Proceedings of the 2022 Fourteenth International Conference on Contemporary Computing最新文献_第9页

Assessment of Modified BAT Algorithm for MOOC Learner Influence Maximization 改进BAT算法对MOOC学习者影响最大化的评估

Proceedings of the 2022 Fourteenth International Conference on Contemporary Computing Pub Date : 2022-08-04 DOI: 10.1145/3549206.3549297

K. Aggarwal, Anuja Arora

{"title":"Assessment of Modified BAT Algorithm for MOOC Learner Influence Maximization","authors":"K. Aggarwal, Anuja Arora","doi":"10.1145/3549206.3549297","DOIUrl":"https://doi.org/10.1145/3549206.3549297","url":null,"abstract":"Identification of a small group of individuals based on their maximal influence cascade is influence maximization. During the COVID-19 pandemic, discussion forums on the Massive Open Online Course (MOOC) platform have become a paramount interaction medium among learners, and the identification of influential learners evolved as a substantial research issue. In this research paper, an optimization function based on an independent cascade is established for the discussion forum influence maximization problem. A modified version of the BAT algorithm is proposed which memorizes the bad experience of the BAT. The proposed Modified algorithm helps the BAT to remember the worst location that has already been traversed for a reliable estimation in an optimized manner while exploring the best solution. Further, the performance of BAT and Modified BAT for influence maximization on the discussion forum network of a MOOC platform is evaluated which shows the excellent performance of modified BAT. Convergence graph for different populations on deviating probability depicts the effective performance of modified BAT over generic BAT algorithm.","PeriodicalId":199675,"journal":{"name":"Proceedings of the 2022 Fourteenth International Conference on Contemporary Computing","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125321639","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

Sample Reduction for Support Vector Data Description (SVDD) by Farthest Boundary Point Estimation (FBPE) using Gradients of Data Density 基于数据密度梯度的最远边界点估计支持向量数据描述(SVDD)的样本约简

Proceedings of the 2022 Fourteenth International Conference on Contemporary Computing Pub Date : 2022-08-04 DOI: 10.1145/3549206.3549287

Pratyush Pareek, Aaryan Bhardwaj, Sanskar Patro, Anirudh Arora, Muskan Deep Kaur Maini, Bagesh Kumar, O. P. Vyas

{"title":"Sample Reduction for Support Vector Data Description (SVDD) by Farthest Boundary Point Estimation (FBPE) using Gradients of Data Density","authors":"Pratyush Pareek, Aaryan Bhardwaj, Sanskar Patro, Anirudh Arora, Muskan Deep Kaur Maini, Bagesh Kumar, O. P. Vyas","doi":"10.1145/3549206.3549287","DOIUrl":"https://doi.org/10.1145/3549206.3549287","url":null,"abstract":"Classification is a quintessential application of machine learning for which support vector machines have been used ubiquitously because of their optimal margins and ease of use. However, they’re rarely used for large datasets due to the cubic time complexity of their training process. This has inspired several papers attempting to reduce the number of features or the number of training samples to lessen the training time of the SVMs. This paper aims to propose a novel approach for reducing the number of training samples for support vector data description (SVDD) while attempting to maximize the knowledge of the target class by selecting the most promising candidates for support vectors, which are the farthest boundary points of the data clusters. The proposed algorithm utilizes the density gradient across the data distribution to uniformly detect the boundary points, which are sampled as potential support vectors to train the support vector machines in a smaller amount of time without significant loss in accuracy. The proposed algorithm is verified via tests conducted on Human Activity Recognition, Breast Cancer Detection, and Heart Disease Detection Datasets.","PeriodicalId":199675,"journal":{"name":"Proceedings of the 2022 Fourteenth International Conference on Contemporary Computing","volume":"148 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116057143","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

Multi-Vehicle Tracking and Speed Estimation Model using Deep Learning 基于深度学习的多车跟踪和速度估计模型

Proceedings of the 2022 Fourteenth International Conference on Contemporary Computing Pub Date : 2022-08-04 DOI: 10.1145/3549206.3549254

Prajwal, Navaneeth, Tharun, Amit Kumar

引用次数: 1

Multi-Domain Network Traffic Analysis using Machine Learning and Deep Learning Techniques 使用机器学习和深度学习技术的多域网络流量分析

