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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 aiDS/HIV Infection using Custom Artificial Neural Networks Model 1

Prediction of AIDS/HIV Infection using Custom Artificial Neu...

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1st International Conference on Intelligent Systems and Computational Networks, ICISCN 2025

作者： Sneha, B.J. Shriyashreshta Kodipalli, Ashwini Martis, Roshan Joy Ushasree, A. Devamane, Shridhar B. Global Academy of Technology Department of Ai & Data Science Bangalore India Manipal Institute of Technology Department of Electronics & Communication Engineering Bangalore India Gokaraju Lailavathi Engineering College Department of Computer Science & Engineering Hyderabad India Amrita Vishwavidyapeetam School of Computing Department of Cs Mysuru India

ISBN: (纸本)9798331529246

HIV is a serious disease that impairs immunity. Without treatment, the infection may progress through three stages, which might drastically shorten a person's life. Artificial neural networks (ANNs) are utilized to detect the HIV infection and even to find treatments for it. The aiDS viral infection is predicted in this review. The aim of ANN is to precisely classify patients according to whether or not they have HIV/aiDS. We have formed a custom ANN model and applied it to the Kaggle dataset that is accessible to the public, compared which optimizers offer the best accuracy and whether employing dropouts yields improved accuracy. With Gradient Descent, we achieved the maximum accuracy of 86.92% it is also one of the most commonly used optimizers due to its simplicity and effectiveness, followed by SGD with 86.45% it is a variation of gradient descent in which a random subset of data as opposed to the complete dataset is used to update the parameter and RMSprop with 86.21% uses the average of recent gradient magnitudes to modify the learning rate for each parameter. This aids with training stabilization, particularly when dealing with sparse or noisy data. The accuracy increased to 87.62% after we introduced dropouts to the hidden layer with optimizer as gradient descent, which produced the best results. Loss vs. Accuracy graphs and ROC curve have been plotted. With dropouts, the custom ANN model performed even better on the Gradient Descent optimizer. © 2025 IEEE.

关键词： ANN dropouts HIV/aiDS optimizers

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Defending Against Adversarial Examples Via Modeling Adversarial Noise

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International Journal of computer Vision 2025年 1-18页

作者： Zhou, Dawei Wang, Nannan Han, Bo Liu, Tongliang Gao, Xinbo State Key Laboratory of Integrated Services Networks Xidian University Shaanxi Xi’an710071 China TMLR Group Department of Computer Science Hong Kong Baptist University Hong Kong Sydney AI Center School of Computer Science Faculty of Engineering The University of Sydney DarlingtonNSW2008 Australia School of Electronic Engineering Organization Xidian University Shaanxi Xi’an710071 China

Adversarial examples have become a major threat to the reliable application of deep learning models. Meanwhile, this issue promotes the development of adversarial defenses. Adversarial noise contains well-generalizing and misleading features, which can manipulate predicted labels to be flipped maliciously. Motivated by this, we study modeling adversarial noise for defending against adversarial examples by learning the transition relationship between adversarial labels (i.e., flipped labels caused by adversarial noise) and natural labels (i.e., real labels of natural samples). In this work, we propose an adversarial defense method from the perspective of modeling adversarial noise. Specifically, we construct an instance-dependent label transition matrix to represent the label transition relationship for explicitly modeling adversarial noise. The label transition matrix is obtained from the input sample by leveraging a label transition network. By exploiting the label transition matrix, we can infer the natural label from the adversarial label and thus correct wrong predictions misled by adversarial noise. Additionally, to enhance the robustness of the label transition network, we design an adversarial robustness constraint at the transition matrix level. Experimental results demonstrate that our method effectively improves the robust accuracy against multiple attacks and exhibits great performance in detecting adversarial input samples. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Adversarial attack Adversarial defense Modeling adversarial noise Transition matrix

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Boli: A dataset for understanding stuttering experience and analyzing stuttered speech

Boli: A dataset for understanding stuttering experience and ...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Batra, Ashita Narang, Mannas Sharma, Neeraj Kumar Das, Pradip K. Department of Computer Science and Engineering Indian Institute of Technology Guwahati Guwahati781039 India Mehta Family School of Data Science and AI Indian Institute of Technology Guwahati Guwahati781039 India

