JOIV : International Journal on Informatics Visualization
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Comparison of Parametric and Nonparametric Forecasting Methods for Daily COVID-19 Cases in Malaysia
Get PDFNumerous research studies are currently examining various measures to control the transmission of COVID-19. One essential task in this regard is predicting or forecasting the number of infected individuals. This predictive capability is crucial for governments to allocate resources effectively. However, the most effective approach to handling time series problems between the parametric and non-parametric methods is unclear. The parametric method utilizes a fixed number of parameters to calculate the value. On the other hand, the non-parametric method increases its parameters along with the number of observations. To address the issue, we conducted a study comparing parametric and non-parametric models for time series forecasting, specifically using Malaysia's daily confirmed COVID-19 cases from 18/3/2020 to 30/12/2020. Since there have been limited comparisons of these models in time series forecasting, we believe our study is beneficial. We considered various models, including persistence, autoregression, ARIMA, SARIMA, single, double, and triple exponential smoothing, multi-linear regression, support vector regression, artificial neural networks (ANN), K-nearest neighbor regression, decision trees regression, random forest regression, and Gaussian processes regression models. Our study revealed significant characteristics of these methods, and we found that exponential smoothing methods were the most effective in capturing the level and trend of the data compared to other methods. Additionally, ANN had the least forecasting error among the machine learning methods. In conclusion, non-parametric methods are not suitable for predicting daily cases of Covid-19 in Malaysia. Enhancing the parametric methods will be preferable in the future.
Text Summarization on Verdicts of Industrial Relations Disputes Using the Cross-Latent Semantic Analysis and Long Short-Term Memory
Get PDFThe information presented in the documents regarding industrial relations disputes constitutes four legal disputes. However, too much information leads to difficulty for readers to find essential points highlighted in industrial relations dispute documents. This research aims to summarize automated documents of court decisions over industrial relations disputes with permanent legal force. This research involved 35 documents of court decisions obtained from Indonesia’s official Supreme Court website and employed an extractive summarization approach to summarize the documents by utilizing Cross Latent Semantic Analysis (CLSA) and Long Short-Term Memory (LSTM) methods. The two methods are compared to obtain the best results CLSA was employed to analyze the connection between phrases, requiring the ordering of related words before they were converted into a complete summary. Then, the use of LSTM is combined with the Attention module to decoder and encoder the information entered so that it becomes a form that can be understood by the system and provides a variety of splitting of documents to be trained and tested to see the highest performance that the system can generate. The research has found out that the CLSA method gave a precision of 79.1%, recall score of 39.7%, and ROUGE-1 score of 50.9%, and the use of LSTM was able to improve the performance of the CLSA method with the results obtained 93.6%, recall score of 94.5 %, and ROUGE-1 score of 93.9% on the variation of splitting 95% training and 5% testing
Measuring the Effect of E-Learning Information Quality on Student’s Satisfaction Using the Technology Acceptance Model
Get PDFThis study analyses a blended e-learning system's information resources. Their quality is assessed based on learners' perceptions using a modified version of the Technology Acceptance Model (TAM). To enable flexible learning and enhance understanding during the COVID-19 epidemic, most Iraqi universities have lately embraced Google Classroom and Moodle in addition to face-to-face (F2F) courses. Based on TAM, individual differences and perspectives were investigated concerning correlations between student satisfaction and technology adoption. There were 270 undergraduate students in the research sample who were enrolled in academic courses at Middle Technical University's (MTU) /Technical College of Management (TCM). A survey was used for data collection. The research was done after developing the model's essential and external variables and selecting their components. Partial least squares structural equation modelling (PLS-SEM) examined path-connected dependent and independent components. The study's results showed how "E-Learning Information Quality" (EIQ) positively impacted students' adoption of e-learning. That is demonstrated by the internal variables' positive correlation, which includes perceived usefulness (PU) and perceived ease of use (PEOU), which can be seen in H1 and H2 by the values of (β = 0.204, β = 0.715), and which both positively influence attitudes toward use (ATU), which can be seen in H5 were value (β = 0.643), and behavioral intention (BIU), which can be seen in H4 was value (β = 0.300). Therefore, e-Learning information sources must have value and meaning for students. However, more research is required to evaluate the system's quality. Furthermore, the acceptability of e-learning may change as pedagogies chang
Ranjana Script Handwritten Character Recognition using CNN
