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29,758 Article Results

State of charge prediction for new and second-life lithium-ion batteries based on the random forest machine learning technique

10.11591/ijpeds.v17.i1.pp487-501
Masoud A. Sahhouk , Mohd Junaidi Abdul Aziz , Mohd Ibthisham Ardani , Nik Rumzi Nik Idris , Tole Sutikno , Bashar Mohammad Othman
Accurate state of charge (SOC) estimation is a critical requirement for the safe and efficient operation of lithium-ion batteries (LIBs), particularly in second-life battery (SLB) applications where battery ageing, nonlinear degradation, and measurement noise introduce uncertainty. Although numerous SOC estimation techniques have been proposed, reliable prediction for new and second-life batteries under varied operating conditions remains challenging. In this study, a comparative investigation of the conventional coulomb counting (CC) method and a data-driven random forest (RF) model is conducted for SOC prediction in new and second-life LIBs. Experimental data are obtained from Murata US18650VTC5D cells under pulse discharge tests (PDT), constant discharge tests (CDT), and dynamic stress tests (DST) across a wide range of C-rates. PDT is conducted at 0.24 C, CDT at 0.2 C, 0.5 C, 1 C, and 2 C, while DST is performed at C-rates ranging from 0.5 C to 4 C at a controlled ambient temperature of 25 °C. The RF model is trained using voltage, current, and time features and evaluated against CC using MAE, MSE, RMSE, and R² metrics. Results show that RF consistently outperforms CC under all conditions, particularly for SLBs, achieving significantly lower errors and R² values approaching 0.998. These findings confirm the effectiveness of RF-based SOC estimation for intelligent battery management systems (BMS).
Volume: 17
Issue: 1
Page: 487-501
Publish at: 2026-03-01

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

Hydrothermal synthesis of ZnFe2O4@g-C3N4 for enhanced adsorption-photocatalytic degradation of ciprofloxacin

10.11591/ijaas.v15.i1.pp313-321
Medya Ayunda Fitri , Muchammad Tamyiz , Eko Prasetyo Kuncoro , Mamlu’atul Nihaya , Muhammad Abdul Basith Thom Thom , Cindy Dwi Cahyani , Bahauddin Alqostolani
The persistence of antibiotic contaminants such as ciprofloxacin (CIP) in aquatic environments poses significant environmental and health risks, necessitating the development of efficient removal strategies. In this work, a zinc ferrite-anchored two-dimensional carbon nitride nanocomposite (ZF@2DCN) was synthesized via a simple calcination and hydrothermal approach to achieve synergistic adsorption–photocatalytic degradation of CIP under visible light. Structural and optical characterizations confirmed the successful formation of a ZF–2DCN heterojunction with high crystallinity, strong interfacial interactions, and enhanced visible-light absorption. The incorporation of ZF reduced the bandgap of 2DCN from 2.8 to 2.6 eV, promoting improved charge separation. Adsorption studies revealed rapid equilibrium within 30 min and multilayer adsorption on heterogeneous active sites, with a maximum adsorption capacity of 11.7 mg g-1. Under visible-light irradiation, ZF@2DCN achieved up to 81% CIP degradation within 60 min, exhibiting an apparent reaction rate approximately 2.5 times higher than that of pristine 2DCN. The enhanced performance is attributed to the strong synergy between adsorption-driven pollutant enrichment and photocatalytic degradation. Overall, ZF@2DCN shows strong potential as an efficient material for antibiotic removal in wastewater treatment.
Volume: 15
Issue: 1
Page: 313-321
Publish at: 2026-03-01

