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Journal of Applied Data Sciences
Published by Bright Publisher
ISSN : -     EISSN : 27236471     DOI : doi.org/10.47738/jads
Core Subject : Science, Engineering,
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management
One of the current hot topics in science is data: how can datasets be used in scientific and scholarly research in a more reliable, citable and accountable way? Data is of paramount importance to scientific progress, yet most research data remains private. Enhancing the transparency of the processes applied to collect, treat and analyze data will help to render scientific research results reproducible and thus more accountable. The datasets itself should also be accessible to other researchers, so that research publications, dataset descriptions, and the actual datasets can be linked. The journal Data provides a forum to publish methodical papers on processes applied to data collection, treatment and analysis, as well as for data descriptors publishing descriptions of a linked dataset.
Arjuna Subject : Ilmu Komputer (semua kategori) - Kecerdasan Buatan
Articles 518 Documents
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Impacting Technology on Employees' Job Performance: A Case Study of Commercial Banks in Vietnam Hien, Lam Thanh; Tam, Phan Thanh
Journal of Applied Data Sciences Vol 6, No 2: MAY 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i2.630

Abstract

Technology is driving rapid transformation in the way people work. Many banks have caught up by introducing technology into their operations and training their personnel to use technology to optimize work productivity. Besides, employee technology competency is one of the most critical issues for organizations, helping them maintain a competitive position in the market. Therefore, the research aimed to measure critical factors impacting on bank employees' job performance and policy recommendations. The methodology of this study applied a structural equation model consisting of five factors: knowledge, attitude, hard skills, soft skills, and technology, and examined the impact of the above factors on tasks, context, and job performance. Data were collected from 900 employees working for 40 branches at 15 commercial banks in Vietnam and processed using SPSS 20.0 and Amos software. After testing the scale’s reliability, convergence, and discrimination, the study's findings showed that the critical factors of knowledge, attitude, skills, and technology positively impact task, contextual, and job performance. In addition, the originality of this research includes the introduction of technological factors into the model, a new factor of the banking industry in the digital transformation period in Vietnam. Based on the results of testing the research model, the authors provided empirical evidence that the career competency framework includes critical factors: knowledge, attitude, skills, and technology that positively impact task performance, contextual performance, and employee job performance. The practical implications of the article proposed management implications to help employees, managers, and policymakers improve employees' knowledge, attitudes, and skills, which play an essential role in performing their duties, and improving job performance in digital competency is one of the required skills in the banking industry.
Dimension-Expanding MLP in Transformer: Inappropriate Sentences and Paragraph Digital Content Filtering Wardhana, Ariq Cahya; Yunus, Andi Prademon; Adhitama, Rifki; Latief, Muhammad Abdul; Sofia, Martryatus
Journal of Applied Data Sciences Vol 6, No 2: MAY 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i2.627

Abstract

The creation of digital content is now a pivotal element of today’s digital environment, driven by the need for both individuals and organizations to engage audiences effectively. As digital platforms grow in scope and impact, ensuring the security, professionalism, and appropriateness of user-generated content has become crucial. This study introduces a new approach for filtering inappropriate digital content by integrating dimension-expanding multi-layer perceptions (MLPs) into transformer architectures. The dimension-expanding MLP processed more high-dimensional features in the Transformers network, giving the ability to understand more specific contexts. Experimental findings reveal that the proposed model outperforms Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU), Transformer (Baseline) in accuracy, computational efficiency, and scalability. The research highlights the model’s practical applications in areas like social media content moderation, legal document compliance monitoring, and filtering harmful content in e-learning and gaming platforms with 0.744 accuracy.
Detection of COVID-19 using EfficientnetV2-XL and Radam Optimizer from Chest X-ray Images Alshalabi, Ibrahim Alkore; Alrawashdeh, Tawfiq; Abusaleh, Sumaya; Alksasbeh, Malek Zakarya; Alemerien, Khalid; Al-Eidi, Shorouq; Alshamaseen, Hamzah
Journal of Applied Data Sciences Vol 6, No 2: MAY 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i2.512