ISBN: (纸本)9798350368741

There is a growing need for diverse, high-quality stuttered speech data, particularly in the context of Indian languages. This paper introduces Project Boli, a multi-lingual stuttered speech dataset designed to advance scientific understanding and technology development for individuals who stutter, particularly in India. The dataset constitutes (a) anonymized metadata (gender, age, country, mother tongue) and responses to a questionnaire about how stuttering affects their daily lives, (b) captures both read speech (using the Rainbow Passage) and spontaneous speech (through image description tasks) for each participant and (c) includes detailed annotations of five stutter types: blocks, prolongations, interjections, sound repetitions and word repetitions. We present a comprehensive analysis of the dataset, including the data collection procedure, experience summarization of people who stutter, severity assessment of stuttering events and technical validation of the collected data. The dataset is released as an open access to further speech technology development. © 2025 IEEE.

关键词： Indian stuttered speech dataset Intelligibility assessment read speech spontaneous speech Stuttering event detection

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Overview of Security Approaches for Quantum Computing in the Current Era 3

Overview of Security Approaches for Quantum Computing in the...

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

作者： Hansora, Pinal Prajapati, Viral Thakkar, Amit Rathod, Ajaysinh Mannanuddin, Khaja Alsarmi, Haitham Said Naseer Cspit Charusat Cse Department Changa388421 India Snpit It Department Umrakh India Department of Computer Science & Design Gujarat Vasad India Sr University School of Cs & Ai Department of Computer Science & Engineering Hasanparthy Warangal India Sohar International Bank Muscat Oman

ISBN: (纸本)9798331509675

Quantum computing are breakthroughs aimed to revolutionize several sectors, although they also present considerable cybersecurity threats. Quantum computers can manage quite greater quantities of information than the most powerful conventional supercomputers and has the capability to do complex computations. Despite being in the early stages of development, quantum computers have the potential to be regarded as cybersecurity concerns. Quantum computing is a phenomenon that should be seen as a technology primed to facilitate significant innovations in the future, while also presenting different concerns. The quantum cybersecurity threat encompasses data breaches of sensitive health and financial information, challenges to the integrity of digital assets, and vulnerabilities in the encryption supporting cryptocurrencies, among other possible risks. This review paper focuses on quantum computing and its effects on different security methods in the present generation. It covers the quantum computer threats to cryptography, post quantum cryptography and a comparison of different approaches to cryptography. This work also analyzes machine learning and ai in quantum security and its challenges and trends for the future. The literature review has incorporated references from more than 25 reputed Scopus-listed journal and conference proceeding. © 2025 IEEE.

关键词： Cybersecurity

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A Comprehensive Review on Deep Learning Based Fall Detection in Elderly People

A Comprehensive Review on Deep Learning Based Fall Detection...

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Machine Learning and Autonomous Systems (ICmlAS), International Conference on

作者： S. Venkata Suryanarayana Lakshmi H.N Posimsetti T Chiranjeevi Swamy D. Bhanu Mahesh Department of Computer Science and Engineering (Data Science) CVR College of Engineering Hyderabad India Department of Computer Science and Engineering (AI&ML) CVR College of Engineering Hyderabad India Department of Artificial Intelligence and Machine Learning Swarnandhra College of Engineering and Technology Narsapuram Andhra Pradesh India Department of Computer Science and Engineering KMIT Hyderabad India

ISBN: (数字)9798331505745

ISBN: (纸本)9798331505752

Elderly people are more vulnerable to falls, which can result in serious injuries, a lower quality of life, and higher medical expenses. Traditional fall detection methods, such as wearable sensors or vision-based systems, face limitations in accuracy, comfort, and practicality. Recent advancements in deep learning have significantly improved fall detection capabilities by leveraging powerful computational models for data analysis and feature extraction. With an emphasis on important methodologies, architectures, and datasets utilized in the area, this paper offers a thorough literature analysis of deep learning-based fall detection strategies. It investigates several deep learning techniques applied to sensor-based and video-based fall detection systems, such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and hybrid models. The review also emphasizes the difficulties encountered in practical application, including data privacy and computing cost, and the need for robust datasets. Finally, future research directions are proposed to address current limitations and improve fall detection systems' dependability and effectiveness for elderly care.

