International Journal of Mathematical Sciences and Computing最新文献_第7页

A Robotic Path Planning by Using Crow Swarm Optimization Algorithm 基于群算法的机器人路径规划

International Journal of Mathematical Sciences and Computing Pub Date : 2021-02-08 DOI: 10.5815/IJMSC.2021.01.03

Mohammed Yousif, A. Salim, Wisam K. Jummar

引用次数: 4

Action Recognition Based on the Modified Twostream CNN 基于改进双流CNN的动作识别

International Journal of Mathematical Sciences and Computing Pub Date : 2020-12-08 DOI: 10.5815/ijmsc.2020.06.03

Dan Zheng, Hang Li, Shoulin Yin

{"title":"Action Recognition Based on the Modified Twostream CNN","authors":"Dan Zheng, Hang Li, Shoulin Yin","doi":"10.5815/ijmsc.2020.06.03","DOIUrl":"https://doi.org/10.5815/ijmsc.2020.06.03","url":null,"abstract":"Human action recognition is an important research direction in computer vision areas. Its main content is to simulate human brain to analyze and recognize human action in video. It usually includes individual actions, interactions between people and the external environment. Space-time dual-channel neural network can represent the features of video from both spatial and temporal perspectives. Compared with other neural network models, it has more advantages in human action recognition. In this paper, a action recognition method based on improved space-time two-channel convolutional neural network is proposed. First, the video is divided into several equal length non-overlapping segments, and a frame image representing the static feature of the video and a stacked optical flow image representing the motion feature are sampled at random part from each segment. Then these two kinds of images are input into the spatial domain and the temporal domain convolutional neural network respectively for feature extraction, and then the segmented features of each video are fused in the two channels respectively to obtain the category prediction features of the spatial domain and the temporal domain. Finally, the video action recognition results are obtained by integrating the predictive features of the two channels. Through experiments, various data enhancement methods and transfer learning schemes are discussed to solve the over-fitting problem caused by insufficient training samples, and the effects of different segmental number, pre-training network, segmental feature fusion scheme and dual-channel integration strategy on action recognition performance are analyzed. The experiment results show that the proposed model can better learn the human action features in a complex video and better recognize the action.","PeriodicalId":312036,"journal":{"name":"International Journal of Mathematical Sciences and Computing","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122137887","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

Vertex Connected Domination Polynomial of some Coalescence of Complete and Wheel Graphs 完备图与轮图若干聚并的顶点连通支配多项式

International Journal of Mathematical Sciences and Computing Pub Date : 2020-12-08 DOI: 10.5815/ijmsc.2020.06.01

N. B. Ibrahim, Hariwan Fadhil M.Salih

引用次数: 1

Numerical Double Integration for Unequal Data Spaces 不等数据空间的数值二重积分

International Journal of Mathematical Sciences and Computing Pub Date : 2020-12-08 DOI: 10.5815/ijmsc.2020.06.04

M. N. Dhali, Nandita Barman, M. Hasan, A. K. M. S. Reza

引用次数: 2

International Journal of Mathematical Sciences and Computing Pub Date : 2020-10-08 DOI: 10.5815/ijmsc.2020.05.05

Ngoc Minh Chau, Nguyễn Thị Lan, N. X. Thao

引用次数: 1

Trend Analysis and Forecasting of Water Level in Mtera Dam Using Exponential Smoothing 基于指数平滑的Mtera大坝水位趋势分析与预测

International Journal of Mathematical Sciences and Computing Pub Date : 2020-08-08 DOI: 10.5815/ijmsc.2020.04.03

Filimon Abel Mgandu, Mashaka Mkandawile, M. Rashid

引用次数: 3

A Flow-Based Technique to Detect Network Intrusions Using Support Vector Regression (SVR) over Some Distinguished Graph Features 基于流的支持向量回归(SVR)检测网络入侵技术

International Journal of Mathematical Sciences and Computing Pub Date : 2020-08-08 DOI: 10.5815/ijmsc.2020.04.01

Yaser Ghaderipour, Hamed Dinari

{"title":"A Flow-Based Technique to Detect Network Intrusions Using Support Vector Regression (SVR) over Some Distinguished Graph Features","authors":"Yaser Ghaderipour, Hamed Dinari","doi":"10.5815/ijmsc.2020.04.01","DOIUrl":"https://doi.org/10.5815/ijmsc.2020.04.01","url":null,"abstract":"Today unauthorized access to sensitive information and cybercrimes is rising because of increasing access to the Internet. Improvement in software and hardware technologies have made it possible to detect some attacks and anomalies effectively. In recent years, many researchers have considered flow-based approaches through machine learning algorithms and techniques to reveal anomalies. But, they have some serious defects. By way of illustration, they require a tremendous amount of data across a network to train and model network’s behaviors. This problem has been caused these methods to suffer from desirable performance in the learning phase. In this paper, a technique to disclose intrusions by Support Vector Regression (SVR) is suggested and assessed over a standard dataset. The main intension of this technique is pruning the remarkable portion of the dataset through mathematics concepts. Firstly, the input dataset is modeled as a Directed Graph (DG), then some well-known features are extracted in which these ones represent the nature of the dataset. Afterward, they are utilized to feed our model in the learning phase. The results indicate the satisfactory performance of the proposed technique in the learning phase and accuracy over the other ones.","PeriodicalId":312036,"journal":{"name":"International Journal of Mathematical Sciences and Computing","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116916966","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: 5

