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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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Early Detection of Female Type-2 Diabetes using Machine Learning and Oversampling Techniques Al-Dabbas, Lana; Abu-Shareha, Ahmad Adel
Journal of Applied Data Sciences Vol 5, No 3: SEPTEMBER 2024
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v5i3.298

Abstract

Early diabetes prediction is crucial as it can save numerous lives and prevent diabetes-related complications. The experiments conducted on diabetes prediction are keen on the limited samples of diabetes and non-diabetes cases provided in the available dataset. Various techniques have been implemented, focusing on the classification technique to improve the accuracy of prediction results. As a significant technique, oversampling has been implemented using SMOTE, which improved the results yet posed limitations due to its naïve technique. In this paper, a framework for diabetes prediction is developed, integrating an advanced oversampling technique using SVMSMOTE with various machine-learning algorithms to achieve the best performance. The proposed framework aims to overcome the problem of inaccurate data and limited samples using preprocessing and oversampling techniques. Besides, these techniques are integrated with other data mining and machine learning algorithms to improve the performance of diabetes prediction. The framework consists of four main stages: data exploration, data preprocessing, data oversampling, and classification. The experiments were conducted on the Pima Indian diabetes dataset, which comprises 768 samples and 9 columns. The results showed that the proposed framework achieved an accuracy of 91%, which improved the accuracy compared to using classification without oversampling, which achieved an accuracy of 90%. In comparison, the best results addressed in the literature were an accuracy of 85.5%. As such, the proposed framework improves the results by approximately 6.4% compared to the existing frameworks. Besides, the proposed framework achieved the best f-measure using the XGBoost classifier and SVMSMOTE, equal to 0.879. The best recall was achieved using RF and SVMSMOTE, which was 0.931. Finally, the best precision was achieved using FR without oversampling, with a value of 0.918.
Insulation Coordination System 150kV Substation and Transmission Line against Lightning Surge Interference in a Nickel Smelting Plant Samad, Busyairi; Manjang, Salama; Kitta, Ikhlas; Utamidewi, Dianti; Amri, Arham
Journal of Applied Data Sciences Vol 5, No 2: MAY 2024
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v5i2.248

Abstract

Related with the increasing demand for electrical energy at nickel smelting plant, a highly reliable electric power system is needed to be able to supply important loads such as electric furnaces and auxiliary equipment. The electric power system delivers electrical power to consumers through substations and transmission lines. The distribution of electrical power through high voltage overhead lines (SUTT) sometimes goes through areas with a high enough lightning strike potential that it can cause sudden blackouts due to direct strikes and back flashovers. Therefore, it is necessary to insulation coordination of the substation and transmission line to avoid damage to electrical equipment. This research aims to determine the magnitude of the voltage due to lightning strikes on GSW and Conductor by varying the location of the lightning protection system on 150 kV overhead line which is useful for obtaining isolation coordination systems on transmission lines and substations in the nickel smelting industry. which is useful for obtaining isolation coordination systems on transmission lines and substations in the nickel smelting plant. This research was carried out by selecting lightning strikes in the current strike of 100 kA on transmission line with simulation use device ATP draw software. This research showed that the installation of lightning protection equipment on high-voltage overhead lines and transmission towers resulted in a significant voltage drop due to lightning strikes lowering under BIL existing insulators.
Data Processing and Optimization in the Development of Machine Learning Systems: Detailed Requirements Analysis, Model Architecture, and Anti-Data Drift Strategies Boyko, Nataliya
Journal of Applied Data Sciences Vol 5, No 3: SEPTEMBER 2024
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v5i3.278

