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A Proposed Feature Selection Particle Swarm Optimization Adaptation for Intelligent Logistics--A Supply Chain Backlog Elimination Framework

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Computers, Materials & Continua 2024年第6期79卷 4081-4105页

作者： Yasser Hachaichi Ayman E.Khedr Amira M.Idrees College of Computing and Information Technology at Khulais Department of Information SystemsUniversity of JeddahJeddah21959Saudi Arabia Faculty of Computers and Information Technology Future University in EgyptCairo11835Egypt

The diversity of data sources resulted in seeking effective manipulation and *** challenge that arises from the increasing dimensionality has a negative effect on the computation performance,efficiency,and stability of *** of the most successful optimization algorithms is Particle Swarm Optimization(PSO)which has proved its effectiveness in exploring the highest influencing features in the search space based on its fast convergence and the ability to utilize a small set of parameters in the search *** research proposes an effective enhancement of PSO that tackles the challenge of randomness search which directly enhances PSO *** the other hand,this research proposes a generic intelligent framework for early prediction of orders delay and eliminate orders backlogs which could be considered as an efficient potential solution for raising the supply chain *** proposed adapted algorithm has been applied to a supply chain dataset which minimized the features set from twenty-one features to ten significant *** confirm the proposed algorithm results,the updated data has been examined by eight of the well-known classification algorithms which reached a minimum accuracy percentage equal to 94.3%for random forest and a maximum of 99.0 for Naïve ***,the proposed algorithm adaptation has been compared with other proposed adaptations of PSO from the literature over different *** proposed PSO adaptation reached a higher accuracy compared with the literature ranging from 97.8 to 99.36 which also proved the advancement of the current research.

关键词： Optimization particle swarm optimization algorithm feature selection logistics supply chain management backlogs

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A TreeSoft Set with Interval Valued Neutrosophic Set in the era of Industry 4.0

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Neutrosophic Sets and systems 2024年 64卷 170-184页

作者： Alqazzaz, Ali Sallam, Karam M. Department of Information Systems and Cybersecurity College of Computing and Information Technology University of Bisha P.O. Box 344 Bisha61922 Saudi Arabia School of IT and Systems Faculty of Science and Technology University of Canberra Canberra Australia

The introduction of Industry 4.0 has brought about a significant shift in the manufacturing and supply chain management sectors, requiring supplier selection procedures to be adjusted to this rapidly changing technical environment. This study aims to improve supplier selection in Industry 4.0. This selection contains various criteria, so multi-criteria decision-making (MCDM) is used to deal with these criteria. The interval-valued neutrosophic sets (IVNSs) are used to deal with uncertainty in the evaluation process. The IVNSs are integrated with the TreeSoft Set. The TOPSIS method is an MCDM method used to rank the alternatives. The results show that the economic criterion is the most important, and supplier 7 is the best. © (2023), (University of New Mexico). All Rights Reserved.

关键词： Industry 4.0

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Evaluation of Sustainable Waste Valorization using TreeSoft Set with Neutrosophic Sets

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Neutrosophic Sets and systems 2024年 65卷 151-160页

This study proposed a neutrosophic set framework with TreeSoft Set for sustainable waste valorization selection. The neutrosophic set is used to overcome uncertainty and vague information in the evaluation process. The neutrosophic set has three membership degrees: truth, indeterminacy, and falsity. The multi-criteria decision-making (MCDM) methodology deals with various criteria to evaluate waste valorization. The VIKOR method is an MCDM method used to rank the alternatives. The numerical example was created with 12 criteria and 10 alternatives. Three decision-makers and experts are invited to evaluate the requirements and options. We used the bipolar neutrosophic numbers to replace the opinions of experts. © (2023), (University of New Mexico). All Rights Reserved.

关键词： Decision making

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Visual-Text Cross Alignment: Refining the Similarity Score in Vision-Language Models 41

Visual-Text Cross Alignment: Refining the Similarity Score i...

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41st International Conference on Machine Learning, ICML 2024

作者： Li, Jinhao Li, Haopeng Erfani, Sarah M. Feng, Lei Bailey, James Liu, Feng School of Computing and Information Systems University of Melbourne Australia Information Systems Technology and Design Pillar Singapore University of Technology and Design Singapore

It has recently been discovered that using a pretrained vision-language model (VLM), e.g., CLIP, to align a whole query image with several finer text descriptions generated by a large language model can significantly enhance zero-shot performance. However, in this paper, we empirically find that the finer descriptions tend to align more effectively with local areas of the query image rather than the whole image, and then we theoretically validate this finding. Thus, we present a method called weighted visual-text cross alignment (WCA). This method begins with a localized visual prompting technique, designed to identify local visual areas within the query image. The local visual areas are then cross-aligned with the finer descriptions by creating a similarity matrix using the pre-trained VLM. To determine how well a query image aligns with each category, we develop a score function based on the weighted similarities in this matrix. Extensive experiments demonstrate that our method significantly improves zero-shot performance across various datasets, achieving results that are even comparable to few-shot learning methods. The code is available at ***/tmlr-group/WCA. Copyright 2024 by the author(s)

