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共 3334 条记录，以下是551-560 订阅

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Inverse Synthetic Aperture Radar Space Target recognition Algorithm Based on Few-Shot Class-Incremental learning 23

Inverse Synthetic Aperture Radar Space Target Recognition Al...

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4th international conference on Computing, Networks and Internet of things, CNIOT 2023

作者： Li, Chenxuan Zhu, Weigang Qiu, Hengde Department of Electronic and Optical Engineering Space Engineering University Department of Electronic and Optical Engineering Space Engineering University

ISBN: (纸本)9798400700705

Identifying the space targets rapidly and accurately is a significant mission in defending national rights and maintaining space security. the continuous emergence of new space targets has led to a dramatic decrease of the performance of Inverse Synthetic Aperture Radar (ISAR) automatic target recognition. this paper proposes a few-shot space target recognition method, which can adaptively excavate the new class features to improve the ISAR target recognition performance. Algorithm proposed in this paper facilitates existing data to predict unseen class and constructs the corresponding feature embedding space, which build the basis for unseen class recognition. Algorithm using memorizing complementary networks to extract global-focus features and local-focus features, which is conductive to increasing recognition accuracy while enhancing feature richness. To address the problem of catastrophic forgetting, this paper proposes an instance mining method based on inductive probability to extract hard-discriminative features at a deep level. Combing with these improving components mentioned above, algorithm of this paper can achieve compatibility and balance between new and old categories effectively. the experimental results demonstrate that algorithm proposed in this paper is superior to other advanced methods in the task of incremental space target recognition. © 2023 ACM.

关键词： Inverse problems

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A Robust Sparse Nonnegative Matrix Factorization with Low Rank representation for clustering 4

A Robust Sparse Nonnegative Matrix Factorization with Low Ra...

引用

4th international conference on machine learning and Computer Application, ICMLCA 2023

作者： Li, Xue Liu, Xinxin Li, Yiwei Zhang, Shuo School of Computer Science and Technology Anhui University of Technology Ma'anshan China Data Communication Product Line Huawei Technologies Co.Ltd Nanjing China College of Science Mathematics and Technology Wenzhou-Kean University Wenzhou China

ISBN: (纸本)9798400709449

Nonnegative Factorization Matrix has achieved excellent results in the field of data mining and machine learning. However, NMF and its extensions, use the least square error function, are sensitive to noises and simultaneously ignore some useful discriminant information in the error matrix. To tackle this issue, we propose a novel method, named Robust Sparse Nonnegative Matrix Factorization with Low Rank (RSLR-NMF), in this paper. RSLR-NMF can effectively remove any redundant information from the input data and extract the hidden information in the noisy error part based on Bilinear Error Matrix Decomposition (BEMD) module. RSLR-NMF takes into account reconstruction error matrix and low rank error matrix simultaneously with 2,1 norm constraint and low rank constraint, respectively. Besides, elegant updating rules are presented to solve the proposed method. the experimental results on several data sets show that the proposed RSLR-NMF provides more faithful basis factors and effective clustering results. © 2023 ACM.

关键词： Matrix algebra

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Protecting the IoT Devices from DDoS Attack Using Blockchain-Based Multi-code-Driven Trust Mechanism 1

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4th international conference on Computer Vision, High-Performance Computing, Smart Devices, and Networks, CHSN 2023

作者： Jayanarayana Reddy, D. Jahnavi, Addula Indhu, Pothula Latha, Mekala Hema Department of CSE G. Pullaiah College of Engineering and Technology Kurnool India Kurnool India

