Pub Date : 2022-03-04DOI: 10.1109/ICACTA54488.2022.9752960
S. Sharanyaa, S. Vijayalakshmi, M. Therasa, U. Kumaran, R. Deepika
To evaluate the Early Gastric Cancer (EGC) detection in humans one of the most common diseases that act as neoplastic disease and second-largest fatal tumor-based disease. Medical imaging techniques and screening equipment such as endoscopy, Computer tomography scanning help the medical industry to detect gastric cancer. The system focuses on implementing a robust prediction scheme that uses image processing techniques to detect the early stage of cancer through lightweight techniques. The test image from the pathology database named BioGPS is preprocessed initially to remove the noisy part of the pixels. The extraction of color features is done using the color threshold algorithm by tuning the image color bands separately. From the R, G, B band the extracted unique feature pixels are mapped in the feature vectors. The cancer part is highlighted by the combination of the R band that associates more with Red pixel points. These formulated pixel vectors are unique and more precise. This is further fetched to the deep Color-Net model (Deep CNET) that compares the training vector with the test vector to find the maximum correlation. The higher the match score the classified results determine the presence of gastric cancer and highlight the spread area from the given test pathology data. Further the system performance is measured using accuracy, precision, recall and F1-Score.
{"title":"DCNET: A Novel Implementation of Gastric Cancer Detection System through Deep Learning Convolution Networks","authors":"S. Sharanyaa, S. Vijayalakshmi, M. Therasa, U. Kumaran, R. Deepika","doi":"10.1109/ICACTA54488.2022.9752960","DOIUrl":"https://doi.org/10.1109/ICACTA54488.2022.9752960","url":null,"abstract":"To evaluate the Early Gastric Cancer (EGC) detection in humans one of the most common diseases that act as neoplastic disease and second-largest fatal tumor-based disease. Medical imaging techniques and screening equipment such as endoscopy, Computer tomography scanning help the medical industry to detect gastric cancer. The system focuses on implementing a robust prediction scheme that uses image processing techniques to detect the early stage of cancer through lightweight techniques. The test image from the pathology database named BioGPS is preprocessed initially to remove the noisy part of the pixels. The extraction of color features is done using the color threshold algorithm by tuning the image color bands separately. From the R, G, B band the extracted unique feature pixels are mapped in the feature vectors. The cancer part is highlighted by the combination of the R band that associates more with Red pixel points. These formulated pixel vectors are unique and more precise. This is further fetched to the deep Color-Net model (Deep CNET) that compares the training vector with the test vector to find the maximum correlation. The higher the match score the classified results determine the presence of gastric cancer and highlight the spread area from the given test pathology data. Further the system performance is measured using accuracy, precision, recall and F1-Score.","PeriodicalId":345370,"journal":{"name":"2022 International Conference on Advanced Computing Technologies and Applications (ICACTA)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114093648","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 : 2022-03-04DOI: 10.1109/ICACTA54488.2022.9753091
A. John, Kalpana Murugan
Wireless Sensor Network (WSN) has created a booming evolution in the past decade. These networks are characterized by their coverage over a large area with small connected sensors. Wireless Body Area Network (WBAN) comes as a subset of WSN. In this type of network, the sensors are deployed over the body of the patient to analyze and collect data regarding various parameters like temperature pressure, etc. The design parameters of antennas are compared in this paper along with the design parameters that is required for designing an antenna in the terahertz frequency range. Various methods that can be used to enhance the performance matrix of the antenna are also discussed.
{"title":"A Survey on Design Parameters of Antenna in Terahertz Wireless Body Area Networks","authors":"A. John, Kalpana Murugan","doi":"10.1109/ICACTA54488.2022.9753091","DOIUrl":"https://doi.org/10.1109/ICACTA54488.2022.9753091","url":null,"abstract":"Wireless Sensor Network (WSN) has created a booming evolution in the past decade. These networks are characterized by their coverage over a large area with small connected sensors. Wireless Body Area Network (WBAN) comes as a subset of WSN. In this type of network, the sensors are deployed over the body of the patient to analyze and collect data regarding various parameters like temperature pressure, etc. The design parameters of antennas are compared in this paper along with the design parameters that is required for designing an antenna in the terahertz frequency range. Various methods that can be used to enhance the performance matrix of the antenna are also discussed.","PeriodicalId":345370,"journal":{"name":"2022 International Conference on Advanced Computing Technologies and Applications (ICACTA)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126018521","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 : 2022-03-04DOI: 10.1109/ICACTA54488.2022.9752921
T. Keerthika, Mohamed Ali Raihan M, Krupaasree K, Kiruthika E, Pradeep Balaji L R, N. S
A fatalform of skin cancer is Melanoma and the fifth most common cancer in the world. It is responsible for the majority of deaths due to skin cancer. Treating and diagnosing melanoma at the initial stages is very crucial as cancer may spread to other organs in the body very quickly which makes it more difficult to treat and may be fatal. Various techniques have been developed for early detection of melanoma like dermatoscopy and it is essential to find the correct set of features and machine learning techniques for classification. The objective of the paper is to exhibit common machine learning algorithms used which is Artificial Neural Network (ANN) and Support Vector Machine (SVM) and techniques of Discrete Wavelet Transform (DWT) that is utilized for feature selection and Gray Level Co-Occurrence Matrix (GLCM) that is implied in feature extraction. The intent of the paper is to show the advantages of using the SVM classifier for the detection of melanoma.
