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Modified simulated annealing for university teacher course assignment considering socio-cultural constraints

International Journal of information technology (Singapore) 2025年第3期17卷 1533-1549页

作者： Brahimi, Samiha Sabba, Sara Elhussein, Mariam Alqahtani, Mohammed Department of Computer Information Systems College of Computer Science and Information Technology Imam Abdulrahman Bin Faisal University Dammam Saudi Arabia Faculty of New Technologies of Information and Communication Laboratory of Data Science and Artificial Intelligence (LISIA) Abdelhamid Mahri Constantine-2 University Constantine 25016 Algeria Department of Mathematics and Computing Mount Royal University Calgary AB Canada

This paper aims to address the complex challenge of course assignment for faculty members within a Saudi university, taking into account the socio-cultural constraints imposed by gender-based segregation between students and faculty. To tackle this challenge, a combinatorial optimization model is proposed, which includes a binary representation of the problem, constraints reflecting gender segregation, and an objective function designed to maximize faculty satisfaction. The model employs a Simulated Annealing (SA) algorithm, supplemented with two neighborhood search heuristics (H1 and H2), to explore diverse combinations. Additionally, a greedy randomized adaptive search procedure Greedy Randomized Adaptive Search Procedure (GRASP) algorithm is implemented for comparative analysis, utilizing heuristic 1 for local search. Through the application of simulated annealing alongside both heuristics (SA_H1_H2), the optimal solution is achieved, resulting in a notable equilibrium of courses among faculty members. Objective 1 demonstrates the standardized deviation in workload distribution among faculty members, yielding a deviation value of 0.08. This study contributes to the field by proposing a novel approach to course assignment that explicitly addresses the gender-based segregation prevalent in Saudi universities. By integrating socio-cultural constraints into the optimization model and employing advanced algorithms, the study offers a pioneering solution to a previously unexplored problem domain. © Bharati Vidyapeeth's Institute of Computer Applications and Management 2024.

关键词： GRASP Mathematical model Operations Research Optimization Simulated annealing Teacher_Course assignment

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Digital Phenotyping: an Epistemic and Methodological Analysis

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Philosophy and technology 2021年第4期34卷 1905-1928页

作者： Coghlan, Simon D’Alfonso, Simon School of Computing & Information Systems Faculty of Engineering and Information Technology The University of Melbourne Victoria Australia

Some claim that digital phenotyping will revolutionize understanding of human psychology and experience and significantly promote human wellbeing. This paper investigates the nature of digital phenotyping in relation to its alleged promise. Unlike most of the literature to date on philosophy and digital phenotyping, which has focused on its ethical aspects, this paper focuses on its epistemic and methodological aspects. The paper advances a tetra-taxonomy involving four scenario types in which knowledge may be acquired from human “digitypes” by digital phenotyping. These scenarios comprise two causal relations and a correlative and constitutive relation that can exist between information generated by digital systems/devices on the one hand and psychological or behavioral phenomena on the other. The paper describes several modes of inference involved in deriving knowledge within these scenarios. After this epistemic mapping, the paper analyzes the possible knowledge potential and limitations of digital phenotyping. It finds that digital phenotyping holds promise of delivering insight into conditions and states as well producing potentially new psychological categories. It also argues that care must be taken that digital phenotyping does not make unwarranted conclusions and is aware of potentially distorting effects in digital sensing and measurement. If digital phenotyping is to truly revolutionize knowledge of human life, it must deliver on a range of fronts, including making accurate forecasts and diagnoses of states and behaviors, providing causal explanations of these phenomena, and revealing important constituents of human conditions, psychology, and experience. © 2021, The Author(s), under exclusive licence to Springer Nature B.V.

关键词： Digital phenotyping Epistemic Ethics Philosophy Psychology Science Wellbeing

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HealthEdge: A Machine Learning-Based Smart Healthcare Framework for Prediction of Type 2 Diabetes in an Integrated IoT, Edge, and Cloud computing System 14

HealthEdge: A Machine Learning-Based Smart Healthcare Framew...