Get PDFThis paper proposes a public image database for Ranjana script Handwritten Character Datasets (RHCD), publicly available for Ranjana script researchers or anyone interested in the subject. To the best of our knowledge, the Ranjana script Handwritten Character Dataset (RHCD) is the first publicly available database for Ranjana script researchers. Ranjana script descended from the Brahmi script, consists of 36 consonant letters, 16 vowel letters, and 10 numerical letters. The focus of this research is three-fold: the first is to create a new database for Ranjana script Handwritten Character Recognition; the second is to test the character recognition accuracy of the created RHCD using existing CNN algorithms like LeNET-5, AlexNET, and ZFNET algorithm; the third is to propose a model by investigating different hyper-tuning parameters to improve the recognition accuracy of the created RHCD. The research method applied in this study is dataset collection, digitization & cropping, pre-processing, dataset splitting, data augmentation, and finally, implementing the CNN model (existing and proposed). Performance evaluation is based on the test accuracy, precision, recall, and F1-score. The experiment result shows that our model ranks first, with a testing accuracy of 99.73% for 64x64 pixels resolution with precision, recall, and F1-score value 1. Creation and recognition of Ranjana script characters, vowel modifiers, and compound characters can be the next milestone to be achieved. Segmentation of words and sentences into characters and recognizing each character individually can be the next research domain
A New Feature Extraction Approach in Classification for Improving the Accuracy in Iris Recognition
Get PDFPersonal identity is becoming increasingly vital to meet the increasing security standards of today's business society. Iris recognition is one of the most accurate biometric technologies currently in use. Iris recognition is employed in high-security sectors due to its dependability and flawless identification rates. The steps of iris identification, comprising image preparation, extraction of features, and classifier creation, are described thoroughly in the primary portion of this research. The feature extraction stage is the most important in an iris identification system since it extracts the iris's distinctive feature. Several methods have been devised to extract the various characteristics that are unique to everyone. Modern iris identification systems frequently use Gabor filters to identify iris textural characteristics. However, in the application, it is necessary to identify the appropriate Gabor modules and to generate a pattern of iris Gabor characteristics. This research aims to provide a novel multi-channel Gabor filter and Wavelet filter for breaking down and extracting iris data from two different iris datasets. Because wavelet is the most scalable method of image processing, the research investigates using it to create a unique pattern for the iris recognition system. The MATLAB program is used to implement these ideas. CASIA and MMU are the datasets used for this purpose, and their comparative analysis is addressed in the research. To show how well the method performs, experimental results are given. We demonstrate through experiments that the suggested approach results in excellent iris identification performance
Classification of Malaria Cell Image using Inception-V3 Architecture
Get PDFMalaria is a severe global public health problem caused by the bite of infected mosquitoes. It can be cured, but only with early detection and effective, quick treatment. It can cause severe conditions if not properly diagnosed and treated at an early stage. In the worst scenario, it can cause death. This study aims at focusing on classifying malaria cell images. Malaria is classified as a dangerous disease caused by the bite of the female Anophles mosquito. As such, it leads to mortality when immediate action and treatment fails to be administered. In particular, this study aims to classify malaria cell images by utilizing the Inception-V3 architecture. In this study, training was conducted on 27,558 malaria cell image data through Inception-V3 architecture by proposing 3 scenarios. The proposed scenario 1 model applies the SGD optimizer to generate a loss value of 0.13 and an accuracy value of 0.95; scenario 2 model applies the Adam optimizer to generate a loss value of 0.09 and an accuracy value of 0.96; and lastly scenario 3 implements the RMSprop optimizer to generate a loss value of 0.08 and an accuracy value of 0.97. Applying the three scenarios, the results of the study apparently indicate that the Inception-V3 model using the RMSprop optimizer is capable of providing the best accuracy results with an accuracy of 97% with the lowest loss value, compared to scenario 1 and scenario 2. Further, the test results confirms that the proposed model in this study is capable of classifying malaria cells effectively
Utilization of Business Analytics by SMEs In Halal Supply Chain Management Transactions
Get PDFHalal supply chain management has transformed beyond food and beverage certification. However, extant literature shows that Halal transaction management still has much to improve in terms of transaction permissibility, with the main gap in understanding Halal businesses and their transactions limited to a system that separately defines e-commerce and financial technology data into its IT business environment. This study aims to demonstrate the usefulness of managing Halal transactions and its permissibility analysis through a proposed Halal Supply Chain Management Transactions (HSCMT) model and prototype by applying a business analytic approach to integrate both e-commerce and financial technology data. The study uses literature analysis to ensure the correct structure of the integrated datasets, before modeling the transaction's permissibility and prototyping its analytics into decision-making analytics. The developed HSCMT prototype uses a payment gateway that can be embedded into a Halal SME owners' e-commerce site. This creates a holistic Halal Financial technology (FinTech) transaction permissibility dashboard, increasing the effectiveness of HSCMT for Malaysia