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检索条件"任意字段=3rd IEEE Intelligent Data Acquisition and Advanced Computing Systems"

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Ensemble Learning for Network data Classification with SDN Clustering Underlying Smart Power Grid-Enabled Smart Cities 3

Ensemble Learning for Network Data Classification with SDN C...

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3rd ieee International Conference on Power Electronics, intelligent Control and Energy systems, ICPEICES 2024

作者： Patel, Man Jain, Naman Patel, Janam Ramoliya, Fenil Gupta, Rajesh Tanwar, Sudeep Institute of Technology Nirma University Department of Comp. Sci. & Engg. Ahmedabad India

ISBN: (纸本)9798350387193

Smart power grid enables smart city to be operated on efficient level for sustainable urban planning, economic growth and become an innovation hub. Various types of energy user or provider plants can contribute to providing services while integrated with physical sensor devices, modules, and software interfaces for efficient communication and control. These integrated software interfaces and computer devices also help us during response for any catastrophic condition, fault detection-diagnosis, remote operation-maintenance, integration with Supervisory Control and data acquisition (SCADA) systems and provide robust cyber security measures. Software defined network (SDN) allows us to have traffic prioritization, traffic engineering and load balancing, Quality of Service (QoS) enforcement and logical segmentation. However, relying on a single SDN entity to handle all network traffic can introduce several challenges such as single point of failure, scalability limitation, limited redundancy, increased complexity and management overhead and delay in serving of highly critical requests during high-load condition. To mitigate this paramount concern, we propose Ensemble Learning (EL)-based network traffic classification approach with clustering of SDNs to carry-out tasks in efficient and effective manner. The performance evaluation of proposed approach has been done through various performance metrics such as receiver operating characteristic (ROC) curve, precision, recall, f'1-score, PR curve and confusion matrix. © 2024 ieee.

关键词： Quality of service

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Ontology-Based Optimization Method for Multi-Source Heterogeneous data Organization in Large Power Grid Planning and Operation Platforms 3

Ontology-Based Optimization Method for Multi-Source Heteroge...

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3rd International Conference on Energy and Electrical Power systems (ICEEPS)

作者： Tang, Yingqi Zhao, Ligang Su, Yinsheng Zhou, Baorong Xu, Yuan Huang, Guanbiao Hu, Jiaming Li, Kaihang CSG Elect Power Res Inst State Key Lab HVDC Technol Guangzhou Peoples R China CSG Power Dispatching & Control Ctr Guangzhou Peoples R China South China Univ Technol Sch Elect Power Engn Guangzhou Peoples R China

ISBN: (纸本)9798350375145;9798350375138

The construction of a large power grid planning and operation platform requires the integration and organization of multi-source heterogeneous data. However, the various types of power grid business have differing levels of importance and high demands for timely response, which current data organization methods struggle to meet. This paper proposes an ontology-based optimization method for multi-source heterogeneous data organization in large power grid planning and operation platforms. A mathematical optimization model is constructed, taking into account both business retrieval efficiency and data redundancy. Additionally, the genetic algorithm is improved based on the specific characteristics of this optimization problem. The method proposed in this paper is then applied to integrate data from multiple power software applications. Experimental results show that this method effectively balances data retrieval efficiency and storage space utilization, achieving automated organization optimization of multi-source heterogeneous data for power grid planning and operation.

关键词： multi-source heterogeneous data organization power grid planning and operation optimization ontology improve genetic algorithm

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A Review of Adversarial Machine Learning for Cyber security in intelligent systems using Image Classification & Signal Classification Analysis 3

A Review of Adversarial Machine Learning for Cyber security ...

引用

3rd ieee International Conference on ICT in Business Industry and Government, ICTBIG 2023

作者： Jahan, Tasneem Lnct University Department of Computer Science & Engineering Bhopal India

ISBN: (纸本)9798350343274

Machine learning models with adversarial examples must be trained to differentiate between genuine and invading data. Cyber computing technology and tools face huge intrusion attacks and malicious programs. Tampered QR code for online payments, malicious face and biometric detection, incorrect facial recognition, object and sign detection are the adversarial examples in real-life scenarios. Convolutional neural networks often fail to image classification tasks due to high vulnerability. The intelligent systems comprising computer vision, self-driving cars, radio signals, IoT Devices, industrial production and automation are prone to cyber attack with adversarial data in machine learning models. The goal of this paper is to summarize the various Convolutional Neural Network models and Recurrent Neural Network approaches used in machine learning for malicious image classification. The analysis aims to identify the area of attacks using adversarial machine learning in cyber physical systems and intrusion detection systems. © 2023 ieee.

关键词： Cybersecurity

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Performance optimization of Learning Trees modules in the ECL repository 3

Performance optimization of Learning Trees modules in the EC...

