bluuebunny/crossref_metadata_2025 huggingface dataset:
Code optimization opportunities in the JavaScript ecosystem with Rust
Authors: Kozub Volodymyr
Published: December 2024

This paper explores the potential of optimizing node.js applications by integrating rust. In particular, in processing cpu-intensive tasks where javascript faces performance limitations due to its single-threaded architecture. Rust's memory safety and parallelism model, which eliminates the need for a garbage collector, makes it an attractive alternative to traditional c/c++ modules for extending the capabilities of node.js. This study explores the performance gains achieved by integrating rust, both through native bindings and WebAssembly, demonstrating significant improvements in computational efficiency, especially in parallel processing scenarios. Rust's ability to efficiently handle computation-intensive workloads with work interception algorithms is emphasized as a key factor in overcoming javascript bottlenecks. The study includes a detailed performance evaluation that compares synchronous and asynchronous modules in node.js with rust implementations. Tests demonstrate how rust optimizations outperform javascript by up to ten times in certain computational tasks. The study also evaluates cross-compiled rust modules using WebAssembly in the browser environment, which once again illustrates the advantages of rust in providing near-native performance. The results emphasize the potential of rust to enhance node.js applications by making them more scalable, reliable, and efficient for high-performance web applications
An Automated Fingerprint Image Detection and Localization Approach-based Unsupervised Learning Algorithms using Low-quality Biometrics Plam Data
Authors: Jadaan Abed Abdulrasool , Abdulhadi Abdullah Dhahir
Published: December 2024

In this study, fingerprint identification and classification of low-quality fingerprints have been analyzed accordingly.  As technology advances and methodologies evolve, staying at the forefront of research and innovation is imperative. The challenges addressed in this paper provide a foundation for future investigations and underscore the importance of developing resilient and adaptable biometric systems for real-world applications. The quest for accurate, efficient, and robust fingerprint identification in adverse conditions is a testament to the continuous evolution and refinement of machine learning and deep learning approaches in biometrics. While deep learning models exhibited improved performance, it is essential to acknowledge the need for further research and development in this domain. Additionally, integrating multimodal biometric systems and combining fingerprint data with other biometric modalities might present a viable avenue for mitigating the limitations associated with degraded fingerprints. In this paper, we develop a fingerprint identification approach for low-quality fingerprint images. The success rate accuracy of the propped algorithm for the low-quality fingerprint images should be significantly better than that of the standard local minutia approach. The main design of our deep learning approach is based on detecting and extracting the primary correlation during the training and using the correlation feature map to calculate the distance between the low-quality fingerprint images during the predicting phase. The experimental results show a very promising repulsing and high prediction accuracy.
Applying Deep Learning Models to Breast Ultrasound Images for Automating Breast Cancer Diagnosis
Authors: Khaleefah Shihab Hamad , Lojungin Eva Cabrini , Mostafa Salama A. , Baharum Zirawani , Aldulaimi Mohammed Hasan , Ghazal Taher M. , Alo Salam Omar , Hidayat Rahmat
Published: December 2024

Breast cancer is a result of uncontrolled human cell division. The vast growth of breast cancer patients has been an issue worldwide. Most of the patients are women, but breast cancer also affects men with a much lesser percentage. Breast cancer might lead to death for those who are suffering from it. Numerous types of research have been done to make an early diagnosis of breast cancer. It has been proven that the tumor can be detected by using an ultrasound image. Artificial Intelligence techniques have been used to detect breast cancer fundamentally. This paper studies the effectiveness of deep learning (DL) techniques in automating breast cancer diagnosis. Subsequently, the paper evaluates the diagnosis performance of three DL models utilizing the criteria of accuracy, recall, precision, and f1-score. The Densenet-169, U-Net, and ConvNet DL models are selected based on the examination of the related work. The DL diagnosis process involves identifying two types of breast cancer tumors: benign and malignant. The evaluation outcomes of the DL models show that the most effective model for diagnosing breast cancer among the three is the ConvNet, which achieves an accuracy of 91%, a recall of 83%, a precision of 85%, and an F1-score of 83%.
Switching On/Off Air Conditioner and Fan Alternately based on IoT Motion Detection and Room Temperature
Authors: Abdul Shukora Nurul Hidayah , Ahmad Baidowi Zaid Mujaiyid Putra , Mohd Isa Mohd Rizal , Darus Mohamad Yusof , Abdullah Muhammad
Published: December 2024

