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31,456 Article Results

Robust deep learning approach for accurate detection of brain tumor and analysis

10.11591/ijece.v15i3.pp3226-3237
Lanke Pallavi , Thati Ramya , Singupurapu Sai Charan , Sirigadha Amith , Thodupunuri Akshay Kumar
Usually, one of the foremost predominant and intricate therapeutic conditions. As broadly perceived, brain tumors are among the foremost significantly harmful circumstances that can radically abbreviate a person’s life expectancy. Various methods are lacking for observing the assortment of tumor sizes, shapes, and areas. When merged with strategies of profound learning, generative adversarial networks (GANs) are competent of catching the measurements, areas, and structures of tumors. Profound learning frameworks will move forward upon the shortage of datasets. It can moreover progress photographs with determination. Classifying and partitioning brain tumors productively is significant. GANs are used in conjunction with an overarching learning handle. A profound learning design called NeuroNet19, could be an intercross of visual geometry group (VGG19) and inverted pyramid pooling module (IPPM) which is utilized to recognize brain tumors. It is clear that, NeuroNet19 employments the foremost exact technique in comparison to all models (DenseNet121, MobileNet, ResNet50, VGG16). The exactness examination gave a Cohen Kappa coefficient of 99% and a F1-score of 99.2%
Volume: 15
Issue: 3
Page: 3226-3237
Publish at: 2025-06-01

Hybrid semantic model based on machine learning for sentiment classification of consumer reviews

10.11591/ijai.v14.i3.pp2001-2011
Palaniraj Rajidurai Parvathy , Nagarajan Mohankumar , Rajendran Shobiga , Gour Sundar Mitra Thakur , Mamatha Bandaru , Velusamy Sujatha , Shanmugam Sujatha
Digital information is regularly produced from a variety of sources, including social media and customer service reviews. For the purpose of increasing customer happiness, this written data must be processed to extract user comments. Consumers typically share comments and thoughts about consumable items, technological goods, and services supplied for payment in the modern period of consumerism with simple access to social networking globe. Each object has a plethora of remarks or thoughts that demand special attention due to their sentimental worth, especially in the written portions. The goal of the current project is to do sentiment prediction on the Amazon Electronics, Kindle, and Gift Card datasets. In order to predict sentiment and evaluate utilizing many executions evaluates admitting accuracy, recall, and F1-score, a hybrid soft voting ensemble method that combines lexical and ensemble methodologies is proposed in this study. In addition to calculating a subjectivity score and sentiment score, this study also suggests a non-interpretive sentiment class label that may be used to assess the sign of the evaluations applying suggested method for sentiment categorization. The effectiveness of our suggested ensemble model is examined using datasets from Amazon customer product reviews, and we found an improvement of 2-5% in accuracy compared to the current state-of-the-art ensemble method.
Volume: 14
Issue: 3
Page: 2001-2011
Publish at: 2025-06-01

Enhancing cybersecurity awareness strategies in organization using Delphi technique

10.11591/ijece.v15i3.pp2986-2997
Anawin Kaewsa-Ard , Nattavee Utakrit
Cybersecurity concerns were once primarily perceived as technical issues, prompting many organizations to prioritize investments in security technologies. However, it has become increasingly evident that cybersecurity is not solely a technical matter. In fact, a significant number of cybersecurity breaches arise from users' lack of awareness about secure technological practices. This research aims to develop a cybersecurity awareness strategy using the Delphi technique over three rounds, involving 15 cybersecurity experts. The findings indicate a consensus among experts that cybersecurity awareness training is an effective strategy to enhance an organization's overall cybersecurity posture. However, the true essence of cybersecurity lies in fostering secure technology usage practices among all users within the organization. To address this, the researcher developed systematic training content for cybersecurity awareness, which was evaluated and refined by experts using the Delphi technique to ensure its effectiveness in promoting genuine cybersecurity awareness.
Volume: 15
Issue: 3
Page: 2986-2997
Publish at: 2025-06-01

