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29,939 Article Results

Comparing machine learning models for Indonesia stock market prediction

10.11591/ijeecs.v38.i1.pp508-516
Selly Anastassia Amellia Kharis , Arman Haqqi Anna Zili , Maulana Malik , Wahyu Nuryaningrum , Agustiani Putri
The financial market hold a significant role in the economy and the ability to accurately predict stock prices poses a major challenge, particularly in volatile markets like Indonesia. This study investigates the application of three supervised machine learning algorithms: random forest (RF), support vector regression (SVR), K-nearest neighbor (KNN) to predict the closing prices of stocks. The data used in this research consists of BBCA, PWON, and TOWR stocks. This study adopted daily historical stock prices from March 2017 to February 2020, which were normalized and segmented into training and testing datasets. The models were trained using machine learning techniques, and their predictive accuracy was evaluated using root mean square error (RMSE) and mean absolute error (MAE). The historical stock data includes Open, High, Low, and Close prices. The result indicated that SVR consistently outperforms RF and KNN in terms of RMSE and MAE across different stocks. The SVR method produced RMSE values of 4.79% for BBCA stock, 10.61% for PWON stock, and 15.14% for TOWR stock, and produces MAE values of 3.52% for BBCA stock, 8.49% for PWON stock, and 13.78% for TOWR stock.
Volume: 38
Issue: 1
Page: 508-516
Publish at: 2025-04-01

Predicting peak demand for electricity consumption using time series data and machine learning model

10.11591/ijeecs.v38.i1.pp668-676
Suriya S. , Agusthiyar R.
Energy consumption is influenced by various factors, including the proliferation of electronic devices, technological advancements, economic growth, agricultural development, and population increase. Each of these factors contributes to the rising demand for energy. This paper addresses the challenge of predicting peak energy demand (ED) by utilizing historical time series data from the past five years, combined with temperature data from Tamil Nadu’s official sources. We employed feature engineering techniques to prepare the data for machine learning models, specifically XGBoost regressor, lasso, and ridge regression. The time series data was then analyzed using both univariate and multivariate models, including auto regressive integrated moving average (ARIMA) and vector autoregressive (VAR) models. The results show that our models can effectively forecast ED, providing critical insights for policymakers and stakeholders involved in energy planning and resource management.
Volume: 38
Issue: 1
Page: 668-676
Publish at: 2025-04-01

Enhancing attack detection in IoT through integration of weighted emphasis formula with XGBoost

10.11591/ijeecs.v38.i1.pp641-648
Januar Al Amien , Hadhrami Ab Ghani , Nurul Izrin Md Saleh , Soni Soni
This research addresses the challenge of detecting attacks in the internet of things (IoT) environment, where minority classes often go unnoticed due to the dominance of majority classes. The primary objective is to introduce and integrate the imbalance ratio formula (IRF) into the XGBoost algorithm, aiming to provide greater emphasis on minority classes and ensure the model's focus on attack detection, particularly in binary and multiclass scenarios. Experimental validation using the IoTID20 dataset demonstrates the significant enhancement in attack detection accuracy achieved by integrating IRF into XGBoost. This enhancement contributes to the consistent improvement in distinguishing attacks from normal traffic, thereby resulting in a more reliable attack detection system in complex IoT environments. Moreover, the implementation of IRF enhances the robustness of the XGBoost model, enabling effective handling of imbalanced datasets commonly encountered in IoT security applications. This approach advances intrusion detection systems by addressing the challenge of class imbalance, leading to more accurate and efficient detection of malicious activities in IoT networks. The practical implications of these findings include the enhancement of cybersecurity measures in IoT deployments, potentially mitigating the risks associated with cyber threats in interconnected smart environments.
Volume: 38
Issue: 1
Page: 641-648
Publish at: 2025-04-01

Flexible hybrid graphene-based NFC tag antenna for temperature monitoring application

10.11591/ijeecs.v38.i1.pp227-242
Najwa Mohd Faudzi , Ahmad Rashidy Razali , Asrulnizam Abd Manaf , Nurul Huda Abd Rahman , Ahmad Azlan Ab Aziz , Syed Muhammad Hafiz , Suraya Sulaiman , Nora’zah Abdul Rashid , Amirudin Ibrahim , Aiza Mahyuni Mozi
A hybrid graphene-based material, composed of reduced graphene oxide (rGO) and silver nanoparticle (AgNP), has been proposed for a near field communication (NFC) tag antenna with an integrated, flexible temperature monitoring circuit. The limited availability of high-conductivity graphene-based materials in the market has restricted the use of graphene in NFC tag applications. Therefore, this paper proposes a hybrid graphene-based composition featuring a high conductivity of 3.95×106 S/m. The feasibility of this material for NFC tags had not been validated previously, which is the main motivation for this research. The synthesis of the materials, along with the design, fabrication, and characterization of the NFC tag, is also presented. Results show that the inkjet-printed tag achieves a good reading range of up to 3 cm and demonstrates robustness against bending from 60⁰ to 190⁰, maintaining a maximum reading range of 1.3 cm. Performance on various materials, such as plastic, paper, and carton, also shows minimal impact on frequency shifting. Additionally, the graphene-based NFC tag integrates well with the temperature circuit, effectively monitoring temperatures in the 20-60 ⁰C range in real-time. This makes the developed tag suitable for applications such as food safety monitoring systems through NFC-integrated packaging.
Volume: 38
Issue: 1
Page: 227-242
Publish at: 2025-04-01