The Internet of Things (IoT) connects electrical appliances that enable data transfer for communication without human intervention. IoT has evolved, and its implementation has been extended to residential areas. It can be said that all residents use fans as cooling appliances. In Malaysia, having a fan is not sufficient due to its hot temperature throughout the year. Therefore, most of the residents use air conditioners as an additional cooling appliance. Using air conditioners regularly could contribute to high energy consumption. Furthermore, excessive energy consumption occurs when an occupant of a residential building forgets to switch off electrical appliances such as fans and air conditioners. In addition, leaving electrical appliances turning on when nobody is at home just wastes energy. This work aims to develop an IoT-based smart home controlling system for minimizing energy consumption. This system enables automatic control that depends on room temperature and motion detection. Various types of sensors, such as temperature sensor, humidity sensor, and motion sensor, are used to switch on/off the air conditioner and fan. The air conditioner and fan will be alternately switched on and off depending on the ideal room temperature. The testing results show a significant reduction in energy consumption and a promising decrease in the electricity bill. Future works should be focusing on determining the over limit energy consumption. On top of that, this research would be best to try on simulation to get better results.
A Framework of Forensic Analysis and Visualization: Using WhatsApp Chat Data as a Case Study
Authors: Pirzada Shahnaz , Ab Rahman Nurul Hidayah , Cahyani Niken Dwi Wahyu , Othman Muhammad Fakri
Published: December 2024

Digital forensic analysis involves studying and analyzing acquired evidence artifacts using methodical approaches. However, unstructured data could be time-consuming and difficult in the forensic examination phase. Automation in digital forensic processes has recently been seen as a potential solution to improve analysis processes. Therefore, we propose a forensic analysis and visualization framework via exploratory data analysis (EDA) using WhatsApp chat datasets as a case study. Univariate and multivariate EDA visualization models were applied to the datasets. The framework's utility was demonstrated through forensic analysis simulation scenarios: linkage (interaction) and attribution (who was responsible). origination (evaluation of source), and sequencing (timeline). It was conducted in a controlled experiment environment using Python scripting. The aim is to test the extent to which EDA visualization models can visualize complete and accurate artifacts based on the scenarios. Our evidence-based findings demonstrated the suitability of specific univariate and multivariate in visualizing complete and accurate data. The framework was able to visualize key metadata such as incoming and outgoing chats, sender identification, communication timeline, and shared media. The findings suggested that the EDA approach aligns with forensic analysis, as it helps describe investigative clues by analyzing data patterns. Additionally, an expert review was conducted, in which the experts confirmed the adequacy of the simulation scenarios and the usefulness of the forensic visualization. Furthermore, the results of this study could aid in presenting evidence in a court of law.
Traffic Violation Detection Using Computer Vision Techniques
Authors: Ong Chin Sin , Connie Tee , Ong Goh Michael Kah
Published: December 2024

The increasing number of road accidents is still a global concern.  Traditional approaches to detecting traffic violators on the road, such as radar guns and sensors, are expensive and time-consuming to maintain and install. This often results in inefficient and ineffective detection of traffic violators. This paper proposes a more cost-effective and efficient approach to traffic violation detection utilizing visual data from CCTV footage. Specifically, the method targets two common violations: crossing red lights and overtaking on double lines. In this study, YOLO is integrated for road object detection, providing the detection of vehicles and traffic lights on the road for our system. Then, the Deep SORT tracker tracks detected vehicles, ensuring continuous monitoring over time. An automated lane detection technique is formulated to identify the stopping line/lane for red light violation detection, enabling precise detection of vehicles that cross the stop lane during red light. For overtaking detection, the system detects the double line to serve as the boundary that vehicles should not cross, identifying illegal overtaking. Furthermore, point-line distance calculation is utilized to detect traffic violators by analyzing their tracked trajectories and positions. The proposed solution is evaluated using real-world CCTV footage from online repositories to reflect the real-world scenarios as closely as possible. Experimental results show that the proposed techniques achieve promising detection of real-time traffic violators, which leads to a safer environment for road users.
Developing and Comparing Machine Learning Algorithms for Music Recommendation
Authors: Bau Yoon-Teck , Mohd Reza Puteri Ainna Ezzurin , Lee Kian-Chin
Published: December 2024

The increasing prevalence of song skipping in music streaming applications negatively impacts user satisfaction and subscription retention. Dissatisfaction often arises when users encounter songs they actively dislike, highlighting a gap between user expectations and the value offered by these services. To address this, music recommendation algorithms were researched and developed. Initially, data collection is started. Data collection is through the Spotify application programming interface. This initiation step sets the stage for subsequent exploratory data analysis. Exploratory data analysis examined the collected data to plot a bar chart for total songs released over the years, plot a bar chart for the popularity of songs based on the year it is released, visualize word cloud on frequently mentioned music genres, chart count plot for explicit songs, and chart count plot for song modalities. Data preprocessing involved cleaning the data, handling missing values, and checking for null values to prepare the application of machine learning algorithms. Four machine learning algorithms were applied, k-means, mini-batch k-means, Gaussian mixture, and density-based spatial clustering of applications with noise (DBSCAN), to analyze musical features like rhythm, tempo, and other relevant music attributes. The results showed that the k-means outperforms all other algorithms evaluated regarding recommendation quality, as measured by the Calinski-Harabasz score. Based on the evaluation, the best machine learning will then be applied to identify similarities between songs and be used to generate music recommendation results.
MobileNet Backbone Based Approach for Quality Classification of Straw Mushrooms (Volvariella volvacea) Using Convolutional Neural Networks (CNN)
Authors: Priyatna Bayu , Abdul Rahman Titik Khawa , Hananto April Lia , Hananto Agustia , Rahman Aviv Yuniar
Published: December 2024

