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2022 OPJU International Technology Conference on Emerging Technologies for Sustainable Development, OTCON 2022

作者： Mandal, Kailash Pati Mukherjee, Prasenjit Chakraborty, Baisakhi Ganguly, Souvik National Institute of Technology Computer Science and Engineering Durgapur India Dr. B.C. Roy Engineering College Bachelor of Computer Application Durgapur India

ISBN: (纸本)9781665492942

Machine intelligence is an essential branch of computer science that imitates human behavior. A human recognizes every real-world object and incident by their distinguishable features. If a computerized system incorporates human cognitive abilities, the system will be able to classify many real-world phenomena accurately. Therefore, an automated system that detects the disease based on symptoms has been addressed. Bidirectional Encoder Representations from Transformers (BERT) has been used to design this system. The BERT model has been developed based on the Transformers concept, forecasting a token by looking at every token in the series. The proposed system recognizes each and every individual's illness in the Bengali language. Punctuation marks are stripped from the user-supplied query which is in form of disease symptoms. Tokens are obtained from the individual's query. Tokens that have been filtered are sent to the proposed system. The proposed method employs filtered tokens to diagnose the disorder. The BERT model is trained using a dataset of diseases and their symptoms obtained from Kaggle. Precision, recall, fl-score, root mean squared error (RMSE), mean squared error (MSE), and mean absolute error (MAE) have been used to describe the proposed system's performance. The accuracy of the proposed system is 98.12% whereas RMSE, MSE, and MAE exhibit 1.8618, 1.0852, and 1.0826 respectively. © 2023 IEEE.

关键词： Machine learning

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Capacitor voltage unbalance minimization for three-phase five-level diode-clamped inverter using hexagonal hysteresis space vector modulation

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MULTISCALE AND MULTIDISCIPLINARY MODELING EXPERIMENTS AND DESIGN 2024年第2期7卷 661-671页

作者： Ramkumar, R. Singh, Manmohan Ramkumar, R. Usha, S. Thentral, T. M. Thamizh Department of Electrical and Electronics Engineering SRM Institute of Science and Technology Chengalpattu India Department of Computer Engineering and Application GLA University Mathura India Department of Electrical and Electronics Engineering School of Engineering and Technology Dhanalakshmi Srinivasan University Trichy India Department of Renewable Energy University of Maroua National Advanced School of Engineering of Maroua Maroua Cameroon

This article suggests a method for diminishing the voltage unbalance in a three-phase five-level diode-clamped inverter (DCI) through the use of hexagonal hysteresis space vector modulation (HHSVM). Capacitor voltage balancing leads to enhanced system efficiency, reduced stress on components, enhanced performance, abridged electromagnetic interference, and reduced total harmonic distortion. The proposed modulation technique and its implementation are thoroughly examined in this study, along with modeling and experiment data that show how efficient the method is at lowering the capacitor voltage unbalance in the proposed five-level DCI. Capacitor voltage unbalance is reduced with the use of this HHSVM approach to 0.95%, which is a superior reduction compared to traditional PWM methods. The paper also discusses the advantages of the proposed method over other existing methods, making it a promising solution for practical applications in power electronics systems.

关键词： Diode-clamped inverter (DCI) Capacitor voltage balancing Hexagonal hysteresis space vector modulation (HHSVM) Total harmonic distortion

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Using Neural Network-Based Time Series Analysis in Wireless Sensor Networks 2

Using Neural Network-Based Time Series Analysis in Wireless ...

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2nd International Conference on Artificial Intelligence and Machine Learning applications, AIMLA 2024

作者： Goyal, Manish Kumar Patil, Nagaraj Bhagat, Akash Kumar Vivekananda Global University Department of Computer Science & Application Jaipur India School of Engineering and Technology Department of Mechanical Engineering Karnataka Bangalore India ARKA JAIN University Department of Computer Science & IT Jharkhand Jamshedpur India

ISBN: (纸本)9798350349221

Neural community-primarily based time series analysis (NNTSA) is an effective technique for research records collected from wireless sensor networks (WSNs). NNTSA uses models stimulated by means of biological neural networks to capture complicated time-various sensor statistics. It requires minimum training of the enter facts and offers a high degree of modelling accuracy. This method has turned out to be increasingly famous for WSN facts evaluation when you consider that it is miles appropriate for streaming statistics and seizing complicated patterns that might not be effortlessly detected with the aid of other techniques. NNTSA fashions are commonly carried out the use of supervised gaining knowledge of processes, together with lower back-propagation, wherein education styles are fed to a neural network at the same time as its parameters are adjusted the use of a fixed of desired output values, together with an average temperature over some time. After the NNTSA c section is complete, new patterns can be evaluated with the trained model to analyze their temporal conduct. NNTSA techniques have been applied in diverse areas, inclusive of fitness monitoring, site visitors management, and weather tracking, with suitable result. © 2024 IEEE.

