2020 5th International Conference on Innovative Technologies in Intelligent Systems and Industrial Applications (CITISIA)最新文献_第9页

A Novel Augmented Reality Approach in Oral and Maxillofacial Surgery: Super-Imposition Based on Modified Rigid and Non-Rigid Iterative Closest Point 一种新的增强现实方法在口腔颌面外科:基于改进的刚性和非刚性迭代最近点的叠加叠加

2020 5th International Conference on Innovative Technologies in Intelligent Systems and Industrial Applications (CITISIA) Pub Date : 2020-11-25 DOI: 10.1109/CITISIA50690.2020.9371785

Sam Manohar, A. Alsadoon, P.W.C. Prasa, R. M. Salah, Angelika Maag, Yahini Murugesan

{"title":"A Novel Augmented Reality Approach in Oral and Maxillofacial Surgery: Super-Imposition Based on Modified Rigid and Non-Rigid Iterative Closest Point","authors":"Sam Manohar, A. Alsadoon, P.W.C. Prasa, R. M. Salah, Angelika Maag, Yahini Murugesan","doi":"10.1109/CITISIA50690.2020.9371785","DOIUrl":"https://doi.org/10.1109/CITISIA50690.2020.9371785","url":null,"abstract":"Background: This paper aim to improve the accuracy of super-imposition and processing time during Oral and Maxillofacial surgery. Methodology: The proposed system consists of Enhanced Tracking Learning Detection (TLD) enhance by an occlusion removal algorithm to remove occlusion in the region of interest. In addition, we propose a Modified Rigid and Non-Rigid Iterative Closest Point (MRaNRICP) for pose refinement. Moreover, this proposed MRaNRICP having a new error metric Boolean function to dictate the Iterative Closest Point (ICP)’s stopping condition. Results: The proposed system using a new error metric being defined as a new MRaNRICP and it gave overlay error from 0.22 - 0.29mm and processing time of 10 – 13 frames per second. Similarly, current system achieved the overlay error from 0.23 - 0.35mm and processing time of 8 – 12 frames per second. Conclusion: This research should reduce the computation time of the TLD algorithm and improve the accuracy of it.","PeriodicalId":145272,"journal":{"name":"2020 5th International Conference on Innovative Technologies in Intelligent Systems and Industrial Applications (CITISIA)","volume":"16 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":"124472609","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

Enhanced the Quality of Telemedicine Real-Time Video Transmission and Distortion Minimization in Wireless Network 提高了无线网络中远程医疗实时视频传输质量和失真最小化

2020 5th International Conference on Innovative Technologies in Intelligent Systems and Industrial Applications (CITISIA) Pub Date : 2020-11-25 DOI: 10.1109/CITISIA50690.2020.9371839

Ajay Ghimire, A. Alsadoon, P. Prasad, Nabil Giweli, Oday D. Jerew, Ghossoon Alsadoon

{"title":"Enhanced the Quality of Telemedicine Real-Time Video Transmission and Distortion Minimization in Wireless Network","authors":"Ajay Ghimire, A. Alsadoon, P. Prasad, Nabil Giweli, Oday D. Jerew, Ghossoon Alsadoon","doi":"10.1109/CITISIA50690.2020.9371839","DOIUrl":"https://doi.org/10.1109/CITISIA50690.2020.9371839","url":null,"abstract":"Achieving good quality and minimum distortion of the video frames is one of the most challenging requirements in the telemedicine system. Transmission process for a real-time video over the wireless network is due to various real-time restrictions, such as encoding mechanism, noise, and bandwidth fluctuations. The restrictions introduce distortions and delay, hence adversely affect the reliability and quality of the video transmission system. This study aims to propose a new system which can fine-tune the encoding process dynamically. The proposed system consists of an Enhanced Video Quality and Distortion Minimization (EVQDM) algorithm to achieve guaranteed quality, minimum distortion, and the minimum delay in the transmission of the video. This system guarantees the video quality by using the adaptive video encoding technique and minimizes the distortion by considering the truncating distortion in the enhanced distortion minimization algorithm. The results of applying the proposed EVQDM algorithm and the state-of-the-art solutions are compared, and it was shown that the proposed algorithm improved the state-of-the-art solution. The video quality has been increased from 47.2 dB to 50.13 dB, the video distortion has been minimized from 0.6802 to 0.3509 and the end-to-end delay has been reduced from 123.58 ms to 112.57 ms. The proposed solution focuses on truncating distortion, to minimize the total distortion of the video. For summarization, this solution addressed the issues of achieving minimum distortion and delay while providing the guaranteed video quality within the boundaries of the real-time constraints that are imposed on the system.","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":"122333708","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

