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3rd International Conference for Emerging technology, INCET 2022

作者： Gadipudi, Praneeth Teja, Palachuri Sai Yelamancheli, Chandana Sowmya. Thaniserikaran, Adith Mohiuddin, Rayyan Joy, Ajin Srm University Department of Computer Science and Engineering Andhra Pradesh Amaravati India Srm Department of Computer Science and Engineering Institute of Science and Technology Tamil Nadu Kattankulathur India Icfai University Department of Computer Science and Engineering Hyderabad India National Institute of Technology Department of Computer Science and Engineering Telangana Warangal India National Institute of Technology Department of Electrical Engineering Telangana Warangal India JSPMs' Rajarshi Shahu College of Engineering Department of Electronics and Telecommunication Engineering Maharashtra Pune India

ISBN: (纸本)9781665494991

The coronavirus sickness (COVID-19) is a worldwide pandemic that was detected in December 2019 by a Chinese physician in Wuhan, Hubei Province, mainland China. There is presently no licensed human vaccine to combat it. When individuals are close together, COVID-19 spreads more fast. As a consequence, travel constraints are in place to minimize the disease from spreading, and regular Washing the hands is encouraged so that the infections that occur due to the virus. Other indications and symptoms include chest discomfort, sputum production, and a sore throat. COVID19 could lead to a very dangerous disease which is pneumonia which occurs due to the virus. When employing CT scans or Xrays to identify the symptoms that are occuring due to the cause of covid-19 in the last region of the lungs then the accuracy is better than when utilizing RT-PCR. But as there are very less radiologists as compared to the new residents or the people that have come and aslo there has been seen many re examinations occurring of these patients. To solve this kind of the issues or problems that is limiting the CT scans and the x-rays the speed of this procedure must be boosted. This may be done by adding artificial intelligence (AI) approaches into contemporary diagnostic systems. The main motive of the paper is to provide the best accuracy to detect the disease using CNN along with a comparison with the transfer learning approach. © 2022 IEEE.

关键词： computerized tomography

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Intelligent Farming in Rural Areas: CBGRU-Based Smart Agriculture and Precision Solutions 4

Intelligent Farming in Rural Areas: CBGRU-Based Smart Agricu...

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4th IEEE International Conference on Mobile Networks and Wireless Communications, ICMNWC 2024

作者： Laxmi, M. Tamilselvan, V. Ramesh, C. Renganathan, K. Sukumar, P. Kaliappan, S. Geetanjali College of Engineering and Technology Department of Electrical and Electronics Engineering Telangana India Karpagam College of Engineering Department of Electrical and Electronics Engineering Coimbatore India M.Kumarasamy College of Engineering Department of Mechanical Engineering Karur India Sri Sairam Engineering College Department of Electronics and Instrumentation Engineering Chennai India Department of Computer Science and Engineering Erode India Lovely Professional University Division of Research and Development Phagwara Jalandhar India

ISBN: (纸本)9798350352931

Climate-Smart Agriculture (CSA) aims to reduce food insecurity, boost rural economies, and protect the environment. CSA programs sometimes ignore small-scale farmers' demands, who face higher challenges. This research introduces Vulnerable-Smart Agriculture (VSA) to address small-scale farmers' needs and create a sustainable farming framework. The recommended method involves preprocessing, feature selection, and model training. Data cleansing, normalization, and correlation analysis are preprocessing steps. MRMR and CFS are used to select features. The CBGRU architecture trains the model. The CBGRU model outperforms CNN and BiGRU with an average accuracy of 93.60%. High accuracy shows that the VSA strategy meets small-scale farmers' needs through data-driven insights. VSA can cover the CSA gap by including small-scale farmers, according to this research. Strong preprocessing, feature selection, and CBGRU-based model training can boost agricultural production, sustainability, and rural lives. © 2024 IEEE.

关键词： Smart agriculture

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Early Epilepsy Diagnosis using Epistemic Neural Networks with Electric Eel Foraging Optimization and Secure EEG Data Sharing via Blockchain 3

Early Epilepsy Diagnosis using Epistemic Neural Networks wit...

