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31,456 Article Results

Advancing network security: a comparative research of machine learning techniques for intrusion detection

10.11591/ijece.v15i2.pp2271-2281
Shynggys Rysbekov , Abylay Aitbanov , Zukhra Abdiakhmetova , Amandyk Kartbayev
In the current digital era, the advancement of network-based technologies has brought a surge in security vulnerabilities, necessitating complex and dynamic defense mechanisms. This paper explores the integration of machine learning techniques within intrusion detection systems (IDS) to tackle the intricacies of modern network threats. A detailed comparative analysis of various algorithms, including k-nearest neighbors (KNN), logistic regression, and perceptron neural networks, is conducted to evaluate their efficiency in detecting and classifying different types of network intrusions such as denial of service (DoS), probe, user to root (U2R), and remote to local (R2L). Utilizing the national software laboratory knowledge discovery and data mining (NSL-KDD) dataset, a standard in the field, the study examines the algorithms’ ability to identify complex patterns and anomalies indicative of security breaches. Principal component analysis is utilized to streamline the dataset into 20 principal components for data processing efficiency. Results indicate that the neural network model is particularly effective, demonstrating exceptional performance metrics across accuracy, precision, and recall in both training and testing phases, affirming its reliability and utility in IDS. The potential for hybrid models combining different machine learning (ML) strategies is also discussed, highlighting a path towards more robust and adaptable IDS solutions.
Volume: 15
Issue: 2
Page: 2271-2281
Publish at: 2025-04-01

Enhancing predictive modelling and interpretability in heart failure prediction: a SHAP-based analysis

10.11591/ijict.v14i1.pp11-19
Niaz Ashraf Khan , Md. Ferdous Bin Hafiz , Md. Aktaruzzaman Pramanik
Predictive modelling plays a crucial role in healthcare, particularly in forecasting mortality due to heart failure. This study focuses on enhancing predictive modelling and interpretability in heart failure prediction through advanced boosting algorithms, ensemble methods, and SHapley Additive exPlanations (SHAP) analysis. Leveraging a dataset of patients diagnosed with cardiovascular diseases (CVD), we employed techniques such as synthetic minority over-sampling technique (SMOTE) and bootstrapping to address class imbalance. Our results demonstrated exceptional predictive performance, with the gradient boosting (GBoost) model achieving the highest accuracy of 91.39%. Ensemble techniques further enhanced performance, with the voting classifier (VC), stacking classifier (SC), and Blending achieving accuracies of 91.00%. SHAP analysis uncovered key features such as time, Serum_creatinine, and Ejection_fraction, significantly impacting mortality prediction. These findings highlight the importance of transparent and interpretable machine learning models in healthcare decision-making processes, facilitating informed interventions and personalized treatment strategies for heart failure patients.
Volume: 14
Issue: 1
Page: 11-19
Publish at: 2025-04-01

Teaching learning based optimization algorithm for effective analysis of power quality using dynamic voltage restorer

10.11591/ijict.v14i1.pp268-275
Soumya Ranjan Das , Surender Reddy Salkuti
In this study, the load voltage is dynamically restored utilising the dynamic voltage restorer (DVR) using the voltage injection approach. The injected voltage is generated using a voltage-source inverter (VSI), which is necessary to correct for the utility network's sag and swell characteristics voltage. The restoration process is dependent on the condition and quality of the utility system, i.e., it injects energy into the external system for the duration of voltage sag, and during voltage swell, energy is absorbed by the compensator from the external system, causing an rise in dc link voltage, which is connected across the VSI. In this study two different controllers are employed based on a learning based optimized algorithm. The simulation results are shown using two different controllers and the performance of the proposed controller is found to be a better one.
Volume: 14
Issue: 1
Page: 268-275
Publish at: 2025-04-01

A unique YOLO-based gated attention deep convolution network-Lichtenberg optimization algorithm model for a precise breast cancer segmentation and classification

