Pub Date : 2021-10-01DOI: 10.1109/GCAT52182.2021.9587806
Jenish Hirpara, Mihir Shah, D. Nanda, Pratik Kanani
The lockdown imposed in many countries due to the deadly effects of COVID-19 caused a huge backlog in offices. A lot of employees have a shift in their working hours and changes in their schedule which makes the manual attendance system less efficient. Employees are preoccupied with the workload so much that it becomes difficult to maintain social distancing in the office space. During these times, where a safe and healthy work environment should be promoted, a mistake from a single person can do irreparable damage to his/her peers. In order to tackle the above problems, we have implemented an automated attendance system to record attendance via QR Scanner, a violation tracker implemented using Internet of Things and Machine Learning which tracks the total number of social distancing and mask violations, a website and an app to display the results. Our software provides a clean and easy to use user-interface which gives the ability to the user to login, view his work calendar, take a note of important announcements made at his/her workplace, keep a track of user’s attendance, and generate a QR code which is unique just to the user.
{"title":"Automated Attendance System, Mask Detection and Social Distancing Violation Tracker for Post Covid Scenarios","authors":"Jenish Hirpara, Mihir Shah, D. Nanda, Pratik Kanani","doi":"10.1109/GCAT52182.2021.9587806","DOIUrl":"https://doi.org/10.1109/GCAT52182.2021.9587806","url":null,"abstract":"The lockdown imposed in many countries due to the deadly effects of COVID-19 caused a huge backlog in offices. A lot of employees have a shift in their working hours and changes in their schedule which makes the manual attendance system less efficient. Employees are preoccupied with the workload so much that it becomes difficult to maintain social distancing in the office space. During these times, where a safe and healthy work environment should be promoted, a mistake from a single person can do irreparable damage to his/her peers. In order to tackle the above problems, we have implemented an automated attendance system to record attendance via QR Scanner, a violation tracker implemented using Internet of Things and Machine Learning which tracks the total number of social distancing and mask violations, a website and an app to display the results. Our software provides a clean and easy to use user-interface which gives the ability to the user to login, view his work calendar, take a note of important announcements made at his/her workplace, keep a track of user’s attendance, and generate a QR code which is unique just to the user.","PeriodicalId":436231,"journal":{"name":"2021 2nd Global Conference for Advancement in Technology (GCAT)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130304874","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 : 2021-10-01DOI: 10.1109/GCAT52182.2021.9587460
Likitha Sr, N. N
Medical images play a critical part in the doctor's ability to make the correct diagnosis and in the patient's treatment. Intelligent algorithms make it possible to swiftly recognize lesions in medical imaging, and extracting features from images is very significant. Various algorithms have been integrated into medical imaging in a number of research. The basic architecture of CNN is constructed by focusing on picture feature extraction using a convolutional neural network (CNN). The research is expanded to multi-channel input CNN for visual feature extraction in order to overcome the constraints of machine vision and human vision. Glioma tumor, meningioma tumor, pituitary tumor, and no tumor are the four classifications investigated in this study, which includes roughly 3300 MRI samples gathered from kaggel. The BrainNet that has been implemented has a 98.31 percent of training accuracy and an 87.80 percent of validation accuracy. Deep architectures such as InceptionNet, ResNet, and XceptionNet were also tested with and without transfer learning to see which strategy performed better.
