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MOCBOA:Multi-Objective Chef-Based Optimization Algorithm Using Hybrid Dominance Relations for Solving engineering Design Problems

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computer Modeling in engineering & sciences 2025年第4期143卷 967-1008页

作者： Nour Elhouda Chalabi Abdelouahab Attia Abdulaziz S.Almazyad Ali Wagdy Mohamed Frank Werner Pradeep Jangir Mohammad Shokouhifar Computer Science Department University of M’silaIchebiliaM’sila28000Algeria Computer ScienceDepartment Mohamed El Bachir El IbrahimiUniversity of Bordj BouArreridjBordj BouArreridj34000Algeria Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityRiyadh11543Saudi Arabia Operations Research Department Faculty of Graduate Studies for Statistical ResearchCairo UniversityGiza12613Egypt Applied Science Research Center Applied Science Private UniversityAmman11931Jordan Faculty of Mathematics Otto-von-Guericke UniversityMagdeburg39016Germany University Centre for Research and Development Chandigarh UniversityMohali140413India Department of Electrical and Computer Engineering Shahid Beheshti UniversityTehran14399Iran DTU AI and Data Science Hub(DAIDASH) Duy Tan UniversityDa Nang550000Vietnam

Multi-objective optimization is critical for problem-solving in engineering,economics,and *** study introduces the Multi-Objective Chef-Based Optimization Algorithm(MOCBOA),an upgraded version of the Chef-Based Optimization Algorithm(CBOA)that addresses distinct *** approach is unique in systematically examining four dominance relations—Pareto,Epsilon,Cone-epsilon,and Strengthened dominance—to evaluate their influence on sustaining solution variety and driving convergence toward the Pareto *** comparison investigation,which was conducted on fifty test problems from the CEC 2021 benchmark and applied to areas such as chemical engineering,mechanical design,and power systems,reveals that the dominance approach used has a considerable impact on the key optimization measures such as the hypervolume *** paper provides a solid foundation for determining themost effective dominance approach and significant insights for both theoretical research and practical applications in multi-objective optimization.

关键词： Multi-objective optimization chef-based optimization algorithm(CBOA) pareto dominance epsilon dominance cone-epsilon dominance strengthened dominance

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Prediction of Healthy and Unhealthy Food Items using Deep Learning 7

Prediction of Healthy and Unhealthy Food Items using Deep Le...

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7th International Conference on Computing Methodologies and Communication, ICCMC 2023

作者： Natesan, P. Thamil Selvan, R. Shrivarshini, M. Dhanya, B. Kalaiselvi, S. Department of Computer Science and Engineering Kongu Engineering College Erode India Department of Information Technology Kongu Engineering College Erode India Department of Computer Technology Kongu Engineering College Erode India

ISBN: (纸本)9781665464086

In recent times, people are mostly attracted to fast food. In this paper, a model for categorizing food products using convolutional neural networks has been built. Deep learning facts are being used in day-to-day life and recognizing objects through image processing. Convolutional neural networks are effective for classifying photographs of food because they exclude distracting characteristics from images. By implementing this model, the layers involved here to convert the three-dimensional input into an output volume and to filter input to a higher level of abstraction. This proposed model may help people to identify healthy and unhealthy food items and save us from causes of diseases. This study used image datasets we built, with the images coming from social media and already-existing image datasets (Kaggle) and (Google). The findings of this study demonstrate how much more effective the system is in classifying images when compared to other approaches. The suggested method includes more visual elements than other method and that it performs better at classifying food images. According to the experimental findings, the classification of foods as healthy or unhealthy is 90% accurate. © 2023 IEEE.

