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10th IEEE Information Technology International Seminar, ITIS 2024

作者： Hasan, Md Mahmudul Mahfuj Hossain, Mirza Sulaiman, Norizam Islam, Md Nahidul Khandaker, Sayma Universiti Malaysia Pahang Al-Sultan Abdullah Faculty of Electrical and Electronics Engineering Technology Pahang Malaysia Jashore University of Science and Technology Department of Computer Science and Engineering Jashore Bangladesh

ISBN: (纸本)9798331521295

Due to the significant variability in electroencephalogram (EEG) signals across individuals and recording sessions, developing a calibration-free system for detecting driver drowsiness presents a considerable challenge. While numerous deep learning techniques have been employed to analyze EEG signals for this purpose, most existing studies rely on deep learning models with limited success in accurately detecting drowsiness. In this research, we explored the application of five deep learning algorithms such as Artificial Neural Networks (ANN), Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Gated Recurrent Units (GRU), and Long Short-Term Memory (LSTM), to improve the detection of driver drowsiness. Our applied models achieve detection accuracies of 73.46% for ANN, 80.09% for CNN, 65.74% for RNN, 83.95% for GRU, and 77.62% for LSTM across 11 subjects, outperforming conventional baseline approaches and state-of-the-art deep learning methods. Notably, the GRU model demonstrated superior accuracy in recognizing drowsiness from EEG signals compared to other applied models. © 2024 IEEE.

关键词： Convolutional neural networks

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A Survey on Digital Twins: Enabling Technologies, Use Cases, Application, Open Issues, and More

IEEE Journal of Selected Areas in Sensors

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IEEE Journal of Selected Areas in Sensors 2025年 2卷 84-107页

作者： Hassija, Vikas Chamola, Vinay De, Rajdipta Das, Soham Chakrabarti, Arjab Sangwan, Kuldip Singh Pandey, Amit Deemed to be University School of Computer Engineering Bhubaneswar751024 India BITS-Pilani Department of Electrical and Electronics Engineering Pilani Campus Pilani333031 India BITS Pilani Department of Mechanical Engineering Pilani Campus Pilani333031 India Bennett University Computer Science Engineering and Technology Department Greater Noida201310 India

Digital Twins, sophisticated digital replicas of physical entities, have been gaining significant attention, especially after NASA's endorsement, and are poised to revolutionize numerous fields, such as medicine and construction. These advanced models offer dynamic, real-time simulations, leveraging enabling technologies, such as artificial intelligence, machine learning, IoT, cloud computing, and Big Data analytics to enhance their functionality and applicability. In the medical field, Digital Twins facilitate personalized treatment plans and predictive maintenance of medical equipment by simulating human organs with precision. In construction, they enable efficient building design and urban planning, optimizing resource use, and reducing costs through predictive maintenance. Startups are innovatively employing Digital Twins in various sectors, from smart cities - where they optimize traffic flow, energy consumption, and waste management - to industrial machinery, ensuring predictive maintenance and minimizing downtime. This survey delves into the diverse use cases, market potential, and challenges of Digital Twins, such as data security and interoperability, while emphasizing their transformative impact on industries. The future prospects are promising, with continuous advancements in AI, ML, IoT, and cloud computing driving further expansion and application of Digital Twin technologies. © 2024 IEEE.

关键词： Predictive maintenance

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Brain Tumor Identification Using Data Augmentation and Transfer Learning Approach

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computer Systems science & engineering 2023年第8期46卷 1845-1861页

作者： K.Kavin Kumar P.M.Dinesh P.Rayavel L.Vijayaraja R.Dhanasekar Rupa Kesavan Kannadasan Raju Arfat Ahmad Khan Chitapong Wechtaisong Mohd Anul Haq Zamil S.Alzamil Ahmed Alhussen Department of Electronics and Communication Engineering Kongu Engineering College638060Tamil NaduIndia Department of Electronics and Communication Engineering Sona College of Technology636005Tamil NaduIndia Department of Computer Science and Engineering Sri Sairam Institute of TechnologyChennai600044Tamil NaduIndia Department of Electrical and Electronics Engineering Sri Sairam Institute of TechnologyChennai600044Tamil NaduIndia Department of Computer Science and Engineering Sri Venkateswara College of EngineeringSriperumbudur602117Tamil NaduIndia Department of Electrical and Electronics Engineering Sri Venkateswara College of EngineeringSriperumbudur602117Tamil NaduIndia Department of Computer Science College of ComputingKhon Kaen UniversityKhonKaen40002Thailand School of Telecommunication Engineering Suranaree University of TechnologyNakhon Ratchasima30000Thailand Department of Computer Science College of Computer and Information SciencesMajmaah UniversityAl-Majmaah11952Saudi Arabia Department of Computer Engineering College of Computer and Information SciencesMajmaah UniversityAl-Majmaah11952Saudi Arabia

