2020 5th International Conference on Innovative Technologies in Intelligent Systems and Industrial Applications (CITISIA)最新文献_第2页

Green Computing: Sustainable Design and Technologies 绿色计算:可持续设计与技术

2020 5th International Conference on Innovative Technologies in Intelligent Systems and Industrial Applications (CITISIA) Pub Date : 2020-11-25 DOI: 10.1109/CITISIA50690.2020.9371781

S. Butt, M. Ahmadi, M. Razavi

{"title":"Green Computing: Sustainable Design and Technologies","authors":"S. Butt, M. Ahmadi, M. Razavi","doi":"10.1109/CITISIA50690.2020.9371781","DOIUrl":"https://doi.org/10.1109/CITISIA50690.2020.9371781","url":null,"abstract":"The Internet of Things (IoT) has generated increasingly dynamic impacts around the world due to its high tech mechanisms in the scientific discipline. IoT is an intelligent infrastructure of numerous technologies which connects small to large-scale devices to the Internet for the purpose of communication. Alternatively, with the appreciation of such an advancement, there have also been numerous environmental issues raised from the operations of IoT. Green IT is a notion that has been envisioned to reduce, and possibly eliminate the environmental issues caused by IoT through its sustainable designs and approaches. This paper proposes four key research questions that will be addressed to evaluate the challenges in IoT and the solutions of Green IT. Additionally, a variety of literature reviews will be provided that will focus on the research questions proposed for this study. The questions will focus on the challenges in IoT, the characteristics of Green IT, the designs of Green IT, and the process of implementing Green designs as a solution to the environmental challenges. The significance of this literature review and the research questions will help to identify the most common practices and propositions on implementing Green IT. This paper attempts to gather past and modern research studies to evaluate and compare the best solutions for Green IT. It also intends to demonstrate a clear and comprehensive analysis of a combination of relevant proposals by different authors in determining the best sustainable IoT designs. Finally, a comparative analysis will be conducted to identify the best solution of implementing Green IT into the daily lifestyle.","PeriodicalId":145272,"journal":{"name":"2020 5th International Conference on Innovative Technologies in Intelligent Systems and Industrial Applications (CITISIA)","volume":"85 9 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":"127989285","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

2020 5th International Conference on Innovative Technologies in Intelligent Systems and Industrial Applications (CITISIA) Pub Date : 2020-11-25 DOI: 10.1109/citisia50690.2020.9371795

引用次数: 0

An evaluation model for Cloud-based Data mining Systems with Hadoop 基于Hadoop的基于云的数据挖掘系统评估模型

2020 5th International Conference on Innovative Technologies in Intelligent Systems and Industrial Applications (CITISIA) Pub Date : 2020-11-25 DOI: 10.1109/CITISIA50690.2020.9371799

Anil Limbu, S. Heiyanthuduwage

{"title":"An evaluation model for Cloud-based Data mining Systems with Hadoop","authors":"Anil Limbu, S. Heiyanthuduwage","doi":"10.1109/CITISIA50690.2020.9371799","DOIUrl":"https://doi.org/10.1109/CITISIA50690.2020.9371799","url":null,"abstract":"The traditional approach of mining is more expensive, slow, and is inefficient in case of big data. This calls the essence of cloud technology which has the capability of discovering knowledge from a huge database at a very high rate. The implementation of Hadoop technology makes the processing more efficient because of its underlying characteristic of parallelism and data locality. The aim is to have a system based on a review that resembles the most efficient cloud data mining technology. The system should have capabilities to mine big data and have greater application whilst address the problems of existing mining technologies. In doing so, the existing technologies described by some of the relevant works were taken to achieve the overall framework. Reviews of related works were performed for a better understanding of the existing technology on cloud data mining. Based on the references, some algorithms perform better in any given circumstance. The scalability, parallelism, and cost-effectiveness play a significant role in making the system more efficient. The data locality feature of Hadoop gives a maximum optimization in the mining process. Data mining is not a single task, and there is nothing like one algorithm fits all the tasks of mining procedures. The assumptions and given circumstance of data mining will define the accuracy of mining and overall performance. Data type and tasks are always changing which indicates the essence in dynamic algorithms and techniques of data mining.","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":"129019774","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

Visualizing Data Using Augmented Head Up Displays in Surgery 在外科手术中使用增强抬头显示器可视化数据

2020 5th International Conference on Innovative Technologies in Intelligent Systems and Industrial Applications (CITISIA) Pub Date : 2020-11-25 DOI: 10.1109/CITISIA50690.2020.9371808

