bluuebunny/crossref_metadata_2025_split huggingface dataset:
Predicting the Neonatal Mortality Using Ensemble Machine Learning Algorithms in Case of Ethiopian Rural Areas
Authors: Mengstie Melaku , Telake Misganaw 
Published: December 2024

Abstract

Each year, approximately 2.5 million newborns die globally, with developing countries behavior the impact of this crisis. Sub-Saharan Africa experiences the highest neonatal mortality rate at 27 deaths per 1,000 live births. In Ethiopia, neonatal mortality remains alarmingly high at 29 deaths per 1,000 live births, with early neonatal mortality reaching 41.8 deaths per 1,000 live births. Rural areas face even more severe disparities, with a prevalence of 45.6 deaths per 1,000 live births compared to 25.5 in urban settings, basically due to inadequate healthcare access, poor maternal and neonatal services, and socioeconomic challenges.
This study aimed to develop a robust predictive model for neonatal mortality in rural Ethiopia, using secondary data from the Ethiopian Demographic and Health Surveys (2000–2019). The dataset, consisting of 29,048 instances and 22 relevant features, was preprocessed to handle missing values and balance the class distribution using SMOTE. Several advanced ensemble machine learning algorithms were applied to build the predictive model, including Random Forest, Gradient Boosting, Extreme Gradient Boosting, Light Gradient Boosting, and CatBoost. The performance of these models was evaluated based on key metrics, including accuracy, precision, recall, F1 score, and ROC-AUC.
Among the ensemble algorithms tested, CatBoost demonstrated the highest performance, achieving 97.5% accuracy, 97.52% precision, 97.5% recall, 97.5% F1 score, and an outstanding ROC-AUC value of 99.57%. The key risk factors for neonatal mortality identified in the study included BCG vaccination status, the number of under-five children in the household, recent episodes of diarrhea, and iron tablet intake during pregnancy. These factors were found to significantly contribute to predicting neonatal mortality, underscoring the importance of targeted healthcare interventions for high-risk neonates.
This study developed a predictive model for neonatal mortality in rural Ethiopia using ensemble machine learning, identifying key risk factors like BCG vaccination and maternal health. It offers actionable insights for targeted interventions, supports healthcare prioritization, and highlights the need for improved access and policy reforms. Mobile health apps and policymaker collaboration can further reduce neonatal mortality.
Developing the Predictive Model for the Level of Food Insecurity Status of Households Using Ensemble Machine Learning Techniques with XAI
Authors: Mengstie Melaku 
Published: December 2024

Abstract

Among the world’s undernourished people, more than 282 million people in Africa and 418 million people in Asia were found. Moderate or severe food insecurity at the global level has risen from 22.6% in 2014 to 26.6% in 2019, with Sub-Saharan Africa experiencing high levels, particularly in Ethiopia. In 2018, 239 million undernourished people were registered in Sub-Saharan Africa particularly high in Kenya, Somalia, Ethiopia, and South Sudan. This study, hence, aimed to develop a predictive model using ensemble machine learning algorithms to assess and classify the levels of food insecurity based on socioeconomic, environmental, and demographic factors in North Western Ethiopia. The dataset, collected from the Dabat Health and Demographic Surveillance from 2014 to 2020, was preprocessed using data cleaning, transformation, and class balancing with SMOTE-ENN. The dataset is split into training, validation, and testing sets with a 90/10 ratio. Relevant features were selected via recursive feature elimination, and class decomposition methods were evaluated. Experiments with ensemble models, including Random Forest, Gradient Boosting, XGBoost, CatBoost, and LightGBM, identified XGBoost as the best-performing algorithm, achieving 91.53% accuracy, 89.39% recall, and a 94.00% micro-average ROC score. SHAP was employed for explainable AI, offering insights into critical factors influencing food insecurity. Key factors that enhance food security include receiving assistance in the form of food or money, the size of cultivated land, altitude, monthly income, and household assets. In contrast, factors that increase the likelihood of food insecurity include the education level of the household head, age, marital status, limited access to irrigation, and reductions in portion sizes and meal frequency for the household head. This study demonstrates the utility of machine learning in identifying key drivers of food insecurity and offers a robust framework for targeted interventions. By bridging predictive analytics with explainable AI, actionable insights are provided to policymakers and stakeholders to alleviate food insecurity in Ethiopia and beyond.
Predictive Modeling and Optimization of TBM Operations: Advanced Techniques Applied to the Jakarta MRT Project
Authors: SalamM Chairul , Kural Orhan 
Published: December 2024

