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

作者： Mathiyalagan, P. Kiruthiga, G. Rasmi, A. Vadivel, M. Mahalakshmi, K.V. Poornappriya, T.S. Sri Ramakrishna Engineering College Department of CSE Coimbatore India Karpagam College of Engineering Dept of Artificial Intelligence and Data Science Coimbatore India RR Institute of Technology Information Science and Engineering Bangalore India Excel Engineering College Department of Information Technology TamilNadu Namakkal India B v Raju Institute of Technology CSE Telangana 502313 India Aspire Systems Pvt Ltd Data and Analytics Karnataka Bengaluru560037 India

ISBN: (纸本)9798331518097

The second most prevalent and leading cause of cancer death is breast cancer, which is the most common malignancy among women. Because the causes of breast cancer are not fully known, there is currently no reliable technique to prevent it. In recent years, breast cancer has become a widespread condition among women. Over the past several years, the issue of breast tumour categorization has become a significant issue. This article offers logical reasoning for the order in which to identify malignant lesions, a significant development in the mammography diagnosis of breast cancer. As a result, we concentrate on studying and analysing cancer imaging, with a particular emphasis on breast cancer. Mammography produces and processes pictures that are used in breast cancer detection. Sensitivity, specificity, and classification accuracy are the metrics used to assess the experimental findings. Results of an improved ANN classifier for differentiating between normal and abnormal cases in pictures from applied advanced mammography. © 2024 IEEE.

关键词： Lung cancer

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Evaluation of Preventive Activities of COVID-19 Using Multi-criteria Decision Making Method 2nd

Evaluation of Preventive Activities of COVID-19 Using Multi-...

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2nd International Conference on Recent Advances in Mechanical engineering Research and Development, ICRAMERD 2021

作者： Sasikumar, Gnanasekaran Ayyappan, Sivasangari Venkatachalam, N. Department of Mechanical Engineering GMR Institute of Technology Andhra Pradesh Srikakulam532127 India Department of Electronics and Communication Engineering GMR Institute of Technology Andhra Pradesh Srikakulam532127 India Department of Mechanical Engineering Excel Engineering College Tamil Nadu Komarapalayam637303 India

ISBN: (纸本)9789811690563

In this paper, a Multi-Criteria Decision Making (MCDM) process based on Analytical Hierarchy Process (AHP) method is presented with an objective of helping decision makers (DMs) to evaluate the important activities that cause the spread of COVID-19 disease. In this study, seven important activities have been identified and a decision hierarchy is constructed based on these criteria. The criteria weights are calculated using the additive normalization method. The consistency of pairwise comparison matrix is calculated and found within the allowable limit. Based on the AHP method, a user friendly decision support system was developed in order to perform the calculations quickly and accurately. It is found that the proposed AHP method deals with evaluation of preventive activities of COVID-19 efficiently. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Analytical hierarchy process COVID-19 Criteria MCDM Pairwise comparison

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Robust Hybrid Artificial Fish Swarm Simulated Annealing Optimization Algorithm for Secured Free Scale Networks against Malicious Attacks

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Computers, Materials & Continua 2021年第1期66卷 903-917页

作者： Ganeshan Keerthana Panneerselvam Anandan Nandhagopal Nachimuthu Anna University Chennai600025India Department of ECE C.Abdul Hakeem College of Engineering and TechnologyVelloreIndia Department of ECE Excel Engineering CollegeSalemIndia

Due to the recent proliferation of cyber-attacks,highly robust wireless sensor networks(WSN)become a critical issue as they survive node ***-free WSN is essential because they endure random attacks *** they are susceptible to malicious attacks,which mainly targets particular significant ***,the robustness of the network becomes important for ensuring the network *** paper presents a Robust Hybrid Artificial Fish Swarm Simulated Annealing Optimization(RHAFS-SA)*** is introduced for improving the robust nature of free scale networks over malicious attacks(MA)with no change in degree *** proposed RHAFS-SA is an enhanced version of the Improved Artificial Fish Swarm algorithm(IAFSA)by the simulated annealing(SA)*** proposed RHAFS-SA algorithm eliminates the IAFSA from unforeseen vibration and speeds up the convergence *** experimentation,free scale networks are produced by the Barabási–Albert(BA)model,and real-world networks are employed for testing the outcome on both synthetic-free scale and real-world *** experimental results exhibited that the RHAFS-SA model is superior to other models interms of diverse aspects.

