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2nd International Conference on Automation, computing and Renewable Systems, ICACRS 2023

作者： Radhika, P. Kamalakkannan, S. Kavitha, P. Department of Computer Science School of Computing Sciences Vels Institute of Science Technology & Advanced Studies Pallavaram Chennai India Department of Information Technology School of Computing Sciences Vels Institute of Science Technology & Advanced Studies Pallavaram Chennai India Department of Computer Applications School of Computing Sciences Vels Institute of Science Technology & Advanced Studies Pallavaram Chennai India

ISBN: (纸本)9798350340235

Cloud computing (CC) is a massive breakthrough in Information Technology (IT) that provides end users to access flexible andvirtualizedsources at affordable infrastructure cost and management. One of the most significant technologies in the big-data era is the CC Data Centres (DCs). The Distributed Denial of Service (DDoS) attacks are one of the most serious issues when it comes to the privacy of DC. DDoS attacks using Transmission Control Protocol (TCP) traffic are taken into consideration, which are becoming more prevalent but challenging to identify. The DDoS attack is the focus of this study, along with the technique used to prevent it and lessen the vulnerability of the big data server side. The system entails the delivery of packets in the form of DDoS attacks to cloud-based websites and even addresses the real-time prediction of software layer DDoS attacks using various Machine Learning (ML) and Deep Learning (DL) techniques. As a result, it stands apart among numerous hosts. Additionally, the objective of this paper was to offer a succinct introduction to attack detection approaches for early researchers working on cloud-based big data applications. As a result, these approaches are categorised according to how they function, their strengths and shortcomings are reviewed, and finally, several research papers that used each method are examined. © 2023 IEEE.

关键词： Big Data Cloud computing Deep learning Distributed Denial of Service attacks Machine Learning

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An Implementation of Photovoltaic Solar Panel Monitoring System Based on Internet of Things 3

An Implementation of Photovoltaic Solar Panel Monitoring Sys...

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3rd IEEE International Conference on Distributed computing and Electrical Circuits and Electronics, ICDCECE 2024

作者： Saranya, J. Divya, V. School Of Computing Sciences Vels Institute Of Science Technology & Advanced Studies Chennai India School Of Computing Sciences Vels Institute Of Science Technology & Advanced Studies Department Of Information Technology Chennai India

ISBN: (纸本)9798350318609

The Internet of Things (IoT) is a contemporary technology that enhances user experience and adds intelligence to objects. Numerous applications have already embraced the Internet of Things. The Internet of Things (IoT) has greatly simplified and increased the accessibility of our lives. Society will be better equipped to safeguard future generations by reducing greenhouse gas emissions and ozone depletion as long as everyone uses renewable energy. These PV technologies are growing in popularity because they are so widely available, reasonably priced, and easy to install and maintain. Monitoring is required to determine the current condition of each solar photovoltaic cell in a solar panel. This monitoring is necessary to detect any defects in the solar cell and implement repair actions in addition to guaranteeing that the solar panel continues to function properly. In this study, an Internet of Things-based solar panel monitoring system is implemented, and the MPPT algorithm is used to track maximum power. © 2024 IEEE.

关键词： Liquid crystal displays

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Feature Extraction to Evaluate the Quality of Data Using Machine Learning Technique 3

Feature Extraction to Evaluate the Quality of Data Using Mac...

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3rd IEEE International Conference on Distributed computing and Electrical Circuits and Electronics, ICDCECE 2024

作者： Pratheesh, R. Divya, V. School Of Computing Sciences Vels Institute Of Science Technology & Advanced Studies Chennai India School Of Computing Sciences Vels Institute Of Science Technology & Advanced Studies Department Of Information Technology Chennai India

ISBN: (纸本)9798350318609

Data quality analysis is a crucial step in ensuring the reliability and accuracy of data used in machine learning models. The proposed approach utilized with machine learning algorithms to identify outliers in the data. Outliers can indicate errors are anomalies that might affect the quality of the dataset. Data Profiling is used to identify patterns and distributions. Missing Data Imputation predict the missing values. Data Validation and Cleansing, Data Duplication Detection, Inconsistency Detection are considered as the primary analysis to be done. The feature extraction process read the sample test dataset, pre-process the dataset by cleaning the data for data duplication, inconsistency through dynamic algorithms principal component analysis (PCA), Linear Discriminant Analysis (LDA) and independent component analysis (ICA) is considered here. The proposed feature extraction data is further opted for pattern validation through isolated forest algorithm. The performance of the system is evaluated using accuracy, precision, and Recall. The LDA model outperforms with accuracy of 99% for the given testset. © 2024 IEEE.

关键词： Feature extraction

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Data Collection and Preprocessing Management for Sugarcane Leaf Disease Identification using by Spatial Relation-Assisted Contrast Enhancement (SRCE) 2

Data Collection and Preprocessing Management for Sugarcane L...

