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International Conference on Advances and Applications of Artificial Intelligence and Machine Learning, ICAAAIML 2022

作者： Cheruvu, Sai Manoj Sesank, Domakonda Sandilya, K. Sriram Gupta, Manu Department of Electronics and Computer Engineering Sreenidhi Institute of Science and Technology Hyderabad India

ISBN: (纸本)9789819959730

Vituperation refers to abuse or something bitter that is against the ethics. Vituperation is disturbing to victims and might lead to consequences like depression, anxiety, fear etc. Due to the COVID-19 outbreak many businesses have chosen online platforms to operate smoothly. The involvement of networks has increased transactions of various multi-media like images, videos, live videos etc. The cyber criminals or people with faulty mindsets might utilize these platforms to transmit multi-media containing different types of abuse to disturb individuals or business. Therefore, a need for preventing, filtering the impairing content has increased exponentially. The proposed work is capable of detecting multiple vituperative activities: Abusive content in image detection, Alcohol detection, Guns detection, Cuss gesture detection, and Religious flags in images and videos. This multi-domain architecture is built upon an Open-Source computer Vision technique using YOLOv5 and optical character recognition methodologies. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2023.

关键词： Network architecture

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Analysing Forecasting of Stock Prices: An Explainable AI Approach 2

Analysing Forecasting of Stock Prices: An Explainable AI App...

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2nd International Conference on Machine Learning and Data engineering, ICMLDE 2023

作者： Kumar, Priyanshu Hota, Lopamudra Tikkiwal, Vinay Anand Kumar, Arun Department of Computer Science and Engineering National Institute of Technology Rourkela769008 India Department of Electronics and Communication Engineering Jaypee Institute of Information Technology Noida201304 India

Predicting stock prices is a well-known and significant problem. We can learn about market behaviour over time and identify trends that might not have been seen without an effective stock prediction model. Machine learning will be a useful approach to solving this issue with the increased processing capacity of computers. Behavioural economics also asserts that the investments made by investors depend on their emotions so psychological theories can also be applied to explain their behaviour and its impact on the market. Combining the analysis of these behavioural patterns with the use of historical financial data sets can result in an approach for accurate stock price predictions. The primary focus of the proposed model is on comparing different models to provide a comparative analysis of the results provided by models used in the literature. The paper provides insight into the improvisation of the current techniques and how different parameters and different error analysis techniques can be implemented. © 2024 Elsevier B.V.. All rights reserved.

关键词： Forecasting

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A Deep Learning Based Enhancing the Power by Reducing the Harmonics in Grid Connected Inverters

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Distributed Generation and Alternative Energy Journal 2024年第1期39卷 137-164页

作者： Sarma, Subramanya S. Sarada, K. Jithendar, P. Maddileti, Telugu Kumar, G. Nanda Kishor Department of EEE Ramachandra College of Engineering Vatluru Andhra Pradesh Eluru534007 India Department of Electrical & Electronics Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram India Department of Electrical and Electronics Engineering MLR institute of Technology Hyderabad India Telangana 500100 India Computer Science and Engineering Malla Reddy University Telangana Hyderabad India

The increasing use of renewable energy systems has led to a rise in the number of grid-connected inverters, which can have a detrimental effect on the superiority and constancy of grid electricity due to the injected current harmonics. In this study, the proportional integral (PI) and proportional resonant (PR) controllers have been investigated for their effectiveness inreducing harmonics in grid-connected inverters. The study also investigates the impact of harmonics compensators (HC) on the control strategies. The results of the study suggest that the implementation of PI and PR controllers in the synchronous frame can effectively reduce the injected current harmonics in grid-connected inverters. The use of harmonics compensators can further enhance the performance of the controllers by reducing the distortion and improving the stability of the grid. The efficiency of the regulator strategies be contingent on the type and level of harmonics in the grid, as well as the design and tuning of the controllers and compensators. The statement that the "PR+HC controller has a superior quality output current" is more specific and suggests that this control method may be more effective than the others in reducing harmonics and enlightening the value of the productivity current. The comparison of the IEEE 1547 standard by three viable inverters from diverse constructors is also noteworthy, as it can provide insights into the compatibility and performance of different types of inverters with the standard. The use of deep learning with the RCNN network for analyzing harmonics and providing information about power is an interesting application of machine learning in power systems research. This approach may have the probable to development the accuracy and competence of harmonics analysis as well as power monitoring in grid-connected inverters. Overall, the study highlights the importance of effective control strategies for managing harmonics in grid-connected inverters, parti

