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检索条件"任意字段=International Conference on Methods and Models in Automation and Robotics"

共 24667 条记录，以下是291-300 订阅

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Advancing Agricultural Sustainability: Machine Learning-Driven Insights for Optimized Crop Yield Forecasting

Advancing Agricultural Sustainability: Machine Learning-Driv...

引用

automation and Computation (AUTOCOM), international conference on

作者： Nitish Kumar Chirag Sharma Tanya Nagpal Department of Computer science and Engineering Lovely Professional University Punjab India Mittal School of Business Lovely Professional University Punjab India

ISBN: (数字)9798331542375

ISBN: (纸本)9798331542382

Sustainable agriculture is crucial for addressing global challenges related to food security, resource efficiency, and environmental preservation. This study examines the potential of machine learning (ML) in forecasting crop production to enhance decision-making and promote sustainable agricultural practices. The research investigates advanced machine learning techniques, including Multilayer Perceptron Artificial Neural Networks (MLP ANN), Decision Trees, Voting Classifiers, and YOLO models, for accurate agricultural yield prediction. These models employ several data sources, encompassing climatic conditions, soil properties, and agricultural practices. Among the assessed models, the MLP ANN achieved a notable accuracy of 91%, illustrating its efficacy in predicting agricultural production. The results demonstrate how these predictive models aid farmers and policymakers in optimizing resource allocation, reducing waste, and aligning agricultural inputs with expected outputs, thereby decreasing environmental impact and improving economic outcomes. Essential insights highlight the effectiveness of data-driven methods in reducing uncertainty and enhancing agricultural productivity. The paper finishes by identifying barriers to the widespread adoption of ML-based solutions and outlining opportunities for integrating these technologies into sustainable agricultural systems, so offering a framework for future implementation.

关键词： YOLO Biological system modeling Decision making Artificial neural networks Predictive models Agriculture Real-time systems Decision trees Forecasting Crop yield

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Intelligent Intrusion Detection in IoT: Integrating Machine Learning and Feature automation

Intelligent Intrusion Detection in IoT: Integrating Machine ...

引用

international conference on Computer and automation Engineering, ICCAE

作者： Nazratun Naim Neha A. A. M. Muzahid Md Shamsuzzoha Imrul Jahid Hua Han Yujin Zhang Md Musabbir Hossain Ferdous Sohel School of Electronic and Electric Engineering Shanghai University of Engineering Science Shanghai China Hawaii Natural Energy Institute University of Hawaii at Manoa Honolulu HI USA School of Information Technology Murdoch University Murdoch WA Australia

ISBN: (数字)9798331533816

ISBN: (纸本)9798331533823

The rapid growth of the internet of things (IoT) raises serious security concerns, demanding effective protection from cyber-attacks. Intrusion detection depends on identifying key features, but despite advances in automation, manual feature selection remains necessary, limiting scalability. To address this limitation, we introduced a hybrid feature selection method that combines filter and wrapper techniques to automatically select important features and enhance the efficiency of machine learning (ML) models for intrusion detection tasks. We utilized the mutual information (MI) algorithm as the filter method and recursive feature elimination (RFE) as the wrapper method. We evaluated the performance of the proposed model on publicly available datasets, HIKARI2021 and UNSW-NB15. We compared the results with several existing methods, and our approach outperformed the state-of-the-art (SOTA) methods in terms of accuracy and training time. We presented comprehensive results, including both quantitative and qualitative analysis, to demonstrate the effectiveness and efficiency of our proposed methods.

关键词： Training automation Machine learning algorithms Limiting Intrusion detection Filtering algorithms Feature extraction Information filters Internet of Things Security

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Attack Detection in Smart Home IoT Networks: A Survey on Challenges, methods and Analysis 14th

Attack Detection in Smart Home IoT Networks: A Survey on Cha...