Straw mushrooms (Volvariella volvacea) are a crucial commodity in Indonesia, with consumption on the rise due to their nutritional value and increasing demand for healthy food options. Despite this growth, farmers often struggle with accurately assessing the post-harvest quality of mushrooms according to market standards, which can diminish their economic value. Manual classification, which relies on human judgment and estimation, is frequently inefficient and susceptible to errors such as inconsistencies in quality assessment and limitations in detecting subtle variations. This study aims to automate the classification of straw mushrooms based on quality using deep learning, specifically by employing MobileNetv3 as the backbone for classifying mushrooms based on their shape and color by the Indonesian National Standards (SNI). The MobileNet-CNN Backbone model implemented in this study demonstrated exceptional performance, achieving a classification accuracy of 99%, thus proving its effectiveness and reliability in replacing traditional manual methods. The results of this research indicate significant potential for applying deep learning models to enhance the efficiency and precision of mushroom quality assessment. However, there remain challenges that require further development, including adding more diverse background data, improving image resolution, and refining data augmentation techniques. Addressing these challenges is essential for achieving optimal results in varying environmental conditions, ensuring the model can be broadly implemented in the agricultural industry. Such advancements could lead to more consistent and accurate quality assessments, benefiting producers and consumers in the mushroom market.
Skin Lesion Classification: A Deep Learning Approach with Local Interpretable Model-Agnostic Explanations (LIME) for Explainable Artificial Intelligence (XAI)
Authors: Hong Sin Yi , Lin Lih Poh
Published: December 2024

The classification of skin cancer is crucial as the chance of survival increases significantly with timely and accurate treatment. Convolution Neural Networks (CNNs) have proven effective in classifying skin cancer. However, CNN models are often regarded as "black boxes”, due to the lack of transparency in the decision-making. Therefore, explainable artificial intelligence (XAI) has emerged as a tool for understanding AI decisions. This study employed a CNN model, VGG16, to classify five skin lesion classes. The hyperparameters were adjusted to optimize its classification performance. The best hyperparameter settings were 50 epochs, a 0.1 dropout rate, and the Adam optimizer with a 0.001 learning rate. The VGG16 model demonstrated satisfactory classification performance. The Local Interpretable Model-Agnostic Explanations (LIME) method was implemented as the XAI tool to justify the predictions made by VGG16. The LIME explanation revealed that the correct predictions made by VGG16 were owing to its truthful extraction of the cancer or lesion area, especially for the “vascular lesion” class. Meanwhile, inaccurate classifications were attributed to VGG16 extraction of the background and insignificant parts of the skin as core features. In conclusion, The LIME model allowed visual inspection of the features selected by VGG16, paving the way for improving the CNN model for better feature extraction and classification of skin lesions, offering a promising direction for future research.
PRiSm: Policy Recommendation Systems in Cadastral Survey Using National Public Opinion Big Data
Authors: Lee Kihoon , Chang Yohan
Published: December 2024

Cadastral surveys are vital for assessing individual properties and generating national statistics. In South Korea, rapid territorial changes have exposed personal-level issues, while current systems limited public engagement capabilities have constrained policymaking. Although efforts have been made to bridge this gap, they have been hindered by the lack of an effective medium. This study introduces a novel framework, PRiSm (Policy Recommendation Systems in Cadastral Survey), which leverages National Public Opinion Big Data. We collected and analyzed two key data sources: 1) public opinion data from 2018 to 2023, which correlates strongly with cadastral resurveys across South Korea, and 2) content from 54 major news media outlets over the same period. The first data set represents bottom-up opinions at the individual level, while the second reflects top-down perspectives on national issues. The PRiSm system, developed in this study, utilizes Natural Language Processing (NLP) and advanced Machine Learning (ML) techniques, including Word2Vec and a Genetic Algorithm for hyperparameter optimization, to process over a thousand inquiries and news articles. Our results highlight how different groups engage in discussions shaped by their interests and concerns, revealing key sensitivities and recommending terms invaluable for stakeholders and policymakers. We anticipate that PRiSm will offer meaningful insights for the public and decision-makers. Additionally, with more advanced ML and/or Deep Learning algorithms, there is significant potential for further advancements in NLP within the PRiSm framework.
A Novel Information Hiding Approach using Selective Quantization Technique in Video Coding
Authors: Hau Joan , Tew Yiqi , Tan Li Peng
Published: December 2024

