Pub Date : 2022-12-08DOI: 10.1109/IBSSC56953.2022.10037394
Samiksha Soni, N. Londhe, RITESH RAJ, Rajendra S. Sonawane
Outstanding performance of the transformer-based model in the field of natural language processing has piqued the interest of researchers in investigating these techniques for computer vision. And the most popular UNet model is considered a major player in the field of image segmentation. Thus, in this paper, we have proposed the transformer-based UNet model for the complex task of psoriasis lesion segmentation from raw color images. One of the major challenges for our segmentation task is the scarcity of datasets and to overcome this challenge we have exploited the EfficientNetB1 transfer learned model as a backbone for our segmentation model. The proposed model is evaluated for the 70:30 hold-out data division technique and the segmentation performance is evaluated using the Dice Score (DS) and Jaccard Index (JI). The value of DS and JI obtained for the intended task are 0.9571 and 0.9201 respectively with the proposed model. Comparative analysis with different derivatives of the UNet model and state-of-the-art literary work shows the better performance of our proposed model.
基于变压器的模型在自然语言处理领域的杰出表现引起了研究人员对这些技术在计算机视觉中的研究兴趣。其中最流行的UNet模型被认为是图像分割领域的主要参与者。因此，在本文中，我们提出了基于变换的UNet模型，用于从原始彩色图像中分割银屑病病变的复杂任务。我们分割任务的主要挑战之一是数据集的稀缺性，为了克服这一挑战，我们利用了EfficientNetB1迁移学习模型作为分割模型的主干。采用70:30 hold- hold数据分割技术对该模型进行了评价，并使用Dice Score (DS)和Jaccard Index (JI)对该模型的分割性能进行了评价。使用所提出的模型得到的预期任务的DS和JI分别为0.9571和0.9201。与UNet模型的不同衍生品和最新文学作品的比较分析表明，我们提出的模型具有更好的性能。
{"title":"TransUnet for psoriasis lesion segmentation","authors":"Samiksha Soni, N. Londhe, RITESH RAJ, Rajendra S. Sonawane","doi":"10.1109/IBSSC56953.2022.10037394","DOIUrl":"https://doi.org/10.1109/IBSSC56953.2022.10037394","url":null,"abstract":"Outstanding performance of the transformer-based model in the field of natural language processing has piqued the interest of researchers in investigating these techniques for computer vision. And the most popular UNet model is considered a major player in the field of image segmentation. Thus, in this paper, we have proposed the transformer-based UNet model for the complex task of psoriasis lesion segmentation from raw color images. One of the major challenges for our segmentation task is the scarcity of datasets and to overcome this challenge we have exploited the EfficientNetB1 transfer learned model as a backbone for our segmentation model. The proposed model is evaluated for the 70:30 hold-out data division technique and the segmentation performance is evaluated using the Dice Score (DS) and Jaccard Index (JI). The value of DS and JI obtained for the intended task are 0.9571 and 0.9201 respectively with the proposed model. Comparative analysis with different derivatives of the UNet model and state-of-the-art literary work shows the better performance of our proposed model.","PeriodicalId":426897,"journal":{"name":"2022 IEEE Bombay Section Signature Conference (IBSSC)","volume":"108 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126566497","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 : 2022-12-08DOI: 10.1109/IBSSC56953.2022.10037520
Sasidharan Vairavasamy, Noel Innocent J, Hamin Mj, S. Ahmed, Seenu N, Ramya MM Dean
This paper focuses on developing a virtually controlled robot by integrating Virtual Reality (VR) and Robot Operating System (ROS). Robots can be used in certain environments where humans cannot be physically present to undertake a task. To increase the safety of humans, controlling a robot virtually is one of the best solutions. This paper shows how the VR-controlled manipulator will help in different fields like hazardous environments, underwater research, and the medical field for surgical operations. Unity 3D is used to develop the virtual environment, and ROS is used for communication with the physical robot. A Virtual environment would be developed where an exact robot (URDF) model can be designed. The interaction with the virtual environment is done with the help of VR headsets and controllers. ROS acts as the communication bridge between virtual and physical robots. A prototype has been developed to be controlled through virtual controllers that can interact through ROS.