Halal SME Owners (MHSO) by an average usability score of 83.67%. Results also indicate that the key basic mechanisms to verify transactional permissibility are the source of the transaction, the use of the transaction, transaction flow, and transaction agreement. Furthermore, its mechanisms must be mapped onto a submodule post-transformation and modeling of the transaction dataset. Further improvements in multisource data points can be further considered, as this research only focuses on local data points from one payment gateway service. This is due to restrictions in data policy when involving overseas supply chain and transaction documentation. This research utilizes available data in business through data management, optimization, mining, and visualization to measure performance and drive a company's growth. The competency of business analytics can be beneficial to Halal SMEs players because it can provide them with insights into the permissibility decision-making process
Biometric Authentication based on Liveness Detection Using Face Landmarks and Deep Learning Model
Get PDFThis paper describes the approach to active liveness detection of the face using facial features and movements. The project aims to create a better method for detecting liveness in real-time on an application programming interface (API) server. The project is built using Python programming with the computer vision libraries OpenCV, dlib and MediaPipe and the deep learning library Tensorflow. There are five modules in active liveness detection progress related to different parts or movements on the face: headshakes, nodding, eye blinks, smiles, and mouths. The functionality of modules runs through face landmarking through dlib and MediaPipe and detection of face features through Tensorflow Convolutional Neural Network (CNN) trained in two different approaches: smile detection and eye-blink detection. The result of implementing face landmarking shows an accurate result through the pre-trained model of MediaPipe and the pre-trained parameter of the dlib 68 landmarking model. And more than 90% classification model accuracy in precision, recall, and f1-score for both trained CNNs in detecting smiles and eyes blinking through the Scikit-Learn classification report. In addition, the prototype API is also implemented using the Python RESTful API library, FastAPI, to test the detection functionality in the prototype Android application. The prototype result is outstanding, as the model excellently requests and retrieves from the API server. The possible research path gives the success of real-time detection on API servers for easy implementation of liveness detection on low-spec client devices
Jaccard-based Random Distribution with Least and Most Significant Bit Hiding Methods for Highly Patients MRI Protected Privacy
Get PDFIn this study, the main goal is to improve patient care by making it easier for patient data and pictures to be sent between medical centers without problems. Still, one of the biggest problems with telemedicine is keeping patient information private and ensuring data is safe. This is especially important because even small changes to patient information could have serious consequences, such as wrong evaluations and lower-quality care. This study develops a new model that uses the unique Jaccard distribution of the least significant bit (LSB) and the most significant bit (MSB) to solve this complex problem. The goal of this model is to hide much information about a patient in the background of an MRI cover picture. The careful creation of this model is a crucial part of the current study, as it will ensure a solid way to hide information securely. A more advanced method is also suggested, which involves randomly putting private text in different places on the cover picture. This plan is meant to strengthen security steps and keep private patient information secret. The peak signal-to-noise ratio (PSNR), the structural similarity index measure (SSIM), and the mean square error (MSE) all improved significantly when this method was tested in the real world. With these convincing results, the study shows telemedicine is more effective than traditional methods for keeping patient data safe. This proves that the model and method shown have the potential to greatly improve patient privacy and data accuracy in telemedicine systems, which would improve the general quality of health care
No-Show Passenger Prediction for Flights
Get PDFIn aviation, “no-show†refers to a customer who booked a reservation but failed to show up. No-shows can result in various resource wastes, such as vacant seats, leading to income loss and flight delays. As a result, no-show passengers can cause considerable problems for airlines, ultimately affecting their bottom line. Recent research has shown the use of machine learning algorithms to reduce the rate of no-shows. For example, a researcher in healthcare is using a predictive model to identify no-shows’ patients to increase efficiency. Therefore, this study aimed to develop prediction models to predict passenger no-shows. In this work, we used a dataset supplied by a local airline company consisting of 1,046,486 rows and 8 columns. Additional datasets like weather data, public holiday data of different countries, aircraft details, and foot traffic data are used to carry out the dataset's feature enrichment task to complement the original dataset. As a result, feature selection has become an important stage in this research to identify and pick the most relevant and useful features from the enormous number of columns. The findings showed that the model built using Random Forest has the highest accuracy of 90.4%, while Decision Tree performed at 90.2%, Gradient Boosting at 86.5%, and Neural Networks at 67.6%. To enhance the accuracy of the models, further research efforts are essential to integrate supplementary passenger information