2020 5th International Conference on Innovative Technologies in Intelligent Systems and Industrial Applications (CITISIA)最新文献

Cloud computing-based Elliptic Curve Augmented Encryption framework for Vehicular Ad-Hoc Networks 基于云计算的车载Ad-Hoc网络椭圆曲线增强加密框架

2020 5th International Conference on Innovative Technologies in Intelligent Systems and Industrial Applications (CITISIA) Pub Date : 2020-11-25 DOI: 10.1109/CITISIA50690.2020.9371811

Rahul Sharma, L. Hourany

{"title":"Cloud computing-based Elliptic Curve Augmented Encryption framework for Vehicular Ad-Hoc Networks","authors":"Rahul Sharma, L. Hourany","doi":"10.1109/CITISIA50690.2020.9371811","DOIUrl":"https://doi.org/10.1109/CITISIA50690.2020.9371811","url":null,"abstract":"The purpose of this research work is to develop a framework, which can provide message authentication for communication that occurs in a vehicular network. The main aim of the research work is to provide a high level of security and encryption for the messages that are being transferred in the vehicular network. The networks are provided with augmented encryption scheme with different approaches to have secure and private communication. The research is based on the Cloud computing-based Elliptic Curve Augmented Encryption framework for Vehicular Ad-Hoc Networks. The research for this project is done with the help of the VEA (VANET network data, Elliptic encryption, Authenticated messages) framework. The given system provides more efficient and accurate system architecture for the communicating messages in the vehicular ad hoc network with the help of different approaches for performing the encryption on the messages that are being transferred. This encryption architecture leads to increased security in the system. State of the art had comparatively lower accuracy in communication. The utilization of various algorithms for providing the encryption to the messages resulting in the increased authentication in communication and improves the efficiency and accuracy of the solution. The research has the goal to provide highlevel security architecture for message authentication with accuracy, sensitivity, specificity, computation and communication with the help of encryption algorithms.","PeriodicalId":145272,"journal":{"name":"2020 5th International Conference on Innovative Technologies in Intelligent Systems and Industrial Applications (CITISIA)","volume":"4 4","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120878275","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

An Unsupervised Machine Learning Technique for Recommendation Systems 推荐系统的无监督机器学习技术

2020 5th International Conference on Innovative Technologies in Intelligent Systems and Industrial Applications (CITISIA) Pub Date : 2020-11-25 DOI: 10.1109/CITISIA50690.2020.9371817

Rupesh Babu Shrestha, M. Razavi, P. Prasad

{"title":"An Unsupervised Machine Learning Technique for Recommendation Systems","authors":"Rupesh Babu Shrestha, M. Razavi, P. Prasad","doi":"10.1109/CITISIA50690.2020.9371817","DOIUrl":"https://doi.org/10.1109/CITISIA50690.2020.9371817","url":null,"abstract":"Due to the advancement of the Internet, various kinds of data are easily found online which helps users to find useful information which are of their interest. However, the exponential growth of data has caused it to be complex and huge, so it has become difficult to filter valuable information from it. Recommendation systems can help to overcome this issue and give recommendations to the users which matches the area of their interest. Most of the systems rely on a rating prediction algorithm where the items are taken as recommended for a user if the user’s predicted rating is high on those items. This research aims to increase the accuracy and reduce the processing time for recommendation using the prediction algorithm based on the unsupervised machine learning method. The proposed solution consists of Autoencoder to enhance the accuracy of prediction and reduce the processing time. Partially observed interaction matrix is used as input for the neural network model which outputs a complete rating matrix. The proposed solution achieved an improvement by 1.83%, 0.85% and 3.72% in cold start case for MSE, RMSE and MAE evaluation metrics respectively. The proposed solution performs better and will be used in cold start cases for datasets where timestamp value (user creation time) is used.","PeriodicalId":145272,"journal":{"name":"2020 5th International Conference on Innovative Technologies in Intelligent Systems and Industrial Applications (CITISIA)","volume":"87 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127177058","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

Use of Wearable Technologies for Analysis of Activity recognition for sports 使用可穿戴技术分析运动的活动识别

2020 5th International Conference on Innovative Technologies in Intelligent Systems and Industrial Applications (CITISIA) Pub Date : 2020-11-25 DOI: 10.1109/CITISIA50690.2020.9371779

Anil Sharma, Thair Al-Dala’in, Ghossoon Alsadoon, Ali A. Alwan

引用次数: 4

Enhanced Advanced Encryption Standard with Randomised S Box 增强的高级加密标准与随机S盒

2020 5th International Conference on Innovative Technologies in Intelligent Systems and Industrial Applications (CITISIA) Pub Date : 2020-11-25 DOI: 10.1109/CITISIA50690.2020.9371788

