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Machine Learning Based Secured Reliable Routing Protocol to Predict and Prevent Byzantine Attack in MANET 5

Machine Learning Based Secured Reliable Routing Protocol to ...

引用

5th International Conference on Electronics and Sustainable Communication Systems, ICESC 2024

作者： Gopinath, D. Karthikeyan, N. Yashoda, M.B. Savitha, K.K. Department of Computer Science Karnataka Bengaluru India Bharathiar University Pg Extension and Research Centre Department of Computer Application Tamilnadu Erode India

ISBN: (纸本)9798350379945

The emergence of Mobile Adhoc Networks (MANETs), in which multiple nodes join alongside one another to provide the necessary real-time services, has garnered increased attention from academics due to recent advancements in wireless communication. The wireless connectivity and decentralized architecture of MANETs, however, make constructing secure routing a challenging task. As a result, a machine learning-based Secured Reliable Routing Protocol (ML-SRRP) is suggested in this study to predict and prevent Byzantine attacks in MANET. In order to prevent unwanted packets with routing information being sent to fake destinations (in order to prevent routed loops), the nodes that serve as intermediates in a network were initially selected using an authority evaluation method at every single node. The node-specific metrics Number of Hop Count (NHC), Residual Energy Level (REL) and Link Validation were used for this. In order to effectively identify attacks and isolate hacked nodes within the network, an intrusion detection system is necessary. In this research, the Ad-hoc On Demand Distance Vector (AODV) routing MANET protocol is used to introduce the Byzantine attacks. Support Vector Machine (SVM) is used to train and test the system to identify illegally nodes in the network. The simulation results show that the proposed ML-based SRRP outperforms current methods in terms of dependability and throughput. Moreover, there is a reduction in delay, routing overhead, and packet loss ratio. R was used for testing and training with the trace file that was taken from the NS-2 simulation, which was run to carry out Byzantine attacks. © 2024 IEEE.

关键词： Support vector machines

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Hyperspectral Images for Crop Prediction using Machine and Deep Learning Models 3

Hyperspectral Images for Crop Prediction using Machine and D...

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

作者： Mahalakshmi, R. Kumutha, K. Sakthivanitha, M. Lakshmi, R. Bagavathi Sirajudeen, Mohamed Padmanabhan, Sankar Vels Institute of Science Technology & Advanced Studies Department of Computer Applications-PG Tamil Nadu Chennai India Vels Institute of Science Technology Advanced Studies Department of Computer Applications-PG Tamil Nadu Chennai India Vels Institute of Science Technology & Advanced Studies Department of Computer Application Tamil Nadu Chennai India Vels Institute of Science Technology & Advanced Studies Department of Information Technology Tamil Nadu Chennai India Nilgiri College of Arts and Science Pg and Research Department of Computer Science Tami Nadu The Nilgiris India Hindustan Institute of Technology and Science Department of Electronics and Communication Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798331509675

Crop classification is essential for local and national governments to make informed agricultural decisions. Remote sensing technology has made it possible to employ high-resolution hyperspectral images (HSIs) for land cover classification for decades. Machine Learning (ML) and Deep Learning (DL) are becoming more popular for HSI categorization due to advances in processing capacity and technology. HSIs can accurately identify crop types due to their narrow and continuous spectral band reflection. This study compares traditional machine learning (ML) models like Support Vector Machines (SVM) and Random Forest (RF) to deep learning (DL) models like Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) for hyperspectral image classification (HSI). The HSI categorization was evaluated using performance criteria such as accuracy, precision, recall, and the F1-Score. CNN achieved the highest accuracy of 96%, demonstrating that it is the most reliable solution for complex HSI classification tasks because it successfully captures spatial and spectral data. © 2025 IEEE.

关键词： Classification Deep Learning(DL) Hyperspectral Images(HSI) Machine Learning(ML)

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Dimensionality Reduction Method for Early Detection of Dementia 1

引用

International Conference on Advances and applications of Artificial Intelligence and Machine Learning, ICAAAIML 2021

作者： Areekara Vasudevan, Ambili Kumar, A. V. Senthil Rajeyyagari, Sivaram PG Research and Computer Application Hindustan College of Arts and Science Coimbatore India Department of Computer Science College of Computing and Information Technology Shaqra University Shaqra Saudi Arabia

ISBN: (数字)9789811948312

ISBN: (纸本)9789811948305

The use of magnetic resonance imaging (MRI) in the diagnosis of dementia is significant because it reveals morphological differences induced by brain decline. The major obstacles faced by compuer assisted systems are: High dimensionality and unbalanced data. Numerous deep learning methods have been used on an MR image dataset for disease classification and prediction, but most of them have refused to endorse data dimensionality reduction, which resulted in skewed outcomes. The UCI machine learning repository provided the MR image of brain dataset utilised in this study. In this article, we suggest an productive dimensional reduction method to ameliorate the attainment of the dementia detection system. The hybrid Linear Discriminant Analysis –Firefly algorithm (LDA-FA) is used for dimensionality reduction. On the reduced data set we apply an optimized convolutional neural network. The method we have proposed is less erroneous and more accurate than other recent work and also produced 95.21% accuracy. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Magnetic resonance imaging

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Machine Learning Based Secured Reliable Routing Protocol to Predict and Prevent Byzantine Attack in MANET

Machine Learning Based Secured Reliable Routing Protocol to ...