关键词： Deep learning Recurrent neural networks Reviews Computational modeling Convolutional neural networks Fall detection Older adults Wearable sensors Long short term memory Injuries

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Emotion Recognition in Visual Art on Color and Texture Analysis using Improved Vision Transformer

Emotion Recognition in Visual Art on Color and Texture Analy...

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Intelligent Systems and Computational Networks (ICISCN), International Conference on

作者： K G Suma Zaed Balasm N. Sasirekha Veera Manikandan P K. Sandhya Department of AI & ML School of Computer Science and Engineering Vellore Institute of Technology Andhra Pradesh India Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Department of Electronics and Communication Engineering Sona College of Technology Salem India Department of Electrical and Electronics Engineering Dhanalakshmi Srinivasan College of Engineering and Technology India Department of Computer Science and Engineering (AI&ML) Mohan Babu University Tirupati India

ISBN: (数字)9798331529246

ISBN: (纸本)9798331529253

Nowadays, Emotion Recognition plays a key role in social communication interaction in which human emotions are interpreted from facial expressions and speech. Several challenges occur in emotion recognition such as capturing context of any emotion also facial attributes make the recognition process complex. To overcome these drawbacks Improved Vision Transformer (IViT) classifier is proposed for emotion recognition. Initially, the input data is collected from the Japanese Female Facial Expression (JAFFE) to collect color features and visual features. Then, the input data is processed using min max scaling method which normalizes pixel values. After that, colour and texture-based features are extracted by utilizing Rotation Invariant Local Binary Patterns (RI-LBP) with Hue Saturation and Value (HSV) and Red Green Blue (RGB). Finally, classification is performed by IViT, which processes images as sequences of patches. Hence, the proposed IViT model achieved better output when compared to existing Visual–Audio Attention Network (VAANet) in terms of accuracy by obtaining 96.6% respectively.

关键词： Emotion recognition Visualization computer vision Art Accuracy Image color analysis Face recognition Speech recognition Feature extraction Transformers

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Groundbreaking taxonomy of metaverse characteristics

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Artificial Intelligence Review 2025年第8期58卷 1-50页

作者： Sadeghi-Niaraki, Abolghasem Rahimi, Fatema Azlan, Nur Alya Emanuelle Binti Song, Houbing Ali, Farman Choi, Soo-Mi Department of Computer Science and Engineering and Convergence Engineering for Intelligent Drone XR Research Center Sejong University Seoul Korea Republic of BaltimoreMD21250 United States Department of Applied AI School of Convergence College of Computing and Informatics Sungkyunkwan University Seoul03063 Korea Republic of

The Metaverse, a dynamic and immersive virtual realm, has captured the imagination of researchers and enthusiasts worldwide. This survey paper aims to introduce a groundbreaking taxonomy for the characteristics of the Metaverse offering a structured and adaptable framework that extends beyond existing categorizations by incorporating dynamic transformations. Unlike prior taxonomies, which often focus on fixed attributes, our approach emphasizes the dynamic evolution of Metaverse characteristics. Through an extensive review of published literature, this study explores key technological, social, economic, and ethical dimensions of the Metaverse. It introduces a process-oriented classification based on 23 distinct characteristics, including immersification, spatiotemporalification, interactification, persistentification, presentification, personification, unification, imaginification, economification, uncertaintification, and credification. By mapping these evolving aspects, we provide a structured and future-proof foundation for understanding the Metaverse’s continuous development. This survey establishes a new standard for comprehensiveness and innovation, shedding light on the diverse facets that have been explored in literature. Through this novel taxonomy, we provide a detailed map of the current landscape and offer insights that pave the way for future research and development in this burgeoning digital frontier. © The Author(s) 2025.

