Journal of Information Systems Research and Practice
Journal Information Journal of Information Systems Research and Practice (JISRP) refers to the academic field and practical application of studying how information technology (IT) can be effectively used to solve real-world problems within organizations. This interdisciplinary field combines elements of computer science, management, psychology, sociology, and other related disciplines to understand how technology can be designed, implemented, and managed to support organizational goals and objectives. The Journal of Information Systems Research and Practice (JISRP) is dedicated to address the challenges in the areas of Information Systems in theoretical aspect and Its Applications, thereby presenting a consolidated view to the interested researchers in the aforesaid fields. The journal looks for significant contributions to Information Systems in theoretical and practical aspects. Journal Summary Journal Title Journal of Information Systems Research and Practice (JISRP) Subjects Information Systems Language English ISSN 1985-3920 Frequency 4 issues per year DOI TBA Editor in Chief Editorial Members Publisher Dept of Information Systems | FCSIT | Uni Malaya Citation Analysis Google Scholar For Special Issue, please send your proposal to jisrp@um.edu.my.
Dept of Information Systems | FCSIT | Uni Malaya
Cultural Tourism and Sustainable Development: A bibliometric Analysis from 2019 to 2024
The intersection between cultural tourism and sustainable development is receiving increasing attention, reflecting the growing body of research aimed at balancing cultural heritage preservation with economic growth. However, there are still challenges in integrating tourism with economic aspects and respecting cultural differences. This bibliometric analysis aims to describe the main thematic groups, identify the largest contributing countries, and highlight the leading authors in this field in 2019-2024 using the Vos Viewer software. By leveraging the Scopus database and focused query strings, this research reveals China, Italy and Indonesia as the main contributors, indicating a large publication output and citation impact. The identification of prominent authors underscores their important role in advancing knowledge in this domain. Visual representations of networks across countries and keywords provide insight into collaborative relationships and common themes. The interconnected nature of research activities between countries underscores the importance of international collaboration in promoting sustainable cultural tourism practices. This study underscores the importance of sustainable cultural tourism in driving economic growth and community engagement while preserving cultural heritage. Overcoming challenges and fostering collaboration is critical to ensuring the continued success of cultural tourism as a driver of sustainable development. By contributing valuable insights to the ongoing discourse on cultural tourism and sustainable development, this analysis informs the direction of future research and discussion, thereby facilitating progress in the field.
Copyright (c) 2024 Journal of Information Systems Research and Practice https://creativecommons.org/licenses/by-sa/4.0/
The Future of Nursing Care with Artificial Intelligence: Balancing Pros and Cons
The integration of artificial intelligence (AI) technologies, such as ChatGPT, into nursing practice, has introduced novel opportunities and challenges. This abstract explores the multifaceted impact of ChatGPT on nursing care, highlighting its potential benefits and pitfalls. ChatGPT serves as a comprehensive knowledge resource, offering nurses instant access to medical information and fostering continuous learning. Additionally, it facilitates educational innovation by providing interactive learning experiences and hands-on training opportunities. This study employed a comprehensive literature review approach to explore the impact of ChatGPT on nursing care. Relevant articles were identified through searches of electronic databases, including PubMed, CINAHL, and Google Scholar, using keywords such as "ChatGPT," "artificial intelligence," "nursing care," and "healthcare." Studies published in peer-reviewed journals between 2010 and 2023 were included in the analysis. The literature review revealed a growing body of evidence on the utilization of ChatGPT in nursing care, highlighting its potential to enhance communication, decision support, and administrative efficiency. Several studies reported positive outcomes associated with ChatGPT adoption, including improved patient satisfaction, increased access to clinical information, and reduced workload for nursing staff. The findings of this study highlight the complex interplay between the promise and pitfalls of ChatGPT integration in nursing care. While AI technologies offer exciting opportunities to augment healthcare delivery, they also present inherent risks that must be addressed proactively. Nurses play a crucial role in navigating the impact of ChatGPT on patient care, advocating for ethical AI use, and preserving the human-centric nature of nursing practice.
