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31,456 Article Results

Computational modelling under uncertainty: statistical mean approach to optimize fuzzy multi-objective linear programming problem with trapezoidal numbers

10.11591/ijece.v15i6.pp5708-5716
Arti Shrivastava , Bharti Saxena , Ramakant Bhardwaj , Aditya Ghosh , Satyendra Narayan
This study presents a comprehensive approach to solving fuzzy multi-objective linear programming problems (FMOLPP) under uncertainty using trapezoidal fuzzy numbers. The authors propose a novel integration of Yager’s ranking method, the Big-M optimization technique, and Chandra Sen’s statistical mean methods to effectively convert fuzzy objectives into crisp values and optimize them. The methodology allows for managing multiple fuzzy objectives by ranking and aggregating them using various statistical means such as arithmetic, geometric, quadratic, harmonic, and Heronian averages. The model is implemented using TORA software and demonstrated through a detailed numerical example. The results validate the robustness and practicality of the proposed approach, showcasing consistent optimal solutions across all statistical methods. This research significantly enhances decision-making processes in uncertain environments by offering a structured, computationally efficient solution strategy for complex real-world optimization problems.
Volume: 15
Issue: 6
Page: 5708-5716
Publish at: 2025-12-01

Advancements in electric vehicle safety and charging infrastructure

10.11591/ijaas.v14.i4.pp1332-1339
Debani Prasad Mishra , Rudranarayan Senapati , Nisha Kedia , Sanchita Sahay , Raj Alpha Swain , Surender Reddy Salkuti
In electric vehicles (EVs), safety measures must be taken to prevent dangerous accidents. Safety regulations must be in place for two important things: electric or EV batteries and EV equipment. Operating an electric vehicle charging stations (EVCS) is a challenging task. This holistic approach is used to evaluate when renewable energy is produced. It's best to focus on the popularity of EVs as more and more people choose this mode of transportation. It is important to know that power plants can be risky. Therefore, safety issues related to EV charging must be addressed quickly and appropriately. Potential safety issues with EVs include overcurrent, ground faults, and overheating. If the charging system does not work, the electric car's battery may heat up and catch fire, and overcharging may cause other problems. To avoid security risks, you must comply with security regulations, use payment devices that meet security requirements, and follow the manufacturer's instructions.
Volume: 14
Issue: 4
Page: 1332-1339
Publish at: 2025-12-01

Generative adversarial network for intelligent haze removal from high quality images

10.11591/ijaas.v14.i4.pp1340-1349
Ali Abdulazeez Mohammed Baqer Qazzaz , Hayfaa T. Hussein , Shroouq J. Al-janabi , Yousif Mudhafar
Suspended atmospheric particulates like haze, mist, and fog greatly degrade captured images, creating considerable challenges for computer vision applications operating in safety-sensitive areas such as autonomous driving, surveillance, and remote sensing. In this paper, we treat the important challenge of single-image haze removal by proposing a novel and robust conditional generative adversarial network (cGAN)-based framework. The proposal utilizes a U-Net-based generator with self-attention and skip connections to preserve spatial fidelity, and a PatchGAN discriminator to enforce local realism. At the heart of our contribution is a carefully weighted multi-component loss function that applies reconstruction, perceptual, edge, structural similarity (SSIM), and adversarial losses to optimize pixel-level accuracy and perceptual quality. We trained and evaluated our proposal on the large-scale real-world LMHaze dataset. Experimental results demonstrate state-of-the-art performance with a peak signal-to-noise ratio (PSNR) of 33.42 dB and SSIM of 0.9590. Our qualitative and comparative analyses further support our claims by assessing our proposed model's capacity to recover clear and artifact-free images from hazy images - outperforming the existing methods on this challenging real-world benchmark.
Volume: 14
Issue: 4
Page: 1340-1349
Publish at: 2025-12-01

An ensemble machine learning based model for prediction and diagnosis of diabetes mellitus

