EAI Endorsed Transactions on Pervasive Health and Technology最新文献_第9页

Artificial Intelligence Application with Contact Tracing for Post COVID -19 Epidemic Management 基于接触者追踪的人工智能在新冠肺炎疫情管理中的应用

EAI Endorsed Transactions on Pervasive Health and Technology Pub Date : 2023-11-10 DOI: 10.4108/eetpht.9.4360

Anasuya Swain, Subhalaxmi Sahu, Monalisha Patel, Pradeep Ranjan Dhal

引用次数: 0

Mental Stress Classification from Brain Signals using MLP Classifier 用MLP分类器对脑信号进行精神压力分类

EAI Endorsed Transactions on Pervasive Health and Technology Pub Date : 2023-11-09 DOI: 10.4108/eetpht.9.4341

Soumya Samarpita, Rabinarayan Satpathy, Pradipta Kumar Mishra, Aditya Narayan Panda

引用次数: 0

Melanoma Skin Cancer Detection using SVM and CNN 基于SVM和CNN的黑色素瘤皮肤癌检测

EAI Endorsed Transactions on Pervasive Health and Technology Pub Date : 2023-11-09 DOI: 10.4108/eetpht.9.4340

Sai Pranav Kothapalli, Panchumarthi Sri Hari Priya, Vempada Sagar Reddy, Botta Lahya, Prashanth Ragam

{"title":"Melanoma Skin Cancer Detection using SVM and CNN","authors":"Sai Pranav Kothapalli, Panchumarthi Sri Hari Priya, Vempada Sagar Reddy, Botta Lahya, Prashanth Ragam","doi":"10.4108/eetpht.9.4340","DOIUrl":"https://doi.org/10.4108/eetpht.9.4340","url":null,"abstract":"In the field of cancer detection and prevention, doctors and patients are facing numerous challenges when it comes to cancer prediction. Melanoma skin cancer is a deadly type of skin cancer with a multitude of variants spread across the world. Traditional methods involved manual inspection followed by various tests of samples. This time-consuming work and inaccurate predictions sometimes risk the overall health of the patient. The two aspects of solving skin cancer detection problems utilising both conventional image-processing techniques and methods based on machine learning and deep learning are elaborated in this article. It gives a review of current skin cancer detection techniques, weighs the benefits and drawbacks of those techniques, and introduces some relevant cancer datasets. The proposed method focuses mainly on Melanoma skin cancer detection and its previous stages (Common Nevus and Atypical Nevus). The methods being proposed employ a blend of colour, texture, and shape characteristics to derive distinguishing attributes from the images. Using CNN (convolutional neural networks) and SVM (support vector machine) algorithms to identify the type of skin cancer the patient is affected with and achieved an accuracy of 92% and 95% respectively.","PeriodicalId":36936,"journal":{"name":"EAI Endorsed Transactions on Pervasive Health and Technology","volume":" 43","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135242106","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

Comparative Analysis of Deep Learning Models for Multiclass Alzheimer’s Disease Classification 深度学习模型在阿尔茨海默病多类别分类中的比较分析

EAI Endorsed Transactions on Pervasive Health and Technology Pub Date : 2023-11-08 DOI: 10.4108/eetpht.9.4334

Raghav Agarwal, Abbaraju Sai Sathwik, Deepthi Godavarthi, Janjhyman Venkata Naga Ramesh

