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YOLOCSP-PEST for Crops Pest Localization and Classification

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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Assessment of ChatGPT in extracting Arabic morphology: active and passive participles

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International Journal of Speech technology 2025年第1期28卷 85-97页

作者： Alian, Marwah Hussein, Ibtisam Al-Khazaleh, Maisa Computer Science Department Faculty of Information Technology The World Islamic Sciences and Education University Amman Jordan Basic Sciences Department Faculty of Science The Hashemite University Zarqa Jordan Arabic Language Department Faculty of Arts Yarmouk University Irbid Jordan

ChatGPT is a language model that has been developed by Open AI. It has the ability to answer any question raised by a participant in a form of conservation. ChatGPT takes advantages of the effective transformers architecture to generate text discussions that are relevant and perceptive. Although ChatGPT was initially trained on English text data, it has also been fine-tuned on other languages including Spanish, French, Chinese, German and Arabic. ChatGPT has been employed in different NLP domains for English language, but specifically it is still slightly limited in languages other than English. In this research, we assess ChatGPT in extracting active participle (ism al-fa’il) and passive participle (ism al-maf’uul) from Arabic verbs of base forms (alfilu alsahih) and verbs with vowels (alfilu almutal) to help the non-native speakers who find it difficult to extract these participles and try to use ChaTGPT as a learning tool and get inaccurate responses. We experiment ChatGPT’s performance in zero-shot learning then with a wide range of prompt templates as well as few-shot learning. The results show a greet enhancement in the ChatGPT performance when tuning the rule-based prompts with k-shot examples especially for passive participle extraction. ChatGPT performed well in extracting participles from verbs without vowels, but after training with k-shot samples, it improved by 6% for active participles and 25% for passive participles. Additionally, extracting passive participles for verbs with vowels significantly improves performance compared to extracting active participles, especially the case when the vowels exist at two positions, which results in a 100% improvement. The performance of ChatGPT improved in extracting active participle for verbs with no vowels by 6% and passive participles by 25%. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Zero-shot learning

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Deep Learning Based Side-Channel Attack Detection for Mobile Devices Security in 5G Networks

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清华大学学报自然科学版（英文版） 2025年第3期30卷 1012-1026页

作者： Amjed A.Ahmed Mohammad Kamrul Hasan Ali Alqahtani Shayla Islam Bishwajeet Pandey Leila Rzayeva Huda Saleh Abbas Azana Hafizah Mohd Aman Nayef Alqahtani Center for Cyber Security Faculty of Information Science and TechnologyUniversiti Kebangsaan MalaysiaBangi 43600Malaysia Imam Alkadhim University College Department of Computer Techniques EngineeringBaghdad 10066Iraq Center for Cyber Security Faculty of Information Science and TechnologyUniversiti Kebangsaan MalaysiaBangi 43600Malaysia Department of Networks and Communications Engineering College of Computer Science and Information SystemsNajran UniversityNajran 61441Saudi Arabia Institute of Computer Science and Digital Innovation UCSI UniversityKuala Lumpur 56000Malaysia Department of Intelligent Systems and Cyber Security Astana IT UniversityAstana 20000Kazakstan Department of Computer Science College of Computer Science and EngineeringTaibah UniversityMadinah 42353Saudi Arabia Department of Electrical Engineering College of EngineeringKing Faisal UniversityAl-Hofuf 31982Saudi Arabia

Mobile devices within Fifth Generation(5G)networks,typically equipped with Android systems,serve as a bridge to connect digital gadgets such as global positioning system,mobile devices,and wireless routers,which are vital in facilitating end-user communication ***,the security of Android systems has been challenged by the sensitive data involved,leading to vulnerabilities in mobile devices used in 5G *** vulnerabilities expose mobile devices to cyber-attacks,primarily resulting from security ***-permission apps in Android can exploit these channels to access sensitive information,including user identities,login credentials,and geolocation *** such attack leverages"zero-permission"sensors like accelerometers and gyroscopes,enabling attackers to gather information about the smartphone's *** underscores the importance of fortifying mobile devices against potential future *** research focuses on a new recurrent neural network prediction model,which has proved highly effective for detecting side-channel attacks in mobile devices in 5G *** conducted state-of-the-art comparative studies to validate our experimental *** results demonstrate that even a small amount of training data can accurately recognize 37.5％of previously unseen user-typed ***,our tap detection mechanism achieves a 92％accuracy rate,a crucial factor for text *** findings have significant practical implications,as they reinforce mobile device security in 5G networks,enhancing user privacy,and data protection.