引用

14th EAI international conference on Broadband Communications, Networks, and Systems, BROADNETS 2024

作者： Rreddy, M. Vinay Kuma Lathigara, Amit Reddy, Muthangi Kantha RK University Gujarat India Computer Engineering RK University Gujarat India Shri Vishnu Engineering College for Women Andhra Pradesh India

ISBN: (纸本)9783031811678

The ubiquity of Internet of Things (IoT) gadgets in smart homes has transformed our interactions with our living environments by providing never-before-seen levels of automation and convenience. However, because IoT devices are becoming possible targets for malicious attacks, this broad connectivity also poses serious security risks. Ensuring the privacy, safety, and integrity of smart home ecosystems requires prompt detection and mitigation of these threats. Data from IoT devices is gathered, pre-processed, feature engineered, labelled, and divided into training, validation, and testing sets as part of a machine learning method to threat detection in smart home IoT networks. The process of choosing and training appropriate machine learning models—which can include everything from classification techniques to anomaly detection algorithms—is crucial. methods are surveyed to review different types of cyber-attacks, such as denial-of-service (DoS), distributed denial-of-service (DDoS), probing, user-to-root (U2R), remote-to-local (R2L), botnet attack, spoofing, and man-in-the-middle (MITM) attacks. To protect user information, data anonymization and encryption techniques are used with privacy considerations. Another strategy that has been put forth aims to improve the security of IoT networks in smart homes by providing a strong defence against new threats and equipping users with the information and resources they need to keep their connected world safe. To provide a full overview of the numerous advancements in this field, a list of all works published in the literature to date is incorporated. Lastly, the study also includes suggestions for future research directions. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2025.

关键词： Smart homes

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A Current Zero-Crossing Distortion Compensation Method Based on BP Neural Network In Fast-Scanning Power Supply

A Current Zero-Crossing Distortion Compensation Method Based...

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Electrical automation and Artificial Intelligence (ICEAAI), international conference on

作者： Shuxian Zhang Jiang Zhao Zhongzu Zhou Yuan He Huan Jia Yuanshuai Qin Shuai Zhang Ye Liu Anhui Feng Institute of Modern Physics Chinese Academy of Sciences University of Chinese Academy of Sciences Lanzhou China Electrical and Electromagnetic Compatibility Group Institute of Modern Physics Chinese Academy of Sciences Lanzhou China

ISBN: (数字)9798331506797

ISBN: (纸本)9798331506803

fast scanning power supply is utilized in many accelerator fields. Current waveform distortion caused by various reasons often occurs at the zero-crossing point, which reduce accuracy of output current. In order to optimize the distortion of current waveform, a waveform compensation method based on BP neural network of Bayesian regularization is introduced. Firstly, the reasons for the current zero-crossing distortion of fast scanning power supply are analyzed theoretically. Then a principle of optimization method is proposed and implemented. Finally, verification of compensation method is carried out on a sine-wave scanning power supply. The result shows that the compensation method can effectively suppress the current distortion. And it is also conducive to achieving the automation of power supplies.

关键词： Accuracy automation Power supplies Neural networks Optimization methods Manuals Predictive models Distortion Testing Intelligent control

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Multi-Task Model Fusion via Adaptive Merging

Multi-Task Model Fusion via Adaptive Merging

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international conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Luming Chen Ziwei Xiang Kai Lei Xu-Yao Zhang School of Artifcial Intelligence University of Chinese Academy of Sciences MAIS Institute of Automation of Chinese Academy of Sciences

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Multi-task model fusion (MTMF) aims to integrate the capabilities of individual models into a unified model. Past approaches either require extensive training and fine-tuning or necessitate that models share the same pre-training and initialization. Recently, several fusion methods have been proposed that do not require extensive training or fine-tuning. These methods can merge multiple independently trained models with different task capabilities into a single multi-task model without increasing the number of parameters. In this work, we identify a common flaw in these fusion methods: they tend to focus on how well the modules of the individual models match before merging while neglecting the representation bias of the merged model. To address this problem, we propose a simple yet effective mitigation method called adaptive merging by representation alignment (AdMbRA). Specifically, we improve the method of weight matching by using representation bias as a constraint and optimize the merging process. Experiments demonstrate that our method can effectively mitigate the representation bias of the merged model, thus improving the performance of each task.