A bibliometric review of critical chain project management in construction

10.11591/ijaas.v15.i1.pp272-280
Dhiraj S. Bachwani , MohammedShakil S. Malek , Deep Shaileshkumar Upadhyaya , Neetu Yadav
This study offers an extensive bibliometric analysis of critical chain project management (CCPM) research over the past twenty years, seeking to elucidate the discipline’s developmental trajectory and pinpoint emerging research frontiers. A comprehensive review of the literature revealed fundamental principles of CCPM, highlighting essential components such as buffer management strategies and resource-constrained scheduling methodologies. This initial analysis established the theoretical framework for the quantitative study and facilitated the identification of suitable metrics to integrate both foundational theories and contemporary advancements in CCPM scholarship. The study examined approximately 1,800 academic publications, including journal articles, conference proceedings, review papers, and book chapters published from 2000-2022, obtained from the Scopus database. The analytical framework encompassed various bibliometric dimensions, including performance metrics, relationship indicators, conceptual frameworks, publication characteristics, and VOSviewer network analysis, as essential elements of the data examination process. The developed framework has two main goals: it helps researchers find important publications, potential collaborators, and new areas of research, and it gives practitioners a structured place to store information about how to use CCPM methods in complicated projects with few resources.
Volume: 15
Issue: 1
Page: 272-280
Publish at: 2026-03-01

Performance comparison of feature extraction methods for electroencephalogram-based recognition of Balinese script

10.11591/ijaas.v15.i1.pp55-64
I Made Agus Wirawan , Ida Bagus Nyoman Pascima , Gede Surya Mahendra , I Made Candiasa , I Nyoman Sukajaya
Recognizing Balinese script from electroencephalogram (EEG) signals remains a challenging problem due to low signal amplitude, non-stationary dynamics, and significant inter-subject variability. Despite previous attempts, no single feature extraction method has been universally effective in addressing these limitations. To fill this gap, this study systematically evaluates five feature extraction techniques—differential entropy (DE), power spectral density (PSD), discrete wavelet transforms (DWT), Hjorth parameters, and statistical features—on the Balinese imagined spelling using electroencephalography (BISE) dataset, which contains EEG recordings specifically designed for Balinese script recognition. For classification, both artificial neural networks (ANN) and support vector machines (SVM) are applied, and their performance is validated across multiple experimental settings. Results demonstrate that DE consistently provides more stable and discriminative features than the other methods, achieving the highest classification accuracy when combined with ANN. These findings highlight the potential of DE-based approaches to advance electroencephalogram driven Balinese script recognition, offering a culturally significant contribution to brain-computer interface (BCI) research and supporting future applications in inclusive artificial intelligence, digital heritage preservation, and assistive technologies.
Volume: 15
Issue: 1
Page: 55-64
Publish at: 2026-03-01

Corporate social responsibility by listed commercial banks in Vietnam: practice and financial performance

10.11591/ijaas.v15.i1.pp261-271
Viet Ha Nguyen , Thi Minh Nguyet Dang
This study examines the impacts of financial performance (FP) on corporate social responsibility (CSR). The article investigates whether FP, as measured by return on assets (ROA) and net interest margin (NIM), influences the likelihood of CSR disclosure, drawing on stakeholder theory and legitimacy theory. The analysis employs binary logistic regression models and an unbalanced panel dataset comprising 26 listed banks between 2014 and 2024. If the bank discloses its CSR practices in its annual or sustainability report, the code for CSR disclosure is 1; otherwise, it is 0. The results show that, while NIM shows a negative relationship, ROA significantly improves CSR. Furthermore, there is a positive correlation between bank size (TA), equity to asset (EA), and CSR; a negative relationship of loan to deposit ratio (LDR) with CSR, and no significant statistical correlation was observed between debt to equity (DTE) and CSR. The study adds to the body of knowledge on CSR in developing nations and offers recommendations for sustainability and bank governance.
Volume: 15
Issue: 1
Page: 261-271
Publish at: 2026-03-01

Effectiveness of iBreast examination for screening breast lesions among women in India

