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29,922 Article Results

Markov-switching and noise-to-signal ratio approach for early detection of currency crises

10.11591/ijaas.v15.i1.pp42-54
Sugiyanto Sugiyanto , Muhammad Bayu Nirwana , Isnandar Slamet , Etik Zukhronah , Syifa’ Salsabila Gita Parahita
Economic instability can easily lead to a currency crisis. Therefore, observing a number of crisis indicators is crucial for building an early warning system (EWS). However, selecting the indicators most responsive to the crisis is the best choice. For this purpose, the noise-to-signal ratio (NSR) method was used. Monthly data from 1990-1925 were used in the autoregressive moving average (ARMA), generalized autoregressive moving average with generalized autoregressive conditional heteroscedasticity (GARMACH), and Markov-switching (MS)-GARMACH hybrid models to explain the crisis. Model interpretation indicates that there will be no crisis from May 2025-April 2026.
Volume: 15
Issue: 1
Page: 42-54
Publish at: 2026-03-01

Analysis of congestion management using generation rescheduling with augmented Mountain Gazelle optimizer

10.11591/ijict.v15i1.pp57-65
Chidambararaj Natarajan , Aravindhan Karunanithy , S. Jothika , R. P. Linda Joice
This study presents an original blockage of the executive’s approach utilizing age rescheduling with the augmented mountain gazelle optimizer (AMGO). Enlivened by the versatility of mountain gazelles, AMGO is applied to enhance age plans for a reasonable power framework situation. The strategy successfully mitigates clogs, taking into account functional imperatives, market elements, and vulnerabilities. Recreation results show AMGO’s heartiness, seriousness, and proficiency in contrast with existing strategies. Notwithstanding its heartiness in blockage the board, the AMGO presents a state-of-the-art versatile element, enlivened by the spryness of mountain gazelles, empowering constant changes in accordance with developing power framework conditions and contrasted and genetic algorithms and PSO. The review adds to propelling streamlining methods for clogging the executives, offering a promising device for improving power framework, unwavering quality and productivity.
Volume: 15
Issue: 1
Page: 57-65
Publish at: 2026-03-01

Towards efficient fog computing in smart cities: balancing energy consumption and delay

10.11591/ijict.v15i1.pp332-342
Ida Syafiza Md Isa , Nur latif Azyze Mohd Shaari Azyze , Haslinah Mohd Nasir , Vigneswara Rao Gannapathy , Ashwini Jayadevan Naidu
In this work, we propose fog-based energy-delay optimization (F-EDO) approach and benchmark its performance against the cloud-based energydelay optimization (C-EDO) method, focusing on energy consumption and delay. Unlike previous studies that optimize energy or delay separately, FEDO minimizes both metrics simultaneously, achieving up to 52.2% energy savings with near-zero delay. Additionally, increasing the number of users also leads to energy savings. This is due to the optimized placement of fog servers at the access layer which reduces network energy consumption compared to C-EDO. F-EDO also significantly reduces delay, with negligible delay compared to C-EDO due to fog servers are placed closer to the users which minimized the transmission distances. Besides, the results also show that the energy saving in F-EDO compared to the C-EDO increased as the processing capacity of the processing server increased while maintaining its minimal delay. Overall, F-EDO proves to be a more energyefficient and lower-delay solution for IoT networks, offering a better alternative to cloud-based offloading.
Volume: 15
Issue: 1
Page: 332-342
Publish at: 2026-03-01

NLP-based fraudulent biomedical news identification using LSTM-SGD deep learning algorithm

10.11591/ijict.v15i1.pp179-188
Siva Dhievaraj , Agusthiyar Ramu
Concern over bio medical fake news is rising, particularly as false information about illnesses, medical procedures, and public health regulations becomes more prevalent. It is essential to recognize such false information, and deep learning (DL) algorithms can offer a potent remedy, especially when paired with sophisticated natural language processing (NLP) methods. This technique improves the model's capacity to ignore frequently used but uninformative terms and concentrate on important terminology. The model's capacity to concentrate on the most pertinent phrases for fake news identification is enhanced by the use of chi-squared, a statistical test that ascertains the dependency between various variables and aids in the removal of unnecessary data. By reducing less significant characteristics to zero, the Lasso approach, a kind of regression, is used for feature selection, guaranteeing that the model only utilizes the most predictive features for classification. A crucial step in getting the data ready for DL models is feature extraction, which turns unprocessed text into numerical data. After the structured data has been analyzed, algorithms like as stochastic gradient descent (SGD), long short-term memory (LSTM) may determine whether or not an article is accurate. The authenticity and dependability of medical information provided across platforms may be ensured by effectively identifying biomedical fake news by fusing DL with sophisticated NLP techniques.
Volume: 15
Issue: 1
Page: 179-188
Publish at: 2026-03-01

