Indonesian Journal of Electrical Engineering and Computer Science
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Enhancing fake profile detection through supervised and hybrid machine learning: a comparative analysis
Get PDFIn modern times, social networks have become ubiquitous platforms facilitating widespread information dissemination, resulting in significant daily data generation. This increase in data production encompasses a wide range of user-generated content, which in turn promotes the proliferation of fraudulent users creating fake profiles and engaging in deceptive activities. This article aims to address this challenge by employing machine learning algorithms to accurately identify fake profiles. The research involves a thorough analysis of various user behaviors, engagement metrics, and content attributes within social platforms. The primary goal is to develop robust models capable of effectively detecting deceptive profiles by meticulously examining user activities and content characteristics. The study explores the application of robust methodologies such as K-means and K-medoids clustering, alongside supervised machine learning classifiers including K-nearest neighbors (KNN), support vector machine (SVM), Bernoulli Naïve Bayes (NB), logistic regression, and linear support vector classification (SVC), specifically tailored for the detection of fake profiles
Particle swarm optimization for beamforming design in a cognitive radio
Get PDFBeamforming is essential for improving transmission in wireless sensor networks (WSNs), particularly in cognitive radio networks (CRNs) with several secondary users (SU) equipped with transmitting antennas. Optimizing beamforming while minimizing interference with primary users (PU) is of great interest. This study proposes an improved particle swarm optimization (PSO) algorithm to enhance beamforming performance. This approach aims to maximize the power of the beam directed to the SU receiver while controlling interference in the PU protection region. The results show that this algorithm constantly improves beam focus and signal-to-noise ratio to effectively optimize beamforming. Firstly, beam focusing becomes narrower as the number of antenna elements increases, generating optimal transmission conditions. Secondly, the algorithm achieves a considerable improvement in signal-to-noise ratio as the number of antenna elements increases. Furthermore, optimization performance improves as the number of antenna elements increases, as shown by the best fitness values. The simulations also illustrate the performance of the proposed method
A predictive model for postpartum depression: ensemble learning strategies in machine learning
Get PDFPostpartum depression (PPD) presents a significant mental health challenge for mothers following childbirth. While the precise cause of this condition remains unknown, preventive measures and treatments are available. This study aims to employ ensemble learning techniques, utilizing C4.5 decision tree (DT), gradient boosting tree (GBT), and extreme gradient boosting (XGBoost), to predict the occurrences of PPD in the Banjarmasin, South Kalimantan, Indonesia. The predictive model developed encompasses a dataset comprising 317 records gathered from postpartum mothers in hospitals, community health services, and midwifery clinics (referred to as Model 1). Furthermore, resampling techniques (Model 2) were employed to address class imbalance. Additionally, feature selection including forward selection and backward elimination (Model 3) were implemented to enhance model performance. The findings reveal that XGBoost, combined with resampling methods, achieved the highest accuracy rate at 87.57%. Feature selection identified five crucial factors associated with PPD incidence: marital status, number of living children, history of depression, fear of delivery, and family relationships. The utilization of ensemble learning strategies for PPD prediction yields reliable outcomes that can be applied within clinical settings. Exploring alternative ensemble learning strategies such as random forest and adaptive boosting could further optimize model performance and warrant consideration in future research endeavours
Predicting student performance using Moodle data and machine learning with feature importance
Get PDFDespite the growing technological advancement in education, poor academic performance of students remains challenging for educational institutions worldwide. The study aimed to predict students’ academic performance through modular object-oriented dynamic learning environment (Moodle) data and tree-based machine learning algorithms with feature importance. While previous studies aimed at increasing model performance, this study trained a model with multiple data sets and generic features for improved generalizability. Through a comparative analysis of random forest (RF), XGBoost, and C5.0 decision tree (DT) algorithms, the trained RF model emerged as the best model, achieving a good ROC-AUC score of 0.77 and 0.73 in training and testing sets, respectively. The feature importance aspect of the study identified the submission actions as the most crucial predictor of student performance while the delete actions as the least. The Moodle data used in the study was limited to 2-degree programs from the University of Southeastern Philippines (USeP). The 22 courses still resulted in a small sample size of 1,007. Future research should broaden its focus to increase generalizability. Overall, the findings highlight the potential of machine learning techniques to inform intervention strategies and enhance student support mechanisms in online education settings, contributing to the intersection of data science and education literature
AI in Moroccan education: evaluating student acceptance using machine learning classification models
Get PDFPersonalized learning is becoming a reality in education thanks to the rise of AI. This study investigates the possibilities of AI within the realm of education, focusing on the individualization of the learning experience. The research is based on the responses of 395 students from various faculties in Morocco. The questionnaire aimed to assess the students’ opinions of AI, their level of knowledge, their previous experiences, and their perception of the application of AI within educational settings. Employing classification techniques such as decision trees (DT), multilayer perceptron (MLP), and random forests (RF), our aim was to predict the receptivity of AI in education. The findings highlight significant differences in how Moroccan students perceive AI, identifying key factors such as familiarity with the technology, ethical concerns, and perception of its potential impact on the learning experience. Classification models showed varied performance in anticipating these attitudes. This study highlights the critical importance of understanding students’ perspectives on AI in education. These findings offer crucial insights for education policymakers as well as designers of educational technology solutions in Morocco. The findings can be used as a guide to adapt the incorporation of AI into the education sector with discernment, taking into account students’ perceptions and preferences