Abstract

Automating the detection of the COVID-19 pandemic has become necessary for assisting radiologists and medical practitioners in the diagnosis process. It enables them not only to save time through early diagnosis but also to ensure that they are making more accurate diagnoses. Therefore, this research presents a novel approach for automatically identifying COVID-19 in chest X-ray images by utilizing the EfficientNetV2-XL model in combination with the Rectified Adam optimizer for training. For conducting the experiments, we used the dataset available on Kaggle, known as the “COVID-19 Radiography Dataset.” The totality of this dataset was 21,165, and it included four patterns: COVID-19, viral pneumonia, lung opacity, and normal cases. The dataset was divided into 80% training and 20% testing. The preprocessing stage included resizing images to 512 × 512 pixels and then applying data augmentation techniques to enhance model robustness. Consequently, a fine-tuned multiclass categorization system was implemented. The proposed system's effectiveness is evidenced by the experimental outcomes, which show a 99.31% accuracy rate and a perfect Area Under the Curve score of 1 for identifying COVID-19. Additionally, the Score-CAM visualization method was utilized to enhance the interpretability of model predictions, identifying key regions within the chest X-ray images that influence the classification outcome. This Localization technique aids healthcare professionals in understanding the reasoning behind the model and confirming the accuracy of the diagnosis. The proposed system outperformed the state-of-the-art models for COVID-19 detection. 
Recognizing Fake Documents by Instance-Based ML Algorithm Tuning with Neighborhood Size S., Prakash; B., Kalaiselvi; K., Sivachandar; Batumalay, Malathy
Journal of Applied Data Sciences Vol 6, No 2: MAY 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i2.654

Abstract

The primary objective of this research paper is to classify spam SMS messages for scamming threats as soon as they are received on a device. The study focuses on evaluating the performance of K-Nearest Neighbors (KNN) classifiers with different neighborhood sizes to determine the most effective machine learning technique for improving accuracy and predictions in SMS spam detection. SMS is a short text messages service that permits mobile phone users to exchange messages.  In today’s world, people are so much tending towards mobile phones and it has become easy to spread spam content through them. One can easily access any person’s details through these social networking websites. No information which is shared and stored in the device is not secure. Numerous anti-spam systems have been developed. In this paper, we compare the classification results against spam SMS data to estimate the effectiveness of the KNN classifiers at different k levels and the comparisons shown. An effective method of classifying spam SMS, based on the metrics like F-measures, Precision, and recall score is recognized from the experiment results. The best performance was achieved with K = 4, where the classifier provided a high accuracy of 94.78% and strong results across all key performance metrics. The research highlights that feature selection plays a crucial role in improving classification efficiency by eliminating irrelevant or redundant features. Although KNN is a simple and effective approach, its scalability and real-time processing limitations suggest that future work should explore deep learning, ensemble models, or heuristic-based optimization for further improvements and support process innovation.
Design of multiband antenna for full screen smartphone using ANSYS HFSS R., Karthick Manoj; K., Suresh Kumar; T., Ananth kumar; R., Nishanth; Joseph, Abin John; Batumalay, Malathy
Journal of Applied Data Sciences Vol 6, No 2: MAY 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i2.718

Abstract

The current scenario and the global trend are rapidly growing in terms of technology and connected through communication. The term communication is an extensive link and has an enormous number of inventions and technologies booming in this field. In mobile communication, numerous antennas are worked and fabricated in mobile phones. Here, in this paper, we focus on communication through mobile phones. It deals with simulating a multi-band slotted microstrip patch antenna for mobile phones with 6 GHz as the operating frequency using FR4 substrate material. This proposed antenna has resonating frequencies such as 2.732 GHz, 3.311 GHz,4.792 GHz,5.373 GHz, 6.462 GHz, 7.476 GHz, and 9.156 GHz. The parameters to be performed are VSWR, gain, directivity, return loss, and radiation pattern with the inputs as dimen-sion and frequency. The FR4 material might be used as the base substrate because flexi-bility is better, easily used for thin substrates and patch antennas, and the cost is meagre. The simulation is performed with the help of the ANSYS HFSS tool. When implemented in real-time in smartphones, the simulated output will be compatible in means of low power consumption and utilizes low area when fabricated. It is very efficient and has good performance. The antenna, thus simulated in this paper, will be proficient in a great way for the fifth-generation telecommunication field when designed further for process innovation
Intelligent Web Search Recommender System: An Application of Ensemble of Convolution Neural Network for Deep Semantic Content Analysis of Web Documents Chawla, Suruchi
Journal of Applied Data Sciences Vol 6, No 2: MAY 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i2.571