10.11591/ijaas.v15.i1.pp178-186
Samuel Ani Grace Kalaimathi , Venkatesan Hemavathy , Sambavadas Kanchana , Radhakrishnan Sudha , Perumal Tamilarasi
The breast has long been a representation of women's identity and an essential component of fertility. The breast lesions refer to an area of abnormal breast tissue. One frequent medical ailment that might worry women is breast lesions. It is estimated that at least 20% of females may develop breast lesions. It may vary in size, shape, and texture can be either benign or malignant. Mammography, clinical breast examination (CBE), and self-breast inspection are the accepted early breast cancer detection techniques. Mammography application in low and middle-income countries is limited because most of the women in these countries cannot afford it. Hence, iBreastExam was identified and validated as an alternative source for screening at the village level to identify breast lesions at an early stage. For the study, a cross-sectional research design using a quantitative research methodology was used. Adopted areas of the selected colleges were the setting for the study: MA Chidambaram College of Nursing, Adyar, Chennai; Sri Balaji College of Nursing, Chrompet, Chennai; Madha College of Nursing, Kundrathur, Chennai; Omayal Achi College of Nursing, Puzhal, Chennai. The sample size consisted of 14,000 women across all the 4 settings. A convenient sampling technique was used to select the samples for the study. A total of 13,988 women were screened, 55 women had positive breast lesions, and out of this 5 were confirmed to have breast cancer through mammogram diagnosis.
Volume: 15
Issue: 1
Page: 178-186
Publish at: 2026-03-01

Technical proposal for the design of a helical conveyor for solid waste handling

10.11591/ijaas.v15.i1.pp333-342
Javier Sinche Ccahuana , Jorge Augusto Sánchez Ayte , Margarita F. Murillo Manrique , Richard Flores-Cáceres
The novelty of this work lies in the design of a helical conveyor for solid waste from the chocolate industry, materials that can be cohesive, with variable density, and potentially corrosive. The objective is to present a validated and replicable technical model that optimizes the transport of 5 metric tons per hour of these wastes at Peru's National Chocolate Company. The goal is to minimize human contact, improve ergonomic safety, and transform waste into exploitable resources under circular economy principles. The methodology employed is an applied type with a quantitative approach, supported by the selection of components through specialized technical catalogs from KWS manufacturing and Martin engineering, which implement ANSI/CEMA 350 standards. Results indicate a total required power of 1.5 HP, with a helicoid diameter of 9", a helical tube of 2", a pitch of 6", and operation at 60 RPM. It is concluded that this design constitutes an efficient and replicable technical solution to improve working conditions in industrial environments, significantly reducing occupational injuries while mitigating environmental impact.
Volume: 15
Issue: 1
Page: 333-342
Publish at: 2026-03-01

Improved seizure detection using optimized time sequence based deep learning framework

10.11591/ijaas.v15.i1.pp198-208
Puspanjali Mallik , Ajit Kumar Nayak , Satyaprakash Swain
Epilepsy disease originates due to the presence of disordered neurons, and epilepsy detection stands as a challenging task for neurologists. With recent advances, electroencephalography (EEG)-based analysis is increasingly supported by deep learning and metaheuristic optimization approaches in order to improve the test results. This experiment uses a convolutional neural network (CNN) model hybridized with bidirectional long short-term memory (BiLSTM). CNN leverages the work with improved feature extraction cum classification supports, and BiLSTM keeps the time sequence of data in both the forward and backward direction for improving signal mapping purposes. To reduce the computational overhead and improve execution accuracy, a hybrid optimization algorithm called secretary bird optimization algorithm (SBOA) is used to fine-tune the execution. Key classification parameters such as accuracy, sensitivity, and specificity reflect the model’s strong predictive capability, with accuracy reaching up to 98.49%. The proposed method demonstrates the potential for high-performance EEG-based seizure detection, paving the way for future integration with edge computing devices to support remote clinical diagnostics and continuous monitoring in real-world healthcare applications.
Volume: 15
Issue: 1
Page: 198-208
Publish at: 2026-03-01

Experimental study on annealing S45C steel: effect of temperature and time on hardness, impact strength

