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International Conference on Power Energy, Environment and Intelligent Control (PEEIC)

作者： Anita Rajvanshi Prerna Mahajan Alka Singh Department of Computer Science and Engineering Tula's Institute Dehradun India Department of Computer Jain (Deemed to be University) Bangalore India Master of Computer Applications Noida Institute Of Engineering and Technology Greater Noida Uttar Pradesh India

This essay delves into the concept of “Smart Cities,” outlining its numerous benefits. It elucidates the diverse applications and services essential for transforming a city into a Smart City, underscoring the pivotal role of Information and Communication technology (ICT) in their realization. Furthermore, it scrutinizes the challenges posed by the deployment of Smart Cities for contemporary ICT systems. The study investigates the promised attributes of 5G technology and its potential as a linchpin for the efficient implementation of urban smart cities. Additionally, it provides a catalog of 5G-enabled Smart City use cases, emphasizing how 5G will serve as the bedrock for the Internet of Things (IoT), fostering the evolution of smart cities.

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A Robust BMS Framework for DC Fast Charging in Smart Grid-Integrated EVs

A Robust BMS Framework for DC Fast Charging in Smart Grid-In...

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Next Generation Communication & Information Processing (INCIP), International Conference on

作者： S.P. Sreeja D. Sreenivasulu Alok Jain Neeraj Chahuan Fatimah Dhari Jabbar Babu Naik Gugulothu Master of Computer Application New Horizon College of Engineering Bangalore India Computer Science and Engineering (Data Science) Institute of Aeronautical Engineering Hyderabad India Lovely Professional University Phagwara India Lloyd Institute of Engineering & Technology Greater Noida Uttar Pradesh Radiology Techniques Department College of Medical Technology The Islamic University Najaf Iraq Department of Electrical and Electronics Engineering BMS Institute of Technology and Management Bengaluru Karnataka India

ISBN: (数字)9798331528140

ISBN: (纸本)9798331528157

Smart grids are now connected to electric vehicles (EVs) so that micro-grids could radically in the future use EV batteries with the acronym V2G and G2V. It presents an electrical architecture of a smart Battery Management System (BMS) for DC fast charging as a Forward and Backward part which is designed to fit both V2G and G2V siding and the topological diagram. Such architecture uses a combined complex hardware associated to smart software algorithms to charge and discharge the battery using optimized all four operations of the energy transfer that are set up in order to achieve nominal state of charge and optimize the time charge for safety purpose. The real time operating software performs control over the system through the use of an on-board processor using dedicatedly designed algorithms, including novel predictive-based machine learning algorithms for smart energy performance optimization and preventive maintenance. Cyber-security protocols and renewable energy are also embedded in the design of the proposed BMS for a side of a renewable energy grid. Our simulation results prove that the proposed BMS improves vehicle range by storing more energy and increasing the battery life. A novel Battery Management System for DC fast charging EVs is presented in this paper.

关键词： Vehicle-to-grid Renewable energy sources Machine learning algorithms Software algorithms Battery management systems computer architecture Software Batteries Fast charging computer security

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Implementing Smart Grids via Cognitive Radio: An Overview

Implementing Smart Grids via Cognitive Radio: An Overview

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International Conference on Power Energy, Environment and Intelligent Control (PEEIC)

作者： Bharti Kalra Febin Prakash Himanshi Sharma Department of Computer Science and Engineering Tula's Institute Dehradun India Department of Computer Jain (Deemed to be University) Bangalore India Master of Computer Applications Noida Institute Of Engineering and Technology Greater Noida Uttar Pradesh India

Calidad of service issues related to antiquated communication frameworks in conventional electrical grids are addressed by the cognition smart grid connectivity paradigm. By incorporating cutting-edge communication and information technology into electric power lines, this novel strategy promotes a two-way exchange of information. However, there is a significant need for SG communications systems to manage large data volumes because to the rise in wireless services and apps, which is being driven by the growing adoption of Internet interconnected Things smart devices. Moreover, our work surpasses the state of the art in that it not only tackles the problems caused by current communication systems but also provides a proactive solution that foresees and handles the growing data needs resulting from the changing wireless application and Internet of Things (loT) landscape. The suggested hybrid design fosters adaptation and resilience in the face of rapidly advancing technology breakthroughs, marking a key step to future-proofing SG communication systems.