{"title":"Classification and Segmentation of Brain MRI images using Deep Learning","authors":"Likitha Sr, N. N","doi":"10.1109/GCAT52182.2021.9587460","DOIUrl":"https://doi.org/10.1109/GCAT52182.2021.9587460","url":null,"abstract":"Medical images play a critical part in the doctor's ability to make the correct diagnosis and in the patient's treatment. Intelligent algorithms make it possible to swiftly recognize lesions in medical imaging, and extracting features from images is very significant. Various algorithms have been integrated into medical imaging in a number of research. The basic architecture of CNN is constructed by focusing on picture feature extraction using a convolutional neural network (CNN). The research is expanded to multi-channel input CNN for visual feature extraction in order to overcome the constraints of machine vision and human vision. Glioma tumor, meningioma tumor, pituitary tumor, and no tumor are the four classifications investigated in this study, which includes roughly 3300 MRI samples gathered from kaggel. The BrainNet that has been implemented has a 98.31 percent of training accuracy and an 87.80 percent of validation accuracy. Deep architectures such as InceptionNet, ResNet, and XceptionNet were also tested with and without transfer learning to see which strategy performed better.","PeriodicalId":436231,"journal":{"name":"2021 2nd Global Conference for Advancement in Technology (GCAT)","volume":"61 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115580440","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 : 2021-10-01DOI: 10.1109/GCAT52182.2021.9587740
Gauri Ramanathan, Diya Chakrabarti, Aarti Patil, Sakshi Rishipathak, S. Kharche
The dominant causes of visual impairment worldwide are Cataract, Glaucoma, and retinal diseases among patients. The alarming cases of these diseases call for an urgent intervention by early diagnosis. The proposed system is designed and developed to easily facilitate the detection of cataract, glaucoma and retinal diseases among patients. The Logistic Regression, Random Forest, Gradient Boosting and Support Vector Machine algorithms are used for detection. The proposed system will help people to get the proper treatment of the aforementioned diseases at an early stage thus reducing the percentage of blindness being caused. The proposed system evaluates the effectiveness and safety of cataract surgery in eyes with age-related degeneration along with glaucoma and retinal diseases detection. This paper shows the accuracy of algorithms and SVM classifiers based upon the glaucoma, retina, cataract and normal eye’s fundus images. The idea of classifying the images based on its fundus and extracting features is widely known now-a-days and also it plays a vital role in the final outcome. This paper talks about the multiclass built models of these classifiers and on the basis of the ROC curves plotted it predicts the output of the images. As far as the algorithms are concerned, the efficiency of algorithms helps it stand best out of many and in our case Gradient boosting proves to give best results for the eye with cataract with 90% accuracy. Then the supervised algorithms logistic regression and random forest gives the accuracy of 89% and 86% respectively.
{"title":"Eye Disease Detection Using Machine Learning","authors":"Gauri Ramanathan, Diya Chakrabarti, Aarti Patil, Sakshi Rishipathak, S. Kharche","doi":"10.1109/GCAT52182.2021.9587740","DOIUrl":"https://doi.org/10.1109/GCAT52182.2021.9587740","url":null,"abstract":"The dominant causes of visual impairment worldwide are Cataract, Glaucoma, and retinal diseases among patients. The alarming cases of these diseases call for an urgent intervention by early diagnosis. The proposed system is designed and developed to easily facilitate the detection of cataract, glaucoma and retinal diseases among patients. The Logistic Regression, Random Forest, Gradient Boosting and Support Vector Machine algorithms are used for detection. The proposed system will help people to get the proper treatment of the aforementioned diseases at an early stage thus reducing the percentage of blindness being caused. The proposed system evaluates the effectiveness and safety of cataract surgery in eyes with age-related degeneration along with glaucoma and retinal diseases detection. This paper shows the accuracy of algorithms and SVM classifiers based upon the glaucoma, retina, cataract and normal eye’s fundus images. The idea of classifying the images based on its fundus and extracting features is widely known now-a-days and also it plays a vital role in the final outcome. This paper talks about the multiclass built models of these classifiers and on the basis of the ROC curves plotted it predicts the output of the images. As far as the algorithms are concerned, the efficiency of algorithms helps it stand best out of many and in our case Gradient boosting proves to give best results for the eye with cataract with 90% accuracy. Then the supervised algorithms logistic regression and random forest gives the accuracy of 89% and 86% respectively.","PeriodicalId":436231,"journal":{"name":"2021 2nd Global Conference for Advancement in Technology (GCAT)","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115623934","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 : 2021-10-01DOI: 10.1109/GCAT52182.2021.9587841
Manjusha Nair, A. R, Arya C. Babu
The different mutated variants of Corona Virus (SARS-CoV-2), affected a large percentage of the world population so far. On this light, any study on understanding the virus’s immunity to vaccines and medicines has greater relevance. Studies on Angiotensin-converting enzyme 2 (ACE2), the main entry receptor for the SARS-COV-2 S protein is significantly important in understanding SARS-COV-2 infection in host cells. The functional implications of various motifs found in the spike glycoprotein and its conformational changes had been studied previously to better understand the pathogenesis. The computational study, described herein, have focused on the disease transmission mechanisms of the virus especially on the receptor recognition mechanisms during viral infection. This study used different computational techniques to identify significant motif of the SARS-CoV-2 S Glycoprotein. Different corona viral genomes were compared against the reference genome (Wuhan seafood market isolate) and the possible intermediate hosts of the virus has been proposed based on the similarity in the motifs which are critical for viral infections. Previous studies on S protein motifs of proteolytic cleavage site are revisited here using computational techniques to suggest the possible intermediate hosts of infection.