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2nd IEEE International Conference on Integrated Intelligence and Communication Systems, ICIICS 2024

作者： Akhil, S. Durga, R. Department of Computer Science Vels Institute of Science Technology & Advanced Studies Chennai India

ISBN: (纸本)9798331504960

Background: Sugarcane, an essential commodity for the global sugar industry, is very vulnerable to several illnesses that significantly impact its output and quality. Methods: The study uses Spatial Relation Assisted Contrast Enhancement (SRCE) to enhance contrast in images of harmed sugarcane leaves. SRCE selectively enhances contrast, allowing for better differentiation between healthy and diseased areas. It focuses on local neighborhoods, highlighting critical symptoms while preserving leaf structure and texture. The enhanced images were analyzed using machine learning classifiers to assess their effectiveness in disease detection. Result: The results demonstrated that SRCE significantly improved the detection accuracy compared to traditional contrast enhancement techniques. This method aids in early disease identification and supports precision agriculture practices by enabling timely interventions. The results of SRCE of CII, LIF, SSIM, and PSNR values are evaluated. Conclusion: Our findings suggest that SRCE can be a valuable agricultural monitoring and management tool, ultimately contributing to increased sugarcane yield and sustainability. © 2024 IEEE.

关键词： CII disease LIF PSNR SRCE SSIM sugarcane leaf

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Predicting and Associating of Spectral Feature Nutritional Intake Level using Bayesian Kernel Machine Regression (BKMR) Method 2

Predicting and Associating of Spectral Feature Nutritional I...

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2nd IEEE International Conference on Integrated Intelligence and Communication Systems, ICIICS 2024

作者： Jeyanthi, P. Durga, R. Department of Computer Science Vels Institute of Science Technology & Advanced Studies Chennai India

ISBN: (纸本)9798331504960

Children's nutrition is an essential determinant of their development, so there is a need to forecast their food consumption adequately. This work presents a model for assessing children's nutritional intake levels from a machine-learning approach. The data cleaning step deals with missing values, outliers, and data inconsistencies to produce high-quality data for analysis from the dataset. Feature selection applies the Level-based Long Short-Term Memory (LLSTM) that captures temporal dependencies in the children's eating patterns. We use Bayesian Kernel Machine Regression (BKMR) for classification since it is not limited to linear relations between food intake and different factors that could influence them. Compared with the other traditional methods, the accuracy of the proposed method is 96.2%. The framework discussed here is located in the field of child nutrition, focusing mainly on the issue of meeting children's basic nutritional needs. This work proves that the probability of nutrition intake can be predicted by the proposed framework, which can help to make better recommendations on children's diet and, therefore, on their future health. © 2024 IEEE.

关键词： BKMR children nutrition data cleaning LLSTM nutritional intake levels

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Feasible Load Balancing for Webserver in Cloud Environment using Energy Efficient Maximal Support Priority Scheduling Approach 2

Feasible Load Balancing for Webserver in Cloud Environment u...

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2nd IEEE International Conference on Integrated Intelligence and Communication Systems, ICIICS 2024

作者： Rajasingh, V.M. Daniel Durga, R. Department of Computer Science Vels Institute of Science Technology & Advanced Studies Chennai India

ISBN: (纸本)9798331504960

Task management and energy efficiency have grown increasingly crucial in cloud computing environments. Effective job scheduling is essential to maximizing resource usage and overall efficiency in cloud computing. However, ensuring energy efficiency is a critical challenge in task scheduling. Traditional task-scheduling methods are often costly and require more adaptability and efficiency. To solve this problem, we propose an Energy Efficient Maximal Support Priority Scheduling Approach (EEMSPS), which schedules tasks in a virtual cloud environment. Furthermore, the Task Completion Time (TCT) algorithm is utilized to calculate the task length and time. Additionally, the Scheduling Based Artificial Bee Colony (SBABC) method is employed to organize the task and reduce data processing time. Lastly, the EEMSPS algorithm assesses the load balancing in the cloud. Additionally, energy efficiency, task execution time, and energy consumption are used to evaluate the proposed method's performance compared to traditional techniques. Therefore, the energy efficiency performance is 96.4% for task scheduling in the cloud. © 2024 IEEE.

关键词： Cloud platforms

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Cyber Attack Detection Using Deep Multi-agent Reinforcement Learning with Beth Dataset

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SN computer science 2025年第5期6卷 1-16页

作者： Manikandan, A. Rajan, S. Deepa Department of Computer Science and Engineering Vels Institute of Science Technology and Advanced Studies VISTAS Tamil Nadu Chennai India

