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Smart Contract Based Carpooling Application for Secure and Efficient Ride Sharing 3

Smart Contract Based Carpooling Application for Secure and E...

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3rd IEEE International Conference on Recent Advances in Systems science and engineering, RASSE 2023

作者： Ramani Bai, V. Sudhir, Pranav Joshi, Rashmi Nair, Vighnesh Anilkumar, Vishnu Vidya Academy of Science and Technology Department of Computer Science Engineering Kerala Thrissur India

ISBN: (纸本)9798350341676

Carpooling offers an effective solution to mitigate pollutant emissions and alleviate traffic congestion. This paper presents a novel system designed to address the shortcomings of centralized ride-sharing services by leveraging smart contracts. By ensuring data security, confidentiality, and privacy, the proposed system initiates the decentralization process for carpooling systems. Users can assume the role of either a driver or a passenger, enabling them to publish rides or search for and book desired rides. Smart contracts facilitate seamless execution of user requests, triggered by events such as ride creation or booking. The fare calculation is based on the passenger's route distance, current fuel price, and the driver's car mileage, resulting in significantly reduced costs. Upon booking a ride, the fare amount is debited from the passenger and credited to the driver only upon the passenger reaching their destination. The system's overarching objective is to simplify people's lives by minimizing travel expenses and traffic congestion, all while upholding user security. © 2023 IEEE.

关键词： Smart contract

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Detection and Classification of Indian Classical Bharathanatyam Mudras Using Enhanced Deep Learning Technique 3

Detection and Classification of Indian Classical Bharathanat...

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3rd International Conference on Innovations in science and technology for Sustainable Development, ICISTSD 2022

作者： Haridas, Sneha Ramani Bai, V. Vidya Academy of Science and Technology Dept. of Computer Science and Engineering Thrissur India Vidya Academy of Science and Technology Computer Science and Engineering Thrissur India

ISBN: (纸本)9781665499361

Indian Classical dance forms like bharathanatyam are composed of advanced hand gestures, facial expressions moreover as body moments. Because of its complexity, identifying each mudra in bharathanatyam is extremely difficult. This paper demonstrates a massive Convolutional Neural Network (CNN) that was trained on Google Colaboratory using a single step model, You Only Look Once version 3 (YOLOv3), to analyze images in the dataset and detect and classify the mudras. Open datasets of mudras are not presently available. So Bharatanatyam mudra dataset of single hand gesture images of 28 classes was created. This proposed system is, as far as we know, the first attempt in this subject. YOLOv3 was never used to detect mudras. YOLOv3 divides the image into sectors, predicts bounding boxes, and calculates probability for each. These bounding boxes are then weighted according to the projected probability, and the model is then able to detect the object based on the final weights. The neural network was able to correctly generate test data after being trained, with a mean average precision (mAP) of 73%. © 2022 IEEE.

关键词： Classification (of information)

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A Wireless Multifunctional Structural Health Monitoring System 3

A Wireless Multifunctional Structural Health Monitoring Syst...

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3rd International Conference on Advances in Computing, Communication, Embedded and Secure Systems, ACCESS 2023

作者： Prabhakaran, Anagha Swapna Kumar, S. Vidya Academy of Science Technology Dept. of Electronics & Communication Engineering Kerala India

ISBN: (纸本)9798350346169

Structural vibrations have a great impact on civil structures. Most of the damage happening to structures that lead to a reduction in their life span is due to vibration. This vibration can be natural or man-made. This paper explains the importance of SHM(Structural health monitoring) and proposes a system for continuously monitoring the structural health status. The prototype consists of an ADXL-335 accelerometer, Arduino Uno, GPS (Global positioning system) and GSM (Global system for mobile communication). The functionalities of the system include detection of variation in the structural parameters, locating the position of the structure by means of GPS and sending the health status of the structure to authority where the GSM consists of a prioritized list of numbers. The paper also focuses on analyzing the dynamic parameters like acceleration and tilt angle in three modes: no vibration mode, normal vibration mode and collapse mode. Also analysis of time-domain features of vibration data in three modes are also analyzed. © 2023 IEEE.

关键词： Structural health monitoring

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Enhancing Security in Communication Networks through Quantum Key Distribution 2

Enhancing Security in Communication Networks through Quantum...

