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International Conference on Circuit, Power and Computing Technologies (ICCPCT)

作者： Divya V Chandran J Anitha Anusree A R Gowri Nair Dept.of Computer Science Engineering Karunya Institute of Technology and Sciences Coimbatore India Dept. Electronics and Communication Engineering Adi Shankara Institute of Engineering and Technology Ernakulam India

ISBN: (数字)9798350372816

ISBN: (纸本)9798350372823

Floods cause immense damage worldwide, resulting in significant financial losses and loss of life. Existing flood detection methods often face limitations, including delays and errors due to manual data interpretation and limited coverage. In this study, we propose a novel flood detection system that leverages deep learning techniques for semantic segmentation. Our system employs a U-Net architecture to analyze aerial and satellite images, identifying flood-affected areas at a pixel level. Trained on a diverse dataset of labeled flood images from various regions, our model is both robust and adaptable. Notably, our system achieves an Intersection over Union (IoU) score of 0.85, surpassing traditional methods in accuracy. This advanced system offers the potential to provide real-time flood information, enhancing disaster response and management. With real-time flood mapping, emergency services can better understand the disaster’s scope and plan rescue operations more effectively. Improved emergency response and resource management become possible with timely, precise data on flooded areas, allowing for informed decisions regarding evacuation, relief distribution, and infrastructure maintenance. Future work will focus on integrating additional data sources, such as weather conditions and water flow rates, to further enhance system performance.

关键词： Accuracy Soft sensors Semantic segmentation Refining computer architecture Real-time systems Data models

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Weibull Distribution based False Data Injection Attack Detection

Weibull Distribution based False Data Injection Attack Detec...

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IEEE General Meeting Power& Energy Society

作者： Purna Kukadiya Trapti Jain Neminath Hubballi Dept. of Electrical Engineering Indian Institute of Technology Indore Indore India Dept. of Computer Science Engineering Indian Institute of Technology Indore Indore India

ISBN: (数字)9798350381832

ISBN: (纸本)9798350381849

False Data Injection (FDI) attack can lead to incorrect state estimation (SE) at control center, thereby giving incorrect information about the system operation. Therefore, the detection of FDI attacks on measurements is crucial. This paper proposes a methodology based on the Weibull Distribution (WD) to detect FDI attacks on single or multiple measurements received from Phasor Measurement Units (PMUs) and Supervisory Control and Data Acquisition (SCADA) system. The Weibull Cumulative Distribution Function (CDF) with two parameters, scale and shape, has been used in this paper. The proposed method estimates the shape parameter of CDF for each measurement by taking a small measurement window. The presence of attack in a measurement window is detected by comparing the shape parameter with a specified threshold value. The performance of the proposed method is validated on the measurements generated for the IEEE 14-bus system using the Real-Time Digital Simulator (RTDS). Furthermore, the proposed method has been validated at various attack magnitudes along with different probability of attack on measurements.

关键词： Systematics Shape Network topology Shape measurement Systems operation SCADA systems Phasor measurement units Real-time systems Weibull distribution State estimation

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Detection of Covid-19 using Transfer Learning Technique 3

Detection of Covid-19 using Transfer Learning Technique

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3rd IEEE International Conference on Computing, Communication, and Intelligent Systems, ICCCIS 2022

作者： Kumar, Nilesh Tripathi, Atul Tripathi, Isha Pathak Mahatma Gandhi Central University Dept. of Computer Science and It Motihari India Indian Institute of Information Technology Dept. of Computer Science and Engineering Kota India

ISBN: (纸本)9781665462006

The whole world is suffering from the wave of the novel coronavirus that causes the large-scale death of a population and is proclaimed a pandemic by WHO. As RT-PCR tests to detect Coronavirus are costly and time taking. So now these days, the purpose of the researcher is to detect these diseases with the help of Artificial Intelligence or Machine learning-based models using CT scan images and X-rays images. So the testing cost, time taken and the number of data required could be minimized. In this paper, transfer learning based on three fine-tuned models has been proposed for Covid detection. The performance of these proposed fine-tuned models has been also compared with other competing models to check the accuracy and other matrices. © 2022 IEEE.

