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Optimization Techniques in University Timetabling Problem:Constraints,Methodologies,Benchmarks,and Open Issues

computers, Materials & Continua 2023年第3期74卷 6461-6484页

作者： Abeer Bashab Ashraf Osman Ibrahim Ibrahim Abakar Tarigo Hashem Karan Aggarwal Fadhil Mukhlif Fuad A.Ghaleb Abdelzahir Abdelmaboud Faculty of Computer Science&Information Technology Alzaiem Alazhari University13311KhartoumSudan Faculty Computing and Informatics Universiti Malaysia SabahKota Kinabalu88400SabahMalaysia Department of Computer Science University of SharjahSharjah27272United Arab Emirates Electronic and Communication Engineering Department Maharishi Markandeshwar Deemed to be UniversityMullanaAmbala133207HaryanaIndia Information Assurance and Security Research Group(IASRG) School of ComputingFaculty of EngineeringUniversiti Teknologi Malaysia81310JohorMalaysia Department of Information Systems King Khaled UniversityMuhayel61913AseerSaudi Arabia

University timetabling problems are a yearly challenging task and are faced repeatedly each *** problems are considered nonpolynomial time(NP)and combinatorial optimization problems(COP),which means that they can be solved through optimization algorithms to produce the aspired optimal *** techniques have been used to solve university timetabling problems,and most of them use optimization *** paper provides a comprehensive review of the most recent studies dealing with concepts,methodologies,optimization,benchmarks,and open issues of university timetabling *** comprehensive review starts by presenting the essence of university timetabling as NP-COP,defining and clarifying the two formed classes of university timetabling:University Course Timetabling and University Examination Timetabling,illustrating the adopted algorithms for solving such a problem,elaborating the university timetabling constraints to be considered achieving the optimal timetable,and explaining how to analyze and measure the performance of the optimization algorithms by demonstrating the commonly used benchmark datasets for the *** is noted that meta-heuristic methodologies are widely used in the ***,recently,multi-objective optimization has been increasingly used in solving such a problem that can identify robust university timetabling ***,trends and future directions in university timetabling problems are *** paper provides good information for students,researchers,and specialists interested in this area of *** challenges and possibilities for future research prospects are also explored.

关键词： University timetabling timetabling approaches meta-heuristics combinatorial optimization

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Cross-View Mutual Learning for Semi-Supervised Medical Image Segmentation 24

Cross-View Mutual Learning for Semi-Supervised Medical Image...

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32nd ACM International Conference on Multimedia, MM 2024

作者： Wu, Song Wei, Xiaoyu Chen, Xinyue Ren, Yazhou He, Jing Pu, Xiaorong School of Computer Science and Engineering University of Electronic Science and Technology of China Sichuan Chengdu China Shenzhen Institute for Advanced Study University of Electronic Science and Technology of China Guangdong Shenzhen China Faculty of Medicine & Biomedical Sciences The University of Queensland BrisbaneQLD Australia

ISBN: (纸本)9798400706868

Semi-supervised medical image segmentation has gained increasing attention due to its potential to alleviate the manual annotation burden. Mainstream methods typically involve two subnets, and conduct a consistency objective to ensure them producing consistent predictions for unlabeled data. However, they often ignore that the complementarity of model predictions is equally crucial. To realize the potential of the multi-subnet architecture, we propose a novel cross-view mutual learning method with a two-branch co-training framework. Specifically, we first introduce a novel conflict-based feature learning (CFL) that encourages the two subnets to learn distinct features from the same input. These distinct features are then decoded into complementary model predictions, allowing both subnets to understand the input from different views. More importantly, we propose a cross-view mutual learning (CML) to maximize the effectiveness of CFL. This approach requires only modifications to the model inputs and supervisory signals, and implements a heterogeneous consistency objective to fully explore the complementarity of model predictions. Consequently, the aggregated predictions can effectively capture both consistency and complementarity across two subnets. Experimental results on three public datasets demonstrate the superiority of CML over previous SoTA methods. Code is available at https://***/SongwuJob/CML. © 2024 ACM.

关键词： Self-supervised learning

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Road Traffic Signs Recognition in Automatic Driving under Extreme Environments 2

Road Traffic Signs Recognition in Automatic Driving under Ex...

