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13th IEEE Global Conference on Consumer Electronic, GCCE 2024

作者： Tsurumi, Kazuki Marutsuka, Ryo Koutaki, Gou Kumamoto University Dept. of Computer Science and Electrical Engineering Kumamoto860-8555 Japan

ISBN: (纸本)9798350355079

Research has primarily focused on fully automatic instrument-playing robots. However, it is difficult to support the experience of playing musical instruments for people with physical disabilities or those who are inexperienced in playing musical instruments. To address this issue, we studied a "semi-automatic"instrument-playing robot based on human-robot collaboration. Our research specifically focused on the guitar, which are stringed instruments with complex pressed strings. The developed robot enables individuals with physical disabilities and those without musical experience to easily play musical instruments. Additionally, the instruments can be easily removed without causing damage. © 2024 IEEE.

关键词： User centered design

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Blockchain Unleashed: Exploring its Multifaceted Applications and the Dynamics of Proof-of-Work

Blockchain Unleashed: Exploring its Multifaceted Application...

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2023 IEEE Global Conference on Information Technologies and Communications, GCITC 2023

作者： Hassan, Inzimam Ul Ahmad Lone, Zeeshan Kaur, Inderdeep Kaur, Gaganjot Siya, Siya Priya, Simran Chandigarh University Dept. of Computer Science and Engineering Mohali India Vivekananda Global University Dept. of Computer Science and Engineering Jaipur India Chandigarh University Dept. of Cse Mohali India

ISBN: (纸本)9798350308167

In a range of sectors, such as banking, healthcare, and the Internet of Things, we have seen several data security and confidentiality issues. So, it is revealed that blockchain can provide a solution to the security problem. Thanks to the distributed database technology known as a blockchain, a trusted third party are not required for secure and transparent transactions between parties. The integrity of the chain is ensured by the cryptographic hash of each block in the chain, which makes each one tamper-proof. Blockchain technology has the potential to revolutionize a wide range of industries, from finance to healthcare, by providing a secure, efficient, and transparent method of storing and transmitting data and value. After reading the research on blockchain applications in different fields, we will highlight some of the major advantages we understand © 2023 IEEE.

关键词： banking finance Blockchain security integrity tamper-proof

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Event Management System Using Generative AI 7

Event Management System Using Generative AI

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7th International Conference on Circuit Power and Computing Technologies, ICCPCT 2024

作者： Thummala, Sruthi Thammishetti, Saketh Varkol, Sharanya Thirunahari, Amarthya Lakshmi Kanthey, V.V.S. B V Raju Institute of Technology Dept. Computer Science and Engineering Narsapur India

ISBN: (纸本)9798350372816

This project depicts how Artificial intelligence is used in developing a "User-Engaging Event Management System". The developed system will spontaneously record all the events and online contests registered by an individual. Motivates the individual to participate the particular event by providing sufficient resources. It implements a persistent alarm or remainder system that operates continuously. The implementation of the system starts with choosing appropriate technologies (Deep learning Libraries, Natural Language Processor, DBMS, Web Development Framework and so on) focusing on generative AI followed by data collection (from existing event data, social media, crowdsourcing and so on) and preprocessing the data to ensure the quality for AI model training which integrates with app architecture. This study evaluates the effectiveness of the generative AI model in Event Management App by employing key numerical metrices. The accuracy metric gauges the model's overall correctness in generating event-related content, while precision focuses on the accuracy of positive predictions. It incorporates a robust feedback mechanism ,allowing users to provide input and receive corresponding responses, enhancing the overall user experience. The system will provide security for user information, access controls, and authentication protocols © 2024 IEEE.

关键词： Access control

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Smart Meter-based Outage Detection and Fault Location in a Microgrid in Gaidouromantra, Greece 14

Smart Meter-based Outage Detection and Fault Location in a M...