{"title":"Simulation And Real Time Of VR Controlled Robotic Manipulator Using ROS","authors":"Sasidharan Vairavasamy, Noel Innocent J, Hamin Mj, S. Ahmed, Seenu N, Ramya MM Dean","doi":"10.1109/IBSSC56953.2022.10037520","DOIUrl":"https://doi.org/10.1109/IBSSC56953.2022.10037520","url":null,"abstract":"This paper focuses on developing a virtually controlled robot by integrating Virtual Reality (VR) and Robot Operating System (ROS). Robots can be used in certain environments where humans cannot be physically present to undertake a task. To increase the safety of humans, controlling a robot virtually is one of the best solutions. This paper shows how the VR-controlled manipulator will help in different fields like hazardous environments, underwater research, and the medical field for surgical operations. Unity 3D is used to develop the virtual environment, and ROS is used for communication with the physical robot. A Virtual environment would be developed where an exact robot (URDF) model can be designed. The interaction with the virtual environment is done with the help of VR headsets and controllers. ROS acts as the communication bridge between virtual and physical robots. A prototype has been developed to be controlled through virtual controllers that can interact through ROS.","PeriodicalId":426897,"journal":{"name":"2022 IEEE Bombay Section Signature Conference (IBSSC)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121750444","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 : 2022-12-08DOI: 10.1109/IBSSC56953.2022.10037515
D. Pawade, Avani M. Sakhapara, Irfan A. Siddavatam, Mishtee Gandhi, Akshata Ingalahalli, A. Dalvi
During the water crisis, water supply through tanker is a common scenario in India. Most of the time, Government hires private tankers on contract basis to distribute the water and pays them based on the number of trips. It is observed that the tanker contractors show fake trips and fraudulently charge for it. To avoid so, roster is maintained which has entry for each trip. Yet this is not sufficient to control the malpractices. It might be possible that, from the source, the entry for the tanker trip is made in roster but there is no guarantee that tanker will reach to the specified destination. In between the tanker driver can sell that water to someone else. Sometimes the tanker reaches to the desired destination but in between half of the water is sold. Thus, the intended beneficiaries don't get enough water. Many times, muddy and contaminated water is being distributed which may lead to many health issues. Thus, there is a need of some automated mechanism using which Government officials can keep track of tanker movement, check the quantity and quality of the water. This motivated us to come up with IoT based solution which comprise of various sensors to collect the data such as GPS location of tanker, level of water, pH and turbidity value of water. This data is stored over a cloud and made available through a web application. This way the Government officials can get consolidated report as well as alerts if any malpractice is carried out.
{"title":"Fraud Detection and Monitoring of Water Tankers using IoT","authors":"D. Pawade, Avani M. Sakhapara, Irfan A. Siddavatam, Mishtee Gandhi, Akshata Ingalahalli, A. Dalvi","doi":"10.1109/IBSSC56953.2022.10037515","DOIUrl":"https://doi.org/10.1109/IBSSC56953.2022.10037515","url":null,"abstract":"During the water crisis, water supply through tanker is a common scenario in India. Most of the time, Government hires private tankers on contract basis to distribute the water and pays them based on the number of trips. It is observed that the tanker contractors show fake trips and fraudulently charge for it. To avoid so, roster is maintained which has entry for each trip. Yet this is not sufficient to control the malpractices. It might be possible that, from the source, the entry for the tanker trip is made in roster but there is no guarantee that tanker will reach to the specified destination. In between the tanker driver can sell that water to someone else. Sometimes the tanker reaches to the desired destination but in between half of the water is sold. Thus, the intended beneficiaries don't get enough water. Many times, muddy and contaminated water is being distributed which may lead to many health issues. Thus, there is a need of some automated mechanism using which Government officials can keep track of tanker movement, check the quantity and quality of the water. This motivated us to come up with IoT based solution which comprise of various sensors to collect the data such as GPS location of tanker, level of water, pH and turbidity value of water. This data is stored over a cloud and made available through a web application. This way the Government officials can get consolidated report as well as alerts if any malpractice is carried out.","PeriodicalId":426897,"journal":{"name":"2022 IEEE Bombay Section Signature Conference (IBSSC)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131432827","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 : 2022-12-08DOI: 10.1109/IBSSC56953.2022.10037516