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2022 2nd International Conference on Artificial Intelligence, Big Data and Algorithms, CAIBDA 2022

作者： Chi, Juntian Feng, Zhuangzhou Zhang, Weipeng Faculty of Electronic Information Engineering South-Central Minzu University Wuhan China Faculty of Computer and Information Engineering Huali College Guangdong University of Technology Guangzhou China Faculty of Computer Science and Technology Shaanxi University of Science and Technology Xi'an China

ISBN: (纸本)9783800760268

Traffic sign recognition (TSR) in automotive driving ensures smooth driving and prevents accidents by collecting and recognizing the road traffic sign information, giving instructions or warnings, and making operations more manageable and safer. Most current traffic recognition methods are based on machine learning methods and perform well. However, Road traffic sign recognition in Automatic extreme environments needs to be further emphasized. In this paper, CNN is used to recognize road traffic signs under extreme weather conditions to help drivers deal with barriers in foggy or highlight situations. This technique provides an efficient way for drivers to identify the traffic signs in the foggy or highlight conditions, decreasing the frequency of traffic wrecks. Firstly, we adopt the methods to blur the picture, and then the processed image is used for training. A multi-layer convolution neural network learning algorithm is proposed. The results of the tests show that the average recognition rate of the algorithm for traffic signs in the natural environment is 97.1%, and the false recognition rate is less than 1%. The results of the experiments suggest that the algorithm we employed in our research works has an accurate recognition degree and can improve the recognition speed of the existing algorithms. © VDE VERLAG GMBH ∙ Berlin ∙ Offenbach.

关键词： Traffic signs

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So Far Yet So Near: Time Series Data Augmentation with Exploring non-Semantic Boundaries based on Reinforcement Learning

So Far Yet So Near: Time Series Data Augmentation with Explo...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Li, Haoran Zhang, Zhibo Kang, Jiarong Jiang, Xun Gong, Xiaoli Zhang, Jin Sun, Zhe Cichocki, Andrzej College of Computer Science Nankai University Tianjin China School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China Faculty of Medicine Faculty of Health Data Science Juntendo University Japan Systems Research Institute Polish Academy of Science Warsaw Poland

ISBN: (纸本)9798350368741

Data augmentation effectively expands feature distribution in time series classification, enhancing downstream task performance. However, existing techniques often fail to maintain semantic consistency between augmented and original time series data, causing label noise and thereby degrading downstream task performance. We argue that data augmentation should preserve time series semantic consistency and expand the non-semantic information space. In this paper, we reformulate data augmentation as a semantic path planning problem between original data and augmented data, modeled as a Markov Decision Process (MDP). We propose a reinforcement learning-based algorithm (RL) named FreqSYN, where the action space is defined by a set of learnable Gaussian kernels that perturbs the frequency domain of the original data to generate augmented samples. The confidence coefficients of augmented data in semantically relevant classification tasks are used as a reward to iteratively refine the FreqSYN. Our method is validated across four datasets, achieving state-of-the-art performance, with a 2% improvement in F1 score over the SimPSI method. The code and models are available at https://***/NKU-EmbeddedSystem/FreqSYN. © 2025 IEEE.

关键词： Data argumentation frequency domain augmentation Gaussian kernel prediction time series

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Construction of PCOS Prediction Model based on BP Neural Network 6

Construction of PCOS Prediction Model based on BP Neural Net...

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6th International Conference on Mobile Computing and Sustainable Informatics, ICMCSI 2025

作者： Hu, Xianqing Yadav, Amit Khan, Asif Sah, Abhishek Pratap Azam, Sami School of Computer and Software Chengdu Neusoft University Chengdu China Faculty of Science and Technology Charles Darwin University NT Australia Department of Computer Application Integral University Lucknow India School of Management Science and Engineering University of Electronic Science and Technology of China Chengdu China

ISBN: (纸本)9798331522667

Polycystic ovary syndrome (PCOS), a common endocrine-metabolic disorder affecting about 10-13% of women during reproductive age worldwide, often leads to irregular menstruation, infertility, obesity, and long-term health risks such as diabetes and cardiovascular disease. In this paper, clinical data from PCOS patients and healthy control group were collected from ten hospitals containing 37 key indicators such as age, weight, BMI, menstrual cycle, etc. BP (Back Propagation) Neural Network prediction models were constructed and compared using the collected data and the cross-validation method was used for parameter tuning. It was found that BP Neural Network performed particularly well on the test set, and both demonstrated high prediction accuracy and generalization ability which provided strong evidence for the early identification and early valuable intervention opportunities for PCOS patients. © 2025 IEEE.

关键词： Prediction models

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High-Resolution Optical Imaging Incorporated Non-Destructive Morphological Boundary Detection of Fruit Disorders: A Pilot Study 6

High-Resolution Optical Imaging Incorporated Non-Destructive...