ISBN: (数字)9789819777945

ISBN: (纸本)9789819777938

the Internet of things (IoT) offers a promising platform for developing mission-critical programs. However, these programs often depend on a centralised storage architecture, which poses significant challenges such as security, privacy, and a single point of failure. Recently, blockchain technology has emerged as a key component in IoT application development. Blockchain can address issues related to data privacy, security, and third-party dependence in IoT applications. Both individuals and communities can benefit from integrating blockchain with IoT. Nonetheless, a DDoS attack on a mining pool in 2017 exposed significant vulnerabilities in blockchain-enabled IoT systems. Additionally, the data generated by these programs is vast. machine learning (ML) is an effective analytical tool that facilitates fully autonomous analysis and decision-making with large datasets. this research proposes a novel distributed intrusion detection system (IDS) utilising fog computing to detect DDoS attacks on mining pools in blockchain-enabled IoT networks, addressing the aforementioned challenges. It introduces an energy-efficient, decentralised, blockchain-based multi-mobile code-driven method for detecting internal threats in sensor node-powered IoT. the results indicate that the proposed approach outperforms existing alternatives. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Blockchain-based multi-mobile code driven solution Distributed Denial of Service Internet of things machine learning

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MambaCPU: Enhanced Correlation mining with State Space Models for CPU Performance Prediction

MambaCPU: Enhanced Correlation Mining with State Space Model...

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2025 IEEE international conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Liu, Xiaoman Intel Corporation Shanghai China

ISBN: (纸本)9798350368741

CPU performance prediction, which involves forecasting the performance scores of a CPU based on its hardware characteristics during the operation process, is a critical technology for computational system design and resource management. However, this research field currently faces two significant challenges. First, collecting real-world data is challenging due to the wide variety of CPU products on the market and the highly specialized nature of relevant hardware characteristics. Second, existing methods based on hardware simulation models or machine learning exhibit notable shortcomings, such as lengthy simulation test cycles, low prediction accuracy, and the ignoration of characteristic correlations. To bridge these gaps, we first collect, preprocess, and standardize historical data from the 4th Generation Intel Xeon Scalable Processors across multiple benchmark suites to create a new dataset, named PerfCastDB. Subsequently, we design a novel network MambaCPU (MaC) as the baseline for the PerfCastDB dataset. this model leverages the mamba structure to mine the global dependencies and correlations between multiple characteristics. the intra- and inter-group attention mechanisms are subsequently utilized to refine the correlations within and across the characteristic type groups. these techniques enhance the analysis and mining capability of MaC for the complex multivariate correlations. Comparative experiments on the PerfCastDB dataset demonstrate that MaC achieves superior results compared to existing methods, validating its effectiveness. Furthermore, we have open-sourced a portion of the dataset and the MaC code at https://***/xiaoman-liu/MaC to facilitate the subsequent research. © 2025 IEEE.

关键词： CPU Performance Prediction Deep learning Mamba State Space

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3rd international conference on Intelligent Edge Processing in the IoT Era, Edge-IoT 2022, and the 4th international conference on Smart Governance for Sustainable Smart Cities, SmartGov 2022

3rd International Conference on Intelligent Edge Processing ...

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3rd international conference on Intelligent Edge Processing in the IoT Era, Edge-IoT 2022, and the 4th international conference on Smart Governance for Sustainable Smart Cities, SmartGov 2022

ISBN: (纸本)9783031359811

the proceedings contain 12 papers. the special focus in this conference is on Intelligent Edge Processing in the IoT Era, and Smart Governance for Sustainable Smart Cities. the topics include: Continuous Measurement of Air Pollutant Concentrations in a Roadway Tunnel in Southern Italy;rating Urban Transport Services Quality Using a Sentiment Analysis Approach;edge Computing with Low-Cost Cameras for Object Detection in Smart Farming;Evaluating Maximum Operating Distance in COTS RFID TAGS for Smart Manufacturing;philippine Stock Direction Forecasting Utilizing Technical, Fundamental, and News Sentiment data;ioT Architectures for Indoor Radon Management: A Prospective Analysis;adversarial Training for Better Robustness;integrating Computer Vision and Crowd Sourcing to Infer Drug Use on Streets: A Case Study with 311 data in San Francisco;machine learning Approach to Crisis Management Exercise Analysis: A Case Study in SURE Project;quantitative Evaluation of Saudi E-government Websites Using a Web Structure mining Methodology.

