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3rd International conference on Innovative Computing and Communication, ICICC 2020

3rd International Conference on Innovative Computing and Com...

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3rd International conference on Innovative Computing and Communication, ICICC 2020

ISBN: (纸本)9789811551123

The proceedings contain 196 papers. The special focus in this conference is on Innovative Computing and Communication. The topics include: A Primer on Opinion Mining: The Growing Research Area;a Generic Framework for Evolution of Deep Neural Networks Using Genetic Algorithms;multimodal Medical Image Fusion Based on Discrete Wavelet Transform and Genetic Algorithm;driver Fatigue and Distraction Analysis Using Machine learning Algorithms;a Hybridized Auto-encoder and Convolution Neural Network-Based Model for Plant Identification;curaBand: Health Monitoring and Warning System;optimization Algorithm-Based Artificial Neural Network Control of Nonlinear Systems;mHealth for Mental Health;elman and Jordan Recurrence in Convolutional Neural Networks Using Attention Window;state of Charge Estimation Using Data-Driven Techniques for Storage Devices in Electric Vehicles;Application of Stochastic Approximation for Self-tuning of PID in Unmanned Surface Vehicles;dark web Activity on Tor—Investigation Challenges and Retrieval of Memory Artifacts;A Heterogeneous Dynamic Scheduling Minimize Energy—HDSME;face Recognition-Based Automated Attendance System;Human Emotion Recognition from Spontaneous Thermal Image Sequence Using GPU Accelerated Emotion Landmark Localization and Parallel Deep Emotion Net;delay Based Multi-controller Placement in Software Define Tactical Network;security in Cloud Computing for Sensitive Data: Challenges and Propositions;Utility of Game Theory in Defensive Wireless Sensor Networks (WSNs);robust Approach for Emotion Classification Using Gait;routing in Internet of Things Using Cellular Automata;depression Detection Among Social Media Users Using Machine learning;Security-Based LEACH Protocol for Wireless Sensor Network;Design of High-Speed FPGA Based CASU Using CBNS Arithmetic: Extension to CFFT Processor;blockchain-Based Boothless E-Voting System;preface.

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Enhanced Movie Recommender system using Deep learning Techniques 3

Enhanced Movie Recommender system using Deep Learning Techni...

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3rd International conference on Computational Modelling, Simulation and optimization, ICCMSO 2024

作者： Tokala, Srilatha Nagaram, Jyothish Enduri, Murali Krishna Lakshmi, T. Jaya SRM University ACT Lab Department of CSE AP India SRM University Department of CSE AP India

ISBN: (纸本)9798350361391

Recommender systems filter user preferences and surfing history to provide recommendations. These recommendations are used to capture the user interests for making decisions. Based on the interaction of like, and dislikes of the user the decisions are made. We are using the deep learning techniques to enhance the movie recommendations. It has the ability to extract meaningful patterns from large volumes of data. This study uses Artificial Neural Networks (ANN) to learn features from user behavior and movie metadata, and Recurrent Neural Networks (RNN) to capture temporal patterns in user preferences and thereby enhance the recommendation accuracy by considering both short-term and long-term factors. Additionally, Convolutional Neural Networks (CNN) enhance model capabilities by focusing on input data spatial correlations. By combining CNN's ability to extract hierarchical representations of structural and visual aspects into our recommendation system, we intend to improve material knowledge. These techniques play a vital role in providing recommendations by enabling the personalized preferences to the users. The models are trained on diverse datasets using user ratings and viewing history. Model performance on datasets shows decreased mean square and mean absolute error. This research shows how ANN, RNN, and CNN algorithms can provide reliable movie suggestions. © 2024 IEEE.

关键词： Contrastive learning

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Mapping learning Algorithms on Data, a Promising Novel Methodology to Compare learning Algorithms 3rd

Mapping Learning Algorithms on Data, a Promising Novel Met...

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3rd International conference on Computer and Communication Engineering, CCCE 2023

作者： Neri, Filippo DIETI University of Naples via Claudio 21 Naples80100 Italy

ISBN: (纸本)9783031352980

The paper describes a novel methodology to compare learning algorithms by exploiting their performance maps. A performance map enhances the comparison of a learner across learning contexts and it also provides insights in the distribution of a learners’ performances across its parameter space. Also some initial empirical findings are commented. In order to explain the novel comparison methodology, this study introduces the notions of learning context, performance map, and high performance function. These concepts are then applied to a variety of learning contexts to show how the methodology can be applied. Finally, we will use meta-optimization as an instrument to improve the efficiency of the parameter space search with respect to its complete enumeration. But, note that meta-optimization is neither an essential part of our methodology nor the focus of our study. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： learning algorithms

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Reliability Analysis Using Artificial Neural Network Based Adaptive Parameter Differential Evolution Algorithm 20

Reliability Analysis Using Artificial Neural Network Based A...

