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25,002 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

Development and analysis of symmetric encryption algorithm

10.11591/ijece.v15i2.pp1900-1911
Ardabek Khompysh , Dilmukhanbet Dyusenbayev , Muratkhan Maxmet
This paper introduces a new block encryption algorithm designed for the cryptographic protection of data. The paper introduces and explains a newly devised exponentiation modulo (EM) transform method, utilized to obtain the S-block, an essential element within the presented algorithm. A method of optimizing the choice of keys and increasing the efficiency of calculation was also used. It is proposed that incorporating characteristics of cryptographic primitives functioning within the Galois field into the algorithm can lead to favorable outcomes. To increase the encryption algorithm's speed, non-positional polynomial notation systems and a working base index table are used. The paper discusses the implementation of an encryption algorithm in C++ and examines the statistical characteristics of the resulting ciphertexts. For experimental testing of statistical safety, a set of statistical tests by National Institute of Standards and Technology (NIST) and D. Knuth was used. Furthermore, the resulting S-box was examined using linear, differential, and algebraic cryptanalysis techniques. In the future, this proposed S-box will be implemented in the encryption algorithm being developed for the preliminary encryption of confidential data.
Volume: 15
Issue: 2
Page: 1900-1911
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

Enhancing mathematics learning in phase E: assessing Wordwall effectiveness

10.11591/ijere.v14i2.30051
Sri Rezeki , Sindi Amelia
The use of technology, classroom atmosphere, facilities, and learning resources can support quality learning outcomes in students. Wordwall, as a gamification tool, has been proven to be effective for elementary and junior high school students in mathematics. However, the effectiveness of Wordwall in enhancing senior high school students’ cognitive abilities in mathematics learning has not been investigated. Previous studies have only shown its effectiveness in improving affective abilities. Therefore, this study endeavors to evaluate the effects of using Wordwall on the mathematics learning outcomes of senior high school students in phase E. Through quasi-experimental research with pre- and post-test group design, 38 experimental class students and 37 control class students were selected as samples in this study. The study found a statistically significant difference (sig. 0.000<0.05) in the mean learning outcomes of students who used Wordwall compared to those who did not. Descriptively, the experimental group displayed superior average mathematics learning outcomes compared to the control group, demonstrating a moderate level of effectiveness (ES=0.57). The strong effect of Wordwall can be realized if it is used not only as an exercise tool within the classroom but also as an instrument for knowledge transformation, incorporating consideration of students’ learning styles.
Volume: 14
Issue: 2
Page: 1246-1252
Publish at: 2025-04-01

Effectiveness of an author’s program for psychopedagogical support in the development of metacognitive abilities

10.11591/ijere.v14i2.30526
Anar Popandopulo , Ainash Kudysheva , Nazgul Kudarova , Berik Matayev , Samal Antikeyeva
In the context of the updated educational system emphasizing autonomy and critical thinking among students, it is necessary to develop effective approaches to support and enhance students’ metacognitive abilities. This research aims to evaluate the effectiveness of a proprietary psychological and pedagogical support program designed to develop metacognitive abilities in schoolchildren. Methods included conducting a quasi-experimental study with control and experimental groups, utilizing the metacognitive awareness assessment questionnaire (MAAQ) to assess outcomes. The study involved 184 students (grades 7-9; M=14.40; SD=0.81) from a school in Kazakhstan, where the experimental group underwent the intervention program. Statistical analysis revealed significant improvements in metacognitive abilities among students in the experimental group compared to the control group, including enhancements in self-awareness, self-regulation, critical thinking, decision-making, and problem-solving. These findings affirm the feasibility of integrating metacognitive approaches into educational programs and suggest further avenues for research in pedagogical support and student development.
Volume: 14
Issue: 2
Page: 1183-1195
Publish at: 2025-04-01

International atmosphere impact on faculty engagement in internationalization: international attitudes mediation

