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Novelty of Different Distance Approach for Multi-Criteria Decision-Making Challenges Using q-Rung Vague Sets

computer Modeling in engineering & sciences 2024年第6期139卷 3353-3385页

作者： Murugan Palanikumar Nasreen Kausar Dragan Pamucar Seifedine Kadry Chomyong Kim Yunyoung Nam Saveetha School of Engineering Saveetha Institute of Medical and Technical SciencesChennai602105India Department of Mathematics Faculty of Arts and ScienceYildiz Technical UniversityEsenlerIstanbul34220Turkey Department of Operations Research and Statistics Faculty of Organizational SciencesUniversity of BelgradeBelgrade11000Serbia College of Engineering Yuan Ze UniversityTaoyuan City320315TaiwanChina Department of Applied Data Science Noroff University CollegeKristiansand4612Norway Artificial Intelligence Research Center(AIRC) Ajman UniversityAjman346United Arab Emirates Department of Electrical and Computer Engineering Lebanese American UniversityByblos1102-2801Lebanon ICT Convergence Research Center Soonchunhyang UniversityAsan31538South Korea Department of Computer Science and Engineering Soonchunhyang UniversityAsan31538South Korea

In this article,multiple attribute decision-making problems are solved using the vague normal set(VNS).It is possible to generalize the vague set(VS)and q-rung fuzzy set(FS)into the q-rung vague set(VS).A log q-rung normal vague weighted averaging(log q-rung NVWA),a log q-rung normal vague weighted geometric(log q-rung NVWG),a log generalized q-rung normal vague weighted averaging(log Gq-rung NVWA),and a log generalized q-rungnormal vagueweightedgeometric(logGq-rungNVWG)operator are discussed in this *** is provided of the scoring function,accuracy function and operational laws of the log q-rung *** algorithms underlying these functions are also described.A numerical example is provided to extend the Euclidean distance and the Humming ***,idempotency,boundedness,commutativity,and monotonicity of the log q-rung VS are examined as they facilitate recognizing the optimal alternative more quickly and help clarify *** chose five anemia patients with four types of symptoms including seizures,emotional shock or hysteria,brain cause,and high fever,who had either retrograde amnesia,anterograde amnesia,transient global amnesia,post-traumatic amnesia,or infantile *** numbers q are used to express the results of the *** demonstrate the effectiveness and accuracy of the models we are investigating,we compare several existing models with those that have been developed.

关键词： Vague set aggregating operators euclidean distance hamming distance decision making

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Robust EEG-based Emotion Recognition Using an Inception and Two-sided Perturbation Model

arXiv

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arXiv 2024年

作者： Sartipi, Shadi Cetin, Mujdat Department of Electrical and Computer Engineering University of Rochester Rochester United States Department of Electrical and Computer Engineering Goergen Institute for Data Science University of Rochester Rochester United States

Automated emotion recognition using electroencephalogram (EEG) signals has gained substantial attention. Although deep learning approaches exhibit strong performance, they often suffer from vulnerabilities to various perturbations, like environmental noise and adversarial attacks. In this paper, we propose an Inception feature generator and two-sided perturbation (INC-TSP) approach to enhance emotion recognition in brain-computer interfaces. INC-TSP integrates the Inception module for EEG data analysis and employs two-sided perturbation (TSP) as a defensive mechanism against input perturbations. TSP introduces worst-case perturbations to the model's weights and inputs, reinforcing the model's elasticity against adversarial attacks. The proposed approach addresses the challenge of maintaining accurate emotion recognition in the presence of input uncertainties. We validate INC-TSP in a subject-independent three-class emotion recognition scenario, demonstrating robust performance. © 2024, CC BY-NC-SA.

关键词： Emotion Recognition

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Evaluation of Adaptive Synthetic Resampling Technique for Imbalanced Breast Cancer Identification

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Procedia computer science 2024年 235卷 1000-1007页

作者： Tsehay Admassu Assegie Ayodeji Olalekan Salau Kanimozhi Sampath Rajkumar Govindarajan Sangeetha Murugan B. Lakshmi Department of Computer Science College of Engineering and Technology Injibara University Injibara Ethiopia Department of Electrical/Electronics and Computer Engineering Afe Babalola University Ado-Ekiti Nigeria Department of Computer Science & Engineering (Cyber Security) Madanapalle Institute of Technology and Science Madanapalle Andhra Pradesh India Department of Computer Science & Engineering (Data Science) Madanapalle Institute of Technology and Science Madanapalle Andhra Pradesh India Department of Computer Science & Engineering Madanapalle Institute of Technology and Science Madanapalle Andhra Pradesh India Department of Computer Science & Technology Madanapalle Institute of Technology and Science Madanapalle Andhra Pradesh India