关键词： Training Degradation Fault diagnosis Adaptation models Analytical models Prevention and mitigation Merging Signal processing Multitasking Speech processing

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Cognitive Flux Detection and Adaptive Emotional Modulation

Cognitive Flux Detection and Adaptive Emotional Modulation

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Advancements in Electrical, Electronics, Communication, Computing and automation (ICAECA), international conference on

作者： K. Mythili Nandhini R Trishla B Vasantha Kumar S Department of Information Technology Sri Krishna College of Technology

ISBN: (数字)9798331525439

ISBN: (纸本)9798331525446

The rapid rise of social networking sites has led to unusual usage patterns that contribute to various social network-related mental disorders, including Cyberrelationship Addiction, Information Overload, and Net Compulsion. These disorders often go unnoticed until they reach advanced stages, resulting in delayed intervention. To address this issue, this project introduces a new depression detection model that identifies signs of depression based on users' online social behavior, particularly through the analysis of tweets. Unlike traditional methods that depend on surveys completed by respondents, this model employs machine learning techniques to analyze social media data, facilitating the early detection of depressive ***, the model incorporates a classification technique that utilizes Term Frequency-Inverse Document Frequency (TF-IDF) for feature extraction. The tweets undergo preprocessing, which includes the removal of Unicode characters, and both KNN and SVM models are utilized to achieve accurate classification. Developed in Python 3.7, the system enhances the detection of depression, serving as an early intervention tool to initiate personalized mental health care. This approach introduces a new method that could serve as a regular and non-intrusive way to identify depressed states and related social network mental disorders by analyzing social media activity.

关键词： Support vector machines Analytical models Frequency modulation Social networking (online) Mental disorders Mental health Machine learning Nearest neighbor methods Depression Data models

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Impedance Modeling Method Considering the Phase of the Grid Connection Point in Grid-Forming and Grid-Following Converter Multi-Machine System

Impedance Modeling Method Considering the Phase of the Grid ...

引用

Electrical automation and Artificial Intelligence (ICEAAI), international conference on

作者： Yue Ma Ning Chen Yibin Tao Luming Ge China Electric Power Research Institute Nanjing China

ISBN: (数字)9798331506797

ISBN: (纸本)9798331506803

In recent years, the rapid growth of renewable energy sources such as wind and solar power has contributed to environmental improvements while introducing new challenges to the safe and stable operation of power systems. Impedance-based methods are widely used for small-signal stability analysis; however, conventional modeling approaches that neglect the grid connection point phase are increasingly inadequate as research on inverters advances. This paper presents a novel modeling method that accounts for the phase of the grid connection point. By incorporating the grid connection phase in the derivation process, the proposed approach ensures applicability even when the inverter phase is not constant. The method is applied to the grid-forming and grid-following converter multi-machine systems to investigate factors influencing its stability, thereby demonstrating the method's versatility and general applicability.

关键词： Analytical models Renewable energy sources automation Grid following Power system stability Stability analysis Impedance Matrix converters Artificial intelligence Grid forming

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Trust Assessment Model for Visual Image-Based Human-Robot Interaction Under Known and Unknown Threats 16th

Trust Assessment Model for Visual Image-Based Human-Robot I...

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16th international conference on Social robotics, ICSR + BioMed 2024

作者： Li, Heqing Li, Xinde Liu, Erfeng Ge, Shuzhi Sam School of Cyber Science and Engineering Southeast University Nanjing China School of Automation Southeast University Nanjing China Department of Electrical and Computer Engineering National University of Singapore Singapore Singapore

ISBN: (纸本)9789819789627

Visual imagery is a critical perceptual source in human-robot interaction (HRI), extensively applied in various domains such as robotic navigation, target detection, and task planning. Aerial robots, also known as UAVs, are the typical representatives of teleoperation robots. The substantial distance between humans and remotely operated robots, coupled with the dynamic and open environments these robots navigate, exposes image-based HRI to significant security risks from both known and unknown threats. The trustworthiness of data sources is a fundamental prerequisite for ensuring the security of HRI systems. However, the multidimensionality of trust factors and the uncertainty of trust evidence have constrained its practical application. To advance this field, we have innovatively proposed a trust evaluation scheme based on multidimensional evidence fusion within the Belief Functions (BFs) framework. This scheme constructs an adaptive trust evaluation model capable of addressing both known and unknown threats. For known threats, we developed coarse-grained multidimensional trust elements and employed multiple lightweight SVM submodels to construct Basic Belief Assignments (BBAs), thereby achieving direct trust modeling and enabling rapid trust evaluation of images. Conversely, for unknown threats, we utilized pretrained models to establish fine-grained multidimensional trust elements, implementing indirect trust modeling through trust recommendation, thus facilitating indirect trust evaluation of images. We further combined direct and indirect trust to derive a overall trust assessment of the images, achieving adaptive dynamic trust evaluation in image-based HRI. Additionally, to more accurately represent image trustworthiness, we introduced a BBAs weighted fusion method, which aids in more rational trust aggregation. To validate the efficacy of our proposed method, we conducted experiments on a real aerial image dataset. The results demonstrate that our approach ef