Abstract

The research relevance is determined by the growing need to use machine learning systems in various industries, which requires reliable data processing and optimization. The study aims to develop a machine learning system for data processing and optimization, that predicts employee departure based on internal company data, analyze the subject area and existing approaches, define model architecture and describe the developed system, validate the application’s performance on test data, and develop strategies to counteract data drift. To achieve this goal, the applied methods are machine learning algorithms, including, decision tree algorithm, logistic regression, neural networks, and architectural approaches used in machine learning systems with low input data information. This study employs multi-generation model architectures, ensemble methods with LightGBM for robust prediction, and dynamic adaptation strategies to handle feature and data drift. The main results of the study are a machine learning and data pre-processing system for recognizing the risk of employee dismissal, which can serve as a basis for implementing similar services in IT companies. The object of the study is the system of predicting the probability of a particular employee’s dismissal within a certain period. It also demonstrates how to cope with all the difficulties of developing a solution based on data of low information content and poor quality. The implemented application, despite the quality of the data and the high imbalance of classes, produces valuable results for the business. The practical significance of this study lies in the possibility of using the developed system to predict and prevent employee losses, which contributes to increasing team stability and improving the efficiency of personnel management, as well as increasing the competitiveness of enterprises.
Analysis Of Product Recommendation Models at Each Fixed Broadband Sales Location Using K-Means, DBSCAN, Hierarchical Clustering, SVM, RF, and ANN Trianasari, Nurvita; Permadi, Thifan Anjar
Journal of Applied Data Sciences Vol 5, No 2: MAY 2024
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v5i2.210

Abstract

The telecommunications industry proliferates in the digitalization era, especially Fixed Broadband services. Fast and stable internet access is essential, especially at sales locations with appropriate products. This research aims to develop an optimal product recommendation model for each sales location, using machine learning with a mixed method approach, with a combination method of clustering and classification, where the clustering method is used for the geographic segmentation stage. Then, the results of each cluster from the geographic segmentation are used as input for the classification method, which is a stage called sales forecasting. Next, the performance analysis measured the accuracy level of each combination of models. The best model combines clustering and classification models, which, on average, across all clusters, gives the best accuracy value. The data used in this research is GIS-based POI data and sales history data, which is internal data from a telecommunications company in Indonesia. From the tests carried out in this research, the best model combination is the K-Means and the Random Forest models, with an accuracy value of 82.08%. Meanwhile, the lowest performance resulted from a combination of the K-Means and ANN models with an accuracy value of 79.50%. With an average combination model performance above 80%, this research shows that using mixed methods with clustering and classification can provide valuable insights in subsequent research, especially in the context of the telecommunications industry, especially in fixed broadband services.
Embedded Image Recognition System for Lightweight Convolutional Neural Networks Fang, Jie; Zhang, Xiangping
Journal of Applied Data Sciences Vol 3, No 3: SEPTEMBER 2022
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v3i3.62

Abstract

In this paper, we design and implement an embedded image recognition system based on STM32 for the problem of limited storage space of embedded systems to run convolutional neural networks efficiently, and for the loading of lightweight convolutional neural network and the hook-up requirement of the quadrotor. The system hardware adopts the idea of modular design to improve the compatibility of the system, and the system software adopts the training of handwritten image recognition based on convolutional neural network, lightweight processing of the convolutional neural network, and transplanting the trained network to the embedded system. Finally, the system can finish the recognition of handwritten images stably and efficiently. This system can provide a low-cost and highly integrated solution for such image processing systems. The lightweight target detection model CED-Det is designed by combining CED-Net and dense feature pyramid network, which firstly performs feature extraction by CED-Net, then performs feature fusion by stacking two layers of dense pyramid network, and finally, the fused feature maps are used for classification prediction and position prediction by two 3×3 convolutions, respectively. CED-Det is used in VOC and Experimental results on COCO datasets show that CED-Det is more suitable for embedded platforms in terms of accuracy, inference speed, and a total number of parameters compared with other target detection models.
Applied Regression Modelling to Perfect Labor Law Policies Contributed to Increasing the Efficiency of Human Resource Management at FDI Enterprises Nga, Tran Thi Bich
Journal of Applied Data Sciences Vol 5, No 3: SEPTEMBER 2024
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v5i3.308

