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Can machines imagine? Critical thinking and cultural reasoning in multimodal-multilingual AI

10.11591/ijict.v15i2.pp823-838
Mohammad Awad AlAfnan , Siti Fatimah MohdZuki , Shefa Mohammad AlAfnan
Effective communication across languages and cultures is essential in today’s interconnected world. Multimodal-multilingual language models (MMMLMs) aim to advance this goal by integrating text, speech, and visual understanding across diverse linguistic contexts. This study evaluates four leading MMMLMs-GIT, mPLUG, CLIP, and Whisper + GPT-4V-on cross lingual and cross-modal tasks, including image captioning, visual question answering, speech-to-image generation, and idiomatic translation. Performance was assessed in high-resource (English, Arabic), medium resource (Malay), and low-resource (Macedonian) settings. Results show strong performance in structured tasks but notable limitations in cultural reasoning, figurative language interpretation, and semantic grounding in low-resource environments. GIT delivered the most consistent multilingual results, while Whisper + GPT-4V excelled in fluency yet lacked cultural sensitivity. To address these gaps, the study proposes culturally informed evaluation protocols that integrate quantitative metrics such as BLEU, CIDEr, and F1 with qualitative, community-centered approaches. These include cross-cultural annotation panels, inter-rater reliability validation using Cohen’s kappa, and a novel “cultural fidelity” metric to measure alignment with culturally specific norms. The findings emphasize the need for inclusive datasets, ethical development, and interdisciplinary collaboration to ensure MMMLMs support equitable and culturally aware global communication.
Volume: 15
Issue: 2
Page: 823-838
Publish at: 2026-06-01

Predicting battery life performance using artificial intelligence techniques in electric vehicles

10.11591/ijict.v15i2.pp805-812
Debani Prasad Mishra , Munavath Pavan Kalyan , Shivam Tyagi , Piyushjeet Piyushjeet , Shiv Grover , Surender Reddy Salkuti
Electric vehicles’ (EVs’ performance and sustainability are significantly influenced by the efficiency and lifespan of their lithium-ion batteries. This paper explores the critical factors affecting battery degradation, focusing on parameters such as charge cycles, thermal management, and voltage dynamics. Utilizing a dataset of 14 batteries, the study employs data-driven machine learning (ML) to predict the remaining useful life (RUL) of batteries. The ensemble-based regression model demonstrated superior predictive accuracy through comprehensive analysis, achieving R² values of 97.89% for training and 94.69% for testing. Feature importance analysis identified cycle index (CI) as the most critical determinant of battery health, followed by discharge time and voltage stability. Visualizations, including correlation heatmaps and residual plots, validate the robustness of the selected model. Additionally, sustainable charging strategies, such as steady current-steady voltage (also known as CC-CV), are highlighted for their role in enhancing battery longevity. This research offers actionable insights into battery management systems, providing a robust foundation for predictive maintenance and the development of sustainable electric mobility solutions.
Volume: 15
Issue: 2
Page: 805-812
Publish at: 2026-06-01

Exploring player interaction and team cooperation in MMOG playability enhancement

10.11591/ijict.v15i2.pp644-654
Gong Xiaoxue , Lili Nurliyana Abdullah , Azrul Hazri Jantan , Noris Mohd Norowi , Fatimah Sidi , Gulmira Abildinova
The massively multiplayer online games (MMOGs) continue to grow in popularity, and it has become particularly important to understand the key factors that influence team playability. While existing research has focused primarily on system functionality and individual player experience, insufficient attention has been paid to the role of team dynamics in player satisfaction. This study focuses on the core variables that influence team playability, including teamwork, task dependency, team loyalty (TLO), and team relationships (TR), and explores how these variables work together to influence player experience. This study used a combination of exploratory research (multi-variates) and a questionnaire survey (N=1064) to initially construct a team playability model, which was validated by structural equation modeling (SEM). The results show that TR have a significant positive effect on teamwork efficiency, and captains with transformational leadership (TL) styles not only enhance TR but also further improve overall team effectiveness (TE) and player satisfaction. This study provides MMOG developers with theoretical support for designing game mechanics centered on team interaction to enhance overall playability and player stickiness.
Volume: 15
Issue: 2
Page: 644-654
Publish at: 2026-06-01