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2nd IEEE International Conference on IoT, Communication and Automation technology, ICICAT 2024

作者： Purushottam, Vaishnavi V Meena, S. Guruprasad, N. Global Academy of Technology Dept. of Computer Science and Engineering Bengaluru India Buddha Institute of Technology Computer Science and Engineering Gorakhpur India

ISBN: (纸本)9798350368109

Quantum Key Distribution or QKD revolutionizes secure communication using quantum mechanics. Unlike traditional cryptographic methods vulnerable to advances in computing power, QKD guarantees unconditional security by detecting any eavesdropping attempt. Quantum Key Distribution (QKD) constitutes a through development in the realm of secure communications by utilizing the ideas of quantum mechanics to provide potentially unbreakable encryption. This study examines the most current advancements in QKD, including the recently announced Measurement-Device-Independent QKD (MDI-QKD) and protocols like BB84 and E91. We analyze the real-world implementation, including satellite-based QKD and quantum repeaters. Furthermore, we discuss the challenges and opportunities of integrating QKD into present communication infrastructure. The significance of QKD in attaining secure communication networks for the next generation is underscored by our findings, which also have ramifications for forthcoming research and applications. Ultimately, this research aims to contribute to the development of robust and impenetrable communication networks in an increasingly interconnected world. © 2024 IEEE.

关键词： Quantum communication

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Advanced Forecasting and Analysis Techniques for Optimizing Website Performance: A Comparative Study of Time Series Models 2

Advanced Forecasting and Analysis Techniques for Optimizing ...

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2nd IEEE International Conference on IoT, Communication and Automation technology, ICICAT 2024

作者： Raikar, Gayathri Harshith, D. Guruprasad, N. Global Academy of Technology Dept. of Computer Science and Engineering Bangalore India Buddha Institute of Technology Computer Science and Engineering Gorakhpur India

ISBN: (纸本)9798350368109

Analyzing website performance is crucial for optimizing a site's functionality, enhancing user engagement, and aligning with business goals. This evaluation focuses on essential metrics such as average engagement time per session, engagement rate, and events per session, which significantly impact user experience and overall business success. Through effective performance analysis, organizations can improve user satisfaction, boost conversion rates, and enhance their profitability and reputation. This paper reviews different approaches for analyzing these key metrics, explores their effects on user behavior and business performance, and offers best practices for optimizing website performance. By employing detailed performance analysis, businesses can make informed decisions and realize their strategic goals in the digital environment. © 2024 IEEE.

关键词： Channel Performance Data Analysis Time Series Forecasting User Engagement Website Performance

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Leveraging XAI and Breakthrough Machine Learning Techniques for Trigeminal Neuralgia Severity Classification

Leveraging XAI and Breakthrough Machine Learning Techniques ...

引用

2024 IEEE Region 10 Symposium, TENSYMP 2024

作者： Abhijna, M.B. Akul, P. Kodipalli, Ashwini Global Academy of Technology Dept. of Information Science and Engineering India Global Academy of Technology Dept. of Computer Science and Engineering India Global Academy of Technology Dept. of Artificial Intelligence and Data Science India

ISBN: (纸本)9798350364866

Trigeminal Neuralgia (TN) is a debilitating chronic pain disorder that significantly diminishes overall well-being, making diagnosis and therapy more challenging. The quick and precise categorization of TN severity is critical to therapy effectiveness. To assess the severity of TN, this study makes use of advanced machine learning techniques and explainable AI (XAI) approaches. We use a range of bagging and boosting strategies, including AdaBoost, Random Forest, Decision Tree, K-Nearest Neighbours (KNN), Gradient Boosting, and Logistic Regression. Random Forest was 95.2% accurate, whereas AdaBoost and Gradient Boosting were 92% and 97.2% in terms of their accuracy. The Decision Tree classifier obtained an accuracy of 84.62%, while K-Nearest Neighbours and Logistic Regression also achieved 92.31% accuracy. F1- score, precision, and recall metrics were used to assess performance on a dataset that included patient demographics and medical histories. XAI techniques, such as LIME and SHAP values, were used to improve the models' readability and transparency, which helped to progress the creation of cutting-edge diagnostic tools and increase clinician and researcher confidence. © 2024 IEEE.

关键词： Logistic regression

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Energy Harvesting based Electromyography Analysis for Muscle Activity 3

Energy Harvesting based Electromyography Analysis for Muscle...

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3rd International Conference on Advances in Computing, Communication, Embedded and Secure Systems, ACCESS 2023

作者： Sidharthan, Pooja Swapna Kumar, S. Vidya Academy of Science & Technology Dept. of Electronics & Communication Engineering Kerala Thrissur India

ISBN: (纸本)9798350346169

Energy is the fundamental part of life and there is great demand of using renewable energy sources. Energy can be harvested from human body movements using piezoelectric transducers. The energy wasted during walking and exercising can be converted into useful energy is considered in this paper. The proposed system consists of the piezoelectric transducers on which the muscle force is applied. The AC signal from piezoelectric transducers is converted to DC using bridge rectifier. Then the DC signal is boosted by a DC-DC boost converter and the energy is stored in a supercapacitor. Due to the fast discharging rate of supercapacitor, it is thereby discharged to a rechargeable battery. This can power up the Arduino and thereby the electromyography sensor which analyses the muscle activity. A mathematical model can be used determine the work-done by lifting some known weights and comparing with electromyographic value. Series-parallel combination of piezoelectric transducers provides more voltage and current. Amplitude variations while lifting different known weights are analyzed using electromyography sensors. Deviation of conceptual work-done and measured value is analyzed. © 2023 IEEE.