10.11591/ijece.v15i2.pp1670-1685
Vinoth Rathinam , Sasireka Rajendran , Valarmathi Krishnasamy
A novel you only look once (YOLO)-based gated attention deep convolution network (GADCN) classification algorithm is developed and utilized in this present study for the detection of breast cancer. In this framework, contrast enhancement-based histogram equalization is applied initially to produce the normalized breast image with reduced noise artifacts. Then, the breast region is accurately segmented from the preprocessed images with low complexity and segmentation error using the YOLO-based attention network model. To diagnose breast cancer with better accuracy, the GADCN model is used to predict the exact class of image (i.e., benign or malignant). During classification, the activation function is optimally computed with the use of the Lichtenberg optimization algorithm (LOA). It aids in achieving improved classification performance with little complexity in training and assessment. The significance of the present study includes the use of a unique, YOLO-based GADCN-LOA model that helps in the prediction of breast cancer with higher accuracy. It was observed that the model exhibited 99% accuracy for the datasets utilized. In addition, the selected model outperforms well with sensitivity, specificity, precision, and F1-score. Hence the proposed model could be exploited for the diagnosis of breast cancer at an early stage to enable preventive care.
Volume: 15
Issue: 2
Page: 1670-1685
Publish at: 2025-04-01

An adaptive audio wave steganography using simulated annealing algorithm

10.11591/ijece.v15i2.pp2237-2253
Atef Ahmed Obeidat , Mohmmed Jazi Bawaneh , Sawsan Yousef Abu Shqair , Hamdi A. Al-Omari , Emad Fawzi Al-shalabi
The science of information security has increased in importance to encounter the espionage and information theft. This research proposes a new steganography framework that utilizes simulated annealing (SA) as an artificial intelligence algorithm to support the process of hiding a binary secret message file within an audio wave file. The best path for embedding the secret data inside the audio file is determined through SA that searches for the preferred path according to the content of the host audio file and secret message to be hidden. The least significant bit (LSB) technique was employed to hide message bytes, in which each audio-chosen byte will hold one bit from a secret message byte. The hiding process constructs the stego audio file and extraction key that will be required in an extraction process. The authorized user requires an extraction key and a decryption key to retrieve the hidden message. On the other hand, the attacker requires knowledge of the aforementioned keys and working algorithms that were employed in the hidden process. Robustness against data extraction, detection, imperceptibility (phonological hearing), security, peak signal to noise ratio (PSNR), mean square error (MSE) and capacity as security performance measures were used to evaluate the system. The maximum size of the data to be hidden may reach 12.5% of the data size of the host audio file, in which the average value of MSE and PSNR are (0.0041, 74.73), respectively.
Volume: 15
Issue: 2
Page: 2237-2253
Publish at: 2025-04-01

Tackling the anomaly detection challenge in large-scale wireless sensor networks

10.11591/ijece.v15i2.pp2479-2490
Tamara Zhukabayeva , Aigul Adamova , Lazzat Zholshiyeva , Yerik Mardenov , Nurdaulet Karabayev , Dilaram Baumuratova
One of the areas of ensuring the security of a wireless sensor network (WSN) is anomaly detection, which identifies deviations from normal behavior. In our paper, we investigate the optimal anomaly detection algorithms in a WSN. We highlight the problems in anomaly detection, and we also propose a new methodology using machine learning. The effectiveness of the k-nearest neighbor (kNN) and Z Score methods is evaluated on the data obtained from WSN devices in real time. According to the experimental study, the Z Score methodology showed a 98.9% level of accuracy, which was much superior to the kNN 43.7% method. In order to ensure accurate anomaly detection, it is crucial to have access to high-quality data when conducting a study. Our research enhances the field of WSN security by offering a novel approach for detecting anomalies. We compare the performance of two methods and provide evidence of the superior effectiveness of the Z Score method. Our future research will focus on exploring and comparing several approaches to identify the most effective anomaly detection method, with the ultimate goal of enhancing the security of WSN.
Volume: 15
Issue: 2
Page: 2479-2490
Publish at: 2025-04-01

Maximum expansion with contiguity constraints scheduling algorithm: enhancing uplink transmission in long-term evolution vehicular environments