{"title":"A Color Based Approach to Detect Melanoma Using SVM Classifier","authors":"T. Keerthika, Mohamed Ali Raihan M, Krupaasree K, Kiruthika E, Pradeep Balaji L R, N. S","doi":"10.1109/ICACTA54488.2022.9752921","DOIUrl":"https://doi.org/10.1109/ICACTA54488.2022.9752921","url":null,"abstract":"A fatalform of skin cancer is Melanoma and the fifth most common cancer in the world. It is responsible for the majority of deaths due to skin cancer. Treating and diagnosing melanoma at the initial stages is very crucial as cancer may spread to other organs in the body very quickly which makes it more difficult to treat and may be fatal. Various techniques have been developed for early detection of melanoma like dermatoscopy and it is essential to find the correct set of features and machine learning techniques for classification. The objective of the paper is to exhibit common machine learning algorithms used which is Artificial Neural Network (ANN) and Support Vector Machine (SVM) and techniques of Discrete Wavelet Transform (DWT) that is utilized for feature selection and Gray Level Co-Occurrence Matrix (GLCM) that is implied in feature extraction. The intent of the paper is to show the advantages of using the SVM classifier for the detection of melanoma.","PeriodicalId":345370,"journal":{"name":"2022 International Conference on Advanced Computing Technologies and Applications (ICACTA)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130181669","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 : 2022-03-04DOI: 10.1109/ICACTA54488.2022.9752920
Ram Kumar M, Gokula Krishnan E, Dharneeshwar R, Dinep Kumar M
Cardio Vascular abnormality is the number of cause of death. Four out of ten people suffers from heart attack around the world according to information from the WHO. At least one life is being taken away by this devastating heart attack every minute in the United States of America. Causes for these may be due to irregular diet, physical inactivity and may be due to tobacco consumption. It contributes to 31% of global death. Detection of heart disease by using computer aided Machine Learning model would make this process easier. There are many methods available but those are not efficient enough but can decide still which model is efficient compared to others that are available.
{"title":"Detection Of Arrhythmia Using Machine Learning(Heart Disease) And ECG","authors":"Ram Kumar M, Gokula Krishnan E, Dharneeshwar R, Dinep Kumar M","doi":"10.1109/ICACTA54488.2022.9752920","DOIUrl":"https://doi.org/10.1109/ICACTA54488.2022.9752920","url":null,"abstract":"Cardio Vascular abnormality is the number of cause of death. Four out of ten people suffers from heart attack around the world according to information from the WHO. At least one life is being taken away by this devastating heart attack every minute in the United States of America. Causes for these may be due to irregular diet, physical inactivity and may be due to tobacco consumption. It contributes to 31% of global death. Detection of heart disease by using computer aided Machine Learning model would make this process easier. There are many methods available but those are not efficient enough but can decide still which model is efficient compared to others that are available.","PeriodicalId":345370,"journal":{"name":"2022 International Conference on Advanced Computing Technologies and Applications (ICACTA)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124139806","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 : 2022-03-04DOI: 10.1109/ICACTA54488.2022.9753620
S. C S, S. S, S. M
In the arena of artificial intelligence, the world is revolutionizing with many technological applications being incorporated with Artificial Intelligence due to improved efficiency and performance. AI has penetrated drastically, delving deep into locker room decisions in many fields like agriculture, healthcare, military, manufacturing, robotics, transportation and so on. AI does a lot more than improving our lives, in most cases, it saves our lives too. Autonomous vehicles, the so-called self-driving cars, are one of the greatest applications of AI and are very instrumental in making the machine work autonomously by observing and interpreting the real-life scenario of the environment. This paper deals with the deployment of an Automatic Traffic sign detection System with voice assistant, which is one of the applications of autonomous vehicles, which can tone down the driver from puzzling traffic conditions significantly increasing driving safety and comfort. This will require an appropriate database and algorithm for improved accuracy in performance. This paper, therefore, compares the features, accuracy, and efficiency of various deep learning algorithms and comes up with a varied model thus saving computational resources.
