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Detection Total Harmonics Distortion in PV Inverters using Various Strategy

Detection Total Harmonics Distortion in PV Inverters using V...

2024 International Conference on Sustainable Computing and Integrated Communication in Changing Landscape of AI, ICSCAI 2024

作者： Pandey, Shiven Mani Husain, Saif O. Kumar, Yogendra Goyal, Himanshu Rai Michaelson, Jacob Aswini, Kilaru Kumar, Manoj Lloyd Institute of Engineering & Technology Greater Noida India Department of Computers Techniques Engineering College of Technical Engineering The Islamic university Najaf Iraq Department of Electrical Engineering GLA University Mathura India Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Institute of Aeronautical Engineering Dundigal Hyderabad India Department of Electronics and Communication Engineering MLR Institute of Technology Hyderabad India

ISBN: (纸本)9798350355338

The fundamental purpose of this research is to develop a photovoltaic-based MLI that implements a pulse width modulation strategy as its mode of operation. Within the context of this specific illustration of a multilayer inverter, two switches are used so that the fundamental frequency and the carrier frequency may be switched. The first three sections of this article focus on analyzing and discussing the impact that varied levels of carrier signal have on inverters. Determine the effect that it has on the voltage, current, and harmonics distortion. Check to determine what type of impact it has. This was one of the comparisons that involved a seven-level inverter. Calculations and results acquired from a modified version of the MATLAB program indicate that the whole harmonics range may be covered by combining the level change technology with multi-stage hybrid inverters. These calculations and results were achieved. © 2024 IEEE.

关键词： MATLAB

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AI Based Virtual Laboratory Using Intelligent Controller with CC-Link Remote IO Module

AI Based Virtual Laboratory Using Intelligent Controller wit...

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Sustainable Computing and Smart Systems (ICSCSS), International Conference on

作者： Pravin Annadate Akshatha Bhat Satish Bojjawar Hariom Patidar P.A. Prassath S. Gokul Department of Electronics and Computer Science Vidyalankar Institute of Technology Mumbai Department of Electronics and Instrumentation Engineering CVR College of Engineering Hyderabad Electronics engineering department Medicaps University Indore Department of Electronics and Communication Engineering Arjun College of Technology Coimbatore Department of Mechanical Engineering Sri Eshwar College of Engineering Coimbatore India

ISBN: (数字)9798350379990

ISBN: (纸本)9798350391558

Most universities and colleges in the world are offering their courses online. Online education alone does not help to develop the full learning potential. Methodology is very important for skill development of students and teachers. By connecting all the machines in the labs to the customized web page, everyone can do the tests from anywhere. This research study determines the efficiency of a transformer using Artificial Intelligence (AI). All types of meters in the circuit of the transformer are compatible with the Internet of Things (IoT). Thus, the quantities displayed by measuring instruments can be measured from a distance. The load applied to the transformer can be increased or decreased as required. All these controls are automatically controlled by the controller. Here, Q03UDE Modular controller is used to control the experimental setup and the readings are monitored in GOT2000 HMI. The readings are measured and tabulated from the remote environment via the Virtual environment.

关键词： Meters Simulation Virtual environments Web pages Transformers Safety Internet of Things

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Deep ensemble optimal classifier-based software defect prediction for early detection and quality assurance

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Journal of Umm Al-Qura University for engineering and Architecture 2025年 1-12页

作者： Srinivas, Kalyanapu Janapati, Krishna Chaithanya Sai Kiran, Mungalched Sudheer Reddy, Davu Vinay, Thota Vamshi Yashwanth, Choula Department of Computer Science and Engineering Dhanekula Institute of Engineering and Technology Andhra Pradesh Vijayawada India Department of Electronics and Communication Engineering Vardhaman College of Engineering Telangana Hyderabad India Department of Computer Science and Engineering (Data Science) Malla Reddy Engineering College and Management Sciences Medchal Telangana Kistapur India