The Internet of Things (IoT) transforms connectivity, enabling devices to share data seamlessly via the cloud or wireless networks. However, IoT systems remain vulnerable to sophisticated cyberattacks, and traditional detection methods often fail due to inadequate fine-tuning and ineffective feature extraction. This study addresses these challenges by proposing a novel cyberattack detection framework based on deep multi-agent reinforcement learning (MARL). The framework employs the deep deterministic policy gradient (DDPG) algorithm to enhance the adaptive capabilities of multiple agents within an IoT network environment. A newly curated dataset, BETH, is introduced, encompassing diverse cyberattack scenarios specifically designed for training and evaluating MARL-based systems. Through deep reinforcement learning, the framework autonomously learns to detect and mitigate known and emerging threats. To assess the robustness and generalizability of the proposed model, a fivefold cross-validation process was conducted. Experimental results reveal that the proposed approach achieves an exceptional detection accuracy of 99.95%, demonstrating its ability to adapt to dynamic attack patterns while minimizing error rates. The findings highlight the framework’s potential to improve network security significantly, advancing the field of intelligent, autonomous cyber defence for IoT systems. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Anomaly detection agents (ADA) BETH dataset Collaboration and communication buffer (CCB) Cyber-attack detection Deep deterministic policy gradient algorithm Internet of Things (IoT) Intrusion detection agents (IDA) Multi-agent reinforcement learning (MARL)

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Clinical Insight: Comparative Analysis of Deep Learning Models for Disease Prediction across Multifaceted Datasets 3

Clinical Insight: Comparative Analysis of Deep Learning Mode...

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3rd IEEE International Conference on Distributed computing and Electrical Circuits and Electronics, ICDCECE 2024

作者： Binu, Shiiu K. Shanthi, C. Vels Institute Of Science Technology & Advanced Studies Department Of Computer Science Chennai India

ISBN: (纸本)9798350318609

This research introduces an advanced model architecture for chronic disease detection, leveraging highly developed artificial intelligence algorithms. The prototype combines the Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) procedure, showcasing significant advancements in predictive capabilities. The proposed hybrid approach, integrating CNN and LSTM with forwarding selection, is adopted for disease presence prediction. Employing a tenfold cross-validation strategy and optimizing with 200 training cycles, the model demonstrates a meticulous training process. Learning rate control plays a pivotal role, and a rate of 0.01 is chosen for optimal learning. The model is applied to datasets related to cancer, heart, kidney, and liver diseases, among other diseases, with feature selection based on information gain. Seven cutting-edge classification algorithms-RF, SVM, KNN, CNN, and CNN-LSTM-are used in a thorough examination. The outcome showcases the superiority of the proposed CNN-LSTM model with notable accuracy rates-98% and 99%, respectively, for chronic kidney disease prediction. © 2024 IEEE.

关键词： Long short-term memory

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An Approach for Optimal Preference of Providers Offering Cloud Services Using Decision Making involving Multiple Criteria 3

An Approach for Optimal Preference of Providers Offering Clo...

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3rd IEEE International Conference on Distributed computing and Electrical Circuits and Electronics, ICDCECE 2024

作者： Krithika, M. Akila, A. Vels Institute Of Science Technology And Advanced Studies Department Of Computer Science Chennai India

ISBN: (纸本)9798350318609

Cloud computing provides on-demand delivery of various services over the Internet and used by many users now days. Numerous IT organization have engaged themselves in providing Cloud service and choosing the best CSP has become great challenge for users, which could be solved by incorporating ranking system. In ranking, multiple criteria should be analyzed for each CSP, thus it comes under MCDM problem where multiple criteria is analyzed for multiple providers to rank the CSPs accordingly. This paper incorporates two proposed MCDM techniques Aggregative and Accumulative Stacked Ordering Model(ASOM) and Absolute Aggregative Model for Stacked Divergence(AAMSD) and normalization technique Stacked Matrix Normalization(SMN). We have compared the proposed approach with the existing MCDM approaches Weighted Sum Model (WSM), Weighted Product Model (WPM) and Technique for Order of Preferences by Similarity to Ideal Solution (TOPSIS). The results clearly show that the proposed approach outperforms the existing approaches in-terms of computational complexity and consistency. © 2024 IEEE.

关键词： Decision making

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Enhanced Multi-Layer Bat Optimization(EMBO) Approach for Data Acquisition and Security Management in Web Platform 2

Enhanced Multi-Layer Bat Optimization(EMBO) Approach for Dat...

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2nd IEEE International Conference on Integrated Intelligence and Communication Systems, ICIICS 2024

作者： Siva, V.R. Durga, R. Department of Computer Science Vels Institute of Science Technology & Advanced Studies Chennai India

ISBN: (纸本)9798331504960

Intrusion Detection Systems (IDS) have grown to be essential to network and computer security. However, it complicates the training phase and makes it time-consuming to detect vulnerabilities in data sharing on the network. To solve this problem, we introduced a Multi-Layer Perceptron Neural Network (MLPNN) technique in data science to classify malicious and non-malicious network traffic. In addition, we preprocess the data by normalizing the mean original value using the Z-score Min-Max Scaling (ZSMMS) technique. In addition, we select the best feature by analyzing the weight and bias values with the Improved Bat Optimization (IBO) algorithm. After that, we propose the MLPNN technique to classify network traffic and improve accuracy. Finally, the Optimal Padding advanced Encryption Standard (OPAES) algorithm will be implemented to enhance the security of the website. Additionally, by producing an IDS model and using metrics such as accuracy, recall, time complexity, and security performance, the website's security performance can be improved to 95.7%. © 2024 IEEE.

关键词： Network intrusion

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