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Novel methodology for apple leaf disease classification with PCNN-IELM

Neural Computing and Applications 2025年第6期37卷 4895-4913页

作者： Navpreet Roul, Rajendra Kumar Department of Computer Science and Engineering Thapar Institute of Engineering and Technology Punjab Patiala147004 India

Agriculture is crucial to the global economy, particularly in ensuring food security. Recent trends indicate that various plant diseases are causing substantial financial losses in the agricultural sector worldwide. Traditional manual inspection methods for detecting fruit and plant diseases are labor-intensive and inefficient. Adopting automated disease detection technologies could significantly enhance early diagnosis and reduce the economic impact of these diseases on agriculture. This study introduces an advanced model for classifying apple diseases by integrating a pre-trained convolutional neural network (PCNN), such as VGG16, VGG19, or ResNet50, with an incremental extreme learning machine (I-ELM) for efficient feature extraction and classification. A key innovation of this model is replacing the PCNN’s fully connected layer with the I-ELM, which eliminates the lengthy back-propagation process and significantly reduces training time. Integrating I-ELM with PCNN harnesses the rapid learning capabilities and robust generalization of I-ELM with the superior feature extraction abilities of CNNs. I-ELM simplifies the network architecture by avoiding the complex neural networks commonly used in other methods. The model’s effectiveness is rigorously evaluated on the well-known Plant Village dataset, demonstrating its ability to identify various apple diseases through performance metrics such as precision, sensitivity, specificity, accuracy, and the F1-score. Comparing existing deep learning models using these metrics highlights its superior performance. This innovation is up-and-coming for intelligent agricultural systems, offering an effective solution for classifying apple diseases and enabling timely and innovative farming practices. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Fruits

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Multi-agent Path Planning Based on Conflict-Based Search (CBS) Variations for Heterogeneous Robots

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Journal of Intelligent and Robotic Systems: Theory and Applications 2025年第1期111卷 1-12页

作者： Bai, Yifan Kotpalliwar, Shruti Kanellakis, Christoforos Nikolakopoulos, George Department of Computer Science Electrical and Space Engineering Luleå University of Technology Luleå797187 Sweden

This article introduces a novel Multi-agent path planning scheme based on Conflict Based Search (CBS) for heterogeneous holonomic and non-holonomic agents, designated as Heterogeneous CBS (HCBS). The proposed methodology employs a hybrid A∗ algorithm for non-holonomic car-like robots and a conventional A∗ algorithm for holonomic robots. Following this, a body conflict detection strategy is utilized to construct the conflict tree, bridging the initial path planning with the resolution of conflicts among agents. Moreover, we present two variants of HCBS: the Enhanced Conflict-Based Search (EHCBS) and the Depth-First Conflict-Based Search (DFHCBS). We evaluate the efficacy of our proposed algorithms—HCBS, EHCBS, and DFHCBS—against a standard prioritized planning algorithm, focusing on success rates and computational efficiency in environments with varying numbers of agents and obstacles. The empirical results demonstrate that EHCBS exhibits superior computational efficiency in small, dense environments, while DFHCBS performs well in larger-scale environments. This highlights the adaptability of our proposed approaches in various settings, proving the computational advantage of EHCBS and DFHCBS over traditional methods. © The Author(s) 2025.

关键词： Multi agent systems

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supported by the National Natural science Foundation of China(Nos.62172051,61772085,and 61877005)and Jiangsu Agriculture science and technology Innovation Fund(***(18)3054).

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Tsinghua science and technology 2023年第1期28卷 82-92页

作者： Jinghui Zhang Yuchen Wang Tianyu Huang Fang Dong Wei Zhao Dian Shen School of Computer Science and Engineering Southeast UniversityNanjing 211189China Hefei Comprehensive National Science Center Hefei 231299China School of Computer Science and Technology Anhui University of TechnologyHefei 230026China Department of Computer Science&Engineering The Chinese University of Hong KongHong Kong 999077China

