Pub Date : 2023-02-22DOI: 10.1109/ICIPTM57143.2023.10118236
Rajitha Jasmine R, P. Nimmagadda, K. Sudhakar, Benitha Christinal J, P. Rajasekar, S. A
The growth of video content in recent years is a challenging problem due to increased memory storage and time consuming for analyzing content of the video. Therefore, there is a need to reduce the content for human usage. This paper presents a keyframes technique that uses co-occurrence matrix and permutation computation for perceptual video summarization (PVS). The Human Visual System (HVS) needs to incorporate the PVS. It helps to allow for the importance of perceptually significant contents. The proposed method uses different kinds of videos to evaluate the effectiveness of the work. The subjective evaluation scores have evaluated the proposed work.
{"title":"Perceptual Video Summarization Using Keyframes Extraction Technique","authors":"Rajitha Jasmine R, P. Nimmagadda, K. Sudhakar, Benitha Christinal J, P. Rajasekar, S. A","doi":"10.1109/ICIPTM57143.2023.10118236","DOIUrl":"https://doi.org/10.1109/ICIPTM57143.2023.10118236","url":null,"abstract":"The growth of video content in recent years is a challenging problem due to increased memory storage and time consuming for analyzing content of the video. Therefore, there is a need to reduce the content for human usage. This paper presents a keyframes technique that uses co-occurrence matrix and permutation computation for perceptual video summarization (PVS). The Human Visual System (HVS) needs to incorporate the PVS. It helps to allow for the importance of perceptually significant contents. The proposed method uses different kinds of videos to evaluate the effectiveness of the work. The subjective evaluation scores have evaluated the proposed work.","PeriodicalId":178817,"journal":{"name":"2023 3rd International Conference on Innovative Practices in Technology and Management (ICIPTM)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117061186","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}
Pub Date : 2023-02-22DOI: 10.1109/ICIPTM57143.2023.10118007
MD Khadimul Islam Zim
If someone showed you a picture of themselves and asked you to describe how they feel, you'd probably have a good idea. Think about how useful it would be if your computer could do that! But what if you could enhance the things you have? It seems like a completely absurd idea. In the past, it was easy to infer a person's emotional state simply by observing their face. However, it is much more challenging for a computer to perform this task. Emotion recognition in photographs is now feasible with the help of machine learning and computer vision. Facial expression recognition is a growing subset of the field of facial recognition. Despite the fact that there are methods that use machine learning and artificial intelligence to accomplish the same goals, this work attempts to use the OpenCV approach to recognise expressions and classify the expressions based on the photos.
{"title":"OpenCV and Python for Emotion Analysis of Face Expressions","authors":"MD Khadimul Islam Zim","doi":"10.1109/ICIPTM57143.2023.10118007","DOIUrl":"https://doi.org/10.1109/ICIPTM57143.2023.10118007","url":null,"abstract":"If someone showed you a picture of themselves and asked you to describe how they feel, you'd probably have a good idea. Think about how useful it would be if your computer could do that! But what if you could enhance the things you have? It seems like a completely absurd idea. In the past, it was easy to infer a person's emotional state simply by observing their face. However, it is much more challenging for a computer to perform this task. Emotion recognition in photographs is now feasible with the help of machine learning and computer vision. Facial expression recognition is a growing subset of the field of facial recognition. Despite the fact that there are methods that use machine learning and artificial intelligence to accomplish the same goals, this work attempts to use the OpenCV approach to recognise expressions and classify the expressions based on the photos.","PeriodicalId":178817,"journal":{"name":"2023 3rd International Conference on Innovative Practices in Technology and Management (ICIPTM)","volume":"92 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127337957","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}
Pub Date : 2023-02-22DOI: 10.1109/ICIPTM57143.2023.10118252
Preethi Samantha Bennet, Deepthi Tabitha Bennet, Anitha D
In mission critical systems, system failure is a major hazard and may cause huge losses including loss or threat to lives. Organisations, industries, hospitals and companies can benefit hugely if an accurate prediction of the impending failure can be made, with enough time to initiate appropriate maintenance routines. Here, we propose and demonstrate that machine failure prediction can be done using suitable machine learning models with high accuracy. We apply the principles of Logistic Regression, Bootstrap Aggregation and Multinomial Logistic Regression to a predictive maintenance dataset of 10,000 data points to predict machine failure under five independent failure modes. Applying ensemble methods like bootstrap aggregation push the accuracy to greater than 99% The machine fails even if one failure mode is true. We are able to predict the possible cause of failure too, with a high accuracy of up to 99%.