Experimental research on text CAPTCHA of fine-grained security features

10.11591/ijeecs.v38.i1.pp535-545
Qian Wang , Shafaf Ibrahim , Xing Wan , Zainura Idrus
CAPTCHA is a cybersecurity measure that distinguishes between humans and automated scripts. Researchers have employed various security features to thwart automated program identification by hackers. However, previous research on the attack resistance of CAPTCHAs has used roughly quantitative analysis instead of a fine-grain quantitative study. This study implemented comparative experiments based on CAPTCHA recognition algorithms to find the best-mixed security features. A multi-stage best parameter selection (MBPS) mechanism was proposed in this study. Experiment results indicated that mixed security features of “overlap + scale + rotate + bg (background)” were the best, with an average machine recognition accuracy of only 4.81%. The contrast experiment result illustrated that the anti-attack ability of mixed security features was better than adding adversarial noise, with machine recognition accuracy decreased by 2.2%. Moreover, by investigating the efficacy of security feature parameters, this study provides practical guidelines for designing robust CAPTCHAs. Furthermore, this study also presents valuable insights into the security of image generation technology.
Volume: 38
Issue: 1
Page: 535-545
Publish at: 2025-04-01

Enhanced hippopotamus optimization algorithm for power system stabilizers

10.11591/ijeecs.v38.i1.pp22-31
Widi Aribowo , Toufik Mzili , Aliyu Sabo
This article presents techniques for modifying the power system stabilizer's (PSS) parameters. An enhanced version of the hippocampal optimization algorithm (HO) is presented here. HO represents a novel approach in metaheuristic methodology, having been inspired by the observed clinging behavior in hippos. The notion of the HO is defined using a trinary-phase model that includes their position updates in rivers or ponds, defensive techniques against predators, and mathematically described evasive methods. To confirm the efficacy of the recommended approach, this article provides comparison simulations of the PSS objective function and transient response. This study employs validation through a comparison between Original HO and conventional methods. Simulation results demonstrate that, when compared to competing algorithms, the suggested approach yields optimal results and, in some cases, exhibits fast convergence. It is known that, in comparison to the original HO approach, the recommended way can lower the average undershoot of the rotor angel and speed by 12.049% and 26.97%, respectively.
Volume: 38
Issue: 1
Page: 22-31
Publish at: 2025-04-01

Multimodal perception for enhancing human computer interaction through real-world affect recognition

10.11591/ijeecs.v38.i1.pp428-438
Karishma Raut , Sujata Kulkarni , Ashwini Sawant
Human-Computer Interaction can benefit from real-world affect recognition in applications like healthcare and assistive robotics. Human express emotions through various modalities, with audio-visual being the most significant. Using a unimodal approach, such as only speech or visual, is challenging in natural, dynamic environments. The proposed methodology integrated a pretrained model with a convolution neural network (CNN) to provide a robust initialization point and address the limited availability of facial expression data. The multimodal framework enhances discriminative power by combining visual scores with speech. This work addresses the challenges at each stage of the real-world affect recognition framework, including data preprocessing, feature extraction, feature fusion, and final classification. A 1D-CNN is employed for training on spectral and prosodic audio features, while deep visual features are processed using a 2D-CNN. The proposed system's performance was evaluated on the extended Cohn-Kanade (CK+), acted-facial-expressions in-the-wild (AFEW), and real-world-affective-face-database (RAF) datasets, which are commonly used in face recognition research. Experimental results indicate that 2% to 5% of visual data from natural settings were undetected, and the inclusion of the audio modality improved performance by providing relevant and supplementary information.
Volume: 38
Issue: 1
Page: 428-438
Publish at: 2025-04-01

Strategic Deployment of EV Charging Infrastructure: An In-Depth Exploration of Optimal Location Selection and CC-CV Charging Strategies

