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31,377 Article Results

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

Predictive modeling and optimization of paper mill using hybrid machine learning techniques

10.11591/ijape.v15.i2.pp692-702
Abhijit Singh Bhakuni , Sandeep Kumar Sunori , Pradeep Juneja
The paper has played a vital role in the life of humans from ancient times covering a vast range of applications such as writing, packaging, and printing. The present paper is presenting a comprehensive review of various optimization and control methodologies, ranging from conventional to advanced ones, pertaining to the paper mill. The final goal of these control strategies is to upgrade the mill’s production and quality in presence of multiple technical challenges such as nonlinear and multivariable nature of the involved processes, various disturbance parameters, and time delays. In this work, the integration of machine learning with paper mill process is illustrated. For any manufacturing process, the final product quality is the key goal. There are various traditional techniques which have already been practiced for final produced paper quality in paper mills. This paper highlights the capability of support vector machine (SVM) algorithm to assess the produced paper quality, capturing the two crucial inputs viz. the pulp consistency and the headbox level. The basic goal of this research is twofold, firstly it presents an exhaustive literature survey exploring various strategies which are practiced currently in the domain of control and optimization of various paper mill processes. Secondly, it intends to develop and evaluate various SVM and SVM-RF hybrid models using MATLAB for assessment of quality of final product on basis of two parameters- pulp consistency and head box level. Finally, genetic algorithm has been employed in MATLAB for multivariate optimization.
Volume: 15
Issue: 2
Page: 692-702
Publish at: 2026-06-01

Optimized resonant capacitor and switching frequency for high-efficiency wireless power transfer in E-bikes using CST Studio Suite

10.11591/ijape.v15.i2.pp514-524
Wan Muhamad Hakimi Wan Bunyamin , Rahimi Baharom
Wireless power transfer (WPT) is increasingly adopted for E-bike charging; however, its performance is often constrained by inaccurate resonant tuning, inefficient capacitor selection, and improper switching-frequency operation, which lead to significant power loss and reduced transfer efficiency. This study addresses these limitations by formulating an optimized design methodology for selecting resonant capacitors and inverter switching frequency to achieve high-efficiency energy transfer. A 40-mm air gap between the transmitter and receiver coils is modeled using CST Studio Suite, where a 3D electromagnetic circuit co-simulation framework is applied to evaluate mutual inductance, resonant behavior, magnetic-field distribution, and S-parameter characteristics. Parametric sweeps combined with a convergence-based optimization algorithm identify the optimal resonant operating point, yielding a peak resonant frequency of 38.1 kHz, a maximum simulated transfer efficiency of 99%, and a deep reflection coefficient of -21.77 dB. The optimized configuration also demonstrates stable voltage and field distribution at resonance, confirming effective impedance matching. The main contributions of this work include: i) establishing a unified EM–circuit optimization workflow for determining resonant capacitance and switching frequency, ii) providing quantitative resonance parameters and performance indicators suitable for compact E-bike WPT systems, and iii) integrating mathematical modelling to validate CST-based predictions and ensure theoretical consistency. The proposed approach significantly enhances design accuracy and efficiency, offering a scalable and high-performance solution for next-generation low-power electric vehicle (EV) and E-bike wireless charging applications.
Volume: 15
Issue: 2
Page: 514-524
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

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

Efficient email classification technique: a comparative study of header-only and full-content approaches

10.11591/ijict.v15i2.pp665-673
Worawit Kitikusoun , Nawaporn Wisitpongphan
The purpose of this research is to explore efficient techniques and sufficient features for organizational email classification, with a focus on identifying emails that are not beneficial for work to reduce the burden of email management. This study proposes a novel approach by comparing the performance of using email header features (Header-Only) versus full email data (Header + Body), aiming to evaluate the accuracy and processing time of widely used machine learning algorithms, including Random Forest, SVM, KNN, XGBoost, and ANN. The experiment was conducted using the Enron dataset, with key features extracted from email headers such as sender and recipient addresses and from the body content. The results show that using only header information provides classification performance comparable to using full email content. In particular, models such as Random Forest, XGBoost, and LightGBM achieved accuracy exceeding 95%, while reducing processing time by up to 21.66% in the Random Forest model. It is evident that classifying emails using header-only features is both highly accurate and resource-efficient. This research offers practical guidance for organizations in developing effective email filtering systems without compromising classification quality.
Volume: 15
Issue: 2
Page: 665-673
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

