International Journal of Advances in Applied Sciences
The International Journal of Advances in Applied Sciences (IJAAS), (p-ISSN 2252-8814, e-ISSN 2722-2594) is a peer-reviewed, open-access international journal dedicated to advancing knowledge, innovation, and technological excellence in applied science, engineering, and computer science. Serving as a global platform for researchers, academics, industry professionals, and innovators, IJAAS publishes high-quality original research and emerging technological developments that address real-world challenges and contribute to a sustainable, digitally connected future. In alignment with its international scholarly profile, the journal particularly emphasizes research in Electrical and Electronic Engineering, interdisciplinary Engineering applications, Computer Science Applications, and Energy-related technologies, while also welcoming contributions that bridge scientific discoveries with practical and industrial implementation. Through the dissemination of impactful, future-oriented research, IJAAS promotes scientific advancement, technological innovation, and cross-disciplinary collaboration that drive progress across academia, industry, and society. Published by Intelektual Pustaka Media Utama (IPMU), IJAAS upholds rigorous editorial standards and international best practices in scholarly publishing. The journal is officially accredited by the Ministry of Education, Culture, Research, and Technology of the Republic of Indonesia under Decree No. 79/E/KTP/2023, reflecting its commitment to academic excellence, research integrity, and global relevance. Since 2019, articles published in IJAAS have been indexed in Scopus, where the journal is classified under the subject areas of Electrical and Electronic Engineering, Engineering (Miscellaneous), Computer Science Applications, and Energy (Miscellaneous). IJAAS is also indexed and evaluated by SCImago Journal Rank (SJR), further strengthening its international visibility, citation impact, and scholarly reputation. By fostering cutting-edge research and emerging technologies, IJAAS continues to position itself as a trusted and influential venue for disseminating high-impact scientific contributions that shape the future of engineering, computing, and energy innovation worldwide. IJAAS Features: Plagiarism: CrossCheck plagiarism powered by iThenticate. DOI: DOI Number is indexed by Crossref. Open Access: Open access provides immediate and unrestricted access to the latest research. Full Archive Available: Every issue of Journal of IJAAS is available online. ISSN Record Information: The ISSN of IJAAS journal is registered at https://portal.issn.org/resource/ISSN/2722-2594 <!--[if mso]> <xml><w:WordDocument xmlns:w="urn:schemas-microsoft-com:office:word"><w:DontUseAdvancedTypographyReadingMail/></w:WordDocument> <o:OfficeDocumentSettings><o:PixelsPerInch>96</o:PixelsPerInch><o:AllowPNG/></o:OfficeDocumentSettings></xml> <![endif]--><!--[if !mso]><!--><!--<![endif]--><!--[if mso ]> <![endif]--> <!--[if mso]> <v:roundrect xmlns:v="urn:schemas-microsoft-com:vml" xmlns:w="urn:schemas-microsoft-com:office:word" href="https://youtu.be/B9lB-4sZNI4?si=o5H0_HS2NaXlxe-Z" style="height:46px;width:210px;v-text-anchor:middle;" arcsize="9%" fillcolor="#3202b6"> <v:stroke dashstyle="Solid" weight="0px" color="#3202b6"/> <w:anchorlock/> <v:textbox inset="0px,0px,0px,0px"> <![endif]-->HOW TO REGISTER<!--[if mso]> </v:textbox></v:roundrect><![endif]--> <!--[if mso]> <v:roundrect xmlns:v="urn:schemas-microsoft-com:vml" xmlns:w="urn:schemas-microsoft-com:office:word" href="https://youtu.be/hQD9-HeNy1k?si=_Q7Dsawb-OEvh2Gl" style="height:46px;width:187px;v-text-anchor:middle;" arcsize="9%" fillcolor="#ff47ce"> <v:stroke dashstyle="Solid" weight="0px" color="#ff47ce"/> <w:anchorlock/> <v:textbox inset="0px,0px,0px,0px"> <![endif]-->HOW TO SUBMIT<!--[if mso]> </v:textbox></v:roundrect><![endif]-->
Institute of Advanced Engineering and Science
COPYRIGHT AND LICENSING AGREEMENT This is to certify that the submitted manuscript is an original work, has not been published previously, and is not being considered for publication elsewhere. Upon acceptance, the article will be published by Intelektual Pustaka Media Utama (IPMU) under the terms of the Creative Commons Attribution-ShareAlike 4.0 International License (CC BY-SA 4.0). The authors retain the copyright to the work. We declare that: This manuscript has not been published previously in the same or substantially similar form. It is not currently under review or consideration for publication by any other journal. All necessary permissions have been obtained for the use of any third-party material included in this manuscript. We agree that upon publication, the article will be distributed under the Creative Commons Attribution-ShareAlike 4.0 International License (CC BY-SA 4.0). License Grant We, the authors, retain all copyrights to the submitted article, and grant to IPMU the non-exclusive right to reproduce, publish, translate, and distribute the article in any form or medium, including print and electronic formats. The article may be used, shared, or adapted by others, provided that: Proper attribution is given to the original author(s) and source, A link to the license is included, Any changes are indicated, and Any derivative works are licensed under the same terms (ShareAlike). Full license details: https://creativecommons.org/licenses/by-sa/4.0 Author Responsibility The corresponding author signs for and accepts responsibility for releasing this material on behalf of any and all co-authors. This agreement is to be signed by at least one of the authors who has obtained the consent of the co-author(s), if any. After submission of this agreement signed by the corresponding author, changes in authorship or order of authors will not be accepted. Retained Rights Authors retain all proprietary rights in any process, method, or material described in the work. Authors may distribute or reuse the work for personal or institutional use, or include it in other works, with appropriate credit and under the same license. Authors may deposit the published version of the article in institutional or public repositories.