关键词： Free scale networks robustness malicious attacks fish swarm algorithm

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Effective Diagnosis of Diabetes Mellitus using Voting Ensemble of Boosting Algorithms - Distinctive Machine Learning Approach

Effective Diagnosis of Diabetes Mellitus using Voting Ensemb...

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2023 International Conference on Data Science, Agents and Artificial Intelligence, ICDSAAI 2023

作者： Karthikeyini, Chokiyan Sundarambal, B. Bommi, R.M. Subramanian, Suresh Excel Engineering College Dept of Ece Chennai India Chennai Institute of Technology Dept of Cse Chennai India Chennai Institute of Technology Department of Ece Chennai India Simats Dept of Ece India

ISBN: (纸本)9798350348910

The rapid growth in the integration of machine learning technology with data exploration modules facilitate processing huge quantum of data in healthcare and develop clinical decision-making system for the diagnosis of pathological condition. Such integrated units assist the physicians to determine the abnormality effectively. In this paper, we have proposed a distinctive integrated Diabetes Mellitus Machine Learning Model (DMMLM) for predicting Diabetes Mellitus (DM) efficiently. The variables considered as a measure of severity are age, insulin level, glucose, pedigree function, body mass index (BMI), skin thickness, pregnancy and blood pressure. Three different machine learning prediction models using boosting algorithms such as Adaboost, Gradient Boosting and XGboost along with voting ensemble are employed to understand their efficacy. Pima Indian Dataset from UCI repository that consists of 758 observations with 8 features is used for the present study. The results obtained shows that the three proposed DMMLM with the voting ensemble for identifying DM provide improved prediction values compared to other models suggested for a similar purpose. The proposed voting ensemble classifier outperforms with higher accuracy of 88.9%, precision 90%, Recall 93%, F1 score 91% and AUC of 0.96. © 2023 IEEE.

关键词： Adaptive boosting

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A comparison and analysis of mechanical properties of glass fiber and banana fiber composite 3

A comparison and analysis of mechanical properties of glass ...

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3rd International Conference on Smart and Sustainable Developments in Materials, Manufacturing and Energy engineering, SME 2021

作者： Karthick, L. Sivakumar, S. Sasikumar, A. Prabhu, A. Senthil Kumar, J. Vadivukarasi, L. Department of Mechanical Engineering Hindusthan College of Engineering and Technology Coimbatore641032 India Department of Aeronautical Engineering Excel Engineering College Namakkal637303 India Department of Mathematics Nandha Arts and Science College Erode India

Natural fibers are plentiful and offer a significant cost savings over fully manufactured composite materials. The usage of natural fibres is widespread in many technological applications requiring great strength and stiffness with little weight. Banana fibre has high mechanical properties and minimal deformation during testing. Comparison of glass fiber and banana fiber with different fibre volume fraction to get the best fibre. In this work, both fibres are prepared using hand lay-up method. The impact of fibre volume fraction on composite fracture toughness and tensile strength is studied. Comparing fracture toughness will help to choose the best composite material for different automotive applications. © 2022 Elsevier Ltd. All rights reserved.

关键词： Tensile strength

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Attention based ConVnet-Recurrent Neural Network for Facial Recognition and Emotion Detection

Attention based ConVnet-Recurrent Neural Network for Facial ...

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2024 International Conference on Intelligent Algorithms for Computational Intelligence Systems, IACIS 2024

作者： Kumar, S. Vinod Sunil, G. Hussein, Ramy Riad Manju Vidhya, S. Sundaram, S. Meenakshi Department of Computer Science and Engineering Secunderabad India Sr University School of Computer Science & Artificial Intelligence Warangal India The Islamic University College of Technical Engineering Department of Computers Techniques Engineering Najaf Iraq Excel Engineering College Department of Artificial Intelligence and Data Science Komarapalayam India Nitte Meenakshi Institute of Technology Department of Cse Bengaluru India