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6th International Conference on Advancements in Computing, ICAC 2024

作者： Hakmana, Nethmi Tharushi Kahatapitiya, Nipun Shantha Weerasinghe, Sasmitha Kalupahana, Deshan Silva, Bhagya Nathali Ravichandran, Naresh Kumar Seong, Daewoon Han, Sangyeob Lee, Seung Yeol June, Hee-Young Jeon, Mansik Kim, Jeehyun Wijenayake, Udaya Wijesinghe, Ruchire Eranga Faculty of Engineering Sri Lanka Institute of Information Technology Department of Mechanical Engineering Malabe Sri Lanka Faculty of Engineering University of Sri Jayewardenepura Department of Computer Engineering Nugegoda Sri Lanka Faculty of Computing Sri Lanka Institute of Information Technology Department of Information Technology Malabe Sri Lanka Sri Lanka Institute of Information Technology Malabe Sri Lanka Center for Scientific Instrumentation Korea Basic Science Institute Daejeon Korea Republic of School of Electronic and Electrical Engineering College of IT Engineering Kyungpook National University Daegu Korea Republic of ICT Convergence Research Center Kyungpook National University Daegu Korea Republic of Kyungpook National University Department of Plant Medicine Daegu Korea Republic of Institute of Plant Medicine Kyungpook National University Daegu Korea Republic of Faculty of Engineering Sri Lanka Institute of Information Technology Department of Electrical and Electronic Engineering Malabe Sri Lanka

ISBN: (纸本)9798331517878

Plant diseases caused by various pathogens, such as fungi, bacteria, and viruses, lead to significant reductions in crop yield and quality. Early detection of these diseases is critical for preventing their spread and optimizing crop yield. Traditional methods of disease detection, while effective, often involve invasive or time-consuming procedures that may not provide real-time insights. This study investigates the use of Swept-Source Optical Coherence Tomography (SS-OCT) for non-destructive, high-resolution imaging to identify morphological abnormalities in fruit specimens affected by diseases. The research focuses on differentiating healthy regions, healthy boundaries, infected boundaries, and fully infected regions in apples. Using a 1310 nm center wavelength laser, the SS-OCT system captured detailed cross-sectional images of fruit tissue, revealing microstructural changes associated with disease progression. A qualitative assessment of the two-dimensional (2D) OCT images highlighted distinct layers in healthy tissues, noticeable structural deformities, and the merging of microstructural layers in the boundary and infected regions. Quantitative depth-profile analysis of axial-direction A-scan profiles further demonstrated the reduction and merging of intensity peaks correlating with tissue abnormalities in infected fruit specimens. These findings underscore the potential of SS-OCT as an effective tool for early disease detection in agricultural products, providing a means for timely intervention and optimized postharvest management. © 2024 IEEE.

关键词： Fruits

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Developing a Test Strategy for Mathematics of Arrays Computation Cores

Developing a Test Strategy for Mathematics of Arrays Computa...

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13th International Electrical engineering Congress, iEECON 2025

作者： Grout, Ian Mullin, Lenore University of Limerick Faculty of Science and Engineering Department of Electronic and Computer Engineering Limerick Ireland University at Albany Suny College of Nanotechnology Science and Engineering Department of Computer Science Albany United States

ISBN: (纸本)9798331543952

Small size, hardware-only, custom data processing cores connected to a host processor are increasingly considered in digital systems design to provide the ability for accelerating data processing operations through hardware acceleration and parallel processing. These can be realized in different forms such as a custom core connected to a host processor on the same die as the processor, as a separate die (e.g., a chiplet) within the same package as the processor or as a separate Integrated Circuit. These require advanced design flows, low-geometry fabrication processes, and suitable test circuit insertion to implement a suitable test strategy. In this paper, the testability of custom hardware cores is considered through test circuit insertion based on the IJTAG standard. The core is to perform data processing operations based on the Mathematics of Arrays and serial communications is based on the Serial Peripheral Interface. This interface is used for both normal operating mode and the IJTAG based test mode. The system design is considered at the pre-synthesis design development stage and identifies architectural considerations for such system designs. © 2025 IEEE.

关键词： Design for testability

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Comorbidity Diseases Diagnosis Using Machine Learning Methods and Chi-Square Feature Selection Technique 16

Comorbidity Diseases Diagnosis Using Machine Learning Method...

