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Remote Sensing Surveillance Using Multilevel Feature Fusion and Deep Neural Network

IEEE Access 2025年 13卷 38282-38300页

作者： Zahoor, Laiba Alhasson, Haifa F. Alnusayri, Mohammed Alatiyyah, Mohammed Alhammadi, Dina Abdulaziz Jalal, Ahmad Liu, Hui Department of Computer Science Air University Islamabad44000 Pakistan Department of Information Technology College of Computer Qassim University Buraydah52571 Saudi Arabia Department of Computer Science College of Computer and Information Sciences Jouf University Sakaka72388 Saudi Arabia Department of Computer Science College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz University Al-Kharj16278 Saudi Arabia Department of Information Systems College of Computer and Information Sciences Princess Nourah bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia Department of Computer Science and Engineering College of Informatics Korea University Seoul02841 Korea Republic of Cognitive Systems Laboratory University of Bremen Bremen28359 Germany

Human action recognition from aerial imagery poses significant challenges due to the dynamic nature of the scenes and the complexity of human movements. In this paper, we present an enhanced system that combines YOLO for human detection with a complete multilevel feature fusion approach to improve recognition of human actions in drone-captured photos. Our system presents a reliable drone-based human action system through the integration of state-of-the-art methods for multilevel feature extraction and object detection. Initially, frames are extracted individually from drone footage sequences. Preprocessing techniques, which include Gaussian blur, grayscale conversion, and background removal, are used for every frame to improve image quality and feature reliability. For object detection, we effectively locate and recognize human subjects in these aerial frames using YOLO approach. Afterward, the framework extracts 14 body landmarks that represent the shape of the human body by keypoint extraction. Four significant features are employed to capture the complexity of human movement effectively: the incorporation of 3D point cloud data adds depth to the image and makes it feasible to construct a more detailed three-dimensional representation;measuring the angles between keypoints provides significant details on joint orientations which are essential for posture analysis;and geodesic distance measure the shortest paths along the surface of the body to provide useful insight into the spatial relationships between keypoints. The extracted features are optimized by using quadratic discriminant analysis. In the end, a deep neural network was trained to perform the action classification. Three benchmark datasets, the UAV Gesture, UAV Human, and UCF-ARG datasets, were used for our experiments and system testing. Our model achieved corresponding action recognition accuracy values of 90.15%, 72.37%, and 76.50% on each of these datasets. © 2013 IEEE.

关键词： Drones

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Implementation on the Price Prediction and Analysis of Chicken Breast with Deep Learning Techniques 14th

Implementation on the Price Prediction and Analysis of C...

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14th International Conference on Frontier Computing, FC 2024

作者： Liu, Chao-Hung Chan, Yu-Wei Lin, Po-Yen Chang, Chih-Hung Yang, Chao-Tung Department of Computer Science and Information Management Providence University Taichung Taiwan Department of Computer Science and Information Engineering Providence University Taichung Taiwan Department of Computer Science and Communication Engineering Providence University Taichung Taiwan Department of Computer Science Tunghai University Taichung City Taiwan

ISBN: (纸本)9789819623945

The Taiwanese livestock industry grapples with escalating costs and challenges such as labor shortages, environmental changes, pollution, and disease threats. Trade liberalization and cheaper imports further highlight the need for industry modernization. In slaughter and food processing, predicting prices requires careful consideration of operational costs and pricing strategies. To analyze historical chicken meat prices, we compared ARIMA and LSTM models, common in time series forecasting. Additionally, we introduced STL-LSTM, combining STL and LSTM. These aimed for accurate real-time price predictions, with MSE used to measure accuracy. The study found that the LSTM model exhibited high predictive accuracy with the available data. Across various prediction horizons (1, 3, and 6 steps), the root mean squared error (RMSE) values were 3.09, 3.27, and 3.34, respectively, and the mean absolute percentage error (MAPE) values were 2.29, 2.95, and 2.92, respectively. These results surpassed those of the STL-LSTM and ARIMA models significantly. The LSTM model demonstrated capability in anticipating and signaling abnormal price fluctuations, offering valuable insights for pricing decisions within the industry. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Deep learning

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Zero Trust Networks: Evolution and Application from Concept to Practice

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computers, Materials & Continua 2025年第2期82卷 1593-1613页

