2022 IEEE International Conference on Distributed Computing and Electrical Circuits and Electronics (ICDCECE)最新文献_第6页

Smart Parking Reservation Mobile Application 智能停车预约移动应用程序

2022 IEEE International Conference on Distributed Computing and Electrical Circuits and Electronics (ICDCECE) Pub Date : 2022-04-23 DOI: 10.1109/icdcece53908.2022.9792684

Chong Chen How, Intan Farhana Kamsin, N. Zainal, Hairul Aysa Abdul Halim Sithiq, Nor Azlina Abd Rahman

{"title":"Smart Parking Reservation Mobile Application","authors":"Chong Chen How, Intan Farhana Kamsin, N. Zainal, Hairul Aysa Abdul Halim Sithiq, Nor Azlina Abd Rahman","doi":"10.1109/icdcece53908.2022.9792684","DOIUrl":"https://doi.org/10.1109/icdcece53908.2022.9792684","url":null,"abstract":"As urbanization has taken place, transportation has become our main way of going around places. Private transportation is the main source of transportation for every individual. With high increasing number of vehicles, the demand for parking for these vehicles increases as well. Metropolitans are surged with vehicles and troubles arise when individuals search for a parking spot in such metropolis. Every industry is trying to implement technological factors in their business. As well as parking system, a parking system that would help with the constant issue of unable to reach the parking ticket booth or having technical difficulties on the machine and customers are unable to back out due to cars behind. An advanced technological system will be implemented in this topic, car plate recognition and QR code. Car plate recognition works as a tool to detect incoming cars which already booked the parking spot, this minimizes the time taken for the car to search a parking spot. Car plate recognition acts as a medium for the car to enter the parking spot, with a condition of having a parking spot booked. The device will recognize the registered car plate and opens the gate whenever the car is trying to enter the parking spot. The booking system works hand in hand with the car plate recognition. The booking system acts as a medium between the consumer and the parking authorities. The consumer will book a parking spot using the system and register their car plate during the process. Therefore, this study is to enhance the experience of the individuals of parking experiences.","PeriodicalId":417643,"journal":{"name":"2022 IEEE International Conference on Distributed Computing and Electrical Circuits and Electronics (ICDCECE)","volume":"84 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128846114","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

Skin Cancer and Oral Cancer Detection using Deep Learning Technique 使用深度学习技术检测皮肤癌和口腔癌

2022 IEEE International Conference on Distributed Computing and Electrical Circuits and Electronics (ICDCECE) Pub Date : 2022-04-23 DOI: 10.1109/icdcece53908.2022.9792688

Geetika Sharma, Raman Chadha

引用次数: 0

Smart Pet Feeder System Based on Google Assistant 基于Google Assistant的智能宠物喂食系统

2022 IEEE International Conference on Distributed Computing and Electrical Circuits and Electronics (ICDCECE) Pub Date : 2022-04-23 DOI: 10.1109/icdcece53908.2022.9792789

Prithviraj V, Sriharipriya K.C

{"title":"Smart Pet Feeder System Based on Google Assistant","authors":"Prithviraj V, Sriharipriya K.C","doi":"10.1109/icdcece53908.2022.9792789","DOIUrl":"https://doi.org/10.1109/icdcece53908.2022.9792789","url":null,"abstract":"Most of the people have pets in their homes and they are emotionally attached to them. People love to take care of their pets by feeding them on time. But the real challenge for most of the pet owners is to feed them whenever they are away from their homes. Then, a machine is needed to take care of things while they are gone. Since the internet of things is known to make the lives of people simpler and better, an automatic smart pet feeder is one of the new technologies to feed pets. In the proposed research work, an IoT-based automated pet feeder system is built to help pet owners to take care of their pets in their busy day-to-day schedule. Whenever the pet owners are not in their homes, the proposed pet feeder system will help them to feed their pets. Working people who have pets in their homes often find it hard to feed their pets as they come home only during the night-time. Till then, the pets can’t be left unfed. This feeding issue has been one of the major reasons for most of the people to not have pets in their homes even though they wish to have one. Pet care should be enjoyable, not stressful, and the purpose of this research work is to make pet care easier for owners by developing a user-friendly pet feeder machine. The goal of the proposed work is to assist pet owners in feeding their pets on time, even while they are not at home.","PeriodicalId":417643,"journal":{"name":"2022 IEEE International Conference on Distributed Computing and Electrical Circuits and Electronics (ICDCECE)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115908271","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: 1

