Articles

Access the latest knowledge in applied science, electrical engineering, computer science and information technology, education, and health.

Filter Icon

Filters article

Years

FAQ Arrow
0
0

Source Title

FAQ Arrow

Authors

FAQ Arrow

29,922 Article Results

Multidrug-resistant Escherichia coli carriage and associated risk factors among healthy individuals in Rural Southwestern Nigeria

10.11591/ijphs.v14i4.26808
Odetoyin Babatunde , Akinde Oluwatoyin
Antimicrobial resistance (AMR), driven by multidrug-resistant (MDR) Escherichia coli, poses a significant public health threat, silently spreading through asymptomatic carriers. Limited data from rural areas highlight the need for focused studies to guide resistance control efforts. This study aimed to isolate faecal Escherichia coli (E. coli) from apparently healthy individuals in a rural community in Southwestern Nigeria, determine their antimicrobial resistance profiles, and evaluate risk factors associated with MDR E. coli carriage. A total of 347 stool samples were collected from healthy, consenting individuals. E. coli was isolated and identified using standard microbiological techniques. Antimicrobial resistance was assessed via the Kirby-Bauer disc diffusion method. Isolates were screened phenotypically for the extended spectrum beta-lactamase (ESBL) production and genotypically for ESBL genes (CTX-M, SHV, TEM). The data were analyzed using WINPEPI. E. coli was isolated from 269 participants (77.5%), yielding 555 isolates. High resistance rates were observed against sulphamethoxazole (94.0%), ampicillin (85.0%), and tetracycline (83.2%), while imipenem showed the lowest resistance (6.7%). Twenty-seven isolates (5%) were confirmed as ESBL producers. Among these, 17 (63%) carried at least one ESBL gene-TEM being the most common (44.4%). Additionally, 50.1% of the isolates were classified as MDR, with MDR E. coli carriage significantly associated with self-medication (p=0.05). The widespread presence of MDR E. coli among healthy rural dwellers, coupled with its association with self-medication, highlights serious public health concerns and raises the urgent need for more evidence-based strategies to combat AMR.
Volume: 14
Issue: 4
Page: 1866-1875
Publish at: 2025-12-01

Clinical dental students' perceptions of difficulties in fixed prosthodontics bridgework denture preparation: a pilot study

10.11591/ijphs.v14i4.24623
Aditya Pratama Sarwono , Khairunnisa Febianti
Preparing abutment teeth for fixed bridgework presents varying challenges to dental students, impacting their training effectiveness and clinical outcomes. Understanding the most difficult stages can help improve educational strategies. This study aims to rank the difficulty of each stage in abutment tooth preparation using student evaluations, identifying the greatest challenges. A quantitative approach was used, analyzing perceptions of 155 clinical dental students from 2021-2023 cohorts at Faculty of Dentistry, Universitas Trisakti, through the non-parametric Friedman’s ANOVA Test. Student evaluations covered seven stages of abutment tooth preparation, identifying variability in perceived difficulty from most difficult to easiest. Results indicate the most difficult stage is proximal reduction (mean rank: 3.01), followed by cervical preparation (mean rank: 3.28), and lingual reduction (mean rank: 3.35). The stages with the lowest difficulty are finishing (mean rank: 5.35), followed by alignment of preparation between 2 abutment teeth (mean rank: 4.85), buccal reduction (mean rank: 4.13), and occlusal reduction (mean rank: 4.03). Proximal reduction is particularly difficult due to the need for high technical skills and precision, requiring accurate space estimation and careful reduction without damaging adjacent teeth. This difficulty is compounded by natural variations in tooth shapes and positions among patients. Findings highlight the importance of refining educational strategies to tackle these challenges, enhancing student learning and clinical skills. This research provides crucial data on which stages need greater emphasis in the curriculum, aiding the creation of more efficient and focused training methods.
Volume: 14
Issue: 4
Page: 1730-1737
Publish at: 2025-12-01

