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26,833 Article Results

Categorizing hyperspectral imagery using convolutional neural networks for land cover analysis

10.11591/ijict.v14i2.pp393-404
Assia Nouna , Soumaya Nouna , Mohamed Mansouri , Achchab Boujamaa
Categorizing hyperspectral imagery (HSI) is crucial in various remote sensing applications, including environmental monitoring, agriculture, and urban planning. Recently, numerous approaches have emerged, with convolutional neural network (CNN)-based algorithms demonstrating remarkable performance in HSI classification due to their ability to learn complex spatial-spectral features. However, these algorithms often require significant computational resources and storage capacity, which can be limiting in practical applications. In this study, we propose a novel CNN architecture tailored for HSI classification within the spectral domain, focusing on optimizing computational efficiency without compromising accuracy. The architecture leverages advanced spectral feature extraction techniques to enhance classification performance. Experimental evaluations on multiple benchmark hyperspectral datasets reveal that the proposed approach not only improves classification accuracy but also achieves a superior balance between performance and computational demand compared to traditional methods like K-nearest neighbors (KNN) and other deep learning-based techniques. Our results demonstrate the potential of the proposed CNN model in advancing the field of HSI classification, offering a viable solution for real-world applications with constrained computational resources.
Volume: 14
Issue: 2
Page: 393-404
Publish at: 2025-08-01

Bridging generations: a scoping review of teaching technology to the elderly using intergenerational strategies

10.11591/ijict.v14i2.pp529-539
Nahdatul Akma Ahmad , Tengku Shahrom Tengku Shahdan , Norziana Yahya
The proportion of the global population aged 60 and above is projected to nearly double by 2050, emphasizing the urgent need for societies to adapt to the challenges posed by an aging population. As the elderly increasingly face difficulties in navigating digital technologies, which are essential for daily tasks and accessing services, the digital divide often leads to digital exclusion. This scoping review investigates intergenerational strategies used to teach technology to older adults. Seventeen studies from 11 countries were analyzed, highlighting six key intergenerational learning strategies: reverse mentoring, virtual learning, collaborative learning, family intergenerational activities, game play learning, and storytelling. These strategies offer diverse methods for enhancing digital literacy and social engagement, with reverse mentoring showing promise in fostering digital competence, and virtual learning promoting inclusivity across generations. However, barriers such as technological access, ongoing support, and cultural differences complicate implementation. This review underscores the importance of adapting instructional approaches to the needs of the elderly while leveraging intergenerational interactions to bridge the digital literacy gap. It calls for sustained efforts to address user needs, provide technical support, and ensure inclusivity, especially for isolated individuals, to maximize the effectiveness and sustainability of these strategies.
Volume: 14
Issue: 2
Page: 529-539
Publish at: 2025-08-01

Advanced predictive models for thyroid disease comorbidities using machine learning and deep learning: a comprehensive review

10.11591/ijict.v14i2.pp673-683
Mohammed Yacoob B. A. , Jayashree J.
With advances in machine learning (ML) and deep learning (DL), the future of thyroid disease diagnosis and prognosis looks very bright. The integration of various data such as imaging and medical record data has increased the accuracy of the model. Advanced DL models such as convolutional neural network (CNN) and recurrent neural network (RNN) further improved disease detection in precision medicine. However, some of the major disadvantages of effective clinical integration include unbalanced samples, unclear sampling, having to communicate in different populations, decreased physician confidence due to the vagueness of current models therefore, and few studies available to identify thyroid comorbidities such as polycystic ovary syndrome (PCOS) and thyroid eye disease (TED) in a variety of different populations to develop the line. It is important to focus future research activities on model definition and validation an improving and thus the diagnosis and prognosis of thyroid comorbidities is of utmost importance. What this will bring is ML and DL, an opportunity to make very significant improvements in the diagnosis, treatment, and management of thyroid diseases, thereby improving patient outcomes and health care by seeking crystals as a group they work interdisciplinary to collaborate in developing flexible solutions, sharing knowledge, and responding to these stated deficiencies.
Volume: 14
Issue: 2
Page: 673-683
Publish at: 2025-08-01

Smart hybrid power management system in electric vehicle for efficient resource utilization with ANN

