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Photoplethysmography Signal Quality Assessment Using Neighbour Edge Restricted Horizontal Visibility Graph and Machine Learning Classifiers 16

Photoplethysmography Signal Quality Assessment Using Neighbo...

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16th International Conference on Electronics, computers and Artificial Intelligence, ECAI 2024

作者： Khan, Zahir Manikandan, M. Sabarimalai Mahmud, Mufti Indian Institute of Technology Palakkad Dept. of Computer Science and Engineering Kerala Palakkad678623 India Indian Institute of Technology Palakkad Dept. of Electrical Engineering Kerala Palakkad678623 India Nottingham Trent University Dept. of Computer Science Nottingham Clifton Lane United Kingdom

ISBN: (纸本)9798350371154

Photoplethysmography (PPG) signals are vital for monitoring pulse rate, blood pressure, and more, but they are prone to motion artefacts and noise, leading to unreliable data. Assessing PPG signal quality is crucial for reliable healthcare and accurate medical diagnoses. By transferring the PPG time domain signal to a Horizontal Visibility Graph (HVG) network and combining extracted features from HVG with machine learning algorithms, we can classify the PPG signal into clean (or high quality) and noisy. We have proposed a new version of HVG called Neighbour Edge Restricted Horizontal Visibility Graph (NERHVG) by invoking some extra conditions for joining edges in HVG for PPG signal quality assessment (SQA). We have used the average degree (AD) of graphs extracted from HVG, and NERHVG algorithms as features in 3 different machine learning classifiers such as Random Forest (RF), Gaussian Naive Bayes (GNB), Decision Tree (DT) to classify 4 standard untrained PPG datasets (DS). The classifier models DT, RF, GNB associated with graph feature AD of HVG, NERHVG algorithms are named as: DT-HVG, DT-NERHVG, RF-HVG, RF-HVG, RF-NERHVG, GNB-HVG and GNB-NERHVG. After all the performance of HVG and NERHVG algorithms using AD feature are compared over the mentioned classifier models. It is observed that the NERHVG algorithm outperformed the HVG algorithm with the AD feature in all 4 datasets with a maximum accuracy of: 99.09%, 95.03%, 96.56 % and 84.63% using the GNB classifier. © 2024 IEEE.

关键词： Decision trees

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Security Constrained Optimal Power Flow using Meta-Heuristic Algorithms 3

Security Constrained Optimal Power Flow using Meta-Heuristic...

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3rd IEEE International Conference on electrical Power and Energy Systems, ICEPES 2024

作者： Kayalvizhi, S. Arulraj, R. National Institute of Technology Dept. of Electrical and Electronics Engineering Tiruchirappalli India K. Ramakrishnan College of Engineering Dept. of Electrical and Electronics Engineering Tiruchirappalli India

ISBN: (纸本)9798350390728

The main goal of this paper is to address the Security Constrained Optimal Power Flow (SCOPF) issue by employing a basic binary Genetic Algorithm (GA). Initially, the Optimal Power Flow (OPF) problem is formulated for normal operating conditions without any contingencies, such as line or generator failures and the optimal generation schedules are obtained using the binary GA and Particle Swarm Optimization (PSO) algorithms. Subsequently, generator outputs are adjusted following a line outage after identifying the worst outage by ranking. The primary objective is to minimize fuel costs while adhering to various constraints related to generator power outputs, bus voltages, shunt capacitors/ reactors, and transformer tap-settings. The binary GA is applied and optimal schedules of generation are found when the system is subjected to line outage i.e. SCOPF is evaluated on the IEEE 30-bus system and its performance is compared with PSO method. It is also evident from the results that increasing the number of bits in the binary string can lead to improved optimal solutions at the cost of computation. © 2024 IEEE.

关键词： Heuristic algorithms

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Modelling of Electric Vehicle and it's components on MATLAB/Simulink 1

Modelling of Electric Vehicle and it's components on MATLAB/...

