Journal of Information Systems Research and Practice

Preface

2024-10-06T07:42:25+08:00

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).

Predicting Kidney Failure Cases Using Machine Learning Approaches

2024-10-06T07:44:19+08:00

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.

A Mobile based Automated Visitors Management Application for Enhancing Security in Student Housing

2024-10-06T08:00:02+08:00

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.

Boundary Detection of Natural Image Objects using Improved High-Pass Filter Algorithm and Morphological Structuring Element

2024-10-06T08:02:55+08:00

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.

Tourism Sustainable Development Factor: A Bibliometric Analysis

2024-10-07T09:28:58+08:00

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.

Negation Detection In Arabic Opinion Reviews: A Comprehensive Annotated Dataset For Sentiment Analysis

2024-10-07T09:38:23+08:00

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.

Partial Derivative Resnet and Xception Neural Network-based Transfer Learning for Students Performance Prediction

2024-10-08T14:47:14+08:00

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.

Gabor Filter and Principal Component Analysis Method-based K-Nearest Neighbor for Orchid Flower Type Classification

2024-10-20T21:38:21+08:00

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.