关键词： Controllers

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Exploratory Data Analysis on Shopping Mall Customers’ Dataset: A Case Study of Marketing Analysis 7th

Exploratory Data Analysis on Shopping Mall Customers’ Datas...

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7th International Conference on Advanced Computing and Intelligent engineering, ICACIE 2022

作者： Swain, K.P. Misra, S. Barik, Shekharesh Samal, S.R. Sahoo, D. Department of Electronics and Communication Engineering GITA Autonomous College Bhubaneswar India Department of Computer Science and Engineering DRIEMS University Cuttack India Department of Electronics and Communication Engineering Silicon Institute of Technology Bhubaneswar India

ISBN: (纸本)9789819950140

Nowadays, data visualizations and data analysis are the most significant fact in providing a quick and clear understanding of the information. An exploratory data analysis of shopping mall customers’ data set is presented in this study to achieve the customers’ segmentation which will beneficial for making marketing strategies. To achieve this, deep data analysis is carried out by extracting specific items from the dataset to know the different groups of customers coming into a mall and their spending behaviors. Depending upon their spending behavior, a machine learning model is created using the K-Means clustering algorithm to serve different marketing strategies for different groups of customers. The developed model can make a business impact to solve similar scenario problems for improving the decision-making related to the best marketing strategies for the customers. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Data visualization

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Exploring Twitter Sentiments for Predicting Match Outcomes in the Game of Cricket 2

Exploring Twitter Sentiments for Predicting Match Outcomes i...

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2nd International Conference on Machine Learning and Data engineering, ICMLDE 2023

作者： Sharma, Karan Hota, Lopamudra Tikkiwal, Vinay Anand Kumar, Arun Department of Computer Science and Engineering National Institute of Technology Rourkela769008 India Department of Electronics and Communication Engineering Jaypee Institute of Information Technology Noida201304 India

In recent years, social media platforms such as Twitter have become popular among fans to discuss and share their opinions about the matches. This research aims to contribute to the growing body of knowledge on utilizing the sentiments expressed through messages on social media platforms to predict outcomes of matches in the sport of cricket. This research work provides insights into the effectiveness of sentiment analysis techniques in forecasting match outcomes, which can be beneficial to cricket fans, and cricket analysts. In this work, sentiment analysis is carried out based on models developed using various machine-learning techniques. The work also explores the impact of different features, such as the number of tweets collected, the timing of tweets, and the sentiment analysis techniques on the accuracy of the prediction. The results of this study have implications for cricket fans, sports analysts, and the broader field of predictive analytics. © 2024 Elsevier B.V.. All rights reserved.

关键词： Sentiment analysis

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Metaheuristic Optimization Algorithm for Signals Classification of Electroencephalography Channels

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computers, Materials & Continua 2022年第6期71卷 4627-4641页

作者： Marwa M.Eid Fawaz Alassery Abdelhameed Ibrahim Mohamed Saber Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Department of Computer Engineering College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Department of Electronics and Communications Engineering Faculty of EngineeringDelta University for Science and TechnologyMansoura11152Egypt