10.11591/ijece.v15i2.pp1709-1719
Shafinaz Ismail , Shajahan Maidin , Darmawaty Mohd Ali , Mohd Kamarulnizam Abdul Rahim
Uplink scheduling has become increasingly important due to increased activities like uploading videos, photos, and file sharing. Many users share or stream live videos and engage with social networks, significantly increasing uplink data traffic volume. Single carrier frequency division multiple access (SC-FDMA) is favored for its power efficiency and high data rates, benefiting user equipment (UE) battery life. However, maintaining the contiguity of resource blocks (RBs) poses challenges in uplink scheduling. The maximum expansion with contiguity constraints (MECC) algorithm has been introduced to address this challenge. MECC prioritizes contiguity, fairness, and throughput for users at the cell edge. The algorithm operates in two phases: initially allocating RBs proportionally and assigning RBs with the highest metrics while ensuring contiguity. Performance evaluation of MECC, conducted under conditions simulating vehicular movement at 30 km/h, demonstrates its superiority over other algorithms. MECC provides high fairness and throughput for both real-time (RT) and non-real-time (NRT) traffic, making it the preferred scheduler for ensuring quality of service (QoS) for both traffic types. Its focus on contiguity, fairness, throughput, and spectral efficiency establishes MECC as a valuable tool for optimizing uplink transmission in mobile networks, addressing the evolving needs of users in today's digital landscape.
Volume: 15
Issue: 2
Page: 1709-1719
Publish at: 2025-04-01

Enhancing engineering education through virtual reality: a systematic study on immersive engineering education practices

10.11591/ijece.v15i2.pp1889-1899
Tarek Riaji , Sanae El Hassani , Fatima Ezzahrae M'hamdi Alaoui
This article explores the integration of virtual reality (VR) and associated technologies in engineering education, focusing on the pedagogical approaches adopted in this integration, which we refer to as immersive engineering education. This study considers the application possibilities and the transformative impact of VR on engineering education. The article addresses the critical collection and analysis of VR applications in engineering education. It covers main VR-related papers published from 2015 to February 2024 and indexed in Scopus, Web of Sciences, or both, and discussing design, development challenges, and collaborative tools. Empirical evidence showcases improved engagement, motivation, and learning outcomes. The findings offer modern insights for educators and researchers on leveraging VR for impactful learning experiences, while also noting the need for further research in this evolving field.
Volume: 15
Issue: 2
Page: 1889-1899
Publish at: 2025-04-01

Classification of brain stroke based on susceptibility-weighted imaging using machine learning

10.11591/ijece.v15i2.pp1602-1611
Shaarmila Kandaya , Norhashimah Mohd Saad , Abdul Rahim Abdullah , Ezreen Farina Shair , Ahmad Sobri Muda , Muhammad Izzat Ahmad Sabri
Magnetic resonance imaging (MRI) is used to identify brain disorders, particularly strokes. Rapid treatment, often referred to as "time is brain," is emphasized in recent studies, stressing the significance of early intervention within six hours of stroke onset to save lives and enhance outcomes. The traditional manual diagnosis of brain strokes by neuroradiologists is both subjective and time-intensive. To tackle this challenge, this study introduces an automated method for classify brain stroke from MRI images based on pre- and post-stroke patients. The technique employs machine learning, with a focus on susceptibility weighted imaging (SWI) sequences, and involves four stages: preprocessing, segmentation, feature extraction, classification and performance evaluation. The paper proposes classification and performance evaluation to determine stroke region according to three types of categories, those are poor improvement, moderate improvement and good improvement stroke patients based on pre and post patients. Then, performance evaluation is verified using accuracy, sensitivity and specificity. Results indicate that the hybrid support vector machine and bagged tree (SVMBT) yields the best performance for stroke lesion classification, achieving the highest accuracy which is 99% and showing significant improvement for stroke patients. In conclusion, the proposed stroke classification technique demonstrates promising potential for brain stroke diagnosis, offering an efficient and automated tool to assist medical professionals in timely and accurate assessments.
Volume: 15
Issue: 2
Page: 1602-1611
Publish at: 2025-04-01