Extension of Hermite-Hadamard type inequalities to Katugampola fractional integrals
In this study, we introduce several new Hermite-Hadamard type general integral inequalities for exponentially (s,m)-convex functions via Katugampola fractional integral. The Katugampola fractional integral is a broader form of the Riemann–Liouville and Hadamard fractional integrals. We utilized the power mean integral inequality, the H¨older inequality and a few additional generalizations to derive these inequalities. Numerous limiting results are derived from the main results presented in the remarks. Furthermore, we provide an example illustrating our theoretical findings, supported by a graphical representation.
Copyright (c) 2026 Dipak Kr Das, Shashi Kant Mishra, Pankaj Kumar, Abdelouahed Hamdi http://creativecommons.org/licenses/by-sa/4.0
Analysis of railway accidents in Nigeria: a decade of insights
This study provides insights into the patterns and dynamics of railway accidents in Nigeria over the past decade. Findings indicate that Nigeria's rail network experiences fewer but more severe accidents than the United States of America (USA) and United Kingdom (UK), with significantly higher fatalities and injuries per million train kilometers 92% and up to 95% more, respectively, in 2023. A top-down approach was employed to establish a risk tree, revealing six railway accident categories recorded over the last decade. The established risk tree could provide a framework for conducting the rail network's comprehensive safety risk assessment. Finally, a root cause analysis of railway intrusion accidents, the most occurring railway accident category in the Nigerian rail network, was conducted. Six immediate and eleven underlying causes (UC) of railway intrusion accidents were identified. About 62% of all intrusion accidents were caused by negligence of road users. Several actionable preventive measures (PM) have been proposed for each identified UC based on best practices in developed rail networks. Infrastructure upgrades and safety awareness campaigns have been identified as the potentially most effective PM for railway intrusion accidents in Nigeria.
Copyright (c) 2026 Aliyu Mani Umar, Mohd Khairul Afzan Mohd Lazi, Sitti Asmah Hassan, Hanini Ilyana Che Hashim, Yinggui Zhang, Nura Shehu Aliyu Yaro, Adam Ado Sabari, Surajo Abubakar Wada http://creativecommons.org/licenses/by-sa/4.0
Enhancing sleep disorder diagnosis through ensemble ML models: a comprehensive study on insomnia and sleep apnea
Sleep disorders are common and can significantly harm human health, with insomnia and sleep apnea being the most prevalent conditions. These disorders are often difficult to detect and treat accurately. Although machine learning (ML) techniques have shown promise in improving diagnostic precision and personalized treatment, most existing studies rely on single source data or conventional ML models, which limit their robustness and generalizability across diverse populations. To address this research gap, this study integrates multi-modal data and ensemble learning techniques to enhance accuracy, interpretability, and real-time applicability in diagnosing insomnia and sleep apnea. A dataset of 400 samples was collected through manual methods and internet of things (IoT) devices from multiple sources. Statistical techniques were applied for data cleaning, followed by principal component analysis (PCA) to reduce dimensionality and improve training efficiency. Four base ML models: decision tree (DT), support vector machine (SVM), naive Bayes (NB), and random forest (RF) were initially trained and evaluated. Subsequently, a boosting-based ensemble model was implemented to further improve performance. The proposed gradient boosting model with RF as the base learner achieved the highest diagnostic accuracy of 96.01%. The results demonstrate that ensemble ML models combined with multi-modal data significantly enhance the accuracy of insomnia and sleep apnea diagnosis.
Copyright (c) 2026 Satyaprakash Swain, Binod Kumar Pattanayak, Mihir Narayan Mohanty, Amiya Kumar Sahoo, Suvendra Kumar Jayasingh http://creativecommons.org/licenses/by-sa/4.0
Markov-switching and noise-to-signal ratio approach for early detection of currency crises
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.
Copyright (c) 2026 Sugiyanto, Muhammad Bayu Nirwana, Isnandar Slamet, Etik Zukhronah, Syifa’ Salsabila Gita Parahita http://creativecommons.org/licenses/by-sa/4.0
Performance comparison of feature extraction methods for electroencephalogram-based recognition of Balinese script
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.
Copyright (c) 2026 I Made Agus Wirawan, Ida Bagus Nyoman Pascima, Gede Surya Mahendra, I Made Candiasa, I Nyoman Sukajaya http://creativecommons.org/licenses/by-sa/4.0
Innovative climate information services: a scoping review and bibliometric analysis for climate change decision-making
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.
Copyright (c) 2026 Jazimatul Husna, Imilia Ibrahim, Ika Wahyuning Widiarti http://creativecommons.org/licenses/by-sa/4.0
Performance enhancement of photovoltaic system integrated with a single-phase grid using advanced controllers
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.
