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检索条件"任意字段=7th International Conference on Computing in Engineering and Technology, ICCET 2022"

共 11096 条记录，以下是1221-1230 订阅

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Edge computing in Network-based Systems: Enhancing Latency-Sensitive Applications 7

Edge Computing in Network-based Systems: Enhancing Latency-S...

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7th international conference on Contemporary computing and Informatics, IC3I 2024

作者： Rajchandar, K. Ramesh, M. Tyagi, Anuj Prabhu, S. Babu, Doddavarapu Sreenu Roniboss, A. Sr University Department of Cs & Ai Telangana Warangal India Marri Laxman Reddy Institute of Technology and Management Dundigal Hyderabad500043 India Northeastern University College of Engineering Boston United States Karpagam Academy of Higher Education Department of Computer Science Tamil Nadu Coimbatore641021 India Bharat Ratna Indira Gandhi College of Engineering Department of Electronics and Communication Engineering Maharashtra Solapur413255 India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science & Technology Department of Chemistry Tamilnadu Chennai600062 India

ISBN: (纸本)9798350350067

Relocating data storage and processing to the network's periphery has revolutionized latency-sensitive applications with edge computing. this study examines how edge computing may boost network-based application performance. For applications with severe latency requirements, we explore how system loads, resource allocation mechanisms, and bandwidth limits across edges affect application response times. We introduce the Enhanced Capacity Edge Cloud Network (ECON) and compare it to a baseline scenario to evaluate its efficacy in different network scenarios. To optimize resource allocation for latency-sensitive applications, we mathematically describe and simulate a city-scale hybrid edge cloud system's capacity. the study's results may help build and implement edge computing technologies to enhance real-world application performance. © 2024 IEEE.

关键词： Cloud platforms

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Bilinear Graph Neural Network-Enhanced Web Services Classification 23

Bilinear Graph Neural Network-Enhanced Web Services Classifi...

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23rd IEEE international conference on High Performance computing and Communications, 7th IEEE international conference on Data Science and Systems, 19th IEEE international conference on Smart City and 7th IEEE international conference on Dependability in Sensor, Cloud and Big Data Systems and Applications, HPCC-DSS-SmartCity-DependSys 2021

作者： Zhang, Lulu Cao, Buqing Peng, Mi Qing, Yueying Kang, Guosheng Liu, Jianxun Fletcher, Kenneth K. School of Computer Science and Engineering Hunan University of Science and Technology Hunan Key Laboratory of Service Computing and New Software Service Technology Xiangtan China University of Massachusetts Boston Department of Computer Science Boston United States

ISBN: (纸本)9781665494571

With the growing number of Web services, classifying Web services accurately and efficiently has become a challenging problem. Effective service classification is conducive to improving the quality of service discovery and the efficiency of service composition. Existing graph neural network methods represent the target service node by a weighted sum of the features of the neighbor service nodes, and have achieved remarkable results in service classification. However, these methods ignore the impact of possible interactions between neighbor service nodes on the target service node. To address this problem, we propose a Web services classification method based on bilinear graph neural network. Our proposed method can model the bilinear interactions between neighbor service nodes and effectively improve the accuracy of service classification. First, our proposed model uses the Word2Vec model to extract the latent semantic vectors from service description documents. We then construct the service relationship network according to tags and shared annotation relationships of Web services. Next, a bilinear aggregator is used to model the pairwise interactions between neighbor service nodes, highlighting the local common attributes. Finally, the bilinear aggregator is combined with the weighted sum aggregator to construct a bilinear graph neural network, which can learn more comprehensive representations of services. the experimental results on the real dataset from ProgrammableWeb show that the classification accuracy of the proposed method is greatly improved compared with DeepWalk, Node2Vec, GCN, and GAT. © 2021 IEEE.

关键词： Smart cities Annotations Mashups Semantics Quality of service Feature extraction Graph neural networks

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High Speed Neural Network MPPT Algorithm for DFIG Based Wind Energy Conversion System 7

High Speed Neural Network MPPT Algorithm for DFIG Based Wind...

