2023 6th International Conference on Information Systems and Computer Networks (ISCON)最新文献_第8页

Cardiovascular Disease Prediction Using Machine Learning 利用机器学习预测心血管疾病

2023 6th International Conference on Information Systems and Computer Networks (ISCON) Pub Date : 2023-03-03 DOI: 10.1109/ISCON57294.2023.10112052

Parise Divyasri, D. Sreelakshmi, Pathuri Sathvika, P. Teja, Tumati Vidya Charan

{"title":"Cardiovascular Disease Prediction Using Machine Learning","authors":"Parise Divyasri, D. Sreelakshmi, Pathuri Sathvika, P. Teja, Tumati Vidya Charan","doi":"10.1109/ISCON57294.2023.10112052","DOIUrl":"https://doi.org/10.1109/ISCON57294.2023.10112052","url":null,"abstract":"The leading cause of death worldwide is heart disease. An effective hybrid classifier model is finally constructed to classify records and produce predictions or identifications based on significant input factors. The findings of this study lower healthcare costs and enable cardiologists to diagnose heart disease more reliably. In this context, an adaptive voting classifier is a type of ensemble learning method that combines the predictions of multiple classifiers to improve accuracy and robustness. This paper presents a heart disease prediction model based on a voting classifier, which combines the predictions of individual classifiers: a decision tree, support vector machine (SVM), k-nearest neighbors (KNN) classifier, Random Forest, and XGBoost. The models used in this study will also be helpful in situations when many patients show up daily. The application would use a few attributes about the patient’s physical state and medical history. On evaluating the proposed adaptive voting-based feature selection for classification has attained an accuracy of 99.83%, and the model has outperformed compared to the other existing state-of-art models considered in the evaluation.","PeriodicalId":280183,"journal":{"name":"2023 6th International Conference on Information Systems and Computer Networks (ISCON)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133026961","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An Approach to Recognize Human Activities based on ConvLSTM and LRCN 基于ConvLSTM和LRCN的人类活动识别方法

2023 6th International Conference on Information Systems and Computer Networks (ISCON) Pub Date : 2023-03-03 DOI: 10.1109/ISCON57294.2023.10112060

Shradha Bhatia, Tushar Chauhan, Sumita Gupta, S. Gambhir, Jitesh H. Panchal

{"title":"An Approach to Recognize Human Activities based on ConvLSTM and LRCN","authors":"Shradha Bhatia, Tushar Chauhan, Sumita Gupta, S. Gambhir, Jitesh H. Panchal","doi":"10.1109/ISCON57294.2023.10112060","DOIUrl":"https://doi.org/10.1109/ISCON57294.2023.10112060","url":null,"abstract":"In recent times, approaches based on deep learning (DL) have been effectively used to predict a variety of human actions using time series data from smartphones and wearable sensors. Time series data handling remains a barrier for DL-based techniques, even though they did quite well in activity detection. Traditional pattern recognition techniques have achieved significant advancements in recent years. However, the performance of the generalization model may be hampered by the approaches’ heavy reliance on human feature extraction. Deep learning methods are becoming more and more successful, and employing these approaches to understand human behaviours in mobile and wearable computing situations or using vision-based technologies has garnered a lot of interest. ConvLSTM and LRCN which is a combination of Convolutional Neural Network (CNN) and Long shirt-term memory (LSTM) are the machine learning methods we employed in this research. With the help of CNNLSTM, it is possible to anticipate human actions more accurately while also simplifying the model and doing away with the necessity for sophisticated feature engineering. Both in terms of space and time, the CNN-LSTM network is deep. In this paper, the LRCN model gets 92% accuracy when we compare the performance of all the models that were utilized against on each other.","PeriodicalId":280183,"journal":{"name":"2023 6th International Conference on Information Systems and Computer Networks (ISCON)","volume":"66 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133981094","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An Optimised Energy Efficient Task Scheduling Algorithm based on Deep Learning Technique for Energy Consumption 一种基于深度学习技术的节能任务调度算法

2023 6th International Conference on Information Systems and Computer Networks (ISCON) Pub Date : 2023-03-03 DOI: 10.1109/ISCON57294.2023.10112019

