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International Journal of Cognitive Computing in engineering 2025年 6卷 462-476页

作者： Alsubai, Shtwai Mosleh, Mogeeb A.A. Al-Hadhrami, Suheer A. Bhatia, Munish College of Computer Engineering and Sciences Prince Sattam bin Abdulaziz University Alkharj Saudi Arabia Faculty of Engineering & Computing University of Science & Technology Aden Yemen Faculty of Engineering and Information Technology Taiz Univeristy Taiz Yemen Department of Computer Engineering College of Engineering Al Mukalla Hadhramout Yemen Department of Computer Applications National Institute of Technology Kurukshetra India

Desertification greatly affects land deterioration, farming efficiency, economic growth, and health, especially in Gulf nations. Climate change has worsened desertification, making developmental issues in the area even more difficult. This research presents an enhanced framework utilizing the Internet of Things (IoT) for ongoing monitoring, data gathering, and analysis to evaluate desertification patterns. The framework utilizes Bayesian Belief Networks (BBN) to categorize IoT data, while a low-latency processing method on edge computing platforms enables effective detection of desertification trends. The classified data is subsequently analyzed using an Artificial Neural Network (ANN) optimized with a Genetic Algorithm (GA) for forecasting decisions. Using cloud computing infrastructure, the ANN-GA model examines intricate data connections to forecast desertification risk elements. Moreover, the Autoregressive Integrated Moving Average (ARIMA) model is employed to predict desertification over varied time intervals. Experimental simulations illustrate the effectiveness of the suggested framework, attaining enhanced performance in essential metrics: Temporal Delay (103.68 s), Classification Efficacy—Sensitivity (96.44 %), Precision (95.56 %), Specificity (96.97 %), and F-Measure (96.69 %)—Predictive Efficiency—Accuracy (97.76 %) and Root Mean Square Error (RMSE) (1.95 %)—along with Reliability (93.73 %) and Stability (75 %). The results of classification effectiveness and prediction performance emphasize the framework's ability to detect high-risk zones and predict the severity of desertification. This innovative method improves the comprehension of desertification processes and encourages sustainable land management practices, reducing the socio-economic impacts of desertification and bolstering at-risk ecosystems. The results of the study hold considerable importance for enhancing regional efforts in combating desertification, ensuring food security, and formulatin

关键词： Life on land Climate action Desertification Internet of things Artificial neural network

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ASL-OOD:Hierarchical Contextual Feature Fusion with Angle-Sensitive Loss for Oriented Object Detection

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computers, Materials & Continua 2025年第2期82卷 1879-1899页

作者： Kexin Wang Jiancheng Liu Yuqing Lin Tuo Wang Zhipeng Zhang Wanlong Qi Xingye Han Runyuan Wen Northwest Institute of Mechanical and Electrical Engineering Xianyang712099China School of Information Engineering Chang’an UniversityXi’an710064China School of Computer Science and Technology Xidian UniversityXi’an710071China

Detecting oriented targets in remote sensing images amidst complex and heterogeneous backgrounds remains a formidable challenge in the field of object *** frameworks for oriented detection modules are constrained by intrinsic limitations,including excessive computational and memory overheads,discrepancies between predefined anchors and ground truth bounding boxes,intricate training processes,and feature alignment *** overcome these challenges,we present ASL-OOD(Angle-based SIOU Loss for Oriented Object Detection),a novel,efficient,and robust one-stage framework tailored for oriented object *** ASL-OOD framework comprises three core components:the Transformer-based Backbone(TB),the Transformer-based Neck(TN),and the Angle-SIOU(Scylla Intersection over Union)based Decoupled Head(ASDH).By leveraging the Swin Transformer,the TB and TN modules offer several key advantages,such as the capacity to model long-range dependencies,preserve high-resolution feature representations,seamlessly integrate multi-scale features,and enhance parameter *** improvements empower the model to accurately detect objects across varying *** ASDH module further enhances detection performance by incorporating angle-aware optimization based on SIOU,ensuring precise angular consistency and bounding box *** approach effectively harmonizes shape loss and distance loss during the optimization process,thereby significantly boosting detection *** evaluations and ablation studies on standard benchmark datasets such as DOTA with an mAP(mean Average Precision)of 80.16 percent,HRSC2016 with an mAP of 91.07 percent,MAR20 with an mAP of 85.45 percent,and UAVDT with an mAP of 39.7 percent demonstrate the clear superiority of ASL-OOD over state-of-the-art oriented object detection *** findings underscore the model’s efficacy as an advanced solution for challenging remote sensing object detection tasks.