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A Survey on Human Activity recognition Using Deep learning Techniques and Wearable Sensor data 4th

A Survey on Human Activity Recognition Using Deep Learning T...

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4th international conference on machine learning, Image Processing, Network Security and data Sciences (MIND)

作者： Dua, Nidhi Singh, Shiva Nand Challa, Sravan Kumar Semwal, Vijay Bhaskar Kumar, M. L. S. Sai Natl Inst Technol Jamshedpur Jharkhand India MANIT Bhopal Bhopal MP India Vignans Inst Informat Technol Visakhapatnam India

ISBN: (纸本)9783031243516;9783031243523

HAR has attained major attention because of its significant use in reallife scenarios like activity and fitness monitoring, rehabilitation, gaming, prosthetic limbs, healthcare, smart surveillance systems, etc. HAR systems provide ways for monitoring human behaviors and detecting body movements and various activities by using sensor data. the collection of sensors available in the mobile and other wearable devices hasmade most of these HAR applications easily possible. Moreover, Deep learning (DL) has further accelerated the research on HAR using the data obtained via wearable devices. In this paper, we have discussed the overview of HAR, its applications, and popular benchmark datasets available publicly. Further, we discussed various DL techniques applied for HAR applications. We have also presented the challenges associated with the field and the future directions for performing more vital research in HAR.

关键词： Wearable sensors pattern recognition HAR Deep learning

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Implementation of SARSA-HMM Technique for Face recognition 4th

Implementation of SARSA-HMM Technique for Face Recognition

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4th international conference on machine learning, Image Processing, Network Security and data Sciences (MIND)

作者： Yadav, Anil Kumar Shukla, Shiv Shankar Prasad Jain, Vikas Kumar Pateriya, Rajesh Kumar VIT Univ Sch Comp Sci & Engn Bhopal India Maulana Azad Natl Inst Technol Comp Sci Dept Bhopal India

ISBN: (纸本)9783031243660;9783031243677

SARSA (State-Action-Reward-State-Action) is a Markov Decision Process Strategy learning method (MDP). there can be discrete, permanent, stationary, time variable or noisy observations in real-time processes. the main difficulty is to characterize observations by estimating their parameters using a welldefined mathematical model as a parametric random process. the hiddenMarkov model is one such statistical model to interpret the (unobserved) process by examining the pattern of a sequence. HMM-based methods frequently used in the study and prediction of the most likely sequence of states, such as usage and activity patterns, as well as transitions between different process stages. In this paper, we proposed SARSA facial recognition technique used to select overlapping subsets from each image, which estimates the hidden attributes for each image. In the result section, represent that our proposed algorithm having better recognition rate than PCA. It can be used for solving complex feature selection and other solution for the development of further image.

关键词： Hidden Markov Models (HMM) Self-learning SARSA learning

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Classification of Distracted Male Driver Based on Driving Performance Indicator (DPI) 4th

Classification of Distracted Male Driver Based on Driving Pe...

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4th international conference on Innovative Manufacturing, Mechatronics and Materials Forum, iM3F2023

作者： Ganasan, Shatiskumar Sazali, Norazlianie Faculty of Manufacturing and Mechatronic Engineering Technology Universiti Malaysia Pahang Al-Sultan Abdullah Pahang Pekan26600 Malaysia

ISBN: (纸本)9789819988181

Distracted driving causes most road accidents and injuries. Cell phones, food, radios, and passenger conversations are all distractions. Distractions may slow a driver's response time and increase the risk of accidents. Studies reveal that even minor distractions may impair a driver's ability to drive safely. this study examines how distracted driving affects male drivers. Using US and Malaysian databases will do this. this research included drivers with at least two years of experience to guarantee a representative sample. Each dataset chose 35 and 58 drivers. Driver distraction level, a new class characteristic, has four levels: no, mild, moderate, and severe. Weka software was used for "data mining" to get insights from a vast dataset. Weka is a strong data mining and machine learning program including algorithms for data preparation, classification, regression, clustering, and visualization. We applied these algorithms on their datasets using its GUI or command-line parameters. Speed, braking, acceleration, steering, lane offset, lane position, and time were used to assess driving performance. Male drivers were more likely to be distracted driving based on their driving skills which is identified by the driving performance indicator (DPI). © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： data mining

