Pub Date : 2022-12-10DOI: 10.1109/STCR55312.2022.10009283
V. E., R. D
There is an enormous increase in number of diseases worldwide. The non-communicable diseases such as cardio vascular disease will leads to death. The second major reason of death in people worldwide occurs due to stroke. It affects any portion of brain due to interruption or reduction of Blood supply. The brain damage can be reduced if required actions taken earlier. So there is necessary requirement to build stroke predictive models. The combined techniques of Machine Learning (ML) and Deep Learning (DL) techniques play the vital role in Disease Prediction. There are many researches has been done for stroke prediction using various ML Algorithms. In order to improve accuracy, the proposed model will work on the hybrid ANNRF (Artificial Neural Network-Random Forest). The proposed method can be reached 94% in classification accuracy.
{"title":"A Systematic Method of Stroke Prediction Model based on Big Data and Machine Learning","authors":"V. E., R. D","doi":"10.1109/STCR55312.2022.10009283","DOIUrl":"https://doi.org/10.1109/STCR55312.2022.10009283","url":null,"abstract":"There is an enormous increase in number of diseases worldwide. The non-communicable diseases such as cardio vascular disease will leads to death. The second major reason of death in people worldwide occurs due to stroke. It affects any portion of brain due to interruption or reduction of Blood supply. The brain damage can be reduced if required actions taken earlier. So there is necessary requirement to build stroke predictive models. The combined techniques of Machine Learning (ML) and Deep Learning (DL) techniques play the vital role in Disease Prediction. There are many researches has been done for stroke prediction using various ML Algorithms. In order to improve accuracy, the proposed model will work on the hybrid ANNRF (Artificial Neural Network-Random Forest). The proposed method can be reached 94% in classification accuracy.","PeriodicalId":338691,"journal":{"name":"2022 Smart Technologies, Communication and Robotics (STCR)","volume":"96 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115633857","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 : 2022-12-10DOI: 10.1109/STCR55312.2022.10009110
G. R, Sathish Kumar N, Senthilkumar B
Mammogram imaging provides very useful support for the radiologist in detecting and treating the breast cancer. All the detection methods need pre-processing support to make the image clear and free from any unwanted information. Filters with high accuracy are the major requirement for all pre-processing methods. Adders are the main building blocks used in the filter design. A new Quality Confirmed Approach (QCA) adder has been proposed by combining the existing Brent Kung, Sklansky and Kogge Stone adder logic by using Tree Grafting Technique (TGT) for improvement in speed, reduction in complexity and power consumption. The proposed new adder performs well in the Modified Low Range Modification (MLRM) filter, which is used for the effective pre-processing of mammogram image towards the detection of breast cancer. The existing and proposed adder based MLRM method has been tested for Power reduction, Power Delay Product (PDP) and accuracy. The proposed QCA adder based MLRM performed well and have consumed 891.842 µW power with 0.21 % of power saving over Brent Kung adder based approach, achieved the PDP value of 16.613 pJ, which is 0.81 % less than that of the Han Carlson Adder based approach. The existing and proposed MLRM methods have been tested for contrast improvement, mean square error (MSE) reduction and peak signal to noise ratio (PSNR) improvement. For the test image mdb072, 7.4 % improvement achieved in contrast percentage than the next best BKA based approach.
{"title":"Analysis and Design of Low Area and Highly Energy Efficient Hybrid Adder for Signal Processing Applications","authors":"G. R, Sathish Kumar N, Senthilkumar B","doi":"10.1109/STCR55312.2022.10009110","DOIUrl":"https://doi.org/10.1109/STCR55312.2022.10009110","url":null,"abstract":"Mammogram imaging provides very useful support for the radiologist in detecting and treating the breast cancer. All the detection methods need pre-processing support to make the image clear and free from any unwanted information. Filters with high accuracy are the major requirement for all pre-processing methods. Adders are the main building blocks used in the filter design. A new Quality Confirmed Approach (QCA) adder has been proposed by combining the existing Brent Kung, Sklansky and Kogge Stone adder logic by using Tree Grafting Technique (TGT) for improvement in speed, reduction in complexity and power consumption. The proposed new adder performs well in the Modified Low Range Modification (MLRM) filter, which is used for the effective pre-processing of mammogram image towards the detection of breast cancer. The existing and proposed adder based MLRM method has been tested for Power reduction, Power Delay Product (PDP) and accuracy. The proposed QCA adder based MLRM performed well and have consumed 891.842 µW power with 0.21 % of power saving over Brent Kung adder based approach, achieved the PDP value of 16.613 pJ, which is 0.81 % less than that of the Han Carlson Adder based approach. The existing and proposed MLRM methods have been tested for contrast improvement, mean square error (MSE) reduction and peak signal to noise ratio (PSNR) improvement. For the test image mdb072, 7.4 % improvement achieved in contrast percentage than the next best BKA based approach.","PeriodicalId":338691,"journal":{"name":"2022 Smart Technologies, Communication and Robotics (STCR)","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126663675","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 : 2022-12-10DOI: 10.1109/STCR55312.2022.10009274
Sanjay V, S. P.