关键词： Oriented object detection transformer deep learning

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Towards a blank design method for manufacturing big-tapered profiled ring disk by spinning-rolling process

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Chinese Journal of Aeronautics 2025年第1期38卷 70-86页

作者： Xuechao LI Lianggang GUO Xiaoqing CHEN Heng LI State Key Laboratory of Solidification Processing School of Materials Science and EngineeringNorthwestern Polytechnical UniversityXi’an 710072China School of Materials Science and Engineering Xinjiang UniversityUrumqi 830017China National Key Laboratory for Precision Hot Processing of Metals Harbin Institute of TechnologyHarbin 150001China CNPC Baoji Oilfield Machinery Co. LtdBaoji 721000China

The big-tapered profiled ring disk is a key component of engines for rockets and missiles.A new forming technology,as called spinning-rolling process,has been proposed previously for the high performance,high efficiency and low-cost manufacturing of the *** design is the key part of plastic forming process *** spinning-rolling process,the shape and size of the blank play a crucial role in process stability,deformation behavior and dimensional *** this work proposes a blank design method to determine the geometry structure and sizes of the *** mathematical model for calculating the blank size has been deduced based on volume conservation and neutral layer length invariance *** FE simulation and corresponding trial production of an actual big-tapered profiled ring disk show that the proposed blank design method is *** order to obtain a preferred blank,the influence rules of blank size determined by different deformation degrees(rolling ratio k)on the spinning-rolling process are revealed by comprehensive FE *** considering the process stability,circularity of the deformed ring disk and forming forces,a reasonable range of deformation degree(rolling ratio k)is recommended for the blank design of the new spinning-rolling process.

关键词： Blank design method Spinning-rolling process Big-tapered profiled ring disk Rolling ratio Intelligent FE simulation

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Clinical deep model to analyse medical multivariate time-series data for health diagnosis

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Cyber-Physical Systems 2025年第2期11卷 139-164页

作者： Almahadeen, Layth Vijay, Richa Shabaz, Mohammad Soni, Mukesh Singh, Pavitar Parkash Patel, Pavan Byeon, Haewon Department of Financial and Administrative Sciences Al- Balqa’Applied University Al-Salt Jordan Department of computer science Amity University UP Noida India Department of Computer Science Engineering Model Institute of Engineering and Technology Jammu Jammu and Kashmir India Department of Computer Science Engineering University Centre for Research & Development Chandigarh University Mohali India Department of Management Lovely Professional University Phagwara India Department of Computer Science Engineering Ahmedabad Institute of Technology Ahmedabad India Department of Digital Anti-Aging Healthcare Inje University Gimhae Korea Republic of

Clinical auxiliary decision-making is related to life and health of patients, so the deep model needs to extract the personalised representation of patients to ensure high analysis and prediction accuracy;and provide a basis for prediction conclusions. In this context, a clinical deep model proposed an interpretable assessment method of patient health status based on contextual learning of medical features, encoding the time-series features of each variable separately, and using a multi-head de-coordination self-attention mechanism for learning Relationships between different features;feature skip-connection encoding based on a compressed excitation mechanism is proposed to improve the sensitivity of the model. © 2024 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Decision making

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Advanced deep learning models for automatic detection of driver’s facial expressions, movements, and alertness in varied lighting conditions: a comparative analysis

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Multimedia Tools and Applications 2025年第6期84卷 2947-2983页

作者： Das, Shiplu Pratihar, Sanjoy Pradhan, Buddhadeb Computer Science and Engineering Indian Institute of Information Technology West Bengal Kalyani India University of Engineering and Management Kolkata West Bengal New Town India

Emotion recognition is vital in understanding human behavior and improving the driving experience. We present a systematic analysis of automatic detection of driver’s facial expressions and movements while crying, feeling sad, experiencing anger, drowsiness, fear and surprise to reduce road accident using a deep learning method in varied lighting conditions. The proposed approach aims to accurately detect and analyze driver emotions in real time, it would help in advancing the growth of smart driver assistance systems. To conduct our analysis, we create a comprehensive dataset comprising annotated facial images of drivers exhibiting crying, sadness, anger, drowsiness, fear and surprise. The dataset is carefully designed to encompass diverse lighting conditions, facial orientations, and driver demographics, ensuring the generalizability and robustness of the proposed method. The CNN-BiLSTM model is compared against the CNN, LSTM, CNN-LSTM, BiLSTM, and CNN-LSTM models in the research to perform precise localization of facial expressions and movements. The trial findings show how well our technique accurately identifies and evaluates a driver’s facial expressions. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Advanced driver assistance systems

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Fruit Quality Monitoring System using CNN 1