Software Defect Prediction (SDP) is critical in identifying fault-prone modules during the software development life cycle, enhancing software quality, and reducing maintenance costs. However, existing SDP models face significant challenges, including high false positive rates and severe class imbalance between defect-prone and non-defect modules, which hinder practical model training and accurate defect detection. To overcome these challenges, this study proposes a robust and efficient SDP architecture that integrates several advanced techniques to enhance predictive accuracy and model reliability. The methodology begins with comprehensive data preprocessing, incorporating normalization to standardize feature scales and outlier detection to eliminate noise and improve data quality. To address the inherent class imbalance in the dataset, the minority oversampling by synthetic data (MOSD) technique is employed to generate synthetic samples for the minority class, thus achieving a more balanced distribution. Following this, an Adaptive sequential K-best (ASKB) feature selection strategy is implemented to identify and retain the most relevant features, effectively reducing dimensionality while preserving critical information. Finally, the weighted random forest (WRF) classifier is utilized, which assigns appropriate weights to training instances, enabling improved classification performance, especially for minority class instances. The proposed methodology achieved an accuracy of 99.119%, a precision of 99.431%, a recall of 99.122%, and an F1-score of 99.333%. © The Author(s) 2025.

关键词： Adaptive sequential K-best Class imbalance Data preprocessing Feature selection Minority oversampling Predictive modeling Software defect prediction Synthetic data Weighted random forest

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Exploring the Influence of Digital Media on Secondary School Students' Daily Lives 7

Exploring the Influence of Digital Media on Secondary School...

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7th IEEE International Conference and Workshop Obuda on Electrical and Power engineering, CANDO-EPE 2024

作者： Gogh, Elod Kovari, Attila Eszterházy Károly Catholic University Institute of Digital Technology Faculty of Computer Science Eger Hungary Obuda University Institute of Electronics and Communication Systems Kandó Kálmán Faculty of Electrical Engineering Budapest Hungary Institute of Computer Science University of Dunaujvaros Dunaujvaros Hungary

ISBN: (纸本)9798331531119

The focus of this study is to investigate how digital media use affects the daily activities, emotional well-being, and general lifestyle of high school students in relation to education amidst concerns about a 'new terrain or venue' for learning in an era moving full swing towards digitization. The study, conducted with students from grades 9-11, seeks to identify overall trends and associations involving digital media interactions: considering both byproducts of using new technology as an educational tool- in addition discursive aspects allowing for different forms of distraction. In summary, the results showed that digital media use is common and often regarded as educationally beneficial or a positive form of relaxation;however, daily activities are it affected by substantial adverse impacts on everyday life and time management. Suggestions focus on supporting healthy digital media habits, including content-wise engagement and time management tools. © 2024 IEEE.

关键词： Daily Activities Digital Media Digital Media Impact

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Fake Review Detection System: A Machine Learning Approach with Linguistic and Semantic Analysis

Fake Review Detection System: A Machine Learning Approach wi...

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Artificial Intelligence and Quantum Computation-Based Sensor Application (ICAIQSA), International Conference on

作者： Sagar Janokar Kiran More Rajesh Raikwar Kalpesh Joshi Prachi Pawar Tanishka Singh Electronics and Telecommunication Vishwakarma Institute of Technology Pune India Electrial Engineering Vishwakarma Institute of Technology Pune India Computer Science Engineering Vishwakarma Institute of Technology Pune India Computer Science Engineering(AI) Vishwakarma Institute of Technology Pune India

ISBN: (数字)9798331517953

ISBN: (纸本)9798331517960

Online platforms heavily rely on user reviews for decision-making, making the identification of fake reviews crucial. This paper presents a novel machine learning-based Fake Review Detection System that incorporates advanced linguistic and semantic analysis. The model's robustness is demonstrated through comprehensive evaluation metrics, showcasing its efficacy in real-world scenarios. Technological innovations in the backend system ensure seamless integration, scalability, and reliability. The outcome contributes to a deeper understanding of linguistic cues, providing a valuable tool for maintaining trust in online platforms. Numerous approaches are employed in detecting fake reviews, predominantly focusing on the linguistic cues of reviewers while overlooking their non-linguistic behaviors. This study identifies various non-linguistic behavioral traits of online reviewers and assesses their significance in detecting fake reviews compared to linguistic cues. Empirical findings from real-world online reviews demonstrate that integrating non-linguistic reviewer characteristics can substantially enhance the efficacy of fake review detection models. Nonverbal features are more important than verbal features in reviews because many reviewers can write fake reviews for promoting a product which is not as good as they portray.