Numerous neural network(NN)applications are now being deployed to mobile *** applications usually have large amounts of calculation and data while requiring low inference latency,which poses challenges to the computing ability of mobile ***,devices’life and performance depend on ***,in many scenarios,such as industrial production and automotive systems,where the environmental temperatures are usually high,it is important to control devices’temperatures to maintain steady *** this paper,we propose a thermal-aware channel-wise heterogeneous NN inference *** contains two parts,the thermal-aware dynamic frequency(TADF)algorithm and the heterogeneous-processor single-layer workload distribution(HSWD)*** on a mobile device’s architecture characteristics and environmental temperature,TADF can adjust the appropriate running speed of the central processing unit and graphics processing unit,and then the workload of each layer in the NN model is distributed by HSWD in line with each processor’s running speed and the characteristics of the layers as well as heterogeneous *** experimental results,where representative NNs and mobile devices were used,show that the proposed method can considerably improve the speed of the on-device inference by 21%–43%over the traditional inference method.

关键词： neural network inference mobile device temperature adjustment channel-wise parallelization

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Adaptable and Dynamic Access Control Decision-Enforcement Approach Based on Multilayer Hybrid Deep Learning Techniques in BYOD Environment

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computers, Materials & Continua 2024年第9期80卷 4663-4686页

作者： Aljuaid Turkea Ayedh M Ainuddin Wahid Abdul Wahab Mohd Yamani Idna Idris Faculty of Computer Science and Information Technology Universiti MalayaKuala Lumpur50603Malaysia Faculty of Computer Science and Information Technology Shaqra UniversityShaqra11961Saudi Arabia Center for Mobile Cloud Computing Faculty of Computer Science and Information TechnologyUniversiti MalayaKuala Lumpur50603Malaysia

Organizations are adopting the Bring Your Own Device(BYOD)concept to enhance productivity and reduce ***,this trend introduces security challenges,such as unauthorized *** access control systems,such as Attribute-Based Access Control(ABAC)and Role-Based Access Control(RBAC),are limited in their ability to enforce access decisions due to the variability and dynamism of attributes related to users and *** paper proposes a method for enforcing access decisions that is adaptable and dynamic,based on multilayer hybrid deep learning techniques,particularly the Tabular Deep Neural Network Tabular DNN *** technique transforms all input attributes in an access request into a binary classification(allow or deny)using multiple layers,ensuring accurate and efficient access *** proposed solution was evaluated using the Kaggle Amazon access control policy dataset and demonstrated its effectiveness by achieving a 94%accuracy ***,the proposed solution enhances the implementation of access decisions based on a variety of resource and user attributes while ensuring privacy through indirect communication with the Policy Administration Point(PAP).This solution significantly improves the flexibility of access control systems,making themmore dynamic and adaptable to the evolving needs ofmodern ***,it offers a scalable approach to manage the complexities associated with the BYOD environment,providing a robust framework for secure and efficient access management.

关键词： BYOD security access control access control decision-enforcement deep learning neural network techniques TabularDNN multilayer dynamic adaptable flexibility bottlenecks performance policy conflict

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Optimized AI and IoT-Driven Framework for Intelligent Water Resource Management

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IEEE Access 2025年 13卷 97628-97646页

作者： Rokaya, Mahmoud Hemdan, Dalia I. Alajmani, Samah Hazzaa Alyami, Raneem Yousif Elmarhomy, Ghada Hashim, Hasan Atlam, El-Sayed College of Computers and Information Technology Taif University Department of Information Technology P.O. Box 11099 Taif21944 Saudi Arabia Taif University Faculty of Science Department of Food Science and Nutrition P.O. Box 11099 Taif21944 Saudi Arabia College of Computer Science and Engineering Taibah University Department of Information Systems Saudi Arabia Tanta University Department Computer Science Faculty of Science Tanta31527 Egypt Taibah University College of Computer Science and Engineering Department of Computer Science Yanbu6421 Saudi Arabia