Abstract

Vietnam is an attractive destination for foreign investors thanks to its stable political environment, strategic geographical location, and abundant labor force. However, to maintain and enhance its attractiveness in the eyes of investors, Vietnam needs to pay special attention to attracting and retaining high-quality human resources, especially in foreign direct investment (FDI) enterprises in Vietnam. Therefore, the research objective explores the critical determinants of employee motivation and loyalty. Besides, the research applies both qualitative and quantitative methods. Qualitative research was conducted with 15 business managers with extensive experience in human resource management to explore the factors affecting employee motivation and loyalty. The study used an official questionnaire to survey research subjects and was conducted on an official data set of 800 employees. Still, it was 785 processed and using SPSS 20.0 and Amos software to test the reliability of the scales using Cronbach's alpha reliability coefficient, exploratory factor analysis, confirmatory factor analysis, and structural modelling model (SEM). The structural equation model results showed that five key factors positively influence employees' job motivation and loyalty at FDI enterprises in Vietnam. In addition, the finding that job motivation also affects employees' loyalty with a significance level of 5%. Unique contribution of this study is beneficial in both theory and practice in managerial implications for business leaders to improve human resource management efficiency by using policies to stimulate and motivate employees. The critical recommendation proposes legal policy implications for enhancing employees' job motivation and loyalty for developing and implementing policies to attract high-quality human resources for FDI enterprises.
IoT based Intrusion Detection for Edge Devices using Augmented System Nagarajan, R.; Batumalay, M.; Xu, Zhengrui
Journal of Applied Data Sciences Vol 5, No 3: SEPTEMBER 2024
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v5i3.358

Abstract

The Edge Computing (EC) paradigm is gaining popularity among users due to its inherent characteristics and expeditious delivery approach. Users may get information from the network's edge thanks to this feature of network architecture. The security of this edge network design, however, is a major issue. Through the Internet and in a shared setting, users can access all EC services. Intrusion detection is a method of network security that searches for threats. It is ineffective to monitor real-time network data, and current detection techniques are unable to identify known dangers. To address this problem, a technique known as augmentation oversampling is proposed, which incorporates the minority classes in the dataset. Our Sort-Augment-Combine (SAC) approach divides the dataset into subsets of the class labels, from which synthetic data is generated for each group. The developed synthetic data was then used to oversample the minority classes. After the oversampling process was complete, the distinct classes were combined to provide improved training data for model fitting. When compared to the original dataset, the models trained using the enhanced datasets perform better in terms of accuracy, recall (sensitivity), and true positives (specificity). SAC fared best in a UNSW-NB15 dataset when compared to the Synthetic Minority Oversampling Technique (SMOTE) and Generative Adversarial Network-Data Augmentation (GAN-DA). Additionally, SAC points to improvements in general sensitivity, specificity, and accuracy. SMOTE, datasets with ROSE enhancements, and Random Over-Sampling Examples for process innovation.
Cutting-Edge AI Approaches with MAS for PdM in Industry 4.0: Challenges and Future Directions Baroud, Shadia Yahya; Yahaya, Nor Adnan; Elzamly, Abdelrafe M.
Journal of Applied Data Sciences Vol 5, No 2: MAY 2024
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v5i2.196

