2020 5th International Conference on Innovative Technologies in Intelligent Systems and Industrial Applications (CITISIA)最新文献_第8页

Strategy-oriented Digital Transformation of Logistics Enterprises: The roles of artificial intelligence and blockchain 物流企业面向战略的数字化转型:人工智能和区块链的作用

2020 5th International Conference on Innovative Technologies in Intelligent Systems and Industrial Applications (CITISIA) Pub Date : 2020-11-25 DOI: 10.1109/CITISIA50690.2020.9371847

Hanwen Liu, S. Islam, Xiaobing Liu, Jia Wang

{"title":"Strategy-oriented Digital Transformation of Logistics Enterprises: The roles of artificial intelligence and blockchain","authors":"Hanwen Liu, S. Islam, Xiaobing Liu, Jia Wang","doi":"10.1109/CITISIA50690.2020.9371847","DOIUrl":"https://doi.org/10.1109/CITISIA50690.2020.9371847","url":null,"abstract":"Currently, affected by the COVID-19, the global economy has experienced varying degrees of impact. The logistics industry, which is the foundation of social and economic operations, has also been affected to varying degrees. Under this economics background, the logistics industry is driven by factors such as resource integration to attract support from technology companies, new demand for contactless delivery, intelligent operating platforms such as blockchain, and artificial intelligence to replace labor shortages. The challenges for the development of logistics enterprises have become more prominent. This paper starts from a new strategic perspective: the strategy-oriented digital transformation of the logistics enterprises analyzes from the three levels of the logistics value chain, delicate operation development, and digital logistics activities. We will present the applications of the most trending digital technology in logistics enterprises. The purpose is to help enterprises better understand how digital transformation through blockchain and artificial intelligence can enhance their competitiveness as a business strategy.","PeriodicalId":145272,"journal":{"name":"2020 5th International Conference on Innovative Technologies in Intelligent Systems and Industrial Applications (CITISIA)","volume":"12 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":"131601368","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

Authentication method to secure cloud data centres using biometric technology 使用生物识别技术保护云数据中心的认证方法

2020 5th International Conference on Innovative Technologies in Intelligent Systems and Industrial Applications (CITISIA) Pub Date : 2020-11-25 DOI: 10.1109/CITISIA50690.2020.9371802

S. Giri, Jinfeng Su, G. Zajko, P. Prasad

{"title":"Authentication method to secure cloud data centres using biometric technology","authors":"S. Giri, Jinfeng Su, G. Zajko, P. Prasad","doi":"10.1109/CITISIA50690.2020.9371802","DOIUrl":"https://doi.org/10.1109/CITISIA50690.2020.9371802","url":null,"abstract":"Biometric technology is an automated process used for the identification of authorized people by personal traits such as the face, palm print, fingerprint, iris pattern, etc., which is a rapidly growing technology for authentication and authorization in the recent development of data storage. This technology provides higher security in efficient way for all types of business. In this method, users use their body parts or physical characteristics as a tool for authentication. This can be used for cloud data authentication as cloud data centers are under continuous threat from malicious attacks. Furthermore, cloud data centers store huge amounts of data, which is crucial for every organization and individual, therefore they are to be preserved and protected from intruders. Biometric authentication is one of the most reliable and efficient means of protection for the cloud data center. In this research, we have studied the input, processing of data, and output in a securely and effectively. The data input, process, and output are studied from recent 30 renowned journals and evaluated them for the understanding of the proposed system. From these journal articles, the best solution is analyzed and evaluated. This research attempts to identify an optimum method of biometric authentication for the cloud data center. In this study we have searched the best, recent top-ranked journals. These articles are analyzed, classified, and evaluated, and proposed the best solution.","PeriodicalId":145272,"journal":{"name":"2020 5th International Conference on Innovative Technologies in Intelligent Systems and Industrial Applications (CITISIA)","volume":"40 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":"123851184","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

Smart Parking Utilizing IoT Embedding Fog Computing Based on Smart Parking Architecture 基于物联网嵌入雾计算的智能停车架构