关键词： Food products

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Privacy-Preserving Machine Learning Models

Privacy-Preserving Machine Learning Models

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2025 International Conference on Multi-Agent Systems for Collaborative Intelligence, ICMSCI 2025

作者： Chodey, Madhuri Devi Gouthami, E. Rao, KakanaboyinaUpendra Ramana, G.V. Shirisha, N. Kumar, Nellore Manoj Department of Electronics and Communication Engineering Karnataka 584101 India Lendi College of Engineering and Technology Department of Science and Humanities Andhra Pradesh Vizianagaram535005 India Anurag Engineering College Department of Computer Science and Engineering Telangana 508206 India Aditya University Department of Mathematics Andhra Pradesh Surampalem533437 India Mlrit Department of Computer Science and Engineering Telangana Hyderabad500043 India Department of Mathematics Thandalam Tamil Nadu Chennai602105 India

ISBN: (纸本)9798331509828

The rapid adoption of machine learning (ML) across several businesses raises serious concerns about data privacy, particularly when sensitive data is involved. By integrating trustworthy techniques in the preprocessing, feature selection, and classification stages, this research provides a comprehensive methodology for developing machine learning models that preserve privacy. Preprocessing uses data anonymisation techniques like k-anonymity and 1-diversity to protect sensitive properties and maintain data utility. Privacy-aware feature selection, or PAFS, is used to identify and retain characteristics that maximise model performance without compromising privacy. In the classification stage, privacy-preserving neural networks handle anonymised or encrypted input while using secure computing frameworks to provide accurate predictions. For privacy-sensitive applications like healthcare and banking, this integrated approach provides a scalable solution, tackling significant challenges in finding a compromise between privacy and usefulness. Experimental results illustrate the utility of the proposed framework and how it could support ethical and safe machine learning in real-world applications. © 2025 IEEE.

关键词： Adversarial machine learning

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Design and Implementation of Alzheimer's Disease Detection using cGAN and CNN

Design and Implementation of Alzheimer's Disease Detection u...

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2023 International Conference on computer Communication and Informatics, ICCCI 2023

作者： Ravikumar, R. Sasipriyaa, N. Thilagaraj, T. Hareesh Raj, R. Abishek, A. Gokula Kannan, G. The Erode College of Pharmacy Tamilnadu India Kongu Engineering College Department of Computer Science and Engineering Tamilnadu India School of Computing Amrita Vishwa Vidyapeetham Mysuru Campus India Kongu Engineering College Department of Computer Science and Engineering India

ISBN: (纸本)9798350348217

The importance of diagnosing Alzheimer's disease (AD) and Mild Cognitive Impairment (MCI), its prodromal form, for patient care, has made it a new study focus because of AD's high prevalence and huge social impact on health. Existing research have used machine learning approaches to diagnose AD early by looking at numerous biomarkers. However, in the bulk of current research, the diagnosis performance is hampered by the inability to efficiently extract high-level brain features that indicate the primary abnormalities of anatomical brain regions connected to AD. In order to diagnose AD brain pictures, the suggested model combines a deep convolutional neural network (CNN) and a conditional generative adversarial network (cGAN).The dataset is preprocessed to create a clear representation of the brain's grey matter, and the cGAN is utilised to create more training instances for the model to improve accuracy. According to the limitations of the discriminator, the generator in cGAN-ADD learns to include the illness category feedback from the classifier into the 2D-brain slice image reconstruction process for image improvement in the training phase. In the prediction phase, the stacked CNN layers extract high-level brain properties from category-enhanced 2D-brain slice pictures. The classifier gets the collected brain features in order to output the posterior probability of disease conditions (Normal, AD, Mild and Very Mild). Tensorflow is used to train the model, and Keras is used to evaluate classification accuracy. The model is trained and tested on ADNI dataset. © 2023 IEEE.

关键词： Brain

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AYURA – AYURVEDIC DIET APP 2

AYURA – AYURVEDIC DIET APP

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2nd International Conference on Advancements in Electrical, Electronics, Communication, Computing and Automation, ICAECA 2023

作者： Shanmathi, M. Arage, Bhavika Premchand Praveena, R. Department of Computer Science Engineering Kumaraguru College of Technology Coimbatore India

ISBN: (纸本)9798350306811

Ayurveda, a system of complementary medicine that has been used for centuries in India, aids in the early treatment of illnesses. Ayurvedic practitioners hold that space, air, fire, water, and earth are the five fundamental components that make up the universe. Three life forces or energies known as doshas are created when these unite in the human body. It manages how a body functions. They are the Pitta (fire and water), Vata (space and air), and Kapha doshas (water and earth). One can identify the dosha using an online programme and can also recommend foods to balance it. The physiological (physical traits, looks, and digestion) and psychological factors will be considered by the application to identify the dosha imbalance. © 2023 IEEE.