{"title":"Recognizing Significant Motifs of Corona Virus Spike Proteins using Computational Approaches","authors":"Manjusha Nair, A. R, Arya C. Babu","doi":"10.1109/GCAT52182.2021.9587841","DOIUrl":"https://doi.org/10.1109/GCAT52182.2021.9587841","url":null,"abstract":"The different mutated variants of Corona Virus (SARS-CoV-2), affected a large percentage of the world population so far. On this light, any study on understanding the virus’s immunity to vaccines and medicines has greater relevance. Studies on Angiotensin-converting enzyme 2 (ACE2), the main entry receptor for the SARS-COV-2 S protein is significantly important in understanding SARS-COV-2 infection in host cells. The functional implications of various motifs found in the spike glycoprotein and its conformational changes had been studied previously to better understand the pathogenesis. The computational study, described herein, have focused on the disease transmission mechanisms of the virus especially on the receptor recognition mechanisms during viral infection. This study used different computational techniques to identify significant motif of the SARS-CoV-2 S Glycoprotein. Different corona viral genomes were compared against the reference genome (Wuhan seafood market isolate) and the possible intermediate hosts of the virus has been proposed based on the similarity in the motifs which are critical for viral infections. Previous studies on S protein motifs of proteolytic cleavage site are revisited here using computational techniques to suggest the possible intermediate hosts of infection.","PeriodicalId":436231,"journal":{"name":"2021 2nd Global Conference for Advancement in Technology (GCAT)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116720829","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 : 2021-10-01DOI: 10.1109/GCAT52182.2021.9587709
Priyam A. Sheth, Soumya, A. Lad, Yash Solanki
The world of the Internet of Things (IoT) has exploded and expanded rapidly in recent years. IoT is made up of several connected devices and sensors that communicate by exchanging data through the internet. With exponential growth in the number of installed devices and sensors, conservation of energy is a buzzing topic in the field. IoT facilitates the conservation of energy by enabling the management of data collected from various sensors. The paper presents an implementation of Energy-aware Smart University focusing on Smart Lighting, Air-conditioning, and Ventilating system, whose scope can be expanded to any electrical appliances. This paper attempts to make a low-cost, energy-efficient system. The proposed solution uses Message Queuing Telemetry Transport (MQTT) Client protocol, an IEEE 802.3 standard Ethernet connectivity shield for internet publishing, and a set of sensors such as PIR Sensor and 5 Megapixel infrared camera supported by the raspberry pi for obtaining real-time data. The electrical appliances are turned on only when motion sensors detect movement, and the presence of humans is confirmed using image processing on pictures captured by the Pi Camera. As a result, a significant amount of energy is saved by preventing the continuous operation of the appliances. The data is stored using the MySQL database, which could be accessed using an Android application remotely, which would make this an easily accessible and operational automation system.