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7th international conference on Circuit Power and computing Technologies, ICCPCT 2024

作者： tharwin Kumar, R. Balaji, V. Sakthidhasan, K. Gomathi, S. Sreedhar, R. Janaki, N. Puducherry Technological University Department of Electrical and Electronics Engineering Puducherry India MAI-NEFHI College of Engineering and Technology Faculty of Electrical and Electronics Engineering Asmara Eritrea Vel Tech Multi Tech Dr. Rangarajan Dr. Sakunthala Engineering College Department of Electrical and Electronics Engineering Chennai India St. Joseph's College of Engineering Department of Electrical and Electronics Engineering Chennai India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Electrical and Electronics Engineering Chennai India Vels Institute of Science Technology and Advanced Studies Department of Electrical and Electronics Engineering Chennai India

ISBN: (纸本)9798350372816

For the generation of electrical energy, breeze energy is a trustworthy and talented renewable vitality basis. An innovative proposal with many scientific and economic benefits is using wind source technology to charge electric vehicles. Worldwide, the market for Wind Energy Conversion System (WECS) has been expanding significantly. High-quality output power is commonly produced when wind energy demand grows. WECS has significant modelling, control, and grid integration issues as a result of the intermittent nature of wind energy. A proper controller mechanism has to be created in order to significantly improve WECS. An innovative method for maximizing the power of wind energy founded on Radial Basis Function Neural Networks (RBFNN) is proposed in this study to increase the power of Double Fed Induction Generators (DFIG). DFIG based wind turbine, and to provide outstanding dynamic performance in response to breeze rapidity fluctuations. MPPT is a strong algorithm that operates around an ideal rotating speed by utilizing RBFNN for mechanical speed control. We evaluated the performance of DFIG founded WECS with MATLAB/Simulink. the RBFNN efficiency is 91% when comparing MPPT controllers. © 2024 IEEE.

关键词： Radial basis function networks

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Prediction of Crop Yield with Feature Dimension Reduction Method Using Regression Models 7

Prediction of Crop Yield with Feature Dimension Reduction Me...

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2023 7th international conference On computing, Communication, Control And Automation, ICCUBEA 2023

作者： Raddekar, Ashwini Chikmurge, Diptee Subhedar, Mansi School of Computer Engineering Mit Academy of Engineering Department of Computer Engineering Maharashtra Alandi India Pillai Hoc College of Engineering and Technology Department of Electronics and Telecommunication Engineering Tal. Khalapur Dit. Raigad Maharashtra Rasayani India

ISBN: (纸本)9798350304268

the important factor in the development of the Indian economy is agriculture. Agricultural production supports more than 70% of rural households. Approximately 17% of the GDP of India is contributed by the agricultural sector, making it a significant economic sector. the main aim of the proposed method is to predict crop yield and analyze the best model to predict the yield of crops among various machine learning algorithms, this model can certainly help farmers to take suitable decisions to grow crops and improve the Indian economy. the dataset employed here includes various parameters such as the amount of fertilizer, the area planted, harvested area, etc. In this work lint crop dataset is considered for crop yield prediction. In the initial step, Principal Component Analysis (PCA) is used for the feature dimension reduction without losing important information/context of the data. It achieves this by reducing the number of dimensions in the data. After reducing the dimension of the dataset using PCA, various machine learning regression algorithms such as Linear rearcssion, Lasso regression, Decision tree, Adaboost, and XGBoost are employed to predict the crop yield. Further, the best model is found by comparing the model's performance metrics which include Mean absolute error (MAE), R2 Score, and Root mean square error (RMSE). © 2023 IEEE.

关键词： crop prediction Machine learning regression algorithms principal component analysis

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Data Encryption: A Compiler Based Approach 7

Data Encryption: A Compiler Based Approach

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7th IEEE international conference on Computational Systems and Information technology for Sustainable Solutions, CSITSS 2023

作者： Jaswanth, Kunisetty Sruthi, S. Ramachandrula, Pranav Belwal, Meena Amrita School of Computing Bengaluru Amrita Vishwa Vidyapeetham Dept. of Computer Science and Engineering India

ISBN: (纸本)9798350343144

In today's age, where data security's a growing concern it is crucial to develop strong encoding and decoding models. In our research paper we introduce a model that combines concepts, from compiler design with diverse hashing algorithms. this model aims to offer an efficient solution for transforming and retrieving data. As technology advances rapidly and the need for data security becomes more prominent in real world scenarios our model tackles the challenges of safeguarding information while ensuring integrity and confidentiality. By utilizing hashing algorithms like SHA, BLAKE2 and MD5 we achieve data encryption. Our experiments demonstrate that our model outperforms approaches showcasing its effectiveness across industries where protecting data privacy is of utmost importance. With its user interface our model provides a solution for secure data transmission and storage contributing significantly to advancements in data security and encryption techniques. © 2023 IEEE.

