2021 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES)最新文献_第10页

Speech Recognition and Separation System using Deep Learning 基于深度学习的语音识别与分离系统

2021 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES) Pub Date : 2021-09-24 DOI: 10.1109/ICSES52305.2021.9633779

Meet Singh Chauhan, R. Mishra, Manish I. Patel

引用次数: 3

Energy Forecasting of Grid Connected Roof Mounted Solar PV Using PV*SOL 基于PV*SOL的并网屋顶太阳能光伏发电能量预测

2021 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES) Pub Date : 2021-09-24 DOI: 10.1109/ICSES52305.2021.9633888

M. Kavitha, D. Immanuel, C. Rex, V. Meenakshi, M. Pushpavalli, Supriya Singari, Vinoba Baskaran

引用次数: 4

A New Sentence Similarity Computing Technique Using Order and Semantic Similarity 一种基于顺序和语义相似度的句子相似度计算新方法

2021 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES) Pub Date : 2021-09-24 DOI: 10.1109/ICSES52305.2021.9633911

Nityam Agarwal, Poorvi Seth, Merin Meleet

引用次数: 0

A time optimization model for the Internet of Things-based Healthcare system using Fog computing 基于雾计算的物联网医疗系统时间优化模型

2021 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES) Pub Date : 2021-09-24 DOI: 10.1109/ICSES52305.2021.9633874

S. Aiswarya, K. Ramesh, B. Prabha, S. Sasikumar, K. Vijayakumar

{"title":"A time optimization model for the Internet of Things-based Healthcare system using Fog computing","authors":"S. Aiswarya, K. Ramesh, B. Prabha, S. Sasikumar, K. Vijayakumar","doi":"10.1109/ICSES52305.2021.9633874","DOIUrl":"https://doi.org/10.1109/ICSES52305.2021.9633874","url":null,"abstract":"Fog computing is a distributed system that works flawlessly among the cloud and the devices. It enables realtime processing and small latency. It is a distributed decentralized system that is situated between the cloud and computing devices. We are living in the age of the Internet of Things (IoT) or (IoE) Everything that needs immediate processing with minimum latency and wide distribution with location awareness. The characteristics of fog include Mobility, Heterogeneousness, and Wireless Access capability. These factors show a huge part in the development of a real and well-organized IoT platform. As healthcare becomes more patient-centric, it needs a multi-layer architecture to manage the enormoussize of data that is generated by the system. In this paper, we deliberate the importance and applicability of fog and IoT in healthcareby giving a general architecture. In this approach, the system needs a multi-layer architecture that consists of IoT devices, fog, and Cloud computing to manage the complex data with different attributes like its speed, latency, variety, and accuracy.","PeriodicalId":6777,"journal":{"name":"2021 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES)","volume":"49 1","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2021-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79777592","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

Object Detection in Self Driving Cars Using Deep Learning 使用深度学习的自动驾驶汽车目标检测

2021 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES) Pub Date : 2021-09-24 DOI: 10.1109/ICSES52305.2021.9633965

P. Prajwal, D. Prajwal, D. H. Harish, R. Gajanana, B. Jayasri, S. Lokesh

{"title":"Object Detection in Self Driving Cars Using Deep Learning","authors":"P. Prajwal, D. Prajwal, D. H. Harish, R. Gajanana, B. Jayasri, S. Lokesh","doi":"10.1109/ICSES52305.2021.9633965","DOIUrl":"https://doi.org/10.1109/ICSES52305.2021.9633965","url":null,"abstract":"In the Computer Vision domain, there has been continuous growth and development with main focus so as to facilitate a smooth interaction between Machines and human. Perception, planning and control are the main aspects that make up the Self-driving system. Perception subsystem converts the raw data collected by sensors or other information capturing devices into a model of the environment surrounding us. Planning subsystem analyses this model of the surrounding environment and makes certain purposeful decisions based on the inferences obtained from the analysis. Finally, the Control Subsystem is responsible for execution of the actions or the decisions planned previously. The scope of this project is to study and analyze the problems faced in the Perception subsystem in the domain of detecting objects for autonomous cars. Previously, technologies like Radar, LiDAR, GPS and various other sensors had been employed for Driverless cars for mapping the surroundings of the car. However, in the recent past, some deep neural network (DNN) architectures like YOLO (You Only Look Once), and SSD (Single Shot MultiBox Detector) have been developed which are capable of detecting objects even when live video is considered as the input, thus having potential to be included as a part of the Driverless car systems. Selection of a model having considerable accuracy and producing results at a faster rate is very much essential so as to meet the requirements of object detection in driverless cars. In this project, we have used Caffe, which is developed by Berkeley AI Research and Community contributors as the deep learning framework. Keeping in mind the factors that contribute to the selection of a good model, we have chosen SSD model along-side MobileNet Neural network as the base architecture as it results in both faster rate of result production and has a moderate accuracy.","PeriodicalId":6777,"journal":{"name":"2021 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES)","volume":"9 1","pages":"1-7"},"PeriodicalIF":0.0,"publicationDate":"2021-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85390383","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