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A Classified Mental Health Disorder (ADHD) dataset Based on Ensemble machine learning from Social Media Platforms 4th

A Classified Mental Health Disorder (ADHD) Dataset Based on...

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4th international conference on Trends in Cognitive Computation Engineering, TCCE 2022

作者： Mumu, Sabrina Mostafij Hoque, Hasibul Sakib, Nazmus Ahsanullah University of Science and Technology Dhaka Tejgaon1208 Bangladesh

ISBN: (纸本)9789811994821

machines in the modern world are becoming more intelligent. Recognizing patterns and categorizing enormous volumes of data into discrete values is one of the typical tasks performed by machine learning systems. Additionally, mental health illnesses are increasingly the most prevalent medical condition nowadays. Additionally, there are other ways that machines are utilized to identify mental health issues. In this study, machine learning is used as the baseline to diagnose the mental health condition known as Attention Deficit Hyperactivity Disorder (ADHD). An unsupervised dataset for ADHD was converted into a classified dataset. To get better results, an ensemble machine learning model made up of five classifiers has been deployed. After that, linguistic models were employed to improve the machine’s ability to predict. this recently created dataset offers the potential for future developments such as chatbots that can be developed to provide people with psychological support and the application of natural language processing to identify the behavior pattern of ADHD patients. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： machine learning

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Deep Insights into Mango Scab: An AI-Powered Approach for Detection and Classification 4

Deep Insights into Mango Scab: An AI-Powered Approach for De...

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4th international conference on Advances in Electrical, Computing, Communication and Sustainable Technologies, ICAECT 2024

作者： Kaur, Arshleen Kukreja, Vinay Rana, Deepak Singh Garg, Ashish Sharma, Rishabh Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Computer Science and Engineering Graphic Era Hill University Uttarakhand Dehradun248002 India Computer Science and Engineering Graphic Era Deemed to Be University Uttarakhand Dehradun248002 India

ISBN: (纸本)9798350343670

the mango, which comes from the plant genus Mangifera indica L., is a fruit crop that is well-regarded all over the world due to its scrumptious flavor and noteworthy nutritional profile. the illness known as mango scab, which is brought on by the fungus Elsinoe mangiferae, is a serious obstacle for the mango business. this disease causes the mango fruit to develop scaly lesions, which in turn results in decreased market value and yield losses. To effectively manage a disease, timely discovery and correct assessment of the severity of the condition are both essential. this study provides a hybrid deep learning (DL) model that combines a Convolutional Neural Network (CNN) for the detection of disease with a Support Vector machine (SVM) for the categorization of the severity of the disease. the model was trained and tested using a broad dataset of mango photos that included a range of disease severity levels as well as different varieties of mango. the model's performance in illness detection resulted in an overall accuracy of 95.93%, with scores of best precisions of 97.17% and best recall of 96.28%, respectively. When it came to the classification of severity, the SVM classifier was able to achieve an accuracy of 95.93%. these findings provide evidence that the model is capable of precisely identifying the degree of mango scab illness and demonstrating its usefulness in detecting the condition. To provide mango farmers with a flexible instrument, the hybrid method makes use of the capability of CNNs to extract features as well as the SVM's skill in classifying data. this research provides growers with up-to-date information that enables targeted disease management. As a result, mango crop yields are increased, and economic losses are decreased. © 2024 IEEE.

关键词： Agriculture Technology Deep learning Image-based recognition Mango Crop Mango Scab Disease Severity Detection

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