关键词： Data privacy

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Implementation of various AI techniques for Autism Spectrum Disorder Detection 7

Implementation of various AI techniques for Autism Spectrum ...

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7th international conference on Circuit Power and computing Technologies, ICCPCT 2024

作者： Asha, P. Raj, Aditya Ramani, Bhavyam Vijay Padma Suresh, L. Prince Mary, S. Mary Posonia, A. Sathyabama Institute of Science and Technology Computer Science and Engineering Chennai India Baselios Mathews II College of Engineering Computer Science and Engineering Kollam India

ISBN: (纸本)9798350372816

A complex neurological disorder affecting behavior, communication, and social interactions is known as autism spectrum disorder (ASD). For people with ASD, improving outcomes requires primordial detection and intervention. the diagnosis of ASD has been transformed by the combination of AI and ML, particularly in young children. In order to create reliable autism screening tools, researchers have explored a variety of machine learning algorithms, including Decision Trees, XGBClassifier, Random Forest, AdaBoost Classifier, CatBoost Classifier, KMeans to Artificial Neural Networks (ANN). Artificial Neural Networks, energised by the intricacies of the human brain, stand out for their remarkable ability to decipher complex patterns within data, thus proving effective in ASD prediction tasks. Nonetheless, ANN models often necessitate substantial data and computational resources for training. It is imperative to undertake rigorous comparative analyses based on accurate and efficient results to determine the most suitable algorithm for ASD prediction. Furthermore, continuous refinement and validation of these varied algorithms along with the representative and comprehensive datasets are vital to ensure their factuality and applicability in real world problems. © 2024 IEEE.

关键词： Random forests

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Embedded Sensor and IoT technology based Substation Monitoring and Control System 7

Embedded Sensor and IoT Technology based Substation Monitori...

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7th international conference on computing Methodologies and Communication, ICCMC 2023

作者： Krishnaswamy, R. Radhika, K. Hamsaveni, V.G. Susila, G. Department of Electronics and Communication University College of Engineering Tamil Nadu Ariyalur India Department of Electronics and Communication Geethanjali Institute of Science and Technology Andhra Pradesh Kovur India Department of Electronics and Communication SITAMS Andra Pradesh Chithoor India Tamilnadu Perambalur India

ISBN: (纸本)9781665464086

Every utility now requires substation automation to raise and improve its efficiency and the calibre of the electricity supplied. Surgery for medical purposes is of utmost importance. IoT has generally been heavily utilized to link cutting-edge medical equipment and provide individuals with smart, efficient healthcare. Automating substations has become necessary for every service organization as the complexity of the distribution network has risen. To enhance the nature of force, it is necessary to identify what type of urgency has occurred. Furthermore, assuming that the security, checking, and control of a power framework are insufficient, consequently, it needs a testing framework capable of recognizing, screening, and grouping the current imperatives on electrical lines. the goal of this framework is to get remote electrical limitations such as voltage, flow, and recurrence and transmit these continuous characteristics, as well as temperature, to the power station over the network. this technology is also intended to protect electrical hardware through a transfer. this transfer is triggered when the electrical boundaries reach the predefined thresholds. this framework is capable of regularly renewing the continuous electrical boundaries. this framework may be set to sound an alarm if the hand-off fails or if the voltage or current exceeds the maximum value. © 2023 IEEE.

关键词： Internet of things

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Cauchy Grasshopper Optimization Algorithm with Deep Learning Model for Cloud Enabled Cyber threat Detection System 7

Cauchy Grasshopper Optimization Algorithm with Deep Learning...