Depth Examination of Deep Learning Applications in Crowd Analysis
Artificial neural networks and machine learning have a long history of tackling diverse problems. However, as problems grew in complexity and datasets expanded, computational demands increased significantly. This paper provides a concise overview of the evolution from artificial neural networks to deep learning, highlighting models, applications, and essential technical details, including hardware, software, and libraries. It particularly focuses on conventional methods employed in crowd analysis and delves deeply into the applications of deep learning in this field, accompanied by an examination of relevant datasets. Additionally, recent studies in crowd analysis are meticulously analyzed and compared. In conclusion, crowd analysis stands as both an academic pursuit and a practical domain where deep learning has led to remarkable achievements.
Internet of Things Performance using Raspberry Pi for Body Temperature and ECG with GPS
This paper aims to discuss how the research produced unique results that are comparable to the results of traditional devices for ECG and blood temperature, as well as the patient's location. It does this by examining previous studies, the study's criteria, and the proposed results. The majority of earlier research findings made it impossible to quickly and effectively compare patient data. Since all sensors must be used to show general results, the majority of earlier studies were unable to display results when only one sensor was used. The results of this study show that the most expensive traditional devices can be replaced with sensors and technologies that connect to the Internet of Things and Raspberry Pi to accomplish similar tasks with more flexibility when handling patient data. Aside from financial savings, integrating multiple sensors into a single device is a unique and innovative process that saves a much of time and effort. When handling his medical cases, the doctor can have more privacy thanks to the desktop system's data security, which also keeps unwanted parties from accessing patient information.
A Review of Transaction Management Algorithms in Distributed Databases
This paper provides a comprehensive review of transaction management algorithms in distributed databases, which are a critical part of modern IT infrastructure. The paper discusses traditional protocols such as Two-Phase Commit (2PC) and Three-Phase Commit (3PC), exploring their limitations in terms of scalability, fault tolerance, and concurrency control. Additionally, new decentralized approaches leveraging blockchain technology and smart contracts are examined, with a focus on how Proof of Work (PoW) and Proof of Stake (PoS) are utilized to enhance transaction transparency and security. Despite significant advancements, challenges remain, particularly in achieving strong concurrency control and ensuring fault tolerance among nodes. The paper concludes with a discussion of emerging trends and the potential of blockchain technology to address these issues, offering a transformative perspective on the management of distributed transactions.
ChatGPT's Healing Words: Navigating the Frontiers of Conversational AI in Healthcare
This comprehensive review paper investigates the unique role of ChatGPT in medical healthcare, exploring its effect on patient engagement, clinical support, and healthcare communication by dissecting the strengths and weaknesses of ChatGPT in this domain. It highlights the potential to enhance patient care, streamlining administrative tasks to foster a more accessible healthcare experience. The paper navigates through real-world applications, shedding light on successful implementations, challenges, and potential ethical considerations. Additionally, we examine the evolving frontiers of conversational AI in healthcare, providing insights into prospects and avenues for further research. This exploration aims to showcase the current state of ChatGPT in healthcare and guide future developments in using conversational AI to improve patient outcomes and overall healthcare delivery. Further, to fully harness the benefits while minimizing the risks of ChatGPT in healthcare, it is essential for policymakers, healthcare organizations, and researchers to carefully evaluate the necessary ethical and regulatory frameworks. This paper serves as a foundation for future research and development by providing valuable insights into the advantages, disadvantages, opportunities, and risks of integrating ChatGPT into healthcare. By effectively utilizing conversational AI, we can improve patient outcomes and enhance overall healthcare services by improving patient care, expediting administrative duties and promoting accessibility.
