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3rd International Conference on Communications, Information, Electronic and Energy Systems, CIEES 2022

作者： Ambrews, Ashley Bryan Gubin Moung, Ervin Farzamnia, Ali Yahya, Farashazillah Omatu, Sigeru Angeline, Lorita Universiti Malaysia Sabah Faculty of Computing And Informatics Kota Kinabalu Sabah Malaysia Universiti Malaysia Sabah Faculty of Engineering Kota Kinabalu Sabah Malaysia Education Research Centre Higashi-Hiroshima739-0046 Japan Taylor's University School of Computer Science Faculty of Innovation And Technology Selangor Malaysia

ISBN: (数字)9781665491495

ISBN: (纸本)9781665491495

The World Health Organization reports that more than 10 million people worldwide die each year due to heart disease. In this project, an ensemble-based machine learning (ML) model is proposed for the prediction of heart disease. The proposed method was evaluated on two datasets: UCI heart disease and Framingham heart study. Various ML such as K-Nearest Neighbors, Naïve Bayes, Decision Tree, Support Vector Machine (SVM), Logistic Regression (LR) and Random Forest (RF) were carried out to predict the outcome of heart disease. Additionally, the Stacking and Voting ensemble approaches are also employed for heart disease prediction. All ML classifiers evaluated on both datasets have their performance metrics analyzed and compared. The results of all ML classifiers assessed on the Framingham dataset is inferior than that of the UCI heart disease dataset. For individual ML classifier, SVM achieved the highest accuracy for UCI heart disease with 91.09%, whereas LR achieved the highest accuracy for the Framingham heart study with 85.38%. However, it should be noted that RF trained on the UCI dataset is the best ML for prioritizing Sensitivity (92.52%). Finally, with the exception of Sensitivity, the Voting approach is strongly suggested because it outperforms the other classifiers noticeably where it achieved Accuracy (91.96%), F1 (91.69%), Sensitivity (91.72%), Specificity (90.77%), and Precision (92.40%) on UCI dataset. © 2022 IEEE.

关键词： Forecasting

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EFFECT OF OPERATING PARAMETERS ON LIGNIN EXTRACTION FROM EMPTY FRUIT BUNCHES AND RICE HUSK USING MALIC ACID-SUCROSE-WATER LOW TRANSITION TEMPERATURE MIXTURE 30th

EFFECT OF OPERATING PARAMETERS ON LIGNIN EXTRACTION FROM EMP...

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30th European Biomass Conference and Exhibition, EUBCE 2022

作者： Cheng, Jonathan Lin Yang Wong, Jung Lin Chew, Jiuan Jing Yeu, Yee Lee Khaerudini, Deni Shidqi Sunarso, Jaka Research Centre for Sustainable Technologies Faculty of Engineering Computing and Science Swinburne University of Technology Jalan Simpang Tiga Sarawak Kuching93350 Malaysia Bld. 440 Kawasan Puspiptek Serpong South Tangerang Banten15314 Indonesia

Low transition temperature mixture (LTTM) is an attractive green solvent alternative to ionic liquids, particularly for lignin extraction from biomass, given its relatively low synthesis cost and environmental compatibility. Lignin is an unwanted by-product of thermochemical processing and is often discarded due to its recalcitrant nature. Given the potential of lignin as a feedstock for pyrolysis, it is desirable to extract lignin in the biomass. Malaysia has tremendous biomass resources from agricultural activities such as empty fruit bunches (EFB) from the palm oil industry and rice husk from the rice mills. The biomass wastes create an opportunity to harness LTTM for the extraction of lignin. However, there is still lack of insight into the operating conditions that govern the lignin extraction efficiency and lignin purity of the extraction process using malic acid-sucrose-water LTTM. This work aims to evaluate the effect of different operating parameters such as temperature, biomass to LTTM ratio, and reaction time on the lignin extraction efficiency and lignin purity. As temperature increases from 70 to 110 °C, the lignin extraction efficiency of EFB increased from 7.67 to 53.70 %, while lignin yield of rice husk increased from 3.80 to 55.74%. The lignin extraction efficiency of EFB increased from 5.57 to 56.82%, while rice husk increased from 6.46 to 38.22 % as biomass to LTTM ratio decreases from 1:10 to 1:40. The extraction efficiency of EFB and rice husk increased from 10.62 to 59.92 % and 4.64 to 20.26 % when the reaction time increased from 2 to 8 h. The highest lignin purity was obtained at 6 h with a fixed operating temperature of 100 °C, and biomass to LTTM ratio of 1:20 for EFB and rice husk were 88.32 and 85.84 %, respectively. © 2022, ETA-Florence Renewable Energies., All rights reserved.