Convolutional neural network based encoder-decoder for efficient real-time object detection

10.11591/ijai.v14.i3.pp1960-1967
Mothiram Rajasekaran , Chitra Sabapathy Ranganathan , Nagarajan Mohankumar , Rajeshkumar Sampathrajan , Thayalagaran Merlin Inbamalar , Nageshvaran Nandhini , Shanmugam Sujatha
Convolutional neural networks (CNN) are applied to a variety of computer vision problems, such as object recognition, image classification, semantic segmentation, and many others. One of the most important and difficult issues in computer vision, object detection, has attracted a lot of attention lately. Object detection validating the occurrence of the object in the picture or video and then properly locating it for recognition. However, under certain circumstances, such as when an item has issues like occlusion, distortion, or small size, there may still be subpar detection performance. This work aims to propose an efficient deep learning model with CNN and encoder decoder for efficient object detection. The proposed model is experimented on Microsoft Common Objects in Context (MS-COCO) dataset and achieved mean average precision (mAP) of about 54.1% and accuracy of 99%. The investigational outcomes amply showed that the suggested mechanism could achieve a high detection efficiency compared with the existing techniques and needed little computational resources.
Volume: 14
Issue: 3
Page: 1960-1967
Publish at: 2025-06-01

Q-learning based active monitoring with weighted least connection round robin load balancing principle for serverless computing

10.11591/ijece.v15i3.pp3171-3179
Yashwanth Balan , Arokia Paul Rajan
Serverless computing is considered one of the most promising technologies for real-time applications, with function as a service (FaaS) managing service requests in serverless computing. Load balancing played a vital role in assigning tasks in serverless computing for customers; user requests were controlled by load balancing algorithms and managed using machine learning techniques to deliver results and performance metrics within specified time limits. All serverless computing applications aimed to achieve optimal performance based on the most effective load balancing techniques, which directed requests to the appropriate servers in a timely manner. This research focused on developing a novel Q-learning based active monitoring with least connection round robin load balancing principle (Q-LAMWLR LB) for serverless computing to address the aforementioned challenge. Also, aimed to intelligently assign requests to serverless computing based on the number of requests arriving at the load balancer and how intelligently they could be directed to the appropriate server. This work utilized standard techniques to calculate the average response time for each scheduling algorithm and develop a novel intelligent load-balancing technique in serverless computing. Required experiment were conducted and the results are giving the improvement as compared to other load balancing principles. The further research in this area also identified and presented.
Volume: 15
Issue: 3
Page: 3171-3179
Publish at: 2025-06-01

A systematic review on software code smells

10.11591/ijece.v15i3.pp3010-3027
Mohammed Ghazi Al-Obeidallah , Dimah Al-Fraihat
This paper provides a systematic review of code smell detection studies published from 2001 to 2023, addressing their significance in identifying underlying issues in software systems. Through stringent inclusion criteria, 116 primary studies were analyzed, focusing on various aspects such as publication venue, code smell categories, subject systems, supported programming languages, evaluation criteria, and detection techniques. The analysis reveals that 50% of the papers were conference proceedings, with 80% utilizing Java-supported techniques and commonly used subject systems like Apache Xerces, GanttProject, and ArgoUML. Metrics-based methods (33%) and search-based approaches (32%) were predominantly employed, with machine learning emerging in 20% and rule-based methods in 15% of the studies. Notably, recent studies have shown an increased adoption of machine learning techniques. The identified code smells include god class, feature envy, long method, and data class, with precision and recall being the most commonly used evaluation metrics. This review aims to inform future research directions and aid the software engineering community in developing novel detection techniques to enhance code quality and system reliability.
Volume: 15
Issue: 3
Page: 3010-3027
Publish at: 2025-06-01

Object detection in printed circuit board quality control: comparing algorithms faster region-based convolutional neural networks and YOLOv8