10.11591/ijict.v14i2.pp488-496
Vinoth Kumar Ponnusamy , Gunapriya Devarajan , Gomathi Easwaram , Geetha Murugesan , Rathinam Marimuthu Sekar , R. Delshi Howsalya Devi
The novel hybrid power system integrating energy storage, electric vehicle (EV) charging infrastructure and renewable energy sources improve sustainability and resilience. This work proposes a power management system controlled by artificial intelligence for EV charging infrastructure. The battery’s state of charge (SoC) is continuously monitored by artificial neural network (ANN) algorithm improves the health of the battery and efficient operation of the system. The buck boost DC-DC converter performs dynamic switching mechanism, which adjusts to changing solar conditions and energy demands, guarantees a steady power supply. Depending on the situation, the ANN algorithm used in the battery’s SoC control mechanism decides whether to priorities the EV charging or the inverter to supply the grid. The work is evaluated with the MATLAB simulation for different conditions and results are compared with different controllers such as PI, PID, and ANN. The experiment performed uses internet of things (IoT) for transferring the data from the EV motor, acts as an input for the controller to perform the novel hybrid power management operation with cloud technology. The experimental prototype evaluates the results connected to the photovoltaic (PV) system and battery management system (BMS) which lowers reliance on non-renewable resources, increases overall energy efficiency, and ensures a steady supply of power under a various condition.
Volume: 14
Issue: 2
Page: 488-496
Publish at: 2025-08-01

Machine learning in detecting and interpreting business incubator success data and datasets

10.11591/ijict.v14i2.pp446-456
Mochammad Haldi Widianto , Puji Prabowo
This research contributes to creating a proposed architectural model by utilizing several machine learning (ML) algorithms, heatmap correlation, and ML interpretation. Several algorithms are used, such as K-nearest neighbors (KNN) to the adaptive boosting (AdaBoost) algorithm, and heatmap correlation is used to see the relationship between variables. Finally, select K-best is used in the results, showing that several proposed model ML algorithms such as AdaBoost, CatBoost, and XGBoost have accuracy, precision, and recall of 94% and an F1-score of 93%. However, the computing time the best ML is AdaBoost with 0.081s. Then, finally, the proposed model results of the interpretation of AdaBoost using select K-best are the best features “last revenue” and “first revenue” with k feature values of 0.58 and 0.196, these features influence the success of the business. The results show that the proposed model successfully utilized model classification, correlation, and interpretation. The proposed model still has weaknesses, such as the ML model being outdated and not having too many interpretation features. The future research might maximize with ML models and the latest interpretations. These improvements could be in the form of ML algorithms that are more immune to data uncertainty, and interpretation of results with wider data.
Volume: 14
Issue: 2
Page: 446-456
Publish at: 2025-08-01

Deep learning for grape leaf disease detection

10.11591/ijict.v14i2.pp653-662
Pragati Patil , Priyanka Jadhav , Nandini Chaudhari , Nitesh Sureja , Umesh Pawar
Agriculture is crucial to India's economy. Agriculture supports almost 75% of the world's population and much of its gross domestic product (GDP). Climate and environmental changes pose a threat to agriculture. India is recognized for its grapes, a commercially important fruit. Diseases reduce grape yields by 10-30%. If not recognized and treated early, grape diseases can cost farmers a lot. The main grape diseases include downy and powdery mildew, leaf blight, esca, and black rot. This work creates an Android grape disease detection app which uses machine learning. When a farmer submits a snapshot of a diseased grape leaf, the smartphone app identifies the ailment and offers grape plant disease prevention tips. In this research, an android app that detects grape plant illnesses use convolutional neural network (CNN) and AlexNet machine learning architectures. We investigated and compared CNN and AlexNet architecture's efficacy for grape disease detection using accuracy and other metrics. The dataset used comes from Kaggle. CNN and AlexNet architectures yielded 98.04% and 99.03% accuracy. AlexNet was more accurate than CNN in the final result.
Volume: 14
Issue: 2
Page: 653-662
Publish at: 2025-08-01

Antioxidant and anticancer activity of Canarium ovatum Engl. (Pili) ethanolic leaf extracts