Impact of artificial light color on microgreen green spinach growth in an IoT-controlled environment
Get PDFThis study investigates the effect of different artificial colors red-blue and white on the growth of green spinach microgreens under an internet of things (IoT) based controlled environment and integrated sensors: DHT22 for temperature and humidity, and YL-69 for soil moisture. The experiment compared plant growth in two lighting scenarios over 10 days evaluating parameters including plant height and number of leaves. Results indicate that spinach microgreens grown under red-blue LED light achieved a slightly higher average height of 4.6cm and more leaves of 50 compared to white LED light with an average height of 4.5cm and 36 leaves. Although the difference between the two lighting conditions appears minor, a t-test was conducted to determine statistical significance. The results show that the difference in the number of leaves is statistically significant, suggesting that morphological responses particularly leaf growth take precedence over vertical steam elongation as an adaptive strategy to optimize environmental conditions
Laryngeal pathology detection using EMD-based voice acoustic features analysis and SVM-RBF
Get PDFTraditional techniques for detecting laryngeal pathologies, such as laryngoscopy and endoscopy, are costly and invasive. This study presents a novel approach for detecting laryngeal disorders using empirical mode decomposition (EMD)-based acoustic features analysis and support vector machine (SVM) with a radial basis function (RBF) kernel. The experiments were conducted using the Saarbrucken voice database (SVD). The voice signals were then decomposed using EMD to extract the intrinsic mode functions (IMFs). The IMF with the highest energy value was selected as the most relevant. A set of acoustic features, including mel-frequency cepstral coefficients (MFCCs), linear predictive cepstral coefficients (LPCCs), Pitch (fundamental frequency), higher-order statistics (HOSs), zero-crossing rate (ZCR), spectral centroid (SC), and spectral roll-off (SRO), is derived from the most relevant IMFs and fed into an SVM classifier to differentiate between healthy and pathological voices. Experimental results demonstrate the effectiveness of the proposed methodology, achieving a high classification accuracy of 94.5%, a sensitivity of 94.2%, a specificity of 95.3%, and an F1 score of 96.1%, outperforming conventional approaches. These results highlight the potential of EMD-based voice analysis as a non-invasive and reliable tool for early diagnosis of laryngeal disorders
Innovative automation and optimization of solar-powered water purification using siemens programmable logic controller and human-machine interface
Get PDFThis study presents a novel approach to optimizing water purification systems at the Zaouiet Kounta solar power plant through the integration of advanced automation and supervision technologies. By utilizing a siemens programmable logic controller (PLC) and human-machine interface (HMI) programmed via the totally integrated automation (TIA) Portal software, the project aimed to significantly enhance the performance of water production and distribution systems. The objectives included improving operational efficiency, reducing manual intervention, and increasing system reliability and precision. The results presented herein show significant improvements in operational efficiency, system reliability, and automation in a challenging environmental context. This research provides a comprehensive case study that not only highlights the feasibility of using Siemens PLC and HMI systems in solar-powered water purification systems but also proposes scalable solutions for similar industrial applications
Precision in 3D positional forecasting with machine learning and deep temporal architectures
Get PDFWe present a comparative analysis of traditional machine learning (ML) models, random forest (RF), support vector machine (SVM), and extreme gradient boosting (XGB), and deep learning (DL) architectures, convolutional neural networks (CNN), and bidirectional long short-term memory (BiLSTM) for high-precision 3D positional forecasting. Conventional approaches often underperform when modeling complex spatiotemporal dependencies, limiting their use in dynamic systems such as robotics and autonomous vehicles. This study highlights BiLSTM's advantage in learning bidirectional temporal features, achieving superior R² scores and stable prediction intervals compared to both classical ML and spatially-focused CNN models. Uncertainty metrics, prediction interval coverage probability (PICP), and mean prediction interval width (MPIW) provide additional insight into model reliability. Experiments on a 22-hour GPS dataset confirm that BiLSTM achieves both high accuracy and predictive confidence, underscoring its suitability for real-world trajectory forecasting
Optimization of photovoltaic pumping system using neuro fuzzy inference system ANFIS control technique
Get PDFIn recent years, artificial intelligence has become increasingly used due to the development of microcontrollers. In this paper, we propose an intelligent technique that employs the adaptive neuro-fuzzy inference system (ANFIS). We use this approach to improve the conventional direct torque control (DTC), which relies on a PI controller for the induction machine, and to enhance the conventional MPPT control based on the Perturb and Observe algorithm. The overall goal is to improve the performance of the photovoltaic pumping system. In this work, we apply ANFIS control to maximum power point tracking (MPPT-ANFIS). Additionally, we simultaneously optimize the efficiency of the DTC by applying ANFIS control (DTC-ANFIS). We present the results by comparing the photovoltaic pumping system using ANFIS control with the conventional photovoltaic pumping system, using MATLAB/Simulink. The results show that ANFIS control significantly improves the photovoltaic system compared to the conventional control, offering excellent dynamic performance of the induction motor and better utilization of photovoltaic solar energy. However, the ANFIS has some drawbacks, such as high computational time consumption and challenges in implementing a database