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International Conference on Software engineering and Computer Systems (ICSECS)

作者： Salwana Mohamad Asmara Noor Azida Sahabudin Nor Syahidatul Nadiah Ismail Ily Amalina Ahmad Sabri Faculty of Computing Universiti Malaysia Pahang Al-Sultan Abdullah Pekan Malaysia Faculty of Ocean Engineering Technology and Informatics Universiti Malaysia Terengganu Terengganu Malaysia

Knowledge representation and reasoning require knowledge graph embedding as it is crucial in the area. It involves mapping entities and relationships from a knowledge graph into vectors of lower dimensions that are continuous in nature. This encoding enables machine learning algorithms to effectively reason and make predictions on graph-structured data. This review article offers an overview and critical analysis specifically about the methods of knowledge graph embedding which are TransE, TransH, and TransR. The key concepts, methodologies, strengths, and limitations of these methods, along with examining their applications and experiments conducted by existing researchers have been studied. The motivation to conduct this study is to review the well-known and most applied knowledge embedding methods and compare the features of those methods so that a comprehensive resource for researchers and practitioners interested in delving into knowledge graph embedding techniques is delivered.

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Group decision making-based TODIM under Linguistic Aggregation Majority Additive operator 28

Group decision making-based TODIM under Linguistic Aggregati...

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28th Simposium Kebangsaan Sains Matematik, SKSM 2021

作者： Rosli, N.N.N.C. Yusoff, B. Wahab, A.F. Madi, E.N. Faculty of Ocean Engineering Technology and Informatics Universiti Malaysia Terengganu Terengganu Kuala Nerus21030 Malaysia Faculty of Informatics and Computing Besut Campus Universiti Sultan Zainal Abidin Malaysia

This paper proposes an extension of TODIM (interactive multi-criteria decision making) under group decision making (GDM) using the Linguistic Aggregation Majority Additive (LAMA) operator. TODIM is an effective method in modelling experts' psychological behaviour in the decision-making process. However, under the GDM, the method is based solely on the average of all experts' judgments without any consideration to soft aggregation processes that include majority and/or minority concepts. In this work, the LAMA operator is used to be integrated with TODIM-GDM to aggregate the experts' opinions with respect to the majority concept under the linguistic domain. This is to provide a greater flexibility in reaching a consensus instead of only considering equally average using the classical averaging operators. Two linguistic representations, namely, symbolic approach and 2-tuple linguistic approach for LAMA operator are proposed to be utilised in the method. A numerical example in investment selection problem is provided to illustrate the applicability of the proposed method. Finally, the comparison of these two linguistic approaches is presented. The results show that LAMA under the 2-tuple linguistic approach is preferable to the symbolic approach in case of there is a tie between alternatives in the final ranking. © Published under licence by IOP Publishing Ltd.

关键词： Linguistics

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An Integrated Model to Email Spam Classification Using an Enhanced Grasshopper Optimization Algorithm to Train a Multilayer Perceptron Neural Network 2nd

An Integrated Model to Email Spam Classification Using an En...

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2nd International Conference on Advances in Cyber Security, ACeS 2020

作者： Ghaleb, Sanaa A. A. Mohamad, Mumtazimah Abdullah, Engku Fadzli Hasan Syed Ghanem, Waheed A. H. M. Faculty of Informatics and Computing Universiti Sultan Zainal Abidin Terengganu Malaysia Faculty of Ocean Engineering Technology and Informatics Terengganu Malaysia Faculty of Education-Aden and Faculty of Education-Saber Aden University Aden Yemen Faculty of Engineering Aden University Aden Yemen

ISBN: (纸本)9789813368347

Email is an important communication that the Internet has made available. One of the significance is seen in the great ease in which immediate transmission of internet data is done during email transmission. This great ease emerges with a major issue which is the continuous increase in spam emails. Thus, the need for a spam email detector. The versatility and adaptability of the nature of spam influenced past innovations. However, previous techniques have been weakened. This study introduces an email detection model that is designed based on use of an improved version of the grasshopper optimization algorithm to train a Multilayer Perceptron in classifying emails as ham and spam. To validate the performance of EGOA, executed on the spam email dataset are utilized, then the performance was relatively compared with popular search algorithms. The implementation demonstrates that EGOA introduces the best results with high accuracy of up to 96.09%. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Electronic mail

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Spam Classification Based on Supervised Learning Using Grasshopper Optimization Algorithm and Artificial Neural Network 2nd

Spam Classification Based on Supervised Learning Using Grass...

