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IMPACT OF A BRAIN-DIRECTED MOTOR PROGRAM ON THE ACCURACY SKILLS OF JORDANIAN university STUDENTS IN THE ACADEMIC YEAR 2022/2023

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Xinan Jiaotong Daxue Xuebao/Journal of Southwest Jiaotong university 2024年第2期59卷 255-263页

作者： Al-Jbour, Nayef Muladi Department of Human Basic Sciences Faculty of Engineering Technology Balqa Applied University Salt Jordan

This study aimed to identify the impact of a brain-oriented motor program on the accuracy element of Jordanian university students. The sample of the study consisted of 50 students from the study community. The researcher used the pilot curriculum to suit this study and the proposed program of 53 paragraphs, using the Statistical Packages Program for Social sciences (SPSS) for data analysis and one-way ANOVA test to detect statistically significant differences at indicative level (α ≤ 0.05) in the accuracy level of the handball test and arrow throwing test, reaching the study results. One of the most important findings of the researcher was that there were no differences in accuracy in the handball test and the arrow throw test between the control group tribal and post measurements, where the t-value did not reach the statistical indicator level. There were also differences in the accuracy level of the handball test and the arrow throw test between the tribal and post measurements of the experimental group according to the tribal and post arrow test where the value t = 6.967 and statistically indicative 0.000 were in favor of remote selection, with the arithmetic average 16.84 and standard deviation 6.69. One of the essential recommendations by the researcher is the need to adopt a brain-oriented exercise program in universities requiring focus and to adopt a brain-oriented exercise program in universities with the beginning of practical lectures for students of colleges of sports education (practical materials) as a form of preparation (warm-up). © 2024 by the authors. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC-BY) license (http://***/licenses/by/4.0/).

关键词： Students

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Novel methods for optimizing CNC aluminum alloy machining parameters in polymer mold cavities

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International Journal of Lightweight Materials and Manufacture 2024年第4期7卷 507-519页

作者： Ikhries, Ibrahim I. Al-Shawabkeh, Ali F. Department of Mechanical Engineering Faculty of Engineering Technology Al-Balqa Applied University Amman11134 Jordan Department of Scientific Basic Sciences Faculty of Engineering Technology Al-Balqa Applied University Amman11134 Jordan

The examination of the machining of 7075-T6 aluminum alloy polymer mold cavities using Taguchi optimization and analysis of variance is presented in this paper. This study identified the best CNC milling cutting parameters and used a mathematical model to quantify the surface roughness of the machined cavities. The findings showed that while using a flat endmill, the spindle speed multiplied by feed rate contributed 28.01% to surface roughness, and when using a ball endmill, the squared depth of cut contributed 41.27%. Using both flat and ball endmills, the depth of the cut contributed 98.53% to the material removal rate. A refined second-order linear regression model was employed to forecast the endmill-machined surface roughness. The Warp Surf Portable tester measured values that were outside the error range of approximately 0.257% and 2.8%, respectively, for the expected values. Surface roughness has a 99.97% correlation coefficient in the regression model, indicating a very significant link. Additionally, the study improved the cutting parameters for a ball endmill, which were 3005 Rpm, 726.7 mm/min, and 0.43 mm, and for a flat endmill, these were spindle speed (2500 Rpm), feed rate (650 mm/min), and axial cut depth (0.5 mm). The outcomes demonstrated how well the techniques enhanced mold cavity machining and cost estimation using Ra and MRR data. Consequently, these results can be applied to future academic studies and industrial applications. © 2024 The Authors

关键词： Surface roughness

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YOLOCSP-PEST for Crops Pest Localization and Classification

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computers, Materials & Continua 2025年第2期82卷 2373-2388页

作者： Farooq Ali Huma Qayyum Kashif Saleem Iftikhar Ahmad Muhammad Javed Iqbal Department of Software Engineering University of Engineering and TechnologyTaxila47050Pakistan Department of Computer Science&Engineering College of Applied Studies&Community ServiceKing Saud UniversityRiyadh11362Saudi Arabia Department of Information Technology Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Computer Science University of Engineering and TechnologyTaxila47050Pakistan