A. Kambang, A. Karim

{"title":"Visualizing Data Using Augmented Head Up Displays in Surgery","authors":"A. Kambang, A. Karim","doi":"10.1109/CITISIA50690.2020.9371808","DOIUrl":"https://doi.org/10.1109/CITISIA50690.2020.9371808","url":null,"abstract":"Several issues, circumstances and hurdles regarding the existing and the possible vulnerabilities in the medical field i.e. surgical field specifically has been a matter of concerned and discussion since years. The risk and disadvantages that might come along during surgery is unavoidable and there are evidences of numerous surgical accidents leading towards death. To mitigate these vulnerabilities, augmented wearable devices were introduced. One can see lots of improvements and success in the field of surgery with the help of these wearable devices. However, shortcomings and limitations still exist in even with these devices. The main objective of this research paper is to point out the specific issues that comes along with the use of these wearable devices and analyses the root cause behind this. Numerous journals, articles and papers were thoroughly read, analyzed and evaluated in order to find the possible solution. Different kinds of issues were figured out on the references of the journals out of them some were complicated. I have collected lots of information and data to analyses the importance and the magic these devices have done so far in the field of surgery. Along with the achievements, I have figured out some difficult issues and circumstances as well. After analyzing all these papers, we came to the conclusion that there are lots of works researchers are still doing for the upliftment and betterment of these devices in the field of medicine.","PeriodicalId":145272,"journal":{"name":"2020 5th International Conference on Innovative Technologies in Intelligent Systems and Industrial Applications (CITISIA)","volume":"5 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":"126056335","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

Detection of Chipless RFID Tag Using a Single Antenna RFID Reader System 用单天线RFID阅读器系统检测无芯片RFID标签

2020 5th International Conference on Innovative Technologies in Intelligent Systems and Industrial Applications (CITISIA) Pub Date : 2020-11-25 DOI: 10.1109/CITISIA50690.2020.9371834

M. A. Bibile, Grishma Khadka, N. Karmakar

{"title":"Detection of Chipless RFID Tag Using a Single Antenna RFID Reader System","authors":"M. A. Bibile, Grishma Khadka, N. Karmakar","doi":"10.1109/CITISIA50690.2020.9371834","DOIUrl":"https://doi.org/10.1109/CITISIA50690.2020.9371834","url":null,"abstract":"The detection of different types of chipless Radio Frequency Identification (RFID) tags using a single antenna reader is presented in this paper. The performance is evaluated through experiment and measured using Chipless RFID reader system with single antenna and compared with results from vector network analyser. It explains the operation of the reader and how the pic-microcontroller is programmed with the detection algorithm. Two types of tags have been tested, a printed tag which is flexible and will be in high demand for commercialisation and a copper tag which has more durability. The post processing of the measured results to obtain the tag ID is performed using MATLAB. Adaptive Wavelet based detection algorithm is used for the decoding of the tag ID. After the tag ID has been decoded it is sent to a display screen on the DSP unit and/ or via RS-232 to the host computer application. There is no need of human interaction for the reader to interrogate the tag as the user is able to save the tag data received by the reader in a specified database/server.","PeriodicalId":145272,"journal":{"name":"2020 5th International Conference on Innovative Technologies in Intelligent Systems and Industrial Applications (CITISIA)","volume":"21 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":"115252178","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

Review of Image encryption techniques using neural network for optical security in the healthcare sector – PNO System 应用神经网络的图像加密技术在医疗保健领域的光学安全综述- PNO系统

2020 5th International Conference on Innovative Technologies in Intelligent Systems and Industrial Applications (CITISIA) Pub Date : 2020-11-25 DOI: 10.1109/CITISIA50690.2020.9371805

Jinfeng Su, Anup Kankani, G. Zajko, A. Elchouemi, Hendra Kurniawan

{"title":"Review of Image encryption techniques using neural network for optical security in the healthcare sector – PNO System","authors":"Jinfeng Su, Anup Kankani, G. Zajko, A. Elchouemi, Hendra Kurniawan","doi":"10.1109/CITISIA50690.2020.9371805","DOIUrl":"https://doi.org/10.1109/CITISIA50690.2020.9371805","url":null,"abstract":"Image encryption is used to encrypt patient images that contain diagnostic information about patients in healthcare. The healthcare sector uses electronic media to support the transmission of scanning results, such as X-rays, MRI scans and ultrasound images. The primary purpose of this paper is to investigate encryption of images through techniques utilising neural networks to maintain security and privacy of patient records. Patient image data, neural network-based encryption, and optical security (PNO) systems are examined in this research work. These components will provide some validation in the use of neural network-enabled image encryption in healthcare. The evaluation of the PNO system is based on different quality factors, which are compared in a classification of the 30 state-of-the-art solutions in image encryption. The effectiveness of the encryption process can be increased in terms of high accuracy, less noise and enhanced security. We conclude that using neural network-based encryption techniques can increase security in visual media in the healthcare sector.","PeriodicalId":145272,"journal":{"name":"2020 5th International Conference on Innovative Technologies in Intelligent Systems and Industrial Applications (CITISIA)","volume":"272 55","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"113959276","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