10.11591/ijece.v15i6.pp5347-5359
Moataz Mohamed El Sherbiny , Asmaa Hamdy Rabie , Mohamed Gamal Abdel Fattah , Ali Elsherbiny Taki Eldin , Hossam El-Din Mostafa
Diabetes mellitus (DM) is a chronic metabolic disorder that poses significant health risks and global economic burdens. Early prediction and accurate diagnosis are crucial for effective management and treatment. This study presents an ensemble machine learning-based model designed to predict and diagnose Diabetes Mellitus using clinical and demographic data. The proposed approach integrates multiple machine learning algorithms, including random forest (RF), extreme gradient boosting (XGB), and logistic regression (LR), to leverage their individual strengths and enhance the entire performance. The ensemble model was trained and validated on multiple comprehensive datasets. Performance measures demonstrate the robustness of proposed model and its reliability in distinguishing diabetic cases from non-diabetic cases after applying several preprocessing steps. This work ensures the capability of machine learning in advancing healthcare by providing efficient, data-driven tools for diabetes management, aiding clinicians in early diagnosis, and contributing to personalized treatment strategies. Comparative analysis against standalone models highlights the superior predictive capabilities of the ensemble approach. Results had shown that ensemble model achieved an accuracy of 96.88% and precision of 89.85% outperforming individual classifiers.
Volume: 15
Issue: 6
Page: 5347-5359
Publish at: 2025-12-01

Enhancing semantic segmentation with a boundary-sensitive loss function: a novel approach

10.11591/ijece.v15i6.pp5327-5335
Ganesh R. Padalkar , Madhuri B. Khambete
Semantic segmentation is crucial step in autonomous driving, medical imaging, and scene understanding. Traditional approaches leveraging manually extracted pixel properties and probabilistic models, have achieved reasonable performance but suffer from limited generalization and the need for expert-driven feature selection. The rise of deep learning architectures has significantly improved segmentation accuracy by enabling automatic feature extraction and capturing intricate object details. However, these methods still face challenges, including the need for large datasets, extensive hyperparameter tuning, and careful loss function selection. This paper proposes a novel boundary-sensitive loss function, which combines region loss and boundary loss, to enhance both region consistency and edge delineation in segmentation tasks. Implemented within a modified SegNet framework, the approach proposed in the paper is evaluated with the semantic boundary dataset (SBD) dataset using standard segmentation metrics. Experimental results indicate improved segmentation accuracy, substantiating to proposed method.
Volume: 15
Issue: 6
Page: 5327-5335
Publish at: 2025-12-01

Intuitive effectiveness degree of research methodologies for spectrum sensing in cognitive radio network

10.11591/ijece.v15i6.pp5699-5707
Pushpa Yellappa , Dr.Keshavamurthy Keshavamurthy
The phenomenon of spectrum sensing plays an essential role in cognitive radio network (CRN) that is performed in real-time for better adaptability to dynamic usage of spectrum. However, efficient decision-making is often noted to be affected by dynamic environmental condition, interference, and noise leading to declination in performance. In recent times, there are proposals for various methodologies addressing such issues targeting towards improving spectrum sensing along with machine learning and energy detection approach, which is gaining its pace for technical research implementation. Irrespective of this advancement, ambiguity shrouds regarding the contrast effectiveness associated with these methods and their appropriateness in different situation. Hence, this manuscript presents a comprehensive and yet crisp review work to offer concise assessment of latest methodologies towards spectrum sensing used in CRN ecosystem. The paper has an inclusion of existing techniques, presents their potentials and shortcomings, exhibited evolving trends of research, extracts key gaps and challenges. The prime intention of this review work is towards guiding the future researchers and scholars by facilitating deeper insight towards the recent state of technologies in spectrum sensing.
Volume: 15
Issue: 6
Page: 5699-5707
Publish at: 2025-12-01

Design and experimental validation of a microstrip Vivaldi antenna-based system for breast tumor detection