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Identification and Segmentation of Polyp Using Deep Learning Approaches

Identification and Segmentation of Polyp Using Deep Learning...

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Innovation in technology (ASIANCON), Asian Conference on

作者： Suman Yaligar Shanta S. Kallur Vijeeta Patil Puneeth N. Thotad Dept. of Computer Science and Engineering K. L. E. Institute of Technology Hubballi India Affiliated to Visvesvaraya Technological University Belagavi Dept. of Master of Computer Applications K. L. E. Institute of Technology Hubballi India

ISBN: (数字)9798350354218

ISBN: (纸本)9798350354225

Colorectal cancer (CRC) is a major health problem, and accurate detection of polyps plays a major role in its prevention and treatment. In the past, people often relied on manually extracted lower-level features such as color, texture, and shape, which often had issues capturing global context and lacked robustness to complex scenarios. With the rise in deep learning, more and more rigorous segmentation of medical images dependent on deep learning networks have emerged, making significant progress in this field. This paper aims to develop an efficient model for polyp detection and segmentation using machine learning techniques. The model will assist clinicians in accurately locating and segmenting polyp regions, which will help in the prevention and treatment of CRC. The work will first review traditional methods that are dependent manually on extracted features and deep segmentation algorithms, then detail benchmark datasets relevant for the topic. The two datasets Kvasir-SEG and CVC-ClinicDB are used for implementation. Specifically, we carried out a comprehensive evaluation of recent deep learning CNN methods. The model gave an accuracy with Pranet method of about 99% in polyp segmentation and identification respectively.

关键词： Deep learning Image segmentation Technological innovation Accuracy Shape Prevention and mitigation Colorectal cancer Feature extraction Convolutional neural networks Medical diagnostic imaging

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Applying a Hybrid Deep Learning Framework to Efficient Stock Price Prediction

Applying a Hybrid Deep Learning Framework to Efficient Stock...

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Mobile Networks and Wireless Communications (ICMNWC), IEEE International Conference on

作者： Eti Khatri Ch Ramesh Babu Nandhini I Sowmya Kethi Reddy G Yoges Pundru Chandra Shaker Reddy Department of Management Studies Nitte Meenakshi Institute of Technology Bengaluru India Department of Computer Science and Engineering Mahatma Gandhi Institute of Technology(MGIT) Hyderabad India Department of Information Technology V.S.B.Engineering College Karur India School of Management Studies Chaitanya Bharathi Institute of Technology Hyderabad India Master of Computer Applications Maharaja Engineering College Avinashi India School of Computer Science and Artificial Intelligence SR University Warangal India

ISBN: (数字)9798331528348

ISBN: (纸本)9798331528355

It is very difficult to make reliable financial predictions using stock market data due to its inherent volatility and non-linearity, especially in the period following a pandemic. In response to these difficulties, this study builds a hybrid framework using deep learning to forecast technology stock prices, financial trends, and risk quantification. An increasing number of individuals have started investing in the stock market due to the fast growth of the economy in the past few years. A novel stock market prediction based on deep learning (SMP-DL) is presented in this research as a method for predicting stock market values. This study forecasted the stock market closing price using a bidirectional gated recurrent unit (BiGRU) in conjunction with long short-term memory (LSTM). When tested against other systems currently in use, the results demonstrated that the suggested system performed admirably. Since the values of R2, RMSE, MDA, MAE, and 0.8, respectively, are 3.4, 6.7, and 3.8.

关键词： Deep learning Wireless communication Pandemics Computational modeling Biological system modeling Predictive models Solids Reliability Stock markets Long short term memory

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Smart Thermal Management and Control of Interleaved Boost Converters in High-Power Photovoltaic Systems

Smart Thermal Management and Control of Interleaved Boost Co...