10.11591/ijaas.v15.i1.pp343-354
Mahadir Sirman , Syahrisal Syahrisal , Henny Pasandang , Rusdi Nur , Muhira Dzar Faraby , Mukhlisin Mukhlisin
Steel generally exhibits poor wear and friction resistance, making it necessary to improve its surface mechanical properties, particularly hardness and microstructure, to enhance performance. Heat treatment is one of the most effective methods for achieving these improvements. This study aimed to optimize the heat treatment parameters of S45C medium-carbon steel to improve hardness and impact strength using response surface methodology (RSM). Experimental trials were conducted at annealing temperatures of 800 °C, 850 °C, and 900 °C with holding times of 30, 60, and 90 minutes, followed by cooling in water, oil, or air. Hardness (HRC) and impact strength (Nm/mm²) were measured, and the data were analyzed using RSM with a central composite design (CCD). Quadratic models were found to be statistically significant for both hardness (Prob > F = 0.0222) and impact strength (Prob > F = 0.0338), confirming their validity. The optimization results indicated that a holding time of 60 minutes within the 850-900 °C range provides the best balance between high hardness (>55 HRC) and adequate impact strength (>0.68 Nm/mm²). These findings not only validate the predictive capability of RSM in heat treatment optimization but also provide practical guidelines for industrial applications of S45C steel in automotive, tooling, and structural components.
Volume: 15
Issue: 1
Page: 343-354
Publish at: 2026-03-01

Designing framework for standardization and testing requirements of rain radar in Indonesia

10.11591/ijaas.v15.i1.pp123-132
Hogan Eighfansyah Susilo , Iqbal Vernando , Amy Reimessa
Indonesia’s tropical environment requires advanced rainfall monitoring systems to strengthen disaster early warning capabilities. However, the absence of a dedicated national standard for rain radar has limited domestic technology growth and interoperability. This study develops a framework for the Indonesian National Standard (SNI) for rain radar by integrating the framework for analysis, comparison, and testing of standards (FACTS) with structural equation modeling (SEM). Stakeholder requirements were systematically analyzed and translated into technical specifications, benchmarked against International Organization for Standardization (ISO) and World Meteorological Organization (WMO) standards, and statistically validated. SEM results indicate that performance parameters (β =0.70) and testing methods (β =0.76) are the most influential components of the framework. The validated model establishes five essential domains—system specifications, testing procedures, calibration and maintenance, installation criteria, and system control. The resulting FACTS-SEM framework provides a robust, evidence-based foundation for developing and validating meteorological instrumentation standards suited to Indonesia’s tropical context.
Volume: 15
Issue: 1
Page: 123-132
Publish at: 2026-03-01

A novel circulant matrix-based McEliece framework for secure digital communication

10.11591/ijaas.v15.i1.pp293-302
Ravikumar Inakoti , James Stephen Meka , Padala Venkata Gopala Durga Prasad Reddy
McEliece cryptosystem is old and well-explored post-quantum cryptography system that offers superior security against quantum attacks. Though the system holds great potential and superior security, the challenge associated with large key sizes has made system impractical for most applications. The first challenge against McEliece cryptosystem remains its large key sizes, which make system impractical, especially when implementing internet of things (IoT) and mobile communication applications. Overcoming challenges and retaining superior security still remains an issue to explore. This paper presents investigation into use of circulant matrices for McEliece encryption system to achieve a considerable reduction in key sizes and enhance fast encryption processes. The use of circulant matrices’ inherent properties boosts performance without focusing much on system’s security. In addition, the paper presents security evaluation process for modified communication system to determine and mitigate weaknesses that might arise, considering use of sophisticated encryption systems. Findings and results explore use of circulant matrices, which achieve great reductions in key sizes and improve efficiency of process. Security evaluation reports that proper scrambling techniques are efficient at mending the vulnerabilities associated with circulant matrix structures. A modified McEliece cryptosystem using circulant matrices offers superior data communication, balancing both strong security and efficient computational processes, making system ideal for use in recent communication systems.
Volume: 15
Issue: 1
Page: 293-302
Publish at: 2026-03-01

Performance enhancement of photovoltaic system integrated with a single-phase grid using advanced controllers