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14th Mediterranean Conference on Power Generation Transmission, Distribution and Energy Conversion, MEDPOWER 2024

作者： Bangali, Sanjeev Athanasios, Vasilakis Dimitrios, Lagos Arya, Prasoon Singhai, Jigyasa Das, Sarasij Dept. of Electrical Engineering Indian Institute of Science Bengaluru India Dept. of Electrical and Computer Engineering National Technical University of Athens Athens Greece

ISBN: (纸本)9781837242689

In the existing literature, Digital relay-based protection schemes are proposed for microgrids (MGs). Digital relays are expensive and uneconomical for practical microgrids. However, in most MGs, smart meters (SMs) with communication facilities are available at the consumer end for energy measurements. These SMs can measure the electrical quantities and communicate the measurements to the local server. This paper demonstrates that SMs can also be used for outage detection and fault location in practical MGs. Gaidouromantra MG is one such MG in Kythnos island, Greece, with SMs and communication facilities. This paper proposes two algorithms to detect outages and fault locations. The first algorithm is a voltage-based algorithm which assumes that SM communication is intact. The second algorithm extends the first. It uses current measurements along with voltage measurements to tackle the problem of SM communication disconnection after the fault. The algorithms use measurements from the SMs to detect the outage in the feeder and locate the fault with the guidance of the MG's topology. Additionally, the extended algorithm can be used to indicate SM communication failure and no-load/disconnection status of houses on the island, if any occur. The algorithms are validated using the Gaidouromantra MG model on Simulink and hardware-in-loop simulations of the Gaidouromantra MG. The proposed algorithms can generally be applied to MGs with radial structures consisting of a single grid-forming inverter and multiple grid-following inverters, as in Gaidouromantra. © The Institution of engineering & Technology 2024.

关键词： Microgrids

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Machine Learning Approach for Parkinson' s disease Prediction through Quantum Computing Techniques 2

Machine Learning Approach for Parkinson' s disease Predictio...

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2nd IEEE International Conference on Advances in Information Technology, ICAIT 2024

作者： Arpitha, K. Lohith, H.R. Nemmadi, H.K. Sujan, K. Yashwanth, C.L. Dept. of Computer Science Engineering BGS Institute of Technology Adichunchanagiri University Mandya India

ISBN: (数字)9798350383867

ISBN: (纸本)9798350383867

One of the most serious illnesses that may impact the neurological system in humans is dementia, of which Parkinson' s disease is a major subtype. Patients with Parkinson' s disease have significant behavioural changes. The suggested system uses quantum computing and machine learning to build a speech signal-based method for Parkinson' s disease prediction. Four main machine learning methods are used in this prediction: Artificial Neural Networks, KNN, Decision Trees, and Naïve Bayes. Furthermore, different ensemble learning models are used to combine numerous models and improve the accuracy. An industry standard dataset from the UCI Machine Learning Repository is used to assess the model' s performance. Ensemble models have proven to outperform extremely efficient techniques like neural networks in terms of accuracy. A multi-layer perceptron, a very significant machine learning optimization technique, is incorporated into the suggested system. © 2024 IEEE.

关键词： Adversarial machine learning

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Weighted butterfly optimization algorithm with intuitionistic fuzzy Gaussian function based adaptive-neuro fuzzy inference system for covid-19 prediction

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Materials Today: Proceedings 2022年 56卷 3317-3324页

作者： Sundaravadivel, T. Mahalakshmi, V. Dept. of Computer Science and Engineering Annamalai University Tamil Nadu India

