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Articles 61 - 90 of 443
Full-Text Articles in Other Computer Sciences
Zero Trust - The Future Of Cybersecurity, Danial Haider, Shougfta Mushtaq, Hasnat Ali, Mazliham Mohd Su’Ud
Zero Trust - The Future Of Cybersecurity, Danial Haider, Shougfta Mushtaq, Hasnat Ali, Mazliham Mohd Su’Ud
Journal of Informatics and Web Engineering
The recent Zero-Trust Architecture (ZTA) is progressively adopted to the develop network security by assuming no implicit trust within or outside an organization’s boundary. Though, ZTA faces substantial challenges in detecting sophisticated and developing cyber threats, particularly due to its trust on traditional security mechanisms that struggle to manage internal threats and sophisticated attack techniques. To report these shortcomings, the proposed study discovers the combination of advanced machine learning (ML) and deep learning (DL) performances to improve the anomaly detection proficiencies within ZTA environments. The study develops the CICIDS2017 dataset, which contains diverse and realistic network traffic patterns, to assess …
The Role Of Generative Ai In E-Commerce Recommender Systems: Methods, Trends And Insights, Kai-Ze Liau, Heru Agus Santoso
The Role Of Generative Ai In E-Commerce Recommender Systems: Methods, Trends And Insights, Kai-Ze Liau, Heru Agus Santoso
Journal of Informatics and Web Engineering
Recommender systems have existed for decades, shaping how people consume digital content, receive information, and engage in day-to-day activities, among others. Undoubtably, recommender systems also play a crucial role in e-commerce applications as well, with industry players like Amazon, AliBaba, eBay using recommender systems within their ecosystems to give suitable and value-driven insights. However, recommender systems face some main concerns such as data sparsity, cold-start problems and so on. As a result, research is currently ongoing to solve these issues and provide high-quality recommendations to consumers. This review aims to identify prevailing gaps surrounding these issues by analysing existing research …
Training The Brain: A Machine Learning Approach To Predicting Wellbeing Through Intentional Thought Pattern Modification, Sellappan Palaniappan, Rajasvaran Logeswaran, Kasthuri Subaramaniam, Oras Baker, Bui Ngoc Dung
Training The Brain: A Machine Learning Approach To Predicting Wellbeing Through Intentional Thought Pattern Modification, Sellappan Palaniappan, Rajasvaran Logeswaran, Kasthuri Subaramaniam, Oras Baker, Bui Ngoc Dung
Journal of Informatics and Web Engineering
This study provides a quantitative framework for wellbeing outcome prediction through intentional cognitive pattern alteration. We demonstrated 81.67% accurate prediction of wellbeing states, in a three-level classification (Low, Medium, High), using a Random Forest classifier with 16 features from psychological, physiological, and behavioural metrics. Our model singles out the gratitude cultivation (21.3%) and peace duration (23.7%) as the strongest predictors of positive well-being outcomes, which provides empirical support to traditional approaches of cognitive training with empirical evidence. Analysis of 1,000 synthetic cases shows that consistent practice of positive thought patterns over 3-6 months can strongly shift wellbeing states, with key …
Hybrid-Based Movie Recommender System: Techniques, Case Studies, Evaluation Metrics, And Future Trends, Cheng-Yung Lai, Noramiza Hashim, Lucia Dwi Krisnawati
Hybrid-Based Movie Recommender System: Techniques, Case Studies, Evaluation Metrics, And Future Trends, Cheng-Yung Lai, Noramiza Hashim, Lucia Dwi Krisnawati
Journal of Informatics and Web Engineering
The necessity for sophisticated recommender systems in the movie recommendation sphere has become particularly pronounced, generating a more personalized movie recommendation due to people nowadays who like to watch movies online. Efficient recommender systems make use of advanced machine learning (ML) techniques in the pursuit of accurate and meaningful recommendations. This paper endeavours to give a comprehensive overview of technologies known as recommender systems, concentrating on ML methods found at the base. Different strategies have been applied in this work, which include collaborative filtering (CF), content-based filtering (CB), hybrid approaches, Generative AI and so on. The merits and demerits of …
Hybrid Filtering For Personalized And Health-Conscious Recipe Recommendations In Unieats, Hui Lek Liew, Xinying Chew, Khai Wah Khaw, Shiuh Tong Lim