10.11591/ijict.v14i1.pp259-267
Debani Prasad Mishra , Pranav Swaroop Nayak , Aman Kumar , Surender Reddy Salkuti
The continued expansion of the electric vehicle (EV) market necessitates strategic planning for the placement of charging stations to ensure efficient access and utilization of electric infrastructure. This paper presents a comprehensive review of the critical factors in optimizing the selection of EV charging station locations, along with the implementation of Constant Current-Constant Voltage (CC-CV) charging models. The study addresses the challenges and opportunities in identifying the most effective locations for charging stations to accommodate the growing demand for sustainable transportation. Furthermore, it examines the benefits of adopting CC-CV charging models to improve the charging process, achieving a balance between charging speed and battery longevity. Through this analysis, the review aims to provide valuable insights to stakeholders involved in the development and expansion of EV charging infrastructure, thereby supporting the transition to a more sustainable and extensive electric mobility ecosystem.
Volume: 14
Issue: 1
Page: 259-267
Publish at: 2025-04-01

A hybrid combination of improved mayfly optimization based modified perturb and observe for solar based water pumping system

10.11591/ijeecs.v38.i1.pp50-62
Dattatray Surykant Sawant , Yerramreddy Srinivasa Rao , Rajendra Ramchandra Sawant
In recent years, solar water pumping systems (WPS) have been fuel-free and environmentally beneficial because they have gained a lot of attention in the agricultural and industrial sectors. Traditional water pumps consume higher amount of energy which make it as frequently unreliable, low efficiency and needs high maintenance. For WPS applications, Brushless DC (BLDC) motors are far superior options than other induction motors because of their high efficiency, high dependability, and low maintenance needs. Thus, in this research, the major goal is to develop a more efficient, reliable, and maintenance-free solar WPS solution. This paper describes a sensorless control strategy that reduces the need for hall sensors and increases system’s overall reliability. Solar system power is typically impacted by partial shadowing and cannot reach the maximum available power because the traditional perturbed and observe (P&O) algorithm fails. This paper integrates the modified P&O (MP&O) algorithm with an improved mayfly optimization (IMO) name called IMO-MP&O to address these issues by efficiently extracts the maximum power from solar. From the results, it clearly shows that IMO-MP&O achieved higher efficiency of 99.58% than the existing P&O MPPT which is analyzed the MATLAB sim-power-system toolboxes.
Volume: 38
Issue: 1
Page: 50-62
Publish at: 2025-04-01

Fake review detection using enhanced ensemble support vector machine system on e-commerce platform

10.11591/ijeecs.v38.i1.pp478-485
Seenia Joseph , S. Hemalatha
Due to the quick growth of online marketing transactions, including buying and selling, fake reviews are created to promote the product market and mislead new customers. E-commerce customers can post reviews and comments on the goods or services they obtained. Before making a purchase, new customers frequently read the feedback and comments posted on the website. Nowadays customers find it very difficult to identify whether the reviews are fake or not, but doing so is essential. So, it's very crucial to develop an online spam detection system to help both consumers and producers in their decision-making. The reviewer's behaviour and important review characteristics can help you identify fake reviews. The importance of this study is to develop a fake review detection system on e-commerce platforms using an enhanced ensemble support vector machine system in which the Euclidean distance is replaced with the Mahalanobis distance metric. Review texts collected from Amazon and Yelp were given as input data sets into the constructed model and classified as fake or real.
Volume: 38
Issue: 1
Page: 478-485
Publish at: 2025-04-01

Automated handwriting analysis and personality attribute discernment using self-attention multi-resolution analysis

10.11591/ijeecs.v38.i1.pp649-656
Yashomati R. Dhumal , Arundhati A. Shinde , Roshnadevi Jaising Sapkal , Satish Bhairannawar
Handwritten document analysis is a method used in academia that examines the patterns and strokes of a person’s handwriting in order to get a deeper understanding of that person’s personality and character. In spite of the fact that there are a number of models and methods that may be used in the investigation of automated graphology, there are a few challenges that need to be solved. Among these challenges is the identification of efficient classification techniques that provide the highest possible degree of accuracy. Within the scope of this study, we propose automated handwriting analysis and personality attribute discernment using self-attention multi-resolution analysis (MRA) where the data is preprocessed using histogram equalization and the spurious line segment section is attached to the genuine line segment portion in order to segment the succeeding line from the authentic picture of the document. A deep dense network is combined with self-attention MRA in order to provide a novel approach to the investigation of authentic handwritten text. Using the most recent and cutting-edge standards that are currently in use, an evaluation is performed to determine whether or not the proposed strategy is feasible. It is observed that the proposed method obtained nearly 98% accuracy with precision of 99%.
Volume: 38
Issue: 1
Page: 649-656
Publish at: 2025-04-01

Boosting industrial internet of things intrusion detection: leveraging machine learning and feature selection techniques