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14th International Conference on Ambient systems, Networks and Technologies Networks, ANT 2023 and The 6th International Conference on Emerging Data and Industry 4.0, EDI40 2023

作者： Hennebelle, Alain Materwala, Huned Ismail, Leila Melbourne Australia Research Laboratory Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates University United Arab Emirates National Water and Energy Center United Arab Emirates University United Arab Emirates Lab School of Computing and Information Systems The University of Melbourne Australia

Diabetes Mellitus has no permanent cure to date and is one of the leading causes of death globally. The alarming increase in diabetes calls for the need to take precautionary measures to avoid/predict the occurrence of diabetes. This paper proposes HealthEdge, a machine learning-based smart healthcare framework for type 2 diabetes prediction in an integrated IoT-edge-cloud computing system. Numerical experiments and comparative analysis were carried out between the two most used machine learning algorithms in the literature, Random Forest (RF) and Logistic Regression (LR), using two real-life diabetes datasets. The results show that RF predicts diabetes with 6% more accuracy on average compared to LR. © 2023 Elsevier B.V.. All rights reserved.

关键词： Edge computing

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A Combined Distance Metric Approach with Weight Adjustment For Improving Mixed Data Clustering Quality

A Combined Distance Metric Approach with Weight Adjustment F...

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IEEE International Conference on Artificial Intelligence in Engineering and technology (IICAIET)

作者： Aung Pyae Yeh-Ching Low Hui Na Chua Department of Computing and Information Systems School of Engineering and Technology Sunway University Subang Jaya Malaysia

ISBN: (数字)9798350389692

ISBN: (纸本)9798350389708

Cluster analysis can be perceived as a problem of grouping data points according to their mutual similarity. Clustering quality largely depends on choosing an effective distance metric, especially when dealing with mixed data that contains both numerical and categorical features. This study introduces a Weighted Combined distance metric using Gower and sqrt-cosine distances to improve clustering quality for mixed data. By combining two individual distance metrics with weights, this unique technique generates an overall similarity score that can improve the clustering quality. Experiments have demonstrated that the Weighted Combined distance metric can yield superior clustering quality compared to individual distance metrics. These findings highlight the potential of this proposed method to effectively cluster mixed data and overcome challenges associated with mixed data clustering.

关键词： Measurement Refining Clustering algorithms Learning (artificial intelligence) Complexity theory Optimization

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Enhancing single-lead electrocardiogram arrhythmia detection with empirical mode decomposition

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Neural computing and Applications 2025年第17期37卷 11583-11605页

作者： Issa, Mohamed F. Yousry, Ahmed Tuboly, Gergely Wang, Zeyu Zoltan, Juhasz Selim, Mazen M. AbuEl-Atta, Ahmed H. Department of Scientific Computing Faculty of Computers and Artificial Intelligence Benha University Benha13511 Egypt Department of Computer Science Faculty of Computers and Artificial Intelligence Benha University Benha13511 Egypt Department of Electrical Engineering and Information Systems University of Pannonia Veszprem8200 Hungary Department of Mechatronics Delta University for Science and Technology Gamasa35712 Egypt

Cardiac arrhythmias pose a significant challenge to health care, requiring accurate and reliable detection methods to enable early diagnosis and treatment. However, traditional ECG beat classification methods often lack robustness and fail to generalize effectively to diverse clinical scenarios, particularly when using single-lead-I recordings, which are less studied compared to lead II. To address this gap, this paper proposes a novel method that combines deep learning and empirical mode decomposition (EMD) for arrhythmia detection using single-lead-I ECG recordings obtained with electrodes on each hand. The hybrid model comprises 149 layers of one-dimensional convolution, max pooling, and residual blocks. By leveraging EMD, it classifies ECG beats into normal beats, premature ventricular contractions, and atrial premature beats with high sensitivity (99.86%, 99.72%, and 99.84%, respectively) using the first four intrinsic mode functions (IMFs). The proposed method is applicable in clinical and out-of-hospital monitoring scenarios, and an accompanying web application provides real-time analysis and diagnostic results. This approach addresses a critical gap in lead-I ECG analysis, enhancing diagnostic precision and expanding the scope of ECG applications in health care. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Empirical mode decomposition

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Project Assessment in Offshore software Maintenance Outsourcing Using Deep Extreme Learning Machines

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Computers, Materials & Continua 2023年第1期74卷 1871-1886页