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2023 International Conference on Power Energy, Environment and Intelligent Control, PEEIC 2023

作者： Dasari, Kavitha Asha, V. Kansal, Lavish Praveen Parashar, Ashish Kumar Alwan, Adil Abbas Institute of Aeronautical Engineering Hyderabad Dundigal India New Horizon College of Engineering Department of Master of Computer Applications Bangalore India Lovely Professional University Phagwara India Lloyd Institute of Engineering & Technology Knowledge Park II Uttar Pradesh Greater Noida201306 India Lloyd Institute of Management and Technology Plot No.-11 Knowledge Park-II Uttar Pradesh Greater Noida201306 India College of Engineering Technology National University of Science and Technology Dhi Qar Iraq

ISBN: (纸本)9798350357769

In the realm of high-power photovoltaic (PV) systems, ensuring efficient and reliable performance demands precise thermal management. This study delves into the intricacies of thermal management and control strategies for interleaved boost converters (IBC), pivotal components in these systems. Interleaved topology, though recognized for its capability to reduce ripple currents and enhance efficiency, presents unique thermal challenges due to uneven current distribution among the parallel converters. This research introduces a smart thermal control methodology that amalgamates real-time temperature monitoring with adaptive control strategies, ensuring optimal thermal performance and preventing hotspot formation. Employing a combination of advanced sensors and sophisticated algorithms, the proposed system autonomously adapts to varying solar irradiances and ambient conditions, thereby ensuring consistent thermal stability. Experimental results demonstrate a significant reduction in temperature deviations among parallel converters and an overall enhancement in system reliability and efficiency. The findings from this research set the groundwork for future designs of PV systems, ensuring their sustainable and efficient operation under diverse conditions. © 2023 IEEE.

关键词： Adaptive Control Strategies Hotspot Prevention. Top of Form Interleaved Boost Converters Photovoltaic Systems Thermal Management

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A Machine Learning-based Diagnosis and Prediction of Diabetes Mellitus Disease

Research Square

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Research Square 2023年

作者： Thotad, Puneeth N. Bharamagoudar, Geeta R. Anami, Basavaraj S. Department of Master of ComputerApplications KLE Institute of Technology Hubballi580027 India Visvesvaraya Technological University Jnana Sangama Belagavi India Department of Computer Science &Engineering KLE Institute of Technology Hubballi580027 India School of Computer Science and Engineering KLE Technological University Hubballi580031 India

Traditional diagnostic techniques are prone to human error and are time-consuming. computer-aided diagnostic procedures improve performance and reduce expenses. This paper presents machine learning-based classifiers to detect diabetes in India, and Indian Demographic & Health Survey (2019–21) dataset is considered for the analysis. Classifiers like Support Vector Machine, Decision Tree, Extreme Gradient Boosting, and Random Forest are considered. The four phases adopted in work include preprocessing, feature extraction, classification, and analysis. Principal Component Analysis is used for feature reduction. Hyper-tuning parameters are tuned to achieve good performance of the classifiers. From the results, Random Forest has given the maximum classification accuracy, precision, recall, and area under the curve compared with other models. The work finds application in healthcare for the predictive analysis of diabetes. © 2023, CC BY.

关键词： Decision trees

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Multi-Criteria Decision Making in Chemical and Process Engineering: Methods, Progress and Potential

arXiv

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arXiv 2024年

作者： Wang, Zhiyuan Nabavi, Seyed Reza Rangaiah, Gade Pandu Department of Computer Science DigiPen Institute of Technology Singapore Singapore828608 Singapore Department of Applied Chemistry Faculty of Chemistry University of Mazandaran Babolsar Iran Department of Chemical and Biomolecular Engineering National University of Singapore Singapore117585 Singapore School of Chemical Engineering Vellore Institute of Technology Vellore632014 India

Multi-criteria decision making (MCDM) is necessary for choosing one from the available alternatives (or from the Pareto-optimal solutions obtained by multi-objective optimization), where the performance of each alternative is quantified against several criteria (or objectives). This paper presents a comprehensive review of the application of MCDM methods in chemical and process engineering. It systematically outlines the essential steps in MCDM, including the various normalization, weighting, and MCDM methods that are critical to the decision-making. The review draws on published papers identified through a search in the SCOPUS database, focusing on works by authors with more contributions to the field and on highly cited papers. Each selected paper is analyzed based on the MCDM, normalization and weighting methods used. Additionally, this paper introduces two readily available programs for performing MCDM calculations. In short, it provides insights into MCDM steps and methods, highlights their applications in chemical engineering, and discusses the challenges and prospects in this area. Copyright © 2024, The Authors. All rights reserved.