The scheme of water resources management is a necessity for reducing water scarcity in arid areas and improving water availability in general [1]. However, water leak detection and irrigation scheduling traditional AI models are often computationally intensive and require complex hyperparameter tuning, making them less scalable. This study presents an artificial intelligence-based optimization framework that improves forecasting accuracy, computational speed, and real-time adaptability. The architecture combines the ensemble-learning algorithms (XGBoost, LightGBM), hybrid AIs (XGBoost + Autoencoder), and metaheuristic feature selection (GA, PSO, SA) for making intelligent decisions. Moreover, ontology-based feature structuring enhances interpretability, while hyperparameter tuning (GridSearchCV, Bayesian Optimization) and model compression techniques (pruning, quantization, knowledge distillation) ensure computational efficiency. A large number of experiments on real-world IoT sensor data testify to the effectiveness of the framework. It achieves 0.992 AUC-ROC scores for leak detection, an RMSE of 0.227 hours for irrigation scheduling, and an overall accuracy of 94.8%. Additional performance measures comprise precision (89.0%), recall (95.2%), F1-score (0.92), and inference speed (0.003 ms/sample). Although quantization has reduced the computational overhead, we still see a 13.02% increase in the model size as seen in Experiment 6, leading to a trade-off that needs to be optimized further. This study offers a deployable AI-based model for sustainable water management by tackling the issues of scalability, computational cost, and limitations in benchmark evaluation. By virtue of the empirical validation and comparative analysis of the framework, it has been shown to perform better than the regular methods, proving that the methodology can act as a step forward in the field of real-time, AI-assisted irrigation and leak detection systems. © 2013 IEEE.

关键词： Resource allocation

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Predictive VM placement algorithm for resource optimization: leveraging deep learning forecasting and resource relationship modeling

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International Journal of computers and Applications 2025年第4期47卷 398-411页

作者： Garg, Rajni Arora, Indu Gupta, Anu Department of Computer Science Lala Lajpat Rai Govt. College Moga India Department of Computer Science Mehr Chand Mahajan DAV College for Women Chandigarh India Department of Computer Science and Applications Panjab University Chandigarh India

The growing demand for cloud computing has made it imperative to optimize the utilization of cloud resources. Resource optimization can be improved through Virtual Machine (VM) Placement. In order to effectively optimize placement of VMs, it becomes necessary to anticipate future resource demand. However, accurate resource forecasting is a major challenge due to the dynamic nature of cloud applications. Furthermore, if VMs are placed on the same server, it can lead to resource contention, especially when they compete for the same resources. This contention can adversely affect VMs performance and potentially increase cost for users, as well as energy consumption by the infrastructure. This work proposes a model named Predictive Disparity-based Virtual Machine Placement (PDVMP) which aims to enhance VM placement decision. The model integrates forecasting techniques grounded in Deep Learning for estimating the future resource needs of VMs. This estimation is incorporated in VM placement decision in order to ensure long-term sustainability of the VM on the destination server. Moreover, the model used in the current research work balances resource optimization and execution performance by packing multiple VMs that exhibit complementary resource demand on a same physical server. The performance of PDVMP model is tested against benchmark placement policies using real workload traces of bitBrains datacenter. The results show that the proposed approach improves resource utilization while reducing both performance bottlenecks and energy consumption. The experimentation shows an improvement in Energy Performance Metric ranging from 49.3% to 62.97%. © 2025 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Bismuth alloys

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Explainable AI model for PDFMal detection based on gradient boosting model

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Neural Computing and Applications 2024年第34期36卷 21607-21622页

作者： Elattar, Mona Younes, Ahmed Gad, Ibrahim Elkabani, Islam Department of Computer Science Faculty of Science Tanta University Tanta31527 Egypt Department of Mathematics and Computer Science Faculty of Science Alexandria University Alexandria21511 Egypt Faculty of Computer Science and Engineering Al Alamein International University Alamein51718 Egypt

Portable document formats (PDFs) are widely used for document exchange due to their widespread usage and versatility. However, PDFs are highly vulnerable to malware attacks, which pose significant security risks. Existing defense mechanisms often struggle to effectively detect and mitigate these threats, highlighting the need for more robust solutions. This paper introduces a robust framework that uses advanced tree-based ensemble models to detect malicious PDFs using the Evasive-PDFMal2022 dataset. The proposed model achieves a recall rate of 100%, an accuracy rate of 99.95%, and a fast inference time of 0.1723 s. Furthermore, the framework exhibits minimal false positive and false negative rates, ensuring a high level of precision in distinguishing between malicious and benign PDFs. Shapley additive explanations are used to improve the interpretability and reliability of the model’s predictions. The results highlight the effectiveness of the proposed model in improving PDF document security and addressing the challenges posed by malware attacks. © The Author(s) 2024.

