International Journal of Computers and Applications最新文献_第4页

TokenLink: a secure loyalty point exchange system powered by smart contracts TokenLink: 由智能合约驱动的安全忠诚度积分兑换系统

International Journal of Computers and Applications Pub Date : 2023-12-20 DOI: 10.1080/1206212x.2023.2293592

Hartik Suhagiya, Hrithik Mistry, Aryan Trivedi, Ramchandra Mangrulkar, Pallavi Chavan

引用次数: 0

Survey on age-related macular degeneration detection in OCT image 关于在 OCT 图像中检测老年黄斑变性的调查

International Journal of Computers and Applications Pub Date : 2023-12-18 DOI: 10.1080/1206212x.2023.2286032

S. R. Deepti, P. C. Karthik

引用次数: 0

Machine learning in concept drift detection using statistical measures 利用统计量进行概念漂移检测的机器学习

International Journal of Computers and Applications Pub Date : 2023-12-15 DOI: 10.1080/1206212x.2023.2289706

Nail Adeeb Ali Abdu, K. O. Basulaim

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MTTF: a multimodal transformer for temperature forecasting MTTF：用于温度预报的多模式变压器

International Journal of Computers and Applications Pub Date : 2023-12-08 DOI: 10.1080/1206212x.2023.2289708

Yang Cao, Junhai Zhai, Wei Zhang, Xuesong Zhou, Feng Zhang

引用次数: 0

Unleashing the power of 2D CNN with attention and pre-trained embeddings for enhanced online review analysis 利用注意力和预训练嵌入释放二维 CNN 的威力，增强在线评论分析能力

International Journal of Computers and Applications Pub Date : 2023-12-06 DOI: 10.1080/1206212x.2023.2283647

Amrithkala M. Shetty, Mohammed Fadhel Aljunid, D. H. Manjaiah

引用次数: 0

Convolutional neural network for intrusion detection using blockchain technology 利用区块链技术检测入侵的卷积神经网络

International Journal of Computers and Applications Pub Date : 2023-11-28 DOI: 10.1080/1206212x.2023.2284443

Ahmed Aljabri, F. Jemili, O. Korbaa

引用次数: 0

Server node video processing based on feature depth analysis algorithm 基于特征深度分析算法的服务器节点视频处理

International Journal of Computers and Applications Pub Date : 2023-11-25 DOI: 10.1080/1206212X.2023.2283648

Yuanhan Du, Ling Wang, Yebo Tao

{"title":"Server node video processing based on feature depth analysis algorithm","authors":"Yuanhan Du, Ling Wang, Yebo Tao","doi":"10.1080/1206212X.2023.2283648","DOIUrl":"https://doi.org/10.1080/1206212X.2023.2283648","url":null,"abstract":"ABSTRACT The complex and diverse server video data leads to the problem of effective retrieval of these data. The current shot edge detection algorithm and key frame extraction algorithm in server node video processing have problems such as poor extraction performance and poor adaptability. Therefore, the research combined the feature depth analysis to improve the two, and the performance was verified by experiments. The shot detection algorithm is verified by modifying the secondary detection model. This method can detect lens mutation, gradual change and other phenomena well, and the accuracy rate can reach 99.7%. The precision under the gradient lens is 92.08%, far higher than 63.50% and 85.39% of ISIFT and CS-DFS. In the verification experiment using Convolution Neural Networks (CNNs) key frame extraction algorithm, the number of key frame extractions of the proposed algorithm can reach up to 88 frames. Compared with other methods, the accuracy of the algorithm studied can reach 99.67%, which is higher than the comparison algorithm. In general, the improved algorithm proposed in the study has high adaptability to edge detection and the ability to express key frame video, and has high practicability in actual server node video processing.","PeriodicalId":39673,"journal":{"name":"International Journal of Computers and Applications","volume":"117 11","pages":"58 - 65"},"PeriodicalIF":0.0,"publicationDate":"2023-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139237218","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 novel multi document summarization with document-elements augmentation for learning materials using concept based ILP and clustering methods 使用基于概念的 ILP 和聚类方法，为学习材料编写带有文档元素增强功能的新型多文档摘要

International Journal of Computers and Applications Pub Date : 2023-11-24 DOI: 10.1080/1206212x.2023.2284446

K. Sakkaravarthy Iyyappan, S. Balasundaram

引用次数: 0

Demystifying the black box: an overview of explainability methods in machine learning 黑箱解密：机器学习中的可解释性方法概述

International Journal of Computers and Applications Pub Date : 2023-11-24 DOI: 10.1080/1206212x.2023.2285533

S. Kinger, Vrushali Kulkarni

引用次数: 0

Hybrid metaheuristic model based performance-aware optimization for map reduce scheduling 基于性能感知模型的混合元搜索优化 map reduce 调度

International Journal of Computers and Applications Pub Date : 2023-11-21 DOI: 10.1080/1206212X.2023.2277553

Vishal Kumar, Sumit Kushwaha

{"title":"Hybrid metaheuristic model based performance-aware optimization for map reduce scheduling","authors":"Vishal Kumar, Sumit Kushwaha","doi":"10.1080/1206212X.2023.2277553","DOIUrl":"https://doi.org/10.1080/1206212X.2023.2277553","url":null,"abstract":"Because of the rapid rise in the count of corporations using cloud-dependent infrastructure as the foundation for big data storing and analysis. The fundamental difficulty in scheduling big data services in cloud-dependent systems is ensuring the shortest possible makespan while simultaneously reducing the number of resources being used. We have created a new, secure map reduce scheduling method that functions as follows. Initially, the cloud architecture is designed and the tasks are generated. In the pre-processing phase, the huge set of tasks was processed by the map-reduce scheduling framework. Afterward, the optimal task scheduling task is conducted which utilizes a hybrid algorithm named Tunicate Combined Moth Flame Algorithm (TCMFA), which provides better task scheduling via providing optimal makespan, execution time, and security. This proposed TCMFA is the hybridization of both Moth Flame Optimization (MFO) and Tunicate Swarm Algorithm (TSA). The error rate of the TCMFA gets reduced to 320 approximately over other conventional methods such as RSA, ACO, GHO, BTS, OWPSO, BES, PRO, SOA, COOT, TSA & MFO which proves the accuracy of our TCMFA and makes it more efficient and secure for optimal map-reduce scheduling.","PeriodicalId":39673,"journal":{"name":"International Journal of Computers and Applications","volume":"7 1","pages":"776 - 788"},"PeriodicalIF":0.0,"publicationDate":"2023-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139253980","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