{"title":"Automatic Traffic Sign Detection System With Voice Assistant","authors":"S. C S, S. S, S. M","doi":"10.1109/ICACTA54488.2022.9753620","DOIUrl":"https://doi.org/10.1109/ICACTA54488.2022.9753620","url":null,"abstract":"In the arena of artificial intelligence, the world is revolutionizing with many technological applications being incorporated with Artificial Intelligence due to improved efficiency and performance. AI has penetrated drastically, delving deep into locker room decisions in many fields like agriculture, healthcare, military, manufacturing, robotics, transportation and so on. AI does a lot more than improving our lives, in most cases, it saves our lives too. Autonomous vehicles, the so-called self-driving cars, are one of the greatest applications of AI and are very instrumental in making the machine work autonomously by observing and interpreting the real-life scenario of the environment. This paper deals with the deployment of an Automatic Traffic sign detection System with voice assistant, which is one of the applications of autonomous vehicles, which can tone down the driver from puzzling traffic conditions significantly increasing driving safety and comfort. This will require an appropriate database and algorithm for improved accuracy in performance. This paper, therefore, compares the features, accuracy, and efficiency of various deep learning algorithms and comes up with a varied model thus saving computational resources.","PeriodicalId":345370,"journal":{"name":"2022 International Conference on Advanced Computing Technologies and Applications (ICACTA)","volume":"103 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117134393","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}
This paper the main focus is on the people who are blind and who cannot see. This prototype leads the blind people to recognize the text before them. The entire paper process of this blind aid. First of all, the blind person will be given with a camera attached to his spectacles. Whenever he wants to read something, he will take a snap of that particular location. Now the text in the image will be detected using an algorithm called EAST (Efficient and Accurate Scene Text Detector) which is an example of FCN with PVANet. In this detection there will be a use of max pooling while feature extraction in images. After detecting the text from image, this project uses Tesseract based OCR Engine to recognize the text in the image. After recognizing the text from the image, the text will be converted to some speech output to the blind person using python package called pytts version 3. The speech converted text will be given as an output to blind person with the aid of speaker. Finally here comes the concept of Modified EAST where the already built in model is extended to increase the accuracy of the prototype or model.
{"title":"Blind Aid: State of the art for Scene Text Detector and Text to Speech","authors":"Srividya Kotagiri, Attada Venkataramana, Gogula Kiran","doi":"10.1109/ICACTA54488.2022.9753094","DOIUrl":"https://doi.org/10.1109/ICACTA54488.2022.9753094","url":null,"abstract":"This paper the main focus is on the people who are blind and who cannot see. This prototype leads the blind people to recognize the text before them. The entire paper process of this blind aid. First of all, the blind person will be given with a camera attached to his spectacles. Whenever he wants to read something, he will take a snap of that particular location. Now the text in the image will be detected using an algorithm called EAST (Efficient and Accurate Scene Text Detector) which is an example of FCN with PVANet. In this detection there will be a use of max pooling while feature extraction in images. After detecting the text from image, this project uses Tesseract based OCR Engine to recognize the text in the image. After recognizing the text from the image, the text will be converted to some speech output to the blind person using python package called pytts version 3. The speech converted text will be given as an output to blind person with the aid of speaker. Finally here comes the concept of Modified EAST where the already built in model is extended to increase the accuracy of the prototype or model.","PeriodicalId":345370,"journal":{"name":"2022 International Conference on Advanced Computing Technologies and Applications (ICACTA)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126893981","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 : 2022-03-04DOI: 10.1109/ICACTA54488.2022.9753141
R. G, Kiran Jangid, Nagarathna Naik, R. Francis
Human bone fractures are becoming more common as a result of elevated pressure and perhaps bone cancer. As a result, a thorough examination and diagnosis are required. X-rays and CT scan images are commonly used to diagnose anomalies in human hard tissues such as bone, dental enamel, dentin, and cementum. In this research paper, image processing techniques were used to reliably diagnose one of the human hard tissue abnormalities, namely bone abnormalities caused by fractures. Preprocessing, segmentation, edge detection, and feature extraction techniques are used to process the obtained X-ray and/or CT scan images. These images are used to contrast the impacts, outcomes, and precision of various edge detection operations. The loading, image processing, and user interface were all programmed in MATLAB. The results reveal that the bone fracture detection system works well, with just minor limitations and high accuracy.
