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Convolutional Neural Networks Based Hybrid Deep Model for Grapevine Leaves Detection and Classification

Traitement du Signal 2025年第2期42卷 835-841页

作者： Atesoglu, Fatih Bingol, Harun Department of Computer Engineering Faculty of Engineering and Naturel Sciences Maltepe University İstanbul34857 Turkey Department of Software Engineering Faculty of Engineering and Naturel Sciences Malatya Turgut Özal University Malatya44200 Turkey

The problem of determining the type of grapevine leave (GL) has an important place in the agricultural field and especially in the field of viticulture. It is a foodstuff that is consumed as a table especially in every Middle Eastern country and its export to Europe has been increasing in recent years. GL are usually consumed as wraps. Considering the economic situation of such a widely used plant, the determination of the plant type is very important. Because early intervention is required for diseases that will occur in the plant. As it is known, early diagnosis will facilitate treatment. In this study, leaf types were classified using artificial intelligence techniques in order to help experts diagnose the type of vine leaf. In addition, a hybrid model was proposed for classification processes. In this proposed hybrid model, feature maps were first extracted from the Resnet50 and Inceptionv3 deep models and these features were combined. Then, the most valuable features in the feature map were selected with the Neighborhood Component Analysis (NCA) method. Then, the feature map containing the most valuable features was classified in the best-known supervised classification methods. The proposed hybrid model reached an accuracy value of 89.2%. Thus, it has been determined that the proposed hybrid model achieves a highly competitive accuracy value in the classification of vine leaf images and can be used for this purpose. ©2025 The authors.

关键词： Supervised learning

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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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Bias analysis of AI models for undergraduate student admissions

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Neural Computing and Applications 2025年第12期37卷 7785-7795页

作者： Van Busum, Kelly Fang, Shiaofen Department of Computer Science and Software Engineering Butler University IndianapolisIN United States Computing and Engineering Indiana University Luddy School of Informatics Indiana University Indianapolis IndianapolisIN United States

Bias detection and mitigation is an active area of research in machine learning. This work extends previous research done by the authors Van Busum and Fang (Proceedings of the 38th ACM/SIGAPP Symposium on Applied Computing, 2023) to provide a rigorous and more complete analysis of the bias found in AI predictive models. Admissions data spanning six years was used to create an AI model to determine whether a given student would be directly admitted into the School of Science under various scenarios at a large urban research university. During this time, submission of standardized test scores as part of a student’s application became optional which led to interesting questions about the impact of standardized test scores on admission decisions. We developed and analyzed AI models to understand which variables are important in admissions decisions, and how the decision to exclude test scores affects the demographics of the students who are admitted. We then evaluated the predictive models to detect and analyze biases these models may carry with respect to three variables chosen to represent sensitive populations: gender, race, and whether a student was the first in his/her family to attend college. We also extended our analysis to show that the biases detected were persistent. Finally, we included several fairness metrics in our analysis and discussed the uses and limitations of these metrics. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Students

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Aligning Instance-Semantic Sparse Representation towards Unsupervised Object Segmentation and Shape Abstraction with Repeatable Primitives

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IEEE Transactions on Visualization and computer Graphics 2025年第5期31卷 2579-2588页

作者： Li, Jiaxin Wang, Hongxing Tan, Jiawei Ou, Zhilong Yuan, Junsong Chongqing University School of Big Data and Software Engineering Chongqing401331 China State University of New York Department of Computer Science and Engineering BuffaloNY14260 United States