New approach of the neighborhood structure of fuzzy points

10.11591/ijaas.v14.i4.pp1083-1088
Amer Himza Almyaly , Jwngsar Moshahary
This paper provides a comparative analysis of the fuzzy Q-neighborhood and the fuzzy neighborhood system of a fuzzy point. Specifically, we investigate the relationship between the elements of these systems when both are defined at the same fuzzy point. We address questions such as: how are these elements interconnected, and which system contains the other? Furthermore, we give the dual of the fuzzy Q-neighborhood system, which is named the fuzzy DQ-neighborhood system, and prove that these two systems are not equivalent. Finally, we examine the properties of these systems to determine whether they satisfy the conditions of fuzzy topology, Supra topology, or filter theory.
Volume: 14
Issue: 4
Page: 1083-1088
Publish at: 2025-12-01

Health literacy on HIV/AIDS and adherence to antiretroviral treatment: the moderating role of social support

10.11591/ijphs.v14i4.26635
Pujaannicha Pujaannicha , Herlina Siwi Widiana
The increasing prevalence of HIV/AIDS continues to pose a significant global health challenge, with developing countries experiencing the fastest growth in transmission rates, including Indonesia. This quantitative correlational study examined the influence of HIV/AIDS-related health literacy on adherence to antiretroviral (ARV) therapy among people living with HIV/AIDS (PLWHA), with peer social support considered as a moderating variable. Participants included 208 PLWHA residing in Majalengka Regency, West Java, Indonesia. Data were gathered using standardized instruments measuring HIV/AIDS health literacy, treatment adherence, and peer social support, and were analyzed through moderation analysis using Jamovi software. The results demonstrated that HIV/AIDS health literacy had a significant positive effect on ARV adherence, and that peer social support strengthened this relationship. Nonetheless, the study is limited by its cross-sectional design and reliance on self-reported measures, which may restrict causal inference and generalizability.
Volume: 14
Issue: 4
Page: 1658-1665
Publish at: 2025-12-01

Association between body shaming and body image in nursing students: implications for health education from Indonesia

10.11591/ijphs.v14i4.24985
Citra Windani Mambang Sari , Rizky Chintya Dewi , Hendrawati Hendrawati
During emerging adulthood, the difference between the ideal body standard and one’s actual body often leads to body-shaming treatment. Persistent experiences of body shaming can significantly harm an individual’s body image. This study aims to identify the association between body-shaming acts and body image in university students. The study population consisted of students aged 18-25 who had experienced body shaming (n = 841). Using a non-probability total sampling method, 448 respondents were included. Data were collected using a body-shaming acts questionnaire and the MBSRQ-AS questionnaire, then analyzed with univariate and bivariate analysis employing the Chi-square test. The results showed a significant relationship between body-shaming acts and body image. Most students received body-shaming treatment at a low level (50.9%), while most body image categories were negative (50.9%). These findings highlight the importance of health workers’ involvement in preventing body-shaming acts and improving students’ body image.
Volume: 14
Issue: 4
Page: 1747-1756
Publish at: 2025-12-01

Likely uptakers of the COVID-19 vaccinations in Cross River South Senatorial District, Nigeria

10.11591/ijphs.v14i4.26130
Veronica Akwenabuaye Undelikwo , Glory Eteng Bassey , Nkoyo Patrick Bassey , Lilian Otu Ubi , Mathew Mike Egong
The COVID-19 outbreak resulted in widespread concern and disruption globally. The development of vaccines was a significant focus in mitigating the impact of the deadly virus. However, vaccine uptake in many regions has been challenging, including the Cross River South Senatorial District. This study examines the socio-demographic variables influencing the adoption of the COVID-19 vaccine. Data was collected from 750 respondents through the questionnaire. Bivariate analysis using Chi-square statistics was used to evaluate the association between COVID-19 vaccination and covariates, including age, sex, location, occupation status, religion, educational status, and availability of COVID-19 vaccination sites. A logistic regression model was used to ascertain this connection. Based on the findings, COVID-19 uptake was 32.3%. Employment status was the only variable statistically significant with the uptake of the COVID-19 vaccine. When promoting the use of vaccines, consideration should be given to variables other than personal characteristics. With the low uptake of the COVID-19 vaccines, continued efforts are needed to improve the vaccination uptake rate by all segments of the study population.
Volume: 14
Issue: 4
Page: 1857-1865
Publish at: 2025-12-01

