This project proposes a pipeline monitoring system that uses a time based Artificial Neural Network method i.e., long short-term memory (LSTM) to predict the pressure measurements and to send an alert mail to the respective higher authorities, so as to take necessary steps in order to prevent a catastrophic situation. The network is trained and tested using the sensor data that is obtained using the experimental setup of a pipeline with and without cracks. The LSTM is trained using the data from the pressure sensor which was collected under normal working conditions of the system. Adding to this, the system is automated using IoT. The platform for IoT is the ThingSpeak. It is to this cloud that we connect our sensor, the ANN system and the system of the higher authority. The data is exchanged and collected here using NodeMCU as the Wi-Fi module. Finally, when trouble arises the IoT sends an alert alarm and mail to the higher authorities.
{"title":"Forecasting Crack Formation Using Artificial Neural Network and Internet of Things","authors":"Nikhil Binoy C, Sukanya G, Anjali Shah, Diljith R, Theiaswikrishna L, Thoufeek M","doi":"10.1109/ICMSS53060.2021.9673595","DOIUrl":"https://doi.org/10.1109/ICMSS53060.2021.9673595","url":null,"abstract":"This project proposes a pipeline monitoring system that uses a time based Artificial Neural Network method i.e., long short-term memory (LSTM) to predict the pressure measurements and to send an alert mail to the respective higher authorities, so as to take necessary steps in order to prevent a catastrophic situation. The network is trained and tested using the sensor data that is obtained using the experimental setup of a pipeline with and without cracks. The LSTM is trained using the data from the pressure sensor which was collected under normal working conditions of the system. Adding to this, the system is automated using IoT. The platform for IoT is the ThingSpeak. It is to this cloud that we connect our sensor, the ANN system and the system of the higher authority. The data is exchanged and collected here using NodeMCU as the Wi-Fi module. Finally, when trouble arises the IoT sends an alert alarm and mail to the higher authorities.","PeriodicalId":274597,"journal":{"name":"2021 Fourth International Conference on Microelectronics, Signals & Systems (ICMSS)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131091344","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 : 2021-11-18DOI: 10.1109/ICMSS53060.2021.9673598
Ganga V Saji, Thasneem Vazim, S. Sundar
Lung cancer is the most common cancer that is fatal if treated late. If the disease could be found at an earlier stage before it's severity, it is more likely to be treated and diagnosed successfully. The presence of lung cancers can be detected from computed tomography and chest x-ray images by locating enlarged lymph nodes. The spread of disease around these nodes can be identified by characterizing size, shape and location; thus, assist doctors in detecting lung cancers at early stages. In many cases, the lung cancer diagnosis is based on doctors' experience, which might lead to misdiagnosis and cause medical issues in patients. There have been numerous strategies and methods for predicting level of cancer malignancy using deep learning and machine learning methods. In this paper, we have studied different Deep Learning methods used for the detection, classification and prediction of cancerous lung nodules and the identification of their malignancy levels. We have analyzed the advantages and limitations of each method along with various datasets used and they are summarized.
{"title":"Deep Learning Methods for Lung Cancer Detection, Classification and Prediction - A Review","authors":"Ganga V Saji, Thasneem Vazim, S. Sundar","doi":"10.1109/ICMSS53060.2021.9673598","DOIUrl":"https://doi.org/10.1109/ICMSS53060.2021.9673598","url":null,"abstract":"Lung cancer is the most common cancer that is fatal if treated late. If the disease could be found at an earlier stage before it's severity, it is more likely to be treated and diagnosed successfully. The presence of lung cancers can be detected from computed tomography and chest x-ray images by locating enlarged lymph nodes. The spread of disease around these nodes can be identified by characterizing size, shape and location; thus, assist doctors in detecting lung cancers at early stages. In many cases, the lung cancer diagnosis is based on doctors' experience, which might lead to misdiagnosis and cause medical issues in patients. There have been numerous strategies and methods for predicting level of cancer malignancy using deep learning and machine learning methods. In this paper, we have studied different Deep Learning methods used for the detection, classification and prediction of cancerous lung nodules and the identification of their malignancy levels. We have analyzed the advantages and limitations of each method along with various datasets used and they are summarized.","PeriodicalId":274597,"journal":{"name":"2021 Fourth International Conference on Microelectronics, Signals & Systems (ICMSS)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126645783","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 : 2021-11-18DOI: 10.1109/ICMSS53060.2021.9673647
A. A., A. B.
The field of medical visualization of organs are needed for accurate diagnosis and treatment of any disease. Brain tumour diagnosis and surgery also requires accurate3D visualization of the brain. Detection and 3D visualization of the brain and possibly tumours from MRI area computationally time consuming and error-prone task. The proposed system presents a 3D reconstruction model of the brain which greatly helps the radiologist to effectively diagnose and analyze brain. If the subject is in motion state or there occurs a movement while taking the scan, there might be distortions in the output scan image. In order to avoid such circumstances, it is better to reconstruct the 2D image into a 3D space as it is more effective. Thus, the quality of the scan image is much better. From such reconstructed images, the diseases associated with the foetus can be identified. By the help of more features the proposed method can be used for the diagnosis of diseases in the organs. By training the proposed system with more organs and features it can be used for the detection of various diseases from the reconstructed images. More than 200 images were used for the training and around 150 images were used for testing. Here the 2D image slices are undergone image preprocessing, image registration, and then reconstruction. For the image registration, the method used is discrete wavelet transform which is more suitable for medical imaging. The proposed system is applicable for the clinical practices.
{"title":"Reconstruction of Brain MRI Images and Detection of Tumour","authors":"A. A., A. B.","doi":"10.1109/ICMSS53060.2021.9673647","DOIUrl":"https://doi.org/10.1109/ICMSS53060.2021.9673647","url":null,"abstract":"The field of medical visualization of organs are needed for accurate diagnosis and treatment of any disease. Brain tumour diagnosis and surgery also requires accurate3D visualization of the brain. Detection and 3D visualization of the brain and possibly tumours from MRI area computationally time consuming and error-prone task. The proposed system presents a 3D reconstruction model of the brain which greatly helps the radiologist to effectively diagnose and analyze brain. If the subject is in motion state or there occurs a movement while taking the scan, there might be distortions in the output scan image. In order to avoid such circumstances, it is better to reconstruct the 2D image into a 3D space as it is more effective. Thus, the quality of the scan image is much better. From such reconstructed images, the diseases associated with the foetus can be identified. By the help of more features the proposed method can be used for the diagnosis of diseases in the organs. By training the proposed system with more organs and features it can be used for the detection of various diseases from the reconstructed images. More than 200 images were used for the training and around 150 images were used for testing. Here the 2D image slices are undergone image preprocessing, image registration, and then reconstruction. For the image registration, the method used is discrete wavelet transform which is more suitable for medical imaging. The proposed system is applicable for the clinical practices.","PeriodicalId":274597,"journal":{"name":"2021 Fourth International Conference on Microelectronics, Signals & Systems (ICMSS)","volume":"127 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121617262","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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