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A trajectory-level robot skill learning method based on fuzzy systems

science China(Information sciences) 2025年第5期68卷 38-52页

作者： Chao ZENG Zhen DENG Boyang TI Yongsheng GAO Department of Computer Science University of Liverpool School of Mechanical Engineering and Automation Fuzhou University State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences State Key Laboratory of Robotics and System Harbin Institute of Technology

Learning from demonstration(LfD) allows for the effective transfer of human manipulation skills to a robot by building a model that represents these skills based on a limited number of demonstrated ***,a skilllearning model that can comprehensively satisfy multiple requirements,such as computational complexity,modeling accuracy,trajectory smoothness,and robustness,is still ***,this work aims to provide such a model by employing fuzzy ***,we introduce an LfD model named Takagi-Sugeno-Kang fuzzy system-based movement primitives(TSKFMPs),which exploits the advantages of the fuzzy theory for effective robotic imitation learning of human *** work formulates the TSK fuzzy system and gradient descent(GD) as imitation learning models,leveraging recent advancements in GD-based optimization for fuzzy *** study takes a two-step strategy.(ⅰ) The input-output relationships of the model are established using TSK fuzzy systems based on demonstration *** this way,the skill is encoded by the model parameter in the latent space.(ⅱ) GD is used to optimize the model parameter to increase the modeling accuracy and trajectory *** further explain how learned trajectories are adapted to new task scenarios through local *** conduct multiple tests using an open dataset to validate our method,and the results demonstrate performance comparable with those of other ***,we implement it in a real-world case study.

关键词： robot skill learning movement primitives TSK fuzzy systems gradient descent robotics

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Privacy Enhanced Mobile User Authentication Method Using Motion Sensors

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computer Modeling in engineering & sciences 2024年第3期138卷 3013-3032页

作者： Chunlin Xiong Zhengqiu Weng Jia Liu Liang Gu Fayez Alqahtani Amr Gafar Pradip Kumar Sharma Shenzhen Institutes of Advanced Technology Chinese Academy of SciencesShenzhen518052China Sangfor Technologies Inc. Shenzhen518055China School of Data Science and Artificial Intelligence Wenzhou University of TechnologyWenzhou325035China College of Computer Science&Technology Zhejiang University of TechnologyHangzhou310023China Software Engineering Department College of Computer and Information SciencesKing Saud UniversityRiyadh12372Saudi Arabia Math&Computer Science Department Faculty of ScienceMenofia UniversityShebin El-KomEgypt Computing Science Department University of AberdeenAberdeenUK

With the development of hardware devices and the upgrading of smartphones,a large number of users save privacy-related information in mobile devices,mainly smartphones,which puts forward higher demands on the protection of mobile users’privacy *** present,mobile user authenticationmethods based on humancomputer interaction have been extensively studied due to their advantages of high precision and non-perception,but there are still shortcomings such as low data collection efficiency,untrustworthy participating nodes,and lack of *** this end,this paper proposes a privacy-enhanced mobile user authentication method with motion sensors,which mainly includes:(1)Construct a smart contract-based private chain and federated learning to improve the data collection efficiency of mobile user authentication,reduce the probability of the model being bypassed by attackers,and reduce the overhead of data centralized processing and the risk of privacy leakage;(2)Use certificateless encryption to realize the authentication of the device to ensure the credibility of the client nodes participating in the calculation;(3)Combine Variational Mode Decomposition(VMD)and Long Short-TermMemory(LSTM)to analyze and model the motion sensor data of mobile devices to improve the accuracy of model *** experimental results on the real environment dataset of 1513 people show that themethod proposed in this paper can effectively resist poisoning attacks while ensuring the accuracy and efficiency of mobile user authentication.

关键词： Mobile authentication blockchain federated learning smart contract certificateless encryption VMD LSTM

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Utilizing the Prophet Model for Time Series Based Stock Price Forecasting 5

Utilizing the Prophet Model for Time Series Based Stock Pric...