关键词： Earth (planet)

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Lightweight Fog Attention Graph Convolutional Network for Secure data Aggregation Mechanism to Fog based Cloud Assisted IoT Network 8

Lightweight Fog Attention Graph Convolutional Network for Se...

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

作者： Akilesh Kumar, S. Hariharan, P. Sukhel Ahamed, N. Gokilavani, S. Karpagam Academy of Higher Education Department of Artificial Intelligence and Data Science Coimbatore India Karpagam Academy of Higher Education Department of Computer Science and Engineering Coimbatore India

ISBN: (数字)9798350376425

ISBN: (纸本)9798350376425

Ever Increasing demand of Cloud Computing paradigm has resulted in widespread development and deployment of multiple fog nodes to cloud assisted internet of things networks as it is highly capable of providing the users with on demand and low latency services through mobile collaborative devices in the edge of multiple clouds. Mobile Collaborative Devices is used for fog computing with integrated storage, computing and communication capabilities. However it offers improved efficiency and increased flexibility. Despite of multiple advantageous of fog based cloud assisted internet of things, security of the user data and their privacy can be compromised during data aggregation. In addition, it faces huge challenges in effectively aggregating the data and transmitting to the cloud server on establishing the strong security. In order to enhance the security of the user data and to preserve the privacy of the user information along establishing a secure data aggregation and transmission, a new light weight convolutional attention network is proposed in this article. It establishes secure data aggregation and data transmission to the distributed cloud data centers efficiently. Fog attention graph convolutional network leverages neighbour fog data efficiently and securely from dataset and represents in form of graph. Attention coefficient is assigned to each fog data represented in graph structure and attention score is calculated to each update of the nodes in graph. Specifically softmax function employs ID3 decision tree classifier and K anonymization mechanism to aggregates fog data on basis of updates of the attributes of the fog nodes in terms of the attention score and transmits aggregated data to the distributed cloud server. Further proposed model enhance the security of the data aggregation and transmission with index function through hash mechanism. Experimental analysis is carried out on the Fog assisted IoT medical (FIoMT) dataset. dataset composed of patient h

关键词： Fog computing

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Fog detection and visibility measurement using SVM 2

Fog detection and visibility measurement using SVM

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2nd International Conference on Advancements in Electrical, Electronics, Communication, Computing and Automation, ICAECA 2023

作者： Apinayaprethi, K.N. Nithya, S. Department of Computer Science and Engineering Kumaraguru College of Technology COIMBATORE India

ISBN: (纸本)9798350306811

Fog which present in the atmosphere due to temperature variance reduces the clear vision of driver or driving applications. Restricted visibility due to fog will affect the accuracy of visualization and it may leads to accident. To overcome this, defogging need to be done in a moving vehicle. We propose a cost-effective fog detection system which can be used in real world. The input images are taken from the camera which is mounted on the dashboard of the car. Density of the fog and visibility distance is calculated using region of interest techniques to provide clear visibility to the driver. In this system, machine learning Algorithm SVM is used to classify the fog and fog-free images. NI Vision Development Software is used for machine vision application for graphical view. © 2023 IEEE.

关键词： Visibility

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Fortified Cuckoo Search Algorithm on training multi-layer perceptron for solving classification problems

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Personal and Ubiquitous Computing 2023年第3期27卷 1039-1049页

作者： Thirugnanasambandam, Kalaipriyan Prabu, U. Saravanan, D. Anguraj, Dinesh Kumar Raghav, R.S. Centre for Smart Grid Technologies School of Computer Science and Engineering Vellore Institute of Technology Tami Nadu Chennai Campus Chennai600127 India Department of Computer Science and Engineering Velagapudi Ramakrishna Siddhartha Engineering College Andhra Pradesh Vijayawada520007 India Department of Computer Science Engineering VIT Bhopal University Bhopal-Indore Highway Madhya Pradesh Bhopal466114 India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Andhra Pradesh Guntur522302 India School of Computing SASTRA Deemed University Tamil Nadu Thanjavur613007 India

