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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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Cognitive Transformation in Personal IoT: Pioneering Intelligent Automation

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Cyber-Physical Systems 2025年第2期11卷 183-240页

作者： Gulzar, Bisma Ahmad Sofi, Shabir Sholla, Sahil Department of Information Technology National Institute of Technology Srinagar India Department of Computer Science Engineering Islamic University of Science & Technology Srinagar India

In recent years, IoT has transformed personal environments by integrating diverse smart devices. This paper presents an advanced IoT architecture that optimizes network infrastructure, focusing on the adoption of MQTT protocol and introducing Cognitive Smart Objects for managing personal IoT applications. These objects use Neural Networks to predict optimal actions based on user behavior patterns. A Continuous Learning mechanism enables real-time adaptation of the network to evolving user interactions. The study highlights the role of Cognitive Transformation in Personal IoT, driving intelligent automation and enhancing user experience. © 2024 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Internet of Things (IoT) Personal Internet of Things (PIoT) Social Internet of Things (SIoT) IoT architecture network infrastructure

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Abnormal event detection in surveillance videos through LSTM auto-encoding and local minima assistance

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Discover Internet of Things 2025年第1期5卷 1-25页

作者： Sengonul, Erkan Samet, Refik Abu Al-Haija, Qasem Alqahtani, Ali Alsemmeari, Rayan A. Alghamdi, Bandar Alturki, Badraddin Alsulami, Abdulaziz A. Department of Computer Engineering Ankara University Ankara06100 Turkey Department of Cybersecurity Faculty of Computer & Information Technology Jordan University of Science and Technology PO Box 3030 Irbid22110 Jordan Department of Networks & Communications Engineering College of Computer Science & Information Systems Najran University Najran61441 Saudi Arabia Department of Information Technology Faculty of Computing and Information Technology King Abdulaziz University Jeddah21589 Saudi Arabia Department of Information Systems Faculty of Computing and Information Technology King Abdulaziz University Jeddah21589 Saudi Arabia

Abnormal event detection in video surveillance is critical for security, traffic management, and industrial monitoring applications. This paper introduces an innovative methodology for anomaly detection in video data, encompassing three primary stages: preprocessing, feature learning, and anomaly detection. We employ background subtraction and noise reduction during preprocessing to refine the data. The feature-learning stage involves training an LSTM autoencoder to capture the essential features of normal video sequences. For anomaly detection, we map video data to a lower-dimensional space (latent code) and compare it against the distribution of codes from normal sequences. We determine regularity scores and identify local minima points exceeding a specified threshold while scrutinizing shadows between adjacent maxima to confirm and pinpoint anomalies. When tested on the CUHK Avenue and UMN datasets, our methodology demonstrated performance with AUCs of 93.8% and 94.1%, respectively, outperforming several baseline models. Our results show the high precision of our method that can detect anomalies, highlighting its potential advantages that it can achieve for enhancing systems of surveillance. © The Author(s) 2025.

关键词： Noise abatement

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An intuitive distributional random forest technique for dementia severity class detection

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Iran Journal of computer Science 2025年 1-17页

作者： Singh, Pawan Kumar Upadhyay, Pawan Kumar Computer Science and Engineering Jaypee Institute of Information Technology Noida India