10.11591/ijere.v14i2.30235
Liu Hai Yan , Yang Su Ping
Globalization continues to reshape higher education, driving increased international exchanges and collaboration. This study investigates the impact of the international atmosphere on faculty engagement in internationalization (FEI), with a focus on local applied universities in China. Despite the recognized importance of faculty in facilitating internationalization efforts, limited research exists on their involvement. Utilizing surveys and structural equation modeling, data was collected from faculty members in local applied universities in China. The survey instrument covered demographics, perceptions of the international environment, engagement in international activities, and attitudes towards internationalization results indicate significant positive correlations between the international atmosphere, faculty international attitudes (FIA), and engagement in international activities. Specifically, the international atmosphere positively influences FIA and subsequent engagement in internationalization. Furthermore, FIA were found to partially mediate the relationship between the international atmosphere and engagement. This study contributes to the understanding of faculty involvement in internationalization efforts, addressing a gap in the literature. By identifying the factors influencing faculty engagement, institutions can develop targeted strategies to promote global engagement in higher education, ultimately enhancing the internationalization process.
Volume: 14
Issue: 2
Page: 1004-1012
Publish at: 2025-04-01

D-RAKE compression for enhanced internet of things data management in air quality monitoring

10.11591/ijece.v15i2.pp2468-2478
Kartika Sari , Rahmi Hidayati
This study addresses the issue of air pollution in Pontianak, marked by high levels of pollutant particles and chemical compounds that cause respiratory health risks. The research involves essential air quality monitoring using various sensors for temperature, humidity (DHT22), O2 (MQ-135), CO (MQ-7), CO2 (MG-811), and dust (GP2Y1010AU0F), collected real-time, leading to a notable increase in data volume. Due to limitations in internet of things (IoT) devices, there is a need for integration between cloud and IoT through data transmission to reduce the communication time and memory usage. The escalation in sensor data volume requires a lossless compression technique to ensure efficient storage without sacrificing crucial information. Compression plays a vital role in overcoming complex storage challenges, facilitating real-time data access for monitoring, and contributing to sustainable efforts to improve air quality in Pontianak. This research applies the D-RAKE compression method based on basic counting procedures with minimal memory requirements, cost-effective, low-speed microcontrollers commonly used in IoT devices. Despite its simplicity, simulation results indicate that the D-RAKE algorithm outperforms well-established compression methods such as gzip, bzip2, and rar, particularly for data sequences with sparse elements. Moreover, when applied to real- world data, D-RAKE achieves superior compression ratios compared to IoT-focused compression techniques.
Volume: 15
Issue: 2
Page: 2468-2478
Publish at: 2025-04-01

Underwater energy harvesting model for agricultural applications using stochastic network calculus

10.11591/ijece.v15i2.pp2031-2041
S. R. Vignesh , Rajeev Sukumaran
Underwater wireless sensor network (UWSN) is a specialized type of wireless sensor network (WSN) designed for underwater communication among sensor nodes deployed in oceans for monitoring purposes such as observing marine life, detecting pollutants, and keeping track of oceanographic conditions. Managing limited energy in harsh underwater environments presents unique challenges compared to terrestrial networks. This research addresses this challenge by developing a reliable energy harvesting model. It analyzes the effects of delay and energy storage constraints on the energy harvesting rate (EHR), a measure of the energy replenished over time to maintain sensor node operations. It quantifies the amount of energy that can be harvested and stored within a given period, which is crucial for sustaining the network's functionality. The study includes analyzing and simulating the model analytically using discrete event simulators to evaluate delay performance bounds. Simulation results indicate that larger packet sizes require a higher minimum EHR, while stricter delay requirements decrease it for a fixed arrival rate.
Volume: 15
Issue: 2
Page: 2031-2041
Publish at: 2025-04-01

Application of machine learning methods to analysis and evaluation of distance education

10.11591/ijece.v15i2.pp2172-2180
Ainur Mukhiyadin , Manargul Mukasheva , Ulzhan Makhazhanova , Aislu Kassekeyeva , Gulmira Azieva , Zhanat Kenzhebayeva , Alfiya Abdrakhmanova
In recent decades, distance learning has become an essential component of the modern educational system, providing students with flexibility and access to knowledge regardless of location. This paper discusses creating a hybrid machine-learning model for assessing the quality of distance learning based on survey data. The model combines two feature extraction methods: Term frequency-inverse document frequency (TF-IDF) and Word2Vec. Combining these methods allows for a more complete and accurate representation of text data, improving the quality of machine learning models. The study aims to develop and evaluate the effectiveness of the proposed hybrid model for analyzing survey data and assessing the quality of distance learning. The paper considers the tasks of collecting and preprocessing text data, experimentally comparing various feature extraction methods and their combinations, training and evaluating a machine learning model based on a combination of TF-IDF and Word2Vec features, as well as analyzing the results and assessing the effectiveness of the proposed model using various metrics. In conclusion, the prospects for further development and application of the proposed model in educational institutions to improve the quality of distance learning are discussed.
Volume: 15
Issue: 2
Page: 2172-2180
Publish at: 2025-04-01