A TOT: tri-optimized-tariff based strategic residential load management with greedy optimization in IEEE33-bus system: a case study with renewable energy penetration

10.11591/ijeecs.v40.i3.pp1199-1211
Kuheli Goswami , Arindam Kumar Sil
The efficiency of a load management system in terms of its energy performance index (EPI) depends on its capacity to enhance the reliability, resilience, and cost effectiveness of the existing system. Artificial intelligence (AI) is crucial in this shift from classical to AI-based power system planning, optimizing renewable energy (RE) and reducing gridstress. On the other hand, proper placement of resources is essential to achieve benefits and reduce transmission losses. Utility sectors of different states has revealed that in certain areas amongst different type of loads, domestic loads accounts for a substantial proportion of energy consumption. Therefore, the present work deals with optimum load scheduling, integration of RE, energy storage (ES) and proposed tri-optimized-tariff (TOT) for prosumers. We have found that the weighted-K-nearest-neighbor (KNN) method excels in selecting features for household appliances and ES scheduling. The composite greedy optimization (CGO) technique outperforms existing methods in optimization. These results demonstrate the efficiency and real-world potential of our model. We have conducted a case study and developed an AI-based strategic-residential-load-managementsystem (SRLMS), which we have tested on the IEEE33 bus system, showing cost effectiveness and improved EPI for prosumers. This work encourages the development of a harmonious relationship between utility-sectors and prosumers.
Volume: 40
Issue: 3
Page: 1199-1211
Publish at: 2025-12-01

Bioecological characteristics of modern soil cover in subtropic regions of Azerbaijan

10.11591/ijaas.v14.i4.pp1200-1207
Farida Verdiyeva Bahram , Turkan Hasanova Allahverdi , Mahsati Ismayilova Eyvaz , Elnur Huseynov Yusif , Telli Jabiyeva Elshad , Gunel Asgarova Farhad
The purpose of this study is to introduce innovation in the field of agriculture in Azerbaijan by determining the abundance of various ecotrophic groups of microorganisms (involved in the formation and mineralization of humic substances) in natural and cultivated gray-brown soils. Studying the microbiological indicators of humic substance transformation in virgin soils and determining the direction of these processes under the influence of anthropogenic factors in agrocenoses soils is considered relevant for the development of the agricultural sector in the Lankaran region. It was found that perennial woody vegetation increased the abundance of pedotrophic microorganisms by 17-21% and humate decomposers by 12-14% compared to completely natural soil. The correlation coefficient between the abundance of humate decomposers and the pedotrophic index was r=-0.685±0.09. Plowing natural gray-brown soils reduces the total humus content and the abundance of micromycetes, which form the peripheral portion of humic substances.
Volume: 14
Issue: 4
Page: 1200-1207
Publish at: 2025-12-01

Machine and deep learning classifiers for binary and multi-class network intrusion detection systems

10.11591/ijai.v14.i6.pp4814-4827
Ahmad Aloqaily , Emad Eddien Abdallah , Esraa Abu Elsoud , Yazan Hamdan , Khaled Jallad
The rapid proliferation of the internet and advancements in communication technologies have significantly improved networking and increased data vol ume. This phenomenon has subsequently caused a multitude of novel attacks, thereby presenting significant challenges for network security in the intrusion detection system (IDS). Moreover, the ongoing threat from authorized entities who try to carry out various types of attacks on the network is a concern that must be handled seriously. IDS are used to provide network availability, confidentiality, and integrity by employing machine learning (ML) and deep learning (DL) algorithms. This research aimed to study the impacts of the binary and multi-attack instances label by establishing IDS that leverages hybrid algorithms, including artificial neural networks (ANN), random forest (RF), and logistic model trees (LMTs). The paper addresses challenges such as data pre processing, feature selection, and managing imbalanced datasets by applying synthetic minority oversampling technique (SMOTE) and Pearson’s correlation methodologies. The IDS was tested using network security laboratory knowledge discovery datasets (NSL-KDD) and catalonia independence corpus intrusion detection system (CIC-IDS-2017) datasets, achieving an average F1-score of 96% for binary classification on NSL-KDD and 85% for binary classification on CIC-IDS-2017, while for multi-classification, the proposed model achieved an average F1-score of 82% and 96% for NSL-KDD and CIC-IDS-2017 successively.
Volume: 14
Issue: 6
Page: 4814-4827
Publish at: 2025-12-01