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5th International Conference on Electronics and Sustainable Communication Systems, ICESC 2024

作者： Pant, Janmejay Pant, Hitesh Kumar Kotlia, Priyanshi Singh, Devendra Sharma, Vaibhav Kumar Bhatt, Jaishankar Graphic Era Hill University Department of Computer Science and Engineering Bhimtal Campus India Kumaun University Department of Management Studies Uttarakhand Nainital India Amrapali Group of Institutes Department of Computer Applications Uttarakhand Haldwani India Graphic Era Deemed to Be University Department of Computer Science and Engineering Uttarakhand Dehradun India

ISBN: (纸本)9798350379945

Time series analysis and forecasting play a crucial role due to their extensive application across various practical domains. Time series data represents a sequential order or collection of data points depicting the values of a variable at regular time intervals. The stock market is widely regarded as one of the most intricate financial systems, comprising numerous components or stocks, whose prices fluctuate significantly over time. Stock market forecasting aims to reveal the trends within the market as they evolve. All investors in the stock market strive to maximize returns on their investments while minimizing associated risks Due to the high sensitivity and rapid changes in stock markets, the primary objective of stock-trend prediction is to devise innovative approaches that can anticipate stocks leading to substantial profits. A statistical model capable of predicting Indian stock prices will be constructed using time series data from the Indian stock market. In our study, we utilize daily stock price data from a prominent company listed on the National Stock Exchange (NSE) of India. © 2024 IEEE.

关键词： Marketplaces

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An Evolutionary Multitasking Algorithm for Efficient Multiobjective Recommendations

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年第3期6卷 518-532页

作者： Tian, Ye Ji, Luke Hu, Yiwei Ma, Haiping Wu, Le Zhang, Xingyi Anhui University Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and Technology Hefei230601 China Anhui University Institutes of Physical Science and Information Technology Hefei230601 China Hefei University of Technology Key Laboratory of Knowledge Engineering with Big Data Hefei230029 China

Represented by evolutionary algorithms and swarm intelligence algorithms, nature-inspired metaheuristics have been successfully applied to recommender systems and amply demonstrated effectiveness, in particular, for multiobjective recommendation. Owing to the population-based search paradigm, these algorithms can produce a number of recommendation lists, making diverse tradeoffs between multiple metrics and meeting the requirements of accuracy, novelty, diversity, and other user preferences. However, these algorithms are criticized for the low efficiency of the optimization process, especially when the number of users is large. To address this issue, this article proposes an evolutionary multitasking-based recommendation method, where each task corresponds to a user and all the tasks are optimized simultaneously, thus highly improving the efficiency of recommendation. To enhance the convergence speed, all the users are divided into multiple populations according to the similarity between their preferences, where each population evolves with internal knowledge transfer between users, and all the populations evolve with external knowledge transfer between populations. Experimental results on various datasets verify that the proposed method can better balance between multiple metrics than classical and deep neural network-based recommendation methods and exhibits significantly higher efficiency than evolutionary multiobjective optimization-based recommendation methods. © 2024 IEEE. All rights reserved.

关键词： Multiobjective optimization

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Lung Cancer Detection Through Filtration and Segmentation via Neural-Network 3

Lung Cancer Detection Through Filtration and Segmentation vi...

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3rd International Conference on Advancement in Electronics and Communication engineering, AECE 2023

作者： Raj, Mrinal Sahare, Parul Pandey, Nachiketa Kalinga Institute of Industrial Technology Department of Computer Science and Engineering Bhubaneswar India Indian Institutes of Information Technology Department of Electronics and Communication Engineering Nagpur India

ISBN: (纸本)9798350330724

Cancer is a threat in which the cell starts to grow abnormally and it does not function like a normal cell. When the cancerous cell begin to develop in lungs it is called as lung cancer. Lung cancer is one of the most significantly diagnosed form of *** is getting hard for many doctors to state the conclusion after looking at the Computed Tomography(CT) image. So, there is a need to detect lung cancer at an early stage possible. An improvised version of VGG-19 Model is proposed for detection of lung cancer in this paper. At first filtration an d segmentation of lesion from CT scan images of lungs is performed. Median and Gaussian filters a re included i n t he fi ltration me thod whereas, segmentation work involves the Gaussian mixture model, Fuzzy-C-Means and proposed mean-based segmentation. The proposed work is evaluated on the publically available Data Bowl 2017 database and compared with other state-of-the-art approaches. It is observed that in the filtration part, Gaussian filter provides the highest accuracy of 99.02% where as mean-based segmentation outperforms other methods with an accuracy of 94.13%. © 2023 IEEE.