Hybrid Filtering For Personalized And Health-Conscious Recipe Recommendations In Unieats, Hui Lek Liew, Xinying Chew, Khai Wah Khaw, Shiuh Tong Lim
Journal of Informatics and Web Engineering
This research paper introduces UniEats, a recipe recommendation website designed to help university students better organize their meals and adopt healthier eating habits. The system aims to address common challenges students face, including time constraints, limited cooking skills, and insufficient nutritional awareness, which often lead to unhealthy food choices. At the core of UniEats is its recommendation engine, which employs a hybrid filtering approach, combining content-based and collaborative filtering techniques to provide personalized recipe suggestions based on users' dietary preferences, rating history, and recipe attributes. UniEats offers a range of features, including recipe search, weekly meal planning, nutritional analysis through …
Implementation Of Lightweight Machine Learning Models For Real-Time Text Classification On Resource-Constrained Devices, Marwah Zaid Mohammed Al-Helali, Naveen Palanichamy, K. Revathi
Implementation Of Lightweight Machine Learning Models For Real-Time Text Classification On Resource-Constrained Devices, Marwah Zaid Mohammed Al-Helali, Naveen Palanichamy, K. Revathi
Journal of Informatics and Web Engineering
This paper addresses the growing need for implementing intelligent Natural Language Processing (NLP) systems on low-power, memory-limited devices such as Raspberry Pi, mobile phones, and IoT edge hardware. As edge computing and smart devices proliferate, there is an urgent need for more advanced NLP technology that does not require constant cloud access and is efficient in computing and provides results in real time. While deep learning and cloud-based models typically offer high text-classification accuracy and have demonstrated exceptional performance across a range of NLP tasks, they are often too resource-intensive for real-time deployment in constrained environments. To overcome these limitations, …
Review On Secure And Efficient Iot-Based Healthcare System With The Integration Of Machine Learning And Firewalls, Muhammad Awais, Syeda Samar Fatima, Jawaid Iqbal
Review On Secure And Efficient Iot-Based Healthcare System With The Integration Of Machine Learning And Firewalls, Muhammad Awais, Syeda Samar Fatima, Jawaid Iqbal
Journal of Informatics and Web Engineering
The integration of the Internet of Things (IoT) into healthcare would mean a revolutionized approach in patient monitoring, diagnosis, and treatment, making this quite some development in healthcare delivery. This review has focused on how the integration of IoT with Machine Learning (ML) and stringent security measures tackle the challenging situation of data privacy and cyber threats in healthcare. Current methodologies point toward how essential advanced sensors, cloud computing, and wireless technologies for IoT-based healthcare systems necessary to secure patient data. patient record kept in files and now forward to the cloud database system so that in any case of …
Addressing Iot Security Challenges Through Advanced Machine Learning And Encryption, Ahmad Aziem Khushairi Anuar, Ahmad Anwar Zainuddin, Ahmad Adlan Abdul Halim, Dek Rina
Addressing Iot Security Challenges Through Advanced Machine Learning And Encryption, Ahmad Aziem Khushairi Anuar, Ahmad Anwar Zainuddin, Ahmad Adlan Abdul Halim, Dek Rina
Journal of Informatics and Web Engineering
The rapid growth of Internet of Things (IoT) devices, including smartwatches, home assistants, and connected appliances, has brought significant convenience to daily life, but it has also introduced serious security challenges. These devices often transmit sensitive data, making them vulnerable to theft, misuse, and unauthorized access. Current security measures are insufficient to address the complex and evolving nature of IoT systems, leaving many of them exposed to potential breaches and cyberattacks. This review explores recent developments in IoT security, focusing on how advanced technologies, such as machine learning, can be utilized to enhance the protection of IoT systems. The main …
Research Article Recommender Systems: A Comprehensive Review Of Models, Approaches And Evaluation Metrics, Sir-Yuean Lim, Noramiza Hashim, Lanh Le Thanh
Research Article Recommender Systems: A Comprehensive Review Of Models, Approaches And Evaluation Metrics, Sir-Yuean Lim, Noramiza Hashim, Lanh Le Thanh
Journal of Informatics and Web Engineering
