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29,939 Article Results

Enhancing facial recognition accuracy through feature extractions and artificial neural networks

10.11591/ijai.v14.i2.pp1056-1066
Adhi Kusnadi , Ivranza Zuhdi Pane , Fenina Adline Twince Tobing
Facial recognition is a biometric system used to identify individuals through faces. Although this technology has many advantages, it still faces several challenges. One of the main challenges is that the level of accuracy has yet to reach its maximum potential. This research aims to improve facial recognition performance by applying the discrete cosine transform (DCT) and Gaussian mixture model (GMM), which are then trained with backward propagation of errors (backpropagation) and convolutional neural networks (CNN). The research results show low DCT and GMM feature extraction accuracy with backpropagation of 4.88%. However, the combination of DCT, GMM, and CNN feature extraction produces an accuracy of up to 98.2% and a training time of 360 seconds on the Olivetti Research Laboratory (ORL) dataset, an accuracy of 98.9% and a training time of 1210 seconds on the Yale dataset, and 100% accuracy and training time 1749 seconds on the Japanese female facial expression (JAFFE) dataset. This improvement is due to the combination of DCT, GMM, and CNN's ability to remove noise and study images accurately. This research is expected to significantly contribute to overcoming accuracy challenges and increasing the flexibility of facial recognition systems in various practical situations, as well as the potential to improve security and reliability in security and biometrics.
Volume: 14
Issue: 2
Page: 1056-1066
Publish at: 2025-04-01

Finite state machine for retro arcade fighting game development

10.11591/ijict.v14i1.pp102-110
Muhammad Bambang Firdaus , Alan Zulfikar Waksito , Andi Tejawati , Medi Taruk , M. Khairul Anam , Akhmad Irsyad
Traditional fighting games are a competitive genre where players engage in one-on-one combat, aiming to reduce their opponent's health points to zero. These games often utilize two-dimensional (2D) graphics, enabling players to execute various character movements such as punching, jumping, and crouching. This research investigates the effectiveness of the finite state machine (FSM) method in developing a combo system for a retro fighting game, focusing on its implementation within the Godot Engine. The FSM method, which structures game behavior through states, events, and actions, is central to the game's control system. By employing the game development life cycle (GDLC) methodology, this study ensures a systematic and structured approach to game design. Special attention is given to the regulation of the combo hit system for the game's protagonist in Brawl Tale. The research culminates in the successful development of the retro fighting game Brawl Tale, demonstrating that the FSM method significantly enhances the fluidity and responsiveness of character movements. The findings suggest that the FSM method is an effective tool for simplifying and improving gameplay mechanics in retro-style fighting games.
Volume: 14
Issue: 1
Page: 102-110
Publish at: 2025-04-01

Teaching learning based optimization algorithm for effective analysis of power quality using dynamic voltage restorer

10.11591/ijict.v14i1.pp268-275
Soumya Ranjan Das , Surender Reddy Salkuti
In this study, the load voltage is dynamically restored utilising the dynamic voltage restorer (DVR) using the voltage injection approach. The injected voltage is generated using a voltage-source inverter (VSI), which is necessary to correct for the utility network's sag and swell characteristics voltage. The restoration process is dependent on the condition and quality of the utility system, i.e., it injects energy into the external system for the duration of voltage sag, and during voltage swell, energy is absorbed by the compensator from the external system, causing an rise in dc link voltage, which is connected across the VSI. In this study two different controllers are employed based on a learning based optimized algorithm. The simulation results are shown using two different controllers and the performance of the proposed controller is found to be a better one.
Volume: 14
Issue: 1
Page: 268-275
Publish at: 2025-04-01

Optimizing the gallstone detection process with feature selection statistical analysis algorithm

10.11591/ijai.v14.i2.pp1183-1191
Musli Yanto , Yuhandri Yuhandri , Muhammad Tajuddin , Vina Tri Septiana
Early detection is one form of early anticipation in treating gallstone disease patients using medical images. However, the problem that exists is that there are still many shortcomings in medical images, such as noise in the image that causes the detection process to not run optimally. Based on this, this study aims to carry out the process of detecting gallstone objects in magnetic resonance cholangiopancreatography (MRCP) images by optimizing the performance of extraction techniques for feature selection. Optimization of extraction techniques in feature selection is carried out using the performance of the feature selection statistics analysis (FSSA) algorithm. The performance of the FSSA algorithm can provide improvements in the feature selection process by excelling in the performance of classification methods such as k-nearest neighbor (KNN), support vector machine (SVM), and artificial neural network (ANN), and the Pearson correlation (PC) method. Based on the tests that have been carried out, the performance of the FSSA algorithm in the detection process provides an accuracy level of 95.69%, a sensitivity of 89.65%, and a specificity of 98.43%. Overall, this study can contribute to the development of extraction and provide a significant technical impact on optimizing the gallstone detection process.
Volume: 14
Issue: 2
Page: 1183-1191
Publish at: 2025-04-01