Alzheimer’s disease affects most of the elderly in today's world. It directly affects the neurotransmitters and leads to dementia. MRI images can spot brain irregularities related to mild cognitive damage. It can be useful for predicting Alzheimer’s disease, though it is a big challenge. In this research, a novel technique is proposed to find to detect Alzheimer’s disease using Adaboost classifier with a hybrid PSO algorithm. Initially, MRI image features are extracted, and the best features are identified by the curvelet transform and Principal Component Analysis (PCA). Adaboost proposed methods yield greater accuracy than the existing systems for analyzing MRI images and give excellent classification accuracy. To evaluate the proposed method three methods metrics namely accuracy, specificity, and sensitivity are used. Based on the results the proposed methods yield greater accuracy than the existing systems.
{"title":"An Enhanced Approach for Detecting Alzheimer’s Disease","authors":"Sanjay V, S. P.","doi":"10.1109/STCR55312.2022.10009274","DOIUrl":"https://doi.org/10.1109/STCR55312.2022.10009274","url":null,"abstract":"Alzheimer’s disease affects most of the elderly in today's world. It directly affects the neurotransmitters and leads to dementia. MRI images can spot brain irregularities related to mild cognitive damage. It can be useful for predicting Alzheimer’s disease, though it is a big challenge. In this research, a novel technique is proposed to find to detect Alzheimer’s disease using Adaboost classifier with a hybrid PSO algorithm. Initially, MRI image features are extracted, and the best features are identified by the curvelet transform and Principal Component Analysis (PCA). Adaboost proposed methods yield greater accuracy than the existing systems for analyzing MRI images and give excellent classification accuracy. To evaluate the proposed method three methods metrics namely accuracy, specificity, and sensitivity are used. Based on the results the proposed methods yield greater accuracy than the existing systems.","PeriodicalId":338691,"journal":{"name":"2022 Smart Technologies, Communication and Robotics (STCR)","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126003968","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 : 2022-12-10DOI: 10.1109/STCR55312.2022.10009414
S. Udaiyakumar, CL Chinnadurrai, C. Anandhakumar, S. Ravindran
In this paper, electricity price forecasting using a hybrid multilayer perceptron, back propagation and modified particle swarm optimization is implemented. Here modified particle swarm optimization technique is used to improve the performance of the backpropagation algorithm while training the multilayer perceptron. Two different MLP are used for electricity price forecasting one MLP is with a single hidden layer and another MLP is with three hidden layers, both the neural networks are trained by BP and initial parameters such as weights between different layers, the bias of the layers, and activation function of each layer except input layer are selected by MPSO. Normally MLP trained by BP uses linear activation functions for all layers and neurons, but in this case, we use three different functions namely linear function, sigmoid function, and tangent function as activation functions. These three different activation functions are independently selected for each neuron by MPSO based on the data set which is used. Because of the independent selection of activation function to each neuron the overall performance, convergence time, and convergence efficiency of the BP are greatly improved. The proposed method is implemented to predict Austria and Northern Italy electricity price.