D. Jat, I. Gill

引用次数: 0

CITISIA 2020 Welcome Message cicisia 2020欢迎辞

2020 5th International Conference on Innovative Technologies in Intelligent Systems and Industrial Applications (CITISIA) Pub Date : 2020-11-25 DOI: 10.1109/citisia50690.2020.9371786

引用次数: 0

Analyzing Vehicle-to-Everything Communication for Intelligent Transportation System: Journey from IEEE 802.11p to 5G and Finally Towards 6G 智能交通系统的车对一切通信分析:从IEEE 802.11p到5G，最终走向6G

2020 5th International Conference on Innovative Technologies in Intelligent Systems and Industrial Applications (CITISIA) Pub Date : 2020-11-25 DOI: 10.1109/CITISIA50690.2020.9371804

G. Munasinghe, Mohsin Murtaza

{"title":"Analyzing Vehicle-to-Everything Communication for Intelligent Transportation System: Journey from IEEE 802.11p to 5G and Finally Towards 6G","authors":"G. Munasinghe, Mohsin Murtaza","doi":"10.1109/CITISIA50690.2020.9371804","DOIUrl":"https://doi.org/10.1109/CITISIA50690.2020.9371804","url":null,"abstract":"The rapid development of the Intelligent Transportation System increases the potential of Vehicular communication in a smart transportation network. Although there are several technological attempts to support vehicular communication, they have limitations from various perspectives including mobility, security, resource allocation, and device-to-device communication. In order to address those limitations, this study evaluates the most appropriate communication technology to transmit data in the future Vehicle to Everything (V2X) communication system. First, we conduct a performance evaluation of the current main vehicular communication system requirements. Then, we analyze the applications of communication technologies such as wi-fi, LTE, 5G, and 6G to fulfill the requirements of future V2X communication. Later, we explore the advanced features of 6G in the context of the Intelligent Transportation System. Then, we suggest that 6G technology as the corresponding communication technology to future V2X communication systems based on the analysis. Finally, we propose an architecture for the 6G V2X network and discuss security challenges and suggested solutions for the architecture.","PeriodicalId":145272,"journal":{"name":"2020 5th International Conference on Innovative Technologies in Intelligent Systems and Industrial Applications (CITISIA)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129515284","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: 4

Survey on Real-Time Tracking and Treatment of Infectious Diseases Using Mixed Reality in Visualisation Technique with Autoimmune Therapy 结合自身免疫治疗的混合现实可视化技术在传染病实时跟踪和治疗中的应用研究

2020 5th International Conference on Innovative Technologies in Intelligent Systems and Industrial Applications (CITISIA) Pub Date : 2020-11-25 DOI: 10.1109/CITISIA50690.2020.9371853

Dharun Teja Vujjini, R. M. Salah, A. Alsadoon, P.W.C. Prasa

{"title":"Survey on Real-Time Tracking and Treatment of Infectious Diseases Using Mixed Reality in Visualisation Technique with Autoimmune Therapy","authors":"Dharun Teja Vujjini, R. M. Salah, A. Alsadoon, P.W.C. Prasa","doi":"10.1109/CITISIA50690.2020.9371853","DOIUrl":"https://doi.org/10.1109/CITISIA50690.2020.9371853","url":null,"abstract":"Healthcare is a key part of the biological process structure in particular semantic recognition of diseases. Critical states of death rates are arranged by determining and activating human epidemics by using smartphone applications for determining and activating human epidemics. Then, it is diagnosed and treat people over autoimmune facility visualized by image processors. The components system is classified into three attributes: Data, Prediction technique, and View. Data are collected from several attributes and resources such as sensors, bit rates, smartphones. While, prediction techniques promote energy responses, decision trees, correlation in the algorithm of mass centric, SVM classifiers, enumeration, error backpropagation, and least square reliefs. Based on several articles, using prediction techniques can be benefited the treating autoimmune therapy by classifying groups and validating criteria. Mixed Reality visualizations based on Image Guided Surgery (IGS) systems increasingly study now. Nevertheless, has not been used in the Operating Room ever so much. It is may due to the result of several factors such as the systems are developed from a technical perspective and rarely evaluated in the field. This paper introduces the Data, Visualization processing, View (DVV) taxonomy which defines each of the major components required for implementing a Mixed Reality IGS system.","PeriodicalId":145272,"journal":{"name":"2020 5th International Conference on Innovative Technologies in Intelligent Systems and Industrial Applications (CITISIA)","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128576582","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

