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28,451 Article Results

Multi-robot coverage algorithm in complex terrain based on improved bio-inspired neural network

10.11591/ijra.v14i3.pp348-360
Fangfang Zhang , Mengdie Duan , Jianbin Xin , Jinzhu Peng
Biological neural network (BNN) algorithms have become popular in coverage search in recent years. However, its edge activity values are weak, and it is simple to fall into a local optimum at a late stage of coverage. When applied to complex environments, the 3D BNN network structure has high computational and storage complexity. In order to solve the above problems, we propose an algorithm for multi-robot cooperative coverage of complex terrain based on an improved BNN. The algorithm models the complex terrain using a 2.5-dimensional (2.5D) elevation map. Combining the dual-layer BNN network with the 2.5D elevation map, we propose an elevation value priority mechanism. This mechanism lets the robot make elevation-based decisions and prioritizes higher terrain areas. The dual neural network's first layer plans the robot's path in normal mode. The second network layer helps the robot escape the local optimum. Finally, the algorithm's full coverage effect in complex terrains and the speed of covering high terrain are verified by simulations. The experiments show that our algorithm preferentially covers high points of the region and eventually covers 100% of complex terrain. Compared with other algorithms, our algorithm covers more efficiently and takes fewer steps than others. The speed of covering high terrain areas has increased by 34.51%.
Volume: 14
Issue: 3
Page: 348-360
Publish at: 2025-09-01

Occupational safety of morticians: A case study of mortuary facilities in Cape Coast, Ghana

10.11591/ijphs.v14i3.25445
James Kojo Prah , Ebenezer Aggrey , Andreas A. Kudom , Mohammed Najimudeen Abdulai , Cecil Banson , Benedict Addo-Yeboa , Richard Pinkrah , Kwasi Sobre Nkrumah , Emmanuel Walker , Elizabeth Atulley
Risk assessment is helpful for risk management because it makes significant workplace concerns easier to understand. Because of the numerous risks involved, the death care sector is regarded as one of the riskiest places to work. Nevertheless, not enough research has been done on the safety of mortuary staff in low-resource environments. This study assessed the risks associated with identified hazards in two mortuary facilities at the University of Cape Coast in Ghana. This was a cross-sectional study that used a combination of methods. Thirty-six morticians participated in the study. Respondents scored low on knowledge, high on attitude, and awareness toward occupational health and safety. Only 9 (25.0%) participants knew the correct concentrations of chlorine solution required to disinfect surfaces. Only two of the six chosen volunteers could reconstitute the chlorine solution for disinfection correctly. Risks of chemical inhalation, musculoskeletal injuries, cuts, and traumatic injuries were evaluated as high. A review of incident registers revealed underreporting of injuries. While the study showed significant gaps in the occupational safety of morticians in low-resource environments, it also presents an opportunity for improvement. Regulatory agencies for morticians in Ghana must set out minimum qualifications for this critical category of health workers.
Volume: 14
Issue: 3
Page: 1540-1551
Publish at: 2025-09-01

Empowering breastfeeding mothers: How self-directed learning boosts confidence-unveiling the two-round Delphi method

10.11591/ijphs.v14i3.25965
Dewi Ariani , Respati Suryanto Dradjat , Kumboyono Kumboyono , Lilik Zuhriyah
Promoting breastfeeding self-efficacy through self-directed learning requires behavior, goal setting, and self-reinforcement. This research aims to collect insights from health professionals on strategies for improving maternal confidence in breastfeeding using self-directed learning and existing knowledge. An in-depth exploration through a two-round Delphi method rooted in the self-efficacy theory of self-directed learning for breastfeeding mothers was conducted, involving expert input and an extensive literature review. Four key documents were identified, each undergoing rigorous expert rating to ensure quality. Six essential elements for health professionals to guide breastfeeding mothers were established, focusing on lactation physiology, successful initiation, confidence building, adversity management, cultural beliefs, and public breastfeeding. Three crucial topics, including prior knowledge, personal attributes, and autonomous processes, were designed to enhance self-efficacy through self-directed learning. In conclusion, the study emphasizes the vital role of health professionals in supporting mothers through comprehensive breastfeeding guidance and encouraging self-directed learning.
Volume: 14
Issue: 3
Page: 1256-1266
Publish at: 2025-09-01

A comparative study of CNN architectures for the detection of tomato leaf diseases