{"title":"Electricity Price Forecasting using Multilayer Perceptron Optimized by Particle Swarm Optimization","authors":"S. Udaiyakumar, CL Chinnadurrai, C. Anandhakumar, S. Ravindran","doi":"10.1109/STCR55312.2022.10009414","DOIUrl":"https://doi.org/10.1109/STCR55312.2022.10009414","url":null,"abstract":"In this paper, electricity price forecasting using a hybrid multilayer perceptron, back propagation and modified particle swarm optimization is implemented. Here modified particle swarm optimization technique is used to improve the performance of the backpropagation algorithm while training the multilayer perceptron. Two different MLP are used for electricity price forecasting one MLP is with a single hidden layer and another MLP is with three hidden layers, both the neural networks are trained by BP and initial parameters such as weights between different layers, the bias of the layers, and activation function of each layer except input layer are selected by MPSO. Normally MLP trained by BP uses linear activation functions for all layers and neurons, but in this case, we use three different functions namely linear function, sigmoid function, and tangent function as activation functions. These three different activation functions are independently selected for each neuron by MPSO based on the data set which is used. Because of the independent selection of activation function to each neuron the overall performance, convergence time, and convergence efficiency of the BP are greatly improved. The proposed method is implemented to predict Austria and Northern Italy electricity price.","PeriodicalId":338691,"journal":{"name":"2022 Smart Technologies, Communication and Robotics (STCR)","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124584250","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 : 2022-12-10DOI: 10.1109/STCR55312.2022.10009100
Siva Satya Sreedhar, R. Anitha, Priya Rachel, S. Suganya, C. Ramesh Babu Durai, G. S. Uthayakumar
Energy distribution is vital in an IoT-based Wireless Sensor Network (WSN).There is no other fuel source for WSN since they deal with battery systems. This means that when the battery runs out, they have no option except to replace it on a regular basis, which isn't always possible. Information may be lost during transmission as another problem with WSNs. Despite the fact that information disasters are rare, it remains a constant threat. The greatest danger lies in a loss of data. B) CH-to-sink data lost. This article saves energy by forecasting missing node values.
{"title":"Energy Conservation for Environment Monitoring System in an IoT based WSN","authors":"Siva Satya Sreedhar, R. Anitha, Priya Rachel, S. Suganya, C. Ramesh Babu Durai, G. S. Uthayakumar","doi":"10.1109/STCR55312.2022.10009100","DOIUrl":"https://doi.org/10.1109/STCR55312.2022.10009100","url":null,"abstract":"Energy distribution is vital in an IoT-based Wireless Sensor Network (WSN).There is no other fuel source for WSN since they deal with battery systems. This means that when the battery runs out, they have no option except to replace it on a regular basis, which isn't always possible. Information may be lost during transmission as another problem with WSNs. Despite the fact that information disasters are rare, it remains a constant threat. The greatest danger lies in a loss of data. B) CH-to-sink data lost. This article saves energy by forecasting missing node values.","PeriodicalId":338691,"journal":{"name":"2022 Smart Technologies, Communication and Robotics (STCR)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130310643","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 : 2022-12-10DOI: 10.1109/STCR55312.2022.10009552
D. Deepa, M. S. Raj, S. Gowthami, K. Hemalatha, C. Poongodi, P. Thangavel
Alzheimer’s Disease is a neurological brain disorder that damages the cells in brain and reduce the ability of the brain from the regular activities. It is a representation of the most common form of adult-onset dementias. Earlier detection of Alzheimer’s disease can be more helpful in predetermining the symptomatic conditions of patients suffering with this problem. By diagnosing the consequences of this disease, with the help of medical scan images, it would be more useful in classifying the patients whether they are suffering from this deadly disease. Machine Learning tends to be more beneficial in diagnosing diseases and implementation of this technique, to Magnetic Resonance Imaging (MRI) inputs in identification of Alzheimer’s disease, resulted in faster prediction of the disease and in the contribution of the evolution of the disease. Carrying out this technique, it is possible to diagnose and predict the individual dementia of adults by screening data of Alzheimer’s disease and inducing Machine Learning classifiers. This work focuses on building an evolving framework to detect Alzheimer’s disease efficiently with the help of neuroimaging technologies and prediction at a very earlier stage by using the data stacked up for Alzheimer’s disease patients.