10.11591/ijece.v15i6.pp5497-5505
Samiya Qanoune , Hassan Ammor , Zakaria Er-Reguig , Zouhair Guennoun
Breast cancer remains one of the leading causes of death among women worldwide, highlighting the critical need for accurate, non-invasive, and cost-effective diagnostic solutions. In light of this, microwave imaging has surfaced as a promising alternative to conventional diagnostic methods. This approach leverages its capability to differentiate between healthy and cancerous tissues by examining their dielectric properties. This study presents the design, implementation, and experimental assessment of a Vivaldi antenna-based system aimed at breast cancer detection. The antenna is designed to operate within the ultra-wideband frequency range, which facilitates high-resolution imaging and effective deep tissue penetration. Data collected from tissue-mimicking phantoms reveals the system’s proficiency in identifying anomalies, showcasing a significant contrast between malignant and normal tissue regions. We analyze various performance metrics, including signal reflection, penetration depth, and imaging resolution to substantiate the system's efficacy. The results underline the significant potential of Vivaldi antennas in improving early- stage breast cancer detection, thus contributing to advancements in microwave imaging technology.
Volume: 15
Issue: 6
Page: 5497-5505
Publish at: 2025-12-01

A systematic review of heuristic and meta-heuristic methods for dynamic task scheduling in fog computing environments

10.11591/ijece.v15i6.pp5986-6000
Hamed Talhouni , Noraida Haji Ali , Farizah Yunus , Saleh Atiewi , Yazrina Yahya
The distributed fog node network and variable workloads make task distribution difficult in fog computing. Optimizing computing resources for dynamic workloads with heuristic and metaheuristic algorithms has shown potential. To address changing workloads, these algorithms enable real-time decision-making. This systematic review examines heuristic, meta-heuristic, and real-time dynamic job scheduling strategies in fog computing. Static methods like heuristic and meta-heuristic algorithms can help modify dynamic task scheduling in fog computing situations. This paper covers a current study area that stresses real-time approaches, meta-heuristics, and fog computing environments' dynamic nature. It also helps build reliable and scalable fog computing systems by spotting dynamic task scheduling trends, patterns, and issues. This study summarizes and analyzes the latest fog computing research on task-scheduling algorithms and their pros and cons to adequately address their issues. Fog computing task scheduling strategies are detailed and classified using a technical taxonomy. This work promises to improve system performance, resource utilization, and fog computing settings. The work also identifies fog computing job scheduling innovations and improvements. It reveals the strengths and weaknesses of present techniques, paving the way for fog computing research to address unresolved difficulties and anticipate future challenges.
Volume: 15
Issue: 6
Page: 5986-6000
Publish at: 2025-12-01

Exploring feature engineering and explainable AI for phishing website detection: a systematic literature review

10.11591/ijece.v15i6.pp5863-5878
Norah Alsuqayh , Abdulrahman Mirza , Areej Alhogail
Detecting phishing websites is a rapidly evolving field aimed at identifying and mitigating cyberattacks targeting individuals, organizations, and governments. Ongoing progress in artificial intelligence (AI) has the potential to revolutionize phishing detection by enhancing model accuracy and improving transparency through eXplainable AI (XAI). However, significant challenges remain, particularly in integrating feature engineering with XAI to address sophisticated phishing strategies including zero-day attacks, that evade traditional detection mechanisms. To overcome these challenges, this examines the impact of feature engineering and XAI in phishing detection, emphasizing their ability to enhance accuracy while providing interpretability. By integrating feature extraction with interpretable models, these techniques improve decision-making transparency and system robustness. This paper presents the first systematic literature review (SLR) focusing on the impact of feature engineering and XAI on state-of-the-art phishing detection approaches. Additionally, it identifies critical research gaps and challenges, including scalability issues, the evolution of phishing techniques, and balancing complexity with interpretability. The findings provide valuable academic insights while offering practical recommendations for developing accurate and interpretable phishing detection systems, aiding organizations in strengthening cybersecurity measures.
Volume: 15
Issue: 6
Page: 5863-5878
Publish at: 2025-12-01