Abstract

Web Information retrieval is widely used for retrieving web documents relevant to the user search query. Search engines retrieve huge collection of web documents for a given search query and an information overload problem arises for the web user.  Web page recommender systems are widely used to deal with the information overload problem. Quality of the web page recommendations for a given search query depends heavily on the document feature representation. In this research a novel method is explained for Intelligent web search based on deep semantic content analysis of clicked web documents using an ensemble of convolution neural network. Deep learning model Convolution neural network has been used in the research for feature generation and it effectively represents the text characterization for classification. The optimized web document feature vector is generated using the ensemble of CNN is finally averaged at the output layer for clustering. The resulting clusters of optimal web documents optimized feature vector therefore groups semantic similar web documents in a given cluster for web page recommendations during web search. Experiment results confirm the improvement in average precision to 93% across all selected domains that shows the relevant web documents are increased in the recommendations based on clusters of web document optimal feature vectors generated using ensemble of CNN. Thus, the proposed system performs the Intelligent web search recommendations based on the deep semantic deep content analysis of web documents using an ensemble of CNN.
Kodein-Penetration: Recommendations of Customer Personalization Level in A CRM using Deep Learning Sudianto, Sudianto; Usman, Muhammad Lulu Latif; Prabowo, Dedy Agung; Gustalika, Muhamad Azrino; Marsally, Silvia Van; Akhmad, Fajar Kamaludin; Rakhma, Nazwa Aulia; Muna, Bunga Laelatul; Wicaksono, Apri Pandu; Rachman, Ari
Journal of Applied Data Sciences Vol 6, No 2: MAY 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i2.597

Abstract

This study aims to develop a personalization-level recommendation model implemented in the Customer Relationship Management (CRM) system at PT Kodegiri, called KodeinPenetration. Personalization in CRM aims to improve customer interaction by providing more relevant recommendations based on their needs and preferences. To achieve this goal, this study tested several classification models using historical customer interaction data as the basis for analysis. The classification models tested included decision tree-based methods such as Random Forest, Gradient Boosting, and AdaBoosting, as well as deep learning models such as Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU). In addition, two main feature extraction techniques were applied to process text data, namely TF-IDF (Term Frequency-Inverse Document Frequency) and Tokenizer Padding. TF-IDF is used to represent words as numeric vectors based on their frequency of occurrence. In contrast, Tokenizer Padding is used in deep learning models to convert text into a numeric format that neural networks can process. The test results showed that the decision tree-based method using the TF-IDF feature produced the best accuracy of up to 82%. On the other hand, the deep learning model with GRU architecture utilizing Tokenizer Padding achieved the highest accuracy of 88.23%. This shows that the deep learning model has greater potential in handling sequential data and providing more accurate results compared to traditional methods. This study provides an important contribution to the development of deep learning-based personalized recommendation systems in CRM. By leveraging historical customer interactions, this system can improve user experience by offering more relevant and targeted services.
Towards Developing an AI Random Forest Model Approach Adopted for Sustainable Food Supply Chain under Big Data Miralam, Maram Saleh
Journal of Applied Data Sciences Vol 6, No 2: MAY 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i2.680

