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8th international conference on Life System Modeling and Simulation, LSMS 2024 and 8th international conference on Intelligent computing for Sustainable Energy and Environment, ICSEE 2024

8th International Conference on Life System Modeling and Sim...

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8th international conference on Life System Modeling and Simulation, LSMS 2024 and 8th international conference on Intelligent computing for Sustainable Energy and Environment, ICSEE 2024

ISBN: (纸本)9789819602315

The proceedings contain 158 papers. The special focus in this conference is on Life System Modeling and Simulation and Intelligent computing for Sustainable Energy and Environment. The topics include: A Method for Evaluating the Operating Health of Wind Turbines Under Wake Effect;toward Next-Generation Buildings: A Review of Renewable Energy Integration and Intelligent Building Energy Management Technologies;improving the Construction of a Multi-modal Underwater Image recognition System for Anomaly Detection in Nuclear Power Plant Cooling Source Areas;A Novel Anti-noise Fault Diagnosis Method for Nuclear Energy Steam Turbine Based on VCformer;Short-Term Photovoltaic Power Forecasting Using Multi-timescale Information Based FFTformer Model;wind Power Forecasting Model Based on Feature Processing and Deep Hybrid Kernel Extreme Learning Machine;optimal User-Side Energy Arbitrage Strategy in Electricity Market with Accurate Battery Model Using Benders Decomposition;a Novel Distributed Economic Dispatch Method for Smart Grid with Time-Varying Feedback Gain;benefits, Barriers, and Human Factors of V2X Implementation – A Brief Review;a General pattern of Permanent Magnet Arrangement for Design Optimization of a Reluctance Magnetic Gear with Enhanced Torque Density for Electric Vehicles;wind Power Scheduling Deviation Compensation Based on Dual-Frequency Pulse Control;a Comprehensive Simulation and Validation of Single Particle Aging Mechanism Model for Lithium-Ion Batteries;semi-supervised State of Health Estimation for Lithium-Ion Batteries via Dynamical Self-encoding;a Reduce-Order Model to Understand the Multiscale Coupling of Battery Electrochemical-Thermal Performance;RUL Prediction for Lithium-Ion Batteries Based on Sequence Decomposition and Transformer Networks;An Estimation Method of State of Health of Sodium-Ion Batteries Based on TCN.

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Women Safety Analysis based on Twitter (Machine Learning)

Women Safety Analysis based on Twitter (Machine Learning)

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MIT Art, Design and Technology School of computing international conference (MITADTSoCiCon)

作者： Gaikwad Nileema Prasad Shinde Pranoti Mohan Sayyad Tabassum Shamshuddin Vadgaonkar Prathamesh Anil Patil Vaishnavi Vijay Department of Computer Engineering K.E. Society’s Rajarambapu Institute of Technology Uran Islampur India

ISBN: (数字)9798350362879

ISBN: (纸本)9798350362886

In today's world, protecting women's safety in public areas is of utmost importance. As social media has grown in popularity, Twitter has emerged as a useful real-time data source reflecting public opinion and incidences pertaining to women's safety. In order to obtain an understanding of the frequency and character of conversations pertaining to women's safety, this study uses machine learning techniques to examine data from Twitter. Using pertinent hashtags and keywords, a sizable dataset of tweets on women's safety is gathered as part of the suggested technique. The text data will be preprocessed using Natural Language Processing (NLP) techniques in order to extract significant characteristics and classify tweets as positive, negative, or neutral using sentiment analysis. Additionally, the usage of Named Entity recognition will be investigated to forecast the sentiment and context of tweets, machine learning models—such as classification algorithms—will be trained on labeled datasets. The purpose of this investigation is to pinpoint hotspots, patterns, and trends pertaining to women's safety issues. Furthermore, temporal analysis will be performed to track changes in event frequency over time. By using Twitter data, the research will help construct a predictive algorithm that can detect any safety hazards. Law enforcement agencies, legislators, and community organizations may find the study's findings useful in putting preventive measures in place to improve women's safety.

关键词： Time-frequency analysis Machine learning algorithms Social networking (online) soft sensors Blogs Machine learning Prediction algorithms

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8th international Workshop on soft computing Applications, SOFA 2018

8th International Workshop on Soft Computing Applications, S...

