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Multi-orthogonal-oppositional enhanced African vultures optimization for combined heat and power economic dispatch under uncertainty

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Neural Computing and Applications 2025年第8期37卷 6097-6123页

作者： Rizk-Allah, Rizk M. Snášel, Václav Hassanien, Aboul Ella Basic Engineering Science Department Faculty of Engineering Menoufia University Shebin El-Kom32511 Egypt Faculty of Electrical Engineering and Computer Science VŠB-Technical University of Ostrava Poruba-Ostrava70800 Czech Republic Faculty of Computers and AI Cairo University Giza Egypt

This paper attempts to conceptualize a potent methodology by combining the African vultures optimization algorithm (AVOA) with a multi-orthogonal-oppositional strategy (M2OS), named AVO-M2OS, to address the nonconvexity and multidimensional nature of the combined heat and power economic dispatch (CHPED) problem under both crisp and uncertainty aspects. The AVO-M2OS uses the M2OS to simultaneously explore the search region, improving solutions’ diversity as well as solution quality. Therefore, AVO-M2OS can perform deeper exploration and exploitation features and thus mitigate the trapping at local optima, especially when tackling the more complicated nature of the CHPED problem. A three-stage analysis is conducted to assess the effectiveness of the proposed AVO-M2OS algorithm. During the first stage, the algorithm’s performance is evaluated on benchmark problems such as CEC 2005 and CEC 2019, employing statistical verifications and convergence characteristics. In the second stage, the significance of the results is evaluated using the nonparametric Friedman test to demonstrate that the results did not occur by chance. The results indicate that the AVO-M2OS algorithm outperforms the best existing algorithm (AVOA) by an average rank of the Friedman test exceeding 26% for the CEC 2005 suite while outperforming the gray wolf optimization (GWO) by 60% for the CEC 2019 suite. Moreover, the AVO-M2OS demonstrates exceptional performance compared to existing state-of-the-art algorithms, surpassing the best algorithm available by an average rank of the Friedman test that exceeds 41%. Finally, the AVO-M2OS’s applicability is achieved by minimizing the operational costs by finding the optimal power and heat generation scheduling for the CHPED problem. The recorded results realize that the AVO-M2OS algorithm offers accurate performance compared to competing optimizers, where it saves the operational cost of the 48-unit system by 24% on the original AVO variant. Furthermore, the u

关键词： Quality control

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Comparative Study of Facial Expression Recognition Using Facial Landmark in e-Learning System 11

Comparative Study of Facial Expression Recognition Using Fac...

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11th International Conference on Electrical engineering, computer science and Informatics, EECSI 2024

作者： Bachtiar, Fitra Abdurrachman Nainggolan, Cesilia Natasya Soetedjo, Riza Setiawan Rokhmawati, Retno Indah Muflikhah, Lailil Brawijaya University Department of Informatics Engineering Faculty of Computer Science Malang Indonesia Brawijaya University Faculty of Computer Science Department of Information Systems Malang Indonesia

ISBN: (纸本)9798350355314

The e-Iearning system or distance learning system has become important, especially during the COVID-19 pan-demic. Several tertiary institutions have made the e-Iearning system an alternative teaching and learning activity. Research on the use of e-Iearning needs to be explored to obtain innovative, creative, and fun learning. One aspect that can be examined in e-learning is students' emotional response to the learning activities. The emotional state of students directly or indirectly affects the learning process. The student's emotional responses can be measured by their facial expressions. In this study, an exploration was carried out on the facial expressions shown by students in HSS Learning, an e-Iearning for learning web programming. The student's facial expressions were extracted using 68 pairs of facial landmarks and additional 6 facial feature vectors. The total number of features used is 142. Machine learning methods for classification are applied to determine the performance of several classification models, including the k-Nearest Neighbor (k- NN) algorithm, Logistic Regression, and Support Vector Machine (SVM). Machine learning performance evaluation is done using the Fl-score and confusion matrix. The results show that the accuracy of the k-NN, Logistic Regression, and SVM are 91.4%, 92.2%, and 92.1 %, respectively. However, the Fl-score macro values are relatively low due to data imbalance with the k- NN, Logistic Regression, and SVM models are 0.250, 0.272, and 0.243, respectively. While the study provides insight into the student learning experience in e-Iearning settings, this study needs to be explored more. © 2024 IEEE.