Abstract

The effectiveness of Earth Pressure Balance (EPB) Tunnel Boring Machines (TBMs) in urban underground construction relies on understanding and optimizing their performance under variable geotechnical conditions. This study investigates the key parameters impacting TBM efficiency during the construction of the Jakarta Mass Rapid Transit (MRT) Underground Section CP106. Data from TBM operation were analyzed using statistical and machine learning techniques, including Mutual Information (MI), Partial Dependence Plots (PDP), and Analysis of Variance (ANOVA), to identify influential parameters such as Tensile Strength, Uniaxial Strength, Spacing, and Penetration. Predictive models, including Gradient Boosting Regressor, Random Forest Regressor, and Linear Regression, were evaluated based on error metrics and R-squared values, with Gradient Boosting Regressor showing the highest predictive accuracy. Clustering analyses using K-Means and Principal Component Analysis (PCA) further classified operational states, identifying conditions that optimize energy efficiency and reduce mechanical wear. The findings suggest that TBM configurations with lower Specific Energy, Normal Force, and Rolling Force contribute to more efficient, less force-intensive tunneling. These insights provide a basis for refining TBM operations and predictive modeling in urban tunneling projects.
A New-fangled Classification Algorithm for Medical Heart Diseases Analysis using Wavelet Transforms
Authors: Nayak Soumya Ranjan , Selvarasu Sivakumar , Sripathy B , Singh Prabhishek , Diwakar Manoj , Gupta Indrajeet , Ravi Vinayakumar , Al Mazroa Alanoud 
Published: December 2024

Background
In this article, the Mixed Mode Database Miner (MMDBM) algorithm is introduced for the classification of data. This algorithm depends on the decision tree classifier, which handles the numerical and categorical attributes. For the experimental analysis in a well-explored heart disease data set collected from the UCI Repository.

Aims
Understanding the fundamentals of every classification method and how to apply them to CUDA is the aim of this study. After reviewing the literature, the approach that is most suited is selected for implementing the suggested algorithm in the MMDM classifier along with the discrete wavelet decomposition. From this experimental analysis, we observed that the use of the wavelet technique in the MMDBM algorithm provides better and more accurate results for data classification.

Objective
The main objective of the manuscript is to identify the early stage of heart attack that was caused either by smoking, smoking with tobacco, or non-smoking. Additionally, this study aims to check the validity of the MMDM classifier along with the discrete wavelet decomposition. From this experimental analysis, we observed that the use of the wavelet technique in the MMDBM algorithm provides better and more accurate results for data classification.

Methods
In the modern digital world today, data has a major impact on everyone’s life. Every database contains a lot of information hidden either in the form of numerical data, characteristic data, or a mixture of both. Moreover, to accurately decode every dataset, a fast and efficient classifier is essential.. Moreover, by using this hybrid technique based on both MMDBM and Wavelet processes, it compresses the data with minimum storage capacity.

Results
The experimental results are compared with the classified data to wavelet data output. These results prove that our presented technique is more prominent and robust in an analysis of heart diseases.

Conclusion
This algorithm is based on a decision tree classifier and tested on a heart disease database. MMDBM is applied to classify a large data set of numerical and categorical attributes. The data are compressed with the help of a one-dimensional wavelet transform. This is one of the new approaches applied to classified data in real-time applications.
The Association of Self-efficacy with Self-care Behaviors, Anxiety, and Academic Motivation in Iranian Medical Sciences Students
Authors: Amiri Mohammad , Sadeghi Elham , Parvane Ayda , Khosravi Ahmad 
Published: December 2024

Background and Objective
Medical students are most susceptible to contracting stressful clinical phenomena and experiencing subsequent psychological consequences. The study aimed to investigate the association between self-efficacy, self-care, and anxiety with academic motivation among students enrolled at Shahroud University of Medical Sciences.

Materials and Methods
This cross-sectional study, conducted in 2022 immediately after the university opening during the COVID-19 pandemic,to investigate anxiety and self-care behaviors related to COVID-19, academic self-efficacy, and motivation among 339 students from diverse medical science disciplines. A multi-stage random sampling technique was employed to choose the participants. The data underwent analysis using one-way ANOVA, Chi-square test, and linear regression model. The significance level for all tests was 0.05.