Antimicrobial resistance profiles of methicillin resistant coagulase negative Staphylococcus at a reference laboratory in Sierra Leone: implications for infection control

10.11591/ijphs.v14i4.26835
Abraham Bwalhuma Muhindo , Adamu Almustapha Aliero , Darlinda F. Jiba , Festo Mwebaze Syalhasha
Methicillin-resistant CoNS (MR-CoNS) are increasingly recognized as significant nosocomial pathogens. Sierra Leone lacks data on the prevalence and antibiotic-resistance patterns of these bacteria, which hinders a cross-sectoral approach to tackling antimicrobial resistance as well as regional and global health surveillance. We report on clinical multidrug-resistant MR-CoNS from Freetown, Sierra Leone, West Africa, as emerging pathogens. This study aimed to explore the prevalence and antimicrobial resistance profiles of MR-CoNS isolated from clinical samples in Freetown, Sierra Leone. A cross-sectional study was conducted at the reference laboratory from January 2025 to June 2025. Clinical samples submitted to the microbiology department were screened for Staphylococcus species, and isolates identified as coagulase-negative Staphylococci (CoNS) using standard microbiological techniques. Methicillin resistance in all isolates was tested with a 30 μg cefoxitin disc and further confirmed through an automated Scenker XK Microbial ID and AST system by measuring the minimum inhibitory concentration (MIC) with oxacillin. Antibiotic susceptibility profiles were determined using the Scenker XK Microbial ID/AST system following the Clinical and Laboratory Standards Institute (CLSI) guidelines, and data were analysed using SPSS ver 16. Findings from our study show a prevalence of 18.2% of MR-CoNS with Staphylococcus schleiferi, (26.9%) predominant. Linezolid, vancomycin, and teicoplanin exhibited 100% activity against all the MR-CoNS isolated. However, there was co-and multidrug resistance exhibited to commonly known antibiotics gentamycin (75-100%), levofloxacin (80-100%), clarithromycin (87-100%), including resistance to newer antibiotics as daptomycin (33-50%).
Volume: 14
Issue: 4
Page: 1666-1674
Publish at: 2025-12-01

Deep learning-based feature selection for lung adenocarcinoma classification and biomarker discovery

10.11591/ijai.v14.i6.pp4703-4710
Sara Haddou Bouazza , Jihad Haddou Bouazza
Lung adenocarcinoma, a leading cause of cancer-related mortality, underscores the need for reliable diagnostic tools. This study proposes a robust multi-stage feature selection and classification framework for biomarker discovery, using the cancer genome atlas lung adenocarcinoma (TCGA-LUAD) as the primary dataset and GSE19188 for independent validation. The framework combines differential expression analysis (Wilcoxon rank-sum test), joint mutual information maximization (JMIM), and sparse autoencoder-based refinement to identify a compact and predictive set of five genes. These genes are involved in key lung cancer pathways, including epidermal growth factor receptor (EGFR) signaling, cell cycle regulation, and immune response, and include biomarkers such as surfactant protein A2 (SFTPA2), napsin an aspartic peptidase (NAPSA), and T-box transcription factor 4 (TBX4). The hybrid deep learning classifier achieved high accuracy (98.4%) and area under the receiver operating characteristic curve (AUC-ROC) (0.996) on TCGA-LUAD, with strong generalization on GSE19188 (accuracy: 96.7%, AUC-ROC: 0.993%). Overall, the framework offers an interpretable and effective solution for LUAD classification and biomarker identification.
Volume: 14
Issue: 6
Page: 4703-4710
Publish at: 2025-12-01