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3rd International Conference on Communication, Control, and intelligent systems, CCIS 2024

作者： Shashank, B. Sudershan, K.S. Shobha, G. Manonmani, S. Dev, Roger Rv College of Engineering Dept. of Computer Science Bangalore India LexisNexis Risk Solutions United States

ISBN: (纸本)9798331528201

Multi-Node computation, also known as distributed computing, is a paradigm that allows for the efficient utilization of multiple interconnected nodes or machines to perform complex computational tasks. By dividing the workload and distributing it across multiple nodes, multi-node computation enables parallel processing, scalability, and improved performance for a wide range of applications. HPCC systems® from LexisNexis Risk Solutions is a proven, open source solution for big data insights that can be implemented by businesses of all sizes. HPCC systems offers a consistent data-centric programming language, two processing platforms and a single, complete end-to-end architecture for efficient processing. ECL is the Enterprise Control Language designed specifically for huge data projects using the HPCC systems platform. It is extremely scalable and a powerful declarative data-processing language with native multi-processing capabilities and used for HPCC systems. Some core ECL functions, like the ones handling Learning Tree algorithms of Machine Learning Bundle are recursive in nature and hence high computational time is needed. This paper thus presents a solution to achieve optimized processing of ML learning tree algorithms in ECL through embedding python libraries. The proposed approach was tested on standard datasets and a significant decrease in computation time. For Real Estate Valuation Regression dataset the computation time was reduced from 11.371 seconds to 0.886 seconds using the proposed method showing 1324% improvement with no impact on the accuracy. Random Forests are also implemented using the proposed Decision Tree method allowing the users to specify the number of decision trees to be used in the forest. © 2024 ieee.

关键词： Parallel processing systems

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Characteristic Analysis of Power Grid Natural Disaster Operation and Maintenance Big data 3

Characteristic Analysis of Power Grid Natural Disaster Opera...

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3rd International Conference on Ubiquitous computing and intelligent Information systems, ICUIS 2023

作者： Li, Shan Zhang, Wei Zhou, Yangjun Ouyang, Jianna Huang, Zhidu Electric Power Research Institute of Guangxi Power Grid Co. Ltd Nanning Guangxi530015 China

ISBN: (纸本)9798350306989

As a recognized clean energy, electric power energy is loved and also welcomed by people because of its characteristics of cleanliness and convenience. It is the most common secondary energy of modern societies. How to carry out long-term operation and maintenance of power grid and disaster warning is the latest research focus. To improve the robustness of the power grid system, this paper conducts the characteristic analysis of the power grid natural disaster operation and maintenance big data. The highlights are: 1. The novel power grid big data analytic paradigm is proposed for analyzing the information effectively. 2. The grid operation and long-term maintenance methods are summarized and the efficient model is proposed. 3. The characteristic analysis of power grid natural disaster operation and maintenance big data is proposed. Through the experimental verification regarding the concurrency test and prediction performance, the performance is presented. © 2023 ieee.

关键词： Big data

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The Hybrid-filtered fuzzy PI controller apply to the automatic tightening system 3

The Hybrid-filtered fuzzy PI controller apply to the automat...

引用

3rd International Conference on computing, Communication, Perception and Quantum Technology, CCPQT 2024

作者： Wang, Shuaixin Zhang, Xiwen Wang, Yu Dong, Fangyan Faculty of Mechanical Engineering & Mechanics Ningbo University Ningbo China China Academy of Safety Science & Technology Beijing China

ISBN: (纸本)9798331528386

The tightening process is a critical component of modern intelligent manufacturing, and the system design and process control of the tightening system have a decisive impact on the efficiency and quality of this *** application of intelligent control in this field has shown great *** this study, a hybrid-filtered fuzzy PI controller for an automatic fastening system was proposed. It employs a conditional filtering mechanism to eliminate the differential explosion issue in the input of the fuzzy controller when there is a large speed deviation, while minimizing data distortion when the speed stabilizes. To test the effectiveness of this controller, a load model and the fastening system were modeled in Matlab/Simulink, which was used to test the controller. In comparison with traditional PI controllers and fuzzy PI controllers, it was found that this controller has significant advantages in terms of precision and stability in speed control. It has great potential for applications in conditions with high noise and rapidly increasing loads. However, further research and verification are needed on the mechanisms by which parameters such as filter width and threshold affect the control system, as well as their deployment effects in hardware. © 2024 ieee.

关键词： Proportional control systems

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Monte Carlo Method and XGBoost Algorithm Based Match Momentum Prediction Study 3

Monte Carlo Method and XGBoost Algorithm Based Match Momentu...

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3rd International Conference on data Analytics, computing and Artificial Intelligence, ICDACAI 2024

作者： Zhang, Putai Lu, Xiaopeng Xie, Chuwei Beihang University Beijing China

ISBN: (纸本)9798331519032

Focusing on the analysis and prediction of momentum in tennis matches, this study explores the feasibility of using reinforcement learning (RL) and machine learning methods to solve the problem of dynamic changes in tennis matches. First, we constructed a MBPE model based on Monte Carlo method, which was trained by Wimbledon tennis tournament data and successfully revealed the momentum differences between different players in the match. The results show that the accuracy and predictive ability of the model in the final match are effectively verified. Secondly, we verified the existence of momentum in the match using Pearson and Spearman correlation analysis and proved its non-randomness. Finally, we proposed the TBMP model, which combines LSTM, Transformers and XGBoost algorithms, to successfully predict the change of player's momentum during the game. By comprehensively analyzing the game state and players' movements, we identified the main factors affecting the momentum shifts and proposed a quick response to the opponent's momentum changes using meta-learning techniques. This study provides practical data analysis tools and strategies for players and coaches, and is expected to play an active role in improving tennis match decision-making and technical training. © 2024 ieee.