10.11591/ijeecs.v39.i3.pp1587-1594
Soumia Benkrama , Benyamina Ahmed , Nour El Houda Hemdani
Recent advancements in computer vision and machine learning (ML) have revolutionised various sectors, including precision agriculture (PA). In our study, we focused on detecting tomato leaf diseases (TLD) using deep learning (DL) techniques. Using a convolutional neural network (CNN) model, we developed an agricultural image index to accurately detect TLD. By utilizing available datasets from Kaggle, we trained our model to recognize various TLDs. To determine the most effective one, we compared multiple architectures, including VGG, ResNet, and EfficientNetB1. The obtained results demonstrated a classification accuracy of over 99% on the test set. This approach has allowed us to accelerate and enhance the disease detection process, positively impacting agricultural communities by reducing crop losses and enabling early intervention in case of disease outbreaks. Our study highlights the effectiveness of CNN models in the detection of TLD, paving the way for future applications in PA.
Volume: 39
Issue: 3
Page: 1587-1594
Publish at: 2025-09-01

An efficient machine learning framework for optimizing hyperspectral data analysis in detecting adulterated honey

10.11591/ijeecs.v39.i3.pp1776-1786
Ashwini N. Yeole , Guru Prasad M. S. , Santosh Kumar
Honey adulteration detection involves employing spectral data, often utilizing machine learning (ML) techniques, to identify the presence of impurities or additives in honey. This study aims to explore ML models through the collection of a hyperspectral honey dataset with limited samples and 128 features. Three distinct feature selection (FS) methods i.e., Boruta, repeated incremental pruning to produce error reduction (RIPPER), and gain ratio attribute evaluator (GRAE) are applied to extract important features for decision-making. Then, the feature-selected dataset is classified through four effective ML algorithms, such as support vector machine (SVM), random forest (RF), logistic regression (LR), and decision tree (DT). Accuracy, F1-score, Kappa Statistics, and Matthews correlation coefficient (MCC) are the performance metrics used to assess the results of ML algorithms. RIPPER FS technique gave the best results by improving its accuracy values from 79.05% (primary data) to 91.89% (augmented data) for the RF classifier model and 74.93% (primary data) to 91.89% (augmented data) for the DT classifier model. These detailed examinations of the experiments demonstrate that proper finetuning of the ML methods can play a vital role in optimizing hyperspectral data analysis for detecting adulteration levels in honey samples.
Volume: 39
Issue: 3
Page: 1776-1786
Publish at: 2025-09-01

Effective vocabulary learning through augmented and virtual reality technologies

10.11591/ijeecs.v39.i3.pp1855-1864
Arifin Arifin , Nofvia De Vega , Syarifa Rafiqa
This study investigates the role of augmented reality (AR) and virtual reality (VR) technologies in enhancing vocabulary learning achievement among students. It addresses the need for innovative instructional methods that improve engagement and retention compared to traditional approaches. Utilizing a survey-based quantitative design supplemented by qualitative interviews, the research involved 220 participants from diverse educational backgrounds, providing a robust dataset for analyzing the impact of these immersive technologies on vocabulary acquisition. Structured questionnaires assessed engagement levels, learning outcomes, and user experiences with AR and VR applications designed explicitly for vocabulary enhancement. The findings reveal that 75% of participants reported improved vocabulary retention, highlighting the interactive nature of AR and VR as a significant factor influencing student attitudes toward vocabulary learning. The study concludes contextualized learning scenarios with interactive features are more effective than passive learning environments. Additionally, it suggests future research directions, including developing personalized learning paths and integrating collaborative features to enhance group learning experiences. The implications for educators emphasize the potential of AR and VR technologies to transform vocabulary instruction and foster deeper engagement among learners.
Volume: 39
Issue: 3
Page: 1855-1864
Publish at: 2025-09-01

Five-Tier BI architecture with tuned decision trees for e-commerce prediction

10.11591/ijeecs.v39.i3.pp1633-1641
Thiruneelakandan Arjunan , Umamageswari A.
In recent times, remarkable performance has been shown by large language models (LLMs) in a range of natural language processing (NLP) such as questioning, responding, document production, and translating languages. In today's competitive business landscape, understanding consumer behaviour in online buying is crucial for the success of e-commerce platforms. The work proposes a novel Five-Tier service-oriented BI architecture (FSOBIA) that leverages advanced tuned decision tree (ATDT) techniques for predicting online buying behaviour. The proposed FSOBIA offers e-commerce platforms a scalable and adaptable solution for gaining insights into consumer preferences and making informed business decisions. The goal of FSOBIA's design and implementation is to meet the needs of evolving users and quicker service. Experimental evaluations on real-world datasets in FSOBIA achieved over 95% prediction accuracy, outperforming traditional models: Decision trees (82%), and XGBoost (91%), while offering better scalability and computational efficiency.
Volume: 39
Issue: 3
Page: 1633-1641
Publish at: 2025-09-01