Proceedings of the 2022 Fourteenth International Conference on Contemporary Computing Pub Date : 2022-08-04 DOI: 10.1145/3549206.3549262

Dincy R. Arikkat, A. RafidhaRehimanK., P. Vinod, S. Yerima, W. Manoja, S. Pooja, Shilpa Sekhar, Sohan James, Josna Philomina

{"title":"Multi-Domain Network Traffic Analysis using Machine Learning and Deep Learning Techniques","authors":"Dincy R. Arikkat, A. RafidhaRehimanK., P. Vinod, S. Yerima, W. Manoja, S. Pooja, Shilpa Sekhar, Sohan James, Josna Philomina","doi":"10.1145/3549206.3549262","DOIUrl":"https://doi.org/10.1145/3549206.3549262","url":null,"abstract":"Recent heterogeneous computing facilities and data explosion introduce challenges in network traffic analysis and demand intelligence-based approaches to ensure cyber security and the protection of online digital services. Researchers have been proposing various machine and deep learning approaches for network traffic analysis in different problem domains. However, it is also crucial to understand how these algorithms perform across the different domains. Hence in this research work we extend an analysis of diverse machine learning and deep learning techniques across three different problem domains: DDoS attack detection, Malicious URL detection and Tor traffic classification. We employ three publicly available datasets to train eight different machine learning and six deep learning models for both multi-class and binary classification in our comparative study. Our experiments show that Random Forest achieved superior performance compared to other machine learning models with F-measure of 92% for multi-class traffic classification and 100% for binary classification problems. For the deep learning models, Autoencoder with Random Forest achieved superior performance with an F-measure of 89% and 100% for multi-class and binary problems respectively.","PeriodicalId":199675,"journal":{"name":"Proceedings of the 2022 Fourteenth International Conference on Contemporary Computing","volume":"71 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115230778","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

A Survey on Surveillance using Drones 无人机监视调查

Proceedings of the 2022 Fourteenth International Conference on Contemporary Computing Pub Date : 2022-08-04 DOI: 10.1145/3549206.3549253

Golla Uday Sai Theja, Motukoori Sai Murari, M. Singha, Ripon Patgiri, A. Choudhury

引用次数: 1

Analyzing Banking Services Applicability Using Explainable Artificial Intelligence 利用可解释的人工智能分析银行服务的适用性

Proceedings of the 2022 Fourteenth International Conference on Contemporary Computing Pub Date : 2022-08-04 DOI: 10.1145/3549206.3549259

Anand Sriram, Sai Srivatsa Gorti, Eshaan Ganesh Amin, Amit Kumar

引用次数: 0

De-Fence: LoRa based Hop-to-Hop Communication 防御:基于LoRa的Hop-to-Hop通信

Proceedings of the 2022 Fourteenth International Conference on Contemporary Computing Pub Date : 2022-08-04 DOI: 10.1145/3549206.3549312

Anav Chaudhary, Maanas Talwar, Avil Goel, Gaurav Singal, Riti Kushwaha

引用次数: 2

Predictive Model for Object Classification and Detection using Deep Learning 基于深度学习的目标分类和检测预测模型

Proceedings of the 2022 Fourteenth International Conference on Contemporary Computing Pub Date : 2022-08-04 DOI: 10.1145/3549206.3549265

P. Singh, R. Krishnamurthi

引用次数: 0

Reward based Video Summarization using Advanced Deep Learning Architectures 使用高级深度学习架构的基于奖励的视频摘要

Proceedings of the 2022 Fourteenth International Conference on Contemporary Computing Pub Date : 2022-08-04 DOI: 10.1145/3549206.3549279

Jaya Gupta, Deepak Garg, V. Mishra

引用次数: 0

Kernel Extreme Learning Machine with Mixture Correntropy for Face Recognition 用于人脸识别的混合熵核极限学习机

Proceedings of the 2022 Fourteenth International Conference on Contemporary Computing Pub Date : 2022-08-04 DOI: 10.1145/3549206.3549260

Bhawna Ahuja, V. P. Vishwakarma

引用次数: 0