An Improved Detection and Diagnosis Methods for Breast Tumor Detection System using IEMD Transform-based CNN Architecture
Mammogram is used to screen the tumor pixels in the breast portion of the human body. This paper develops a methodology for detecting and diagnosing the tumor regions in mammogram images using deep learning algorithm. The methodology consists of data augmentation, transform, and Convolutional Neural Networks (CNN) classifier with segmentation process for detecting the malignant mammogram image from the benign mammogram image. Then, the malignant mammogram image is analyzed for its severity levels using CNN classifier. The proposed system diagnosed the malignant case mammogram images into either Early or Advance using the developed CNN architecture. The simulation results of the proposed methods are compared with respect to other similar studies in this field.
Performance Evaluation using IPL Performance Impact Model
In the context of the Indian Premier League (IPL), assessing player performance is crucial for team success and strategic planning, as the tournament demands players who can balance high-scoring rates with consistency and reliability. Player performance evaluations help teams identify top-performing individuals who contribute significantly to both offense and defense, supporting optimal team compositions. This study delves into evaluating IPL 2024 player performances through a machine learning-driven model, designed to calculate impact scores that reveal each player’s contribution across batting, bowling, and all-rounder roles. By integrating Euclidean and perpendicular distances from origin-referenced metrics, the model identifies players with a balanced performance profile across key indicators like strike rate, batting and bowling averages, and consistency. This data-driven analysis helps identify potential retention candidates for the IPL 2025 Mega Auction, offering IPL teams objective insights into players who bring strategic value and performance reliability, thus optimizing team compositions for future tournaments.
Chaos-Driven Encryption: A New Frontier For IoT Image Security Using Multi-Linear Systems
In the context of an increasingly digital world, safeguarding sensitive visual information from unauthorized access is essential, particularly within resource-constrained Internet of Things (IoT) environments. This study introduces a novel image encryption method leveraging a Pseudo Hadamard Transformation (PHT), designed to provide a lightweight and efficient alternative to conventional pixel scrambling techniques. The proposed approach integrates chaos-based diffusion methods, which significantly enhance the encryption framework by effectively obscuring the correlation between the original image (plaintext) and the encrypted image (ciphertext). Through rigorous evaluations, the method demonstrates impressive statistical security metrics, achieving a Number of Pixels Change Rate (NPCR) of 99.6064 for the Lenna image, indicating a high degree of pixel alteration in response to single-pixel changes. Additionally, a Unified Average Changing Intensity (UACI) value of 33.4682 for the Lenna image highlights considerable intensity variations, further reinforcing the encryption's robustness. Compared to existing encryption techniques, the proposed method excels in both NPCR and UACI values, underscoring its superior performance and security capabilities. This hybrid encryption scheme, characterized by its efficient computational requirements and strong security features, is particularly well-suited for IoT applications, where maintaining a balance between data protection and resource limitations is paramount. The findings suggest that PHT, coupled with chaos-based diffusion, offers a promising solution for enhancing visual data security in modern digital environments.
Hyperparameter Tuning Modeling for Socioeconomic based Academic Analysis
This study analyzes the impact of hyperparameter tuning in improving the performance of predictive models for academic success based on socioeconomic data. To analyze their predictive capabilities, this research focuses on two ML algorithms, Gradient Boosting Machine (GBM) and Random Forest (RF). Using a UCI Machine Learning Repository dataset, this study implements grid search for hyperparameter tuning, optimizing parameters such as learning speed and number of estimators. Results show that GBM consistently outperforms RF, with higher average accuracy (78.64% vs. 77.45%), lower standard deviation (0.0077 vs. 0.0091), and better stability. Paired t-test results (p-value = 0.0081) confirmed the statistical significance of the superiority of GBM. This research contributes to the field by integrating socioeconomic factors into academic performance prediction models, providing valuable insights for data-driven educational decision-making.
The Volume 3 Issue 1 of JISRP has been available online on January 4th, 2025. The issue has been available online for accepted papers (uncorrected proofs).