关键词： COVID-19

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Recognition of Wh-Question Sign Gestures in Video Streams using an Attention Driven C3D-BiLSTM Network

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Procedia computer science 2024年 235卷 2920-2931页

作者： Arnab Dey Samit Biswas Dac-Nhuong Le Dept. of Computer Science and Technology Indian Institute of Engineering Science and Technology Shibpur Howrah 711103 India Faculty of Information Technology Haiphong University Haiphong 180000 Vietnam

Human sign language is a visual and gestural means of communication used by people with hearing impairments to interact with others. It has the potential to enable interaction between individuals who struggle with verbal communication and those who lack the essential proficiency to comprehend sign language. Wh-questions constitute a substantial portion of daily sign language interactions. Automatic recognition of Wh-question words in sign language, encompassing interrogative words, has received limited attention in gesture recognition. In this context, a novel dataset named the American Question Sign Video dataset (AQSVd) has been introduced as a significant contribution. Recognizing Wh-question signs in video streams empowers individuals to effectively convey messages through hand movements and gestures, fostering inclusivity and accessibility in communication for deaf and hearing-impaired individuals. The paper proposes a Deep Convolutional-3D BiLSTM Multi-head Attention network for recognizing American Wh-Question word sign gestures in video streams. Incorporating Multi-head attention in the proposed model enhances its ability to capture intricate spatial and temporal features, essential for accurate gesture recognition. The proposed model is thoroughly evaluated across a range of datasets, such as INCLUDE50, WLASL-100, and our AQSVd dataset, achieving remarkable results. Specifically, our model demonstrated an outstanding 98.91% validation accuracy on the AQSVd dataset. The C3D-BiLSTM MHAttention model outperforms other state-of-the-art models, demonstrating its superiority in sign language recognition tasks. The proposed dataset and C3D-BiLSTM Multi-head Attention model contribute significantly to this field, offering potential benefits in education, human-robot interaction, and overall communication for the hearing-impaired community.

关键词： Gesture Recognition Wh-Question American Sign Language Video Streams Multi-head Attention C3D-BiLSTM

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Taxi Origin-Destination Demand Prediction using DNN Algorithm with NYU Database 2

Taxi Origin-Destination Demand Prediction using DNN Algorith...

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

作者： Shukla, Amogh Reddy, Apoorva Meena, Radhey Shyam Almal, Shubh School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Vellore India Indian Institute of Technology Dept. of Industrial and Management Engineering Kanpur India

ISBN: (纸本)9781665437899

Because of its colossal possible application in huge scope keen transportation frameworks, taxi request expectation has as of late drawn in expanded research interest. Most past methodologies, then again, just considered taxi request forecast in beginning regions, neglecting the demonstrating of the interesting circumstance of objective travellers. We accept that assigning taxicabs to every area ahead of time founded exclusively on taxi beginning interest is wasteful. In this paper, we present a significant and fascinating errand called taxi beginning objective interest expectation, which means to estimate taxi interest between all-area matches from now on. Its key hardships come from delineating how to proficiently gather a wide scope of relevant information to learn request designs. In this paper, a clever Deep Neural Network (DNN) with Deep learning-based models outflanked conventional AI techniques in an assortment of characterization undertakings, including beginning and objective perspectives. Broad tests and examinations on an expansive dataset plainly show that our DNN beats other looked at techniques for foreseeing taxi beginning objective interest. © 2022 IEEE.

关键词： Deep neural networks

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Bi-Directional Gated Recurrent Unit Approach for Detecting and Classifying High Impedance Fault in Power Distribution 3

Bi-Directional Gated Recurrent Unit Approach for Detecting a...

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3rd IEEE International Conference on Distributed Computing and Electrical Circuits and Electronics, ICDCECE 2024

作者： Irfan, Md. Mujahid Supriya, R. Reddy, P. Malleswara Alabdeli, Haider Gurumoorthy, Sasikumar Sr University Department Of Electrical And Electronics Engineering Warangal India Nitte Meenakshi Institute Of Technology Dept.of Ise Bengaluru India Department Of Eee Ananthapuramu Gooty India College Of Technical Engineering The Islamic University Department Of Computers Techniques Engineering Najaf Iraq School Of Engineering And Technology Dhanalakshmi Srinivasan University Computer Science And Engineering Tamil Nadu Tiruchirappalli India

ISBN: (纸本)9798350318609

In the recent days, detecting the High Impedance Faults (HIF) is an extremely difficult task because it is unpredictable, asymmetric, and nonlinear. Due to the size of the fault current is typically much lower than the normal load current, these faults are typically undetectable and impossible to isolate using traditional over-current approaches. Therefore, this research introduced a novel technique name called Bi-directional Gated Recurrent Unit (Bi-GRU) for power distribution-related HIF detection and isolation. The devised technique makes use of voltage as well as data from current sensors. In order to clarify the procedure, many types of algorithms were utilized during the detecting phase. For classification purposes, the algorithm Bi-GRU is applied on IEEE 13- distribution of node networks to test this method's ability to recognize and detect HIF with both damaged and unbroken wires. The suggested Bi-GRU provides fast and flawless output when troubleshooting high impedance issues. Bi-GRU considerably increases input processing and enhances classification accuracy. From the overall analysis, suggested Bi-GRU combines the forward and reverse GRUs in reverse order, which allows it to complete the operation more quickly and accurately than existing Morphological Fault Detector by achieving 95.63 % accuracy rate. © 2024 IEEE.