{"title":"Detection of Abnormality in Human Hard Tissue using Edge Detection Operators","authors":"R. G, Kiran Jangid, Nagarathna Naik, R. Francis","doi":"10.1109/ICACTA54488.2022.9753141","DOIUrl":"https://doi.org/10.1109/ICACTA54488.2022.9753141","url":null,"abstract":"Human bone fractures are becoming more common as a result of elevated pressure and perhaps bone cancer. As a result, a thorough examination and diagnosis are required. X-rays and CT scan images are commonly used to diagnose anomalies in human hard tissues such as bone, dental enamel, dentin, and cementum. In this research paper, image processing techniques were used to reliably diagnose one of the human hard tissue abnormalities, namely bone abnormalities caused by fractures. Preprocessing, segmentation, edge detection, and feature extraction techniques are used to process the obtained X-ray and/or CT scan images. These images are used to contrast the impacts, outcomes, and precision of various edge detection operations. The loading, image processing, and user interface were all programmed in MATLAB. The results reveal that the bone fracture detection system works well, with just minor limitations and high accuracy.","PeriodicalId":345370,"journal":{"name":"2022 International Conference on Advanced Computing Technologies and Applications (ICACTA)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116874405","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 : 2022-03-04DOI: 10.1109/ICACTA54488.2022.9753159
L. K., Raja Sathasivam, S. P., D. R, P. K R, M. Sj, Gunasekar M, M. Sd
In recent decades, Internet of Things devices have grown in popularity across a wide range of industries and uses. As a result, vast amounts of data are created and generated. Despite the fact that the collected data contains a great quantity of crucial information, most current and general pattern mining algorithms simply analyses a single item and exact information to identify the needed data. Because the amount of data gathered is so huge, it is vital to identify meaningful and updated data in a short period of time. In this paper, we use a multi-objective evolutionary framework to effectively mine the interesting Potential High Utility Itemset (PHUI) in a limited period, with the majority of items being PHUI utility and uncertainty. In an unpredictable context, the benefits of the proposed model (dubbed MOPSO-PHUIM) can identify lucrative PHUIs without pre-defined threshold values (i.e., minimal utility and minimum uncertainty). To illustrate the efficiency of the created MOPSO-PHUIM, two encoding techniques are also taken into account. Using the developed MOPSO-PHUIM model for decision-making, a set of non-dominated PHUIs may be found in a short amount of time. Studies are then carried out to demonstrate the utility and performance of the built MOPSO-PHUIM model in terms of velocity, hyper volume, and the different result discovered when compared to generic techniques.
{"title":"Discovery of Potential High Utility Itemset from Uncertain Database using Multi Objective Particle Swarm Optimization Algorithm","authors":"L. K., Raja Sathasivam, S. P., D. R, P. K R, M. Sj, Gunasekar M, M. Sd","doi":"10.1109/ICACTA54488.2022.9753159","DOIUrl":"https://doi.org/10.1109/ICACTA54488.2022.9753159","url":null,"abstract":"In recent decades, Internet of Things devices have grown in popularity across a wide range of industries and uses. As a result, vast amounts of data are created and generated. Despite the fact that the collected data contains a great quantity of crucial information, most current and general pattern mining algorithms simply analyses a single item and exact information to identify the needed data. Because the amount of data gathered is so huge, it is vital to identify meaningful and updated data in a short period of time. In this paper, we use a multi-objective evolutionary framework to effectively mine the interesting Potential High Utility Itemset (PHUI) in a limited period, with the majority of items being PHUI utility and uncertainty. In an unpredictable context, the benefits of the proposed model (dubbed MOPSO-PHUIM) can identify lucrative PHUIs without pre-defined threshold values (i.e., minimal utility and minimum uncertainty). To illustrate the efficiency of the created MOPSO-PHUIM, two encoding techniques are also taken into account. Using the developed MOPSO-PHUIM model for decision-making, a set of non-dominated PHUIs may be found in a short amount of time. Studies are then carried out to demonstrate the utility and performance of the built MOPSO-PHUIM model in terms of velocity, hyper volume, and the different result discovered when compared to generic techniques.","PeriodicalId":345370,"journal":{"name":"2022 International Conference on Advanced Computing Technologies and Applications (ICACTA)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116039999","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 : 2022-03-04DOI: 10.1109/ICACTA54488.2022.9752925
Kanakaprabha. S., A. P., S. R
Parkinson disease is a neural disease. It prompts shaking of the hands, difficulty to walk, balance with coordination. No medical treatment is available in the high-level stage. X-ray, CT scan and blood tests report are not sufficiently results available in the early stage. About two trillion community are alive in Parkinson's disease (PD) in the U.K., which is the highest number of people affected. are pinpointed to have different sclerosis, solid dystrophy and Lou Gehrig's illness. This is relied upon to ascend to 1.5 million by 2040. Around the 75,000 Americans are diagnosis PD with every year. It is very important to predict Parkinson's disease early so that important treatment can be done. The purpose of the proposed work is to detect Parkinson disease, where we aimed to identify disease in early prediction using clinical imaging that incorporate the use of Machine learning techniques. A comparative analysis done with various Machine Learning classifier algorithms like XGBoost, Random Forest, KNN, SVM are the best model is proposed which is used to make predictions and find accuracy. We are observed that Random Forest provides better performance with an accuracy 90%. Automatic detection with more accuracy will make screening for Parkinson disease as cost effective and efficient manner facilitates to use appropriate and fast solutions.
