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Enhancing Precision Agriculture with YOLOv8: A Deep Learning Approach to Potato Disease Identification

International Journal of Advanced Computer Science and Applications 2025年第4期16卷 1156-1166页

作者： Aleinzi, Mohammed Faculty of Computing and Information Technology Information System Department Northern Border University Saudi Arabia

Timely and precise identification of potato leaf diseases plays a critical role in improving crop productivity and reducing the impact of plant pathogens. Conventional detection techniques are often labor-intensive, dependent on expert anal ysis, and may not be practical for widespread agricultural use. This paper introduces an automated detection system based on YOLOv8, a cutting-edge deep learning framework specialized in object detection, to accurately recognize multiple potato leaf diseases. The proposed model is trained on a carefully prepared dataset that includes both healthy and infected leaves, utilizing robust feature learning to distinguish between different disease types. Our experimental evaluation reveals that the YOLOv8-based method achieves superior performance in terms of accuracy and processing speed when compared to traditional approaches. This work contributes to the ongoing transformation of agriculture through smart technologies by offering an AI powered tool that facilitates real-time crop monitoring. Future research may focus on deploying this solution on edge devices, such as smartphones or drones, to enable scalable, on-field disease diagnostics. Ultimately, this study supports the vision of sustainable agriculture by integrating intelligent systems into everyday farming operations. © (2025), (Science and information Organization). All Rights Reserved.

关键词： Deep learning

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Optimizing Medical Image Analysis: A Performance Evaluation of YOLO-Based Segmentation Models

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International Journal of Advanced Computer Science and Applications 2025年第4期16卷 1167-1174页

作者： Alanazi, Haifa Department of Information Systems-Faculty of Computing and Information Technology Northern Border University Saudi Arabia

Instance segmentation is a critical component of medical image analysis, enabling tasks such as tissue and organ delineation, and disease detection. This paper provides a detailed comparative analysis of two fine-tuned one-stage object detection models, YOLOv11-seg and YOLOv9-seg, tailored for instance segmentation in medical imaging. Leveraging transfer learning, both models were initialized with pretrained weights and sub sequently fine-tuned on the NuInsSeg dataset, which comprises over 30,000 manually segmented nuclei across 665 image patches from various human and mouse organs. This approach facilitated faster convergence and improved generalization, particularly given the limited size and high complexity of the medical dataset. The models were evaluated against key performance metrics. The experimental results reveal that YOLOv11n-seg outperforms YOLOv9c-seg with a precision of 0.87, recall of 0.84, and mAP50 of 0.89, indicating superior segmentation quality and more accurate delineation of nuclei contours. This study highlights the robust performance and efficiency of YOLOv11n seg, demonstrating its superiority in medical image segmentation tasks, with notable advantages in both accuracy and real-time processing capabilities. © (2025), (Science and information Organization). All Rights Reserved.

关键词： Image segmentation

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Securing IIoT operations with recurrent federated network-based enhanced local search grasshopper

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Neural computing and Applications 2024年第25期36卷 15893-15906页

作者： Alassafi, Madini O. Department of Information Technology Faculty of Computing and Information Technology King Abdulaziz University Jeddah Saudi Arabia

The integration of the Industrial Internet of Things (IIoT) brings about a significant improvement in the efficiency and productivity of industrial processes. The speed and accuracy of various tasks have been greatly enhanced with the utilization of IIoT which allows smoother operations and cost-effective solutions, to protect uninterrupted intrusions and avoid potential threats. This article proposes a novel recurrent federated network-based enhanced local search grasshopper (RFN-ELG) algorithm. Datasets like UNSW-NB15 and MQTT-IoT-IDS2020 datasets are employed to determine the performances of IIoT via two diverse phases, namely the data preprocessing phase as well as attack detection phase. The recurrent neural networks (RNNs) are integrated with federated learning (FL) to overcome the gradient issues during the training process. In addition to this, the hyperparameters of RNN-FL are tuned via a grasshopper optimization algorithm with a local search strategy. Accuracy, precision, recall, F1-score, and false alarm rate (FAR) are the performance metrics taken for attack detection, where accuracy attains the value of 98.1%, precision attains the value of 97.4%, recall attains the value of 96.1%, F1-score attains the value of 97.2%, and FAR attains the value of 96.1%, respectively. Therefore, the proposed RFN-ELG algorithm attains high performance in detecting the attacks in IIoT. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Internet of things