关键词： Comprehensive analysis Futuristic exploration Metaverse characteristics Paradigm-shifting taxonomy Revolutionary categorization

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Novel Computational Photography for Soft-Focus Effect in Automatic Post Production

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IEEE Transactions on Image Processing 2025年 34卷 2560-2574页

作者： Tsai, Hao-Yu Tsai, Morris C.-H Huang, Scott C.-H Wu, Hsiao-Chun National Tsing Hua University Department of Electrical Engineering Hsinchu300044 Taiwan Louisiana State University School of Electrical Engineering and Computer Science Baton RougeLA70803 United States Yuan Ze University Innovation Center for AI Applications Chungli Taoyuan City320315 Taiwan

The well-known soft-focus effect, which relies on either special optical filters or manual post-production techniques, has been intriguing and powerful in photography for quite a while. Nonetheless, how to impose the soft-focus effect automatically simply using sophisticated image-processing (computational photography) algorithms has never been addressed in the literature to the best of our knowledge. In this work, we would like to make the first-ever attempt to design an automatic, optical-filter-free approach to create the appropriate soft-focus effects desired by individual users. Our approach is first to investigate the physical optical filter, namely Kenko Black Mist No. 5, and estimate the corresponding kernel matrix (i.e., the system impulse response matrix) using our proposed novel irradiance-domain kernel-matrix estimation framework. Furthermore, we demonstrate that it is not feasible to find a kernel matrix that precisely characterizes the soft-focus effect by just using a pixel-value-domain image (a regular photo) in post production. To combat the aforementioned problem, we establish a novel pixel-value-to-pseudo-irradiance map such that the pseudo irradiance-domain image can be obtained directly from any pixel-value-domain image. Finally the soft-focus effect can be created from the two-dimensional convolution between the pseudo irradiance-domain image and the estimated kernel. To evaluate our proposed automatic scheme for soft-focus effect, we compare the results from our proposed new scheme and the physical optical filter in terms of the DCT-KLD (Kullback-Leibler divergence of discrete cosine transform) and the conventional PSNR (peak-signal-to-noise ratio). Experiments show that our proposed new scheme can achieve very small DCT-KLDs and very large PSNRs over the ground truth, namely the results from the physical optical filter. © 1992-2012 IEEE.

关键词： Tomography

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Machine Learning and Artificial Intelligence for Predictive Modeling in Antimicrobial Resistance Data Sets, Challenges, and Future Directions

Machine Learning and Artificial Intelligence for Predictive ...

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Smart Systems for applications in Electrical sciences (ICSSES), International Conference on

作者： Ajeeb Sagar Vinothkumar Kolluru Ujjwal Jaiswal Gaurav Kumavat Shreyas Rajendra Hole Ankit Kumar Computer science Engineering (AI&ML) Dayananda Sagar University Bangalore India Department of Data science Stevens Institute of Technology Hoboken U.S.A. Computer Science and Engineering PES UNIVERSITY Bangalore India

ISBN: (数字)9798331507671

ISBN: (纸本)9798331507688

The phenomenon of Antimicrobial Resistance (AMR) is emerging strategically rather quickly and efficiently on a global scale. Because of due AMR Corona-virus outbreaks, previously effective antibiotics are becoming obsolete. The situation is only becoming dire as a result of chronic infections, increased deaths and subsequent healthcare expenditures. Increasingly, Artificial Intelligence solutions and Machine Learning strategies are contributing towards optimizing large volume data sets and offer introduction of new intervention methodologies. Within the scope of the current work, we aim to focus on data set amalgamation through Open-Source genomic data along with phenotype and environmental data sets within ml frameworks. While genomic datasets explain portion of resistance around genes, phenotype datasets gather ‘recorded’ datasets of resistance patterns and Environmental datasets monitor the distribution of AMR loops. Opportunities for improving model ethics, integration of data, and addressable generalization gaps to subsequently increase prediction accuracy are studied in focus. Predictability in AMR detection through leveraging ai increases accuracy and optimizes clinical decision-making alongside public health responsiveness. With the help of exact models, it is also possible to predict alterations in the resistance evolution timeline with new resistance genes and while clinically useful to determine right treatment options, they can aid clinical decisions. ai and ml mechanisms enable streamlined AMR surveillance, effectively guiding the direction for inline policy and containment control centers for antibiotic sustainability frameworks. ai, along with ml developments and emergence, is looked upon to take the lead in delays in AMR deployment. ml would intensively work in maintaining the effectiveness of antibiotics and redefining infection healthcare management.

关键词： Ethics Phenotypes Explainable ai Antibiotics Genomics Predictive models Data models Bioinformatics Public healthcare Immune system

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