Copyright (c) 2025 Journal of Information Systems Research and Practice https://creativecommons.org/licenses/by-sa/4.0/
Detection and Tracking of People in a Dense Crowd through Deep Learning Approach: A Systematic Literature Review
Crowd-related incidents, such as the Hillsborough Disaster and the Kanjuruhan Stadium stampede, often result from poor crowd management, leading to tragedies like suffocation and crushing. To mitigate human error in crowd control, this research explores the use of deep learning for the detection and tracking of individuals in dense crowds. The study focuses on implementing artificial intelligence for automated crowd monitoring through a localization map, with an emphasis on re-identification accuracy and auto-annotation of targets in datasets. A systematic literature review (SLR) was conducted following the PRISMA guidelines, analyzing 4384 articles published between 2019 and 2024 across five databases. 13 primary studies met the inclusion criteria and were analyzed to address questions related to the accuracy of crowd tracking and detection. This SLR aims to provide insights and reference points for further research in artificial intelligence, particularly in the areas of auto annotation and re-identification for crowd monitoring.
Emerging Environmental, Social, and Governance Funding for Malaysian Micro, Small, and Medium Enterprises
The adoption of Environmental, Social, and Governance practices by Micro, Small, and Medium Enterprises is challenging on several levels, particularly due to financial constraints. With the onset of the digital age, technology driven funding mechanisms are paving the way for new funding pathways that are more accessible to financially constrained enterprises. As these new mechanisms are still in an early developmental phase in Malaysia, it remains uncertain how important they are in fostering Environmental, Social, and Governance practices among Micro, Small, and Medium Enterprises. Therefore, this study examines emerging Environmental, Social, and Governance funding mechanisms, such as micro-financing and crowdfunding, for Micro, Small, and Medium Enterprise in Malaysia. Surveys, interviews, and document analyses were used to investigate the role these funding mechanisms have in advancing Environmental, Social, and Governance among Malaysian Micro, Small, and Medium Enterprises. Institutional theory, employed to analyse the findings, emphasises the importance of institutional mechanisms in the development of emerging funding for Environmental, Social, and Governance. The future development of emerging funding pathways and their impact on Environmental, Social, and Governance adoption among Malaysian Micro, Small, and Medium Enterprises will depend heavily on the interplay of key institutions at the national level.
The Volume 2 Issue 5 of JISRP: SUPPLEMENTARY ISSUE on Soft Computing and Data Mining (SCDM) availables online on December 14th, 2024. The issue has been available online for accepted papers (uncorrected proofs).
Feathers and Pixels: A Comparative Analysis of CNN Models for Efficient Bird Species Identification
Bird species identification is crucial for biodiversity conservation, ecosystem monitoring, and avian research. It helps assess population trends, monitor ecosystem health, and track environmental changes. Birds are key indicators of habitat quality and climate impacts. Accurate identification supports studies on migration, breeding, and foraging, aiding conservation efforts and identifying threatened species. Deep learning technology, specifically Convolutional Neural Networks (CNNs), is used for bird species identification by training models on large labeled datasets of bird images. The CNN architecture is designed to extract features from images, and the model is trained to associate these features with bird species labels. The paper navigates through the nuances of model performance, facilitating a comprehensive understanding of their strengths and limitations. Metrics such as accuracy, recall, and precision are scrutinized, offering valuable insights for researchers and practitioners. The few models that were picked out for performance analysis are VGG16, VGG19, ResNet50, DenseNet, InceptionV3, and EfficientNet. The analysis was performed on 200 sample images, each of 50 different species, taken from Google’s dataset. This comparative study welcomes the reader to the intersection of avian ecology and deep learning, where feathers meet pixels to unlock new dimensions in bird species identification.