{"title":"Comparative Analysis of Deep Learning Models for Multiclass Alzheimer’s Disease Classification","authors":"Raghav Agarwal, Abbaraju Sai Sathwik, Deepthi Godavarthi, Janjhyman Venkata Naga Ramesh","doi":"10.4108/eetpht.9.4334","DOIUrl":"https://doi.org/10.4108/eetpht.9.4334","url":null,"abstract":"INTRODUCTION: The terrible neurological condition is known Worldwide; millions of individuals are affected with Alzheimer's disease (AD). Effective treatment and management of AD depend on early detection and a precise diagnosis. An effective method for identifying anatomical and functional abnormalities in the brain linked to AD is magnetic resonance imaging (MRI). OBJECTIVES: However, manual MRI scan interpretation requires a lot of time and is inconsistent between observers. The automated analysis of MRI images for AD identification and diagnosis using deep learning techniques has shown promise. METHODS: In this paper, we present a convolutional neural network (CNN)-based deep learning model for automatically classifying MRI images for Alzheimer's (AD) and a healthy control group. A huge dataset of MRI scans was used to train the CNN, which distinguished between AD and healthy control groups with excellent accuracy. RESULTS: Additionally, we looked into how transfer learning may be used to enhance pre-trained models and boost CNN performance. We discovered that transfer learning considerably increased the model's accuracy and decreased overfitting. Our findings show that MRI scans may be used to precisely detect and diagnose AD utilizing approaches to deep learning and machine learning. CONCLUSION: These techniques may improve the efficiency and accuracy of AD diagnosis and enable early disease identification, resulting in better AD management and therapy.","PeriodicalId":36936,"journal":{"name":"EAI Endorsed Transactions on Pervasive Health and Technology","volume":"71 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135342626","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

A Novel Approach to Identify the Brain Tumour Using Convolutional Neural Network 利用卷积神经网络识别脑肿瘤的新方法

EAI Endorsed Transactions on Pervasive Health and Technology Pub Date : 2023-11-08 DOI: 10.4108/eetpht.9.4337

Suraj Khari, Deepa Gupta, Alka Chaudhary, Ruchika Bhatla

引用次数: 0

Enhancing Image Recognition: Leveraging Machine Learning on Specialized Medical Datasets 增强图像识别:在专业医疗数据集上利用机器学习

EAI Endorsed Transactions on Pervasive Health and Technology Pub Date : 2023-11-08 DOI: 10.4108/eetpht.9.4336

Nidhi Agarwal, Nitish Kumar, None Anushka, Vrinda Abrol, Yashica Garg

{"title":"Enhancing Image Recognition: Leveraging Machine Learning on Specialized Medical Datasets","authors":"Nidhi Agarwal, Nitish Kumar, None Anushka, Vrinda Abrol, Yashica Garg","doi":"10.4108/eetpht.9.4336","DOIUrl":"https://doi.org/10.4108/eetpht.9.4336","url":null,"abstract":"INTRODUCTION: Image recognition plays a pivotal role in numerous industries, ranging from healthcare to autonomous vehicles. Machine learning techniques, especially deep learning algorithms, have revolutionized the field of image recognition by enabling computers to identify and classify objects within images with high accuracy. OBJECTIVES: This research paper provides an in-depth exploration of the application of machine learning algorithms for image recognition tasks, including supervised learning, convolutional neural networks (CNNs), and transfer learning. METHODS: The paper discusses the challenges associated with image recognition, such as dataset size and quality, overfitting, and computational resources.  RESULTS: It highlights emerging trends and future research directions, including explainability and interpretability, adversarial attacks and robustness, and real-time and edge-based recognition.  CONCLUSION: In conclusion, the study emphasizes the transformative impact of deep learning algorithms, addressing challenges in image recognition. Ongoing focus on emerging trends is vital for enhancing accuracy and efficiency in diverse applications.","PeriodicalId":36936,"journal":{"name":"EAI Endorsed Transactions on Pervasive Health and Technology","volume":"59 1‐2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135341843","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

Diabetic Retinopathy Classification Using Deep Learning 基于深度学习的糖尿病视网膜病变分类

EAI Endorsed Transactions on Pervasive Health and Technology Pub Date : 2023-11-08 DOI: 10.4108/eetpht.9.4335

Abbaraju Sai Sathwik, Raghav Agarwal, Ajith Jubilson E, Santi Swarup Basa

引用次数: 0

Usage of Web Scraping in the Pharmaceutical Sector 网络抓取在制药行业的使用

EAI Endorsed Transactions on Pervasive Health and Technology Pub Date : 2023-11-06 DOI: 10.4108/eetpht.9.4312