Copyright (c) 2026 Madhu Babu Thiruveedula, Thiramdasu Chandana, Meghavath Mahesh, Avinash Udala, Yerra Praveen, Mohammed Asaduzzama http://creativecommons.org/licenses/by-sa/4.0
Google Play review analysis on Sharia pawnshop applications in Indonesia
Digital transformation opens opportunities for Sharia pawnshops to develop innovative application-based services following Sharia principles. This study analyzes the perception and experience of users of the Sharia pawnshop application on the Google Play Store using a netnography approach. It collects and analyzes 395 user reviews between June and December 2024, which consist of 219 positive reviews and 176 negative reviews. The analysis shows that 59.82% of users gave positive reviews regarding satisfaction with using the application, especially regarding transaction security and ease of use. As many as 17.35% of positive reviews emphasized the benefits of the application in transforming Islamic financial services. The main challenges identified included update system problems (35.23%), technical and server problems (30.11%), and registration complexity (5.68%). There was also a discrepancy between numerical ratings and review content. Important concerns include service problems (9.66%) and limited choice of Islamic banks (5.11%). This research provides important insights for the development of digital Islamic finance applications in the future, especially in the aspects of improving technological infrastructure, simplifying processes, and improving the quality of customer service. The results of this study contribute to a better understanding of user needs in the context of the digitization of Islamic financial services in Indonesia.
Copyright (c) 2026 Azhar Alam, Adityo Wiwit Kurniawan, Muhammad Sholahuddin http://creativecommons.org/licenses/by-sa/4.0
Enhancing service reliability in heavy-duty commercial vehicles industry
Reducing breakdown lead time is a critical factor in ensuring customer productivity and sustaining competitiveness in the heavy-duty commercial vehicle (HDCV) industry. This was tackled by applying a methodology called define, measure, analyze, improve, and control (DMAIC), which stands for DMAIC. By deploying it, the breakdown lead time of an Indonesian HDCV company can be minimized. Before the initiative, the lead time was 4 days with 81.54% or 815,400 defects per million opportunities (DPMO) or less than 1 sigma with only 303 parts within target. The reduction target was 2 days as required by its customers, with 40% or 400,000 DPMO or less than 2 sigmas, with 658 parts within target. After following the methodology, the lead time was less than 2 days, meeting customer requirements with 31.2% or 312,000 DPMO, or about 2 sigmas. It shows an improved lead time, which is less than 2 days from 4 days, and a sigma level which is less than 2 sigmas from less than 1 sigma, with 908 parts within target. The study demonstrates how integrating digital applications, remanufactured spare parts, and a centralized command center significantly shortens breakdown handling.
Copyright (c) 2026 Jonny, Januar Nasution http://creativecommons.org/licenses/by-sa/4.0
Modeling of solar and wind energy using MATLAB/Simulink: a review
This paper presents a concise review of solar (photovoltaic (PV)) and wind (horizontal axis) energy systems, focusing on their modeling and simulation using MATLAB)/Simulink. The advantages, disadvantages, strengths, and weaknesses of each system are discussed, providing a comprehensive overview of their characteristics. The review explores the mathematical modeling approaches for PV cells and modules specific for single diode model, as well as horizontal-axis wind turbine systems, highlighting the key equations and parameters involved. Furthermore, the paper discusses the emerging trend of hybrid solar-wind energy systems and their potential for optimizing power output, efficiency, and reliability. The review emphasizes the importance of accurate modeling based on fundamental knowledge, which serves as a practical implication for readers to understand the mechanism. Future research directions and challenges in the field of renewable energy modeling and simulation are also outlined. This review serves as a valuable resource for researchers, engineers, and decision-makers involved in the development and implementation of solar and wind energy systems.
Copyright (c) 2026 Fahmy Rinanda Saputri, Nicholas Pranata http://creativecommons.org/licenses/by-sa/4.0
Designing framework for standardization and testing requirements of rain radar in Indonesia
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.
Copyright (c) 2026 Hogan Eighfansyah Susilo, Iqbal Vernando, Amy Reimessa http://creativecommons.org/licenses/by-sa/4.0
An ensemble-based approach for breast cancer identification using mammography
Breast cancer is among the most common cancers in women worldwide; timely detection is vitally important for improving chances of survival. The present study examines an innovative machine learning technique for the diagnosis of breast cancer using the breast cancer Wisconsin (diagnostic) dataset from Kaggle. The dataset includes 569 instances, and each instance has 30 attributes derived from digitized fine needle aspiration (FNA) images of masses found in the breast. We will present an ensemble deep learning (DL) model fusing a convolutional neural network (CNN) and LRAlexNet architectures to increase the accuracy and robustness of this type of cancer diagnosis. CNN models are well-known for their power to capture spatial hierarchies in image data, and LRAlexNet is a specialized deep CNN that excels at image classification due to its depth and parameter optimization. In this work, we use the ability to extract features of CNNs along with the superior classification performance of LRAlexNet to distinguish between benign and malignant cancers. The model will be trained and validated on the curated breast imaging subset of the digital database for screening mammography (CBIS-DDSM) dataset, and performance will be evaluated using sensitivity, accuracy, specificity, and the area under the curve (AUC) for the receiver operating characteristic. The results show that the ensemble CNN-LRAlexNet model achieved superior accuracy for breast cancer prediction when compared to traditional machine learning methods.