Huntington’s disease and other fatal neurodegenerative disorders have a significant global impact, affecting millions and often leading to dementia or death. Dementia is a general term with multiple symptoms that reduce a person’s cognitive abilities. The common symptoms of dementia include memory loss, difficulty concentrating, mood swings, and a confused state. This paper aims to improve dementia severity class detection by managing two key phases. The first phase involves analyzing the significant predictors from two benchmark versions of the dataset. The second phase focuses on enhancing the classification accuracy using machine learning techniques. Our novel approach involves two methods. The first method is the selection of clinically and anatomically significant predictors out of the total predictors from the benchmark dataset using the Z-distributional test. The second method governs a joint conditional estimate-based distributional random forest, which is used to classify the different severity levels of dementia. The evaluated results indicate that the resultant dataset clinically and anatomically significant predictors are more closely aligned with the distribution test line for each dementia class. These significant predictors with high correlations are well visualized in the quantile-quantile plot and are considered to have greater significance for detecting dementia severity classes. The proposed approach effectively detects heterogeneity in multivariate data, achieving an accuracy of 93.20%. The performance of other measures is improved in terms of precision, recall, and F-measures, making it more robust than existing methods. This methodology enhances early dementia severity detection and effectively attains multivariate data heterogeneity. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： Clinical dementia rating Intuitive random forest Neurodegenerative disease Open access series of imaging studies dataset Q–Q plot

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A Generative Model-Based Network Framework for Ecological Data Reconstruction

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computers, Materials & Continua 2025年第1期82卷 929-948页

作者： Shuqiao Liu Zhao Zhang Hongyan Zhou Xuebo Chen School of Electronic and Information Engineering University of Science and Technology LiaoningAnshan114051China School of Computer Science and Software Engineering University of Science and Technology LiaoningAnshan114051China

This study examines the effectiveness of artificial intelligence techniques in generating high-quality environmental data for species introductory site selection *** Strengths,Weaknesses,Opportunities,Threats(SWOT)analysis data with Variation Autoencoder(VAE)and Generative AdversarialNetwork(GAN)the network framework model(SAE-GAN),is proposed for environmental data *** model combines two popular generative models,GAN and VAE,to generate features conditional on categorical data embedding after SWOT *** model is capable of generating features that resemble real feature distributions and adding sample factors to more accurately track individual sample *** data is used to retain more semantic information to generate *** model was applied to species in Southern California,USA,citing SWOT analysis data to train the *** show that the model is capable of integrating data from more comprehensive analyses than traditional methods and generating high-quality reconstructed data from them,effectively solving the problem of insufficient data collection in development *** model is further validated by the Technique for Order Preference by Similarity to an Ideal Solution(TOPSIS)classification assessment commonly used in the environmental data *** study provides a reliable and rich source of training data for species introduction site selection systems and makes a significant contribution to ecological and sustainable development.

关键词： Convolutional Neural Network(CNN) VAE GAN TOPSIS data reconstruction

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IensNet: A novel and efficient approach for iris spoof detection via ensemble of deep models

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Multimedia Tools and Applications 2025年 1-30页

作者： Sharma, Deepika Selwal, Arvind School of Computer Science Engineering and Technology Bennett University Greater Noida201310 India Department of Computer Science and Information Technology Central University of Jammu Samba India

Iris biometrics allow contactless authentication, which makes it widely deployed human recognition mechanisms since the couple of years. Susceptibility of iris identification systems remains a challenging task due to diversity in spoof or presentation attacks (PAs) that fails to assure consistency while adopting them in real life scenarios. Hence, iris PAs are the growing concerns that gained significant attention in recent past decade. To alleviate these attacks or recognize presentation attack instruments (PAIs), iris presentation attacks detection (IPAD) algorithms are designed to distinguish a real and fabricated iris trait. Aiming at the efficient iris spoof detection mechanism, in this research work we expound a novel ensemble learning-enabled model (IensNet) that learns three pre-trained and fined-tuned deep models (i.e. DenseNet161, ResNet and VGGNet) for better accuracy and generalized performance. The novel IensNet approach offers several merits (i.e. consolidated strengths of multiple models, improved generalization ability, etc.) as compared to a simple transfer learning strategy where the knowledge is drawn from single pre-trained model. Finally, our approach learns a novel fully-connected dual layer classifier via outcome of three fine-tuned models to yield a final classification result as bonafide or spoof iris trait. Our approach is evaluated on Notre Dame LivDet iris 2017 and Notre Dame contact lenses 2015 anti-spoofing datasets. The experimental analysis of IensNet offers outstanding performance with a lower ACER of 0.2% and 1.4% for Iris-LivDet-2017 and Notre Dame contact lenses 2015 dataset respectively. Besides, IensNet exhibit promising results in cross-dataset environment with an ACA of 91.46%. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Contrastive Learning