Enhancing Bitcoin price forecasting: a comparative analysis of advanced time series models with hyperparameter optimization

10.11591/ijict.v15i2.pp535-544
Amine Batsi , Mohamed Biniz , Rachid El Ayachi
This paper evaluates state-of-the-art time series forecasting to predict next day Bitcoin prices via distinct architectures and methodologies in a real-time setting. We study six advanced models, KAN, TimesNet, NBEATS, NHITS, PatchTST and BiTCN, applied to a Jan 1, 2023, to Dec 1, 2024. We simulate real world applications via a rolling forecast strategy, in which we predict daily prices from the most recent data. The dataset consists of daily Bitcoin closing prices and data preprocessing and integrity checks for its constituent data. Additionally, rigorous accuracy and reliability were investigated using performance metrics such as the MAE, RMSE, MAPE, and R². NBEATS and NHITS were the top performers, achieving an R² score of 0.967, explaining complex patterns in volatile cryptocurrency data. The specific importance of model architecture and further hyperparameter optimization in achieving higher forecasting accuracy is highlighted in this study. The practical implications of these findings for the advancement of time series forecasting in financial markets are leveraged here, where timely and accurate forecasts are critical.
Volume: 15
Issue: 2
Page: 535-544
Publish at: 2026-06-01

Enhanced transfer learning framework for brain tumor detection from MRI scans using attention-based feature fusion

10.11591/ijict.v15i2.pp497-507
Smita Bharne , Ekta Sarda , Shamal Salunkhe
Due to the complexity of the different tumor types in medical imaging detection of brain tumor is still as prominent challenge. This paper present the innovative technique enhanced transfer learning framework (ETLF) which integrating the advanced pre-processing with hybrid fine-tuned method for accurate brain tumor detection from magnetic resonance imaging (MRI) scans. The proposed model combine the strength of pre-trained convolutional neural networks (CNNs) such as EfficientNetB0 through domain specific transfer learning and attention based fine tuning. A novel feature fusion layer and adaptive learning rate scheduler are key indicators for model performance and prevent overfitting. The methodology is assessed on the benchmark dataset BraTS and Kaggle brain tumor datasets. The main contribution of work lies in development of domain- adaptive transfer learning with different datasets. The ETLF shows the high accuracy of 98.76% which able outperforms effectively in diagnosing tumor suitable of clinical purpose.
Volume: 15
Issue: 2
Page: 497-507
Publish at: 2026-06-01

Advanced IoT-integrated real-time fire detection and automated mitigation system

10.11591/ijict.v15i2.pp861-868
Rama Krishna Peddarapu , Ajimera Abhinav , Gnana Sathwika V. N. V. , Poosa Brijesh , Amrutha Varshini Ravula
In the field of industry and commerce safety, tackling the most challenging and ongoing fire threats requires the advance internet of things (IoT) integrated real-time fire detection and automated mitigation system. Leveraging IoT and multi-modal sensing in fire safety, the system combines flame, gas, and humidity sensors and cameras to provide continuous real time monitoring and appropriate management of the threats. Real-time automated hazard interventions, such as sprinkler system engagement and geocoded alerts to fire departments, significantly improve life safety outcomes of the system. Active damage mitigation IoT devices provide integrated damage mitigation safety and individual IoT device remote monitoring. In the scope of industry and commerce, this system is a demonstration of the impact of IoT on improving fire safety.
Volume: 15
Issue: 2
Page: 861-868
Publish at: 2026-06-01

Intelligent home automation framework using sensor fusion and machine learning for energy efficiency and thermal comfort