Hate Speech Detection: A Social Network Story that Needs Serious Attention
Hate speech detection is an important task in natural language processing that has significant implications for online safety and social harmony. In this paper, we introduce a new benchmark composed of existing datasets for multilingual hate speech detection, comprising 24 datasets from 14 different languages covering a range of hate speech types, including racism, Islamophobia, misogyny, and more. The dataset includes numerous instances of data that have been carefully curated and annotated to ensure high quality and representation. We evaluated several machine learning models on this dataset, including mBERT, XLM-Roberta, and tree-based models such as XGBoost and we also investigated several aspects such as LASER being found to be resilient to biased data. Our results show that these models achieve high F1 scores in detecting hate speech in different languages. We also compare the performance of these models to other state-of-the-art models on our benchmark dataset, demonstrating that our dataset provides a valuable addition to the multilingual hate speech detection field. Our benchmark dataset and evaluation results provide a useful resource for future researchers working on multilingual hate speech detection. They can use this dataset to benchmark and compare the performance of different models, and to develop new models that can detect hate speech more effectively in different languages. Overall, our work contributes to advancing the field of natural language processing and promoting online safety and social harmony.
Gabor Filter and Principal Component Analysis Method-based K-Nearest Neighbor for Orchid Flower Type Classification
Orchid flowers are plants with highly diverse variations worldwide. Many orchid species appear visually similar, making visual identification less accurate. Therefore, more advanced scientific methods are required to distinguish and classify different orchid species accurately. This research aims to apply Principal Component Analysis (PCA) to classify orchid species using the K-Nearest Neighbor (K-NN) algorithm as the foundation for developing the model, supported by Gabor Filter techniques to extract features from the image dataset. The dataset used was obtained from an online dataset provider, Kaggle, and consists of 6,500 images. This dataset includes five orchid species: Cattleya, Dendrobium, Oncidium, Phalaenopsis, and Vanda. The data processing involves image augmentation, feature extraction using Gabor Filter, and PCA application for dimensionality reduction. The model evaluation results using the K-NN algorithm, Gabor Filter, and PCA demonstrate good performance with an accuracy of 95.69%, precision of 95.72%, recall of 95.77%, f1-score of 95.74%, and specificity of 98.92%. Additionally, the average Area Under the Curve (AUC) for the five classes is 97.35%, indicating that the model is highly effective in identifying and distinguishing various orchid species.
Partial Derivative Resnet and Xception Neural Network-based Transfer Learning for Students Performance Prediction
Despite deep learning-based methods have made a notable advancement involving both cognitive and non-cognitive skills, a good deal of these methods require to encapsulate the complicated associations present in learning activities, that are mandatory for boosting prediction accuracy. There is a growing proof that numerous characteristics, both cognitive and non-cognitive skills play a paramount part in the analysis of students’ performance prediction. Correspondingly a small amount of information is investigated about how cognitive features and non-cognitive features correlate with performance prediction among students. The objective of the present study is to check out the contribution of cognitive factors (general cognitive abilities) and non-cognitive factors (discipline or school absences, social factors like, going out with friends, alcohol consumption) on analyzing students’ performance prediction using a method called, Partial Derivative ResNet and Xception Neural Network-based Transfer Learning for students’ performance prediction (PDRN-XNNTL). The PDRN-XNNTL method is split into three sections, feature extraction, fine-tuning and prediction. First, the input from student performance dataset is subjected to the feature extraction model using Partial Derivative ResNet-based Pre-trained Network model. Second with the extracted features as input, fine-tuning is performed using Xception Neural Network-based Transfer Learning model. Finally, with the fine-tuned results students’ performance prediction is made using Normalized Exponential function. Student performance dataset is chosen for the performance evaluation and analysis of the PDRN-XNNTL method. The proposed PDRN-XNNTL method has significantly improved the accuracy compared with related works. To examine the contributions of the individual components of the proposed PDRN-XNNTL method, extensive studies are conducted to confirm the efficiency via training time and prediction error. The research implications are to confirm the feasibility of Neural Network-based Transfer Learning to enhance the optimal solution of the target model and to provide a generic approach to bridge the gap between the cognitive skills and non-cognitive skills using Normalized Exponential function.