关键词： Time series analysis

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Blockchain and Machine Learning for Data-Driven Insights in Consumer Behavior Analytics 13

Blockchain and Machine Learning for Data-Driven Insights in ...

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13th IEEE International Conference on Communication Systems and Network Technologies, CSNT 2024

作者： Rawat, Bhupender Singh Thapliyal, Nitin Pal, Sunil Kumar Mishra, Nikhil Verma, Rohan College of Smart Computing Coer University Roorkee India Graphic Era Hill University Department of Computer Science & Engineering Dehradun India Axis Colleges Department of Computer Science & Application Kanpur India Department of Computer Science & Engineering Dehradun India

ISBN: (纸本)9798350305463

Comparing with neural network method to more known methods, this article assesses their simplicity and effectiveness. Our approach uses new network pruning, interpretability-based regularization, and gradient-based sensitivity analysis. Numerous dataset trials show that the suggested method is more accurate, faster, and more durable than others. Also easier to understand. It turns out that small neural networks may simplify models without compromising accuracy. This comparative study shows how neural networks can handle complex data and make good predictions. These networks will be increasingly accessible in daily life. © 2024 IEEE.

关键词： Sensitivity analysis

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Leveraging the Strengths of Long Short-Term Memory and Gated Recurrent Unit Architectures for Indian Stock Market Prediction 4

Leveraging the Strengths of Long Short-Term Memory and Gated...

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4th International Conference on Emerging Systems and Intelligent Computing, ESIC 2024

作者： Jena, Prem Purusottam Das, Kaberi Mishra, Debahuti Lenka, Arundhati Mishra, Sashikala Mallick, Pradeep Kumar Siksha 'o' Anusandhan [Deemed to Be University Department of Computer Science and Engineering Bhubaneswar India Siksha 'o' Anusandhan [Deemed to Be University Department of Computer Application Bhubaneswar India Pune India Kiit Deemed to Be University School of Computer Engineering Bhubaneswar India

ISBN: (纸本)9798350349856

The study recognizes the multifaceted nature of the stock market and the challenges inherent in predicting market movements accurately. Focused on the Indian stock market, the research harnesses the capabilities of deep learning, specifically the long short-term memory (LSTM) and gated recurrent unit (GRU) architectures, known for their prowess in processing sequential data with predictions over a 10-day horizon. The models are systematically evaluated, considering parameters such as root mean square error (RMSE), mean absolute error (MAE), regression variance, and R2-score, with a comparative analysis highlighting the effectiveness of the proposed hybrid models - LSTM-GRU and GRU-LSTM. The experimental analysis reveals that the GRU-LSTM model demonstrates superior performance on the HDFC bank Ltd. dataset, while the LSTM-GRU model excels on the TATA Motors Ltd. dataset, aligning with the predictive performance and accuracy metrics observed in earlier experiments. © 2024 IEEE.

关键词： Long short-term memory

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Replication-Based Query Management for Resource Allocation Using Hadoop and MapReduce over Big Data

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Big Data Mining and Analytics 2023年第4期6卷 465-477页

作者： Ankit Kumar Neeraj Varshney Surbhi Bhatiya Kamred Udham Singh Department of Computer Engineering and Application GLA UniversityMathura 281406India. College of Computer Sciences and Information Technology King Faisal UniversityHofuf 31982Saudi Arabia School of Computing Graphic Era Hill UniversityDehradun 248002India.

We live in an age where everything around us is being *** generation rates are so scary,creating pressure to implement costly and straightforward data storage and recovery *** model functionality is used for creating a cluster parallel,distributed algorithm,and large *** MapReduce strategy from Hadoop helps develop a community of non-commercial use to offer a new algorithm for resolving such problems for commercial applications as expected from this working algorithm with insights as a result of disproportionate or discriminatory Hadoop cluster *** results are obtained in the work and the exam conducted under this job;many of them are scheduled to set schedules,match matrices’data positions,clustering before determining to click,and accurate mapping and internal reliability to be closed together to avoid running and execution *** output and proponents have been implemented,and the map has been used to reduce the *** execution input key/value pair and output key/value pair have been *** paper focuses on evaluating this technique for the efficient retrieval of large volumes of *** technique allows for capabilities to inform a massive database of information,from storage and indexing techniques to the distribution of queries,scalability,and performance in heterogeneous *** results show that the proposed work reduces the data processing time by 30%.