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

Vector-logic models of digital circuits for simulation and rendering

10.11591/ijra.v15i2.pp319-330
Vladimir Hahanov , Svetlana Chumachenko , Eugenia Litvinova , Andrii Voronov , Oleh Demchenko , Nataliya Maksymova
Vector-logical in-memory computing for solving modelling for simulation (MOSI) problems by using read-write transactions free of processor instructions is proposed. A parser mechanism has been developed for converting the HDL code of the logical circuit into the internal vector-logical data structures of the MOSI service, addresses of logical vectors of elements. Deductive vectors are generated from the vector-logic model of the digital circuit for fault as address simulation of the input test sets. A mechanism for modelling a fault simulation matrix as the addresses of the deductive vector bits of each element on the test set has been created. The results of good-value and fault as address simulation are rendered and synchronized in the good-value simulation, fault as address simulation, fault simulation matrices on the input set, and on the lines of the logical circuit displayed on the monitor. Modeling and simulation mechanisms encoded and verified using examples of logical circuits of the ISCAS library. The scientific novelty is represented by vector-logical models of digital circuit elements, good-value simulation of test set as address and fault as address simulation of a digital circuit, and fault as address simulation of the input set, using a quadratic simulation matrix.
Volume: 15
Issue: 2
Page: 319-330
Publish at: 2026-06-01

Personalized learning with learning style using fuzzy for university students performance

10.11591/ijai.v15.i3.pp2216-2228
Endina Putri Purwandari , Endang Widi Winarni , Siti Soraya Abdul Rahman , Jafar Nashrudin Al Azam
The main challenges of traditional learning systems are time-space constraints and teacher-centeredness. The emergence of information technology has given rise to e-learning systems characterized by teacher centred strategy components and one-size-fits-all strategies. Furthermore, the concept of personalization is presented through learning technology that provides educational content to the students learning style. This research develops a personalized system that aligns teaching strategies with students' learning styles using the Myers-Briggs Type Indicator (MBTI). The emphasis is on adaptive and revising teaching strategies to improve student learning performance. The system is developed to create student profiles to determine their learning styles based on the MBTI and fuzzy. The system was tested with undergraduate students at the information systems department in University of Bengkulu. Research shows that students in the experimental group have higher post-test scores, greater learning achievement and performance than the control group. Fuzzy clustering based personalized e-learning could improve university student performance. The use of personalized online learning significantly affects learning management system (LMS) integration, lecturers, and curriculum development.
Volume: 15
Issue: 3
Page: 2216-2228
Publish at: 2026-06-01

Transforming electric vehicle charging through solar integration and high-frequency magnetic induction for seamless wireless power transfer

10.11591/ijece.v16i3.pp1118-1131
Selvan Chinnaiyan , Prabhakar Manickam , Madhu Chandra G. , Aarthi V. , Narendra Babu C. R.
The rapid adoption of electric vehicles (EVs) is constrained by limited charging infrastructure, prolonged charging duration, grid dependency, and inefficiencies in conventional wireless charging systems. To address these challenges, this paper proposes a solar-integrated high-frequency inductive wireless charging framework that enables efficient, contactless, and partially dynamic EV charging. The proposed system combines a photovoltaic (PV) energy harvesting subsystem with maximum power point tracking (MPPT), a high-frequency resonant inductive coupling mechanism using a series–series (SS) topology, and an intelligent solar inductive synergy optimization algorithm (SISOA) for adaptive power and energy storage management. The theoretical foundation of the system is based on Faraday’s law of electromagnetic induction and resonant magnetic coupling to enhance mutual inductance and power transfer efficiency. Simulation studies conducted in MATLAB/Simulink demonstrate that the proposed approach achieves a mutual inductance of 82.5, an output voltage of 500 V, and an output power of 4,800 W, while reducing overall power losses to 21.18% and improving system efficiency to 94.5%. The results further reveal that vehicle speed and the number of receiver coils significantly influence charging effectiveness and state-of-charge performance.
Volume: 16
Issue: 3
Page: 1118-1131
Publish at: 2026-06-01