关键词： Lignin

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Quantifying the Benefits of Implementing Big Data Analytics on Consumers’ Online Purchase Intentions in e-Commerce

Quantifying the Benefits of Implementing Big Data Analytics ...

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2024 IEEE International Conference on computing, ICOCO 2024

作者： Wei, Justin Ang Kang Mansor, Mahayaudin M. Derasit, Zuraidah Bakar, Mohd Aftar Abu Ul-Saufie, Ahmad Zia Hamzah, Firdaus Mohamad Faculty of Business Economics and Accounting HELP University Kuala Lumpur Malaysia School of Mathematical Sciences College of Computing Informatics and Mathematics Universiti Teknologi MARA Malaysia Institute for Big Data Analytics and Artificial Intelligence Universiti Teknologi MARA Selangor Shah Alam Malaysia Department of Mathematical Sciences Faculty of Science & Technology Universiti Kebangsaan Malaysia Malaysia Centre for Defence Foundation Studies Universiti Pertahanan Nasional Malaysia Kuala Lumpur Malaysia

ISBN: (纸本)9798331530303

The digital revolution has reshaped the global economy, especially e-commerce. Innovative technologies such as big data analytics, artificial intelligence, and cloud computing drive this transformation, enabling companies to make informed business decisions and influence consumers’ online purchasing behaviour. This study explores empirical evidence for the benefits of using big data analytics (BDA) to influence consumers’ online purchasing decisions in e-commerce. Primary data was collected via an online survey targeting 300 customers with online shopping experience on platforms that have adopted the digital technologies of BDA. Statistical methods used in this study included questionnaire design, reliability test, descriptive analysis, and multiple regression modelling and diagnostics. The findings indicate that the benefits of implementing BDA in e-commerce for information search, recommendation systems, consumer services, dynamic pricing, security and fraud detection are strongly associated with consumers’ online purchasing intentions. The study underscores the importance of businesses leveraging BDA, as the benefits significantly and positively impact consumers’ purchasing intentions in e-commerce while addressing security and fraud detection concerns that undermine its advantage. Nevertheless, we encourage all companies to leverage BDA implementation in their digital marketplace, which ultimately contributes to achieving e-commerce goals, such as increased market reach, cost reduction, and revenue growth. ©2024 IEEE.

关键词： Marketplaces

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Accuracy and performance analysis for classification algorithms based on biomedical datasets 7

Accuracy and performance analysis for classification algorit...

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7th International Conference on Software Engineering and Computer Systems and 4th International Conference on Computational science and Information Management, ICSECS-ICOCSIM 2021

作者： Al-Hameli, Bassam Abdo Alsewari, AbdulRahman A. Khubrani, Mousa Fakhreldin, Mohammoud Universiti Malaysia Pahang Faculty of Computing Pahang Pekan26600 Malaysia Universiti Malaysia Pahang Centre for Software Development and Integrated Computing Faculty of Computing Pahang Pekan26600 Malaysia Jazan University Computer Science and Information Technology Jazan Saudi Arabia