关键词： Contrastive Learning

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Novel Optimized Feature Selection Using Metaheuristics Applied to Physical Benchmark Datasets

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computers, Materials & Continua 2023年第2期74卷 4027-4041页

作者： Doaa Sami Khafaga El-Sayed M.El-kenawy Fadwa Alrowais Sunil Kumar Abdelhameed Ibrahim Abdelaziz A.Abdelhamid Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt School of Computer Science University of Petroleum and Energy StudiesDehradun248001India Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Department of Computer Science Faculty of Computer and Information SciencesAin Shams UniversityCairo11566Egypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia

In data mining and machine learning,feature selection is a critical part of the process of selecting the optimal subset of features based on the target *** are 2n potential feature subsets for every n features in a dataset,making it difficult to pick the best set of features using standard ***,in this research,a new metaheuristics-based feature selection technique based on an adaptive squirrel search optimization algorithm(ASSOA)has been *** using metaheuristics to pick features,it is common for the selection of features to vary across runs,which can lead to *** of this,we used the adaptive squirrel search to balance exploration and exploitation duties more evenly in the optimization *** the selection of the best subset of features,we recommend using the binary ASSOA search strategy we developed *** to the suggested approach,the number of features picked is reduced while maximizing classification accuracy.A ten-feature dataset from the University of California,Irvine(UCI)repository was used to test the proposed method’s performance *** other state-of-the-art approaches,including binary grey wolf optimization(bGWO),binary hybrid grey wolf and particle swarm optimization(bGWO-PSO),bPSO,binary stochastic fractal search(bSFS),binary whale optimization algorithm(bWOA),binary modified grey wolf optimization(bMGWO),binary multiverse optimization(bMVO),binary bowerbird optimization(bSBO),binary hybrid GWO and genetic algorithm 4028 CMC,2023,vol.74,no.2(bGWO-GA),binary firefly algorithm(bFA),and *** results confirm the superiority and effectiveness of the proposed algorithm for solving the problem of feature selection.

关键词： Metaheuristics adaptive squirrel search algorithm optimization methods binary optimizer

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Industrial revolution 4.0 in water supply, wastewater and stormwater management: opportunities, challenges, and impacts

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Environmental Technology Reviews 2024年第1期13卷 144-168页

作者： Kuok, Kuok King Chiu, Po Chan Bakri, Muhammad Khusairy Bin Rahman, Md. Rezaur Yun, Chin Mei Faculty of Engineering Computing and Science Swinburne University of Technology Sarawak Campus Kuching Malaysia Faculty of Computer Science and Information Technology Universiti Malaysia Sarawak Kota Samarahan Malaysia Faculty of Engineering Universiti Malaysia Sarawak Kota Samarahan Malaysia

Industrial Revolution 4.0 (IR 4.0) is the revolution that promotes customized and flexible mass production technologies in automation and manufacturing processes. The application of industrial revolution 4.0, such as the Internet of Things (IoT), Cyber-Physical System (CPS), and big data, are used in the water industry to provide better water quality, accurate real-time data, etc. This paper compiles IR 4.0 technology applications, advantages, disadvantages, impacts, and challenges in the water sector, including water supply, wastewater treatment, and stormwater management. It is found that the current water industry is facing a few challenges, such as managing water services, balancing water costs with energy prices, maintaining aging water and wastewater infrastructure, and environmental issues. However, with the application of numerous IR 4.0 innovations, all the challenges can be solved or minimized. Meanwhile, the advantages and impacts of adopting IR 4.0 in water industries are thoroughly discussed. Despite the numerous advantages of IR 4.0, significant drawbacks have restricted the adoption of IR 4.0 in the water industry. The high start-up cost, reluctance, uncertainty, and problems of adopting IR 4.0 are some key challenges that need to be addressed. Generally, IR 4.0 provides more benefits to the community as it helps improve the water system’s performance and efficiencies and creates a more sustainable environment. © 2023 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Water supply

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An Improved Robust Sparse Convex Clustering

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Tsinghua science and Technology 2023年第6期28卷 989-998页

作者： Jinyao Ma Haibin Zhang Shanshan Yang Jiaojiao Jiang Gaidi Li Beijing Institute for Scientific and Engineering Computing Faculty of ScienceBeijing University of TechnologyBeijing 100124China School of Computer Science and Engineering University of New South WalesSydney 2052Australia

Convex clustering,turning clustering into a convex optimization problem,has drawn wide *** overcomes the shortcomings of traditional clustering methods such as K-means,Density-Based Spatial Clustring of Applications with Noise(DBSCAN)and hierarchical clustering that can easily fall into the local optimal ***,convex clustering is vulnerable to the occurrence of outlier features,as it uses the Frobenius norm to measure the distance between data points and their corresponding cluster centers and evaluate *** accurately identify outlier features,this paper decomposes data into a clustering structure component and a normalized component that captures outlier *** from existing convex clustering evaluating features with the exact measurement,the proposed model can overcome the vast difference in the magnitude of different features and the outlier features can be efficiently identified and *** solve the proposed model,we design an efficient algorithm and prove the global convergence of the *** on both synthetic datasets and UCI datasets demonstrate that the proposed method outperforms the compared approaches in convex clustering.

