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31,456 Article Results

Intrusion detection and prevention using Bayesian decision with fuzzy logic system

10.11591/ijece.v15i1.pp1200-1208
Satheeshkumar Sekar , Palaniraj Rajidurai Parvathy , Gopal Kumar Gupta , Thiruvenkadachari Rajagopalan , Chethan Chandra Subhash Chandra Basappa Basavaraddi , Kuppan Padmanaban , Subbiah Murugan
Nowadays, intrusion detection and prevention method has comprehended the notice to decrease the effect of intruders. denial of service (DoS) is an attack that formulates malicious traffic is distributed into an exacting network device. These attackers absorb with a valid network device, the valid device will be compromised to insert malicious traffic. To solve these problems, the Bayesian decision model with a fuzzy logic system based on intrusion detection and prevention (BDFL) is introduced. This mechanism separates the DoS packets based on the type of validation, such as packet and flow validation. The BDFL mechanism uses a fuzzy logic system (FLS) for validating the data packets. Also, the key features of the algorithm are excerpted from data packets and categorized into normal, doubtful, and malicious. Furthermore, the Bayesian decision (BD) decide two queues as malicious and normal. The BDFL mechanism is experimental in a network simulator environment, and the operations are measures regarding DoS attacker detection ratio, delay, traffic load, and throughput.
Volume: 15
Issue: 1
Page: 1200-1208
Publish at: 2025-02-01

Correlation between learning style, gender, and university location toward environmental problem-solving skills

10.11591/ijere.v14i1.30140
Rima Meilita Sari , Ridhwan Ridhwan , Tengku Muhammad Sahudra , Faiz Urfan
Environmental degradation in technological development is accelerating. Therefore, it requires environmental problem-solving skills. This study is aimed to determine university students’ learning style and skill to solve environmental problems based on gender and university location. In addition, this study also aims to find the correlation between learning styles, gender, and university location towards environmental problem-solving skills. The population of this study was the university student in Indonesia. This study employed a descriptive and correlational study. The researchers used the VARK test to find out the learning style. Meanwhile, the data on the environmental problem-solving skills collected by using a validated and tried out test instrument that refers to indicators from Ridhwan. The results of this study showed that the students’ learning styles tended to be visual at 50.49%. The environmental problem-solving skill was in the low category with an average score of 41.47. There was no any correlation between learning styles and gender with the ability to solve environmental problems, but in variable of university location, there was a significant correlation. This is because university differences are closely related to location differences and have an impact on access to learning facilities.
Volume: 14
Issue: 1
Page: 279-288
Publish at: 2025-02-01

Machine learning for the detection of soil pH, macronutrients, and micronutrients with crop and fertilizer recommendations

10.11591/ijai.v14.i1.pp439-446
John Joshua Montañez , Jeffrey Sarmiento
The study aims to determine the levels of soil parameters such as soil pH, macronutrients, and micronutrients. After determining said parameters, the system appropriately recommends crops and fertilizers suitable for the soil samples. For soil pH and macronutrient levels, i.e., nitrogen, phosphorus, and potassium, these parameters can be detected using the soil test kit. Meanwhile, for soil micronutrients, i.e., copper, iron, and zinc, there is a need for the development of appropriate assays for colorimetric processes that can be done for the appropriate determination of said micronutrients. Comparison of available machine learning such as support vector machine algorithm, naïve Bayes algorithms, and K-nearest neighbor algorithm is a must to determine the well-fit algorithm that is considered fast and has high predictive power in classification and regression. The outputs of the colorimetric and spectrometric processes are the inputs in the machine learning activities intended for crop and fertilizer recommendation.
Volume: 14
Issue: 1
Page: 439-446
Publish at: 2025-02-01

Enhancing routing efficiency in highway environments of vehicular ad hoc networks through fuzzy logic-based protocols

10.11591/ijece.v15i1.pp493-504
Mahmoud A. Al Shugran , Ahmad Abu-Al-Aish , Ghaith M. Jaradat , Fahad Ali Alghamdi , Jehad Saad Alqurni , Mutasem Khalil Alsmadi , Majd AL Hawamdeh , Hayat Alfagham , Usama A. Badawi , Mutaz Falah Gharaibeh
The predictive directional greedy routing (PDGR) protocol is widely utilized in highway settings within vehicular ad hoc networks (VANETs). However, PDGR encounters a notable challenge when packets lack a suitable vehicle directionally, leading to network disconnections. This triggers a shift to carry and forward recovery mode due to outdated neighbor information in the vehicle's neighbor table (VNT). To address this, our study proposes an improved fuzzy logic-based improved PDGR (IPDGR). This novel algorithm dynamically adjusts beaconing intervals based on real-time network dynamics. Through comprehensive evaluation using VANET simulators, IPDGR demonstrates superior performance compared to PDGR and directional greedy routing (DGR) protocols across various metrics including Inconsistency of vehicle's neighbor's table (IVNT), packet delivery ratio (PDR), routing path length (RPL), and number of hole problem occurrence (NHPO).
Volume: 15
Issue: 1
Page: 493-504
Publish at: 2025-02-01