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7th international conference on Intelligent computing and Control Systems, ICICCS 2023

作者： Ravi, C.N. Karthik, T.S. Manikandan, K. Kalaivaani, P.C.D. Chopkar, Priyanka Nandkishor Srivastava, Aviral CMR Engineering College Computer Science & Engineering Department Telangana Hyderabad India Aditya College of Engineering & Technology Surampalem E.G.Dist. Andhra Pradesh 533437 India Government College of Engineering Thanjavur India Kongu Engineering College Computer Science and Engineering Perundurai India G H Raisoni Institute of Engineering & Technology Electronics & Telecommunication Engineering Nagpur India Amity University Rajasthan Amity University Rajasthan NH 11 C kant kalwar Jaipur300202 India

ISBN: (纸本)9798350397253

the tremendous growth of internet technology has drastically improved the large amount of connected devices. To secure network infrastructure from the damage that cyberattacks might cause, this has made an enormous attack surface that needs the deployment of practical and effective counter measures. In the contemporary era of active network transmission and throughput, Intrusion Detection System (IDS) plays a vital role in ensuring secure network resource and data from outside invasion. In recent times, IDS becomes an essential tool to enhance the efficiency and flexibility for unpredictable and unexpected invasions of the network. Deep learning (DL) is a well-known and essential method to resolve challenges and could learn rich features of massive information. therefore, the study focuses on the design and development of the Cauchy Grasshopper Optimization Algorithm with Deep Learning for Cloud Enabled IDS (CGOA-DLCIDS) technique. the presented CGOA-DLCIDS method aims to recognize the presence of intrusions in the cloud platform. To achieve this, the CGOA-DLCIDS technique performs feature subset election by CGOA which reduces the feature subset and enhances the intrusion detection rate. Next, the CGOA-DLCIDS technique employs attention based long short-term memory (ALSTM) module for automated and accurate intrusion detection and classification. the simulations analysis of the CGOA-DLCIDS method on benchmark dataset highlighted the increasing results compared to recent IDS approaches. © 2023 IEEE.

关键词： Intrusion detection

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A Machine Learning Framework for Classification of Expert and Non-Experts Radiologists using Eye Gaze Data 7

A Machine Learning Framework for Classification of Expert an...

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7th IEEE international conference on Recent Advances and Innovations in engineering, ICRAIE 2022

作者： Shamyuktha, R.S. Amudha, J. Aiswariya Milan, K. Dept of Computer Science and Engineering Amrita School of Computing Bengaluru Amrita Vishwa Vidyapeetham India

ISBN: (纸本)9781665489102

Artificial intelligence (AI) techniques enhance the efficiency of radiology data interpretation and reporting process. the paper focuses on classifying a participant as an expert or non-expert based on the features related to saccade, such as saccade latency, saccade amplitude, etc. these features help to understand whether the participant knows the respective field or not. the data used here is how the participant looks at an optic disc image and whether they know where to look precisely. the proposed work used different machine learning models to classify experts and non-expert radiologists and observed that the decision tree gave the highest accuracy of 96%. Finally, we create a dashboard with the help of powerbi, which displays the insights about the expert and non-expert viewing the optic discs. © 2022 IEEE.

关键词： Eye movements

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Transient Analysis of Doubly Fed Induction Generator in Grid Connected Mode 7

Transient Analysis of Doubly Fed Induction Generator in Grid...

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7th IEEE international conference for Convergence in technology, I2CT 2022

作者： Kumar, Rajesh Singh, Sachin Yadav, Anchal Kumar Singh, Rajiv Kumar Singh, Indra Institute of Engineering & Technology Department of Electrical Engineering Lucknow India Institute of Engineering & Technology Department of Electronics & Communicatioon Engineering Lucknow India

ISBN: (纸本)9781665421683

In this paper wind energy conversion systems (WECS) is getting more attention to diversify the increased renewable energy penetration. DFIG has emerged as a preferred choice for WECS due to capability for reactive power control and variable speed operation with lower rating of power converters. In this research a comprehensive grid-connected of DFIG system wind turbine in a dq-frame has been developed in TYPHOON HIL and MATLAB software platforms. the results are come by in different fault scenarios and results are compared. With increased penetration of renewable distributed energy generations WECS are getting more attention from industry and researchers. © 2022 IEEE.

关键词： Transient analysis

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