10.11591/ijaas.v15.i1.pp77-85
Madhu Babu Thiruveedula , Thiramdasu Chandana , Meghavath Mahesh , Avinash Udala , Yerra Praveen , Mohammed Assaduzzama
This study offers a thorough examination of a photovoltaic (PV) system using a variety of maximum power point tracking (MPPT) methods, including fuzzy logic control (FLC), adaptive neuro-fuzzy inference systems (ANFIS), perturb and observe (P&O), and artificial neural networks (ANN). Optimizing power extraction from PV systems under various environmental circumstances, including temperature variations and irradiance, is the main goal of these MPPT algorithms. Despite its widespread use and affordability, the P&O algorithm may have performance issues in dynamic circumstances. By using fuzzy logic to adjust to non-linear changes in environmental conditions, FLC improves P&O and offers more dependable and seamless operation. Although they demand a large amount of data and processing power, ANN-based MPPT approaches provide sophisticated capabilities by predicting optimal operating points by learning from historical system actions. By fusing fuzzy logic and neural networks, ANFIS offers a reliable solution that can more accurately adjust in real time to changing circumstances. These algorithms' incorporation into a PV system allows for more flexible and effective power management, guaranteeing peak performance in a range of climatic conditions. By combining many MPPT techniques, hybrid approaches can further reduce the drawbacks of individual approaches and improve the overall dependability and efficiency of PV systems.
Volume: 15
Issue: 1
Page: 77-85
Publish at: 2026-03-01

Adaptive sentiment analysis for stock markets using deep learning

10.11591/ijaas.v15.i1.pp416-426
Talent Mawere , Selvaraj Rajalakshmi , Venu Madhav Kuthadi , Othlapile Dinekanyane
Stock markets are highly volatile, making price prediction very difficult. One of the factors influencing the volatility of financial markets is rapidly changing news sentiment. This study presents a novel adaptive deep learning (DL) framework for sentiment analysis with concept drift capabilities. The proposed model combines convolutional neural networks (CNN), bidirectional long short-term memory (BiLSTM), and attention mechanisms in its processing architecture. The model inputs preprocessed news headlines into both the CNN and BiLSTM-Attention networks to extract local features, model contextual dependencies, and prioritizes important sentiment cues in its prediction mechanism. We use FastText and Word2Vec for word embeddings, while incremental learning is used to manage concept drift. One key advantage of handling concept drift is that the model can continuously learn new patterns in data streams without needing to fully retrain the model. The model is validated on a curated dataset from various sources with superior performance across all metrics, like accuracy (0.9753) and an F1-score (0.98). It significantly outperforms benchmarks like distilled bidirectional encoder representations from transformers (DistilBERT), LSTM, and valence aware dictionary and sentiment reasoner (VADER). A run of ten iterations validated that the real-time pipeline did not exceed 200 ms in processing and classifying headlines. This signifies the practical viability of our model in fintech applications such as algorithmic trading and risk management.
Volume: 15
Issue: 1
Page: 416-426
Publish at: 2026-03-01

Integrating swarm intelligence with CMIP climate models for ecocritical environmental analysis

10.11591/ijaas.v15.i1.pp168-177
Pavithra R. , S. Mahadevan
This research establishes a cohesive swarm intelligence framework used for climate simulations derived from the coupled model intercomparison project phase 6 (CMIP6), obtained from the earth system grid federation (ESGF). The study examines essential environmental variables such as near-surface air temperature (tas), sea-level pressure (psl), precipitation (pr), surface shortwave radiation (rsds), and longwave radiation (rlds). The system specifically evaluates a global mean surface temperature rise of 1.72 °C, a psl range of 980-1,030 hPa, pr anomalies averaging ±1.3 mm/day, rsds values fluctuating between 140-280 W/m², and rlds values reaching a maximum of 350 W/m² for high-emission shared socioeconomic pathways (SSP)5-8.5 scenarios. The characteristics served as inputs for decentralized particle swarm architecture aimed at identifying ecological stress signs via geographic anomaly divergence, entropy deviation, and signal intensity thresholds. The model simulated swarm behavior across temporal CMIP grids, effectively capturing changes in climatic feedback and highlighting areas of ecological instability. The swarm framework dynamically analyzes pattern-based fluctuations in model output, facilitating ecocritical evaluation of environmental risk. This hybrid method integrates physically based climate data with adaptive artificial intelligence (AI) modeling, providing an ecologically contextual understanding of earth system changes and improving predictive insights for sustainability and policy formulation.
Volume: 15
Issue: 1
Page: 168-177
Publish at: 2026-03-01
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