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Multi-trial Vector-based Whale Optimization Algorithm

Journal of Bionic engineering 2024年第3期21卷 1465-1495页

作者： Mohammad H.Nadimi-Shahraki Hajar Farhanginasab Shokooh Taghian Ali Safaa Sadiq Seyedali Mirjalili Faculty of Computer Engineering Najafabad BranchIslamic Azad UniversityNajafabad***Iran Big Data Research Center Najafabad BranchIslamic Azad UniversityNajafabad***Iran Department of Computer Science Nottingham Trent UniversityNottinghamNG118NSUK Centre for Artificial Intelligence Research and Optimisation Torrens UniversityBrisbane4006Australia

The Whale Optimization Algorithm(WOA)is a swarm intelligence metaheuristic inspired by the bubble-net hunting tactic of humpback *** spite of its popularity due to simplicity,ease of implementation,and a limited number of parameters,WOA’s search strategy can adversely affect the convergence and equilibrium between exploration and exploitation in complex *** address this limitation,we propose a new algorithm called Multi-trial Vector-based Whale Optimization Algorithm(MTV-WOA)that incorporates a Balancing Strategy-based Trial-vector Producer(BS_TVP),a Local Strategy-based Trial-vector Producer(LS_TVP),and a Global Strategy-based Trial-vector Producer(GS_TVP)to address real-world optimization problems of varied degrees of ***-WOA has the potential to enhance exploitation and exploration,reduce the probability of being stranded in local optima,and preserve the equilibrium between exploration and *** the purpose of evaluating the proposed algorithm's performance,it is compared to eight metaheuristic algorithms utilizing CEC 2018 test ***,MTV-WOA is compared with well-stablished,recent,and WOA variant *** experimental results demonstrate that MTV-WOA surpasses comparative algorithms in terms of the accuracy of the solutions and convergence ***,we conducted the Friedman test to assess the gained results statistically and observed that MTV-WOA significantly outperforms comparative ***,we solved five engineering design problems to demonstrate the practicality of *** results indicate that the proposed MTV-WOA can efficiently address the complexities of engineering challenges and provide superior solutions that are superior to those of other algorithms.

关键词： Swarm intelligence algorithms Metaheuristic algorithms Optimization engineering design problems Whale optimization algorithm

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AKDC: Ambiguous Kernel Distance Clustering Algorithm for COVID-19 CT Scans Analysis

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IEEE Transactions on Systems, Man, and Cybernetics: Systems 2024年第10期54卷 6218-6229页

作者： Singh, Pritpal Huang, Yo-Ping The Quantum Optimization Research Lab Department of Data Science and Analytics Central University of Rajasthan Ajmer305817 India National Taipei University of Technology Department of Electrical Engineering Taipei10608 Taiwan National Taipei University Department of Computer Science and Information Engineering New Taipei City23741 Taiwan Chaoyang University of Technology Department of Information and Communication Engineering Taichung41349 Taiwan

Conventional soft clustering algorithms perform well on linearly distributed features, but their performance degrades on nonlinearly distributed features in high-dimensional space. In this study, a novel soft clustering algorithm, the ambiguous kernel distance clustering (AKDC) algorithm, is presented. This algorithm is developed by applying ambiguous set theory and the Gaussian kernel function. The ambiguous set theory defines the ambiguities inherent in each feature with four membership values: 1) true;2) false;3) true-ambiguous;and 4) false-ambiguous. The degree of membership values here forms a low-dimensional feature space that is not linearly distributed. Therefore, these nonlinearly distributed membership values are mapped into a high-dimensional feature space using the Gaussian kernel function. This study focuses on performing cluster analysis of computerized tomography scans of COVID-19 (CTSC-19) cases using AKDC. COVID-19, recognized as one of the most life-threatening diseases of this century, is highly contagious, and early diagnosis may prevent one-to-one transmission. Extensive empirical studies have been conducted with different types of CTSC-19 to demonstrate its effectiveness against existing kernel-based clustering and nonkernel-based clustering algorithms, namely mercer kernel fuzzy c-mean (MKFCM), kernel generalized FCM (KGFCM), kernel intuitionistic fuzzy entropy c-means (KIFECMs), morphological reconstruction and membership filtering clustering (FRFCM), and intuitionistic FCM based on membership information transferring and similarity measurements (IFCM-MS). The effectiveness of the proposed algorithm compared to the existing algorithms is evaluated using standard statistical metrics, such as dice index (DI), Jaccard index (JI), structural similarity index (SI), and correlation coefficient (CC). The empirical results show that AKDC is more effective than existing algorithms based on DI, JI, SI, and CC. © 2013 IEEE.