With the advent of the current digital era, individuals across the developed world are commonly equipped with devices that can access vast amounts of information at their fingertips. What was considered an impossible feat was realized through remarkable technological advancements. This positive transformation has had a profound impact on education, where traditional knowledge management, such as libraries, are no longer a primary determinant of a student’s academic success. Instead, it has been replaced by the internet as a medium for learning, practicing, and topic exploration. However, the sheer volume of the ever-increasing information available online can easily overwhelm a user, …
Optimised Heterogeneous Handover In Mobile Ipv6 Using Enhanced Predictive Fast Proxy With Media Independent Handover (Mih) Support, Aliyu Aminu Abdulhadi, Nura Mukhtar
Optimised Heterogeneous Handover In Mobile Ipv6 Using Enhanced Predictive Fast Proxy With Media Independent Handover (Mih) Support, Aliyu Aminu Abdulhadi, Nura Mukhtar
Journal of Informatics and Web Engineering
In wireless networks, handover performance is essential for enabling real-time traffic applications. Long handover delays make it impossible for a Mobile Node (MN) to send and receive data packets, which is very undesirable for real-time applications like video conferencing and VoIP. Therefore, in order to guarantee better handover performance, decreasing handover duration is crucial. The Internet Engineering Task Force (IETF) has standardized Fast Proxy Mobile IPv6 (FPMIPv6) as an enhancement to the novel Proxy Mobile IPv6 (PMIPv6) to attain better handover performance. FPMIPv6 functions in two modes: predictive and reactive, using a link-layer triggering mechanism. The predictive mode uses link-layer …
Developing A Predictive Model For Football Players’ Market Value Using Machine Learning, Muhammad Afif Jazimin Idris, Sew Lai Ng
Developing A Predictive Model For Football Players’ Market Value Using Machine Learning, Muhammad Afif Jazimin Idris, Sew Lai Ng
Journal of Informatics and Web Engineering
Football is the world’s most popular sport, and evaluating the market value of players is crucial for clubs and managers in making informed decisions regarding transfers, contracts, and financial planning. This study aims to develop a predictive model to estimate the market value of football players using machine learning (ML) algorithms and real-life statistics performance data from the top five European leagues such as English Premier League, Italian Serie A, Spanish La Liga, German Bundesliga, and French Ligue 1 between the 2017/18 and 2019/20 seasons. By reviewing past research, various ML methods such as Random Forest, LightGBM, XGBoost, and Gradient …
Deep Learning Approaches To Autocorrelation Function And Signal-To-Noise Ratio Estimation In Noisy Images, Kai Liang Lew, Kok Swee Sim, Shing Chiang Tan
Deep Learning Approaches To Autocorrelation Function And Signal-To-Noise Ratio Estimation In Noisy Images, Kai Liang Lew, Kok Swee Sim, Shing Chiang Tan
Journal of Informatics and Web Engineering
Accurate estimation of signal-to-noise ratio (SNR) in Scanning Electron Microscopy (SEM) is crucial because it evaluates the image quality. SEM images faced a challenge whereby Gaussian noise commonly appears in the images. Thus, researchers have developed several methods to estimate the SNR value. With the introduction of deep learning, most of the limitations in the classical methods can be addressed. This paper proposes a novel deep learning, CNN-based Calibration Map Network (CalibNet) to estimate the SNR value from SEM images using a calibration map between classical SNR and autocorrelation function SNR. The architecture consists of convolutional layers, rectified linear unit …
Loan Default Prediction Using Machine Learning Algorithms, Zhi Zheng Kang, Sin Yin Teh, Samuel Yong Guang Tan, Wei Chien Ng
Loan Default Prediction Using Machine Learning Algorithms, Zhi Zheng Kang, Sin Yin Teh, Samuel Yong Guang Tan, Wei Chien Ng
Journal of Informatics and Web Engineering
Financial institutions constantly face at the risk of default by borrowers which can result in significant financial losses. It is essential to develop an appropriate predictive model for loan default to reduce these risks and minimise financial losses. The objective of this study is to identify the most suitable machine learning model to predict loan default by comparing four models which are Random Forest, Decision Tree, Extreme Gradient Boosting (XGBoost), and Light Gradient Boosting Machine (LightGBM). Additionally, it also examines the key features influencing loan default prediction. The dataset used in this study is sourced from Kaggle and it consists …