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16th International Conference on Developments in eSystems engineering, DeSE 2023

作者： Mukhlif, Dheyauldeen M. Abd, Dhafar Hamed Ejbali, Ridha Alimi, Adel M. Sfax Tunisia University of Anbar College of Computer Science and Information Technology Ramadi Iraq Gabes Tunisia Sfax Tunisia University of Johannesburg Faculty of Engineering and the Built Environment Department of Electrical and Electronic Engineering Science Johannesburg South Africa

ISBN: (纸本)9798350381344

The diagnosis of common diseases, in which people suffer from several bad health conditions, is a complex medical challenge. This study investigated the use of machine learning methods combined with the Chi-square feature selection technique to improve the accuracy and efficiency of comorbidity diseases diagnosis. Using various decision trees of machine learning algorithms, random forest, Gradient Boost, AdaBoost, Bagging, and extra trees, this research aimed to improve the identification and prediction of comorbid conditions and ultimately advance early detection and treatment strategies. Using the selection of Chi-Square features helped give priority to the most relevant attributes, reduce noise, and refine the models. The results showed that both the AdaBoost and Gradient Boost algorithms obtained an accuracy rate of 91.33%, confirming their efficacy in comorbidity diseases diagnosis. However, it is essential to ensure their reliability and efficacy in the real clinical environment, including the practical implementation and validation of these methods, to improve patient care and medical decisions. © 2023 IEEE.

关键词： Adaptive boosting

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ANN-Based Modeling and Control of a Pick and Place Manipulator 1

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8th International Congress on Information and Communication Technology, ICICT 2023

作者： Mostafa, Mohamed Essam Mostafa, Aya Essam Ammar, Hossam Hassan Shalaby, Raafat School of Engineering and Applied Science NU Giza Egypt School of Information Technology and Computer Science NU Giza Egypt SESC Center School of EAS NU Giza Egypt Faculty of Electronic Engineering Menofia University Menouf Egypt

ISBN: (数字)9789819930913

ISBN: (纸本)9789819930906

Pick-and-place robots are used in the manufacturing industry. This paper will focus on one of the most used functionalities in the industry. In addition, it will focus on this functionality and how to achieve more precision with the least cost. The robot is used for packaging, picking, placing factory jobs, and inspection. This paper is focusing on the design of the linear pick-and-place robot. The target of this paper is to tackle the design, construction, control, and analysis of the linear pick-and-place robot. The main target of this paper is to find the best design and the most cost-effective way to develop it. It will be done by comparing several delta robots designs to find which one will be the most economical. It will be done via a survey. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Cost effectiveness

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Advance Path Loss Model for Distance Estimation Using LoRaWAN Network's Received Signal Strength Indicator (RSSI)

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IEEE Access 2024年 12卷 83205-83216页

作者： Vo, Hoang Hoang Long Nguyen, Van Tran, Van Lic Ferrero, Fabien Lee, Fang-Yi Tsai, Meng-Hsun The University of Danang-University of Science and Technology Faculty of Advance Science and Technology Da Nang550000 Viet Nam The University of Danang-University of Science and Technology Department of Electronic and Telecommunication Engineering Da Nang550000 Viet Nam LEAT CNRS Universite Cote d'Azur Sophia Antipolis06903 France National Cheng Kung University Department of Computer Science and Information Engineering Tainan70101 Taiwan National Yang Ming Chiao Tung University Department of Computer Science Hsinchu30010 Taiwan

This work introduces a novel approach to improve the precision of distance estimation in localization systems by using existing LoRaWAN and RSSI-based techniques. Despite the benefits of range and power efficiency, these systems exhibit limited accuracy in practical situations. To address the limitation, this study provides an innovative technique that greatly improves the precision of distance estimations, particularly in urban environments. The fundamental basis of this approach lies in the use of a dynamic path loss model. An additional element is to accommodate the varied and dynamic conditions of signal transmission in metropolitan areas. A better Kalman filter is also used in the study. This is important because it reduces the effects of multipath fading and environmental noise that often make RSSI-based localization in LoRaWAN networks less accurate. The study further examines the influence of the environmental exponent, also known as the path loss exponent, on the RSSI results and the precision of the distance measurements. This methodology achieves the average error under 1 meters for indoor environments and under 7 meters for outdoor environments. Finally, the Cumulative Density Function (CDF) shows 90 % of the distance estimation algorithm error for indoor environment is lower than 1.08 meters while for outdoor environment is lower than 7.55 meters. Based on these improvements, the introduced methodology not only enhances and improves existing approaches but also optimizes the precision and dependability of urban localization technologies, with substantial implications for a variety of practical applications. © 2013 IEEE.

关键词： Kalman filters

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