A systematic analysis on machine learning classifiers with data pre-processing to detect anti-pattern from source code

10.11591/ijai.v14.i1.pp376-384
Nazneen Akhter , Afrina Khatun , Md. Sazzadur Rahman , A. S. M. Sanwar Hosen , Mohammad Shahidul Islam
Automatic detection of anti-patterns from source code can reduce software maintenance costs massively. Nowadays, machine learning approaches are very commonly used to identify anti-patterns. Hence, it is very crucial to choose a classifier that can be useful for detecting anti-patterns. This work aims to help practitioners to choose a suitable classifier to detect anti-patterns. In this paper, we highlight 16 classifiers in four different categories to detect anti-patterns. Furthermore, the performance of these classifiers is identified with the data pre-processing (DPP) to detect four commonly occurring anti-patterns from the three commonly used open-source Java projects’ source code. The accuracy of Dagging classifiers is 98.4%. Kernel logistic regression (KLR) also performs well i.e., 97%. In the case of time complexity, naive Bayes (NB), decision trees (DT), support vector machines (SVM), library for support vector machines (LibSVM), logistic, and LightGBM (LB) have less time complexity to build a model in all the projects.
Volume: 14
Issue: 1
Page: 376-384
Publish at: 2025-02-01

Enhancing traffic flow through multi-agent reinforcement learning for adaptive traffic light duration control

10.11591/ijai.v14.i1.pp500-515
Nada Faqir , Jaouad Boumhidi , Chakir Loqman , Youness Oubenaalla
This study addresses urban traffic congestion through deep learning for traffic signal control (TSC). In contrast to previous research on single traffic light controllers, our approach is tailored to the TSC challenge within a network of two intersections. Employing convolutional neural networks (CNN) in a deep Q-network (DQN) model, our method adopts centralized training and distributed execution (CTDE) within a multi-agent reinforcement learning (MARL) framework. The primary aim is to optimize traffic flow in a twointersection setting, comparing outcomes with baseline strategies. Overcoming scalability and partial observability challenges, our approach demonstrates the efficacy of the CTDE-based MARL framework. Experiments using urban mobility simulation (SUMO) exhibit a 68% performance enhancement over basic traffic light control systems, validating our solution across diverse scenarios. While the study focuses on two intersections, it hints at broader applications in complex settings, presenting a promising avenue for mitigating urban traffic congestion. The research underscores the importance of collaboration within MARL frameworks, contributing significantly to the advancement of adaptive traffic signal control (ATSC) in urban environments for sustainable transportation solutions.
Volume: 14
Issue: 1
Page: 500-515
Publish at: 2025-02-01

Assessment for learning practices among Malay language teachers in Malaysia National Type Chinese School

10.11591/ijere.v14i1.31404
Siti Aishah Wan Oya , Zaimuariffudin Shukri Nordin
This study investigates the issues Malay language teachers in Malaysia National Types Chinese School (SJKC) regarding the inconsistent application assessment for learning (AfL) practices, the persistence of traditional teachercentered, and the challenge of aligning AfL practices with curriculum standards and assessment criteria. These issues have a critical impact on the effectiveness of assessment, teaching, and learning. Given AfL’s potential to address educational challenges and enhance student outcomes, this study is highly relevant. To achieve this goal, a qualitative case study design approach, six participants were selected using the following criteria in purposive sampling, using data gathered from in-depth semi-structured interviews, informal conversations, lesson plans and assessment methods. Thematic analysis conducted using Atlas.ti, revealed three main themes: knowledge of AfL, AfL practice, and obstacles and challenges. The results show that most teachers primarily use belief AfL to improve student outcomes, but there are significant differences between beliefs and practices. Moreover, lack of knowledge which poses challenges due to shifts in curriculum standards and assessment administration in school. Given the results, suggestions embedding AfL principle into AfL practices to develop an objective framework for AfL and tracking second language learners’ language proficiency and progress, improving learning outcomes and language acquisition.
Volume: 14
Issue: 1
Page: 365-372
Publish at: 2025-02-01

Augmented reality board game in promoting financial literacy among Malaysia secondary school students

