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Magnificent Frigatebird Optimization: A New Bio-Inspired Metaheuristic Approach for Solving Optimization Problems

Computers, Materials & Continua 2024年第8期80卷 2721-2741页

作者： Tareq Hamadneh Khalid Kaabneh Ibraheem AbuFalahah Gulnara Bektemyssova Galymzhan Shaikemelev Dauren Umutkulov Sayan Omarov Zeinab Monrazeri Frank Werner Mohammad Dehghani Department of Mathematics Al Zaytoonah University of JordanAmman11733Jordan Faculty of Information Technology Al-Ahliyya Amman UniversityAmman19328Jordan Department of Mathematics Faculty of ScienceThe Hashemite UniversityP.O.Box 330127Zarqa13133Jordan Department of Computer Engineering International Information Technology UniversityAlmaty050000Kazakhstan Department of Big Data Analytics and Data Science Software Development Company《QazCode》Almaty050063Kazakhstan Department of Electrical and Electronics Engineering Shiraz University of TechnologyShiraz***Iran Faculty of Mathematics Otto-von-Guericke UniversityP.O.Box 4120Magdeburg39016Germany

This paper introduces a groundbreaking metaheuristic algorithm named Magnificent Frigatebird Optimization(MFO),inspired by the unique behaviors observed in magnificent frigatebirds in their natural *** foundation of MFO is based on the kleptoparasitic behavior of these birds,where they steal prey from other *** this process,a magnificent frigatebird targets a food-carrying seabird,aggressively pecking at it until the seabird drops its *** frigatebird then swiftly dives to capture the abandoned prey before it falls into the *** theoretical framework of MFO is thoroughly detailed and mathematically represented,mimicking the frigatebird’s kleptoparasitic behavior in two distinct phases:exploration and *** the exploration phase,the algorithm searches for new potential solutions across a broad area,akin to the frigatebird scouting for vulnerable *** the exploitation phase,the algorithm fine-tunes the solutions,similar to the frigatebird focusing on a single target to secure its *** evaluate MFO’s performance,the algorithm is tested on twenty-three standard benchmark functions,including unimodal,high-dimensional multimodal,and fixed-dimensional multimodal *** results from these evaluations highlight MFO’s proficiency in balancing exploration and exploitation throughout the optimization *** studies with twelve well-known metaheuristic algo-rithms demonstrate that MFO consistently achieves superior optimization results,outperforming its competitors across various *** addition,the implementation of MFO on four engineering design problems shows the effectiveness of the proposed approach in handling real-world applications,thereby validating its practical utility and robustness.

关键词： Optimization metaheuristic magnificent frigatebirds exploration exploitation

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Hybrid Deep Learning and Fuzzy Matching for Real-Time Bidirectional Arabic Sign Language Translation: Toward Inclusive Communication Technologies

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IEEE Access 2025年 13卷 94118-94136页

作者： Mogeeb A. A. Mosleh Ahmed A. A. Mohammed Ezzaldeen E. A. Esmail Rehab A. A. Mohammed Basheer Almuhaya Department of Information Technology Faculty of Engineering and Computing University of Science and Technology Aden Yemen Department of Software Engineering Taiz University Taizz Yemen

Technological advances and AI tools can help address the challenges faced by individuals who are deaf or nonverbal in different areas of social interaction. Existing tools mainly focus on one-way translation and are limited by small vocabulary datasets, require significant computational power, and often lack a real-time implementation. Therefore, a bidirectional real-time translation application for Arabic Sign Language and written Arabic text was developed in this research to improve communication and learning experiences for individuals who are deaf. The proposed system designed with two primary translation modules includes sign-to-text and text-to-sign. The sign-to-text module employs transfer learning models to translate Arabic sign images into text, while the text-to-sign module integrates a fuzzy string-matching tool to convert Arabic text into sign images. The system was customized using six CNN-based deep learning architectures: AlexNet, ResNet152V2, YOLOv8n, Swin Transformer, InceptionV3, and Xception. Additionally, the ArSL dataset and an Arabic data dictionary were employed to enhance the diversity, accuracy, and completeness of the selected CNN models, thereby improving the system’s adaptability across various users and contexts. Experimental evaluations were conducted to assess the system’s performance in terms of both accuracy and processing efficiency. The results demonstrated exceptionally high accuracy across all investigated CNN models, but YOLOv8n-cls demonstrated the highest accuracy with a score of 99.9%, followed by Xception, Swin Transformer, and AlexNet at 99.0%, and InceptionV3 and ResNet152V2 at 98.0%. These closely aligned results are attributed to the inherent characteristics of the dataset, as well as the shared methodologies employed, including preprocessing, data augmentation, cross-validation, and hyperparameter tuning. In terms of the real-time adaptability in recognizing each sign image, the InceptionV3, AlexNet, and YOLOv8n models