Jenifer Mahilraj, P. Sivaram, Ns Lokesh, B. Sharma

{"title":"An Optimised Energy Efficient Task Scheduling Algorithm based on Deep Learning Technique for Energy Consumption","authors":"Jenifer Mahilraj, P. Sivaram, Ns Lokesh, B. Sharma","doi":"10.1109/ISCON57294.2023.10112019","DOIUrl":"https://doi.org/10.1109/ISCON57294.2023.10112019","url":null,"abstract":"The information technology (IT) and mobile computing industries are now in the development stages of cloud computing (CC). Instead of being purchased, resources such as software, CPUs, memory, I/O hardware, and others are used and charged as needed. The massive expansion of CC necessitates enormous energy consumption, or data centers house a diverse spectrum of computers. Consequently, cloud service providers are exploring low-cost strategies for reducing energy use and carbon emissions. Therefore, work planning has garnered great attention and critical consideration about effective resources and bad energy consumption. This paper proposes a machine learning technique called short-term or Long-Term Memory (LSTM) for efficient power task scheduling to address growing carbon or energy emissions. The recommended strategy for scheduling considers the finish time or exclusive usage of a resource task, as well as the standardizing process. The Novel Black Window is used to reduce weight and improve the performance of LTSM. The simulated analysis is used to evaluate the efficiency of the LSTM-NBW in aspects of makes pan, power consumption, task completion time, and resource utilization. The findings show that the suggested model only obtained 400KWh more for the 80kB user job than the original LSTM model.","PeriodicalId":280183,"journal":{"name":"2023 6th International Conference on Information Systems and Computer Networks (ISCON)","volume":"353 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124460441","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Traditional Indian Textiles Classification using Deep feature fusion with Curvelet transforms 基于深度特征融合和Curvelet变换的传统印度纺织品分类

2023 6th International Conference on Information Systems and Computer Networks (ISCON) Pub Date : 2023-03-03 DOI: 10.1109/ISCON57294.2023.10112134

S. Varshney, Sarika Singh, C. V. Lakshmi, C. Patvardhan

{"title":"Traditional Indian Textiles Classification using Deep feature fusion with Curvelet transforms","authors":"S. Varshney, Sarika Singh, C. V. Lakshmi, C. Patvardhan","doi":"10.1109/ISCON57294.2023.10112134","DOIUrl":"https://doi.org/10.1109/ISCON57294.2023.10112134","url":null,"abstract":"With the increasing demand for online shopping in the competitive fashion market, new designs come as early birds, and fabric texture plays a crucial role in selecting the correct fabric design. The Indian traditional textile patterns are varied and vibrant, and it exhibits the culture of the area of its origin. They are widely in demand in the international market. Unfortunately, with the incursion of mechanization and the low yield of handmade textiles, artisans for such art forms are dwindling. Generating new designs in sync with the market stream is time-consuming, and the art must be learned meticulously. Hence, every single attempt at developing technology to conserve these art forms is the moment’s need. This paper proposes a Traditional Indian Textiles Classification based on a fusion of deep features and Curvelet transforms. The traditional motif images are complex. Rather than straight lines, it contains curves; hence, the curvelet transform is designed to handle it. Compared with other transforms, Curvelet transforms allow a more systematic representation of other singularities and edges along lines. This work was tested with the Indian art forms datasets. It utilized the pre-trained CNN architecture (InceptionresNetV2, VGG16) as a feature extractor and concatenated these features with curvelet features. In this experiment, the XGB classifier provided the best results (precision 98.24%, recall 97.15%, F1Score 97.15%, accuracy 97.15%, and specificity 99.52%) with 4scale Curvelet and InceptionResNetV2 feature sets. These initial results are promising and motivate further work on larger and more complex datasets.","PeriodicalId":280183,"journal":{"name":"2023 6th International Conference on Information Systems and Computer Networks (ISCON)","volume":"05 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129024202","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Deep Learning-Based Classification of Indian Classical Music Based on Raga 基于拉格的印度古典音乐深度学习分类

2023 6th International Conference on Information Systems and Computer Networks (ISCON) Pub Date : 2023-03-03 DOI: 10.1109/ISCON57294.2023.10111985