作者： Atif Ikram Masita Abdul Jalil Amir Bin Ngah Saqib Raza Ahmad Salman Khan Yasir Mahmood Nazri Kama Azri Azmi Assad Alzayed Faculty of Ocean Engineering Technology and Informatics University Malaysia TerengganuKuala NerusMalaysia Department of Computer Science&Information Technology Faculty of Information TechnologyThe University of LahoreLahore54000Pakistan Department of Software Engineering Faculty of Information TechnologyThe University of LahoreLahore54000Pakistan Advanced Informatics Department Razak Faculty of Technology and InformaticsUniversiti Teknologi Malaysia54100Kuala LumpurMalaysia Computer Science and Information Systems Department College of Business Studies Public Authority for Applied Education and Training(PAAET)AdailiyahKuwait Department of Computer Science Sharif College of Engineering&TechnologyLahore54000Pakistan

software maintenance is the process of fixing,modifying,and improving software deliverables after they are delivered to the *** can benefit from offshore software maintenance outsourcing(OSMO)in different ways,including time savings,cost savings,and improving the software quality and *** of the hardest challenges for the OSMO vendor is to choose a suitable project among several clients’*** goal of the current study is to recommend a machine learning-based decision support system that OSMO vendors can utilize to forecast or assess the project of OSMO *** projects belong to OSMO vendors,having offices in developing countries while providing services to developed *** the current study,Extreme Learning Machine’s(ELM’s)variant called Deep Extreme Learning Machines(DELMs)is used.A novel dataset consisting of 195 projects data is proposed to train the model and to evaluate the overall efficiency of the proposed *** proposed DELM’s based model evaluations achieved 90.017%training accuracy having a value with 1.412×10^(-3) Root Mean Square Error(RMSE)and 85.772%testing accuracy with 1.569×10^(-3) RMSE with five DELMs hidden *** results express that the suggested model has gained a notable recognition rate in comparison to any previous *** current study also concludes DELMs as the most applicable and useful technique for OSMO client’s project assessment.

关键词： software outsourcing deep extreme learning machine(DELM) machine learning(ML) extreme learning machine assessment

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DA-Ada: Learning Domain-Aware Adapter for Domain Adaptive Object Detection 38

DA-Ada: Learning Domain-Aware Adapter for Domain Adaptive Ob...

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38th Conference on Neural information Processing systems, NeurIPS 2024

作者： Li, Haochen Zhang, Rui Yao, Hantao Zhang, Xin Hao, Yifan Song, Xinkai Li, Xiaqing Zhao, Yongwei Li, Ling Chen, Yunji Intelligent Software Research Center Institute of Software CAS Beijing China State Key Lab of Processors Institute of Computing Technology CAS Beijing China State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation CAS Beijing China University of Chinese Academy of Sciences Beijing China

Domain adaptive object detection (DAOD) aims to generalize detectors trained on an annotated source domain to an unlabelled target domain. As the visual-language models (VLMs) can provide essential general knowledge on unseen images, freezing the visual encoder and inserting a domain-agnostic adapter can learn domain-invariant knowledge for DAOD. However, the domain-agnostic adapter is inevitably biased to the source domain. It discards some beneficial knowledge discriminative on the unlabelled domain, ***-specific knowledge of the target domain. To solve the issue, we propose a novel Domain-Aware Adapter (DA-Ada) tailored for the DAOD task. The key point is exploiting domain-specific knowledge between the essential general knowledge and domain-invariant knowledge. DA-Ada consists of the Domain-Invariant Adapter (DIA) for learning domain-invariant knowledge and the Domain-Specific Adapter (DSA) for injecting the domain-specific knowledge from the information discarded by the visual encoder. Comprehensive experiments over multiple DAOD tasks show that DA-Ada can efficiently infer a domain-aware visual encoder for boosting domain adaptive object detection. Our code is available at https://***/Therock90421/DA-Ada. © 2024 Neural information processing systems foundation. All rights reserved.

