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Performance analysis of REST API in a real-time IoT-based vehicle monitoring system

10.11591/ijres.v14.i3.pp766-784
Rizki Ananta Dwiyanto , Giva Andriana Mutiara , Marlindia Ike Sari
This study studies the design and implementation of a REST API and its performance analysis for an internet of things (IoT)-based vehicles monitoring system. This system incorporates brake pad sensors, a tire pressure monitoring system (TPMS) for assessing tire pressure and temperature, light detection and ranging (LIDAR) for measuring tire thickness, and radio frequency identification (RFID) for tire identification. Data is gathered using an ESP32 microcontroller and transmitted in real-time to the server via a REST API over a wireless network. The JSON Web Token (JWT) authentication mechanism is employed to ensure data security. Testing indicates that this system has an average response time of 4–11 ms, with optimal performance recorded at 3.93 ms for the RFID sensor and peak performance at 9.19 ms for the LIDAR sensor. Load testing with 100 concurrent users demonstrates that the system maintains stability with a 100% data delivery success rate. Authentication testing demonstrates that the API is accessible solely with a valid token, hence preventing unauthorized access. This study's results demonstrate that integrating REST API with IoT monitoring systems facilitates real-time vehicle monitoring, enhances maintenance efficiency, and offers viable solutions for future predictive maintenance systems.
Volume: 14
Issue: 3
Page: 766-784
Publish at: 2025-11-01

Development and evaluation of a generalized ontology framework for software requirement specification

10.11591/ijeecs.v40.i2.pp1050-1064
Sourav Kundu , Soumay Kanti Das , Abu Rafe Md Jamil , Md Kamrul Islam , SK. Shalauddin Kabir , Mostafijur Rahman Akhond
This paper presents an ontology developed to address challenges such as com munication gaps, risks of errors, and inconsistencies during the manual process of creating software requirement specifications (SRS). The proposed ontology offers a systematic and formal depiction of the requirements, enhancing consis tency and communication among stakeholders. The ontology has been devel oped from the software requirements documents to facilitate the development process. This paper discusses the process of creating the ontology and demon strates using Pellet Reasoner for inference and Prot´eg´e for ontology construction to save and reuse information. The ontology seems to be efficient in manag ing complex software projects, enabling accurate requirement retrieval through SPARQL queries. This study emphasizes how incorporating ontologies into re quirement engineering can significantly enhance the quality and reliability of SRS.
Volume: 40
Issue: 2
Page: 1050-1064
Publish at: 2025-11-01

The smart e-bike ecosystem integrates internet of things and artificial intelligence

10.11591/csit.v6i3.p307-314
Tole Sutikno , Hendril Satrian Purnama
The smart e-bike ecosystem, a combination of internet of things (IoT) and artificial intelligence (AI), has transformed urban mobility. This study aims to shed light on the transformative potential of the smart e-bike ecosystem in the context of urban transportation solutions. It includes real-time navigation, crash detection, and a smart electric drive to encourage sustainable practices and reduce reliance on traditional vehicles. The use of smart locks and parking beacon systems creates a safe and efficient urban infrastructure, encouraging e-bike use. This approach reduces traffic congestion and carbon emissions. IoT frameworks in smart e-bikes improve the user experience and contribute to urban mobility solutions. Real-time monitoring of critical parameters, such as battery levels, speed, and maintenance requirements, keeps riders informed and safe at all times. IoT-enabled features, such as navigation assistance, shorten travel times and improve the efficiency of urban transportation systems. The evolution of smart e-bikes is consistent with the anticipated improvements of 6G networks, which promise to transform communication infrastructures. AI-powered features such as real-time navigation and crash detection make rides safer. The use of smart electric drives and cloud server technology promotes a data-driven approach to transportation. Future research and development should look into the use of advanced localization techniques to improve user experience while addressing accuracy and energy consumption issues.
Volume: 6
Issue: 3
Page: 307-314
Publish at: 2025-11-01

A dual-model machine learning approach to medicare fraud detection: combining unsupervised anomaly detection with supervised learning