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2nd International Conference on Advances in Cyber Security, ACeS 2020

ISBN: (纸本)9789813368347

The electronic mailing system has in recent years become a timely and convenient way for the exchange of multimedia messages across the cyberspace and computer networks in the global sphere. This proliferation has prompted most (if not all) inboxes receiving junk email messages on numerous occasions every day. Due to these surges in spam attacks, a number of approaches have been proposed to lessen the attacks across the globe significantly. The effect of previous detection techniques has been weakened due to the adaptive nature of unsolicited email spam. Hence, resolving spam detection (SD) problem is a challenging task. A regular class of the Artificial Neural Network (ANN) called Multi-Layer Perceptron (MLP) was proposed in this study for email SD. The main idea of this research is to train a neural network by leveraging a new nature-inspired metaheuristic algorithm referred to as a Grasshopper Optimization Algorithm (GOA) to categorize emails as ham and spam. Evaluation of its performance was performed on an often-used standard dataset. The results showed that the proposed MLP model trained by GOA achieves high accuracy of up to 94.25% performance compared to other optimization. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Multilayer neural networks

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Relationship of Rainfall and IOD Phenomena by using Spectral Analysis and SARIMA 4

Relationship of Rainfall and IOD Phenomena by using Spectral...

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4th International Conference on Mathematics, Science, Education and technology, ICOMSET 2020, in Conjunction with the 2nd International Conference on Biology, Science and Education, ICoBioSE 2020

作者： Miftahuddin Furqany, N.E. Bakar, M.A. Department of Statistics Faculty of Mathematics and Sciences Syiah Kuala University Banda Aceh Indonesia Faculty of Ocean Engineering Technology and Informatics Universiti Malaysia Terengganu

Rainfall is very essential for life and it has complexity process. To obtain the relationship between rainfall in time-spatial of Aceh region and the Indian ocean Dipole (IOD) phenomenon, we used spectral-analysis and SARIMA model. Identification results show that the rainfall data monthly in Aceh Barat (ABR), Aceh Besar (ABS), Sabang (S) and Aceh Utara (AU) has a similar pattern. However, due to the sensitivity of the occurrence of the IOD, the ABR region has higher rainfall compared to ABS, S and AU regions. Rainfall in all regions observation has a peak time, namely at the end of the year (October-November-December). It has the negative peak of the IOD event occurred in September-2016 while the positive peak point occurred in October-2019. Overall, the normal phase occurred from January to April-2019. In peak season in ABR and ABS, the rainfall intensity increased. The results showed that the rainfall model in ABR and S has normal distribution, whereas in ABS and AU has nonnormal distribution. Furthermore, the rainfall model in ABR with SARIMA(2,0,2)(0,1,1)6 is the best model compared to model of three other regions with high accuracy forecasting. © Published under licence by IOP Publishing Ltd.

关键词： Rain

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An LSTM Based Forecasting for Major Stock Sectors Using COVID Sentiment

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Computers, Materials & Continua 2021年第4期67卷 1191-1206页

作者： Ayesha Jabeen Sitara Afzal Muazzam Maqsood Irfan Mehmood Sadaf Yasmin Muhammad Tabish Niaz Yunyoung Nam Department of Computer Science COMSATS University IslamabadAttock CampusPakistan Department of Media Design and Technology Faculty of Engineering&InformaticsUniversity of BradfordBradfordBD71AZUK Department of Smart Device Engineering School of Intelligent Mechatronics EngineeringSejong UniversitySeoulKorea Department of Computer Science&Engineering Soonchunhyang UniversityAsan36538Korea

Stock market forecasting is an important research area,especially for better business decision *** stock predictions continue to be significant for business *** short-term stock market forecasting is usually based on historical market data analysis such as stock prices,moving averages,or daily ***,major events’news also contains significant information regarding market *** effective stock market forecasting system helps investors and analysts to use supportive information regarding the future direction of the stock *** research proposes an efficient model for stock market *** current proposed study explores the positive and negative effects of coronavirus events on major stock sectors like the airline,pharmaceutical,e-commerce,technology,and *** use the Twitter dataset for calculating the coronavirus sentiment with a Long Short-Term Memory(LSTM)model to improve stock *** LSTM has the advantage of analyzing relationship between time-series data through memory *** performance of the system is evaluated by Mean Absolute Error(MAE),Mean Squared Error(MSE),and Root Mean Squared Error(RMSE).The results show that performance improves by using coronavirus event sentiments along with the LSTM prediction model.

关键词： Business intelligence decision making stock prediction long short-term memory COVID-19 event sentiment

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Kco: Balancing Class Distribution in Just-in-Time Software Defect Prediction Using Kernel Crossover Oversampling

SSRN

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SSRN 2023年

作者： Ismail, Ahmad Muhaimin Hamid, Siti Hafizah Ab Sani, Asmiza Abdul Daud, Nur Nasuha Mohd Department of Software Engineering Faculty of Computer Science and Information Technology University of Malaya Kuala Lumpur50603 Malaysia Faculty of Ocean Engineering Technology and Informatics University Malaysia Terengganu Kuala Terengganu Terengganu21030 Malaysia

ContextThe performance of the defect prediction model by using balanced and imbalanced datasets makes a big impact on the discovery of future defects. Current resampling techniques only address the imbalanced datasets without taking into consideration redundancy and noise inherent to the imbalanced *** address the imbalance issue, we propose Kernel Crossover Oversampling (KCO), an oversampling technique based on kernel analysis and cross-over interpolation. Specifically, the proposed technique aims to generate balanced datasets by increasing data diversity in order to reduce redundancy and *** first represents multidimensional features into two-dimensional features by employing Kernel Principal Component Analysis (KPCA). KCO then divides the plotted data distribution by deploying spectral clustering to select the best region for interpolation. Lastly, KCO generates the new defect data by cross-interpolating different data templates within the selected data *** to the prediction evaluation conducted, KCO consistently produced F-scores ranging from 21% to 63% across six datasets, on average. According to the experimental results presented in this study, KCO provides more effective prediction performance than other baseline *** experimental results show that KCO within project and cross project predictions especially consistently achieve higher performance of F-score results. © 2023, The Authors. All rights reserved.