A. Thakare, Pranjali G. Gulhane, S. Chaudhari, H. Baradkar
The Novel Coronavirus Illness 2019 (COVID-19) was found in Wuhan, Hubei, China, in December 2019 and has since spread globally. When the patient's corona sickness worsened, his life was in danger. Coronavirus assaults the lungs. Diagnostic kits today only search for viral illnesses, which deceives doctors. All patients receiving the same treatment harm patients with less infection. This publication describes non-invasive treatment for infected people. Dissecting chest X-ray pictures to examine the coronavirus helps investigate and predict COVID-19 patients. We offer a hybrid method for detecting Covid. CNN and SVM identify Covid. Because X-ray pictures are inconsistent, CNN is used for feature extraction. To construct a training dataset before CNN, we used data augmentation. Data augmentation increases the training dataset's amount and quality. SVM is used for classification since it tolerates feature differences. The main goal is to help clinical doctors determine the severity of a chest infection so they can administer life-saving treatment. Deep learning and machine learning-based techniques will determine the degree of chest infection and lead to optimal medication, avoiding expensive treatment for all patients.
{"title":"Performance Analysis of Machine Learning Algorithms for COVID-19 Detection","authors":"A. Thakare, Pranjali G. Gulhane, S. Chaudhari, H. Baradkar","doi":"10.1109/IBSSC56953.2022.10037516","DOIUrl":"https://doi.org/10.1109/IBSSC56953.2022.10037516","url":null,"abstract":"The Novel Coronavirus Illness 2019 (COVID-19) was found in Wuhan, Hubei, China, in December 2019 and has since spread globally. When the patient's corona sickness worsened, his life was in danger. Coronavirus assaults the lungs. Diagnostic kits today only search for viral illnesses, which deceives doctors. All patients receiving the same treatment harm patients with less infection. This publication describes non-invasive treatment for infected people. Dissecting chest X-ray pictures to examine the coronavirus helps investigate and predict COVID-19 patients. We offer a hybrid method for detecting Covid. CNN and SVM identify Covid. Because X-ray pictures are inconsistent, CNN is used for feature extraction. To construct a training dataset before CNN, we used data augmentation. Data augmentation increases the training dataset's amount and quality. SVM is used for classification since it tolerates feature differences. The main goal is to help clinical doctors determine the severity of a chest infection so they can administer life-saving treatment. Deep learning and machine learning-based techniques will determine the degree of chest infection and lead to optimal medication, avoiding expensive treatment for all patients.","PeriodicalId":426897,"journal":{"name":"2022 IEEE Bombay Section Signature Conference (IBSSC)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125519517","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 : 2022-12-08DOI: 10.1109/IBSSC56953.2022.10037262
S. Umamaheswari, D. Sangeetha, S. Sriram, J. Nandhinipriva
Cardiac Arrhythmia is a heart disease that corresponds to abnormal rhythm of heart. It means that the heart is either beating too quickly, too slowly, or sporadically. Arrhythmia is recognized and categorized effectively so as to improve the living conditions of the patients. The Electro Cardiogram (ECG) is a tool for recording electrical activity and determining the electrical impulses in the heart. There are four main classes of arrhythmia which occur due to abnormal heartbeat which are being classified. The main objective of this proposed work is to provide better performance in predicting arrhythmia since even a small error can become dangerous to a person's life. The existing methods uses CNN as the feature extraction model which delays the time of prediction. Here, a novel feature extraction method is introduced based on 1D-Convolutional Neural Networks using the Eigen Vectors functionality. This feature extraction model proves to outperform the existing works in accurately classifying the different classes of arrhythmia. Finally, the ANN model is trained using the K-fold Cross Validation method to achieve this performance and is compared with an ensemble model containing a SVM, ANN and a Decision Tree.