Results
The mean score of students' self-care behavior was 56.5±13.7, the mean academic self-efficacy was 106.5±17.6, and the mean academic motivation score was 98.5±13.0. The mean score of students' anxiety about COVID-19 was 6.9±6.9, suggesting a low level of anxiety. A total of 70 participants (20.6%) demonstrated good self-care behaviors against COVID-19, 316 (93.2%) reported reduced anxiety related to COVID-19, and 154 (45.4%) reported high levels of academic motivation. Only the self-care behaviors and academic motivation variables in the faculty and COVID-19 setting were shown to be substantially correlated with the sense of academic self-efficacy in the final model.

Conclusion
Medical students have been found to have significantly lower motivation and academic self-efficacy than students in other fields of study, so the need for identification of causes and implementation of intervention measures has been deemed necessary.
Analysis of Oral and Gut Microbiota Composition in Children with Dental Caries by NGS Approaches
Authors: Cianetti Stefano , Marchianò Silvia , Wijeratne Dhanushka Frank , Carino Adriana , Biagioli Michele , Bordoni Martina , Roselli Rosalinda , Di Giorgio Cristina , Bellini Rachele , Valenti Chiara , Coniglio Maddalena , Lomurno Giuseppe , Lomurno Anna Palma , Pagano Stefano 
Published: December 2024

Objectives:
This study aimed to characterize oral and gut microbiota of children with
high dmft index and caries-free children at phylum, family and species levels as well as to evaluate
the effect of Streptococcus salivarius M18 DSM 14685 (Carioblis) administration on microbiota
composition of caries active children.

Materials and Methods:
Ten children with active caries and nine caries-free children have been recruited.
Four samples from different oral niches and stools were collected from each patient for
the NGS sequencing of 16s Microbiota rDNA by S5 Ion Torrent.

Results:
Our results revealed modifications in the microbiota composition of teeth, saliva and
vestibular regions of the oral cavity and faecal samples in the presence of dental caries. These
changes were evident at the family and species levels, with no significant differences found at the
phylum composition level. In particular, Streptococcaceae were positively correlated to the high
degree of caries in all niches, and the analysis at the species level led to the identification of 39
bacterial species significantly modulated in the analyzed groups. The use of probiotic seemed to
exert beneficial effects on oral but not on faeces dysbiosis. The intestinal tract was confirmed to
have a different microbiota composition compared to the oral cavity.

Conclusions:
Dental caries mainly lead to modifications in the oral microbiota composition. Streptococcus
salivarius M18 DSM 14685 administration determines a shift in the oral microbiota composition
towards a healthier state. Concerning the gastrointestinal tract, our study found for the
first time that caries cause the increase of two bacterial species, related to other disorders: Bifidobacterium
adolescentis and Ruminococcus torques.
Pemodelan kesesuaian habitat macan dahan kalimantan (Neofelis diardi borneensis) di Taman Nasional Kayan Mentarang
Authors: Gumilar Jefry Frihardian , Husainar Husien Dwi , Nababan Josua 
Published: December 2024

Macan dahan kalimantan (Neofelis diardi borneensis Wilting, Christiansen, Kitchener, Kemp, Ambu and Fickel, 2007) merupakan satwa prioritas di Taman Nasional Kayan Mentarang (TNKM) dan predator puncak di Kalimantan. Data dan informasi tentang habitat macan dahan kalimantan di alam masih sulit untuk dipahami. Penelitian ini bertujuan untuk membuat pemodelan spasial kesesuaian habitat macan dahan kalimantan di TNKM. Penelitian ini dilakukan dengan metode pemodelan spasial dengan Maximum Entropy (MaxEnt). Variabel yang digunakan diantaranya adalah variabel ketinggian, kelerengan, jarak dari lahan pertanian, jarak dari lahan terbuka, jarak dari savana, dan jarak dari sungai. Hasil pemodelan menunjukkan wilayah TNKM yang sesuai untuk habitat macan dahan kalimantan adalah seluas 514.327,15 ha. Hasil uji jackknife menunjukkan variabel dengan kontribusi tertinggi adalah variabel ketinggian dan variabel yang memiliki pengaruh tertinggi adalah variabel jarak dari lahan pertanian, ketinggian dan jarak dari savana. Evaluasi kinerja model menunjukkan nilai AUC sebesar 0,872 dan standar deviasi sebesar 0,038 dengan kategori baik.
Challenges of the first six months COVID-19 among students : an Experience at Yogyakarta State University
Authors: Prasastiawati Diana , Andhika Muhammad , Sumunar Dyah Respati Suryo 
Published: December 2024

