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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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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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Local Closeness Gravity Model to Identify the Vital Nodes in Complex Networks 16

Local Closeness Gravity Model to Identify the Vital Nodes in...

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16th International Conference on COMmunication Systems and NETworkS, COMSNETS 2024

作者： Nandi, Suman Dutta, Animesh National Institute of Technology Durgapur Dept. of Computer Science and Engineering West Bengal India

ISBN: (纸本)9798350383119

In complex network analysis, identifying the viral nodes is a major concern of the research domain by which any kind of information or infection is controlled throughout the entire network. Several algorithms have been developed over the past few years to identify the viral nodes (influential spreaders) considering many properties of the network. Among them, some authors proposed gravity-based centrality to identify the vital nodes based on the law of gravity with certain limitations. The major limitation of existing gravity-based methods is the mass of the object (i.e. node) is considered as the degree or kshell index only, which does not always signify the spreading ability of the nodes. To address this research challenge, we propose an innovative Local Closeness Gravity method (named LCG) to measure the influential ability of individual nodes, facilitating the identification of the vital nodes in the network. To minimize the computational complexity of Closeness centrality, at first, we measure the local Closeness centrality of individual nodes considering all the nodes residing in the truncation radius. Thereafter we introduce a new parameter 'information sharing ability' based on connectivity strength to measure the distance between the nodes. Finally, the influential ability of each node is measured based on the gravity model considering the local closeness centrality, kshell index, and the distance. The efficiency of LCG is compared with the existing baseline centrality methods by using the Susceptible-Infectious-Recovered (SIR) simulator. The correlation between the LCG method and the baseline centrality methods with the SIR method is compared by Kendalls' tau method considering various infection probabilities and various percentages of seed nodes respectively. The ranking uniqueness of the LCG method and the baseline centrality methods are also measured by the monotonicity metrics. Through the obtained results and various analyses, it becomes evident that t

关键词： Complex Networks Information sharing ability Local Closeness centrality SIR epidemic simulator Vital nodes

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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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Enhancing the Coexistence of 802.11 WLAN and 5G Networks using a Multi-Task Learning with Convolutional Neural Network Model 2

Enhancing the Coexistence of 802.11 WLAN and 5G Networks usi...

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2nd International Symposium on Information Technology and Digital Innovation, ISITDI 2024

作者： Putra, Dody Ichwana Ekariani, Shelvi Universitas Andalas Dept. of Computer Engineering Padang Indonesia Universitas Andalas Dept. of Mathematics and Data Science Padang Indonesia

ISBN: (纸本)9798350389241

The increasing need for high-speed internet for wireless devices has driven the development of 5G New Radio (NR) technology. The 5G NR operates in various spectrums such as sub-1 GHz, sub-6 GHz, and millimeter wave (mmWave) in the 26-28 GHz range. Conversely, the new standard Wi-Fi 6E operates on the unlicensed band and currently utilizes 2.4 GHz, 5 GHz, and 6 GHz. The coexistence of 5G NR sub-6 GHz and Wi-Fi 6E networks can result in interference and reduced performance for both technologies. To address this challenge, we introduce a Multi-Task Learning (MTL) approach using Convolutional Neural Networks (CNN) to improve the coexistence of 5G and Wi-Fi 6E networks. This proposes identifying and classifying packets transmitted and its modulation over both types of networks. The MTL-CNN model is trained to classify packets based on their type (e.g., 5G or Wi-Fi) and to identify the specific protocol used within each packet. We confirm the accuracy of this method with Wi-Fi and 5G signals produced by the MATLAB waveform simulator and subsequently deploy it on a hardware testbed using SDR. Our experimental results show that this method improves the accuracy of packet identification and classification, thus enhancing the coexistence between 5G and Wi-Fi 6E networks. MTL-CNN potentially leads to more efficient use of the wireless spectrum and improves the overall performance of wireless communication systems. © 2024 IEEE.