10.11591/ijphs.v14i2.25343
Rafael Joseph Itao Terrazola , Djamae Librado Manzanares , Lady Jane Gacasan Morilla , Loren Grace Jaranilla Anunciado , Lilybeth Flores Olowa , Olive Anies Amparado
The Pili (Canarium ovatum Engl.) tree, native to the Philippines, has been reported to have medicinal properties because of the biological and chemical properties it exhibits. This paper aimed to investigate the presence of phytochemicals, antioxidant, and anticancer activities of the ethanolic leaf extract of C. ovatum. To determine the phytochemicals, present in the extract, standard procedures for qualitative phytochemical screening were performed. The antioxidant activity of the extract was assessed in vitro using 2,2-Diphenyl-1-picrylhydrazyl (DPPH) free radical scavenging assay. The anticancer activity of the extract was assessed in vitro using 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) cell viability assay against the HCT116 cancer cell line. Phytochemical screening of C. ovatum ethanolic leaf extract detected alkaloids, steroids, flavonoids, saponins, and tannins. The extract had extremely high antioxidant activity (IC50=11.44 mg/L). The MTT assay revealed moderate cytotoxic activity of the leaf extract to HCT116 cancer cell line (IC50=94.43 mg/L). These findings suggest that the C. ovatum ethanolic leaf extract has therapeutic potential because of the presence of beneficial phytochemicals, strong antioxidant activity, and anticancer capacity. Futher research is recommended to comprehensively evaluate the medicinal potential of Pili leaf extracts, including exploring other biological activities using various assays and employing different solvents for leaf extraction.
Volume: 14
Issue: 2
Page: 827-835
Publish at: 2025-06-01

Knowledge, attitude, and practice among the Indian population regarding COVID-19 using LASI-DAD COVID-19 data

10.11591/ijphs.v14i2.23836
Ala Saritha , Rohit Jangra , Lekha Bhat , Neena Elezebeth Philip
During the COVID-19 pandemic, India was one of the worst-hit countries in terms of the number of cases and deaths. Knowledge, attitude, and practice play an important role in reducing disease transmission. This study uses a nationally represented large data set to understand the knowledge, attitude, and practice (KAP) of COVID-19 among the Indian population. This cross-sectional study utilized Diagnostic Assessment of Dementia for the Longitudinal Aging Study in India (LASI-DAD) COVID-19 data which provides information on respondents’ demographics, socioeconomic effects, health status, behaviour, perceptions, and attitudes toward COVID-19. Descriptive statistics, correlation, and regression were performed to find the results. Out of 3,797 respondents, 1,929 (50.8%) accounted for males and 1,868 (49.2%) for females; rural respondents were 40.3% and urban were 59.7%. The findings show that the respondent’s knowledge about COVID-19 was improved from rounds 1 to 4 and 7, but attitude from round 2 to round 8 and behaviour from round 1 to round 9 were poor. In multivariate analysis, males (AOR=1.855; CI=1.129-3.048; p=0.015) and people residing in urban areas (AOR=1.698; CI=1.050-2.745; p=0.031) had a good level of knowledge towards COVID-19 when compared to their females, and rural counterparts. Despite a good level of knowledge about COVID-19 among the Indian population, attitudes and practices towards COVID-19 were poor. There is a need to establish and implement effective policies and interventions to improve people’s behaviour towards COVID-19 and similar pandemics that the world might encounter in the future.
Volume: 14
Issue: 2
Page: 634-642
Publish at: 2025-06-01

Maternal and child factors of stunted children: a case control study

10.11591/ijphs.v14i2.24473
Apriyani Puji Hastuti , Tintin Sukartini , Yuni Sufyanti Arief , Nursalam Nursalam , Ratna Roesardhyati , Elyk Dwi Mumpuningtias , Syaifurrahman Hidayat , Ardhiles Wahyu Kurniawan
Due to the transition or weaning process and infant feeding patterns, infants under the age of five (IYCF) are susceptible to nutritional problems, especially with regard to food diversity, diet quality, availability, and accessibility. According to fundamental health research, the mother’s capacity to supply nourishment (sources from animal and vegetable protein) and the main meal as an energy source during the first two years of life is associated. Community based case-control study was conducted among children 6-24 month. The study participant used multistage random sampling procedure, with a sample size of 180 mothers who have stunted children. This study used a questionnaire as its data research instrument, which was examined for validity and reliability utilizing data analysis methods like linear regression and SPSS 16.0 statistical software. Factor associated the role of mothers in the feeding of stunted children to nutritional status are age, educational level, occupation, motivation, mobility, decision making, knowledge, self-esteem, self-efficacy, family type, family role, stress of family, coping of family, family social support, weight of birth, responsive feeding. The all of factors can affected roles of mothers in the feeding of stunted children to nutritional status with p-value <0.005. While the child’s age, birth length, breastfeeding, feeding children are not factors associated with the role of mothers in the feeding of stunted children to nutritional status.
Volume: 14
Issue: 2
Page: 852-859
Publish at: 2025-06-01

Organizational and leadership factors affecting the quality of nurse performance in hospitals