10.11591/csit.v6i3.p245-252
Jesu Marcus Immanuvel Arockiasamy , Gowrishankar Bhoopathi
Medicare fraud, costing $54.35 billion in improper payments in 2024, undermines U.S. healthcare by draining resources meant for vulnerable populations. Traditional detection methods struggle with reactive designs, high false positives, and reliance on scarce labeled data, exacerbated by a 0.017% fraud prevalence. This paper proposes a dual-model machine learning framework to tackle these challenges. Unsupervised anomaly detection uses cluster-based local outlier factor (CBLOF) and empirical cumulative outlier detection (ECOD) to identify novel fraud patterns across 37 million records. These findings are validated by the list of excluded individuals/entities (LEIE). Supervised classification, with C4.5 decision trees and logistic regression, refines these anomalies using an 80:20 balanced dataset, reducing false positives by 63%. Key innovations include hybrid sampling to address class imbalance, LEIE integration for labeled validation, and parallelized processing of 2.1 million claims hourly. Achieving an area under the curve (AUC), a measure of model accuracy, of 88.3%, this approach outperforms single-model systems by 24%, blending exploratory detection with actionable precision. This scalable, interpretable framework potentially advances fraud detection, safeguarding public funds and Medicare’s integrity with a practical, adaptable solution for evolving threats.
Volume: 6
Issue: 3
Page: 245-252
Publish at: 2025-11-01

Water quality monitoring using soft computing techniques in Udupi Region, Karnataka, India

10.12928/telkomnika.v23i5.26228
Krishnamurthy; Manipal Academy of Higher Education Nayak , Sumukha K.; Birla Institute of Technology and Science (BITS) Nayak , Supreetha Balavalikar; Manipal Academy of Higher Education Shivaram
A monitoring of water quality index parameters using soft computing technology is the current research focus as the main challenge of which is to design a soft computing algorithm with the highest accuracy and less computation time. For the secondary dataset obtained by the government database, this research proposes a water quality prediction and classification method based on decision tree algorithm. The comparative analysis is made for the different highest accuracy algorithms like decision tree algorithm with support vector machine (SVM), k-nearest neighbour (KNN) classifier, linear discriminant analysis, Naïve Bayes classifier and logistic regression. Decision tree algorithm had the highest accuracy compared to other algorithms. The KNN algorithm used as clustering algorithm to plot the two classes good and bad. The trend analysis of the water quality is performed with various water quality parameters like pH, fluoride and total dissolved solids (TDS) test results are plotted and observed for the variations of the values with respect to increase in time. The performance is measured with statistical indices and the prediction accuracy of 0.99 and mean squared error of 0.05. The results prove that the KNN algorithm found to be better for clustering purposes.
Volume: 23
Issue: 5
Page: 1333-1341
Publish at: 2025-10-10

Comparison of long short-term memory and deep neural network optimized neural networks for maximum power tracking of wind turbines

10.11591/ijece.v15i5.pp4454-4464
Ezzitouni Jarmouni , Ahmed Mouhsen , Mohamed Lamhamdi , Ennajih Elmehdi , Naoual Ajedioui , En-Naoui Ilias
In wind energy conversion systems, maximum power point tracking (MPPT) performance is crucial, as it is directly related to wind speed variability and the characteristics of the equipment used. Maximum power point tracking controllers are essential for optimizing the efficiency of wind power generation. This paper presents the development of three distinct approaches to maximum power point tracking: the classical perturb and observe (P&O) method, and two other techniques based on artificial intelligence, namely long short-term memory (LSTM) networks and deep neural networks (DNNs). Rather than focusing solely on the development of an intelligent neural network-based maximum power point tracking model, our work emphasizes the design of a deep neural network controller with an optimized architecture and a reduced number of layers and neurons per layer, thereby simplifying its implementation in embedded process control units while maintaining high maximum power point tracking performance. The results obtained show that our optimized deep neural network model identifies the point of maximum power more effectively than other techniques, demonstrating remarkable performance in terms of response time, accuracy, and the quality of the generated power.
Volume: 15
Issue: 5
Page: 4454-4464
Publish at: 2025-10-01

A hybrid extreme learning machine and sine cosine algorithm model for accurate electricity price forecasting