关键词： Human robot interaction

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Leveraging Cloud-Based Deep Learning for Cardiovascular Diseases Prediction: Challenges and Solutions

Leveraging Cloud-Based Deep Learning for Cardiovascular Dise...

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Advancements in Electrical, Electronics, Communication, Computing and automation (ICAECA), international conference on

作者： S. Kanagaraj Department of Information Technology Kumaraguru College of Technology Coimbatore India

ISBN: (数字)9798331525439

ISBN: (纸本)9798331525446

Cardiovascular diseases CVDs are a growing threat to global mortality therefore the need for accurate early prediction of diseases is imperative to improve patient outcome. The cloud-based deep learning model provides powerful CVD prediction solutions using large volumes of multi-structured medical data. But even then there are considerable challenges regarding data privacy, model interpretability, and effectiveness which inhibit effective application in clinics. The objective of this paper is to evaluate the role of explainable AI in the prediction of cardiovascular diseases using cloud based deep learning. Important shortcomings such as lack of model interpretability and trust are also discussed along with solutions to counter them. Here, XAI methods, for example SHAP and LIME may be used to enhance model interpretability. Clarifying the basis for AI predictions can enhance clinician's trust in AI-based decision making systems. Utilizing data augmentation techniques, multi-modal data integration, transfer learning, and other approaches can overcome data challenges and improve prediction outcomes. The paper further addresses the issues of data confidentiality and time multi-structured prediction by exploring federated learning and hybrid cloud models. This paper offers a new perspective in building XAI enhanced cardiovascular disease (CVD) predictive Deep Learning models through the integration of interpretability techniques and cloud infrastructure that will ultimately lead to better results, greater access to the systems, all while allowing the use of more personalized and effective healthcare methods.

关键词： Deep learning Accuracy Federated learning Explainable AI Transfer learning Data integration Medical services Predictive models Data models Cardiovascular diseases

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Major Depressive Disorder Detection using Dung Beetle Optimization based QGNN Classification

Major Depressive Disorder Detection using Dung Beetle Optimi...

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Advancements in Electrical, Electronics, Communication, Computing and automation (ICAECA), international conference on

作者： R.K Shalini V. Sujitha P. Parameswari Department of Information Technology Kumaraguru College of Technology Coimbatore TamilNadu India

ISBN: (数字)9798331525439

ISBN: (纸本)9798331525446

Major Depressive Disorder (MDD) is prevalent and incapacitating mental health disorder which mandates correct and early diagnosis for appropriate intervention. Conventional diagnostic methods are based on subjective assessments, often resulting in discrepancies and delayed treatment. Into our research, we offer novel framework based upon Quantum Graph Neural Networks (QGNN) for automatic detection of MDD, which focuses on leveraging deep learning and optimization techniques to improve accuracy in the diagnostic process. The DLC integrates QGNN with Dynamic Bayesian Optimization (DBO) for hyperparameter fine-tuning, enhancing model's predictive accuracy. Further, enhanced data preprocessing methods are applied, like Hanning Window function, for filtering out noise and enhancing input features. Experimental evaluations on the MDD patient dataset demonstrate that proposed approach achieves an accuracy of 86.62%, higher than existing graph-based models. Quantum-Inspired Schemes for Graph Learning and Optimization Techniques for NMPDD Classification and Recognition Expanding datasets, optimizing deep learning architectures, and incorporating clinical data are potential avenues for future research for further improving model generalization and diagnostic reliability. These conclusions specify a QGNN-DBO has probability to be powerful, scalable, and efficient solution for automatic MDD detection, potentially enhancing the landscape of mental health diagnostics and treatment planning

关键词： Deep learning Accuracy Data preprocessing Mental health Predictive models Depression Graph neural networks Data models Telecommunication computing Optimization

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