Emperor Journal of Applied Scientific Research最新文献_第5页

An Application of a Deep Learning Algorithm for Detection of Accidents under bad CCTV Monitoring Conditions in Tunnels using ODTS &R-CNN 基于ODTS &R-CNN的深度学习算法在隧道监控不良条件下事故检测中的应用

Emperor Journal of Applied Scientific Research Pub Date : 1900-01-01 DOI: 10.35338/ejasr.2022.4902

D. V. Kumar, L. Y. Reddy, Y. Vinnie, N. Tejaswini, A. Raju

{"title":"An Application of a Deep Learning Algorithm for Detection of Accidents under bad CCTV Monitoring Conditions in Tunnels using ODTS &R-CNN","authors":"D. V. Kumar, L. Y. Reddy, Y. Vinnie, N. Tejaswini, A. Raju","doi":"10.35338/ejasr.2022.4902","DOIUrl":"https://doi.org/10.35338/ejasr.2022.4902","url":null,"abstract":"In this project, an Object Detection and Tracking System (ODTS) can be brought and used together with a famous deep gaining knowledge of community, the Faster Regional Convolution Neural Network (Faster R-CNN), for Object Detection and a Conventional Object Tracking set of rules for computerized detection and tracking of surprising occasions on CCTVs in tunnels, which can be probable to be (1) Wrong-Way Driving (WWD), (2) Stop, (three) Person out of the automobile in ODTS takes a video body in time as enter and makes use of Object Detection to generate Bounding Box (BBox) findings, evaluating the BBoxes of the modern and former video frames to assign a completely unique ID wide variety to every shifting and diagnosed item. This technique lets in you to display a shifting item in real-time, that's hard to do with conventional item detection frameworks. With a group of occasion pix in tunnels, a deep gaining knowledge of version in ODTS changed into educated to Average Precision (AP) values of zero.8479, zero.7161, and zero.9085 for goal items Car, Person, and Fire, respectively. The ODTS-primarily based totally Tunnel CCTV Accident Detection System changed into then examined the use of 4 twist of fate recordings, one for every twist of fate, the use of a educated deep gaining knowledge of version. As a consequence, inside 10 seconds, the gadget can locate all injuries. The maximum important truth is that once the education dataset grows larger, the detection functionality of ODTS can be robotically multiplied with none adjustments to the programme codes.","PeriodicalId":112326,"journal":{"name":"Emperor Journal of Applied Scientific Research","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121276462","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

A Study on Awareness of Digital Currencies on Digital ERA in reference with Nagercoil Town 数字时代数字货币意识研究——以Nagercoil镇为例

Emperor Journal of Applied Scientific Research Pub Date : 1900-01-01 DOI: 10.35337/eijfmr.2019.1605

Dr. S. Muthulekshmi, Dr. C. Subathra

引用次数: 0

Traffic Incident Detection Method Based on Machine Learning 基于机器学习的交通事件检测方法

Emperor Journal of Applied Scientific Research Pub Date : 1900-01-01 DOI: 10.35338/ejasr.2022.4405

B. Nalini, K. Himabindu, Dr. S. Jansi

{"title":"Traffic Incident Detection Method Based on Machine Learning","authors":"B. Nalini, K. Himabindu, Dr. S. Jansi","doi":"10.35338/ejasr.2022.4405","DOIUrl":"https://doi.org/10.35338/ejasr.2022.4405","url":null,"abstract":"This paper aims to scale back the Traffic incidents entitled “TRAFFIC INCIDENT DETECTION technique supported MACHINE LEARNING” . Timely and actual detection of traffic incidents will effectively cut back personal casualties and property losses, and improve the capability of macro-control and scientific decision-making of traffic. The unbalance of traffic incident knowledge features a nice impact on the detection impact. Therefore, a traffic incident detection technique supported machine learning (FA-WRF) is intended. Through the analysis of the amendment rule of traffic flow framework to make the initial incident variable. The correlational analysis (FA) technique is employed to scale back the extent of the initial incident variables. victimization Bootstrap improved algorithmic rule to fate the information extraction normal of the coaching set. The Medical counseling Committee constant worth is calculated for the classification impact of the choice tree when coaching, and is allotted to every tree as a weight worth, therefore on make sure that the trees with higher classification capability have additional ballot power within the ballot method, so improve the classification performance of the random forest (RF) algorithmic rule for unbalanced knowledge.","PeriodicalId":112326,"journal":{"name":"Emperor Journal of Applied Scientific Research","volume":"2012 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128206957","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

Facial Expression Recognition based Restaurant Scoring System Using Deep Learning 基于面部表情识别的深度学习餐厅评分系统

Emperor Journal of Applied Scientific Research Pub Date : 1900-01-01 DOI: 10.35338/ejasr.2022.4801

M. A. Srinivas, Vikas Reddy Kambam, Sai keerthana Pinnu, Sriram Edla, Arun Kumar Komera

引用次数: 0

Deep Learning for Traffic Prediction: Methods, Analysis, and Future Directions 交通预测的深度学习:方法、分析和未来方向

Emperor Journal of Applied Scientific Research Pub Date : 1900-01-01 DOI: 10.35338/ejasr.2022.4903