ISBN: (纸本)9798350360660

Facial Emotion Recognition (FER) plays a crucial role in analysing and understanding individual's emotions in the form of non-verbal communication. Existing Deep Learning (DL) based FER methods offered diverse contribution in analysing human emotions but several challenges like extracting accurate spatial and temporal features for accurate classification of emotions is a major concern. To address this, a DL based Facial Recognition and Emotion Detection (FRED) system is proposed by using extended Cohn-Kanade (CK+) dataset as the input. The data is pre-processed first where the Histogram Equalization (HE) is applied and then Convolutional Neural Network (CNN) is used to extract the spatial features while reducing the dimensionality. The extracted features are given to Recurrent Neural Network (RNN) to learn long term dependencies from a temporal sequence of data. The final classification is performed where the Attention Mechanism (AM) is integrated in the RNN to determine the higher weights for better outcome. The results of proposed AMConVnet-RNN showed a superior classification accuracy of 99.54%, precision of 98.63%, and f1-score of 97.39% when compared to existing FER methods like Residual Network-50 (ResNet-50) and Channel Based Attention Mechanism (CBAM) based ResNet50. © 2024 IEEE.

关键词： Convolutional neural networks

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Research and Application of Boundary Optimization Algorithm of Forest Resource Vector Data Based on Convolutional Neural Network 2

Research and Application of Boundary Optimization Algorithm ...

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2nd International Conference on Smart Technologies for Smart Nation, SmartTechCon 2023

作者： Thangarasan, T. Surendhiran, V. Keerthana, R. Moheshkumar, G. Namasivayam, M. Saratha, M. Department of Computer Science & Engineering Madanapalle Institute of Technology & Science Andhra Madanapalle India Department of Computer Science and Engineering Excel Engineering College Komarapalayam India Department of Computer Science and Engineering K.S.R.College of Engineering Tamilnadu Tiruchengode India Department of Information Technology KSR Institute for Engineering and Technology Tamilnadu Tiruchengode India Department of Computer Science and Engineering K.S.R. College of Engineering Tamilnadu Tiruchengode India M.Kumarasamy College of Engineering Thalavapalayam Tamilnadu Karur India

ISBN: (纸本)9798350305418

With the deepening of the research and application of GIS (Geographic Information System), the boundary quality of GIS data is becoming more and more important for production and application, and the high accuracy of vector data is the trend of future development. The second-class survey results have the characteristics of accurate and reliable survey data, rich contents and diverse expressions, and can establish forest resources archives for local areas. Therefore, this study established a boundary optimization algorithm for vector data of forest resources based on CNN (Convolutional Neural Network). The network structure proposed in this paper consists of encoder and decoder, and the input dimension of source data is 64×64. The encoder consists of CL(Convolution layer), active layer and PL(Pool layer). The dimension of CL is 24×64×256, which respectively represents the width of convolution kernel, the number of input data columns and the number of convolution kernels. The results show that the vector data processed by the algorithm basically keeps the original shape unchanged, and reduces the redundancy of vector data. At the same time, from the test results, CNN has achieved the best results. Compared with GA (Genetic Algorithm), the accuracy of this method is 3.6% higher. The results verify the reliability of this algorithm. © 2023 IEEE.

关键词： Convolutional Neural Network Data updating Forest resources Source vector data

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Soft Computing based Machine Learning Techniques for Optical Communication Networks 1

Soft Computing based Machine Learning Techniques for Optical...

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1st IEEE International Conference on Automation, Computing and Renewable Systems, ICACRS 2022

作者： Malini, P. Vasantharaj, A. Preethi, S. Karpakam, S. Sasikala, U. Irfanbasha, S. Vivekananda College of Technology for Women Triuchengode India Excel Engineering College Namakkal India Kongu Engineering College Erode India Sri Eshwar College of Engineering coimbatore India J.K.K.Munirajah College of Technology Coimbatore India K.Ramakrishnan College of Engineering India