{"title":"Parkinson Disease Detection Using Various Machine Learning Algorithms","authors":"Kanakaprabha. S., A. P., S. R","doi":"10.1109/ICACTA54488.2022.9752925","DOIUrl":"https://doi.org/10.1109/ICACTA54488.2022.9752925","url":null,"abstract":"Parkinson disease is a neural disease. It prompts shaking of the hands, difficulty to walk, balance with coordination. No medical treatment is available in the high-level stage. X-ray, CT scan and blood tests report are not sufficiently results available in the early stage. About two trillion community are alive in Parkinson's disease (PD) in the U.K., which is the highest number of people affected. are pinpointed to have different sclerosis, solid dystrophy and Lou Gehrig's illness. This is relied upon to ascend to 1.5 million by 2040. Around the 75,000 Americans are diagnosis PD with every year. It is very important to predict Parkinson's disease early so that important treatment can be done. The purpose of the proposed work is to detect Parkinson disease, where we aimed to identify disease in early prediction using clinical imaging that incorporate the use of Machine learning techniques. A comparative analysis done with various Machine Learning classifier algorithms like XGBoost, Random Forest, KNN, SVM are the best model is proposed which is used to make predictions and find accuracy. We are observed that Random Forest provides better performance with an accuracy 90%. Automatic detection with more accuracy will make screening for Parkinson disease as cost effective and efficient manner facilitates to use appropriate and fast solutions.","PeriodicalId":345370,"journal":{"name":"2022 International Conference on Advanced Computing Technologies and Applications (ICACTA)","volume":"140 11-12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129409597","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 : 2022-03-04DOI: 10.1109/ICACTA54488.2022.9753563
S. R, Gokul Prasanth M, B. R, Abhishek J, Ajay D
IoT is an emerging technology which will change our future by transforming real world applications into a virtual world. IoT brings a lot of benefits to mankind by providing smart services that can be used anytime anywhere. Nowadays usage of IoT in designing the devices has been increased tremendously but many applications using IoT needs lot of sensors to spread over a wide area and connecting billions of IoT devices is also a great challenge. The range of communication is a major drawback in Wi-Fi and Bluetooth based IoT devices. This drawback can be controlled by using a technology with long range wireless communication with low power consumption. LPWAN is a wireless technology that can be used to communicate over long distance with low power consumption. LPWAN technology plays significant and crucial role in making this possible by increasing the connectivity range at lower cost. This paper explains usage of various LPWAN technologies in real time and explains the technology which will fit best for numerous IoT applications.
{"title":"LPWAN for IoT","authors":"S. R, Gokul Prasanth M, B. R, Abhishek J, Ajay D","doi":"10.1109/ICACTA54488.2022.9753563","DOIUrl":"https://doi.org/10.1109/ICACTA54488.2022.9753563","url":null,"abstract":"IoT is an emerging technology which will change our future by transforming real world applications into a virtual world. IoT brings a lot of benefits to mankind by providing smart services that can be used anytime anywhere. Nowadays usage of IoT in designing the devices has been increased tremendously but many applications using IoT needs lot of sensors to spread over a wide area and connecting billions of IoT devices is also a great challenge. The range of communication is a major drawback in Wi-Fi and Bluetooth based IoT devices. This drawback can be controlled by using a technology with long range wireless communication with low power consumption. LPWAN is a wireless technology that can be used to communicate over long distance with low power consumption. LPWAN technology plays significant and crucial role in making this possible by increasing the connectivity range at lower cost. This paper explains usage of various LPWAN technologies in real time and explains the technology which will fit best for numerous IoT applications.","PeriodicalId":345370,"journal":{"name":"2022 International Conference on Advanced Computing Technologies and Applications (ICACTA)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130111944","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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