{"title":"Identification and Analysis of Alzheimer’s Disease using DenseNet Architecture with Minimum Path Length Between Input and Output Layers","authors":"D. Deepa, M. S. Raj, S. Gowthami, K. Hemalatha, C. Poongodi, P. Thangavel","doi":"10.1109/STCR55312.2022.10009552","DOIUrl":"https://doi.org/10.1109/STCR55312.2022.10009552","url":null,"abstract":"Alzheimer’s Disease is a neurological brain disorder that damages the cells in brain and reduce the ability of the brain from the regular activities. It is a representation of the most common form of adult-onset dementias. Earlier detection of Alzheimer’s disease can be more helpful in predetermining the symptomatic conditions of patients suffering with this problem. By diagnosing the consequences of this disease, with the help of medical scan images, it would be more useful in classifying the patients whether they are suffering from this deadly disease. Machine Learning tends to be more beneficial in diagnosing diseases and implementation of this technique, to Magnetic Resonance Imaging (MRI) inputs in identification of Alzheimer’s disease, resulted in faster prediction of the disease and in the contribution of the evolution of the disease. Carrying out this technique, it is possible to diagnose and predict the individual dementia of adults by screening data of Alzheimer’s disease and inducing Machine Learning classifiers. This work focuses on building an evolving framework to detect Alzheimer’s disease efficiently with the help of neuroimaging technologies and prediction at a very earlier stage by using the data stacked up for Alzheimer’s disease patients.","PeriodicalId":338691,"journal":{"name":"2022 Smart Technologies, Communication and Robotics (STCR)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130563487","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 : 2022-12-10DOI: 10.1109/STCR55312.2022.10009430
S. Sanjayprabu, R. Sathish Kumar, K. Somasundaram, R. Karthikamani
In December 2019, the SARS-CoV-2 virus, often referred to as COVID-19, was discovered in Wuhan, China. It is very virulent and has spread very quickly throughout the world. With COVID-19, people have described a wide variety of symptoms, from little discomfort to life-threatening respiratory illness. In this study, chest X-ray scan images are preprocessed using an anisotropic diffusion filter and three classifiers, and the Covid-19 cases are classified from the chest X-ray images using the GLRLM feature extraction approach. Common metrics like sensitivity, selectivity, and accuracy are utilized to compare the performance of the classifiers. When compared to other classifiers in this study, the Gaussian Mixture Model had the best accuracy of 91.07%.
{"title":"Mathematical Model for Anisotropic diffusion Filter and GLRLM Feature Extraction to Detect Covid-19 from Chest X-Ray Images","authors":"S. Sanjayprabu, R. Sathish Kumar, K. Somasundaram, R. Karthikamani","doi":"10.1109/STCR55312.2022.10009430","DOIUrl":"https://doi.org/10.1109/STCR55312.2022.10009430","url":null,"abstract":"In December 2019, the SARS-CoV-2 virus, often referred to as COVID-19, was discovered in Wuhan, China. It is very virulent and has spread very quickly throughout the world. With COVID-19, people have described a wide variety of symptoms, from little discomfort to life-threatening respiratory illness. In this study, chest X-ray scan images are preprocessed using an anisotropic diffusion filter and three classifiers, and the Covid-19 cases are classified from the chest X-ray images using the GLRLM feature extraction approach. Common metrics like sensitivity, selectivity, and accuracy are utilized to compare the performance of the classifiers. When compared to other classifiers in this study, the Gaussian Mixture Model had the best accuracy of 91.07%.","PeriodicalId":338691,"journal":{"name":"2022 Smart Technologies, Communication and Robotics (STCR)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132383815","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 : 2022-12-10DOI: 10.1109/STCR55312.2022.10009089
Kalpesh P. Modi, S. Chakole, Sandeep R Sonaskar, Neema Ukani
This paper describes the design and development of SpO2 based simple healthcare system, as an application of embedded system and Internet of Things (IOT). In this paper, minimal open-source hardware based on Infrared (IR) and LEDs is integrated to perform tasks related to healthcare monitoring such as measurement of oxygen level in blood (SpO2) and recording heart rate (beats per minute). It is demonstrated that the prototype is working and reliable readings are obtained repeatedly, through the assembled device. Although it is common to achieve such a prototype [1],[2], this work also illustrates the feasibility of viewing the measurements in real-time on a portable device such as a mobile or PDA, which is suitable for early diagnosis and preventive healthcare. The prototype is further designed and implemented into a compact wearable device, conducive for trials.