关键词： Fifth Generation(5G)networks smartphone information leakage Side-Channel Attack(SCA) deep learning

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Enhanced MADM Strategy with Heptapartitioned Neutrosophic Distance Metrics

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Neutrosophic Sets and Systems 2025年 78卷 74-96页

作者： Eassa, Mohamed Myvizhi, M. Elkholy, Mohamed Abdelhafeez, Ahmed Elbehiery, Hussam 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 Mathematics KPR Institute of Engineering and Technology Tamilnadu Coimbatore India Faculty of Computer Science& Engineering Alamein International University Egypt

In this work, a novel methodological approach to multi-attribute decision-making problems is developed and the notion of Heptapartitioned Neutrosophic Set Distance Measures (HNSDM) is introduced. By averaging the Penta-Partitioned Neutrosophic Distance Measures under particular conditional criteria, the suggested technique ranks the options. Additionally, we explore the fundamental characteristics of HNSDM and provide several illustrative theorems. The Heptapartitioned Neutrosophic Set Distance Measures are formulated based on the Hepta Neutrosophic Set. Lastly, a practical numerical example is provided to illustrate the efficacy of the suggested methodology. © 2025, University of New Mexico. All rights reserved.

关键词： Heptapartitioned Neutrosophic set Heptapartitioned Neutrosophic set and MADM strategy Heptapartitioned set Neutrosophic sine distance measure

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LLMs Encounter Critical Elements Prompts: Semantically Guided Partial Supervision Skeleton-Based Action Recognition

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IEEE Sensors Journal 2025年第10期25卷 17350-17363页

作者： Xin, Wentian Liu, Yi Fu, Xianping Wang, Huibing Miao, Qiguang Liu, Ruyi Li, Long Pun, Chi-Man Dalian Maritime University School of Information Science and Technology Liaoning Dalian116026 China Xidian University School of Computer Science and Technology Shanxi Xi’an710071 China Guilin University of Electronic Technology Guangxi Key Laboratory of Trusted Software Guangxi Guilin541004 China University of Macau Faculty of Science and Technology Department of Computer and Information Science Macau China

Semantically guided skeleton-based action recognition is a crucial but challenging task in the data processing with RGB and depth sensors. The generation of action descriptions is facilitated by the pretrained language model, thereby imbuing the skeleton with essential contextual semantics. However, it also presents two challenges that require careful consideration: 1) efficiently and automatically generating the core semantics of actions and 2) extending the applicability of supervised learning when only partial labels are available. Therefore, we propose critical elements prompts for partial supervision in skeleton-based action contrastive learning (CEPCLR) as a significant attempt to break through the existing limitations of skeleton and semantic multimodal fusion. First, we design action-specific prompts that enable large language models (LLMs) to generate general semantic descriptions both efficiently and accurately, followed by iterative prompting that mines discriminative descriptions through controlled, rule-based emergence. Second, we adopt language-skeleton contrastive learning for multimodal fusion, which significantly enhances the integration of positive and negative action pairs. Notably, we introduce a novel partially supervised learning task for skeleton data, where labels are used selectively for semantic generation but are not involved in the classification tasks. The proposed CEPCLR achieves strong results on standard datasets like NTU RGB+D, NTU RGB+D 120, and PKU-MMD, effectively addressing missing contextual information in human skeleton and action expressions. © 2001-2012 IEEE.

关键词： Musculoskeletal system

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Securing the Mobile Future: An Extensive Analysis of the Threat Landscape from Mobile Devices User Perspectives

International Journal of Interactive Mobile Technologies

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International Journal of Interactive Mobile Technologies 2025年第8期19卷 140-158页

作者： Aljamal, Mahmoud Alsarhan, Ayoub Aljaidi, Mohammad Mughaid, Ala Al-Jamal, Wafa’ Q. Twairesh, Abdullah Ewayed Department of Cybersecurity Irbid National University Irbid Jordan Department of Information Technology Faculty of Prince Al-Hussien bin Abdullah for IT The Hashemite University Zarqa Jordan Department of Computer Science Faculty of Information Technology Zarqa University Zarqa Jordan Department of Computer Science Gulf University of Science and Technology Mubarak Al-Abdullah Kuwait Nilai Malaysia Department of Finance and Insurance College of Business Administration Northern Border University Arar Saudi Arabia

Mobile devices are becoming increasingly popular in the modern era, posing a significant threat to the security of organizations. As a result, mobile devices have become more and more vul-nerable to cyberattacks. The current study aims to assess the threat landscape of mobile devices from the perspectives of users, highlighting the importance of user-centered perspectives in designing solutions to mobile security threats. A total of 47 participants of different demographic profiles were recruited as respondents, and the primary data were collected through an online questionnaire. The study concludes with a discussion of the findings’ implications for research and practice in mobile security. The paper outlines the current state of the art in mobile device security, including vulnerability to security threats, privacy risks, trust in security measures, awareness of potential threats, and satisfaction with the security provided by mobile operating systems. The results demonstrated that users are unaware of data protection and mobile device security, which have a substantial impact on an organization’s performance. However, this study lays a foundational perspective for future advancements in security audits, aiming to enhance cybersecurity practices in an increasingly mobile-centric corporate landscape. © 2025 by the authors of this article. Published under CC-BY.

关键词： Cyber attacks

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Comparison of Nine Deep Regressors in Continuous Blood Pressure Estimation Using Single-Channel Photoplethysmograms under the PulseDB

Comparison of Nine Deep Regressors in Continuous Blood Press...