引用

Electronics and Sustainable Communication Systems (ICESC), 2020 International Conference on

作者： Gopinath D N. Karthikeyan Yashoda M B K.K. Savitha Department of Computer Science Kristu Jayanti College (Autonomous) Bengaluru Karnataka India Department of Computer Application Bharathiar University PG Extension and Research Centre Erode Tamilnadu India

ISBN: (数字)9798350379945

ISBN: (纸本)9798350379952

关键词： Support vector machines Wireless communication Intrusion detection Routing Throughput Routing protocols Vectors Reliability Mobile computing AODV

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Classification of Satellite Agriculture Imagery to Identify Crop Type Using Deep Learning Techniques - A Review

Classification of Satellite Agriculture Imagery to Identify ...

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International Conference on computer Communication and Informatics (ICCCI)

作者： N. Thamaraikannan S Manju PG & Research Department of Computer Science PSG College of Arts & Science Coimbatore India Department of Computer Application (MCA) PSG College of Arts & Science Coimbatore India

DL is a subdivision of machine learning, which is a subset of *** the past two decades, deep learning has gained a great deal of attention. Deep learning requires large quantities of data and produces accurate results. Agriculture is a new field of study; yield prediction is particularly critical in agriculture and is largely dependent on soil and climate elements including moisture, temperature, and climate. The potential of planetary exploration is becoming extremely relevant in crop monitoring. As the incidence with which satellites acquire remote sensing images increases. Today, Rapid changes in environmental circumstances, on the other hand, have made it impossible for the farming community to continue doing so. Crop prediction in agriculture is a complicated process one of main challenges that researchers in this field face is the lack of cultural labeled data that is synchronized throughout spatial and temporal. Evolutionary computation models have piqued the interest of many agricultural experts due to their assurance in machine vision. Effectiveness in hyperspectral imagery (HSI) recognition for a variety of earth environmental monitoring, including Agro - based measuring and land surface identification Limited frequency components associated with process entity attributes are blocked by extract features. Retrieved and embedded high bonding ghastly spatial highlights addressing the progressive design of target entity attributes, providing the model with improved classifier achievement and reduce computational Complexity.

关键词： Deep learning Temperature sensors Temperature dependence Satellites Computational modeling Crops Feature extraction

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Hyperspectral Images for Crop Prediction using Machine and Deep Learning Models

Hyperspectral Images for Crop Prediction using Machine and D...

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Electronics and Renewable Systems (ICEARS), International Conference on

作者： R. Mahalakshmi K. Kumutha M. Sakthivanitha R. Bagavathi Lakshmi Mohamed Sirajudeen Sankar Padmanabhan Department of Computer Applications-PG Vels Institute of Science Technology & Advanced Studies Chennai Tamil Nadu India Department of Computer Applications-PG Vels Institute of Science Technology Advanced Studies Chennai Tamil Nadu India Department of Computer Application Vels Institute of Science Technology & Advanced Studies Chennai Tamil Nadu India Department of Information Technology Vels Institute of Science Technology & Advanced Studies Chennai Tamil Nadu India PG and Research Department of Computer Science Nilgiri College of Arts and Science The Nilgiris Tami Nadu India Department of Electronics and Communication Engineering Hindustan Institute of Technology and Science Chennai Tamil Nadu India

ISBN: (数字)9798331509675

ISBN: (纸本)9798331509682

关键词： Support vector machines Radio frequency Deep learning Recurrent neural networks Accuracy Crops Spatial databases Convolutional neural networks Reliability Hyperspectral imaging

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Optimization Techniques for Machine Learning Models to Improve the Efficiency of Classification

Optimization Techniques for Machine Learning Models to Impro...