Augmented reality for ancient attractions

10.11591/ijece.v15i6.pp5717-5727
Numtip Trakulmaykee , Katchaphon Janpetch , Patchanee Ladawong , Atitaya Khamouam
The study focuses on augmented reality (AR) understanding, development and evaluation. For evaluation, this paper assesses the role of multimedia types in perceived enjoyment, and investing in how perceived usefulness, ease-of-use, and enjoyment affect the adoption of AR by tourists. A quantitative approach was employed to collect data from 115 participants who experienced an AR application designed for 14 ancient attractions in Songkhla, Thailand. The multimedia content included 3D models, historical videos, drone videos, billboard navigations, and text animations. Structural equation modeling (SEM) was used to test the proposed relationships. The findings revealed that perceived ease-of-use and enjoyment significantly influence behavioral intention (BI) as significant factors at 0.01, while perceived usefulness did not affect BI in the context of ancient attractions. Moreover, the multimedia types directly impacted the perceived enjoyment at a significant level of 0.05, and indirectly impacted BI. This study contributes to the theoretical understanding of AR adoption in tourism by integrating multimedia types with tourist perceptions and BI. Practically, it provides insights for designing AR applications that enhance visitor engagement and satisfaction in heritage tourism.
Volume: 15
Issue: 6
Page: 5717-5727
Publish at: 2025-12-01

Development of a leakage detection and alert system for liquefied petroleum gas via a mobile application

10.11591/ijaas.v14.i4.pp1099-1110
Wael A. Salah , Anees Abu Sneineh
Nowadays, ensuring the comfort and safety of house users is a top priority, and this may be accomplished by implementing smart technology to lead a convenient and safe life. Leakage of liquefied petroleum gas (LPG), which is mostly utilized in the home kitchen for cooking, is one of the frequent risks. Using a gas sensing device, a gas control system, and wireless communication units, the goal of this study is to create an LPG gas leakage warning and management system to prevent the gas from exploding by detecting the leak. When LPG gas is brought near the sensor, it detects the leakage and the buzzer is activated by activating the audio-visual alarm and closing the gas cylinder valve. The system also generates alert messages and sends them to the fire station when the LPG gas leakage has reached a critical level. Testing results of the proposed LPG leakage system show a satisfactory performance of the developed device with a quick response to LPG gas leakage. In addition, powerful audio and visual alarms are activated. An immediate message was sent to homeowners and the fire station department regarding the leakage incident to prevent the risk of gas leakage.
Volume: 14
Issue: 4
Page: 1099-1110
Publish at: 2025-12-01

Influence of potassium bromide phosphor on optical properties of white light-emitting diodes

10.11591/ijaas.v14.i4.pp1359-1366
Pham Hong Cong , Nguyen Thi Phuong Loan , Nguyen Doan Quoc Anh , Hsiao-Yi Lee
Conventional phosphor-converted light-emitting diodes (LEDs) using silicone binders often suffer from yellowing, moisture degradation, and limited spectral tunability, restricting their performance in high-power street lighting. To overcome these limitations, this study aims to develop an advanced LED illumination system integrating a KBr-doped sol-gel/silica phosphor with total internal reflection (TIR) lenses and a reflective housing, encapsulated by an atomic layer deposition (ALD)-coated minilens panel. The sol-gel matrix, synthesized from MTEOS, TEOS, and silica granules, was engineered to achieve uniform KBr particle dispersion, reduced thermal quenching, and improved chromatic stability. The ALD laminate provides an additional moisture and heat barrier, sealing micro-defects and minimizing stress-induced cracking. Optical performance was quantitatively assessed using Monte Carlo beam-tracking simulations under various street configurations, including focal, zigzag, and single-plane pole layouts. Results demonstrated enhanced luminous efficacy, precise glare control, and high uniformity in street illumination. Overall, this integrated sol-gel/ALD LED design effectively addresses the durability and color instability problems of traditional silicone systems, offering a scalable and energy efficient solution for next-generation street lighting.
Volume: 14
Issue: 4
Page: 1359-1366
Publish at: 2025-12-01

