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Leveraging feature fusion ensemble of VGG16 and ResNet-50 for automated potato leaf abnormality detection in precision agriculture

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Soft Computing 2025年第4期29卷 2263-2277页

作者： Trivedi, Amit Kumar Mahajan, Tripti Maheshwari, Tanmay Mehta, Rajesh Tiwari, Shailendra Department of Computer Science and Engineering Thapar Institute of Engineering and Technology Punjab Patiala147001 India

In the era of advancement in technology and modern agriculture, early disease detection of potato leaves will improve crop yield. Various researchers have focussed on disease due to different types of microbial infection in potato leaves using computer vision and machine learning approaches. In this paper, a data science approach for multiclass classification of potato normal and abnormal leaves due to fungal infection like early blight and late blight is performed using the ensembling of deep learning (DL) CNN models. Firstly, the performance of classification on potato disease is verified separately on VGG16 and ResNet-50 CNN models after pre-processing of the leaf dataset. The pre-processing includes noise removal and normalization. Further improvement in classification accuracy is achieved by the ensembling of VGG16 and ResNet-50 CNN models. The ensembling of CNN models is performed on the feature level by fusing features extracted using VGG16 and ResNet-50. From the experimental results, performed on publicly available datasets consisting of 2152 number of normal and abnormal images it is observed that the average classification accuracy of 98.22%, 96.16% and 95.68% is achieved using the proposed ensemble, VGG16 and ResNet-50 models respectively. The efficacy of the proposed approach (ensemble technique at feature level fusion) is verified in comparison with recently reported DL model-based approaches. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： Classification (of information)

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Constraint Learning-based Optimal Power Dispatch for Active Distribution Networks with Extremely Imbalanced Data

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CSEE Journal of Power and Energy Systems 2024年第1期10卷 51-65页

作者： Yonghua Song Ge Chen Hongcai Zhang State Key Laboratory of Internet of Things for Smart City and Department of Electrical and Computer Engineering University of MacaoMacao 999078China School of Electrical and Computer Engineering Purdue UniversityWest LafayetteIN 47907 USA

Transition towards carbon-neutral power systems has necessitated optimization of power dispatch in active distribution networks(ADNs)to facilitate integration of distributed renewable *** to unavailability of network topology and line impedance in many distribution networks,physical model-based methods may not be applicable to their *** tackle this challenge,some studies have proposed constraint learning,which replicates physical models by training a neural network to evaluate feasibility of a decision(i.e.,whether a decision satisfies all critical constraints or not).To ensure accuracy of this trained neural network,training set should contain sufficient feasible and infeasible ***,since ADNs are mostly operated in a normal status,only very few historical samples are ***,the historical dataset is highly imbalanced,which poses a significant obstacle to neural network *** address this issue,we propose an enhanced constraint learning ***,it leverages constraint learning to train a neural network as surrogate of ADN's ***,it introduces Synthetic Minority Oversampling Technique to generate infeasible samples to mitigate imbalance of historical *** incorporating historical and synthetic samples into the training set,we can significantly improve accuracy of neural ***,we establish a trust region to constrain and thereafter enhance reliability of the *** confirm the benefits of the proposed method in achieving desirable optimality and feasibility while maintaining low computational complexity.

关键词： Deep learning demand response distribution networks imbalanced data optimal power flow

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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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An Empirical Model Informed Neural Network Core Loss Predictor for Soft Magnetic Materials

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IEEE Transactions on Power Electronics 2025年第8期40卷 11257-11267页

作者： Rajput, Neha Sandhibigraha, Himanshu Bhusan Agrawal, Neeraj Iyer, Vishnu Mahadeva Indian Institute of Science Department of Electrical Engineering Bengaluru560012 India