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3rd International conference on Robot Systems and Applications (ICRSA)

作者： Bui, N. T. Nguyen, T. T. Nguyen, V. T. Tao, N. L. Bui, N. T. Hasegawa, H. Hanoi Univ Sci & Technol Sch Mech Engn Hanoi Vietnam Shibaura Inst Technol Dept Machinery & Control Syst Tokyo Japan

ISBN: (纸本)9781450387644

Reliability analysis is one of the methods to consider the safety and stability of an engineering system. It is very important to determine whether a system is safe or not. We need to solve the complex nonlinear and implicit the limit state functions to obtain the reliability index. Traditional reliability analysis methods, First-Order Reliability Method (FORM), Second-Order Reliability Method (SORM), and Monte Carlo simulation (MCS), are not effective and have many limitations. In this paper, at the first step, an artificial neural network was used to model the limit state function. After that, the elite opposition-based learning differential evolution algorithm was selected to solve nonlinear equality constrained optimization problem to find the reliability index and the failure probability of problems in terms of random variables. The proposed method and some reference methods were applied to analyze the test problems in the literature to compare their effectiveness.

关键词： Reliability analysis neural network differential evolution optimization algorithm global search

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Analysis of Life Expectancy Rate Post Lung Surgery Using Machine learning 3

Analysis of Life Expectancy Rate Post Lung Surgery Using Mac...

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3rd International conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Shreya Jain, Tarun Sharma, Vijay Shankar Manipal University Jaipur Department of Computer Science and Engineering India Manipal University Jaipur Department of Iot & Intelligent Systems India

ISBN: (纸本)9798350319125

It has become really important for patients to know the precise results that can happen post the treatment of lung cancer or that if they have any underlying health problem which can be the cause of death post lung cancer surgery. Doctors also need to know the full detailed report of patients' health with the risk factors that can affect their survival rate for better treatment of their patients. As such results are crucial, we need to implement such techniques which can generate the most accurate results. For such accurate analysis, we use machine learning techniques to generate better results. Several machine learning and deep learning approaches are being used to estimate the survival rate of lung cancer patients post the surgery. Examination of various health factors is used for prediction, as health factors could be significant predictors. Seven machine learning techniques that are linear regression, XGBooster, random forests, Decision Tree, KNN, SVM, voting classifier and two deep learning techniques that are ANN and BILSTM are used for analyzing performance. Analysis of accuracy has been done using F1 score, precision, and recall as measuring factors. For the analysis of outcome prediction, we have taken accuracy. © 2023 IEEE.

关键词： ANN Decision Tree Machine learning Random Forest Thoracic Surgery

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Application of Hybrid PSO and LS-SVR intelligent Recognition of Dual Linear Axis Dynamic Synchronization Error Characteristics

Application of Hybrid PSO and LS-SVR Intelligent Recognition...

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3rd International Symposium on intelligent Information Technology Application

作者： Lin, Xiankun Yuan, Bo Han, Shizhuo Univ Shanghai Sci & Technol Coll Mech Engn Shanghai 200093 Peoples R China

ISBN: (纸本)9780769538594

The synchronization error of feed axis has direct relation with contour error in interpolation motions. Recognition of synchronization error of dual linear motor driven axis with gantry frame has an important role in compensating the error to avoid the effect of contour precision. In the contribution, a combination of Particle Swarm optimization (PSO) algorithm and Least Squares Support Vector Regression (LS-SVR) machine technique for intelligent recognition of dynamic synchronization error in dual linear axis feed process is presented. The 2d-time function of laser interferometer is applied in acquisition of the dynamic synchronization error data as recognition learning patterns. In recognition of various error characteristics during different process in the feed motion, dual important parameters of LS-SVR, support vector number and error punishment factor, are tuned with PSO in an intelligent way. To demonstrate the procedure of the proposed approach, an illustration is discussed in detail. The result shows that the combination technique can decrease support number 38.5% and effectively recognize the characteristics with 9.04 mu m error under 40m/min feed rate motion condition.