关键词： Clustering algorithms

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EmoSync: Facial Emotion Detection for Adaptive Music Playback 3

EmoSync: Facial Emotion Detection for Adaptive Music Playbac...

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3rd International Conference on Artificial Intelligence For Internet of Things, AIIoT 2024

作者： Devarajan, Devikanniga Rajkumar, K. GITAM Deemed to be University Computer Science and Engineering Nagadenahalli Doddabalpur Karnataka Bangalore561203 India SRM Institute of Science and Technology Department of Data Science and Business Systems School of Computing Tamil Nadu Kattankulathur603 203 India

ISBN: (纸本)9798350372120

This work investigates using Convolutional Neural Networks (CNN) and VGGNet architecture, for realtime emotion detection. Raw data from the FER-2013 dataset is transformed from CSV format into organized image directories, segmented by distinct emotions. The resulting model, equipped with convolutional, pooling, and dense layers, effectively classifies emotions from 48x48 grayscale images. In application, the model overlays detected emotions onto a webcam stream. An integrated music player module synchronizes music with the subject's emotions. Overall, this research explores a comprehensive interactive system focused on recognizing and responding to human emotions. Without further training data, our model, to the best of our understanding, achieves modern single-network accuracy of 74.57% on FER2013 © 2024 IEEE.

关键词： Python

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EMPA-YOLO:A Lightweight Real-time Weed Detection Method Suitable for Natural

EMPA-YOLO:A Lightweight Real-time Weed Detection Method Suit...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Wang, Yuzhe Chen, Jiahao Duan, Xiaodong Li, Zhuohui College of Mechanical and Electronic Engineering Dalian China College of Computer Science and Engineering Dalian China Seac Key Laboratory of Big Data Applied Technology Dalian China

ISBN: (纸本)9781665410205

Weed detection is crucial for the healthy growth of crops, yet existing detection models struggle to perform high-accuracy real-time detection of weeds in natural environments on edge computing devices. This paper introduces EMPA-YOLO, a model designed for rapid, accurate, real-time weed detection on low-performance edge computing devices. It incorporates an efficient multi-scale convolutional structure, C3EMSC, and a lightweight, adaptive weight subsampling layer, LAWDS, into YOLOv5s. Additionally, a logical distillation algorithm, AlignSoftTarget, is proposed for knowledge distillation. Validation on a mixed dataset of crops and weeds showed that EMPA-YOLO improved mAP50 by 11.2%, reduced parameter count by 4.8M, decreased computational load by 8.7GFLOPs, and increased inference frame rate by 58% compared to the original YOLOv5s algorithm. When compared to YOLOv3, YOLOv5s, YOLOv6, YOLOv8s, and RT-DETR, inference speed improved by 89.2%, 60.8%, 77.5%, 76.5%, and 94.7%, respectively, with mean accuracy enhancements of 7.3%, 11.2%, 16.7%, 0.9%, and 1.2%. Real-world testing on edge computing devices met real-time detection requirements, proving its efficacy and practicality in weed detection. © 2024 IEEE.

关键词： edge computing knowledge distillation lightweight model model compress ion YOLO

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Expert Patient Interaction Language Model (EPILM)

Expert Patient Interaction Language Model (EPILM)

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Advances in Computing, Communication and Networking (ICAC2N), International Conference on

作者： Pratham Gala Pooja Vartak Jay Vithlani Naman Chheda Dept. of Computer Science and Engineering (Data Science) Dwarkadas J Sanghvi College of Engineering Mumbai India Dept. of Computer Science and Engineering (Data Science) Mumbai India