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3rd International Conference on Automation, Computing and Renewable Systems, ICACRS 2024

作者： Kader, Mohideen Abdul M. Ali, Baba Fakruddin B.H. Hariharan, R. Ledalla, Sukanya Kadam, Swaraj Department of Computing Technologies School of Computing SRM Institute of Science and Technology Tamilnadu Kattankulathur India Department of AIDE Jain deemed to be university Karnataka Bangalore India Department of Computer Science and Engineering Koneru Lakshmaiah Foundation Telangana Hyderabad India Department of Electrical Engineering Dr. D Y Patil Institute of Technology Maharashtra Pune India

ISBN: (纸本)9798331532420

Epilepsy is still quite common, affecting over 50 million individuals worldwide, despite the tremendous advances in contemporary therapy. Early detection and treatment can greatly lower the chance of long-term harm and raise living standards for those with epilepsy. Electroencephalography (EEG), a non-invasive method that tracks electrical activity in the brain, is frequently used to diagnose this neurological disorder. Yet, using EEG data for diagnosis and study raises a number of issues with patient privacy and security protocols for such private digital data. An early epilepsy diagnostic system and a decentralized, secure environment for exchanging EEG data based on a block chain-based system with DL integration are presented in this work. The proposed method makes it possible to maintain patients’ personal data confidentiality and, at the same time, increase the efficiency of early epilepsy diagnosis. The most frequently used preprocessing technique in the strategy is Multiple Discrete Orthonormal S-Transforms (MDOSTs), and for feature extraction, Memory Efficient Vision Transformer-based Feature Extraction is employed. In classification, we use an Epistemic Neural Network (ENN) to achieve high precision and reliable prediction through the Electric Eel Foraging Optimization (EEFO) algorithm. The storage and dissemination of the data are both proactively secured through blockchain;through smart contracts, patients completely govern their data. The analysis of the Temple University Hospital EEG Corpus (TUH EEG) dataset proves that this method is diagnosed more than the traditional one, while the protection level of this data is also high. By achieving above 99% performance on all evaluation measures, such as F1-score, accuracy, recall, specificity, and precision, the suggested method demonstrates its remarkable efficacy in both secure EEG data sharing and early epilepsy detection. The method's higher performance over conventional approaches can be attributed to it

关键词： Electroencephalography

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An Empirical Investigation in Applying Reliable Industry 4.0 Based Machine Learning (ML) Approaches in Analysing and Monitoring Smart Meters using Multivariate Analysis of Variance (Manova) 2

An Empirical Investigation in Applying Reliable Industry 4.0...

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2nd International Conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2022

作者： Rana, Md. Sohel Dixit, Anil Kumar Rajan, M.Sundar Malhotra, Sumit Radhika, S. Pant, Bhasker Nanjing University of Information and Science Technology Computer Science and Technology India Uttaranchal University Dehradun Department of Lcd India Arbaminch University Faculty of Electrical and Computer Engineering Ethiopia Chandigarh University Computer Science and Engineering Gharuan Mohali India Raghu Institute of Technology Department of CSE AndhraPradesh Visakhapatnam India Graphic Era Deemed to Be University Computer Science & Engineering Uttarakhand Dehradun India

ISBN: (纸本)9781665437899

Conventional power systems have been evolving towards prospective smart grids in recent decades as market needs have changed and independent innovations, such as the 'Internet of Things' (IoT), have developed. The further automation of traditional development of industrial operations with a variety of current advanced techniques is termed as Industry 4.0. The most efficient strategy to boost the productivity of the enterprise environment, such as smart grids, is to use contemporary control technology that regulates and supervises interaction between smart devices. Furthermore, the advancement of loT -enabled smart meters is aided by a separate data connection, such as 'Bluetooth' or the 'Global System for Mobiles' (GSM), for remotely inspecting and operating smart meters. In this context, SPSS software is used for analysing the role of Industry 4.0 based Machine Learning or ML in analysing Smart Meters and MANOV A (and correlation) approach is considered to get proper result. This study proposes a machine learning-based architecture for analysing and monitoring smart meter data. The advantages of loT integration into Industry 4.0 include the ability to exchange actual information across a wide range of smart meters for various applications, such as factories, hospitals and residences, which is critical for smart grids. Results suggested that Smart Grid is beneficial to the extent of reducing electricity cost;however, maintenance cost and security issues are the major challenging factors in the new technology. © 2022 IEEE.

关键词： Internet of things

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PrivShape: Extracting Shapes in Time Series Under User-Level Local Differential Privacy

PrivShape: Extracting Shapes in Time Series Under User-Level...

