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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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A Novel Soft Clustering Approach for Gene Expression Data

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computer systems science & engineering 2022年第12期43卷 871-886页

作者： E.Kavitha R.Tamilarasan Arunadevi Baladhandapani M.K.Jayanthi Kannan A Constituent College of Anna University University College of EngineeringVillupuram605103India A Constituent College of Anna University University College of EngineeringPattukkottai614701India Department of Electronics and Communication Engineering Dr.N.G.P Institute of TechnologyCoimbatore641048India Department of Computer Science Engineering Faculty of Engineering and TechnologyJAIN(Deemed to be University)Bangalore562112India

Gene expression data represents a condition matrix where each rowrepresents the gene and the column shows the condition. Micro array used todetect gene expression in lab for thousands of gene at a time. Genes encode proteins which in turn will dictate the cell function. The production of messengerRNA along with processing the same are the two main stages involved in the process of gene expression. The biological networks complexity added with thevolume of data containing imprecision and outliers increases the challenges indealing with them. Clustering methods are hence essential to identify the patternspresent in massive gene data. Many techniques involve hierarchical, partitioning,grid based, density based, model based and soft clustering approaches for dealingwith the gene expression data. Understanding the gene regulation and other usefulinformation from this data can be possible only through effective clustering algorithms. Though many methods are discussed in the literature, we concentrate onproviding a soft clustering approach for analyzing the gene expression data. Thepopulation elements are grouped based on the fuzziness principle and a degree ofmembership is assigned to all the elements. An improved Fuzzy clustering byLocal Approximation of Memberships (FLAME) is proposed in this workwhich overcomes the limitations of the other approaches while dealing with thenon-linear relationships and provide better segregation of biological functions.

关键词： Reinforcement membership centroid threshold statistics bioinformatics gene expression data

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Beamforming Design Towards Sum-Rate Maximization for Holographic Active RIS-Aided Uplink Near-Field communications

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IEEE Internet of Things Journal 2025年第12期12卷 18582-18595页

作者： Singh, Sandeep Singh, Keshav Singh, Sandeep Kumar Shin, Hyundong Duong, Trung Q. Kaohsiung80424 Taiwan National Sun Yat-sen University Institute of Communications Engineering Kaohsiung80424 Taiwan Kyung Hee University Department of Electronic Engineering Gyeonggi-do Yongin-si17104 Korea Republic of Motilal Nehru National Institute of Technology Allahabad Department of Electronics and Communication Engineering Prayagraj India Kyung Hee University Department of Electronics and Information Convergence Engineering Deogyeong-daero Gyeonggi-do Yongin-si1732 Korea Republic of Memorial University Faculty of Engineering and Applied Science St. John'sNLA1C 5S7 Canada Queen's University Belfast School of Electronics Electrical Engineering and Computer Science BelfastBT7 1NN United Kingdom

Holographically-driven active reconfigurable intelligent surface (HARIS), leveraging densely packed sub-wavelength elements, overcomes the limitations of conventional RIS in signal processing, unlocking advanced capabilities for next-generation networks. Thus, to exploit its full potential, this work proposes the integration of HARIS into an Internet of Things (IoT) multi-user uplink near-field-driven wireless communication system. A sum-rate maximization problem is formulated to provide efficient resource utilization by jointly optimizing the equalizer design, power allocation at each IoT user, and the HARIS phase shift, while satisfying strict constraints of quality of service (QoS) requirement and limited power budget at each IoT user and HARIS. Due to the non-convex nature of the problem, we propose an alternating optimization (AO)-based algorithm, incorporating techniques such as minimum mean square error (MMSE), convex upper bound approximation, and semidefinite relaxation (SDR). Then, extensive simulations validate the algorithm's efficacy and convergence, demonstrating up to 63% higher performance with HARIS than passive RIS. Additionally, we highlight that near-field communication yields up to 90% higher sum-rate than hybrid 76% and far-field model 73%. Moreover, we demonstrate the impact of imperfect channel state information (iCSI) on the system performance. © 2014 IEEE.