2020 5th International Conference on Innovative Technologies in Intelligent Systems and Industrial Applications (CITISIA) Pub Date : 2020-11-25 DOI: 10.1109/CITISIA50690.2020.9371848

Amir Man Singh Maharjan, A. Elchouemi

{"title":"Smart Parking Utilizing IoT Embedding Fog Computing Based on Smart Parking Architecture","authors":"Amir Man Singh Maharjan, A. Elchouemi","doi":"10.1109/CITISIA50690.2020.9371848","DOIUrl":"https://doi.org/10.1109/CITISIA50690.2020.9371848","url":null,"abstract":"Rapid population growth and skyrocketing demand of private transportation bloom the market of automakers worldwide. The concept of automated parking system under smart city arises with the availability of the technology like fast internet connections, offloading computational resources, IoT devices communicating various devices each other, to uplift the quality of life. Revolution of cloud computing and ease of this technology also open the door for better opportunities for smart parking where a system can be served from the remote area, but there is an issue with cloud computing for cost of operation and latency of the services. In urban cities there is rapid growth of population and there is advancement of automotive industries, there are lots of vehicles that are being used in all the cities which creates a lot of issues like road congestions, issue in parking spaces. This paper proposes the fog computing architecture to reduce the latency and efficiently utilise all the available technologies together by building fog computing architecture network which is a multi-tier structure where applications runs jointly, communicates and compute with each other. Smart parking has gain massive attention due to ease and outcome from those technologies are exponential. The role of Internet of Things and fog computing enable the platform to minimize the take duration for finding the parking space, this reduces the time and excess use of fuel and emission of CO2, these are the consequence of over and unmanaged vehicles in the urban areas and unmanaged parking areas.","PeriodicalId":145272,"journal":{"name":"2020 5th International Conference on Innovative Technologies in Intelligent Systems and Industrial Applications (CITISIA)","volume":"11 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":"125324499","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

A DFC taxonomy of Speech emotion recognition based on convolutional neural network from speech signal 基于卷积神经网络的语音情感识别的DFC分类

2020 5th International Conference on Innovative Technologies in Intelligent Systems and Industrial Applications (CITISIA) Pub Date : 2020-11-25 DOI: 10.1109/CITISIA50690.2020.9371841

Surendra Malla, A. Alsadoon, Simi Bajaj

{"title":"A DFC taxonomy of Speech emotion recognition based on convolutional neural network from speech signal","authors":"Surendra Malla, A. Alsadoon, Simi Bajaj","doi":"10.1109/CITISIA50690.2020.9371841","DOIUrl":"https://doi.org/10.1109/CITISIA50690.2020.9371841","url":null,"abstract":"Speech is an efficient agent to explicit attitude and emotions via language. The crucial task for the researchers is to find out the emotions through the speech utterance and eliminating the noise from a raw speech data. The goal of this research paper is to explore the latest journal papers in the field of convolutional neural network-based speech emotion recognition (SER) models related with the specific problem and provide a best solution which can recognize emotion in the speech from the speech signal.The components of this proposed system are data, feature extraction and classification (DFC) that helps to assist in the implementation and evaluating the system. We propose the DFC taxonomy which will assist the end users in recognition of the emotion from the speech signal and making the artificial intelligence (AI) more robust by using convolutional neural network, facilitating a huge presence in the future system.The system evaluates a state-of-the-art model that is associated to the convolutional neural network-based speech emotion recognition which presents and validates the DFC components. Based on system completeness, system acceptance, and by classifying 30 state-of-the-art journal research papers in the domain, components are evaluated, verified and validated.The benefaction of this research paper is the critical analysis in the latest literature that are available on the convolutional neural network-based system which can recognize the emotion by extracting the features from the speech signal so that accurate recognition of emotion can be made. Also, highlighting the importance of DFC taxonomy.","PeriodicalId":145272,"journal":{"name":"2020 5th International Conference on Innovative Technologies in Intelligent Systems and Industrial Applications (CITISIA)","volume":"8 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":"129738443","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

Strokes Classification in Cricket Batting Videos 板球击球视频中的击球分类

2020 5th International Conference on Innovative Technologies in Intelligent Systems and Industrial Applications (CITISIA) Pub Date : 2020-11-25 DOI: 10.1109/CITISIA50690.2020.9371776

