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检索条件"任意字段=4th International Conference on Intelligent Computing in Data Sciences, ICDS 2020"

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4th international conference on intelligent computing in data sciences, icds 2020

4th International Conference on Intelligent Computing in Dat...

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4th international conference on intelligent computing in data sciences, icds 2020

ISBN: (纸本)9781728180847

the proceedings contain 75 papers. the topics discussed include: new color and texture features coding method combined to the simulated annealing algorithm for content based image retrieval;CBPM: an approach for contextual business process modeling;deep learning using genetic algorithm optimization for short term solar irradiance forecasting;grid based approach (GBA): a new approach based on the grid-clustering algorithm to solve a CPP type problem for air surveillance using UAVs;big data processing: batch-based processing and stream-based processing;fast 3D image reconstruction by separable moments based on Hahn and Krawtchouk polynomials;dynamic vulnerability detection approaches and tools: state of the art;and deep Q-learning approach for congestion problem in smart cities.

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Robust Facial Emotion Detection in Low-Light Conditions: A Novel Approach Using Real-Time WebSocket data Transfer 6

Robust Facial Emotion Detection in Low-Light Conditions: A N...

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6th international conference on intelligent computing in data sciences, icds 2024

作者： Patel, Dhruv Qurashi, Abdul Wahab Johnson, Anju P. School of Computing and Engineering University of Huddersfield Huddersfield United Kingdom Advanced Manufacturing Research Centre University of Sheffield Sheffield United Kingdom

ISBN: (纸本)9798350351200

Facial emotion detection plays a crucial role in human-computer interaction and psychological research. However, existing methods often falter in low-light environments, limiting their real-world applicability. this paper presents a novel approach to enhance the robustness of facial emotion detection systems in low-light conditions using a standard camera and real-Time data transfer via WebSocket. Our method employs advanced computer vision techniques, including adaptive histogram equalization and deep learning-based feature extraction, to ensure robust facial feature detection and emotion classification across varied lighting conditions. We will integrate our system with a TurtleBot 4 mobile platform to demonstrate its practical applications in assistive technologies and interactive systems. Experimental results show that our approach achieves 85.2% accuracy in low-light conditions, a significant improvement over baseline models. this research contributes to the development of more versatile and reliable emotion recognition systems for real-world applications. © 2024 IEEE.

关键词： Human robot interaction

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Classification of Diabetic data using Novel Optimized Deep Ensemble Learning 4

Classification of Diabetic Data using Novel Optimized Deep E...

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4th international conference on Emerging Systems and intelligent computing, ESIC 2024

作者： Mohapatra, Saumendra Kumar Khilar, Rashmita Mohanty, Mihir Narayan Sri Sri University Faculty of Emerging Technology India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Chennai India Department of Ece India

ISBN: (纸本)9798350349856

Machine learning application in medical data analysis is one of the most popular approaches in the diagnosis of various diseases. Authors in this work have classified the diabetic data collected from the UCI machine learning repository by using a modified ensemble machine learning approach. In the proposed method, a stacked ensemble model with a different meta-classifier is considered for classification. In the first stage, the performance is compared with the Support vector machine and gradient boosting meta-model. Again the learning rate for the meta model has been changed to get better accuracy. From the result, it is observed that the modified model is providing better results compared to others. © 2024 IEEE.

关键词： data mining

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Risk Analysis of Enterprise Cross-network data via DAE-BiLSTM and Fuzzy Bayesian Network 4

Risk Analysis of Enterprise Cross-network Data via DAE-BiLST...

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4th international conference on intelligent Communications and computing

作者： Li, Wei Li, Jianjun Shan, Yuxiang Li, Junjie Nie, Guibing China Tobacco Zhejiang Ind Co Ltd Informat Ctr Hangzhou Peoples R China Zhejiang Univ Sch Engn Hangzhou Peoples R China Hangzhou DBAPPSecur Co Ltd Hangzhou Peoples R China

ISBN: (纸本)9798331530372;9798331530365

With the increasing severity of enterprise information security issues, the identification and evaluation of cross-network data risk has become one of the essential technologies to safeguard enterprise data and network security. However, traditional rule-based or signature-based anomaly detection methods are increasingly unable to cope with the complex and evolving forms of attacks, necessitating more intelligent and flexible detection mechanisms to counter these constantly changing threats. To address this, a method integrating denoising autoencoder (DAE) and bidirectional long short-term memory network (BiLSTM) is first proposed for monitoring the enterprise cross-network data traffic and identify the occurring abnormal risks. On this basis, we use a fuzzy Bayesian network to effectively measure these known abnormal risks. In the end, experimental results demonstrate that the proposed method outperforms traditional machine learning algorithms and other deep learning models across various evaluation metrics, exhibiting high accuracy and efficiency.

