2023 International Conference on Emerging Smart Computing and Informatics (ESCI)最新文献_第3页

Hybrid Gradient Boost based Heart Failure Prediction System 基于混合梯度升压的心力衰竭预测系统

2023 International Conference on Emerging Smart Computing and Informatics (ESCI) Pub Date : 2023-03-01 DOI: 10.1109/ESCI56872.2023.10099903

Gargee Athalye, Atharva Sarde, Mayur Badgujar, Vijay Gaikwad, S. Sondkar

{"title":"Hybrid Gradient Boost based Heart Failure Prediction System","authors":"Gargee Athalye, Atharva Sarde, Mayur Badgujar, Vijay Gaikwad, S. Sondkar","doi":"10.1109/ESCI56872.2023.10099903","DOIUrl":"https://doi.org/10.1109/ESCI56872.2023.10099903","url":null,"abstract":"Heart diseases are prevalent in today's world due to many factors like lipid disorder (hypercholesterolemia), corpulence (obesity), increase in triglycerides levels (lipids obtained from esterification fatty acids to glycerol), hypertension, etc. It is estimated that nearly 18 million lives are affected yearly due to various heart diseases. Early detection of such diseases could help save several lives. In the proposed system, heart failure prediction is estimated using the combination of Gradient boost detection and decision trees. The parallel handling approach is used for feature processing to speed up the results and for optimal performance. The generation and discrimination approach are used to verify the outcomes concerning other algorithms and pseudo-codes. This paper uses the data file from the University of California, Irvine Intelligent Systems Repository to test the results. It is observed from several experiments that it provides optimal performance compared to the remaining predictors in the context of f1 score, recall, and accuracy. The ROC curve of Gradient Boost provides a higher deviation for low false positives. The Gradient Boost shows a 0.919 ROC value and 92 % of accuracy with an F1 score of 0.928 and a recall of 0.934.","PeriodicalId":441215,"journal":{"name":"2023 International Conference on Emerging Smart Computing and Informatics (ESCI)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126896066","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

Insight on Human Activity Recognition Using the Deep Learning Approach 使用深度学习方法对人类活动识别的洞察

2023 International Conference on Emerging Smart Computing and Informatics (ESCI) Pub Date : 2023-03-01 DOI: 10.1109/ESCI56872.2023.10099759

Smita S. Kulkarni, Sangeeta Jadhav

{"title":"Insight on Human Activity Recognition Using the Deep Learning Approach","authors":"Smita S. Kulkarni, Sangeeta Jadhav","doi":"10.1109/ESCI56872.2023.10099759","DOIUrl":"https://doi.org/10.1109/ESCI56872.2023.10099759","url":null,"abstract":"This work proposes a video understanding technique that primarily focuses on the individual action recognition appearing in the video. The state-of-the-art showed promising work in video understanding. Though, it's essential to require inclusive information on human action in real-time CCTV video surveillance, sports video analysis, health care, etc. This paper proposed a transfer learning deep neural network model designed for recognizing individual actions accomplished by multiple people in a video sequence. This research established a deep model which uses Region-Of-Interest (RoI) pooling layer to capture automated features from a specified video frame to recognize individual actions. The MobileNet model accomplishes this as the backbone to recognize individual actions from each video frame. The accuracy score of the model was compared with the CNN models VGG-19,InceptionV3, and MobileNet. The MobileNet is computationally low-cost and enhances the performance of individual action recognition performed by multiple humans in a video frame. The investigational results were evaluated by varying learning parameters, and optimizer of deep neural network. The experimental results of the proposed model for individual action recognition demonstrate the improved efficiency of the standard benchmark collective activity dataset. This research illustrates the progress of action recognition by employing the transfer learning CNN model along with RoI pooling layer.","PeriodicalId":441215,"journal":{"name":"2023 International Conference on Emerging Smart Computing and Informatics (ESCI)","volume":"140 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114174375","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

Meta-heuristic Artificial Humming Bird Algorithm Based Energy Efficient Cluster Head Selection (MAHA-EECHS) in Wireless Sensor Networks 基于元启发式人工蜂鸟算法的无线传感器网络高效簇头选择(MAHA-EECHS)

2023 International Conference on Emerging Smart Computing and Informatics (ESCI) Pub Date : 2023-03-01 DOI: 10.1109/ESCI56872.2023.10100064