An innovative Arabic light stemmer developed using a hybrid approach

10.11591/ijece.v15i2.pp2356-2363
Driss Namly , Karim Bouzoubaa
Our study introduces an innovative light stemming tool tailored for Arabic morphology challenges. In conformance with the templatic and concatenative structures, our stemmer utilizes a combination of clitic stripping, lexicon-based, and statistical disambiguation techniques to ensure accurate stemming. To accomplish this, we rely on our clitic rules lexicon to detect all potential combinations of clitics for each input entry. Subsequently, we depend on an extensive lexicon of over 7 million stems to verify the potential stems. Lastly, we employ a statistical model to ascertain the most likely stem based on the sentence's context. Experimental results demonstrate the effectiveness of the proposed stemmer in comparison with existing ones. Using different datasets, our stemmer achieves higher accuracy and F1 scores, highlighting its efficiency in Arabic stemming tasks.
Volume: 15
Issue: 2
Page: 2356-2363
Publish at: 2025-04-01

A novel scalable deep ensemble learning framework for big data classification via MapReduce integration

10.11591/ijai.v14.i2.pp1386-1400
Kesavan Mettur Varadharajan , Josephine Prem Kumar , Nanda Ashwin
Big data classification involves the systematic sorting and analysis of extensive datasets that are aggregated from a variety of sources. These datasets may include but are not limited to, electronic records, digital imaging, genetic information sequences, transactional data, research outputs, and data streams from wearable technologies and connected devices. This paper introduces the scalable deep ensemble learning framework for big data classification (SDELF-BDC), a novel methodology tailored for the classification of large-scale data. At its core, SDELF-BDC leverages a Hadoop-based map-reduce framework for feature selection, significantly reducing feature-length and enhancing computational efficiency. The methodology is further augmented by a deep ensemble model that judiciously applies a variety of deep learning classifiers based on data characteristics, thereby ensuring optimal performance. Each classifier's output undergoes a rigorous optimization-based ensemble approach for refinement, utilizing a sophisticated algorithm. The result is a robust classification system that excels in predictive accuracy while maintaining scalability and responsiveness to the dynamic requirements of big data environments. Through a strategic combination of classifiers and an innovative reduction phase, SDELF-BDC emerges as a comprehensive solution for big data classification challenges, setting new benchmarks for predictive analytics in diverse and data-intensive domains.
Volume: 14
Issue: 2
Page: 1386-1400
Publish at: 2025-04-01

Data analysis and visualization on titanic and student’s performance datasets-an exploratory study

10.11591/ijict.v14i1.pp68-76
Seong-Cheol Kim , Surender Reddy Salkuti , Alka Manvayalar Suresh , Madhu Sree Sankaran
Exploratory data analysis (EDA) is all about exploring the data in order to identify any underlying pattern before you try to use it to make a predictive model. It also plays a major role in the data discovery process as it is used to analyze data and to recapitulate their different characteristics, which is displayed efficiently with the help of data visualization methods. This paper aims to identify errors in the dataset, to understand the existing hidden structure and to identify new ones, to detect points in a dataset that deviate to a greater extent from the collected data (outliers), and also to find any relationship or intersection between the variables and constants. Two datasets are used namely ‘Titanic’ and ‘student’s performance’ to perform data analysis and ‘data visualization’ to depict ‘exploratory data analysis’ which acts as an important set of tools for recognizing a qualitative understanding. The datasets were explored and hence it assisted with identifying patterns, outliers, corrupt data, and discovering the relationship between the fields in the dataset.
Volume: 14
Issue: 1
Page: 68-76
Publish at: 2025-04-01

Enhancing spatiotemporal weather forecasting accuracy with 3D convolutional kernel through the sequence to sequence model