关键词： network security cross-network data risk analysis deep learning

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Machine Learning-Based Crop Recommendation System 4

Machine Learning-Based Crop Recommendation System

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4th international conference on Ubiquitous computing and intelligent Information Systems, ICUIS 2024

作者： Ebenezer, V. Nair, Akash Stewart Kirubakaran, S. Praising Linijah, N.L. Rajan, Jerlin Santhiya, P. Karunya Institute of Technology and Sciences Division of Data Sciences and Cyber Security Coimbatore India Karunya Institute of Technology and Sciences Division of Computer Science and Engineering Coimbatore India Karunya School of Management Karunya Institute of Technology and Sciences Coimbatore India

ISBN: (纸本)9798331529635

the time-honored way that farmers have been used to apply to the identification of the best crops for better yield is not exhaustive. Under this technique, the management process of powerful and productive resources, cannot be done properly. the crop recommendation system comprising a machine learningbased model can be deployed to solve the problem. While using techniques such as Logistic Regression, Decision Trees, Random Forests, XGB boost, SVM and Stacking Classifier, the machine learning model attempts to predict the most suitable crops by taking and observing soil contents and environmental parameters. that study is devoted to helping farmers have comprehensive prescriptions and appropriate crop selections to optimize productivity and guarantee sustainability. © 2024 IEEE.

关键词： Adversarial machine learning

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Text Semantic Analysis and intelligent Interaction in Parallel Dispatching Systems for Technical Station 4

Text Semantic Analysis and Intelligent Interaction in Parall...

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4th IEEE international conference on Digital Twins and Parallel Intelligence, DTPI 2024

作者： Jiao, Yuantao Wang, Jian Li, Runmei Xiong, Gang Chen, Shichao Beijing Jiaotong University School of Automation and Intelligence Beijing China Institute of Automation Chinese Academy of Sciences Beijing Engineering Research Center of Intelligent Systems and Technology Beijing China Cloud Computing Center Chinese Academy of Sciences Guangdong Engineering Research Center of 3D Printing and Intelligent Manufacturing Dongguan China Chinese Academy of Sciences State Key Laboratory for Multimodal Artificial Intelligence Systems Beijing China

ISBN: (纸本)9798350349252

Technical station dispatching system plays an important role in cargo operation, but due to the large number of dispatching systems and complex operation, dispatchers rely on manual experience to complete the task, and many scenarios cannot be applied in the real system. therefore, this paper utilizes the data-driven form to establish a parallel dispatching system for technical stations, semantically analyzes text data rich in dispatcher experience, completes the Computational Experiments on the textual semantic data in the artificial dispatching system, and synchronizes the Parallel Execution of data interaction with the real dispatching system. © 2024 IEEE.

关键词： parallel intelligence railway dispatching system technical station

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Research on neutron source accelerators visual-real fusion fault diagnosis with Digital Twin 4

Research on neutron source accelerators visual-real fusion f...

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4th international conference on Electronic Information Engineering and Computer Communication, EIECC 2024

作者： Yang, Sheng Zhang, Xiaodan Liang, Lizhen Liu, Shaoqing Wang, Xiaolong Qinghai University School of Computer Technology and Applications Xining China Intelligent Computing and Application Laboratory of Qinghai Province Xining China Institute of Energy Hefei Comprehensive National Science Center Hefei China

ISBN: (纸本)9798331534622

To address the intelligent upgrading needs of neutron source accelerators, a digital twin visualization system was designed and developed based on digital twin technology. the virtual mapping model was constructed in the digital space using actual parameters of physical space equipment, establishing a mapping connection between the physical and digital spaces centered around twin data. And a multi-channel integrated learning loop diagnostic model with virtual-physical fusion was proposed. Focusing on the experimental operational data of the equipment, the diagnostic model features data monitoring and multi-channel diagnostic functions. Input thresholds for each diagnostic channel of the model were configured based on the data scale. the final diagnostic result with higher accuracy was obtained by comprehensively comparing the results of each channel, significantly improving the real-time performance and efficiency of the diagnostic process. the effectiveness of the multichannel diagnostic model was experimentally validated using vibration signals generated during equipment operation. Additionally, the digital twin visualization system implemented data information visualization and intelligent management of accelerator equipment. © 2024 IEEE.

关键词： Neutrons

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Adaptive Federated Learning for Efficient Network Traffic Management in Edge computing 4

Adaptive Federated Learning for Efficient Network Traffic Ma...

