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Cross-Layer Optimization Strategies for Energy-Efficient and Reliable Machine-Type Communication in LPWANs 2

Cross-Layer Optimization Strategies for Energy-Efficient and...

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2nd IEEE International Conference on Emerging Research in Computational science, ICERCS 2024

作者： Amanullah, M. Manoranjini, J. Sathiya, V. Helen Prabbu Sankar, P. Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Computer Science & Engineering Chennai India Rajalakshmi Engineering Department of Artificial Intelligence and Data Science Chennai India Panimalar Engineering College Department of Computer Science & Engineering Chennai India

ISBN: (纸本)9798331534950

In the massive Machine-Type Communication (mMTC), the exponential growth of Internet of Things (IoT) devices over Low Power Wide Area Networks (LPWANs) presents substantial issues regarding energy efficiency and stability. The existing frameworks frequently fails in the the intricate relationships among the MAC-layer and physical parameters. To enhanced cross-layer evaluation methodology that integrates adaptive data rate (ADR) optimization, real-time energy consumption monitoring, and dynamic downlink message handling to address the problems. To maintain stability and performance with the network situations, the framework makes use of a Control-Lyapunov function. To enable effective resource allocation uses the Successive Convex Approximation that improve the effects of various transmission systems under quasi-static and dynamic channel conditions. To verify the cross-layer interactions and it impacts the energy efficiency, the proposed method begins with the data collection and it moves to payload size and packet length, are regularly changed to examines the affect throughput and energy consumption. The findings show the high-density node situations, energy consumption that reduced while retaining reliable communication. Furthermore, the results show the interdependencies among network parameters, highlighting the necessity of an integrated strategy. This framework helps to ensure the mMTC scenarios that optimized by improving the long-term viability and dependability of IoT networks. © 2024 IEEE.

关键词： Resource allocation

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Innovative Approach for Early Heart Disease Prediction: Gaussian Mixture Model and K-Nearest Neighbour 3

Innovative Approach for Early Heart Disease Prediction: Gaus...

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3rd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Surendran, R. Anandaraj, B. Deepa, P. Mageshwari, M. Balamurugan, K.S. Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Department of Computer Science and Engineering Madnapalle Institute of Technology & Science Andhra Pradesh Madanapalle India Department of Computer Science and Engineering Panimalar Engineering College Chennai India Department of Computer science and engineering Karpaga Vinayaga College of Engineering and Technology Tamil Nadu Chengalpattu India Department of Electronics and communication engineering Karpaga Vinayaga College of Engineering and Technology Tamil Nadu Chengalpattu India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527549

An Electrocardiogram (ECG) contributes significantly to early diagnosis and classification of heart diseases, arrhythmia which means irregular heart rate. Regrettably, the process became difficult due to asymmetric and minimal amplitude from recordings of ECG and cannot do the extraction from multiple signals. By using machine learning technology, it can classify and extract multiple samples, but cannot extract the data features from complex data structures. To overcome the limitations, previously introduced metaheuristic optimization (MHO) to improve the classification and extraction. Proposed a method which supports and K-Nearest Neighbour (KNN) Gaussian Mixture Model (GMM) algorithms for determine the abnormalities, predicting cardiac events, and improve diagnostic accuracy. As a result, this can help us to get results more quickly and accurately with sensitivity 99.7% and accuracy 99.2%, for helping healthcare professionals in diagnosing arrythmias, myocardial infarction and any other heart conditions. © 2025 IEEE.

关键词： Adversarial machine learning

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Key Observation to Prevent IP Spoofing in DDoS Attack on Cloud Environment 8th

Key Observation to Prevent IP Spoofing in DDoS Attack on Clo...

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8th International Conference on Soft Computing: Theories and Applications, SoCTA 2023

作者： Sunitha, T. Vijayashanthi, V. Navaneethakrishan, M. Mohanaprakash, T.A. Ashwin, S. Harish, T.R. Stanes, Emmanuel A. Department of Computer Science and Engineering P. B. College of Engineering Chennai India Chennai India Department of Computer Science and Engineering St. Joseph College of Engineering Sriperumbudur Chennai India Department of Computer Science and Engineering Panimalar Engineeering College Varadharajapuram Poonamallee Chennai India Department of Computer Science and Engineering Panimalar Insitute of Technology Varadharajapuram Poonamallee Chennai India