Digital Fiat Currency (DFC): A Taxonomy for Automatic Sleep Stage Classification 数字法定货币(DFC):自动睡眠阶段分类的分类法

2020 5th International Conference on Innovative Technologies in Intelligent Systems and Industrial Applications (CITISIA) Pub Date : 2020-11-25 DOI: 10.1109/CITISIA50690.2020.9371800

A. Kaur, O. H. Alsadoon, S. Aloussi

{"title":"Digital Fiat Currency (DFC): A Taxonomy for Automatic Sleep Stage Classification","authors":"A. Kaur, O. H. Alsadoon, S. Aloussi","doi":"10.1109/CITISIA50690.2020.9371800","DOIUrl":"https://doi.org/10.1109/CITISIA50690.2020.9371800","url":null,"abstract":"Deep learning is the latest phenomena, which is being used to get the results for automatic classification, segmentation, image processing in various medical fields. This technology basically helps in reducing processing time and to avoid manual classification and identification. In recent years, convolution neural network in deep learning has been used for getting automatic results from the raw data. [1], [2] This technology is quite popular in automatic sleep stage classification, these days. It is basically used for automatic sleep stage classification, as manual classification is very time consuming and complex. [3] In previous times, the classification of sleep stages, was done with the help of manual human vision inspection, which were very time consuming and complex. To fasten this process and to reduce complexity, deep learning neural network models are used for classification. These neural network models help to improve this process and give better results than manual scoring of sleep stages. [4], [5] In this proposed DFC taxonomy, these components are implemented to validate the sleep stage classification in deep convolution neural network. [6]. After validation, evaluation and verification of this Digital Fiat Currency (DFC) taxonomy, it can improve the results of classification to large extent, which involves the major components of deep learning to improve the accuracy. In addition, this proposed method, is simple and easy to adapt for other methods.","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":"127947365","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

Software Defect Prediction Using Atomic Rule Mining and Random Forest 基于原子规则挖掘和随机森林的软件缺陷预测

2020 5th International Conference on Innovative Technologies in Intelligent Systems and Industrial Applications (CITISIA) Pub Date : 2020-11-25 DOI: 10.1109/CITISIA50690.2020.9371797

Suroj Thapa, A. Alsadoon, P. Prasad, Thair Al-Dala’in, Tarik A. Rashid

{"title":"Software Defect Prediction Using Atomic Rule Mining and Random Forest","authors":"Suroj Thapa, A. Alsadoon, P. Prasad, Thair Al-Dala’in, Tarik A. Rashid","doi":"10.1109/CITISIA50690.2020.9371797","DOIUrl":"https://doi.org/10.1109/CITISIA50690.2020.9371797","url":null,"abstract":"This research aims to improve software defect prediction in terms of accuracy and processing time. The new proposed algorithm is based on the Random Forest Algorithm that classifies and distributes the data based on tree module. It has value either 1 for defective module or 0 for the non-defective module. Random Forest Algorithm selects a feature from a subset of features which has been already classified. Random Forest Algorithm uses a number of trees for the prediction. For this research, datasets were tested with 10 and 15 sets of trees. Results showed an improvement in accuracy and processing time when the proposed system was used compared to the current solution for the software defect model generation and prediction. The proposed solution achieved an accuracy of 90.09% whereas processing time dropped by 54.14%. Processing time decreased from 19.78s to 9.07s during the prediction for over 100 records. Accuracy was improved from 89.97% to 90.09%. The proposed solution uses Atomic Rule Mining with Random Forest Algorithm for software defect prediction. It consists of classification and prediction process by using the Random Forest Algorithm during storing data that is carried out using Atomic Rule Mining.","PeriodicalId":145272,"journal":{"name":"2020 5th International Conference on Innovative Technologies in Intelligent Systems and Industrial Applications (CITISIA)","volume":"57 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":"128940076","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

DFA Taxonomy for the classification of ECG data for effective health monitoring using ML technology 利用ML技术对心电数据进行有效的健康监测的DFA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/CITISIA50690.2020.9371859