ISBN: (数字)9798350356816

ISBN: (纸本)9798350356823

Today’s youth encounter numerous mental health challenges, many of which can be effectively addressed through professional intervention. However, there is a notable reluctance among young individuals to seek professional mental health services. The integration of language models in providing mental health support offers a non-intrusive and accessible approach for young people to engage with professional assistance for their mental health concerns. Although existing language models like GPT 3.5, Falcon-7B model have been effective, they also present certain challenges like high dependability of data from sources like Reddit, computational obstacles hindering access, and a preference for certain models over superior alternatives have impeded advancements in this area. that require thorough examination. Issues related to biases and the model’s performance across diverse scenarios underscore the necessity for more intelligent strategies. The proposed method involves enhancing the Falcon-7B model through the application of quantization low rank adaptation (Q-LoRA). This technique reduces the computational requirements and simplifies the accessibility for individuals equipped with the necessary technology while providing the average bertSCORE 85%. By addressing these issues, paper’s objective is to refine mental health support systems to benefit a broader audience.

关键词： Training Adaptation models Quantization (signal) Social networking (online) Computational modeling Graphics processing units Mental health Chatbots data models Tuning

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FedTop:a constraint-loosed federated learning aggregation method against poisoning attack

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Frontiers of computer science 2024年第5期18卷 233-235页

作者： Che WANG Zhenhao WU Jianbo GAO Jiashuo ZHANG Junjie XIA Feng GAO Zhi GUAN Zhong CHEN School of Computer Science Peking UniversityBeijing 100871China Peking University Chongqing Research Institute of Big Data Chongqing 401329China China Unicom Beijing 100033China National Engineering Research Center for Software Engineering Peking UniversityBeijing 100871China

Federated learning(FL)is a decentralized machine learning paradigm,which has significant advantages in protecting data privacy[1].However,FL is vulnerable to poisoning attacks that malicious participants perform attacks by injecting dirty data or abnormal model parameters during the local model training and aim to manipulate the performance of the global model[2].

关键词： constraint loose aggregation

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LESaET: Low-Dimensional Embedding Method for Link Prediction Combining Self-Attention and Enhanced-TuckER

LESaET: Low-Dimensional Embedding Method for Link Prediction...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Ding, Lichao Zhao, Jing Lu, Kai Hao, Zenghao Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (纸本)9781665410205

Knowledge graphs (KGs) provide a structured representation of the real world through entity-relation triples. However, current KGs are often incomplete, typically containing only a small fraction of all possible facts. This involves inferring missing content from existing information, a task known as link prediction. Existing methods in the field of link prediction struggle with controlling the dimensionality of embedding vectors or suffer from overly complex models. In order to tackle these challenges, we introduce a method in this paper, named Low-Dimensional Embedding Method for Link Prediction Combining Self-Attention and Enhanced-TuckER (LESaET). LESaET leverages both self-attention mechanisms and tensor factorization to learn expressive contextual-enhanced representations of KGs. Specifically, LESaET employs the multi-head self-attention mechanism of Transformer as an encoder to capture the mutual information between entities and relationships, and utilizes Enhanced-TuckER as a decoder, ultimately achieving expressive low-dimensional embeddings for link prediction tasks. LESaET demonstrates competitive performance compared to advanced methods on standard datasets. © 2024 IEEE.

关键词： Prediction models

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Enhanced Heart Disease Diagnosis in MRI Images Using Swan Adapt Optimized Thresholding Segmentation with U-Net Model 5

Enhanced Heart Disease Diagnosis in MRI Images Using Swan Ad...

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

作者： Steephan Amalraj, J. Kumaresan, T. Pravin Savaridass, M. Vaanathi, S. Bannari Amman Institute of Technology Computer Science and Engineering Sathyamangalam India Bannari Amman Institute of Technology Electronics and Instrumentation Engineering Sathyamangalam India Bannari Amman Institute of Technology Atrificial Intelligence and Data Science Sathyamangalam India

ISBN: (数字)9798350379945

ISBN: (纸本)9798350379945

The diagnosis of heart disease in MRI images is a critical application within the area of medical imaging. Conventional segmentation algorithms have difficulty in the accurate delineation of normal and sick heart tissues due to problems such as noise sensitivity, inaccuracy in boundaries, and sensitivity to parameters. In this work, we propose a novel strategy of thresholding based on image clusters, which we term SwanAdaptOpti thresholding for the segmentation of MRI images. The proposed system also utilizes Watershed segmentation with markers refined to enhance the segmentation of diseased heart tissues. Indeed, our approach is capable of achieving satisfactory performance in delineating normal and sick heart images, avoiding issues like overfitting, underfitting, and local optima. The overall robustness of our approach is demonstrated by extensive experimentation, which reveals the potential of our approach for the accurate and reliable diagnosis of heart disease from MRI images with an accuracy of 96%. © 2024 IEEE.

