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IAENG International Journal of computer science 2025年第3期52卷 793-807页

作者： BEN ALLA, Hicham BEN ALLA, Said EZZATI, Abdellah Computer Science Mathematics and Computer Science Department Science and Technical Faculty Hassan 1 University Settat Morocco Embedded Systems and Computer Science National School of Applied Sciences of Berrechid Hassan 1 University Settat Morocco

Efficient task scheduling and resource allocation are essential for optimizing performance in cloud computing environments. The presence of priority constraints necessitates advanced solutions capable of addressing the increased complexity in the task scheduling process and resource allocation management. In this paper, we propose a novel approach, Dynamic Prioritization and Adaptive Resource Allocation and Task Scheduling (DP-ARTS), to tackle these challenges by integrating dynamic task prioritization with adaptive resource allocation and scheduling strategies. Our approach introduces a scoring mechanism to prioritize tasks based on deadlines, resource requirements, and other critical characteristics. Furthermore, we utilize K-means clustering to group Virtual Machines (VMs) according to their capabilities, facilitating more efficient task-to-VM mapping. Through simulations conducted with real-world workload traces in the CloudSim toolkit, DP-ARTS demonstrates superior performance in reducing makespan and energy consumption, while also improving resource utilization and load balancing compared to existing solutions. © (2025), (International Association of Engineers). All rights reserved.

关键词： K-means clustering

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Analysis, Trends, and Utilization of Security Information and Event Management (SIEM) in Critical Infrastructures 10

Analysis, Trends, and Utilization of Security Information an...

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10th International Conference on Advanced Computing and Communication systems, ICACCS 2024

作者： Sashwin, K. Nithika, K.S. Priyadharshini, S. Maduvant, S.P. Saranya Sri Eshwar College of Engineering Department of Computer Science and Business Systems Computer Science and Business Systems Coimbatore India Sri Eshwar College of Engineering Department of Computer Science and Business Systems Coimbatore India

ISBN: (数字)9798350384369

ISBN: (纸本)9798350384369

Security Information and Event Management (SIEM) systems have become essential assets in the realm of cybersecurity. They fulfill a central role in the prevention, detection, and response to cyber threats. Over time, SIEM solutions have matured into comprehensive platforms that offer extensive visibility, enabling organizations to pinpoint high-risk areas and take proactive measures to mitigate potential threats. This proactive approach not only bolsters security but also streamlines incident response, reducing both costs and response times. Recent developments in the field have witnessed the convergence of SIEM systems with big data analytics tools, opening doors to more sophisticated threat detection and response capabilities. This survey delves into the core features of widely adopted SIEM platforms and evaluates the external factors influencing the trajectory of the SIEM landscape over the medium and long term. Furthermore, the study presents a catalog of potential enhancements for the next generation of SIEMs, offering a thorough analysis of their advantages and practical applications within critical infrastructure settings. © 2024 IEEE.

关键词： Critical infrastructures

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Teaching Tip Rethinking How We Teach Git: Pedagogical Recommendations and Practical Strategies for the Information systems Curriculum

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Journal of Information systems Education 2025年第1期36卷 1-12页

作者： Wagner, Gerit Thurner, Laureen Faculty Information Systems and Applied Computer Science Otto-Friedrich-Universität Bamberg Bamberg Germany

Git, as the leading version-control system, is frequently employed by software developers, digital product managers, and knowledge workers. Information systems (IS) students aspiring to fill software engineering, management, or research positions would therefore benefit from familiarity with Git. However, teaching Git effectively can be challenging, as students in IS and other disciplines report themselves overwhelmed by the plethora of Git’s detailed commands and options, including those involved in local setup and secure shell (SSH) connections. From our view, such technical considerations distract students, and even prevent them from developing a deeper understanding of the Git model and its underlying concepts. Ideally, teaching efforts should convey a solid understanding of the Git model and thereby enable students to ask the right questions and look up the relevant commands. With this teaching tip, we therefore challenge the common approach to organizing Git teaching materials. In particular, we draw on established pedagogical theory to propose a novel approach that employs a new, macro-level ordering of contents beginning with the concept of branches and then proceeding to committing and collaboration. We present several practical strategies that make this approach feasible. In addition, we recommend that teachers clearly separate conceptual from applied learning and present the more challenging transfer questions at the end of the course. Our hope is to stimulate reflection on the most effective ways to teach Git to future professionals. © (2025), (ISCAP- Information systems and Computing Academic Professionals). All rights reserved.

