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Innovative Research in Applied science, Engineering and Technology (IRASET),Conference on

作者： Syarifah Diana Permai Austin Ordell Salomo Fauzan Hafizh Zulfikar Muhammad Farras Fadhilah Ryan Bukhari Muhammad Fadlan Hidayat Statistics Department School of Computer Science Bina Nusantara University Jakarta Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indoensia

ISBN: (数字)9798331532970

ISBN: (纸本)9798331532987

This research concentrates on utilizing multiclass classification methodology for heart disease prediction. Specifically, this paper explores the utilization of Support Vector Machine (SVM) and Multi-Layer Perceptron (MLP) classifiers to classify various cardiac conditions. This research is used to get the best results in predicting heart disease that has a classification of more than two categories using machine learning methods. The main goal is to contribute insights to medical diagnostics by explaining the effectiveness of SVM and MLP in improving the accuracy of heart disease prediction models in various classes using several factors that influence heart disease. The data used in this research has missing values, therefore in this research, several methods were used to overcome missing values. Determining the best model is done by comparing precision, recall, and accuracy values. The best model in this research is the SVM model because it has the highest accuracy, precision, and recall. It is hoped that this research can provide knowledge to optimize its application in the identification and classification of heart disease to make it more efficient.

关键词： Heart Support vector machines Training Analytical models Accuracy Machine learning Medical diagnosis Kernel Tuning Diseases

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Intra Class Flower Recognition System Using Machine Learning and Neural Networks

Intra Class Flower Recognition System Using Machine Learning...

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2023 IEEE International Conference on Network, Multimedia and Information Technology, NMITCON 2023

作者： Duth P, Sudharshan Nischitha, H.S. Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science Mysuru570026 India

ISBN: (纸本)9798350300826

This comparative study of three machine learning algorithms, namely Support Vector Machines (SVM), K-Nearest Neighbours (KNN), and Recurrent Neural Networks (RNN), for Intra class flower classification. The study's objective is to identify the most effective approach for classifying different types of flowers within the same class. To evaluate the performance of these algorithms, the study uses a collected dataset of Intra class flower images and measures their accuracy, precision, and recall. The evaluation is based on the confusion matrix, which shows the number of correct and incorrect predictions for each class. SVM achieved the highest accuracy rate of 95.43%, followed by RNN with an accuracy rate of 97.009%, and KNN with an accuracy rate of 93.96%. While KNN has faster training and prediction time, RNN is better suited for sequential data, such as temporal aspects of flower growth. SVM, on the other hand, excels in linearly separable data. The study provides insights into the strengths and limitations of each algorithm for Intraclass flower classification. The results of this study have significant implications for the development of automated flower recognition systems. By identifying the most effective approach for classifying different types of flowers within the same class, this research can help improve the accuracy of such systems, which are increasingly being used in various fields, including agriculture, ecology, and conservation. In conclusion, the comparative study of Deep learning technique i.e, RNN and Machine learning techniques i.e, SVM and KNN demonstrates the strengths and limitations of each algorithm, providing valuable insights for researchers and practitioners in the field of machine learning. This study's findings can be used to improve the development of more effective automated flower recognition systems, with potential applications in diverse fields. © 2023 IEEE.

关键词： Support vector machines

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UltraStar:A Lightweight Simulator of Ultra-Dense LEO Satellite Constellation Networking for 6G

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IEEE/CAA Journal of Automatica Sinica 2023年第3期10卷 632-645页

作者： Xiaoyu Liu Ting Ma Zhixuan Tang Xiaohan Qin Haibo Zhou Xuemin(Sherman)Shen School of Electronic Science and Engineering Nanjing UniversityNanjing 210023China Department of Electrical and Computer Engineering University of WaterlooWaterloo N2L 3G1Canada IEEE

The mega-constellation network has gained significant attention recently due to its great potential in providing ubiquitous and high-capacity connectivity in sixth-generation(6G)wireless communication ***,the high dynamics of network topology and large scale of mega-constellation pose new challenges to the constellation simulation and performance *** this paper,we introduce UltraStar,a lightweight network simulator,which aims to facilitate the complicated simulation for the emerging mega-constellation of unprecedented ***,a systematic and extensible architecture is proposed,where the joint requirement for network simulation,quantitative evaluation,data statistics and visualization is fully *** characterizing the network,we make lightweight abstractions of physical entities and models,which contain basic representatives of networking nodes,structures and protocol ***,to consider the high dynamics of Walker constellations,we give a two-stage topology maintenance method for constellation initialization and orbit ***,based on the discrete event simulation(DES)theory,a new set of discrete events is specifically designed for basic network processes,so as to maintain network state changes over ***,taking the first-generation Starlink of 11927 low earth orbit(LEO)satellites as an example,we use UltraStar to fully evaluate its network performance for different deployment stages,such as characteristics of constellation topology,performance of end-to-end service and effects of network-wide traffic *** simulation results not only demonstrate its superior performance,but also verify the effectiveness of UltraStar.

