Current Trends in Computer Sciences & Applications最新文献

Retrofitting Mobile First Design, Responsive Design: Driving Factors, Approach, Best Practices and Design Considerations 改进移动优先设计，响应式设计:驱动因素，方法，最佳实践和设计考虑

Current Trends in Computer Sciences & Applications Pub Date : 2020-12-02 DOI: 10.32474/ctcsa.2020.02.000131

Bhanu Prakash

引用次数: 0

Random Graph Models with Non-Independent Edges 非独立边随机图模型

Current Trends in Computer Sciences & Applications Pub Date : 2020-11-30 DOI: 10.32474/ctcsa.2020.02.000130

Zohre R. Mojaveri, A. Faragó

引用次数: 1

Analysis and Review of Average Fuzzy Inference Technique and Other AI Techniques Used for Robot Control and Navigation 平均模糊推理技术及其他人工智能技术在机器人控制与导航中的应用分析与综述

Current Trends in Computer Sciences & Applications Pub Date : 2020-11-23 DOI: 10.32474/ctcsa.2020.02.000129

R. ParhiDayal

{"title":"Analysis and Review of Average Fuzzy Inference Technique and Other AI Techniques Used for Robot Control and Navigation","authors":"R. ParhiDayal","doi":"10.32474/ctcsa.2020.02.000129","DOIUrl":"https://doi.org/10.32474/ctcsa.2020.02.000129","url":null,"abstract":"Finite element analysis [34-37] can be utilized for evaluating the mechanical properties of various structures used for fabricating bodies and frames of the robots. Nature driven Fire Fly algorithm is one of the promising AI technique to address many optimization problems. Engineers have used Fire Fly algorithm [38-40] for path planning of robotic agents in uncertain environments. Abstract In this paper application of average fuzzy inference technique has been analysed for navigation control of robotic agent. Also, the reviews of other AI techniques for control of robots are carried out. The robotic agent uses sensors to map the surroundings and take the decision with the help of Fuzzy AI technique to avoid obstacles. In this paper a novel averaging method has been deployed to optimize the results obtained from various fuzzy membership functions. Using the Average Fuzzy Inference technique robot navigates from start position to goal position avoiding obstacles while reaching the target. The simulation results agree with experimental results. The methodology can be used for various applications by the scientific communities to address various engineering problems.","PeriodicalId":303860,"journal":{"name":"Current Trends in Computer Sciences & Applications","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126100194","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 New Mechanism for Generating an Ipv6 Address in IoT Context 在物联网环境下生成Ipv6地址的新机制

Current Trends in Computer Sciences & Applications Pub Date : 2020-11-16 DOI: 10.32474/ctcsa.2020.02.000128

Ali El Ksimi

引用次数: 0

Tendency of Educational Data Mining in Digital Learning Platform 数字化学习平台中教育数据挖掘的发展趋势

Current Trends in Computer Sciences & Applications Pub Date : 2020-11-02 DOI: 10.32474/ctcsa.2020.02.000127

G. Sharma

引用次数: 0

Modeling and Analysis of LTE Downlink System-3GPP LTE下行链路系统3gpp的建模与分析

Current Trends in Computer Sciences & Applications Pub Date : 2020-03-04 DOI: 10.32474/CTCSA.2020.01.000125

F. FarhanAlta, M. HaseebZafar

引用次数: 0

Design of a Smart Military Fatigue 智能军用疲劳系统设计

Current Trends in Computer Sciences & Applications Pub Date : 2019-12-19 DOI: 10.32474/ctcsa.2019.01.000124

Ogboru Or

引用次数: 0

Duplicate Detection Models for Bug Reports of Software Triage Systems: A Survey 软件分类系统Bug报告的重复检测模型:综述

Current Trends in Computer Sciences & Applications Pub Date : 2019-12-17 DOI: 10.32474/ctcsa.2019.01.000123

Behzad Soleimani Neysiani

引用次数: 4

Stock Market Trend Prediction Model Using Data Mining Techniques 基于数据挖掘技术的股票市场趋势预测模型

