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31,456 Article Results

Influence of potassium bromide phosphor on optical properties of white light-emitting diodes

10.11591/ijaas.v14.i4.pp1359-1366
Pham Hong Cong , Nguyen Thi Phuong Loan , Nguyen Doan Quoc Anh , Hsiao-Yi Lee
Conventional phosphor-converted light-emitting diodes (LEDs) using silicone binders often suffer from yellowing, moisture degradation, and limited spectral tunability, restricting their performance in high-power street lighting. To overcome these limitations, this study aims to develop an advanced LED illumination system integrating a KBr-doped sol-gel/silica phosphor with total internal reflection (TIR) lenses and a reflective housing, encapsulated by an atomic layer deposition (ALD)-coated minilens panel. The sol-gel matrix, synthesized from MTEOS, TEOS, and silica granules, was engineered to achieve uniform KBr particle dispersion, reduced thermal quenching, and improved chromatic stability. The ALD laminate provides an additional moisture and heat barrier, sealing micro-defects and minimizing stress-induced cracking. Optical performance was quantitatively assessed using Monte Carlo beam-tracking simulations under various street configurations, including focal, zigzag, and single-plane pole layouts. Results demonstrated enhanced luminous efficacy, precise glare control, and high uniformity in street illumination. Overall, this integrated sol-gel/ALD LED design effectively addresses the durability and color instability problems of traditional silicone systems, offering a scalable and energy efficient solution for next-generation street lighting.
Volume: 14
Issue: 4
Page: 1359-1366
Publish at: 2025-12-01

AI-based federated learning for heart disease prediction: a collaborative and privacy-preserving approach

10.11591/ijict.v14i3.pp751-759
Stuti Bhatt , Surender Reddy Salkuti , Seong-Cheol Kim
People with symptoms like diabetes, high BP, and high cholesterol are at an increased risk for heart disease and stroke as they get older. To mitigate this threat, predictive fashions leveraging machine learning (ML) and artificial intelligence (AI) have emerged as a precious gear; however, heart disease prediction is a complicated task, and diagnosis outcomes are hardly ever accurate. Currently, the existing ML tech says it is necessary to have data in certain centralized locations to detect heart disease, as data can be found centrally and is easily accessible. This review introduces federated learning (FL) to answer data privacy challenges in heart disease prediction. FL, a collaborative technique pioneered by Google, trains algorithms across independent sessions using local datasets. This paper investigates recent ML methods and databases for predicting cardiovascular disease (heart attack). Previous research explores algorithms like region-based convolutional neural network (RCNN), convolutional neural network (CNN), and federated logistic regressions (FLRs) for heart and other disease prediction. FL allows the training of a collaborative model while keeping patient info spread out among various sites, ensuring privacy and security. This paper explores the efficacy of FL, a collaborative technique, in enhancing the accuracy of cardiovascular disease (CVD) prediction models while preserving data privacy across distributed datasets.
Volume: 14
Issue: 3
Page: 751-759
Publish at: 2025-12-01

A qualitative study on factors of management institute: alumni association contributing to the institute’s growth

10.11591/ijere.v14i6.32690
Raghav Sandhane , Kanchan Patil
Institutes imparting management education in India are rising in numbers. They are struggling to keep in pace with the competition and are facing several challenges. After studying these challenges, researchers have proposed several approaches including better advertisement, having experienced faculty and attracting companies for recruitment as possible areas for improvements. One of the proposed areas that seems to address a variety of the challenges, is having a better institute-alumni association. The research conducted on the alumni-institute association thus far have highlighted only a limited number of areas. Hence, this grounded study was conducted to bring forth all the possible factors and the sub factors of association between the alumni and the institute. This is a qualitative study that uses dialogic theory as an underpinning theory to aid data collection and analysis. The 13 decision makers with minimum of 15 years of teaching/administration experience in post graduate institutes were interviewed. The empirical data was analyzed using code analysis technique. The analysis presented a model of factors and sub factors to enable better institute-alumni association. The model clearly identifies the areas to focus on for developing a strong alumni association. One of the foremost findings of the research is the identification of the need for institute’s contribution in alumni growth and the ways to make this happen. Institute risk identification is another important area for alumni contribution. The developed model serves as a ready reckoner for the management institutes in its pursuit of growth.
Volume: 14
Issue: 6
Page: 4340-4352
Publish at: 2025-12-01

