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Studies in Big Data 2020年 80卷 91-105页

作者： Ali, Abbas M. Ghafoor, Kayhan Zrar Maghdid, Halgurd S. Mulahuwaish, Aos Department of Software Engineering Salahaddin University-Erbil Erbil Iraq School of Mathematics and Computer Science University of Wolverhampton Wulfruna Street WolverhamptonWV1 1LY United Kingdom Department of Software Engineering Faculty of Engineering Koya University Kurdistan Region Koysinjaq Iraq Department of Computer Science and Information Systems Saginaw Valley State University 7400 Bay Rd Science East 174 University Center Itta BenaMI48710 United States

In this paper, we performed a comparative analysis using machine learning algorithms named support vector machine (SVM), decision tree (DT), k-nearest neighbor (kNN), and convolution neural network (CNN) to classify pneumonia level (mild, progressive, and severe stage) of the COVID-19 confirmed patients. More precisely, the proposed model consists of two phases: first, the model computes the volume and density of lesions and opacities of the CT images using morphological approaches. In the second phase, we use machine learning algorithms to classify the pneumonia level of the confirmed COVID-19 patient. Extensive experiments have been carried out and the results show the accuracy of 91.304%, 91.4%, 87.5%, 95.622% for kNN, SVM, DT, and CNN, respectively. © Springer International Publishing AG 2018.

关键词： COVID-19

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Integrating Privacy Enhancing Techniques into Blockchains Using Sidechains

Integrating Privacy Enhancing Techniques into Blockchains Us...

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2019 IEEE Canadian Conference of Electrical and computer engineering, CCECE 2019

作者： Parizi, Reza M. Homayoun, Sajad Yazdinejad, Abbas Dehghantanha, Ali Choo, Kim-Kwang Raymond Department of Software Engineering and Game Development Kennesaw State University GA United States Department of Computer Engineering and Information Technology Shiraz University of Technology Shiraz Iran Faculty of Computer Engineering University of Isfahan Iran School of Computer Science University of Guelph ON Canada Department of Information Systems and Cyber Security University of Texas at San Antonio TX United States

ISBN: (纸本)9781728103198

Blockchains are turning into decentralized computing platforms and are getting worldwide recognition for their unique advantages. There is an emerging trend beyond payments that blockchains could enable a new breed of decentralized applications, and serve as the foundation for Internet's security infrastructure. The immutable nature of the blockchain makes it a winner on security and transparency;it is nearly inconceivable for ledgers to be altered in a way not instantly clear to every single user involved. However, most blockchains fall short in privacy aspects, particularly in data protection. Garlic Routing and Onion Routing are two of major Privacy Enhancing Techniques (PETs) which are popular for anonymization and security. Garlic Routing is a methodology using by I2P Anonymous Network to hide the identity of sender and receiver of data packets by bundling multiple messages into a layered encryption structure. The Onion Routing attempts to provide low-latency Internet-based connections that resist traffic analysis, deanonymization attack, eavesdropping, and other attacks both by outsiders (e.g. Internet routers) and insiders (Onion Routing servers themselves). As there are a few controversies over the rate of resistance of these two techniques to privacy attacks, we propose a PET-Enabled Sidechain (PETES) as a new privacy enhancing technique by integrating Garlic Routing and Onion Routing into a Garlic Onion Routing (GOR) framework suitable to the structure of blockchains. The preliminary proposed GOR aims to improve the privacy of transactions in blockchains via PETES structure. © 2019 IEEE.

关键词： Blockchain

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Identifying the pornographic video on youtube using vlog stream 4

Identifying the pornographic video on youtube using vlog str...