{"title":"Energy Aware IoT based Green Smart University with Automated Lighting and CCTV System using MQTT and MySQL","authors":"Priyam A. Sheth, Soumya, A. Lad, Yash Solanki","doi":"10.1109/GCAT52182.2021.9587709","DOIUrl":"https://doi.org/10.1109/GCAT52182.2021.9587709","url":null,"abstract":"The world of the Internet of Things (IoT) has exploded and expanded rapidly in recent years. IoT is made up of several connected devices and sensors that communicate by exchanging data through the internet. With exponential growth in the number of installed devices and sensors, conservation of energy is a buzzing topic in the field. IoT facilitates the conservation of energy by enabling the management of data collected from various sensors. The paper presents an implementation of Energy-aware Smart University focusing on Smart Lighting, Air-conditioning, and Ventilating system, whose scope can be expanded to any electrical appliances. This paper attempts to make a low-cost, energy-efficient system. The proposed solution uses Message Queuing Telemetry Transport (MQTT) Client protocol, an IEEE 802.3 standard Ethernet connectivity shield for internet publishing, and a set of sensors such as PIR Sensor and 5 Megapixel infrared camera supported by the raspberry pi for obtaining real-time data. The electrical appliances are turned on only when motion sensors detect movement, and the presence of humans is confirmed using image processing on pictures captured by the Pi Camera. As a result, a significant amount of energy is saved by preventing the continuous operation of the appliances. The data is stored using the MySQL database, which could be accessed using an Android application remotely, which would make this an easily accessible and operational automation system.","PeriodicalId":436231,"journal":{"name":"2021 2nd Global Conference for Advancement in Technology (GCAT)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117108113","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 : 2021-10-01DOI: 10.1109/GCAT52182.2021.9587770
Midhun Manoj, S. Ananthakrishnan, Palagati Sriharshitha, V. Pandi
This paper presents a working and Simulation of a four-axis welding robot. The Robotic aspects have many applications in industries including welding. These tasks are described according to the end effector function. This work deals with handling a robotic arm or welding arm by a master manipulator where the end effector is used to hold. For the movement of the robotic arm, a fuzzy logic controller is used, and its performance is compared to that of a PID controller. Forward kinematics deal with the problem of finding end-effector pose (position + orientation) with given joints variables using two methods: Homogeneous transformation and Denavit-Hartenberg Representation. Actuation modes include Torque and Motion which are described through simulation showing effects on working of machine due to dynamics. The model has been done based on MATLAB/Simulink software.
{"title":"Four Axis Welding Robot Control using Fuzzy Logic","authors":"Midhun Manoj, S. Ananthakrishnan, Palagati Sriharshitha, V. Pandi","doi":"10.1109/GCAT52182.2021.9587770","DOIUrl":"https://doi.org/10.1109/GCAT52182.2021.9587770","url":null,"abstract":"This paper presents a working and Simulation of a four-axis welding robot. The Robotic aspects have many applications in industries including welding. These tasks are described according to the end effector function. This work deals with handling a robotic arm or welding arm by a master manipulator where the end effector is used to hold. For the movement of the robotic arm, a fuzzy logic controller is used, and its performance is compared to that of a PID controller. Forward kinematics deal with the problem of finding end-effector pose (position + orientation) with given joints variables using two methods: Homogeneous transformation and Denavit-Hartenberg Representation. Actuation modes include Torque and Motion which are described through simulation showing effects on working of machine due to dynamics. The model has been done based on MATLAB/Simulink software.","PeriodicalId":436231,"journal":{"name":"2021 2nd Global Conference for Advancement in Technology (GCAT)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123283543","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 : 2021-10-01DOI: 10.1109/GCAT52182.2021.9587659
Atharva Gondkar, Jeevan Thukrul, Raghav Bang, S. Rakshe, S. Sarode
The highly volatile nature of the stock market has made stock price prediction as challenging as weather forecasting. Consequently, as a hint of this dread, people don’t invest in the stock market. In this paper, we have discussed hybrid networks and a stacked LSTM network for stock price prediction. Additionally, it also focuses on portfolio optimization done using six different techniques, which focuses on creating best performing portfolios categorized on the basis of sectors. One hybrid neural network consists of 1D-Convolutional layers and LSTM layers, and the other is a combination of GRU and LSTM layers. The stock prices of SBI, Indian Bank, Bank of India are predicted using stacked LSTM and Hybrid Neural Networks and compared using the sliding window of time steps with variable width. The neural networks predict the following day’s closing price using a variable sliding window. The RMSE, MSE, and MAE are used to evaluate the efficiency of these neural networks. The hybrid network is proving to be more competent in various situations.