Negation Detection In Arabic Opinion Reviews: A Comprehensive Annotated Dataset For Sentiment Analysis
Negation detection plays a vital role in Natural Language Processing (NLP), especially in sentiment analysis. In this paper, we introduce a comprehensive dataset of Arabic opinion reviews, specifically annotated for negation detection. The dataset consists of 84,000 reviews collected from TripAdvisor, Booking.com, and Agoda, spanning the period from June 2013 to June 2023. It is evenly divided between 42,000 'negated positive' reviews and 42,000 positive reviews. The reviews focus on hotels and travel accommodations across the Middle East and North Africa and are written in various Arabic dialects. The data collection process involved web scraping, language filtering, and both automatic and manual annotation of negation cues, such as ‘لا’ (no) and ‘ليس’ (not). The quality of the annotations was verified through expert review and inter-annotator agreement, ensuring high consistency. This dataset offers valuable insights into negation structures in both Modern Standard (MSA) and Dialectal Arabic (DA), providing a foundation for developing and evaluating negation detection methods. It will be made available to the Arabic research community to help address these key linguistic challenges.
Tourism Sustainable Development Factor: A Bibliometric Analysis
Tourism has a significant impact on economic, environmental, and social aspects, hence the growth of the tourism industry is crucial in supporting sustainable development. This study analyzes the evolution of the concept of sustainable tourism using a bibliometric approach to 1858 research documents (2013–2023). The study uses bibliometric analysis to identify key tourism-related trends and issues as factors of sustainable development, including publication trends, author collaborations, and core journal sources. The growth of scientific publications reached 17.81% per year, focusing on the third Global Tourism Code of Conduct (GCET) Principles. The results of the analysis show thematic evolutionary trends, providing practical implications for responsible tourism management. The identification of core journals and the distribution of scientific production provide a holistic view of the global contribution to sustainable tourism development. This research provides an in-depth understanding for decision makers and practitioners in designing sustainable tourism policies in the future.
ASGBOOKSYS: All Star Gym Booking System with Usability Heuristic Theory Adaptation
All Star Gym Booking System (ASGBookSys) is a system that is made for All Star Gym that is located at Kuala Terengganu, Terengganu. This system's main objective is to simplify the process of handling gym bookings. Initially, customers had difficulty booking the gym because there was no automated system to handle the booking. Customers must contact the gym's staff before making a booking to find out the available date and time. The staff also has difficulties keeping and tracking information about customer bookings in the logbook. Because there is no automated system in place, the system administrator, who is also the gym's owner, finds it challenging to monitor the gym process. The existence of ASGBookSys enables the resolution of all identified issues, including the system's ability to verify availability prior to customer booking, store data in an unlimited system database, and establish user-level data access restrictions. The booking process is easier and faster for the customers. ASGBookSys was developed using the Adapted Waterfall Model, where four stages were included which are requirement analysis, design, implementation, and testing. The project also employs the Ten Usability Heuristics as its theoretical framework. It has served as the guideline for developing this system. The system has passed all the requirements in the test plan. We used expert and usability evaluations to assess the system, considering all feedback. The evaluation reveals that the system interface is the cornerstone of system usability, boasting a mean score of 4.57. In the future, some enhancements will be made to improve the ASGBookSys such as online payment after booking confirmation.
Boundary Detection of Natural Image Objects using Improved High-Pass Filter Algorithm and Morphological Structuring Element
Object boundary detection involves finding instances of real image objects such as humans, animals, vehicles, flowers, mountains, etc. The boundary detection process of images is a preprocessing technique in digital image processing. Enhanced image objects include stairs, roofs, ramps, and peak boundaries. Traditional boundary detection methods for color flower images are sensitive to noise and cannot correctly identify boundaries. Photon noise, blur, and irregularities in the surface structure of objects are factors that affect modern boundary detection techniques. This paper affords an advanced edge detection approach that makes use of high-pass frequency filtering algorithms, and morphological elements. The Structuring elements are used to reconstruct the damaged areas of the border of the experimented images. The boundary detection technique of Roberts, Prewitt, Sobel, Laplacian of Gaussian (LoG), Canny, and the proposed method has experimented on the created image dataset. Experimental results are validated using different image quality evaluation methods. The proposed method shows better results in the objective image quality evaluation methods.