关键词： Optimal systems

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Multi-Objective Optimization and Multi-Criteria Decision-Making Approach to Design Multi-Tubular Packed-Bed Membrane Reactor in Oxidative Dehydrogenation of Ethane

arXiv

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arXiv 2024年

作者： Nabavi, Seyed Reza Wang, Zhiyuan Rodríguez, María Laura Department of Applied Chemistry Faculty of Chemistry University of Mazandaran Babolsar Iran Department of Computer Science DigiPen Institute of Technology Singapore Singapore139660 Singapore UNSL-CONICET Almirante Brown 1455 San LuisD5700HGC Argentina

Ethylene is a crucial chemical in manufacturing numerous consumer products. In recent years, the oxidative dehydrogenation of ethane (ODHE) technique has garnered significant interest as a means for ethylene production due to its high ethylene selectivity and energy efficiency, the availability of ethane as a feedstock, and catalysts that can improve the performance of the process. This work delves into determining the optimal operating conditions for the ODHE process within a multi-tubular packed-bed membrane reactor by formulating and resolving multi-objective optimization (MOO) problems. The elitist non-dominated sorting genetic algorithm II (NSGA-II) method is employed in conjunction with three different multi-criteria decision-making (MCDM) methods: (i) technique for order of preference by similarity to ideal solution (TOPSIS), (ii) preference ranking on the basis of ideal-average distance (PROBID), and (iii) simple additive weighting (SAW), to solve the MOO problems, obtain the optimal Pareto frontier, and recommend one solution for ultimate implementation. Through this approach, this work elucidates the trade-offs between conflicting objectives, deepens the comprehension of their interrelationships, and underscores the effects of decision variables on the optimization objectives. Consequently, this work provides practitioners with the insights needed for informed decision-making in the ODHE process operation. Copyright © 2024, The Authors. All rights reserved.

关键词： Ethylene

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An Intelligent Method for Train Accident Prevention and Detection Using ResNet and Long Short-Term Memory Network

An Intelligent Method for Train Accident Prevention and Dete...

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2023 International Conference on Self Sustainable Artificial Intelligence Systems, ICSSAS 2023

作者： Jeyaselvi, M. Kavitha, V. Kalaiarasi, S. Sekhar, Savanam Chandra Girdharwal, Nitin Sindhu, K. Srm Institute of Science and Technology Department of Networking and Communications Chennai Kattankulathur India Vardhaman College of Engineering Department of Mathematics Hyderabad India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Data Science Chennai India Koneru Lakshmaiah Education Foundation Kl University Department of Bba Telangana Vijayawada India Faculty of Management Indore India M.Kumarasamy College of Engineering Master of Computer Applications Tamilnadu Karur India

ISBN: (纸本)9798350300857

At unattended railroad crossings, numerous technologies have been developed to monitor trains, identify objects, and automatically open and close gates. In order to prevent accidents at the crossing, the currently planned systems may effectively close and open the gates upon the arrival and departure of trains. However, there are frequently incidents at railroad crossings caused by the unexpected arrival of trains. Indulging in headphone use is a leading cause of accidents on railroad tracks. The literature review revealed that no viable approach for preventing such mishaps had been developed. The purpose of this proposed system is to provide a comprehensive analysis of the current systems dedicated to the prevention of accidents in real time. Preprocessing, feature selection, and model performance evaluation are all used in the suggested method. The DWT method is used for preprocessing in the suggested method. PCA and LDA are used for feature selection. The suggested method employs ResNet LS TM to assess the efficiency of the model. Two conventional approaches, LSTM and GRU, are contrasted with the proposed methodology. The proposed method outperforms the other two. © 2023 IEEE.

关键词： Discrete wavelet transforms

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