Analysis of cryptographic methods for ensuring security in the field of internet of things

10.11591/ijeecs.v37.i3.pp1596-1606
Temirbekova Zhanerke Erlanovna , Abdiakhmetova Zukhra Muratovna , Tynymbayev Sakhybay
The number of internet of things (IoT) devices continues to grow, and so do the associated concerns regarding their security and privacy. Evaluating the efficacy of cryptographic solutions within IoT systems emerges as a crucial endeavor to uphold the integrity and reliability of these systems. Amidst the rapid evolution of IoT technology, safeguarding the confidentiality, integrity, and availability of data emerges as a top priority. This article underscores the significance of deploying robust cryptographic algorithms to fortify IoT devices against a myriad of potential threats. Effective evaluation of cryptographic solutions within IoT systems entails a comprehensive analysis and comparison of diverse algorithms, coupled with an assessment of their performance, resilience against attacks, and resource utilization. Central to evaluating the effectiveness of cryptographic solutions within IoT systems is a consideration of various factors including computational complexity, power consumption of devices, ease of implementation, and compatibility with existing infrastructures. This article reviews a number of cryptographic solutions including Rivest–Shamir–Adleman (RSA), El-Gamal, Paillier. These algorithms are implemented on the ATmega2560 microcontroller, which allows for a comprehensive assessment of key parameters such as efficiency in terms of encryption and decryption time, power consumption, and memory usage of IoT devices.
Volume: 37
Issue: 3
Page: 1596-1606
Publish at: 2025-03-01

Quantitation of new arbitrary view dynamic human action recognition framework

10.11591/ijeecs.v37.i3.pp1797-1803
Anh-Dung Ho , Huong-Giang Doan
Dynamic action recognition has attracted many researchers due to its applications. Nevertheless, it is still a challenging problem because the diversity of camera setups in the training phases are not similar to the testing phases, and/or the arbitrary view actions are captured from multiple viewpoints of cameras. In fact, some recent dynamic gesture approaches focus on multiview action recognition, but they are not resolved in novel viewpoints. In this research, we propose a novel end-to-end framework for dynamic gesture recognition from an unknown viewpoint. It consists of three main components: (i) a synthetic video generation with generative adversarial network (GAN)-based architecture named ArVi-MoCoGAN model; (i) a feature extractor part which is evaluated and compared by various 3D CNN backbones; and (iii) a channel and spatial attention module. The ArVi-MoCoGAN generates the synthetic videos at multiple fixed viewpoints from a real dynamic gesture at an arbitrary viewpoint. These synthetic videos will be extracted in the next component by various three-dimensional (3D) convolutional neural network (CNN) models. These feature vectors are then processed in the final part to focus on the attention features of dynamic actions. Our proposed framework is compared to the SOTA approaches in accuracy that is extensively discussed and evaluated on four standard dynamic action datasets. The experimental results of our proposed method are higher than the recent solutions, from 0.01% to 9.59% for arbitrary view action recognition.
Volume: 37
Issue: 3
Page: 1797-1803
Publish at: 2025-03-01

Utilizing logistic regression in machine learning for categorizing social media advertisement

10.11591/ijeecs.v37.i3.pp1954-1963
Hari Gonaygunta , Geeta Sandeep Nadella , Karthik Meduri
The purpose of this paper is to investigate the use of logistic regression in machine learning to distinguish the types of social media advertisements. Since the logistic regression algorithm is designed to classify data with a target variable that has categorical results, it is the one selected. As a result, this research intends to measure the efficiency of logistic regression for the classification of social media advertisements. This research centers on the social media advertisements dataset and employs logistic regression for classification purposes. The model is evaluated against performance metrics to measure the extent to which it can categorize social media advertisements. As a result, the findings of this study show that logistic regression is fit for classifying social media advertisements. Logistic regression is important for machine learning when it comes to classifying social media advertisements because it supports categorizing advertisements according to their characteristics and precisely predicts the categorical results.
Volume: 37
Issue: 3
Page: 1954-1963
Publish at: 2025-03-01

