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A Phonology of Vowel Insertion to Malay Cluster Consonants by Native Speaker of Kashmir
No full textThis study explores the phonetic intricacies of the Kashmiri sound system, characterized by a rich inventory of vowels and consonants, resembling other Indo-Aryan languages. Kashmiri syllables typically comprise a vowel accompanied by one or two optional consonants, contributing to the distinct melodic quality of Kashmiri speech. Notably, Kashmiri exhibits a variety of short and long vowels, where vowel length and nasalization play pivotal roles in word meaning differentiation, enriching the phonetic landscape. The research focuses on vowel insertion processes into Malay consonant clusters by native Kashmiri speakers, aiming to identify instances of epenthesis in Malay words with consonant clusters. Employing the Optimality Theory framework, the studyanalyzes these processes and elucidates the hierarchy of constraints shaping Kashmiri speakers' Malay communication. Findings suggest prevalent vowel insertion and phoneme substitution among native Kashmiri speakers, particularly in Malay words with consonant clusters, underscoring the significance of phonological processes in interlinguistic communication
Enhancing Narrative Writing through the 4C’s to WRITE Module: A Mixed-methods Study in Malaysian ESL Classrooms
No full textThere are four language skills that learners acquire when learning, which are listening, speaking, reading, and writing. However, learners often face a barrier in writing skills compared to the other three language skills. Therefore, the 4C's to WRITE Module was created in this study. Moreover, this Module emphasises the amalgamation of communication, collaboration, creativity, and critical thinking (4C's) based on the Online Collaborative Learning Theory (OLC). The learners use this Module to practise their narrative writing elements. The ASSURE Model, comprising six steps, was used in the development and assessment phases of the Module. Fifty-five Form 4 English as a Second Language (ESL) learners from two upper secondary schools in Malaysia participated in the study. Pre-test, post-test, and interviews were conducted to assess the effectiveness of the Module in developing narrative writing skills. The findings showed that the Module could assist learners in constructing main ideas, organising these ideas into a few paragraphs, and applying appropriate grammar and vocabulary features to write a story. Moreover, the findings showed that the t-value was 21.64 and a partial Eta squared was .875, indicating that 87.5 per cent of the variation in the overall scores for the post-test is attributed to the 4C’s to WRITE Module. The participants also disclosed that they would need more grammar and vocabulary practices according to their language proficiency. Therefore, it is recommended that future researchers design more grammar and vocabulary assessments aligned with learners' ability levels. Overall, this study presents a novel framework that integrates 4C's skills with narrative writing instruction, demonstrating effectiveness in enhancing ESL learners' writing competence and higher-order thinking skills
Does Media Appeal Matter? Investigating the Motives for Using Social Media and Its Addiction among Malaysian Youth
No full textSocial networking sites (SNS) have mushroomed in this digital era, which has caused various media psychology issues, such as addiction. Youth is a segment that is significantly impacted by the phenomenon, which is alarming. Thus, it is crucial to understand the motives that motivate youth to use SNS platforms. Coined within the Uses and Gratifications Theory (UGT), this research examines social media motives, namely enjoyment, information seeking, social interaction, and media appeal, on social media addiction among youth. This study applied a quantitative research methodology using the Statistical Package for Social Sciences for data analysis. A questionnaire was distributed, and 194 valid responses were gathered via purposive sampling. The findings revealed that enjoyment and media appeal motives were predictors of social media addiction; however, social interaction and information seeking were not predictors. The present research contributes to UGT by expanding the media appeal motive for social media addiction. Implications for policymakers, educators, and mental health professionals are discussed to identify targeted interventions to ameliorate emerging social media addiction problems among youth for the betterment of society
Factors Influencing the Cooperative Preschool Education in Ulanqab Ethnic Area: Insights from the Input-Process-Outcome Perspective
No full textThe integrated education model of multiparty cooperative education has become the development trend of preschool education, and preschools, families, and communities cover the main scope of children's activities. There is limited awareness of family-preschool-community cooperative education, and a lack of practical cooperative education management systems. Moreover, the existing research shows a lack of relevant experience in ethnic areas. For this reason, we established a family-preschool-community cooperative preschool education framework. The proposed framework adopts the input-process-output (IPO) model to examine the factors affecting preschool education quality and assess its applicability within the ethnic minority area of Ulanqab. This study collected and analysed data from 378 parents of preschool children in the Ulanqab area using SmartPLS for data analysis. The results show that family, preschool, and community have a direct and positive effect on preschool education quality. These inputs indirectly contribute to quality improvement through the mediating role of cooperative teaching and learning. The research results confirm the applicability of the family-preschool-community cooperation framework in Ulanqab and provide a reference for future preschool cooperative education policies and practices in similar ethnic regions