{"title":"Major and Sub-Class Classification of Arrhythmia using Eigen Vectors in ConvNet","authors":"S. Umamaheswari, D. Sangeetha, S. Sriram, J. Nandhinipriva","doi":"10.1109/IBSSC56953.2022.10037262","DOIUrl":"https://doi.org/10.1109/IBSSC56953.2022.10037262","url":null,"abstract":"Cardiac Arrhythmia is a heart disease that corresponds to abnormal rhythm of heart. It means that the heart is either beating too quickly, too slowly, or sporadically. Arrhythmia is recognized and categorized effectively so as to improve the living conditions of the patients. The Electro Cardiogram (ECG) is a tool for recording electrical activity and determining the electrical impulses in the heart. There are four main classes of arrhythmia which occur due to abnormal heartbeat which are being classified. The main objective of this proposed work is to provide better performance in predicting arrhythmia since even a small error can become dangerous to a person's life. The existing methods uses CNN as the feature extraction model which delays the time of prediction. Here, a novel feature extraction method is introduced based on 1D-Convolutional Neural Networks using the Eigen Vectors functionality. This feature extraction model proves to outperform the existing works in accurately classifying the different classes of arrhythmia. Finally, the ANN model is trained using the K-fold Cross Validation method to achieve this performance and is compared with an ensemble model containing a SVM, ANN and a Decision Tree.","PeriodicalId":426897,"journal":{"name":"2022 IEEE Bombay Section Signature Conference (IBSSC)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128345875","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 : 2022-12-08DOI: 10.1109/IBSSC56953.2022.10037478
R. Adsul, Vedant Misra, Saumya Pailwan
Lung cancer is one of the most common types of cancer, which is the main cause of death in humans. In order to be cured, cancer must be diagnosed at an early stage. Lung cancer, also known as lung carcinoma, is a malignant tumor that forms in the lungs and is characterized by unchecked cell proliferation. Non-small cell lung cancer (NSCLC) and small cell lung cancer (SCLC) are the two main subtypes of lung cancer. This research examines lung cancer symptoms and risk factors and uses Machine Learning algorithms to identify lung cancer patients from healthy people. These algorithms also distinguish pathological non-small cell lung carcinoma's three types. During pre-diagnosis, this classification helps choose the next step. The optimal data mining strategy is chosen by comparing its results. For the two datasets, SVM and XGBoost methods perform best.
{"title":"A Comparative Study on Data Mining Classifiers to Predict Lung Cancer and Types of NSCLC","authors":"R. Adsul, Vedant Misra, Saumya Pailwan","doi":"10.1109/IBSSC56953.2022.10037478","DOIUrl":"https://doi.org/10.1109/IBSSC56953.2022.10037478","url":null,"abstract":"Lung cancer is one of the most common types of cancer, which is the main cause of death in humans. In order to be cured, cancer must be diagnosed at an early stage. Lung cancer, also known as lung carcinoma, is a malignant tumor that forms in the lungs and is characterized by unchecked cell proliferation. Non-small cell lung cancer (NSCLC) and small cell lung cancer (SCLC) are the two main subtypes of lung cancer. This research examines lung cancer symptoms and risk factors and uses Machine Learning algorithms to identify lung cancer patients from healthy people. These algorithms also distinguish pathological non-small cell lung carcinoma's three types. During pre-diagnosis, this classification helps choose the next step. The optimal data mining strategy is chosen by comparing its results. For the two datasets, SVM and XGBoost methods perform best.","PeriodicalId":426897,"journal":{"name":"2022 IEEE Bombay Section Signature Conference (IBSSC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134211552","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 : 2022-12-08DOI: 10.1109/IBSSC56953.2022.10037363
Manan Doshi, Harsh Shah, Neha Katre
Research for autonomous cars has now been close to a decade and still it is not possible to employ these cars everywhere around the world, for one major reason being clear lane line detection. However, there is constant discovery to improve the method of lane detection, especially in real-time. For lane detection, various computer-vision techniques and deep learning models have been devised, but for practical use it is necessary to find an efficient solution in real-time. Our technique is based on the real-time efficient detection of straight lanes using a canny edge detector followed by finding a region of interest and Hough transformation. This method takes video as an input and gives outputs in the form of images with slopes and marked lines of lanes. For long highways with straight lanes, this algorithm can prove to be extremely efficient for detection, which can be easily employed in real-time using camera sensors that provide a video feed. Furthermore, there is no requirement for training the algorithm. Hence, this system works on most of the scenarios without any prior data training.