Classification of breast cancer using a precise deep learning model architecture

10.11591/ijict.v14i3.pp933-940
Mohammed Ghazal , Murtadha Al-Ghadhanfari , Fajer Fadhil
Breast cancer is an important topic in medical image analysis because it is a high-risk disease and the leading cause of death in women. Early detection of breast cancer improves treatment outcomes, which can be achieved by identifying it using mammography images. Computer-aided diagnostic systems detect and classify medical images of breast lesions, allowing radiologists to make accurate diagnoses. Deep learning algorithms improved the performance of these diagnoses systems. We utilized efficient deep learning approaches to propose a system that can detect breast cancer in mammograms. The proposed approach adopted relies on two main elements: improving image contrast to enhance marginal information and extracting discriminatory features sufficient to improve overall classification quality, these improvements achieved based on a new model from scratch to focus on enhancing the accuracy and reliability of breast cancer detection. The model trained on the digital database for screening mammography (DDSM) dataset and compared with different convolutional neural network (CNN) models, namely EfficientNetB1, EfficientNetB5, ResNet-50, and ResNet101. Moreover, to enhance the feature selection process, we have integrated adam optimizer in our methodology. In evaluation, the proposed method achieved 96.5% accuracy across the dataset. These results show the effectiveness of this method in identifying breast cancer through images.
Volume: 14
Issue: 3
Page: 933-940
Publish at: 2025-12-01

Artificial intelligence-based multi-key security for protected and transparent medical cloud storage

10.11591/ijaas.v14.i4.pp1241-1250
Ravi Kiran Bagadi , Neelima Santoshi Koraganji , Bandreddi Venkata Seshukumari , Kavya Ramya Sree Karuturi , Sireesha Abotula , Bodapati Venkata Rajanna , Mahalakshmi Annavarapu , Nitalaksheswara Rao Kolukula , Jayasree Pinajala , James Stephen Meka
Ensuring the security and privacy for the patient medical records and medical reports data is a crucial challenge as cloud-based healthcare technologies become more prevalent. For cloud-hosted medical data, internet of things (IoT) and artificial intelligence (AI) technologies shows best solutions for the challenges in the medical domain. This study suggests a Secure and Transparent Multi-Key Authentication Framework that makes use of AI. Using Z-score normalization, the framework first preprocesses the data before clustering to create a multi-level multi-key security structure. The physics-informed triangulation aggregation neural network (PITANN) model in the study reduces computation costs by minimizing overhead, ensuring secure handling of location-based and medical data for enhanced data classification and encryption effectiveness. A multi-key derivation of an elliptic curve, the ElGamal cryptography scheme is presented, which allows for safe multi-key encryption with little increase in the length of the ciphertext. This method guarantees safe, confidential access to cloud-hosted encrypted health information. An envisioned amalgamation improves flexibility by enhancing performance metrics such as speed of computation while safeguarding patient information through enhanced security measures and ensuring precise medical record integrity within virtual healthcare systems.
Volume: 14
Issue: 4
Page: 1241-1250
Publish at: 2025-12-01

AI-integrated pharmacy systems: bridging technology, ethics, and patient care

10.11591/ijaas.v14.i4.pp1305-1321
Adi El-Dalahmeh , Nevien Nedal , Khulood Abu Maria , Sara Abu Tarboosh
The operation of pharmacy systems undergoes transformation through artificial intelligence (AI), which advances from manual procedures to intelligent adaptive tools. These technologies enhance daily operations through prescription verification, drug interaction alerts, and inventory management while decreasing human mistakes. Through AI, patients gain access to customized medication recommendations, automatic appointment alerts, and virtual support services. The advancement of technology creates multiple new difficulties for healthcare systems. The increasing integration of AI in healthcare creates growing concerns about data privacy alongside algorithmic bias and the requirement for decision-making explanations. This paper evaluates AI systems against conventional pharmacy methods through an assessment of their precision and speed and their impact on patient safety and ethical preparedness. The adoption of AI systems requires strong ethical protections together with defined regulatory frameworks to maintain human clinical decision-making authority in patient care.
Volume: 14
Issue: 4
Page: 1305-1321
Publish at: 2025-12-01