A brain tumor is a lethal neurological disease that affects the average performance of the brain and can be *** India,around 15 million cases are diagnosed *** mitigate the seriousness of the tumor it is essential to diagnose at the ***,the manual evaluation process utilizing Magnetic Resonance Imaging(MRI)causes a few worries,remarkably inefficient and inaccurate brain tumor ***,the examination process of brain tumors is intricate as they display high unbalance in nature like shape,size,appearance,and ***,a precise and expeditious prognosis of brain tumors is essential for implementing the of an implicit *** computer models adapted to diagnose the tumor,but the accuracy of the model needs to be *** all the above mentioned things,this work aims to identify the best classification system by considering the prediction accuracy out of Alex-Net,ResNet 50,and Inception *** augmentation is performed on the database and fed into the three convolutions neural network(CNN)models.A comparison line is drawn between the three models based on accuracy and *** accuracy of 96.2%is obtained for AlexNet with augmentation and performed better than ResNet 50 and Inception V3 for the 120th *** the suggested model with higher accuracy,it is highly reliable if brain tumors are diagnosed with available datasets.

关键词： AlexNet brain tumor data augmentation inception V3 ResNet 50

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Deep Learning Based Corona Discharge Severity Classification for High Voltage Equipment 50

Deep Learning Based Corona Discharge Severity Classification...

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50th Annual Conference of the IEEE Industrial electronics Society, IECON 2024

作者： Messaoudi, Maher Kameli, Sayed Mohammad Refaat, Shady S. Abu-Rub, Haitham Trabelsi, Mohamed Department of Electrical and Computer Engineering Texas A&M University at Qatar Doha Qatar School of Physics Engineering and Computer Science University of Hertfordshire Hatfield United Kingdom Electronics and Communications Engineering Department Kuwait College of Science and Technology Kuwait

ISBN: (纸本)9781665464543

Discharges, such as partial discharges (PDs) and corona discharges (CDs) are the most common faults that occur in insulation materials used in high voltage (HV) equipment. A high repetition rate of discharge activity indicates the severity of the defects that shorten the lifetime of electrical equipment, leading to insulation failure. To solve this, this paper proposes an efficient classification technique for corona discharge defect intensity using features obtained from statistical parameters such as the ignition voltage of CDs. The Recurrent Neural Network (RNN) is proposed to identify the intensity of corona discharges. A comprehensive experimental evaluation is conducted, to demonstrate the capabilities of the proposed solution. The exceptional predictive abilities of the long short-term memory (LSTM) method are the primary benefit of the proposed approach presented, with a potential for enhancing the performance of CD detection systems. The obtained results demonstrate the accuracy of the proposed model, indicating its potential for deployment in practical applications. The innovative approaches utilized in this paper will help engineers and operators quickly determine the severity (sharpness and curvature) of the protrusions or surface defects that cause CDs, solely based on measurements of the ignition voltage. © 2024 IEEE.

关键词： Long short-term memory

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LSTM Based Model Predictive Control Approach for Energy Management System in PV-Battery Integrated Microgrid Network 10

LSTM Based Model Predictive Control Approach for Energy Mana...