Indonesian Language Sign Detection Using Mediapipe With Long Short-Term Memory (Lstm) Algorithm, Wargijono Utomo, Yogasetya Suhanda, Harun Ar-Rasyid, Andy Dharmalau
Indonesian Language Sign Detection Using Mediapipe With Long Short-Term Memory (Lstm) Algorithm, Wargijono Utomo, Yogasetya Suhanda, Harun Ar-Rasyid, Andy Dharmalau
Journal of Informatics and Web Engineering
People with disabilities mostly communicate using sign language, but the public still has little understanding of the Indonesian Sign Language System (ISLS). This causes obstacles in daily interactions. Advances in artificial intelligence technology, especially artificial neural networks, open opportunities in sign language recognition, but are still in the development stage. This study aims to build a ISLS sign language recognition model using the LSTM approach and MediaPipe Hands. The method of collecting hand keypoint data, 25 sequences per gesture, and 36 alphabetic and numeric gestures. The dataset is divided into three categories, namely 80% training, 10% validation, and 10% testing. …
Mapping Relational Database To Full-Text Xml For Open Journal System Cross-Platform Article Distribution, Chee-Xiang Ling, Kok-Why Ng, Heru Agus Santoso
Mapping Relational Database To Full-Text Xml For Open Journal System Cross-Platform Article Distribution, Chee-Xiang Ling, Kok-Why Ng, Heru Agus Santoso
Journal of Informatics and Web Engineering
In academic publications, the automation of full-text eXtensible Markup Language (XML) is increasingly essential, as generating full-text XML for article distribution is a complex and time-consuming process that requires metadata extraction from a relational database and transformation into hierarchical structures such as Journal Article Tag Suite (JATS). The lack of automation in this transformation process may cause inconsistencies and inaccuracies and may cause errors due to human error. The primary aim is to develop an automation system for transforming metadata from a relational database to full-text XML by reducing errors and speeding the process of generating full-text XML. This is …
Exploring Generative Ai Recommender Systems In E-Commerce: Model, Evaluation Metric, And Comparative Review, Wan-Er Kong, Tong-Ern Tai, Palanichamy Naveen, Kok-Why Ng, Lucia Dwi Krisnawati
Exploring Generative Ai Recommender Systems In E-Commerce: Model, Evaluation Metric, And Comparative Review, Wan-Er Kong, Tong-Ern Tai, Palanichamy Naveen, Kok-Why Ng, Lucia Dwi Krisnawati
Journal of Informatics and Web Engineering
Generative Artificial Intelligence (GAI) is changing what can be done with Recommender Systems (RS) in e-commerce by allowing much more interactive, situationally aware, and highly tailored experiences for users. The purpose of this paper is to provide overall insight into how GAI, including Large Language Models (LLMs), Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and other emerging methods, is affecting the building and running of modern e-commerce RS. This paper classifies generative models into groups based on the type of models used, data modality, and specific domain of application. Their involvement in tasks such as personalized product ranking, content generation, …
User Behaviour Prediction In E-Commerce Using Logistic Regression, Wei-Wen Lee, Noramiza Hashim, Shaymaa Al-Juboori
User Behaviour Prediction In E-Commerce Using Logistic Regression, Wei-Wen Lee, Noramiza Hashim, Shaymaa Al-Juboori
Journal of Informatics and Web Engineering
From a psychological perspective, human behaviour reflects underlying thoughts and decision-making patterns, for example, consumer behaviour may correlate with the purchase decisions. In the fast-evolving e-commerce industry, predicting user behaviour is essential for enhancing marketing strategies, improving customer experiences, and increasing sales. However, traditional heuristic (e.g. market basket analysis) approaches to analyse buyer behaviour are often rigid and fail to adapt to complex consumer interactions. This research work develops a predictive model that analyses user behaviour based on data such as historical purchasing patterns and demographic attributes. Based on a review of previous studies, Logistic Regression (LR) is utilized as …
Identifying The Barriers To Digital Financial Inclusion In The Most Financially Excluded Country Using Machine Learning Algorithm, Yin Ting Chin, Hui Shan Lee