10.11591/ijece.v15i5.pp4366-4375
Udaiyakumar Sambathkumar , Sangeetha Shanmugam , Kannayeram Ganapathiya Pillai
Electricity demand is continually rising due to the advancement of new technology, the switch to greener energy, and the popularity of electric vehicles over conventional ones. The proliferation of businesses in the generation and distribution sectors has increased competition in the electricity market. Forecasting electricity prices enables consumers to control their monthly electricity bills and consumer-owned distributed generation by knowing the forecasted hourly price. For demand management, generation scheduling, and bidding price quotations, electricity price forecasting is crucial for buyers, generation businesses, and bidders alike. Electricity price data is highly nonlinear and affected by numerous factors because of which EPF models are more complex, highly volatile and slow in convergence. A range of neural network models, training algorithms, and hybrid systems comprising two or more models have been suggested for precise and efficient electricity price forecasting by researchers over the decade. This study involves the development of a hybrid neural network model with two intelligent algorithms sine cosine algorithm (SCA) and extreme learning machine (ELM) to predict electricity price for a particular duration. The newly developed network model is trained and tested with real-time Indian electricity price data from the year 2022. The selected annual price data set is divided into three different sets to explore seasonal variations and all the sets are given as the input to the model for training and testing to obtain the effective price forecasting.
Volume: 15
Issue: 5
Page: 4366-4375
Publish at: 2025-10-01

Enhancing network resilience and energy efficiency in the El Abiodh Sidi Cheikh grid through load flow analysis

10.11591/ijece.v15i5.pp4774-4784
Ali Abderrazak Tadjeddine , Soumia Djelaila , Ridha Ilyas Bendjillali , Sofiane Mohammed Bendelhoum , Abdelyamine Boukhobza , Salih Lachache
This study investigates the performance of the El Abiodh Sidi Cheikh (ESC) electrical grid, focusing on energy efficiency, system stability, and the integration of renewable energy sources. Numerical methods, including the Newton-Raphson (NR), accelerated Newton-Raphson (ANR), and fast decoupled (FD) load flow methods, were employed to evaluate power flow, voltage stability, and active power losses. Key results reveal that the NR method achieves the lowest power loss, with a minimal value of 2.32 MW, while voltage violations at specific nodes, such as buses 4 and 13, emphasize the necessity for voltage regulation. Analysis of the sun trajectory and temperature profiles highlights correlations between climatic conditions and energy demand, aiding renewable energy optimization. Additionally, photovoltaic (PV) measurements demonstrate diurnal variations in energy output, critical for enhancing renewable energy integration. These findings underscore the importance of advanced power flow analysis and strategic planning to ensure network resilience, energy efficiency, and reliability in the ESC region.
Volume: 15
Issue: 5
Page: 4774-4784
Publish at: 2025-10-01

Systematic review: the application of ChatGPT on Arabic language text processing

10.11591/ijece.v15i5.pp4837-4847
Ali Mousa AlSbou , Fadzli Syed Abdullah , Ashanira Mat Deris
Over 420 million people speak Arabic, and it is the official language of 22 countries. Its complex morphology and dialectal diversity present unique challenges for natural language processing (NLP) models like ChatGPT. This systematic review investigates the application of ChatGPT in Arabic language text processing, examining its potential uses, accuracy, and limitations. Covering literature published between 2021 and 2024, this review synthesizes findings from 21 articles, addressing four key research questions: ChatGPT’s applications in Arabic text processing, its performance in terms of accuracy and reliability, the challenges and limitations encountered, and future directions to enhance its utilization. Results indicate that ChatGPT has potential in several applications, including educational tools, machine translation, text generation, and sentiment analysis. Despite current limitations, ChatGPT's potential in Arabic text processing is promising. While it shows high accuracy in structured tasks, it struggles with dialectal variations and cultural nuances, especially in complex text types. Primary limitations include a lack of high-quality Arabic datasets, difficulty handling dialects, and a need for more nuanced contextual understanding. Future research should focus on improving data quality, expanding dialectal coverage, fine-tuning models for specific linguistic tasks, and integrating AI with human teaching methods. Addressing these areas will enhance ChatGPT's accuracy and reliability for Arabic NLP.
Volume: 15
Issue: 5
Page: 4837-4847
Publish at: 2025-10-01