Assessing the user experience of marker-based 3D WebAR applications using user experience questionnaire

10.11591/ijict.v14i1.pp31-41
Nooralisa Mohd Tuah , Wan Nooraishya Wan Ahmad , Ryan MacDonell Andrias , Dg. Senandong Ajor , Suaini Sura , Ahmad Rizal Ahmad Rodzuan
Marker-based 3D web-based augmented reality (WebAR) applications are an emerging field that merges web technologies with augmented reality. WebAR has gained popularity because of its ability to provide users with a reliable and autonomous platform. Yet, a limited investigation has verified its application and user perspective on its ability to function. This study is designed to evaluate the user experiences of marker-based 3D WebAR applications using the user experience questionnaire (UEQ). This study assesses various elements of the user experience, including attractiveness, clarity, engagement, efficiency, and innovation, utilizing the UEQ. This study aims to analyze user perceptions and interaction patterns thoroughly to get useful insights into the usability and user satisfaction aspects of marker-based 3D WebAR apps. The findings reveal that the WebAR app is both appealing and efficient, instilling confidence in its users. This underscores the pivotal role of user experience in shaping the effectiveness and reception of WebAR applications. This research has the potential to influence the creation of more user-focused and engaging marker-based 3D WebAR experiences, improving user engagement and immersion in web-based augmented reality environments.
Volume: 14
Issue: 1
Page: 31-41
Publish at: 2025-04-01

The integration of discrete contourlet transform in OFDM framework for future wireless communication

10.11591/ijict.v14i1.pp182-194
Mohamed Hussien Mohamed Nerma , Adam Mohamed Ahmed Abdo
In the upcoming era, the forthcoming sixth-generation (6G) wireless communication network will demand highly efficient technology to support extensive capacity, ultra-high speeds, low latency, scalability, and adaptability. While the current fifth-generation (5G) wireless communication system relies on OFDM technology, the evolution towards a beyond 5G wireless communication system necessitates a new OFDM framework. This study introduces a novel OFDM system that integrates the discrete Contourlet transform. A comparative analysis has been conducted among the proposed system, conventional OFDM, and curvelet-based OFDM systems. The results indicate that the proposed system offers improvements in bit error rate (BER), reduced computational complexity, decreased peak-to-average power ratio (PAPR), and enhanced power spectrum density (PSD) when contrasted with both the traditional and curvelet-based systems.
Volume: 14
Issue: 1
Page: 182-194
Publish at: 2025-04-01

Planar hexagonal patch multiple input multiple output 4x4 antenna for UWB applications

10.11591/ijict.v14i1.pp174-181
Nasrul Nasrul , Firdaus Firdaus , Nurraudya Tuz Zahra , Maulidya Rachmawati
The combination of Multiple Input Multiple Output (MIMO) antennas and Ultra-Wideband (UWB) technology offers several advantages, including reduced interference, improved isolation, and optimized dual paths. These benefits extend the range and enhance signal quality. However, designing UWB-MIMO antennas presents challenges, such as achieving low mutual coupling for high isolation and creating small-sized antennas suitable for portable devices while being effective for UWB frequencies in a MIMO configuration. The proposed antenna is a 4x4 planar MIMO antenna with a hexagon-shaped patch, a partial ground plane featuring an inverted L-stub on the left side, and a plus-shaped slot in the centre ground. It has dimensions of 32 x 32 x 1.6 mm³ and is capable of achieving a wide bandwidth of 3-12.5 GHz. The antenna's performance measurements are impressive: return loss below -10 dB at frequencies of 3-12.5 GHz, mutual coupling below -16.5 dB, Envelope Correlation Coefficient (ECC) bellow 0.005, Diversity gain of more than 9.97, Total Active Reflection Coefficient (TARC) below -10 dB. Based on these results, the proposed antenna offers excellent performance for UWB applications, featuring high efficiency, minimal interference between antenna elements, and optimal diversity performance.
Volume: 14
Issue: 1
Page: 174-181
Publish at: 2025-04-01

Schedule-free optimization of the transformers-based time series forecasting model

10.11591/ijai.v14.i2.pp1067-1076
Kyrylo Yemets , Michal Greguš
The task of time series forecasting is important for many scientific, technical, and applied fields, such as finance, economics, meteorology, medicine, transportation, and telecommunications. Existing methods, such as autoregressive models and moving average models, have their limitations, especially when working with non-stationary and seasonal data. In this work, the basic architecture of transformers was modified to solve time series forecasting problems. Additionally, state-of-the-art optimizers were investigated and experimentally compared, including AdamW, stochastic gradient descent (SGD), and new methods such as schedule-free SGD and schedule-free AdamW, to improve forecasting accuracy and the efficiency of the training procedure for the transformer architecture. Modeling was conducted on meteorological data that included seasonal time series. The accuracy evaluation of the optimization methods studied in this work was performed using a range of different performance indicators. The results showed that the new optimization methods significantly improve forecasting accuracy compared to the use of traditional optimizers.
Volume: 14
Issue: 2
Page: 1067-1076
Publish at: 2025-04-01