10.11591/ijere.v14i1.29032
Yin Yin Khoo , Saedah Siraj , Mohd Asri Mohd Noor , Wong Yoke Seng , Mohamad Rohieszan Ramdan , Fatimah Salwah Abd. Hadi
About two third of working millennials have not saved and only 5% of millennials have enough saving. This study aimed the aim of the study is to promote financial literacy among secondary students using board games. Besides that, this study also focuses to enhance students’ financial planning skills after using the board game. On the other hand, this study also investigates whether there is any gender difference between male and female students. The study employs the survey design. A total of 235 secondary school students aged between 15-16 years old from three different states in Malaysia was taken as samples in this study. A board game with augmented reality named FinPlan was used in this study. The study found that there was no difference between test score of financial literacy among male and female. However, there were significant between personal financial planning, practices, and interest towards financial literacy. Female students were outperformed than male students in each criteria. Conceptually, this study contributes to the development of the board game in financial literacy and give insight to the potential of mobile application with augmented reality. This learning method allows students to learn the daily situation that happen such as rewards, penalties and life event. It also brings a message to students, many daily activities that we need to spend money rather than saving the money; therefore, the students must plan the money properly.
Volume: 14
Issue: 1
Page: 472-481
Publish at: 2025-02-01

High body temperature detection solution through touchless machine for health monitoring

10.11591/ijai.v14.i1.pp166-171
Siddharth Swami , Lalit Mohan Joshi , Mohammed Ismail Iqbal , Meera Sharma , Amar Jeet Rawat , Sameer Dev Sharma , Rajesh Singh
The demand for reliable health monitoring systems has surged in today's health-conscious society. Body temperature monitoring is crucial for preserving health and preventing infectious disease outbreaks. In this study an Arduino uno hardware board with a touchless temperature sensor is proposed to detect elevated body temperature, indicating fever and early signs of illness. The system prioritizes real-time health surveillance, accessibility, and usability, blending seamlessly with normal life. Arduino's versatility allows the system to function covertly, uphold privacy and autonomy, and foster wellbeing. The goal is to highlight the system's ability to function covertly, uphold privacy and autonomy, and foster wellbeing. This technology exemplifies the synergy between personal wellness and contemporary technologies, offering a useful and adaptable fever detection solution for various contexts, including homes and public areas.
Volume: 14
Issue: 1
Page: 166-171
Publish at: 2025-02-01

Instructional leadership as a catalyst for organizational commitment: insights from a systematic literature review

10.11591/ijere.v14i1.30474
Cammellia Othman , Abdul Halim Busari
This research intends to provide insight on the relationship between instructional leadership and organizational commitment, with a specific emphasis on the timeframe spanning from 2009 to 2023. Using the preferred reporting items for systematic reviews and meta-analyses (PRISMA) approach, this study conducted a systematic search of academic databases, including Web of Science (WoS), Scopus, and ERIC. The search produced a large selection of papers, reviews, and articles about organizational commitment and instructional leadership. The analysis demonstrated the critical role that the impact of instructional leadership on organizational commitment played by using an advanced search strategy involving keywords, such as “instructional leadership,” “educational leadership,” “school leadership,” “organizational commitment,” “employee commitment,” and “workplace commitment.” The review identified three main themes from the final dataset, which included 33 items. These themes were: i) instructional leadership and teachers; ii) organizational commitment and school leadership styles; and iii) leadership in diverse educational contexts. The key themes were then validated by experts. The analysis reveals school leaders’ potential contributions to organizational commitment, which has consequences for educational leadership and management.
Volume: 14
Issue: 1
Page: 198-206
Publish at: 2025-02-01

Advancing integrity and privacy in cloud storage: challenges, current solutions, and future directions

10.11591/ijai.v14.i1.pp12-18
Shrinivasa Shrinivasa , Chandrakala Beturpalya Muddaraju , Annapurna Prashanth Patil
The rapid expansion of cloud computing has steered in an era where cloud storage is increasingly prevalent, offering significant advantages in terms of reducing local storage burden. However, this technological shift has also introduced complex security challenges, including data integrity and privacy concerns. In response to these challenges, various data integrity auditing (DIA) protocols have been developed, aiming to enable efficient and secure verification of data stored in cloud environments. This survey paper provides a comprehensive analysis of existing DIA mechanisms, focusing on methods like homomorphic linear authentication, dynamic hash tables, and watermarking techniques for integrity and privacy preservation. It critically evaluates these methods in terms of their advantages, limitations, and the unique challenges they face in practical applications, such as scalability, efficiency in multi-owner contexts, and real-time auditing. Furthermore, the paper identifies key research gaps, including the need for optimizing largescale data handling, balancing watermarking imperceptibility with embedding capacity, and developing comprehensive solutions for decentralized public auditing. The survey serves as a critical resource for researchers to understand the current background of cloud data integrity auditing and the future directions in this evolving field.
Volume: 14
Issue: 1
Page: 12-18
Publish at: 2025-02-01