Fuzzy clustering optimization based artificial bee colony algorithm for brain magnetic resonance imaging image segmentation

10.11591/ijece.v15i5.pp4916-4932
Chakir Mokhtari , Mohammed Debakla , Boudjelal Meftah
In brain magnetic resonance imaging (MRI) analysis, image clustering is regarded as one of the most crucial tasks. It is frequently employed to estimate and visualize brain anatomical structures, identify pathological regions, and assist in guiding surgical procedures. Fuzzy c-means algorithm (FCM) is widely used in the MRI image segmentation process. However, it has been several weaknesses such as noise sensitivity, stuck in local optimum and issues with parameters initialization. To address these FCM problems, this paper presents a novel fuzzy optimization method that enhances brain MRI image segmentation by integrating the artificial bee colony (ABC) algorithm with FCM clustering techniques. The proposed method seeks to optimize multiple FCM parameters simultaneously, including the objective function, number of clusters, and cluster center values. The method was evaluated on both simulated and clinical brain MR images, with an emphasis on segmenting white matter, grey matter, and cerebrospinal fluid regions. Experimental results demonstrate significant improvements in segmentation accuracy, achieving a Jaccard similarity (JS) of nearly 1, a partition coefficient index (PCI) of 0.92, and a Davies-Bouldin index (DBI) of 0.41, outperforming other stats of the arts methods.
Volume: 15
Issue: 5
Page: 4916-4932
Publish at: 2025-10-01

Optimizing internet of things based gas sensors: deep learning and performance optimization strategies

10.11591/ijece.v15i5.pp4813-4828
Mariam M. Abdellatif , Mehmet Akif Çifçi , Asmaa A. Ibrahim , Hany M. Harb , Abeer S. Desuky
The rapid growth of industrialization and internet of things (IoT) driven advancements in Industry 5.0 necessitates efficient and user-friendly engineering solutions. Gas leakage incidents in coal mines, chemical enterprises, and households pose significant risks to ecosystems and human safety, emphasizing the need for automated and rapid gas-type detection. Traditional detection methods rely on single-source data and focus on isolated spatial or temporal features, limiting accuracy. This paper proposes a multimodal artificial intelligence (AI) fusion technique combining pre-trained convolutional neural networks (CNNs), such as VGG16, with a deep neural network (DNN) model. The particle swarm optimization (PSO) algorithm optimizes CNN hyperparameters, outperforming traditional trial-and-error methods. The system addresses challenges posed by gases being odorless, colorless, and tasteless, which limit conventional human detection methods. By leveraging sensor fusion, the late fusion technique integrates distinct network architectures for unified gas identification. Experimental results demonstrate 95% accuracy using DNN with gas sensor data, 96% with optimized VGG16 using thermal imaging, and 99.5% through multimodal late fusion. This IoT-enhanced solution outperforms single-sensor approaches, offering a robust and reliable gas leakage detection system suitable for industrial and smart city applications.
Volume: 15
Issue: 5
Page: 4813-4828
Publish at: 2025-10-01

A memory improved proportionate affine projection algorithm for sparse system identification

10.11591/ijece.v15i5.pp4605-4619
Senthil Murugan Boopalan , Sarojini Raju , Krithiga Sukumaran , Manimegalai Munisamy , Kalphana Ilangovan , Sudha Ramachandran , Janani Munisamy , Bharathiraja Ramamoorthi , Sakthivel Pichaikaran
For cluster sparse system identification, it is known that the cluster sparse improved proportionate affine projection algorithm (CS-IPAPA) outperforms the standard IPAPA. However, since CS-IPAPA does not retain past proportionate factors, its performance can be further improved. In this paper, a modification to CS-IPAPA is proposed by utilizing the past instant proportionate elements based on its projection order. Steady-state performance of the proposed memory cluster sparse improved proportionate affine projection algorithm (MCS-IPAPA) is studied by deriving the condition for mean stability. Different simulation setups show that the proposed algorithm outperforms different versions of IPAPA in terms of convergence rate, normalized misalignment (NM) and tracking, for different types of inputs like colored noise, white noise, and speech signal. By incorporating past proportionate factors, the proposed MCS-IPAPA significantly reduces computational complexity for higher projection orders.
Volume: 15
Issue: 5
Page: 4605-4619
Publish at: 2025-10-01