Wireless Sensor Networks using image processing for fire detection 利用图像处理的无线传感器网络进行火灾探测

2020 5th International Conference on Innovative Technologies in Intelligent Systems and Industrial Applications (CITISIA) Pub Date : 2020-11-25 DOI: 10.1109/CITISIA50690.2020.9371798

Zaighum Ateeq, Mohammad Momani

{"title":"Wireless Sensor Networks using image processing for fire detection","authors":"Zaighum Ateeq, Mohammad Momani","doi":"10.1109/CITISIA50690.2020.9371798","DOIUrl":"https://doi.org/10.1109/CITISIA50690.2020.9371798","url":null,"abstract":"Wireless Sensor Network (WSN) is an influential technology predominantly suitable for eco-friendly monitoring. It provides a low-cost fine-grained detection of hazardous wildfire locations like urban interfaces. Compared with early forest techniques of fire detection, some of the systems are less effective and early prevention system for indications of forest fire is necessary. There is a need for a quick response to the fire detection in hazard and WSN is used to provide a systematic gateway for detection of exact fire location and spreading location. Flame identification based on image processing has been widely used in fire detection. The purpose of this work is to propose a system based on WSN using image processing for fire detection. Remote Image data, segmentation and evaluation is the taxonomy used in this research work that defines each of the necessary components used to implement a fire detection using WSN. It is proposed that the component of the proposed taxonomy is used for the validation criteria. For implementing the WSN system for fire detection using image processing. The solution will act as a pool for the user to understand the necessary elements of WSN for the fire detection system.","PeriodicalId":145272,"journal":{"name":"2020 5th International Conference on Innovative Technologies in Intelligent Systems and Industrial Applications (CITISIA)","volume":"101 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":"121496929","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

MRI-based Diagnosis of Brain Tumours Using a Deep Neural Network Framework 基于mri的脑肿瘤诊断的深度神经网络框架

2020 5th International Conference on Innovative Technologies in Intelligent Systems and Industrial Applications (CITISIA) Pub Date : 2020-11-25 DOI: 10.1109/CITISIA50690.2020.9371831

Mrs M Acharya, A. Alsadoon, Shahd Al-Janabi, P. Prasad, A. Dawoud, Ghossoon Alsadoon, M. Paul

{"title":"MRI-based Diagnosis of Brain Tumours Using a Deep Neural Network Framework","authors":"Mrs M Acharya, A. Alsadoon, Shahd Al-Janabi, P. Prasad, A. Dawoud, Ghossoon Alsadoon, M. Paul","doi":"10.1109/CITISIA50690.2020.9371831","DOIUrl":"https://doi.org/10.1109/CITISIA50690.2020.9371831","url":null,"abstract":"The median survival time of patients with high grade glioma, a form of brain tumour, is 1-3 years. The current best practice adopts Convolutional Neural Network (CNN) for image classification and tumour detection. This method provides a significant improvement in brain tumour segmentation of Magnetic Resonance Imaging (MRI) images in comparison to other frameworks, but it is nonetheless slow and lacks precision. We sought to build upon the current best practice model by utilising a Deep Neural Network (DNN) model, which entailed modification of the segmentation and feature-extraction stages in order to improve the accuracy of those stages and the resulting segmentation. We contrasted the accuracy and efficiency of our model to the current best practice model using 10 brain tumour patient MRI datasets. First, the segmentation accuracy of our proposed model (M= 90%) outperformed that of the current best practice (M=78%). Second, the tumour detection processing time of our proposed model (M=34 ms) also outperformed that of the current best practice (M=73 ms). We, therefore, replicated previous studies by showing that automatic segmentation can aid in brain tumour detection. Importantly, we extended previous studies by proposing a model that classifies a brain tumour with greater accuracy and within lower processing times. Validation of the model with a larger dataset is recommended.","PeriodicalId":145272,"journal":{"name":"2020 5th International Conference on Innovative Technologies in Intelligent Systems and Industrial Applications (CITISIA)","volume":"55 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":"114262006","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

Predicting Early Phase of Type 2 Diabetic by Deep Learning 利用深度学习预测2型糖尿病的早期阶段

2020 5th International Conference on Innovative Technologies in Intelligent Systems and Industrial Applications (CITISIA) Pub Date : 2020-11-25 DOI: 10.1109/CITISIA50690.2020.9371843

Prabir Pathak, A. Elchouemi

引用次数: 0

Towards Adapting Autonomous Vehicle Technology for the Improvement of Personal Mobility Devices 面向改进个人移动设备的自动驾驶汽车技术

2020 5th International Conference on Innovative Technologies in Intelligent Systems and Industrial Applications (CITISIA) Pub Date : 2020-11-25 DOI: 10.1109/CITISIA50690.2020.9371836

Maleen Jayasuriya, Janindu Arukgoda, Ravindra Ranasinghe, G. Dissanayake

引用次数: 0