Predictive analysis of the supply chain management using Machine learning approaches: Review and Taxonomy 使用机器学习方法的供应链管理预测分析:回顾和分类法

2020 5th International Conference on Innovative Technologies in Intelligent Systems and Industrial Applications (CITISIA) Pub Date : 2020-11-25 DOI: 10.1109/CITISIA50690.2020.9371842

X. Pham, Angelika Maag, Sunthatalingam Senthilananthan, Moshiur Bhuiyan

{"title":"Predictive analysis of the supply chain management using Machine learning approaches: Review and Taxonomy","authors":"X. Pham, Angelika Maag, Sunthatalingam Senthilananthan, Moshiur Bhuiyan","doi":"10.1109/CITISIA50690.2020.9371842","DOIUrl":"https://doi.org/10.1109/CITISIA50690.2020.9371842","url":null,"abstract":"Currently, there are many literature reviews on the application of predictive analytics in Supply Chain Management (SCM). However, most of them focus only on some specific functions in supply chain management, including Procurement, Demand Management, Logistics and Transportation, or purely technical aspects. The purpose of this paper is twofold: first, it aims to provide an overview of the outstanding supply chain management functions (SCMF) that apply predictive analytics; and second, to highlight practical approaches, algorithms, or models in SCM via a comparative review of machine learning approach for aspect-based predictive analysis. For these purposes, details of relevant literature were gathered and reviewed. Accordingly, this article will present the data, algorithms, and models applied in predictive analytics along with its performance, SCM result taxonomy, which includes all the necessary components in the effective implementing of SCMF. Via the result of the recent related publications and papers (2018- 2020), Demand management and Procurement are the two main areas of SCM, in which predictive analytics is often applied. Particularly, accurate demand forecasting and sensing (Demand management) and sourcing risk management and supplier selection (Procurement) are among the foremost applications of BDA-enabled predictive models. This taxonomy not only helps scientists to have a steppingstone to provide more valuable articles in the future but also allows manufacturers to gain an in-depth understanding of these elaborate scenarios and better manage the supply chain management functions (SCMF) via the application of predictive analytics.","PeriodicalId":145272,"journal":{"name":"2020 5th International Conference on Innovative Technologies in Intelligent Systems and Industrial Applications (CITISIA)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124583859","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: 2

Modelling Environmental Impact on Public Health using Machine Learning: Case Study on Asthma 使用机器学习模拟环境对公众健康的影响:哮喘案例研究

2020 5th International Conference on Innovative Technologies in Intelligent Systems and Industrial Applications (CITISIA) Pub Date : 2020-11-25 DOI: 10.1109/CITISIA50690.2020.9397488

Lakmini Wijesekara, L. Liyanage

{"title":"Modelling Environmental Impact on Public Health using Machine Learning: Case Study on Asthma","authors":"Lakmini Wijesekara, L. Liyanage","doi":"10.1109/CITISIA50690.2020.9397488","DOIUrl":"https://doi.org/10.1109/CITISIA50690.2020.9397488","url":null,"abstract":"Environmental conditions such as weather and pollution have direct links with public health. It is estimated that the global burden of disease attributed to environmental factors is 24%. A plethora of research has been carried out to investigate the links between the environment and public health. Most of them are clinical or experimental studies. In addition to the investigations of causal effects, it is always useful to study associations of weather and pollution with diseases to manage and mitigate the burden of diseases as well as other environmental issues holistically. Environmental conditions could be used to provide an alarm of a future episode of a disease such as asthma so that risky individuals can take precautions to minimize the risk. This study involves a case study of asthma which applies several machine learning techniques to build a classification model predicting the risk of getting future episodes of asthma based on weather and pollution conditions. Support Vector Machine, Artificial Neural Network, Decision Tree and Random Forest models were considered for the classification. Random forest model produced the best performance compared to other models with an accuracy of 77%. Decision tree model exhibits the highest sensitivity of 70%. Even though ANN gives the lowest accuracy of 59%, its learning curve shows a good fit.","PeriodicalId":145272,"journal":{"name":"2020 5th International Conference on Innovative Technologies in Intelligent Systems and Industrial Applications (CITISIA)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116492137","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

Abnormal Activity Detection in Healthcare 医疗保健中的异常活动检测

2020 5th International Conference on Innovative Technologies in Intelligent Systems and Industrial Applications (CITISIA) Pub Date : 2020-11-25 DOI: 10.1109/CITISIA50690.2020.9371790

Jack William Moore, Hongen Lu

引用次数: 0