{"title":"Intelligent Machine-Failure Prediction System (IMPS)","authors":"Preethi Samantha Bennet, Deepthi Tabitha Bennet, Anitha D","doi":"10.1109/ICIPTM57143.2023.10118252","DOIUrl":"https://doi.org/10.1109/ICIPTM57143.2023.10118252","url":null,"abstract":"In mission critical systems, system failure is a major hazard and may cause huge losses including loss or threat to lives. Organisations, industries, hospitals and companies can benefit hugely if an accurate prediction of the impending failure can be made, with enough time to initiate appropriate maintenance routines. Here, we propose and demonstrate that machine failure prediction can be done using suitable machine learning models with high accuracy. We apply the principles of Logistic Regression, Bootstrap Aggregation and Multinomial Logistic Regression to a predictive maintenance dataset of 10,000 data points to predict machine failure under five independent failure modes. Applying ensemble methods like bootstrap aggregation push the accuracy to greater than 99% The machine fails even if one failure mode is true. We are able to predict the possible cause of failure too, with a high accuracy of up to 99%.","PeriodicalId":178817,"journal":{"name":"2023 3rd International Conference on Innovative Practices in Technology and Management (ICIPTM)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130150152","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}
Pub Date : 2023-02-22DOI: 10.1109/ICIPTM57143.2023.10118147
R. K. Revulagadda, Sanjeev Kumar, H. Olasiuk, Sudhanshu Singh, N. Vihari, Satvik Vats
The current study presents a bibliometric and visual analysis of the literature on Financial Technology (fintech) applications to find out the publication patterns across publication medium, countries and relevant applications through bibliometric analysis and visual study. The study finds that China is the leading publisher of the fintech articles followed by U.S.A and U.K followed by India at fourth place. Also, it is seen that fintech overlaps between engineering and management disciplines where one leads in innovation and other in disbursement of technology. Hence, fintech leading journals are in management discipline. Fintech is studied in close association with financial inclusion, blockchain, cryptocurrency, artificial intelligence in majority along with some other minor applications. It is also evident that the next fintech revolution would be led by countries like India and China as the growth of articles and studies in these countries is increasing at a faster pace.
{"title":"A Bibliometric and Visual Analysis of Financial Technology Applications","authors":"R. K. Revulagadda, Sanjeev Kumar, H. Olasiuk, Sudhanshu Singh, N. Vihari, Satvik Vats","doi":"10.1109/ICIPTM57143.2023.10118147","DOIUrl":"https://doi.org/10.1109/ICIPTM57143.2023.10118147","url":null,"abstract":"The current study presents a bibliometric and visual analysis of the literature on Financial Technology (fintech) applications to find out the publication patterns across publication medium, countries and relevant applications through bibliometric analysis and visual study. The study finds that China is the leading publisher of the fintech articles followed by U.S.A and U.K followed by India at fourth place. Also, it is seen that fintech overlaps between engineering and management disciplines where one leads in innovation and other in disbursement of technology. Hence, fintech leading journals are in management discipline. Fintech is studied in close association with financial inclusion, blockchain, cryptocurrency, artificial intelligence in majority along with some other minor applications. It is also evident that the next fintech revolution would be led by countries like India and China as the growth of articles and studies in these countries is increasing at a faster pace.","PeriodicalId":178817,"journal":{"name":"2023 3rd International Conference on Innovative Practices in Technology and Management (ICIPTM)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132713686","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}
Pub Date : 2023-02-22DOI: 10.1109/ICIPTM57143.2023.10118251
Aman Kumar, Vishrut Kumar, P. Rajakumar
In the field of human-computer interaction, identifying the emotions conveyed in a person's speech is a challenge that is both incredibly fascinating and challenging. Recent times have seen a significant uptick in people's interest in this topic. In the subject of speech emotion recognition, a wide variety of techniques have been applied in order to extract emotions from signals. These techniques include a number of well-known speech analysis and classification strategies. This paper provides an overview of the deep learning technique, which is based on a simple algorithm based on feature extraction and model creation that recognizes emotion. In the traditional method of speech emotion recognition, features are first extracted from the speech signals, then the features themselves are picked, collectively known as the selection module, and then the emotions are recognized.