Abstract

Big data presents a transformative solution for addressing operational challenges and emerging risks in the food industry while unlocking new opportunities. It enables the analysis and integration of complex, large-scale datasets that often suffer from poor quality and unstructured formats. Although big data is a well-established technique in supply chain management, several areas remain unexplored, particularly in the global food supply chain, which faces significant limitations such as environmental impact, resource wastage, and operational inefficiencies. Achieving sustainability requires enhancing food supply chain operations through data-driven methods. The integration of big data with artificial intelligence models, such as Random Forest, offers a more efficient and sustainable approach to optimizing resource utilization, minimizing waste, and improving overall efficiency. This study develops and implements an artificial intelligence-based Random Forest model, demonstrating its effectiveness in improving sustainability in the food supply chain. The model achieves an accuracy of 96%, outperforming traditional Linear Regression, which records 91% accuracy. Additionally, the F1-score for Random Forest is 0.89, compared to 0.84 for Linear Regression, highlighting its superior balance between precision and recall. The model also improves waste reduction by 17% and optimizes resource utilization by 22%, contributing to more efficient food supply chain operations. These findings underscore the potential of integrating big data analytics and AI-driven approaches to enhance sustainability and decision-making in global food supply chains.
A Proposed Model for Detecting Learning Styles Based on the Felder-Silverman Model Using KNN and LR with Electroencephalography (EEG) Hasibuan, Muhammad Said; Isnanto, R Rizal; Dewi, Deshinta Arrova; Kurniawan, Tri Basuki; Yeh, Ming-Lang; Wijaya, Adi
Journal of Applied Data Sciences Vol 6, No 2: MAY 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i2.659

Abstract

The identification of learning styles plays a crucial role in enhancing personalized education and optimizing learning outcomes. This research proposes a model for detecting learning styles based on the Felder-Silverman model using two machine learning algorithms: K-Nearest Neighbors (KNN) and Linear Regression (LR). Electroencephalography (EEG) data, known for its ability to capture cognitive and neural activity, serves as the primary dataset for this study. The proposed model was tested on a dataset comprising EEG signals collected during various learning tasks. Feature extraction and preprocessing techniques were employed to ensure high-quality input for the learning algorithms. The experimental results revealed that the LR-based model achieved an accuracy of 96.4%, significantly outperforming the KNN-based model, which obtained an accuracy of 89.9%. These findings highlight the potential of EEG-based models for accurately identifying learning styles, offering valuable insights for educators and researchers aiming to implement adaptive learning systems. This study demonstrates the feasibility and effectiveness of combining EEG data with machine learning techniques for learning style detection, paving the way for more personalized and efficient educational approaches. Future research will explore the integration of additional physiological data and advanced machine learning methods to further improve model accuracy and applicability.
Power Quality Assessment in Grid-Connected Solar PV Systems Using Deep Learning Techniques S., Dhivya; S., Prakash; Batumalay, Malathy
Journal of Applied Data Sciences Vol 6, No 2: MAY 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i2.655

Abstract

To address challenges in stability, power quality, and computational demands while supporting sustainable energy goals in grid-connected solar PV systems, this research introduces a novel deep learning approach: Adaptive Graph-Aware Reinforced Autoencoder with Attention-Based Neural Architecture Search (AGRAAN). AGRAAN simplifies and accelerates the development of neural networks by automatically identifying optimal architectures through Neural Architecture Search (NAS), enabling efficient learning from limited data using Few-Shot Learning, and enhancing performance through attention mechanisms for time-series forecasting. This integrated approach reduces manual tuning and adapts effectively to various tasks. High levels of solar PV integration in power grids introduce variability due to weather conditions and limited forecasting, often resulting in high operational costs. To address this, the AGRAAN model enhances real-time solar variability prediction, improving adaptability, cost-efficiency, and grid stability. NAS supports architectural optimization, Few-Shot Learning improves adaptability with minimal data, and attention mechanisms enhance forecasting accuracy. Additionally, high PV penetration causes voltage fluctuations and harmonic distortions in diverse grid environments. To mitigate these effects, a complementary system named Graph-Aware Reinforced Autoencoder Control System (GRAACS) is proposed. GRAACS detects and manages power quality issues using Autoencoders for anomaly detection, Graph Convolutional Networks (GCNs) for spatial prediction, and Reinforcement Learning for adaptive real-time control. The combined AGRAAN and GRAACS models significantly enhance performance, achieving a high efficiency score of 0.98, an F1-Score of 0.97, and a low Mean Absolute Error (MAE) of 0.11. These results demonstrate the effectiveness of the proposed AI-driven framework in optimizing solar PV grid integration for energy efficiency.

Page 35 of 52 | Total Record : 518

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