关键词： Sign language Accuracy Translation Real-time systems Deep learning Transformers Hands Data models Convolutional neural networks Computational modeling

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Utilizing Support Vector Machine to Help with Social Robot Therapy for Autism Spectrum Disorder 21

Utilizing Support Vector Machine to Help with Social Robot T...

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21st International Multi-Conference on Systems, Signals and Devices, SSD 2024

作者： Ibrahim, Hemin Al-Talabani, Abdulbasit K. Loo, Chu Kiong Tishk International University Information Technology Department Erbil Iraq Koya University Faculty of Engineering Department of Software Engineering Koya Iraq University of Malaya Department of Artificial Intelligence Kuala Lumpur Malaysia

ISBN: (纸本)9798350374131

Children with autism spectrum disorder (ASD) require long-term care, support, and empathy from experienced therapists. However, there is a shortage num-ber of highly experienced therapists available to provide consistent and high-quality care for all ASD children and teach them effectively. The Robotic-based assessment and treatment offer promising advantages for ASD children. However, many robotic and virtual therapies rely on pre-programmed that may not consider individual needs and cultural differences. This study aims to develop and test the hypothesis that robots can effectively assist in conducting therapy sessions for children with ASD. A Support Vector Machine (SVM) classifier has been used to predict out-comes from sckeleton and gaze basaed features of data generated during both robot-child and therapist-child in-teractions. A comprehensive evaluation across five distinct scenarios, incorporating various combinations of Robot-Enhanced Therapy (RET), Standard Human Treatment (SHT), and their concatenation have been conducted. The article results for both skeleton-based features and gaze-based data reveal a significant performance advantage over existing state-of-the-art methodologies. © 2024 IEEE.

关键词： Support vector machines

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Improved Hybrid Deep Collaborative Filtering Approach for True Recommendations

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Computers, Materials & Continua 2023年第3期74卷 5301-5317页

作者： Muhammad Ibrahim Imran Sarwar Bajwa Nadeem Sarwar Haroon Abdul Waheed Muhammad Zulkifl Hasan Muhammad Zunnurain Hussain Department of Computer Science The Islamia University of BahawalpurBahawalpurPakistan Department of Computer Science Bahria University Lahore CampusLahorePakistan Software Engineering Department Faculty of ITUniversity of Central PunjabLahorePakistan Faculty of Computer Science and Information Technology Universiti Putra MalaysiaSelangorMalaysia

Recommendation services become an essential and hot research topic for researchers *** data such asReviews play an important role in the recommendation of the *** was achieved by deep learning approaches for capturing user and product information from a short ***,such previously used approaches do not fairly and efficiently incorporate users’preferences and product *** proposed novel Hybrid Deep Collaborative Filtering(HDCF)model combines deep learning capabilities and deep interaction modeling with high performance for True *** overcome the cold start problem,the new overall rating is generated by aggregating the Deep Multivariate Rating DMR(Votes,Likes,Stars,and Sentiment scores of reviews)from different external data sources because different sites have different rating scores about the same product that make confusion for the user to make a decision,either product is truly popular or *** proposed novel HDCF model consists of four major modules such as User Product Attention,Deep Collaborative Filtering,Neural Sentiment Classifier,and Deep Multivariate Rating(UPA-DCF+NSC+DMR)to solve the addressed *** results demonstrate that our novel model is outperforming state-of-the-art IMDb,Yelp2013,and Yelp2014 datasets for the true top-n recommendation of products using HDCF to increase the accuracy,confidence,and trust of recommendation services.

关键词： Neural sentiment classification user product attention deep collaborative filtering multivariate rating artificial intelligence

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TRIPORA: Intelligent Machine Learning Solution for Sri Lanka Touring Access and Updates 4

TRIPORA: Intelligent Machine Learning Solution for Sri Lanka...