A Mobile based Automated Visitors Management Application for Enhancing Security in Student Housing
The security of student housing remains a pressing concern for educational institutions due to increasing incidents of unauthorized access and safety threats. Traditional paper-based visitor management systems are inefficient, prone to errors, and lack real-time data accessibility. This study presents a Mobile-Based Automated Visitor Management Application (AVMA) designed to enhance security in student housing. The AVMA leverages modern mobile technology, integrating visitor registration, and advanced access control to streamline the management of visitor activities. Key features include a user-friendly interface for registering visitors, push notifications, and visitor logs, which enhance communication between residents and housing management. The application is developed using the Flutter framework, Dart programming language, and Google Firebase for backend services, ensuring a secure and scalable solution. Initial testing enhanced user experience, making the AVMA a vital application for safeguarding student accommodations.
Predicting Kidney Failure Cases Using Machine Learning Approaches
This study presents an in-depth analysis of the application of machine learning approaches for predicting kidney failure. This is known as a critical condition that leads to severe health conditions if not properly diagnosed and treated. Although various diagnostic methods are available, diagnosing kidney failure early on is still challenging. The objective of this study is to predict the likelihood of kidney failure in patients by applying machine learning techniques. Our results demonstrate how machine learning approaches have a great deal of potential to increase kidney failure detection accuracy. Our algorithms predicted kidney failure in individuals with an outstanding 98% accuracy rate. These findings provide significant implications for the early detection and treatment of kidney failure, potentially leading to better patient outcomes and reduced expenses for healthcare. This study demonstrates the potential of machine learning techniques in improving the accuracy and effectiveness of kidney failure diagnosis. We used seven different classifiers: Support Vector Machine (SVM), K-nearest neighbors (KNN), Neural Network (NN), Naïve Bayes, Decision Tree (DT), Stack, and Logistic Regression. The SVM classifier achieved the best results, with an accuracy rate of 99.7%, closely followed by the Decision Tree classifier, which had an accuracy rate of 99.2%. Nonetheless, additional study is required to validate these findings and develop more thorough predictive models for clinical application.
The Volume 2 Issue 4 of JISRP will be available online on October 14th, 2024 for the regular issue of December 2024. The issue has been available online for accepted papers (uncorrected proofs).
Enhancing Profitability: Multi-Utility Range Algorithm for High Utility Itemset Mining
Association rule mining is a critical domain within data mining, aimed at uncovering interesting relationships between variables in large datasets. Traditional algorithms for association rule mining primarily rely on support and confidence metrics, which often fall short of addressing the complexities of real-time applications. High-utility itemset mining has emerged as a promising solution, offering significant insights by identifying items of substantial value to users. In this paper, we introduce a novel multi-utility range algorithm for computing high-utility itemsets. This algorithm is particularly advantageous for profit-oriented applications as it allows for the adjustment of utility ranges, thereby enabling the extraction of more valuable items. Our approach not only enhances the relevance of the mined itemsets but also provides flexibility in targeting different utility thresholds, making it highly effective for dynamic and real-time data analysis scenarios.