As one of the most common types of cancer among women, breast cancer is a serious health concern worldwide. Early detection is crucial for successful treatment and improved survival rates. However, detecting breast cancer is challenging due to imbalanced classification, where the minority class (cancerous) is ominously smaller than the majority class (non-cancerous). In this paper, we explore the use of logistic regression (LR) and the adaptive synthetic resampling (ADASYN) technique to address imbalanced classification in breast cancer detection. To that end, we collected the Wisconsin Breast Cancer dataset, which contains 569 instances. The dataset is imbalanced, with 212 malignant (cancerous) cases and 357 benign (non-cancerous) cases. Then, we trained support vector machine, LR, K-nearest neighbor, gradient, and adaptive boosting on the imbalanced dataset. Finally, we trained these algorithms on resampled data with the ADASYN oversampling and we evaluated their performance using cross-validation score with 5-folds. The results of the experiment showed that using ADASYN with LR significantly improved the performance the LR model. The LR model achieves 99.46% accuracy on breast cancer diagnosis. Moreover, the confusion matrix shows that among the 188 samples, the model misclassified one cancerous instance. Thus, we concluded that the proposed model is effective for breast cancer diagnosis.

关键词： Breast cancer detection imbalanced classification machine learning Adaptive synthetic oversampling

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Enhancing Jailbreak Attacks via Compliance-Refusal-Based Initialization

arXiv

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arXiv 2025年

作者： Levi, Amit Himelstein, Rom Nemcovsky, Yaniv Mendelson, Avi Baskin, Chaim Department of Computer Science Technion - Israel Institute of Technology Department of Data and Decision Science Technion - Israel Institute of Technology School of Electrical and Computer Engineering Ben-Gurion University of The Negev

Jailbreak attacks aim to exploit large language models (LLMs) and pose a significant threat to their proper conduct;they seek to bypass models’ safeguards and often provoke transgressive behaviors. However, existing automatic jailbreak attacks require extensive computational resources and are prone to converge on suboptimal solutions. In this work, we propose Compliance Refusal Initialization (CRI), a novel, attack-agnostic framework that efficiently initializes the optimization in the proximity of the compliance subspace of harmful prompts. By narrowing the initial gap to the adversarial objective, CRI substantially improves adversarial success rates (ASR) and drastically reduces computational overhead—often requiring just a single optimization step. We evaluate CRI on the widely-used AdvBench dataset over the standard jailbreak attacks of GCG and AutoDAN. Results show that CRI boosts ASR and decreases the median steps to success by up to ×60. The project page, along with the reference implementation, is publicly available at . © 2025, CC BY-NC-SA.

关键词： Crime

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AI-Optimized Placement and Routing for PCB Design 9

AI-Optimized Placement and Routing for PCB Design

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9th International Conference on science, Technology, engineering and Mathematics, ICONSTEM 2024

作者： Sathyaraj, P. Arulkumar, S. Ajina, A. Beshir, Kedir Umasankar, L. Arularasan, A.N. Rmk College of Engineering And Technology Department of Electronics And Communication Engineering Tamil Nadu Puduvoyal601206 India Wachemo University Department of Electrical And Computer Engineering Ethiopia M S Ramaiah Institute of Technology Department of Artificial Intelligence And Machine Learning Bangalore560054 India S.A. Engineering College Department of Electrical And Electronics Engineering Tamil Nadu Tiruvallur600077 India Panimalar Engineering College Department of Artificial Intelligence And Data Science Tamil Nadu Chennai600123 India

ISBN: (纸本)9798350365092

Rapid electronic device development requires more complicated and densely packed PCB designs. These systems need properly placed and connected electrical components for best performance and reliability. Complexity and time restrictions are rendering manual methods ineffective. Therefore, AI-based PCB insertion and routing automation methods have been developed. This research suggests using AI to optimize PCB component arrangement and routing. The system uses machine learning and optimization to improve design efficiency. The AI system creates layouts that match performance requirements, decrease signal interference, and meet manufacturing limits using previous design data and user preferences. A learning module that updates its knowledge base, an algorithm that chooses between design objectives, and real-time user input are examples. Signal integrity, thermal issues, and manufacturability are considered while modifying design requirements. Evaluation of the suggested methodology shows considerable gains in design efficiency, time-to-market, and electronic system performance. This research improves AI-automated PCB design, enabling more complex and effective electrical systems. AI in PCB design will revolutionize the industry by solving complicated design problems and speeding up the development of cutting-edge electronics. © 2024 IEEE.