10.11591/ijict.v15i2.pp545-552
Franklin Ovuolelolo Okorodudu , Gracious Chukwuweike Omede , Etinosa Eugene Osawe
This paper presents an innovative, intelligent home automation framework integrating sensor fusion and machine learning to promote energy efficiency and thermal comfort in residential settings. Utilising low-cost hardware such as the Arduino Uno R3, passive Infrared (PIR) sensors, KY-018 photoresistors, and KY-028 temperature sensors, the system achieves a human presence detection accuracy of 95.3% via a random forest classifier. Over a three-month period, testing in several homes showed that the system is 99.7% reliable, responds in 1.2 seconds, and costs 85% less than commercial options. This research lays the groundwork for sustainable smart homes by providing a mathematical model for optimizing energy use and a unified modeling language (UML) model of the system architecture. These results show how important it is to have open-source technology that is cheap and could help smart building systems spread around the world. The study utilized a controlled experimental design featuring five families, with sensor data gathered at 10-second intervals over a three-month period. A random forest classifier trained on 10,000 labeled data points could correctly guess whether or not a person was present 94.8% of the time and 95.7% of the time. The framework is useful because it combines cheap sensors with a lightweight machine-learning pipeline that can work on small microcontrollers. This solves the long-standing problem of the cost performance gap seen in prior smart-home deployments.
Volume: 15
Issue: 2
Page: 545-552
Publish at: 2026-06-01

Utilizing the machine learning-driven techniques used to ECG dataset for predicting coronary heart disease

10.11591/ijict.v15i2.pp719-728
Mohd Osama , Rajesh Kumar , Chandrakant Kumar Singh
The worldwide cause of mortality is cardiovascular heart disease. The automatic prediction of heart disease can be made to possible for accurate detection in initial stage. In recent year, the artificial intelligence approaches giving promising outcomes in predicting various types of cardiovascular conditions. The main focous of this work is to implementation of various machine learning techniques used to predict cardiovascular heart disease (CHD) using electrocardiogram (ECG) datasets. ECG provide the electrical Signal from the heart that identify the presence of disease or not. The preprocessing method are used for improving the quality of ECG signals and extract the features from ECG of patients. There are several well-established machine learning techniques, including support vector machine (SVM) and K-nearest neighbour (KNN)., logistic regression and decision tree classifier used for prediction of the disease. So, our finding of this paper will provide the new understanding regarding CHD prediction using different machine learning techniques. The Decision Tree-based machine learning model demonstrated excellent performance, achieving 98% accuracy, 96% precision, 100% recall, and an F1-score of 97%, which is better than rest of other comparative machine learning models. Finaly expermental results shows that decision tree approach providing better outcome amongs all the algorithms with respect to all above mensioned parameter.
Volume: 15
Issue: 2
Page: 719-728
Publish at: 2026-06-01

Semantic interoperability in IoT for Industry 4.0: Review, taxonomy, challenges, and future research

10.11591/ijict.v15i2.pp909-924
Devamekalai Nagasundaram , Erum Ashraf , Selvakumar Manickam , Shams Ul Arfeen Laghari , Shankar Karuppayah
Semantic interoperability is a critical enabler for achieving the Industry 4.0 vi sion, ensuring that heterogeneous IoT devices, systems, and applications can ex change and interpret data consistently. Despite its importance, achieving seman tic interoperability continues to pose significant challenges due to the diversity of data formats, standards, and ontologies used across industrial IoT environ ments. This paper presents a comprehensive review and taxonomy of semantic interoperability within Industry 4.0, analyzing existing frameworks, protocols, and ontological models. We classify current approaches based on their architec tural layers, semantic technologies, and application domains. Additionally, this study identifies the limitations of prevailing solutions, highlights open research challenges, and proposes future directions for enhancing semantic interoperabil ity in industrial IoT systems. The insights provided aim to support researchers and practitioners in developing scalable, secure, and semantically aligned IoT ecosystems for Industry 4.0.
Volume: 15
Issue: 2
Page: 909-924
Publish at: 2026-06-01