{"title":"An Application of Embedded System and IOT: Development of SpO2 based Simple Healthcare System","authors":"Kalpesh P. Modi, S. Chakole, Sandeep R Sonaskar, Neema Ukani","doi":"10.1109/STCR55312.2022.10009089","DOIUrl":"https://doi.org/10.1109/STCR55312.2022.10009089","url":null,"abstract":"This paper describes the design and development of SpO2 based simple healthcare system, as an application of embedded system and Internet of Things (IOT). In this paper, minimal open-source hardware based on Infrared (IR) and LEDs is integrated to perform tasks related to healthcare monitoring such as measurement of oxygen level in blood (SpO2) and recording heart rate (beats per minute). It is demonstrated that the prototype is working and reliable readings are obtained repeatedly, through the assembled device. Although it is common to achieve such a prototype [1],[2], this work also illustrates the feasibility of viewing the measurements in real-time on a portable device such as a mobile or PDA, which is suitable for early diagnosis and preventive healthcare. The prototype is further designed and implemented into a compact wearable device, conducive for trials.","PeriodicalId":338691,"journal":{"name":"2022 Smart Technologies, Communication and Robotics (STCR)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132324204","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 : 2022-12-10DOI: 10.1109/STCR55312.2022.10009508
Amay Gada, Dishant Zaveri, Pratham Bhoir, Tushar Deshpande, Arpit Palod, Aniket Kore
Census is the process of gathering, analyzing, compiling, and spreading social, cultural, demographic, and economic data relating to all the people in a country. A census gives a statistically accurate view which is important to fill the gaps in the system. Enumerators are majorly responsible for the credibility of the census, and to maintain its reliability, it is important to monitor their location to confirm no random form fills. However, there is a lack of GeoJSON data for small, remote villages, districts, and talukas. This hinders the monitoring process. Hence, we devise a method to retrieve GeoJSON data from an image of the map and the border GeoJSON of the parent map in the hierarchy, using computationally efficient image processing. The proposed pipeline involves a 5 step process that includes preprocessing, extracting boundary coordinates, determining the scaling factor, inner boundary localization, and mapping. The results are computed by comparing the areas of the predicted and actual polygons of the retrieved regions whilst confirming that there is a massive overlap between the two polygons. An error rate of 4.87% is achieved (95.13% accuracy).
{"title":"Estimating GeoJSON Coordinates using Image Processing to Improve Census Credibility","authors":"Amay Gada, Dishant Zaveri, Pratham Bhoir, Tushar Deshpande, Arpit Palod, Aniket Kore","doi":"10.1109/STCR55312.2022.10009508","DOIUrl":"https://doi.org/10.1109/STCR55312.2022.10009508","url":null,"abstract":"Census is the process of gathering, analyzing, compiling, and spreading social, cultural, demographic, and economic data relating to all the people in a country. A census gives a statistically accurate view which is important to fill the gaps in the system. Enumerators are majorly responsible for the credibility of the census, and to maintain its reliability, it is important to monitor their location to confirm no random form fills. However, there is a lack of GeoJSON data for small, remote villages, districts, and talukas. This hinders the monitoring process. Hence, we devise a method to retrieve GeoJSON data from an image of the map and the border GeoJSON of the parent map in the hierarchy, using computationally efficient image processing. The proposed pipeline involves a 5 step process that includes preprocessing, extracting boundary coordinates, determining the scaling factor, inner boundary localization, and mapping. The results are computed by comparing the areas of the predicted and actual polygons of the retrieved regions whilst confirming that there is a massive overlap between the two polygons. An error rate of 4.87% is achieved (95.13% accuracy).","PeriodicalId":338691,"journal":{"name":"2022 Smart Technologies, Communication and Robotics (STCR)","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114330398","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 : 2022-12-10DOI: 10.1109/STCR55312.2022.10009132
S. Mahalakshmi, S. Nizar, B. Elizabeth Caroline, K. Sagadevan, K. Loga