Tejaswi Parasapogu, Indra Seher, R. M. Salah, Ali A. Alwan

{"title":"DFA Taxonomy for the classification of ECG data for effective health monitoring using ML technology","authors":"Tejaswi Parasapogu, Indra Seher, R. M. Salah, Ali A. Alwan","doi":"10.1109/CITISIA50690.2020.9371859","DOIUrl":"https://doi.org/10.1109/CITISIA50690.2020.9371859","url":null,"abstract":"ECG data of patients are collected using sensors which are further classified for monitoring their health. There are certain pitfalls of the existing classification schemes used for health monitoring that are poor extraction of features, ineffective filtering of data, improper access control, and issues related to dimensionality reduction. In this study, Machine learning (ML) is used to perform an early diagnosis of diseases in order to achieve the aim of effective and timely health monitoring of patients. Data preprocessing, Feature extraction, and Activity classification (DFA) are the major components utilised for the implementation of Health monitoring system based on ECG data classification using ML technology. This system classifies recorded activities based on extracted ECG data using Hidden Markov Model (HMM) and Support Vector Machine (SVM) and is integrated with Internet of Medical Things (IoMT) in order to diagnose patient’s disease at early stages. The DFA taxonomy is evaluated based on the effectiveness and performance of the solution. It contributes to the reduction of dimensionalities that facilitates effective feature extraction and improves the accessibility of the model for better health monitoring. The importance of DFA taxonomy is demonstrated by classifying 30 research papers in the domain of health monitoring system. The classification depicts that few components of the ML-based ECG Data Classification system are validated and even fewer are evaluated to depict the effectiveness of the taxonomy.","PeriodicalId":145272,"journal":{"name":"2020 5th International Conference on Innovative Technologies in Intelligent Systems and Industrial Applications (CITISIA)","volume":"19 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":"127487978","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

Maximum Power Point Tracking with Modified Incremental Conductance Technique in Grid-Connected PV Array 基于改进增量电导技术的并网光伏阵列最大功率点跟踪

2020 5th International Conference on Innovative Technologies in Intelligent Systems and Industrial Applications (CITISIA) Pub Date : 2020-11-25 DOI: 10.1109/CITISIA50690.2020.9371803

M. Siddique, M. A. Khan, Adeel Asad, A. Rehman, R. M. Asif, S. Rehman

引用次数: 7

Automatic Camera Switching in Soccer Game using Decision Tree 基于决策树的足球比赛镜头自动切换

2020 5th International Conference on Innovative Technologies in Intelligent Systems and Industrial Applications (CITISIA) Pub Date : 2020-11-25 DOI: 10.1109/CITISIA50690.2020.9371815

H. Najeeb, R. F. Ghani

{"title":"Automatic Camera Switching in Soccer Game using Decision Tree","authors":"H. Najeeb, R. F. Ghani","doi":"10.1109/CITISIA50690.2020.9371815","DOIUrl":"https://doi.org/10.1109/CITISIA50690.2020.9371815","url":null,"abstract":"Cameras management is challenging due in to the need of professional director for controlling and switching between cameras to select the best scéne among them. The aim of this paper is substituting the director of soccer game broadcasting through building automatic camera switching system that works as a TV director that discovers the best scene from a set of cameras and broadcasts it over the network in real time, taking into account the cinematic instructions represented by using the 180-degree rule when filming and avoiding jumping between scenes. Three cameras broadcasting the soccer game in real-time. Every scene in each camera is been analyzed and evaluated by using five parameters are frame direction, previous frame direction, finding ball, number of players, and their position. Many algorithms were used for analyzing the frame such as optical flow for determining the direction of frame, Euclidean distance and Extended Kalman filter for finding the ball, and contour and moment for detecting the players and determining their position. The results of the research were the proposed work was able to create one video stream from three video sources.","PeriodicalId":145272,"journal":{"name":"2020 5th International Conference on Innovative Technologies in Intelligent Systems and Industrial Applications (CITISIA)","volume":"14 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":"123699093","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 Novel Solution of an Enhanced Error and Loss Function using Deep Learning for Hypertension Classification in Traditional Medicine 一种基于深度学习增强误差和损失函数的传统医学高血压分类新方法

2020 5th International Conference on Innovative Technologies in Intelligent Systems and Industrial Applications (CITISIA) Pub Date : 2020-11-25 DOI: 10.1109/CITISIA50690.2020.9371809

Srijan Karki, A. Ali, O. H. Alsadoon, Tarik A. Rashid

{"title":"A Novel Solution of an Enhanced Error and Loss Function using Deep Learning for Hypertension Classification in Traditional Medicine","authors":"Srijan Karki, A. Ali, O. H. Alsadoon, Tarik A. Rashid","doi":"10.1109/CITISIA50690.2020.9371809","DOIUrl":"https://doi.org/10.1109/CITISIA50690.2020.9371809","url":null,"abstract":"Deep Learning in traditional medicine has different ways to detect and classify hypertension. However, not many researches have combined those ways to classify hypertension more accurately. This research aims to combine two of the most popular ways i.e. Tongue image and symptoms to increase the accuracy of detecting hypertension.The proposed system consists of training the parameters using error function with a Rectified Linear Unit (ReLU) Function and combining the learned features of both tongue image and symptoms using vector outer product. The proposed solution was tested on different data samples and provides the classification accuracy of 94.25% against the current average accuracy of 90.75%. The proposed solution only focused on increasing the classification accuracy. However, the proposed solution has not increased the processing time while doing so, instead the average processing time has decreased from 0.3774 to 0.3482.The proposed solution has increased the classification accuracy and decreased the processing time for classifying the hypertension in traditional medicine. The enhanced error function and loss function with ReLU activation function solves the vanishing gradient problem to achieve the accuracy of 94.25%.","PeriodicalId":145272,"journal":{"name":"2020 5th International Conference on Innovative Technologies in Intelligent Systems and Industrial Applications (CITISIA)","volume":"24 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":"116975254","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