{"title":"Speech Emotion Recognition Using Machine Learning","authors":"Aman Kumar, Vishrut Kumar, P. Rajakumar","doi":"10.1109/ICIPTM57143.2023.10118251","DOIUrl":"https://doi.org/10.1109/ICIPTM57143.2023.10118251","url":null,"abstract":"In the field of human-computer interaction, identifying the emotions conveyed in a person's speech is a challenge that is both incredibly fascinating and challenging. Recent times have seen a significant uptick in people's interest in this topic. In the subject of speech emotion recognition, a wide variety of techniques have been applied in order to extract emotions from signals. These techniques include a number of well-known speech analysis and classification strategies. This paper provides an overview of the deep learning technique, which is based on a simple algorithm based on feature extraction and model creation that recognizes emotion. In the traditional method of speech emotion recognition, features are first extracted from the speech signals, then the features themselves are picked, collectively known as the selection module, and then the emotions are recognized.","PeriodicalId":178817,"journal":{"name":"2023 3rd International Conference on Innovative Practices in Technology and Management (ICIPTM)","volume":"90 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131512815","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}
Pub Date : 2023-02-22DOI: 10.1109/ICIPTM57143.2023.10118224
R. Raman, Radha. H. R, T. Inbamalar, D. A. Subhahan, Ashok Kumar, S. Bathrinath, Swagata B. Sarkar
Computational medicine has emerged as a result of the advancement of medical technology, which has led to the emergence of the big data era in the biomedical area, which is supported by artificial intelligence technology. To advance the development of precision medicine, people must be able to extract the valuable information from this vast biomedical data. In the past, professionals in the field of feature engineering and domain knowledge were typically utilised to extract the features from the biological data using machine learning techniques, which took a lot of time and resources. Modern machine learning techniques like deep learning (DL) have an advantage over them in that they can automatically find strong, complex features from fresh data without the necessity for succeeding engineering. The study of DL's applications in the fields of genomics, drug development, electronic health records, and medical imaging suggests that deep learning has clear advantages in maximising the use of biomedical data. Deep learning is becoming increasingly important in the field of medicine and health due to its large range of potential applications. The lack of data, interpretability, data privacy, and heterogeneity are some of the limitations of deep learning in computational medical health. A resource for improving the use of deep learning in medical health is provided by the analysis and discussion of these difficulties.
{"title":"Penetration of Deep Learning in Human Health Care and Pharmaceutical Industries; the Opportunities and Challenges","authors":"R. Raman, Radha. H. R, T. Inbamalar, D. A. Subhahan, Ashok Kumar, S. Bathrinath, Swagata B. Sarkar","doi":"10.1109/ICIPTM57143.2023.10118224","DOIUrl":"https://doi.org/10.1109/ICIPTM57143.2023.10118224","url":null,"abstract":"Computational medicine has emerged as a result of the advancement of medical technology, which has led to the emergence of the big data era in the biomedical area, which is supported by artificial intelligence technology. To advance the development of precision medicine, people must be able to extract the valuable information from this vast biomedical data. In the past, professionals in the field of feature engineering and domain knowledge were typically utilised to extract the features from the biological data using machine learning techniques, which took a lot of time and resources. Modern machine learning techniques like deep learning (DL) have an advantage over them in that they can automatically find strong, complex features from fresh data without the necessity for succeeding engineering. The study of DL's applications in the fields of genomics, drug development, electronic health records, and medical imaging suggests that deep learning has clear advantages in maximising the use of biomedical data. Deep learning is becoming increasingly important in the field of medicine and health due to its large range of potential applications. The lack of data, interpretability, data privacy, and heterogeneity are some of the limitations of deep learning in computational medical health. A resource for improving the use of deep learning in medical health is provided by the analysis and discussion of these difficulties.","PeriodicalId":178817,"journal":{"name":"2023 3rd International Conference on Innovative Practices in Technology and Management (ICIPTM)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114709054","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}
Pub Date : 2023-02-22DOI: 10.1109/ICIPTM57143.2023.10118098
M. P, Varun C M, K. N, Pradip Padhye, K. A. Kumari, Puja Das, Swagata B. Sarkar
Due to the lack of a mechanism in place to check online for parking availability, it is now quite difficult to find a parking spot in congested regions. Imagine being able to access information about parking slot availability on your phone and not having to move around to do so. The cutting-edge parking system powered by IoT can overcome this issue. The IoT-based parking system makes it simple to check the availability of parking spaces online. With the aid of this device, the parking system might be fully automated. You might have automatic admission, payment, and exit processes. A NodeMCU-based IOT-based car parking system is being created using five IR sensors, two servo motors, and NodeMCU. Three IR sensors are utilised to determine whether parking spaces are available, and two IR sensors are used at the entry and exit gates to identify vehicles. Based on the sensor value, servo motors control the gates' opening and closing. In this part, we'll show you how to upload data to the cloud that is reachable from anywhere using the Adafruit IO platform.