An innovative and efficient approach for searching and selecting web services operations

10.11591/ijece.v15i1.pp827-835
Sara Rekkal , Kahina Rekkal
The marketing of web services on the internet continues to increase, resulting in an increasing number of web services and, therefore, operations offering equivalent functionalities. As a consequence, finding an appropriate web service (operation) for a particular task has become a difficult challenge, taking a lot of time and leading to an insufficient selection of relevant services. This work aims to propose a new approach facilitating the search and localization of relevant web services (operations) in an acceptable time while ensuring the totality of the response. This approach is divided into three crucial phases. The first step involves collecting web services from various universal description, discovery, and integration (UDDI) registries and different domains and forming specialized sub-registries. The second phase involves the extraction of operations from various services, followed by a similarity study whose goal is the formation of clusters of similar operations. The third phase processes user requests by identifying the desired features. A list of operations is then provided to the client, including the non-functional properties, from which they select the one that best meets their needs and begin to invoke it.
Volume: 15
Issue: 1
Page: 827-835
Publish at: 2025-02-01

Harnessing deep learning for medicinal plant research: a comprehensive study

10.11591/ijece.v15i1.pp908-920
Vidya Hullekere Ananda , Narasimha Murthy Madiwala Sathyanarayana Rao , Thara Dharmapura Krishnamurthy
In today’s world, people are more prone to diseases due to food adulteration and pollution in the environment, and people have found a way of using herbal medicine as an alternative to allopathic medicine, especially since coronavirus disease 2019 (COVID-19). Medicinal plants are the source of herbal medicines that increase the immunity of humans. Medicinal plants are used in many applications, like pharmaceuticals, cosmetics, and drugs. Medicinal plants are of great importance, and hence this work presents a review of the medicinal plants grown in Karnataka State, India. The work also highlights species identification and disease detection of medicinal plants employing machine learning and deep learning approaches. The paper provides information about datasets available for various medicinal plant leaf images. The deep learning models used for species identification and disease detection in medicinal plants have been discussed along with the results.
Volume: 15
Issue: 1
Page: 908-920
Publish at: 2025-02-01

Detection of diabetic retinopathy and classification of its stages by using convolutional neural network

10.11591/ijeecs.v37.i2.pp1284-1293
Sachin Gaur , Anirudh Kandwal , Bhaskar Pandey
Diabetes detection is pivotal in disease management and complication prevention. Traditional screening methods, like blood tests, are invasive and time-consuming. Deep learning has emerged as a non-invasive and automated alternative for diabetes detection. Convolutional neural networks (CNNs) excel in image analysis tasks, making them ideal for this purpose. This paper employs a CNN-based method for diabetes prediction using retinal images, utilizing the DenseNet169 architecture for feature extraction and diabetic retinopathy (DR) prediction. The APTOS 2019 blindness detection dataset from Kaggle, containing around 13,000 retinal images, is used for training. Pre-processing and normalization precede feature extraction, followed by the prediction of the DR stage. The model aims to classify retinal images into five stages of DR (0 to 4), ranging from no DR to proliferative DR. Our model achieved over 82% accuracy, outperforming advanced algorithms. Model evaluation includes accuracy, precision, recall, and F1 score measures.
Volume: 37
Issue: 2
Page: 1284-1293
Publish at: 2025-02-01

Education and smart technologies: towards a new pedagogical paradigm

10.11591/ijere.v14i1.30470
Amine Dehbi , Abdellah Bakhouyi , Al Mahdi Khaddar , Mohamed Talea
Smart education, a new field of technology related to education, has emerged as a unique response to current educational challenges. This is becoming increasingly important for academic progress and aligns with the transformative impact of technology. This study addresses the transformative impact of smart technologies on education, focusing on the integration of the internet of things, big data, and artificial intelligence. Through a bibliometric and content analysis based on Scopus and Web of Science databases, we identify the most active researchers, leading universities, and the countries that contribute most significantly to the field of smart education. The findings reveal a significant increase in related publications, highlighting the growing importance of these technologies in enhancing teaching and learning experiences. The study shows the advantages and challenges of adopting such technologies, providing insights into their practical applications and the future direction of educational innovations. Integrating smart technologies in education is crucial for improving quality of life and academic outcomes, necessitating further research and development to fully realize their potential. This research contributes to the understanding of technological impacts on education and supports the development of strategies for their effective implementation.
Volume: 14
Issue: 1
Page: 297-309
Publish at: 2025-02-01
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