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30,033 Article Results

Evaluating user experience of a mobile website and redesigning its user interface using goal-directed design method

10.11591/ijict.v15i2.pp634-643
Aang Subiyakto , Muhammad R. Alghifari , Nuryasin N. , Muhammad Q. Huda , Nashrul Hakiem , Viva Arifin , Dwi Yuniarto , Hadi Rahman , Thosporn Sangsawang , Naeem Atanda Balogun
This study evaluated the usability of the user interface (UI) of a mobile website using its user experience (UX) perspectives. The website serves as an information portal intended for access via smartphones and other handheld devices. The objective of the study was to assess the usability of its current interface, redesign it using the goal-directed design (GDD) method, and compare the usability performance before and after the redesign. The study was conducted in five main steps using the cognitive walkthrough, think-aloud, post-study system usability questionnaire (PSSUQ), and interview techniques with five representative participants and 50 respondents. The most important findings of the study were that the redesigned mobile website showed improved usability of the website, as indicated by increased effectiveness and efficiency values, enhanced PSSUQ satisfaction scores, and more positive user feedback.
Volume: 15
Issue: 2
Page: 634-643
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

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

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

Early prediction of myocardial infarction using proposed score tree algorithm

10.11591/ijict.v15i2.pp813-822
Nusrat Parveen , Utkarsha Pacharaney , Gayatri Hegde , Mohammad Rafique , Sana Firoj Nalband , Shamim Akhtar , Satish Devane
Early detection and diagnosis of a diseases will have a big impact on the medical field and help to prevent loss of life. This study begins by gathering information on myocardial infraction patients from hospitals and focuses on earlier diagnostics. In fact, the pre-processed, confirmed data from a qualified doctor is used for this research. Early prediction of myocardial infarction (MI) is proposed by many researchers. They have used Kaggle datasets that is not recent, and they work on post MI. We have proposed early myocardial infraction detection works on unsupervised datasets. To identify myocardial infraction, numerous machines learning supervised algorithms, including decision tree (DT), random forest (RF), are employed in the literature. In this study, we use the score tree algorithm (STA), which operates on an unsupervised dataset, to present a unique early MI prediction method.
Volume: 15
Issue: 2
Page: 813-822
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

Trust-based secure routing in IoT networks using machine learning for enhanced anomaly detection and risk mitigation

10.11591/ijict.v15i2.pp839-849
Sangeetha Krishnaswamy , Arulanandam Karalagan
The rapid growth of the internet of things (IoT) has led to the development of new challenges in ensuring secure and reliable data transmission. This paper proposes a trust-based secure routing protocol (TBSRP) designed to mitigate security threats such as routing attacks in IoT networks. The core innovation lies in the dual-layer trust evaluation mechanism, which combines reputation-based trust and behavioral analysis to dynamically adjust routing decisions based on real-time performance and historical behavior of network nodes. To enhance security, the protocol incorporates an adaptive threshold mechanism that adjusts trust criteria based on observed network conditions and an anomaly detection system utilizing machine learning (ML) algorithms for real-time monitoring of node behavior. Experimental evaluation demonstrates that TBSRP outperforms existing protocols (such as Ad hoc on-demand distance vector (AODV), trust-based AODV (TB-AODV), energy-efficient secure routing (ESR), and Secure AODV (SEC-AODV)) in key performance metrics, including packet delivery ratio (PDR), end-to-end delay, throughput, and routing overhead. The proposed protocol exhibits strong resilience to the increasing number of malicious nodes and varying network conditions, making it highly effective for securing IoT networks. This work contributes to the development of adaptive, scalable, and secure routing protocols for IoT environments, with the potential for further optimization through advanced ML techniques and real-world implementation.
Volume: 15
Issue: 2
Page: 839-849
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

Multiclass classification using variational quantum circuit on benchmark dataset

10.11591/ijict.v15i2.pp578-587
Muhammad Hamid , Bashir Alam , Om Pal
Classification is a major task in data science. Data classification is required in many industries such as healthcare, transport, and finance. Noisy intermediate-scale quantum (NISQ) era. Quantum computers are capable of solving complex data challenges and can be used for the classification of the data with minimum features. In this regard, quantum neural networks are being used extensively for data classification. In this paper, we employ variational quantum circuits for the task of multiclass classification. A hybrid approach is used for building the neural network. In which quantum circuits are used for the feedforward architecture, while in back-propagation, parameters are updated using a classical optimizer on classical computers. We have successfully demonstrated multiclass classification using the proposed approach on benchmark data sets. Our results show that variational quantum circuit (VQC) are a promising candidate for classification problems with fewer features. We have performed experiments on International Business Machines Corporation (IBM) quantum hardware and simulators.
Volume: 15
Issue: 2
Page: 578-587
Publish at: 2026-06-01

Artificial intelligence-based battery management systems in electric vehicles: models, optimization, and future directions

10.11591/ijece.v16i3.pp1645-1654
Hassan Kassem , Tariq Bishtawi
The electric vehicle (EV) depends on the capabilities and durability of the main element of the car — the battery. Conventional battery management systems (BMS) can generally be challenged with regards to state estimation and lifespan forecasting in the face of complicated real-world scenarios. To address these limitations, this study examines how artificial intelligence (AI) has the potential to transform BMS operations. We introduce an in-depth discussion of AI-controlled BMS by examining the state-of-the-art models of precise state-of-charge and state-of-health estimation. The paper also goes into details of how machine learning and deep learning methods can optimize charging strategy, improve thermal management, and predictive diagnostics. The comparison between the data-driven solutions and the traditional methods is going to reveal that there is a high safety, efficiency, and battery life improvement. Lastly, we map the way ahead, taking into consideration issues such as edge computing, explainable AI, and the way of making the BMS a truly self-optimizing system, essential to the next generation of electric cars.
Volume: 16
Issue: 3
Page: 1645-1654
Publish at: 2026-06-01