Vipan Kusla, Gurbinder Singh Brar, Vikas K. Garg, Ankit Bansal, R. Kaushal

{"title":"Meta-heuristic Artificial Humming Bird Algorithm Based Energy Efficient Cluster Head Selection (MAHA-EECHS) in Wireless Sensor Networks","authors":"Vipan Kusla, Gurbinder Singh Brar, Vikas K. Garg, Ankit Bansal, R. Kaushal","doi":"10.1109/ESCI56872.2023.10100064","DOIUrl":"https://doi.org/10.1109/ESCI56872.2023.10100064","url":null,"abstract":"A wireless sensor network (WSN) improves wireless communication by using hundreds or thousands of nodes to gather data. The lifespan of the nodes and balanced energy consumption are the major issues in the WSN. Long-term WSN efficiency requires optimising node energy. Selecting the optimal node as the cluster head improves energy usage in wireless sensor networks. The Artificial Hummingbird algorithm is used in this paper to identify the best cluster head selection in homogenous wireless sensor networks. The proposed algorithm's innovation lies in the fact that it takes into account a number of parameters like residual energy, intra-cluster distance, and balanced cluster formation while choosing a CH from a homogeneous sensor network. The performance analysis of the proposed algorithm considers four parameters: average energy consumption, total energy consumption, first node death, and residual energy. When compared to other algorithms, MATLAB-based simulation analyses show that the proposed algorithm MAHA-EECHS outperforms them.","PeriodicalId":441215,"journal":{"name":"2023 International Conference on Emerging Smart Computing and Informatics (ESCI)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115214896","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

Harmonic Analysis of Mild steel plate 低碳钢板的谐波分析

2023 International Conference on Emerging Smart Computing and Informatics (ESCI) Pub Date : 2023-03-01 DOI: 10.1109/ESCI56872.2023.10100307

Vijaykumar R. Bhanuse, S. Patankar, J. Kulkarni

引用次数: 0

Image Fusion using Xilinx System Generator for MRI and CT Medical Image Modalities 使用Xilinx System Generator进行MRI和CT医学图像模式的图像融合

2023 International Conference on Emerging Smart Computing and Informatics (ESCI) Pub Date : 2023-03-01 DOI: 10.1109/ESCI56872.2023.10099923

Neelima K, Kumar Raja Meruva, C. Subhas

引用次数: 2

Mobile Robot Control Based on Virtual Impedance Force Feedback 基于虚拟阻抗力反馈的移动机器人控制

2023 International Conference on Emerging Smart Computing and Informatics (ESCI) Pub Date : 2023-03-01 DOI: 10.1109/ESCI56872.2023.10099666

Masaru Sasaki, T. Shibanoki, H. Tonooka

引用次数: 0

Protection of Hazardous Places in Industries using Machine Learning 使用机器学习保护工业中的危险场所

2023 International Conference on Emerging Smart Computing and Informatics (ESCI) Pub Date : 2023-03-01 DOI: 10.1109/ESCI56872.2023.10100323

Praveen Sankarasubramanian

引用次数: 0

Longitudinal Control for closed loop simulation of Autonomous driving Vehicle 自动驾驶车辆闭环仿真纵向控制

2023 International Conference on Emerging Smart Computing and Informatics (ESCI) Pub Date : 2023-03-01 DOI: 10.1109/ESCI56872.2023.10099753

Bhuvana Kumbhare, K. Akant, M. Khanapurkar, P. Chandankhede

引用次数: 0

Mine Detecting Military Bot Using IoT 使用物联网的地雷探测军用机器人

2023 International Conference on Emerging Smart Computing and Informatics (ESCI) Pub Date : 2023-03-01 DOI: 10.1109/ESCI56872.2023.10100211

M. Rane, Manas Jain, Aryan Kashyap, Adhip Jajoo, Harshvardhan Kadam, Devika Kadam

引用次数: 2

A Review on Traditional and Deep Learning based Object Detection Methods 基于传统和深度学习的目标检测方法综述

2023 International Conference on Emerging Smart Computing and Informatics (ESCI) Pub Date : 2023-03-01 DOI: 10.1109/ESCI56872.2023.10099639

B. Solunke, S. Gengaje

{"title":"A Review on Traditional and Deep Learning based Object Detection Methods","authors":"B. Solunke, S. Gengaje","doi":"10.1109/ESCI56872.2023.10099639","DOIUrl":"https://doi.org/10.1109/ESCI56872.2023.10099639","url":null,"abstract":"Fast and accurate object detection systems are in high demand due to the advent of autonomous vehicles, smart video surveillance, facial detection, and numerous people counting applications. These systems not only detect and classify every object in an image or video, but also locate each one by creating a bounding box around it. This paper analyses the traditional and recent deep learning-based object detection methods from different perspectives, incorporating features recognition on many scales, data expansion, training approach, and perspective detection, in order to make it easier to deeply understand object detection. Some commonly used standard datasets for object detection are discussed. It also addressed the challenges and possible research scope in the future from the perspective of evolving object detection datasets and the framework for object detection tasks. From the analysis, it is observed that the performance of the methods in use for object detection is moderate and requires improvement, especially in difficult environments such as large object scale variance, obstructed object view, and horrific mild prerequisites. Therefore, the possible research scope for inventions and implementation of more novel deep learning methods to enhance object detection and classification accuracy is discussed.","PeriodicalId":441215,"journal":{"name":"2023 International Conference on Emerging Smart Computing and Informatics (ESCI)","volume":"43 13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114299177","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