Fetal electrocardiogram extraction and signal quality assessment using statistical method

10.11591/ijict.v15i1.pp217-227
Li Mun Ng , Nur Anida Jumadi , Farah Najidah Noorizan
Abdominal electrocardiogram (aECG) can be used to monitor fetal heart rate (fHR), providing critical insights into fetal health during pregnancy. However, separating the mixed signals of fetal ECG (fECG) and maternal ECG (mECG) within the aECG remains a critical challenge. This paper investigates the integration of statistical metrics, including signal-to-noise ratio (SNR), skewness, kurtosis, standard deviation, and variance to assess fECG signal quality during extraction using three adaptive filtering metods ((Least mean square (LMS), normalized LMS (NLMS), and recursive least square (RLS)) and independent component analysis (ICA). The findings reveal that RLS achieves the best performance among the three AF methods, with the highest SNR of 5.6 dB at the step size, µ of 0.9. For ICA with a bandpass Chebyshev filter (low-cut frequency = 1 Hz, high-cut frequency = 50 Hz) produces an SNR of 0.86 dB. Additionally, both RLS and ICA yield similar fHR values of 133 bpm with a PE measurement of 0.9%. In conclusion, integrating statistical metrics with ICA and RLS effectively extracts fECG with good signal quality. Future research could explore other ECG datasets and incorporate machine learning to further improve fECG extraction and signal quality assessment.
Volume: 15
Issue: 1
Page: 217-227
Publish at: 2026-03-01

Innovative climate information services: a scoping review and bibliometric analysis for climate change decision-making

10.11591/ijaas.v15.i1.pp65-76
Jazimatul Husna , Imilia Ibrahim , Ika Wahyuning Widiarti
This research aims to develop innovative information services to strengthen decision-making in climate change mitigation through a scoping review and bibliometric analysis (ScoRBA). A systematic search of the Scopus database identified 1,214 publications from 2009 to 2023, with 383 meeting inclusion criteria. Using the patterns, advances, gaps, evidence, and recommendations (PAGER) framework, this research provides a transparent synthesis of evidence on climate information services (CIS). The analysis reveals four major thematic clusters: i) emerging technologies and innovations, ii) climate and environmental studies, iii) information systems and decision making, and iv) context awareness and applications. Technologies such as service-oriented architecture (SOA), internet of things (IoT), and cloud computing are key enablers for improving CIS accuracy and efficiency. Evidence shows that these technologies have been successfully applied in agriculture and aquaculture across Vietnam, Bangladesh, and Australia. North African countries have adopted IoT-based water management systems to address water scarcity, while India employs similar technologies to optimize agricultural resources. Integrating local knowledge with scientific data—particularly in Africa, Southeast Asia, and South America—has proven essential for effective adaptation strategies. This research advances theoretical and practical understanding of CIS, offering evidence-based insights to guide the development of adaptive and equitable climate information frameworks.
Volume: 15
Issue: 1
Page: 65-76
Publish at: 2026-03-01

Soft fuzzy partial metric and some results on fixed point theory under soft set

10.11591/ijaas.v15.i1.pp427-436
Rohini R. Gore , Renu P. Pathak
This research paper establishes a new concept of soft fuzzy partial metric spaces, combining soft sets, partial metric spaces, and fuzzy sets to handle uncertainty and imprecision. This paper's primary goal is to use soft fuzzy partial metric spaces to examine various fixed-point theory conclusions. A few fixed-point results are defined under the 𝛹 −contraction mapping on soft fuzzy partial metric space and the soft fuzzy contraction mapping. Also, illustrate the related example of fixed-point theorem. Soft fuzzy partial metric spaces have applications in various fields, including image processing, decision-making analysis.
Volume: 15
Issue: 1
Page: 427-436
Publish at: 2026-03-01

Investigation of efficiency and safety in wireless capacitive power transfer through a single-layer tissue phantom

10.11591/ijpeds.v17.i1.pp502-517
Yusmarnita Yusop , Amy Sarah Ngu , Cheok Yan Qi , N. B. Asan , Huzaimah Husin , Shakir Saat , Peter Adam Hoeher
Wireless power transfer (WPT) is a promising solution for implantable biomedical devices, offering an alternative to traditional implanted batteries and percutaneous connections, which are limited by short lifespans and high infection risks. Existing capacitive power transfer (CPT) systems for biomedical implants often utilize media such as animal meat or liquids to validate power transfer across the human body, but these materials exhibit inconsistent and inaccurate dielectric properties. To address this limitation, this study proposes a CPT system designed to operate with a single-layer tissue phantom that closely mimics the dielectric characteristics of human tissue. The system is integrated with a class-E LCCL resonant topology to enhance power transfer efficiency. In addition to evaluating performance, this work also investigates safety aspects in terms of electric field emission and specific absorption rate (SAR). Simulations using MATLAB Simulink and ANSYS HFSS reveal that at a 1 mm tissue gap, the electric field reaches 298.09 V/m and the SAR is 1.14 W/kg, which are both within established safety limits (614 V/m and 2 W/kg per 10 g of tissue). Furthermore, a 5 W, 1 MHz system operating across a 2 mm tissue gap demonstrates power transfer efficiencies of 40.61% for skin tissue and 20.53% for muscle tissue. These results validate the system’s safety and efficiency for powering deeply implanted biomedical devices.
Volume: 17
Issue: 1
Page: 502-517
Publish at: 2026-03-01