关键词： Information use

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An Efficient Two-State Feature Attention-Based GRU for Text Classification

Informatica (Slovenia)

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Informatica (Slovenia) 2025年第15期49卷 95-112页

作者： Mohammed, Dhurgham Ali Patel, Kalyani A. Faculty of Computer Science Gujarat University Ahmedabad India Department of Computer Science Faculty of Education for Girl University of Kufa Iraq K.S. School of Business Management and Information Technology Gujarat University India

Text classification has become crucial for mechanically sorting documents into specific categories. The goal of classification is to assign a predefined group or class to an instance based on its characteristics. To attain precise text categorization, a feature selection scheme is employed to categorize significant features and eliminate irrelevant, undesirable, and noisy ones, thereby reducing the dimensionality of the feature space. Many advanced deep learning algorithms have been developed to handle text classification drawbacks. Recurrent neural networks (RNNs) are broadly employed in text classification tasks. In this paper, we referred to a novel Two-state GRU based on a Feature Attention strategy, known as Two-State Feature Attention GRU (TS-FA-GRU). The proposed framework identifies and categorizes word polarity through consecutive mechanisms and word-feature capture. Furthermore, the developed study incorporates a pre-feature attention TS-FA-GRU to capture essential features at an early stage, followed by a post-feature attention GRU that mimics the decoder’s function to refine the extracted features. To enhance computational performance, the reset gate in the ordinary GRU is replaced with an update gate, which helps to reduce redundancy and complexity. The effectiveness of the developed model was tested on five benchmark text datasets and compared with five well-established traditional text classification methods. The proposed TS-FA-GRU model demonstrated superior performance over several traditional approaches regarding convergence rate and accuracy. Experimental outcomes revealed that the TS-FA-GRU model achieved excellent text classification accuracies of 93.86%, 92.69%, 94.73%, 92.46%, and 88.23 on the 20NG, R21578, AG News, IMDB, and Amazon review dataset respectively. Moreover, the results indicated that the proposed model effectively minimized the loss function and captured long-term dependencies, leading to exceptional outcomes when compared to the

关键词： Recurrent neural networks

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Enhancing connectivity and coverage in wireless sensor networks: a hybrid comprehensive learning-Fick’s algorithm with particle swarm optimization for router node placement

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Neural Computing and Applications 2024年第34期36卷 21671-21702页

作者： Amer, Dina A. Soliman, Sarah A. Hassan, Asmaa F. Zamel, Amr A. Computer Science Department Higher Technological Institute Tenth of Ramadan44228 Egypt Management Information Systems Department Faculty of Management Economics and Business Technology Egyptian Russian University Badr11829 Egypt Computer and Systems Engineering Department Faculty of Engineering Zagazig University Zagazig44519 Egypt

Wireless Sensor Networks (WSNs) are essential for collecting and transmitting data in modern applications that rely on data, where effective network connectivity and coverage are crucial. The optimal placement of router nodes within WSNs is a fundamental challenge that significantly impacts network performance and reliability. Researchers have explored various approaches using metaheuristic algorithms to address these challenges and optimize WSN performance. This paper introduces a new hybrid algorithm, CFL-PSO, based on combining an enhanced Fick’s Law algorithm with comprehensive learning and Particle Swarm Optimization (PSO). CFL-PSO exploits the strengths of these techniques to strike a balance between network connectivity and coverage, ultimately enhancing the overall performance of WSNs. We evaluate the performance of CFL-PSO by benchmarking it against nine established algorithms, including the conventional Fick’s law algorithm (FLA), Sine Cosine Algorithm (SCA), Multi-Verse Optimizer (MVO), Salp Swarm Optimization (SSO), War Strategy Optimization (WSO), Harris Hawk Optimization (HHO), African Vultures Optimization Algorithm (AVOA), Capuchin Search Algorithm (CapSA), Tunicate Swarm Algorithm (TSA), and PSO. The algorithm’s performance is extensively evaluated using 23 benchmark functions to assess its effectiveness in handling various optimization scenarios. Additionally, its performance on WSN router node placement is compared against the other methods, demonstrating its competitiveness in achieving optimal solutions. These analyses reveal that CFL-PSO outperforms the other algorithms in terms of network connectivity, client coverage, and convergence speed. To further validate CFL-PSO’s effectiveness, experimental studies were conducted using different numbers of clients, routers, deployment areas, and transmission ranges. The findings affirm the effectiveness of CFL-PSO as it consistently delivers favorable optimization results when compared to existing meth

关键词： Particle swarm optimization (PSO)

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Phishing Attacks Detection Using EnsembleMachine Learning Algorithms

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computers, Materials & Continua 2024年第7期80卷 1325-1345页

