NSGA-III-Based Deep-Learning Model for Biomedical Search Engines

Gupta, Manish; Kumar, Naresh; Singh, Bhupesh Kumar; Gupta, Neha

doi:10.1155/2021/9935862

Cited by 16 publications

(5 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Cubic SVM provides the highest efficiency of 0.99. Several machine learning approaches as described in [29][30][31][32][33][34][35][36][37] can be utilized in a similar way. Authors in [38] proposed a classroom activity detection approach using video surveillance.…”

Section: Literature Reviewmentioning

confidence: 99%

Recognition of Suspicious Human Activity in Video Surveillance: A Review

Gupta

Agarwal

2023

Eng. Technol. Appl. Sci. Res.

View full text Add to dashboard Cite

Over the past few years, there has been a noticeable growth in the use of video surveillance systems, frequently functioning as integrated systems that remotely monitor key locations. In order to prevent terrorism, theft, accidents, illegal parking, vandalism, fighting, chain snatching, and crime, human activities can be observed through visual surveillance in sensitive and public places like buses, trains, airports, banks, shopping centers, schools, and colleges. In this paper, a review of the state-of-the-art is provided, showing the overall development of identifying suspicious behavior from surveillance recordings over the past few years. We give a quick overview of the issues and difficulties associated with recognizing suspicious human activity. The purpose of this publication is to give this field's scholars a literature evaluation of several suspicious activity recognition systems along with their general structure.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Recognition of Suspicious Human Activity in Video Surveillance: A Review

Gupta

Agarwal

2023

Eng. Technol. Appl. Sci. Res.

View full text Add to dashboard Cite

show abstract

“…MobileNetV3-Large is more accurate than ImageNet with less latency than MobileNetV2 [10]. In [16][17][18][19][20][21][22][23][24][25][26][27] one can review some machine learning models that show its importance and improved results.…”

Section: A Modelsmentioning

confidence: 99%

Suspicious Activity Classification in Classrooms using Deep Learning

Gupta,

Agarwal

2023

Eng. Technol. Appl. Sci. Res.

Self Cite

View full text Add to dashboard Cite

Video processing is attracting the attention of both research and industry. The existence of intelligent surveillance cameras with high processing power has paved the way for designing intelligent visual surveillance systems. Along with analyzing video for information recovery, it is nowadays used to analyze live surveillance video to detect activities. These systems are implemented in real time. The proposed work's goal is to create a method that can examine and discover suspicious behaviors in the lecture room environment. Video analytics offers the most efficient answer because it enables pointing an occasion and retrieves applicable statistics from the video recorded. The method aims to identify suspicious activities like fighting, sleeping, looking elsewhere, eating, etc. that the students might be doing. The proposed method involves breaking a video input into frames and converting it into image data because the model has been trained on images collected from the internet. Several models were tested and experimented with, including efficientnet_b2, spnasnet_100, efficientnet_b3, and mobilenetv3_large_100. Parameters such as the Learning Rate were optimized to find out the best method and create a system with the best results.

show abstract

“…Crawling. Nowadays, many companies do not list their job openings on the common job listing portals on the web [35][36][37][38][39][40][41][42][43][44][45][46][47][48][49]. It was decided to individually crawl the API calls [50,51] of these companies' personal job portals.…”

Section: Standalone Company Websitementioning

confidence: 99%

Technical Job Recommendation System Using APIs and Web Crawling

Kumar¹,

Gupta²,

Sharma

et al. 2022

Computational Intelligence and Neuroscience

Self Cite

View full text Add to dashboard Cite

There has been a sudden boom in the technical industry and an increase in the number of good startups. Keeping track of various appropriate job openings in top industry names has become increasingly troublesome. This leads to deadlines and hence important opportunities being missed. Through this research paper, the aim is to automate this process to eliminate this problem. To achieve this, Puppeteer and Representational State Transfer (REST) APIs for web crawling have been used. A hybrid system of Content-Based Filtering and Collaborative Filtering is implemented to recommend these jobs. The intention is to aggregate and recommend appropriate jobs to job seekers, especially in the engineering domain. The entire process of accessing numerous company websites hoping to find a relevant job opening listed on their career portals is simplified. The proposed recommendation system is tested on an array of test cases with a fully functioning user interface in the form of a web application. It has shown satisfactory results, outperforming the existing systems. It thus testifies to the agenda of quality over quantity.

show abstract

NSGA-III-Based Deep-Learning Model for Biomedical Search Engines

Cited by 16 publications

References 29 publications

Recognition of Suspicious Human Activity in Video Surveillance: A Review

Recognition of Suspicious Human Activity in Video Surveillance: A Review

Suspicious Activity Classification in Classrooms using Deep Learning

Technical Job Recommendation System Using APIs and Web Crawling

Contact Info

Product

Resources

About