Identifying The Barriers To Digital Financial Inclusion In The Most Financially Excluded Country Using Machine Learning Algorithm, Yin Ting Chin, Hui Shan Lee
Journal of Informatics and Web Engineering
Despite the call of digital financial services (DFS) to improve inclusive growth and reduce poverty, the adoption of DFS remains low in Nigeria. The objective of this study is to examine the barriers of ability, access and usage of DFS in Nigeria. This study uses secondary data Global Findex year 2017 and year 2021 to predict the socioeconomic factors on the target variables of DFS (ability, access and usage). Using a machine learning (ML) algorithm, namely the J48 decision tree in the Waikato Environment for Knowledge Analysis (WEKA) software, this study analyses the predictive strength of variables such as gender, …
Robust Lane Detection Under Varying Lighting Conditions Using Adaptive Vision-Based Techniques, Habibatelrahman Mosaad Abdelfattah Mohamed Abdelaal, Tee Connie, Michael Kah Ong Goh
Robust Lane Detection Under Varying Lighting Conditions Using Adaptive Vision-Based Techniques, Habibatelrahman Mosaad Abdelfattah Mohamed Abdelaal, Tee Connie, Michael Kah Ong Goh
Journal of Informatics and Web Engineering
Reliable lane detection is crucial to autonomous driving but continues to be challenging with varying lighting conditions. Fluctuations in illuminations due to bright sunlight, shadows or low lighting at night can degrade the visual quality and adversely affect the accuracy of the lane detection results. This research proposes an adaptive approach for lane detection under different lighting scenarios. For daytime, a Region of Interest (ROI) masking and line averaging technique help in the stability and visibility of the lane markings. For nighttime conditions, a Probabilistic Hough Transform-based method improves lane detection in low-light environments. An evaluation tool has been developed …
Editorial: Ai-Enhanced Computing And Digital Transformation, Hairulnizam Mahdin
Editorial: Ai-Enhanced Computing And Digital Transformation, Hairulnizam Mahdin
Journal of Informatics and Web Engineering
Artificial intelligence (AI), machine learning (ML), and advanced computing techniques are now central to the digital transformation journey, reshaping industries, academia, and society. This special issue of the Journal of Informatics and Web Engineering on AI-Enhanced Computing and Digital Transformation brings together contributions that reflect both technical innovation and societal applications. The featured articles span optimization methods, software quality improvements, data augmentation techniques, intelligent mobile applications, blockchain-based governance systems, and disaster management platforms. Together, they illustrate how computational advances not only strengthen efficiency and accuracy but also enable resilience in the face of global challenges. From a technical standpoint, metaheuristic …
Real-Time Read And Analysis Of Air Pollution Produced From Private Electrical Generators In Mosul City Using Lorawan, Mahmood Alfathe, Awfa Aladwani, Ali Abduljabbar
Real-Time Read And Analysis Of Air Pollution Produced From Private Electrical Generators In Mosul City Using Lorawan, Mahmood Alfathe, Awfa Aladwani, Ali Abduljabbar
Journal of Informatics and Web Engineering
This study presents a novel, site-specific deployment of a Long Range Wide Area Network (LoRaWAN)-driven air pollution monitoring network specifically for the Iraqi city of Mosul, which is beset by widespread power outages and extensive utilization of decentralized diesel generators. While these generators mitigate electricity shortages, they are enormous contributors to urban air pollution, emitting high levels of CO2 and particulates. As opposed to previous studies, which concentrate on affluent urban areas, this research addresses a very deprived locale using an extensible low-power, low-cost LoRaWAN network and high-precision CO2 sensors (Sensirion SCD30 and MH-Z19) and The Things Network (TTN) for …
A Scoping Review Of Artificial Intelligence Research Trends In Mobile Applications, Asmau Usman, Ibrahim Anka Salihu, Abdulrahman Aminu Ghali, Bilkisu Larai Muhammad Bello, Aminu Aminu Mu'azu, Amina Sambo Magaji
A Scoping Review Of Artificial Intelligence Research Trends In Mobile Applications, Asmau Usman, Ibrahim Anka Salihu, Abdulrahman Aminu Ghali, Bilkisu Larai Muhammad Bello, Aminu Aminu Mu'azu, Amina Sambo Magaji
Journal of Informatics and Web Engineering