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4th International Conference on Advancements in Computing, ICAC 2022

作者： Legrand, T.R. Bandara, K.M.R.A.I. Stefania Crishani, J.A.D. Uvindu, L.W.P. Amarasena, Nelum Kasthurirathna, Dharshana Department of Software Engineering Faculty of Computing Sri Lanka Institute of Information Technology Malabe Sri Lanka Department of Information Technology Faculty of Computing Sri Lanka Institute of Information Technology Malabe Sri Lanka Department of Computer Science and Software Engineering Faculty of Computing Sri Lanka Institute of Information Technology Malabe Sri Lanka

ISBN: (纸本)9798350398090

Sri Lanka is one of the top tourist destinations in the world. However, tourists face various inconveniences due to the obsolescence of facilities. There are various tools designed to solve such problems. But they are scattered in different places and users have to use different tools. The biggest issue in the tourist sector is that travelers are unable to get the most out of their tours since there may be days when a large number of people visit the same location, causing the location to become overcrowded, and preventing tourists from enjoying their visit as anticipated. There are seasons when natural disasters occur, as well as human-centered crises. Also, there are situations when travelers feel helpless because they are unable to find the best tour guide for them. We developed a cost-effective, automatic, and efficient Machine Learning-based recommendation system as a result of this research. Based on past data on tourists and data received from the SLTDA, this research can provide the best trip plan with the tour guide and provide destination news alerts on regular basis. Furthermore, in order to achieve the best accuracy through the system, unique machine learning approaches were used in this study. © 2022 IEEE.

关键词： Obsolescence

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Assessing Cybersecurity Awareness among the Hashemite University Students in Terms of Computer Usage 16

Assessing Cybersecurity Awareness among the Hashemite Univer...

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16th International Conference on Electronics, Computers and Artificial Intelligence, ECAI 2024

作者： Aljammal, Ashraf H. Qawasmeh, Ahmad Taamneh, Salah Wedyan, Fadi Obiedat, Mamoon Salameh, Hani Bani The Hashemite University Prince Al Hussein Bin Abdullah Ii Faculty of Information Technology Department of Computer Science and Applications Zarqa Jordan Lewis University Department of Engineering Computing and Mathematical Sciences RomeovilleIL United States The Hashemite University Prince Al Hussein Bin Abdullah Ii Faculty of Information Technology Department of Information Technology Zarqa Jordan The Hashemite University Prince Al Hussein Bin Abdullah Ii Faculty of Information Technology Department of Software Engineering Zarqa Jordan

ISBN: (纸本)9798350371154

Internet usage among information technology users has expanded substantially, as have cybercrimes with potentially disastrous repercussions. Therefore, internet users have to employ the available security measures and policies during internet usage. Nowadays, University students make up a sizable portion of internet users. This study assesses the cybersecurity awareness among The Hashemite University students In terms of Computer Usage. A questionnaire survey is used to assess students' cybersecurity knowledge while using their PCs and connecting to the internet, concentrating on their behaviors and use of security measures. © 2024 IEEE.

关键词： Cybersecurity

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Research on Faster R-CNN material micro-defect detection method for small data volume

Research on Faster R-CNN material micro-defect detection met...

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Artificial Intelligence and Electromechanical Automation (AIEA), International Conference on

作者： Zhili Wang Hairong Yan Weiyu Zhou Software Engineering Faculty of Information Technology Beijing University of Technology Beijing China

ISBN: (数字)9798350366174

ISBN: (纸本)9798350366181

Defect detection in material micro-images has a significant impact on the study of the relationship between the micro-structure and macro-properties, however, material microdefects are usually relatively small and span a wide range of scales, which increases the difficulty of defect detection. Meanwhile, since defects exist in a small number, overfitting becomes another challenge. In this paper, based on the Faster R-CNN algorithm, automated data enhancement is used to solve the overfitting, and a feature pyramid model is proposed for the defects multi-scale problem, and finally, the feasibility of the above viewpoint is verified by experiments.