ISBN: (纸本)9781665414074

Diseases chronic, including heart disease, cancer, diabetes, and obesity, are the main causes of mortality in the United States and accounting for and consuming the majority of the country's healthcare expenditure. As indicated by recent researches. The main reason for the emergence of these diseases prominently is their relationship to each other, where diabetes is one of the causes of cancer and heart disease, hepatitis also is associated with diabetes, and heart disease. This paper focuses on data mining and machine learning techniques in healthcare classification and prediction of diseases and rebuild disease detection systems (DDS). The study suggests finding a classifier among the most common kinds of classification algorithms within a combined approach represent in Bayesian, Trees, Rules, Function, and lazy algorithms to automate a better performance of early detection of diseases from the medical datasets. This paper presents and analyzes five different machine learning (ML) algorithms: Function-based Neural Network (MLP) algorithm. Trees based Decision Tree (ID3) algorithm, Bayesian Theorem based Hidden Naïve Bayes (HNB) algorithm. Lazy based k-nearest neighbors (IBK) algorithm, and Rules-based OneR algorithm. The analysis is based on four benchmark datasets in the healthcare sector, including the Pima Indian Diabetes PID, the Breast Cancer, Heart Cleveland, and Hepatitis Datasets, which were obtained from several ML repositories. The results show that the HNB predicts the best result with a relatively higher Precision, AUROC Statistic, highest accuracy, and performance when compared to MLP, IBK, OneR, ID3 algorithms. © 2021 IEEE.

关键词： Diseases

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Kinetic and equilibrium adsorption study of anionic dyes using oil palm trunk-derived activated carbon

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Materials Today: Proceedings 2022年 64卷 1627-1638页

作者： Lim, Adeline Chew, Jiuan Jing Ismadji, Suryadi Khaerudini, Deni Shidqi Darsono, Nono Sunarso, Jaka Research Centre for Sustainable Technologies Faculty of Engineering Computing and Science Swinburne University of Technology Jalan Simpang Tiga Sarawak Kuching 93350 Malaysia Department of Chemical Engineering Widya Mandala Surabaya Catholic University Kalijudan 37 Surabaya 60114 Indonesia Bld. 440 Kawasan Puspiptek Serpong Banten South Tangerang 15314 Indonesia

Oil palm trunk (OPT) was chemically activated to produce high surface area activated carbon (AC) in this work. The adsorption performances of OPT-derived AC in removing two different anionic dyes, i.e. acid blue 25 (AB 25) and sunset yellow (SY) from the aqueous solution, were evaluated individually and as a binary system. The effects of adsorbate concentration, pH, temperature, and duration were examined via batch adsorption studies. Both AB 25 and SY adsorption were most favourable at pH of 2, with the absence of electrostatic repulsion between AC and the dye components. The adsorption isotherm of AB 25 and SY can be well fitted using the non-linear Langmuir isotherm model, while the adsorption kinetics can be correlated well using the non-linear pseudo-second order model. The adsorption process of both anionic dyes was spontaneous and endothermic. The OPT-derived AC endured at least seven regeneration cycles during reusability tests with at least 80 % contaminant removal retained throughout. © 2022

关键词： Activated carbon

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A Great Deluge Algorithm for University Course Timetabling

A Great Deluge Algorithm for University Course Timetabling

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IEEE International Conference on Automatic Control and Intelligent Systems (I2CACIS)

作者： Zi Xuan Loke Say Leng Goh James Mountstephens Jonathan Likoh Faculty of Computing and Informatics Universiti Malaysia Sabah Labuan International Campus Labuan Malaysia Faculty of Computing and Informatics Universiti Malaysia Sabah Kota Kinabalu Malaysia Preparatory Centre for Science and Technology Universiti Malaysia Sabah Kota Kinabalu Malaysia

Here we propose a two-stage approach to address the University Course Timetabling Problem. In the first stage, we utilize an existing algorithm called Tabu Search with Sampling and Perturbation to generate a feasible solution, and in stage two, we use a Great Deluge algorithm to improve the quality of this feasible solution. The proposed methodology was tested on a recent instance of real university timetabling data from Universiti Malaysia Sabah. Experiments were conducted to determine the most suitable GD algorithm parameter values that resulted in optimal performance. Additionally, the performance of the GD algorithm was compared with that of a Genetic Algorithm and was found to achieve a lower minimum and lower average cost values.