Factors associated with risk scores among stone mortar workers exposed to high noise levels in Lampang province, Thailand

10.11591/ijphs.v14i3.24814
Yuwadee Khunsaard , Arroon Ketsakorn
Noise pollution is an undesirable phenomenon that affects human health and can lead to occupational hearing loss. This study was to assess associations of risk scores from exposure to noise related to their variables from noise exposure among stone mortar workers who exposed to high noise levels during their work in Lampang, Thailand. The study was conducted between August and September 2023. Data collection involved using standardized questionnaires which were developed by researchers and used scientific instruments for noise measurement. The questionnaires contained items related to population characteristics and work information, knowledge, attitude, and practice for preventing noise exposure, noise exposure measurement, and risk scores from exposure to noise. Pearson’s correlation coefficient was used to analyze data. The results showed that seven factors significantly associated with risk scores from exposure to noise while performing their work. Apparently, there were four influential variables which included height of workers, ear symptoms, working hour per day, and noise measurement as tested using multiple regression analysis. Therefore, efforts should be made to manage those variables by drafting policies and creating tools for risk prediction to control the influential variables related to risk level from exposure to noise in the working area.
Volume: 14
Issue: 3
Page: 1394-1403
Publish at: 2025-09-01

Relationship between shift work and the risk of colorectal cancer among Moroccan women

10.11591/ijphs.v14i3.25572
Hamza Elbaylek , Soumia Ammor
Colorectal cancer (CRC) is a public health problem worldwide, and also in Morocco, with 7.9% of new cancer cases. Dietary factors have been linked to CRC risk; however, several modifiable risk factors have not been studied in Morocco. This study aimed to explore the association between shift work and the risk of colorectal cancer among Moroccan women. A case-control study was conducted at CHU Mohamed VI Marrakech, involving 165 cases and 165 controls. Data were collected using a self-administered questionnaire. For general characteristics, we used the Chi-square test for categorical variables and student’s t-test or Mann-Whitney U for continuous variables to select confounding factors, we ran logistic regression analysis to estimate odds ratios and 95% confidence intervals. Findings from our study show an increased risk of CRC for rotating shift workers ORb:1.74 (95% CI:1.05-2.91) (p-value = 0.01). When stratified by tumor location, night shift work was correlated with an increased risk of rectal cancer, while stratified by age, rotating shift work was also correlated with an increased risk of CRC among those aged 45 to 65 years ORb: 2.18 (95% CI:1.03-4.79) (p-value = 0.048). Findings from this study may be helpful for future research in Morocco and North African countries.
Volume: 14
Issue: 3
Page: 1109-1118
Publish at: 2025-09-01

Graphene-based reconfigurable FSS for dynamic millimeterwave OAM beam generation

10.11591/ijeecs.v39.i3.pp1608-1621
Nidal Qasem
This research paper explores the dynamic generation of orbital angular momentum (OAM) beams at millimeter-wave (mm-wave) frequencies using intelligent reconfigurable metasurfaces (IRM). The ability to dynamically control OAM properties is crucial for unlocking these beams’ full potential. This paper proposes a novel method utilizing a frequency-selective surface (FSS) integrated with reconfigurable graphene to generate an IRM. By carefully designing the FSS elements and controlling the graphene’s electrical conductivity, the system can generate and manipulate mm-wave OAM beams with different topological charges. With the suggested IRM structure, a conversion efficiency of nearly 80% can be achieved in converting the circularly polarized incident wave into its cross-polarized component at 30.7 GHz, with an overall thickness of 0.067 λ. This research has significant implications for advancing mm-wave communications by providing additional spatial dimensions for multiplexing and enhancing system capacity.
Volume: 39
Issue: 3
Page: 1608-1621
Publish at: 2025-09-01

Simulation of reactive flow over a parabolic vertical plate using MATLAB

10.11591/ijeecs.v39.i3.pp1673-1682
Sivakumar Pushparaj , Balaji Ramalingam , Ramesh Adhimoolam , P. Venkata Mohan Reddy , Andal Srinivasan , Muthucumaraswamy Rajamanickam
This article examines how fluid flows around an infinitely large, parabolic-shaped vertical plate, which is heated at an exponentially accelerating rate and undergoes a chemical reaction with the fluid. The plate’s temperature increases at an exponential rate, adding complexity to the heat transfer process. Additionally, the fluid undergoes a chemical reaction in this environment, impacting both the flow and concentration of chemical species. The article includes graphs that show how different parameters such as the rate of temperature increase, strength of thermal radiation, and reaction rate, effect the flow, heat, and concentration profiles. This graphical analysis provides a visual understanding of how each parameter influences the behavior of the fluid.
Volume: 39
Issue: 3
Page: 1673-1682
Publish at: 2025-09-01