Copyright (c) 2026 Naveen Ananda Kumar Joseph Annaiah, Nakka Thirupathi Rao, Balakesava Reddy Parvathala, Banana Omkar Lakshmi Jagan, Bodapati Venkata Rajanna http://creativecommons.org/licenses/by-sa/4.0
Ensemble machine learning based model to estimate irrigation water requirement for wheat crop
India faces a serious water shortage issue, as its population grows faster than the percentage of fresh water available, with only 4% of the world's fresh water available to 18% of the world's population. Agriculture sector is more water-consuming sector in India. India's irrigation system still faces two significant problems: low irrigation efficiency and a lack of optimization during irrigation. To address these problems, agriculturists ought to be aware of the water requirements for crops beforehand. Innovative fields like machine learning, a branch of artificial intelligence, have a big potential to improve irrigation. Verifying the suitability of the gradient boosting regressor machine learning algorithm-based model for estimating irrigation water requirements (IWR) is the aim of this research. The experiment is conducted in Ludhiana, a city in Central Punjab, India, with a hot, semi-arid climate that features scorching summers and chilly winters. The results demonstrate the remarkably high accuracy rate with coefficient of determination (R2) =0.98 for predicting IWR. The suggested model, which is based on a gradient boosting regression, allows the stakeholders to accurately estimate the amount of water needed for irrigation, the number of irrigation applications for the growing season of wheat crops, and the interval between irrigations.
Copyright (c) 2026 Satendra Kumar Jain, Anil Kumar Gupta http://creativecommons.org/licenses/by-sa/4.0
Comparative analysis of YOLO variants and EfficientNet for detecting bone fractures in X-ray images
A bone fracture is a serious medical problem, and accurate and prompt diagnosis is crucial for optimal treatment. This study highlights the progress of automatic bone fracture detection using deep learning (DL) models. A dataset containing 17 different fracture classes was used to train and evaluate the models. The dataset had class imbalance and minor fracture detection challenges. Extensive preprocessing, including data augmentation and resizing, has been applied to solve these problems, which has helped to increase the robustness of the model. Seven state-of-the-art models—you only look once (YOLO)v8, YOLOv9, YOLOv10, YOLOv11, EfficientNetB0, DenseNet169 and ResNet50—are trained and evaluated. Precision, recall, F1-score, and mean average precision (mAP) were used to evaluate the performance of the models. Among all models, YOLOv11 leads the others by achieving the highest precision, mAP, and precision-recall balance. YOLOv11 adds architectural improvements such as a deep backbone network and hybrid feature fusion, which make the model more reliable in different types of fracture detection. It is capable of reducing false detections and maintaining stable memory usage consistency even under different imaging conditions. Overall, YOLOv11 showed promising results and highlighted the potential of AI-powered diagnostic tools to improve clinical processes and patient care. As future work, the application field of the model can be extended to larger medical imaging tasks, and it can be further refined for effective use in resource-limited environments.
Copyright (c) 2026 Shatabdi Sarker, Avizit Roy, Shaila Sharmin, Shakila Rahman, Jia Uddin http://creativecommons.org/licenses/by-sa/4.0
Integrating swarm intelligence with CMIP climate models for ecocritical environmental analysis
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.
Copyright (c) 2026 Pavithra R., S. Mahadevan http://creativecommons.org/licenses/by-sa/4.0
Effectiveness of iBreast examination for screening breast lesions among women in India
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.
Copyright (c) 2026 Samuel Ani Grace Kalaimathi, Venkatesan Hemavathy, Sambavadas Kanchana, Radhakrishnan Sudha, Perumal Tamilarasi http://creativecommons.org/licenses/by-sa/4.0
Artificial intelligence-powered image recognition retail checkout systems
The integration of artificial intelligence (AI) with big data analytics leads to substantial transformations in the retail sector. This research explores the impact of AI-powered image recognition checkout systems on the retail industry, focusing on operational efficiency, customer experience, and resource waste. Employing a mixed-methods approach, this study combines usability testing and data analytics to assess the viability of this technology in attaining automation and accuracy in retail operations. The study focuses on the creation of robust, resource-efficient systems that foster long-term industrial growth. The findings show that AI-powered solutions not only speed the checkout process but also contribute to sustainable infrastructure by reducing resource consumption and increasing energy efficiency. This report offers significant information, like the impact of AI-powered image recognition checkout systems on operational efficiency, customer experience, and the role of AI in promoting sustainable infrastructure for retailers and governments looking to advance the digitalization of the retail industry.
Copyright (c) 2026 Malyssa Alias, Dhaifina Saidi, Lim Jia Huey, Lee Qing Fang, Durghaashini S. Ragunathan, JosephNg Poh Soon, Phan Koo Yuen, Lim Jit Theam, Wong See Wan http://creativecommons.org/licenses/by-sa/4.0
Improved seizure detection using optimized time sequence based deep learning framework
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.