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Encryption with Image Steganography Based Data Hiding Technique in IIoT Environment

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Computers, Materials & Continua 2022年第7期72卷 1323-1338页

作者： Mahmoud Ragab Samah Alshehri Hani A.Alhadrami Faris Kateb Ehab Bahaudien Ashary SAbdel-khalek Information Technology Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Centre of Artificial Intelligence for Precision Medicines King Abdulaziz UniversityJeddah21589Saudi Arabia Department of Mathematics Faculty of ScienceAl-Azhar UniversityNaser City11884CairoEgypt Department of Pharmacy Practice Faculty of PharmacyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Medical Laboratory Technology Faculty of Applied Medical SciencesKing Abdulaziz UniversityJeddah21589Saudi Arabia Molecular Diagnostic Lab King Abdulaziz University HospitalKing Abdulaziz UniversityJeddah21589Saudi Arabia Special Infectious Agent Unit King Fahd Medical Research CenterKing Abdulaziz UniversityJeddah21589Saudi Arabia Electrical and Computer Engineering Department Faculty of EngineeringKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Mathematics Faculty of ScienceTaif UniversityTaif21944Saudi Arabia Department of Mathematics Faculty of ScienceSohag UniversitySohag82524Egypt

Rapid advancements of the Industrial Internet of Things(IIoT)and artificial intelligence(AI)pose serious security issues by revealing secret ***,security data becomes a crucial issue in IIoT communication where secrecy needs to be guaranteed in real ***,AI techniques can be utilized to design image steganographic techniques in *** addition,encryption techniques act as an important role to save the actual information generated from the IIoT devices to avoid unauthorized *** order to accomplish secure data transmission in IIoT environment,this study presents novel encryption with image steganography based data hiding technique(EISDHT)for IIoT *** proposed EIS-DHT technique involves a new quantum black widow optimization(QBWO)to competently choose the pixel values for hiding secrete data in the cover *** addition,the multi-level discrete wavelet transform(DWT)based transformation process takes ***,the secret image is divided into three R,G,and B bands which are then individually encrypted using Blowfish,Twofish,and Lorenz Hyperchaotic *** last,the stego image gets generated by placing the encrypted images into the optimum pixel locations of the cover *** order to validate the enhanced data hiding performance of the EIS-DHT technique,a set of simulation analyses take place and the results are inspected interms of different *** experimental outcomes stated the supremacy of the EIS-DHT technique over the other existing techniques and ensure maximum security.

关键词： IIoT security data hiding technique image steganography encryption secure communication

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Fabricating an adsorbent and micro-nano bubble catalyst through confining maghemite in the β cage of NaY zeolite

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Chemosphere 2024年 350卷 141103页

作者： Duan, Yalong Zhang, Ruxia Han, Pengfei Wong, Ngie Hing Sunarso, Jaka Liu, Shaomin Yu, Jiang Research Group of Environmental Catalysis & Separation Process College of Chemical Engineering Beijing University of Chemical Technology Beijing100029 China Research Centre for Sustainable Technologies Faculty of Engineering Computing and Science Swinburne University of Technology Jalan Simpang Tiga Sarawak Kuching93350 Malaysia

This work reports the ion exchange fabrication of maghemite (γ-Fe2O3) modified NaY zeolite (Fe2O3@Y) with bifunction of adsorption and catalysis. The Fe3+ successfully replaced the Na+ in the β cage of zeolite in the ion exchange process and coordinated with framework oxygens to form magnetic γ-Fe2O3. Therefore, most of the γ-Fe2O3 particles were confined in the β cages, which resulted in the high dispersal and stability of the catalyst. The Fe2O3@Y could remove methylene blue (MB) model pollutants up to 59.02 and 61.47% through the adsorption and catalysis process, respectively. The hydrogen bond between the OH− ions around the Fe2O3@Y surface and the N and O presented in the MB molecules enabled the chemical adsorption to MB, which accorded with the pseudo-second-order kinetic model. Further, the H+ existed in the solution and the β cage of zeolite promoted the collapse of micro-nano bubbles (MNBs). Then, the γ-Fe2O3 catalyst would be activated by high temperature and oxidated OH− to produce hydroxyl radicals for pollutant degradation. Thus, pollutant removal was attributed to the combined effects of adsorption and catalysis in the Fe2O3@Y + MNB system. In this work, the Fe2O3@Y was demonstrated as a potentially magnetic adsorbent or MNB catalyst for wastewater treatment. © 2024 Elsevier Ltd

