Pub Date : 2020-12-16DOI: 10.1109/PuneCon50868.2020.9362465
Arshit Jain, Anshul Dubey
Data on the internet is growing nonstop. Every platform has millions of active users, searching for something specific. LinkedIn is one such platform, known for career opportunities, company and employee information and more. In this paper, we make a graph database system, collecting data from LinkedIn, IBM new feed, DNB. Using data crawling the data is gathered followed by data cleaning and, building APIs for graph database. A graph database is made of nodes and relationships, Cypher query language is used to store and retrieve the data from graph database. Neo4j and cypher query language are used for visual representation, with Neovis library. The system shows company details, employee details such as skills, experience, education background, contact information, certifications, licenses and more. The system is resourceful for companies and employees, provides easy and quick relevant information about the company and a person, such as a company’s employees, it’s blogs and articles, and further about the employee’s details. The project holds great future scope, with bigger, multiple sources.
互联网上的数据不断增长。每个平台都有数百万的活跃用户，他们在搜索特定的东西。LinkedIn就是这样一个平台，以提供职业机会、公司和员工信息等著称。本文通过对LinkedIn、IBM new feed、DNB的数据采集，构建了一个图形数据库系统。使用数据爬行收集数据，然后进行数据清理，并为图数据库构建api。图数据库由节点和关系组成，使用Cypher查询语言存储和检索图数据库中的数据。使用Neo4j和cypher查询语言进行可视化表示，并使用Neovis库。该系统显示公司详细信息、员工详细信息，如技能、经验、教育背景、联系信息、证书、执照等。该系统为公司和员工提供了丰富的资源，可以方便快捷地提供有关公司和个人的相关信息，例如公司的员工，博客和文章，以及员工的详细信息。该项目拥有巨大的未来空间，拥有更大的、更多的资源。
{"title":"Graph Database using Data Crawling","authors":"Arshit Jain, Anshul Dubey","doi":"10.1109/PuneCon50868.2020.9362465","DOIUrl":"https://doi.org/10.1109/PuneCon50868.2020.9362465","url":null,"abstract":"Data on the internet is growing nonstop. Every platform has millions of active users, searching for something specific. LinkedIn is one such platform, known for career opportunities, company and employee information and more. In this paper, we make a graph database system, collecting data from LinkedIn, IBM new feed, DNB. Using data crawling the data is gathered followed by data cleaning and, building APIs for graph database. A graph database is made of nodes and relationships, Cypher query language is used to store and retrieve the data from graph database. Neo4j and cypher query language are used for visual representation, with Neovis library. The system shows company details, employee details such as skills, experience, education background, contact information, certifications, licenses and more. The system is resourceful for companies and employees, provides easy and quick relevant information about the company and a person, such as a company’s employees, it’s blogs and articles, and further about the employee’s details. The project holds great future scope, with bigger, multiple sources.","PeriodicalId":368862,"journal":{"name":"2020 IEEE Pune Section International Conference (PuneCon)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127233324","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 : 2020-12-16DOI: 10.1109/PuneCon50868.2020.9362406
Sanjivani Jambhale, Shekhar Malani, Alka S. Barhatte
Electric Vehicles are becoming the future of the automotive industry and battery is the main component of Electric vehicles. The life span of the electric vehicle mainly depends on the performance of the battery which can be increased by the proper design of the battery management system (BMS). One of the factors that affect the battery performance and life span of an electric vehicle is the driving style. So, the supporting system to the BMS is developed that considers the effect of driving style on the performance of the battery. The system is implemented using MATLAB/Simulink tool which takes different driving cycles as input and obtained state of charge (SoC) as the output parameter. The main focus is to analyze the battery performance and its effect based on the different driving styles. The parameters like acceleration and deceleration account to determine the driving style like very aggressive, aggressive, and gentle. Finally based on battery capacity, the state of charge (SoC) is calculated for a different driving cycle. These observations are stored and analyzed to generate reports. These reports would be used to avoid the driving styles that are resulting in a decrease in battery performance and thus increases life span.