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4th IEEE International Conference on Computing Communication and Automation, ICCCA 2018

作者： Islam, Md Kamrul Ahmed, Md Manjur Zamli, Kamal Zuhairi Faculty of Computer Systems and Software Engineering Universiti Malaysia Pahang Pahang Malaysia

ISBN: (纸本)9781538669471

Today, YouTube is the most used platform for sharing videos from individual to organizations. Along with the sharing videos, YouTube creates a social networking platform by allowing sharing of feelings, views, and opinions about a video called vlog. It also allows uploading of videos of all ages (child, young and old people). YouTube has its policy for defining age-restricted content on YouTube. But a lot of uploaded videos are not marked as age restricted though they actually contain pornographic contents. YouTube has a team for reviewing the video content upon receiving a report from the user only. All the existing methods identify the pornographic video based on the visual or audio-visual features of video. However, there is no automatic method exist the author aware of identifying them based on vlogger sentiment. Thus, the challenge of automatically detecting this kind of video content is significant in applications like content filtering, detection of illegal content, etc. In this paper, we present an automatic method for identifying the pornographic content on YouTube based on the vlog stream. The experiment has been executed on both the age-restricted vlog stream and non-restricted vlog stream to validate the proposed method. The experimental result shows the effectiveness of the proposed methodology with nearly 80% accuracy. © 2018 IEEE.

关键词： Sentiment analysis

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A Review of Artificial Intelligence in Serious Game for Public Health

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Journal of Physics: Conference Series 2021年第1期1830卷

作者： Abdul Rafiq Tuty Asmawaty Abdul Kadir Siti Normaziah Ihsan Soft Computing and Intelligent System Research Group (SPINT) Faculty of Computer Systems and Software Engineering Universiti Malaysia Pahang Kuantan Pahang Malaysia

Serious games used in public health for purposes including training, learning, prediction, coaching, diagnostic, rehabilitation and supporting. However, the serious game focuses mainly on user experience and graphical application, but less attention on applying artificial intelligence (AI). By enhancing artificial intelligence, it will have the capability to solve issues, especially in the field of public health. This article discussed a review of the use of artificial intelligence in a serious game for public health. The main idea of this paper is to gather all the related articles and create a trend analysis of the use of Artificial Intelligence (AI) in a serious game for public health. The related articles were applied to Artificial intelligence (AI) in the area of decision-making. The final section discussed the new trend and the future of Artificial intelligence using current AI techniques.

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Exploring the integration of multi criteria decision analysis in the clean energy biodiesels applications: A systematic review and gap analysis

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engineering Applications of Artificial Intelligence 2024年 133卷

作者： Alamoodi, A.H. Garfan, Salem Al-Zuhairi, Omar Zaidan, B.B. Zaidan, A.A. Albahri, O.S. Y.Y. Ahmaro, Ibraheem Albahri, A.S. Yussof, Salman Magableh, Aws A. Institute of Informatics and Computing in Energy Universiti Tenaga Nasional Malaysia Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman19328 Jordan Perak Malaysia Applied Science Research Center Applied Science Private University Amman Jordan Low Dimensional Materials Research Centre Department of Physics Faculty of Science Universiti Malaya Kuala Lumpur50603 Malaysia SP Jain School of Global Management Lidcombe SydneyNSW2141 Australia International Graduate School of Artificial Intelligence National Yunlin University of Science and Technology Douliu Taiwan Victorian Institute of Technology Australia Computer Techniques Engineering Department Mazaya University College Nasiriyah Iraq Computer Science Department College of Information Technology Hebron University Hebron Palestine Technical College Imam Ja'afar Al-Sadiq University Baghdad Iraq Department of Information Systems Yarmouk University Irbid Jordan Baghdad Iraq Department of Computing College of Computing and Informatics Universiti Tenaga Nasional Malaysia Departments of Software Engineering Prince Sultan University Riyad Saudi Arabia