Understanding 3D object shapes necessitates shape representation by object parts abstracted from results of instance and semantic segmentation. Promising shape representations enable computers to interpret a shape with meaningful parts and identify their repeatability. However, supervised shape representations depend on costly annotation efforts, while current unsupervised methods work under strong semantic priors and involve multi-stage training, thereby limiting their generalization and deployment in shape reasoning and understanding. Driven by the tendency of high-dimensional semantically similar features to lie in or near low-dimensional subspaces, we introduce a one-stage, fully unsupervised framework towards semantic-aware shape representation. This framework produces joint instance segmentation, semantic segmentation, and shape abstraction through sparse representation and feature alignment of object parts in a high-dimensional space. For sparse representation, we devise a sparse latent membership pursuit method that models each object part feature as a sparse convex combination of point features at either the semantic or instance level, promoting part features in the same subspace to exhibit similar semantics. For feature alignment, we customize an attention-based strategy in the feature space to align instance- and semantic-level object part features and reconstruct the input shape using both of them, ensuring geometric reusability and semantic consistency of object parts. To firm up semantic disambiguation, we construct cascade unfrozen learning on geometric parameters of object parts. Experiments conducted on benchmark datasets confirm that our approach results in instance- and semantic-level joint segmentation and shape abstraction with repeatable primitives, providing coherent semantic interpretations of 3D object shapes across categories in a one-stage, fully unsupervised manner, without relying on annotations or heuristic semantic priors. Code will be

关键词： Semantic Segmentation

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Priority-Aware Resource Allocation for VNF Deployment in Service Function Chains Based on Graph Reinforcement Learning

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computers, Materials & Continua 2025年第5期83卷 1649-1665页

作者： Seyha Ros Seungwoo Kang Taikuong Iv Inseok Song Prohim Tam Seokhoon Kim Department of Software Convergence Soonchunhyang UniversityAsan31538Republic of Korea School of Digital Technologies American University of Phnom PenhPhnom Penh12106Cambodia Department of Computer Software Engineering Soonchunhyang UniversityAsan31538Republic of Korea

Recently,Network Functions Virtualization(NFV)has become a critical resource for optimizing capability utilization in the 5G/B5G *** decomposes the network resource paradigm,demonstrating the efficient utilization of Network Functions(NFs)to enable configurable service priorities and resource *** Service Providers(TSPs)face challenges in network utilization,as the vast amounts of data generated by the Internet of Things(IoT)overwhelm existing *** applications,which generate massive volumes of diverse data and require real-time communication,contribute to bottlenecks and *** this context,Multiaccess Edge Computing(MEC)is employed to support resource and priority-aware IoT applications by implementing Virtual Network Function(VNF)sequences within Service Function Chaining(SFC).This paper proposes the use of Deep Reinforcement Learning(DRL)combined with Graph Neural Networks(GNN)to enhance network processing,performance,and resource pooling *** facilitates feature extraction through Message-Passing Neural Network(MPNN)*** with DRL,Deep Q-Networks(DQN)are utilized to dynamically allocate resources based on IoT network priorities and *** focus is on minimizing delay times for VNF instance execution,ensuring effective resource placement,and allocation in SFC deployments,offering flexibility to adapt to real-time changes in priority and *** results demonstrate that our proposed scheme outperforms reference models in terms of reward,delay,delivery,service drop ratios,and average completion ratios,proving its potential for IoT applications.

关键词： Deep reinforcement learning graph neural network multi-access edge computing network functions virtualization software-defined networking

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Ethical Aspects of ChatGPT in software engineering Research

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年第2期6卷 254-267页

作者： Akbar, Muhammad Azeem Khan, Arif Ali Liang, Peng Lappeenranta-Lahti University of Technology Software Engineering Department Lappeenranta15210 Finland University of Oulu M3S Empirical Software Engineering Research Unit Oulu90014 Finland Wuhan University School of Computer Science Wuhan430072 China

ChatGPT can improve software engineering (SE) research practices by offering efficient, accessible information analysis, and synthesis based on natural language interactions. However, ChatGPT could bring ethical challenges, encompassing plagiarism, privacy, data security, and the risk of generating biased or potentially detrimental data. This research aims to fill the given gap by elaborating on the key elements: motivators, demotivators, and ethical principles of using ChatGPT in SE research. To achieve this objective, we conducted a literature survey, identified the mentioned elements, and presented their relationships by developing a taxonomy. Furthermore, the identified literature-based elements (motivators, demotivators, and ethical principles) were empirically evaluated by conducting a comprehensive questionnaire-based survey involving SE researchers. In addition, we employed an interpretive structure modeling approach to analyze the relationships between the ethical principles of using ChatGPT in SE research and develop a level-based decision model. We further conducted a cross-impact matrix multiplication applied to classification analysis to create a cluster-based decision model. These models aim to help SE researchers devise effective strategies for ethically integrating ChatGPT into SE research by following the identified principles by adopting the motivators and addressing the demotivators. The findings of this study will establish a benchmark for incorporating ChatGPT services in SE research with an emphasis on ethical considerations. © 2020 IEEE.

