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31,456 Article Results

Sign language recognition and classification using blended ensemble machine learning

10.11591/ijai.v14.i3.pp2035-2043
Akash Rajan Rai , Sujata Rajesh Kadu
An efficient sign language recognition system (SLR) is the most significant for hearing-impaired people for communication. The body movements and hand gestures are utilized to characterize the vocabulary in dynamic sign language. The SLR is a challenging problem because the computational model requires simultaneous spatial-temporal modelling for a number of sources. To overcome this problem, this research proposes the blended ensemble machine learning (ML) approaches for SLR. Initially, the Indian sign language (ISL) dataset is collected for evaluating the effectiveness of the model. Then, the pre-processing is done by using data augmentation and normalization techniques. Then, the pre-processed data is provided to the segmentation process which is done by using multi-threshold entropy function. Then, VGG-16 is used for the feature extraction process to extract the features and finally, classification is carried out using ensemble ML. An effectiveness of the proposed method is validated based on accuracy, precision, recall, and F1-score, wherein it achieves better results of 99.57%, 0.92%, 0.95%, and 0.99% as compared to the existing works like support vector machine (SVM) and convolutional neural network (CNN).
Volume: 14
Issue: 3
Page: 2035-2043
Publish at: 2025-06-01

Artificial intelligence multilingual image-to-speech for accessibility and text recognition

10.11591/ijai.v14.i3.pp1743-1751
Rosalina Rosalina , Hasanul Fahmi , Genta Sahuri
The primary challenge for visually impaired and illiterate individuals is accessing and understanding visual content, which hinders their ability to navigate environments and engage with text-based information. This research addresses this problem by implementing an artificial intelligence (AI)-powered multilingual image-to-speech technology that converts text from images into audio descriptions. The system combines optical character recognition (OCR) and text-to-speech (TTS) synthesis, using natural language processing (NLP) and digital signal processing (DSP) to generate spoken outputs in various languages. Tested for accuracy, the system demonstrated high precision, recall, and an average accuracy rate of 0.976, proving its effectiveness in real-world applications. This technology enhances accessibility, significantly improving the quality of life for visually impaired individuals and offering scalable solutions for illiterate populations. The results also provide insights for refining OCR accuracy and expanding multilingual support.
Volume: 14
Issue: 3
Page: 1743-1751
Publish at: 2025-06-01

Modeling sentiment analysis of Indonesian biodiversity policy Tweets using IndoBERTweet

10.11591/ijai.v14.i3.pp2389-2401
Mohammad Teduh Uliniansyah , Asril Jarin , Agung Santosa , Gunarso Gunarso
This study develops and evaluates a sentiment analysis model using IndoBERTweet to analyze Twitter data on Indonesia’s biodiversity policy. Twitter data focusing on topics such as food security, health, and environmental management were collected, with a representative subset of 13,435 tweets annotated from a larger dataset of 500,000 to ensure reliable sentiment labels through majority voting. IndoBERTweet was compared to seven traditional machine-learning classifiers using TF-IDF and BERT embeddings for feature extraction. Model performance was assessed using mean accuracy, mean F1 score, and statistical significance (p-values). Additionally, sentiment analysis included word attribution techniques with BERT embeddings, enhancing relevance, interpretability, and consistent attribution to deliver accurate insights. IndoBERTweet models consistently outperformed traditional methods in both accuracy and F1 score. While BERT embeddings boosted performance for conventional models, IndoBERTweet delivered superior results, with p-values below 0.05 confirming statistical significance. This approach demonstrates that the model’s outputs are explainable and align with human understanding. Findings underscore IndoBERTweet’s substantial impact on advancing sentiment analysis technology, showcasing its potential to drive innovation and elevate practices in the field.
Volume: 14
Issue: 3
Page: 2389-2401
Publish at: 2025-06-01

Indoor navigation for mobile robots based on deep reinforcement learning with convolutional neural network

10.11591/ijece.v15i3.pp2748-2757
Khoa Nguyen Dang , Van Tran Thi , Nguyen Van Thang
The mobile robot is an intelligent device that can achieve many tasks in life. For autonomous, navigation based on the line on the ground is often used because it helps the robot to move along a predefined path, simplifies the path planning, and reduces the computational load. This paper presents a method for navigating the four-wheel mobile robot to track a line based on a deep Q-network as a control algorithm to desire the action of the mobile robot and a camera as a feedback sensor to detect the line. The control algorithm uses a convolution neural network (CNN) to generate the mobile robot action, defined as an agent of deep Q-network. CNN uses images from the camera to define the state of the deep Q network. The simulations are performed based on Gazebo software which includes a 3D environment, mobile robot model, line, and Python programming. The results demonstrate the high-performance tracking of mobile robots with complex line trajectories, achieving errors of less than 100 px, which is compared with the traditional vision method (VNS), the MSE of the proposal method is 0.0264 lower than VNS with 0.0406. Showcases proved convincingly that effectiveness suggested a control approach.
Volume: 15
Issue: 3
Page: 2748-2757
Publish at: 2025-06-01