关键词： Measurement Reviews Semantics User-generated content Machine learning Linguistics Feature extraction Distortion Electronic commerce Fake news

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Comparative Analysis of Wireless Communication Technologies for IoT Applications 3rd

Comparative Analysis of Wireless Communication Technologies ...

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3rd International Conference on Recent Trends in Advanced Computing - Artificial Intelligence and Technologies, ICRTAC-AIT 2020

作者： Shilpa, B. Radha, R. Movva, Pavani Department of Electronics and Communication IcfaiTech Hyderabad Shankarpally India School of Computer Science and Engineering Vellore Institute of Technology Chennai India

ISBN: (纸本)9789811664472

The Internet of things (IoT) is no longer a new word, because it got to be necessarily portioned of our daily life since of its gigantic applications in every domain. In simple terms, IoT refers to things connecting to the Internet;here things do not mean only electronic devices, and it can be anything like physical, biological, and environmental objects. In present technological world, every second, 127 new devices are connected to Internet. But in the next few years, we can hope to see the IoT-linked devices touching hundreds of billions. There are growing wireless communication technologies are evolving to handle these large connected devices. We have gone through many research and survey papers which were explaining about the IoT technologies and their performance comparison. In this paper, we tried to facilitate the end user with efficient selection of the communication technology for their IoT application by investigating the available technologies and matching with the application requirements. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Internet of things

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Detection of Disease Through Clinical Data Using Light Intensity Based Firefly Algorithm 1

Detection of Disease Through Clinical Data Using Light Inten...

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1st International Conference on Futuristic Technologies, INCOFT 2022

作者： Sahu, Bibhuprasad Pattanaik, Swarupa Ravindra, Jvr Pani, Sasmita Department of Artificial Intelligence and Data Science Hyderabad India Gandhi Institute for Education & Technology Department of Computer Science and Engineering Odisha Bhubaneswar India Department of Electronics & Communication Engineering Hyderabad India Balasore College of Engineering and Technology Department of Computer Science and Engineering Odisha Balasore India

ISBN: (纸本)9781665450461

In this study, we presented a hybrid gene selection model based on a novel multi-filter(MF) ensemble technique and light intensity difference-based Firefly Algorithm. A multifilter (ReliefF, Fisher Score, SU (Symmetrical Uncertainty), CFS (Correlation-based Feature Selection)) is applied to the original micro-array dataset to evaluate the best-ranked feature subsets individually. Ensemble multi-filer coupled with light intensity difference based firefly algorithm to achieve the optimized feature subset and at the last performance, evaluation is done using Support vector machine (SVM) by considering three different kernel parameters. From the experimental analysis, it is noteworthy to assure that the proposed methodology performs better than the existing approach. In this paper ensemble, a multi-filter method is used by implementing the concept of correlation-based redundancy and a Modified firefly algorithm (FA) called Light Intensity Difference Based Firefly Algorithm(LFA) is used as a wrapper to identify the optimal high-ranked feature subsets. So the selected subsets will be more robust for classification purposes. The correlation-based redundancy approach is used for the multi-filter case, where the top n genes of individuals are identified and a new dataset is prepared, which will be considered as input for the wrapper method. The main drawback behind considering this single filter may be trapped into the local optimum and may increase the complexity of the wrapper which may reduce the performance of the proposed model. Using multifilter (MF) not only considers all possible combinations of feature subsets by considering more than one filer but also provides a better opportunity to achieve better classification accuracy. © 2022 IEEE.

关键词： Redundancy

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Hybrid Quantum Model for Digital Media Processing for Metal Surface Defect

Hybrid Quantum Model for Digital Media Processing for Metal ...

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Advancements in Electrical, electronics, Communication, Computing and Automation (ICAECA), International Conference on

作者： Joy Saha Shreyansh Jain Roopa M Department of Computer Science and Engineering SRM Institute of Science and Technology Chennai India Department of Electronics and Communication Engineering SRM Institute of Science and Technology Chennai India

ISBN: (数字)9798331525439

ISBN: (纸本)9798331525446

Processing Digital media such images is a crucial step for any image classification task such as surface defect detection or any deep learning task which include Digital Media Processing. Ensuring the detail capturing of each and every feature of the input image from each and every pixel is must in order to build a robust system with minimal error or no error. While it may not be possible and causes the struggle for classical computing methods to efficiently capture and process complex patterns and subtle details. Utilising quantum computing for digital image processing gives an advantage over classical methods as it operates on high dimensional space and leverages quantum parallelism to explore numerous possibilities simultaneously. This enable the extraction of more complex features that might be otherwise missed improving the model’s capability to flag defects with greater accuracy and efficiency. Quantum computing’s potential to enhance feature extraction can significantly boost the performance of classification models, leading to dependable and precise surface defect detection.