关键词： software engineering

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Predictive Caching of File System for Database Workload Management 1

Predictive Caching of File System for Database Workload Mana...

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1st International Conference on AIML-Applications for engineering and Technology, ICAET 2025

作者： Jadhav, Rohini Jadhav, Veena Pawar, Prajakta Kannaujia, Abhishek Kumar, Abhishek Gohil, Jayesh College of Engineering Department of Information Technology Pune India College of Engineering Department of Computer Engineering Pune India Bharati Vidyapeeth College of Engineering Department of Computer Engineering Lavale Pune India Veritas Principal Software Engineering India

ISBN: (纸本)9798350355611

The proactive caching technique known as 'predictive caching' attempts to improve file system performance by anticipating and pre-fetching data that is likely to be requested in the future. Conventional caching strategies, such LRU (Least Recently Used) and LFU (Least Frequently Used), base their decision on which material to keep in the cache on historical access patterns. Although these methods work well in many situations, they could not completely take advantage of the temporal and geographical proximity of file system access patterns, which could result in less-than-ideal cache use. Utilizing machine learning algorithms, statistical analysis, or heuristics, predictive caching makes predictions about future access patterns based on past data, system status, file properties, or user activity. So we propose a predictive caching technique which reduce access latency and boost system performance by pre-fetching and storing pertinent data ahead of time in anticipation of incoming data accesses. © 2025 IEEE.

关键词： Dynamic Data File Access Pattern Predictive Caching Random Forest Classifier

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CNN-based cancer prediction scheme using 5G-assisted federated learning for healthcare Industry 5.0

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Alexandria engineering Journal 2025年 126卷 131-142页

作者： Kumari, Aparna Patadia, Divya Tanwar, Sudeep Pau, Giovanni Alqahtani, Fayez Tolba, Amr Department of Computer Science and Engineering Institute of Technology Nirma University Ahmedabad382481 India Faculty of Engineering and Architecture Kore University of Enna Enna94100 Italy Software Engineering Department College of Computer and Information Sciences King Saud University Riyadh12372 Saudi Arabia Computer Science Department Community College King Saud University Riyadh11437 Saudi Arabia

Healthcare Industry 5.0 has witnessed a transformative change by incorporating Artificial Intelligence (AI), Internet-of-things (IoT), and genomics to provide individualized and patient-centered healthcare. It prioritizes comprehensive health, proactive strategies for chronic diseases like tumors, cancer, etc., and effective cooperation among participants. Much work has been done for cancer prediction using machine learning and deep learning approaches. However, it has yet to be fully explored to protect data privacy and security by limiting data sharing. So, this paper proposes a Convolutional Neural Networks (CNN)-based Cancer Prediction scheme, i.e., CNN-CPS within the context of Healthcare Industry 5.0. The proposed scheme harnesses the capabilities of 5G-assisted Federated Learning (FL), a revolutionary method for predicting cancer occurrences with data privacy, security, and limiting data sharing. Then, it also leverages model data from dispersed systems to boost efficiency. By capitalizing on the speed and efficiency of 5G networks and the collaborative nature of FL, precise cancer predictions can be attained, all while upholding the confidentiality of sensitive data. This integration of technologies has the potential to significantly reshape cancer diagnosis and therapeutic approaches in the Industry 5.0 landscape. Here, 5G can enable real-time data interchange across remote data centers with rapid transmission of data capabilities and low-latency connectivity, enabling more effective and precise model training. Hence, this study examines the FL to predict breast cancer and combines the optimizer weights from several clients to continually enhance the performance of the overall model. The experimental results show the efficacy of the CNN-CPS compared to the existing approaches based on several parameters like low latency (40.77% improvement), accuracy (75%), and loss (0.54). © 2025 The Authors