Prediction index drought use neural network based rainfall

10.11591/ijaas.v14.i4.pp1146-1154
Nur Nafiiyah , Ali Mokhtar
Prolonged dry seasons compared to rainy seasons often lead to drought, making drought index observations essential. In Indonesia, drought monitoring commonly uses the standardized precipitation index (SPI), yet there is no common standard for drought index measurement. Therefore, this research applies the Z-score index (ZSI) and China-Z index (CZI), which, like SPI, are rainfall-based drought indices but have rarely been explored in previous research. To predict ZSI and CZI, this research compares the weighted moving average (WMA) and multilayer perceptron (MLP) methods. Two input scenarios are tested: the previous two periods (t-2, t-1) and the previous three periods (t-3, t-2, t-1). The results show that MLP outperforms WMA, with the best performance achieved by the MLP model at a mean absolute percentage error (MAPE) of 4.177% using the three variable input scenario and MLP architecture 3-6-10-1.
Volume: 14
Issue: 4
Page: 1146-1154
Publish at: 2025-12-01

Psychometric validation of Arabic attention control scale: reliability, correlation, PCA, and CFA analyses

10.11591/ijere.v14i6.34383
Amine Khadraoui , Abdellah Barebzi
The scarcity of reliable tools to measure attention control among Arabic speaking learners represents a key research problem. To address this gap, this study aimed to develop and evaluate the Arabic version of the attention control scale (ACS). The research was conducted on a sample of (N=210) Moroccan students in their second year of middle school (2023/2024), using precise translation methods, including “forward and backward translation”. The reliability of the scale was confirmed through Cronbach’s alpha (α=0.832), McDonald’s omega (Ʊ=0.820), and split-half reliability (ρ=0.870). The factor structure of the scale was verified using principal component analysis (PCA), while confirmatory factor analysis (CFA) confirmed the model’s goodness of fit (χ²=1689.398, df=66, *p<.001; Kaiser-Meyer-Olkin (KMO)=0.672). The results showed statistically significant correlations between the scale’s dimensions: general attention control (GA), attention focusing (AF), and attention shifting (AS) (*p<.001). The findings confirmed that the 12 item Arabic version of the ACS (short version) has strong psychometric properties with factor loadings ranging from 0.311 to 0.825. This study is considered an important step in developing attention assessment tools for learners in the Arab educational context. It provides a standardized scale to measure the attention process, which helps enable teachers to accurately diagnose attention difficulties and provide targeted educational interventions. These interventions contribute to improving learners’ focus and engagement, thereby enhancing the overall effectiveness of the educational process.
Volume: 14
Issue: 6
Page: 4456-4467
Publish at: 2025-12-01

Digital control of plant development through sensors and microcontrollers in Kosova

10.11591/ijict.v14i3.pp1072-1084
Ragmi M. Mustafa , Kujtim R. Mustafa , Refik Ramadani
The plant monitoring system aims to develop an automated solution for optimizing plant growth. Using the Arduino Uno ATMEGA328P microcontroller module and various sensors, this system regulates environmental conditions to promote optimal plant development. It requires adequate software to operate effectively, enabling the microcontroller to monitor and regulate climatic conditions. The primary goal of this paper is to present a comprehensive system that continuously measures parameters such as light intensity, air humidity, and soil moisture in real time within a vegetable greenhouse or a plastic-covered plant environment. This scientific paper provides an in-depth description of the hardware components used, their electronic connections, and the implementation of program code written in C++. Based on the measured physical parameters, the plant monitoring system performs specific actions, such as watering the plants and regulating the ambient temperature. In conclusion, this system effectively supports healthy plant growth and enhances the quality and yield of plant products. The paper serves as a practical example for improving plant cultivation in the agricultural sector in the Republic of Kosova.
Volume: 14
Issue: 3
Page: 1072-1084
Publish at: 2025-12-01

Detection of heavy metals concentration in vegetables and analyze the health risks