Multi-layer perceptron (MLP) in artificial neural networks (ANN) is one among the trained neural models which can hold several layers as a hidden layer for intensive training to obtain optimal results. On the other hand, the classification problem has a high level of attraction towards researchers to increase the accuracy in classification. In ANN, feedforward neural network (FNN) is one model that possesses the art of solving classification and regression problems. When input data is given to FNN, it will apply the sum of product rule and the activation function to map the input with its appropriate output. In the sum of product rule, a term called weights is to be chosen appropriately to map between the input and output. In standard FNN, the weights are chosen in a random way which may lead to slower convergence towards the optimal choice of weight values. In this paper, an effective optimization model is proposed to optimize the weights of MLP of FNN for effective classification problems. Four different datasets were chosen, and the results are interpreted with statistical performance measures. © 2023, The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature.

关键词： Machine learning

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A Survey Based on Privacy-Preserving Over Health Care data Analysis 2nd

A Survey Based on Privacy-Preserving Over Health Care Data A...

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2nd International Conference on Emerging Trends in Expert Applications and Security, ICE-TEAS 2023

作者： Panimalar, S.P. Gunasundari, S. Department of Information Technology Saveetha Engineering College Chennai India Department of Computer Science and Engineering Velammal Engineering College Chennai India

ISBN: (纸本)9789819919451

Modern communications, sensors, and cloud services have recently been revolutionizing the traditional public health system. However, privacy concerns have been growing due to the convergence of advancements. Therefore, it is essential to thoroughly investigate the privacy concerns related to e-health information. To implement quality and effective health services seamlessly, hospitals in India and worldwide have shifted from paper-based human data processing to Healthcare Information Systems (HIS) and Digital Health Records (DHR). Because patient information is now nearly instantly accessible from anywhere globally, the increasing use of digital data in the healthcare sector has the potential to yield tremendous benefits. However, making DHR accessible online and linking HIS to the internet expose data to security risks and compromise patient privacy. This paper provides a survey on the privacy of healthcare data and information on healthcare systems (HIS), their automation, threats to the privacy protection of healthcare information as a result of automation, and how to identify these threats using new technologies to promote the quality of healthcare. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Automation

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Fusion Strategy for Improving Medical Image Segmentation

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computers, Materials & Continua 2023年第2期74卷 3627-3646页

作者： Fahad Alraddady E.A.Zanaty Aida HAbu bakr Walaa M.Abd-Elhafiez Department of Computer Engineering College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia Information System Department Faculty of Computers and InformationSohag UniversitySohagEgypt Mathematical and Computer Science Department Faculty of ScienceAswan UniversityAswanEgypt College of Computer Science&Information Technology Jazan UniversityJazanKingdom of Saudi Arabia Computer Science Department Faculty of Computers and Artificial IntelligenceSohag UniversitySohagEgypt

In this paper,we combine decision fusion methods with four metaheuristic algorithms(Particle Swarm Optimization(PSO)algorithm,Cuckoo search algorithm,modification of Cuckoo Search(CS McCulloch)algorithm and Genetic algorithm)in order to improve the image *** proposed technique based on fusing the data from Particle Swarm Optimization(PSO),Cuckoo search,modification of Cuckoo Search(CS McCulloch)and Genetic algorithms are obtained for improving magnetic resonance images(MRIs)*** algorithms are used to compute the accuracy of each method while the outputs are passed to fusion *** order to obtain parts of the points that determine similar membership values,we apply the different rules of incorporation for these *** proposed approach is applied to challenging applications:MRI images,gray matter/white matter of brain segmentations and original black/white images Behavior of the proposed algorithm is provided by applying to different medical *** is shown that the proposed method gives accurate results;due to the decision fusion produces the greatest improvement in classification accuracy.

关键词： Decision fusion particle swarmoptimization(PSO) cuckoo search algorithm CS McCulloch algorithm genetic algorithm CT and MRI

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