关键词： Reinforcement learning

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p-Mean Regret for Stochastic Bandits 39

p-Mean Regret for Stochastic Bandits

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Krishna, Anand John, Philips George Barik, Adarsh Tan, Vincent Y.F. Department of Electrical and Computer Engineering NUS Singapore CNRS-CREATE Department of Computer Science NUS Singapore Institute of Data Science NUS Singapore Department of Mathematics NUS Singapore

ISBN: (纸本)157735897X

In this work, we extend the concept of the p-mean welfare objective from social choice theory to study p-mean regret in stochastic multi-armed bandit problems. The p-mean regret, defined as the difference between the optimal mean among the arms and the p-mean of the expected rewards, offers a flexible framework for evaluating bandit algorithms, enabling algorithm designers to balance fairness and efficiency by adjusting the parameter p. Our framework encompasses both average cumulative regret and Nash regret as special cases. We introduce a simple, unified UCB-based algorithm (EXPLORE-THEN-UCB) that achieves novel p-mean regret bounds. Our algorithm consists of two phases: a carefully calibrated uniform exploration phase to initialize sample means, followed by the UCB1 algorithm of Auer et al. Under mild assumptions, we prove that our algorithm achieves a p-mean regret bound of O^{∼(rT2^k|1p|)} for all p ≤ −1, where k represents the number of arms and T the time horizon. When −1 < p < 0, we achieve a regret bound of O^{∼(k⁴³ T⁻⁴¹)}. For the range 0 < p ≤ 1, we achieve a p-mean regret scaling as O^{∼(qT^k)}, which matches the previously established lower bound up to logarithmic factors. This result stems from the fact that the p-mean regret of any algorithm is at least its average cumulative regret for p ≤ 1. In the case of Nash regret (the limit as p approaches zero), our unified approach differs from prior work of Barman et al., which requires a new Nash Confidence Bound algorithm. Notably, we achieve the same regret bound up to constant factors using our more general method.

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Establishment of an Effective Brain Tumor Classification System through Image Transformations and Optimization Techniques 1

Establishment of an Effective Brain Tumor Classification Sys...

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1st IEEE International Conference on Innovations in High Speed Communication and Signal processing, IHCSP 2023

作者： Arora, Gaurav Kumar Taj Mahaboob, Shaik Adilakshmi, S. Rani, Sasi Kala Kasthuri, A. Mamodiya, Udit IBM India Pvt. Ltd Uttar Pradesh Greater Noida India Jntua College of Engineering Department of ECE Andhra Pradesh Pulivendula India Vignana Bharathi Institute of Technology Department of Data Science Telangana India Sri Krishna College of Engineering and Technology Department Computer Science and Engineering Coimbatore India Vinayaka Mission's Kirupananda Variyar Engineering College Department Computer Science and Engineering Salem India Poornima Institute of Engineering and Technology Department of Electrical Engineering Rajasthan Jaipur India

ISBN: (纸本)9798350345957

In recent years, researchers and health care professionals have been interested in medical image fusion, which combines images of the human body, organs, and cells with general information to address medical difficulties. computer-aided imaging techniques offer quantitative assessment of images under validation, which helps doctors make unbiased, objective decisions quickly. Multi-sensor and multi-source image fusion models also diversify healthcare analysis characteristics. It led to strong information processing that reveals otherwise invisible details. The fused pictures can help pinpoint anomalies. Multimodal image fusion combines complementing pictures into one. The fused image is ideal for visual perception or computer segmentation, feature extraction, and object recognition. The goal is to create a single fused image with more information than the source photos alone. The fused image combines the source images' complementary and shared elements, delivering enhanced subjective and objective detail. In biomedical imaging, fusing multimodal images has shown useful for disease characterisation. Medical image analysis uses CT, MRI, and PET (PET). In this research paper, we developed a Multimodality Image Fusion using Centre based genetic algorithm (CBGA) and fuzzy logic (FL) technique with lifting wavelet transform (LWT) for fusion of spatially registered MRI and CT images. We also proposed an efficient Multimodality Medical Image Fusion system based on DWT and binary crow search optimization (BCSO) algorithm for fusing MRI and CT images. The performance of the suggested models is validated against many benchmark medical datasets. The experiment showed that the proposed models outperformed all others. © 2023 IEEE.

关键词： Magnetic resonance imaging

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Building Intelligent Automated Real-Time data Analysis Systems Through Machine Learning 2

Building Intelligent Automated Real-Time Data Analysis Syste...