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Lockstep Replacement: Fault-Tolerant Design

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IEEE Access 2025年 13卷 94302-94318页

作者： Mach, Ján Kohútka, Lukáš Slovak University of Technology Institute of Informatics Information Systems and Software Engineering Bratislava Slovakia DrASIC S.r.o Bratislava Slovakia

System-level lockstep, interconnecting two original cores, is nowadays the state-of-the-art approach for protecting processor systems against random hardware faults. However, the lack of information outside the cores necessitates many compromises in dependability, software complexity, power consumption, performance, and system cost. We propose a redundancy-based protection scheme integrated into the microarchitecture of the core. The scheme separates the execution pipeline into sections with fault detection and fault tolerance while the large predictor components remain unprotected. This approach results in fault-tolerant hardware with only an 8% area penalty compared to the dual-core lockstep, which provides only fault detection. Since the hardware recovers from the faults automatically, it does not necessitate software support and additional memory for checkpoints. The scheme comprises independent protection approaches, resulting in negligible frequency impact. It also provides interface and memory protection and is designed to be applicable in most of the embedded-class cores. Simulation-based fault injection campaigns were performed, considering physical synthesis data, to assess the fault-tolerance. We also analyze how software compilation affects dependability and provide a hardware solution to mitigate the undesired effects. © 2013 IEEE.

关键词： Program compilers

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Feature selection using modified chaotic satin bowerbird algorithm with deep transfer learning for Multispectral Image Classification

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International Journal of information technology (Singapore) 2024年 1-13页

作者： Rajakani, M. Kavitha, R.J. Rajesh, S. Department of Data Science and Business Systems School of Computing Faculty of Engineering and Technology SRM Institute of Science and Technology SRM Nagar Kattankulathur Chennai India Department of Electronics and Communication Engineering University College of Engineering Panruti Panruti 607106 India Department of Information Technology Mepco Schlenk Engineering College Sivakasi India

Multispectral image classification has received significant attention among research communities and academicians. Recently, several artificial intelligence (AI) models can be used for the extraction of prominent features. Besides, deep learning (DL) methods become more familiar and gained interest in the remote sensing community for the classification of multispectral and hyperspectral images. With this motivation, this paper presents an optimized feature learning with deep transfer learning enabled multispectral image classification (OFLDTL-MSIC) technique. The proposed OFLDTL-MSIC technique aims to categorize the different class labels of the multispectral images. Besides, the OFLDTL-MSIC technique applies multi-level discrete wavelet transform (DWT) based image decomposition technique. Moreover, the EfficientNet technique is applied as a feature extractor to generate a collection of features. Furthermore, selecting optimal features extracted by a EfficientNet involves identifying and retaining the most relevant and informative features The improved chaotic satin bowerbird optimization (CSBO) algorithm which uses dynamic population size is proposed for feature selection and kernel extreme learning machine (KELM) model is applied for the classification process. In order to ensure the enhanced performance of the OFLTDL-MSIC technique, a wide range of simulations take place using the Madurai LISS IV multispectral images and the results are examined under several aspects. The extensive comparative study highlighted the better performance of the OFLTDL-MSIC technique over the recent methods. © Bharati Vidyapeeth's Institute of Computer Applications and Management 2024.

关键词： Deep learning Image classification Multispectral images Parameter tuning Remote sensing

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Enhancing Soft Skills in Autistic Children: The Next Generation of Mind Champ's Technological Approach

Enhancing Soft Skills in Autistic Children: The Next Generat...

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Advancements in computing (ICAC), International Conference on

作者： Kalna Peiris Ruchira Nelaka Kalpani Manathunga Department of Computer Systems Engineering Department of Information Technology Department of Computer Science and Software Engineering

ISBN: (数字)9798331517878

ISBN: (纸本)9798331517885

Autism Spectrum Disorder (ASD) significantly impacts a child's ability to navigate social interactions, regulate emotions, and develop adaptive skills crucial for daily functioning. While various interventions exist to address cognitive and academic skills, the development of soft skills such as communication, emotional regulation, social interaction, and creativity remains an under explored area. This paper builds upon the foundational work of the Mind Champ platform, which originally targeted both learning and soft skills, by focusing exclusively on enhancing the soft skills development of autistic children. This enhanced version of the Mind Champ platform leverages advanced behavioral and emotional analysis techniques to offer a comprehensive technological solution. Through interactive activities centered on painting and music, the platform creates a structured, engaging, and supportive environment tailored to the unique needs of children with ASD. By prioritizing emotional engagement and creative expression, the platform empowers children to improve their social abilities, emotional regulation, and adaptability. The results from our continued research indicate that these technology-driven interventions contribute significantly to the holistic development of soft skills in autistic children, providing them with valuable tools to navigate social environments more effectively.

关键词： Autism Navigation Variable speed drives Motors Regulation Real-time systems Creativity Next generation networking Painting Resilience

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