This research examines the different areas of information hiding in current and emerging video compression standards. In the subsequent sections, we provide a detailed comparison of these techniques based on partition modes, prediction units, transform coding, and syntax elements. It shows the engineer and the reader that none of the methods are perfect but are the best for selected applications. We also consider the new video coding standards that have recently appeared, H.266/Versatile Video Coding (VVC) and H.265/High-Efficiency Video Coding (HEVC) and stress the fact that information hiding is critical in attaining such high compression efficacy. To facilitate the reader's understanding of all the relative information, the table that provides the analysis of each technique is presented in the form of a simple listing containing information about each technique's advantages, disadvantages, impacts, and practical applications. The current resource is intended to assist researchers and practitioners in optimizing information hiding for improved video compression. The study's outcome can contribute to enhancing knowledge of information hiding and the new developments of information hiding in video compression beyond what current research offers now, as well as provide a foundation for fresh advances in the field. Further, it is introduced to selective quantization techniques as the approach to information hiding. This method also minimizes this distortion while putting the information into the compressed stream. Finally, we evaluate the performance of this introduced approach towards information hiding capacity and maintaining video quality, with the potential to inspire further research and development in the field.
Comprehensive Review of Security Requirements for Mitigating Threats and Attacks on IoT Assets
Authors: Janisar Aftab Alam , Shafee bin Kalid Khairul , Sarlan Aliza Bt , Iqbal M. Aqeel , Amir Khan Muhammad
Published: December 2024

Machine learning and artificial intelligence are increasingly being utilized to automate identifying and defining security requirements (SR) and addressing diverse IoT security issues. Despite its extensive environment, IoT-focused cyberattacks had the largest attack surface. IoT security requirements include data confidentiality, integrity, authentication, access control, and privacy. Inadequate emphasis on assessing security requirements leads to attacks and threats. To address the security issues that threaten the IoT environment, additional security measures are required to protect IoT-based applications from threats and other vulnerabilities. However, the absence of the security requirement assessment in IoT systems architecture jeopardizes security, exposing the system to vulnerabilities and risking organizational assets and reputation while also escalating the cost and time required to address security issues.  In this study major threats and attacks are identified relevant to the assets of IoT security requirements. To systematically identify, analyze, and address potential security threats and attacks related to IoT assets, this research proposes a three-step methodology: (1) analysis of the IoT security requirements, (2) Identification of threats and attacks in IoT, and (3) IoT assets centric security threats and attacks. An illustrative example of IoT asset security is provided to highlight potential attacks and threats relevant to IoT assets. This approach offers a practical and clear foundation for the early identification of IoT security requirements and their seamless integration into requirements engineering (RE) activities, contributing to a more secure and resilient IoT system architecture.
Batik Image Representation using Multi Texton Co-occurrence Histogram
Authors: Minarno Agus Eko , Soesanti Indah , Nugroho Hanung Adi
Published: December 2024

This paper introduces a novel approach to batik image representation using the texton-based and statistical Multi Texton Co-occurrence Histogram (MTCH). The MTCH framework is leveraged as a robust batik image descriptor, capable of encapsulating a comprehensive range of visual features, including the intricate interplay of color, texture, shape, and statistical attributes. The research extensively evaluates the effectiveness of MTCH through its application on two well-established public batik datasets, namely Batik 300 and Batik Nitik 960. These datasets serve as benchmarks for assessing the performance of MTCH in both classification and image retrieval tasks. In the classification domain, four distinct scenarios were explored, employing various classifiers: the K-Nearest Neighbors (K-NN), Support Vector Machine (SVM), Decision Tree (DT), and Naïve Bayes (NB). Each classifier was rigorously tested to determine its efficacy in correctly identifying batik patterns based on the MTCH descriptors. On the other hand, the image retrieval tasks were conducted using several distance metrics, including the Euclidean distance, City Block, Bray Curtis, and Canberra, to gauge the retrieval accuracy and the robustness of the MTCH framework in matching similar batik images. The empirical results derived from this study underscore the superior performance of the MTCH descriptor across all tested scenarios. The evaluation metrics, including accuracy, precision, and recall, indicate that MTCH not only achieves high classification performance but also excels in retrieving images with high similarity to the query. These findings suggest that MTCH is a highly effective tool for batik image analysis, offering significant potential for applications in cultural heritage preservation, textile pattern recognition, and automated batik classification systems.
Deep Metric Learning with Augmented Latent Fusion and Response-Based Knowledge Distillation on Edge Device for Paddy Pests and Disease Identification
Authors: Darmawan Hendri , Yuliana Mike , Hadi Mochammad Zen Samsono , Sangaiah Arun Kumar
Published: December 2024