MOERID: enhancing open educational resource discoverability through an artificial intelligence-powered chatbot recommender system

10.11591/ijece.v15i3.pp3107-3117
Sandoussi Rima , Hnida Meriem , Daoudi Najima , Ajhoun Rachida
Open educational resources (OER) are valuable assets in learning and teaching. They ensure cost-effectiveness and customizability, and contribute to global collaboration in the education realm. Hence, education stakeholders face a critical challenge in locating suitable OERs that meet their needs and pedagogical objectives. To cater to this issue, researchers propose diverse digital solutions, one being recommender systems (RS). While a wide array of the suggested tools focused on learners only, this study introduces MOERID, an AI-powered OER Recommender Chatbot aimed at the teaching community. It aspires to facilitate resource discoverability, allowing instructors to save time and energy to concentrate on other pedagogical duties. MOERID engages NLP and recommendation filtering techniques to locate and deliver relevant OERs to instructors. The study describes the implementation of MOERID, highlights its efficacy and provides actionable recommendations for future research by outlining the research gaps.
Volume: 15
Issue: 3
Page: 3107-3117
Publish at: 2025-06-01

A review on ischemic heart disease prediction frameworks using machine learning

10.11591/ijaas.v14.i2.pp361-372
Kabo Clifford Bhende , Tshiamo Sigwele , Chandapiwa Mokgethi , Aone Maenge , Venu Madhav Kuthadi
Ischemic heart disease (IHD) is a leading cause of mortality worldwide, calling for advanced predictive models for timely intervention. Current literature reviews on machine learning (ML)-based IHD prediction frameworks often focus on predictive accuracy but lack depth in areas like dataset diversity, model interpretability, and privacy considerations. Existing IHD prediction frameworks face limitations, including reliance on small, homogenous datasets, limited critical analysis, and issues with model transparency, reducing their clinical utility. This review addresses these gaps through a systematic, comparative analysis of popular ML models, such as random forest (RF) and support vector machines (SVM), noting their strengths and limitations. Key contributions include a qualitative examination of prevalent tools, datasets, and evaluation metrics, identification of gaps in dataset diversity and interpretability; and recommendations for improving model transparency and data privacy. Major findings reveal a trend toward ensemble models for accuracy but highlight the need for explainable artificial intelligence (AI) to support clinical decisions. Future directions include using federated learning to enhance data privacy, integrating unstructured data for comprehensive prediction, and advancing explainable AI to build trust among healthcare providers. By addressing these areas, this review aims to guide future research toward developing robust, transparent ML frameworks that can be more effectively deployed in clinical settings.
Volume: 14
Issue: 2
Page: 361-372
Publish at: 2025-06-01

Development of a digital-based fiber tensile testing apparatus to enhance fiber testing accuracy

10.11591/ijaas.v14.i2.pp552-561
Muhammad Iswar , Muhammad Arsyad Suyuti , Rusdi Nur , Ahmad Nurul Muttaqin
Natural fibers are increasingly used in various industries due to their eco-friendly properties and cost-effectiveness. However, current methods for testing the mechanical properties of these materials, such as tensile strength, often face limitations in accuracy and efficiency. This study aims to develop an innovative digital-based fiber tensile testing apparatus to enhance the precision of tensile testing. The research involves the design and construction of the apparatus, utilizing components such as ST37 steel, stepper motors, and Arduino technology. The apparatus was tested using two types of natural fibers, Cocos nucifera L. (coconut fiber) and Sansevieria, to assess their tensile properties. The results showed that although Sansevieria fibers have a smaller diameter, they exhibited higher tensile stress compared to coconut fibers. The developed digital testing apparatus enables more accurate and efficient fiber testing, contributing to the development of stronger and more sustainable materials for industrial applications. The findings of this study highlight the potential of advanced testing equipment in supporting the use of natural fibers in manufacturing and environmental sustainability.
Volume: 14
Issue: 2
Page: 552-561
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

A gamified online learning environment with comprehensive assessments and software integration