10.11591/ijece.v15i2.pp2022-2030
Renaldy Fredyan , Karli Eka Setiawan , Kelvin Asclepius Minor
Accurate weather forecasting is important when dealing with various sectors, such as retail, agriculture, and aviation, especially during extreme weather events like heat waves, droughts, and storms to prevent disaster impact. Traditional methods rely on complex, physics-based models to predict the Earth's stochastic systems. However, some technological advancements and the availability of extensive satellite data from beyond Earth have enhanced meteorological predictions and sent them to Earth's antennae. Deep learning models using this historical data show promise in improving forecast accuracy to enhance how models learn the data pattern. This study introduces a novel architecture, convolutional sequence to sequence (ConvSeq2Seq) network, which employs 3D convolutional neural networks (CNN) to address the challenges of spatiotemporal forecasting. Unlike recurrent neural network (RNN)--based models, which are time-consuming due to sequential processing, 3D CNNs capture spatial context more efficiently. ConvSeq2Seq overcomes the limitations of traditional CNN models by ensuring causal constraints and generating flexible length output sequences. Our experimental results demonstrate that ConvSeq2Seq outperforms traditional and modern RNN-based architectures in both prediction accuracy and time efficiency, leveraging historical meteorological data to provide a robust solution for weather forecasting applications. The proposed architecture outperforms the previous method, giving new insight when dealing with spatiotemporal with high density.
Volume: 15
Issue: 2
Page: 2022-2030
Publish at: 2025-04-01

Evaluating ChatGPT’s Mandarin “yue” pronunciation system in language learning

10.11591/ijai.v14.i2.pp1634-1641
Yoke Lian Lau , Swee Mee Tan , Anna Lynn Abu Bakar , Zi Xian Yong , Zi Hong Yong , Ernahwatikah Nasir , Chen Jung Ku
By incorporating voice control technology into ChatGPT, it becomes possible to engage in conversations or dialogues with individuals who are actively engaged in the process of acquiring language skills. Our study team conducted a modest experiment to evaluate the efficacy of a voice control feedback system in facilitating the mastery of the most challenging pronunciation of the Mandarin syllable "yue". The objective of this study is to evaluate the effectiveness of voice-controlled ChatGPT in aiding learners to acquire accurate pronunciation of the Mandarin phoneme "yue". Furthermore, the study seeks to investigate the methods utilised by the ChatGPT model in identifying and distinguishing the word "yue" when it is used alone or in combination with "ye" and "yi". We employed many testing approaches, including single-word instances, paired instances, and the integration of phrases. In addition, we evaluated the model's ability to accurately detect the term "yue" in short sentences and, ultimately, in a longer sentence.
Volume: 14
Issue: 2
Page: 1634-1641
Publish at: 2025-04-01

Nonlinear regression analysis to predict mandibular landmarks on panoramic radiographs

10.11591/ijece.v15i2.pp2098-2108
Nur Nafiiyah , Ayu Ismi Hanifah , Edy Susanto , Eha Renwi Astuti , Chastine Fatichah , Ramadhan Hardani Putra , Agus Subhan Akbar
An automatic system for determining mandibular landmark points on panoramic radiography can reduce errors due to differences in expert professionalism and save time. Previous research has shown that the linear regression method is ineffective at predicting condyle and gonion landmark points in panoramic radiography. So, this research proposes an analysis of nonlinear regression methods (support vector machine (SVM) kernel=‘polynomial’, polynomial regression, ensemble regression) for predicting condyle and gonion landmark points. There are four predicted landmark points, namely the right condyle, left condyle, right gonion, and left gonion. The nonlinear regression methods used are SVM, polynomial regression, and ensemble regression. The Dental and Oral Hospital, within the Faculty of Dentistry at Universitas Airlangga, provides the research data. The research encompasses 119 patients between the ages of 19 and 70, dividing 103 into training and 16 into testing. The research results show that the SVM method is only good at predicting the right condyle point with a mean radial error (MRE) of 4,724 pixels. Meanwhile, to predict the left condyle, right gonion, and left gonion points, it is better to use the polynomial regression method and ensemble regression with an order of success detection rate (SDR) of 37.5%, 18.75%, and 12.5%, respectively.
Volume: 15
Issue: 2
Page: 2098-2108
Publish at: 2025-04-01
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