{"title":"Fashionable Four Wheeler Parking to Reduce the Human Intervention and to Enhance the Flexibility in Parking System Using IoT","authors":"M. P, Varun C M, K. N, Pradip Padhye, K. A. Kumari, Puja Das, Swagata B. Sarkar","doi":"10.1109/ICIPTM57143.2023.10118098","DOIUrl":"https://doi.org/10.1109/ICIPTM57143.2023.10118098","url":null,"abstract":"Due to the lack of a mechanism in place to check online for parking availability, it is now quite difficult to find a parking spot in congested regions. Imagine being able to access information about parking slot availability on your phone and not having to move around to do so. The cutting-edge parking system powered by IoT can overcome this issue. The IoT-based parking system makes it simple to check the availability of parking spaces online. With the aid of this device, the parking system might be fully automated. You might have automatic admission, payment, and exit processes. A NodeMCU-based IOT-based car parking system is being created using five IR sensors, two servo motors, and NodeMCU. Three IR sensors are utilised to determine whether parking spaces are available, and two IR sensors are used at the entry and exit gates to identify vehicles. Based on the sensor value, servo motors control the gates' opening and closing. In this part, we'll show you how to upload data to the cloud that is reachable from anywhere using the Adafruit IO platform.","PeriodicalId":178817,"journal":{"name":"2023 3rd International Conference on Innovative Practices in Technology and Management (ICIPTM)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116523428","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}
Pub Date : 2023-02-22DOI: 10.1109/ICIPTM57143.2023.10117966
Kashif Anjum, Mohd. Sameer, Santosh Kumar
Usually, people are not conscious of the several treatments or manifestations of a particular illness. They usually have to go to the hospital for minor health problems, which needs an extra amount of time. Also, answering phone calls for complaints is quite hectic. However, this matter can be fixed by utilizing a medical Chatbot called MedBot, which can give proper advice on leading a healthy lifestyle. The basic idea is to construct a healthcare chatbot (MedBot) based on Artificial Intelligence and Natural Language Processing, which can identify the illness and provide necessary information about it prior to consulting or visiting a doctor, thereby making the MedBot more reachable and reducing healthcare costs. Some of these chatbots act as virtual medical assistants, teaching patients regarding their sickness and motivating them to have better health. A text-to-text medical Chatbot involves users in an online conversation about their medical issues and offers a range of personalized diagnoses depending on the symptoms that have been presented. The MedBot interacts with potential patients who come to the application.[6]
{"title":"AI Enabled NLP based Text to Text Medical Chatbot","authors":"Kashif Anjum, Mohd. Sameer, Santosh Kumar","doi":"10.1109/ICIPTM57143.2023.10117966","DOIUrl":"https://doi.org/10.1109/ICIPTM57143.2023.10117966","url":null,"abstract":"Usually, people are not conscious of the several treatments or manifestations of a particular illness. They usually have to go to the hospital for minor health problems, which needs an extra amount of time. Also, answering phone calls for complaints is quite hectic. However, this matter can be fixed by utilizing a medical Chatbot called MedBot, which can give proper advice on leading a healthy lifestyle. The basic idea is to construct a healthcare chatbot (MedBot) based on Artificial Intelligence and Natural Language Processing, which can identify the illness and provide necessary information about it prior to consulting or visiting a doctor, thereby making the MedBot more reachable and reducing healthcare costs. Some of these chatbots act as virtual medical assistants, teaching patients regarding their sickness and motivating them to have better health. A text-to-text medical Chatbot involves users in an online conversation about their medical issues and offers a range of personalized diagnoses depending on the symptoms that have been presented. The MedBot interacts with potential patients who come to the application.[6]","PeriodicalId":178817,"journal":{"name":"2023 3rd International Conference on Innovative Practices in Technology and Management (ICIPTM)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122393214","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}
