INTRODUCTION: Post COVID -19 epidemics is in a critical situation which has to be properly managed with right preventive and curative measures to protect the economy and welfare of the Human beings.
OBJECTIVES: Effective management of this terrific situation may be possible through the help of contact tracing and its application of AI mechanism. Here the authors as taken the available data for the testing of the significance of AI approach for contract tracing proper management of the post COVID epidemic situation.
METHODS: Here contact tracing data are collected analysed interpreted and validity is tested with the help of statistical tools like egression, coefficient and Annova for the testing of the available data with its further application.
R ESULTS: AI application creates more awareness, vaccination, self-testing, isolation and intake medicine
CONCLUSION: Artificial Intelligence &social media plays a vital role for the creation of social awareness and proper manage of post COVID-19 epidemics.
{"title":"Artificial Intelligence Application with Contact Tracing for Post COVID -19 Epidemic Management","authors":"Anasuya Swain, Subhalaxmi Sahu, Monalisha Patel, Pradeep Ranjan Dhal","doi":"10.4108/eetpht.9.4360","DOIUrl":"https://doi.org/10.4108/eetpht.9.4360","url":null,"abstract":"INTRODUCTION: Post COVID -19 epidemics is in a critical situation which has to be properly managed with right preventive and curative measures to protect the economy and welfare of the Human beings.
 OBJECTIVES: Effective management of this terrific situation may be possible through the help of contact tracing and its application of AI mechanism. Here the authors as taken the available data for the testing of the significance of AI approach for contract tracing proper management of the post COVID epidemic situation.
 METHODS: Here contact tracing data are collected analysed interpreted and validity is tested with the help of statistical tools like egression, coefficient and Annova for the testing of the available data with its further application.
 R ESULTS: AI application creates more awareness, vaccination, self-testing, isolation and intake medicine
 CONCLUSION: Artificial Intelligence &social media plays a vital role for the creation of social awareness and proper manage of post COVID-19 epidemics.","PeriodicalId":36936,"journal":{"name":"EAI Endorsed Transactions on Pervasive Health and Technology","volume":"118 35","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135137057","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}
Soumya Samarpita, Rabinarayan Satpathy, Pradipta Kumar Mishra, Aditya Narayan Panda
INTRODUCTION: The most common and widespread mental condition that unavoidably affects people's mood and conduct is stress. The physiological reaction to powerful emotional, intellectual, and physical obstacles might be viewed as stress. As a result, early stress detection can result in solutions for potential improvements and ultimate event suppression.
OBJECTIVES: To classify mental stress from the EEG signals of humans using an MLP classifier.
METHODS: We examine the EEG signal analysis techniques currently in use for detecting mental stress using Multi-layer Perceptron (MLP).
RESULTS: The suggested technique has a 95% classification accuracy performance.
CONCLUSION: In our study, the use of MLP classifiers for stress detection from EEG signals has shown promising results. The high accuracy and precision of the classifiers, as well as the informative nature of certain EEG frequency bands, suggest that this approach could be a valuable tool for stress detection and management.
{"title":"Mental Stress Classification from Brain Signals using MLP Classifier","authors":"Soumya Samarpita, Rabinarayan Satpathy, Pradipta Kumar Mishra, Aditya Narayan Panda","doi":"10.4108/eetpht.9.4341","DOIUrl":"https://doi.org/10.4108/eetpht.9.4341","url":null,"abstract":"INTRODUCTION: The most common and widespread mental condition that unavoidably affects people's mood and conduct is stress. The physiological reaction to powerful emotional, intellectual, and physical obstacles might be viewed as stress. As a result, early stress detection can result in solutions for potential improvements and ultimate event suppression.
 OBJECTIVES: To classify mental stress from the EEG signals of humans using an MLP classifier.
 METHODS: We examine the EEG signal analysis techniques currently in use for detecting mental stress using Multi-layer Perceptron (MLP).
 RESULTS: The suggested technique has a 95% classification accuracy performance.
 CONCLUSION: In our study, the use of MLP classifiers for stress detection from EEG signals has shown promising results. The high accuracy and precision of the classifiers, as well as the informative nature of certain EEG frequency bands, suggest that this approach could be a valuable tool for stress detection and management.","PeriodicalId":36936,"journal":{"name":"EAI Endorsed Transactions on Pervasive Health and Technology","volume":" 24","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135285693","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}
Sai Pranav Kothapalli, Panchumarthi Sri Hari Priya, Vempada Sagar Reddy, Botta Lahya, Prashanth Ragam
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.
{"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}
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.
{"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}
INTRODUCTION: Determining the possibility that an individual is affected by a tumour is an intricate process in today's modern technological and biological age, when feats are reaching unprecedented levels with every passing second. Machine learning modalities could dramatically enhance the accuracy of diagnosis.
OBJECTIVES: Our research makes it feasible to detect tumours early, aiding in early diagnosis, and is a necessity for the curative efforts of cancer patients.
METHODS: In our research model Convolutional Neural Network (CNN) was implemented using Jupiter to give an accurate result.
RESULTS: In our proposed model we got 99% accuracy that is higher than the other results.
CONCLUSION: Our research demonstrates the potential of using machine learning techniques to improve the accuracy and efficiency of medical diagnosis.