关键词： Zero-shot learning

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Inverse kinematics solution for a six-degree-of-freedom upper limb rehabilitation robot using deep learning models

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Neural computing and Applications 2025年 1-19页

作者： Shah, Muhammad Faizan Khan, Naveed Ahmad Jamwal, Prashant K. Chetty, Girija Goecke, Roland Hussain, Shahid School of Information Technology & Systems University of Canberra Canberra Australia Department of Electrical and Computer Engineering Nazarbayev University Astana Kazakhstan School of Systems and Computing University of New South Wales Canberra Australia

The inverse kinematics problem in serially manipulated upper limb rehabilitation robots implies the usage of the end-effector position to obtain the joint rotation angles. In contrast to the forward kinematics, there are no systematic approaches for solving the inverse kinematics problem. Furthermore, for some morphology of the upper limb rehabilitation robots, the inverse kinematics problem is particularly challenging to solve. Conventional methods to solve the inverse kinematics problem reported in the literature are computationally expensive. In the present work, we propose a deep learning-based model to acquire the joint angles for a given end-effector position. The proposed approach exhibits high efficacy in determining the joint angles for various target positions and can accurately predict the end-effector positions once trained, improving the ability of the upper limb rehabilitation robot to adapt to varying patient needs. Due to its improved capability and effectiveness to track positions, the proposed algorithm lays the foundation for the development of efficient controllers in future. © The Author(s) 2025.

关键词： Inverse kinematics

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Priority Detector and Classifier Techniques Based on ML for the IoMT

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Computers, Materials & Continua 2023年第8期76卷 1853-1870页

作者： Rayan A.Alsemmeari Mohamed Yehia Dahab Badraddin Alturki Abdulaziz A.Alsulami Department of Information Technology Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Computer Science Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Information Systems Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia

Emerging telemedicine trends,such as the Internet of Medical Things(IoMT),facilitate regular and efficient interactions between medical devices and computing *** importance of IoMT comes from the need to continuously monitor patients’health conditions in real-time during normal daily activities,which is realized with the help of various wearable devices and *** major health problem is workplace stress,which can lead to cardiovascular disease or psychiatric ***,real-time monitoring of employees’stress in the workplace is *** levels and the source of stress could be detected early in the fog layer so that the negative consequences can be mitigated ***,overwhelming the fog layer with extensive data will increase the load on fog nodes,leading to computational *** study aims to reduce fog computation by proposing machine learning(ML)models with two *** first phase of theMLmodel assesses the priority of the situation based on the stress *** the second phase,a classifier determines the cause of stress,which was either interruptions or time pressure while completing a *** approach reduced the computation cost for the fog node,as only high-priority records were transferred to the ***-priority records were forwarded to the *** MLapproaches were compared in terms of accuracy and prediction speed:Knearest neighbors(KNN),a support vector machine(SVM),a bagged tree(BT),and an artificial neural network(ANN).In our experiments,ANN performed best in both phases because it scored an F1 score of 99.97% and had the highest prediction speed compared with KNN,SVM,and BT.

关键词： Machine learning priority detector Internet of Medical Things IoMT fog computing cloud computing

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Inclusive Gamification: An Exploratory Study in software Development Enterprises 35

Inclusive Gamification: An Exploratory Study in Software Dev...

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35th International Conference on software Engineering and Knowledge Engineering, SEKE 2023

作者： Elsalmy, Fayrouz Sherief, Nada Hany Abdelmoez, Walid Information Systems College of Computing and Information Technology Arab Academy for Science and Technology Alexandria Egypt Software engineering College of Computing and Information Technology Arab Academy for Science and Technology Alexandria Egypt

Gamification is the incorporation of game elements into non-game settings. software designers increase user motivation by introducing adequately engaging elements, such as leaderboards and badges, into an existing system. In recent years, few studies have examined the risks associated with gamification inside the business systems sector, especially within software development organizations. Yet, this issue must be addressed more systematically to build gamified solutions that take individual gamification risks into account. This research introduces "Inclusive Gamification" as contextually aware gamification that highlights individual risks. The purpose of this paper is twofold: 1) to identify the gamification risk factors that could cause the emergence of risks in software development organizations;and 2) to develop a list of risks with consideration of various contextual factors, namely tasks, personality type, adopted game element, and organizational cultural aspects. 15 employees from three software development companies participated in an exploratory study in which their perspectives on the negative effects of gamification were collected. As a result, we were able to identify several risk factors and associated risks. Our findings assist software engineers throughout the design process in identifying potential threats. Thus, create gamified solutions that better situate employees at their workplaces. © 2023 Knowledge systems Institute Graduate School. All rights reserved.

关键词： Personnel

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Comparative Study of Classification Algorithms Using Big Data Analytics for Flight Delays Prediction 5th

Comparative Study of Classification Algorithms Using Big Dat...