关键词： big data hadoop mapreduce resource allocation query management

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A self-driving solution for resource-constrained autonomous vehicles in parked areas

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High-Confidence Computing 2024年第1期4卷 32-42页

作者： Jin Qian Liang Zhang Qiwei Huang Xinyi Liu Xiaoshuang Xing Xuehan Li College of Information Engineering Taizhou UniversityTaizhou 225300China Engineering Research Center of Integration and Application of Digital Learning Technology Ministry of EducationBeijing 100039China School of Computer Science and Engineering Changshu Institute of TechnologyChangshu 215500China The School of Electronic and Information Engineering Beijing Jiaotong UniversityBeijing 100044China

Autonomous vehicles in industrial parks can provide intelligent,efficient,and environmentally friendly transportation services,making them crucial tools for solving internal transportation *** the characteristics of industrial park scenarios and limited resources,designing and implementing autonomous driving solutions for autonomous vehicles in these areas has become a research *** paper proposes an efficient autonomous driving solution based on path planning,target recognition,and driving decision-making as its core *** designs for path planning,lane positioning,driving decision-making,and anti-collision algorithms are *** analysis and experimental validation of the proposed solution demonstrate its effectiveness in meeting the autonomous driving needs within resource-constrained environments in industrial *** solution provides important references for enhancing the performance of autonomous vehicles in these areas.

关键词： Autonomous vehicles Path planning Target recognition Driving decision-making Self-driving

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A Unified Framework for Image Caption Generation Using Deep Reinforcement Learning 15

A Unified Framework for Image Caption Generation Using Deep ...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Shree, Ritu Narmadha, T. Joseph, Vishakha Vivekananda Global University Department of Computer Science & Application Jaipur India School of Engineering and Technology Department of Computer Science Engineering Karnataka Bangalore India Arka Jain University Department of Management Jharkhand Jamshedpur India

ISBN: (纸本)9798350370249

This paper introduces a unified framework for picture caption generation with the use of deep reinforcement mastering (RL) that combines convolution neural networks (CNNs) with RL. The paper first introduces a country representation as a remark for RL by means of combining CNN-primarily based photo representation and natural language processing (NLP) natural language representation. Then, the paper describes an RL-based reinforcement mastering (RL-RL) set of rules which uses the statement to generate captions. Eventually, the paper shows the experimental consequences of the proposed RL-RL set of rules on real-world datasets and shows that the RL-RL set of rules outperforms the present RL-NLP and CNN-RL algorithms. The paper additionally demonstrates the very best overall performance on the benchmark dataset COCO with a BLEU score of 0.645 and a METEOR score of 0.348. The proposed framework affords a sensible technique to generate interesting and affordable captions for photographs. © 2024 IEEE.

关键词： Benchmark Consequences Convolution Photographs Reinforcement Representation

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A Study Of Cyclone Tracking Mechanism Using Deep Learning Techniques

A Study Of Cyclone Tracking Mechanism Using Deep Learning Te...

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2023 International Conference on System, Computation, Automation and Networking, ICSCAN 2023

作者： Paul, Tushar Dhanalakshmi, R. Masters of Computer Application School of Computer Science and Engineering Vellore Institute of Technology Chennai India School of Computer Science and Engineering Vellore Institute of Technology Centre for Cyber Physical Systems Chennai India

ISBN: (纸本)9798350315127

With over 90 storms emerging worldwide each year, tropical cyclones (TCs) are the most damaging weather systems to emerge over the tropical oceans. For the purpose of providing advanced warning to the impacted areas, fast TC detection and tracking is essential. Remote sensing is essential for the detection of these storms since they originate over open oceans that are far from the continents. Here, we describe an innovative deep learning approach-based automated technique for TC detection from satellite photos. Our research presents a three-phase deep learning architecture for TC detection that consists of three components: 1) a classifier - convolutional neural network (CNN);2) a wind speed filter;and 3) a detector - Mask convolutional neural network (CNN). Using Bayesian optimization, the hyperparameters of the pipeline as a whole are tuned to display optimal performance. Results show that the suggested method produces test images with specificity (97.59%), high precision (97.10%) and accuracy (86.55%). © 2023 IEEE.

关键词： Tropical cyclone

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Utilizing Mixture Methods for Classifier in NLP: An Essential Consideration

Utilizing Mixture Methods for Classifier in NLP: An Essentia...

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2023 International Conference on Artificial Intelligence and Smart Communication, AISC 2023

作者： Kavita Singh, Harveer Chandigarh School of Business Department of Master of Computer Application India Sanskriti University Department of Computer Science Engineering Uttar Pradesh Mathura India

ISBN: (纸本)9798350322309

The primary purpose of natural language program is to use it for desktop programming tool. This is very effective, and it can transform common biological conversation into a device which can be easily usable form. The robot may receive inputs in the form of data, voice, or visual. Currently, the machine earning is growing and finding usage in a variety of fields. The machine learning tool is developed by leading companies are having very big effect on the mobile and communication industry. Previous generations' daily tasks will be made easier by chat bots that are educated for all kind of issues which need to be discussed and or taken guidance. This well-known platform was mostly constructed using machine learning algorithms (ML) methods. In order to obtain the best outcomes and also the accuracy rate, the study intends to offer a principle component analysis gives the answer to a text categorization issue. © 2023 IEEE.

关键词： Natural language processing systems

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