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2nd IEEE International Conference on Futuristic Technologies, INCOFT 2023

作者： Rathee, Sahil Vigenesh, M. Priydarshini, Kala Vijaykumar, R. Singh, Shweta Seth, Jyoti Department of Computer Science and Information Technology Karnataka Bangalore India Karpagam Academy of Higher Education Department of Computer Science Engineering Coimbatore641021 India Prince Shri Venkateshwara Padmavathy Engineering College Department of Electrical and Electronics Engineering Chennai127 India Vishwakarma Institute of Information Technology Department of Artificial Intelligence & Data Science Pune India Maharishi University of Information Technology Maharishi School of Engineering and Technology Uttar Pradesh India Vivekananda Global University Department of Computer Science & Engineering Jaipur India

ISBN: (纸本)9798350308846

This paper explores the capacity of using gadget-get-to-know-based facts evaluation structures to build intelligent, automated real-time statistics analysis systems. It examines the demanding situations present points evaluation systems pose and proposes a unique remedy. The proposed method leverages supervised and unsupervised devices and learning strategies to enable deep information on complex datasets. It then makes use of predictive skills to make clever selections with the aid of comparing input facts about databases. ultimately, the paper describes a case to demonstrate the effectiveness of this proposed machine and outlines implications for comparable implementations in the enterprise. This work is precious for information scientists and commercial enterprise decision-makers, as it outlines an ability to deploy reliable and accurate records analysis structures. © 2023 IEEE.

关键词： Accurate Deploying Enterprise Structures

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An Investigation into Spectral Efficiency Enhancement of Fiber-Optic Communication via High-Speed Spectrum Management 2

An Investigation into Spectral Efficiency Enhancement of Fib...

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2nd IEEE International Conference on Futuristic Technologies, INCOFT 2023

作者： Singh, Khushboo Lalitha, Sai Ravivarman, G. Sasirekha, R. Shinde, Swapnil K. Singh, Akhilendra Pratap Vivekananda Global University Department of Computer Science & Engineering Jaipur India Department of Computer Science and Information Technology Karnataka Bangalore India Department of Electrical and Electronics Engineering Karpagam Academy of Higher Education Coimbatore641021 India Prince Shri Venkateshwara Padmavathy Engineering College Department of Computer Science and Engineering Chennai India Vishwakarma Institute of Information Technology Department of Artificial Intelligence & Data Science Pune India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India

ISBN: (纸本)9798350308846

This research appears into the effectiveness of high-pace spectrum management as a way to beautify the spectral performance of fiber-optic conversation. that is finished by means of forecasting a dynamic optical visitors arrival pattern to reduce carrier-degree blocking off opportunity, and with the aid of allocating the maximum green bandwidth to every request based totally on a non-blocking priority-based totally scheduling algorithm. This study assesses the overall performance of modern-day systems, analyzes their shortcomings, and uses experimental consequences to evaluate their effectiveness for improving spectral efficiency. The findings endorse that there's ability for improvement in spectral efficiency with excessive-speed spectrum control. © 2023 IEEE.

关键词： Control Efficiency Excessive Improvement Spectrum

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A Hybrid GAN-CNN Framework for Low-Light Image Enhancement Combining Structural Noise Reduction and Perceptual Quality

A Hybrid GAN-CNN Framework for Low-Light Image Enhancement C...

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

作者： Gobinath, R. Boopathi, R. Vijayalakshmi, S. Disney, D. Anu Priyadharshini, P. Nirmala Maheshwari, K. E.G.S. Pillay Engineering College Nagapattinam611002 India Karpaga Vinayaga College of Engineering and Technology Department of Electrical and Electronic Engineering Tamilnadu 603308 India Sona College of Technology Department of Electronics and communication Engineering Tamilnadu Salem636005 India Sathyabama Institute of Science and Technology Department of Computer Science and Engineering Chennai600119 India Adithya Institute of Technology Department of Information Technology Coimbatore641107 India Sir Issac Newton College of Engineering and Technology Department of Artificial Intelligence and Data Science Nagapattinam611 102 India

ISBN: (纸本)9798331509828

Low light region is one of the difficult scenarios for surveillance system, especially noisy, low-quality images may lead to poor object detection and scene analysis. To tackle this problem, we introduce a hybrid GAN - CNN framework for low light image denoising and quality enhancement. We develop a framework that combines the strengths of Generative Adversarial Networks (GANs) for photorealistic image generation and Convolutional Neural Networks (CNNs) for robust noise reduction. First denoising and feature extraction is done by the CNN module, and the GAN part further refines the denoised output to improve the perceptual quality of the generated images. We train and validate the proposed model in standard low light image dataset and show superior PSNR, SSIM, and visual clarity compared to state of art methods. We show that the hybrid GAN-CNN model successfully trades between image denoising accuracy and visual realism, thereby providing a viable alternative for enhancing surveillance performance in lowlight conditions. Keywords: Image denoising, GAN-CNN hybrid, low-light enhancement, PSNR, SSIM, surveillance systems. © 2025 IEEE.

关键词： Gallium nitride

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