{"title":"Impact of Driving Style on Battery Life of the Electric Vehicle","authors":"Sanjivani Jambhale, Shekhar Malani, Alka S. Barhatte","doi":"10.1109/PuneCon50868.2020.9362406","DOIUrl":"https://doi.org/10.1109/PuneCon50868.2020.9362406","url":null,"abstract":"Electric Vehicles are becoming the future of the automotive industry and battery is the main component of Electric vehicles. The life span of the electric vehicle mainly depends on the performance of the battery which can be increased by the proper design of the battery management system (BMS). One of the factors that affect the battery performance and life span of an electric vehicle is the driving style. So, the supporting system to the BMS is developed that considers the effect of driving style on the performance of the battery. The system is implemented using MATLAB/Simulink tool which takes different driving cycles as input and obtained state of charge (SoC) as the output parameter. The main focus is to analyze the battery performance and its effect based on the different driving styles. The parameters like acceleration and deceleration account to determine the driving style like very aggressive, aggressive, and gentle. Finally based on battery capacity, the state of charge (SoC) is calculated for a different driving cycle. These observations are stored and analyzed to generate reports. These reports would be used to avoid the driving styles that are resulting in a decrease in battery performance and thus increases life span.","PeriodicalId":368862,"journal":{"name":"2020 IEEE Pune Section International Conference (PuneCon)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114923586","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 : 2020-12-16DOI: 10.1109/PuneCon50868.2020.9362466
R. Ravikumar, Saudamini Ghatge, Ratan Soni, Jonathan Nadar
The paper reviews the necessity and design of battery management circuitry and describes tests required for characterisation of Li-ion cell. The suggested design implements a novel cell balancing circuit comprising of only two active components. An individual cell monitoring board is economical when compared to open source solutions provided by Texas Instruments and Analog Devices, along with offering improved stack-ability. Design and analysis of cell balancing circuitry and electronic load, which can also be used as a battery-charger is discussed. The effect of the discharging rate on the capacity of a cell is analysed. The proposed design would aid to sustainable development and clean energy systems.
{"title":"Design of Battery Management System","authors":"R. Ravikumar, Saudamini Ghatge, Ratan Soni, Jonathan Nadar","doi":"10.1109/PuneCon50868.2020.9362466","DOIUrl":"https://doi.org/10.1109/PuneCon50868.2020.9362466","url":null,"abstract":"The paper reviews the necessity and design of battery management circuitry and describes tests required for characterisation of Li-ion cell. The suggested design implements a novel cell balancing circuit comprising of only two active components. An individual cell monitoring board is economical when compared to open source solutions provided by Texas Instruments and Analog Devices, along with offering improved stack-ability. Design and analysis of cell balancing circuitry and electronic load, which can also be used as a battery-charger is discussed. The effect of the discharging rate on the capacity of a cell is analysed. The proposed design would aid to sustainable development and clean energy systems.","PeriodicalId":368862,"journal":{"name":"2020 IEEE Pune Section International Conference (PuneCon)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125436748","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 : 2020-12-16DOI: 10.1109/PuneCon50868.2020.9362463
Humera Shaziya, K. Shyamala
Image Segmentation performs segregation of distinct segments of an image. Lung segmentation separate different elements of thoracic region. It is an essential prerequisite to several analysis tasks performed on the Computed Tomography (CT) images of lungs. Computational complexity is greatly reduced only when the required area is segregated from the entire CT image. Automated segmentation facilitates quick processing since it requires relatively less time to process more images. Conventional computer based segmentation methods require extensive support for determining the features. Users develop the features and provide to the system which then utilize those features to delineate the required regions. Recent advancements in deep learning showed optimal results in solving numerous image recognition and segmentation problems. The significant characteristic of deep learning is that the model itself learns the features from the input images and then apply the learned features to process new images. The most successful model of deep learning is Convolutional Neural Network (CNN) has outperformed earlier techniques for image recognition, object and face detection and is considered to be the most successful architecture of deep learning. CNN has also been applied for segmentation tasks. In this proposed work, CNN and UNet models have been implemented to evaluate the processing of medical images. The focus of the work is on CT images of lungs. Results obtained on the lungs dataset of 267 images on CNN is 81.34% and UNet is 82.61%. Thus U-Net has improved the dice coefficient by 1.27%. The experiments show that UNet model outperforms CNN model to segment the lung fields in CT images.