作者： Nisreen Innab Ahmed Abdelgader Fadol Osman Mohammed Awad Mohammed Ataelfadiel Marwan Abu-Zanona Bassam Mohammad Elzaghmouri Farah H.Zawaideh Mouiad Fadeil Alawneh Department of Computer Science and Information Systems College of Applied SciencesAlmaarefa UniversityDiriyahRiyadh13713Saudi Arabia Applied College King Faisal UniversityAl-Ahsa31982Saudi Arabia Department of Management Information Systems College of Business AdministrationKing Faisal UniversityAl-Ahsa31982Saudi Arabia Department of Computer Science Faculty of Computer Science and Information TechnologyJerash UniversityJerash26110Jordan Department of Business Intelligence and Data Analysis Faculty of Financial Sciences and BusinessIrbid National UniversityIrbid21110Jordan Faculty of Information Technology Ajloun National UniversityAjloun26767Jordan

Phishing,an Internet fraudwhere individuals are deceived into revealing critical personal and account information,poses a significant risk to both consumers and web-based *** indicates a persistent rise in phishing ***,these fraudulent schemes are progressively becoming more intricate,thereby rendering them more challenging to ***,it is imperative to utilize sophisticated algorithms to address this *** learning is a highly effective approach for identifying and uncovering these harmful *** learning(ML)approaches can identify common characteristics in most phishing *** this paper,we propose an ensemble approach and compare it with six machine learning techniques to determine the type of website and whether it is normal or not based on two phishing *** that,we used the normalization technique on the dataset to transform the range of all the features into the same *** findings of this paper for all algorithms are as follows in the first dataset based on accuracy,precision,recall,and F1-score,respectively:Decision Tree(DT)(0.964,0.961,0.976,0.968),Random Forest(RF)(0.970,0.964,0.984,0.974),Gradient Boosting(GB)(0.960,0.959,0.971,0.965),XGBoost(XGB)(0.973,0.976,0.976,0.976),AdaBoost(0.934,0.934,0.950,0.942),Multi Layer Perceptron(MLP)(0.970,0.971,0.976,0.974)and Voting(0.978,0.975,0.987,0.981).So,the Voting classifier gave the best *** in the second dataset,all the algorithms gave the same results in four evaluation metrics,which indicates that each of them can effectively accomplish the prediction ***,this approach outperformed the previous work in detecting phishing websites with high accuracy,a lower false negative rate,a shorter prediction time,and a lower false positive rate.

关键词： Social engineering attacks phishing attacks machine learning security artificial intelligence

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Analysis of Statistical and Deep Learning Techniques for Temperature Forecasting

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Recent Advances in computer science and Communications 2024年第2期17卷 49-65页

作者： Kruthika, Sriram Ganesan Rajasekaran, Umamaheswari Alagarsamy, Malini Sharma, Vandana Department of Computer Science and Business Systems Thiagarajar College of Engineering Tamil Nadu Madurai India NCR Delhi India

In the field of meteorology, temperature forecasting is a significant task as it has been a key factor in industrial, agricultural, renewable energy, and other sectors. High accuracy in temperature forecasting is needed for decision-making in advance. Since temperature varies over time and has been studied to have non-trivial long-range correlation, non-linear behavior, and seasonal variability, it is important to implement an appropriate methodology to forecast accurately. In this paper, we have reviewed the performance of statistical approaches such as AR and ARIMA with RNN, LSTM, GRU, and LSTM-RNN Deep Learning models. The models were tested for short-term temperature forecasting for a period of 48 hours. Among the statistical models, the AR model showed notable performance with a r2 score of 0.955 for triennial 1 and for the same, the Deep Learning models also performed nearly equal to that of the statistical models and thus hybrid LSTM-RNN model was tested. The hybrid model obtained the highest r2 score of 0.960. The difference in RMSE, MAE and r2 scores are not significantly different for both Statistical and Vanilla Deep Learning approaches. However, the hybrid model provided a better r2 score, and LIME explanations have been generated for the same in order to understand the dependencies over a point forecast. Based on the reviewed results, it can be concluded that for short-term forecasting, both Statistical and Deep Learning models perform nearly equally. © 2024 Bentham science Publishers.

关键词： Decision making

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Enforcing Image Encryption using Cryptographic Algorithms 15

Enforcing Image Encryption using Cryptographic Algorithms

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Kumar, K. Siva Rathinavel, S. Dharun Raghavendharan, M. Lilian, Felicia Department of computer science and Business systems Madurai15 India

ISBN: (纸本)9798350370249

This paper proposes a methodology to ensure the user Image privacy in social media platform using image encryption method. Social media has long been a significant part of online communication. However, image and user privacy concerns soared due to the increasing sensitivity of social media users. The paper offers a new methodology that suggests using this encryption technique that ensure the privacy protection for images in social media platform. A client-side image encryption solution is integrated into a blog profile website. When a user wants to download someone else's profile picture, the website uses an on-the-fly encryption algorithm like chaotic behaviour that shuffles the pixels of the original image before delivery. By making the downloaded images unusable in their original form, users are protected with their privacy while maintaining the browsing capabilities of the platform. The paper proposes on-the-fly image encryption algorithm in detail, including the a random pixel shuffling map and maps to generate the encrypted image. This ensures the CIA by performing metrics. © 2024 IEEE.