ISBN: (纸本)9789811998577

The utilization of Cloud to put together and execute Distributed Denial of Service (DDoS) assaults is well-known among programmers in the current scenario. In the existing framework, networks can use Border Gateway Protocol (BGP) declaration setups to change the internet courses and an assortment of other sources in a precise manner. In this paper investigates in detail how an organisation can regulate this data source and the connection where the traffic enters an organisation. In this proposed work, PKI-Public key validation techniques with Advanced Encryption Standard were utilized (AES) and technique works on the basis that the courses are influenced by network control, thus the organization getting the caricature traffic affects it when it gets traffic, rather than depending on switches that are not influenced at all. The proposed method which is does not correspond to the existing follow back approaches requires no progressions to send hardware or participation from different organizations. This procedure work best when the caricature traffic begins from not many sources, as is normal in intensification DDoS assaults and also the experimental findings demonstrated that the approach has a 99.3% accuracy rate for detecting internal and external low/high rate faked DDoS assaults and offers higher performance. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Network security

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Game Therapy for Specially-Abled Individuals with PPO Reinforcement Learning in VR-based Educational Games 5

Game Therapy for Specially-Abled Individuals with PPO Reinfo...

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5th International Conference on Data Intelligence and Cognitive Informatics, ICDICI 2024

作者： Joypriyanka, M. Surendran, R. Sathish Kumar, P.J. Sivasangari, A. Saveetha Institute of Medical and Techinical Sciences Saveetha School of Engineering Department of Computer Science and Engineering Chennai India Panimalar Engineering College Department of Computer Science and Engineering Chennai India Sathyabama Institue of Science and Technology School of Computing Chennai India

ISBN: (数字)9798350389609

ISBN: (纸本)9798350389609

This research study presents a new application of reinforcement learning (RL), specifically Proximal Policy Optimization (PPO), in creating immersive virtual reality (VR) games aimed at aiding skill acquisition and emotional regulation for individuals with anger management difficulties. People with Down syndrome, Autism Spectrum Disorder, Cerebral Palsy (CP), Intellectual Disabilities (ID), and Stroke Survivors are mainly considered. The study examines how PPO-trained agents improve cognitive, social, and emotional regulation. Techniques include thorough hyperparameter optimization for the PPO algorithm with two sets. Iterative agent training in Unity and AWS Sagemaker uses cloud computing resources for parallelized training. Emotional regulation and positive behaviour change are assessed empirically and through user studies. Virtual reality (VR) cooperative RL games may offer comprehensive treatments for anger management, according to the results, which show promising gains in emotional regulation and teamwork. By enhancing interest and effectiveness in satisfying emotional regulation demands, the study demonstrates that AI-driven therapies and "gamified therapy,"which mixes game components into therapy, can revolutionize mental health. © 2024 IEEE.

关键词： Reinforcement learning

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Evaluation of Logistic Regression and Decision Tree Models for Lung Cancer Prediction Using Survey Data

Evaluation of Logistic Regression and Decision Tree Models f...

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2024 IEEE International Conference on Electronic Systems and Intelligent Computing, ICESIC 2024

作者： Narayana, K.E. Jayashree, K. ThavaVinu, A. Sindhuja, M. Jaeyalakshmi, M. Rajalakshmi Engineering College Department of Information Technology Tamilnadu Chennai India Panimalar Engineering College Department of Artificial Intelligence and Data Science Tamilnadu Chennai India Prathyusha Engineering College Department of Information Technology Chennai India Acharya University Department of Computer Science and Engineering Karakul Uzbekistan Rajalakshmi Engineering College Department Of Computer Science And Engineering Chennai India

ISBN: (纸本)9798331522988

Lung cancer is one of the most deadly and ubiquitous forms of cancer globally. Early detection can make a significant difference in survival rates, prognosis, etc. Background The present study compares the performance of logistic regression and decision trees using a survey-based dataset for predicting lung cancer. This dataset has a total of 284 cases with 16 features;behavioral patterns, symptomatic data, and demographic details. Logistic regression is a binary classification algorithm that uses input features to model the probability of lung cancer presence given these input features using for example a sigmoid function. Using Decision Trees can identify significant fields in a dataset while reducing Gini Impurity for models that categorize patients based on feature values. The performance of both models is evaluated according to accuracy, F1-score, precision, and recall measures. From the preliminary results, it looks like the Decision Tree works a little better than the logistic regression model which gets an accuracy of 94%, but this one does with more accuracy (97%). This result emphasizes the inclusion of logistic regression within this context, as it may be able to predict lung cancer with greater accuracy. Additionally, to ensure the generalization and robustness of the model, further investigation and trials will focus on optimizing performance through ensemble approaches, hyperparameter tuning, and cross-validation. This comparative analysis demonstrates the effectiveness of machine learning techniques in developing prediction models for early lung cancer detection, an important step toward a quick and appropriate intervention. © 2024 IEEE.