Electric Propulsion Motors: A Comparative Review for Electric and Hybrid Electric Vehicles 电力推进电机:电动和混合动力汽车的比较综述

2022 IEEE International Conference on Distributed Computing and Electrical Circuits and Electronics (ICDCECE) Pub Date : 2022-04-23 DOI: 10.1109/icdcece53908.2022.9793099

Dharmendra Singh Yadav, M. Manisha

引用次数: 5

Efficient Off Board DC Charger Design Comparing with Onboard DC Charger for Electric Vehicles 高效车载直流充电器与车载直流充电器的比较

2022 IEEE International Conference on Distributed Computing and Electrical Circuits and Electronics (ICDCECE) Pub Date : 2022-04-23 DOI: 10.1109/icdcece53908.2022.9793316

Shivalingaswamy G D, Kothaiandal C, R. Selvamathi

引用次数: 0

Driver Drowsiness Estimation Based on Hybrid Feature Extraction and Light weighted Dense Convolutional Network 基于混合特征提取和轻加权密集卷积网络的驾驶员困倦估计

2022 IEEE International Conference on Distributed Computing and Electrical Circuits and Electronics (ICDCECE) Pub Date : 2022-04-23 DOI: 10.1109/icdcece53908.2022.9792965

Sharanabasappa, S. Nandyal

{"title":"Driver Drowsiness Estimation Based on Hybrid Feature Extraction and Light weighted Dense Convolutional Network","authors":"Sharanabasappa, S. Nandyal","doi":"10.1109/icdcece53908.2022.9792965","DOIUrl":"https://doi.org/10.1109/icdcece53908.2022.9792965","url":null,"abstract":"Researchers propose a fully automated method of detecting drowsiness using driving images with a focus on fatigue driving detection. Kanade – Lucas – Tomasi - ViolaJones (KLT-ViolaJones) is used to locate feature points and detect faces in the proposed algorithm and feature points are used to extract the region of interest (ROI). In order to determine the status of the eye from the ROI images, Histogram oriented Gradient (HoG) is used. Two parameters with which fatigue can be detected are percentage of eyelid closure over pupil over time (PERCLOS) ratio and Eyes Aspect Ratios (EAR). Experimental results demonstrate that the proposed Light Weighted Dense Convolution Network (Li-DenseNet) can detect drowsiness levels in drivers using the National Tsing Hua University Driver Drowsiness Detection dataset (NTHU-DDD). The proposed algorithm Li-DenseNet outperforms other CNN-based methods, AlexNet, VGG, RNN, and ResNet showing accuracy, sensitivity, specificity, precision and F1-Score rates of 98.44%, 91.5%,92.3%,98.2 and 97.02%, respectively.","PeriodicalId":417643,"journal":{"name":"2022 IEEE International Conference on Distributed Computing and Electrical Circuits and Electronics (ICDCECE)","volume":"71 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116138598","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

K-Means Clustering driven Deep Spatiotemporal Learning Model for PM2.5 Prediction 基于k均值聚类驱动的PM2.5深度时空学习模型

2022 IEEE International Conference on Distributed Computing and Electrical Circuits and Electronics (ICDCECE) Pub Date : 2022-04-23 DOI: 10.1109/icdcece53908.2022.9793281