Ishara Bandara, B. Bačić

引用次数: 6

CITISIA 2020 Table of Contents cicisia 2020目录

2020 5th International Conference on Innovative Technologies in Intelligent Systems and Industrial Applications (CITISIA) Pub Date : 2020-11-25 DOI: 10.1109/citisia50690.2020.9371827

引用次数: 0

A Novel Hybrid Fall Detection Technique Using Body Part Tracking and Acceleration 一种新的基于身体部位跟踪和加速度的混合跌倒检测技术

2020 5th International Conference on Innovative Technologies in Intelligent Systems and Industrial Applications (CITISIA) Pub Date : 2020-11-25 DOI: 10.1109/CITISIA50690.2020.9371850

Ahmed Ahsan Khan, A. Alsadoon, Shatha Habeeb Al-Khalisy, P. Prasad, Oday D. Jerew, Paul Manoranjan

{"title":"A Novel Hybrid Fall Detection Technique Using Body Part Tracking and Acceleration","authors":"Ahmed Ahsan Khan, A. Alsadoon, Shatha Habeeb Al-Khalisy, P. Prasad, Oday D. Jerew, Paul Manoranjan","doi":"10.1109/CITISIA50690.2020.9371850","DOIUrl":"https://doi.org/10.1109/CITISIA50690.2020.9371850","url":null,"abstract":"Falls by elderly individuals are a major issue in modern health care. A significant amount of research has been done in this domain. In this paper, we have proposed a hybrid solution for fall detection by using body part tracking and human body acceleration. The paper finds that in most cases vision-based fall detection systems work better and give a more accurate result when compared to non-vision-based systems because of the limitations of non-vision based systems (e.g., people forget to wear the wearable detection devices). The proposed system improves the accuracy of the state-of-the-art solution and reduces its computation cost. The vertical distances between head and body center, and human body acceleration are the features used in the proposed method and a Support Vector Machine (SVM) classifier is used to classify the outcome into two classes. The depth image from a Kinect Camera was used as an input to avoid any privacy issues that may occur by using RGB-based texture images, and the events were classified as an activity of daily living (ADL) or a fall.","PeriodicalId":145272,"journal":{"name":"2020 5th International Conference on Innovative Technologies in Intelligent Systems and Industrial Applications (CITISIA)","volume":"64 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":"128809787","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

Analysing Stock Market Trend Prediction using Machine & Deep Learning Models: A Comprehensive Review 使用机器和深度学习模型分析股票市场趋势预测:全面回顾

2020 5th International Conference on Innovative Technologies in Intelligent Systems and Industrial Applications (CITISIA) Pub Date : 2020-11-25 DOI: 10.1109/CITISIA50690.2020.9371852

Doan Yen Nhi Le, Angelika Maag, Suntharalingam Senthilananthan

{"title":"Analysing Stock Market Trend Prediction using Machine & Deep Learning Models: A Comprehensive Review","authors":"Doan Yen Nhi Le, Angelika Maag, Suntharalingam Senthilananthan","doi":"10.1109/CITISIA50690.2020.9371852","DOIUrl":"https://doi.org/10.1109/CITISIA50690.2020.9371852","url":null,"abstract":"The applications of intelligent financial forecasting play an utmost important role in facilitating the investment decisions activities of many investors. With the right insight information, the investors can tailor their portfolio to maximise return while minimising risks. However, not every investment guarantees a good return, and this is mainly because most investors have limited information and skills to predict the stock trend. Nevertheless, the complex, chaotic and volatile nature of the stock market make any prediction attempts extremely difficult. This paper aims to provide a comprehensive review of the exiting researches which related to the application of Machine Learning and Deep Learning models in financial market forecasting domain. To prepare for this project, more than sixty research papers were analysed in-depth to extract required quantitative information, applications, and results on different methodologies. It is found from this project that Deep Learning outperformed Machine Learning in all the collected research papers, and it is the most suitable methodologies to apply to the stock market forecasting domain.","PeriodicalId":145272,"journal":{"name":"2020 5th International Conference on Innovative Technologies in Intelligent Systems and Industrial Applications (CITISIA)","volume":"31 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":"133381069","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