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International Conference on Inventive Computation Technologies (ICICT)

作者： J. Anita Smiles M. Sakthivanitha A. Bharathi D. Narayani. S. Sudha M. Mohamed Sirajudeen Department of Computer Applications SRM Institute of Science and Technology Chennai Tamil Nadu India Department of Computer Application Vels Institute of Science Technology & Advanced Studies Chennai Tamil Nadu India Department of Computer Applications Vels institute of Science Technology and Advanced Studies (VISTAS) Tamil Nadu India Department of Computer Applications Hindustan Institute of Technology and Science Chennai Tamil Nadu India Department of Computer Science PG and Research Nilgiri College of Arts and Science (Autonomous) Thaloor The Nilgiris Tamil Nadu India

ISBN: (数字)9798331512248

ISBN: (纸本)9798331512255

The objective of the study is to identify the most optimal set of hyperparameters for a machine learning (ML) or deep learning(DL) algorithms that improves its performance on a certain task.. This study uses five machine learning methods like Decision Tree(DT), Random Forest(RF), Gradient Boost Models(XGBoost), Support Vector Machine(SVMs) and K-Nearest Neighbhor(KNN). The model specific parameters were applied to all these ML methods to improve the accuracy of the models. The ML models performance with its hyperparameter tuning are evaluated for performance using the performance metrics like accuracy, precision, Recall and F-score. These findings indicate that XGBoost models performed significantly in terms of accuracy, precision, recall, and F1-score. Gradient boosting models are extremely adaptable, but they are also sensitive to hyperparameters such as the learning rate, number of estimators, and tree depth. Tuning these parameters can dramatically improve performance. The optimal model and tuning method are determined by the dataset, task specifications, and computing power.. The contribution of the study to suggests a suitable with right hyperparameter settings to develop a highly flexible model that can adapt to a variety of datasets. The study and application of model-specific hyperparameters in ML continues to evolve, resulting to advances that improve productivity, durability, and generalization.

关键词： Support vector machines Adaptation models Accuracy Machine learning algorithms Computational modeling Forestry Vectors Decision trees Tuning Random forests

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A Budget Balanced Energy Distribution Mechanism among Consumers and Prosumers in Microgrid

A Budget Balanced Energy Distribution Mechanism among Consum...

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9th IEEE Int Conf on Internet of Things / 12th IEEE Int Conf on Green Comp and Communicat / 9th IEEE Int Conf on Cyber, Phys, and Social Comp / IEEE Int Conf on Smart Data

作者： Faqiry, M. Nazif Das, Sanjoy PG and Research Department of Computer Science&Application Quaid-E-Millath Government College for Women (A) Chennai India

ISBN: (纸本)9781509058808

Energy trading is a key feature of the emerging smart grid system. In this paper, we propose an energy allocation mechanism for energy trading among competing consumers and prosumers, i.e. agents, in a microgrid. The proposed mechanism is modeled for rational agents with hidden private information, aiming to optimize self-utility and payoff. We achieve this goal through the presence of a central impartial aggregator whose aim is to accomplish an efficient, price uniform, individually rational, and budget balanced trading among agents.

关键词： agents trading aggregator bid social welfare

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CTCHABC- hybrid online sequential fuzzy Extreme Kernel learning method for detection of Breast Cancer with hierarchical Artificial Bee

CTCHABC- hybrid online sequential fuzzy Extreme Kernel learn...

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IEEE International Advance Computing Conference, IACC

作者： S. Mythili A.V. Senthil Kumar PG & Research Department of Computer Application Hindusthan College of Arts & Science Coimbatore Tamil Nadu India

Circulating Tumor Cells (CTCs) in Peripheral Blood (PB) testing is measured as significant investigative and promising microarray technology for breast cancer examination. Few numbers of the work have been proposed in earlier for the role of CTCs detection in breast cancer; but still the development of novel method for identification of CTC becomes difficult because of hundreds and thousands of indicative genes is presented. The main intention of the work is to the identification of CTC in PB during Breast Cancer (BC) regarding to Metastatic (MS), Non-Metastatic (NMS) and hybrid MS and NMS. The proposed method is not only the identification of CTC in BC, in addition it solves gene selection by proposing hybrid fuzzy online sequential Particle Swarm Genetic (PSG) kernel extreme learning machine finally named as (FOP-KELM) classification. The proposed FOP-KELM method calculates the mean values for each gene features and it is compared objective function of KELM to select and remove unimportant gene features. In order to reduce the fuzzy membership assumption value in ELM, it is optimized using PSG algorithm. The impact of selected features from FOP-KELM has been investigated using clustering method. To perform classification task for selected gene features, a novel Hierarchical Artificial Bee clustering algorithm (HABCA) is proposed. It capably distinguishes the CTC through the separation of tumor samples into a hierarchical tree structure in a top-down manner, where the distance between two gene tumor samples is determined by using ABC. Clustering results are classified into MS, NMS, MS and NMS.

关键词： Breast cancer Tumors Clustering algorithms Classification algorithms Mathematical model Accuracy Kernel

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