Investigation of Mobile Machine Learning Models to Preserve the Effectiveness of User Privacy 保护用户隐私有效性的移动机器学习模型研究

2020 5th International Conference on Innovative Technologies in Intelligent Systems and Industrial Applications (CITISIA) Pub Date : 2020-11-25 DOI: 10.1109/CITISIA50690.2020.9397489

Danial Motahari, Samrah Arif, Arash Mohboubi, S. Rehman

{"title":"Investigation of Mobile Machine Learning Models to Preserve the Effectiveness of User Privacy","authors":"Danial Motahari, Samrah Arif, Arash Mohboubi, S. Rehman","doi":"10.1109/CITISIA50690.2020.9397489","DOIUrl":"https://doi.org/10.1109/CITISIA50690.2020.9397489","url":null,"abstract":"Machine Learning (ML) has become one of the dominant technologies in the research world. It is being applied without exception in every field where automation and future predictions are required such as cyber security, computer vision, data science, search engines and various other disciplines. The application of ML in search engines creates a high risk of breaching user’s privacy because this involves using data gathered from user’s browsing history, purchase transactions, searching videos and queries. The user’s information gathered from the search engine queries stored in computers, mobiles, other handheld devices is privately uploaded to a centralised cloud location and is then utilised in designing various ML models. As most ML models use a trained model that requires large datasets, user data gathered this way plays an important role in the development of the ML models. This however creates a significant privacy issue for individuals who may not want to reveal their personal information for ML training yet, prevention of this is beyond their access and control. In this article, we focus on the use of ML in mobile devices and address privacy concerns that can be raised by practising ML in mobile devices. The primary area of study in this research is the comparison of ML on mobile devices with ML on the cloud and figuring out its feasibility of becoming an essential ML for preserving user’s privacy. Sequentially, this study first explores the need for using the ML algorithm to address privacy issues. Next, a pre-chosen ML algorithm will be tested on mobile devices and cloud to get the comparison outcome that justifies the adoption of privacy-preserving ML model on mobile devices to preserve the user’s privacy.","PeriodicalId":145272,"journal":{"name":"2020 5th International Conference on Innovative Technologies in Intelligent Systems and Industrial Applications (CITISIA)","volume":"15 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":"126294659","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

Application of Machine learning algorithms in diagnosis and detection of psychological disorders 机器学习算法在心理障碍诊断和检测中的应用

2020 5th International Conference on Innovative Technologies in Intelligent Systems and Industrial Applications (CITISIA) Pub Date : 2020-11-25 DOI: 10.1109/CITISIA50690.2020.9371801

Yamu Aryal, Angelika Maag, Nirosha Gunasekera

{"title":"Application of Machine learning algorithms in diagnosis and detection of psychological disorders","authors":"Yamu Aryal, Angelika Maag, Nirosha Gunasekera","doi":"10.1109/CITISIA50690.2020.9371801","DOIUrl":"https://doi.org/10.1109/CITISIA50690.2020.9371801","url":null,"abstract":"A psychological disorder can be described as the disturbance of the natural state of the mind that affects the cognitive and social behaviour of the individual. The rapid modernization of society and the lack of social and personal interactions are further assisting in the increasing number new cases of psychological disorders. This paper intends to provides a brief overview of existing research being carried out in the field of machine learning and diagnosis, classification and prediction of psychological disorders and will present a sample framework which uses the data from the electronic health records to extract different text-based documents and reports to produce a tagged list of words relevant to disorder which is matched against the symptoms and signs of different psychological disorders to predict the disorder. To validate this prediction, it is further checked against the output of the machine learning models that will predict the psychological disorder based on the patient’s fMRI image and PET images extracted from the patient’s EHR. Through this paper, readers will be able to get an overview of the recent developments in the field of diagnosis of mental disorders by utilizing the machine learning algorithms and techniques to process the relevant unstructured data for improving the accuracy of the diagnosis to reduce the risk of misdiagnosis.","PeriodicalId":145272,"journal":{"name":"2020 5th International Conference on Innovative Technologies in Intelligent Systems and Industrial Applications (CITISIA)","volume":"30 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":"126534262","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