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4th international conference on Ubiquitous computing and intelligent Information Systems, ICUIS 2024

作者： Babu, Kuricheti Suresh Venkatasubramanian, S. Kishore, S. Sree, K. Kavya Ramya Susanna, Ch.Lavanya Rao, Goda Srinivasa Lakkshmanan, Ajanthaa Pace Institute of Technology and Sciences Department of Computer Science and Engineering AndhraPradesh Valluru India Saranathan college of engineering Department of Computer Science and Business Systems Trichy India B V Raju Institute Of Technology Department of Csbs Narsapur Telangana Medak502313 India Aditya University Department of Ai&ml Surampalem India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Andhra Pradesh Vaddeswaram India Kl University Department of Cse Vaddeswaram Andhra Pradesh Guntur India School of Computing Srm Institute of Science and Technology Department of Computing Technologies Kattankulathur Campus Kattankulathur603 203 India

ISBN: (纸本)9798331529635

With the increasing prevalence of edge computing environment, there is a need to establish effective network traffic management that are able to manage vast data while also protecting the privacy of users. the proposed method enhances efficiency of bandwidth, reduce the network latency and manages computational demands without managing data privacy. this is achieved by utilizing the design of edge devices. the adaptable nature of the method enables it to react in real time to different traffic situations. It optimizes the distribution of resources and improves the overall performance of the network. Additionally, it can reduce the possibility of centralized data exposure, providing robust privacy preservation through the allowance of local model training on edge devices. Finally, this method is superior. the employment of federated learning to network traffic management provide efficiency to optimize the traffic in edge computing which is both scalable and safe. this makes it suitable for a wide variety of applications that are utilized in next generation networks © 2024 IEEE.

关键词： Federated learning

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4th international conference on Advances in computing and data sciences, ICACDS 2020

4th International Conference on Advances in Computing and Da...

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4th international conference on Advances in computing and data sciences, ICACDS 2020

ISBN: (纸本)9789811566332

the proceedings contain 46 papers. the special focus in this conference is on Advances in computing and data sciences. the topics include: intelligent Mobile Edge computing: A Deep Learning Based Approach;analysis of Clustering Algorithms in Machine Learning for Healthcare data;securing Mobile Agents Migration Using Tree Parity Machine with New Tiny Encryption Algorithm;an Approach to Waste Segregation and Management Using Convolutional Neural Networks;open Source Intelligence Initiating Efficient Investigation and Reliable Web Searching;a Neural Network Based Hybrid Model for Depression Detection in Twitter;Unleashing the VEP Triplet Count of Virtually Created 3D Bangla Alphabet to Integrate with Augmented Reality Application;a Hybrid Machine Learning Framework for Prediction of Software Effort at the Initial Phase of Software Development;chronic Disease Prediction Using Deep Learning;the Moderating Effect of Demographic Factors Acceptance Virtual Reality Learning in Developing Countries in the Middle East;a Deep Learning Based Method to Discriminate Between Photorealistic Computer Generated Images and Photographic Images;Load Balancing Algorithm in Cloud computing Using Mutation Based PSO Algorithm;statistical Model for Qualitative Grading of Milled Rice;measuring the Effectiveness of Software Code Review Comments;proposed Model for Feature Extraction for Vehicle Detection;analysis of Feature Selection Methods for P2P Botnet Detection;ELM-MVD: An Extreme Learning Machine Trained Model for Malware Variants Detection;real-Time Biometric System for Security and Surveillance Using Face Recognition;an Effective Block-Chain Based Authentication Technique for Cloud Based IoT;early Detection of Autism Spectrum Disorder in Children Using Supervised Machine Learning;digital Marketing Effectiveness Using Incrementality.

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Design and Implementation of a Web-based Collaborative Manufacturing System Based on Knowledge Graph 4

Design and Implementation of a Web-based Collaborative Manuf...

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4th IEEE international conference on Digital Twins and Parallel Intelligence, DTPI 2024

作者： Yuan, Meng Wu, Huaiyu Shen, Zhen Han, Yunjun Zhou, Jiehan Yue, Zhijian Xiong, Gang Chinese Academy of Sciences State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Beijing100190 China University of Chinese Academy of Sciences School of Artificial Intelligence Beijing100049 China Chinese Academy of Sciences Guangdong Engineering Research Center of 3D Printing and Intelligent Manufacturing Cloud Computing Center Dongguan523808 China Qingdao Academy of Intelligent Industries Intelligent Manufacturing Center Qingdao266109 China University of Oulu Faculty of Information Technology and Electrical Engineering OuluTS352 Finland

ISBN: (纸本)9798350349252

Collaborative manufacturing is central to agile, intelligent, and cloud-based manufacturing paradigms. this paper presents the design and implementation of a web-based collaborative manufacturing system based on a knowledge graph (KG). the system achieves the full spectrum of knowledge extraction, fusion, and reasoning across the industrial chain, thereby enhancing efficiency and reducing operational costs. the proposed system constructs an industrial chain KG and multi-agent systems, offering comprehensive digital services across the manufacturing value chain. It supports the integration of business processes, data, and standard information for enterprise groups, presenting collaborative manufacturing data of the industrial chain in real-time visualization. the paper further discusses a method for real-time perception of anomalies in the collaborative manufacturing process, utilizing expert knowledge and data-driven insights to construct an operational risk KG. the system's architecture, workflow, and functional design are detailed, along with application cases in visual dynamic management. the conclusion underscores the system's potential to improve supply-demand chain management and operational efficiency in collaborative manufacturing. © 2024 IEEE.

关键词： Smart manufacturing

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