Covid-19 cases are increasing each day, however none of the countries successfully came up with a proper approved vaccine. Studies suggest that the virus enters the body causing a respiratory infection post contact with a disease. Measures like screening and early diagnosis contribute towards the management of COVID- 19 thereby reducing the load of health care systems. Recent studies have provided promising methods that will be applicable for the current pandemic situation. The previous system designed a various Machine Learning (ML) algorithms such as Decision Tree (DT), Random Forest (RF), XGBoost, Gradient Boosting Machine (GBM) and Support Vector Machine (SVM) for predicting COVID-19 disease with symptoms. However, it does not produce satisfactory results in terms of true positive rate. And also, better optimization methods are required to enhance the precision rate with minimum execution time. To solve this problem the proposed system designed a Weighted Butterfly Optimization Algorithm (WBOA) with Intuitionistic fuzzy Gaussian function based Adaptive-Neuro Fuzzy Inference System (IFGF-ANFIS) classifier for predicting the magnitude of COVID- 19 disease. The principle aim of this method is to design an algorithm that could predict and assess the COVID-19 parameters. Initially, the dataset regarding COVID-19 is taken as an input and preprocessed. The parameters included are age, sex, history of fever, travel history, presence of cough and lung infection. Then the optimal features are selected by using Weighted Butterfly Optimization Algorithm (WBOA) to improve the classification accuracy. Based on the selected features, an Intuitionistic fuzzy Gaussian function based Adaptive-Neuro Fuzzy Inference System (IFGF-ANFIS) classifier is utilized for classifying the people having infection possibility. The studies conducted on this proposed system indicates that it is capable of producing better results than the other systems especially in terms of accuracy, precision,

关键词： COVID-19

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Sea Erosion Prediction and Monitoring Using IOT and ML 2

Sea Erosion Prediction and Monitoring Using IOT and ML

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2nd International Conference on Recent Trends in Microelectronics, Automation, Computing, and Communications Systems, ICMACC 2024

作者： Usha Bala, V. Harshavardhan Sai, Y. Slipa Shramila, V. Asritha, M. Bharath, M. Chandu, A. Anil Neerukonda Institute of Technology & Sciences Dept. of Computer Science & Engineering Visakhapatnam India

ISBN: (纸本)9798350366570

One of the major environmental issues that the world is currently facing is sea erosion, which requires an innovative solution for early detection and mitigation. Our paper focuses on the integrated system that combines both IoT and Machine Learning. The four main important environmental factors that are responsible for sea erosion are temperature, wave height, wave speed, and soil moisture. IoT sensors deployed along the prototype design are used to monitor these environmental parameters. Another key component is the machine learning algorithm, which is used analyse the interactions, patterns, and relations among the environmental data. By analysing the patterns among the previous data, the model can predict the likelihood of sea erosion events. This allows for the early detection of erosion events and timely interventions that can minimize the damage or loss caused by sea erosion. The main aim of this project is to help the coastal management authority take smart decisions and proactive steps using both real-time and past data analysis. © 2024 IEEE.

关键词： Soil erosion

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Enhancing Exercise Efficacy: A Predictive EMG Analysis for Dumbbell Press Workouts 6

Enhancing Exercise Efficacy: A Predictive EMG Analysis for D...

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6th IEEE International Conference on Artificial Intelligence in engineering and Technology, IICAIET 2024

作者： Pattanashetty, Pranav T. Chaurasia, Shreya Upadhyaya, Sujatha R. Visweswara, Ramesh Debur Pes University Dept. of Computer Science and Engineering Bengaluru India Ramaiah Memorial Hospital Dept. of Physiotherapy Bengaluru India

ISBN: (纸本)9798350389692

The current paper describes a preliminary model that would help improve the dumbbell press exercise through the use of electromyography (EMG). The model recognizes the optimal and marginal repetitions in sets based on the analysis of the EMG data from the pectoral and deltoid muscles. The model provides features like load, rest, and technique hints for set modifications that will improve set quality and minimize non-optimal repetitions in order to provide safer and more efficient training. The findings of this study can be used to give customized training advice by presenting immediate feedback according to individual EMG patterns. The data used in this study is self-reported and obtained from two participants who have previous experience exercising, so they are aware of the movements done. Additionally, a positive feedback system was established, a formula for dynamic adjustment of the exercise plan, taking into account the fitness and experience of the participants was introduced, which improved the effectiveness of the exercises. The proposed paradigm helps to optimize training outcomes in strength and conditioning programs regarding the maximization of exercise performance and the minimization of the injury risk. The study entails three series of exercise tasks at varying angles with participants completing multiple sets of exercises and EMG data collected. A three-layers Artificial Neural Network, Random Forest Classifier, Logistic Regression and Long Short-Term Memory (LSTM) models were used, and it was observed that the models were highly accurate in the classification of exercise quality and the feedback generated could help in improving performance. © 2024 IEEE.