Developing a BERT-Enhanced Blockchain Model for HealthInsurance Fraud Detection
The health insurance fraud issue has been a significant challenge in the healthcaresystem. Worldwide organisations have been actively searching for effective strate-gies to either prevent or manage this problem. For this purpose, we are introducingHI-BERT (Health Insurance Bidirectional Encoder Representations from Transform-ers), which is based on BERT with adjustments made to vocabulary. We also useNaive Bayes as a supervised classification machine learning algorithm to label pre-scriptions by disease type. We check the similarity between the prescriptions andInternational Classification of Diseases (ICD) codes, which are designated by theWorld Health Organisation (WHO) to enhance the fraud detection process. Withthe objective of securing access to patients’ medical records, we have developed ahybrid blockchain in order to create a strong system for securely storing and retriev-ing data. Consequently, we can make sure that health information is protected to anextent that makes healthcare services more trustworthy. By integrating HI-BERTwith Naive Bayes, the performance is enhanced in detecting medical prescriptionsemantic similarity, and we get more accurate results compared to the basic BERT.Also, by using a hybrid blockchain instead of a single public blockchain, we increasedsecurity and reduced network load.
A Bibliometric Analysis of Sustainable Village Tourism Development Strategy Researches from 2019 to 2024
This study presents a bibliometric analysis which focusing on village tourism development strategy. It presents a comprehensive abstract, offering a synopsis of both research findings and methodological approaches employed in the analysis. The core objective revolves around the discernment of pivotal themes and emerging trends within the sphere of sustainable tourism, with a specific focus on rural locales. Utilizing VOS Viewer as our main vehicle tool for bibliometric analysis and leveraging data from Scopus, we accentuate the significance of environmental, economic, and social sustainability in the developmental trajectory of village tourism. Furthermore, the study underscores the integral role of community participation and collaborative efforts in realizing the objectives of sustainable tourism, aligning with the broader goals of environmental conservation and community well-being. These findings contribute substantively to the ongoing scholarly discourse surrounding sustainable tourism practices, thereby furnishing valuable insights for policymakers, researchers, and practitioners engaged in this dynamic and critical field.
Web-based Learning Media for Alphabet Recognition in Early Childhood using LeNet-5
A child's ability to learn to read, write and count in their early years is an indicator of the developmental milestones that must be achieved, particularly in the areas of cognitive and language development. The process of acquiring reading ability entails the initial recognition of vowels and consonants, which are subsequently integrated to form syllables, and then words and simple sentences. The use of learning media such as posters, cards, letter puzzles and letter boxes has been used to help improve letter recognition skills. However, the disadvantages of this learning media are that the use of digital technology in learning media is not optimal and requires teacher support, while the school we observed has a limited number of teachers. These problems are solved by creating a web-based learning media for letter recognition, which can help students to learn how to write and read letters and to learn two languages (English and Indonesian). This learning media applies computer vision technology with the Convolutional Neural Network (CNN) method of LeNet-5 architecture. The optimal parameter is a learning rate of 0.1 with a maximum epoch of 100, resulting in a system accuracy of 100% and an application user acceptance test result of 92.8%.
Automated Machine Learning Algorithms for Predicting Anxiety and Depression in Bangladeshi University Students
Stress is a mental health issue that results in a persistent sense of hopelessness and boredom. It affects one's thoughts, feelings, and behavior and sets off a host of psychological and medical problems. The global rate of increase in mental stress is unparalleled, particularly among students. This problem is caused by a number of factors, and the infection is causing an increase in linked illnesses. Not only does gloom increase the likelihood of health hazards, but it can also lead to dangerous social offenses such as self-harm and abuse within the family. We employed machine learning tools such as logistic regression (LR), K-Nearest Neighbors (KNN), Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), Gradient Boosting Machine (GBM), Adaboost, and Bagging to forecast psychological stress or anxiety and determined the correlation heat map for observing related features. We have attempted to look at eight machine learning algorithms concerning performance time and accuracy. In our proposed framework RF, DT, and Adaboost show 100% accuracy and precision, but in the perspective of performance time, SVM is the best because it takes only 0.007 seconds. The primary objective of this study is to forecast the anxiety and depression disorders of Bangladeshi university students using a machine learning algorithm. We assessed the performance of eight different machine learning algorithm compositions using a 10-fold verification technique. Random forest classifiers typically outperform other machine learning classifiers in terms of performance.