Comprehensive Insights into Smart Contracts: Architecture, Sectoral Applications, Security Analysis, and Legal Frameworks
No full textMost conventional contract systems have issues with middlemen, drawn-out implementation procedures, fraud risk, and human error. Considering this, the project uses smart contract technology to provide a decentralized, automated, and safe solution in an effort to address such inefficiencies and the trust issues they raise. Smart contracts enable self-execution of contracts whose conditions are expressed explicitly in lines of code by presenting solutions using blockchain technology. The concept behind a smart contract is that each party may carry out their portion of the duties without depending on a third party and the contract will automatically execute in the meantime. This automation significantly reduces transaction costs while simultaneously improving security and transparency. With the use of this underlying technology, smart contracts may be used to directly code parties' compliance with their duties under the agreement and the blockchain will keep an immutable record of every transaction. For smooth and dependable transactions, smart contracts offer a dependable and effective substitute for conventional contract methods. Furthermore, integrating smart contracts with cutting-edge technologies like machine learning and artificial intelligence could improve decision-making and accelerate operations in a variety of sectors. Their application extends beyond financial transactions to areas such as supply chain management, energy trading, and healthcare, showcasing their versatility. Despite these advantages, issues like energy consumption, scalability, and regulatory compliance still need creative solutions. Ongoing research and development aim to address these issues, fostering the evolution of smarter, more sustainable contract systems. By leveraging these advancements, smart contracts keep opening the door for a revolution in the digital economy that will increase productivity and confidence
Anomaly Detection in Network Traffic for Insider Threat Identification: A Comparative Study of Unsupervised and Supervised Machine Learning Approaches
No full textInsider threats pose a significant and growing risk to organizational cybersecurity, with recent studies indicating a 47% increase in insider incidents from 2018 to 2022. This paper presents a comparative analysis of unsupervised and supervised machine learning approaches for detecting potential insider threats through network traffic anomaly identification. We develop and evaluate an Isolation Forest (unsupervised) and a Random Forest (supervised) model, training them on a simulated dataset representing six months of network logs from a mid-sized company. Our study introduces a unique feature set combining traditional network metrics with temporal and behavioral indicators, enhancing the models' detection capabilities. Results show that the Random Forest classifier outperforms the Isolation Forest, with F1-scores of 0.6425 and 0.4624, respectively. However, the unsupervised approach shows promise in scenarios lacking labeled data. Key findings reveal that increased connection frequency and data transfer volume are critical indicators of potential threats, with temporal patterns also playing a significant role. This study provides valuable insights into the strengths and limitations of each approach, offering practical implications for real-world digital forensics investigations. We contribute to the field by proposing a hybrid approach that leverages the strengths of both methods, potentially improving the accuracy and adaptability of insider threat detection systems. These findings pave the way for more robust, context-aware cybersecurity measures in the digital age
Machine Learning Model for Predicting Net Environmental Effects
No full textEnvironmental sustainability is a global challenge in the face of increasing incidences of disasters affecting communities worldwide. This requires predicting net environmental effects accurately. While various approaches exist, we need more sophisticated prediction models that account for both environmental and social factors. This study presents a proof-of-concept machine learning model for predicting net environmental effects using synthetic data. We developed a multiple linear regression model incorporating nine key features: renewable energy usage, carbon emissions, air quality index, water usage, biodiversity impact, land use, public awareness, and environmental attitudes. We generated a synthetic dataset of 1000 samples using probability distributions and correlation structures derived from environmental literature and expert knowledge. Our model achieved an R-squared value of 0.67, demonstrating moderate predictive