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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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Comparison of *** and Spring Boot in Web Development 15

Comparison of *** and Spring Boot in Web Development

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15th International Conference on Electronics, computers and Artificial Intelligence, ECAI 2023

作者： Novac, Ovidiu Constantin Ghiurau, David Novac, Mihaela Cornelia Gordan, Cornelia Emilia Oproescu, Mihai Bujdoso, Gyongyi Mihalca, Vlad Ovidiu University of Oradea Electrical Engineering and Information Technology Faculty Department of Computers and Information Technology Oradea Romania University of Oradea Management in Information Technology Electrical Engineering and Information Technology Faculty Oradea Romania University of Oradea Electrical Engineering and Information Technology Faculty Department of Electrical Engineering Oradea Romania University of Oradea Electrical Engineering and Information Technology Faculty Department of Electronics and Telecommunications Oradea Romania University of Pitesti Faculty of Electronics Communication and Computer Science Department of Electronics Computers and Electrical Engineering Pitesti Romania University of Debrecen Faculty of Informatics Department of Computer Science and Library and Information Science Debrecen Hungary School of Engineering Sciences University of Oradea Electrical Engineering and Information Technology Faculty Oradea Romania

ISBN: (纸本)9798350321388

In this paper we present two types of server implementation, we will look at the advantages and disadvantages of each technology. *** server application, built with ***, a single threaded server which will treat the requests in an asynchronous manner, with a non-blocking principle, whilst Spring Boot is a more robust and more common approach of a server technology with the benefits of having a strongly typed architecture and multiple threads available. These types of application will behave the same, making almost impossible to tell the difference between a *** and a Spring Boot web server. © 2023 IEEE.

关键词： Node.Js non-blocking single-multi threaded Spring Boot web server

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EfficientViT: An Efficient Vision Transformer for Fire and Smoke Image Classification 1

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4th International Conference on Smart systems: Innovations in Computing, SSIC 2023

作者： Gourisaria, Mahendra Kumar Patel, Ayush V. Chatterjee, Rajdeep Singh, Vijander School of Computer Engineering KIIT Deemed to Be University Odisha Bhubaneswar751024 India Department of Computer Science and Engineering Manipal University Jaipur Rajasthan Jaipur302034 India Department of ICT and Natural Science Faculty of Information Technology and Electrical Engineering NTNU—Norwegian University of Science and Technology Aalesund Norway

ISBN: (数字)9789819736904

ISBN: (纸本)9789819736898

Given the variety of fire and smoke, which are distinguished by variances in texture and color, it is extremely difficult to detect fire and smoke from visual imagery. A significant amount of economic and environmental harm has resulted from fire and smoke incidents around the world. In order to prevent property damage and save lives, fires must be detected as soon as possible, for example, forest fires, which frequently occur in the summer yet are easily avoidable. Using cutting-edge deep learning models including Convolutional Neural Network (CNN), VGG16, Vision Transformer (ViT), and MobileViT, we offer a thorough method for classifying fire and smoke in this study. We also provide an innovative model dubbed EfficientViT, which combines the advantages of the MobileViT and Inverted Residual layers. The correct classification of visual images into the categories of fire and smoke is our main goal. We show that EfficientViT beats the other developed models, obtaining an amazing accuracy of 0.92, through a thorough experimentation and evaluation. This demonstrates how well our suggested model works at correctly differentiating between incidents of fire and smoke. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Convolutional neural networks

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SFA-Net: A Selective Features Absorption Network for Object Detection in Rainy Weather Conditions

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IEEE Transactions on Neural Networks and Learning systems 2023年第8期34卷 5122-5132页

作者： Huang, Shih-Chia Hoang, Quoc-Viet Le, Trung-Hieu National Taipei University of Technology Department of Electronic Engineering Taipei10608 Taiwan National Taipei University of Technology International Graduate Program in Electrical Engineering and Computer Science Taipei10608 Taiwan Hung Yen University of Technology and Education Faculty of Information Technology Hungyen 17000 Viet Nam

In recent years, object detection approaches using deep convolutional neural networks (CNNs) have derived major advances in normal images. However, such success is hardly achieved with rainy images due to lack of visibility. Aiming to bridge this gap, in this article, we present a novel selective features absorption network (SFA-Net) to improve the performance of object detection not only in rainy weather conditions but also in favorable weather conditions. SFA-Net accomplishes this objective by utilizing three subnetworks, where the feature selection subnetwork is concatenated with the object detection subnetwork through the feature absorption subnetwork to form a unified model. To promote further advancement in object detection impaired by rain, we propose a large-scale rainy image dataset, named srRain, which contains both synthetic rainy images and real-world rainy images for training and testing purposes. srRain is comprised of 25 900 rainy images depicting diverse driving scenarios in the presence of rain with a total of 181 164 instances interpreting five common object categories. Experimental results display that our SFA-Net reaches the highest mean average precision (mAP) of 77.53% on a normal image set, 62.52% on a synthetic rainy image set, 37.34% on a collected natural rainy image set, and 32.86% on a published real rainy image set, surpassing current state-of-the-art object detectors and the combination of image deraining and object detection models while retaining a high speed. © 2012 IEEE.