Performance improvement of DC microgrids via adaptive neuro-fuzzy inference system -optimized AI-tuned fractional order proportional-integral-derivative controllers

10.11591/ijict.v15i2.pp797-804
Debani Prasad Mishra , Sarita Samal , Manas Ranjan Sahu , Sonna Murari , Piyuskant Das , Surender Reddy Salkuti
This paper presents a novel approach to enhance the dynamic performance of direct current (DC) microgrids using an artificial intelligence (AI)-tuned fractional order proportional-integral-derivative (FO-PID) controller, further optimized through an adaptive neuro-fuzzy inference system (ANFIS). Conventional PID controllers tend to fail when it comes to dealing with microgrid environment-related non-linearities and uncertainties, particularly under changing load and generation situations. To remedy this, the suggested approach combines AI-tuned tuning algorithms for selecting initial parameters, and then ANFIS optimization to fine-tune the FOPID gains adaptively for better control precision. The performance of the hybrid control approach is tested through MATLAB simulations on a generic DC microgrid model that includes distributed energy resources, power electronic converters, and dynamic loads. Comparative evaluation against standard PID and independent FOPID controllers verifies remarkable advantages in terms of voltage regulation, stability, and transient response in various operating conditions. Amongst the achieved outcomes, it highlights the strength of the proposed ANFIS-optimized AI-tuned FOPID controller as a smart and robust strategy for real-time control of DC microgrids.
Volume: 15
Issue: 2
Page: 797-804
Publish at: 2026-06-01

Hybrid deep neural network model for aspect and opinion extraction with multi-head attention-driven sentiment analysis

10.11591/ijict.v15i2.pp769-777
Abhinandan Shirahatti , Ramesh Medar , Vijay Rajpurohit , Sanjeev Kaulgud , Mrutyunjaya Mathad Shivamurthaiah
Finding and extracting significant features from review sentences is known as aspect triplet extraction, and it provides succinct information on the elements that users have addressed. This method makes sentiment analysis and opinion mining easier, which helps to provide an adequate understanding of user opinions in reviews. This research presents a novel approach to achieve aspect-sentiment triplet-extraction (ASTE) using a deep neural network and transformer-based multi-head attention model. The proposed hybrid model adopts a pipeline methodology, concurrently extracting opinions and aspects while performing sentiment classification. The study addresses the intricate challenge of identifying triplets that capture nuanced relationships between terms and sentences, employing a deep neural network for joint extraction of aspects and opinions using a sequential tagging method. Sentiment classification is seamlessly integrated into the pipeline, treating sentiment recognition as a classification task, and aspect and opinion extraction as text-extraction challenges. Evaluations was out experimentally on the SemEval 2016 restaurant dataset demonstrate the effectiveness of the model, despite issues with unequal distribution of data.
Volume: 15
Issue: 2
Page: 769-777
Publish at: 2026-06-01

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

A novel Lucas-based adaptive sampling optimization for enhancing network lifetime

10.11591/ijict.v15i2.pp607-615
Kanaka Raju Rajana , Shanmuk Srinivas Amiripalli
This paper introduced to enhance network lifetime using a novel Lucas based adaptive sampling methodology by sampling network condition to dynamically modifying sampling intervals using the Lucas sequence, this sequence not only used for sampling but also used to modify data collection, optimizing accuracy and energy efficiency. This technique aims to reduce superfluous data transmissions and conserve network resources by monitoring network utilization and adjusting sample with low medium and high rates. We enhance the network performance and longevity using Lucas based technique via simulation and demonstrating its potential. This may effectively approach novel address to challenges associated with constrained networks, particularly in the domain of IoT and wireless sensor networks (WSNs).
Volume: 15
Issue: 2
Page: 607-615
Publish at: 2026-06-01
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