An model for structured the NoSQL databases based on machine learning classifiers

10.11591/ijict.v14i1.pp229-239
Amine Benmakhlouf
Today, the majority of data generated and processed in organizations is unstructured. NoSQL database management systems perform the management of this data. The problem is that these unstructured databases cannot be analyzed by traditional OLAP analytical treatments. The latter are mainly used in structured relational databases. In order to apply OLAP analyses on NoSQL data, the structuring of this data is essential. In this paper, we propose a model for structuring the data of a document-oriented NoSQL database using machine learning (ML). This method is broken down into three steps, first the vectorization of documents, then the learning via different ML algorithms and finally the classification, which guarantees that documents with the same structure will belong to the same collection. Therefore, the modeling of a data warehouse can be carried out in order to create OLAP cubes. Since the models found by learning allow the parallel computation of the classifier, our approach represents an advantage in terms of speed since we will avoid doubly iterative algorithms, which rely on textual comparisons (TC). A comparative study of the performances is carried out in this work in order to detect the most efficient methods to perform this type of classification.
Volume: 14
Issue: 1
Page: 229-239
Publish at: 2025-04-01

Data analysis and visualization on titanic and student’s performance datasets-an exploratory study

10.11591/ijict.v14i1.pp68-76
Seong-Cheol Kim , Surender Reddy Salkuti , Alka Manvayalar Suresh , Madhu Sree Sankaran
Exploratory data analysis (EDA) is all about exploring the data in order to identify any underlying pattern before you try to use it to make a predictive model. It also plays a major role in the data discovery process as it is used to analyze data and to recapitulate their different characteristics, which is displayed efficiently with the help of data visualization methods. This paper aims to identify errors in the dataset, to understand the existing hidden structure and to identify new ones, to detect points in a dataset that deviate to a greater extent from the collected data (outliers), and also to find any relationship or intersection between the variables and constants. Two datasets are used namely ‘Titanic’ and ‘student’s performance’ to perform data analysis and ‘data visualization’ to depict ‘exploratory data analysis’ which acts as an important set of tools for recognizing a qualitative understanding. The datasets were explored and hence it assisted with identifying patterns, outliers, corrupt data, and discovering the relationship between the fields in the dataset.
Volume: 14
Issue: 1
Page: 68-76
Publish at: 2025-04-01

A proposed approach for plagiarism detection in Myanmar Unicode text

10.11591/ijai.v14.i2.pp1616-1624
Sun Thurain Moe , Khin Mar Soe , Than Than Nwe
Around the world, with technology that improves over time, almost everyone can access the internet easily and quickly. With the increase in the use of the internet, the plagiarism of information that is easily available on the internet has also increased. Such plagiarism seriously undermines originality and ethical principles. In order to prevent these incidents, there is plagiarism detection software for many countries and languages, but there is no plagiarism detection software for the Myanmar language yet. In an attempt to fill that gap, this study proposed a deep learning model with Rabin-Karp hash code and Word2vec model and built a plagiarism detection system. Our deep learning model was trained by randomly obtaining information from Myanmar Wikipedia. According to the experiments, our proposed model can effectively detect plagiarism of educational content and information from Myanmar Wikipedia. Moreover, it is possible to distinguish plagiarized texts by rearranging words or substituting words with some synonyms. This study contributes to a broader understanding of the complexities of plagiarism in the Myanmar academic area and highlights the importance of measures to effectively prevent plagiarism. It maintains the credibility of education and promotes a culture that values originality and intellectual integrity.
Volume: 14
Issue: 2
Page: 1616-1624
Publish at: 2025-04-01

Explainable zero-shot learning and transfer learning for real time Indian healthcare

10.11591/ijict.v14i1.pp91-101
Swati Saigaonkar , Vaibhav Narawade
Clinical note research is globally recognized, but work on real-time data, particularly from India, is still lagging. This study initiated by training models on medical information mart for intensive care (MIMIC) clinical notes, focusing on conditions like chronic kidney disease (CKD), myocardial infarction (MI), and asthma using the structured medical domain bidirectional encoder representations from transformers (SMDBERT) model. Subsequently, these models were applied to an Indian dataset obtained from two hospitals. The key difference between publicly available datasets and real-time data lies in the prevalence of certain diseases. For example, in a real-time setting, tuberculosis may exist, but the MIMIC dataset lacks corresponding clinical notes. Thus, an innovative approach was developed by combining a fine-tuned SMDBERT model with a customized zero-shot learning method to effectively analyze tuberculosis-related clinical notes. Another research gap is the lack of explainability because deep learning (DL) models are inherently black-box. To further strengthen the reliability of the models, local interpretable model-agnostic explanations (LIME) and shapley additive explanations (SHAP) explanations were projected along with narrative explanations which generated explanations in a natural language format. Thus, the research provides a significant contribution with ensemble technique of zero-shot learning and SMDBERT model with an accuracy of 0.92 as against the specialized models like scientific BERT (SCIBERT), biomedical BERT (BIOBERT) and clinical BioBERT.
Volume: 14
Issue: 1
Page: 91-101
Publish at: 2025-04-01