Enhancing support vector machine performance using particle swarm optimization for sentiment analysis

10.11591/ijict.v15i2.pp523-534
Christofer Satria , Anthony Anggrawan , Peter Wijaya Sugijanto , Husain Husain , I Nyoman Yoga Sumadewa , Victoria Cynthia Rebecca
Recently, social media has established itself as a leading platform in various sectors. Meanwhile, text extraction and sentiment analysis classification have attracted significant attention in research. Regrettably, traditional sentiment analysis often falls short of accurately capturing sentiment nuances. At the same time, machine learning has enabled more effective sentiment analysis, data mining, and classification, as well as the development of models that incorporate artificial intelligence. Therefore, the purpose of this study is to optimize sentiment analysis of public opinion in social media regarding Grand Prix motorcycle racing (MotoGP) and World Superbike (WSBK) events using machine learning and an optimized machine learning method. This study applies the support vector machine (SVM) machine learning method and enhances its performance through optimization by integrating it with the particle swarm optimization (PSO) algorithm. This study found that the SVM method achieved 80.15% accuracy, 75.63% recall, and 76.89% F1-score. In contrast, the SVM method combined with PSO achieves accuracies of 81.82%, 79.9%, and 79.62% for recall, precision, and F1-score, respectively, in classifying the sentiment of sporting events. The implications suggest that applying Hybrid SVM with PSO significantly enhances classification accuracy in sentiment analysis.
Volume: 15
Issue: 2
Page: 523-534
Publish at: 2026-06-01

A review of sensemaking design elements: towards an affordances typology

10.11591/ijict.v15i2.pp488-496
Fadzlin Ahmadon , Murni Mahmud , Muna Azuddin
This study explores the intersection of interaction design and sensemaking within digital systems, aiming to identify and categorize key affordances that enhance user sensemaking. Starting with a focused literature review, key design elements such as tagging and annotation are identified, important for effective sensemaking in interaction design. Drawing on Maier's construct of affordances, the behaviours of these design elements are analyzed to derive specific affordances integral to enhancing user experience. The primary objective is to develop a generalized affordance typology that supports sensemaking across various digital systems. This typology organizes the derived affordances into broad themes such as effortless discovery, expressive freedom, collaborative engagement, cognitive support, insight enhancement, and user empowerment. This typology serves as a tool for interaction designers, facilitating the application of these themes in various design scenarios to create more intuitive and effective digital environment for sensemaking.
Volume: 15
Issue: 2
Page: 488-496
Publish at: 2026-06-01

Stacking of machine learning classifiers for bot detection using account level data

10.11591/ijict.v15i2.pp477-487
Jwala Sharma , Samarjeet Borah
Social media is a platform for individuals to connect, share, and create information. Social bots produce automated content and interact with humans; in the process, they learn and mimic humans’ behaviour. This research study addresses the challenge of identifying social media bots (SMB) that can rapidly disseminate information or misinformation on platforms like Twitter. It contributes to the field by reviewing literature to define bot behaviours and exploring advanced machine learning classifiers for effective bot detection using account-level data. The study employed Spearman's rank correlation coefficient to select relevant features for SMB classification, then trained six different machine learning models: decision tree (DT), random forest (RF), logistic regression (LR), support vector machine (SVM), and k-nearest neighbour (KNN). To further improve accuracy, a classifier stacking technique was applied. Key findings revealed that while individual classifiers performed variably, with RF leading at 89% accuracy, the stacked classifier approach outperformed all single-classifier methods with an impressive 90% accuracy rate. The results underscore the potential of combining multiple classifiers to enhance the precision of social media bot detection efforts.
Volume: 15
Issue: 2
Page: 477-487
Publish at: 2026-06-01

Deep learning-based optimization techniques for network lifetime enhancement in wireless sensor networks