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International Conference on Data engineering

作者： Yulian Mao Qingqing Ye Haibo Hu Qi Wang Kai Huang Department of Computer Science and Engineering Southern University of Science and Technology Department of Electrical and Electronic Engineering The Hong Kong Polytechnic University Research Institute of Trustworthy Autonomous Systems Southern University of Science and Technology School of Computer Science and Engineering Macau University of Science and Technology

ISBN: (数字)9798350317152

ISBN: (纸本)9798350317169

Time series have numerous applications in finance, healthcare, IoT, and smart city. In many of these applications, time series typically contain personal data, so privacy infringement may occur if they are released directly to the public. Recently, local differential privacy (LDP) has emerged as the state-of-the-art approach to protecting data privacy. However, existing works on LDP-based collections cannot preserve the shape of time series. A recent work, PatternLDP, attempts to address this problem, but it can only protect a finite group of elements in a time series due to ω-event level privacy guarantee. In this paper, we propose PrivShape, a trie-based mechanism under user-level LDP to protect all elements. PrivShape first transforms a time series to reduce its length, and then adopts trie-expansion and two-level refinement to improve utility. By extensive experiments on real-world datasets, we demonstrate that PrivShape outperforms PatternLDP when adapted for offline use, and can effectively extract frequent shapes.

关键词： Privacy Differential privacy Shape Smart cities Frequency-domain analysis Time series analysis Transforms

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A Deep Learning Approach for In-Network Synchrophasor Missing Data Recovery Using Programmable Network Switches

A Deep Learning Approach for In-Network Synchrophasor Missin...

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IEEE International Conference on Smart Grid Communications (SmartGridComm)

作者： Xiaoyu Guo Yanfeng Qu Gong Chen Dong Jin Electrical Engineering and Computer Science University of Arkansas Fayetteville AR USA Computer Science Illinois Institute of Technology Chicago IL USA

ISBN: (数字)9798350318555

ISBN: (纸本)9798350318562

Phasor measurement unit (PMU) networks deliver accurate and timely measurements, which is essential for managing today’s electric power systems. To ensure data quality and enhance the cyber-resilience of PMU networks against malicious attacks and data errors, this study presents an online PMU missing data recovery scheme by leveraging P4 programmable switches. The data plane incorporates a customized PMU protocol parser that abstracts the necessary payload data for recovery. Recovery processes are executed in the control plane using a pre-trained machine learning model. Both traditional and advanced ML models, such as transformer and TimeGPT, are explicitly employed for data prediction. This approach ensures rapid and precise data recovery. Performance evaluations focus on recovery speed and accuracy, using a real dataset from a campus microgrid. With 20% missing PMU data, the mean absolute percentage error for voltage magnitude is 0.0384%, and the phase angle error discrepancy is approximately 0.4064%.

关键词： Performance evaluation Accuracy Protocols Power measurement Process control Predictive models Transformers Phasor measurement units Smart grids Payloads

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Optimized Control of IoT-Monitored Microgrid Systems Using Genetic Algorithm

Optimized Control of IoT-Monitored Microgrid Systems Using G...

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

作者： Vijayalakshmi, N. Yuvaraj, S. Baskar, V. Vijaya Krishnaswamy, R. Sasikala, K. Srinivasan, C. Government Polytechnic College Deputed from Annamalai University Department of Electrical and Electronics Engineering Tamilnadu Coimbatore India Srm Institute of Science and Technology Faculty of Engineering and Technology Department of Electronics and Communication Engineering Tamil Nadu Chennai India School of Electrical and Electronics Engineering Sathyabama Institute of Science and Technology Tamil Nadu Chennai India University College of Engineering Department of Electronics and Communication Engineering Tamil Nadu Ariyalur India Department of Electrical and Electronics Engineering Tamil Nadu Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350357769

Microgrid (MG) systems have become a good way to make power generation and distribution more efficient, especially when Distributed Energy Resources (DERs) are introduced. The inverter is a critical part of MGs since it regulates the voltage output to the grid. A unique method is proposed to tune the settings of a Proportional-Integral-Derivative (PID) controller for the inverter using Genetic Algorithm (GA). The main aim is to use GAs to improve the inverter control in an MG that is monitored by the Internet of Things (IoT). GA's are very useful for resolving difficult optimization issues. Optimization of voltage regulation, stability, and power transmission to the grid may be achieved by using GA-based tuning of PID controller settings. From the simulation results, the GA-based methodology provides better control performance than conventional tuning approaches, providing exact voltage regulation and improving the MG's stability and dependability. © 2023 IEEE.