关键词： Solid modeling Analytical models Automation Data models Artificial intelligence Defect detection

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OMP-ENGINEER: BRIDGING SYNTAX ANALYSIS AND IN-CONTEXT LEARNING FOR EFFICIENT AUTOMATED OPENMP PARALLELIZATION

arXiv

引用

arXiv 2024年

作者： Wang, Weidong Zhu, Haoran Department of Software Engineering Faculty of Information Technology Beijing University of Technology China

In advancing parallel programming, particularly with OpenMP, the shift towards NLP-based methods marks a significant innovation beyond traditional S2S tools like Autopar and Cetus. These NLP approaches train on extensive datasets of examples to efficiently generate optimized parallel code, streamlining the development process. This method’s strength lies in its ability to swiftly produce parallelized code that runs efficiently. However, this reliance on NLP models, without direct code analysis, can introduce inaccuracies, as these models might not fully grasp the nuanced semantics of the code they parallelize. We build OMP-Engineer, which balances the efficiency and scalability of NLP models with the accuracy and reliability of traditional methods, aiming to enhance the performance of automating parallelization while navigating its inherent challenges. © 2024, CC BY.

关键词： Application programming interfaces (API)

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Hybrid Color Texture Features Classification Through ANN for Melanoma

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Intelligent Automation & Soft Computing 2023年第2期35卷 2205-2218页

作者： Saleem Mustafa Arfan Jaffar Muhammad Waseem Iqbal Asma Abubakar Abdullah S.Alshahrani Ahmed Alghamdi Faculty of Computer Science&Information Technology Superior UniversityLahore54600Pakistan Department of Software Engineering Superior UniversityLahore54600Pakistan Department of Computer Science&Artificial Intelligence College of Computer Science&EngineeringUniversity of Jeddah21493Saudi Arabia Department of Software Engineering College of Computer Science and EngineeringUniversity of Jeddah21493Saudi Arabia

Melanoma is of the lethal and rare types of skin *** is curable at an initial stage and the patient can survive *** is very difﬁcult to screen all skin lesion patients due to costly *** are requiring a correct method for the right treatment for dermoscopic clinical features such as lesion borders,pigment networks,and the color of *** challenges are required an automated system to classify the clinical features of melanoma and non-melanoma *** trained clinicians can overcome the issues such as low contrast,lesions varying in size,color,and the existence of several objects like hair,reﬂections,air bubbles,and oils on almost all *** contour is one of the suitable methods with some drawbacks for the segmentation of irre-gular *** entropy and morphology-based automated mask selection is pro-posed for the active contour *** proposed method can improve the overall segmentation along with the boundary of melanoma *** this study,features have been extracted to perform the classiﬁcation on different texture scales like Gray level co-occurrence matrix(GLCM)and Local binary pattern(LBP).When four different moments pull out in six different color spaces like HSV,Lin RGB,YIQ,YCbCr,XYZ,and CIE L*a*b then global information from different colors channels have been ***,hybrid fused texture features;such as local,color feature as global,shape features,and Artiﬁcial neural network(ANN)as classiﬁers have been proposed for the categorization of the malignant and *** had been carried out on datasets Dermis,DermQuest,and *** results of our advanced method showed super-iority and contrast with the existing state-of-the-art techniques.

关键词： Gray level co-occurrence matrix local binary pattern artiﬁcial neural networks support vector machines color skin cancer dermoscopic

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Traffic Anomaly Detection Using Optimized Faster R-CNN

Traffic Anomaly Detection Using Optimized Faster R-CNN

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2024 International Conference on Electronics, Electrical and information engineering, ICEEIE 2024

作者： Karim, Shahid Qadir, Akeel Han, Gao Li, Jing Ibrar, Muhammad Bibi, Irfana School of Information Engineering Xi'an Eurasia University 710065 China Software College Shenyang Normal University Shenyang China Department of Computer Science Faculty of Computing and Information Technology University of Punjab Punjab Lahore Pakistan

ISBN: (数字)9781510686809

ISBN: (纸本)9781510686793

This paper introduces a new method for traffic anomaly identification within the Faster R-CNN framework by combining the DenseNet module with the region proposals (RPs) processing block. The suggested approach seeks to improve feature extraction and RP creation in aerial imagery or CCTV frames to get beyond the drawbacks of the current methods. Discriminative features are extracted through DenseNet, and the RPs block makes it easier to provide a wide range of accurate and varied area proposals. Contrast correction techniques are applied to overcome the difficulties presented by low-resolution and low-contrast images frequently seen in surveillance scenarios. Comparing experimental results to state-of-the-art techniques, one can observe notable resilience and detection accuracy increases. The suggested method works exceptionally well in various traffic scenarios and environmental circumstances, indicating its potential for practical implementation in traffic surveillance systems. Overall, the study contributes to advancing the efficacy and reliability of anomaly detection in urban traffic environments. © 2024 SPIE.

关键词： Aerial photography

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