Preservation of the crops depends on early and accurate detection of pests on crops as they cause several diseases decreasing crop production and quality. Several deep-learning techniques have been applied to overcome the issue of pest detection on crops. We have developed the YOLOCSP-PEST model for Pest localization and classification. With the Cross Stage Partial Network (CSPNET) backbone, the proposed model is a modified version of You Only Look Once Version 7 (YOLOv7) that is intended primarily for pest localization and classification. Our proposed model gives exceptionally good results under conditions that are very challenging for any other comparable models especially conditions where we have issues with the luminance and the orientation of the images. It helps farmers working out on their crops in distant areas to determine any infestation quickly and accurately on their crops which helps in the quality and quantity of the production yield. The model has been trained and tested on 2 datasets namely the IP102 data set and a local crop data set on both of which it has shown exceptional results. It gave us a mean average precision (mAP) of 88.40% along with a precision of 85.55% and a recall of 84.25% on the IP102 dataset meanwhile giving a mAP of 97.18% on the local data set along with a recall of 94.88% and a precision of 97.50%. These findings demonstrate that the proposed model is very effective in detecting real-life scenarios and can help in the production of crops improving the yield quality and quantity at the same time.

关键词： Deep learning classification of pests YOLOCSP-PEST pest detection

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Fuzzy C-mean clustering technique based visual features fusion for automatic video summarization method

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Multimedia Tools and Applications 2024年第40期83卷 87673-87696页

作者： Khalid, Eman Thabet Jassim, Salwa Alwan Saqaeeyan, Sasan Department of Computer Sciences Education College for Pure Sciences University of Basrah Basrah6100 Iraq Department of Computer Engineering Faculty of Technology and Engineering Islamic Azad University Abadan Iran

Video Summarization is one of the most important processes in multimedia applications. It is the process of taking a few segments of each scene to create a video summary that describes the story of an entire video in a short amount of time. Automatic video summarization has many applications which would benefit several domains of healthcare, security, surveillance, and other many applications. However, creating a comprehensive video summary that encompasses keyframes of video shots is a challenging task in terms of providing well representative features extraction and features classification techniques. The main objective of this study is to produce informative video summarization, conveying interestingness, representativeness, and visual semantic information. That’s in addition to preserving the continuous flow of motion information for video sequence. Therefore, this paper proposed SURF and Hog techniques for features description, and Covariance matrix (CM) for features reduction. Fuzzy C-mean clustering algorithm is proposed for features classification and generation summary based keyframes selection. The experiments are conducted using annotated video sequences SumMe and VSUMM datasets. The obtained results showed good performance of proposed method achieving average F-score of 0.5197 on SumMe videos, and 0.9221 on VSUMM videos. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Covariance matrix

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Privacy-preserving explainable AI: a survey

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Science China(Information sciences) 2025年第1期68卷 23-56页

作者： Thanh Tam NGUYEN Thanh Trung HUYNH Zhao REN Thanh Toan NGUYEN Phi Le NGUYEN Hongzhi YIN Quoc Viet Hung NGUYEN School of Information and Communication Technology Griffith University School of Computer and Communication Sciences Ecole Polytechnique Federale de Lausanne Faculty of Mathematics and Computer Science University of Bremen Faculty of Information Technology HUTECH University Department of Computer Science Hanoi University of Science and Technology School of Electrical Engineering and Computer Science The University of Queensland

As the adoption of explainable AI(XAI) continues to expand, the urgency to address its privacy implications intensifies. Despite a growing corpus of research in AI privacy and explainability, there is little attention on privacy-preserving model explanations. This article presents the first thorough survey about privacy attacks on model explanations and their countermeasures. Our contribution to this field comprises a thorough analysis of research papers with a connected taxonomy that facilitates the categorization of privacy attacks and countermeasures based on the targeted explanations. This work also includes an initial investigation into the causes of privacy leaks. Finally, we discuss unresolved issues and prospective research directions uncovered in our analysis. This survey aims to be a valuable resource for the research community and offers clear insights for those new to this domain. To support ongoing research, we have established an online resource repository, which will be continuously updated with new and relevant findings.