Empirical analysis of Bitcoin investment strategy: a comparison of machine learning and deep learning approach

10.11591/ijeecs.v39.i3.pp1745-1754
Nrusingha Tripathy , Yugandhar Manchala , Rajesh Kumar Ghosh , Biswajit Dash , Archana Rout , Nirmal Keshari Swain , Subrat Kumar Nayak
A digital currency known as a cryptocurrency uses blockchain technology to record transactions electronically, guaranteeing security and transparency. Cryptocurrencies, in contrast to conventional hard currency, are virtual or soft currencies; that do not exist in the actual world like coins or banknotes. Since all transactions occur digitally, cryptocurrencies are decentralized and frequently stand-alone from conventional financial institutions. Peer-to-peer transfers, increased anonymity, and often quicker transaction processing without middlemen are made possible by this. In this study, two machine learning models; autoregressive integrated moving average (ARIMA), extreme gradient boosting (XGBoost), and two deep learning models; long short-term memory (LSTM), bidirectional LSTM (Bi-LSTM) were compared. By employing past Bitcoin data from 2012 to 2020, we evaluated the models' mean absolute error (MAE) and root mean squared error (RMSE). Compared to other models, the Bi-LSTM model yields minimal RMSE scores of 67.18 and MAE scores of 24.73. This aids in capturing all temporal correlations, which are important for forecasting the price of Bitcoin.
Volume: 39
Issue: 3
Page: 1745-1754
Publish at: 2025-09-01

An innovative approach to Raga pattern identification

10.11591/ijeecs.v39.i3.pp1865-1876
Sudipta Chakrabarty , Prativa Rai , Md Ruhul Islam , Hiren Kumar Deva Sarma
Raga is a fundamental element of Indian classical music (ICM), crucial for identifying the unique characteristics of a given song. Recognizing the embedded Raga allows for various applications, including music therapy, and leveraging the therapeutic effects of different Ragas. The use of mathematical techniques such as fast fourier transform (FFT) and fundamental frequency measurement (FFM) in calculating note values has proven effective for Raga pattern recognition. Both methods yield nearly identical results, facilitating accurate identification of Ragas. Once identified, these Ragas can be used for specific therapeutic purposes, harnessing their healing potential.
Volume: 39
Issue: 3
Page: 1865-1876
Publish at: 2025-09-01

Pairing mobile users using K-means algorithm on PD-NOMA-based mmWaves communications system

10.11591/ijeecs.v39.i3.pp1595-1607
Litim Abdelkhaliq , Bendimerad Mohammed Yassine
In this research, we study the effectiveness of the K-means machine learning (ML) clustering approach for pairing mobile users on a power domain nonorthogonal multiple access (PD-NOMA) single input single output (SISO) downlink-based millimeter-wave (mmWave) communication system. The basic concept is to pair the mobile users by using a data set that contains essential information about the mobile users in the micro cell base station (BS) (e.g., the SNR, the distance between the mobile users and the BS, the channel gain, and the data rate of each mobile user). The study conducted in this paper demonstrates that the proposed K-means clustering-based scheme achieves a balance between computational complexity and performance metrics. It outperforms single carrier NOMA (SC-NOMA), the conventional NOMA pairing scheme, and time division multiple access (TDMA), offering an effective trade-off between system efficiency and implementation feasibility.
Volume: 39
Issue: 3
Page: 1595-1607
Publish at: 2025-09-01

Prediction of broiler shear force using near infrared spectroscopy with second derivative linear modeling

10.11591/ijeecs.v39.i3.pp1787-1794
Rashidah Ghazali , Herlina Abdul Rahim , Syahidah Nurani Zulkifli
This study explores the use of linear predictive models, specifically principal component regression (PCR) and partial least squares (PLS), in combination with a cost-effective near infrared spectroscopy (NIRS) system to noninvasively assess the texture of raw broiler meat. The findings demonstrate that appropriate pre-processing techniques, such as excluding the visible spectrum and applying the second-order Savitzky-Golay (SG) derivative with an optimal filter length (FL), enhance model performance. Notably, the PLS model outperformed PCR, requiring fewer latent variables (LVs) to achieve accurate predictions. This suggests that PLS more effectively captures key spectral features associated with meat texture, making it a promising approach for assessing raw broiler meat quality in a practical, cost-efficient, and non-invasive manner. These results highlight the potential of integrating linear predictive models with NIRS technology for reliable texture analysis in the poultry industry.
Volume: 39
Issue: 3
Page: 1787-1794
Publish at: 2025-09-01
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