10.11591/ijece.v15i3.pp2796-2808
Jaja Kustija , Diki Fahrizal , Muhamad Nasir , Andi Adriansyah , Muhammad Husni Muttaqin
Along with the development of electronic technology, the integration of numerous components on printed circuit board (PCB) boards has resulted in increasingly complex and intricate layouts. Small defects in traces can lead to failures in electronic functions, making the inspection of PCB surface layouts a critical process in quality control. Given the limitations of manual inspection, which struggles to detect such defects due to their size and complexity, there is a growing need for a PCB inspection system that utilizes automated optical inspection (AOI) based on deep learning detection. This research develops and compares two deep learning algorithms, faster region-based convolutional neural networks (R-CNN) and YOLOv8, to identify the most effective algorithm for detecting defects on PCB layouts. The findings of this study indicate that the YOLOv8 algorithm outperforms faster R-CNN, with the YOLOv8x variant emerging as the best model for defect detection. The YOLOv8x model achieved performance scores of 0.962 (mAP@50), 0.503 (mAP@50:95), 0.953 (Precision), 0.945 (Recall), and 0.949 (F1-score). These results provide a strong foundation for further research into the application of AOI for PCB defect detection and other quality control processes in manufacturing, using optimized deep learning models.
Volume: 15
Issue: 3
Page: 2796-2808
Publish at: 2025-06-01

A review of recent deep learning applications in wood surface defect identification

10.11591/ijai.v14.i3.pp1696-1707
Martina Ali , Ummi Raba’ah Hashim , Kasturi Kanchymalay , Aji Prasetya Wibawa , Lizawati Salahuddin , Rahillda Nadhirah Norizzaty Rahiddin
Wood is widely used in construction, art, and home applications due to its aesthetic appeal and favorable mechanical properties. However, environmental factors significantly affect the growth and preservation of wood, often leading to defects that can reduce its performance and ornamental value. Researchers have introduced machine vision and deep learning methods to address the challenges of high labor costs and inefficiencies in identifying wood defects. Deep learning has shown great success in image recognition tasks, yielding impressive results. This paper reviews previous work on deep-learning strategies for identifying wood surface defects. It also discusses data augmentation techniques to address limited defect data and explores transfer learning to enhance classification accuracy on small datasets. Finally, the paper examines the potential limitations of deep learning for defect identification and suggests future research directions.
Volume: 14
Issue: 3
Page: 1696-1707
Publish at: 2025-06-01

Road pavement deformation using remote sensing technique

10.11591/ijaas.v14.i2.pp345-351
Kishan Patel , Rajesh Gujar
The road surface reflects the status of the city’s infrastructure. Road safety and driving comfort can be affected by the rough surface. To minimize road hazards, pavement conditions must be periodically inspected for damaged surfaces. A quick and efficient data collection can be provided by the radar images. For a large spatial coverage, radar image provides a non-destructive data collection technique for analyzing road conditions and classifying distress. The surface distress can be correlated by analyzing the images collected from high-resolution cameras and satellites. This article outlines the applicability of synthetic aperture radar (SAR) and interferometric synthetic aperture radar (InSAR) based images to manage and monitor pavement infrastructure. Therefore, the detection of deteriorating surfaces can be improved by analyzing the radar images timely. The results showed the deficiencies on the surface that can be used to mitigate bad pavement conditions and allow road users to use good road infrastructure with safety and comfort.
Volume: 14
Issue: 2
Page: 345-351
Publish at: 2025-06-01

Augmented reality learning media for electrical motor: case study in electrical engineering education

