining trust in e-commerce platforms. This study presents a novel approach for detecting fake reviews using machine learning (mL) and deep learning (DL) models, with a focus on achieving high accuracy through advanced feature extraction techniques and data augmentation. Various models, including classical mL algorithms such as Support Vector machine (SVm) and Random Forest, alongside DL models like Convolutional Neural Networks (CNN), Bidirectional Long Short-Term memory (BiLSTm), and Cascaded multi-Layer Perceptron (mLP), were evaluated. Feature extraction methods like TF-IDF, GloVe, and combined approaches were used to enhance performance. The DL model RoBERTa achieved the highest accuracy of 99.5%, while Cascaded mLP reached 99.2%. Classical models like Random Forest and SVm also performed well, achieving accuracies of 97.1 % and 96.8%, respectively. Data augmentation techniques, such as back-translation, further improved the models' ability to handle imbalanced datasets. The results demonstrate that combining DL architectures with advanced feature extraction leads to superior performance in fake review detection. © 2024 IEEE.

关键词： Adversarial machine learning

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An Enhanced Traffic Incident Detection using Factor Analysis and Weighted Random Forest Algorithm 5

An Enhanced Traffic Incident Detection using Factor Analysis...

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5th International Conference on IoT Based Control Networks and Intelligent Systems, ICICNIS 2024

作者： Kanna, P. Rajesh Vanithamani, S. Karunakaran, P. Pandiaraja, P. Tamilarasi, N. Nithin, P. Bannari Amman Institute of Technology Department of Computer Science and Engineering Tamil Nadu Erode India M. Kumarasamy College of Engineering Department of Master of Computer Applications Tamil Nadu Karur India Nandha Engineering College Department of Artificial Intelligence and Data Science Tamil Nadu Erode India Vel Tech Rangarajan Dr. Sagunthala Department of Computer Science and Engineering R&D Institute of Science and Technology Tamil Nadu Chennai India Nehru Institute of Technology Department of Electronics and Communication Engineering Tamil Nadu Coimbatore India Bannari Amman Institute of Technology Department of Artificial Intelligence and Machine Learning Tamil Nadu Erode India

ISBN: (纸本)9798331518097

Efficient and precise traffic incident detection is essential to reduce casualties and property damage. To address the issue of unbalanced event data, this work offers a novel methodology known as FA-WRF (Factor Analysis and Weighted Random Forest). This approach combines dimensionality reduction through factor analysis with classification using weighted random forests, data preparation through the Synthetic minority Over-Sampling technique (SmOTE), and data preparation with SmOTE. The included feature of severity detection is highlighted, as are the performance metrics of the FA-WRF model using well-established metrics such as detection level rate, number of false count alarm rate, classification type rate, and area specification under the receiver operating characteristic curve (AUC). The superiority of the FA-WRF model is illustrated using real-world expressway traffic data represented by imbalanced incidents through thorough comparisons with various machine learning-based algorithms such as Support Vector machine, k-nearest neighbours algorithms, Logistic Regression algorithms, and decision-making trees. In addition to advancing incident detection, this technique has encouraging prospects for enhancing traffic management procedures and well-informed decision-making procedures in transportation networks. © 2024 IEEE.

关键词： Dimensionality reduction

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A Superficial Learning Strategy to Identify Email Spams Based on Hybrid Data Filtration and Classification methodology

A Superficial Learning Strategy to Identify Email Spams Base...

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2024 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems, ICSES 2024

作者： Alekhya, A. Tagore Kumar, P. Bharaneedharan, m. Kavitha, S. Geetha, C. Thyagarajan, C. Qis College of Engineering and Technology Department of Electrical and Electronics Engineering Andhra Pradesh Ongole India Qis College of Engineering and Technology Department of Mechanical Engineering AndhraPradesh Ongole India S.A. Engineering College Department of Ece Chennai India Vel Tech High Tech Dr. Rangarajan Dr. Sakunthala Engineering College Department of Cse Chennai India R.M.K. Engineering College Chennai Department of Computer Science and Engineering Chennai India Panimalar Engineering College Department of Cse Chennai India