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10th IEEE International Symposium on Smart Electronic Systems, iSES 2024

作者： Preetha Roselyn, J. Sundaravadivel, Prabha Vignesh Babu, V. Nithya, C. Devaraj, D. SRM Institute of Science and Technology Kattankulathur Dept of Electrical and Electronics Engineering Chennai603203 India The University of Texas Department of Electrical and Computer Engineering TylerTX75701 United States SRM Institute of Science and Technology Kattankulathur Department of Electrical and Electronics Engineering Chennai603203 India Kalasalingam Academy of Research and Education Srivilliputhur India

ISBN: (纸本)9798331533229

This paper proposes an intelligent energy management system in grid-connected microgrid with renewable energy and battery storage systems. The battery charging and discharging strategy is regulated so that the overall energy cost, along with battery degradation cost, is minimized considering the variation in grid tariff, inconsistent renewable power output and changing load demand. The proposed model is developed as an optimization-based problem over a 24-hour horizon using Long Short-Term Memory (LSTM) based forecasting Energy Management-based Model Predictive Control. The proposed model provides information about the renewable power generation and demand for next 24 hours through LSTM based prediction model. The Long Short-Term Memory network is developed for time series forecasting of renewable generation and its performance is compared with other regression models. The Receding Horizon strategy is proposed to reduce the forecasting results of LSTM is fed as inputs to the Model Predictive Control based Energy management system forecasts and enable the implementation of energy management system. The proposed algorithm is developed and implemented in microgrid environment in MATLAB/Simulink 2022b under three case studies and the results claim that the proposed LSTM-MPC control strategy is successful in terms of reducing the grid consumption cost without much degrading the energy storage. The proposed model is also validated in 1kW microgrid hardware setup and promising results are obtained © 2024 IEEE.

关键词： Microgrids

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IoT-based Automatic Pan Evaporimeter Monitoring and Irrigation Control System for Crops 8

IoT-based Automatic Pan Evaporimeter Monitoring and Irrigati...

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8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2024

作者： Kabir, Md Shahriar Niloy, Khalid Mahmud Hossen, Md Imon Afrose, Sumaiya Mofazzol, Md. Ismail Hossain American International University-Bangladesh Dept. of Electrical & Electronics Engineering Dhaka Bangladesh Uttara University Dept. of Computer Science Engineering Dhaka Bangladesh

ISBN: (纸本)9798350376425

This research study intends to automate the manual pan evaporimeter system and scheduling irrigation by considering soil water content and crop evapotranspiration. Automating the pan evaporimeter means performing tasks without human intervention, such as measuring the daily water evaporation amount, calculating and storing data, and refilling the pan with water. A waterproof ultrasonic sensor was used to measure the water evaporation amount. The ultrasonic sensor was installed on an innovatively designed structure facing the pan's water surface. The sensor measured the distance between the water surface and the sensor. The surface level decreased as the water evaporated over time, which caused an increment in the distance between the ultrasonic and water surfaces. By this, the sensor calculated the daily evaporation amount. The pan needed to be refilled when all water evaporated;a submersible pump was used to refill the pan automatically. A relay switch controlled the pump, and a microcontroller turned on the switch based on sensor value. The entire setup was IoT-enabled to monitor and control the system remotely on a mobile device. The entire system was backed up by solar energy to run on clean energy. A solenoid valve was used to control the flow of irrigation water. Pan evaporation estimated the amount of water that could be lost owing to evaporation and transpiration. Soil moisture sensors provided a direct measurement of the amount of water present in the soil. The two combined generated a dynamic and responsive irrigation plan that maximized water use and promoted the healthy development of crops. That approach was used. The ultrasonic sensor accurately measured water evaporation, but the installation process was complex. So, a need arises to develop a simple system or scale-type sensors to overcome the complexity. © 2024 IEEE.

关键词： Solenoid valves

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Stock Recommendation System for Better Investment Plan

Stock Recommendation System for Better Investment Plan

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2024 International Conference on Signal Processing, Computation, electronics, Power and Telecommunication, IConSCEPT 2024

作者： Lokesh, D. Aravind Vasan, G. Vani, V. Karthik, N. National Institute of Technology Puducherry Department of Computer Science and Engineering Karaikal India National Institute of Technology Puducherry Department of Electrical and Electronics Engineering Karaikal India

ISBN: (数字)9798331540685

ISBN: (纸本)9798331540685

In the volatile landscape of financial markets, making well-informed decisions for intraday trading and long-term investment necessitates an integrated analysis of real-time stock data and the contextual information provided by news articles. This paper presents an innovative stock recommendation system that leverages advanced Machine Learning (ML) techniques and Natural Language Processing (NLP) to deliver timely, data-driven recommendations by assessing both risk and profit potential. Our system employs ML algorithms to process live stock price, trading volume, and technical indicators, enabling real-time assessment of a stock's performance. Developing a stock recommendation system is a challenging undertaking that frequently calls for a multidisciplinary team with experience in data science, machine learning, and finance. Simultaneously, it harnesses NLP models to extract sentiment and contextual information from news articles, providing insights into market sentiment and potential impact on stock behavior. By merging these two streams of data, our system offers a comprehensive view of stock's dynamics. © 2024 IEEE.