关键词： Deep learning Analytical models Quantum computing Computational modeling Digital images Media Parallel processing Feature extraction Surface treatment Defect detection

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An Efficient Weed Detection using Adaboost and CNN Hybrid Model

An Efficient Weed Detection using Adaboost and CNN Hybrid Mo...

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Advancement in Computation & computer Technologies (InCACCT), International Conference on

作者： Divya R Heena SK Shailaja Y Ramesh S. M Ashok Kumar S Kumar V Department of Electrical and Electronics Engineering PSG College of Technology Coimbatore India Department of Computer Science Engineering QIS College of Engineering and Technology Ongole India Department of ME QIS College of Engineering and Technology Ongole India Department of Electronics and Communication Engineering KPR Institute of Engineering and Technology Coimbatore India Department of Electronics and Communication Engineering Karpagam Academy of Higher Education Coimbatore India Department of Electronics and Communication Engineering Builders Engineering College Kangeyam India

ISBN: (数字)9798331543891

ISBN: (纸本)9798331543907

The detection of weeds is an essential component of precision agriculture because it provides farmers with the tools necessary to efficiently manage and control weed infestation in agricultural areas. Manual work is laborious and error-prone in traditional weed-detecting methods. computer vision and machine learning have shown promise in automating weed detection in recent years. This study introduces a hybrid model that combines AdaBoost and CNN to detect weeds. The hybrid model uses AdaBoost and CNN to boost weed detection accuracy. AdaBoost is used as a feature selector to identify input images with discriminative properties. CNNs, or convolutional neural networks, are popular deep learning models that can extract complex spatial features from images. This CNN receives the selected features. The CNN learns and improves features, enabling precise weed classification. A large dataset from Kaggle with crop field images of various weed species and backdrops is used to evaluate the hybrid model. The dataset is hand-annotated to provide ground truth labels for training and testing. The dataset trains the hybrid model, which is optimized for precision, recall, and F1 score. Experimental comparisons show that the proposed AdaBoost and CNN hybrid model outperforms individual models and other weed detection methods in accuracy and computing economy.

关键词： Precision agriculture Training Accuracy Automation Computational modeling Crops Production Feature extraction Data models Convolutional neural networks

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Efficient Data Compression on Healthcare Images Using Improved Hadamard Transform Based Filter

Efficient Data Compression on Healthcare Images Using Impro...

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International Conference on Intelligent Systems and Networks, ICISN 2024

作者： Thi, Thu Nguyen Haridoss, G. Rubia, Jency Dilipkumar, S. Chowdhury, Subrata Nguyen, Anh Phuong Duc, Tan Tran Faculty of Electronic Engineering Hanoi University of Industry Hanoi10000 Viet Nam Department of Electronics and Communication Engineering M.A.M College of Engineering and Technology Tiruchirappalli India Department of Electronics and Communication Engineering Vel Tech University Chennai India Department of Analytics SCOPE Vit Vellore Vellore India Department of Computer Sciences and Engineering Sreenivasa Institute of Technology and Management Studies AP Chittoor India Foreign Language Specialized School University of Languages and International Studies Hanoi Viet Nam Faculty of Electrical and Electronic Engineering Phenikaa University Hanoi Viet Nam

ISBN: (纸本)9789819755035

The Hadamard transform is an ancient method for efficient data compression, especially for medical image data. Thus, the proposed work consists of analyzing the filter performance using a novel Hadamard transform to apply data compression. The parametric evaluation has been employed for factors such as PSNR, compression time, and compression ratio. For the application purpose, the proposed modified Hadamard transform is applied for the encryption and decryption process and retrieves the original image. The experimental analysis reveals that the proposed Novel Hadamard Transform gives the best performance. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Compression ratio Data compression and decompression Hadamard transform Vector Merging Adder PSNR

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