ISBN: (纸本)9798331543617

Spam email detection is a critical task in improving email security, especially with the increasing volume of spam emails that threaten users' privacy and productivity. In this study, we propose a hybrid approach combining Gated Recurrent Units (GRU) and Support Vector machine (SVm) to identify spam emails effectively. The GRU component captures temporal dependencies in the email text, while SVm classifies the extracted features into spam or non-spam categories. The model was evaluated using various performance metrics, including accuracy, precision, recall, and F1-score, and compared with other models like standalone GRU, SVm, CNN, and Naive Bayes. The proposed hybrid GRU-SVm model achieved a superior accuracy of 96.8%, outperforming GRU (93.2%), SVm (91.5%), CNN (94.1%), and Naive Bayes (89.4%). Additionally, the model demonstrated a high true positive rate (98.2%) and a low false negative rate (1.8%) on a spam-heavy dataset, indicating its robustness in identifying spam emails. The results suggest that the hybrid model can significantly enhance spam email detection systems by improving classification accuracy and reducing false positives. © 2024 IEEE.

关键词： Support vector machines

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Diagnostic Framework for mumps Based on Deep Learning with Enhanced mayfly Optimization Algorithm 1

Diagnostic Framework for Mumps Based on Deep Learning with E...

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1st IEEE International Conference on Ambient Intelligence, Knowledge Informatics and Industrial Electronics, AIKIIE 2023

作者： mercy, S. Sudha madhavi, S. Keerthika, A. Prathima, Ch Prabhu, R.T. R.M.K Engineering College Department of Computer Science & Design Chennai India Vel Tech Multi Tech Dr.Rangarajan Dr Sakunthala Engineering College Department of It Chennai India Aarupadai Veedu Institute of Technology Department of Ece Chennai India Department of Data Science Tirupati India Saveetha School of Engineering Simats Department of Ece Chennai India

ISBN: (纸本)9798350316469

The mumps virus causes a very infectious sickness. The enlargement of the parotid salivary glands can be quite uncomfortable. mumps treatment centers on symptom management. The illness must take its natural course. While most people experience very moderate symptoms, some develop severe consequences. The mumps virus can be avoided with the use of the mmR vaccination. In this research, we employ a convolutional neural network (CNN) technique to better automate mumps detection. In order to automate mumps detection, the authors of this research suggest a convolutional neural network (CNN) trained using XGBoost Classifier data. Incorporating machine learning (mL) technique comparisons. A mumps dataset consisting of 50×50 RGB picture patches served as the basis for all the models. Quantitative findings from each approach were put through validation testing based on their respective effectiveness metrics. To improve the efficiency of the model, the mayfly Optimization Algorithm is implemented. Results show that the suggested method is effective, with 97% accuracy, suggesting that it might help decrease diagnostic errors caused by human error. And the suggested system we've developed is so precise that it outperforms the CNN model, which only gets around 92% of the time. Consequently, the suggested method is 5% more accurate than the CNN model. © 2023 IEEE.

关键词： Convolutional Neural Network mayfly Optimization Algorithm mumps Classification XGBoost Classifier

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Exploring Augmented Reality Triage Tools to Support mass Casualty Incidents 66th

Exploring Augmented Reality Triage Tools to Support Mass Cas...

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66th International Annual meeting of the Human Factors and Ergonomics Society, HFES 2022

作者： Nelson, Cassidy Rae Conwell, Joseph Lally, Samuel Cohn, madelyn Tanous, Kyle moats, Jason Gabbard, Joseph L. Department of Industrial and Systems Engineering Virginia Tech United States Department of Mathematics Virginia Tech United States Electrical and Computer Engineering Virginia Tech United States School of Visual Arts Virginia Tech United States Department of Computer Science Virginia Tech United States Texas A and M Engineering Extension Service Texas A and M University United States