作者： Yongjun Ren Zhiming Wang Pradip Kumar Sharma Fayez Alqahtani Amr Tolba Jin Wang School of Computer Science School of Cyber Science and EngineeringEngineering Research Center of Digital ForensicsMinistry of EducationNanjing University of Information Science and TechnologyNanjing210044China Department of Computing Science University of AberdeenAberdeenAB243FXUK Software Engineering Department College of Computer and Information SciencesKing Saud UniversityRiyadh12372Saudi Arabia Computer Science Department Community CollegeKing Saud UniversityRiyadh11437Saudi Arabia Sanya Institute of Hunan University of Science and Technology Sanya572024China

In the context of an increasingly severe cybersecurity landscape and the growing complexity of offensive and defen-sive techniques,Zero Trust Networks(ZTN)have emerged as a widely recognized *** Trust not only addresses the shortcomings of traditional perimeter security models but also consistently follows the fundamental principle of“never trust,always verify.”Initially proposed by John Cortez in 2010 and subsequently promoted by Google,the Zero Trust model has become a key approach to addressing the ever-growing security threats in complex network *** paper systematically compares the current mainstream cybersecurity models,thoroughly explores the advantages and limitations of the Zero Trust model,and provides an in-depth review of its components and key ***,it analyzes the latest research achievements in the application of Zero Trust technology across various fields,including network security,6G networks,the Internet of Things(IoT),and cloud computing,in the context of specific use *** paper also discusses the innovative contributions of the Zero Trust model in these fields,the challenges it faces,and proposes corresponding solutions and future research directions.

关键词： Zero trust cybersecurity software-defined perimeter micro-segmentation internet of things

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On Shaping Gain of Multidimensional Constellation in Linear and Nonlinear Optical Fiber Channel

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IEEE Journal on Selected Areas in Communications 2025年第5期43卷 1455-1468页

作者： Chen, Bin Liang, Zhiwei Lei, Yi Deng, JingXin Li, Shen Liga, Gabriele Hefei University of Technology School of Computer Science and Information Engineering Hefei China Chalmers University of Technology Department of Electrical Engineering Gothenburg Sweden Department of Electrical and Computer Engineering Québec Canada Eindhoven University of Technology Department of Electrical Engineering Eindhoven Netherlands

Utilizing the multi-dimensional (MD) space for constellation shaping has been proven to be an effective approach for achieving shaping gains. Despite there exists a variety of MD modulation formats tailored for specific optical transmission scenarios, there remains a notable absence of a dependable comparison method for efficiently and promptly re-evaluating their performance in arbitrary transmission systems. In this paper, we introduce an analytical nonlinear interference (NLI) power model-based shaping gain estimation method to enable a fast performance evaluation of various MD modulation formats in coherent dual-polarization (DP) optical transmission system. In order to extend the applicability of this method to a broader set of modulation formats, we extend the established NLI model to take the 4D joint distribution into account and thus able to analyze the complex interactions of non-iid signaling in DP systems. With the help of the NLI model, we conduct a comprehensive analysis of the state-of-the-art modulation formats and investigate their actual shaping gains in two types of optical fiber communication scenarios (multi-span and single-span). The numerical simulation shows that for arbitrary modulation formats, the NLI power and relative shaping gains in terms of signal-to-noise ratio can be more accurately estimated by capturing the statistics of MD symbols. Furthermore, the proposed method further validates the effectiveness of the reported NLI-tolerant modulation format in the literature, which reveals that the linear shaping gains and modulation-dependent NLI should be jointly considered for nonlinearity mitigation. © 1983-2012 IEEE.

关键词： Optical fibers

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An overview on IRS-enabled sensing and communications for 6G: architectures, fundamental limits, and joint beamforming designs

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science China(information sciences) 2025年第5期68卷 170-193页

作者： Xianxin SONG Yuan FANG Feng WANG Zixiang REN Xianghao YU Ye ZHANG Fan LIU Jie XU Derrick Wing Kwan NG Rui ZHANG Shuguang CUI School of Science and Engineering (SSE) Shenzhen Future Network of Intelligence Institute (FNii-Shenzhen)and Guangdong Provincial Key Laboratory of Future Networks of Intelligence The Chinese University of Hong Kong Department of Electrical Engineering City University of Hong Kong School of Information Engineering Guangdong University of Technology Key Laboratory of Wireless-Optical Communications Chinese Academy of Sciences School of Information Science and Technology University of Science and Technology of China Computer School Beijing Information Science and Technology University National Mobile Communications Research Laboratory School of Information Science and Engineering Southeast University School of Electrical Engineering and Telecommunications University of New South Wales School of Science and Engineering Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Department of Electrical and Computer Engineering National University of Singapore