关键词： privacy-preserving explainable AI privacy attacks privacy defences PrivEx PPXAI

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An Encoding-Decoding Framework Based on CNN for circ RNA-RBP Binding Sites Prediction

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Chinese Journal of Electronics 2024年第1期33卷 256-263页

作者： Yajing GUO Xiujuan LEI Yi PAN School of Computer Science Shaanxi Normal University Faculty of Computer Science and Control Engineering Shenzhen Institute of Advanced TechnologyChinese Academy of Sciences Department of Computer Science Georgia State University

Predicting RNA binding protein(RBP) binding sites on circular RNAs(circ RNAs) is a fundamental step to understand their interaction mechanism. Numerous computational methods are developed to solve this problem, but they cannot fully learn the features. Therefore, we propose circ-CNNED, a convolutional neural network(CNN)-based encoding and decoding framework. We first adopt two encoding methods to obtain two original matrices. We preprocess them using CNN before fusion. To capture the feature dependencies, we utilize temporal convolutional network(TCN) and CNN to construct encoding and decoding blocks, respectively. Then we introduce global expectation pooling to learn latent information and enhance the robustness of circ-CNNED. We perform circ-CNNED across 37 datasets to evaluate its effect. The comparison and ablation experiments demonstrate that our method is superior. In addition, motif enrichment analysis on four datasets helps us to explore the reason for performance improvement of circ-CNNED.

关键词： Circular RNAs (circRNAs) RNA binding proteins Convolutional neural network Temporal convolutional network Encoder-decoder network

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Electricity demand forecasting at distribution and household levels using explainable causal graph neural network

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Energy and AI 2024年第2期16卷 385-395页

作者： Amir Miraki Pekka Parviainen Reza Arghandeh Department of Computer Science Electrical Engineering and Mathematical SciencesWestern Norway University of Applied SciencesBergenNorway Department of Informatics University of BergenBergenNorway

Forecasting electricity demand is an essential part of the smart grid to ensure a stable and reliable power grid. With the increasing integration of renewable energy resources into the grid, forecasting the demand for electricity is critical at all levels, from the distribution to the household. Most existing forecasting methods, however, can be considered black-box models as a result of deep digitalization enablers, such as deep neural networks, which remain difficult to interpret by humans. Moreover, capture of the inter-dependencies among variables presents a significant challenge for multivariate time series forecasting. In this paper we propose eXplainable Causal Graph Neural Network (X-CGNN) for multivariate electricity demand forecasting that overcomes these limitations. As part of this method, we have intrinsic and global explanations based on causal inferences as well as local explanations based on post-hoc analyses. We have performed extensive validation on two real-world electricity demand datasets from both the household and distribution levels to demonstrate that our proposed method achieves state-of-the-art performance.

关键词： Causal inference Electricity demand forecasting Explainable artificial intelligence(XAI) Graph neural network

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Hybrid Approach for Cost Efficient Application Placement in Fog-Cloud Computing Environments

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computers, Materials & Continua 2024年第6期79卷 4127-4148页

作者： Abdulelah Alwabel Chinmaya Kumar Swain Department of Computer Sciences College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAl-Kharj11942Saudi Arabia Department of Computer Science and Engineering Institute of Management and Information TechnologyCuttackBPUTOdishaIndia