ARTTS is a head-worn Augmented Reality (AR) Triage Tool Suite containing an initial sorting tool, virtual assessment tool, and virtual triage tag to assist first responders in mass casualty incidents. The initial sorting tool prompts stunned responders through first-wave tasks to aid recalibration from shock to triage. The virtual assessment tool provides novice responders, potentially confused by the chaos, with a walkthrough of the SALT triage flowchart. Finally, current emergency medical triage processes leverage static paper tags susceptible to loss or illegible damage. ARTTS’ virtual triage tags are dynamic, can be updated automatically with vital sensors or manually through responder interaction, and employ user interface emergent features based on individual patient conditions. This paper describes the UX process used to develop ARTTS. © 2022 by Human Factors and Ergonomics Society. All rights reserved.

关键词： Augmented reality

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Cloud-Based AI Robotics for Precision Pruning of Fruit Trees to Improve Sustainable Farming 3

Cloud-Based AI Robotics for Precision Pruning of Fruit Trees...

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3rd International Conference on Intelligent Systems, Advanced Computing, and Communication, ISACC 2025

作者： Ilambirai, R.C. Barkathulla, A. mahalakshmi, G. Babu, J. Jagan Prabu, K. Srinivasan, C. SRM Institute of Science and Technology Department of Electrical and Electronics Engineering Tamil Nadu Chennai India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology School of Computing Department of Computer Science and Engineering Tamil Nadu Chennai India Chennai Institute of Technology Department of Computer Science and Engineering Tamil Nadu Chennai India R.M.D Engineering College Department of Electronics and Communication Engineering Tamil Nadu Chennai India Gojan School of Business and Technology Department of Electronics and Communication Engineering Tamil Nadu Chennai India Saveetha University Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798331523893

The crucial agricultural practice of precision pruning fruit trees greatly impacts fruit production yield and quality. Traditional pruning techniques take a lot of time and money because they need skilled workers and are labor-intensive. It suggests a novel cloud-based AI robotics system intended to precisely prune fruit trees to address these issues. This system uses cutting-edge robotics fitted with Convolutional Neural Networks (CNNs) to carry out precise and effective tasks related to pruning. Cloud computing integration makes it possible to process and make real-time decisions, improving the system's flexibility in responding to changing tree conditions and pruning needs. The system's primary parts are robotic arms for accurate cuts, various sensors for analyzing tree structures, and CNN models for maximizing pruning techniques. Early field trials show noticeable gains in operational efficiency and pruning accuracy compared to conventional techniques. This study demonstrates how cloud-integrated AI robotics can transform agricultural practices and improve fruit farming's sustainability and productivity. © 2025 IEEE.

关键词： Robotic arms

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Data Visualization for Corona Patients Globally using Real-Time APIs 2

Data Visualization for Corona Patients Globally using Real-T...

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2nd International Conference on Sustainable Computing and Data Communication Systems, ICSCDS 2023

作者： Durai, S. Iqbal, m. mohamed Kumar, S Niresh Alagappan, Chockalingam Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Avadi Dept. of Cse Chennai India VIT-AP University School of Computer Science and Engineering Amaravati India Dhanalakshmi Srinivasan College of Engineering and Technology Department of Computer Science and Engineering Mamallapuram India M.Kumarasamy College of Engineering Department of Electrical and Electronics Engineering Tamilnadu Karur639113 India

ISBN: (纸本)9781665491990

All the nations' administrative units are concerned about the COVID-19 outbreak in different regions of the world. India is also trying to control the spread of the virus with strict measures and has managed to slow down its growth rate. The administration of each country faces the challenge of maintaining records of corona patients. To address this challenge, this work builds a website from scratch using real-time APIs for data visualization. The website provides information on the number of active cases, death cases, recovery cases, and total cases of COVID-19 patients in each country. The data can be visualized using graphs, making it easier to compare the situation in different countries. The website allows for monitoring which country has a higher number of deaths, patients, favorable recovery rates, and a large number of active cases. The COVID-19 status regarding patients can be analyzed through graphical representation using real-time data. © 2023 IEEE.

关键词： COVID-19

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