This study presents an overview on intelligent reflecting surface(IRS)-enabled sensing and communication for the forthcoming sixth-generation(6G) wireless networks, in which IRSs are strategically deployed to proactively reconfigure wireless environments to improve both sensing and communication(S&C) performance. First, we exploit a single IRS to enable wireless sensing in the base station's(BS's) non-line-of-sight(NLoS) area. In particular, we present three IRS-enabled NLoS target sensing architectures with fully-passive, semi-passive, and active IRSs, respectively. We compare their pros and cons by analyzing the fundamental sensing performance limits for target detection and parameter estimation. Next, we consider a single IRS to facilitate integrated sensing and communication(ISAC), in which the transmit signals at the BS are used for achieving both S&C functionalities, aided by the IRS through reflective beamforming. We present joint transmit signal and receiver processing designs for realizing efficient ISAC, and jointly optimize the transmit beamforming at the BS and reflective beamforming at the IRS to balance the fundamental performance tradeoff between S&C. Furthermore, we discuss multi-IRS networked ISAC, by particularly focusing on multi-IRS-enabled multi-link ISAC, multi-region ISAC, and ISAC signal routing, respectively. Finally, we highlight various promising research topics in this area to motivate future work.

关键词： integrated sensing and communication (ISAC) intelligent reflecting surface (IRS) non-line-of-sight (NLoS) sensing sensing and communication tradeoff

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Unguided Machine Learning-Based Computation Offloading for Near-Memory Processing 38

Unguided Machine Learning-Based Computation Offloading for N...

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38th International Conference on VLSI Design, VLSID 2025

作者： Maity, Satanu Ghose, Manojit Kumar, Avinash Chakraborty, Anol Chakraborty, Ankit Indian Institute of Information Technology Department of Computer Science and Engineering Assam Guwahati India Jorhat Engineering College Department of Computer Science and Engineering Assam India

ISBN: (纸本)9798331522445

Recent data-intensive applications encounter memory wall bottlenecks in the traditional processor-centric computing architecture due to the need for frequent and extensive off-chip data movement. The emerging 3D-enabled near-memory processing (NMP) is a potential solution where computation can be offloaded and performed near memory, eliminating the need for expansive off-chip data transfer. Recently, some crucial works in this context have been proposed that use a supervised machine learning approach that requires a well-labeled dataset, which necessitates creating a time-consuming simulation process. Furthermore, their major focus was on profiling and offloading the entire application to the NMP side, causing a sub-optimal performance because of the low processing power on the NMP cores. This paper presents an unguided machine-learning-based approach (UCO) for computation offloading in NMP architecture that offloads only the memory-intensive regions of an application. The approach extracts several application performance characteristics and creates two clusters using them - one for the CPU side and another for the NMP side. The proposed approach's effectiveness is demonstrated through a series of comprehensive experiments with standard simulators and dataintensive benchmark applications. The results indicate that UCO improves performance (1.31x and 1.10x), saves energy (16% and 12%), and reduces off-chip data transfer (78% and 73%) compared to conventional and state-of-the-art approaches. © 2025 IEEE.

关键词： Unsupervised learning

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Evaluation of Reliability in Request-segregated Clouds through Remiadiations Predicting Hardware Failure Scheme

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Recent Patents on engineering 2025年第1期19卷 112-127页

作者： Sharma, Rohit Yadav, Vibhash Singh, Raghuraj Department of Computer Science and Engineering Dr. Ambedkar Institute of Technology for Handicapped U.P. Kanpur208024 India Department of Information Technology Rajkiya Engineering College Banda210201 India Department of Computer Science and Engineering Harcourt Butler Technical University Kanpur208002 India

Background: Cloud services have become a popular approach for offering efficient services for a wide range of activities. Predicting hardware failures in a cloud data center can minimize downtime and make the system more reliable and fault-tolerant. Objective: This research aims to analyze a predictive hardware failure model based on machine learning that anticipates the required remediations for undiagnosed failures in a cloud computing system serving multiclass requests. Methods: The model is tested on a carefully designed cloud data center that categorizes incoming requests as web, compute, storage, and dedicated server requests. To demonstrate improved reliability, a carefully designed test case is run on ReliaCloud-NS, which is a simulator for creating a CCS and computing its reliability. Results: The work found that using this model considerably enhanced the reliability of cloud computing systems when compared to not using the model. Conclusion: Although various estimation methods are patented to evaluate the system reliability of a cloud computing network, the emphasis of this study was mostly on improving the reliability of request-segregated clouds upon failing hardware resources like CPU, memory, bandwidth, and hard disc. Moreover, the prediction model might potentially be expanded to other system resources such as GPUs, software, and database packages. © 2025 Bentham science Publishers.