Fog computing has recently developed as a new paradigm with the aim of addressing time-sensitive applications better than with cloud computing by placing and processing tasks in close proximity to the data ***,the majority of the fog nodes in this environment are geographically scattered with resources that are limited in terms of capabilities compared to cloud nodes,thus making the application placement problem more complex than that in cloud *** approach for cost-efficient application placement in fog-cloud computing environments that combines the benefits of both fog and cloud computing to optimize the placement of applications and services while minimizing *** approach is particularly relevant in scenarios where latency,resource constraints,and cost considerations are crucial factors for the deployment of *** this study,we propose a hybrid approach that combines a genetic algorithm(GA)with the Flamingo Search Algorithm(FSA)to place application modules while minimizing *** consider four cost-types for application deployment:Computation,communication,energy consumption,and *** proposed hybrid approach is called GA-FSA and is designed to place the application modules considering the deadline of the application and deploy them appropriately to fog or cloud nodes to curtail the overall cost of the *** extensive simulation is conducted to assess the performance of the proposed approach compared to other state-of-the-art *** results demonstrate that GA-FSA approach is superior to the other approaches with respect to task guarantee ratio(TGR)and total cost.

关键词： Placement mechanism application module placement fog computing cloud computing genetic algorithm flamingo search algorithm

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Enhanced Neutrosophic Set and Machine Learning Approach for Kidney Disease Prediction

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Neutrosophic Sets and Systems 2025年 80卷 465-477页

作者： Al-Doori, Humam M. Alkellezli, Tareef S. Abdelhafeez, Ahmed Eassaa, Mohamed Sawah, Mohamed S. El-Douh, Ahmed A. Cybersecurity Sciences Department College of Science Ashur University Baghdad Iraq Cybersecurity Engineering Department College of Engineering Ashur University Baghdad Iraq Computer Science Department Faculty of Information System and Computer Science October 6 University Giza12585 Egypt Applied Science Research Center Applied Science Private University Amman Jordan Department of Computer Science Faculty of Information Technology Ajloun National University P.O.43 Ajloun26810 Jordan Cybersecurity Technology Engineering Department College of Engineering Technology Ashur University Baghdad Iraq

Kidney disease (KD) is a gradually increasing global health concern. It is a chronic illness linked to higher rates of morbidity and mortality, a higher risk of cardiovascular disease and numerous other illnesses, and expensive medical expenses. The machine learning (ML) models are applied for KD prediction with higher accuracy and precision. The KD dataset has uncertainty and vague information, so, we used the neutrosophic set (NS) to deal with vague and uncertainty information in the KD dataset. The KD dataset is converted into the N-KD dataset with three membership functions: truth, indeterminacy, and falsity. Three ML models are used in this study such as logistic regression (LR), support vector machine (SVM), and k-nearest neighbor (KNN). These ML models are applied to the N-KD dataset. The results show the LR has higher accuracy and precision on the N-KD dataset than the original KD dataset. © 2025, university of New Mexico. All rights reserved.

关键词： Logistic regression

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Avoid Suspicious Route of Blackhole Nodes in MANET’s:Using A Cooperative Trapping

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computer Systems Science & engineering 2023年第5期45卷 1901-1915页

作者： Abdllkader Esaid Mary Agoyi Department of Computer Engineering Faculty of EngineeringCyprus International UniversityNicosiaCyprus Information Technology Department School of Applied SciencesCyprus International UniversityNicosiaCyprus

Mobile Ad hoc Network(MANET)is decentralized wireless network and can communicate without existing infrastructure in many *** is vulnerable to various attacks that affect its performance such as blackhole *** attacker,inject fault routing information to persuade the source node to select the path with malicious node as the shortest *** eliminate malicious nodes from launching any collaborative attack.A cooperative Trapping Approach(CTA)was proposed based on modifying Ad-hoc On-demand Distance Vector(AODV)routing protocol and trapping the malicious nodes by responding to the trap request *** approach aims to eliminate and rule out both single and collaborative malicious blackhole nodes from any *** approach realizes a backward tracking mechanism to perform the elimination *** proposed algorithm(CTA)was executed using NS-2 network *** performance metrics that has been considered to evaluate the performance of the proposed algorithm such as throughput,end to end delay,packet delivery ratio,and consuming *** experimental results have shown the performance metrics of the proposed approach outperformed other state of at algorithms.

关键词： AODV Blackhole MANET Malicious node

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