{"title":"ROI based real time straight lane line detection using Canny Edge Detector and masked bitwise operator","authors":"Manan Doshi, Harsh Shah, Neha Katre","doi":"10.1109/IBSSC56953.2022.10037363","DOIUrl":"https://doi.org/10.1109/IBSSC56953.2022.10037363","url":null,"abstract":"Research for autonomous cars has now been close to a decade and still it is not possible to employ these cars everywhere around the world, for one major reason being clear lane line detection. However, there is constant discovery to improve the method of lane detection, especially in real-time. For lane detection, various computer-vision techniques and deep learning models have been devised, but for practical use it is necessary to find an efficient solution in real-time. Our technique is based on the real-time efficient detection of straight lanes using a canny edge detector followed by finding a region of interest and Hough transformation. This method takes video as an input and gives outputs in the form of images with slopes and marked lines of lanes. For long highways with straight lanes, this algorithm can prove to be extremely efficient for detection, which can be easily employed in real-time using camera sensors that provide a video feed. Furthermore, there is no requirement for training the algorithm. Hence, this system works on most of the scenarios without any prior data training.","PeriodicalId":426897,"journal":{"name":"2022 IEEE Bombay Section Signature Conference (IBSSC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133809953","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 : 2022-12-08DOI: 10.1109/IBSSC56953.2022.10037417
Mihir Nikam, Ameya Ranade, R. Patel, Prachi Dalvi, Aarti M. Karande
Plant identification has a wide array of applications in the fields of agronomy and the discovery of natural and medicinal products. This research aims to explore various deep learning techniques like InceptionV3, Xpection, and ResNet to identify plants. Highly accurate machine learning models generally lack explainability and interpretability. Neural networks are usually opaque systems and thus a direct understanding of the interpretations becomes necessary. We aim to remove this ambiguity of how the model reaches its conclusion by introducing Explainable AI (XAI) techniques. Explainability aims to break such barriers by diminishing the lack of transparency in Artificial Intelligence and Machine Learning models, thus taking a step toward making AI reliable. In this paper, Convolutional Neural Network has been used to identify Vietnamese medicinal plant images based on the characteristics of the leaves, stems and other parts of the plant. Upon identification, our paper also elaborates on how each model predicts which part of the image helps the CNN model to make a prediction by integrating Explainable AI (XAI) using the Lime package. Through this research, we generated images using LIME package which highlight pixels that determine the result of our plant identification process.
{"title":"Explainable Approach for Species Identification using LIME","authors":"Mihir Nikam, Ameya Ranade, R. Patel, Prachi Dalvi, Aarti M. Karande","doi":"10.1109/IBSSC56953.2022.10037417","DOIUrl":"https://doi.org/10.1109/IBSSC56953.2022.10037417","url":null,"abstract":"Plant identification has a wide array of applications in the fields of agronomy and the discovery of natural and medicinal products. This research aims to explore various deep learning techniques like InceptionV3, Xpection, and ResNet to identify plants. Highly accurate machine learning models generally lack explainability and interpretability. Neural networks are usually opaque systems and thus a direct understanding of the interpretations becomes necessary. We aim to remove this ambiguity of how the model reaches its conclusion by introducing Explainable AI (XAI) techniques. Explainability aims to break such barriers by diminishing the lack of transparency in Artificial Intelligence and Machine Learning models, thus taking a step toward making AI reliable. In this paper, Convolutional Neural Network has been used to identify Vietnamese medicinal plant images based on the characteristics of the leaves, stems and other parts of the plant. Upon identification, our paper also elaborates on how each model predicts which part of the image helps the CNN model to make a prediction by integrating Explainable AI (XAI) using the Lime package. Through this research, we generated images using LIME package which highlight pixels that determine the result of our plant identification process.","PeriodicalId":426897,"journal":{"name":"2022 IEEE Bombay Section Signature Conference (IBSSC)","volume":"63 5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125944090","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 : 2022-12-08DOI: 10.1109/IBSSC56953.2022.10037329
Nicole D'Souza, K. Shah, Pranav Singh
Diabetes is a serious illness. Predicting this disease in a timely manner is necessary to avoid severe side effects. Current medical practise dictates that a patient undergoes a battery of tests in order to obtain the information necessary for diagnosis, after which treatment is administered based on the diagnosis. However, in many cases, the early stages go undetected, and it is quite difficult for physicians to diagnose due to the interdependence of numerous factors. A single parameter is commonly inadequate for the accurate diagnosis of diabetes and may lead to erroneous decisions. To accurately forecast diabetes at an early stage, multiple criteria must be combined. This study proposes the development of an early diabetes detection model. The model will not only be more accurate than humans, but it will also reduce the workload of medical professionals.