Ms. Mutyala Keerthi, OLETI. Gayatri Srinitya, A. Raju, K. Praveena, P. Jagadesh

{"title":"Deep Learning for Traffic Prediction: Methods, Analysis, and Future Directions","authors":"Ms. Mutyala Keerthi, OLETI. Gayatri Srinitya, A. Raju, K. Praveena, P. Jagadesh","doi":"10.35338/ejasr.2022.4903","DOIUrl":"https://doi.org/10.35338/ejasr.2022.4903","url":null,"abstract":"In an clever transportation gadget, visitors prediction is critical. Accurate visitors forecasting can assist with direction planning, car dispatching, and visitors congestion reduction. Due to the complicated and dynamic spatial-temporal relationships among one of a kind elements in the street community, this trouble is tough to solve. Recently, a considerable quantity of studies paintings has been dedicated to this area, specifically the deep mastering technique, which has extensively stepped forward visitors prediction abilities. The intention of this have a take a observe is to offer a whole evaluation of deep mastering-primarily based totally on visitors prediction algorithms from numerous angles. In particular, we offer a taxonomy and a precis of acknowledged visitors prediction algorithms. Second, we offer a listing of contemporary-day today's methodologies for numerous visitors forecast programs. Third, we acquire and set up normally used public datasets from the literature to make it simpler for different researchers. Furthermore, we adopt thorough experiments to evaluate the overall performance of various methods on a real-global public dataset to offer an assessment and analysis. Finally, we discover the field's unsolved problems.","PeriodicalId":112326,"journal":{"name":"Emperor Journal of Applied Scientific Research","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124790442","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

Biological Treatment of Municipal Solid Waste in Erode Corporation, Tamilanadu, India 城市生活垃圾的生物处理，泰米尔纳德邦

Emperor Journal of Applied Scientific Research Pub Date : 1900-01-01 DOI: 10.35338/ejasr.2022.4101

Shuvendra Mohan, N. Muralimohan, K. Vidhya, P. Tamilchelvan, K. N. D. Mary

{"title":"Biological Treatment of Municipal Solid Waste in Erode Corporation, Tamilanadu, India","authors":"Shuvendra Mohan, N. Muralimohan, K. Vidhya, P. Tamilchelvan, K. N. D. Mary","doi":"10.35338/ejasr.2022.4101","DOIUrl":"https://doi.org/10.35338/ejasr.2022.4101","url":null,"abstract":"In this article, we see the limit of utilising different city solid waste streams as feedstock for effective power energy creation. These waste streams include ordinarily degradable waste, yet are not limited to mixed burnable waste, versatile and plastic waste, clinical waste with benefits, normal biodegradable waste, biomass, and sewage grime. Current advancements such as anaerobic dealing, gasification, and pyrolysis have been investigated in close proximity to the area and waste stream sums in the chosen test region.It was seen that there are run of the mill, social and monetary benefits in the waste to energy approach for the waste streams kept an eye out for. The reachability of executing such advances is, on an exceptionally fundamental level, dependent upon the fundamental capital hypothesis and the important cost of the work space. Various factors blend the size of the waste stream, the cost of things, and deals. The quick urbanisation and change in lifestyle has increased the waste weight and, as required, ruining loads on the metropolitan environment to unmanageable and upsetting degrees. This assessment revelation embraced existing to foster waste dumping fights are fully past their end and under unsanitary conditions, impelling defiling of water sources, augmentation of vectors of communicable contamination, foul smells and aromas, the appearance of disastrous metabolites, sedative environment and imperfection, etc.","PeriodicalId":112326,"journal":{"name":"Emperor Journal of Applied Scientific Research","volume":"150 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116345363","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

Instagram Filtering Hashtags using the hits Algorithm and Crowd Tagging Instagram过滤标签使用命中算法和人群标签

Emperor Journal of Applied Scientific Research Pub Date : 1900-01-01 DOI: 10.35338/ejasr.2022.4603

Dr. Vignesh Janarthanan, S. Keerthana, M. Manideep, Y. Sowmya, Prasanna Kumar

{"title":"Instagram Filtering Hashtags using the hits Algorithm and Crowd Tagging","authors":"Dr. Vignesh Janarthanan, S. Keerthana, M. Manideep, Y. Sowmya, Prasanna Kumar","doi":"10.35338/ejasr.2022.4603","DOIUrl":"https://doi.org/10.35338/ejasr.2022.4603","url":null,"abstract":"Instagram is a great place to look for descriptive tags for photographs and other types of information. Inaccordance with the learning by example paradigm, the tags–image pairs can be utilised to train automated image annotation (AIA) systems. In earlier research, we found that, on average. Approximately 22% of Instagram hashtags are related to the image's visual content,accompany, in the sense that they are descriptive hashtags, whereas there are many irrelevant hashtags, in the sense that they are not descriptive hashtags.Stop using hashtags on completely different photographs merely to get more clicks and likes.Enhancement of searchability We provide a revolutionary methodology in this study that is based on the collective intelligence principles that aid in the discovery of those hashtags. We demonstrate this in particular that the use of a modified version of the widely used hyper link induced topic search. In the context of crowd tagging, the (HITS) algorithm provides an effective and consistent method for locating pairs of Instagram photographs and hashtags, resulting in representative and noise-free results. Content-based image retrieval training sets We used crowdsourcing as a proof of concept platform Figure-eight to enable for the collection of collective intelligence in the form of tag selection.For Instagram hashtags, this is known as (crowdtagging). Figure-crowdtagging eight's data is utilised to create bipartite networks in which the first kind of node relates to the annotators and the second type of node corresponds to the annotations input the hashtags they've chosen. The HITS algorithm is used to rank the annotators in the first place,in terms of their efficiency in the crowdtagging activity, and then to find the appropriate hashtags for each situation image.","PeriodicalId":112326,"journal":{"name":"Emperor Journal of Applied Scientific Research","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123632965","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

Automatically Identifying & Counting Animals in Camera Trap Image Using DL 利用深度学习自动识别和计数相机陷阱图像中的动物

Emperor Journal of Applied Scientific Research Pub Date : 1900-01-01 DOI: 10.35338/ejasr.2022.4502

M. I. Prasad, Dr. J. Sreenivasan

引用次数: 0