Sustainable supply chain modeling: a review based on the application of the system dynamics approach

10.11591/ijeecs.v37.i3.pp1637-1649
Julia Kurniasih , Zuraida Abal Abas , Siti Azirah Asmai , Agung Budhi Wibowo
Sustainable supply chains, evolving with supply chain 5.0 revolution, are crucial for achieving sustainable development goals (SDGs) by balancing economic growth, environmental protection, and social responsibility. They help reduce environmental impacts, promote ethical labor practices, and ensure financial viability. Sustainable supply chains involve complex interactions and external influences. The system dynamics approach effectively captures these intricate interactions through feedback loops and non-linear relationships. This review seeks to identify issues in modeling sustainable supply chains using system dynamics and offer insights for developing sustainable, flexible, responsive, and resilient models. This paper reviews literature from 2020 to 2023 using thematic analysis. It examines dynamics, behaviors, management, sustainability strategies, decision-making, and future directions for sustainable supply chain modeling. The findings suggest that a comprehensive framework can enhance management practices, support policymaking, and promote sustainability. Integrated risk management is essential for resilient, adaptable supply chains, while financial viability and scalability are essential for the widespread adoption of sustainability practices. Understanding the roles of various actors and integrating supply chain components can improve support systems, and exploring green energy, technology adoption, and consumer behavior can advance sustainability goals. Future research should also better integrate sustainability aspects and explore a broader range of literature for deeper insights.
Volume: 37
Issue: 3
Page: 1637-1649
Publish at: 2025-03-01

Tomato leaf disease detection using Taguchi-based Pareto optimized lightweight CNN

10.11591/ijeecs.v37.i3.pp1772-1784
Bappaditya Das , C. S. Raghuvanshi
The prospect of food security becoming a global danger by 2050 due to the exponential growth of the world population. An increase in production is indispensable to satisfy the escalating demand for food. Considering the scarcity of arable land, safeguarding crops against disease is the best alternative to maximize agricultural output. The conventional method of visually detecting agricultural diseases by skilled farmers is time-consuming and vulnerable to inaccuracies. Technology-driven agriculture is an integral strategy for effectively addressing this matter. However, orthodox lightweight convolutional neural network (CNN) models for early crop disease detection require fine-tuning to enhance the precision and robustness of the models. Discovering the optimal combination of several hyperparameters might be an exhaustive process. Most researchers use trial and error to set hyperparameters in deep learning (DL) networks. This study introduces a new systematic approach for developing a less sensitive CNN for crop leaf disease detection by hyperparameter tuning in DL networks. Hyperparameter tuning using a Taguchi-based orthogonal array (OA) emphasizes the S/N ratio as a performance metric primarily dependent on the model’s accuracy. The multi-objective Pareto optimization technique accomplished the selection of a robust model. The experimental results demonstrated that the suggested approach achieved a high level of accuracy of 99.846% for tomato leaf disease detection. This approach can generate a set of optimal CNN models’ configurations to classify leaf disease with limited resources accurately.
Volume: 37
Issue: 3
Page: 1772-1784
Publish at: 2025-03-01

Approach for modelling and controlling of autonomous cruise control system through machine learning algorithms

10.11591/ijeecs.v37.i3.pp1532-1542
R. Kiruba , S. Prince Samuel , N. Kavitha , K. Srinivasan , V. Radhika
Automated cruise control installation is one of the utmost significant phases in the auto industry's pursuit of autonomous vehicles. The controller of choice is one of the key factors in determining whether a design will be durable and cost-effective. The model-based controller and a cutting-edge algorithmic optimization method are both presented inside the framework of this proposed study. The suggested controller may achieve the desired characteristics of the design, including a faster rise time, a faster settle time, a smaller peak overshoot, and a smaller steady-state error. A MATLAB-executed and -simulated system model using a control method based on a hybrid genetic algorithm and reinforcement learning has been used to effectively and automatically regulate the vehicle's velocity in compliance with all design parameters.
Volume: 37
Issue: 3
Page: 1532-1542
Publish at: 2025-03-01
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