关键词： computerized tomography

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Botanic Alchemy: Cultivating Insight through Extensive Plant Disease Dataset and Reweighted Visual Analysis 2

Botanic Alchemy: Cultivating Insight through Extensive Plant...

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2nd International Conference on computer, Communication and Control, IC4 2024

作者： Mustafa Nawaz, S.M. Maharajan, K. Anand, J. Vijay Malathi, G. School of Computing Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Tamil Nadu India J.N.N Institutes of Engineeriung Department of Electronics and Communication Engineering Tamil Nadu India Sri Sai Ram Institute of Technology Department of Computer Science and Engineering Kanchipuram Tamil Nadu Chennai India

ISBN: (纸本)9798350387933

Diagnosing plant diseases research tackles the vital challenge of identifying plant diseases in agriculture by focusing on the intricate complexities found in images displaying diverse symptoms, scattered lesions, and complex backgrounds. To aid this endeavor, a substantial dataset of 220,592 images is assembled. The proposed method uses a reweighting strategy that targets both visual regions and loss. Initially, weights for segmented patches are calculated based on their discriminative potential obtained from cluster distributions. These weights are then seamlessly integrated into the training process, assigning them to individual patch-label pairs during weakly-supervised training, aiding in recognizing disease-specific regions. Features extracted from this trained network create a comprehensive feature representation. This integration incorporates the GRU algorithm to encode the sequence of weighted patch features. Thorough evaluations on the newly curated dataset and a public dataset demonstrate the effectiveness and superiority of this methodology. This research significantly advances the field of plant disease recognition in image processing, opening doors for further innovative exploration. © 2024 IEEE.

关键词： Learning algorithms

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PotatoLeafGuard: A Novel Hybrid CNN Architecture for Advanced Detection of Potato Leaf Diseases

PotatoLeafGuard: A Novel Hybrid CNN Architecture for Advance...

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Emerging Innovations and Advanced Computing (INNOCOMP), International Conference on

作者： Meenakshi Sharma Raman Babal Naman Sharma Varun Navpreet Department of Computer Science Engineering Global Group of Institutes Amritsar India

ISBN: (数字)9798350376470

ISBN: (纸本)9798350376487

Potatoes assume a crucial role as a global food crop, contending with challenges posed by various pathogens affecting their growth. Ranked as the third-largest agricultural food crop, potatoes are indispensable for delivering essential nutrients. One significant threat to potato health arises from leaf diseases. The accurate detection of these diseases is imperative for effective intervention. This research introduces a novel hybrid model integrating deep learning and transfer learning to predict the risks associated with potato leaf diseases. To train the model, a dataset comprising 3,076 images categorized into seven classes is used. These classes delineate various types of leaf diseases, including those affected by viruses, bacteria, fungi, pests, nematodes, phytophthora, and healthy leaves. The methodology leverages Convolutional Neural Networks (CNNs), recognized for their efficacy in identifying potato leaf diseases through image analysis. To enhance accuracy, Transfer Learning algorithms such as VGG16 and Xception are incorporated. This hybrid model addresses the intricate classification challenges arising from the distinct characteristics and data limitations inherent in potato leaf diseases. Furthermore, data augmentation techniques are employed in conjunction with CNNs to mitigate classification challenges associated with unique features and limited data. The stacking of outputs from these models substantially enhances the accuracy of potato leaf disease detection. In summary, this research endeavors to provide a novel methodology for the precise classification of diverse types of potato leaf diseases, contributing to improved crop management and food security. The proposed model achieves an overall classification accuracy of 94.70%, accompanied by commendable precision, recall, and F1-score values of 93.75%, 93.6%, and 92.75%, respectively. These results validate the model's efficacy in advancing research on potato leaf disease detection, attesting to its

关键词： Technological innovation Pathogens Accuracy Transfer learning Stacking Crops Predictive models

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Lung Cancer Detection: A Machine Learning Approach for Early Diagnosis 8

Lung Cancer Detection: A Machine Learning Approach for Early...