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2025 IEEE/SICE International Symposium on System Integration, SII 2025

作者： Yamamoto, Takumi Kanoga, Suguru Sugiura, Yuta Tokyo Japan Keio University Faculty of Science and Technology Department of Information and Computer Science Kanagawa Japan

ISBN: (纸本)9798331531614

Blood pressure (BP) is a vital parameter in medical treatment and diagnosis, and as a non-invasive method to measure BP, some deep learning models have been proposed to estimate BP from photoplethysmograms (PPGs). However, the datasets and the method of dividing them into training and testing subsets are not uniform, making it difficult to compare them fairly. In this study, we compared the performance of nine deep learning models for estimating systolic and diastolic BP using PPGs. We used PulseDB, which has "calibration-based subset"and "calibration-free subset"as test subsets. The calibration-based subset has the same subject's data in the training subset, and the calibration-free subset does not have the same subject's data in the training subset. The results showed that ST-ResNet performed the best, and it is important to evaluate the models using both calibration-based and calibration-free subsets, and to prevent the overfitting by using the weight decay. © 2025 IEEE.

关键词： Blood pressure

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Feature Selection in Socio-Economic Analysis: A Multi-Method Approach for Accurate Predictive Outcomes

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International Journal of Crowd science 2025年第1期9卷 64-78页

作者： Al-Qerem, Ahmad Ali, Ali Mohd Jebreen, Issam Nabot, Ahmad Rajab, Mohammed Alauthman, Mohammad Aldweesh, Amjad Aburub, Faisal Alangari, Someah Alzgol, Musab Computer Science Department Faculty of Information Technology Zarqa University Zarqa13110 Jordan Communications and Computer Engineering Department Faculty of Engineering Al-Ahliyya Amman University Amman19328 Jordan University Headquarter University of Anbar Ramadi31001 Iraq Department of Information Security University of Petra Amman11196 Jordan College of Computing and Information Technology Shaqra University Shaqra11911 Saudi Arabia Department of Business Intelligence and Data Analytics University of Petra Amman11196 Jordan Computer Information Systems Department Faculty of Information Technology Isra University Amman11622 Jordan

Feature selection is a cornerstone in advancing the accuracy and efficiency of predictive models, particularly in nuanced domains like socio-economic analysis. This study explores nine distinct feature selection methods, utilizing a heart disease dataset as a representative model for complex socio-economic systems. Our findings identified four universally recognized features as critical across all selection methods. However, the divergence in significance attributed to other features by different methods underscores the inherent variability in selection techniques. When the top four features were incorporated into twelve classification models, a noticeable surge in predictive accuracy was observed, emphasizing their foundational role in enhancing model outcomes. The variations among methods stress the need for a methodical and discerning approach to feature selection, especially in data-rich socio-economic landscapes. As we venture further into an era defined by data-driven decision-making, rigour and precision in feature selection become indispensable. Future research should extend this approach to broader datasets, ensuring the robustness and adaptability of our findings. © The author(s) 2025.

关键词： Prediction models

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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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Drone-Based Public Surveillance Using 3D Point Clouds and Neuro-Fuzzy Classifier

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computers, Materials & Continua 2025年第3期82卷 4759-4776页

作者： Yawar Abbas Aisha Ahmed Alarfaj Ebtisam Abdullah Alabdulqader Asaad Algarni Ahmad Jalal Hui Liu Faculty of Computing and AI Air UniversityIslamabad44000Pakistan Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Information Technology College of Computer and Information SciencesKing Saud UniversityRiyadh12372Saudi Arabia Department of Computer Sciences Faculty of Computing and Information TechnologyNorthern Border UniversityRafha91911Saudi Arabia Department of Computer Science and Engineering College of InformaticsKorea UniversitySeoul02841Republic of Korea Cognitive Systems Lab University of BremenBremen28359Germany

Human Activity Recognition(HAR)in drone-captured videos has become popular because of the interest in various fields such as video surveillance,sports analysis,and human-robot ***,recognizing actions from such videos poses the following challenges:variations of human motion,the complexity of backdrops,motion blurs,occlusions,and restricted camera *** research presents a human activity recognition system to address these challenges by working with drones’red-green-blue(RGB)*** first step in the proposed system involves partitioning videos into frames and then using bilateral filtering to improve the quality of object foregrounds while reducing background interference before converting from RGB to grayscale *** YOLO(You Only Look Once)algorithm detects and extracts humans from each frame,obtaining their skeletons for further *** joint angles,displacement and velocity,histogram of oriented gradients(HOG),3D points,and geodesic Distance are *** features are optimized using Quadratic Discriminant Analysis(QDA)and utilized in a Neuro-Fuzzy Classifier(NFC)for activity ***-world evaluations on the Drone-Action,Unmanned Aerial Vehicle(UAV)-Gesture,and Okutama-Action datasets substantiate the proposed system’s superiority in accuracy rates over existing *** particular,the system obtains recognition rates of 93%for drone action,97%for UAV gestures,and 81%for Okutama-action,demonstrating the system’s reliability and ability to learn human activity from drone videos.

关键词： Activity recognition geodesic distance pattern recognition neuro fuzzy classifier

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