Energy-aware inertial measurement units scheduling for wearable LoRa systems using quaternion features

10.11591/ijece.v16i3.pp1449-1465
Yudhi Adhitya , Indri Septiani
Wearable Internet of Things systems increasingly depend on inertial measurement units (IMUs) to capture human motion, yet continuous high-frequency sensing, on-device processing, and long-range (LoRa) communication impose significant energy and latency challenges for battery-powered devices. This study formulates a practical scheduling framework that optimizes IMU sampling, quaternion-based feature extraction, and transmission decisions within the wearable/LoRa architecture. The framework operates in discrete time windows of W=0.5−1 s, within which sensing, processing, and communication decisions are updated at the window level to balance energy consumption and responsiveness. The method models energy consumption, accuracy degradation at lower sampling rates, and communication constraints to define feasible operating modes and determine optimal configurations under varying activity levels. An empirical accuracy–frequency mapping and component-wise energy model support both offline optimization and lightweight online scheduling. The results show that the proposed framework can balance accuracy, responsiveness, and battery life by dynamically shifting between high-performance, balanced, and low-power surveillance states. This scheduling strategy extends operational lifetime while preserving motion-detection reliability and ensuring timely event transmission. The findings demonstrate the importance of energy-aware IMU management in long-range wearable systems and provide a foundation for adaptive sensing strategies in real-world deployments.
Volume: 16
Issue: 3
Page: 1449-1465
Publish at: 2026-06-01

MLP-DT: a deep learning model for early prediction of diabetes and thyroid disorders

10.11591/ijict.v15i2.pp778-788
Aouatef Chaib , Ouahiba Djama , Sabar Messaoudi
In this paper we present an intelligent and automated system for controlling diabetes and thyroid disorders. This system is designed to self-diagnose autoim mune diseases as early as possible in order to treat them quickly and thus slow down or stop their progression and thus provide a tool for self-control of dis eases. Our system is based on deep neural networks (DNNs), it contains several layers and it is classified as multi-layer perceptron (MLP). The proposed model called MLP model for early prediction of diabetes and thyroid disorders (MLP DT)uses a set of biomedical variables, allowing the system to formulate person alized treatment recommendations. To improve diagnostic accuracy and facili tate early screening, the system also incorporates machine learning techniques. The optimization in MLP-DT is provided by the adam optimizer algorithm, it is always applied to adjust the weights of the three hidden layers and the output layer (Sigmoid or Softmax). Experimental results demonstrate that the proposed MLP-DT model achieves reliable predictive performance and supports effective early screening of diabetes and thyroid disorders. These findings highlight the potential of the proposed approach as an intelligent decision-support tool for personalized healthcare and preventive medicine.
Volume: 15
Issue: 2
Page: 778-788
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

Machine learning centered energy optimization in mobile edge computing: a review

10.11591/ijict.v15i2.pp465-476
Chandapiwa Mokgethi , Tshiamo Sigwele , Kabo Clifford Bhende , Aone Maenge , Selvaraj Rajalakshmi
Current literature reviews on machine learning-based approaches for mobile edge computing (MEC) energy optimization often lack in-depth gap analysis and fail to identify trends or offer actionable insights. Most focus narrowly on comparing MEC frameworks without critically evaluating or benchmarking prior research. This review contributes by addressings these gaps via analysis of existing reviews and related studies, with a focus on ML models, research objectives, evaluation metrics, datasets, tools, and gap identification. The review method follows a systematic literature review (SLR) using the PRISMA framework for transparency and reproducibility. Key findings reveal persistent challenges in energy consumption, computational overhead, cost, and poor performance in accuracy, QoS, latency, scalability, and carbon footprint. Deep reinforcement learning (DRL) emerges as the most commonly used model (55%), while TensorFlow (35%) is the most adopted tool, valued for its flexibility and robust community support. The AudioSet dataset is frequently used (28%) due to its compatibility. However, methodology limitations include dependency on study quality and exclusion of grey literature, context sensitivity. The review concludes by recommending advanced solutions such as serverless computing, liquid cooling, containerization, software-defined power, quantum computing, and blockchain to drive future MEC energy optimization.
Volume: 15
Issue: 2
Page: 465-476
Publish at: 2026-06-01

A new modified characteristic equation for optimal coordination of directional overcurrent relays

10.11591/ijict.v15i2.pp789-796
Neelakanteshwar Rao Battu , Surender Reddy Salkuti
The integration of distributed generation (DG) into power systems is increasing to meet the requirements of the utility system. Renewable energy sources are given priority due to their clean energy and high consistency advantages. Integration of DG into the system makes the bi-directional flow of current. Directional type overcurrent relays are usually used for protection of lines associated with bidirectional power flows. The installation of DGs, (especially, inverter-based) invites challenges to the existing protection schemes. A new modified characteristic equation-based approach is proposed in this paper to obtain the faster operational time of relays. The relay coordination scheme proposed in this paper is applied to an 8-bus test system integrated with the solar-based photovoltaic integrated distributed generator (PVIDG). The comparative analysis between the conventional and proposed approaches is done.
Volume: 15
Issue: 2
Page: 789-796
Publish at: 2026-06-01
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