关键词： Federated learning

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OptiMobileX: Optimizing Deep Transfer Learning Model for Accurate Human Posture Recognition Using a Deep Feature Fusion Technique

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IEEE Sensors Journal 2025年第6期25卷 9759-9766页

作者： Ogundokun, Roseline Oluwaseun Damasevicius, Robertas Maskeliunas, Rytis Kaunas University of Technology Department of Software Engineering Kaunas44249 Lithuania Kaunas University of Technology Centre of Real Time Computer Systems Kaunas44249 Lithuania

Human posture recognition (HPR) has garnered growing interest given the possibility of its use in various applications, including healthcare and sports fitness. Interestingly, achieving accurate pose recognition on mobile devices with very little computing power is still tricky. The precise identification of human posture on mobile devices with constrained computational resources is of utmost importance to bring about significant advancements in healthcare, fitness, and technology engagement. By facilitating real-time monitoring and providing personalized solutions, this technology improves accessibility and well-being, revolutionizing our interaction with digital applications. In this work, we used the deep feature concatenation (DFC) mechanism to combine features extracted from input images using two modern pretrained DTL models, MobileNetV2 and Xception, which led to our proposed OptiMobileX: A deep transfer learning (DTL) model based on a DFC of the MobileNetV2 and Xception models for the overall improvement of the identification capability of this posture. The classification accuracy of 96.42% is achieved by the OptiMobileX model, demonstrating the superiority of the proposed model over MobileNetV2, Xception, AlexNet, and InceptionV3, which only manage to obtain 88.17%, 92.47%, 90.32%, and 73.48% accuracy, respectively. The OptiMobileX model exhibits notable capabilities but faces significant limitations. Inadequate details on detecting inappropriate postures and a lack of information on required computing resources hinder the study's comprehensiveness and practical implementation. The absence of insufficient discussion on dataset biases and a lack of information on the model's generalization capacity create obstacles, raising concerns about its fairness, applicability, and real-world utility. © 2001-2012 IEEE.

关键词： Deep learning

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An aspect-based sentiment analysis model for Arabic game reviews based on hybrid transformers models

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Neural Computing and Applications 2025年 1-23页

作者： Hammad, Mahmoud AbuEnnab, Noor Al-Refai, Mohammed IT Department Ajman University Ajman United Arab Emirates Software Engineering Department Jordan University of Science and Technology Irbid Jordan Computer Science Department Jordan University of Science and Technology Irbid Jordan

Aspect-based sentiment analysis (ABSA) is a natural language processing (NLP) technique to determine the various sentiments of a customer in a single comment regarding different aspects. The increasing online data content generated by interested customers and reviewers motivated researchers and data scientists to conduct ABSA. ABSA has become increasingly popular in recent years due to its versatility in e-commerce, social media, and customer feedback analysis. However, ABSA faces several significant challenges, including determining the aspects and their sentiment polarities (positive, negative, or neutral) in a given text. Moreover, ABSA faces particular challenges in non-English languages such as Arabic due to the lack of resources and mature models. Typically, ABSA tackles one or more of the ABSA research tasks: (T1) aspect term extraction, (T2) aspect term polarity, (T3) aspect category identification, and (T4) aspect category polarity. To identify the aspects and their corresponding sentiment polarities in a given text, accurate and efficient NLP techniques are required. Despite growing interest in Arabic ABSA, the lack of annotated datasets and pre-trained models has hindered its development. In this research, we have collected a dataset of Arabic game reviews and annotated them using three annotators, and then we trained an ABSA deep learning model based on the BERT pre-trained model combined with zero-shot learning (ZSL) to tackle all the four aforementioned tasks. Our best performing model achieved a high accuracy on all four tasks with an accuracy of 91.61% on T1, 90.99% on T2, 79.08% on T3, and 88.17% on T4. Finally, we compared our model’s accuracy with the state-of-the-art Arabic-based ABSA models on different datasets. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Sales

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