关键词： Recommender systems

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computer Vision based Panoptic Driving Perception under Various Weather Conditions

Computer Vision based Panoptic Driving Perception under Vari...

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2023 International Conference on Industry sciences and computer science Innovation, iSCSi 2023

作者： Sridevi, M. Harish, M. Department of Computer Science & Engineering National Institute of Technology Tiruchirappalli620015 India Department of Electrical and Electronics Engineering National Institute of Technology Tiruchirappalli620015 India

Learning models that have been known to multi-task are gaining too much attention in various fields. One such area of work where Multi-tasking models are extracting hopeful outcomes is Autonomous Driving. Panoptic Driving Perception has become an inevitable aspect of autonomous driving systems which demands a very good accuracy and speed. Over a decade, various multi-tasking models have been proposed to simultaneously carry out traffic object detection, drivable area segmentation, and lane detection. Among all of them, YOLOP, YOLOPv, and HybridNets are the 3 models that have accomplished the above tasks using the BDD100K Dataset with high excellent accuracy and efficiency, thus achieving the new State-Of-The-Art (SOTA) performance. This paper focuses mainly on comparing the performance of the above-mentioned models in various weather conditions based on accuracy, robustness, and computational speed. © 2024 The Authors. Published by ELSEVIER B.V.

关键词： Object detection

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Unveiling Sentiments: Exploring Tweet and Text Analysis for Deeper Insights

Unveiling Sentiments: Exploring Tweet and Text Analysis for ...

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2024 International Conference on Signal Processing, Computation, electronics, Power and Telecommunication, IConSCEPT 2024

作者： Ranjan, Ritik Karthik, N. Vani, V. National Institute of Technology Puducherry Department of Electrical and Electronics Engineering Karaikal India National Institute of Technology Puducherry Department of Computer Science and Engineering Karaikal India

ISBN: (数字)9798331540685

ISBN: (纸本)9798331540685

This work presents a comprehensive exploration of sentiment analysis on social media data, with a specific focus on Twitter. The study evaluates three state-of-the-art classifiers-Support Vector Machine (SVM), Naive Bayes (NB), and Random Forest (RF)-to categorize sentiments (positive, negative, neutral) based on specific criteria. Data cleansing and classification methods are applied to extract meaningful insights from unstructured social media data. Results show that SVM achieves the highest accuracy (92.67%) among the classifiers. It also discusses the strengths and limitations of each classifier in capturing the nuances of sentiment expressions. This work contributes to the understanding of sentiment analysis techniques on social media platforms © 2024 IEEE.

关键词： Tweets

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Digital Twin-based Flight Dynamics Simulation

Digital Twin-based Flight Dynamics Simulation

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2024 IEEE Conference on engineering Informatics, ICEI 2024

作者： Talluri, Geetam Konjengbam, Anand Indian Institute of Technology Dharwad Dept of Electrical Electronics & Communication Engineering Dharwad India Indian Institute of Technology Dharwad Computer Science & Engineering Dharwad India

ISBN: (纸本)9798331505776

Unmanned Aerial Vehicle (UAV) systems face significant challenges during critical flight phases, such as post-liftoff stabilization and mid-flight disturbance response. This paper presents a Virtual Reality (VR) based simulation and control system integrated with a digital twin to enhance the understanding of UAV dynamics. The system uses real-time data from the UAV's Onboard computer (OBC) to create a dynamic 3D environment where users can interact with flight parameters such as position, rotational dynamics, and vibration amplitude. This approach enables the analysis of UAV performance and stability under changing conditions by allowing real-time adjustments. This immersive system supports research, training, and operational optimization of aerospace systems. © 2024 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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