Advancements in materials, improved modeling, and optimized design of power magnetic components are imperative to push the efficiency and power density bounds of power electronic converters. The MagNet Challenge was launched in 2023 as the first IEEE Power Electronics Society International Challenge on Design Methods in Power Electronics, to leverage data-driven approaches for modeling losses in power magnetics with a goal to "Upgrade the Steinmetz Equation."The nonlinear magnetic characteristics, material properties, tolerances introduced by material processing and manufacturing of ferrites, and circuit and component level nonidealities that impact core-loss measurements cannot be easily modeled using traditional methods. However, some of these effects can be captured using a neural network since they are good at deciphering the complex relationships between the input and output data. We presented an EMPirical model informed neural network (EMPINN) approach for core loss modeling of soft ferrites for the 2023 MagNet Challenge. In this work, we have radically improved our approach and reduced the size of our model by a factor of 20 to present an EMPINN core loss predictor. This article also delves deep into data preparation, choice of empirical models, custom training loss function, and how the neural network can find the localized fit Steinmetz parameters across a wide range of operating frequencies and peak flux-density ranges. The proposed model is compared with empirical and neural network-based approaches to showcase the benefits of combining these approaches. The proposed EMPINN core loss predictor outperforms state-of-the-art core loss models in terms of accuracy and size. © 1986-2012 IEEE.

关键词： Ferrites

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PierGuard: A Planning Framework for Underwater Robotic Inspection of Coastal Piers

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IEEE Transactions on Automation science and engineering 2025年 22卷 15941-15952页

作者： Wang, Pengyu Lin, Hin Wang Li, Jialu Wang, Jiankun Shi, Ling Meng, Max Q.-H Southern University of Science and Technology Shenzhen Key Laboratory of Robotics Perception and Intelligence Department of Electronic and Electrical Engineering Shenzhen China Hong Kong University of Science and Technology Department of Electronic and Computer Engineering Hong Kong Southern University of Science and Technology Jiaxing Research Institute Jiaxing China Hong Kong University of Science and Technology Department of Chemical and Biological Engineering Hong Kong The Chinese University of Hong Kong Department of Electronic Engineering Hong Kong University of Alberta Canada Department of Electrical and Computer Engineering Canada

Using underwater robots instead of humans for the inspection of coastal piers can enhance efficiency while reducing risks. A key challenge in performing these tasks lies in achieving efficient and rapid path planning within complex environments. Sampling-based path planning methods, such as Rapidly-exploring Random Tree* (RRT*), have demonstrated notable performance in high-dimensional spaces. In recent years, researchers have begun designing various geometry-inspired heuristics and neural network-driven heuristics to further enhance the effectiveness of RRT*. However, the performance of these general path planning methods still requires improvement when applied to highly cluttered underwater environments. In this paper, we propose PierGuard, which combines the strengths of bidirectional search and neural network-driven heuristic regions. We design a specialized neural network to generate high-quality heuristic regions in cluttered maps, thereby improving the performance of the path planning. Through extensive simulation and real-world ocean field experiments, we demonstrate the effectiveness and efficiency of our proposed method compared with previous research. Our method achieves approximately 2.6 times the performance of the state-of-the-art geometric-based sampling method and nearly 4.9 times that of the state-of-the-art learning-based sampling method. Our results provide valuable insights for the automation of pier inspection and the enhancement of maritime safety. (Video1). © 2004-2012 IEEE.

关键词： Motion planning

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Oversampling-Enhanced Feature Fusion-Based Hybrid ViT-1DCNN Model for Ransomware Cyber Attack Detection

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computer Modeling in engineering & sciences 2025年第2期142卷 1667-1695页

作者： Muhammad Armghan Latif Zohaib Mushtaq Saifur Rahman Saad Arif Salim Nasar Faraj Mursal Muhammad Irfan Haris Aziz Department of Computer and Information System Cleveland State UniversityOhio44115USA Department of Electrical Electronics and Computer SystemsCollege of Engineering and TechnologyUniversity of SargodhaSargodha40100Pakistan Electrical Engineering Department College of EngineeringNajran UniversityNajran61441Saudi Arabia Department of Mechanical Engineering College of EngineeringKing Faisal UniversityAl Ahsa31982Saudi Arabia Department of Mechanical Industrial and Energy System EngineeringUniversity of SargodhaSargodha40100Pakistan

Ransomware attacks pose a significant threat to critical infrastructures,demanding robust detection *** study introduces a hybrid model that combines vision transformer(ViT)and one-dimensional convolutional neural network(1DCNN)architectures to enhance ransomware detection *** common challenges in ransomware detection,particularly dataset class imbalance,the synthetic minority oversampling technique(SMOTE)is employed to generate synthetic samples for minority class,thereby improving detection *** integration of ViT and 1DCNN through feature fusion enables the model to capture both global contextual and local sequential features,resulting in comprehensive ransomware *** on the UNSW-NB15 dataset,the proposed ViT-1DCNN model achieved 98%detection accuracy with precision,recall,and F1-score metrics surpassing conventional *** approach not only reduces false positives and negatives but also offers scalability and robustness for real-world cybersecurity *** results demonstrate the model’s potential as an effective tool for proactive ransomware detection,especially in environments where evolving threats require adaptable and high-accuracy solutions.