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Detection of Left Ventricular Cavity from Cardiac MRI Images Using Faster R-CNN

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computers, Materials & Continua 2023年第1期74卷 1819-1835页

作者： Zakarya Farea Shaaf Muhammad Mahadi Abdul Jamil Radzi Ambar Ahmed Abdu Alattab Anwar Ali Yahya Yousef Asiri Faculty of Electrical and Electronic Engineering Universiti Tun Hussein Onn MalaysiaParit RajaBatu Pahat86400JohorMalaysia Department of Computer Science College of Science and ArtsSharurahNajran UniversityNajran61441Saudi Arabia Department of Computer Science Faculty of Computer Science and Info.SystemsThamar UniversityDhamar87246Yemen Department of Computer Science College of Computer Science and Information SystemsNajran UniversityNajran61441Saudi Arabia

The automatic localization of the left ventricle(LV)in short-axis magnetic resonance(MR)images is a required step to process cardiac images using convolutional neural networks for the extraction of a region of interest(ROI).The precise extraction of the LV’s ROI from cardiac MRI images is crucial for detecting heart disorders via cardiac segmentation or ***,this task appears to be intricate due to the diversities in the size and shape of the LV and the scattering of surrounding tissues across different ***,this study proposed a region-based convolutional network(Faster R-CNN)for the LV localization from short-axis cardiac MRI images using a region proposal network(RPN)integrated with deep feature classification and *** was trained using images with corresponding bounding boxes(labels)around the LV,and various experiments were applied to select the appropriate layers and set the suitable *** experimental findings showthat the proposed modelwas adequate,with accuracy,precision,recall,and F1 score values of 0.91,0.94,0.95,and 0.95,*** model also allows the cropping of the detected area of LV,which is vital in reducing the computational cost and time during segmentation and classification ***,itwould be an ideal model and clinically applicable for diagnosing cardiac diseases.

关键词： Cardiac short-axis MRI images automatic left ventricle localization deep learning models faster R-CNN

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A State-Migration Particle Swarm Optimizer for Adaptive Latent Factor Analysis of High-Dimensional and Incomplete Data

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IEEE/CAA Journal of Automatica Sinica 2024年第11期11卷 2220-2235页

作者： Jiufang Chen Kechen Liu Xin Luo Ye Yuan Khaled Sedraoui Yusuf Al-Turki MengChu Zhou IEEE the College of Computer Science and Technology Chongqing University of Posts and Telecommunications the Department of Computer Science Columbia University the College of Computer and Information Science Southwest University the Department of Electrical and Computer Engineering Faculty of Engineeringand also with K.A.CARE Energy Research and Innovation CenterKing Abdulaziz University the Center of Research Excellence in Renewable Energy and Power Systems the Department of Electrical and Computer EngineeringFaculty of Engineeringand also with K.A.CARE Energy Research and Innovation CenterKing Abdulaziz University the Department of Electrical and Computer Engineering New Jersey Institute of Technology

High-dimensional and incomplete(HDI) matrices are primarily generated in all kinds of big-data-related practical applications. A latent factor analysis(LFA) model is capable of conducting efficient representation learning to an HDI matrix,whose hyper-parameter adaptation can be implemented through a particle swarm optimizer(PSO) to meet scalable ***, conventional PSO is limited by its premature issues,which leads to the accuracy loss of a resultant LFA model. To address this thorny issue, this study merges the information of each particle's state migration into its evolution process following the principle of a generalized momentum method for improving its search ability, thereby building a state-migration particle swarm optimizer(SPSO), whose theoretical convergence is rigorously proved in this study. It is then incorporated into an LFA model for implementing efficient hyper-parameter adaptation without accuracy loss. Experiments on six HDI matrices indicate that an SPSO-incorporated LFA model outperforms state-of-the-art LFA models in terms of prediction accuracy for missing data of an HDI matrix with competitive computational ***, SPSO's use ensures efficient and reliable hyper-parameter adaptation in an LFA model, thus ensuring practicality and accurate representation learning for HDI matrices.

关键词： Data science generalized momentum high-dimensional and incomplete(HDI)data hyper-parameter adaptation latent factor analysis(LFA) particle swarm optimization(PSO)

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