关键词： Feature extraction

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Face Mask Detection and Alert system 1

Face Mask Detection and Alert System

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1st IEEE International Conference on Electronics, Computing, Communication and Control technology, ICECCC 2024

作者： Singh, Sukhvir Rawat, Daksh Narang, Harshit Das, Purushottam Dhanalakshmi, R. Yadav, Satya Prakash Graphic Era Hill University Computer Science and Engineering Dehradun India Indian Institute of Information Technology Department of Computer Science and Engineering Tamil Nadu Tiruchirappalli India Department of Computer Science and Engineering Greater Noida201306 India

ISBN: (纸本)9798350371802

The COVID-19 pandemic has underscored the importance of preventive measures, with the use of face masks being a crucial aspect of controlling the virus's spread. Thus, in the response to the global imperative to curb the spread of infectious diseases, particularly in the context of the COVID-19 pandemic, this research paper delves into the realm of face mask detection and alert systems. With the ubiquity of technology, the development of automated solutions for ensuring adherence to mask-wearing protocols has become most necessary. This paper provides a thorough examination of existing methodologies for face mask detection, ranging from image& video-based approaches to sophisticated deep learning models. Additionally, explores the integration of alert systems into these detection frameworks, emphasizing real- time processing considerations and user interface design. The review incorporates case studies, ethical considerations, and privacy implications, offering a comprehensive overview of the current state of the art. By elucidating the challenges, successes, and future directions, this research paper contributes to the discourse on leveraging technology for public health measures and sets the stage for continued advancements in face mask detection systems and also provides a comprehensive review of the state-of-the-art in face mask detection technologies and explores the design. © 2024 IEEE.

关键词： COVID-19

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Who Cites Whom and How It Impacts the Knowledge Production Process across Disciplines?: A Methodological Insight

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science and technology Libraries 2023年第2期42卷 201-214页

作者： Iqbal, Sobia Safder, Iqra Aljohani, Naif Radi Alfakeeh, Ahmed S. Visvizi, Anna Nawaz, Raheel Hassan, Saeed-Ul Department of Computer Science Information Technology University Lahore Pakistan Department of Computer Science National University of Computing and Emerging Sciences Lahore Pakistan Faculty of Computing and Information Technology King Abdulaziz University Jeddah Saudi Arabia Effat College of Business Effat University Jeddah Saudi Arabia SGH Warsaw School of Economics Warsaw Poland Manchester Metropolitan University Manchester United Kingdom

This paper proposes two novel approaches to measure the similarity of co-cited authors for the task of document clustering, a) paragraph-level content-based author co-citation analysis (PCACA) and b) section-level content-based author co-citation analysis (SCACA), by mining the textual cited reference at the paragraph and the section level within a given scientific publication, respectively. Using over 2000 full-text publications, indexed in the field of computer and information sciences, indexed in ***, we extract the useful information from a full-text publication such as citing sentences, location of citing sentences, cited first author name, and title of cited. We show that our proposed SCACA method of clustering outperforms existing clustering methods by exhibiting more optimal clusters with minimum graph density and an average degree in SCACA, i.e., 0.327 and 27.143, respectively. Finally, we show that SCACA produces the optimum number of clusters that comprehensively explains sub-disciplines of co-cited author pairs. © 2022 The Author(s). Published with license by Taylor & Francis Group, LLC.

关键词： information management

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An Intelligent Motorcycle Safety Driving and Automated Accident Reporting system 13

An Intelligent Motorcycle Safety Driving and Automated Accid...

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13th IEEE Global Conference on Consumer Electronic, GCCE 2024

作者： Chung, Ming-Ying Lu, Yi-Da Lee, Cheng-Han Kuo, Szu-Yu Chen, Liang-Bi National Penghu University of Science and Technology Department of Computer Science and Information Engineering Penghu Taiwan National Kaohsiung University of Science and Technology Department of Shipping and Transportation Management Kaohsiung Taiwan

ISBN: (纸本)9798350355079

While driving on roads in Taiwan, it's crucial to be vigilant when riding a motorcycle due to common hazards such as illegal parking, sudden opening of doors, abrupt lane changes, disregarding traffic lights, and failing to maintain a safe distance. These factors contribute to the rising number of car accidents. To tackle these issues, this paper proposes an intelligent system to promote safe motorcycle driving and automate the reporting of accidents. The proposed system detects vehicle distances, monitors vehicle conditions, provides hazard warnings, and facilitates cloud-based accident reporting. © 2024 IEEE.

关键词： Motorcycles

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Bridging Machine Learning and Formal Concept Analysis for Effective Crowd Detection 25

Bridging Machine Learning and Formal Concept Analysis for Ef...