关键词： convex clustering outlier features block coordinate descent Newton’s method

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Cyber-Syndrome:Concept,Theoretical Characterization,and Control Mechanism

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Tsinghua science and Technology 2024年第3期29卷 721-735页

作者： Feifei Shi Huansheng Ning Liming Chen Sahraoui Dhelim School of Computer and Communication Engineering University of Science and Technology BeijingBeijing 100083China Faculty of Computing Ulster UniversityNewtownabbeyBT370QBUK School of Computer Science University College DublinDublinD04 V1W8Ireland

The prevalence of social media and mobile computing has led to intensive user engagement in the emergent Cyber-Physical-Social-Thinking(CPST)***,the easy access,the lack of governance,and excessive use has generated a raft of new behaviors within CPST,which affects users’physical,social,and mental *** this paper,we conceive the Cyber-Syndrome concept to denote the collection of cyber disorders due to excessive or problematic Cyberspace interactions based on CPST *** we characterize the Cyber-Syndrome concept in terms of Maslow’s theory of Needs,from which we establish an in-depth theoretical understanding of Cyber-Syndrome from its etiology,formation,symptoms,and ***,we propose an entropy-based Cyber-Syndrome control mechanism for its computation and *** goal of this study is to give new insights into this rising phenomenon and offer guidance for further research and development.

关键词： Cyber-Syndrome cyber-physical systems Cyber-Physical-Social-Thinking(CPST)space Maslow’sHierarchy of Needs entropy

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AWS Server Less Luggage Tracking System Integrating IOT 2

AWS Server Less Luggage Tracking System Integrating IOT

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2nd International Conference on Networking, Embedded and Wireless Systems, ICNEWS 2024

作者： Maharjan, Aditi Yadav, Prabhesh Rai, Chulindra Mandal, Ajay Kumar Basavaraju, D.R. Computer Science and Engineering- Internet of Things Faculty of Engineering & Technology' Karnataka Bengaluru India

ISBN: (纸本)9798350367461

The global airline industry serves over two billion traveller annually and faces continuous challenges about luggage handling. Lost luggage, mishandling of luggage are most common problem for the traveller. With the number of traveller even small percentage of baggage mishandling translates to significant number of effected *** system examines the current implementation of Amazon Web Services (AWS) and Radio Frequency Identification (RFID) in in luggage handling and proposes the adoption of Internet of Things (IoT) technology to develop reusable luggage tags. The main goal of this paper is to explore the problems and limitations in existing luggage handling systems and propose and IoT-based solution to enhance the baggage tracking efficiency. Existing systems did not provide the facility to name the luggage in the application or end-user interfaceThe research focuses on three main goals: evaluating the effectiveness of current RFID and AWS-based systems, developing a reusable IoT-enabled luggage tag and also evaluating the impact on reducing mishandling of luggage. The proposed system integrates smart, reusable tags with IoT capabilities allowing real time tracking of the *** proposed system demonstrated that the IoT based luggage tracking system outperformed RFID systems in terms of tracking efficiency and accuracy. Overall, integrating IoT technology into luggage tracking systems provides a better solution to the challenges faced by the passenger of air travel. The system also reduces the changes of lost luggage incidents and promotes environmental sustainability by the use of reusable tags. © 2024 IEEE.