Biodiesel has gained tremendous attention from researchers due to its environmentally friendly and renewable fuel manufactured domestically from vegetable oils or animal fats. Multiple factors, including emission and fuel efficiency, measure the performance of biodiesel. These characteristics make the process of evaluation and benchmarking a multi-criteria decision-making (MCDM) problem. This research attempts to survey the literature for multi-criteria decision-making applications on biodiesel. This systematic review was carried out on four scientific databases: PubMed, ScienceDirect, IEEE Xplore, and WoS. The collected articles were summarized and mapped into a coherent taxonomy, while the main challenges, motivations, and recommendations were collected and categorized for better understanding and finding out essential characteristics. The final set of included articles was 33, and they were categorized into six different categories. Most articles (n=15/33) investigated the evaluation and selection of biodiesel alternatives using MCDM. Furthermore, a state-of-art bibliometric analysis is provided. The analysis demonstrates several challenges in this area, such as source selection, optimum fuel blending, and uncertainty. This article is expected to assist future researchers in addressing the current gaps and contribute to advancing this area. © 2024 Elsevier Ltd

关键词： Biodiesel

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Modelling and Optimisation of Oil Palm Trunk Core Biodelignification using Neural Network and Genetic Algorithm

Modelling and Optimisation of Oil Palm Trunk Core Biodeligni...

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作者： Abdul Sahli Fakharudin Norazwina Zainol Zulsyazwan Ahmad Khushairi SPINT Faculty of Computer Systems & Software Engineering Universiti Malaysia Pahang Faculty of Chemical and Natural Resources Engineering Universiti Malaysia Pahang

In this paper,artificial neural network had been implemented to model the biodelignification process of oil palm trunk core using Pleurotus *** generated model was used as the fitness function for the genetic algorithm to obtain the optimise lignin left *** 4-10-5-2-1 network architecture had been used to model the process and 10 models were generated *** models were used to find the optimised the network output using genetic algorithm *** modelling results had improved the accuracy and error when using the artificial neural network modelling with training MSE of 0.0096 and testing MSE of *** results also show an improved lignin left around 7.55% when the network output was optimised by the genetic *** application of neural network and genetic algorithm had improved the delignification process.

关键词： Modelling optimisation biodelignification artificial neural network genetic algorithm

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Influence of Network Speed on Structured Query Language (SQL) Database Data Transmission Performance in an Internet of Things (IoT) Sensing Device on Single Board computers (SBC)

Influence of Network Speed on Structured Query Language (SQL...

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International Conference on Green engineering Technology and Applied Computing 2019, IConGETech2 019 and International Conference on Applied Computing 2019, ICAC 2019

作者： Nor Azhar, A. Nur Ayuni Hasna, S. Roslina, A.H. Fauziah, Z. Rahmah, M. Department of Computer Science Universiti Malaysia Pahang Lebuhraya Tun Razak Gambang Pahang Kuantan26300 Malaysia Department of Computer Systems and Networking Universiti Malaysia Pahang Lebuhraya Tun Razak Gambang Pahang Kuantan26300 Malaysia Department of Software Engineering Universiti Malaysia Pahang Lebuhraya Tun Razak Gambang Pahang Kuantan26300 Malaysia Department of Graphic and Multimedia Technology Faculty of Computer Systems and Software Engineering Universiti Malaysia Pahang Lebuhraya Tun Razak Gambang Pahang Kuantan26300 Malaysia

One of the crucial elements in an Internet of Things (IoT) environment is a database. IoT performance will be at stake if the wrong database is adopted. In this research, three Structured Query Language (SQL) databases were tested against multiple network speeds in an IoT device. Single board computers (SBC) were used as a media of testing instead of an ordinary computer. A controlled wireless sensor network (WSN) was developed with several uniform constants and variable network speeds. A new data transmission rate equation also was proposed and tested. From the result, it was found that not all SQL databases can cater IoT devices. Some of them showed that performance decreased along with network speed. The correlation, between 0.90 and 0.96, proved the strong influence between the research subjects. In addition, file-type database is the best option available for SQL-wise IoT storage. © 2019 Published under licence by IOP Publishing Ltd.