关键词： Ransomware attacks cybersecurity vision transformer convolutional neural network feature fusion encryption threat detection

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Text-driven clothed human image synthesis with 3D human model estimation for assistance in shopping

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Multimedia Tools and Applications 2025年第1期84卷 167-200页

作者： Karkuzhali, S. Aasim, A. Syed StalinRaj, A. Department of Computer Science and Engineering Mepco Schlenk Engineering College Tamil Nadu Sivakasi India

Online shopping has become an integral part of modern consumer culture. Yet, it is plagued by challenges in visualizing clothing items based on textual descriptions and estimating their fit on individual body types. In this work, we present an innovative solution to address these challenges through text-driven clothed human image synthesis with 3D human model estimation, leveraging the power of Vector Quantized Variational AutoEncoder (VQ-VAE). Creating diverse and high-quality human images is a crucial yet difficult undertaking in vision and graphics. With the wide variety of clothing designs and textures, existing generative models are often not sufficient for the end user. In this proposed work, we introduce a solution that is provided by various datasets passed through several models so the optimized solution can be provided along with high-quality images with a range of postures. We use two distinct procedures to create full-body 2D human photographs starting from a predetermined human posture. 1) The provided human pose is first converted to a human parsing map with some sentences that describe the shapes of clothing. 2) The model developed is then given further information about the textures of clothing as an input to produce the final human image. The model is split into two different sections the first one being a codebook at a coarse level that deals with overall results and a fine-level codebook that deals with minute detailing. As mentioned previously at fine level concentrates on the minutiae of textures, whereas the codebook at the coarse level covers the depictions of textures in structures. The decoder trained together with hierarchical codebooks converts the anticipated indices at various levels to human images. The created image can be dependent on the fine-grained text input thanks to the utilization of a blend of experts. The quality of clothing textures is refined by the forecast for finer-level indexes. Implementing these strategies can result

关键词： Vector quantization

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Transforming Sustainable Aquaculture: Synergizing Fuzzy Systems and Deep Learning Innovations

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International Journal of Fuzzy Systems 2024年第8期26卷 2536-2552页

作者： Haobijam, Basanta Huang, Yo-Ping Chang, Yue-Shan Chang, Tsun-Wei Department of Electrical Engineering National Taipei University New Taipei City23741 Taiwan Department of Electrical Engineering National Penghu University of Science and Technology Penghu88046 Taiwan Department of Electrical Engineering National Taipei University of Technology Taipei10608 Taiwan Department of Computer Science and Information Engineering National Taipei University New Taipei City23741 Taiwan Department of Information and Communication Engineering Chaoyang University of Technology Taichung41349 Taiwan Department of Computer Science and Information Engineering HungKuo Delin University of Technology New Taipei City236302 Taiwan

Pisciculture encounters an array of intricate challenges that span disease management, preservation of water quality, prevention of genetic hybridization, ensuring the integrity of net systems, sourcing sustainable aquatic feed, and comprehending fish growth and reproductive dynamics. Addressing these multifaceted challenges necessitates a comprehensive research approach. This study employs an innovative synergy of fuzzy logic and deep learning techniques, resulting in a robust strategy to tackle these obstacles effectively. Fuzzy logic excels in assessing stressed fish conditions by handling inherent uncertainties. Simultaneously, YOLOv7 with fuzzy color enhancement (YOLOv7FCE) is used to detect damaged fish nets, thereby mitigating losses and upholding the integrity of the net infrastructure. This approach also leverages YOLOv7FCE for identifying Cobia fish within shoals, streamlining the identification process. Subsequently, DeepLabv3 is implemented to meticulously segment the recognized Cobia fish, facilitating precise measurements of their physical attributes. This comprehensive methodology yields profound insights into growth patterns and feeding tendencies within the confined aquatic environment. By embracing this approach, the research presents a versatile and adaptive framework that not only enhances our comprehension of piscine dynamics but also holds the potential to revolutionize the aquaculture industry. © The Author(s) under exclusive licence to Taiwan Fuzzy Systems Association 2024.