Over the past decade, mobile devices have become an integral part of our daily routines, offering a broad spectrum of applications that enhance everyday tasks. As more people adopt smartphones, developers are increasingly focusing on improving app quality, particularly by incorporating artificial intelligence (AI) features. This growing trend has led to a surge of interest from both researchers and industry experts, who are aiming to explore AI integration in sectors such as healthcare, education, agriculture, and e-commerce. This study conducts a thorough review of AI applications on mobile platforms by analysing 98 scholarly articles published between 2014 and 2024 from …
Impact Of Data Quality Types On Computational Time In Data Source Selection Using Ant Colony Optimization, Nor Amalina Mohd Sabri, Abd Samad Hasan Basari, Nurul Akmar Emran
Impact Of Data Quality Types On Computational Time In Data Source Selection Using Ant Colony Optimization, Nor Amalina Mohd Sabri, Abd Samad Hasan Basari, Nurul Akmar Emran
Journal of Informatics and Web Engineering
Data quality varies dramatically from source to source, even within the same domain. Given these challenges, data source selection has emerged as a crucial step in information integration. It demands efficient and scalable approaches that can handle massive data volumes while ensuring the quality of results. Adapting the ACO algorithm to solve the data sources selection problems may lead to inconsistent computational time if the data sources provided are vary in quality. These challenges bring the issues of time consuming in selecting the required data sources. However, how much the computational time needed in solving the data sources selection is …
Lightweight String Similarity Approaches For Duplicate Detection In Academic Titles, Fahrudin Mukti Wibowo, Muhammad Zidny Nafan, Muhamad Azrino Gustalika, Harinda Fernando, Muhammad Hussain, Nur Afiqah Sahadun
Lightweight String Similarity Approaches For Duplicate Detection In Academic Titles, Fahrudin Mukti Wibowo, Muhammad Zidny Nafan, Muhamad Azrino Gustalika, Harinda Fernando, Muhammad Hussain, Nur Afiqah Sahadun
Journal of Informatics and Web Engineering
This study addresses the critical challenge of detecting duplicate final year project (FYP) titles in academic institutions, where minor variations like reordering, synonyms, and paraphrasing often obscure plagiarism. We systematically evaluate four string similarity algorithms - Jaro-Winkler, Levenshtein Edit Distance, TF-IDF with Cosine Similarity, and Jaccard Similarity - using a synthetic dataset of 250 title pairs representing common duplication patterns. Our experiments reveal that character-based methods (Jaro-Winkler and Edit Distance) achieve perfect detection (F1-score=1.0) for literal matches, including typographical variations and phrase reordering. At the same time, TF-IDF demonstrates strong semantic capability (F1-score=0.95), albeit with some false positives. Jaccard Similarity …
Improving Code Effectiveness Through Refactoring: A Case Study, Abdullah Almogahed, Manal Othman, Mazni Omar, Samera Obaid Barraood, Abdul Rehman Gilal
Improving Code Effectiveness Through Refactoring: A Case Study, Abdullah Almogahed, Manal Othman, Mazni Omar, Samera Obaid Barraood, Abdul Rehman Gilal
Journal of Informatics and Web Engineering
Software refactoring is a crucial practice in modern software development methodologies, such as Agile and DevOps, as it enables teams to iteratively improve and evolve their codebases while minimizing the risk of introducing bugs or regressions. It fosters a culture of continuous improvement and code hygiene, ultimately leading to more robust, maintainable, and scalable software systems. However, research examining the impact of refactoring on code effectiveness is scarce. This study, therefore, seeks to investigate the impact of refactoring methods on the code’s effectiveness. The study was carried out in four distinct phases: refactoring methods selection, case study selection, software metric …
Societies’ Funds Management System Using Blockchain, Jun Xiang Lau, Nur Ziadah Harun, Suziyanti Marjudi, Abd Samad Hasan Basari, Nur Amlya Abd Majid, Roziyani Setik
Societies’ Funds Management System Using Blockchain, Jun Xiang Lau, Nur Ziadah Harun, Suziyanti Marjudi, Abd Samad Hasan Basari, Nur Amlya Abd Majid, Roziyani Setik
Journal of Informatics and Web Engineering