Multiple intelligence based tasks for enhancing reading motivation of university students in Ethiopia

10.11591/ijere.v14i2.30195
Teshale Alemu Gebremeskel , Mebratu Mulatu Bachore , Elias Woemego Bushisso
The purpose of the study was to investigate the effectiveness of multiple intelligence-based tasks in enhancing students’ motivation towards reading. It employed a quasi-experimental design. A total of 60 communicative English class university students, who were selected purposefully participated as treatment and comparison groups. The research process was carried out with reading tasks that were designed based on a model for teaching using multiple intelligence-driven tasks for the treatment group while the comparison group followed the conventional approach for 12 weeks. English-reading motivation questionnaires and focused group discussions were used to gather data. Data normality check was carried out using Shapiro-Wilk tests, and a p value of 0.05 was used to determine the level of significance. T-tests were used to compare the scores between the two groups. It was found that multiple intelligence-based reading tasks (MIBRT) brought a significant difference in the students’ motivation, with the effect size value ranging from low (for importance), moderate (for efficacy and for extrinsic), and strong (for intrinsic). It was suggested that university teachers should use multiple intelligence-driven reading tasks as an alternative scaffolding tools to raise the motivational levels of struggling readers in the context of the study.
Volume: 14
Issue: 2
Page: 1548-1556
Publish at: 2025-04-01

A hybrid machine learning approach for improved ponzi scheme detection using advanced feature engineering

10.11591/ijict.v14i1.pp50-58
Fahad Hossain , Mehedi Hasan Shuvo , Jia Uddin
Ponzi schemes deceive investors with promises of high returns, relying on funds from new investors to pay earlier ones, creating a misleading appearance of profitability. These schemes are inherently unsustainable, collapsing when new investments wane, leading to significant financial losses. Many researchers have focused on detecting such schemes, but challenges remain due to their evolving nature. This study proposes a novel hybrid machine-learning approach to enhance Ponzi scheme detection. Initially, we train an XGBoost classifier and extract its features. Meanwhile, we tokenize opcode sequences, train a gated recurrent unit (GRU) model on these sequences, and extract features from the GRU. By concatenating the features from the XGBoost classifier and the GRU, we train a final XGBoost model on this combined feature set. Our methodology, leveraging advanced feature engineering and hybrid modeling, achieves a detection accuracy of 96.57%. This approach demonstrates the efficacy of combining XGBoost and GRU models, along with sophisticated feature engineering, in identifying fraudulent activities in Ethereum smart contracts. The results highlight the potential of this hybrid model to offer more robust and accurate Ponzi scheme detection, addressing the limitations of previous methods.
Volume: 14
Issue: 1
Page: 50-58
Publish at: 2025-04-01

Connected caregiving: investigating mothers in the era of digital access

10.11591/ijict.v14i1.pp347-354
Anissa Saidi , Wong Yee Von , Tirzah Zubeidah Zachariah@ Omar , Lim Seong Pek , Rita Wong Mee Mee , Khoo Kim Leng
Mothers have embraced and utilized digital access for nurturing and personal use to enhance their roles while balancing newfound demands. The Internet has provided mothers access to information on various topics, including pregnancy, childbirth, and infant care. Social media tools and platforms have also provided mothers with a space to connect with other mothers, share experiences, and seek support. This scoping review aims to identify the relationship of the focus skills among mothers in utilizing digital access. Four databases, including Scopus, web of science (WOS), education resources information centre (ERIC), and ScienceDirect, were used in this research, which found 36 articles for eligibility. Only 16 articles are eligible for analysis and reference after the exclusion and inclusion process for data collection. Based on the 16 publications examining mothers’ use of internet access, four essential skills have been identified. These included social, digital, cultural, and problem-solving skills and are acknowledged as being related to digital access mothering. The findings show these skills are offered to mothers through digital access, fostering diverse skill sets, contributing to their empowerment, and supporting sustainable development goal 5: gender equality, aiming to enhance women’s roles and ensure equal opportunities through digital inclusion.
Volume: 14
Issue: 1
Page: 347-354
Publish at: 2025-04-01
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