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MDNET: A Novel Neural Network Based on CNN and Fuzzy Rough Set with Adaptive Parameters for Intrusion Detection in the Internet of Things

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International Journal of engineering, Transactions B: Applications 2025年第12期38卷 2965-2993页

作者： Zendehdel, M. DehakiToroghi, A. Hamidzadeh, J. Faculty of Computer Engineering and Information Technology Sadjad University Mashhad Iran

The internet of things (IoT) has become increasingly widespread, finding applications in various fields like transportation fleet management, smart homes, traffic management, and smart greenhouses. However, a significant concern with IoT devices is the vast amount of data they store. Sensitive data, including personal information, requires strong security, as unauthorized access can lead to cyberattacks. Attack scenarios, including data theft, system hijacking, and unauthorized access, pose significant threats, making early detection crucial to minimize potential damage. This paper proposes the MDNET neural network for enhanced IoT intrusion detection. The MDNET network aims to achieve high accuracy in detecting IoT network intrusions without requiring manual hyperparameter tuning. To achieve this, the CEO Optimization Algorithm was employed to adaptively select the hyperparameters for the proposed network. Our proposed method employs fuzzy rough set theory to select appropriate features from the network traffic dataset. This process removes features significantly impacted by noise, thereby enhancing the overall efficiency of the *** proposed approach was evaluated using five standard metrics accuracy, average precision, Kappa coefficient, Hamming loss, and Jaccard similarity;compared with seven existing models (FWP-SVM-GA, FBiNN, GA-FR-CNN, APSO-CNN, LSTM, GRU, ANN). The proposed method achieved an impressive 99.57% accuracy, 99.7% average-precision, and 0.99 Kappa coefficient, significantly outperforming the best-performing existing algorithm (FBiNN) with 97.28% accuracy and 0.96 Kappa coefficient. Additionally, MDNET exhibited lower Hamming loss (0.003) and a higher Jaccard similarity (0.99), confirming its superior classification accuracy and robustness in IoT attack detection. ©2025 The author(s).

关键词： Network intrusion

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Hybrid CNN-ViT architecture to exploit spatio-temporal feature for fire recognition trained through transfer learning

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Multimedia Tools and Applications 2025年第8期84卷 4703-4732页

作者： Shahid, Mohammad Wang, Hong-Cyuan Chen, Yung-Yao Hua, Kai-Lung Dept. of Computer Science and Information Engineering National Taiwan University of Science and Technology Taipei Taiwan Dept. of Electronic and Computer Engineering National Taiwan University of Science and Technology Taipei Taiwan

Fires are becoming one of the major natural hazards that threaten the ecology, economy, human life and even more worldwide. Therefore, early fire detection systems are crucial to prevent fires from spreading out of control and causing destruction. Based on vision sensors, many fire detection techniques have evolved with the recent surge of curiosity in deep learning, which exploits the spatial features of individual images. However, fire can take different forms, scales, and combustion materials can produce different colors, making accurate fire detection from an image challenging. Small fires captured from long-distance cameras lack salient features, further complicating detection. This paper proposes a hybrid structure that uses attention-enhanced convolutional neural networks and vision transformers (CNN-ViT) to detect fire. The proposed CNN-ViT first pays spatial attention to every frame and then aggregates temporal contextual information from neighboring frames to improve detection performance. Due to the limited availability of training fire datasets, the study employs deep transfer learning for feature extraction using pre-trained CNN. We used various metrics to examine the efficacy of the proposed approach. The results showed that the CNN-ViT method outperformed previous models based on spatial-temporal characteristics by achieving a relative improvement in accuracy and F1 score. The satisfactory results on images contaminated with different intensities of noise confirm the robustness of the approach. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Fires