关键词： Interpolation

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Review on Advance Monitoring of Electrical and Mechanical Failure in Submersible Pump

Review on Advance Monitoring of Electrical and Mechanical Fa...

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International Conference on Electrical, Control and Instrumentation engineering (ICECIE)

作者： T. S. N. F. Tuan Mohamad N. Afrizal M.Z. Daud M.R. Awal Faculty of Ocean Engineering Technology and Informatics Universiti Malaysia Terengganu Kuala Terengganu Terengganu Malaysia

ISBN: (纸本)9781665480772

Electrical Submersible Pump (ESP) is one of the most efficient and reliable devices for lifting moderate to high volumes of fluids from wellbores. It is commonly used in the oil and gas industry, where it provides high volumes of lifting with the flexibility to cover a range of sizes output flow capacities at various well conditions. Due to its challenging operation environment, ESP prompt to face a fatigue failure, undesirables disruption that lead to shorten its life time. Hence, this work attempts to review and summarise recent research and development in ESP performance monitoring and early fault detection focusing on mechanical and electrical failures. The paper starts with a brief explaination of pump operating mechanism followed by detail discussion on ESP failures and detail explanation of various methods on ESP performance monitoring. A recent and advance failure detection methods applied on ESP also discussed including the main principles of the motor currents signature analysis (MCSA) method. Finally, the authors discuss practical aspects and challenges related to data processing and real-world implementation on ESP monitoring condition.

关键词： Shafts Condition monitoring Vibrations Induction motors Fluids Fault detection Pumps

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MalCov: Covid-19 Fake News Dataset in the Malay Language

MalCov: Covid-19 Fake News Dataset in the Malay Language

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Visualization, informatics and technology Conference (IVIT), International

作者： N. H. Abd Rahim M. S. H. Basri Faculty of Ocean Engineering Technology and Informatics Universiti Malaysia Terengganu Kuala Nerus Terengganu Malaysia

ISBN: (纸本)9781665486491

The COVID-19 pandemic has drastically changed the world's atmosphere. The virus itself has spread worldwide, so the misinformation related to COVID-19 also created chaos in society. The inaccurate use of infodemic terminology created misleading info about the disease. This tragedy caused panic, confusion among the public, and miscommunication between government information and the public. Several attempts using automated classification via machine learning models have been recently made to avoid the spread of this fake news. These methods require the use of labeled data. However, the scarcity of available corpora for predictive modeling, particularly in languages other than English, is a big barrier challenge in this area. To date, our proposed research may be the first step in an extensive study of fake news detection in the Malay language. We introduce MalCov (Malaysia Covid) fake news dataset for the purpose. The MalCov which contains 79.5% fake articles or approximately 171 statements are gathered from main social media platforms. The remaining statements are valid articles that have been checked and manually validated by the local authorities. All these articles are gathered from a single portal called "***" Since we are using a non-English language for this dataset, the data has been separated into contents and titles. The most frequent words used are then analyzed. Several machine learning models such as Naïve Bayes, SVM, and Logistic Regression are utilized to build the classifiers. As a result, the decision tree achieves the highest performance, which is 93.48%. Keywords—dataset; fake news; fake news detection; machine learning classification; Malay language.

关键词： COVID-19 Support vector machines Terminology Social networking (online) Pandemics Machine learning Predictive models

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Design and Implement of REST API for Data Integration 8

Design and Implement of REST API for Data Integration

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8th International Conference on engineering and Emerging Technologies, ICEET 2022

作者： Yusof, Mohd Kamir Man, Mustafa Ismail, Azli Universiti Sultan Zainal Abidin Faculty of Informatics and Computing Terengganu Besut 22200 Malaysia Universiti Malaysia Terengganu Faculty of Ocean Engineering Technology and Informatics Terengganu Kuala Nerus21030 Malaysia Wisma Dataran Rimba Square Lieos SDN BHD Lot 37744 Tingkat 1 Terengganu Kuala Nerus21300 Malaysia

ISBN: (纸本)9781665491068

Data integration is currently most important to organization to integrate data from different data sources using different platform or programming languages. A suitable approach is required to allow one system send the data to another system using standard data format. REST API is one approach can used for data integration using JSON format. Three processes are involved in this approach. There are extract data, send data, received and dump data. In this approach, SAP system will be sending the data in JSON to FEMS system. FEMS system will be exploded and dump the data into database FEMS. Based the experiment, the result indicates, REST API is successfully implemented by showing less response time and CPU usage during sending and receiving the data in JSON format. © 2022 IEEE.

关键词： Data integration

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