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Computing and Information Technology (ICCIT-1441), 2020 International Conference on

作者： Qamar H. Naith Department of Software Engineering College of Computer Science and Engineering University of Jeddah Jeddah Saudi Arabia

ISBN: (数字)9798350353839

ISBN: (纸本)9798350353846

Managing the classroom and lab activities in the university and reporting process for the problems that occur during lectures is a hard process that needs more consideration. This study addresses challenges faced by technicians and academic staff in university classrooms, emphasizing the need for a streamlined automated issue reporting system. This paper proposes a comprehensive system aimed at improving communication, efficiency, and effectiveness in addressing classroom issues, benefiting technicians, students, and lecturers. Quantitative Analysis and evaluation of the system's user experience from instructors and technicians showed highly positive outcomes, indicating its high effectiveness in managing classroom issues and improving overall classroom management. Moreover, the statistical test revealed no statistically significant differences (α= 0.05) in the willingness to use the classroom issue management system between technicians and academic staff, indicating similar user perceptions and readiness for both groups to utilize the system.

关键词： Surveys Productivity Bridges Statistical analysis User experience Stakeholders Usability Information technology

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Energy Oriented Three-Tier Computation Offloading Scheme in Maritime Edge Computing Network

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IEEE Transactions on Vehicular Technology 2025年第5期74卷 8126-8140页

作者： Zhang, Hongxia Xi, Shiyu Shang, Bodong Zhang, Peiying Wu, Sheng Jiang, Chunxiao Qingdao Institute of Software College of Computer Science and Technology Qingdao266580 China Eastern Institute of Technology Eastern Institute for Advanced Study Ningbo315200 China Beijing University of Posts and Telecommunications School of Information and Communication Engineering Beijing100876 China Tsinghua University Beijing National Research Center for Information Science and Technology Beijing100084 China

With the development of maritime continuing to grow, large-scale maritime wireless devices (MWDs) are being deployed for various maritime applications. The rapid development of maritime Internet of Things (IoT) and growing demand for these applications have led to an increasing number of computation-intensive and resource-sensitive tasks. Considering the limited computing capacity and energy of MWDs, it is challenging to transmit and process massive maritime computing tasks in an energy efficient manner. In this paper, we propose a three-tier maritime edge computing system in which low earth orbit (LEO) satellite and offshore base station (OBS) support to provide communication and computing services for MWDs. The system optimizes association variable, task partitioning, transmission power, and computing resource allocation to reduce system energy consumption under latency constraints. The optimization problem is formulated as a mixed integer non-linear programming (MINLP) optimization problem. We decompose the original problem into four subproblems and design algorithms to solve them. Firstly, the association variable optimization subproblem is solved by slack variable method. Then, we adopt quadratic transformation and design a difference of convex algorithm to solve transmission power optimization subproblem. Further, task partitioning subproblem is solved by obtaining upper and lower bounds of offloading task size by strict deduction, and then solved by the standard convex method. Finally, Lagrangian dual method is utilized to deal with joint computing resource optimization subproblem of LEO satellite and OBS. In simulation results, the proposed algorithm saves 39.3% of system energy consumption compared to benchmark schemes. © 1967-2012 IEEE.

关键词： Computation offloading

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Enhanced Federated RL with NEAT, Markov Chains, and Gaussian Processes

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Procedia computer science 2025年 260卷 742-751页

作者： Robert McMenemy Kallinatha H D Hamsaveni M Sachin D N Software Engineer Independent Researcher Glasgow Area Scotland UK Dept of Computer Science and Engineering Siddaganga Institute of Technology Tumakuru India Dept of Computer Science and Engineering Vidyavardhaka College of Engineering Mysuru India

Reinforcement Learning (RL) is a machine learning approach in which an agent learns to make decisions in an environment to maximize a cumulative reward. When combined with NeuroEvolution of Augmenting Topologies (NEAT), RL offers several advantages. NEAT is a genetic algorithm that optimizes the development of artificial neural networks by modifying both their structure and weights. When integrated with RL, NEAT can improve the learning process by merging evolutionary optimization with RL techniques. NEAT has demonstrated significant potential in evolving neural networks for RL tasks. However, traditional centralized training methods encounter scalability and data privacy issues. This paper investigates the integration of NEAT with Federated Learning (FL) and its enhancement with Markov Chains and Gaussian Processes to address certain issues. We propose a new framework that combines NEAT for neural network evolution with TensorFlow Federated (TFF) for decentralized training across multiple clients. Our approach is assessed using the BipedalWalker-v3 environment from OpenAI Gym. The experimental results show that our federated NEAT framework, augmented with Markov Chains and Gaussian Processes, achieves competitive performance while maintaining data privacy and reducing computational overhead on central servers. Additionally, we implement parallelization techniques using concurrent futures to enhance the efficiency of NEAT generations.