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8th International Conference on Electronics, Communication and Aerospace Technology, ICECA 2024

作者： Joshi, Praveen Kumar Prakash, Rahul Singh, Devendra Dhanik, Saurabh Tamta, Abhishek Sharma, Vaibhav Kumar Graphic Era Hill University School of Computing Department Bhimtal India National Institutes of Technology Department of Electronics Engineering Srinagar India Graphic Era Hill University Department of Computer Science and Engineering Bhimtal India

ISBN: (纸本)9798350367904

Lung cancer poses a significant health challenge, with early detection being crucial for better outcomes. Traditional diagnostic methods have limitations, leading to missed diagnoses. Machine learning offers promise in revolutionizing disease detection by leveraging data and advanced algorithms. This paper explores using machine learning for early lung cancer detection. Sophisticated algorithms enable accurate diagnostic systems, improving efficiency and scalability. Challenges like data quality and interpretability also need to be addressed. This study evaluates machine learning algorithms on real datasets to assess their effectiveness in clinical practice, contributing to the field of healthcare applications. We have found highest accuracy (96.77%), precision (98.33%), recall (98.33%) and F1-score (98.33%) in the Random Forest classifier. © 2024 IEEE.

关键词： Lung cancer

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CHALLENGES AND CONCEPT FOR AN INTEROPERABLE DIGITAL TWIN ARCHITECTURE FOR PV-INTEGRATED HEATING NETWORKS

CHALLENGES AND CONCEPT FOR AN INTEROPERABLE DIGITAL TWIN ARC...

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2023 ISES Solar World Congress, SWC 2023

作者： Paredes, Theo Papadimitriou, Melachroinos Timoudas, Thomas Ohlson Peng, Zeng Wang, Qian KTH Royal Institute of Technology Department of Civil and Architectural Engineering Stockholm Sweden RISE Research Institutes of Sweden Department of Computer Science Stockholm Sweden University of Toulouse IMT Mines Albi Albi France University of Thessally Department of Electrical and Computer Engineering Volos Greece

ISBN: (纸本)9783982040899

Existing data-driven approaches are not always supported, or only partly implemented, by existing tools, platforms, and services. The study focuses on proposing a novel, theoretical and general model to describe the heating network of buildings. This study will consolidate and abstract access to standardized interfaces, using a transparent interoperability mechanism, the "meta" interface, supporting translation modules to translate from different components or hardware that are commonly used in current heating networks of buildings in Europe. The ICT architecture will be based on state-of-the-art digital twin technologies and methodologies, offering a unified ecosystem for orchestrated deployment of different input/output configurations, and data flow and energy exchanges. This study will further validate the reference ICT architecture using data-driven approaches, applied in a use case that could be considered a typical low-temperature district heating network. © 2023. The Authors.

关键词： District heating

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IntelliCrop: Advanced Security in RPL-Enabled IoT Networks Through Machine Learning

IntelliCrop: Advanced Security in RPL-Enabled IoT Networks T...

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International Conference on Inventive Computation Technologies (ICICT)

作者： Mukul Shukla Lalji Prasad Department of Computer Science & Engineering Institutes of Advance Computing SAGE University Indore India

ISBN: (数字)9798331512248

ISBN: (纸本)9798331512255

Smart irrigation systems have emerged as a novel technology due to the influence of the Internet of Things (IoT) on contemporary agriculture. T he Internet of Things (IoT) is vulnerable to significant security threats due to its reliance on the Routing Protocol for Low-Power and Lossy Networks (RPL). Network performance may deteriorate, data integrity may be jeopardized, and automatic irrigation systems may be interrupted by attacks like rank, Sybil, blackhole, and wormhole. This research presents an adaptive methodology named IntelliCrop, utilizing the XGBoost machine learning algorithm, which employs machine intelligence to enhance the security of RPL-based smart irrigation networks. IntelliCrop enables rapid categorization of network events, identification of errant or malicious users, and automated updates to routing tables, so ensuring optimal overall throughput throughout the process. Skymote devices, arbitrary topologies, and actual attack scenarios are employed to evaluate the system before to its implementation in IoT-based irrigation systems. In comparison to standard models like Random Forest and Decision Tree, our assessment results indicate that our model achieves superior accuracy (99.92%), perfect recall (100%), and a high F1-score (99.2%) on the test data. IntelliCrop enhances IoT-enabled irrigation networks through real-time threat assessment, adaptive delivery routing, and scalability, therefore safeguarding against routing threats and promoting dependable automated smart agricultural ecosystems.

关键词： Irrigation Adaptive systems Machine learning algorithms Biological system modeling Heuristic algorithms Routing Real-time systems Security Internet of Things Random forests

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