关键词： Lung cancer

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Enhancing Data Security and Efficiency: A Hybrid Cloud Approach to Secure Authorized Deduplication

Enhancing Data Security and Efficiency: A Hybrid Cloud Appro...

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2024 International Conference on System, Computation, Automation and Networking, ICSCAN 2024

作者： Suresh Kumar, K. Preethi, S.T. Mahalakshmi, P. Hariharasudhan, S. Chennai Institute of Technology Department of Computer Science and Engineering Tamil Nadu Chennai India S A Engineering College Department of Computer Science and Engineering Tamil Nadu Chennai India Panimalar Engineering College Department of Computer Science and Engineering Tamil Nadu Chennai India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Computer Science and Engineering India

ISBN: (数字)9798331510022

ISBN: (纸本)9798331510022

As cloud services grow, data deduplication has become crucial for reducing storage costs and improving resource utilization by eliminating duplicate data. However, maintaining data security and user privacy in deduplication-enabled environments remains a significant challenge due to increasing risks of unauthorized access and data breaches. To address this, convergent encryption has been proposed, allowing data encryption before outsourcing while supporting deduplication. This paper introduces a hybrid cloud approach for secure authorized deduplication, combining public and private clouds to optimize efficiency, data integrity, and confidentiality. Our solution uses a dual-layer authentication and encryption mechanism, where key generation is tied to user privileges and file ownership. The control plane is managed by the private cloud, handling sensitive metadata and keys, while the public cloud stores deduplicated data to ensure cost efficiency. We further introduce differential user privileges in duplicate checks, a feature lacking in traditional systems. To validate our approach, we developed a prototype and conducted testbed experiments. The results show substantial reductions in storage overhead and latency, with minimal performance impact. Security analysis confirms that our scheme meets the required security guarantees, providing a scalable and efficient solution for cloud deduplication while addressing privacy and performance concerns. © 2024 IEEE.

关键词： Data integrity

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Development of Cancer Drug Targets and Potential Anti-Cancer Agents using Deep Learning and Screening Processes 5

Development of Cancer Drug Targets and Potential Anti-Cancer...

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5th International Conference on Data Intelligence and Cognitive Informatics, ICDICI 2024

作者： Balamurugan, K.S. Ramesh, T. Sathish Kumar, P.J. Surendran, R. Tamilvizhi, T. Karpaga Vinayaga College of Engineering and Technology Department of Electronics and Communication Engineering Tamil Nadu Chengalpattu India R.M.K Engineering College Department of Computer Science & Engineering Tamil Nadu Chennai India Panimalar Engineering College Department of Computer Science and Engineering Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350389609

Cancer is one of the global health issues with various complications that lead to fatal. A novel approach is initiated using deep learning techniques. They help in the analysis of complex biological structures which helps to provide potential anti-cancer drugs. They help in the screening of emergent compounds through therapeutic potential. The first stage in the proposed system involves the development of comprehensive datasets that include multi-omics data and protein-protein interaction network structures. They proceeded with preprocessing and feature extraction that led to the contribution of cancer progression stages. The deep learning model involves learning from cancer-associated genes and pathways. They help in the prediction of protein targets that are decisive for cancer development. In the subsequent stage, the deep learning model helps in employing a screen for the collection of emerging compounds from diverse platforms ranging from synthetic libraries. Another stage involves the validation process that leads to in vivo and in vitro experiments. They are proceeded with cellular assays. Compound efficacy is obtained through xenograft and mouse models followed by molecular biology techniques such as protein profiling and gene analysis. Thus the system provides a cumulative approach in obtaining anti-cancer drug targets. © 2024 IEEE.

关键词： Mammals

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Recognizing Human Emotions by using Lazy classifier with selected algorithm 9

Recognizing Human Emotions by using Lazy classifier with sel...