关键词： Mean square error

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K-Banhatti Sombor Invariants of Certain computer Networks

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computers, Materials & Continua 2022年第10期73卷 15-31页

作者： Khalid Hamid Muhammad Waseem Iqbal Abaid Ur Rehman Virk Muhammad Usman Ashraf Ahmed Mohammed Alghamdi Adel A.Bahaddad Khalid Ali Almarhabi Department of Computer Science Superior UniversityLahore54000Pakistan Department of Software Engineering Superior UniversityLahore54000Pakistan Department of Mathematics University of SialkotSialkot51013Pakistan Department of Computer Science GC Women UniversitySialkot53310Pakistan Department of Software Engineering College of Computer Science and EngineeringUniversity of Jeddah21493Saudi Arabia Department of Information System Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Computer Science College of Computing in Al-QunfudahUmm Al-Qura UniversityMakkah24381Saudi Arabia

Any number that can be uniquely determined by a graph is called a graph *** the last twenty years’countless mathematical graph invariants have been characterized and utilized for correlation ***,no reliable examination has been embraced to decide,how much these invariants are related with a network graph or molecular *** this paper,it will discuss three different variants of bridge networks with good potential of prediction in the field of computer science,mathematics,chemistry,pharmacy,informatics and biology in context with physical and chemical structures and networks,because k-banhatti sombor invariants are freshly presented and have numerous prediction qualities for different variants of bridge graphs or *** study solved the topology of a bridge graph/networks of three different types with two invariants KBanhatti Sombor Indices and its reduced *** deduced results can be used for the modeling of computer networks like Local area network(LAN),Metropolitan area network(MAN),and Wide area network(WAN),backbone of internet and other networks/structures of computers,power generation,bio-informatics and chemical compounds synthesis.

关键词： Bridge networks invariants k-banhatti sombor indices maple network graph molecular graph

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Epileptic seizure detection in EEG signals via an enhanced hybrid CNN with an integrated attention mechanism

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Mathematical Biosciences and engineering 2025年第1期22卷 73-105页

作者： Mekruksavanich, Sakorn Phaphan, Wikanda Jitpattanakul, Anuchit Department of Computer Engineering School of Information and Communication Technology University of Phayao Phayao56000 Thailand Department of Applied Statistics Faculty of Applied Science King Mongkut’s University of Technology North Bangkok Bangkok10800 Thailand Department of Mathematics Faculty of Applied Science King Mongkut’s University of Technology North Bangkok Bangkok10800 Thailand Intelligent and Nonlinear Dynamic Innovations Research Center Science and Technology Research Institute King Mongkut’s University of Technology North Bangkok Bangkok10800 Thailand

Epileptic seizures, a prevalent neurological condition, necessitate precise and prompt identification for optimal care. Nevertheless, the intricate characteristics of electroencephalography (EEG) signals, noise, and the want for real-time analysis require enhancement in the creation of dependable detection approaches. Despite advances in machine learning and deep learning, capturing the intricate spatial and temporal patterns in EEG data remains challenging. This study introduced a novel deep learning framework combining a convolutional neural network (CNN), bidirectional gated recurrent unit (BiGRU), and convolutional block attention module (CBAM). The CNN extracts spatial features, the BiGRU captures long-term temporal dependencies, and the CBAM emphasizes critical spatial and temporal regions, creating a hybrid architecture optimized for EEG pattern recognition. Evaluation of a public EEG dataset revealed superior performance compared to existing methods. The model achieved 99.00% accuracy in binary classification, 96.20% in three-class tasks, 92.00% in four-class scenarios, and 89.00% in five-class classification. High sensitivity (89.00–99.00%) and specificity (89.63–99.00%) across all tasks highlighted the model’s robust ability to identify diverse EEG patterns. This approach supports healthcare professionals in diagnosing epileptic seizures accurately and promptly, improving patient outcomes and quality of life. © 2025 American Institute of Mathematical sciences. All rights reserved.

关键词： Electroencephalography

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Feature Map Guided Strategy for Robust Multi-Resolution Image Dehazing

Feature Map Guided Strategy for Robust Multi-Resolution Imag...