10.11591/ijece.v15i3.pp2545-2555
Bima Mustaqim , Abdul Muin Sibuea , Sahat Siagian , Agus Junaidi , Muhammad Amin , Sriadhi Sriadhi , Wan Ahmad Jaafar Wan Yahaya , Harun Sitompul
The impact of augmented reality (AR) learning tools and students' critical thinking abilities on learning outcomes in electrical engineering education is the focus of this study. The study explores the ways in which these factors, both independently and in combination, influence student performance. Findings reveal that AR-based learning materials significantly enhance understanding and retention across self-directed and guided learning models. Critical thinking skills emerge as a key determinant of success, with students exhibiting strong critical thinking consistently outperforming peers with lower-level skills, regardless of the instructional model. The study also highlights variations in AR tools' effectiveness depending on the learning model and students’ critical thinking abilities. Guided learning with AR tools benefits all students, while self-directed AR tools prove most effective for those with advanced critical thinking skills. Students with lower critical thinking abilities face challenges in navigating less structured AR environments. These results underscore the importance of fostering critical thinking and adopting tailored strategies when integrating AR technology into engineering education. By considering both the learning model and critical thinking levels, educators can optimize AR’s potential to enhance student learning outcomes in technical fields.
Volume: 15
Issue: 3
Page: 2545-2555
Publish at: 2025-06-01

Energy consumption prediction methods in a cyber-physical system

10.11591/ijece.v15i3.pp3054-3063
Kenzhegali Nurgaliyev , Akylbek Tokhmetov
In recent decades, cyber-physical systems (CPS) have become an essential part of modern industry and daily life. These systems integrate physical processes with computer and network components, allowing them to interact with their environment and manage their components autonomously. One of the most significant aspects of CPS efficiency is managing energy consumption, which significantly affects their reliability, efficiency, and economic performance. CPS devices generate vast amounts of diverse data, which is crucial to accurately model. Researchers use predictive analysis to develop models that forecast trends and simulate real-world conditions, enabling them to make better-informed decisions. This article presents a comparative analysis of different predictive models for CPS data analytics, focusing on energy consumption in smart buildings. Short-term models include gradient-boosted regressor (XGBoost), random forest (RF) and long short-term memory (LSTM). The comparative results have been studied in terms of prediction errors to determine accuracy.
Volume: 15
Issue: 3
Page: 3054-3063
Publish at: 2025-06-01

A review of open-source energy system modeling tools

10.11591/ijaas.v14.i2.pp469-480
Nguyen Binh Khanh , Phuong Le Ngo , Luong Ngoc Giap , Truong Nguyen Tuong An , Trung Bui Tien , Tran The Vinh , Le Van Nghia , Tran Trong Dat
Nowadays, the transition to open markets, the rapid growth of renewable energy sources like wind and solar, and the shift towards electrification in transportation and industry for decarbonization have increased the demand for advanced energy system models with detailed spatial and temporal data. This paper utilizes a comprehensive literature review and selects a representative set of open-source tools for evaluation. A comparative analysis of 17 open-source energy system modeling tools and their commercial alternatives was conducted. The paper analyzes many open-source aspects such as code commits, updates, programming languages, license details, citations, and energy system modeling features such as power flows (PFs), continuation PF, dynamic analysis, short-circuit analysis, contingency analysis, transportation model, optimal PF (OPF), multi-period OPF, unit commitment (UC), investment optimization, and graphic user interface. Based on the results, the paper suggests appropriate tools used for according power/energy system analysis objective: MATPOWER for power system analysis and Python for power system analysis (PyPSA) for energy system analysis.
Volume: 14
Issue: 2
Page: 469-480
Publish at: 2025-06-01

Bridging technology and healthcare: user acceptance of a surgical site infection system