关键词： intelligent systems

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A Patient-Centric Blockchain-Based Framework with Layer-2 Integration for Secure and Scalable Healthcare data Management 3

A Patient-Centric Blockchain-Based Framework with Layer-2 In...

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3rd International Conference on intelligent systems, advanced computing, and Communication, ISACC 2025

作者： Tarannum, Wajda Abidin, Shafiqul Aligarh Muslim University Department of Computer Science Aligarh India

ISBN: (纸本)9798331523893

Today, personal data is vulnerable in the digital era, especially in the medical sector, where healthcare data is sensitive. Yet, it plays a significant role in deciding the health conditions of patients, doctors, and researchers. Healthcare data is associated with patients' history, adding more value and utility to patients, doctors, and Researchers. These data are usually stored in centralized databases, creating vulnerabilities and leading to cyber-attacks. Furthermore, some healthcare providers rely on third-party contractors for data storage, which can be expensive and unreliable since vendors use diverse security procedures. Ensuring the secure protection of data from cyber threats and breaches is vital. This study aims to design a framework that manages large volumes of data from numerous sources like laboratories and hospitals. As we know, the volume of healthcare data is persistently growing. To address these issues, we propose a blockchain-based Patient-Centric framework with layer-2 solutions for healthcare data management that enhances security, privacy, and transparency in handling sensitive medical information. The concept integrates blockchain technology and the Interplanetary File System (IPFS), a distributed cloud storage system. We created smart contracts in Solidity to promote safe interactions between patients and healthcare professionals, and a React application communicates with them using MetaMask. Layer-2 technology decreases congestion, allowing the system to process a wide range of transactions more effectively while maintaining performance and security. Furthermore, by keeping cryptographic proofs on-chain and huge files off-chain, our strategy improves data security and privacy while lowering storage costs. © 2025 ieee.

关键词： Sensitive data

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Enhancing Skin Cancer Detection with Novel data Augmentation and Transfer Learning Techniques 3

Enhancing Skin Cancer Detection with Novel Data Augmentation...

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3rd International Conference on intelligent systems, advanced computing, and Communication, ISACC 2025

作者： Vullam, Nagagopiraju Rani, Gummadi Sudha Prabhakara Rao, T. Babu, Vunnava Dinesh Subbaiah Desanamukula, Venkata Rao, A. Lakshmana Chalapathi Institute of Engineering and Technology Department of CSE A.P Guntur India Dhanekula Institute of Engineering & Technology Department of CSE Vijayawada India Aditya University Department of CSE Surampalem India RV Institute of Technology Department of CSE Guntur India Lakireddy Bali Reddy College of Engineering Department of CSE Mylavaram India Aditya University Department of IT Surampalem India

ISBN: (纸本)9798331523893

Skin cancer is one of the most prevalent and life-threatening diseases worldwide, requiring early detection for effective treatment. This study investigates a deep learning-based approach for classifying skin cancer into multiple categories, utilizing CNNs and transfer learning models. Initially, a CNN model with reduced complexity was applied to classify skin cancer images from a dataset containing various classes, including melanoma, basal cell carcinoma, and nevus. Due to the limited size of the dataset, data augmentation techniques were used to generate additional samples for each class, improving model performance. Transformations such as rotation, zoom, shifting, and flipping were applied to simulate real-world variability. To further enhance classification accuracy, four pre-trained transfer learning models - VGG16, ResNet50, InceptionV3, and EfficientNet - were fine-tuned and compared. The results shown a substantial improvement in accuracy with the combination of data augmentation and transfer learning. © 2025 ieee.

关键词： Deep learning

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Principal Component Analysis Visualization and State Discovery with Soil data 12

Principal Component Analysis Visualization and State Discove...

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12th ieee International Conference on intelligent data acquisition and advanced computing systems: Technology and Applications, IDAACS 2023

作者： Sirola, Miki Koskinen, Markku Polvinen, Tatu Pihlatie, Mari University of Helsinki HelsinkiFI-00014 Finland

ISBN: (纸本)9798350358056

data exploration helps to gain understanding of the dataset and the system itself. There are methodologies to handle large number of sensors as well. In this paper operational states are defined to interpret physical behaviour in a soil ecosystem. Dimensionality reduction is achieved with Principal Component Analysis (PCA) method giving another view to the soil dataset from spring term. K-means algorithm groups data densities by clustering the data. This grouping is the basis for defining operational states in the system. Soil data as a part of an ecosystem involves specific features. In the applied approach dynamic visualization including animations constitute an important exploration view. All experiments are realized in Jupyter programming environment with Python 3 programming language. Related literature about data visualization is reviewed. Combining methods and tools with this data as a result soil ecosystem features are recognized. © 2023 ieee.

关键词： Principal Component Analysis Soil data State Discovery Visualization

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