Students and residents who live in residential areas around the campus are one of the groups vulnerable to the spread of COVID-19. This study aims to analyze the level of anticipation of vulnerability and capacity in Yogyakarta in facing the threat of COVID-19 This research is categorized as quantitative research, with a research sample of 395 students of Yogyakarta State University. The survey method through questionnaires was used to collect primary data in the form of community vulnerability and student capacity. The research sampling technique in this research is using purposive sampling technique. The validity used is content validity and construct validity. The results of the reliability analysis of this study are 0.95 for Likert scale items (Cronbach Alpha), 0.708 for Guttman scale items (KR-20), and 0.575 for multiple choice items (Split-Half Coefficient). The results showed that in general the score of anticipating the vulnerability of Universitas Negeri Yogyakarta to the threat of COVID-19 reached 69.98% (good category). The level of capacity of students in dealing with the threat of COVID-19 is also classified as good (90.76%).
Knowledge of elderly cadres on dementia in Yogyakarta, Indonesia
Authors: Mulyani Sri , Cahyani Kadek Dewi , Listyaningrum Desy , Ulayya Aisyah Iffah 
Published: December 2024

Introduction: Dementia can cause disability, in addition to physical, mental, and financial stress for caregivers, and affect the nation’s healthcare system and economy. Efforts are needed to improve the quality of life of people with dementia involving various sectors, especially elderly cadres. Elderly cadres are volunteers who provide screening and health education for elderly persons at the community level. Accordingly, it is crucial to identify the knowledge levels about dementia in elderly cadres to improve public awareness and interventions concerning this topic. This study aimed to determine the level of knowledge of elderly cadres about dementia. Methods: This quantitative study was conducted with a cross-sectional design. There were 103 elderly cadres recruited using convenience sampling from November to December 2022. A demographic questionnaire and Dementia Knowledge Assessment Scale (DKAS) were used to collect the data. The data were analyzed using Spearman’s rho and Mann Whitney with significance set as p < 0.05. Results: The median score of the elderly cadres’ knowledge score was 22 (minimum-maximum = 0-44). Elderly cadres’ knowledge was related significantly to training about dementia (p = .002). There was no significant correlation between elderly cadres' knowledge and age (p = .271), gender (p = .073), and education level (p = .106). Conclusions: In general, the knowledge of elderly cadres about dementia is relatively low. However, there was a significant change in knowledge scores after dementia training, so a program to increase understanding about dementia through training is recommended for elderly cadres.
Marriage Traditions and Family Resilience in Bugis Bone Society: A Study of Islamic Law and Islamic Education
Authors: Said Wardana , Hukmiah Hukmiah , Nur Suriani , Wahyuni Sri , Akbar Rahmatul 
Published: December 2024

Indonesia, with the highest Muslim population globally, has experienced significant changes in its legal landscape throughout history. These changes have resulted in the coexistence of various legal systems, including the Islamic law, the customary law, and the state law. The three variations of societal practices, including marriage, persist to be the topic of academic discussion. Marriage has both legal and customary aspects, as well as philosophical and educational components. This study is an empirical sociological investigation that specifically examines the marriage tradition within the Bugis society, employing the theoretical framework of the Islamic law and Islamic education. The data collection approaches employed include interviews, observations, and document analysis. This study finds that the marriage process, both before and after implementation, is conducted with wisdom and is imbued with profound values within the family and the community involved in the marriage. The relationship between marriage traditions and family resilience is rooted in the ideals and meanings of Islamic legal philosophy. The marriage rituals, from preparation to implementation and beyond, serve to build and bind bonds between families. The marriage tradition within the Bugis Bone group encompasses educational principles, including aqidah education, moral education, and social education. The educational ideals serve as the bedrock for enhancing the resilience of families in the Bugis Bone community.
Em perspectiva hermenêutica: arte, existência e tragédia da cultura
Authors: Rajobac Raimundo 
Published: December 2024