关键词： Explainable artificial intelligence (XAI) Malicious PDF Malware detection Tree-based ensemble models

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Object Segmentation in Thermal Images Using YOLOv8 5th

Object Segmentation in Thermal Images Using YOLOv8

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5th International Conference on Computing and Network Communications, CoCoNet 2023

作者： Muften, Haider Ali Khayeat, Ali Retha Hasoon College of Computer Science and Information Technology University of Kerbala Computer Science Department Karbala Iraq

ISBN: (纸本)9789819747108

Thermal images are crucial for object detection in surveillance, security, industrial automation, and vehicular navigation due to their ability to capture heat signatures. However, complexities like low contrast, fluctuating thermal patterns, and lack of thermal-specific datasets necessitate the use of specialized algorithms for precise object identification and location. This research paper delves into the domain of object detection within thermal images, a crucial aspect of applications like surveillance and autonomous systems. Leveraging the YOLOv8 algorithm renowned for real-time and accurate object detection, this study focuses on enhancing detection performance in the challenging realm of thermal imagery. The methodology involves training on a meticulously curated dataset of 1898 thermal images, comprising 1518 for training and 380 for validation. The algorithm’s efficacy is demonstrated through a mean average precision (mAP50) of (82%) and map50–90 of (59.3%). These results validate YOLOv8 suitability for thermal imaging applications, propelling advancements in fields reliant on accurate object detection in thermally challenging conditions. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Thermography (imaging)

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Health management of young and middle-aged residents based on probabilistic optimisation BP neural network

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International Journal of Information and Communication technology 2025年第14期26卷 87-103页

作者： Wen, Yuanyuan College of Computer Science and Engineering Taizhou Institute of Science and Technology NJUST Taizhou225300 China

The health status of the young and middle-aged population has a significant impact on the stable functioning of society. To address the issue of poor prediction accuracy in the current study, the BP neural network (BPNN) is first improved based on Bayesian optimisation (BO), and the parameter combination that maximises the conditional probability is selected to improve the fitting accuracy of the model. Then the factors affecting the health status are analysed, the influencing factors are decomposed and reconstructed using the improved variational modal decomposition (VMD) and fuzzy entropy algorithm, and the objective function is iteratively searched through BO probability theory to obtain the degree of the BPNN parameter that minimises the prediction error. Finally, corresponding health management suggestions are proposed for the prediction results. The simulation results indicate that the accuracy of the proposed method is 95.37%, which significantly improves the prediction accuracy. Copyright © The Author(s) 2025.

关键词： Bayesian optimisation BP neural network BPNN health prediction probability theory variational modal decomposition VMD

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Cultivating road safety: A comprehensive examination of intelligent ensemble-based road crack detection

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Multimedia Tools and Applications 2025年第12期84卷 10747-10780页

作者： Navpreet Roul, Rajendra Kumar Rani, Rinkle Department of Computer Science and Engineering Thapar Institute of Engineering & Technology Punjab Patiala147004 India

Detecting and promptly identifying cracks on road surfaces is of paramount importance for preserving infrastructure integrity and ensuring the safety of road users, including both drivers and pedestrians. Presently, the predominant approach for crack detection relies on labor-intensive and costly manual inspections, exacerbated by the enduring challenge of concrete road cracks susceptible to environmental factors like temperature fluctuations, heavy traffic loads, and prolonged exposure to harsh weather conditions. This paper presents a comprehensive research initiative aimed at advancing road crack detection techniques. The proposed method leverages a classical Convolutional Neural Network (CNN) in conjunction with the Extreme Learning Machine (ELM) for efficient feature extraction and classification. A pivotal breakthrough is achieved by replacing the fully connected layer of the CNN with ELM, thus circumventing the time-consuming backpropagation process and significantly expediting the training process. This integration harnesses ELM’s swift learning capabilities and strong generalization performance, complemented by the CNN’s exceptional feature extraction abilities. The model’s effectiveness is rigorously assessed using the wide recognized CCIC, SDNET2018, and Synthesized datasets, and its proficiency in distinguishing between Crack and No-Crack regions is demonstrated through various performance metrics. Furthermore, the model’s performance is benchmarked against existing deep learning models using these performance metrics, showcasing its superior performance. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Roads and streets

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