{"title":"Diabetes Detection Using Machine Learning Algorithms","authors":"Nicole D'Souza, K. Shah, Pranav Singh","doi":"10.1109/IBSSC56953.2022.10037329","DOIUrl":"https://doi.org/10.1109/IBSSC56953.2022.10037329","url":null,"abstract":"Diabetes is a serious illness. Predicting this disease in a timely manner is necessary to avoid severe side effects. Current medical practise dictates that a patient undergoes a battery of tests in order to obtain the information necessary for diagnosis, after which treatment is administered based on the diagnosis. However, in many cases, the early stages go undetected, and it is quite difficult for physicians to diagnose due to the interdependence of numerous factors. A single parameter is commonly inadequate for the accurate diagnosis of diabetes and may lead to erroneous decisions. To accurately forecast diabetes at an early stage, multiple criteria must be combined. This study proposes the development of an early diabetes detection model. The model will not only be more accurate than humans, but it will also reduce the workload of medical professionals.","PeriodicalId":426897,"journal":{"name":"2022 IEEE Bombay Section Signature Conference (IBSSC)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127974561","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 : 2022-12-08DOI: 10.1109/IBSSC56953.2022.10037388
Sandeep Ushkewar, Gaurav B. Patil, Vishal Moyal
Electrical vehicles require too much time to recharge their batteries, so to accommodate our busy schedule the conventional method of electric vehicle battery charging is replaced by “Dynamic Charging” The work presented involved the expansion of a novel type of wireless power transmission device to ensure high-efficiency battery charging stations for electric cars. A research project will look at the efficiency of traditional battery charging systems. In this paper, the finite element analysis is done by ANSYS simulation software. The static and dynamic modeling of the suggested wireless power transfer technique is the study's most important finding. A new model is created and describedthat takes into account both static and dynamic issues. This article will aid in the growth of future electric vehicle infrastructure.
{"title":"Wireless Charging in a Dynamic Environment for Electric Vehicles","authors":"Sandeep Ushkewar, Gaurav B. Patil, Vishal Moyal","doi":"10.1109/IBSSC56953.2022.10037388","DOIUrl":"https://doi.org/10.1109/IBSSC56953.2022.10037388","url":null,"abstract":"Electrical vehicles require too much time to recharge their batteries, so to accommodate our busy schedule the conventional method of electric vehicle battery charging is replaced by “Dynamic Charging” The work presented involved the expansion of a novel type of wireless power transmission device to ensure high-efficiency battery charging stations for electric cars. A research project will look at the efficiency of traditional battery charging systems. In this paper, the finite element analysis is done by ANSYS simulation software. The static and dynamic modeling of the suggested wireless power transfer technique is the study's most important finding. A new model is created and describedthat takes into account both static and dynamic issues. This article will aid in the growth of future electric vehicle infrastructure.","PeriodicalId":426897,"journal":{"name":"2022 IEEE Bombay Section Signature Conference (IBSSC)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117230904","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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