关键词： Discrete event simulation(DES) mega-constellation network dynamics performance evaluation simulation architecture design

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Automated diabetes diagnosis and risk assessment using machine learning 2nd

Automated diabetes diagnosis and risk assessment using machi...

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2nd International Conference on Next-Generation Computing and Information Systems, ICNGCIS 2023

作者： Mathukiya, Deep Kumar, Yogesh Koul, Apeksha Department of Computer Science and Engineering School of Technology Pandit Deendayal Energy University Gandhinagar India Punjabi University Punjab Patiala India

ISBN: (纸本)9781032738659

Currently, Diabetes is a prevalent issue worldwide, affecting millions of people. The detection machinery for this disease is only available at healthcare centers, so individuals with diabetes are only aware of their condition when they visit and undergo a checkup. Prolonged diabetes can lead to various complications like heart disorders, kidney disease, nerve damage, and diabetic retinopathy. However, the risk can be reduced if the disease at its early stage is predicted. By utilizing a deep learning approach, this model can easily determine if an individual is diabetic or not. The aim of the research is to develop machine learning model that can identify diabetic people on the basis of their clinical data. The machine learning algorithms utilized in this study include Naive Bayes (NB), k-nearest neighbor (KNN), Stochastic Gradient Descent (SGD), Decision tree (DT), Support Vector Machine (SVM), Random Forest (RF), and Logistic Regression (LR). We have implemented efficient pre-processing techniques to detect as well as handle missing/ NAN and duplicate values and later trained our models with the cleaned dataset. During execution, we found that Random Forest as well as Decision Tree performed well for both diabetic and non-diabetic class by computing 100% and 99% accuracy respectively. © 2025 The Author(s).

关键词： Decision trees

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Adaptive Fusion Techniques for Image Super-Resolution 2

Adaptive Fusion Techniques for Image Super-Resolution

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2nd IEEE International Conference on Futuristic Technologies, INCOFT 2023

作者： Ga, Pranesh Vadakkan, Akshay Anto Jisha, R.C. Amrita School of Computing Department of Computer Science and Applications Amrita Vishwa Vidyapeetham Amritapuri India

ISBN: (纸本)9798350308846

This research paper presents a thorough analysis of Image Super-Resolution (ISR) methods, covering their historical evolution, challenges, recent advances, and significance across domains. It explores both traditional interpolation-based and data-driven machine learning approaches, examining factors like image priors, network architectures, and the importance of domain-specific datasets and data augmentation. The paper also delves into ensemble models and addresses ongoing challenges in the field. It concludes with a comprehensive comparison of ISR methods, considering strengths, weaknesses, and applicability, offering a valuable resource for researchers and practitioners in the computer vision community and paving the way for future developments in image super-resolution. © 2023 IEEE.

关键词： Convolutional Neural Networks (CNNs) for Super Resolution Deep Learning Super Resolution Enhance Image Quality Image Reconstruction Image Super Resolution

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Deep Convolution Neural Network-Based Classification and Diagnosis of Heart Disease using ElectroCardioGram (ECG) Images 8

Deep Convolution Neural Network-Based Classification and Dia...

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8th IEEE International Conference for Convergence in Technology, I2CT 2023

作者： Kurian, Thanu Thangam, S. Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9798350334012

A cardiovascular disease, if identified correctly at an early stage, could reduce the critical consequences in patients , including fatality. One of the best diagnostic tool for detecting heart disease is through an ECG test. Models trained using signal data related to ECG is difficult to be implemented in an actual healthcare scenario. A CNN model is proposed which makes use of 12-lead ECG images to diagnose cardiac conditions such as myocardial infarction, abnormal heart beat, history of myocardial infarction and normal heartbeat. The ECG image can be taken by scanning the image using a smart phone. This would be very helpful in small healthcare centers where there are no experts for diagnosis. The proposed model was efficiently trained with an accuracy of 99% and cardiac condition was diagnosed using ECG images scanned using a mobile with a superior performance. The work also compares the performance of model with pretrained models as ResNet and EfficientNet-B0 for the same ECG image dataset. © 2023 IEEE.

关键词： Electrocardiograms

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Decision Theory and Risk Simulation Analysis for Optimizing Profit in PayLater Services

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Procedia computer science 2023年 219卷 60-67页

作者： Nicholas Dominic Bens Pardamean Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department BINUS Graduate Program Master of Computer Science Program Bina Nusantara University Jakarta Indonesia 11480

The projected increase in PayLater utilization reaches up to five million people by 2025. To optimize the yearly profit from their PayLater service, fintech companies must examine all possible risks before a unanimous decision is taken. Therefore, we proposed a unified decision framework derived from decision theory and the Monte Carlo simulation technique. Two schemes were coined: (1) a decision-making scheme, and (2) a risk simulation scheme. Throughout experiments, the framework was able to estimate several alternative decisions and their impacts, analyze the causes of failure and delays in the development of the PayLater service, and execute Monte Carlo simulations in up to 10,000 trials. Outputs of this study will benefit decision-makers in the fintech initiative before launching their PayLater products.