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8th international Workshop on soft computing Applications, SOFA 2018

ISBN: (纸本)9783030519919

The proceedings contain 69 papers. The special focus in this conference is on soft computing Applications. The topics include: Design of intelligent stabilizing controller and payload estimator for the electromagnetic levitation system: DOBFLC approach;on the concept of fuzzy graphs for some networks;fuzzy logic in predictive harmonic systems;particle swarm optimization ear identification system;the enhanced crow search algorithm for fuel-cost function parameters assessment of the cogeneration units from thermal power plants;puzzle learning trail generation using learning blocks;design of an extended self-tuning adaptive controller;assessing the quality of bread by fuzzy weights of sensory attributes;catalan numbers associated to integer partitions and the super-exponential;emotional control of wideband mobile communication;modelling a photovoltaic power station;identification of fault locations on tree structure systems;probability distribution functions for short-term wind power forecasting;feedback seminar analysis - An introductory approach from an intelligent perspective;flexible fuzzy numbers for likert scale-based evaluations;inventory optimization model parameter search speed-up through similarity reduction;preface;daily load curve forecasting. comparative analysis: Conventional vs. unconventional methods;on posets for reliability: How fine can they be?;comparison of neural network models applied to human recognition;a genetic deep learning model for electrophysiological soft robotics;blockchain. Today applicability and implications;integrating technology in training: System dynamic-based model considerations regarding smart training and its relationship with educational components;a survey on nonlinear second-order diffusion-based techniques for additive denoising;cathedral and indian mughal monument recognition using tensorflow;analysis of interrelationship for lean and sustainability principles and implications.

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Intelligent False Data Injection Attack Detection Using soft computing in Cyber-Physical Power Systems

Intelligent False Data Injection Attack Detection Using Soft...

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international conference of Electronics, Communication and Aerospace Technology (ICECA)

作者： Prerana Nilesh Khairnar Bindu K V Md. Abul Ala Walid S. Jothimani Subha B Aviral Srivastava Department of ETE Sir Visvesvaraya Institute of Technology Nashik Department of EEE RMK College of Engineering And Technology Puduvoyal Department of Computer Science & Engineering Khulna University of Engineering & Technology (KUET) Khulna Bangladesh Bannari Amman Institute of Technology Sathyamangalam Department of Management Kristu Jayanti College Bangalore Department of CSE Amity University Rajasthan Jaipur

The modern power system has developed into a cyber-physical system with the incorporation of smart meters, Phasor Measurement Units (PMUs), extensive area monitoring, control systems and substation automation. False Data Injection Attack (FDIA) has gained popularity in Cyber-Physical Power System (CPPS). In particular, there is an increasing need to examine the possible behavior of adversaries to enhance the cybersecurity nature of CPPS but the FDIA model is frequently built on the effects of attacker or the effect of attack. Therefore, this study presents an Intelligent False Data Injection Attack Detection using soft computing (IFDIAD-SD) technique in the CPPS environment. The aim of the IFDIAD-SD technique is the effectual recognition and classification of FDIAs in the CPPS environment. In the IFDIAD-SD method, fuzzy min-max normalization is used to scale the input data. For FDIA detection and classification, convolutional neural network with autoencoder (CNN-AE) method is applied. At final stage, the multiverse optimization (MVO) technique is used to optimally select the parameter values of the CNN-AE model. A series of experiments were implemented for identifying the superior performance of the IFDIAD-SD technique. The comprehensive comparative result analysis revealed the supremacy of the IFDIAD-SD method when compared to other models.

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8th international conference on Life System Modeling and Simulation, LSMS 2024 and 8th international conference on Intelligent computing for Sustainable Energy and Environment, ICSEE 2024

8th International Conference on Life System Modeling and Sim...

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8th international conference on Life System Modeling and Simulation, LSMS 2024 and 8th international conference on Intelligent computing for Sustainable Energy and Environment, ICSEE 2024

ISBN: (纸本)9789819602933

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Attention-based Deep Learning Methods for Document Layout Analysis 22

Attention-based Deep Learning Methods for Document Layout An...

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8th international conference on computing and Artificial Intelligence, ICCAI 2022

作者： Sun, Hao-Yue Zhong, Ying Wang, Da-Han School of Computer and Information Engineering Xiamen University of Technology Xiamen361024 China Fujian Key Laboratory of Pattern Recognition and Image Understanding Xiamen361024 China

ISBN: (纸本)9781450396110

Deep learning has unique advantages in document layout analysis. In this paper, we propose to use the attention mechanism to improve the deep learning based methods for document layout analysis, and conducted comprehensive experiments to evaluate the effects of different models/modules/backbones. Firstly, the evaluated backbones include ResNet-50 and ResNet-101 for their high performance, and the popularly used Fast RCNN and Faster RCNN based methods. Secondly, for the loss function, the Focal loss function is also adopted to improve the performance of document layout analysis. Most importantly, we propose to integrate the attention mechanism into the deep learning-based methods to improve the performance. Specifically, when calculating the residual using this ResNet network, we use the attention to perform a dot product operation on the degraded accuracy to correct the residual error of the network calculation, which obtains a higher detection accuracy. We conducted experiments on the PubLayNet, DocBank and ICDAR-2013 datasets for evaluation. Experimental results show that, with the attention mechanism, the basic deep learning-based methods has been significantly improved in the task of document layout analysis, demonstrating the effectiveness of the proposed method. The focal loss that is designed to overcome the problem of imbalance number of training samples is also effective for document layout analysis. © 2022 Association for computing Machinery. All rights reserved.