关键词： Piezoelectricity

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Federated Learning for Cybersecurity: A Promising Frontier 2

Federated Learning for Cybersecurity: A Promising Frontier

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2nd IEEE International Conference on IoT, Communication and Automation technology, ICICAT 2024

作者： Ananya, G. Bindu Madhavi, V. Murthy, Monisha Guruprasad, N. Global Academy of Technology Dept. of Computer Science and Engineering Karnataka Bengaluru India Buddha Institute of Technology Computer Science and Engineering Uttar Pradesh Gorakhpur India

ISBN: (纸本)9798350368109

In today's world, Artificial Intelligence and Machine Learning are transforming many industries, but they rely on huge amounts of data leading to privacy issues. A better alternative is Federated learning. Federated Learning involves training the model from multiple sources using the decentralization technique, meaning each device trains the model on its local data thereby reducing the strain on the single server. This is useful in cases where the data is too large to be sent and maintained on a central server or in handling privacy and security concerns. Cybersecurity is a prime domain where vulnerable attacks and breaches can be prevented using this technique. Other domains like finance, healthcare, IoT etc. have transformed the idea of data-driven decisions. This paper gives an insight into the concept of Federated learning and why it is a better choice. It includes the various algorithms that can be implemented in multiple applications depicted through a comparative analysis. The choice of algorithm depends upon its efficiency and the desired cybersecurity application. © 2024 IEEE.

关键词： Federated learning

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Advancements in Cancer Disease Prediction Using Modified Multi Model Deep Neural Networks 3

Advancements in Cancer Disease Prediction Using Modified Mul...

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3rd IEEE Asian Conference on Innovation in technology, ASIANCON 2023

作者： Balakrishnan, D. Mariappan, Umasree Reddy, Sangam Sravya Nikitha, Ishnumolakala Likitha, S. Sirisha, S. Kalasalingam Academy of Research and Education Department of Computer Science and Engineering TamilNadu Krishnankoil626126 India Sri Vidya College of Engineering and Technology Department of Computer Science and Engineering TamilNadu Virudhunagar626001 India

ISBN: (纸本)9798350302288

Cancer disease prediction is a critical task in healthcare that can significantly impact patient outcomes. This abstract presents a multimodal deep neural network (DNN) approach for cancer disease prediction, implemented in MATLAB. The proposed model leverages the power of DNNs to handle diverse types of data, such as medical images, genomic profiles, clinical records, and patient demographics. By integrating these multimodal data sources, the model aims to capture a comprehensive view of the disease, incorporating both genetic and phenotypic information. The MATLAB implementation allows for efficient data processing, model training, and evaluation. The performance of the proposed multimodal DNN model is assessed using benchmark datasets, and the results demonstrate its potential in accurate cancer disease prediction. Further research is needed to explore the interpretability of the model's predictions and expand its application to real-world clinical settings. Overall, this work contributes to advancing the field of cancer diagnostics by utilizing a multimodal DNN approach implemented in MATLAB for improved disease prediction. © 2023 IEEE.

关键词： Deep neural networks

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Real Time Eye-Tracking Mouse Control Using Recurrent Neural Network

Real Time Eye-Tracking Mouse Control Using Recurrent Neural ...

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2023 International Conference on Integrated Intelligence and Communication Systems, ICIICS 2023

作者： Balakrishnan, D. Mariappan, Umasree Niteesh, Vedula Reddy, Yeruva Akhileswara Reddy, Vangala Mahesh Reddy, Velagala Venkata Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Tamil Nadu Krishnakoil India Sri Vidya College of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu Virudhunagar India

ISBN: (纸本)9798350315455

Machine Learning (ML) techniques, specifically Support Vector Machine (SVM) and Extreme Gradient Boosting (XGBoost), were employed to achieve precise and intuitive real-time eye tracking mouse control through computer vision. However, XGBoost may suffer from overfitting when dealing with a large number of features compared to the training data size, or when dealing with noisy or imbalanced data. To address this issue, this paper introduces a Recurrent Neural Network (RNN), for eye tracking mouse control in Human computer Interaction (HCI). Gaze Direction Estimation (GDE) is initially employed to estimate gaze direction, utilizing pupil positions and camera calibration parameters. The estimated gaze direction is then used as input for the RNN to eye-tracking mouse control for HCI. The experimental results shows that the GDE-RNN has 26.03% and 8.24% superior accuracy, 24.93% and 86.76% better precision, and 27.36% and 8.75% high recall in comparison to SVM and XGBoost for eye tracking mouse control. © 2023 IEEE.

关键词： Eye tracking gaze direction estimation human computer interaction mouse control recurrent neural network

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