10.11591/ijict.v15i2.pp623-633
Abhay Raghunath Gaidhani , Amol D. Potgantwar
Wireless sensor networks (WSNs) are integral to applications like environmental monitoring, healthcare, and surveillance, yet they face the critical challenge of limited energy resources, which shortens the network's operational lifespan. Addressing this issue, this paper explores deep learning-based optimization techniques as a solution to enhance network lifetime by efficiently managing energy consumption. We present a detailed review of the existing challenges in WSNs and examine various deep learning methods, including neural networks, deep reinforcement learning (DRL), and generative adversarial networks, specifically tailored for WSN optimization. In this study, we introduce a new reinforcement learning (RL) based optimization algorithm to prolong the network lifetime. The proposed technique is intended to smartly distribute the energy consumption among the network elements, leading to desirable performance with regard to the battery lifetime. The paper ends with a summary of design aspects and future research directions to improve the WSN performance further based on deep learning.
Volume: 15
Issue: 2
Page: 623-633
Publish at: 2026-06-01

Diabetic retinopathy detection using SWIN transformer

10.11591/ijict.v15i2.pp750-758
Sheetal J. Nagar , Nikhil Gondaliya
Diabetic retinopathy (DR) is a diabetes related eye disorder that damages the retina. DR is among the most specific complications of diabetes. A vital challenge for automated detection systems in medical image diagnosis is to minimize the false negative rate for patients’ timely treatment. This paper presents a novel strategy employing the shifted window (SWIN) Transformer for efficiently modeling local and global visual information to address this challenge. We have proposed our work to maximize the true positive ratio and minimize the false negative ratio for the automated process of diagnosing the level of DR, so that patients with positive signs of DR can be predicted most accurately and can save vision. The results suggest that SWIN Transformer architecture, along with the contrast-limited adaptive histogram equalization (CLAHE) technique, provides a robust option for developing a reliable DR detection system. The results indicate that the proposed approach achieves 96% weighted recall across all the levels of DR detection and 97.45% validation accuracy for the eyePACS DR detection dataset, as well as 99% weighted recall across all the levels of DR detection, along with 99.26% validation accuracy for APTOS 2019 Blindness Detection dataset. Thus, this study aimed to develop a DR detection system focused on minimizing false negatives using the SWIN transformer.
Volume: 15
Issue: 2
Page: 750-758
Publish at: 2026-06-01

Business intelligence and its impact on organizational decision-making: a systematic review

10.11591/ijict.v%vi%i.pp%p
Cesar Patricio-Peralta , Hernan Peña Carnero , Jesús Mondragon , Adan Eugenio Contreras Angeles , Marina Vargas Vega , Walter Patricio Peralta , Marco Mayor Ravines , Juan Mayor Gamero , Cesar Paccha Rufasto
This research examines in detail how business intelligence (BI) supports and guides organizations in decision-making for their plans. The paper warns that the BI tool must be adapted to users' real needs. It's super crucial to keep all the important info in one spot. This optimizes resources and boosts the system's capabilities. The study used a set approach to tackle its main question. This included much searching through big science lists. Scopus and Web of Science were on the list. The search term was a particular word used to pinpoint documents. The review looked at studies from 2019 to 2025. Initially, we found 77 papers. Rules were then applied to include or exclude papers. These descartes criteria take into account the kind of paper, the language used, and how relevant it is to the subject. In the end, 24 papers went through the peer review process. These were reviewed following the preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines. The findings indicate that the application of BI considerably improves the group’s ability to attain superior goals. Some research showed a 93% boost in productivity. Profits went up by 65%, too. These results come only from articles written in English, Spanish, and Portuguese. They mainly focus on explaining the functioning in wealthier nations. The results really show off the main perks of BI. It facilitates informed decision-making more easily for all organisations.
Volume: 15
Issue: 2
Page: 741-749
Publish at: 2026-06-01
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