关键词： Linear motor Synchroniztion Error LS-SVR Recognition

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Machine learning based computational electromagnetic methods for intelligence CAD/CAE application 3

Machine learning based computational electromagnetic methods...

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3rd International Youth conference on Radio Electronics, Electrical and Power Engineering, REEPE 2021

作者： Dubovitskiy, Maxim A. Pashaev, Soltanbek Yu. Zhukov, Aleksandr O. Department of Radio Devices and Antenna Systems National Research University Moscow Power Engineering Institute Moscow Russia

ISBN: (纸本)9781728183985

The key issues of combined electrodynamic methods construction in relation to the analysis of antenna systems and microwave elements are considered. Based on combined methods, the general algorithms of the analysis and functioning of modern CAD in the field of electrodynamic modeling are constructed. Further development of computer technologies shows trends towards the creation of intelligent CAD/CAE systems using Machine learning to automate computing processes. In this paper, we propose to consider three levels of intelligence achieved in the development of CAD entirely on the organizational principles of Artificial Intelligence systems using both formal and heuristic design procedures. © 2021 IEEE.

关键词： Computer aided design

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A study on the application of named entity recognition in resume parsing 3

A study on the application of named entity recognition in re...

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3rd International conference on intelligent Computing and Human-Computer Interaction, ICHCI 2022

作者： Liang, Haifeng Chengdu University of Information Technology Shuangliu Chengdu610225 China

ISBN: (纸本)9781510661301

In recent years, with the development of machine learning as well as deep learning, named entity recognition has also been rapidly developed. Currently, with the huge saturation of the talent market, resulting in a dramatic increase in the number of resumes, it has become more difficult to screen resumes, and company personnel have taken on a huge burden of screening work. The application of named entity recognition on resume parsing can greatly shorten the time of resume screening and apply more time to the screening of target talents. Based on the problems faced by named entity recognition in resume parsing, this paper proposes traditional machine learning models HMM, CRF and deep learning model BiLSTM+CRF to solve them. After a series of experiments, the effectiveness of the proposed method is proved. © 2023 SPIE.

关键词： Deep learning

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3rd International conference on Applied Machine learning, ICAML 2021

作者： Li, Xuanda Zhou, Lanjiang School of Information Engineering and Automation Kunming University of Science and Technology Kunming650500 China Key Laboratory of Intelligent Information Processing Kunming University of Science and Technology Yunnan Kunming650500 China

ISBN: (纸本)9781665421256

Similarity œmputing of short texts in botli Ctiinese and Lao is a basic t»sk in bilingual research of the Chinese and Lao, which is widely used in the fields of text matching and QA question answering. The corpus resources of the Lao language are scarce, and the research results by current methods are poor. Therefore, we propose a similarity calculation method by incorporating bilingual language features and combining the common part-of-speech features of Chinese and Lao bilinguals to obtain more semantic information from the limited training corpus^ At the same time, we get the text-matching information by the Poly network to further improve the accuracy of semantic vector representation, and finally, the similarity calculation model of Chinese and Lao biUngual short texts is constructed. Experimental results show that the proposed method achieves better results than the existing methods (74.95%). © 2021 IEEE

关键词： Calculations

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Short-term Wind Power Forecasting with Evolutionary Deep learning 3

Short-term Wind Power Forecasting with Evolutionary Deep Lea...

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3rd IEEE conference on Energy Internet and Energy System Integration, EI2 2019

作者： Yi, Lingzhi Sun, Haoyi Qiu, Dongzhou Chen, Zhang Chang, Fengming Zhao, Jian Xiangtan University School of Information Engineering Xiangtan China CHD Tieling Power Generation Co. LTD Zhuzhou China CRRC Zhuzhou Electric Locomotive Co. LTD Zhuzhou China

ISBN: (纸本)9781728131375

Accurate short-term wind power forecasting is essential for economic dispatching and secure operation of power system with large-scale wind power integrated. The traditional prediction model performs poorly on the feature extraction of high dimensional non-linear wind power data, hence, a prediction model based on evolutionary deep-learning feature extraction which is used to improve the wind power forecasting accuracy was proposed in this paper. Using the global optimization characteristics of evolutionary algorithms, the deep-learning optimization reconstruction error function constructed a feature extraction prediction model with good performance, and reduced the hidden layer structure. The real-world data collected from a wind farm in Liaoning Province was applied to verify the conclusions. Results show that the proposed method outperform BP neural network, support vector machine and DBN with respect to forecasting accuracy. © 2019 IEEE.

关键词： Wind power

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