{"title":"Stock Market Prediction and Portfolio Optimization","authors":"Atharva Gondkar, Jeevan Thukrul, Raghav Bang, S. Rakshe, S. Sarode","doi":"10.1109/GCAT52182.2021.9587659","DOIUrl":"https://doi.org/10.1109/GCAT52182.2021.9587659","url":null,"abstract":"The highly volatile nature of the stock market has made stock price prediction as challenging as weather forecasting. Consequently, as a hint of this dread, people don’t invest in the stock market. In this paper, we have discussed hybrid networks and a stacked LSTM network for stock price prediction. Additionally, it also focuses on portfolio optimization done using six different techniques, which focuses on creating best performing portfolios categorized on the basis of sectors. One hybrid neural network consists of 1D-Convolutional layers and LSTM layers, and the other is a combination of GRU and LSTM layers. The stock prices of SBI, Indian Bank, Bank of India are predicted using stacked LSTM and Hybrid Neural Networks and compared using the sliding window of time steps with variable width. The neural networks predict the following day’s closing price using a variable sliding window. The RMSE, MSE, and MAE are used to evaluate the efficiency of these neural networks. The hybrid network is proving to be more competent in various situations.","PeriodicalId":436231,"journal":{"name":"2021 2nd Global Conference for Advancement in Technology (GCAT)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114954747","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}
The motive behind conceptualizing and implementing the project is to leverage the availability of technological advances to cater to the needs of a COVID-19 vaccination center. The built cross-stage system assists users to navigate through the waiting, vaccination and monitoring room using the indoor navigation map of the vaccination center based on the availability of the rooms and live location of other people. The approach of using the application also helps us maintain the social distancing norms and corroborates the compulsory use of masks in the center.
{"title":"Accessible Self-Care and Automated Indoor Navigation for COVID-19 Vaccination Centre","authors":"Param Batavia, Isha Gajera, Shakshi Gandhi, Prem Mody, Sagar D. Korde","doi":"10.1109/GCAT52182.2021.9587773","DOIUrl":"https://doi.org/10.1109/GCAT52182.2021.9587773","url":null,"abstract":"The motive behind conceptualizing and implementing the project is to leverage the availability of technological advances to cater to the needs of a COVID-19 vaccination center. The built cross-stage system assists users to navigate through the waiting, vaccination and monitoring room using the indoor navigation map of the vaccination center based on the availability of the rooms and live location of other people. The approach of using the application also helps us maintain the social distancing norms and corroborates the compulsory use of masks in the center.","PeriodicalId":436231,"journal":{"name":"2021 2nd Global Conference for Advancement in Technology (GCAT)","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126266103","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 : 2021-10-01DOI: 10.1109/GCAT52182.2021.9587793
S. Sunori, P. Negi, P. Juneja, M. Niranjanamurthy, P. G. Om Prakash, Amit Mittal, Dr Sudhanshu Maurya
As far as, air quality index (AQI) is concerned, the long duration lockdown that was applied in India in year 2020 due to Covid-19 pandemic was very fruitful. The reason being, due to complete ban on the movement of people and automobiles, the air became so pure and clean, and AQI value went much down. The secondary air pollution data of the lockdown duration, for Uttarakhand, is the base of this research work. This work attempts to design unsupervised and supervised classification models to classify the provided data into two classes i.e class 1 (‘clean’) and class 2 (‘hazardous’) using MATLAB. The techniques used are FCM clustering and Probabilistic neural network (PNN). Eventually, a comparative study of the performance of both models is performed.