10.11591/ijphs.v14i2.23542
Retno Twistiandayani , Rizki Dwi Prameswari , Daviq Ayatulloh , Diah Priyantini
The purpose of this study was to analyze organizational and leadership factors that affect the quality of nurse performance in hospitals. A cross-sectional study in 162 respondents conducted at Universitas Airlangga Hospital in May-June 2021. Variable organizational factors (resources, rewards, work structure and design), leadership factors (competence, job meaning, autonomy and impact), and the quality of nurse performance were measured using a questionnaire that had been modified by the researcher and declared valid (r table=0.351-1.000) and reliable (0.919-0.980), the data were then analyzed using logistic regression. Organizational factors consisting of resources (p=0.001), rewards (p=0.012), structure (p=0.029) and work design (p=0.013) showed a significant relationship with the quality of work, as well as organizational factors consisting of competence (p=0.043), job meaning (0.035), autonomy (0.021) and impact (0.025). The strongest results are shown in the quality of nurse resources. Improving the quality of performance of nurses must pay attention to organizational factors and influencing leadership factors, with the most dominant factor being the quality of nurse resources.
Volume: 14
Issue: 2
Page: 790-798
Publish at: 2025-06-01

Epidemiological analysis of the incidence of endometrial hyperplasia in a large city of Kazakhstan

10.11591/ijphs.v14i2.24971
Imasheva Bayan Imashkyzy , Kamaliev Maksut Adilkhanovich , Lokshin Vyacheslav Notanovich , Kiselyova Marina Viktorovna , Turekhanova Aizhan Dzhambylbayevna , Jexembekova Alfiya Ernazarovna , Uteshova Malika , Saparaliyeva Aizhan Mukhtarkyzy
Endometrial hyperplasia (EH) is an abnormality of uterus, characterized by excessive proliferation of endometrium, and in case of lack of timely diagnostics and treatment, it may rapidly progress to endometrial cancer (EC). According to the World Cancer Research Fund, EC ranks 6th among all female malignancies in the world. EC takes the 3rd place in the Republic of Kazakhstan according to the state statistics. However, there is no determining statistical data on dynamics of EH incidence in our region, which will show the extent of exposure of the population to this disease. Therefore, this research provides assessment and analysis of all registered cases of EH from the Republican Center for Electronic Healtcare for the period from 2012 to 2022 for presentation of accurate and correct information on the trends of EH incidence and its histologic types, taking into account age differences of the female population of our city. The study has showed the increase of indicators of incidence of EH, especially, non-atypical endometrial hyperplasia (NAEH) and atypical endometrial hyperplasia (AEH). It was found that the peak incidence of EH occurred in the age group of 65-69 years, where the main increase was due to an increase in number of cases of NAEH. The age group of 45-49 years is at the peak of the incidence of AEH. The conducted research has identified the trends of EH incidence, reflecting population changes in the EH risk factors, and that requires their comprehensive study for development of strategies of treatment and prevention measures.
Volume: 14
Issue: 2
Page: 586-593
Publish at: 2025-06-01

Understanding HIV transmission and prevention among men who have sex with men in a sexual health clinic

10.11591/ijphs.v14i2.24552
Suriya Kumareswaran , Bala Murali Sundram
This comprehensive study investigates the knowledge, attitudes, and practices (KAP) concerning human immunodeficiency virus (HIV) transmission and prevention among men who have sex with men (MSM) in a sexually transmitted infections (STI)-friendly clinic in Johor, Malaysia. Utilizing a cross-sectional design, the study analyses data from 421 MSM patients, emphasizing the critical role of sociodemographic factors in influencing HIV-related behaviours. The majority of participants, mostly with tertiary education, displayed a sound understanding of HIV, with 71% showing good knowledge on HIV prevention and transmission. However, there remain gaps in knowledge, particularly among those with lesser education, and in practices related to condom use and pre-exposure prophylaxis (PrEP). The study highlights a stark disparity in HIV-related attitudes and practices based on education level, employment status, and income. Notably, those with higher education and income levels demonstrate more responsible practices and better knowledge, underscoring the need for tailored educational programs. The findings call for comprehensive and targeted interventions, considering the diverse backgrounds of individuals, to effectively mitigate the risk of HIV infection. This research is vital in the absence of a definitive cure for HIV and acquired immune deficiency syndrome (AIDS), emphasizing preventive measures based on accurate information and positive attitudes towards the disease.
Volume: 14
Issue: 2
Page: 569-575
Publish at: 2025-06-01