关键词： Electromyography

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Revolutionizing Pandemic Healthcare: Mask Detection and Patient Face Recognition 14

Revolutionizing Pandemic Healthcare: Mask Detection and Pati...

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14th International Conference on Cloud Computing, Data science and engineering, Confluence 2024

作者： Saraswat, Shipra Singh, Sofia Middha, Parth Thirwani, Parth Rohilla, Harsh Amity University Dept. of Computer Science & Engineering Noida India Amity University Dept. of Artificial Intelligence Noida India

ISBN: (纸本)9798350344837

During the COVID-19 pandemic, Delhi, India, faced a pressing issue where approximately 1,500 COVID-19-positive patients went missing. In public health emergencies, such as pandemics, natural disasters, or other calamities, hospitals, and medical centres experience a sudden influx of patients, and hospital management faces difficulties in keeping track of patients, especially when they need to be moved between facilities or when new temporary healthcare facilities are set up. As a consequence of these challenges, there can be an increase in missing person cases. Patients may be inadvertently separated from their families. The human face is a unique biometric system that can determine the age, gender, mood, of an individual, and even identity for verification purposes. Harbouring the power of deep learning and artificial intelligence, one of the most important applications of computer vision is Patient Identification. In this study, we have proposed a state-of-the-art patient face detection model using a twofold model that uses MTCNN short for 'multi-task cascaded convolution neural network' for face detection and alignment purposes with a FaceNet Convolutional Neural Network (CNN) a renowned face embedding algorithm finally with KNN algorithm as a classifier to get an accuracy of 97.1%. Also, to ensure public safety during the pandemic we have constituted a Resnet34 model for mask detection trained on the Face Mask Detection dataset with an accuracy of 97%. This study not only addresses the immediate challenges of patient identification and safety during crises but also carries implications for broader healthcare applications. The proposed models offer promising avenues for enhancing patient care and security. © 2024 IEEE.

关键词： Face recognition

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Emotion Classification to Augment the Emotional Intelligence of Autistic Individuals 5

Emotion Classification to Augment the Emotional Intelligence...

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5th IEEE International Conference on Communication, Computing and Industry 6.0, C2I6 2024

作者： Guruvammal, S. Kesavamoorthy, R. Anju, M.I. Chellatamilan, T. Jegatha Deborah, L. Jain University Dept. of Cse Kanakapura India Cmr Institute of Technology Dept. of Cse Bengaluru India Srm Institute of Science and Technology Dept. of Ece Chennai India Vellore Institute of Technology School of Computer Science and Engineering Vellore India University College of Engineering Computer Science and Engineering Tindivanam India

ISBN: (数字)9798350379884

ISBN: (纸本)9798350379884

The collaboration of machine learning (ML) algorithms and cognitive psychological frameworks supports the enhancement of emotional intelligence. In light of Plutchik's emotional wheel, this study investigates how to identify human emotions. By utilizing 24 different emotions, 8 high-intensive (secondary) emotions are identified. We employed 9 different classification algorithms K-nearest neighbors (KNN), decision trees (DT), naïve bayes (NB), support vector machine (SVM), Logistic regression, random forest (RF), artificial neural network (ANN), gradient boosting machines (GBM), and bagging classifier (BC) to identify the emotions accurately and the performance of them are compared. This research uses the labelled dataset consisting of 11 columns as similar word 1, similar word 2, typical sensation 1, typical sensation 2, opposite intensity low, opposite intensity medium, opposite intensity high, tells, helps, emotion and cluster. © 2024 IEEE.

关键词： Random forests

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