Ruby Dahiya, None Nidhi, Kajal Kumari, Shruti Kumari, Nidhi Agarwal

{"title":"Usage of Web Scraping in the Pharmaceutical Sector","authors":"Ruby Dahiya, None Nidhi, Kajal Kumari, Shruti Kumari, Nidhi Agarwal","doi":"10.4108/eetpht.9.4312","DOIUrl":"https://doi.org/10.4108/eetpht.9.4312","url":null,"abstract":"INTRODUCTION: Web scraping is a technique that provides organizations with the ability to analyse large amounts of information and gather new information.  OBJECTIVES: Find a group that is a health check, a full body test, a blood test, and so on. In this way, the pharmaceutical industry should consider how to improve information, information storage, information retrieval, and capture. For example, the healthcare system may decide to standardize the assessment of speech and allow information to be shared across organizations to improve treatment outcomes in web scraping applications. METHODS: Web scraping is based on the pharmaceutical industry. From here, we get information about pharmacies, such as drug names in different categories or drug sales. However, we are dealing with diseases and common medicines. Using this information, we can find the most common viruses. There are many factors to consider when creating a junk website for the pharmaceutical industry, such as drug names, tablet categories, and syrups found in the pharmaceutical industry. RESULTS: As is clearly visible from the output, there are columns for drug names, manufacturers, drug types, and prices. This is the information we get from a website called Net meds, a pharmacy site. With the help of this information, we learn which drugs are most needed, and then we can find the most common diseases today. CONCLUSION: The results of this web scraping can be very useful and powerful. However, the industry's success in web scraping and data extraction techniques depends on the availability of clean chemical data.","PeriodicalId":36936,"journal":{"name":"EAI Endorsed Transactions on Pervasive Health and Technology","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135634593","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

Early Detection of Monkeypox Skin Disease Using Patch Based DL Model and Transfer Learning Techniques 基于贴片的深度学习模型和迁移学习技术的猴痘皮肤病早期检测

EAI Endorsed Transactions on Pervasive Health and Technology Pub Date : 2023-11-06 DOI: 10.4108/eetpht.9.4313

Abbaraju Sai Sathwik, Beebi Naseeba, Jinka Chandra Kiran, Kokkula Lokesh, Venkata Sasi Deepthi Ch, Nagendra Panini Challa

{"title":"Early Detection of Monkeypox Skin Disease Using Patch Based DL Model and Transfer Learning Techniques","authors":"Abbaraju Sai Sathwik, Beebi Naseeba, Jinka Chandra Kiran, Kokkula Lokesh, Venkata Sasi Deepthi Ch, Nagendra Panini Challa","doi":"10.4108/eetpht.9.4313","DOIUrl":"https://doi.org/10.4108/eetpht.9.4313","url":null,"abstract":"In the field of medicine, it is very important to prognosticate diseases early to cure them from their initial stages. Monkeypox is a viral zoonosis with symptoms similar to the smallpox as it spreads widely with the person who is in close contact with the affected. So, it can be diagnosed using various new age computing techniques such as CNN, RESNET, VGG, EfficientNet. In this work, a prediction model is utilized for better classification of Monkeypox. However, the implementation of machine learning in detecting COVID-19 has encouraged scientists to explore its potential for identifying monkeypox. One challenge in using Deep learning (DL) and machine learning (ML) for this purpose is the lack of sufficient data, including images of monkeypox-infected skin. In response, Monkeypox Skin Image Dataset is collected from Kaggle, the largest of its kind till date which includes images of healthy skin as well as monkeypox and some other infected skin diseases. The dataset undergoes through different data augmentation phases which is fed to different DL and ML algorithms for producing better results. Out of all the approaches, VGG19 and Resnet has got the best result with 92% recognition accuracy.","PeriodicalId":36936,"journal":{"name":"EAI Endorsed Transactions on Pervasive Health and Technology","volume":"668 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135636300","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

Big data-analysis, map reduced framework, security & privacy challenges and techniques in health sector 大数据分析、地图简化框架、安全卫生部门的隐私挑战和技术

EAI Endorsed Transactions on Pervasive Health and Technology Pub Date : 2023-11-02 DOI: 10.4108/eetpht.9.4292

Rajarshi Sarkar, Mokshith Telugu, Nooharika Kuntla

引用次数: 0