A review on long short-term memory combination development

10.11591/ijai.v14.i6.pp4427-4441
Ahmad Riyadi , Nur Rokhman , Lukman Heryawan
Long short-term memory (LSTM) has continued to develop since it was proposed in 1997. LSTM has optimized solutions to various problems. The LSTM cell, architecture, and memory model have been reviewed. A review of LSTM implementation has been carried out in various problem domains. There are combinations of LSTM with other methods to optimize solutions. However, there is no review on the development of LSTM combination (LC). This research reviews the development of the LC model on nine research questions, namely: development framework, data, preprocessing, learning process, tasks, optimization and evaluation, domain problems, trends, and challenges. The results show that the LC model is increasingly widespread in solving problems. The LC model has completed 26 types of tasks. Prediction, detection, forecasting, classification, and recognition are the most frequently performed tasks. LC model development trends show that LSTM is increasingly collaborative with other methods on a wider scope. The challenges identified include research areas, data, model developments, the area of implementation, performance, and efficiency.
Volume: 14
Issue: 6
Page: 4427-4441
Publish at: 2025-12-01

Computer vision syndrome prevention: detection of expression and eye distance with monitor screens

10.11591/ijai.v14.i6.pp4533-4540
Aufaclav Zatu Kusuma Frisky , Elang Arkanaufa Azrien , Raden Sumiharto , Sri Hartati
Computer vision syndrome (CVS) is a vision-related complaint caused by computer usage. CVS can be analyzed through facial expressions detected by a camera. Expression detection is categorized into two groups: safe and dangerous. The safe category comprises happy, neutral, disgusted, sad, angry, and surprised, while the dangerous category includes sad and fearful emotions. This division is based on the similarity of CVS symptoms to facial emotion characteristics. Additionally, an additional feature is implemented to detect the distance between the screen and the user's eyes using the FaceMeshModule to prevent the user's eyes from getting too close to the screen. Both detections will provide warning notifications when a dangerous category expression is detected ≥70% every minute, and when the distance between the screen and the eyes is ≤40 cm. Notifications in this program use the Tkinter library as a graphical user interface (GUI) message box. In this research, facial expressions are detected using the CascadeClassifier for face detection and the extreme inception (Xception) as the facial expression classifier. The results of expression detection achieved an accuracy of 94%, an F1-score of 94%, precision of 95%, and recall of 94%.
Volume: 14
Issue: 6
Page: 4533-4540
Publish at: 2025-12-01

Enhancing cross-site scripting attack detection by using FastText as word embeddings and long-short term memory

10.11591/ijai.v14.i6.pp4923-4932
Muhammad Alkhairi Mashuri , Nico Surantha
Cross-site scripting (XSS) is one of the dangerous cyber-attacks and the number of attacks continues to increase. This study takes a new approach to detect attacks by utilizing FastText as word embedding, and long-short term memory (LSTM), which aims to improve the performance of deep learning. This method is proposed to capture the broader meaning and context of the data used, leading to better feature extraction and model performance. This study not only improves the detection of XSS attacks, but also highlights the potential for better text processing techniques. The results obtained showing this method achieves higher results than other methods, with an accuracy of 99.89%.
Volume: 14
Issue: 6
Page: 4923-4932
Publish at: 2025-12-01

Enhancing waste management through municipal solid waste classification: a convolutional neural network approach

10.11591/ijai.v14.i6.pp4775-4786
Md. Tarequzzaman , Mojahidul Alom Akash , Zakir Hossain Nayon , Md. Sabbir Reza , Shajjadul Haque
The escalation of population, economic expansion, and industrialization has resulted in an increase in waste production. This has made waste management more challenging and has resulted in environmental deterioration, negatively impacting the quality of life. Recycling, reducing, and reusing are viable methods to eradicate the escalating waste issue, requiring the appropriate classification of municipal solid waste. This study focuses on comparing six advanced waste classification systems that employ a pre-trained convolutional neural network (CNN) designed to recognize twelve distinct categories of municipal waste. It has been determined that DarkNet53 is the most effective classifier among these six models. To assess the effectiveness of each waste classifier, the confusion matrix, precision, recall, F1 score, the area under the receiver operating characteristic curve, and the loss function are examined. It has been found that DarkNet53 has an F1 score of 98.7% and validation accuracy of 99%, respectively. The suggested approach will be useful in promoting garbage recovery and reuse in the direction of a circular and sustainable economy.
Volume: 14
Issue: 6
Page: 4775-4786
Publish at: 2025-12-01
Show 92 of 1995

Discover Our Library

Embark on a journey through our expansive collection of articles and let curiosity lead your path to innovation.

Explore Now
Library 3D Ilustration