{"title":"Pulmonary CT Images Segmentation using CNN and UNet Models of Deep Learning","authors":"Humera Shaziya, K. Shyamala","doi":"10.1109/PuneCon50868.2020.9362463","DOIUrl":"https://doi.org/10.1109/PuneCon50868.2020.9362463","url":null,"abstract":"Image Segmentation performs segregation of distinct segments of an image. Lung segmentation separate different elements of thoracic region. It is an essential prerequisite to several analysis tasks performed on the Computed Tomography (CT) images of lungs. Computational complexity is greatly reduced only when the required area is segregated from the entire CT image. Automated segmentation facilitates quick processing since it requires relatively less time to process more images. Conventional computer based segmentation methods require extensive support for determining the features. Users develop the features and provide to the system which then utilize those features to delineate the required regions. Recent advancements in deep learning showed optimal results in solving numerous image recognition and segmentation problems. The significant characteristic of deep learning is that the model itself learns the features from the input images and then apply the learned features to process new images. The most successful model of deep learning is Convolutional Neural Network (CNN) has outperformed earlier techniques for image recognition, object and face detection and is considered to be the most successful architecture of deep learning. CNN has also been applied for segmentation tasks. In this proposed work, CNN and UNet models have been implemented to evaluate the processing of medical images. The focus of the work is on CT images of lungs. Results obtained on the lungs dataset of 267 images on CNN is 81.34% and UNet is 82.61%. Thus U-Net has improved the dice coefficient by 1.27%. The experiments show that UNet model outperforms CNN model to segment the lung fields in CT images.","PeriodicalId":368862,"journal":{"name":"2020 IEEE Pune Section International Conference (PuneCon)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114525576","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 : 2020-12-16DOI: 10.1109/PuneCon50868.2020.9362403
J. Patil, M. Prabhu, Dhaval Walavalkar, Vivian Brian Lobo
Accidents through roadways have been a great threat to developed as well as underdeveloped countries. Road accidents and its safety have been a major concern for the world, and everyone is trying to handle this since years. Road traffic and reckless driving occur in every part of the world. Because of this, many pedestrians are affected too. With no fault, they become victims. Many road accidents occur because of numerous factors like atmospheric changes, sharp curves, and human faults. Injuries caused by road accidents are major but sometimes imperceptible, which later on affect health too. This study aims to analyze road accidents in one of the popular metropolitan cities, i.e., Bengaluru, through k-means algorithm and machine learning by scrutinizing accident-prone or hotspot areas and their root causes.