关键词： Internet of things

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COVID-19 pneumonia level detection using deep learning algorithm

TechRxiv

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TechRxiv 2020年

作者： Ghafoor, Kayhan Zrar Ali, Abbas M. Mulahuwaish, Aos Maghdid, Halgurd S. Hussein, Safwan Mawlood Mohammed, Marwan A. Department of Software Engineering Salahaddin University-Erbil Iraq School of Mathematics and Computer Science University of Wolverhampton Wulfruna Street WolverhamptonWV1 1LY United Kingdom Department of Computer Science and Information Systems Saginaw Valley State University University Center 7400 Bay Rd Science East 174 MI48710 United States Department of Software Engineering Faculty of Engineering Koya University Kurdistan Iraq Computer Engineering Department Tishk International University Iraq

The first COVID-19 confirmed case is reported in Wuhan, China and spread across the globe with unprecedented impact on humanity. Since this pandemic requires pervasive diagnosis, it is significant to develop smart, fast and efficient detection technique. To this end, we developed an Artificial Intelligence (AI) engine to classify the lung inflammation level (mild, progressive, severe stage) of the COVID-19 confirmed patient. In particular, the developed model consists of two phases;in the first phase, we calculate the volume and density of lesions and opacities of the CT images of the confirmed COVID-19 patient using Morphological approaches. In the second phase, the second phase classifies the pneumonia level of the confirmed COVID-19 patient. To achieve precise classification of lung inflammation, we use modified Convolution Neural Network (CNN) and k-Nearest Neighbor (kNN). The result of the experiments show that the utilized models can provide the accuracy up to 95.65% and 91.304 % of CNN and kNN respectively. © 2020, CC BY.

关键词： COVID-19

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arXiv

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arXiv 2019年

作者： Netisopakul, Ponrudee Wohlgenannt, Gerhard Pulich, Aleksei Bangkok Thailand Faculty of Software Engineering and Computer Systems Itmo University St. Petersburg Russia

Distributional semantics in the form of word embeddings are an essential ingredient to many modern natural language processing systems. The quantification of semantic similarity between words can be used to evaluate the ability of a system to perform semantic interpretation. To this end, a number of word similarity datasets have been created for the English language over the last decades. For Thai language few such resources are available. In this work, we create three Thai word similarity datasets by translating and re-rating the popular WordSim-353, SimLex-999 and SemEval-2017-Task-2 datasets. The three datasets contain 1852 word pairs in total and have different characteristics in terms of difficulty, domain coverage, and notion of similarity (relatedness vs. similarity). These features help to gain a broader picture of the properties of an evaluated word embedding model. We include baseline evaluations with existing Thai embedding models, and identify the high ratio of out-of-vocabulary words as one of the biggest challenges. All datasets, evaluation results, and a tool for easy evaluation of new Thai embedding models are available to the NLP community online. Copyright © 2019, The Authors. All rights reserved.

关键词： Embeddings

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2M-SELAR: A Model for Mining Sequential Least Association Rules 2nd

2M-SELAR: A Model for Mining Sequential Least Association Ru...

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2nd International Conference on Advanced Data and Information engineering, DaEng 2015

作者： Abdullah, Zailani Adam, Omer Herawan, Tutut Noraziah, Ahmad Saman, Md Yazid Mohd Hamdan, Abdul Razak School of Informatics and Applied Mathematics Universiti Malaysia Terengganu Kuala Terengganu21030 Malaysia Faculty of Computer Science and Information Technology Universiti Malaya Kuala Lumpur50603 Malaysia Faculty of Computer Systems and Software Engineering Universiti Malaysia Pahang KuantanPahang26300 Malaysia Faculty of Information Science and Technology Universiti Kebangsaan Malaysia BangiSelangor43600 Malaysia

ISBN: (纸本)9789811317972

Recently, mining least association rule from the sequential data becomes more important in certain domain areas such as education, healthcare, text mining, etc. due to its uniqueness and usefulness. However, discovering such rule is a great challenge because it involves with a set of least items which usually holds a very low in term of support. Therefore, in this paper propose a model for mining sequential least association rule (2M-SELAR) that embedded with SELAR algorithm, and Critical Relative Support (CRS) and Definite Factor (DF) measures. The experimental results reveal that 2M-SELAR can successfully generate the desired rule from the given datasets. © Springer Nature Singapore Pte Ltd. 2019.

关键词： Data mining

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