Artificial intelligence model for the prediction of cannabis addiction

10.11591/ijphs.v14i2.25786
Abdelilah Elhachimi , Mohamed Eddabbah , Abdelhafid Benksim , Ibanni Hamid , Mohamed Cherkaoui
A novel approach for predicting cannabis addiction has been introduced by integrating combined machine learning (ML) algorithms, specifically K-means clustering and linear regression (LR). The study, conducted in Marrakech, Morocco, at a center linked to the National Association for drug-risk reduction (DRR), involved 146 participants. Among those with prior cannabis use, one subgroup included passive users, while another exhibited cannabis dependence. The research utilised features derived from patient data, emphasising psycho-cognitive state, addiction status, and socio-demographic factors. The goal was to evaluate the effectiveness of the combined ML algorithms (K-means + LR) in distinguishing between addicted and non-addicted individuals using real-world data from a primary care addiction center. The findings indicate that the proposed method delivers balanced results, achieving an overall accuracy of 70%, a sensitivity of 65%, and a specificity of 86%. These results are particularly noteworthy when compared to other ML studies in addiction research. The combined algorithm demonstrates promising potential with competitive accuracy and high specificity. Further efforts to improve sensitivity and validate the model in diverse settings will be essential for advancing predictive modeling in this field. Our findings contribute to existing research by developing simple and effective tools for early detection of cannabis addiction, potentially aiding in the creation of preventive and therapeutic strategies to reduce its prevalence.
Volume: 14
Issue: 2
Page: 1076-1087
Publish at: 2025-06-01

Do gender, age, and emotional intelligence affect the emotional regulation of adolescents involved in cyberbullying?

10.11591/ijphs.v14i2.24335
Nia Agustiningsih , Ah Yusuf , Ahsan Ahsan , Dwi Indah Iswanti , I Made Moh. Yanuar Saifudin
The objective of this study was to explore how age, gender, and emotional intelligence impact emotion regulation in adolescents participating in cyberbullying. A cross-sectional study was conducted in January 2023, involving 108 teenagers selected through purposive sampling. Data, gathered through a demographic questionnaire, Revised Cyberbullying Inventory II, and an emotional intelligence questionnaire, were analyzed using descriptive and multiple regression methods. The findings revealed that age, gender, and emotional intelligence collectively influence situation modification, accounting for a 2.52% impact (p-value=0.024). Emotional intelligence demonstrated effects on both situation modification and attentional deployment in individuals playing the roles of victims and perpetrators (p-value=0.018). In the case of adolescents acting as perpetrators, age, gender, and emotional intelligence collectively exhibited significant influence on attention deployment, contributing to a 9.83% impact (p-value=0.01). For adolescents who abstain from participating, the modulation response is simultaneously affected by age, gender, and emotional intelligence (p-value<0.001), resulting in a 4.44% influence. Notably, age, gender, and emotional intelligence were identified as factors influencing emotion regulation at various stages, depending on whether adolescents played the roles of victims, victims-perpetrators, or perpetrators. it is recommended that mental health nurses implement tailored emotion regulation interventions for adolescents involved in cyberbullying.
Volume: 14
Issue: 2
Page: 692-700
Publish at: 2025-06-01

Millet consumption in type 2 diabetics in urban slums of India: a pilot study

10.11591/ijphs.v14i2.25422
Pooja Sohil , Sudhanshu Mahajan , Rupeshkumar Deshmukh , Jayashree Gothankar , Saibal Adhya
Diabetes is a leading cause of death and disability worldwide, affecting people regardless of their country, age group, or sex. Considering the growing prevalence of diabetes among low socioeconomic groups in developing countries like India, the present study aims to determine the prevalence of millet consumption in urban slums in Pune, India. A pilot cross-sectional study was conducted at a private medical college’s field practice area in Pune from January to March 2024. The study focused on individuals with type 2 diabetes mellitus. A pre-designed, pretested semi-structured paperless questionnaire in the Kobo tool app was used to collect information on socio-demographic information, consumption of millet, and diabetes-related information. A total of 30 type 2 diabetics were interviewed as a pilot study. Of these, 53.57% were females, and 46.43% were males. The prevalence of millet consumption was 93% in type 2 diabetics. Sorghum was the most consumed millet amongst all other millet. Age >50 years, education >10th pass, and non-alcoholics were significantly associated with satisfactory consumption of millet. Diversifying diets with nutritious foods like millet can help reduce health-related burdens, including type 2 diabetes. This is important for policy-making and prioritizing diabetes self-care interventions.
Volume: 14
Issue: 2
Page: 904-911
Publish at: 2025-06-01
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