Digital signal processing of electroencephalography(EEG)data is now widely utilized in various applications,including motor imagery classification,seizure detection and prediction,emotion classification,mental task classification,drug impact identification and sleep state *** the increasing number of recorded EEG channels,it has become clear that effective channel selection algorithms are required for various *** Whale Optimization Method(Guided WOA),a suggested feature selection algorithm based on Stochastic Fractal Search(SFS)technique,evaluates the chosen subset of *** may be used to select the optimum EEG channels for use in Brain-computer Interfaces(BCIs),the method for identifying essential and irrelevant characteristics in a dataset,and the complexity to be *** enables(SFS-Guided WOA)algorithm to choose the most appropriate EEG channels while assisting machine learning classification in its tasks and training the classifier with the ***(SFSGuided WOA)algorithm is superior in performance metrics,and statistical tests such as ANOVA and Wilcoxon rank-sum are used to demonstrate this.

关键词： Signals metaheuristics optimization feature selection multilayer perceptron support vector machines

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Digital Twin-Based Automated Fault Diagnosis in Industrial IoT Applications

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computers, Materials & Continua 2023年第4期75卷 183-196页

作者： Samah Alshathri Ezz El-Din Hemdan Walid El-Shafai Amged Sayed Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science and Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Computer Science Department Security Engineering LabPrince Sultan UniversityRiyadh11586Saudi Arabia Department of Electronics and Electrical Communications Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Department of Industrial Electronics and Control Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Department of Electrical Energy Engineering College of Engineering&TechnologyArab Academy for Science Technology&Maritime TransportSmart Village CampusEgypt

In recent years, Digital Twin (DT) has gained significant interestfrom academia and industry due to the advanced in information technology,communication systems, Artificial Intelligence (AI), Cloud Computing (CC),and Industrial Internet of Things (IIoT). The main concept of the DT isto provide a comprehensive tangible, and operational explanation of anyelement, asset, or system. However, it is an extremely dynamic taxonomydeveloping in complexity during the life cycle that produces a massive amountof engendered data and information. Likewise, with the development of AI,digital twins can be redefined and could be a crucial approach to aid theInternet of Things (IoT)-based DT applications for transferring the data andvalue onto the Internet with better decision-making. Therefore, this paperintroduces an efficient DT-based fault diagnosis model based on machinelearning (ML) tools. In this framework, the DT model of the machine isconstructed by creating the simulation model. In the proposed framework,the Genetic algorithm (GA) is used for the optimization task to improvethe classification accuracy. Furthermore, we evaluate the proposed faultdiagnosis framework using performance metrics such as precision, accuracy,F-measure, and recall. The proposed framework is comprehensively examinedusing the triplex pump fault diagnosis. The experimental results demonstratedthat the hybrid GA-ML method gives outstanding results compared to MLmethods like LogisticRegression (LR), Na飗e Bayes (NB), and SupportVectorMachine (SVM). The suggested framework achieves the highest accuracyof 95% for the employed hybrid GA-SVM. The proposed framework willeffectively help industrial operators make an appropriate decision concerningthe fault analysis for IIoT applications in the context of Industry 4.0.

关键词： Automated fault diagnosis control system ML AI CC IIoT digital twins genetic algorithm GA-ML technique

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Brain Tumor Classification using MR Images and Transfer Learning 2

Brain Tumor Classification using MR Images and Transfer Lear...

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2nd International Conference on Edge Computing and Applications, ICECAA 2023

作者： Gupta, Manu Shirisha, Gangadi Chandana, Aela Mani Ankitha, Belide Sreenidhi Institute of Science and Technology Department of Electronics and Computer Engineering Hyderabad India

ISBN: (纸本)9798350347579

A brain tumor is the irregular development of a mass tissue within the brain or close to it, which has the capacity to spread and duplicate the wildly affecting organs within the body. Brain tumor classification is a vital assignment to assess the tumors and make a treatment choice concurring with their classes. In this study, a model for the classification of brain tumors from MRI images utilizing transfer learning is developed. The convolution neural network (CNN), VGG16 and InceptionV3 techniques are applied to extract features and perform classification with brain MRI image dataset as input. The precision, accuracy, recall, and f1-score metrics are computed to evaluate the performance of the developed model. The results obtained show that the proposed pre-trained model VGG16 achieved better results than other models with an accuracy of 98%. © 2023 IEEE.