关键词： Federated Learning NEAT Reinforcement Learning TensorFlow Federated NeuroEvolution Privacy Decentralized Training Parallelization Markov Chains Gaussian Processes

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A modified grey wolf optimizer with multi-solution crossover integration algorithm for feature selection

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Journal of Ambient Intelligence and Humanized Computing 2025年第1期16卷 329-345页

作者： Ihsan, Muhammad Din, Fakhrud Zamli, Kamal Z. Ghadi, Yazeed Yasin Alahmadi, Tahani Jaser Innab, Nisreen Faculty of IT Department of Computer Science and IT University of Malakand Khyber Pakhtunkhwa Chakdara18800 Pakistan Faculty of Computing College of Computing and Applied Sciences Universiti Malaysia Pahang Pahang Pekan26600 Malaysia Department of computer science and software engineering Al Ain University Abu Dhabi12555 United Arab Emirates Department of Information SystemsCollege of Computer and Information Sciences Princess Nourah bint Abdulrahman University Riyadh84428 Saudi Arabia Department of Computer Science and Information SystemsCollege of Applied Sciences Al-Maarefa University Diriyah Riyadh13713 Saudi Arabia

Feature selection helps eradicate redundant features which is essential to mitigate the curse of dimensionality when a machine-learning model deals with high-dimensional datasets. Grey Wolf Optimizer (GWO) is a swarm-based algorithm that simulates the wolves’ hunting behavior. Although very efficient, GWO faces some limitations which may cause premature convergence and/or local optima trapping. Moreover, GWO relies mainly on the three best wolves, limiting its potential for diverse exploration and exploitation. This work proposes an improved version of GWO namely, a modified grey wolf optimizer with multi-solution crossover integration (MGWO-MCI) algorithm. MGWO-MCI algorithm incorporates a multi-solution strategy that evolves new potential solutions in the optimization process. A crossover operation is performed between the new wolves and the existing hierarchy, reforming the position-updating process. MGWO-MCI utilizes this position-updating process using two different approaches. The first approach named MGWO-MCI-I expands the additional wolves’ role to both exploration and exploitation whereas the second approach named MGWO-MCI-II incorporates their role to exploration only. These approaches are evaluated and tested using 18 datasets and an Intrusion detection dataset NSL-KDD for feature selection. Statistically, the results are analyzed through the Wilcoxon test, which shows the superiority of MGWO-MCI-II. MGWO-MCI-II outperforms others with an accuracy of 98.6% on NSL-KDD and achieves 55.5% overall best outcomes on other datasets. Moreover, the MGWO-MCI was evaluated on two constrained optimization problems, the pressure vessel and welded beam design validating its effectiveness and adaptability in solving different optimization problems. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： Constrained optimization

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Using Machine Learning to Enhance Personalization in E-Learning

Using Machine Learning to Enhance Personalization in E-Learn...

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2025 International Conference on Pervasive Computational Technologies, ICPCT 2025

作者： Palimkar, Sneha S. Kunnathully, Kannan Thota, Srinivasarao Joshi, Meeta Imran Akkalkot, Afsha Al Said, Nidal Coep Technological University Computer Engineering and Technology Maharashtra Pune India Veltech School of Law India Capital One Software Engineering VA United States Marwadi University Faculty of Management Studies India Pune Technological University Computer Science Engineering College of Engineering Maharashtra Pune India Ajman University College of Mass Communication Ajman United Arab Emirates

ISBN: (纸本)9798331508685

Numerous diverse learning materials can be found on e-learning sites. Students in today's e-learning platforms invest a lot of time and energy in locating pertinent learning materials. The student's actual requirements must be taken into account based on a variety of characteristics, including choices, expertise, and learning style. Education must be pertinent to the necessary concept's environment. This study aims to develop an efficient approach for detecting e-learning style and then customizing the e-learning contents to match that style, using machine learning (ML) algorithms to enhance personalization in e-learning. The blended ensemble method combined with the XGBoost meta-learning approach produced the most prominent results for enhancing e-learning style, with an accuracy of 97.6%. Further, the textual material of the e-files is altered using various natural language processing (NLP) approaches. The spaCy NLP-oriented labeled entity identification (LEI) algorithm achieves a 94.2% F1 value and a 0.92 precise match ratio while color-coded textual production of 10 e-files with 790 different phrases. These alterations are intended to suit students' tastes, resulting in an additional personalized and interactive teaching encounter. © 2025 IEEE.