关键词： Genetic algorithm and Optimization technique Microgrid Renewable energy Voltage control

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Smart Controller for Air Conditioning in Car using IoT 2

Smart Controller for Air Conditioning in Car using IoT

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2nd International Conference on Electronics and Renewable Systems, ICEARS 2023

作者： Pradeepkumar, G. Manikandan, P. Rathika, P. Senthil, T. Thurai Raaj, B.V. Ponmurugan, P. Kpr Institute of Engineering and Technology Department of Electronics and Communication Engineering Coimbatore India K S R Institute for Engineering and Technology Department of Electrical and Electronics Engineering Tiruchengode India K S R Institute for Engineering and Technology Department of Computer Science and Engineering Tiruchengode India Bannari Amman Institute of Technology Department of Electronics and Communication Engineering Tamilnadu Sathyamangalam638401 India Madanapalle Institute of Technology & Science Madanapalle Department of Electrical and Electronics Engineering Andhra Pradesh India Meenakshi Academy of Higher Education and Research Department of Research Chennai600078 India

ISBN: (纸本)9798350346640

The vast majority of automobiles now have intelligent controllers. During the hot summer months, the interior temperature of the car rapidly rises, necessitating the use of the air conditioner to maintain comfort. This necessitates the need to turn ON the AC before getting into the car. The proposed mobile application is mainly developed to monitor the temperature range inside the car by using the Internet of Things (IoT). With the proposed mobile application, the air conditioning can be switched ON before getting into the vehicle as the AC controller is linked to the mobile app. On the other hand, it is highly required to ensure that the air conditioner has sufficient power. This necessitates the use of a lithium-ion battery. electrical energy is created by converting kinetic energy. The wingtips will generate kinetic energy as the vehicle moves. The intelligent air conditioner's energy consumption will decrease as the battery is charged. The results show that the proposed system can contribute significantly to the proposed temperature reduction. The proposed strategy will monitor the temperature inside the vehicle as simple as possible. © 2023 IEEE.

关键词： Controllers

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EEG based stress classification by using difference plots of variational modes and machine learning

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Journal of Ambient Intelligence and Humanized Computing 2023年第12期14卷 16347-16360页

作者： Salankar, Nilima Qaisar, Saeed Mian School of Computer Science University of Petroleum and Energy Studies Dehradun India Persistent System Limited IT Park Maharashtra Nagpur India Department of Electrical and Computer Engineering EFfat University Jeddah22332 Saudi Arabia Communication and Signal Processing Lab Energy and Technology Research Center Effat University Jeddah22332 Saudi Arabia

The recent trend in healthcare is to use the automated biomedical signals processing for an augmented and precise diagnosis. In this context, an original approach is presented for categorization of stress and non-stress classes by processing the multichannel Electroencephalogram (EEG) signals. The EEG signals are decomposed by using the "Empirical Mode Decomposition" (EMD) and "Variational Mode Decomposition" (VMD), respectively render the "Intrinsic Mode Functions" (IMFs) and Modes. The Second Order Difference Plots (SODPs) are traced for each IMF and Mode. Shapes of SODPs are used to discriminate the two intended categories. The feature space includes areas, Central Tendency Measures (CTMs) and means of SODPs. It is derived from first 7 IMFs and Modes. The identification of stress and non-stress categories is performed by using the robust machine learning algorithms namely, the Support Vector Machine (SVM), Boosting with Random Forest (RF) and Multilayer Perceptron Neural Network (MLPNN). The applicability is tested by using a publicly available EEG signals dataset, recorded during mental arithmetic test. A detailed evaluation is carried out at the subset, individual channel and lobes wise. Noteworthy highest accuracies of subset wise 99.89%, channel wise 98.89% and lobe wise 99.99% are obtained respectively. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Empirical mode decomposition

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Isolation Improvement in FMCW Radar Using Double Sided RF PCB

Isolation Improvement in FMCW Radar Using Double Sided RF PC...

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Microwave, Antennas & Circuits (ICMAC), International Conference on

作者： Muhammad Luqman Nazar Nosherwan Shoaib Muhammad Umar Khan School of Electrical Engineering and Computer Science (SEECS) National University of Sciences and Technology (NUST) Islamabad Pakistan

ISBN: (数字)9798331518424

ISBN: (纸本)9798331518431

This paper presents a novel technique to improve isolation in FMCW Radar transceiver by preventing leakage from transmitter chain into receiver chain. The proposed idea is to implement transmitter and receiver chains on opposite sides of a simple double sided printed circuit board (PCB) to improve isolations in microstrip topology. The ground planes are also stitched using plated through holes (PTH). Isolation of more than 90 dB has been achieved. The measurement results are in close agreement with simulation results in terms of isolation improvement. Consequently, an improvement in the dynamic range has been observed which helps in better detection of weak and close targets.

关键词： Radio frequency Couplings Transmitters Simulation Printed circuits Radar Receivers Dynamic range Transceivers Topology

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