2023 International Conference on Advancement in Computation & Computer Technologies (InCACCT)最新文献_第2页

Automated Classification of Heart Disease using Deep Learning 使用深度学习的心脏病自动分类

2023 International Conference on Advancement in Computation & Computer Technologies (InCACCT) Pub Date : 2023-05-05 DOI: 10.1109/InCACCT57535.2023.10141725

Ayush Pandey, R. Joshi, M. Dutta

{"title":"Automated Classification of Heart Disease using Deep Learning","authors":"Ayush Pandey, R. Joshi, M. Dutta","doi":"10.1109/InCACCT57535.2023.10141725","DOIUrl":"https://doi.org/10.1109/InCACCT57535.2023.10141725","url":null,"abstract":"Heart murmurs are irregular heartbeat patterns that may be a sign of a serious cardiac problem. These conditions can only be diagnosed by qualified professionals using a stethoscope. Given that the patient-to-doctor ratio is low in developing countries, there is a requirement for an automated system that can classify heart sounds and analyses the phonocardiogram (PCG) recording in real-time. A critical step in the diagnosis of cardiovascular disorders (CVDs) is the computerized classification of cardiac sounds. Particularly when applied to heart sound spectrograms, Deep learning techniques have been quite successful in automating the detection of CVDs. Such a system might be created using a variety of available techniques, transfer learning is one of such utilities. A modern machine learning technique that has gained popularity due to its quick training time and improved accuracy. The lack of sufficient data, effective models and ineffective training pose certain limitations. This paper aims at developing a lightweight, fast and reliable alternative for heart sound classification. The data is cleaned, processed, and transformed into an image using spectrogram signal representation. Based on the obtained experimental outcomes of this research paper, a transfer learning pipeline could make heart sound classification and CVD detection easier while requiring less training time and resources.","PeriodicalId":405272,"journal":{"name":"2023 International Conference on Advancement in Computation & Computer Technologies (InCACCT)","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132744190","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

Issues and Challenges of Wormhole Attack Detection for Secure Localization in WSNs 无线传感器网络安全定位中虫洞攻击检测的问题与挑战

2023 International Conference on Advancement in Computation & Computer Technologies (InCACCT) Pub Date : 2023-05-05 DOI: 10.1109/InCACCT57535.2023.10141721

Ruchi Garg, Tarun Gulati

{"title":"Issues and Challenges of Wormhole Attack Detection for Secure Localization in WSNs","authors":"Ruchi Garg, Tarun Gulati","doi":"10.1109/InCACCT57535.2023.10141721","DOIUrl":"https://doi.org/10.1109/InCACCT57535.2023.10141721","url":null,"abstract":"The field sensed data always requires to have location of the sensors for approximating the place of events in a field of observation. However, localization is a well-established research problem in wireless sensor networks (WSNs). Further, considering a network under ideal conditions free from all network snags, for the localization, is an erratic postulate. Therefore, localization should consider some common network snags like wormhole attack. Though wormhole attack is comparatively easy to implement yet it has a devastating effect on localization. There are a few algorithms to address the wormhole attack detection in the literature. Some of the recent time wormhole attack detection algorithms are studied here. The study categorizes the existing algorithms based on various attributes. The analysis of the existing algorithms is comprehended with the future issues and challenges in wormhole attack detection. Further, the study reveals that most of the existing algorithms get affected by varying network traffic, increased computation and communication efforts, additional hardware cost, distance among the sensors, and mobility rate. In this manner, it is established that the secure localization requires detection of wormhole attack before localizing sensors where sensors should not get distressed due to wormhole attack detection.","PeriodicalId":405272,"journal":{"name":"2023 International Conference on Advancement in Computation & Computer Technologies (InCACCT)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134628879","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

Parkinson’s Disease Prediction using Fisher Score based Recursive Feature Elimination 基于Fisher评分递归特征消除的帕金森病预测

2023 International Conference on Advancement in Computation & Computer Technologies (InCACCT) Pub Date : 2023-05-05 DOI: 10.1109/InCACCT57535.2023.10141768

Ravi Aishwarya, K. Pavitra, Primal Viola Miranda, K. Keerthana, L. Kamatchi Priya

引用次数: 0

Augmented GPS Navigation: Enhancing the Reliability of Location-Based Services 增强GPS导航:增强基于位置服务的可靠性

2023 International Conference on Advancement in Computation & Computer Technologies (InCACCT) Pub Date : 2023-05-05 DOI: 10.1109/InCACCT57535.2023.10141739

S. Sayeedunnisa, Khaja Hameeduddin Saberi, Masood Ahmed Mohiuddin

引用次数: 0

A Survey on the role of ML and AI in fighting Covid-19 ML和AI在抗击新冠肺炎中的作用综述

2023 International Conference on Advancement in Computation & Computer Technologies (InCACCT) Pub Date : 2023-05-05 DOI: 10.1109/InCACCT57535.2023.10141732