10.11591/ijphs.v14i4.26823
Solomon Legesse Gurmu , Fekede Weldekidan Mengistu , Atsedu Yeshwalul Beyene , Bizunesh Ketema , Birhanu Million Tadesse
Heavy metals are elements found in Earth’s crust but introduced into soil and water bodies by human activities. They are not biodegradable, so they persist for a long time in the environment. Heavy metals are incorporated into to human body through the food chain, resulting in various health problems. Akaki Rivers, which are major water sources in Addis Ababa, are contaminated with various wastes, including heavy metals. This research aimed to detect heavy metal concentration in cabbage, potato, tomato, and beetroot irrigated with the Akaki Rivers and evaluate associated health risks. Following the vegetable sample collection, a laboratory-based study was used in sample processing, digestion, and heavy metal detection. Mean concentration (mg/kg dry weight) of Cd (26.11-26.34), Pb (17-33.84) in all samples, and Hg (0.124) in beetroot exceeded the permissible limits set by WHO/FAO. The HRI of Cd (28.3-140.96), Pd (10.9-27.35), both in adults and children, and Hg (1.727 for children) exceeded the safe limit (<1). The health of adults and children is at risk due to Cd, Pb, and Hg, with children facing approximately 2.5 times higher. Minimization of the release of wastewater into the Akaki Rivers, and dietary diversification should be encouraged, and the health of permanent consumers should be checked.
Volume: 14
Issue: 4
Page: 1849-1856
Publish at: 2025-12-01

A hybrid approach using VGG16-EffcientNetV2B3-FCNets for accurate indoor vs outdoor and animated vs natural image classification

10.11591/ijict.v14i3.pp903-913
Meghana Deshmukh , Amit Gaikwad , Snehal Kuche
The paper introduces a hybrid approach that synergistically combines the strengths of VGG16, EfficientNetV2B3, and fully connected networks (FCNets) to achieve precise image classification. Specifically, our focus lies in discerning between basic indoor and outdoor scenes, further extended to distinguish between animated and natural images. Our proposed hybrid architecture harnesses the unique characteristics of each component to significantly enhance the model’s overall performance in fine-grained image categorization. In our methodology, we utilize VGG16 and EfficientNetV2B3 as the feature extractors. During evaluation, we examined various classification algorithms, such as VGG16, EfficientNet, Feature_Aggr_Avg, and Feature_Aggr_max, among others. Notably, our hybrid feature aggregation approach demonstrates a remarkable improvement of 0.5% in accuracy compared to existing solutions employing VGG16 and EfficientNet as feature extractors. Notably, for indoor versus outdoor image classification, feature_aggr_avgachieves an accuracy of 98.51%. Similarly, when distinguishing between animated and natural images, Feature_Aggr_Avgachieves an impressive accuracy of 99.20%. Our findings demonstrate improved accuracy with the hybrid model, proving its adaptability across diverse classification tasks. The model is promising for applications like automated surveillance, content filtering, and intelligent visual recognition, with its robustness and precision making it ideal for realworld scenarios requiring nuanced categorization.
Volume: 14
Issue: 3
Page: 903-913
Publish at: 2025-12-01

Advanced control techniques for performance improvement of axial flux machines

10.11591/ijict.v14i3.pp1095-1107
Kalpana Anumala , Ramesh Babu Veligatla
The topological advancements in twin rotor axial flux induction motors (TRAxFIMs) have spurred the interest in performance optimization and control strategies for electric vehicle (EV) applications in particular. This paper investigates for the enhanced performance of multi-level inverters (MLIs) fed TRAxFIMs with different advanced control techniques. The performance evaluation is done under variable speed conditions at constant torque and vice versa. The TRAxFIMs offer unique advantages like high power density, high efficiency and most suitable for EV applications. The performance analysis of MLIs fed TRAxFIM has been carried out with proportional-integral (PI), fuzzy controllers, and artificial neural network (ANN) controllers. The PI controller provides a conventional control approach, while the fuzzy and ANN controllers serve as advanced control strategies. The integration of MLIs and advanced control techniques with TRAxFIMs aims to enhance dynamic response, stability and efficiency. The proposed control strategies are evaluated through extensive MATLAB simulations and the potential of MLIs fed TRAxFIMs is emphasized for EV applications.
Volume: 14
Issue: 3
Page: 1095-1107
Publish at: 2025-12-01

Influencing factors of patient safety in anesthesia services in a low- and middle-income country