Current Trends in Computer Sciences & Applications Pub Date : 2019-12-12 DOI: 10.32474/ctcsa.2019.01.000122

Oyelade Iyinoluwa

{"title":"Stock Market Trend Prediction Model Using Data Mining Techniques","authors":"Oyelade Iyinoluwa","doi":"10.32474/ctcsa.2019.01.000122","DOIUrl":"https://doi.org/10.32474/ctcsa.2019.01.000122","url":null,"abstract":"Stock market prediction is essential and of great interest because successful prediction of stock prices may promise smart benefits. These tasks are highly complicated and very difficult. Many researchers have made valiant attempts in data mining to devise an efficient system for stock market movement analysis. This research has developed an efficient approach to stock market trend prediction by employing Frequent Pattern growth and Fuzzy C-means clustering algorithms. This research has been encouraged by the need of predicting the stock market to facilitate investors about when to buy, sell or hold a stock in order to make profit. Firstly, the original stock market data were converted into interpreted historical (financial) data via technical indicators. Based on these technical indicators, datasets that are required for analysis was created. Subsequently, Frequent Pattern Growth algorithm was used to generate frequent patterns. Based on these frequent patterns, Fuzzy C-means clustering technique was used to formulate the prediction model. Finally, a classification technique, K-Nearest Neighbor classifier was employed to predict the stock market trends. The results from the stock market trend prediction were validated through Hit ratio evaluation metric to estimate the prediction accuracy. Comparative analysis was carried out for the proposed model and a neural network model was used to benchmark the proposed model. The obtained results showed that proposed model produced better results than the neural network model in terms of accuracy. This paper has provided a novel approach which combines FP-Growth, Fuzzy C-means and K-Nearest Neighbor algorithms for stock market trend prediction.","PeriodicalId":303860,"journal":{"name":"Current Trends in Computer Sciences & Applications","volume":"78 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123433158","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

Multidimensional: User with File Content and Server’s Status Based Authentication for Secure File Operations in Cloud 多维:具有文件内容的用户和基于服务器状态的云端安全文件操作认证

Current Trends in Computer Sciences & Applications Pub Date : 2019-11-22 DOI: 10.32474/CTCSA.2019.01.000121

Jims Marchang, Jing Wang, Abayomi Otebolaku, Timibloudi S. Enamamu, Daniel Porter, Benjamin Sanders

{"title":"Multidimensional: User with File Content and Server’s Status Based Authentication for Secure File Operations in Cloud","authors":"Jims Marchang, Jing Wang, Abayomi Otebolaku, Timibloudi S. Enamamu, Daniel Porter, Benjamin Sanders","doi":"10.32474/CTCSA.2019.01.000121","DOIUrl":"https://doi.org/10.32474/CTCSA.2019.01.000121","url":null,"abstract":"The popularity of data storage in cloud servers is getting more and more favoured in recent times. Its ease of storage, availability and synchronization of personalized cloud file storage using client applications made cloud storage more popular than ever. In cloud storage system, using a basic authentication method like username and password are still one of the most popular forms of authentication. However, the security ensure by such traditional authentication method is weak and vulnerable because the user name and password can be compromised by intruders or the user account can be left open by forgetting to logoff in public computers, leading to exposure of information to unauthorised users and hackers. In recent years, using a two-factor authentication has become a trend throughout network-based cloud services, online banking system and any form of services that requires user authentication. Here, in this paper a second layer authentication in the form of session key is used to ensure the authenticity of the activities of the user after user’s web-based account is logged-in successfully. The interesting and the critical contribution in this paper is the way the session key is generated and delivers to the authentic user. The key is generated by using the hash value of the file content, file size, file last modified, pseudo-random generated by the server using CPU temperature, clock speed, system time, and network packet timings, and user based 8 digit random position selection from a 32 digit Hex to mitigate against the attacker while performing vital file activities which may lead to data lost or data destruction or when user’s credentials are compromised.","PeriodicalId":303860,"journal":{"name":"Current Trends in Computer Sciences & Applications","volume":"66 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115664314","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