10.11591/ijaas.v14.i2.pp523-532
Afan Fatkhur Akhmad , Maria Ulfa
Surgical site infections (SSI) continue to be a problem for surgeons, and unfortunately, SSI information systems are underutilized. This study analyzed the user acceptance of the SSI information system based on the extended technology acceptance model (TAM2). A cross-sectional questionnaire-based study. The variables studied intention to use (IU), perceived ease of use (PEOU), demographic factors (FD), subjective norm (SN), Image (I), job relevance (JR), output quality (OQ), result demonstrability (RD), perceived usefulness (PU). Data were collected by filling out questionnaires and then analyzed using smart-partial least squares (PLS). In total, 61 nurses were included. Most respondents are aged 31-35 (26.23%), and most working periods are between 11-15 years (27.87%). There were significant positive effects on SN to PU (β=0.12; p 0.05). This study concluded that PEOU is the most influential variable in the IU the SSI information system.
Volume: 14
Issue: 2
Page: 523-532
Publish at: 2025-06-01

Enhancing Alzheimer’s disease diagnosis through metaheuristic feature selection and advanced classification techniques

10.11591/ijece.v15i3.pp3382-3395
Arar Al-Tawil , Worood Al-Muhtaseb , Laiali Almazaydeh , Hanaa Fathi
A diverse array of diagnostic and detection methods has been developed as a result of the advent of Alzheimer’s disease (AD) as a significant global health issue. This study employs bio-inspired algorithms, such as the parrot optimization algorithm (POA), grey wolf optimizer (GWO), and differential evolution (DE), to identify the most effective feature selection techniques for AD diagnosis. The predictive accuracy of these algorithms was improved by the simple keywords: Alzheimer’s disease optimization classification machine learning metaheuristic mentation of the Alzheimer’s disease Dataset. This was achieved by integrating a personalized fitness function and optimizing parameter settings with decision tree classifiers. To evaluate the algorithms’ effectiveness in machine learning models with population sizes of 30 and 60, precision, recall, accuracy, and F1-score were evaluated at 5, 15, and 30 iterations. The gradient boosting and XGBoost classifiers consistently obtained the highest results, while DE, GWO, and parrot optimization (PO) achieved maximal accuracy rates of 0.94, 0.93, and 0.94, respectively. These findings underscore the efficacy of integrating metaheuristic algorithms with robust classifiers to enhance the predictive accuracy of AD diagnosis. Furthermore, they illustrate that artificial intelligence (AI) algorithms that are operated by biological processes can accurately forecast AD, with the success rates and stability of the proposed methods serving as metrics for evaluating their efficacy.
Volume: 15
Issue: 3
Page: 3382-3395
Publish at: 2025-06-01

Investigation of microwave absorption performance of anti-radiation plastic cement brick

10.11591/ijece.v15i3.pp2900-2910
Linda Mohd Kasim , Hasnain Abdullah Idris , Mohd Nasir Taib , Norhayati Mohamad Noor , Azizah Ahmad , Noor Azila Ismail , Nazirah Mohamat Kasim , Nur Qaisarah Anuar
The increasing demand for effective anti-microwave radiation materials motivates the exploration of sustainable and eco-friendly alternatives. This research investigates the microwave absorption properties of various brick compositions, including commercial brick (CB) and solid bricks (SB1, SB2 and SB3) incorporating recycled materials, polyethylene terephthalate (PET) and palm oil fuel ash (POFA). The dimension of the developed brick is 200×100×60 mm (length×width×height). The absorption performance of the bricks was measured in 100 mm and 60 mm thickness across the frequency range of 1 to 12 GHz using the naval research laboratory (NRL) free space arch method. At 100 mm thickness, SB3 shows the highest absorption up to-32.2061 dB at 1.98 GHz. At 60 mm thickness, SB1 achieved the maximum absorption at -57.6511 dB at 2.505 GHz. SB2 shows consistent average absorption performance at 15.2064 dB at 100 mm thickness and -19.5 dB at 60 mm thickness respectively. The compressive strength of the brick was measured, and it was shown that SB2 exhibited the highest average compressive strength of 7.17 MPa. Considering the standard wall thickness and brick strength, SB2 shows the most effective performance due to its enhanced composition and consistent performance across frequencies.
Volume: 15
Issue: 3
Page: 2900-2910
Publish at: 2025-06-01
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