Deepti Malhotra, G. Sodhi

{"title":"A Survey on the role of ML and AI in fighting Covid-19","authors":"Deepti Malhotra, G. Sodhi","doi":"10.1109/InCACCT57535.2023.10141732","DOIUrl":"https://doi.org/10.1109/InCACCT57535.2023.10141732","url":null,"abstract":"Believed to have been originated Chinese province Wuhan in December 2019, the coronavirus has said to cause 95 million cases with overall death rate of 2% of overall cases (as per Jan 2022). As per today China is still facing the threat of the virus emerging again. This fast-spreading pandemic virus poses a challenge at world level and proposes serious danger to people’s health as well as the economy. With time and regions this virus has undergone several mutations resulting in rise of various other viruses, OMICRON being the latest. The most common and widely faced threat in this disease was in the case of asymptomatic patients, the ones who showed no symptoms and yet were carriers of this deadly virus. In recent times, many researchers have started exploring various methods for predicting the disease using various medical parameters. Few of the commonly used technologies for the same are Machine Learning and Artificial Intelligence. The present paper aims to exhibit the role of these technologies in predicting the virus presence. Various models used by the researchers in the prediction of the corona virus have been compiled and presented in this paper.","PeriodicalId":405272,"journal":{"name":"2023 International Conference on Advancement in Computation & Computer Technologies (InCACCT)","volume":"2011 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114625169","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

Behaviour Analysis Using Machine Learning Algorithms In Health Care Sector 在医疗保健领域使用机器学习算法的行为分析

2023 International Conference on Advancement in Computation & Computer Technologies (InCACCT) Pub Date : 2023-05-05 DOI: 10.1109/InCACCT57535.2023.10141829

Anukriti Yadav, Deepak Kumar, Y. Hasija

{"title":"Behaviour Analysis Using Machine Learning Algorithms In Health Care Sector","authors":"Anukriti Yadav, Deepak Kumar, Y. Hasija","doi":"10.1109/InCACCT57535.2023.10141829","DOIUrl":"https://doi.org/10.1109/InCACCT57535.2023.10141829","url":null,"abstract":"A behavioral analytics approach uses big data analytics in combination with machine learning (ML) to identify patterns, trends, aberrations, and other useful insights. The behavior of an individual can be analyzed by expressions, postures, and activity levels. Using ML algorithms could revolutionize the way clinicians make decisions in health care sector. Studies of human behavior have been conducted in a range of scientific disciplines (e.g sociology, psychology, computer science). ML algorithms have the potential to transform the way doctors and instructors make choices. This methodology has been slow to be adopted by behavior analysis experts to maximize its application to practical issues and to aid them in learning more about human behavior. ML algorithms are dominating the healthcare industry. Recent researches have indicated that these techniques can be used to anticipate disease based on health data. Our study examines several machine learning algorithms used in early disease detection and identifies key trends in their performance. The analysis suggests that human behavior may play a role in a variety of conditions, including diabetes, cancer, heart disease, autism, mental illness, Alzheimer’s, and others. A number of daily habits are associated with this behavior, including food, respiration rate, blood pressure, voice output, social abnormalities, insomnia, and so on. A few examples of ML applications integrated into healthcare services are naive bayes (NB), support vector machines (SVM), random forest (RF), and convolutional neural networks (CNN). In a variety of cancer classification applications, these models are proved to be highly efficient in diagnosing various cancer types. This review includes a number of research investigations that employ ML to analyze behavioral data. As we gain further insights into the factors influencing organisms’ behavior, we are able to create computational models which allow disease prediction and management to become more accurate.","PeriodicalId":405272,"journal":{"name":"2023 International Conference on Advancement in Computation & Computer Technologies (InCACCT)","volume":"67 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114995439","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

Securing and Visualizing Sensor Data on Private Blockchain 在私有区块链上保护和可视化传感器数据

2023 International Conference on Advancement in Computation & Computer Technologies (InCACCT) Pub Date : 2023-05-05 DOI: 10.1109/InCACCT57535.2023.10141766

Pawan Kumar Pal, Siddhant Khanna, Siddharth Shukla, Virat Shukla

引用次数: 0

Voice-based Gender and Age Recognition System 基于语音的性别和年龄识别系统

2023 International Conference on Advancement in Computation & Computer Technologies (InCACCT) Pub Date : 2023-05-05 DOI: 10.1109/InCACCT57535.2023.10141801

Vinayak Sudhakar Kone, A. Anagal, Swaroop Anegundi, Pranali Jadhav, Uday Kulkarni, M. M