10.11591/ijaas.v14.i2.pp416-429
Swati Shilaskar , Shripad Bhatlawande , Rupali Deshpande , Shivam Shinde , Jyoti Madake , Anjali Solanke
The National Achievement Survey (NAS), conducted by the Ministry of Education, India, highlighted a concerning decline in mathematics proficiency among students in Maharashtra as they advance through grades. This trend is further aggravated by the limited availability of online resources in Marathi, hindering their learning progress. To address this, a pilot study was proposed to develop a specialized online platform tailored for Marathi medium students, integrating gamification and artificial intelligence (AI)-driven feedback to enhance engagement and comprehension. The pilot project, conducted at a Marathi medium school with approval from the principal, focused on polynomial division tests for 8th-grade students over four days. Results revealed that despite the easy level test's higher difficulty, students scored higher on the medium level test, possibly due to an adjustment period to the online platform. Notably, some students performed better on the hard-level test, indicating the platform's potential to improve performance. While promising, the study's limitations, including a small sample size, highlight the need for further research with a larger cohort and the integration of automatic suggestions for concept-specific games and assessments in future iterations to optimize the platform's effectiveness.
Volume: 14
Issue: 2
Page: 416-429
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

Birth data clustering to segmentation delays in birth certificate registration

10.11591/ijaas.v14.i2.pp513-522
Erfan Hasmin , Aedah Abd Rahman
Timely and accurate birth registration is essential for ensuring access to vital public services. This study focuses on clustering birth data to identify patterns in registration delays, using data mining techniques such as the K-means algorithm. By clustering birth data from Makassar City, Indonesia, based on various demographic and birth-related criteria, the study segments the data into groups that reflect both timely and delayed registrations. The optimal number of clusters is determined using the elbow and silhouette methods. Results show that a three-cluster configuration effectively captures patterns in birth registration delays, offering critical insights for policymakers. These findings provide a foundation for improving birth registration processes, ensuring more timely registration, and guiding data-driven public policy decisions.
Volume: 14
Issue: 2
Page: 513-522
Publish at: 2025-06-01

The future of healthcare: exploring internet of things and artificial intelligence applications, challenges, and opportunities

10.11591/ijece.v15i3.pp3075-3083
Kamal Elhattab , Driss Naji , Abdelouahed Ait ider , Karim Abouelmehdi
The internet of things (IoT) refers to a network of physical devices embedded with sensors, software, and communication tools, which allow for seamless exchange and collection of data. This technology enables automation, continuous monitoring, and data-driven decision-making across a variety of fields. In the healthcare sector, the integration of IoT with artificial intelligence (AI) is transforming how patient care is delivered, providing real-time health monitoring, personalized treatment options, and more efficient management of healthcare resources. This study investigates the significant influence of the IoT and AI on the healthcare system, focusing on how these technologies improve patient outcomes and streamline healthcare operations. It also highlights emerging challenges in the adoption of these technologies and suggests potential solutions to address these obstacles and enhance healthcare delivery. The research is based on an in-depth review of AI and IoT applications in healthcare, uncovering advancements in patient monitoring, disease management, and operational efficiency, while also identifying key challenges such as data privacy concerns and issues with system interoperability.
Volume: 15
Issue: 3
Page: 3075-3083
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

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

Crowdfunding platform integrated with cryptocurrency payment support

10.11591/ijaas.v14.i2.pp598-608
Rosalina Rosalina , Genta Sahuri
Crowdfunding platforms often face challenges such as high transaction fees, limited global accessibility, and reliance on traditional banking systems, which restrict participation and efficiency. These limitations hinder the full potential of crowdfunding, particularly for global contributors and projects. This research addresses these issues by proposing the development of a mobile crowdfunding platform integrated with cryptocurrency payment support. By incorporating cryptocurrency, the platform aims to reduce transaction costs, remove geographical barriers, and enhance transaction security through blockchain technology. The platform is built using a cross-platform mobile framework to ensure broad accessibility while integrating cryptocurrency gateways for decentralized financial transactions. This allows for real-time, secure, and low-cost payments, offering a transparent and efficient process for both contributors and fundraisers. Additionally, the platform's design supports scalability to accommodate various cryptocurrencies and an expanding user base. The findings demonstrate that cryptocurrency payment integration significantly improves transaction speed, reduces fees, and enhances security compared to traditional payment methods. It also fosters global participation, increasing engagement in crowdfunding initiatives.
Volume: 14
Issue: 2
Page: 598-608
Publish at: 2025-06-01
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