关键词： and Natural language processing Artificial intelligence E-learning Machine learning Students

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Pansharpening Based on Adaptive Spatial Detail Fusion and Injection Weights Refinement

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IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2025年 18卷 12194-12208页

作者： Dai, Huan Yang, Yong Huang, Shuying Lu, Hangyuan Wan, Weiguo Wang, Kang Jiangxi University of Finance and Economics School of Computer and Artificial Intelligence Nanchang 330032 China Jiangxi Science & Technology Infrastructure Center Nanchang 330003 China Tiangong University School of Computer Science and Technology Tianjin 300387 China Tiangong University School of Software Tianjin 300387 China Jinhua University of Vocational Technology College of Information Engineering Jinhua 321007 China Jiangxi University of Finance and Economics School of Software and Internet of Things Engineering Nanchang 330032 China

Pansharpening refers to the process of extracting spatial details from a panchromatic (PAN) image and incorporating them into a multi-spectral (MS) image to produce a high spatial resolution MS (HRMS) image. Current pansharpening methods fail to consider the high-frequency spatial details in MS images, leading to spectral distortion in the fused results. To address this issue, the paper proposes a pansharpening method based on adaptive spatial detail fusion and injection weights refinement. First, a multi-stage intensity enhancement module (MIEM) is constructed to enhance the intensity component I of an MS image for spatial-spectral preservation. Second, an adaptive fusion strategy for spatial detail is designed by measuring the correlation and discrepancies between the spatial details of the PAN and I images, ensuring that spatial details from the source images are fused effectively. Third, the jointly weighted injection coefficients are used to refine the fused spatial details based on edge and spectral correlation. Last, the refined spatial details are incorporated into upsampled MS(UPMS) images to produce the final pansharpened images. Both simulated and real experiments have demonstrated that the proposed method outperforms several traditional and deep learning-based approaches. Specifically, the new method enhances the RASE metric by 12.67% and 15.99% relative to the second-best method on the reduced-scale Pléiades and IKONOS datasets, respectively. © 2008-2012 IEEE.

关键词： injection weight refinement multi-stage intensity enhancement Pansharpening spatial detail fusion

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Efficient Byzantine-Robust Federated Learning Based on the Multi-Message Shuffle Protocol for Consumer Internet of Things

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IEEE Internet of Things Journal 2025年

作者： Jin, Chunhua Li, Lulu Wang, Jiahao Li, Jinbiao Liu, Junyi Chen, Guanhua Zhang, Hao Weng, Jian Faculty of Computer Software Engineering with Huaiyin Institute of Technology Huai’an China Huaiyin Institute of Technology Faculty of Computer Software Engineering Huai’an China Huaiyin Institute of Technology Faculty of Computer and Software Engineering Huaian China Jinan University College of Information Science and Technology China

The accelerated advancement of consumer Internet of Things (CIoT) devices has resulted in their pervasive integration into the fabric of contemporary lifestyles. As these devices are capable of collecting vast quantities of data pertaining to users, they present a significant risk to the privacy and security of individuals. Federated learning is an advanced machine learning paradigm that prioritises the preservation of user data privacy and enhances security by eliminating the need for centralised data collection. As a result, it is a promising approach to address user data privacy and security concerns. However, federated learning still faces challenges such as privacy attacks, Byzantine attacks and high communication overheads. To address these challenges, we propose an efficient Byzantine-robust federated learning method based on multi-message shuffle protocol. To achieve privacy protection and Byzantine robustness, we introduce an innovative aggregation strategy inspired by the multi-message shuffle protocol for differential privacy. To improve communication efficiency, we develop an adaptive gradient compression scheme. Theoretical analysis indicates that our multi-message shuffle protocol is capable of satisfying the requisite differential privacy conditions. Compared with the state-of-the-art methods, our proposed scheme demonstrates superior convergence capabilities on the real-world dataset when approximately 40% of participants are Byzantine, achieving accuracies of 96.78%, which illustrates its strong robustness. Upon reduction of the transmitted model parameters to 1% of the original size, the proposed scheme exhibits a maximum accuracy degradation of 0.35%. which significantly reduces the communication overhead while achieving high accuracy. In addition, the scheme also achieves an accuracy of 88.73% on the Non-iid MotionSense dataset, which is superior to other schemes. © 2014 IEEE.

关键词： Federated learning

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A Review of Link Prediction on Heterogeneous Networks 8th

A Review of Link Prediction on Heterogeneous Networks

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8th International Conference on Cognitive Computing, ICCC 2024, Held as Part of the Services Conference Federation, SCF 2024

作者： Mi, Rui Wang, Changbo Zhang, Liang-Jie Wu, Yirui Chen, Junyang Wang, Huan College of Informatics Huazhong Agricultural University Wuhan China Center of Health Administration and Development Studies Hubei University of Medicine Shiyan China College of Computer Science and Software Engineering Shenzhen University Shenzhen China Hohai University Nanjing China