关键词： Baggage handling

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Text Augmentation-Based Model for Emotion Recognition Using Transformers

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computers, Materials & Continua 2023年第9期76卷 3523-3547页

作者： Fida Mohammad Mukhtaj Khan Safdar Nawaz Khan Marwat Naveed Jan Neelam Gohar Muhammad Bilal Amal Al-Rasheed Department of Computer Science The University of HaripurHaripur22620Pakistan Department of Computer Systems Engineering Faculty of Electrical and Computer EngineeringUniversity of Engineering and Technology PeshawarPeshawar25120Pakistan Department of Electronics Engineering Technology University of TechnologyNowshera24100Pakistan Department of Computer Science Shaheed Benazir BhuttoWomen UniversityPeshawar25000Pakistan Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia

Emotion Recognition in Conversations(ERC)is fundamental in creating emotionally ***-BasedNetwork(GBN)models have gained popularity in detecting conversational contexts for ERC ***,their limited ability to collect and acquire contextual information hinders their *** propose a Text Augmentation-based computational model for recognizing emotions using transformers(TA-MERT)to address *** proposed model uses the Multimodal Emotion Lines Dataset(MELD),which ensures a balanced representation for recognizing human *** used text augmentation techniques to producemore training data,improving the proposed model’s *** encoders train the deep neural network(DNN)model,especially Bidirectional Encoder(BE)representations that capture both forward and backward contextual *** integration improves the accuracy and robustness of the proposed ***,we present a method for balancing the training dataset by creating enhanced samples from the original *** balancing the dataset across all emotion categories,we can lessen the adverse effects of data imbalance on the accuracy of the proposed *** results on the MELD dataset show that TA-MERT outperforms earlier methods,achieving a weighted F1 score of 62.60%and an accuracy of 64.36%.Overall,the proposed TA-MERT model solves the GBN models’weaknesses in obtaining contextual data for ***-MERT model recognizes human emotions more accurately by employing text augmentation and transformer-based *** balanced dataset and the additional training samples also enhance its *** findings highlight the significance of transformer-based approaches for special emotion recognition in conversations.

关键词： Emotion recognition in conversation graph-based network text augmentation-basedmodel multimodal emotion lines dataset bidirectional encoder representation for transformer

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Fluid Antenna-Assisted Simultaneous Wireless Information and Power Transfer Systems

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IEEE Transactions on Vehicular Technology 2025年第5期74卷 8285-8290页

作者： Zhou, Liaoshi Yao, Junteng Wu, Tuo Jin, Ming Yuen, Chau Adachi, Fumiyuki Ningbo University faculty of Electrical Engineering and Computer Science Ningbo315211 China Nanyang Technological University School of Electrical and Electronic Engineering 639798 Singapore Sendai Japan

This paper examines a fluid antenna (FA)-assisted simultaneous wireless information and power transfer (SWIPT) system. Unlike traditional SWIPT systems with fixed-position antennas (FPAs), our FA-assisted system enables dynamic reconfiguration of the radio propagation environment by adjusting the positions of FAs. This capability enhances both energy harvesting and communication performance. The system comprises a base station (BS) equipped with multiple FAs that transmit signals to an energy receiver (ER) and an information receiver (IR), both equipped with a single FA. Our objective is to maximize the communication rate between the BS and the IR while satisfying the harvested power requirement of the ER. This involves jointly optimizing the BS's transmit beamforming and the positions of all FAs. To address this complex convex optimization problem, we employ an alternating optimization (AO) approach, decomposing it into three sub-problems and solving them iteratively using first and second-order Taylor expansions. Simulation results validate the effectiveness of our proposed FA-assisted SWIPT system, demonstrating significant performance improvements over traditional FPA-based systems. © 2025 IEEE.

关键词： Convex optimization

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Fingerprint Fraud Explainability Using Grad-Cam for Forensic Procedures 23

Fingerprint Fraud Explainability Using Grad-Cam for Forensic...

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23rd International Conference on New Trends in Intelligent Software Methodologies, Tools and Techniques, SoMeT 2024

作者： Manai, Elyes Mejri, Mohamed Fattahi, Jaouhar Department of Computer & Software Engineering Faculty of Science and Engineering Laval University Quebec Canada

ISBN: (纸本)9781643685380

This paper investigates the application of GradCAM, an explainable AI (XAI) technique, to enhance the transparency and precision of fingerprint authentication systems in forensics, particularly in detecting fingerprint mutilation-a common method used to evade biometric security measures. Employing the SOCOfing dataset, which contains both unaltered and synthetically altered fingerprint images, we apply GradCAM to visualize and understand the decision-making process of a convolutional neural network (CNN) model trained to recognize and classify these alterations. Our study not only demonstrates the model's effectiveness in identifying different types of fingerprint modifications but also identifies areas where the model's performance can be enhanced. Through detailed visual analysis, we uncover the model's focus points and assess its reliability across various alteration types and difficulty levels. The insights gained underline the potential of XAI in improving the robustness and reliability of biometric verification systems, paving the way for more secure and equitable AI applications in high-stakes environments. © 2024 IOS Press. All rights reserved.

关键词： Convolutional neural networks

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