ISBN: (纸本)9781665460842

Massive amounts of extremely far-off data are now being sourced from the telecommunications networks. Additionally, this information may be acquired via network business traces, network logs, warnings, signal quality indicators, drug addicts' behavioral data, and other sources. Advanced tools are required to collect useful data.. Machine learning is one of these fantastic technologies (ML), which is considered as one of the most promising tactical answers for analyzing network data and automation. The recent increase in network difficulty contributes to the abandoning of ML techniques in the context of optical communication networks. In this study high-level overview of networking and ML to optic dispatches are analyzed. This article, outlines the problem, review the relevant literature, and provide experimenters and analysts interested in this fieldan introduction to machine learning. By providing new possible exploration guidelines to encourage more advancement in this field, we infer the research. Even if recently reasonable investigation documents have appeared, machine learning's application to optical networks is still in its infancy. © 2022 IEEE

关键词： Optical communication

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Assessing Secure Cloud Information Sharing Through Authentication and Encoded Indexing 8

Assessing Secure Cloud Information Sharing Through Authentic...

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

作者： Pandiaraja, P. Karthik, K. Rajesh Kanna, P. Ushapriya, C. Mahesh, P.C.Senthil Muthumanickam, K. Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology School of Computing Department of Computer Science and Engineering Chennai600 062 India M. Kumarasamy College of Engineering Department of Computer Science and Engineering Karur Tamilnadu Thalavapalayam639113 India Bannari Amman Institute of Technology Department of Computer Science and Engineering Tamilnadu Erode638401 India St. Martin's Engineering College Department of Ai & Ml Telangana Secunderabad500100 India Excel Engineering College Department of Computer Science and Engineering Tamilnadu Komarapalayam637303 India Kongunadu College of Engineering and Technology Department of Information Technology Tamil Nadu Trichy621215 India

ISBN: (纸本)9798350376425

The high usage of storage in cloud, ensuring the privacy and security of information stored in the cloud has come up with a difficult concern. One approach to address this issue is the use of techniques which are encrypted, where data is already encrypted being uploaded to the storage of cloud. However, traditional encryption methods hinder the ability to find and retrieve particular information from the secured information, as the information needs to be decrypted first. This limitation hampers the usability and efficiency of storage of cloud systems. To overcome the challenge, this research says a novel approach for keyword search on secured information in storage of cloud using an AES approach. The algorithm of AES is a maximum adopted symmetric encryption method familiar for efficiency and robustness. By leveraging the AES encryption approach, the solution assures the confidentiality of information stored while enabling efficient keyword-based search operations without the need for decryption. The proposed approach utilizes a combination of cryptographic techniques such as AES encryption and index structures to access efficient information findings on the secured information. A secure index is created during encryption process that enables for potent retrieval of encrypted data related to specific keywords. The encrypted data remains confidential, as only authorized users possess the necessary decryption keys to retrieve the plaintext information. During data outsourcing, data owner can set the access permission based on their user identity. Users should have specific identity with decryption key for accessing data from cloud. Here data manager can also provide particular period for accessing information from cloud. The results demonstrate that the keyword findings on secured information in storage of using the AES approach achieve a balance between data privacy and search functionality. The solution offers an effective means to securely store and retrieve data

关键词： Cloud storage

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Enhancing Security and Transparency Through the Integration of Blockchain and Machine Learning

Enhancing Security and Transparency Through the Integration ...

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2023 International Conference on Data Science, Agents and Artificial Intelligence, ICDSAAI 2023

作者： Bommi, R.M. Sundarambal, B. Karthikeyini, Chokiyan Subramanian, Suresh Chennai Institute of Technology Department of Ece Chennai India Chennai Institute of Technology Dept of Cse Chennai India Excel Engineering College Dept of Ece Chennai India Sri Indu Inst. of Engg & Technology Dept of Ece Chennai India

ISBN: (纸本)9798350348910

This article examines a new strategy in improving security and trust by fusing blockchain technology with machine learning techniques. This synergy exploits the decentralisation and immutability properties of blockchains alongside analytical capabilities provided by ML models to achieve optimal value creation. Through strict scrutiny, the employed technique reveals efficacy as regards boosting security measures as well as fostering transparency in all sorts of operations. It shows how such an integrated approach can take up issues pertaining to the problem of securing contemporary data which will pave way for reliable, trustable, strong systems. For this reason, it becomes possible to widen the application of these safe and transparent technologies as a contribution to enlargement into broader fields of science. © 2023 IEEE.

关键词： Transparency

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