PCF is a photonic crystal-based optical fibre. For sensing HIV (Human Immunodeficiency Virus) in the human body, a photonic crystal fiber-based biosensor is proposed.The hollow core photonic crystal fibre (HCPCF) is utilised in this article to elucidate the HIV virus contaminated cells in the body. This model gives higher sensitivity in detecting contaminated HIV virus by minimising confinement loss. To analyse the output of the PCF sensor the Sample cells are inserted in the core.The hollow core with air ring enhances the sensing for biomedical analytes.Relative Sensitivity(Rs), Effective mode area (Aeff), Confinement loss (αCL) and effective mode index can be determined by using this Comsol multiphysics software. This Software is used to design the high complexity fabrication model.Pathogen impacts the defensive mechanism of the human individual.AIDS (acquired immunodeficiency syndrome) can develop from HIV if it is not adequately managed). To examine the PCF sensor's operation, sample cells typically placed into the core. The hollow core with air ring optimizes the sensing of biological analytes. At Certain Wavelength the laser light passes through a core. This Software is employed in construct the high complexity fabrication.TheHIV pathogen develops the HIV/AIDS epidemic spectrum.The human immune system's CD4+ T cells, macrophages, and dendritic cells are frequently contaminated by that of the retrovirus designated as HIV. It suppresses CD4+ T cells both intrinsically and extrinsically. The three levels of HIV infectioncompriseof acute, chronic, and acquired immunodeficiency syndrome (AIDS).Although there is no cure for HIV, medication can help to standstill or quit the progression of the disease. Antiretroviral therapy is a type of treatment for HIV infection (ART). HIV is spurred on by a virus. Through sexual contact, illicit drug use, reusing needles,in touch with contaminated blood, or contact with infected blood, it can be spread from mother to infant, delivery, or suckling. HIV attacks CD4 T cells, is some kind of white blood cell that is essential for disease resistance. The design of the previous work is quite complicated and lacks precision. So, using the COMSOL Multiphysics software, we created a basic structure PCF-based Bio Sensor for HIV sensing. We reached a relative sensitivity of around 96.827 for wavelengthλ = 0.7m by interpreting the simulation findings. Our concept appears to be fairly basic and accuratepremised on the results we have obtained.
{"title":"Design and Investigation of Photonic Crystal Fiber for the Detection of HIV Virus","authors":"S. Mahalakshmi, S. Nizar, B. Elizabeth Caroline, K. Sagadevan, K. Loga","doi":"10.1109/STCR55312.2022.10009132","DOIUrl":"https://doi.org/10.1109/STCR55312.2022.10009132","url":null,"abstract":"PCF is a photonic crystal-based optical fibre. For sensing HIV (Human Immunodeficiency Virus) in the human body, a photonic crystal fiber-based biosensor is proposed.The hollow core photonic crystal fibre (HCPCF) is utilised in this article to elucidate the HIV virus contaminated cells in the body. This model gives higher sensitivity in detecting contaminated HIV virus by minimising confinement loss. To analyse the output of the PCF sensor the Sample cells are inserted in the core.The hollow core with air ring enhances the sensing for biomedical analytes.Relative Sensitivity(Rs), Effective mode area (Aeff), Confinement loss (αCL) and effective mode index can be determined by using this Comsol multiphysics software. This Software is used to design the high complexity fabrication model.Pathogen impacts the defensive mechanism of the human individual.AIDS (acquired immunodeficiency syndrome) can develop from HIV if it is not adequately managed). To examine the PCF sensor's operation, sample cells typically placed into the core. The hollow core with air ring optimizes the sensing of biological analytes. At Certain Wavelength the laser light passes through a core. This Software is employed in construct the high complexity fabrication.TheHIV pathogen develops the HIV/AIDS epidemic spectrum.The human immune system's CD4+ T cells, macrophages, and dendritic cells are frequently contaminated by that of the retrovirus designated as HIV. It suppresses CD4+ T cells both intrinsically and extrinsically. The three levels of HIV infectioncompriseof acute, chronic, and acquired immunodeficiency syndrome (AIDS).Although there is no cure for HIV, medication can help to standstill or quit the progression of the disease. Antiretroviral therapy is a type of treatment for HIV infection (ART). HIV is spurred on by a virus. Through sexual contact, illicit drug use, reusing needles,in touch with contaminated blood, or contact with infected blood, it can be spread from mother to infant, delivery, or suckling. HIV attacks CD4 T cells, is some kind of white blood cell that is essential for disease resistance. The design of the previous work is quite complicated and lacks precision. So, using the COMSOL Multiphysics software, we created a basic structure PCF-based Bio Sensor for HIV sensing. We reached a relative sensitivity of around 96.827 for wavelengthλ = 0.7m by interpreting the simulation findings. Our concept appears to be fairly basic and accuratepremised on the results we have obtained.","PeriodicalId":338691,"journal":{"name":"2022 Smart Technologies, Communication and Robotics (STCR)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114892395","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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