关键词： Wi-Fi

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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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Integration of Q-Learning with Maximal Ratio Combining to Enhance IoT Communications in Dynamic Fading Environments 12

Integration of Q-Learning with Maximal Ratio Combining to En...

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12th International Conference on Internet of Everything, Microwave, Embedded, Communication and Networks, IEMECON 2024

作者： Upadhyay, Deepak Sharma, Kunj Bihari Gupta, Mridul Sharma, Rashmi Upadhyay, Abhay Dept. of Computer Science and Engineering Graphic Era Hill University Dehradun India Dept. of Computer Science and Engineering Graphic Era deemed to be University Dehradun India Dept. of Electronics and Communication Engineering Graphic Era deemed to be University Dehradun India Nagpur Campus Pune India

ISBN: (纸本)9798350387315

This paper serves to show how Q-learning can be used in complement with Maximal Ratio Combining to better IoT communication systems under dynamic fading, high-density networks, and severe interference sources. The ML-enhanced MRC demonstrated promising improvements in signal strength and quality, allowing for performance that was either above or at least equal to the regular MRC. The ML-enhanced presented the added benefit of dynamic adaptation, modifying its performance to meet real-life needs by adapting to issues such as increased density and interference. These results support that integrating Q-learning into MRC-based frameworks significantly increases the reliability and efficiency of IoT communication protocols. Future investigations will aim to improve ML algorithms more appropriate for devices with few resources and evaluate their potential with state-of-the-art technology to foster future applications across intelligent IoT networks. These approaches have the potential to support more efficient operations and increased connectivity in IoT systems. © 2024 IEEE.

关键词： Fading MRC Q-Learning Wireless

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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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Industrial 3D Printing Quality Control System Based on Machine Vision

Industrial 3D Printing Quality Control System Based on Machi...

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2025 International Russian Smart Industry Conference, SmartIndustryCon 2025

作者： Tagirova, Klara Vulfin, Alexey Kachkaeva, Polina Ufa University of Science and Technology Dept. of Technical Cybernetics Ufa Russia Ufa University of Science and Technology Dept. of Computer Engineering and Information Security Ufa Russia

ISBN: (纸本)9798331511241

The work is devoted to improving the efficiency of the 3D printing quality control using machine learning and network technologies. Its relevance stems from the inherent limitations of quality control systems in additive manufacturing and the need to refine machine vision methods, models, and algorithms to automate and improve the control of the additive manufacturing process for technical products. Significant practical results have been obtained, demonstrating the effectiveness of a machine learning-based web service for 3D printing quality control. According to the results of calculating metrics for assessing the effectiveness of the proposed solution to classification and detection problems within the framework of a computational experiment, the F1-measure indicator is 0.94. © 2025 IEEE.

关键词： Model predictive control

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An Effective Way to Detect Phishing Website Using Machine Learning 6

An Effective Way to Detect Phishing Website Using Machine Le...

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6th International Conference on Electrical engineering and Information and Communication Technology, ICEEICT 2024

作者： Toyeer-E-Ferdoush Shorab, Mst. Sumaiya Ritu, Saima Akter International University of Business Agriculture and Technology Dept. of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798350385779

In today's interconnected digital landscape, phishing represents a significant threat to both individuals and organizations. Phishers cleverly utilize deceptive techniques to extract sensitive information from unsuspecting users, posing a risk to critical account details. Despite various proposed methods to detect phishing websites, the adaptability of phishers continually challenges the efficacy of existing approaches. Recognizing the vital role of Machine Learning (ML) in combating these evolving threats, this research investigates the application of ML techniques. Common characteristics in phishing attacks can be identified through accurate ML analysis. This research employs a range of ML models and transformer models, including Catboost (Cat), XGBoost (XGB), Random Forest (RF), Support Vector Machine (SVM), Decision Tree (DT), TabNet, K-nearest neighbor (KNN), and Logistic Regression (LR) with grid-search. Among these models, the Catboost classifier achieved a remarkable accuracy of 97%. This study shows dedication to utilizing diverse methodologies to advance the field of phishing website detection. © 2024 IEEE.

关键词： Decision trees

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