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Pervasive Attentive Neural Network for Intelligent Image Classification Based on N-CDE’s

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Computers, Materials & Continua 2024年第4期79卷 1137-1156页

作者： Anas W.Abulfaraj Department of Information Systems Faculty of Computing and Information TechnologyKing Abdulaziz UniversityRabigh21911Saudi Arabia

The utilization of visual attention enhances the performance of image classification *** attentionbased models have demonstrated notable performance,but many of these models exhibit reduced accuracy when confronted with inter-class and intra-class similarities and ***-Controlled Differential Equations(N-CDE’s)and Neural Ordinary Differential Equations(NODE’s)are extensively utilized within this ***’s possesses the capacity to effectively illustrate both inter-class and intra-class similarities and differences with enhanced *** this end,an attentive neural network has been proposed to generate attention maps,which uses two different types of N-CDE’s,one for adopting hidden layers and the other to generate attention *** distinct attention techniques are implemented including time-wise attention,also referred to as bottom N-CDE’s;and element-wise attention,called topN-CDE’***,a trainingmethodology is proposed to guarantee that the training problem is sufficiently *** classification tasks including fine-grained visual classification andmulti-label classification,are utilized to evaluate the *** proposedmethodology is employed on five publicly available datasets,including CUB-200-2011,ImageNet-1K,PASCAL VOC 2007,PASCAL VOC 2012,and MS *** obtained visualizations have demonstrated that N-CDE’s are better appropriate for attention-based activities in comparison to conventional NODE’s.

关键词： Differential equations neural-controlled DE image classification attention maps N-CDE’s

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Dawwen: An Arabic Mental Health Mobile App Based on Natural Language Processing

International Journal of Interactive Mobile Technologies

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International Journal of Interactive Mobile Technologies 2025年第4期19卷 108-131页

作者： Wali, Arwa Almagrabi, Hana El-Feky, Sarah Jokhdar, Maryah Department of Information Systems Faculty of Computing and Information Technology King Abdulaziz University Jeddah Saudi Arabia

People are increasingly concerned about their mental health wellness. Scientific studies suggest that online counselling for anxiety and depression is just as effective as in-person treatment. Additionally, journaling interventions have shown promise for individuals dealing with mental and psychological issues. In recent years, a growing number of mobile applications have been developed to improve people’s mental wellness and emotional commu-nication. However, many of these applications are not available in Arabic but are available only in English or the native languages of their users, while other applications have feature limitations. This study presents a prototype of an interactive mental health mobile application, called Dawwen, to assist Arab individuals in freely expressing their feelings through online journaling, receiving recommendations for practices and activities, and locating nearby therapy centers based on their geolocation. Dawwen is augmented with an easy-to-use interface, a natural language processing (NLP) technique for analyzing the user input, and integrated features. The system is implemented using Android Studio and various cloud-based tools, making it adaptable for the integration of more advanced artificial intelligence (AI) technologies in the future. The System Usability Scale (SUS) scored an average of 88.25%, indicat-ing high user satisfaction with the app’s functionality and ease of use. The study highlights Dawwen’s effectiveness in improving mental health support for Arabic-speaking users, filling a critical gap in existing digital mental health resources. © 2025 by the authors of this article. Published under CC-BY.