power. Feature importance analysis revealed renewable energy usage (coefficient = 0.71) and public awareness (coefficient = 0.44) as significant positive factors influencing environmental outcomes. Model validation included residual analysis and feature importance assessment, with results suggesting reasonable performance within linear regression constraints. Limitations of our study include reliance on synthetic data, assumption of linear relationships between variables, and limited environmental factors. Notwithstanding, our findings provide insights for environmental policymaking, particularly regarding renewable energy adoption and public awareness campaigns. Future work could focus on incorporating real-world data, exploring non-linear modeling approaches, and expanding the feature set to capture more complex environmental interactions. Our research contributes to data-driven environmental assessment by demonstrating the feasibility of combining both physical and social factors in predictive modeling
Predicting Short-Range Weather in Tropical Regions Using Random Forest Classifier
No full textIn this paper, we present a Random Forest classifier machine learning model for predicting short-range weather in in tropical regions like Malaysia. Our model uses environmental factors such as temperature, humidity, wind speed, and cloud cover to predict weather conditions like clear skies, rain, and thunderstorms. Tropical weather, influenced by high humidity, fluctuating temperatures, and frequent rainfall, present unique challenges for forecasting accurately. To address these challenges, we trained a Random Forest classifier on a synthetic (simulated) dataset comprising 1,500 samples, each representing a specific weather scenario. Our model achieved an accuracy of 98.66% in predicting short-term weather conditions, identifying cloud cover, precipitation intensity, and humidity as the most influential factors. Our model’s high accuracy demonstrates its potential for predicting short-range weather conditions in tropical regions. Potential applications of the model spans sectors like agriculture, energy, tourism, disaster management, and public health. In agriculture, the model can be used to optimize irrigation schedules and crop management. In the energy sector, it can be used to optimize energy production and distribution. In disaster management, it can alert residents of impending bad weather, so they are more prepared. In the health sector, it can provide timely weather alerts and assist those who are more prone to arthritis and migraine attacks. We can enhance the model by using real-world data and regional customization
Diabetes Risk Prediction using Shapley Additive Explanations for Feature Engineering
No full textDiabetes is prevalent globally, expected to increase in the next few years. This includes people with different types of diabetes including type 1 diabetes and type 2 diabetes. There are several causes for the increase: dietary decisions and lack of exercise as the main ones. This global health challenge calls for effective prediction and early management of the disease. This research focuses on the decision tree algorithm utilization to predict the risk of diabetes and model interpretability with the integration of SHapley Additive exPlanations (SHAP) for feature engineering. Random forest and gradient boosting models were developed to identify the risk factors and compare the prediction with the decision tree model. The performance of these classifiers was evaluated using the metrics for accuracy, f1-score, precision, and recall. Understanding the features that drive predictions can enhance clinical decision-making as much as predictive accuracy. With the use of a comprehensive dataset having 520 instances with 17 features including the target output, the proposed decision tree model had an accuracy of 97%. The decision tree model’s categorical variables enable straightforward data visualization. The SHAP tool was applied to interpret the model’s prediction after developing the model. This is crucial for healthcare practitioners as it provides specific health metrics to identify high-risk diabetic patients. Preliminary results indicate that a combination of polyuria, polydipsia, and age are predictors of diabetes risk. This study highlights the benefits that the integration of SHAP and decision trees algorithm provides predictive capability and transparent model interpretability. It also contributes to the growing body of literature on machine learning in the healthcare industry. The results advocate for the application of this methodology in clinical settings for prediction fostering trust between the approach and practitioners and patients alike
Editorial Preview for February 2025 Issue
No full textEffective from Volume 3, JIWE has transitioned to a triannual publication release effort. Specifically, releases occur each February, June, and October. This change would thus ensure steady and timely publication of research articles in the fast-changing domains of informatics and web engineering. This issue contains a diverse collection of 24 papers that demonstrate the recent developments and innovative applications in various fields such as Information Systems (IS), Web Technologies, Artificial Intelligence (AI), Machine Learning (ML), Data Mining (DM), Blockchain, IoT, Cybersecurity, Healthcare and Software Engineering that persist in moulding the digital landscape