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2024 International Conference on Electrical, Electronics and Computing Technologies, ICEECT 2024

作者： Kumar, Avishek Kumar, Nagendra Mishra, Akhilesh Kumar Pathak, Gouransh Chatterjee, Amit IIT Delhi Centre for Sensors Instrumentation and Cyber-Physical System Engineering New Delhi110016 India NIT Jamshedpur Department of Electronics and Communication Engineering Jamshedpur831014 India IILM Univeristy Department of Electrical and Electronics Engineering Greater Noida201306 India PIEMR-Indore Department of Computer Science and Engineering Indore452010 India DIT University School of Computing Dehradun248009 India

ISBN: (纸本)9798350378092

Presence of haze in digital images poses significant challenges in computer vision applications, adversely impacting tasks like image segmentation and object detection. To tackle this issue, this paper introduces a fusion-based dehazing strategy by integrating color conversion, adaptive gamma correction, and parameter estimation. Through multi resolution fusion of chromatic and brightness maps, the algorithm efficiently removes haze, surpassing conventional single-image approaches while maintaining computational efficacy. From comparative analysis it is evident that, the proposed strategy is highly effective in terms of improving image quality for diverse applications. © 2024 IEEE.

关键词： Demulsification

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Reversible Watermarking Method with Low Distortion for the Secure Transmission of Medical Images

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computer Modeling in engineering & sciences 2022年第3期130卷 1309-1324页

作者： Rizwan Taj Feng Tao Shahzada Khurram Ateeq Ur Rehman Syed Kamran Haider Akber Abid Gardezi Saima Kanwal School of Computer and Communication Lanzhou University of TechnologyLanzhouChina Faculty of Computing The Islamia University of BahawalpurBahawalpurPakistan Department of Electrical Engineering Government College UniversityLahorePakistan College of Internet of Things Engineering Hohai UniversityChangzhouChina Department of Computer Science COMSATS University IslamabadIslamabadPakistan

In telemedicine,the realization of reversible watermarking through information security is an emerging research ***,adding watermarks hinders the distribution of pixels in the cover image because it creates distortions(which lead to an increase in the detection probability).In this article,we introduce a reversible watermarking method that can transmit medical images with minimal distortion and high *** proposed method selects two adjacent gray pixels whose least significant bit(LSB)is different from the relevant message bit and then calculates the distortion *** use the LSB pairing method to embed the secret matrix of patient record into the cover image and exchange pixel *** results show that the designed method is robust to different attacks and has a high PSNR(peak signal-to-noise ratio)*** MRI image quality and imperceptibility are verified by embedding a secret matrix of up to 262,688 bits to achieve an average PSNR of 51.657 *** addition,the proposed algorithm is tested against the latest technology on standard images,and it is found that the average PSNR of our proposed reversible watermarking technology is higher(i.e.,51.71 dB).Numerical results show that the algorithm can be extended to normal images and medical images.

关键词： LSB reversible watermarking medical images security data distortion

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Smart Ports Solutions for Cargo Container Tracking and Vessel Traffic Counting systems using IoT and Cloud Computing 10

Smart Ports Solutions for Cargo Container Tracking and Vesse...

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10th International Conference on communication and Signal Processing, ICCSP 2024

作者： Priya, S. Sairam, A. Azath, H. Manivannan, S.K. Mohankumar, N. Vedasundara Vinayagam, P. Deemed To Be University Department of Electrical and Electronics Engineering Tamil Nadu Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Tamil Nadu Chennai India Vit Bhopal University School of Computing Science and Engineering Bhopal India Faculty of Management Srm Institute of Science and Technology Chennai Tamil Nadu Kattankulathur India Symbiosis Institute of Technology Maharashtra Nagpur India Adhiparasakthi Engineering College Department of Electronics and Communication Engineering Tamil Nadu Melmaruvathur India

ISBN: (纸本)9798350353068

This research examines current port operations that use Internet of Things (IoT) technologies for cargo container monitoring and vessel traffic counts. Effective port management is crucial for smooth commodities movement and congestion reduction as global commerce grows. Addressing these issues with IoT solutions is promising. It analyzes cargo container IoT sensors and devices for real-time location, condition, and security monitoring. These IoT-enabled containers provide port status data for better logistical planning and security. IoT devices are used to count and control vessel traffic in the research. Sensors and data analytics at important port locations provide real-time vessel tracking and traffic monitoring. This data improves safety and optimizes vessel traffic flow, lowering congestion and wait times. The pros and downsides of using IoT in port operations include data privacy, security, and infrastructure needs. Smart port technologies may enhance marine efficiency, cost savings, and sustainability. The IoT can revolutionize port operations, notably cargo container monitoring and vessel traffic counts. Port capacity, security, and the environmental and economic viability of worldwide trade may all be improved with the help of the IoT. © 2024 IEEE.