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1st IEEE International Conference on Smart Power Control and Renewable Energy, ICSPCRE 2024

作者： Kumar, Gautam Kumar, Sanjay Sankalp, Kumar Raj, Ashish National Institute of Technology Jamshedpur Engineering Dept. Jamshedpur India National Institute of Technology Jamshedpur Electrical Engineering Dept. Jamshedpur India

ISBN: (纸本)9798350377002

This literature commences with an overview of electric vehicles, encompassing their diverse types, charging topologies, and future prospects. Subsequently, the paper delves into the dynamics of vehicle motion, elucidating the forces at play during acceleration, with simulations conducted using MATLAB/Simulink. Visual representations of electric vehicle systems are provided, serving as a precursor to forthcoming Simulink realizations. Furthermore, the paper outlines the dynamic mathematical modelling of electric vehicles in subsequent sections. The discussion transitions to the simulation and mathematical analysis of various converters utilized in electric vehicles, including rectifiers, inverters, and choppers. Battery-related calculations are then explored, addressing the requisite number of cells in series and parallel configurations for optimal vehicle operation. Finally, the project scrutinizes three potential issues - cell equalization, voltage limits violation, and thermal anomalies in the battery - during vehicle operation or charging. A simulation model is presented as a proposed solution to mitigate these challenges, underscoring the comprehensive approach of this research endeavor. © 2024 IEEE.

关键词： Equalizers

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Churn Buster: Uncovering Patterns and Predicting Churn in OTT Platforms 6

Churn Buster: Uncovering Patterns and Predicting Churn in OT...

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6th Parul International Conference on engineering and technology, PICET 2024

作者： Anand, Akshat Sarika, D. Malviya, Harshit Vatsa, Dipti Yadwad, Sunita Adarsh Dholariya, Shreya M. Dept. Computer Science Engineering Parul Institute of Engineering and Technology Vadodara India

ISBN: (纸本)9798350369748

Churn, a common concern among business leaders, poses a significant challenge in today's fast-paced world where consumer attention spans are short. This research focuses on churn analysis within the OTT streaming platform sector, essential for retaining subscribers and ensuring business longevity. Utilising data preprocessing methods and machine learning algorithms, this study aims to identify factors influencing churn and provide insights for targeted retention strategies. The dataset includes 16 variables, with churn serving as the binary-dependent variable. Data preprocessing involved addressing missing values and converting categorical data into numerical format. Four machine learning models-Random Forest, XGBoost, K-Nearest Neighbors, and Support Vector Machine-were applied, with Random Forest achieving the highest accuracy of 89%. Evaluation metrics like confusion matrices, ROC curves, and Precision-Recall curves were used to evaluate model performance, revealing Random Forest's effectiveness in churn prediction. Principal Component Analysis (PCA) was employed to handle the complexity of user data, aiding in the identification of influential churn factors. This study emphasises the importance of understanding customer behaviour and utilising machine learning to forecast subscription choices. The insights gained provide valuable guidance to OTT companies for enhancing customer satisfaction and retention strategies, ensuring competitiveness in a dynamic industry landscape. © 2024 IEEE.

关键词： Sales

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MeQANet: Deep Learning-based Framework for Classifying Medical Query Answering in Bengali

MeQANet: Deep Learning-based Framework for Classifying Medic...

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2024 IEEE International Conference on Computing, Applications and Systems, COMPAS 2024

作者： Tasnia, Fairooz Hoque, Mohammed Moshiul Chittagong University of Engineering and Technology Dept. of Computer Science and Engineering Chattagram Bangladesh

ISBN: (纸本)9798331529765

The automatic classification of medical QA pairs is crucial for improving healthcare efficiency, allocating specialized care, optimizing resource distribution, and facilitating robust data analysis. Bengali, the 7th most widely spoken language globally, has a growing volume of health-related queries. However, unlike English, there is no benchmark large-scale medical QA dataset, pre-trained language model, or human baseline score for medical QA in Bengali. This paper introduces a deep learning-based framework called MeQANet that leverages CNN and BiLSTM techniques for classifying medical QA texts in Bengali. It also presents a novel medical QA corpus in Bengali (BnMedQA), consisting of approximately 4500 medical QA pairs across eight domains: Orthopedic, ENT, Gastroenterology and Hepatology, Gynecology, Pediatrics, General Medicine, Dermatology, and Cardiology. Various techniques (including LR, SVM, RF, MNB, AdaBoost (AB), CNN, BiL-STM, and CNN+BiLSTM) are employed to perform the classification tasks. Experimental results indicate that the CNN+BiLSTM approach achieved the highest F1-score of 0.85 among all employed techniques. © 2024 IEEE.