Securing Defi: a comprehensive review of ML approaches for detecting smart contract vulnerabilities and threats

10.11591/ijict.v15i1.pp438-446
Dhivyalakshmi Venkatraman , Manikandan Kuppusamy
The rapid evolution of decentralized finance (DeFi) has brought revolutionary innovations to global financial systems; however, it has also revealed some major security vulnerabilities, especially of smart contracts. Traditional auditing methods and static analysis tools are prone to fail in identifying sophisticated threats, including reentrancy attacks, front-running, oracle manipulation, and honeypots. This review discusses the growing role of machine learning (ML) in enhancing the security of DeFi systems. It provides a comprehensive overview of modern ML-based methods related to the detection of smart contract vulnerabilities, transaction-level fraud detection, and oracle trust assessment. The paper also provides publicly available datasets, necessary toolkits, and architectural designs used for developing and testing these models. Additionally, it provides future directions like federated learning, explainable AI, real-time mempool inspection, and cross-chain intelligence sharing. While it is full of promise, the application of ML in DeFi security is plagued by issues like data scarcity, interoperability, and explainability. This paper concludes by highlighting the need for standardised benchmarks, shared data initiatives, and the integration of ML into development pipelines to deliver secure, scalable, and reliable DeFi ecosystems.
Volume: 15
Issue: 1
Page: 438-446
Publish at: 2026-03-01

Privacy-preserving fitness recommendation system using modified seagull monarch butterfly optimized deep learning model

10.11591/ijict.v15i1.pp393-404
Esmita Gupta , Shilpa Shinde
This paper presents a novel modified seagull monarch butterfly optimization (MSMBO) algorithm, with a multi-objective focus on privacy and personalization in the fitness recommender system using a refined three-tier deep learning structure. The method is divided into three phases. In the first phase, fitness data from wearable devices undergoes preprocessing to eliminate noise and standardize features. The second phase incorporates improved elliptic curve cryptography (IECC) alongside the MSMBO to encrypt user data securely, ensuring privacy in cloud storage. This phase also enhances neural network performance by optimizing weights and hyperparameters through feature selection, effectively reducing data complexity while boosting accuracy. In the third phase, ConvCaps extracts spatial data features, while Bi-LSTM identifies temporal dependencies. The proposed system balances multiple objectives like novelty, accuracy, and precision, while safeguarding user data through robust encryption. With the experimental findings, our suggested method performs better than current existing models, especially in heart rate prediction and fitness pattern identification. The overall outcome makes the system ideal for privacyconscious, personalized fitness recommendations. The model’s shows significant improvement in mean squared error (MSE), normalized mean squared error (NMSE), and mean absolute percentage error (MAPE), thus verifying its effectiveness in secure, real-time fitness tracking.
Volume: 15
Issue: 1
Page: 393-404
Publish at: 2026-03-01

Exploring diverse perspectives: enhancing black box testing through machine learning techniques

10.11591/ijict.v15i1.pp238-246
Heba Nafez Jalal , Aysh Alhroob , Ameen Shaheen , Wael Alzyadat
Black box testing plays a crucial role in software development, ensuring system reliability and functionality. However, its effectiveness is often hindered by the sheer volume and complexity of big data, making it difficult to prioritize critical test cases efficiently. Traditional testing methods struggle with scalability, leading to excessive resource consumption and prolonged testing cycles. This study presents an AI-driven test case prioritization (TCP) approach, integrating decision trees and genetic algorithms (GA) to optimize selection, eliminate redundancy, and enhance computational efficiency. Experimental results demonstrate a 96% accuracy rate and a 90% success rate in identifying relevant test cases, significantly improving testing efficiency. These findings contribute to advancing automated software testing methodologies, offering a scalable and efficient solution for handling large-scale, data-intensive testing environments.
Volume: 15
Issue: 1
Page: 238-246
Publish at: 2026-03-01

A decision support system for mushroom classification using Naïve Bayesian algorithm