Investigating relationships between reading comprehension and oral reading fluency through AI-driven tool reading progress

10.11591/ijaas.v14.i4.pp1192-1199
Pham Duc Thuan , Pham Thi Tam
This study investigates the relationship between reading comprehension and oral reading fluency components—accuracy and rate—among 113 Vietnamese EFL university students using the AI-powered tool Microsoft Reading Progress. Over 14 weeks, students engaged in weekly oral reading and comprehension tasks using integrated Microsoft Teams features. Fluency metrics (accuracy and rate) and comprehension scores were automatically collected and analyzed using Pearson correlation. The results revealed weak but statistically significant positive correlations between reading comprehension and accuracy (r = .257, p < .01), and between comprehension and rate (r = .289, p < .01), suggesting that improvements in fluency modestly support comprehension. A strong correlation between accuracy and rate (r = .765, p < .01) was also observed. The study highlights the effectiveness of Reading Progress in capturing fluency data and promoting self-paced improvement. However, limitations such as the short duration, localized sample, and constraints of accent recognition in AI-based speech analysis affect the generalizability and validity of results. The findings support the pedagogical integration of AI tools in EFL instruction while calling for future research with larger samples, extended timelines, and diversified digital tools to further validate and expand on these results.
Volume: 14
Issue: 4
Page: 1192-1199
Publish at: 2025-12-01

Hybrid artificial intelligence approach to counterfeit currency detection

10.11591/ijece.v15i6.pp5804-5814
Monther Tarawneh
The use of physical money continues, posing ongoing challenges in the form of counterfeit money. This problem not only poses a threat to economic stability but also undermines confidence in the financial systems in use. Traditional methods such as manual inspections and testing of security features have become ineffective in detecting advanced counterfeiting techniques on an ongoing basis. This study proposes a hybrid model that harnesses the power of artificial intelligence, combining convolutional neural networks (CNNs), long short-term memory networks (LSTMs), and support vector machines (SVMs) for counterfeit detection. The proposed model leverages the diverse strengths of a number of artificial intelligence techniques, combining the ability to detect counterfeiting, analyse visual aspects, and sequences of banknotes. The proposed model was tested using real Jordanian currency sets of different denominations and datasets generated using generative adversarial networks (GANs). The results showed that the model was able to detect counterfeiting with high accuracy of 98.6%. and minimal errors compared to other methods. This outstanding performance demonstrates the benefits of integrating artificial intelligence (AI) technologies and that there is room for development and solutions that can keep up with advanced counterfeiting strategies. The study demonstrates the importance of integrating AI in maintaining the integrity of physical currency transactions.
Volume: 15
Issue: 6
Page: 5804-5814
Publish at: 2025-12-01

AI-integrated pharmacy systems: bridging technology, ethics, and patient care

10.11591/ijaas.v14.i4.pp1305-1321
Adi El-Dalahmeh , Nevien Nedal , Khulood Abu Maria , Sara Abu Tarboosh
The operation of pharmacy systems undergoes transformation through artificial intelligence (AI), which advances from manual procedures to intelligent adaptive tools. These technologies enhance daily operations through prescription verification, drug interaction alerts, and inventory management while decreasing human mistakes. Through AI, patients gain access to customized medication recommendations, automatic appointment alerts, and virtual support services. The advancement of technology creates multiple new difficulties for healthcare systems. The increasing integration of AI in healthcare creates growing concerns about data privacy alongside algorithmic bias and the requirement for decision-making explanations. This paper evaluates AI systems against conventional pharmacy methods through an assessment of their precision and speed and their impact on patient safety and ethical preparedness. The adoption of AI systems requires strong ethical protections together with defined regulatory frameworks to maintain human clinical decision-making authority in patient care.
Volume: 14
Issue: 4
Page: 1305-1321
Publish at: 2025-12-01
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