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ProxaDyn: A Proximity-Aware Dynamic Caching Approach for Named Data Networks

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IEEE Transactions on Network Science and engineering 2025年第3期12卷 2360-2372页

作者： Kumari, Matta Krishna Tripathi, Nikhil Joshi, Piyush Indian Institute of Information Technology Computer Science and Engineering Group Sri City517 646 India Department of Computer Science and Engineering Dhanbad826 004 India

Named Data Network (NDN), a future Internet architecture is introduced to address the shortcomings of the current Internet architecture. NDN supports in-network caching to facilitate scalable content distribution and enhance overall network performance. However, the known NDN caching strategies suffer from a few common drawbacks, such as inefficient cache utilization, high content redundancy, and overhead due to lookup repetition. To address these issues, in this paper, we propose a novel caching strategy called ProxaDyn for efficient content lookup, placement, and replacement. During the content lookup phase, ProxaDyn interacts exclusively with the router responsible for caching a particular content. This eliminates interaction with other intermediate routers, thereby significantly reducing content access latency. For content placement, ProxaDyn strategically selects an on-path router based on content popularity. Popular content is placed in the cache of a router closer to the consumer, while less popular content is cached in a router away from the consumer. This approach significantly improves the cache hits and reduces the access latency. We test ProxaDyn over a diverse range of real-world network topologies. Using extensive experiments, we show that ProxaDyn could achieve significantly better results compared to the state-of-the-art NDN caching strategies. © 2013 IEEE.

关键词： Data communication systems

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TMT-FL: Enabling Trustworthy Model Training of Federated Learning With Malicious Participants

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IEEE Transactions on Dependable and Secure Computing 2025年第3期22卷 2723-2740页

作者： Lu, Zhongkai Wang, Lingling Zhang, Zhengyin Huang, Mei Wang, Jingjing Li, Meng Qingdao University of Science and Technology School of Information Science and Technology Shandong Qingdao266061 China Hefei University of Technology School of Computer Science and Information Engineering Anhui Hefei230601 China

Federated learning is a widely used method for collaborative machine learning without sharing local data. In this approach, participants train models using their local data, and the model updates are aggregated into a global model. However, ensuring trustworthy model training is crucial because malicious participants may not use their actual local data or may not train the model as intended, which makes it challenging to guarantee the authenticity of the data and the integrity of the model training. To address these issues, we propose a trustworthy model training scheme (TMT-FL) with verifiable authenticity and integrity. Specifically, we leverage zero-knowledge succinct non-interactive argument of knowledge (zk-SNARK) based proofs to verify the integrity of the training execution. To deal with the performance bottleneck in generating zk-SNARK proofs, we use the Chinese Remainder Theorem to optimize the convolution operation, and present an improved zk-SNARK based proof generating scheme which significantly reduces the online proving time. Besides, we adopt matrix commitment along with bloom filter to ensure the authenticity and integrity of the training datasets. Extensive experimental results demonstrate that our improved zk-SNARK scheme performs nearly 3.1× faster than the state-of-the-art in online proving time. Moreover, we experimentally confirm the efficiency of TMT-FL under diverse datasets in terms of computational costs, storage costs, and communication overheads. © 2004-2012 IEEE.

关键词： Training Data Models Computational Modeling Security Data Integrity Data Privacy Costs Threat Modeling Kernel Gold Federated Learning Trustworthiness Model Training Authenticity Integrity Zk SNARK Federated Learning Trustworthy Model Malicious Participants Computational Cost Local Data Global Model Convolution Operation Updated Model Storage Cost Message Authentication Communication Overhead Performance Bottleneck Bloom Filter Convolutional Neural Network Data Integration Convolutional Layers Matrix Multiplication Convolutional Neural Network Model Dimensional Matrix Privacy Protection Local Training Local Updates Zero Knowledge Proof Integrity Verification MNIST Dataset Training Round Public Parameters Differential Privacy Sample Dimensions Training Subsets

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