10.11591/ijict.v15i1.pp138-151
Vilchor G. Perdido , Thelma D. Palaoag
Mushrooms are rich in vitamins and proteins, a well-known superfood, however, cases of harmful mushroom consumption worldwide result in hallucinations, illness, or death. A significant challenge is that some poisonous mushrooms closely resemble edible varieties, making it difficult for mushroom foragers to distinguish between them. This study introduced KabuTeach, a decision support system (DSS) designed to classify mushrooms based on their morphological characteristics using the Naïve Bayes (NB) algorithm. The classification model was applied to a real-world dataset of 8,124 instances from Kaggle, containing 23 attributes. Evaluation metrics, including accuracy, recall, precision, specificity, and F1-score, were used to assess the classifier’s performance. Results indicated that the NB classification algorithm integrated into KabuTeach achieved a high accuracy level of 89.13%, using a 70:30 data split and 5-fold cross-validation approaches. The 0.98 AUC (area under the curve) value further concluded that the model was excellent in classifying between edible and poisonous mushrooms. These findings showed that KabuTeach is a reliable classification tool that aids mushroom foragers in differentiating mushrooms and promoting safer consumption practices. This innovation in agricultural technology could potentially reduce health risks by minimizing accidental ingestion of toxic mushrooms, ultimately contributing to public health safety.
Volume: 15
Issue: 1
Page: 138-151
Publish at: 2026-03-01

Enhancing intellectual property rights management through blockchain integration

10.11591/ijict.v15i1.pp111-119
Raghavan Sheeja , Sherwin Richard R. , Shreenidhi Kovai Sivabalan , Srinivas Madhavan
The generational improvement has significantly converted several industries, and the area of intellectual property rights (IPR) isn’t any exception. IPRs, being as important as they are, need to be securely managed in some way. Blockchain, with its decentralized and immutable nature, gives a promising answer for enhancing the management of intellectual property (IP). This paper explores the strategic integration of blockchain generation for the control of IPR. The proposed system consists of a complete system, from registration and validation to predictive evaluation and royalty distribution, all facilitated through clever contracts. The use of zero-knowledge proofs guarantees the safety and confidentiality of sensitive information. The paper discusses the advantages and future implications of implementing this type of device.
Volume: 15
Issue: 1
Page: 111-119
Publish at: 2026-03-01

Fuzzy logic-based driver fatigue prediction system for safe and eco-friendly driving

10.11591/ijict.v15i1.pp84-92
Raghavan Sheeja , Chidambaranathan Bibin , Selvaraj Vanaja , Shakeela Joy Arul Dhas , Alex Arockia Abins , Padmavathi Balasubramaniam
The advancement of intelligent car systems in recent years has been significantly influenced by developments in information technology. Driver fatigue is a dominant problem in car accidents. The goal of advanced driving assistance is to develop an advanced driving assistance system (ADAS) a eco-friendly model which focuses on the detection of drowsy driver, to notify drivers of their fatigued condition to prevent accidents on the roads. With relation to driving, the driver mustn’t be distracted by alarms when they are not tired. The answer to this unanswered question is provided by 60- second photograph sequences that were taken when the subject’s face was visible. To reduce false positives, two alternative solutions for determining whether the driver is drowsy have been developed. To extract numerical data from photos and feed it into a fuzzy logic-based system, convolutional network is applied initially; later deep learning technique is followed. The fuzzy logic-based solution avoids the false alarm of the system.
Volume: 15
Issue: 1
Page: 84-92
Publish at: 2026-03-01

Practice-based teaching using an AI platform to strengthen faculty competency

10.11591/ijict.v15i1.pp171-178
Angsana Phonsuk , Phakharach Plirdpring
This research aimed to i) analyze faculty members’ knowledge, understanding, and skills in using AI for practice-based teaching enhancement, ii) evaluate factors affecting faculty readiness in integrating AI into teaching processes, and iii) design and develop an AI platform to enhance faculty competency in practice-based teaching. The questionnaire, validated by five experts, was administered to 200 respondents divided into two groups: 100 faculty members from public universities and 100 from private universities. Comparative analysis revealed that public university faculty and private university faculty statistically significant differences in challenges and concerns at the 05 level, with public university faculty expressing higher concerns. Significant differences were found in AI experience and skills, attitudes toward AI use, and challenges and concerns. However, no significant differences were observed in three other areas: AI knowledge and understanding, AI readiness, and belief in AI’s effectiveness for practice-based learning enhancement. Data from both groups were utilized in designing and developing the AI platform to enhance practicebased teaching competency in higher education. Expert evaluation of the platform’s suitability showed high levels of demand for the AI platform and high appropriateness of the technology used in platform development.
Volume: 15
Issue: 1
Page: 171-178
Publish at: 2026-03-01
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