A computational study of passive cooling of photovoltaic panels using hybrid material heat sink

10.11591/ijece.v15i5.pp4487-4499
Dang Van Binh , Pham Quang Vu , Pham Manh-Hai
Photovoltaic panels generate electricity from solar energy based on the photovoltaic effect. The conversion efficiency of photovoltaic panels depends on many factors such as solar radiation, wind speed, dust, orientation, tilt angle, and operating temperature. When the operating temperature increases by 1 C, the conversion efficiency of photovoltaic panels decreases by 0.4% - 0.5%. Heat sink is a device used to cool electrical and electronic equipment, including photovoltaic panels. This paper presents calculating the cooling capability of hybrid heat sink made from two materials in steady state using heat transfer theory. Heat sink base is constructed from aluminum and copper layers, with copper layer thickness is 1 and 2 mm. Under different conditions of radiation intensity, wind speed, and tilt angle of photovoltaic panel, results show that heat sink added copper layers of 1 and 2 mm, the operating temperature decreases by about 0.6 K and 1.2 K compared to the aluminum base. Accordingly, the conversion efficiency of photovoltaic panel increased by 0.1% and 0.2%.
Volume: 15
Issue: 5
Page: 4487-4499
Publish at: 2025-10-01

Detecting autism with Vietnamese child facial images using deep learning

10.11591/ijece.v15i5.pp4762-4773
Tran Van Thanh , Lam Thanh Hien , Do Nang Khoa , Le Anh Tu , Ha Manh Toan , Do Nang Toan
Deep learning techniques created a significant increase in intelligent systems, especially in the medical field. Among mental problems, autism is a dangerous neurodevelopmental disorder and it needs to be diagnosed early because of the malleability of child brain development. In our study, we focused on autism detection by using the Vietnamese facial child image and studied the role of international data and Vietnamese data when applying deep learning approach to diagnose autism. To do that, we proposed different strategies based on our hypothesis about factors of the transfer learning and training set types. To conduct the experiment, we prepared a Vietnamese facial child image set from several kindergartens in Ho Chi Minh City, Vietnam and we applied different deep architectures such as ResNet, DenseNet, and AlexNet in the autism classification experiment with both Vietnamese and international facial child images. We analyzed important factors from the experiment results with area under the curve (AUC), accuracy, sensitivity, and specificity, including applying transfer learning and the appearance of Vietnamese data in the training set. Besides, we also discussed the difference of international and Vietnamese data domains. The exposure of data distribution differences in the proposed strategies also highlights the importance of collecting facial data of Vietnamese children.
Volume: 15
Issue: 5
Page: 4762-4773
Publish at: 2025-10-01

Enhancing diabetes prediction through probability-based correction: a methodological approach

10.11591/ijece.v15i5.pp4933-4941
Aitouhanni Imane , Berqia Amine
Predictive healthcare analytics demands accurate predictions from interpretable models for early diagnosis and intervention on diabetes prognosis, which remains a well-established challenge. This study presents a new probability-based correction method to enhance the performance of a model in diabetes prediction. Initial model comparisons are performed using the PyCaret framework to identify the baseline model. Logistic regression was selected due to its simplicity, interpretability, and its higher accuracy, which outperformed other models. To further facilitate future research in this field, this study was conducted using a noisy dataset without any changes or preprocessing steps other than those available in the dataset from the producer. This intentional decision meant that the new probability-based method could be evaluated in isolation without any additional modifications being applied. The proposed correction method adjusts predictions into borderline probability intervals to obtain more accurate classifications. This approach increased the model accuracy by 6% from 75% to 81%, thus proving successful in resolving the misclassification problem with higher risk. This approach outperforms state-of-the-art methods and demonstrates its generalizability in enhancing the certainty of downstream clinical decisions.
Volume: 15
Issue: 5
Page: 4933-4941
Publish at: 2025-10-01
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