10.11591/ijphs.v14i4.26631
Aldy Heriwardito , Andi Ade Wijaya Ramlan , Raihanita Zahra , Amelia Martira , Bintang Pramodana , Agung Bintartho , Raden Besthadi Sukmono , Joshua Eldad Frederich Lasanudin
Patient safety in anesthesia remains a concern in low- and middle-income countries due to workforce shortages, limited equipment, and inconsistent protocols. In Jakarta, where demand for anesthesia is rising, baseline data on these parameters are lacking. This study aimed to identify gaps in human resources, equipment availability, and safety protocol adherence. A cross-sectional survey of all actively practicing anesthesiologists in Jakarta was conducted in January 2024, yielding 115 responses. The questionnaire, developed and face-validated through a focus group with senior anesthesiologists, covered three domains: human resources, facilities and equipment, and protocols. Internal reliability was assessed using Cronbach’s alpha. Participation was voluntary, responses were anonymous, and data were analyzed using descriptive statistics. Prolonged work hours were reported by a minority of respondents (6.1-7%), with 22.7% agreeing that anesthesiologists' workload is too high. Most rated human resource parameters positively (median 4/5), but access to basic monitoring devices for oxygenation, ventilation, circulation, and temperature was limited. Protocol adherence was generally high (median 4-5/5), though a small minority of institutions lacked incident reporting pathways, patient risk assessment, and post-surgical recovery rooms. Improving patient safety in Jakarta requires ensuring essential monitoring equipment, optimizing staffing to manage workload, and mandating full perioperative safety protocols across facilities.
Volume: 14
Issue: 4
Page: 1826-1836
Publish at: 2025-12-01

Spatial analysis of tuberculosis based on geographic information systems in Sleman district, Special Region Yogyakarta

10.11591/ijphs.v14i4.26826
Makhrum Irmaningsih , Angga Eko Pramono
The number of tuberculosis cases continues to rise annually, with Sleman Regency recording 2,372 cases in 2024, making it one of the highest in the Special Region of Yogyakarta Province. This study aims to analyze spatial autocorrelation and spatial relationships of tuberculosis cases in Sleman Regency in 2024 using geographic information systems (GIS) and spatial analysis. A quantitative cross-sectional design was applied to 1,406 tuberculosis cases across 86 villages. Bivariate local indicators of spatial association (LISA) analysis were performed using GeoDa software, while geographically weighted regression (GWR) in R Studio examined local environmental influences. Bivariate LISA results showed no significant spatial autocorrelation for population density, air temperature, air humidity, precipitation, and altitude (p-values: 0.173, 0.265, 0.138, 0.312, and 0.401, respectively). GWR revealed negative correlations between these variables and tuberculosis cases. Findings highlight spatial patterns and inform targeted interventions, recommending enhanced tuberculosis awareness and treatment access in low-density, high-incidence areas, along with public education on ventilation and preventive measures during colder seasons, and strengthened prevention in high-risk lowland villages.
Volume: 14
Issue: 4
Page: 1876-1885
Publish at: 2025-12-01

The components and indicators of professional learning community: the guidelines for educational quality improvement

10.11591/ijere.v14i6.33511
Korakeng Klinthaisong , Jatuphum Ketchatturat , Charuni Samat
A professional learning community (PLC) is a group of educational personnel who come together to exchange knowledge, develop, and collaboratively solve problems related to improving instructional practices and school issues. The common goal is to develop students’ competencies. PLCs have the potential to transform the quality of educational results significantly. Consequently, studying the components and indicators of a PLC and conducting confirmatory factor analysis (CFA) of PLC models can help schools understand the key factors and practices that contribute to PLC success. This knowledge can be applied to develop a strong PLC within the school. The study of components and indicators of the PLC is exploratory research. The samples consisted of 1,080 administrators and heads of the subject group from 120 schools under the Secondary Educational Service Area Office, randomized by multi-stage random sampling. The research tool was a 4-level rating scale questionnaire. Research findings indicate that the CFA of PLCs aligns with empirical evidence. All components in the PLC model have significant factor loadings ranging from 0.916 to 0.945 at the 0.01 level (p<0.01). When ranked from highest to lowest, these components are: i) supportive conditions–structures; ii) shared values and vision; iii) collective learning and application; iv) Shared and supportive leadership; v) supportive conditions–relationships; and vi) shared personal practice. All of these are essential components of a PLC and serve as an important mechanism for educational personnel to work together to improve the quality of schools.
Volume: 14
Issue: 6
Page: 4498-4508
Publish at: 2025-12-01
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