Hybrid Cryptography: Performance Analysis of Various Cryptographic Combinations for Secure Communication 混合密码学:用于安全通信的各种密码组合的性能分析

International Journal of Mathematical Sciences and Computing Pub Date : 2020-08-08 DOI: 10.5815/ijmsc.2020.04.04

Zuhi Subedar, KarnatakaIndia Belagavi, A. Ashwini

{"title":"Hybrid Cryptography: Performance Analysis of Various Cryptographic Combinations for Secure Communication","authors":"Zuhi Subedar, KarnatakaIndia Belagavi, A. Ashwini","doi":"10.5815/ijmsc.2020.04.04","DOIUrl":"https://doi.org/10.5815/ijmsc.2020.04.04","url":null,"abstract":"The amount of data that is transmitted across the internet is continuously increasing. With the transmission of this huge volume of data, the need of an encryption algorithm that guarantees the data transmission speedily and in a secure manner is a must. Hence, to achieve security in wireless networks, cryptography plays a very important role. In this paper, several hybrid combinations, which combines both symmetric and asymmetric cryptographic techniques to offer high security with minimum key maintenance is presented. This hybrid combination offers several cryptographic primitives such as integrity, confidentiality and authentication, thereby enhancing the security. Various combinations of Advanced Encryption Standard (AES), Elliptical Curve Cryptography (ECC) and Rivest, Shamir and Adleman (RSA) algorithms are used to provide hybrid encryption. Secure Hash Algorithm (SHA-256) is also used to provide authentication and integrity. The experimental results show that the proposed hybrid combinations gives better performance in terms of computation time compared to individual cryptographic schemes.","PeriodicalId":312036,"journal":{"name":"International Journal of Mathematical Sciences and Computing","volume":"102 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134173729","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: 12

Comparative Analysis of Steganography Technique for Information Security 信息安全隐写技术的比较分析

International Journal of Mathematical Sciences and Computing Pub Date : 2020-08-08 DOI: 10.5815/ijmsc.2020.04.05

Pooja Yadav, Sangeeta Dhall

引用次数: 2

Modifying one of the Machine Learning Algorithms kNN to Make it Independent of the Parameter k by Re-defining Neighbor 修改一种机器学习算法kNN，通过重新定义邻居使其独立于参数k

International Journal of Mathematical Sciences and Computing Pub Date : 2020-08-08 DOI: 10.5815/ijmsc.2020.04.02

P. Sinha

{"title":"Modifying one of the Machine Learning Algorithms kNN to Make it Independent of the Parameter k by Re-defining Neighbor","authors":"P. Sinha","doi":"10.5815/ijmsc.2020.04.02","DOIUrl":"https://doi.org/10.5815/ijmsc.2020.04.02","url":null,"abstract":"When we are given a data set where in based upon the values and or characteristics of attributes each data point is assigned a class, it is known as classification. In machine learning a very simple and powerful tool to do this is the kNearest Neighbor (kNN) algorithm. It is based on the concept that the data points of a particular class are neighbors of each other. For a given test data or an unknown data, to find the class to which it is the neighbor one measures in kNN the Euclidean distances of the test data or the unknown data from all the data points of all the classes in the training data. Then out of the k nearest distances, where k is any number greater than or equal to 1, the class to which the test data or unknown data is the nearest most number of times is the class assigned to the test data or unknown data. In this paper, I propose a variation of kNN, which I call the ANN method (Alternative Nearest Neighbor) to distinguish it from kNN. The striking feature of ANN that makes it different from kNN is its definition of neighbor. In ANN the class from whose data points the maximum Euclidean distance of the unknown data is less than or equal to the maximum Euclidean distance between all the training data points of the class, is the class to which the unknown data is neighbor. It follows, henceforth, naturally that ANN gives a unique solution to each unknown data. Where as , in kNN the solution may vary depending on the value of the number of nearest neighbors k. So, in kNN, as k is varied the performance may vary too. But this is not the case in ANN, its performance for a particular training data is unique. For the training data [1] considered in this paper, the ANN gives 100% accurate result.","PeriodicalId":312036,"journal":{"name":"International Journal of Mathematical Sciences and Computing","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116884774","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: 5