Copyright (c) 2026 Puspanjali Mallik, Ajit Kumar Nayak, Satyaprakash Swain http://creativecommons.org/licenses/by-sa/4.0
Robust multi-faces recognition and tracking via fuzzy genetic algorithms and deep coupled features
In real-world surveillance environments, face recognition and tracking remain challenging due to partial occlusion, pose variation, illumination changes, and background clutter. This paper presents a robust hybrid framework that integrates fuzzy genetic algorithms (FGA) with deep coupled feature learning for multi-face recognition and tracking. The proposed system comprises three main modules: i) face detection and pre processing using the multi-task cascaded convolutional network (MTCNN), ii) deep coupled ResNet embeddings that jointly learn identity and appearance-invariant representations, and iii) a fuzzy rule-based genetic optimizer that adaptively refines tracking decisions based on uncertainty in motion, appearance similarity, and occlusion levels. The novelty of this work lies in the fusion of fuzzy inference with evolutionary search to guide the genetic optimization process—allowing dynamic adaptation to noisy and uncertain visual conditions. Moreover, probabilistic data association filters (PDAF) and conditional joint likelihood filters (CJLF) are employed to further enhance temporal consistency under occlusion and appearance variation. The results confirm that fuzzy evolutionary optimization, when coupled with deep feature learning, significantly improves robustness and stability for real-time face tracking in complex, dynamic scenes.
Copyright (c) 2026 Adil Abdulhur Abushana, Yousif Samer Mudhafar http://creativecommons.org/licenses/by-sa/4.0
Hybrid energy storage systems as a sustainable energy source
The use of fossil fuel power plants will contribute to emissions and environmental pollution, which has an impact on air and environmental pollution. Applying hybrid energy systems can help reduce the emission footprint and improve the stability of local electricity networks, especially in services with high energy consumption. Hybrid optimization of multiple energy resources (HOMER) is a simulator that simulates using renewable energy with the hybrid renewable energy systems (HRES). The simulation produces a system with the most appropriate combination of photovoltaic (PV), wind power (WP), and converter. The combination of PV-WP produces an economical choice for providing electrical energy in a particular location. The hybrid PV-WP model can save about 40.8% less than the current condition. The investment can be returned in 10.11 years, which is recommended for similar conditions in other areas. This positive impact can provide incentives for policymakers in the implementation of a hybrid system that can neutralize emissions and environmental pollution.
Copyright (c) 2026 Muhammad Adam, Suwarno, Catra Indra Cahyadi http://creativecommons.org/licenses/by-sa/4.0
Temperature and pH effects on bioethanol production from wild cassava (Manihot glaziovii Muell. Arg) using simultaneous co-fermentation
Bioethanol is a clean alternative energy source, with wild cassava (Manihot glaziovii Muell. Arg) as a potential feedstock. Fermentation converts glucose from hydrolysis into ethanol. This study examines the effects of pH and fermentation temperature on bioethanol characteristics using a simultaneous saccharification and co-fermentation (SSCF) technique. A factorial randomized block design (RBD) was used with two factors: pH (4.5, 5.0, and 5.5) and fermentation temperature (30, 32.5, and 35 °C). Data were analyzed using variance and Duncan’s test. Results showed that pH and temperature significantly affected pH value, total soluble solids, reducing sugar, and ethanol content. The optimal conditions for bioethanol production were pH 4.5 and temperature 32.5 °C, yielding a pH of 3.55±0.07, total soluble solids of 9.3±0.57 °Brix, reducing sugar of 3.038±0.10 mg/mL, and ethanol content of 3.48±0.37 (%w/v). Based on the results of this study, wild cassava can be utilized as bioethanol by considering the effect of fermentation conditions.
Copyright (c) 2026 Ida Ayu Pridari Tantri, Ida Bagus Wayan Gunam, Anak Agung Made Dewi Anggreni, I Gede Arya Sujana http://creativecommons.org/licenses/by-sa/4.0
Application of fuzzy logic for the evaluation of student academic performance in biomedical subjects
Conventional educational systems primarily use rigid assessment models that narrowly define student achievement through examination scores, categorizing outcomes into success or failure. Fuzzy logic, a mathematical approach derived from set theory, provides a more flexible framework capable of capturing uncertainty and gradations in performance. Initially applied in engineering and artificial intelligence, fuzzy logic has shown significant promise in educational contexts where nuanced evaluation is essential. This study applies a fuzzy logic-based methodology to the evaluation of biomedical course performance at the Sports University of Tirana, Faculty of Rehabilitation Sciences. Data were collected from fifty students enrolled in biomedical subjects and analyzed through both classical examination grading and fuzzy logic evaluation. Comparative analysis revealed that while classical assessment remains constrained by static calculations, fuzzy logic introduces dynamic adaptability. The findings highlight the superiority of fuzzy logic over traditional methods in providing a multidimensional picture of academic achievement. This approach not only refines evaluation accuracy but also supports fairer and more individualized assessment practices. Consequently, fuzzy logic emerges as a powerful tool for modernizing educational evaluation systems, particularly in biomedical disciplines where learning outcomes often extend beyond conventional metrics.