{"title":"Voice-based Gender and Age Recognition System","authors":"Vinayak Sudhakar Kone, A. Anagal, Swaroop Anegundi, Pranali Jadhav, Uday Kulkarni, M. M","doi":"10.1109/InCACCT57535.2023.10141801","DOIUrl":"https://doi.org/10.1109/InCACCT57535.2023.10141801","url":null,"abstract":"The ability to detect gender and age from voice is a valuable tool in a variety of applications, like voice-based biometric identification, natural language processing, and speech recognition. Recent advances in Deep Learning have enabled the development of highly accurate gender and age detection models. In this paper, the discussion is about the Machine Learning based gender and age detection model using voice. The various approaches used to extract features from speech, and the data-set used for model evaluation and classification are obtained using different Machine Learning algorithms. The discussion is about the opportunities and challenges in this area of research. It is concluded by highlighting some of the open challenges and future directions in this field. Age prediction from voice using a grid search pipeline is a Machine Learning technique that uses a range of algorithms to detect the age of a person using their voice. In the proposed model, RobustScalar, Principal component analysis (PCA), and Logistic Regression algorithms are used. The grid search pipeline uses a combination of models to identify the best age prediction algorithm for a given data-set. For Gender prediction sequential model with 5 hidden layers has been used. The results were obtained based on the trained model for the common voice data-set with an accuracy of around 91% for gender and 59% for age.","PeriodicalId":405272,"journal":{"name":"2023 International Conference on Advancement in Computation & Computer Technologies (InCACCT)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124047272","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

Web-based Advertisements and Behavioral Impacts on Adolescents: A review 网络广告与青少年行为影响研究综述

2023 International Conference on Advancement in Computation & Computer Technologies (InCACCT) Pub Date : 2023-05-05 DOI: 10.1109/InCACCT57535.2023.10141792

Sangeet Kaur Sandhu, G. Madaan

{"title":"Web-based Advertisements and Behavioral Impacts on Adolescents: A review","authors":"Sangeet Kaur Sandhu, G. Madaan","doi":"10.1109/InCACCT57535.2023.10141792","DOIUrl":"https://doi.org/10.1109/InCACCT57535.2023.10141792","url":null,"abstract":"Adolescents and teens are vulnerable to behavioral impacts by unrestricted exposure to web-based advertisements. The behavioral impacts may be through addiction to drugs, tobacco products, alcohol and unrestricted consumerism, gambling, and unhealthy food habits. These behavioral impacts are severe and many of the popular remedial measures of parental regulation, and law reforms may not be effective. Comprehensive measures by way of regulations, law reforms, enforcement and education and improved awareness level among adolescence can only avoid the negative behavioral impacts of web-based advertisements among adolescents. The United States of America leads the research behavioral impacts of web-based advertisements among adolescents, with its dominance in document publication, and citations. The leading research affiliations and funding agencies are also from the United States of America. This review includes a bibliometric review and thematic analysis of the behavioral impacts of web-based advertisements among adolescents, based on Scopus publications since 2005. A limited set of documents and poor collaborations are the major limitations of the research related to the behavioral impacts of web-based advertisements among adolescents.","PeriodicalId":405272,"journal":{"name":"2023 International Conference on Advancement in Computation & Computer Technologies (InCACCT)","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128199628","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

Classification Of Chest X-ray Images Of Covid-19 By Deep Learning Based CNN Model and Attention Mechanism 基于CNN模型和注意机制的Covid-19胸片图像深度学习分类

2023 International Conference on Advancement in Computation & Computer Technologies (InCACCT) Pub Date : 2023-05-05 DOI: 10.1109/InCACCT57535.2023.10141775

A. Agrawal

{"title":"Classification Of Chest X-ray Images Of Covid-19 By Deep Learning Based CNN Model and Attention Mechanism","authors":"A. Agrawal","doi":"10.1109/InCACCT57535.2023.10141775","DOIUrl":"https://doi.org/10.1109/InCACCT57535.2023.10141775","url":null,"abstract":"Covid-19 is a highly infectious viral disease that has been found in a broad range of animal species, including humans. This fatal virus threatens not just people’s lives, but also their health and the country’s economy. Although Covid-19 is a serious and widespread disease, there is presently no vaccine available to protect against it. Clinical research conducted on people who contracted COVID-19 found that the respiratory system was the most common location of infection after exposure to the virus. When it comes to the diagnosis of lung-related illnesses, imaging modalities such as chest CT and chest x-ray (also known as radiography) are superior. The cost of a chest CT scan is more than that of a thorough chest x-ray, but the latter is much cheaper. When it comes to machine learning, deep learning provides the most impressive results. It provides valuable insight that may be used to the investigation of a large number of chest x-ray images, which may have a substantial impact on the Covid19 screening procedure. Specifically, this research will apply the attention method on the resnet50 features. Six thousand four hundred thirty-two chest x-ray scan samples were generated once the feature learning process was finished using the Xgboost method for validation in the Kaggle repository. These were split between 965 validation examples and 5467 training examples. The proposed model (resnet-attention-xgboost) obtained 98.34 percent, while the supplemented dataset reached 99 percent, when it came to identifying chest X-ray pictures. This is in comparison to earlier models. This study is purely concerned with prospective categorization methodologies for patients infected with covid-19.","PeriodicalId":405272,"journal":{"name":"2023 International Conference on Advancement in Computation & Computer Technologies (InCACCT)","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128276112","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