Asymmetrical Cascaded H- Bridge 31 Level Inverter with Low THD for PV Application 用于光伏的低THD非对称级联H桥31电平逆变器

2021 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES) Pub Date : 2021-09-24 DOI: 10.1109/ICSES52305.2021.9633836

A. Rameshbabu, G. Sundarrajan, J. B. Paul Glady

引用次数: 1

Automatic Feature Extraction and Traffic Management Using Machine Learning and Open CV Model 基于机器学习和开放CV模型的自动特征提取和流量管理

2021 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES) Pub Date : 2021-09-24 DOI: 10.1109/ICSES52305.2021.9633856

M. Prakash, C. Saravanakumar, S. Lakshmi, J. Rose, B. Praba

{"title":"Automatic Feature Extraction and Traffic Management Using Machine Learning and Open CV Model","authors":"M. Prakash, C. Saravanakumar, S. Lakshmi, J. Rose, B. Praba","doi":"10.1109/ICSES52305.2021.9633856","DOIUrl":"https://doi.org/10.1109/ICSES52305.2021.9633856","url":null,"abstract":"Artificial intelligence covers a vast area of the real time domain which supports humans for all activities. Machine learning (ML) techniques learn the data and react based on the properties of these data. The properties are identified by extracting the features from the extracted data. Image and video processing methods are essentials in real time application due the IoT (Internet of Things) devices. The data of these types of data is more complex and also high dimensional in nature. These dimensions are reduced by performing reduction techniques before performing the classification process. The proposed ML model targets the traffic management by automating the traffic light based on the flow in the road. The traffic priority is assigned based on the congestion level on the road. The traffic classification is done by considering different features and infrastructure maintained by the city. Existing system suffers the problem due to the following reasons such as traffic congestion, longer waiting time, improper maintenance of the traffic signal, and high carbon emission and so on. The objective of the proposed model is to reduce the traffic congestion by performing traffic flow conditions and make the people comfortable level during the travel.","PeriodicalId":6777,"journal":{"name":"2021 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES)","volume":"72 1","pages":"1-5"},"PeriodicalIF":0.0,"publicationDate":"2021-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80328761","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: 4

Wearable Device for Commuting Ladies Using IoT 使用物联网的通勤女性可穿戴设备

2021 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES) Pub Date : 2021-09-24 DOI: 10.1109/ICSES52305.2021.9633946

K. Vasanth, GPoralla Pradhyumna, Shivani Peram, P. K. Reddy, Tarun Dandetikar, C. Ravi

引用次数: 1

Forecasting the lung diseases from Rediography scans with hybrid Transfer Learning Techniques 利用混合迁移学习技术预测肺部疾病

2021 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES) Pub Date : 2021-09-24 DOI: 10.1109/ICSES52305.2021.9633887

Bhargavinath Dornadula, S. Geetha, A. Phamila, R. Priscilla, K. Vijayakumar

{"title":"Forecasting the lung diseases from Rediography scans with hybrid Transfer Learning Techniques","authors":"Bhargavinath Dornadula, S. Geetha, A. Phamila, R. Priscilla, K. Vijayakumar","doi":"10.1109/ICSES52305.2021.9633887","DOIUrl":"https://doi.org/10.1109/ICSES52305.2021.9633887","url":null,"abstract":"Lung related issues are rapidly increasing day by day as it is very important to identify the disease and get treated earliest possible as lungs are part of very complex system, expanding and relaxing thousands of times each day allow us to breathe by bringing oxygen into our bodies and sending carbon dioxide out. Lung related issues are directly preoperational to breathing problems. X-rays are one of the important ways of identifying the status of lungs. As there are many communicable diseases like Covid-19, the person should be identified early and should be treated to control the spread of virus. Lung Opacity is one of the major problem faced by many people and also a very serious problem if not treated early it will spread entire lungs and which leads to cancer similarly Pneumonia is another disease which is an infection to one's lungs caused by spread of virus. All these diseases directly affect Respiratory system of human. The paper aims to lung diseases classification among Pneumonia, Lung opacity, Normal and Covid-19 using the proposed hybrid model. The Deep Transfer Learning model helps to extract good features which helps for better learning and greater results. The Ensembled model of Deep Transfer Learning is used in this paper, which is a combination of VGG, EfficientNet and DenseNet. Considering the output of image augmentation as input for Ensembled model and classification of lung disease. The accuracy of the proposed hybrid model is very much accurate when compared to individual base models.","PeriodicalId":6777,"journal":{"name":"2021 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES)","volume":"19 1","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2021-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78501380","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: 0

Quotidian Sales Forecasting using Machine Learning 使用机器学习进行日常销售预测

2021 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES) Pub Date : 2021-09-24 DOI: 10.1109/ICSES52305.2021.9633975

M. Spuritha, Cheruku Sai Kashyap, Tejas Rakesh Nambiar, D. Kiran, N. Rao, G. Reddy

引用次数: 2