Lack of transparency of the funds and lack of immutability of the funds’ records are large problems today, especially in the societies’ funds. Leveraging the Ethereum Blockchain, the system ensures complete transparency and security in recording and accessing financial transactions for any society. Advanced encryption and blockchain consensus protocols guarantee data privacy and resilience against fraud or tampering. The information entered in the system helps the treasurer to effectively manage funds and accurately and transparently pass appropriate financial transactions. This project meets the challenges of transparency and immutability in society’s fund records, providing an assurance system and promoting growth for …
Implementation Of Conjugate Gradient Method For Estimating Inflation Rate In Malaysia, Shin Yi Wong, Nur Syarafina Mohamed, Norhaslinda Zullpakkal
Implementation Of Conjugate Gradient Method For Estimating Inflation Rate In Malaysia, Shin Yi Wong, Nur Syarafina Mohamed, Norhaslinda Zullpakkal
Journal of Informatics and Web Engineering
Optimization methods are valuable for making decisions and identifying the most suitable alternative based on a given objective function. One of the mathematical optimization methods is Conjugate Gradient (CG) method which is commonly used to solve large-scale unconstrained optimization systems with less storage space. Recently, various optimization methods have been studied and used in economics estimating. However, just a few studies have predicted inflation rate using modified CG method. Random initial points are tested on New Three-Terms (NTT) which are modified Rivaie-Mustafa-Ismail-Leong (RMIL+) and Umar Mustapha Waziri (UMW) CG method with ten optimization test functions suggested by Andrei using MATLAB. …
Conjugate Gradient Methods In Fitting Precipitation Of Rainfall Data In Malaysia, Hua Ru Tang, Nur Syarafina Mohamed, Nurul Hajar Mohd Yusoff, Norhaslinda Zullpakkal
Conjugate Gradient Methods In Fitting Precipitation Of Rainfall Data In Malaysia, Hua Ru Tang, Nur Syarafina Mohamed, Nurul Hajar Mohd Yusoff, Norhaslinda Zullpakkal
Journal of Informatics and Web Engineering
Conjugate gradient method (CGM) is one of the most efficient numerical methods for solving unconstrained optimization problems. It is also known as an iterative method with simple formulation. The classical CGM has always been an interest to the current researchers in improving the formulation which are categorized into three-term (TTCGM), spectral (SCGM), hybrid and scaled CGM. Although there are many variations of the CGM available, choosing the most efficient and effective one for a particular problem can be a time-consuming process. In this study, spectral Hestenes-Stiefel (sHS) CGM with the greatest NOI and central processing time per unit (CPU time) …
Enhancing Imbalanced Data Augmentation: A Comparative Study Of Ganified-Smote And Latent Factor Integration, Rusma Anieza Ruslan, Nureize Arbaiy, Pei-Chun Lin
Enhancing Imbalanced Data Augmentation: A Comparative Study Of Ganified-Smote And Latent Factor Integration, Rusma Anieza Ruslan, Nureize Arbaiy, Pei-Chun Lin
Journal of Informatics and Web Engineering
One such serious problem in machine learning (ML) is imbalanced datasets. Minority class samples are usually sparse but hold significant meaning. The model can become biased toward the majority class due to unbalanced class distribution. This results in fraudulently high accuracy without being able to detect minority cases. This bias is also most perilous in critical applications, where ignoring minority cases can be highly destructive. To overcome this problem, the Synthetic Minority Oversampling Technique (SMOTE) is one of the most widely used. SMOTE creates balanced class distribution by interpolating between existing minority samples. It creates samples that are too close …
Coordinating Instruments For Multi-Messenger Astrophysics, Daisy Wang, Ye Htet, Marion Sudvarg, Roger Chamberlain, Jeremy Buhler, James Buckley
Coordinating Instruments For Multi-Messenger Astrophysics, Daisy Wang, Ye Htet, Marion Sudvarg, Roger Chamberlain, Jeremy Buhler, James Buckley
Computer Science Faculty Research & Creative Works
In multi-messenger astrophysics, signals of multiple types (e.g., gravitational waves, neutrinos, electromagnetic waves) are combined in an effort to learn more about the observed phenomena of interest. The Advanced Particle-astrophyics Telescope (APT) is a mission concept for a space-borne instrument that detects gammaray bursts (GRBs) omnidirectionally, facilitating multi-messenger observations by identifying and localizing celestial events of interest. Here, we describe the on-instrument computations for APT and its Antarctic Demonstrator (ADAPT) as well as techniques for follow-up observations of transient events.