关键词： Magnetic resonance

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Virtual Twin Analytics: Advancing IoT Security through AI-Powered Software Informatics 1

Virtual Twin Analytics: Advancing IoT Security through AI-Po...

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1st International Conference on Software, Systems and Information technology, SSITCON 2024

作者： Nanthini, S. Jain, Upma Arora, Renuka Bhatia, Rishu Sutaria, Kruti Patil, Harshal Saveetha School of Engineering Department of Computer Science and Engineering Chennai India Graphic Era Deemed to Be University Department of Computer Science and Engineering Dehradun India Jagannath University Department of Computer Science and Engineering Haryana India Ganga Institute of Technology and Management Department of Electronics and Communication Engineering Haryana India Parul Institute of Engineering and Technology Faculty of Engineering and Technology Gujarat India School of Computer Science and Engineering Uttar Pradesh India

ISBN: (纸本)9798350352931

The Internet has grown in importance and impact over the years, causing people to become more reliant on it. The Internet has evolved into a major vector for cybercrime because to its ever-increasing user base. Over the last decade, the number of these computing systems - including desktops, laptops, smartphones, and the Internet of Things (IoT) - has skyrocketed. Among them, cell phones are practically integral to modern life. The popularity of web-based assaults has skyrocketed with the exponential growth in the number of individuals using the Internet. These web-based assaults are increasingly being combatted by security corporations. Unfortunately, new forms of these assaults are appearing all the time, making it hard for older security measures to stay up. Artificial intelligence (AI) is a source of optimism in the current cybersecurity landscape, offering a potential solution to the ever-changing digital dangers. The fast development of AI over the last decade has given rise to this optimism, because it is now impacting the expansion of every industry. With AI bringing so many advantages in every field, online security is one sector that just cannot afford to ignore it. This planned effort's work represents an advance in that direction. Critical online security issues have been the focus of this proposed work's study, which aims to address these issues using AI. Web security issues for desktop and mobile devices have been addressed in the proposed work. The planned work's contributions to online security are as follows: The 'MalCrawler' web crawler is a targeted tool for finding and exploring the web. This crawler makes it easy to gather websites, particularly ones that are harmful. It does a better job of collecting dangerous websites than a typical crawler. Additionally, it is built to circumvent the evasion strategies used by rogue websites. The crawler's ability to gather webpages - particularly dangerous ones - in order to provide datasets for ML-based an

关键词： Web crawler

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Magnetics Design Optimization for LLC Converter employing Machine Learning 11

Magnetics Design Optimization for LLC Converter employing Ma...

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11th International Conference on Power electronics - ECCE Asia, ICPE 2023-ECCE Asia

作者： Tung, Nguyen Tan Pong, Man-Hay Chiu, Huang-Jen National Taiwan University of Science and Technology Department of Electronics and Computer Engineering Taiwan

ISBN: (纸本)9788957083505

This paper proposes an intelligent and machine-learning based optimization method that targets to optimal windings layer setup for LLC converter transformer with small number of optimizing iterations. The research utilizes Bayesian optimization (BO) to guide designers toward winding layers setup that achieves the maximum efficiency at nominal operating point of LLC converters. Furthermore, a new multiphysics simulation platform is proposed for feeding efficiencies to optimizing iterations. This simulation platform is able to provide entire losses estimation, high simulation accuracy is verified by measurement. An LLC converter prototype with 33V input and 400V/250W output is implemented to demonstrate effectiveness of proposed optimization method. © 2023 The Korean Institute of Power electronics.

关键词： Simulation platform

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