关键词： Electronic health record

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Grey Wolf-Based Method for an Implicit Authentication of Smartphone Users

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Computers, Materials & Continua 2023年第5期75卷 3729-3741页

作者： Abdulwahab Ali Almazroi Mohamed Meselhy Eltoukhy University of Jeddah College of Computing and Information Technology at KhulaisDepartment of Information TechnologyJeddahSaudi Arabia

Smartphones have now become an integral part of our everyday *** authentication on smartphones is often accomplished by mechanisms(like face unlock,pattern,or pin password)that authenticate the user’s *** technologies are simple,inexpensive,and fast for repeated ***,these technologies are still subject to assaults like smudge assaults and shoulder ***’touch behavior while using their cell phones might be used to authenticate them,which would solve the *** performance of the authentication process may be influenced by the attributes chosen(from these behaviors).The purpose of this study is to present an effective authentication technique that implicitly offers a better authentication method for smartphone usage while avoiding the cost of a particular device and considering the constrained capabilities of *** began by concentrating on feature selection methods utilizing the grey wolf optimization *** random forest classifier is used to evaluate these *** testing findings demonstrated that the grey wolf-based methodology works as a better optimum feature selection for building an implicit authentication mechanism for the smartphone environment when using a public *** achieved a 97.89%accuracy rate while utilizing just 16 of the 53 characteristics like utilizing minimum mobile resources mainly;processing power of the device and memory to validate ***,the findings revealed that our approach has a lower equal error rate(EER)of 0.5104,a false acceptance rate(FAR)of 1.00,and a false rejection rate(FRR)of 0.0209 compared to the methods discussed in the *** promising results will be used to create a mobile application that enables implicit validation of authorized users yet avoids current identification concerns and requires fewer mobile resources.

关键词： Smartphone authentication implicit authentication grey wolf random forest feature selection

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A Novel Method Based on Nonlinear Binary Grasshopper Whale Optimization Algorithm for Feature Selection

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Journal of Bionic Engineering 2023年第1期20卷 237-252页

作者： Lingling Fang Xiyue Liang Department of Computing and Information Technology Liaoning Normal UniversityDalian116081China

Feature Selection(FS)is considered as an important preprocessing step in data mining and is used to remove redundant or unrelated features from high-dimensional *** optimization algorithms for FS problems are not balanced in search.A hybrid algorithm called nonlinear binary grasshopper whale optimization algorithm(NL-BGWOA)is proposed to solve the problem in this *** the proposed method,a new position updating strategy combining the position changes of whales and grasshoppers population is expressed,which optimizes the diversity of searching in the target *** distinct high-dimensional UCI datasets,the multi-modal Parkinson's speech datasets,and the COVID-19 symptom dataset are used to validate the proposed *** has been demonstrated that the proposed NL-BGWOA performs well across most of high-dimensional datasets,which shows a high accuracy rate of up to ***,the experimental results on the medical datasets also demonstrate the advantages of the proposed method in actual FS problem,including accuracy,size of feature subsets,and fitness with best values of 0.913,5.7,and 0.0873,*** results reveal that the proposed NL-BGWOA has comprehensive superiority in solving the FS problem of high-dimensional data.

关键词： Feature selection Hybrid bionic optimization algorithm Biomimetic position updating strategy Nature-inspired algorithm-High-dimensional UCI datasets-Multi-modal medical datasets

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Priority Detector and Classifier Techniques Based on ML for the IoMT

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Computers, Materials & Continua 2023年第8期76卷 1853-1870页

作者： Rayan A.Alsemmeari Mohamed Yehia Dahab Badraddin Alturki Abdulaziz A.Alsulami Department of Information Technology Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Computer Science Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Information Systems Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia