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5th Doctoral Symposium on Computational Intelligence, DoSCI 2024

作者： Asiri, Mona Hassan AL-Malaise AL-Ghamdi, Abdullah S. Fayoumi, Ayman G. Ragab, Mahmoud Department of Information Systems Collage of Computing and Information Technology King Abdulaziz University Jeddah Saudi Arabia Information Systems Department School of Engineering Computing and Design Dar Al-Hekma University Jeddah34801 Saudi Arabia Department of Information Systems Faculty of Computing and Information Technology King Abdulaziz University Jeddah21589 Saudi Arabia Information Technology Department Faculty of Computing and Information Technology King Abdulaziz University Jeddah21589 Saudi Arabia

ISBN: (纸本)9789819771776

Flight delays are the most pressing cause of concern in the airline sector because they can weaken and harm airlines, passengers, and airports. Flight delay prediction has become an essential step in decision-making processes within commercial aviation. Due to the developments of recent technologies, there are many prediction models for flight delays;however, more development of accurate prediction models is still needed. Machine learning, statistical models, and other techniques can be used to create quick and accurate flight delay prediction models. Furthermore, methods for feature selection can be developed to get rid of dimensionality issues. This study aimed to compare four classification algorithms using "big data analytics" to predict flight delays, a function that can improve the whole airline sector. The selected classification algorithms were the "Gaussian Naïve Bayes," "Gradient Boosted Tree" classifier, "Logistic Regression," and "Random Forest" classifier. Many assessment techniques were applied to each one of the suggested models to show the better performance of these models. The comparison used the resulting confusion matrix to summarize prediction results for the following classification problems: "F1 Score", "Accuracy," "Recall," "Precision," "Area Under ROC," and "Area Under PR." The assessment results showed the enhanced outcomes of the "Gradient Boosted Tree" Classifier with existing techniques, and all results of "Logistic Regression" and "Naïve Bayes" are equal. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Prediction models

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Optimizing AES S-Box Implementation:A SAT-Based Approach with Tower Field Representations

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Computers, Materials & Continua 2025年第4期83卷 1515-1531页

作者： Jingya Feng Ying Zhao Tao Ye Wei Feng School of Computer Science and Information Security Guilin University of Electronic TechnologyGuilin541004China School of Computing and Information Systems The University of MelbourneMelbourne3010Australia Guangxi Wangxin Information Technology Co. Ltd.Nanning530000China

The efficient implementation of the Advanced Encryption Standard(AES)is crucial for network data *** paper presents novel hardware implementations of the AES S-box,a core component,using tower field representations and Boolean Satisfiability(SAT)*** research makes several significant contri-butions to the ***,we have optimized the GF(24)inversion,achieving a remarkable 31.35%area reduction(15.33 GE)compared to the best known ***,we have enhanced multiplication implementa-tions for transformation matrices using a SAT-method based on local *** approach has yielded notable improvements,such as a 22.22%reduction in area(42.00 GE)for the top transformation matrix in GF((24)2)-type S-box ***,we have proposed new implementations of GF(((22)2)2)-type and GF((24)2)-type S-boxes,with the GF(((22)2)2)-type demonstrating superior *** implementation offers two variants:a small area variant that sets new area records,and a fast variant that establishes new benchmarks in Area-Execution-Time(AET)and energy *** approach significantly improves upon existing S-box implementations,offering advancements in area,speed,and energy *** optimizations contribute to more efficient and secure AES implementations,potentially enhancing various cryptographic applications in the field of network security.

关键词： AES S-box SAT optimization tower field hardware implementation area efficiency energy consumption

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Social Media-Based Surveillance systems for Health Informatics Using Machine and Deep Learning Techniques:A Comprehensive Review and Open Challenges

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Computer Modeling in Engineering & Sciences 2024年第5期139卷 1167-1202页

作者： Samina Amin Muhammad Ali Zeb Hani Alshahrani Mohammed Hamdi Mohammad Alsulami Asadullah Shaikh Institute of Computing Kohat University of Science and TechnologyKohat26000Pakistan Department of Computer Science College of Computer Science and Information SystemsNajran UniversityNajran61441Saudi Arabia Department of Information System College of Computer Science and Information SystemsNajran UniversityNajran61441Saudi Arabia

Social media(SM)based surveillance systems,combined with machine learning(ML)and deep learning(DL)techniques,have shown potential for early detection of epidemic *** review discusses the current state of SM-based surveillance methods for early epidemic outbreaks and the role of ML and DL in enhancing their ***,every year,a large amount of data related to epidemic outbreaks,particularly Twitter data is generated by *** paper outlines the theme of SM analysis for tracking health-related issues and detecting epidemic outbreaks in SM,along with the ML and DL techniques that have been configured for the detection of epidemic *** has emerged as a promising ML technique that adaptsmultiple layers of representations or features of the data and yields state-of-the-art extrapolation *** recent years,along with the success of ML and DL in many other application domains,both ML and DL are also popularly used in SM *** paper aims to provide an overview of epidemic outbreaks in SM and then outlines a comprehensive analysis of ML and DL approaches and their existing applications in SM ***,this review serves the purpose of offering suggestions,ideas,and proposals,along with highlighting the ongoing challenges in the field of early outbreak detection that still need to be addressed.

关键词： Social media epidemic machine learning deep learning health informatics pandemic

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