Copyright (c) 2026 Elda Maraj, Anila Peposhi, Aida Bendo http://creativecommons.org/licenses/by-sa/4.0
Financial distress prediction for batik small and medium enterprises credit financing based on deep learning algorithm
One of the biggest obstacles that any finance provider has when evaluating a borrower's creditworthiness is the prediction of financial trouble. The credit decision-making process is made more difficult for small and medium enterprises (SMEs) due to their inherent ambiguity, which raises financing costs and lowers the chance of approval. In order to estimate a binomial classifier for predicting financial hardship using logistic regression (LR), extreme gradient boosting (XGBoost), and artificial neural network (ANN) techniques, this study examines data from batik SMEs in Pekalongan city. Financial ratios predict the first period and grow in a multi-period model based on temporal factors, credit history, and age. Financial distress is defined as a substantial obstacle to a business's capacity to pay its debts as opposed to the potential for bankruptcy. The long short-term memory (LSTM) algorithm with more variables yields the best prediction accuracy. The study's conclusion indicates that in order to guarantee the accuracy of financial distress prediction, the time at risk must be adjusted.
Copyright (c) 2026 Taryadi, Bambang Sudiyatno, Robertus Basiya, Era Yunianto http://creativecommons.org/licenses/by-sa/4.0
Effect of binder variations on the performance of one-phase induction motors in bio-pellet production process
Indonesia has many oil palm plantation areas. One of the negative impacts is the large amount of empty fruit bunch (EFB) waste. Utilizing EFB as a bio pellet as a renewable energy source is one of the solutions to reduce waste while supporting the green energy transition. EFB bio-pellets have the potential to replace fossil fuels, but face challenges in setting good quality standards. The production process of EFB bio-pellets uses a variety of binder contents. This study aims to analyze the influence of different levels of binder content on the quality of bio-pellet products. Statistical analysis of linear regression was performed to measure energy consumption and motor performance in the production process of EFB bio-pellets. This study provides recommendations to help maximize the quality and efficiency of the bio-pellet production process from palm oil EFB waste.
Copyright (c) 2026 Ediwan Ediwan, Arnawan Hasibuan, Abubakar Abubakar, Muhammad Daud, Adi Setiawan, Fajar Syahbakti Lukman, Gandi Supriadi http://creativecommons.org/licenses/by-sa/4.0
Corporate social responsibility by listed commercial banks in Vietnam: practice and financial performance
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.
Copyright (c) 2026 Ha Viet Nguyen, Nguyet Thi Minh Dang http://creativecommons.org/licenses/by-sa/4.0
A bibliometric review of critical chain project management in construction
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.
Copyright (c) 2026 Dhiraj S. Bachwani, MohammedShakil S. Malek, Deep Shaileshkumar Upadhyaya, Neetu Yadav http://creativecommons.org/licenses/by-sa/4.0
DCNNVA: a deep convolutional neural network for volcanic activity classification using satellite imagery
Monitoring and classifying volcanic activity are a critical task for disaster risk reduction and hazard management. Recent discoveries in machine learning and deep learning have proved excellent satellite image classification and volcanic anomaly identification capabilities, yet the majority of existing methods suffer from small datasets, particularly on solitary data modalities or particular cases, merely as examples. In this research work, we put forward develop deep convolutional neural network for volcanic activity (DCNNVA) classification specifically designed for satellite imagery on volcanic activity. We rigorously benchmarked DCNNVA model's strength against a total of eight state-of-the-art transfer learning models: ResNet50, NASNetLarge, DenseNet121, MobileNet, InceptionV3, Xception, VGG19, and VGG16. Comparative experimental results show that proposed DCNNVA framework's overall performance significantly surpasses its competitors with an accuracy of 99.33%, precision of 100%, recall of 98.67%, and F1-score of 99.33%, significantly beating existing state-of-the-art methods. Also, we create a deployable graphical user interface (GUI) system that is capable of real-time monitoring on volcanic activity and generates multi-modal alert processing that can make this research directly applicable for practical use on disaster management as well as in early warning systems. This research contributes a scalable, strong, as well as practical solution towards volcanic hazard identification as well as a baseline system toward developing future multi-modal as well as real-time geohazard tracking system frameworks.
Copyright (c) 2026 Yasir Hussein Shakir, Reem Ali Mutlag, Eshaq Aziz Awadh AL Mandhari, Mohamed Shabbir Abdulnabi http://creativecommons.org/licenses/by-sa/4.0
A novel circulant matrix-based McEliece framework for secure digital communication
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.
Padala Venkata Gopala Durga Prasad Reddy
Copyright (c) 2026 Ravikumar Inakoti, James Stephen Meka, Padala Venkata Gopala Durga Prasad Reddy http://creativecommons.org/licenses/by-sa/4.0
Hybrid deep learning and ensemble learning approach for high accuracy thyroid disease classification
Thyroid disease is a common endocrine disorder affecting the thyroid gland, a small butterfly-shaped organ at the base of the neck. According to the World Health Organization (WHO), nearly one billion people worldwide are affected by thyroid-related conditions. Conventional diagnostic methods, such as thyroid scans and function tests, are often costly, time-consuming, and complex for clinicians to interpret. To overcome these limitations, this study introduces a novel temporal conditional-Markov random field (TC MRF) framework for early detection and classification of thyroid disease. The multi-modality images computed tomography (CT), magnetic resonance imaging (MRI), and ultrasound (US) are collected from the ImageNet database and preprocessed using contrast stretching adaptive Gaussian star (CSAGS) filter to improve image clarity. The enhanced images are then processed over a convolutional neural network (CNN) for feature extraction. These features are classified using a random forest (RF) model to determine whether the thyroid condition is normal or abnormal. The proposed TC MRF achieves a classification accuracy of 98.27% and F1-score of 96.05%. The TC-MRF enhances the total accuracy range of 6.30%, 4.11%, and 5.36% better than naive Bayes, multilayer perceptron (MLP), and decision tree, respectively.