关键词： Zeolites

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A Multi-Site Photometric Analysis of Biomass Burning Activity in South-East Asia During October 2023

A Multi-Site Photometric Analysis of Biomass Burning Activit...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Santo V. Salinas Tan Li Fernando Santos Liew Soo Chin Moritz Müeller Cheah Wee Centre For Remote Imaging Sensing and Processing (CRISP) National University of Singapore Singapore Faculty of Engineering Computing and Science Swinburne University of Science and Technology Kuching Malaysia Institute of Ocean and Earth Sciences Universiti Malaya Kuala Lumpur Malaysia

ISBN: (数字)9798350360325

ISBN: (纸本)9798350360332

Satellite and ground level observations during the period of August to October 2023 captured an elevated number of fire hot spots and biomass burning (smoke) activity over the South-East Asia landmasses of Sumatra and Borneo. Over this region, fire activity can be a yearly occurrence due to prevalent regional dryness and low rainfall, such activity can also be enhanced by other tropical phenomena like El Nino Southern Oscillation. In this paper, we present a multi—site photometric analysis of smoke emissions during this period with emphasis on aerosol physical and optical characteristics that would allow us to differentiate the source levels of emissions, aerosol composition and possibly transport pathways to downwind receptor sites.

关键词： Satellites Rain Stimulated emission Asia Geoscience and remote sensing Aerosols Optical variables control

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The Effectiveness of Integrated Online Problem-Based Learning (iON-PBL) Physics Module on Pre-university Students’ Motivation and Problem-Solving Skills 11th

The Effectiveness of Integrated Online Problem-Based Learnin...

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11th International Workshop on Learning technology for Education Challenges, LTEC 2023

作者： Eldy, Elnetthra Folly Sulaiman, Fauziah Ishak, Mohd. Zaki Uden, Lorna Netto-Shek, Jo-Ann Preparatory Centre for Science and Technology Universiti Malaysia Sabah Jalan UMS Kota Kinabalu Sabah88400 Malaysia Faculty of Science and Natural Resources Universiti Malaysia Sabah Jalan UMS Kota Kinabalu Sabah88400 Malaysia Faculty of Psychology and Education Universiti Malaysia Sabah Jalan UMS Kota Kinabalu Sabah88400 Malaysia Faculty of Computing and Digital Technologies Staffordshire University Stoke-on-TrentST4 2DF United Kingdom English Language and Literature National Institute of Education Nanyang Technology University Singapore Singapore

ISBN: (纸本)9783031347535

Dropping number of enrolments in physics is the primary motivation of this study. Students’ view of physics as a difficult subject contributed to this scenario, and problem-solving skills and motivation directly influenced them. Physical practice proved that problem-based learning (PBL) improves students’ motivation and problem-solving skills. Hence, main objective of this study is to investigate the impact of the integrated online problem-based learning (iON-PBL) module of physics on students’ motivation and problem-solving skills in learning physics. Quasi-experimental research approach was used with a pre-test, post-test, and delayed post-test control group design. The target population comprises of 166 students, 88 females and 28 males, amongst pre-university students of the Preparatory centre for science and technology (PPST), Universiti Malaysia Sabah. Motivated Strategies for Learning Questionnaire (MSLQ) by Pintrich & Groot and Problem-Solving Inventory (PSI) by Heppner & Petersen were adopted as the instruments to measure students’ motivation and problem-solving skills towards learning physics after intervention with the iON-PBL module of physics. Data collected was analysed using SPSS, with paired sample t-test to test the research questions and hypotheses raised at an alpha level of 0.05. Study shows significant difference in students’ motivation towards learning physics, i.e., intrinsic value, and cognitive strategy use components. Similar results in problem-solving skills, i.e., students’ personal control, where this component shows significant difference favour to the experimental group. Some suggestions on how to implement iON-PBL effectively are also highlighted. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Motivation

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