Emerging telemedicine trends,such as the Internet of Medical Things(IoMT),facilitate regular and efficient interactions between medical devices and computing *** importance of IoMT comes from the need to continuously monitor patients’health conditions in real-time during normal daily activities,which is realized with the help of various wearable devices and *** major health problem is workplace stress,which can lead to cardiovascular disease or psychiatric ***,real-time monitoring of employees’stress in the workplace is *** levels and the source of stress could be detected early in the fog layer so that the negative consequences can be mitigated ***,overwhelming the fog layer with extensive data will increase the load on fog nodes,leading to computational *** study aims to reduce fog computation by proposing machine learning(ML)models with two *** first phase of theMLmodel assesses the priority of the situation based on the stress *** the second phase,a classifier determines the cause of stress,which was either interruptions or time pressure while completing a *** approach reduced the computation cost for the fog node,as only high-priority records were transferred to the ***-priority records were forwarded to the *** MLapproaches were compared in terms of accuracy and prediction speed:Knearest neighbors(KNN),a support vector machine(SVM),a bagged tree(BT),and an artificial neural network(ANN).In our experiments,ANN performed best in both phases because it scored an F1 score of 99.97% and had the highest prediction speed compared with KNN,SVM,and BT.

关键词： Machine learning priority detector Internet of Medical Things IoMT fog computing cloud computing

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Enhancing Ulcerative Colitis Diagnosis:A Multi-Level Classification Approach with Deep Learning

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Computer Modeling in Engineering & Sciences 2024年第7期140卷 1129-1142页

作者： Hasan J.Alyamani Department of Information Systems Faculty of Computing and Information Technology in RabighKing Abdulaziz UniversityJeddah21589Saudi Arabia

The evaluation of disease severity through endoscopy is pivotal in managing patients with ulcerative colitis,a condition with significant clinical ***,endoscopic assessment is susceptible to inherent variations,both within and between observers,compromising the reliability of individual *** study addresses this challenge by harnessing deep learning to develop a robust model capable of discerning discrete levels of endoscopic disease *** initiate this endeavor,a multi-faceted approach is embarked *** dataset is meticulously preprocessed,enhancing the quality and discriminative features of the images through contrast limited adaptive histogram equalization(CLAHE).A diverse array of data augmentation techniques,encompassing various geometric transformations,is leveraged to fortify the dataset’s diversity and facilitate effective feature extraction.A fundamental aspect of the approach involves the strategic incorporation of transfer learning principles,harnessing a modified ResNet-50 *** augmentation,informed by domain expertise,contributed significantly to enhancing the model’s classification *** outcome of this research endeavor yielded a highly promising model,demonstrating an accuracy rate of 86.85%,coupled with a recall rate of 82.11%and a precision rate of 89.23%.

关键词： Ulcerative colitis deep learning CLAHE transfer learning geometric transformations

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Ensemble Deep Learning Approaches in Health Care:A Review

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Computers, Materials & Continua 2025年第3期82卷 3741-3771页

作者： Aziz Alotaibi Department of Computer Science College of Computing and Information TechnologyTaif UniversityTaif21974Saudi Arabia

Deep learning algorithms have been rapidly incorporated into many different applications due to the increase in computational power and the availability of massive amounts of ***,both deep learning and ensemble learning have been used to recognize underlying structures and patterns from high-level features to make predictions/*** the growth in popularity of deep learning and ensemble learning algorithms,they have received significant attention from both scientists and the industrial community due to their superior ability to learn features from big *** deep learning has exhibited significant performance in enhancing learning generalization through the use of multiple deep learning *** ensemble deep learning has large quantities of training parameters,which results in time and space overheads,it performs much better than traditional ensemble *** deep learning has been successfully used in several areas,such as bioinformatics,finance,and health *** this paper,we review and investigate recent ensemble deep learning algorithms and techniques in health care domains,medical imaging,health care data analytics,genomics,diagnosis,disease prevention,and drug *** cover several widely used deep learning algorithms along with their architectures,including deep neural networks(DNNs),convolutional neural networks(CNNs),recurrent neural networks(RNNs),and generative adversarial networks(GANs).Common healthcare tasks,such as medical imaging,electronic health records,and genomics,are also ***,in this review,the challenges inherent in reducing the burden on the healthcare system are discussed and ***,future directions and opportunities for enhancing healthcare model performance are discussed.

关键词： Deep learning ensemble learning deep ensemble learning deep learning approaches for health care health care

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