{"title":"Unsupervised and Supervised Learning based Classification Models for Air Pollution Data","authors":"S. Sunori, P. Negi, P. Juneja, M. Niranjanamurthy, P. G. Om Prakash, Amit Mittal, Dr Sudhanshu Maurya","doi":"10.1109/GCAT52182.2021.9587793","DOIUrl":"https://doi.org/10.1109/GCAT52182.2021.9587793","url":null,"abstract":"As far as, air quality index (AQI) is concerned, the long duration lockdown that was applied in India in year 2020 due to Covid-19 pandemic was very fruitful. The reason being, due to complete ban on the movement of people and automobiles, the air became so pure and clean, and AQI value went much down. The secondary air pollution data of the lockdown duration, for Uttarakhand, is the base of this research work. This work attempts to design unsupervised and supervised classification models to classify the provided data into two classes i.e class 1 (‘clean’) and class 2 (‘hazardous’) using MATLAB. The techniques used are FCM clustering and Probabilistic neural network (PNN). Eventually, a comparative study of the performance of both models is performed.","PeriodicalId":436231,"journal":{"name":"2021 2nd Global Conference for Advancement in Technology (GCAT)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126519518","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 : 2021-10-01DOI: 10.1109/GCAT52182.2021.9587732
D. S. Harsha, S. Praneetha, V. Swetha, P. Dinesh, K. Vani
During More than 10.5 million individuals in India live in kutcha houses and are described by helpless everyday environments, the consistent convergence of the rustic populace to urban communities looking for occupations is causing issues on metropolitan lodging. To improve this Government of India has as of late dispatched a moderate lodging plan, Pradhan Mantri Awas Yojana – Housing for All (Urban) Mission” for metropolitan territory is being executed during 2015-2022. This Mission gives focal help to carrying out organizations through States and Union Territories for giving houses to every single qualified family/recipient by 2022. The aim is to analyze the beneficiaries for the EWS provided by the government under this scheme. To review various literatures and understand PMAY, an affordable housing scheme for especially Economically Weaker Section (EWS) beneficiaries in India analyzing how Central Government funds are being utilized and contrast the progress of these beneficiaries to the public. The entire process aims at understanding all these activities by clustering (Machine Learning technique) of housing data using GIS coordinates and mapping these clusters to disclose the stages of houses at corresponding location/area.
{"title":"Evaluation of Support to Beneficiaries Under PMAY using Clustering Techniques","authors":"D. S. Harsha, S. Praneetha, V. Swetha, P. Dinesh, K. Vani","doi":"10.1109/GCAT52182.2021.9587732","DOIUrl":"https://doi.org/10.1109/GCAT52182.2021.9587732","url":null,"abstract":"During More than 10.5 million individuals in India live in kutcha houses and are described by helpless everyday environments, the consistent convergence of the rustic populace to urban communities looking for occupations is causing issues on metropolitan lodging. To improve this Government of India has as of late dispatched a moderate lodging plan, Pradhan Mantri Awas Yojana – Housing for All (Urban) Mission” for metropolitan territory is being executed during 2015-2022. This Mission gives focal help to carrying out organizations through States and Union Territories for giving houses to every single qualified family/recipient by 2022. The aim is to analyze the beneficiaries for the EWS provided by the government under this scheme. To review various literatures and understand PMAY, an affordable housing scheme for especially Economically Weaker Section (EWS) beneficiaries in India analyzing how Central Government funds are being utilized and contrast the progress of these beneficiaries to the public. The entire process aims at understanding all these activities by clustering (Machine Learning technique) of housing data using GIS coordinates and mapping these clusters to disclose the stages of houses at corresponding location/area.","PeriodicalId":436231,"journal":{"name":"2021 2nd Global Conference for Advancement in Technology (GCAT)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125893514","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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