关键词： Internet of things

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Reinforcement Learning for Dynamic Power Management in Embedded systems 5

Reinforcement Learning for Dynamic Power Management in Embed...

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5th IEEE International Conference on Recent Trends in computer science and Technology, ICRTCST 2024

作者： Panigrahi, Bhawani Sankar Pattanaik, Balachandra Pattanaik, Ojasvi Tilak Babu, S.B.G. Pavithra, G. Shaik, Bazani Vardhaman College of Engineering Department of IT Telangana Hyderabad India Saveetha Institute of Medical and Technical Sciences Adjunct Faculty Saveetha School of Engineering Tamilnadu Chennai India JNTU Vignan Institute of Management and Technology for Women Department of Computer Science and Engineering Hyderabad Ghatkesar India Aditya Engineering College Department of ECE Surampalem India Dayananda Sagar College of Engineering Department of Electronics & Communication Engineering Karnataka Bangalore India Ramachandra College Of Engineering Department of Mechanical Engineering Andhra Pradesh Eluru India

ISBN: (纸本)9798350351378

Itaddresses the challenges that are associated with dynamic power management in embedded systems by utilizing techniques that are derived from the discipline of reinforcement learning (RL). Given the increasing complexity of embedded systems and the need for solutions that are efficient for energy consumption, RL is a potential technology that can dynamically optimize power utilization. This is particularly pertinent in light of the requirement for solutions that are efficient for energy consumption. The dynamic workload fluctuations that are inherent in embedded systems are taken into consideration in this research, which studies the integration of RL algorithms to control power levels in real time in an adaptive manner. This research also takes into account the variable workloads that are endemic to embedded systems. To evaluate the efficacy of RL-based dynamic power management strategies in comparison to traditional methods, with a specific emphasis on the potential for enhanced energy efficiency and system responsiveness, the goal of this is to evaluate the effectiveness of these strategies. The findings not only contribute to the improvement of understanding of the use of reinforcement learning in the setting of embedded systems, but they also provide insights into the utility of RL in meeting the evolving power management requirements in contemporary computing environments. © 2024 IEEE.

关键词： Reinforcement learning

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Extended Kalman Filter Algorithm to Detect False Data Injection in Wireless Sensor Network 2

Extended Kalman Filter Algorithm to Detect False Data Inject...

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2nd International Conference on Augmented Intelligence and Sustainable systems, ICAISS 2023

作者： Babuji, R. Ganesan, J. Jeya Lekha, D. Karthikeyan, M. Kalai Selvi, D.M. Rmk Engineering College Department of Electronics and Communication Engineering Tamil Nadu Chennai India Sri Sairam Institute of Technology Department of Artificial Intelligence and Data Science Tamil Nadu Chennai India Rmk College of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Srm Institute of Science and Technology School of Computing Faculty of Engineering and Technology Department of Computing Technologies Tamil Nadu Chennai India R.M.D. Engineering College Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350325799

Wireless Sensor Networks (WSN) contains spatially distributed sensor nodes that collaborate with each other. However, the WSN is susceptible since the wireless medium is unpredictable. Several conventional approaches are used to distinguish the abnormal in the WSN, and all have difficulties. An Extended Kalman Filter Algorithm (EKFA) to detect false inject data in WSN is proposed in this paper. It addresses two issues such as abnormality recognition and obstacle detection. It also distinguishes false insert data. Particularly, it observes neighbors' behaviors and then applying EKFA to forecast their future states. The node behavior is observed by incorporating abnormal node examination and system examination elements. The Path Optimization Method (POM) is a shortest route that is used to discover the obstacle in the WSN. The EKFA mechanism minimizes the forwarder count and delay by applying POM. Simulation results demonstrate that the EKFA approach enhances abnormal node detection and minimizes the false negative ratio. © 2023 IEEE.

关键词： Extended Kalman filters

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