Copyright (c) 2026 Shuriya Balusamy, Balajishanmugam Vivekanadhan, Prathima Mabel John, Sushma Sunil Bhosle http://creativecommons.org/licenses/by-sa/4.0
Hydrothermal synthesis of ZnFe2O4@g-C3N4 for enhanced adsorption-photocatalytic degradation of ciprofloxacin
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.
Copyright (c) 2026 Medya Ayunda Fitri, Muchammad Tamyiz, Eko Prasetyo Kuncoro, Mamlu’atul Nihaya, Muhammad Abdul Basith Thom Thom, Cindy Dwi Cahyani, Bahauddin Alqostolani http://creativecommons.org/licenses/by-sa/4.0
Hybrid deep learning approach for Indonesian hoax detection: a comparative evaluation with IndoBERT
The spread of hoaxes in Indonesia has escalated significantly, with over 12,547 cases recorded between 2018 and 2023. Low public literacy and uncontrolled information flow contribute to the rapid dissemination of false content that fuels disinformation and social unrest. Previous studies have utilized artificial intelligence (AI) approaches such as Indonesia bidirectional encoder representations from Transformers (IndoBERT) and deep learning models like long short-term memory (LSTM), bidirectional LSTM (BiLSTM), convolutional neural network (CNN), and Transformer-based methods. However, most relied on a single modeling paradigm and did not address the trade-offs between classification performance and computational efficiency. This study proposes a hybrid architecture that integrates IndoBERT with bidirectional gated recurrent unit (BiGRU) and BiLSTM to enhance Indonesian hoax detection. Using 4,312 news articles and 10-fold cross-validation, we compare the performance of IndoBERT–BiGRU, IndoBERT–BiLSTM, and the proposed hybrid IndoBERT–BiGRU BiLSTM model. Evaluation metrics include accuracy, precision, recall, F1 score, and training time. The hybrid model achieved the best performance with 98.73% accuracy, 99.01% recall, 98.04% precision, and 98.98% F1 score, while also reducing training time compared to single models. These findings demonstrate that combining BiGRU and BiLSTM within the IndoBERT framework effectively balances performance and efficiency, making it a robust solution for Indonesian text classification.
Copyright (c) 2026 Siti Mujilahwati, Moh. Rosidi Zamroni, Miftahus Sholihin http://creativecommons.org/licenses/by-sa/4.0
Technical proposal for the design of a helical conveyor for solid waste handling
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.
Copyright (c) 2026 Javier Sinche Ccahuana, Jorge Augusto Sánchez Ayte, Margarita F. Murillo Manrique, Richard Flores-Cáceres http://creativecommons.org/licenses/by-sa/4.0
Experimental study on annealing S45C steel: effect of temperature and time on hardness, impact strength
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.
Copyright (c) 2026 Mahadir Sirman, Syahrisal Syahrisal, Henny Pasandang, Rusdi Nur, Muhira Dzar Faraby, Mukhlisin Mukhlisin http://creativecommons.org/licenses/by-sa/4.0
State evolution approach for the axion conversion probability in magnetosphere of a neutron star
Neutron stars (NS), with their extreme gravitational and magnetic fields, provide an exceptional astrophysical laboratory for studying axion dark matter (DM). Through the Primakoff effect, axions can convert into photons within the magnetospheres of NS, a process that may produce observable radio and X-ray signals. In this work, we investigate axion-photon conversion using a novel, time-dependent state evolution formalism, moving beyond the commonly used stationary-path approximations. We derive a generic analytical expression for the conversion probability and calculate the associated radiated power. Our analysis demonstrates that this approach allows NS to strongly constrain the axion-photon coupling constant, reaching sensitivities of gaγγ ≃ 10−14 −10−15 GeV−1 for axion masses of ma ≃ 10−3 −10−10 eV. These results establish a new pathway to constrain gaγ via NS observations. Future campaigns using powerful observatories like the James Webb Space Telescope (JWST), Green Bank Telescope (GBT), and More Karoo Array Telescope (MeerKAT) array will be ideally suited to probe the distinct spectral signatures predicted by our model across multiple frequency domains.
Copyright (c) 2026 Bilal Ahmad, Shehreyar Ali http://creativecommons.org/licenses/by-sa/4.0
Miniaturized circular fractal patch antenna with defected ground structure for high-selectivity dual-band X-band applications
Microstrip patch antennas are easily fabricated and have a low profile, making them widely used in radar, satellite, and defence applications. Achieving high selectivity and miniaturization in X-band dual-band operation remains a challenge. Conventional designs using simple patch geometries and defected ground structures (DGS) often exhibit limited bandwidth, poor impedance matching, and reduced gain. To address these limitations, this work presents a miniaturized circular fractal patch antenna with an optimized DGS to enhance frequency selectivity, improve impedance matching, and maintain compact size. Circular fractal slots are introduced in the radiating patch to extend the effective current path while preserving the footprint. A centrally placed diamond-shaped slot provides capacitive loading that aids impedance tuning. Electromagnetic simulations were conducted in Ansys HFSS 2023 R2, and a prototype was fabricated on an FR-4 substrate with εr = 4.4, loss tangent = 0.02, and thickness 1.6mm. Measurements verify two passbands: 8.637–9.173GHz (center 8.8025GHz, return loss −22.0267dB, voltage standing wave ratio (VSWR) 1.1720, gain 4.82dB, efficiency 63.51%) and 10.121–10.956GHz (center 10.3700GHz, return loss −25.2864dB, VSWR 1.1199, gain 3.42dB, efficiency 72.58%). The antenna shows steady radiation and improved matching across both bands, supporting use in compact X-band front ends.