O ensaio tem como objetivo investigar a temática arte, existência e tragédia da cultura no contexto da racionalidade mitológica, a partir da perspectiva hermenêutica gadameriana. A discussão se insere no âmbito da crítica mais ampla dos problemas da compreensão e interpretação, e dirige-se ao campo da crítica metodológica à moderna teoria da ciência. São articulados conceitos centrais como mito, logos e razão em direção à narrativa mitológica como modo de acesso ao saber. Nessa esteira explicita-se a relação fundamental entre arte, vida e cultura, de forma a fundamentar já na mitologia a experiência da arte inserida na tragicidade da existência como forma de conferir sentido à própria vida. AbstractThe aim of this essay is to investigate art, existence and culture’s tragedy, within the context of mythological rationality, from a Gademerian hermeneutic perspective. This discussion is linked to the field of methodological criticism of the modern theory of science. Central concepts are integrated, such as myth, logos and reason, with mythological narrative as a means to access knowledge. Within this orientation, the fundamental connection between art, life and culture is clarified so as to establish – starting in mythology - the experience of art within the tragedy of existence as a way to give meaning to life itself.
A coleção didática da Pinacoteca Barão de Santo Ângelo e a longa duração do ensino acadêmico na arte brasileira
Authors: Gomes Pereira Sonia 
Published: December 2024

O artigo analisa, a partir da Coleção Didática da Pinacoteca Barão de Santo Ângelo, a repercussão e permanência de procedimentos acadêmicos no ensino da arte no Brasil.
Magliani: alfabeto para gritar em silêncio
Authors: Fonseca Bohns Neiva Maria 
Published: December 2024

O artigo analisa a trajetória, o processo criativo e aspectos da produção de Maria Lídia Magliani (1946-2012), uma artista com umaprodução marcada pela presença da figura feminina e seus embates na sociedade contemporânea.
PESQUISA-INTERVENÇÃO NA EXTENSÃO UNIVERSITÁRIA
Authors: Eloy de Novaes Rafaella , Santos Lopes de Oliveira Maria Cláudia 
Published: December 2024

Este artigo é um convite a um pensar/fazer decolonial, pela articulação entre pesquisa-intervenção e extensão universitária, enquanto uma das muitas opções possíveis à colonialidade que pode atravessar as pesquisas em Ciências Humanas e Sociais. A partir de um estudo em Psicologia do Desenvolvimento e Escolar, realizado junto a membros de grupos historicamente marginalizados das universidades públicas (periféricos, negros, indígenas, assentados, campesinos, etc.) em um projeto de extensão, evidencia-se que o envolvimento com ações extensionistas oferece possibilidades interessantes de exercício do protagonismo e do diálogo nas trajetórias dos(as) envolvidos(as), dando visibilidade e audibilidade a saberes e experiências desqualificados. Enfatiza-se o papel do(a) pesquisador(a) na constituição de dinâmicas decoloniais para produção de conhecimento, junto aos subalternizados, para superação da colonialidade do saber e do ser.
DIS-MOI SI TU LIS, JE TE DIRAI QUI TU ES
Authors: Noël Audrey 
Published: December 2024

L’illettrisme, concept apparu en France dans les années 70, est plus que jamais d’actualité. Si les derniers chiffres ont pointé une diminution du phénomène, les médias évoquent quant à eux la propagation de l’illettrisme, qui revêt alors l’apparence d’une pathologie sociale. Certaines régions de France sont tout particulièrement concernées par l’illettrisme : c’est le cas de l’île de La Réunion, département d’Outre-mer et ancienne colonie française. Territoire multiculturel et multilingue, où la créolisation a permis l’hybridation de cultures, de modes de vie et de pratiques linguistiques, La Réunion est également qualifiée de société à tradition orale. Dans ce contexte, quelle vision est portée sur la littératie ? En quels termes parle-t-on d’illettrisme ? Cet article explore et confronte discours dominant (médiatique et institutionnel), et discours individuels de personnes dites en situation d’illettrisme ou en difficulté dans leurs pratiques de l’écrit. Ces discours minoritaires apparaissent plus nuancés, voire contestataires, et prônent dans une certaine mesure la normalité de l’état de non-littératie. Cela nous permet de questionner l’imaginaire autour de l’illettrisme et le statut singulier de ce concept, qui apparait emblématique d’une fracture épistémologique entre deux modèles sociétaux et deux rapports aux savoirs.