{"title":"A Novel Approach to Identify the Brain Tumour Using Convolutional Neural Network","authors":"Suraj Khari, Deepa Gupta, Alka Chaudhary, Ruchika Bhatla","doi":"10.4108/eetpht.9.4337","DOIUrl":"https://doi.org/10.4108/eetpht.9.4337","url":null,"abstract":"INTRODUCTION: Determining the possibility that an individual is affected by a tumour is an intricate process in today's modern technological and biological age, when feats are reaching unprecedented levels with every passing second. Machine learning modalities could dramatically enhance the accuracy of diagnosis.
 OBJECTIVES: Our research makes it feasible to detect tumours early, aiding in early diagnosis, and is a necessity for the curative efforts of cancer patients.
 METHODS: In our research model Convolutional Neural Network (CNN) was implemented using Jupiter to give an accurate result.
 RESULTS: In our proposed model we got 99% accuracy that is higher than the other results.
 CONCLUSION: Our research demonstrates the potential of using machine learning techniques to improve the accuracy and efficiency of medical diagnosis.","PeriodicalId":36936,"journal":{"name":"EAI Endorsed Transactions on Pervasive Health and Technology","volume":"117 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":"135342121","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}
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.
{"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}
Abbaraju Sai Sathwik, Raghav Agarwal, Ajith Jubilson E, Santi Swarup Basa
One of the main causes of adult blindness and a frequent consequence of diabetes is diabetic retinopathy (DR). To avoid visual loss, DR must be promptly identified and classified. In this article, we suggest an automated DR detection and classification method based on deep learning applied to fundus pictures. The suggested technique uses transfer learning for classification. On a dataset of 3,662 fundus images with real-world DR severity labels, we trained and validated our model. According to our findings, the suggested technique successfully detected and classified DR with an overall accuracy of 78.14%. Our model fared better than other recent cutting-edge techniques, illuminating the promise of deep learning-based strategies for DR detection and management. Our research indicates that the suggested technique may be employed as a screening tool for DR in a clinical environment, enabling early illness diagnosis and prompt treatment.
{"title":"Diabetic Retinopathy Classification Using Deep Learning","authors":"Abbaraju Sai Sathwik, Raghav Agarwal, Ajith Jubilson E, Santi Swarup Basa","doi":"10.4108/eetpht.9.4335","DOIUrl":"https://doi.org/10.4108/eetpht.9.4335","url":null,"abstract":"One of the main causes of adult blindness and a frequent consequence of diabetes is diabetic retinopathy (DR). To avoid visual loss, DR must be promptly identified and classified. In this article, we suggest an automated DR detection and classification method based on deep learning applied to fundus pictures. The suggested technique uses transfer learning for classification. On a dataset of 3,662 fundus images with real-world DR severity labels, we trained and validated our model. According to our findings, the suggested technique successfully detected and classified DR with an overall accuracy of 78.14%. Our model fared better than other recent cutting-edge techniques, illuminating the promise of deep learning-based strategies for DR detection and management. Our research indicates that the suggested technique may be employed as a screening tool for DR in a clinical environment, enabling early illness diagnosis and prompt treatment.","PeriodicalId":36936,"journal":{"name":"EAI Endorsed Transactions on Pervasive Health and Technology","volume":"5 12","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135390337","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}
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.
{"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}
Abbaraju Sai Sathwik, Beebi Naseeba, Jinka Chandra Kiran, Kokkula Lokesh, Venkata Sasi Deepthi Ch, Nagendra Panini Challa
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.
{"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}
INTRODUCTION: Data is increasing exponentially. Data processing is an essential component in all industries, including health care. Even though a lot of progress has been made, it has been noted that in the recent decade, the health industry is capable of efficiently utilizing data and providing perfect Advancements in therapies.
OBJECTIVES: the main objectives include of finding the right problems in the security systems and to review the methods of present data processing methods.
METHODS: Methods involved are Quantitive analysis, Descriptive analysis, Data cleaning and Extraction.
RESULTS: The outputs of the reduce function are combined across all reducer nodes to produce the final output.
CONCLUSION: Big data analytics has enormous potential to accelerate the health care industry and that can only be done with some innovative methods and security plays a crucial role and can be a good catalyst in the user experience elements.
{"title":"Big data-analysis, map reduced framework, security & privacy challenges and techniques in health sector","authors":"Rajarshi Sarkar, Mokshith Telugu, Nooharika Kuntla","doi":"10.4108/eetpht.9.4292","DOIUrl":"https://doi.org/10.4108/eetpht.9.4292","url":null,"abstract":"INTRODUCTION: Data is increasing exponentially. Data processing is an essential component in all industries, including health care. Even though a lot of progress has been made, it has been noted that in the recent decade, the health industry is capable of efficiently utilizing data and providing perfect Advancements in therapies.
 OBJECTIVES: the main objectives include of finding the right problems in the security systems and to review the methods of present data processing methods.
 METHODS: Methods involved are Quantitive analysis, Descriptive analysis, Data cleaning and Extraction.
 RESULTS: The outputs of the reduce function are combined across all reducer nodes to produce the final output.
 CONCLUSION: Big data analytics has enormous potential to accelerate the health care industry and that can only be done with some innovative methods and security plays a crucial role and can be a good catalyst in the user experience elements.","PeriodicalId":36936,"journal":{"name":"EAI Endorsed Transactions on Pervasive Health and Technology","volume":"67 9","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135933883","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}
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