{"title":"Road Accident Analysis using Machine Learning","authors":"J. Patil, M. Prabhu, Dhaval Walavalkar, Vivian Brian Lobo","doi":"10.1109/PuneCon50868.2020.9362403","DOIUrl":"https://doi.org/10.1109/PuneCon50868.2020.9362403","url":null,"abstract":"Accidents through roadways have been a great threat to developed as well as underdeveloped countries. Road accidents and its safety have been a major concern for the world, and everyone is trying to handle this since years. Road traffic and reckless driving occur in every part of the world. Because of this, many pedestrians are affected too. With no fault, they become victims. Many road accidents occur because of numerous factors like atmospheric changes, sharp curves, and human faults. Injuries caused by road accidents are major but sometimes imperceptible, which later on affect health too. This study aims to analyze road accidents in one of the popular metropolitan cities, i.e., Bengaluru, through k-means algorithm and machine learning by scrutinizing accident-prone or hotspot areas and their root causes.","PeriodicalId":368862,"journal":{"name":"2020 IEEE Pune Section International Conference (PuneCon)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125905606","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 : 2020-12-16DOI: 10.1109/PuneCon50868.2020.9362449
R. Patil, S. Patil
In the current scenario, the technology of the Internet of Things (IoT) acting a vital part in Precision agriculture, Military, Engineering applications. The main resource of our country is the agriculture field. IoT is widely adopted in the Precision Agriculture field to count the dissimilar environmental constraints such as soil moisture, humidity, temperature and the PH rate of soil for increasing the yield of crop. While using the IoT in Precision Agriculture it aided to decrease the consumption of the natural assets (freshwater, clean air, healthy soils, etc.) used in agricultural. Therefore, the purpose of work is to implement the several IoT skills accepted for smart agriculture This work has also points to the various communication tools and wireless sensors existing for Precision Farming. This work will very helpful to our farmers whose resources have limited. So farmers using these technologies with these limited resources will be enhancing the yield with improving quality which is the aim of Precision Agriculture.
{"title":"Cognitive Intelligence of Internet of Things in Smart Agriculture Applications","authors":"R. Patil, S. Patil","doi":"10.1109/PuneCon50868.2020.9362449","DOIUrl":"https://doi.org/10.1109/PuneCon50868.2020.9362449","url":null,"abstract":"In the current scenario, the technology of the Internet of Things (IoT) acting a vital part in Precision agriculture, Military, Engineering applications. The main resource of our country is the agriculture field. IoT is widely adopted in the Precision Agriculture field to count the dissimilar environmental constraints such as soil moisture, humidity, temperature and the PH rate of soil for increasing the yield of crop. While using the IoT in Precision Agriculture it aided to decrease the consumption of the natural assets (freshwater, clean air, healthy soils, etc.) used in agricultural. Therefore, the purpose of work is to implement the several IoT skills accepted for smart agriculture This work has also points to the various communication tools and wireless sensors existing for Precision Farming. This work will very helpful to our farmers whose resources have limited. So farmers using these technologies with these limited resources will be enhancing the yield with improving quality which is the aim of Precision Agriculture.","PeriodicalId":368862,"journal":{"name":"2020 IEEE Pune Section International Conference (PuneCon)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128163661","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 : 2020-12-16DOI: 10.1109/PuneCon50868.2020.9362402
Jaychand Upadhyay, R. Paranjpe, H. Purohit, Rohan P. Joshi
Generally, biometric authentication has been implemented on human features like voice,fingerprint,iris scan, and facial recognition [1]. Humans can recognize other people based on their walking pattern based on their previous learning experience [1]. This walking pattern of the person is termed GAIT [1]. This GAIT can be used for biometric authentication of a person [1]. The system is meant in such some way that it focuses on simple usage, utility, and measurability [1]. The project can be used in facilities which require accurate identification of a person for security purpose.