Abstract

Integrating Artificial Intelligence (AI) within Industry 4.0 has propelled the evolution of fault diagnosis and predictive maintenance (PdM) strategies, marking a significant shift towards smarter maintenance paradigms in the mechatronics sector. With the advent of Industry 4.0, mechatronic systems have become increasingly sophisticated, highlighting the critical need for advanced maintenance methodologies that are both efficient and effective. This paper delves into the confluence of cutting-edge AI techniques, including machine learning (ML) and deep learning (DL), with multi-agent systems (MAS) to enhance fault diagnosis precision and facilitate PdM in the context of Industry 4.0. Specifically, we explore the use of various ML models, including Support Vector Machines (SVMs) and Random Forests (RFs), and DL architectures like Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), which have been effectively oriented to analyses complex industrial data. Initially, the study examines the progress in AI algorithms that accelerate fault identification by leveraging data from system operations, sensors, and historical trends. AI-enabled fault diagnosis rapidly detects irregularities and discerns the fundamental causes, thereby minimizing downtime and enhancing system reliability and efficiency. Furthermore, this paper underscores the adoption of AI-driven PdM approaches, emphasizing prognostics that predict the Remaining Useful Life (RUL) of machinery. This predictive capability allows for the strategic scheduling of maintenance activities, optimizing resource use, prolonging the lifespan of expensive assets, and refining the management of spare parts inventory. The tangible advantages of employing AI for fault diagnosis and PdM are showcased through a case study from authentic mechatronics implementations. This case study highlights successful implementations, documenting real-world challenges such as data integration issues and system interoperability, and elaborates on the strategies deployed to navigate these obstacles. The results demonstrate improved operational reliability and cost savings and shed light on the pragmatic considerations and solutions that facilitate the adoption of AI and MAS in industrial applications. The paper also navigates the challenges and prospective research avenues in applying AI within the mechatronics domain of Industry 4.0, setting the stage for ongoing innovation and exploration in this transformative domain.
The Mediating Role of Psychological Ownership and Job Satisfaction in Human Resource Management Practices and Employee Loyalty: A Case Study of Sichuan University of Technology He, Juncai; Tresirichod, Teetut
Journal of Applied Data Sciences Vol 5, No 3: SEPTEMBER 2024
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v5i3.266

Abstract

The objective of this research is to examine the influence of human resource management practices on employee loyalty, with a focus on the mediating effects of psychological ownership and job satisfaction. This investigation involves the collection of survey data from 600 educators at Sichuan University of Technology, located in the western region of China, to serve as a case study. The hypotheses posited within the theoretical framework were evaluated through the application of Partial Least Squares Structural Equation Modeling (PLS-SEM). The findings of this research indicate that efficacious Human Resource Management practices not only directly augment employee loyalty but also substantially bolster employee loyalty through the enhancement of psychological ownership and the improvement of job satisfaction. This study corroborates the combined mediating role of psychological ownership and job satisfaction in the relationship between Human Resource Management practices and employee loyalty. This research offers novel perspectives for comprehending and addressing the issue of disparate resource allocation within Chinese higher education, considering the unique aspects of China's imbalanced educational resources. It furnishes critical insights for the enhanced management and motivation of faculty in higher educational institutions in the western regions, alongside pragmatic recommendations for educational policymakers and university administrators.
Gum Disease Identification Using Fuzzy Expert System Nasir, Muhammad; Kurniawan, Tri Basuki; Dewi, Deshinta Arrova; Zakaria, Mohd Zaki; Bujang, Nurul Shaira Binti
Journal of Applied Data Sciences Vol 5, No 3: SEPTEMBER 2024
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v5i3.346

Abstract

Gum disease, including Gingivitis and Periodontitis, is among the most common dental conditions, primarily caused by dental plaque, a bacterial biofilm. These conditions are strongly linked to various systemic illnesses, including cancer, atherosclerosis, hypertension, stroke, and respiratory and cardiovascular conditions like aspiration pneumonia, as well as adverse pregnancy outcomes. Gum inflammation is typically characterized by symptoms such as increased redness, swelling (edema), and a loss of surface texture (stippling; gum fiber attachment). These symptoms are site-specific, meaning that an individual can have both healthy and diseased areas within their mouth. In this research, we developed a fuzzy expert system using MATLAB to identify gum diseases. The system was tested on various cases and produced an output value of 0.133, which successfully identified Gingivitis. This value was derived using a fuzzy logic system that processes input data through predefined rules within the Fuzzy Expert System (FES). The system utilizes several input variables such as the frequency of gum bleeding, the extent of plaque accumulation, the depth of gum recession, and the degree of tooth mobility. The key contribution of this study lies in the integration of fuzzy logic to handle the inherent uncertainties in clinical diagnosis, providing a more nuanced assessment compared to traditional methods. The novelty of this research is the application of a fuzzy expert system in dental diagnostics, offering a promising tool for improving the accuracy and efficiency of gum disease identification in clinical settings. This system has the potential to assist dentists in making more informed decisions, ultimately leading to better patient outcomes.

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