关键词： PayLater decision-making Bayesian analysis fault tree analysis critical path method Monte Carlo simulation

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Novelty Detection of Text Using Spectral Graphs and Visualization 4

Novelty Detection of Text Using Spectral Graphs and Visualiz...

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4th IEEE Global Conference for Advancement in Technology, GCAT 2023

作者： Menon, Remya R.K. Fawaz-Al-Faizi Sooraj, U.L. Department of Computer Science and Applications Amrita School of Computing Amrita Vishwa Vidyapeetham Amritapuri India

ISBN: (纸本)9798350305258

Novelty detection is the process of identifying the presence of new or unknown information in a given text dataset that does not conform to the existing patterns or patterns seen in the past. There have been some studies on the application of spectral graph-based methods for novelty detection in text data. There is still a gap in the literature on the integration of these techniques with visualization methods to enhance the interpretability of the results. Our work aims to fill this gap in the literature by proposing a novel approach that combines spectral graph-based methods with visualization techniques for the task of novelty detection in text data. The proposed approach of combining spectral graph-based methods with visualization techniques can effectively detect novel documents among text data collection while also providing interpretable results. Our contribution is a novel framework that integrates these two methods, which can be applied to various domains that require novelty detection in text data. © 2023 IEEE.

关键词： Fiedler vectors Novelty Detection PCA Spectral clustering t-SNE Visualization

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Predicting Dementia Risk Factors Based on Feature Selection and Neural Networks

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computers, Materials & Continua 2023年第5期75卷 2491-2508页

作者： Ashir Javeed Ana Luiza Dallora Johan Sanmartin Berglund Arif Ali Peter Anderberg Liaqat Ali Aging Research Center Karolinska InstitutetStockholm17165Sweden Department of Health Blekinge Institute of TechnologyKarlskrona37141Sweden School of Health Sciences University of SkövdeSkövdeSE 54128Sweden Department of Computer Science University of Science and Technology BannuBannu28100Pakistan Department of Electrical Engineering University of Science and Technology BannuBannu28100Pakistan

Dementia is a disorder with high societal impact and severe consequences for its patients who suffer from a progressive cognitive decline that leads to increased morbidity,mortality,and *** there is a consensus that dementia is a multifactorial disorder,which portrays changes in the brain of the affected individual as early as 15 years before its onset,prediction models that aim at its early detection and risk identification should consider these *** study aims at presenting a novel method for ten years prediction of dementia using on multifactorial data,which comprised 75 *** are two automated diagnostic systems developed that use genetic algorithms for feature selection,while artificial neural network and deep neural network are used for dementia *** proposed model based on genetic algorithm and deep neural network had achieved the best accuracy of 93.36%,sensitivity of 93.15%,specificity of 91.59%,MCC of 0.4788,and performed superior to other 11 machine learning techniques which were presented in the past for dementia *** identified best predictors were:age,past smoking habit,history of infarct,depression,hip fracture,single leg standing test with right leg,score in the physical component summary and history of TIA/*** identification of risk factors is imperative in the dementia research as an effort to prevent or delay its onset.

关键词： Dementia prediction feature selection genetic algorithm neural networks

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Computational Analysis of Quran Text Using Machine Learning and Large Language Models 8

Computational Analysis of Quran Text Using Machine Learning ...

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8th International Conference on data science and Machine Learning Applications, CDMA 2025

作者： Shahid, Usama Hussain, Muhammad Zunnurain Sayers, William School of Business Computing and Social Sciences University of Gloucestershire Cheltenham United Kingdom Bahria University Department of Computer Science Lahore Pakistan

ISBN: (纸本)9798331539696

The Quran verses are foundational for Muslims worldwide. Significant research has been dedicated to information retrieval (IR) from Quran;however, multiple studies have focused on descriptive analysis and topic modelling of the Quran in Arabic and translated versions. This study presents a comprehensive framework for analysing large textual data using an English translation of the Quran. Initially, it conducts a descriptive analysis of the verses to uncover various features, including readability, word clouds, significant n-grams, and network graphs illustrating word associations. The framework then applies machine learning techniques, specifically clustering models based on numerical vectors from text-embedding-3-large, to identify effective groupings of verses. Additionally, GPT-4-turbo is used for topic modelling within each cluster through prompt engineering, aiming to enhance the understanding of these clusters. The results include statistical information graphs and concise knowledge summaries that are beneficial to both domain experts and wider populace. © 2025 IEEE.

关键词： Adversarial machine learning

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