关键词： Deep learning

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Multi-Scale Channel Attention for Chinese Scene Text recognition 22

Multi-Scale Channel Attention for Chinese Scene Text Recogni...

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Proceedings of the 2022 11th international conference on computing and pattern recognition

作者： Haiqing Liao Xia Du Yun Wu Da-Han Wang Fujian Key Laboratory of Pattern Recognition and Image Understanding Xiamen University of Technology China

ISBN: (纸本)9781450397056

Scene text recognition have proven to be highly effective in solving various computer vision tasks. Recently, numerous recognition algorithms based on the encoder-decoder framework have been proposed for handling scene texts with perspective distortion and curve shape. Nevertheless, most of these methods only consider single-scale features while not taking multi-scale features into account. Meanwhile, the existing text recognition methods are mainly used for English texts, whereas ignoring Chinese texts' pivotal role. In this paper, we proposed an end-to-end method to integrate multi-scale features for Chinese scene text recognition (CSTR). Specifically, we adopted and customized the Dense Atrous Spatial Pyramid Pooling (DenseASPP) to our backbone network to capture multi-scale features of the input image while simultaneously extending the receptive fields. Moreover, we added Squeeze-and-Excitation Networks (SE) to capture attentional features with global information to improve the performance of CSTR further. The experimental results of the Chinese scene text datasets demonstrate that the proposed method can efficiently mitigate the impacts of the loss of contextual information caused by the text scale varying and outperforms the state-of-the-art approaches.

关键词： Chinese Scene Text recognition Multi-scale Dense Atrous Spatial Pyramid Pooling

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8th international conference on Life System Modeling and Simulation, LSMS 2024 and 8th international conference on Intelligent computing for Sustainable Energy and Environment, ICSEE 2024

8th International Conference on Life System Modeling and Sim...

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8th international conference on Life System Modeling and Simulation, LSMS 2024 and 8th international conference on Intelligent computing for Sustainable Energy and Environment, ICSEE 2024

ISBN: (纸本)9789819602247

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Review of Place recognition Approaches: Traditional and Deep Learning Methods 14th

Review of Place Recognition Approaches: Traditional and Deep...

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14th international conference on Applications of Fuzzy Systems, soft computing, and Artificial Intelligence Tools, ICAFS 2020

作者： Taha, Mohammed Abdulghani Şah, Melike Direkoğlu, Cem Department of Computer Engineering Tishik International University Erbil Iraq Department of Computer Engineering Near East University via Mersin 10 Nicosia North Cyprus Turkey Department of Electrical and Electronics Engineering Middle East Technical University - Northern Cyprus Campus via Mersin 10 Kalkanli Guzelyurt North Cyprus Turkey

ISBN: (纸本)9783030640576

Place recognition is an important topic in many vision-based applications. For this purpose, many traditional and artificial intelligence-based methods have been proposed. Despite years of expertise accrued in this area, it still remains a challenging problem due to the various ways in which the appearance of places in the real world can take place. Recently, rapid improvements have been made in image processing and recognition using deep neural networks. In particular, convolutional neural frameworks (CNN) are widely used in object detection, image classification, as well as in place recognition. The advantage of CNN-based place recognition is that CNN methods can automatically learn image patterns using sample images without any pre-processing and can handle appearance variations better than traditional methods. In this paper, we review and discuss traditional and deep learning-based place recognition algorithms, as well as existing datasets that can be used for performance measurement. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Object detection

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Local Binary pattern for Binary Object Classification using Coordination Number (CN)∗ and Hu's Moments 9

Local Binary Pattern for Binary Object Classification using ...

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9th IEEE international conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions), ICRITO 2021

作者： Kumar, Ratnesh Mali, Kalyani University of Kalyani Dept. of Computer Science and Engineering West Bengal Nadia India

ISBN: (纸本)9781665417037

Hu's Moment corresponding to the Local Binary pattern (LBP) features vector have been frequently used for binary objects pattern recognition and classification. Hu's Moments are used in a variety of applications due to its geometrically invariant features on object translation, rotation and scaling. In this paper, we have formulated the methodology of the Hu's Moment by using Local Binary patterns. The minimum responding decimal values of the developed model for the Local Binary pattern (LBP) corresponding to the Coordination Number (CN)∗ of each contour point of an object are used to compute Hu's seven moments. We have incorporated the local binary patterns corresponding to the coordination number of object contour points in a modified model for computing similarity between two binary objects. The experimental results conducted on Kimia-99 and MPEG-7 CE datasets demonstrate that our approach shows better results compared to the other existing algorithms. © 2021 IEEE.

关键词： Local binary pattern

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