The health of paddy fields significantly impacts rice yields and the economic stability of farmers. Limited number of experts available to watch these issues poses a challenge. Consequently, a reliable diagnostic system is necessary to find pests and diseases in rice crops. In this study, we propose deep metric learning with augmented latent fusion (FADMAKA) combined with a response-based knowledge distillation (KD) approach. The student model, which processes single RGB input images, is trained using soft latent labels derived from four augmented input from the teacher model. Our method delivers a high validation accuracy of 0.973, keeps an accuracy of 0.782 on the unseen data, and with rapid inference time of 38.911 milliseconds. This approach’s accuracy outperforms SoftMax deep learning classification with fine-tuning, which only has a maximum accuracy of 0.739 on the unseen data with computation time of 36.224 ms, and the DML with augmented latent fusion with k-NN classifier on the same base model, which achieves an accuracy of 0.78 with computation time of 124.977 ms. Our proposed model has 0.12 giga floating point operations per second (GFLOPs) that is suitable for edge devices with low computational resources. Following the modeling phase, we deployed the highest-accuracy student model to a Raspberry Pi 4B device equipped with a camera. This system can provide biological agent-based recommendations for identified pest and disease threats in rice fields. Our approach not only improved accuracy but also proved efficiency, enabling farmers to identify pests and disease without relying on internet connectivity.
Virtual Reality in Algorithm Programming Course: Practicality and Implications for College Students
Authors: Dewi Ika Parma , Ambiyar - , Mursyida Lativa , Effendi Hansi , Giatman Muhammad , Efrizon - , Hanafi Hafizul Fahri , Ali Siti Khadijah
Published: December 2024

Virtual Reality (VR) is one of the revolutionary technologies in education that can simulate learning interactively and realistically. The reliability of VR supports various variations in learning, including learning programming algorithms. This research aims to design and develop VR in learning programming algorithms. The ADDIE model is applied in this study to develop products through the stages of analysis, design, development, implementation, and evaluation.  The analysis of learning problems shows the unavailability of interactive learning media that can support various learning styles of students in programming algorithm materials. Therefore, this VR learning design integrates various elements of students' learning styles, including visual, audiovisual, kinesthetic, and reading.  The product developed is in the form of a VR application that presents learning by having a variety of learning styles. Audiovisual displays are presented in a VR environment, while kinesthetic interactions are obtained through user movements, and material information and readings are presented in a VR environment.  The data collected included the results of a practicality and effectiveness test involving sixty-six students. The results of the practicality test show that this VR product is practical to use based on the indicators of convenience, attractiveness, efficiency, and benefits. Meanwhile, the effectiveness test shows that this VR product can improve learning outcomes better than traditional learning methods.  Overall, the VR products developed have proven to be practical and effective in learning programming algorithms. The presence of VR, which presents a variety of realistic displays in a virtual environment, opens opportunities for further research related to distance learning through VR technology as well as an analysis of the impact of VR on health.
Uncovering the Most Effective Pedagogical Techniques for Math Education Using Machine Learning
Authors: Elnaffar Said Selim , Fawey Mohamed
Published: December 2024

Many new math educators express that their first years in the teaching field are extremely challenging. They struggle to discover and apply the most effective teaching techniques and behaviors, often without the support of more experienced colleagues. In this study, we use machine learning to find the strategies that novice teachers can adopt to enhance their teaching effectiveness. The core aim is to uncover the relationship between teachers’ performance, as assessed by student evaluations, and their pedagogical methods. These strategies are derived from the final decision tree model, which is trained on a large dataset of empirical data from schools. The data consists of input from 72 math teachers of grades 7-9 and their students in Dubai, used to train two decision tree models: a classification tree and a regression tree. The structure of these trees is analyzed to identify and rank the effectiveness of nine teaching techniques—such as Visualization, Practice, Math Rules, Gamification, Collaboration, Problem-Solving, Case Studies, Assessments, and Language Switching—and four behavioral methods—such as Inspiration, Engagement, Entertainment, and Bonding— in relation to the Student Evaluation Index (SEI), which is derived from student feedback. Results indicate that techniques such as "gamification" and "inspirational behavior" are consistently associated with higher SEI scores across different tree configurations. However, factors such as the demographics and culture of both students and teachers may need to be considered when generalizing these findings to other regions of the world.
Preliminary Development of Vircadia Virtual Reality Platform for Monitoring Water Quality Powered by Solar Panels
Authors: Rante Hestiasari , Achmad Zacky Maulana , Suaib Norhaida Mohd , Prasetya Sonki , Avianto Tiyo , Hermanu Adhi Indra , Alfarezi Fitratama , Wijaya Ray Yusra
Published: December 2024