关键词： Cloud platforms

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PULSE: Progressive Utilization of Log-Structured Techniques to Ease SSD Write Amplification in B-epsilon-tree 25

PULSE: Progressive Utilization of Log-Structured Techniques ...

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30th Asia and South Pacific Design Automation Conference, ASP-DAC 2025

作者： Peng, Huai-De Chen, Yi-Shen Chen, Tseng-Yi Chang, Yuan-Hao Department of Computer Science and Information Engineering National Central University Taiwan Department of Electronic and Computer Engineering National Taiwan University of Science and Technology Taiwan Institute of Information Science Academia Sinica Taiwan

ISBN: (纸本)9798400706356

During the unprecedented expansion of global data, efficient storage solutions are essential for processing massive datasets stored on modern storage devices. B-epsilon-tree (Bϵ-tree) is one of the most well-known techniques that provides a write-optimized structure for database file systems. With the excellent access performance and high energy efficiency of solid-state drives (SSDs), they are expected to yield promising outcomes for large-scale data computation. However, their integration into storage systems has the challenges of write amplification, which impacts SSD endurance and reliability. This work identifies significant write amplification issues with Bϵ-tree implementations on SSDs due to the complicated management of key-value pairs. To mitigate the impact of write amplification, we propose PULSE, a novel scheme that rethinks Bϵ-tree designs by leveraging log-structured techniques optimized for SSDs. Moreover, PULSE integrates auxiliary indexing and a dual flush selector to minimize write amplification. Experimental results demonstrate that PULSE significantly mitigates write amplification by more than 62.6% on SSDs for the representative benchmarks compared to the Bϵ-tree. © 2025 Copyright is held by the owner/author(s). Publication rights licensed to ACM.

关键词： Flash-based SSDs

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A Comparative Analysis of LIME and SHAP Interpreters With Explainable ML-Based Diabetes Predictions

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IEEE Access 2025年 13卷 37370-37388页

作者： Ahmed, Shamim Kaiser, M. Shamim Shahadat Hossain, Mohammad Andersson, Karl Jahangirnagar University Institute of Information Technology Savar Dhaka1342 Bangladesh University of Chittagong Department of Computer Science and Engineering Chattogram4331 Bangladesh Luleå University of Technology Electrical and Space Engineering Department of Computer Science Skellefteå931 87 Sweden

Explainable artificial intelligence is beneficial in converting opaque machine learning models into transparent ones and outlining how each one makes decisions in the healthcare industry. To comprehend the variables that affect decision-making regarding diabetes prediction that can be accounted for by model-agnostic techniques. In this project, we investigate how to generate local and global explanations for a machine-learning model built on a logistic regression architecture. We trained on 253,680 survey responses from diabetes patients using the explainable AI techniques LIME and SHAP. LIME and SHAP were then used to explain the predictions produced by the logistic regression and Random forest-based model on the validation and test sets. With a discussion of future work, the comparative analysis and discussion of various experimental findings between LIME and SHAP are provided, along with their strengths and weaknesses in terms of interpretation. With a high accuracy of 86% on the test set, we used LR architecture with a spatial attention mechanism, demonstrating the possibility of merging machine learning and explainable AI to improve diabetes prediction, diagnosis, and treatment. We also focus on various applications, difficulties, and probable future directions of machine learning models for LIME and SHAP interpreters. © 2013 IEEE.

关键词： Forecasting

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Domain Adaptation for Vehicle Detection Under Adverse Weather

IEEE Open Journal of Intelligent Transportation Systems

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IEEE Open Journal of Intelligent Transportation Systems 2025年 6卷 568-578页

作者： Lin, Huei-Yung Huang, Yi-Chao Lai, Jing-Xian You, Ting-Ting National Taipei University of Technology Department of Computer Science and Information Engineering Taipei106 Taiwan National Chung Cheng University Department of Electrical Engineering Chiayi621 Taiwan

The images captured under varying illumination or adverse weather conditions exhibit distinct distributions in the high-dimensional feature space, hindering the performance of object detection networks. To address this issue, we propose a domain adaptation method based on adversarial learning. This approach ensures that extracted features have a similar distribution, even when input images originate from different data acquisition domains. Due to the lack of driving images recorded under a variety of weather conditions in existing datasets, we incorporate a semi-supervised learning framework to enhance detection performance by training with unlabeled images. Experimental results on public and our latest datasets demonstrate that the proposed adversarial learning technique surpasses recent traffic scene object detection networks across various driving scenarios. © 2020 IEEE.

关键词： Adversarial machine learning

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