Anupam Singha, N. R. Rajalakshmi, J. Arun Pandian, Swaminathan Saravanan

{"title":"Deep Learning-Based Classification of Indian Classical Music Based on Raga","authors":"Anupam Singha, N. R. Rajalakshmi, J. Arun Pandian, Swaminathan Saravanan","doi":"10.1109/ISCON57294.2023.10111985","DOIUrl":"https://doi.org/10.1109/ISCON57294.2023.10111985","url":null,"abstract":"Classical music is an integral part of Indian culture’s lineage. The raga serves as the foundation for all Indian classical music, including both Hindustani and Carnatic music. The term “raga” is typically used to refer to the melodic structure in Indian classical music. Traditional approaches to the classification of ragas are time consuming and inefficient. In this work, a convolutional neural network was proposed for raga classification. The proposed convolutional neural network was trained on 70 classes of ragas. The proposed convolutional neural network takes the spectrograms of the audio note and identifies the raga of the note. The proposed convolutional neural network model achieved a precision of 97.9% on raga classification. The performance of the proposed convolutional neural network was compared with the traditional classification techniques using standard performance metrics such as precision, recall, F1 score, and AUC-ROC. The comparison results show that the classification performance of the proposed convolutional neural network was superior to the state-of-the-art machine learning techniques.","PeriodicalId":280183,"journal":{"name":"2023 6th International Conference on Information Systems and Computer Networks (ISCON)","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115629364","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Arrhythmia Detection from ECG Signals using CNN Model 基于CNN模型的心电信号心律失常检测

2023 6th International Conference on Information Systems and Computer Networks (ISCON) Pub Date : 2023-03-03 DOI: 10.1109/ISCON57294.2023.10112173

G. Kumar, S. Pandey, Neeraj Varshney, R. Janghel, K. Singh, Ankit Kumar

引用次数: 0

A Model for Translation of Text from Indian Languages to Bharti Braille Characters 印度语言文本到巴蒂盲文的翻译模型

2023 6th International Conference on Information Systems and Computer Networks (ISCON) Pub Date : 2023-03-03 DOI: 10.1109/ISCON57294.2023.10112021

Nisheeth Joshi, Pragya Katyayan

引用次数: 1

Optimising Feature Selection: A Comparative Study of mRMR-Boruta/RFE Hybrid Approach 优化特征选择:mRMR-Boruta/RFE混合方法的比较研究

2023 6th International Conference on Information Systems and Computer Networks (ISCON) Pub Date : 2023-03-03 DOI: 10.1109/ISCON57294.2023.10112125

Manu Sharma, D. Sharma

引用次数: 0

Analysis of Factors Influencing The Use of QRIS Through Mobile Banking in Jakarta and Tangerang 雅加达和橘子市手机银行使用QRIS的影响因素分析

2023 6th International Conference on Information Systems and Computer Networks (ISCON) Pub Date : 2023-03-03 DOI: 10.1109/ISCON57294.2023.10111978

Johnson Husin, Serafine Cordelia, Vivaldi Darren Christophilus, Natalia Limantara

{"title":"Analysis of Factors Influencing The Use of QRIS Through Mobile Banking in Jakarta and Tangerang","authors":"Johnson Husin, Serafine Cordelia, Vivaldi Darren Christophilus, Natalia Limantara","doi":"10.1109/ISCON57294.2023.10111978","DOIUrl":"https://doi.org/10.1109/ISCON57294.2023.10111978","url":null,"abstract":"The growth of Indonesia’s payment via mobile phones has increased lately. In 2019, QRIS was launched by Bank Indonesia and it has been used to make payments using every application that utilizes the QRIS system. By the year of 2025, Bank Indonesia expects to focus on digital finance, including banking digitalization. However, the number of QRIS transactions are still less than other payment methods. Therefore, in order to better understand users’ acceptance of QRIS as a payment method through mobile banking applications, this research is conducted. This study uses the TAM (Technology Acceptance Model) with other independent variables using a quantitative method by distributing questionnaires to people that uses QRIS on mobile banking applications in Jakarta and Tangerang with 355 respondents. The data was examined using the Partial Least Square (PLS) approach of the Structural Equation Model (SEM) using the analytical tool SMART PLS. The findings in this study show that perceived compatibility affects both perceived usefulness and perceived ease of use. Effort expectancy affects perceived ease of use, all three factors of perceived security, perceived usefulness, and perceived ease of use affects attitude toward using, and attitude toward using affects behavioral intention to use. However, social influence does not affect both perceived usefulness and perceived ease of use. The result of this study may give the QRIS payment system room for improvements to be more impactful in their users’ daily lives therefore they are able to influence other people to use QRIS as a payment system.","PeriodicalId":280183,"journal":{"name":"2023 6th International Conference on Information Systems and Computer Networks (ISCON)","volume":"70 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127015169","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Implications of Multi-Word Expressions on English to Bharti Braille Machine Translation 多词表达对英语到巴蒂盲文机器翻译的启示

2023 6th International Conference on Information Systems and Computer Networks (ISCON) Pub Date : 2023-03-03 DOI: 10.1109/ISCON57294.2023.10112137

Nisheeth Joshi, Pragya Katyayan

引用次数: 0