Pub Date : 2023-02-22DOI: 10.1109/ICIPTM57143.2023.10117824
D. Gunapriya, N. Pusphalatha, S. Sudharsan, S. Pandi, L. Catherine, Vandana Sharma, A. Alkhayyat
Battery demand is increasing as new energy-powered electric vehicles and smart grid technology advance significantly. Mechanized potential lithium battery research has grown in popularity as it has numerous applications in industries. Batteries that can retain chemical energy and translate it to electrical energy when desired are known as rechargeable batteries. These batteries increase system efficiency and offer cost savings for later usage. The BMS plays a valuable part in maintaining the reliability of the battery. A better BMS incorporates additional system functions, most notably SOC estimation and battery cell voltage equalization.
{"title":"An Exhaustive Investigation of Battery Management System (BMS)","authors":"D. Gunapriya, N. Pusphalatha, S. Sudharsan, S. Pandi, L. Catherine, Vandana Sharma, A. Alkhayyat","doi":"10.1109/ICIPTM57143.2023.10117824","DOIUrl":"https://doi.org/10.1109/ICIPTM57143.2023.10117824","url":null,"abstract":"Battery demand is increasing as new energy-powered electric vehicles and smart grid technology advance significantly. Mechanized potential lithium battery research has grown in popularity as it has numerous applications in industries. Batteries that can retain chemical energy and translate it to electrical energy when desired are known as rechargeable batteries. These batteries increase system efficiency and offer cost savings for later usage. The BMS plays a valuable part in maintaining the reliability of the battery. A better BMS incorporates additional system functions, most notably SOC estimation and battery cell voltage equalization.","PeriodicalId":178817,"journal":{"name":"2023 3rd International Conference on Innovative Practices in Technology and Management (ICIPTM)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122978575","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}
Pub Date : 2023-02-22DOI: 10.1109/ICIPTM57143.2023.10118103
S. Subha, T. Inbamalar, K. R, Lakshmi R Suresh, S. Boopathi, K. Alaskar
The Internet of Medical Things (IoMT) is one of the most promising technology solutions that is currently being developed to monitor health status remotely. A risk-stratified data transmission protocol has been used to construct the IoMT architecture for remote patient monitoring. All the sub-systems have undergone performance tests as well as clinical validation. Clinical validation of IoMT software on 100 patients was successful. Digital representations' size and complexity are reduced by up to 80%, making them appropriate for use in developing narrow-band IoT networks. In particular for low-power devices, performance measurement revealed that bandwidth and energy were reduced to 97% and 95%, respectively.
{"title":"A Remote Health Care Monitoring system using internet of medical things (IoMT)","authors":"S. Subha, T. Inbamalar, K. R, Lakshmi R Suresh, S. Boopathi, K. Alaskar","doi":"10.1109/ICIPTM57143.2023.10118103","DOIUrl":"https://doi.org/10.1109/ICIPTM57143.2023.10118103","url":null,"abstract":"The Internet of Medical Things (IoMT) is one of the most promising technology solutions that is currently being developed to monitor health status remotely. A risk-stratified data transmission protocol has been used to construct the IoMT architecture for remote patient monitoring. All the sub-systems have undergone performance tests as well as clinical validation. Clinical validation of IoMT software on 100 patients was successful. Digital representations' size and complexity are reduced by up to 80%, making them appropriate for use in developing narrow-band IoT networks. In particular for low-power devices, performance measurement revealed that bandwidth and energy were reduced to 97% and 95%, respectively.","PeriodicalId":178817,"journal":{"name":"2023 3rd International Conference on Innovative Practices in Technology and Management (ICIPTM)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124556028","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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