Fruit Quality Monitoring System using CNN

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1st International Conference on Intelligent Systems and Computational Networks, ICISCN 2025

作者： Ravikumar, B.N. Vidya, M.S. Gowda, Naveen Chandra NarendraBabu, C.R. Veena, H.N. Ramani, D. Roja Bms Institute of Technology and Management Department of Information Science and Engineering Bengaluru India Bms Institute of Technology and Management Department of Computer Science and Engineering Bengaluru India School of Computer Science and Engineering Reva University Bengaluru India Sjb Institute of Technology Department of Computer Science and Engineering Bengaluru India New Horizon College of Engineering Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798331529246

The grading of fruits relies on inspections, experiences, and observations, with a proposed system integrating machine learning techniques to assess fruit freshness. By analyzing 2D fruit portrayals based on shape and color, limitations arise as different fruits may share similar color, size, and shape values. To enhance accuracy, the system combines size, shape, and color analysis with Convolutional Neural Network (CNN) technology. Upon capturing the fruit image, properties like size, shape, and color are extracted, followed by CNN training and testing processes. The proposed work have implements UNet and En-UNet chosen over other architectures like ResNet or MobileNet for segmentation. This innovative approach utilizes CNN to extract comprehensive features, yielding promising results in fruit freshness detection with accuracy of 99% and loss rate of 0.0123. © 2025 IEEE.

关键词： Convolutional neural networks

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A Membership Inference and Adversarial Attack Defense Framework for Network Traffic Classifiers

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年第2期6卷 317-332页

作者： Liu, Guangrui Zhang, Weizhe Wang, Xurun King, Stephen Yu, Shui Harbin Institute of Technology School of Cyberspace Science Heilongjiang Harbin150001 China Peng Cheng Laboratory Guangdong Shenzhen518055 China Leland High school San JoseCA95120 United States University of Technology Sydney School of Computer Science UltimoNSW2007 Australia

Malicious traffic identification methods in intrusion detection systems have evolved from rule-based matching to machine learning. However, security risks such as membership inference and adversarial attacks hinder the practical deployment of machine learning-based network intrusion detection systems (ML-NIDSs). In this work, we design a defense framework called hierarchical differential privacy (HierarchicalDP) to safeguard ML-NIDS against membership inference and adversarial attacks. First, we analyze the principles of membership inference and adversarial attacks to find their correlation. Based on this, we propose the feature distribution security metric (FDSM) to measure the risk of membership inference and adversarial attacks on ML-NIDS. Then, we design the HierarchicalDP framework, which partitions network traffic sample features according to security levels and introduces distinct noise on each security level feature to satisfy FDSM, thus defensing against membership inference and adversarial attacks. Finally, we evaluate the defensive performance of the HierarchicalDP framework on two network traffic datasets and four machine-learning models. The HierarchicalDP defense framework, based on Laplace noise, reduces the success rate of membership inference from 64.9% to 54.4% (ineffective binary classification), the evasion rate of adversarial samples from 86.1% to 23.2%, and maintains model accuracy (ACC) fluctuations within 4.2%. Furthermore, the HierarchicalDP framework adjusts sample features without modifying the model, thereby not affecting the inference speed. HierarchicalDP offers efficient and convenient defenses for ML-NIDS deployed in a network. © 2020 IEEE.

关键词： Machine learning

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Prioritization and meta-heuristic approach for efficient congestion control in vehicular ad hoc networks (VANETs)

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International Journal of Information technology (Singapore) 2025年 1-9页

作者： Sharma, Swati Khanum, Saba Madhumala, R.B. Maroju, Abhilash Aggarwal, Saurabh Department of Computer Science and Engineering Thapar Institute of Engineering and Technology Punjab India Department of Computer Science and Engineering MSIT Janakpuri Delhi India Department of Computer Science and Engineering Dayanand Sagar Academy of Technology and Management (DSATM) Bengaluru India Department of Computer Science and Engineering University of Cumberlands Williamsburg United States Department of Information Technology University of the Cumberlands Williamsburg United States

Vehicular ad hoc networks (VANETs) have emerged as a key area of interest in the research community due to their wide range of applications. As the number of vehicles increases, VANETs encounter challenges with access control, which affects communication between the source vehicle and other vehicles. To overcome this issue, the prioritization and meta-heuristic-based congestion control (PMCC) scheme is proposed in the given paper. It addresses this congestion by prioritizing messages and dynamically adjusting the transmission rate of safety messages. In the prioritization phase, message priority is determined using both dynamic and static factors. Then, to control channel load within VANETs, the PMCC scheme employs a Tabu Search optimization (TSO) algorithm to adjust the transmission rate of beacon messages. Comparative analysis of the PMCC scheme with existing schemes such as urban vehicular broadCAST (UV-CAST), distributed-fair power adjustment for the vehicular environment (D-FPAV), and loss differentiation rate adaptation (LORA) is conducted based on metrics including reception probability, average delay, and vehicle coverage ratio. The results show the superior performance of the proposed PMMC scheme in all aspects. © Bharati Vidyapeeth's institute of computer Applications and Management 2025.