Climate change is a global issue that significantly challenges water resources, especially in regions with limited public awareness about water conservation. It manifests through rising global temperatures, shifting weather patterns, more frequent and intense natural disasters, and instability in water availability. These problems are worsened by low public awareness and the reliance on steam power plants for water pumps. Addressing these challenges requires educational media that raises awareness about the causes and impacts of climate change. This study introduces the early development of a Virtual Reality (VR) platform utilizing Vircadia, focused on creating a 3D world and monitoring water quality with the support of solar power. Vircadia, an open-source platform, offers developers the flexibility to build and host virtual worlds on their servers, providing greater control over the environment, scalability, and customization. With Vircadia, we can rapidly implement a VR platform that integrates custom assets from Blender and personalized avatars from Ready Player Me. Vircadia can seamlessly connect to IoT platforms via weblink, allowing for real-time monitoring of water quality parameters and enabling users to interact directly with and oversee IoT devices within the VR environment. This paper discusses why we chose Ready Player Me and Blender as platforms for building 3D avatars and assets, and Vircadia as the VR Platform. Additionally, it addresses challenges encountered when using Vircadia, such as asset optimization and IoT device integration. Future research will focus on optimizing asset quality, enhancing IoT integration, and implementing carbon emissions monitoring within the VR platform.
Analyzing Rupiah-USD Exchange Rate Dynamics: A Study with ARCH and GARCH Models
Authors: Ahmar Ansari Saleh , Al Idrus Salim , Asmar -
Published: December 2024

The study aims to analyze the volatility of the Rupiah-USD exchange rate and predict future fluctuations using the Autoregressive Conditional Heteroskedasticity (ARCH) and Generalized Autoregressive Conditional Heteroskedasticity (GARCH) models. The exchange rate data, spanning from January 2010 to December 2023, is sourced from Bank Indonesia (BI) and adheres to the Jakarta Interbank Spot Dollar Rate (JISDOR) regulations, focusing solely on business days. ARCH and GARCH models are widely applied in financial time series analysis because they capture and forecast time-varying volatility. This study analyzes historical exchange rate data to evaluate the persistence of volatility and detect any structural breaks that could impact future exchange rate behavior. The findings reveal that both models effectively capture the volatility of the Rupiah-USD exchange rate, but the GARCH (1,1) model demonstrates superior forecasting accuracy. This model's ability to account for long-term volatility clustering makes it particularly useful for predicting exchange rate dynamics. The research contributes to a deeper understanding of the factors driving exchange rate fluctuations, offering valuable insights for policymakers, investors, and businesses. These insights can help stakeholders manage exchange rate risks more effectively within Indonesia's open economy, where global financial conditions and external shocks significantly shape currency movements. The study emphasizes the importance of using advanced econometric models for accurate volatility predictions and informed decision-making.
Optimizing Genetic Algorithm by Implementation of An Enhanced Selection Operator
Authors: BinJubier Mohammed , Ismail Mohd Arfian , Othman Muhaini , Kasim Shahreen , Amnur Hidra
Published: December 2024

The Traveling Salesman Problem (TSP) represents an extensively researched challenge in combinatorial optimization. Genetic Algorithms (GAs), recognized for their nature-inspired approach, stand as potent heuristics for resolving combinatorial optimization problems. Nevertheless, GA exhibits inherent deficiencies, notably premature convergence, which diminishes population diversity and consequential inefficiencies in computational processes. Such drawbacks may result in protracted operations and potential misallocation of computational resources, particularly when confronting intricate NP-hard optimization problems. To address these challenges, the current study underscores the pivotal role of the selection operator in ameliorating GA efficiency. The proposed methodology introduces a novel parameter operator within the Stochastic Universal Selection (SUS) framework, aimed at constricting the search space and optimizing genetic operators for parent selection. This innovative approach concentrates on selecting individuals based on their fitness scores, thereby mitigating challenges associated with population sorting and individual ranking while concurrently alleviating computational complexity. Experimental results robustly validate the efficacy of the proposed approach in enhancing both solution quality and computational efficiency, thereby positioning it as a noteworthy contribution to the domain of combinatorial optimization.
Enhanced Adverse Drug Event Extraction Using Prefix-Based Multi-Prompt Tuning in Transformer Models
Authors: Modi Salisu , Kasmiran Khairul Azhar , Mohd Sharef Nurfadhlina , Sharum Mohd Yunus
Published: December 2024