关键词： Principal component analysis

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Digital Twin-based Flight Dynamics Simulation

Digital Twin-based Flight Dynamics Simulation

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engineering Informatics, IEEE

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

ISBN: (数字)9798331505776

ISBN: (纸本)9798331505783

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.

关键词： Vibrations Three-dimensional displays Data visualization Virtual reality Autonomous aerial vehicles Aerodynamics Real-time systems Digital twins Vehicle dynamics Optimization

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Bidirectional Laser Based OCDMA-VLC System for Smart Home Applications 6

Bidirectional Laser Based OCDMA-VLC System for Smart Home Ap...

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6th International Conference on Contemporary Computing and Informatics, IC3I 2023

作者： Arya, Vivek Kumari, Meet Chauhan, Rahul Sharma, Neha Dept. of ECE FET Haridwar India Chandigarh University Dept.of ECE Mohali Gharuan India Graphic Era Hill University Computer Science and Engineering Dehradun India Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura India

ISBN: (纸本)9798350304480

A bidirectional symmetric 3 x35Gbps optical code division multiple access (OCDMA) with visible light communication (VLC) system is presented. The performance is measured using multi-diagonal (MD) code for different users with channel impairments. The simulation results indicate that faithful VLC range of 45m is achieved successfully. Also, the system employing MD code can supports 80 numbers of end users in both downlink as well as upstream transmission. Also, the system shows that best performance over other existing ones for smart home applications. © 2023 IEEE.

关键词： multi-diagonal (MD) optical code division multiple access (OCDMA) smart home visible light communication (VLC)

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Plagiarism Detection and Similarity Checking Program using Machine Learning and String Matching Algorithm

Plagiarism Detection and Similarity Checking Program using M...

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International Conference on Communication and Electronics Systems (ICCES)

作者： Munugapati Bhavana Koppula Srinivas Rao Gagan Kumar Koduru K.Vijay Krupa Vatsal S. Parvathi Mogiligidda Sravani Dept. of Computer Science & Information Technology MLR Institute of Technology Affiliated to JNTU Hyderabad Department of Computer Science & Engineering MLR Institute of Technology Affiliated to JNTU Hyderabad MLR Institute of Technology Hyderabad Department of Computer Science & Engineering Malla Reddy University Hyderabad Dept.of Computer Science & Information Technology MLR Institute of Technology Hyderabad Department of Information Technology MVSR Engineering College

ISBN: (数字)9798350377972

ISBN: (纸本)9798350377989

This research proposes a system for detecting plagiarism in academic submissions. The system utilizes advanced text comparison algorithms to identify instances of plagiarism, ensuring academic integrity and promoting a culture of originality among students. By providing educators with a tool to efficiently detect plagiarism, this system aims to streamline the evaluation process and enhance the overall learning experience for students. This system has the potential to significantly reduce plagiarism incidents and foster a more ethical and productive academic environment.

关键词： Measurement Ethics Machine learning algorithms Plagiarism Machine learning Complexity theory

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Real-Time Medical IoT for Continuous Glucose Monitoring

Real-Time Medical IoT for Continuous Glucose Monitoring

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2024 IEEE International Conference on Intelligent Systems and Advanced Applications, ICISAA 2024

作者： Goel, Akanksha Tamrakar, Poi Lal, Mily Bhende, Manisha Wani, Minal Saoji, Saurabh Dr.D.Y. Patil School of Science & Technology Dr.D.Y. Patil Vidyapeeth Pimpri Pune India Dr.D.Y. Patil Biotechnology and bioinformatics Institute Dr.D.Y. Patil Vidyapeeth Pimpri Pune India Maharashtra Institute of Engineering and Technology Dept.of Computer Engineering Nutan Pune India

ISBN: (纸本)9798331539948

This research is about Continuous Glucose Monitoring (CGM) technology and its pivotal role in revolutionizing diabetes care. Leveraging data obtained from a comprehensive study focused on evaluating CGM's efficacy in diabetes management, this study investigates the impact and potential benefits of CGM usage on patients' health after collecting the real secondary data through online forms. It encompasses a diverse array of patient-centric variables, including medical history, medication intake, physiological parameters, and CGM-derived glucose *** integrating machine learning techniques and statistical analysis, this study aims to derive valuable insights into the interplay between CGM data patterns and real-time activities, patients' physiological profiles, and diabetes management outcomes. The extracted features and processed data serve as a foundation for subsequent predictive modeling, classification, and regression analysis. This research aims to contribute to the expansion of knowledge regarding the effectiveness of CGM technology in diabetes treatment. By exploring correlations between CGM data, patient attributes, and clinical outcomes, this study endeavors to pave the way for personalized and optimized diabetes management strategies. © 2024 IEEE.

关键词： CGM Diabetes IoT Data Monitoring Real-time

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