关键词： Decentralized congestion control (DCC) Distributed-fair power adjustment for the vehicular environment (D-FPAV) Loss differentiation rate adaptation (LORA) PMCC TSO Urban vehicular broadCAST (UV-CAST) VANETs

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PerfCam: Digital Twinning for Production Lines Using 3D Gaussian Splatting and Vision Models

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IEEE Access 2025年 13卷 85390-85406页

作者： Khan, Michel Gokan Guarese, Renan Johnson, Fabian Wang, Xi Vincent Bergman, Anders Edvinsson, Benjamin Romero, Mario Vachier, Jeremy Kronqvist, Jan KTH Royal Institute of Technology School of Engineering Sciences Stockholm Sweden KTH Royal Institute of Technology Digital Futures Stockholm Sweden KTH Royal Institute of Technology School of Electrical Engineering and Computer Science Stockholm Sweden AstraZeneca Slovakia KTH Royal Institute of Technology School of Industrial Engineering and Management Stockholm Sweden Linköping University Department of Science and Technology Sweden

We introduce PerfCam, an open source Proof-of-Concept (PoC) digital twinning framework that combines camera and sensory data with 3D Gaussian Splatting and computer vision models for digital twinning, object tracking, and Key Performance Indicators (KPIs) extraction in industrial production lines. By utilizing 3D reconstruction and Convolutional Neural Networks (CNNs), PerfCam offers a semi-automated approach to object tracking and spatial mapping, enabling highly accurate digital twins that capture real-time KPIs such as availability, performance, Overall Equipment Effectiveness (OEE), and rate of conveyor belts in the production line. We validate the effectiveness of PerfCam through a practical deployment within realistic test production lines in the pharmaceutical industry and contribute an openly published dataset to support further research and development in the field. The results demonstrate PerfCam’s ability to deliver actionable insights through its precise digital twin capabilities, underscoring its value as an effective tool for developing usable digital twins in smart manufacturing environments and extracting operational analytics. © 2013 IEEE.

关键词： Convolutional neural networks

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Diffusion Models in Low-Level Vision: A Survey

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IEEE Transactions on Pattern Analysis and Machine Intelligence 2025年第6期47卷 4630-4651页

作者： He, Chunming Shen, Yuqi Fang, Chengyu Xiao, Fengyang Tang, Longxiang Zhang, Yulun Zuo, Wangmeng Guo, Zhenhua Li, Xiu Tsinghua University Tsinghua Shenzhen International Graduate School Shenzhen518055 China Duke University Department of Biomedical Engineering DurhamNC27708 United States Shanghai Jiao Tong University MoE Key Lab of Artificial Intelligence AI Institute Shanghai200240 China Harbin Institute of Technology School of Computer Science and Technology Harbin150006 China Tianyijiaotong Technology Ltd. Suzhou215131 China

Deep generative models have gained considerable attention in low-level vision tasks due to their powerful generative capabilities. Among these, diffusion model-based approaches, which employ a forward diffusion process to degrade an image and a reverse denoising process for image generation, have become particularly prominent for producing high-quality, diverse samples with intricate texture details. Despite their widespread success in low-level vision, there remains a lack of a comprehensive, insightful survey that synthesizes and organizes the advances in diffusion model-based techniques. To address this gap, this paper presents the first comprehensive review focused on denoising diffusion models applied to low-level vision tasks, covering both theoretical and practical contributions. We outline three general diffusion modeling frameworks and explore their connections with other popular deep generative models, establishing a solid theoretical foundation for subsequent analysis. We then categorize diffusion models used in low-level vision tasks from multiple perspectives, considering both the underlying framework and the target application. Beyond natural image processing, we also summarize diffusion models applied to other low-level vision domains, including medical imaging, remote sensing, and video processing. Additionally, we provide an overview of widely used benchmarks and evaluation metrics in low-level vision tasks. Our review includes an extensive evaluation of diffusion model-based techniques across six representative tasks, with both quantitative and qualitative analysis. Finally, we highlight the limitations of current diffusion models and propose four promising directions for future research. This comprehensive review aims to foster a deeper understanding of the role of denoising diffusion models in low-level vision. © 1979-2012 IEEE.

关键词： Stochastic systems

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