Extracting mentions of adverse drug events and relationships between them is crucial for effective pharmacovigilance and drug safety surveillance. Recently, transformer-based models have significantly improved this task through fine-tuning. However, traditional fine-tuning of transformer models, especially those with many parameters, is resource-intensive, memory-inefficient, and often leaves a gap between pre-training and downstream task-specific objectives. Soft prompting is a lightweight approach that updates a trainable prompt to guide task-specific fine-tuning, showing comparable performance to traditional fine-tuning for large language models on simple tasks. However, its effectiveness on complex tasks like token-based sequence labeling requiring multiple predictions for a single input sequence remains underexplored, particularly in multi-task settings. In addition, using holistic prompts in multi-task learning settings may be biased to other subtasks. Additionally, some prompt tokens hurt the model prediction. This study proposes a prefix-based multi-prompt soft tuning method with attention-driven prompt token selection for tuning transformer models on multi-task dual sequence labelling for concept and relation extraction. We experimented with BERT and SciBERT models using frozen and unfrozen parameter strategies. Our approach achieved state-of-the-art performance on the n2c2 2018 and TAC 2017 datasets for adverse drug event extraction, with multi-prompt tuning in unfrozen models surpassing traditional fine-tuning. Moreover, it outperforms the largest clinical natural language processing model, GatorTron, on the n2c2 2018 dataset. This research highlights the potential of soft prompts in efficiently adapting large language models to complex downstream NLP tasks.
The Rewardable Persuasive Model: A Mobile Exergame Conceptual Design Model that Facilitates Youths to Exercise through Mobile Gaming
Authors: Hashim Hasdina Lynn , Kamaruddin Azrina , Jantan Azrul Hazri , Sulaiman Puteri Suhaiza
Published: December 2024

This study seeks to assess current design models to propose a conceptual design model for a mobile exergame that promotes physical activity among youths. Encouraging youths to engage in physical activity rather than remaining sedentary is essential due to the numerous health benefits associated with regular exercise. Exercise is a subset of physical activity structured for fitness. Exergames have the potential to inspire youths to be active by combining exercise and gaming engagingly and enjoyably. The proliferation of mobile gaming has further broadened the availability and accessibility of exergames. In this study, we conducted four stages of information gathering to assess and propose a mobile exergame conceptual design model: a literature review, a user survey, expert reviews, and in-depth user interviews. Based on the study's findings, we identified appropriate components and their rationale by adapting an existing design model to our conceptual design model for a mobile exergame. This conceptual design model is called the Rewardable Persuasive Model (RPM). This model aims to help youths achieve their weekly physical activity targets using an engaging and functional mobile application (app). The app incentivizes exercise by integrating it as a key element for unlocking gameplay. With the introduction of these components, an exergame can be designed to engage youths and facilitate physical activity effectively. In a future study, youths will use an app to assess the RPM's effectiveness over time. This assessment will ascertain its appropriateness for facilitating exercise during inactivity.
Problem-Frame-Oriented Requirements Traceability to Enhance Requirements Management
Authors: ShengWen Xiao , Hassan Sa'adah , Che Pa Noraini
Published: December 2024

Managing software requirements is a challenge in software development and maintenance. Requirements changes are inevitable, particularly in a rapid iterative development approach that leads to occasional changes in software requirements. Unable to manage this properly will impact the overall quality of the software. Thus, requirements traceability is essential because it ensures that all requirements are adequately addressed, changes are managed effectively, and that there's a clear linkage between business requirements and the system's functionality. Inadequate traceability mechanisms can make changing the requirements and detecting their impact difficult. Thus, it is crucial to establish precise requirements traceability and maintain clear links to manage the requirement changes effectively. Our research explores using a problem frames modeling approach to address this issue.  It starts by representing requirements as problems, creating a requirements relationship diagram, and generating a corresponding relationship matrix. The values in the traceability matrix help identify which elements are most affected by requirement changes, allowing developers to prioritize changes that minimize overall system impact. Furthermore, using problem frame modeling, complex problems can be broken down into manageable sub-problems, providing a clear structure for understanding the requirements. Additionally, a tool has been created to streamline the process, and a case study is used to demonstrate the functionalities. An evaluation has been conducted to assess the usability of the proposed work.  The requirements relationship diagrams and relationship matrices visually and quantitatively map the links between requirements, enabling traceability and identifying the impact of changes in requirements.
Automated UML Class Diagram Generation from Textual Requirements Using NLP Techniques
Authors: Meng Yang , Ban Ainita
Published: December 2024