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9th IEEE International Conference on Smart Structures and Systems, ICSSS 2023

作者： Loganathan, V. Mohanaprakash, T.A. Bhavani, R. Savija, J. Saveetha Engineering College Department of Computer Science Engineering Chennai India Panimalar Engineering College Department of Computer Science and Engineering Chennai India St Joseph College of Engineering Department of It Chennai India Velammal Engineering College Department of Ai & Ds Chennai India

ISBN: (纸本)9798350384208

Music predates language, and humans are born with the ability to communicate themselves via music. Music has played a vital part in human history, and it is incorporated into many facets of human life Music is showing tremendously significant role in people's production and life. This research is focusing a n identification of human emotions like happiness, sad, relaxation, tension on Turkish music emotion dataset through inductive learning approach. This work is to focus on the various lazy learning approaches are implemented to recognize human emotions while listening music. The K=3 with Ball Tree NN search model acquires highest performance which as accuracy 85.75%, kappa 0.66, precision 0.86, F-Measure0.86, recall 0.86, Matthews Correlation Coefficient 0.65 which generated the best outcomes based on their efficacy in comparison to other models. The K=3 with Ball Tree model produces best result for recognizing human emotions compare with other models. © 2023 IEEE.

关键词： Trees (mathematics)

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Efficient Plant Disease Detection using K-Means clustering and DenseNet-based Classification 3

Efficient Plant Disease Detection using K-Means clustering a...

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3rd International Conference on Electronics and Renewable Systems, ICEARS 2025

作者： Yashodha, G. Rajakumar, B. Krishnamoorthy, M. Surendran, R. Kg College of Arts and Science Department of Computer Technology Coimbatore641035 India J.N.N. Institute of Engineering Department of Artificial Intelligence and Data Science Chennai601102 India Panimalar Engineering College Department of Computer Science and Engineering Chennai600 123 India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Chennai India

ISBN: (数字)9798331509675

ISBN: (纸本)9798331509675

Early detection is vital in crop health, yet improvement in productivity faces time-consuming and inefficient challenges due to traditional manual techniques of plant disease detection. Thus, we present a deep learning-based novel framework with a method called k-means clustering with DenseNet Classification denoted by Kmc-Densenet. The approach streamlines this plant disease detection process involving the overall pipeline of extensive preprocessing, segmentation, and classification. In the preprocessing stage, we employ contrast enhancement techniques to improve the visibility of disease symptoms, ensuring clearer and more consistent leaf images for further analysis. For the segmentation task, we leverage a k-means clustering which effectively isolates regions of interest, distinguishing between healthy and diseased areas of the plant leaf. In the classification stage, we use a DenseNet variant of CNN, known for its feature reuse capabilities, to classify the plant diseases with high accuracy. The DenseNet model helps overcome the challenges posed by class imbalance, enhancing the overall detection performance. Finally the parameters of the Densenet model is fine-tuned using the Lyrebird Optimization Algorithm. The proposed Kmc-Densenet method is evaluated on the publicly available Plant village dataset, demonstrating superior performance in both segmentation and classification tasks. This framework offers a more efficient and reliable solution for early plant disease detection in precision agriculture, with potential applications in improving crop yield and quality. © 2025 IEEE.

关键词： K-means clustering

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Big Data Analytics on Thyroid: A Review on Algorithms, Attributes and Future 1

Big Data Analytics on Thyroid: A Review on Algorithms, Attri...

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1st International Conference on Computational science and Technology, ICCST 2022

作者： Priya, K. Hema Valarmathi, K. Panimalar Engineering College Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9781665476553

The healthcare industry struggles with diagnosing health disorders and finding the right treatment for diseases at an earlier stage. Many diseases can be detected early and treated well. Thyroid malfunction is one such disease. One of the most important organs in both men and women is the thyroid gland, a vascular gland. This gland secretes the hormones that help to control the body's metabolism. Hyperthyroidism and hypothyroidism are two types of thyroid ailments. Clinical examination and numerous blood tests are necessary for the traditional methods of detecting thyroid disorders. The main aim is to more precisely identify thyroid dysfunction at an early stage. This research makes a study of a variety of classification approaches, including Decision Tree (DT), Support Vector Machine (SVM), Random Forest (RF), Naive Bayes (NB), Logistic Regression (LR), Linear Discriminant Analysis (LDA), K Nearest Neighbour (KNN) and Multi-Layer Perceptron (MLP), to predict thyroid illness as early as possible. This study majorly focuses on the performance metric accuracy observed from various Machine Learning (ML) classification Algorithms. © 2022 IEEE.

关键词： Diseases

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