关键词： Query languages

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Rotor Poles Analysis of a Novel Wound Field Flux Switching Machine with π - Shaped Stator 33

Rotor Poles Analysis of a Novel Wound Field Flux Switching M...

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33rd Southern African Universities Power engineering Conference, SAUPEC 2025

作者： Raafi, Rafiullah Shirani, Habiburahman Ullah, Wasiq Khan, Wasiullah Akuru, Udochukwu Bola Khan, Faisal Kabul Polytechnic University Faculty of Electromechanics Dept. of Electrical Engineering Kabul Afghanistan Kabul University Faculty of Engineering Dept. of Electrical and Electronics Engineering Kabul Afghanistan Kabul Polytechnic University Vice Chancellor of Research and Journals Kabul Afghanistan Comsats University Islamabad Abbottabad Campus Dept. of Electrical and Computer Engineering Abbottabad Pakistan Tshwane University of Technology Pretoria West Campus Dept. of Electrical Engineering Pretoria South Africa

ISBN: (纸本)9798331535162

In this paper, the performance of various rotor pole configurations in a novel π-shaped stator wound-field flux switching machine (WFFSM) is investigated to address concerns with cost and environmental concerns associated with permanent magnet machines. By employing the finite element analysis (FEA) method, the study focuses on the no-load, on-load and overload performance characteristics of the proposed machines based on four different rotor poles-13P, 14P, 16P and 17P. The 14P rotor configuration emerges as superior machine, showcasing highest torque, lowest torque ripple and best overload capability. The future study will cover full optimization of all four machines with experimental validation. © 2025 IEEE.

关键词： Stators

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Implementation of Load Side Coordination Control through Cuve Fitting 5

Implementation of Load Side Coordination Control through Cuv...

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5th IEEE International Conference on Recent Trends in computer Science and technology, ICRTCST 2024

作者： Mahato, Gopal Chandra Kumar, Deepak Kumari, Shalini Mohan, Anand RVS College of Engineering and Technology Dept. of Electrical and Electronics Engineering Jamshedpur India RVS College of Engineering and Technology Dept. of Electronics and Communication Engineering Jamshedpur India

ISBN: (纸本)9798350351378

The process of Maximum Power Point Tracking (MPPT) aims to extract the highest possible power output from photovoltaic (PV) panels. However, due to the intermittent nature of solar energy, MPPT can result in significant fluctuations in power output from the PV panel. In systems with a high penetration of PV, these fluctuations can create stability issues for the grid. To address this challenge, a Flexible Power Point Tracking (FPPT) scheme has been developed to regulate the fluctuating power injection, thereby enhancing system reliability. Traditionally, FPPT schemes have focused on optimizing PV panel parameters while overlooking the demand from the load side. In this study, we propose an FPPT algorithm that incorporates load coordination control, achieved through curve fitting in addition to utilizing feedback from the PV panel. This integrated approach aims to effectively manage power fluctuations. However, it's important to note that this strategy involves curtailing power and is effective only when the available power exceeds the load demand. Furthermore, this approach may also contribute to the development of the Demand Side Integration (DSI) technique for future Smart grid systems. © 2024 IEEE.

关键词： Solar panels

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Effective Pest Detection by YOLOv5 with BottleneckCSP 2

Effective Pest Detection by YOLOv5 with BottleneckCSP

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2nd International Conference on Power, electrical, Electronics and Industrial Applications, PEEIACON 2024

作者： Tani, Ishrat Zahan Jannat, Tasmia Rajshahi University of Engineering & Technology Dept. of Computer Science & Engineering Rajshahi Bangladesh