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25th International Arab Conference on information technology, ACIT 2024

作者： Al-Oraiqat, Anas M. Drieiev, Oleksandr Samara, Ghassan Almatarneh, Sattam Al-Oraiqat, Karim A. Alrawashdeh, Hazim Drieieva, Hanna Elrashidi, Ali Hasan, Yassin M. Y. Zarqa University Faculty of Information Technology Department of Computer Science Jordan Central Ukrainian National Technical University Department of Cybersecurity and Software Kropyvnytskyi Ukraine Zarqa University Faculty of Information Technology Department of Data Science and Artificial Intelligence Jordan Zarqa University Faculty of Information Technology Department of Software Engineering Jordan University of Business and Technology Department of Electrical Engineering Jeddah21432 Saudi Arabia Assiut University Electrical Engineering Department Egypt

ISBN: (纸本)9798331540012

Crowd detection and prevention systems have become essential for managing densely populated areas. Modern systems leverage the combined power of machine learning, data mining, and image processing to extract and analyse features from crowded zones, enabling the identification of behavioural patterns and anomalies. However, most current solutions primarily focus on detection and lack robust decision-making and recommendation mechanisms for selecting appropriate crowd-prevention strategies. To address this gap, we integrate the predictive capabilities of machine learning models with the analytical and clustering strengths of Fuzzy Formal Concept Analysis (fuzzy FCA). Machine learning is employed to extract valuable insights from images of the area, with a neural network used to identify human figures, track individuals' positions, and predict crowd levels. This data is fed into an FFCA-based decision system, where crowd information is structured and clustered using lattice theory. This latter helped exclude low-crowd zones and cluster the rest of the monitored areas based on their crowd features. Additionally, we define bottom-up parsing algorithms to recommend suitable crowd-prevention plans based on crowd density levels within fuzzy formal concepts. Experiments confirmed not only the ability of fuzzy FCA to "exclude"low-crowd zones thanks to a used crowd threshold but also the efficient "feature-based clustering"of crowded zones into hierarchical formal concepts and these latter's "bottom-up parsing"to finally identify the dense zones. © 2024 IEEE.

关键词： Adversarial machine learning

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PADAAV: Enhancing Perception systems using GAN-generated Adversarial Augmented Domains for Autonomous Vehicles

PADAAV: Enhancing Perception Systems using GAN-generated Adv...

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2023 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events, PerCom Workshops 2023

作者： Rawlley, Oshin Gupta, Shashank Birla Institute of Technology and Science Department of Computer Science & Information Systems Rajasthan Pilani India

ISBN: (纸本)9781665453813

In the field of autonomous vehicles (AV), it is crucial for the perceptual systems of the AVs to learn inter-domain adaptations in the absence of paired examples for detecting vehicular instances in unstructured real-world scenarios. One straightforward approach is to train the models directly on labeled synthetic datasets. However, this approach usually fails to achieve generality, owing to the domain bias between the real and fake databases of images. We therefore, propose a novel architecture that produces synthetic images based on cycle consistency, in the absence of labeled pair of images. We test the object detectors Detectron and SSD on four types of curated benchmark datasets to evaluate their robustness in detecting objects such as cars, bikes, and pedestrians on road. The four benchmark datasets contain a diversified set of image corruptions and a few variations are built using the proposed framework such as weather variations. The four datasets are PASCAL VOC and SYNTHIA dataset images, weather-translated images, variation-augmented images, and stylized renderings using Adain style transfer. After conducting extensive investigations, we observe a decreased classification loss when exposed to variable image quality. We also witness that augmenting the training datasets with variations in the wild aided in boosting the generalizing capability of the object detectors. The boost in performance is testified by the testing results showing better mAP. © 2023 IEEE.

关键词： Object detection

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A Quick and Effective Loan Eligibility Analysis Model Using LightGBM 1st

A Quick and Effective Loan Eligibility Analysis Model Using ...

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1st International Conference on Intelligent Computing, Smart Communication, and Network Technologies, ICICSCNT 2023

作者： Gowdham, C. Ashwin, R. Raj, T. S. Senthil Department of Computer Science Hindustan Institute of Technology and Science Tamil Nadu Chennai India School Of Computing & Information Technology REVA University Bangalore560064 India

ISBN: (纸本)9783031759567

In recent times, it is extremely common for the people to make use of bank credits to satisfy their day to day needs. This is increasing rapidly. There are lot of schemes are provided by the banks. Lending money is one of the most crucial bank programmes. Banks generally provide loans to customers based on their requirements. But, due to some situation, some clients cannot repay their debts on time or they will make it delay because of lack of finances. There are so many people who take advantage of this and misuse the facilities that are provided by the bank. To overcome this issue, Banks should utilize certain methods to help in predicting the status of loan repayment. The financial system needs an appropriate modelling system to handle a variety of problems. For any bank, one of the most challenging tasks is the task of anticipating loan late payers. However, the banks will undoubtedly be able to minimise their non-profit assets in order to decrease their loss as a result of the expected bankruptcies. As a result, it enables the repayment of approved debts to proceed with no losses and contributes to the credit report. This demonstrates the significance of researching this technique to foresee lending decisions. The proposed method utilizes machine learning methods for predicting default on loans. This is because they provide greater accuracy on prediction challenges. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Decentralized finance

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