关键词： Image segmentation

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Heart Diseases Prediction Using Machine Learning Techniques: A Case Study from Jordan 25

Heart Diseases Prediction Using Machine Learning Techniques:...

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25th International Arab Conference on Information Technology, ACIT 2024

作者： Alazaidah, Raed Safaqah, Mohammed Abu Alzoubi, Haneen Omar, Hani Al-Safarini, Maram Y. Elsoud, Esraa Abu Elrashidi, Ali Alluwaici, Mo'Ath Owaida, Suhaila Abu Zarqa University Faculty of It Department of Data Science and Ai Zarqa Jordan Department of Mechanical Engineering Muscat Oman Applied Science Private University Faculty of It Department of Computer Science Amman Jordan Zarqa University Faculty of It Department of Computer Science Zarqa Jordan University of Business and Technology Department of Electrical Engineering Jeddah21432 Saudi Arabia Al-al-Bayt University Faculty of Information Technology Department of Computer Science Mafraq Jordan

ISBN: (纸本)9798331540012

Heart disease is one of the most common diseases in Jordan. It is a major reason of death among Jordanian adult citizens. Worldwide, an average of 56,000 people dies each day or one death every 1.5 seconds. Hence, this research is interested in the early prediction of this disease among Jordanian people. To achieve this main objective, Machine Learning (ML) is utilized through a large number of classification models and considering five well-known evaluation metrics. These classification models have been trained on a primary dataset that has been collected for the purpose of the research. The results revealed that RandomForest and RandomCommittee are the best classification models to handle the task of the early prediction of Heart disease in Jordan. © 2024 IEEE.

关键词： Adversarial machine learning

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Diabetic Foot Ulcer Detection and Classification using ConvMixer Model 5

Diabetic Foot Ulcer Detection and Classification using ConvM...

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5th International Conference on Smart Electronics and Communication, ICOSEC 2024

作者： Anusha, N. Vidhya, R. Jani Anbarasi, L. Dhanya, D. Pushparani, S. Mohan, Geetha Vellore Institute of Technology School of Computer Science and Engineering Department of IoT Vellore Campus Vellore India Agni College of Technology Department of Information Technology Chennai India Vellore Institute of Technology School of Computer Science and Engineering Chennai India Mar Ephraem College of Engineering and Technology Department of Artificial Intelligence and Data Science Elavuvilai India Meenakshi College of Engineering Department of Cse Chennai India Institute of Applied Technology Computer Science Abu Dhabi United Arab Emirates

ISBN: (纸本)9798331504403

Diabetic foot ulcer (DFUs) are a common and significant consequence of diabetes, frequently resulting in severe illness and amputations if not promptly identified and treated. The growing occurrence of DFUs is a substantial obstacle to healthcare globally. Precise and prompt categorization of DFUs is essential for efficient treatment and avoidance of consequences. This research analyzed ConvMixer model for comprehensive classification designed to accurately evaluate DFU. The DFU images are preprocessed using the watershed algorithm that helps in separating different regions of an image, such as the ulcer and surrounding tissues, to improve the neural network's ability to extract high-quality information. The framework utilizes depthwise and pointwise convolutions to mix spatial and channel-wise information in an efficient manner to effectively extract pertinent information from various clinical DFU dataset. The proposed work features a multi-layered depthwise and pointwise convolutional architecture, created especially to manage the complex patterns and variances found in various diabetic foot ulcer stages and variants (DFUs). By integrating these convolutions and skip associations, the network facilitates hierarchical extraction of features and robust representation of features. Experimental results on a substantial and varied DFU dataset demonstrate that the ConvMixer model achieves an impressive accuracy rate of 97.16%, with a precision, F1-score and a sensitivity of 97%, 97% and 99.08% respectively. © 2024 IEEE.

关键词： Diseases

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