Copyright (c) 2026 Raju Thommandru, Rengaraj Saravanakumar http://creativecommons.org/licenses/by-sa/4.0
Adaptive sugarcane monitoring in Mojokerto using a hybrid powered IoT multi-sensor system and machine learning
This study develops a hybrid-powered IoT multi-sensor system integrated with machine learning for sugarcane monitoring in Mojokerto. Four sensors—soil moisture, pH, LM35 temperature, and LDR light—are connected to an Arduino UNO R4 WiFi microcontroller. A hybrid power supply (mains electricity and solar panels) and dual data storage (real-time transmission to Google Sheets and local SD backup) ensure resilience and reliability under field conditions. Sensor data are normalized and smoothed prior to analysis using K-Means clustering to map environmental states and a Random Forest classifier to predict crop health. Field validation demonstrates soil moisture as the most influential parameter, followed by temperature, pH, and light intensity. The Random Forest model achieved 93.01% accuracy, 93.88% precision, 99.02% recall, and a 96.38% F1-score on held-out data. By combining hybrid power, multi-sensor integration, dual storage, and machine learning, the system provides robust, data-informed monitoring that supports timely irrigation and management decisions in sugarcane cultivation.
Copyright (c) 2026 Sekar Sari, Oktavia Citra Resmi Rachmawati, Tole Sutikno http://creativecommons.org/licenses/by-sa/4.0
SAIDI and SAIFI indicators for the control of feeder A4502 of the Concepción transformer electrical substation
This study evaluated the reliability of feeder A4502 of the Concepción substation (Huancayo, Peru) through the analysis of system average interruption duration index (SAIDI) and system average interruption frequency index (SAIFI) indicators. The 46-year-old infrastructure presented 805 structural deficiencies (59%), with a predominance of corrosion in iron poles. Automatic recloser devices were implemented at strategic points, based on the fact that 67% of the 73 interruptions in 2021 were transient faults. Post-intervention results (2024) showed significant improvements: SAIDI was reduced from 9.87 to 7.39 hours (25%), nearing the regulatory limit of 7 hours; SAIFI decreased from 4.29 to 2.71 events (37%), falling within the limit of 4. Pearson correlation analysis confirmed a statistically significant relationship between structural deficiencies and the indicators (r =0.62 SAIDI, r =0.58 SAIFI, p <0.05). The integrated approach—diagnosis of deficiencies + automation with reclosers—proved to be technically viable and economically justifiable, also allowing for the meeting of new energy demands (240 kVA available). The results constitute a replicable model for other aging Latin American networks, validating the viability of regulatory compliance without prohibitive investments.
Copyright (c) 2026 Jorge Augusto Sanchez Ayte, Margarita Fredesvinda Murillo-Manrique http://creativecommons.org/licenses/by-sa/4.0
Crop prediction in Tamil Nadu according to environmental and soil factors using hybrid machine learning architecture
Mathuranthagam, Tamil Nadu, India is the site of this research initiative that employs state-of-the-art hybrid machine learning (ML) architectures to forecast crop suitability in relation to environmental and soil characteristics. The model takes advantage of the strengths of linear support vector machine (SVM) classifier, bidirectional long short-term memory (BiLSTM), and convolutional LSTM (ConvLSTM) networks, and the data to capture complicated temporal and spatial correlations. To prepare the dataset for model training, it is normalized using min-max scaling and then feature selected using a Jaya optimization technique. The dataset contains variables such as humidity, rainfall, temperature, and pH. Both the BiLSTM and the ConvLSTM improve the model's comprehension of context from both previous and subsequent time steps. The ConvLSTM also records spatial dependencies. A powerful decision-making tool for differentiating across crop varieties is the linear SVM classifier. Comparing the hybrid model's performance to that of traditional LSTM approaches using measures such as recall, accuracy, precision, and F1-score shows that it performs much better. Using this approach can see how deep learning (DL) can supplement more conventional ML methods and see how important local environmental data is for agricultural policy and planning.
Copyright (c) 2026 Sundaraj Kannan Susee, Shenbagaramasubramanian Shenbaga Vadivu, Murugesan Senthil Kumar http://creativecommons.org/licenses/by-sa/4.0
Adaptive sentiment analysis for stock markets using deep learning
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.
Copyright (c) 2026 Talent Mawere, Selvaraj Rajalakshmi, Venu Madhav Kuthadi, Othlapile Dinekanyane http://creativecommons.org/licenses/by-sa/4.0
Soft fuzzy partial metric and some results on fixed point theory under soft set
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.
Copyright (c) 2026 Rohini R. Gore, Renu P. Pathak http://creativecommons.org/licenses/by-sa/4.0