ISBN: (纸本)9783031779534

Link prediction is a central downstream assignment in network analysis, which denotes an attempt to assess the probability of a connection between two nodes based on observed link and node properties, which can be utilized across diverse categories of graph system, including knowledge graphs, network traffic, social networks, biological networks, etc. Heterogeneous networks usually contain diverse nodes and a variety of edges’ type, which can preserve more information than homogeneous information networks. However, the majority of researches focus on graph representation analysis of homogeneous networks. Limited research has systematically reviewed advanced techniques for link prediction and applications on Heterogeneous Information Networks (HIN). Building upon this, we propose a classification summary for link prediction methods in heterogeneous information graphs. We will introduce some classical methods and some newly proposed methods for link prediction on heterogeneous graphs. We provide general technical ideas behind each method in classes and analysis performance of models. We further discuss the applications of link prediction on complex heterogeneous graphs across various domains. Additionally, the research challenges are discussed, and several possible research avenues for link prediction on heterogeneous network are pointed out. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Prediction models

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COSIS: An AI-Enabled Digital Transformation Framework Integrating Large Language Models and Key Performance Indicators 21st

COSIS: An AI-Enabled Digital Transformation Framework Integr...

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21st International Conference on Services Computing, SCC 2024, Held as Part of the Services Conference Federation, SCF 2024

作者： Zhang, Liang-Jie Chen, Huan He, Sheng Li, Changhu Chen, Junyang Zhang, Haodi Du, Wenfeng Center for AI Services Computing College of Computer Science and Software Engineering Shenzhen University Shenzhen China SF Technology Co. Ltd. Shenzhen China Kingdee Research Kingdee International Software Group Co. Ltd. Shenzhen China

ISBN: (纸本)9783031769993

The rapid advancement of Artificial Intelligence (AI) and Large Language Models (LLMs) has significantly transformed various sectors of society, compelling enterprises to undertake comprehensive digital transformations to remain competitive in the AI era. This paper introduces COSIS, an AI-enabled digital trans-formation framework comprising five key dimensions: Culture, Operation, Strategy, Innovation, and Service. The COSIS framework provides a structured approach for enterprises to navigate the complexities of digital transformation by integrating AI and LLM technologies into their core functions. For each dimension, the paper identifies specific key performance indicators (KPIs) to measure the effectiveness of AI-enabled transformations. These KPIs enable enterprises to quantitatively assess progress and outcomes, ensuring alignment with strategic objectives and facilitating continuous improvement. Additionally, the paper discusses the implications of AI and LLM technologies on enterprise management team building in the AI era. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Human resource management

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Forecasting land use changes in crop classification and drought using remote sensing

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Journal of Arid Land 2025年第5期17卷 575-589页

作者： Mashael MAASHI Nada ALZABEN Noha NEGM Venkatesan VEERAMANI Sabarunisha Sheik BEGUM Geetha PALANIAPPAN Department of Software Engineering College of Computer and Information SciencesKing Saud UniversityRiyadh 11451Saudi Arabia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh 11671Saudi Arabia Department of Computer Science Applied College at MahayilKing Khalid UniversityAbha 61421Saudi Arabia Department of Civil Engineering University College of EngineeringAnna UniversityAriyalur 621731India Department of Biotechnology P.S.R.Engineering CollegeSivakasi 626140India Department of Electronics and Communication Engineering School of Engineering and TechnologyDhanalakshmi Srinivasan UniversitySamayapuram 621112India

Challenges in land use and land cover(LULC)include rapid urbanization encroaching on agricultural land,leading to fragmentation and loss of natural ***,the effects of urbanization on LULC of different crop types are less *** study assessed the impacts of LULC changes on agriculture and drought vulnerability in the Aguascalientes region,Mexico,from 1994 to 2024,and predicted the LULC in 2034 using remote sensing data,with the goals of sustainable land management and climate resilience *** increasing urbanization and drought,the integration of satellite imagery and machine learning models in LULC analysis has been underutilized in this *** Landsat imagery,we assessed crop attributes through indices such as normalized difference vegetation index(NDVI),normalized difference water index(NDWI),normalized difference moisture index(NDMI),and vegetation condition index(VCI),alongside watershed delineation and spectral *** random forest model was applied to classify LULC,providing insights into both historical and future *** indicated a significant decline in vegetation cover(109.13 km^(2))from 1994 to 2024,accompanied by an increase in built-up land(75.11 km^(2))and bare land(67.13 km^(2)).Projections suggested a further decline in vegetation cover(41.51 km^(2))and continued urban land expansion by *** study found that paddy crops exhibited the highest values,while common bean and maize performed *** analysis revealed that mildly dry areas in 2004 became severely dry in 2024,highlighting the increasing vulnerability of agriculture to climate *** study concludes that sustainable land management,improved water resource practices,and advanced monitoring techniques are essential to mitigate the adverse effects of LULC changes on agricultural productivity and drought resilience in the *** findings contribute to the understanding of how remote sensing can be effectively used for long-t

关键词： land use and land cover(LULC) crop attributes drought vulnerability machine learning models remote sensing

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