10.11591/ijai.v14.i2.pp1232-1241
Lahcen Idouglid , Said Tkatek , Khalid Elfayq
The rapid integration of industrial internet of things (IIoT) technologies into Industry 4.0 has revolutionized industrial efficiency and automation, but it has also exposed critical vulnerabilities to cyber threats. This paper delves into a comprehensive evaluation of machine learning (ML) classifiers for detecting anomalies in IIoT environments. By strategically applying feature selection techniques, we demonstrate significant enhancements in both the accuracy and efficiency of these classifiers. Our findings reveal that feature selection not only boosts detection rates but also minimizes computational demands, making it a cornerstone for developing resilient intrusion detection systems (IDS) tailored for Industry 4.0. The insights garnered from this study pave the way for deploying more robust security frameworks, safeguarding the integrity and reliability of IIoT infrastructures in modern industrial settings.
Volume: 14
Issue: 2
Page: 1232-1241
Publish at: 2025-04-01

Identification of ocular disease from fundus images using CNN with transfer learning

10.11591/ijeecs.v38.i1.pp613-621
Fatima Zohra Berrichi , Abderrahim Belmadani
Eye diseases are one of the serious health problems affecting human life. Detecting and diagnosing them early is critical to prompt treatment and preventing vision loss. However, all studies in the field of eye disease classification using machine learning models are limited to the detection of single diseases, and the accuracy rate is still low in multi-class systems. In this study, we propose a multi-class classification model using four pre-trained CNNs (DenseNet121, ResNet50, EfficientNetB3 and VGG16). The model classified eye diseases into four categories: diabetic retinopathy, cataract, glaucoma, and normal. To improve the training process, another data augmentation technique is applied to increase the amount of data. The performance metrics of the system are calculated using the confusion matrix. DenseNet-121 shows excellent performance in retinal disease classification in 30 epochs of training, with training and test accuracy reaching 99.97% and 96.21% respectively. The implementation of this system should be considered as a very useful means to help ophthalmologists to rapid and precision detection of various eye diseases in the future.
Volume: 38
Issue: 1
Page: 613-621
Publish at: 2025-04-01

Link adaptation techniques for throughput enhancement in LEO satellites: a survey

10.11591/ijeecs.v38.i1.pp262-271
Habib Idmouida , Khalid Minaoui Minaoui
In addition to the rapid geometric change of low earth orbit (LEO) satellites, the earth-to-space channel suffers from various attenuations that affect the communication link. To overcome this challenge, the link adaptation technique emerges as a key solution to optimize the transmission performance of LEO satellites, especially the data throughput. The existing contributions in the literature remain scattered across the research board, and a comprehensive survey of this research area still lacks at this stage. The present survey examines various link adaptation methods, mainly variable coding and modulation, adaptive coding and modulation, and hybrid methods using artificial intelligence. In addition, this study explains how this technique leverages a set of recommended standards and cost-effective technologies, such as software-defined radio (SDR) and field programmable gate arrays (FPGA), to fine-tune transmission strategies. Lastly, the paper provides a comparative study of the current research on this field and sheds light on future directions, where the need for higher data throughput makes emerging learning-based techniques and new experimental standards a necessity.
Volume: 38
Issue: 1
Page: 262-271
Publish at: 2025-04-01

Optimizing FBMC/OQAM: Hermite filter and DFT-based precoding for PAPR reduction

10.11591/ijeecs.v38.i1.pp76-87
Anupriya Anupriya , Vikas Nandal
In the ever-evolving landscape of wireless communication, there is a persistent quest for modulation schemes that optimize spectral efficiency, reduce interference, and enhance overall system performance. This paper introduces a novel modulation technique that synergistically improves on the strengths of filter bank multi-carrier (FBMC). A distinctive feature of our approach is the deployment of the Hermite prototype filter in the FBMC system, diverging from traditional FBMC architectures. An advanced precoding strategy leveraging a pruned discrete fourier transform (pDFT) paired with scaling is also introduced. This combination promises reduced inter-symbol interference and heightened spectral efficiency. As the management of the peak-to-average power ratio (PAPR) is a significant challenge in FBMC systems to addressing this iterative particle swarm optimization (IPSO) algorithm is proposed. Evaluations are carried out to demonstrate the efficiency of the proposes scheme in reducing PAPR substantially for FBMC/OQAM framework. Experiments are conducted and comparisons are performed among several prominent multicarrier modulation schemes. The results from the experiments indicate that the application of IPSO algorithm with Hermite functions and applied to an FBMC/OQAM system using pruned DFT has been successful in reducing the PAPR also a 6-13% decrease in error rate has been shown across varying QAM orders regardless of SNR level.
Volume: 38
Issue: 1
Page: 76-87
Publish at: 2025-04-01
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