ISBN: (纸本)9798331517984

Pest damage poses a significant risk to crop quality and productivity, underscoring the necessity for accurate and efficient detection methods for farmers. Artificial intelligence (AI) facilitates targeted pest management with reduced reliance on broad-spectrum insecticides and enables early detection of insect infestations. Object detection, a computer vision approach, is revolutionizing pest identification in agriculture by locating pests in digital crop photographs. However, the detection of tiny objects within these datasets presents challenges due to sparse spatial information. In response to this challenge, we have replaced the C3 block of YOLOv5 with the BottleneckCSP module, which is more effective for feature mapping of tiny objects. Our enhanced model demonstrates superior performance on the IP102 dataset, which is characterized by the presence of tiny objects. Comparative analysis reveals that our BottleneckCSP-integrated model significantly improves feature mapping, resulting in a notable increase in mean average precision (mAP). Specifically, our modified model achieves an mAP of 63.9%, outperforming many state-of-the-art methods on this dataset. This advancement in the YOLOv5 architecture sets a new benchmark for AI-powered precision agriculture, providing farmers with a highly efficient tool for crop protection and pest management. The significant performance gains highlight the potential of our approach to transform current agricultural practices, paving the way for more sustainable and effective pest control strategies. © 2024 IEEE.

关键词： Damage detection

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Carotid plaque stroke risk assessment using multiscale AM-FM analysis based on DoG filterbanks 24

Carotid plaque stroke risk assessment using multiscale AM-FM...

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24th International Conference on Digital Signal Processing, DSP 2023

作者： Constantinou, Kyriacos P. Constantinou, Ioannis P. Pattichis, Marios S. Kyriacou, Efthyvoulos Pattichis, Constantinos S. University of Cyprus Dept. of Computer Science Nicosia Cyprus Istognosis Ltd Nicosia Cyprus University of New Mexico Dept. of Electrical and Computer Engineering AlbuquerqueNM United States Cyprus University of Technology Dept. of Electrical Engineering Computer Engineering and Informatics Limassol Cyprus

ISBN: (纸本)9798350339598

The objective of this work was the investigation of multiscale Amplitude Modulation - Frequency Modulation (AM-FM) analysis based on Difference of Gaussians (DoG) filterbanks representations in order to predict the risk of stroke by analysing carotid plaques ultrasound images of individuals with asymptomatic carotid stenosis. We computed the instantaneous amplitude, instantaneous phase and the magnitude of instantaneous frequency to extract histogram features on each plaque region. The Support Vectors Machine classifier was implemented to classify asymptomatic versus symptomatic plaques. A dataset of 100 carotid plaque images (50 asymptomatic and 50 symptomatic) were tested, and showed that the AM-FM features based on DoG filterbanks and simple histograms performed better than the traditional AM-FM features. Best results were obtained when an eight scale filterbank with a combination of scales was used reaching the accuracy of 75%. © 2023 IEEE.

关键词： Frequency modulation

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Deep Feature Fusion Based Brain Tumor Classification From Imbalance MRI Dataset Using Weighted Loss Functions 2

Deep Feature Fusion Based Brain Tumor Classification From Im...

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2nd International Conference on Power, electrical, Electronics and Industrial Applications, PEEIACON 2024

作者： Tasnim, Taieba Parvin, Farjana Rajshahi University of Engineering & Technology Dept. of Computer Science & Engineering Rajshahi Bangladesh

ISBN: (纸本)9798331517984

Deep learning using MRI images to classify brain tumours presents a significant challenge due to imbalanced training datasets, where one tumor type may have far more samples than others. The overrepresented class may perform worse in classifiers as a result of this imbalance. In order to tackle this issue, the paper examines how feature learning in CNNs is affected by weighted loss functions to manage data imbalance. The article propose deep feature fusion, where particular CNN model features that were trained using three distinct loss functions are fused and classified using softmax classifier. This approach lead to notable improvements in brain tumor classification compared to a CNN trained with standard cross-entropy loss. Notably, the proposed methods substantially reduce classification errors between uneven class samples. The proposed model achieved an impressive 94.5% classification accuracy, accompanied by an average Fl-score of 94.6%, average precision of 94.6 %, and average recall of 95%. These findings underscore the effectiveness of our methodology in improving brain tumor classification accuracy, offering a promising avenue for more reliable diagnosis and treatment planning in clinical settings, © 2024 IEEE.

关键词： Deep learning

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