Soft Real Time Data Driven IoT for Knee Rehabilitation 软实时数据驱动物联网膝关节康复

2020 5th International Conference on Innovative Technologies in Intelligent Systems and Industrial Applications (CITISIA) Pub Date : 2020-11-25 DOI: 10.1109/CITISIA50690.2020.9371780

S. A. Arosha Senanayake, Putri Wulandari

{"title":"Soft Real Time Data Driven IoT for Knee Rehabilitation","authors":"S. A. Arosha Senanayake, Putri Wulandari","doi":"10.1109/CITISIA50690.2020.9371780","DOIUrl":"https://doi.org/10.1109/CITISIA50690.2020.9371780","url":null,"abstract":"This article presents soft real time data driven Internet of Things (IoT) for knee rehabilitation using cyber physical sensory information system interfaced with cloud storage. Custom made wearable wireless motion capture suit interfaced to smart watch as the IoT are built for biofeedback visualization. Mullti-sensor integration and data fusion mechanisms are employed to obtain input vectors of knowledge base and the output vector is based on patient classification defined using multivariate statistics by the healthcare professionals. Case based reasoning is applied for the established reference standard in order to produce patient centric actual knee rehabilitation status and classification using semi supervised deep learning method. Wearable IoT is automatically updated the actual knee rehabilitation status and classification of a patient using relevant cyber physical sensory information retrieved from the cloud storage connected vis AWS cloud. Hence, a soft real time data drive IoT for knee rehabilitation system is successfully tested and validated using semi supervised deep learning cyber physical sensory information database subject to statistically quantified parameters by health professionals based on principle component analysis and patient centric parameters based on independent component analysis. The data driven IoT built has been validated in rehabilitation clinics by relevant physiotherapists and patients with the average age of ±36.8.","PeriodicalId":145272,"journal":{"name":"2020 5th International Conference on Innovative Technologies in Intelligent Systems and Industrial Applications (CITISIA)","volume":"139 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":"114357517","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

Design & Evaluation of Layout-Agnostic Tactile Guides for In-Vehicle Touchscreens 车载触摸屏布局不可知触觉导向的设计与评价

2020 5th International Conference on Innovative Technologies in Intelligent Systems and Industrial Applications (CITISIA) Pub Date : 2020-11-25 DOI: 10.1109/CITISIA50690.2020.9371782

Sarmad Soomro, A. Cockburn

{"title":"Design & Evaluation of Layout-Agnostic Tactile Guides for In-Vehicle Touchscreens","authors":"Sarmad Soomro, A. Cockburn","doi":"10.1109/CITISIA50690.2020.9371782","DOIUrl":"https://doi.org/10.1109/CITISIA50690.2020.9371782","url":null,"abstract":"Touchscreens are commonly used to provide access to a wide range of vehicle functions. However, interacting with touchscreens can require more attention than physical controls due to their lack of tactile sensation, potentially causing driver distraction. Prior research has indicated that stencils overlays mounted on a touchscreen can ease these problems. However, the previous stencils studies used holes that were cut to the size and location of underlying interface controls, meaning that they could only be used with a single interface layout, which is unrealistic for typical in-vehicle use. In this paper, we examine the use of layout agnostic stencils that can be used with different user interface layouts, with the aim of reducing visual attention on the touchscreen while driving. We conducted an experiment in which two layout agnostic stencil designs were evaluated in comparison to a normal touchscreen during simulated driving. Contrary to our intention, the new stencil designs increased attentional demands and impaired driving performance compared to the normal touchscreen. To understand the causes of this failure, we developed a framework for understanding low-level human activities while interacting with in-vehicle controls. The framework suggests the need for improved understanding of the acuity of the human proprioceptive target approach and of the human ability to discriminate between tactile objects.","PeriodicalId":145272,"journal":{"name":"2020 5th International Conference on Innovative Technologies in Intelligent Systems and Industrial Applications (CITISIA)","volume":"238 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":"116181894","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