Translating textual requirements into precise Unified Modeling Language (UML) class diagrams poses challenges due to the unstructured and often ambiguous nature of text, which can lead to inconsistencies and misunderstandings during the initial stages of software development. Current methods often struggle with effectively addressing these challenges due to limitations in handling diverse and complex textual requirements, which may result in incomplete or inaccurate UML diagrams. This study aims to propose a Natural Language Processing (NLP) model that analyzes and comprehends textual requirements to extract relevant information for generating UML class diagrams, ensuring accuracy and consistency between the diagrams and requirement descriptions. The research employs a four-step approach: preprocessing to handle text noise and redundancy, sentence classification to distinguish between "class" and "relationship" sentences, syntactic analysis to examine grammatical structures, and UML class diagram generation based on predefined rules. The results show that the model achieved a classification accuracy of 88.46% with a high Area Under the Curve (AUC) value of 0.9287, indicating robust performance in distinguishing between class definitions and relationships. This study highlights that existing methods may not fully address the nuances of translating complex textual requirements into accurate UML diagrams. This study successfully demonstrates an automated method for generating UML class diagrams from textual requirements and suggests that future research could expand datasets, optimize feature extraction, explore advanced models, and develop automated rule generation methods for further improvements.
Factors Influencing Information Quality of Information Systems: A Systematic Literature Review
Authors: Aziz Azwan Abd , Haizan Nor Rozi Nor , Jusoh Yusmadi Yah , Wan Ab. Rahman Wan Nurhayati , Mohd. Ali Norhayati
Published: December 2024

In today's digital era, information quality in information systems is essential for organizational effectiveness and decision-making. This systematic literature review aims to assess and synthesize factors influencing information quality across various systems, focusing on key dimensions such as reliability, accessibility, usability, accuracy, completeness, and timeliness. The existing literature is fragmented, lacking an integrated theory that comprehensively addresses the significance of information quality. Thus, a systematic review was conducted following the PRISMA framework to address this gap and provide evidence-based recommendations for research and practice. Studies were identified, screened, and selected from Scopus and Web of Science. After an initial search using specific keywords, a total of 1,548 articles were found that contained specified terms or strings in various combinations. Of these, 31 studies were chosen for full review based on predefined inclusion and exclusion criteria. The analysis was organized into three primary themes: i) Core Information Quality Factors, ii) Synergizing Information Quality with System and Service Quality, and iii) Impact of Information Quality on User Satisfaction and Organizational Outcomes. The results emphasize the significant role of high information quality in enhancing user satisfaction and operational efficiency. Different industries prioritize various quality dimensions according to their specific needs. Therefore, this review elucidates the imperative function of good quality information in reinforcing information systems' proper functioning, calling for empirical studies to develop holistic frameworks that incorporate multiple dimensions and impact analysis across different domains.
Technologies on Intelligent Financial Risk Early Warning in Higher Education Institutions: A Systematic Review
Authors: Chao Yu , Elias Nur Fazidah , Yahya Yazrina , Jenal Ruzzakiah
Published: December 2024

Financial risk early warning (FREW) is critical for developing Higher Educational Institutions (HEIs). This review uses the Systematic Literature Review (SLR) method to discuss the current research status, leading causes, early warning techniques, and algorithms of financial risk management in HEIs. Based on the WoS database, 139 articles meeting the research criteria were selected from 451 relevant literature for in-depth analysis. The results show that the current research on financial risk management in HEIs mainly focuses on developing risk identification, assessment, and early warning models. The primary sources of university financial risk include the instability of fundraising and distribution, decreased financial allocation, and intensified market competition. In response to these risks, scholars have proposed various early warning models and technologies, such as univariate, multivariable, and artificial neural network models, to predict and manage these risks better. In terms of methodology, this review provides a comprehensive perspective on the study of university financial risk through quantitative and qualitative analysis. This study reveals this field's main research trends and gaps through literature screening and cluster analysis. Finally, this study discusses the practical significance of financial risk management in HEIs, highlighting its role in the stability and growth of these institutions. It suggests future research directions, especially in improving the accuracy and applicability of the Early Warning System (EWS), to further enhance the financial stability of HEIs. This literature review has crucial theoretical value for the academic community and provides practical guidance for HEI administrators.
Relevance of Ancient Sundanese Manuscript "Bujangga Manik" to Aspects of Sustainable Development Goals (Sdg’s) Through Literary Anthropology Study
Authors: Isnendes Chye Retty , Haerudin Dingding , Hendrayana Dian , Kusumawardhana Gelar Taufiq , Harya Karguna Purnama
Published: December 2024

The research was motivated by two things, namely the scientific concept of literary anthropology which is interdisciplinary with one of the characteristics of focusing on ethnography and the significance of the ancient Sundanese literary manuscript Bujangga Manik in the Sundanese literary and cultural treasures as a presentation of human thought in its day and an inexhaustible source of inspiration. The problem that arises is whether the manuscript can become a hypogram for the awakening of new thoughts and developments in addressing issues of environmental problems, especially climate change that is rapidly leading to global warming. Therefore, the world community is simultaneously calling for a green campaign, also known as the issue of Sustainable Development Goals or SDG's. To answer this, the descriptive analytical method will be used with the steps of collecting data and analyzing it in two stages. The problems to be answered are: 1) the main character's activities, reasons, and consequences, and 2) the relevance of the research results to the SDG's program. The character's activities are very important because they are related to signs that represent humans in their time. The relevance of SDG's will show that this manuscript is able to be empowered and cross the current time span.