Articles

An machine learning-enhanced reconfigurable software defined radio architecture for adaptive 5G wireless systems

10.11591/ijict.v15i2.pp699-706

Vijaya Bhaskar Chalampalem , Sancarapu Nagaraju , Venkata Vara Prasad , R. Kiran Kumar , Shanmugham Balasundaram

This paper presents a machine learning (ML)-enhanced software defined radio (SDR) architecture optimized for adaptive 5G wireless communication. The system incorporates predictive ML algorithms to enable real-time modulation selection, finite impulse response (FIR) filter reconfiguration, and spectrum adaptation based on dynamic channel parameters such as bit error rate (BER), received signal strength indicator (RSSI) and signal-to-noise ratio (SNR). A decision tree classifier and a deep Q-learning agent dynamically determine optimal modulation schemes (BPSK, QPSK, 16-QAM, OQAM) and filter tap configurations (4/8/16 taps), ensuring efficient communication under varying network conditions. Implemented on a Xilinx Zynq SoC using Verilog for datapath design and Python for ML control via AXI4-Lite, the architecture achieves a maximum operating frequency of 182.4 MHz, 40.7% logic utilization, and only 122.3 mW power consumption. Compared to existing SDR implementations, the system demonstrates a 17% frequency improvement, 28% power reduction, and 21% area savings. Real-time electrocardiogram (ECG) transmission confirms the system’s adaptability, achieving BER < 10⁻³ at 22 dB SNR and < 10⁻⁵ at 26 dB. These results affirm the viability of the proposed ML-SDR for edge-based biomedical and ultra-reliable low-latency communications (URLLC) applications in 5G networks.

Volume: 15

Issue: 2

Page: 699-706

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

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

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

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

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

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

Analyzing learners' perceptions of engagement and learning interaction in gamified massive open online courses for TVET using SEM-PLS

10.11591/ijece.v16i3.pp1319-1328

Azizul Mohd Yusoff , Sazilah Salam , Siti Nurul Mahfuzah Mohamad , Rujianto Eko Saputro

The introduction of gamified massive open online courses (G-MOOCs) represents a novel advancement in technical and vocational education and training (TVET). The use of gamification in education has been shown to increase engagement and motivation, which are crucial for effective learning. However, there is limited research on the specific impacts of G-MOOCs on learner outcomes in TVET. A key feature of G-MOOCs is the integration of gamification elements to enhance learner engagement and interest. This research employs structural equation modelling with partial least squares (SEM-PLS) to examine learners' perceptions of their participation and learning experiences in G-MOOCs for TVET. Specifically, the study aims to identify how gamification approaches such as fun, engagement, and learner interaction influence knowledge acquisition, skills development, satisfaction, and overall learning outcomes. The analysis reveals that G-MOOCs have a strong positive correlation (0.505) with learning engagement. Additionally, learning engagement significantly moderates learning outcomes (p=0.002). Interaction also has a significant impact (p=0.381) on learning outcomes. Overall, the findings indicate a significant positive relationship between learners' activities and their performance in G-MOOCs.

Volume: 16

Issue: 3

Page: 1319-1328

Publish at: 2026-06-01

Applications of machine intelligence and decision analytics in hospitality: a systematic review

10.11591/ijai.v15.i3.pp2024-2040

Omar Jawabreh , Ehab Abdul Raheem Alshatnawi , Rami Mahmoud

This systematic review synthesizes how machine intelligence (MI) and decision analytics (DA) are deployed across hospitality functions (customer relationship management (CRM)/personalization, revenue management (RM) and pricing, and operations) and clarifies barriers and research frontiers. Following a PRISMA-oriented protocol for the 2008–2025 period, the study searched Scopus, Web of Science, IEEE/Elsevier, and SpringerLink using combined terms for MI/artificial intelligence (AI)/machine learning (ML) and hospitality/tourism with CRM, revenue/pricing, forecasting, and operations. The inclusion criteria focused on peer-reviewed English studies reporting concrete models/applications or empirical evidence. Studies were thematically coded into four streams: CRM and personalization; RM and dynamic pricing; operations and scheduling; and governance (privacy/ethics/skills). The findings consistently show that MI/DA improves customer segmentation, demand forecasting, dynamic pricing (revenue per available room (RevPAR)), and workforce/maintenance efficiency. Yet, adoption is slowed by data governance (privacy/general data protection regulation (GDPR)) and siloed systems. Unlike prior broad IT/e Tourism reviews, this study integrates recent MI advances (spatiotemporal deep learning, real-time decisioning, and human–AI teaming) into a hospitality-specific decision pipeline and outlines a testable research agenda around scalability/drift, privacy-by-design, and controlled field experiments.

Volume: 15

Issue: 3

Page: 2024-2040

Publish at: 2026-06-01

SMAC: System for monitoring and automatic control of water, nutrients, and pH in hydroponic nutrient film technique

10.11591/ijra.v15i2.pp365-376

Frengki Simatupang , Istas Pratomo Manalu , Eka Stephani Sinambela , Marojahan Mula Timbul Sigiro , Gerry Italiano Wowiling

Manual regulation of nutrient solution parameters in hydroponic systems often causes instability and delayed corrective actions. This study presents an Internet of Things (IoT) based system monitoring and automatic control (SMAC) for a nutrient film technique (NFT) hydroponic system to automatically regulate pH, total dissolved solids (TDS), and temperature. The proposed system integrates real-time sensors, automatic actuators, and dual-microcontroller architecture, in which an Arduino Uno performs local control while an ESP32 enables wireless IoT monitoring. An experimental systems engineering approach was applied for system design and performance evaluation. Automatic temperature compensation (ATC) was incorporated into pH and TDS measurements to improve reliability under varying thermal conditions. Experimental results indicate that the temperature sensor achieved an average error of 0.13 °C. The control algorithm corrected pH deviations gradually by approximately ±0.31 pH units (pH Up) and ±0.38 pH units (pH Down) per cycle without overshoot. Nutrient concentration control increased TDS by about 75 ppm per cycle under low-TDS conditions. Stability testing confirmed that pH and TDS remained within optimal ranges after disturbances, while safety mechanisms operated reliably under abnormal temperatures. The results demonstrate that the proposed SMAC system provides accurate, stable, and adaptive control suitable for precision and sustainable hydroponic applications.

Volume: 15

Issue: 2

Page: 365-376

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

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

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

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

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

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31,377 Article Results

An machine learning-enhanced reconfigurable software defined radio architecture for adaptive 5G wireless systems

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

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

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

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

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

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

Analyzing learners' perceptions of engagement and learning interaction in gamified massive open online courses for TVET using SEM-PLS

Applications of machine intelligence and decision analytics in hospitality: a systematic review

SMAC: System for monitoring and automatic control of water, nutrients, and pH in hydroponic nutrient film technique

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

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

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

A review of sensemaking design elements: towards an affordances typology

Diabetic retinopathy detection using SWIN transformer

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