{"title":"Biometric Identification using Gait Analysis by Deep Learning","authors":"Jaychand Upadhyay, R. Paranjpe, H. Purohit, Rohan P. Joshi","doi":"10.1109/PuneCon50868.2020.9362402","DOIUrl":"https://doi.org/10.1109/PuneCon50868.2020.9362402","url":null,"abstract":"Generally, biometric authentication has been implemented on human features like voice,fingerprint,iris scan, and facial recognition [1]. Humans can recognize other people based on their walking pattern based on their previous learning experience [1]. This walking pattern of the person is termed GAIT [1]. This GAIT can be used for biometric authentication of a person [1]. The system is meant in such some way that it focuses on simple usage, utility, and measurability [1]. The project can be used in facilities which require accurate identification of a person for security purpose.","PeriodicalId":368862,"journal":{"name":"2020 IEEE Pune Section International Conference (PuneCon)","volume":"2180 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130092508","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 : 2020-12-16DOI: 10.1109/PuneCon50868.2020.9362471
Seema Yadav, G. Bhole
Real world dataset consists of normal instances with lesser percentage of interesting or abnormal instances. The cost of misclassifying an abnormal instance as normal instance is very high. The majority class is normal class whereas minority class is the abnormal one. Researchers in data mining and machine learning are looking out numerous strategies to resolve issues associated with dataset that is unbalanced and also the challenges featured in way of life. Irregular distribution in the dataset is the motive behind declining performance of classifier. There are mainly two methods, algorithm based and data level based, the utmost widespread methodology associated to the current is hybrid method. The task of decision making and overall classification accuracy is affected due to bias for majority class. Ensemble technique is an effective technique. The objective of study is providing background related to imbalance class issues, way out to confront the disputes and challenges in studying unbalanced data. In support to experimental result accompanied on one of the dataset, ensemble technique in adjacent to different strategies of data-level offers improved outcomes. The fusion of techniques is going to be advantageous for several applications in real-life like intrusion detection, medical diagnosis, software defect prediction, etc.
{"title":"Handling Imbalanced Dataset Classification in Machine Learning","authors":"Seema Yadav, G. Bhole","doi":"10.1109/PuneCon50868.2020.9362471","DOIUrl":"https://doi.org/10.1109/PuneCon50868.2020.9362471","url":null,"abstract":"Real world dataset consists of normal instances with lesser percentage of interesting or abnormal instances. The cost of misclassifying an abnormal instance as normal instance is very high. The majority class is normal class whereas minority class is the abnormal one. Researchers in data mining and machine learning are looking out numerous strategies to resolve issues associated with dataset that is unbalanced and also the challenges featured in way of life. Irregular distribution in the dataset is the motive behind declining performance of classifier. There are mainly two methods, algorithm based and data level based, the utmost widespread methodology associated to the current is hybrid method. The task of decision making and overall classification accuracy is affected due to bias for majority class. Ensemble technique is an effective technique. The objective of study is providing background related to imbalance class issues, way out to confront the disputes and challenges in studying unbalanced data. In support to experimental result accompanied on one of the dataset, ensemble technique in adjacent to different strategies of data-level offers improved outcomes. The fusion of techniques is going to be advantageous for several applications in real-life like intrusion detection, medical diagnosis, software defect prediction, etc.","PeriodicalId":368862,"journal":{"name":"2020 IEEE Pune Section International Conference (PuneCon)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126809450","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 : 2020-12-16DOI: 10.1109/PuneCon50868.2020.9362380
Tejashri Kelhe, Chaitanyasuma Jain, M. Bhandarkar, A. Deshpande
In this paper, we propose a circular, smart system involving participation of the government, health services and citizens, via a mobile application, with the analysis of the collected data being performed in a hierarchical manner in Cloud Storage. We performed a case study on the COVID-19 India dataset to validate the system. The proposed system will aid early detection of infectious disease outbreaks thus reducing the ultimate size of the outbreak, with lower overall morbidity and mortality.
{"title":"A Smart Early Warning System for Disease Outbreak with a Case Study of COVID-19 in India","authors":"Tejashri Kelhe, Chaitanyasuma Jain, M. Bhandarkar, A. Deshpande","doi":"10.1109/PuneCon50868.2020.9362380","DOIUrl":"https://doi.org/10.1109/PuneCon50868.2020.9362380","url":null,"abstract":"In this paper, we propose a circular, smart system involving participation of the government, health services and citizens, via a mobile application, with the analysis of the collected data being performed in a hierarchical manner in Cloud Storage. We performed a case study on the COVID-19 India dataset to validate the system. The proposed system will aid early detection of infectious disease outbreaks thus reducing the ultimate size of the outbreak, with lower overall morbidity and mortality.","PeriodicalId":368862,"journal":{"name":"2020 IEEE Pune Section International Conference (PuneCon)","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122668356","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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