2020 IEEE Pune Section International Conference (PuneCon)最新文献

Graph Database using Data Crawling 使用数据爬行的图数据库

2020 IEEE Pune Section International Conference (PuneCon) Pub Date : 2020-12-16 DOI: 10.1109/PuneCon50868.2020.9362465

Arshit Jain, Anshul Dubey

引用次数: 0

Impact of Driving Style on Battery Life of the Electric Vehicle 驾驶方式对电动汽车电池寿命的影响

2020 IEEE Pune Section International Conference (PuneCon) Pub Date : 2020-12-16 DOI: 10.1109/PuneCon50868.2020.9362406

Sanjivani Jambhale, Shekhar Malani, Alka S. Barhatte

{"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}

引用次数: 3

Design of Battery Management System 电池管理系统的设计

2020 IEEE Pune Section International Conference (PuneCon) Pub Date : 2020-12-16 DOI: 10.1109/PuneCon50868.2020.9362466

R. Ravikumar, Saudamini Ghatge, Ratan Soni, Jonathan Nadar

引用次数: 0

Pulmonary CT Images Segmentation using CNN and UNet Models of Deep Learning 基于CNN和UNet深度学习模型的肺部CT图像分割

2020 IEEE Pune Section International Conference (PuneCon) Pub Date : 2020-12-16 DOI: 10.1109/PuneCon50868.2020.9362463

Humera Shaziya, K. Shyamala

{"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}

引用次数: 9

Road Accident Analysis using Machine Learning 使用机器学习进行道路事故分析

2020 IEEE Pune Section International Conference (PuneCon) Pub Date : 2020-12-16 DOI: 10.1109/PuneCon50868.2020.9362403

J. Patil, M. Prabhu, Dhaval Walavalkar, Vivian Brian Lobo

引用次数: 10

Cognitive Intelligence of Internet of Things in Smart Agriculture Applications 物联网认知智能在智慧农业中的应用

2020 IEEE Pune Section International Conference (PuneCon) Pub Date : 2020-12-16 DOI: 10.1109/PuneCon50868.2020.9362449

R. Patil, S. Patil

引用次数: 7

Biometric Identification using Gait Analysis by Deep Learning 基于深度学习的步态分析生物特征识别

2020 IEEE Pune Section International Conference (PuneCon) Pub Date : 2020-12-16 DOI: 10.1109/PuneCon50868.2020.9362402

Jaychand Upadhyay, R. Paranjpe, H. Purohit, Rohan P. Joshi

引用次数: 1

Handling Imbalanced Dataset Classification in Machine Learning 机器学习中不平衡数据集分类的处理

2020 IEEE Pune Section International Conference (PuneCon) Pub Date : 2020-12-16 DOI: 10.1109/PuneCon50868.2020.9362471

Seema Yadav, G. Bhole

{"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}

引用次数: 2

A Smart Early Warning System for Disease Outbreak with a Case Study of COVID-19 in India 疾病爆发的智能预警系统——以印度新冠肺炎疫情为例

2020 IEEE Pune Section International Conference (PuneCon) Pub Date : 2020-12-16 DOI: 10.1109/PuneCon50868.2020.9362380

Tejashri Kelhe, Chaitanyasuma Jain, M. Bhandarkar, A. Deshpande

引用次数: 1

PuneCon 2020 Conference Committee PuneCon 2020会议委员会

2020 IEEE Pune Section International Conference (PuneCon) Pub Date : 2020-12-16 DOI: 10.1109/punecon50868.2020.9362493

引用次数: 0