Kunal G. Srinivas, N. Giri

{"title":"K-Means Clustering driven Deep Spatiotemporal Learning Model for PM2.5 Prediction","authors":"Kunal G. Srinivas, N. Giri","doi":"10.1109/icdcece53908.2022.9793281","DOIUrl":"https://doi.org/10.1109/icdcece53908.2022.9793281","url":null,"abstract":"This paper proposed a novel and robust clustering driven deep spatiotemporal learning model for PM2.5 concentration prediction. Unlike classical approaches of PM2.5 prediction, our proposed model emphasizes on both feature improvement as well as feature learning to achieve a generalizable BigData analytics solution for PM2.5 prediction. More specifically, in this paper four Chinese city’s data (Chengdu, Guangzhou, Shenyang, and Shanghai) have been considered where each city possesses three monitoring stations providing spatiotemporal features like timestamp, wind-direction, wind-speed, temperature, dew, humidity, precipitation and corresponding PM2.5 concentration. To alleviate missing element problem, at first it performs data wrangling and missing element removal, which is then followed by clustering using K-Means algorithm. Unlike classical methods, where input spatiotemporal features are directly learnt, we clustered the non-zero instances or features for the different time-periods so as to make learning more efficient. Once clustering the dataset, we applied three different deep spatiotemporal learning models derived using deep Long- and Short-Term Memory (LSTM) architecture to perform PM2.5 prediction. The performance in terms of prediction results and allied mean square error exhibit that the proposed model performs superior over other existing techniques, including classical LSTM methods. Results confirm that the use of clustered features can yield more accurate performance than the random feature learning. The overall proposed model was implemented over Apache Spark platform, which makes it suitable for the decentralized computation or BigData analytics purposes.","PeriodicalId":417643,"journal":{"name":"2022 IEEE International Conference on Distributed Computing and Electrical Circuits and Electronics (ICDCECE)","volume":"78 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126214751","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

IoT Based Parameters Calculation of Electric Bicycle using OpenModelica Simulation Tool with Data Analytics Technology 基于物联网的电动自行车参数计算——基于OpenModelica仿真工具和数据分析技术

2022 IEEE International Conference on Distributed Computing and Electrical Circuits and Electronics (ICDCECE) Pub Date : 2022-04-23 DOI: 10.1109/icdcece53908.2022.9792637

V. K, Amarjeet Singh, Darshini Machamma M S, M. T. Ali, Appaji

引用次数: 0

Data augmented Approach to Optimizing Asynchronous Actor-Critic Methods 优化异步Actor-Critic方法的数据增强方法

2022 IEEE International Conference on Distributed Computing and Electrical Circuits and Electronics (ICDCECE) Pub Date : 2022-04-23 DOI: 10.1109/icdcece53908.2022.9792764

S. N., Pradyumna Rahul K, Vaishnavi Sinha

引用次数: 1

Ranking the Customer Reviews from Mobile Commerce Big data: K means Clustering 移动商务大数据客户评论排序:K表示聚类

2022 IEEE International Conference on Distributed Computing and Electrical Circuits and Electronics (ICDCECE) Pub Date : 2022-04-23 DOI: 10.1109/icdcece53908.2022.9792800

C. Kumaresan, P. Thangaraju

{"title":"Ranking the Customer Reviews from Mobile Commerce Big data: K means Clustering","authors":"C. Kumaresan, P. Thangaraju","doi":"10.1109/icdcece53908.2022.9792800","DOIUrl":"https://doi.org/10.1109/icdcece53908.2022.9792800","url":null,"abstract":"Big data analytics in the field of mobile commerce gathers huge measures of data, yet it doesn't use the information to settle on constant choices. Rather, there is ordinarily a slack between when the data is gathered and when the data is dissected. In short, such data is so substantial and complex that none of the conventional data the executives’ devices can store it or procedure it effectively. The moto of this article is to analyze the big data analytics in mobile commerce field. In m commerce area customer reviews is an important thing to purchase products. Here we mine the high customer reviews based on K means clustering algorithm to cluster the reviews as per the features. The proposed work optimizes the features by using Salp Swarm Algorithm (SSA) to find the efficient features. The performance of the proposed work relates to group the reviews, and ranking the reviews for particular sites based on some products. The result depicted that Amazon and flip kart performs better reviews from customers in mobile commerce sites compared to other shopping sites. The proposed result gives minimum cost, high quality and best brand performs in Amazon platform than others and recognize optimally utilizing the K-means clustering algorithm.","PeriodicalId":417643,"journal":{"name":"2022 IEEE International Conference on Distributed Computing and Electrical Circuits and Electronics (ICDCECE)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131739499","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