关键词： Fish

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Semi‑supervised contour‑driven broad learning system for autonomous segmentation of concealed prohibited baggage items

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Visual Computing for Industry,Biomedicine,and Art 2024年第1期7卷 1-18页

作者： Divya Velayudhan Abdelfatah Ahmed Taimur Hassan Muhammad Owais Neha Gour Mohammed Bennamoun Ernesto Damiani Naoufel Werghi Department of Electrical Engineering and Computer Sciences Center for Cyber‑Physical SystemsKhalifa University of Science and TechnologyAbu Dhabi 127788United Arab Emirates Department of Electrical Computer and Biomedical EngineeringAbu Dhabi UniversityAbu Dhabi 59911United Arab Emirates Department of Computer Science and Software Engineering the University of Western AustraliaPerthWA 6009Australia

With the exponential rise in global air traffic,ensuring swift passenger processing while countering potential security threats has become a paramount concern for aviation *** X-ray baggage monitoring is now standard,manual screening has several limitations,including the propensity for errors,and raises concerns about passenger *** address these drawbacks,researchers have leveraged recent advances in deep learning to design threatsegmentation ***,these models require extensive training data and labour-intensive dense pixelwise annotations and are finetuned separately for each dataset to account for inter-dataset ***,this study proposes a semi-supervised contour-driven broad learning system(BLS)for X-ray baggage security threat instance segmentation referred to as *** research methodology involved enhancing representation learning and achieving faster training capability to tackle severe occlusion and class imbalance using a single training routine with limited baggage *** proposed framework was trained with minimal supervision using resource-efficient image-level labels to localize illegal items in multi-vendor baggage *** specifically,the framework generated candidate region segments from the input X-ray scans based on local intensity transition cues,effectively identifying concealed prohibited items without entire baggage *** multi-convolutional BLS exploits the rich complementary features extracted from these region segments to predict object categories,including threat and benign *** contours corresponding to the region segments predicted as threats were then utilized to yield the segmentation *** proposed C-BLX system was thoroughly evaluated on three highly imbalanced public datasets and surpassed other competitive approaches in baggage-threat segmentation,yielding 90.04%,78.92%,and 59.44%in terms of mIoU on GDXray,SIXray,and Compass-XP,***,the lim

关键词： Baggage X-ray imagery Broad learning systems Threat detection Threat segmentation

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A Novel Tensor Decomposition-Based Efficient Detector for Low-Altitude Aerial Objects With Knowledge Distillation Scheme

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IEEE/CAA Journal of Automatica Sinica 2024年第2期11卷 487-501页

作者： Nianyin Zeng Xinyu Li Peishu Wu Han Li Xin Luo Department of Instrumental and Electrical Engineering Xiamen UniversityXiamen 361005China IEEE College of Computer and Information Science Southwest UniversityChongqing 400715China

Unmanned aerial vehicles(UAVs) have gained significant attention in practical applications, especially the low-altitude aerial(LAA) object detection imposes stringent requirements on recognition accuracy and computational resources. In this paper, the LAA images-oriented tensor decomposition and knowledge distillation-based network(TDKD-Net) is proposed,where the TT-format TD(tensor decomposition) and equalweighted response-based KD(knowledge distillation) methods are designed to minimize redundant parameters while ensuring comparable performance. Moreover, some robust network structures are developed, including the small object detection head and the dual-domain attention mechanism, which enable the model to leverage the learned knowledge from small-scale targets and selectively focus on salient features. Considering the imbalance of bounding box regression samples and the inaccuracy of regression geometric factors, the focal and efficient IoU(intersection of union) loss with optimal transport assignment(F-EIoU-OTA)mechanism is proposed to improve the detection accuracy. The proposed TDKD-Net is comprehensively evaluated through extensive experiments, and the results have demonstrated the effectiveness and superiority of the developed methods in comparison to other advanced detection algorithms, which also present high generalization and strong robustness. As a resource-efficient precise network, the complex detection of small and occluded LAA objects is also well addressed by TDKD-Net, which provides useful insights on handling imbalanced issues and realizing domain adaptation.

关键词： Attention mechanism knowledge distillation(KD) object detection tensor decomposition(TD) unmanned aerial vehicles(UAVs)

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