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Predictive Modeling of Solar Energy Production: A Comparative Analysis of Machine Learning and Time Series Approaches
In this study, we dive into the world of renewable energy, specifically focusing on predicting solar energy output, which is a crucial part of managing renewable energy resources. We recognize that solar energy production is heavily influenced by a range of environmental factors. To effectively manage energy usage and the power grid, it's vital to have accurate forecasting methods. Our main goal here is to delve into various predictive modeling techniques, encompassing both machine learning and time series analysis, and evaluate their effectiveness in forecasting solar energy production. Our study seeks to address this by developing robust models capable of capturing these complex dynamics and providing dependable forecasts. We took a comparative route in this research, putting three different models to the test: Random Forest Regressor, a streamlined version of XGBoost, and ARIMA. Our findings revealed that both the Random Forest and XGBoost models showed similar levels of performance, with XGBoost having a slight edge in terms of RMSE.. By providing a comprehensive comparison of these different modeling techniques, our research makes a significant contribution to the field of renewable energy forecasting. We believe this study will be immensely helpful for professionals and researchers in picking the most suitable models for solar energy prediction, given their unique strengths and limitations. 2024 IEEE. -
Predictive Modeling of Student Learning Outcomes Through Cognitive and Emotional Skill Integration
The interplay of factors, including both cognitive and non-cognitive, plays a significant role in the learning patterns of students. However, the majority of the research conducted on such issues mainly puts forward the role of cognitive skills but forgets that a very important role is played by the non-cognitive factor, specifically motivation and emotional intelligence. Therefore, this study focuses on bridging that gap by investigating the combined influence of cognitive and non-cognitive factors on the learning capacities of engineering students during their transition to higher education. A two-year longitudinal study on engineering students of AITAM, Tekele, India was considered in relation to their academic performance, learning preference, and socio-emotional aspects. The approach adopted makes use of predictive analytics. It is deployed here as machine learning algorithms in the form of Logistic Regression (LR), Naive Bayes, k-Nearest Neighbors (k-NN), Decision Trees (DT), and Support Vector Machines (SVM) to classify the learners into very fast, fast, average, and slow learners. The algorithm of k-NN also achieved the highest accuracy classification and showed good robustness for learning the students' learning rates. This study underscores the combination of new teaching approaches as well as personalized self-learning methods to enhance learning performance, especially for slow learners. Indeed, the outcome gives avenues for much more extensive studies done on large datasets using advanced algorithms which can be applied across a range of educational fields to support tailored learning interventions. 2025, Iquz Galaxy Publisher. All rights reserved. -
Predictive Modeling of Substance Abuse Risks using Big Data Analytics and Social Media Mining
The worldwide increase in substance abuse among teenagers and young adults has become serious concern in recent times. One way this pattern has developed is through the evolution of social media. Social media has transformed people's attitudes towards certain behaviors and has encouraged risky behavior to the point of actually causing addiction by exposing them to drug-related material. Despite the existence of preventative measures, such as education programs in schools, many children and youth have not had adequate access to educational interventions or evidence-based measures due to barriers created by geography, economic circumstances, and social factors, particularly in less developed countries. The research proposed is focusing on addressing this gap using a big data approach. This research employs a unique analytical framework that integrates multiple large data sets from a variety of sources to better identify and assess the effectiveness of interventions. This model employs an analytical approach that uses statistical learning techniques and predictive analytics to identify historical patterns and anticipate future trends, and assess the effectiveness of various interventions conducted in different countries. The results of the analysis suggest that this big data approach will provide decision-makers with clearly documented evidence related to various risk-taking behaviors as they relate to available prevention interventions, and will assist decision-makers in developing targeted prevention intervention strategies. This study demonstrates the revolutionary aspect behind the application of computational intelligence in preventing substance abuse and informing evidence-based community health interventions. 2025 IEEE. -
Predictive Modelling of Heart Disease: Exploring Machine Learning Classification Algorithms
In addressing the critical challenge of early and accurate heart failure diagnosis, this study explores the application of five machine learning models, including XGBoost, Decision Tree, Random Forest, Logistic Regression, and Gaussian Naive Bayes. Employing cross-validation and grid search techniques to enhance generalization, the comparative analysis reveals XGBoost as the standout performer, achieving a remarkable accuracy of 85%. The findings emphasize the significant potential of XGBoost in advancing heart failure diagnosis, paving the way for earlier intervention, and potentially improving patient prognosis. The study suggests that integrating XGBoost into diagnostic processes could represent a valuable and impactful advancement in the realm of heart failure prediction, offering promising avenues for improved healthcare outcomes. 2024 IEEE. -
Predictive Modelling of Microwave Link Failures Using Machine Learning and Deep Learning Techniques
Microwave radio links play a vital role in keeping mobile networks running, especially when it comes to backhaul-the part of the network that connects base stations to the core. Gradual failure in these links could disrupt services and cost providers a lot in both revenue and customer trust. In this study, we explore how machine learning can help predict such failures before they happen. Network performance data from a mobile network operator in Nigeria was collected, cleaned and used to achieve the purpose of the study. Four algorithms belonging to machine learning (ML) and deep learning (DL) were adopted and used for training the dataset and predicting link failures. Results show that the Long ShortTerm Memory (LSTM - a deep learning model effective for handling time-series data) performed best with prediction accuracy of 92%, distantly followed by others. These findings indicate that the LSTM is better in modelling temporal patterns in network behaviours. This study provides a practical framework for automating microwave link monitoring and maintenance, thereby reducing manual diagnostics, preventing outages, and improving service reliability. The proposed solution supports the integration of predictive intelligence into network operations, enhancing the quality of service and operational efficiency for telecom providers. 2025 IEEE. -
Predictive value of IL-6, IL-1?, TNF-?, and vaginal pH in diagnosing vaginal microbial infections: A host-inflammatory axis perspective
Microbial-associated vaginal infections are common among women of reproductive age and are linked to alterations in the local immune environment. Inflammatory biomarkers such as IL-6, IL-?, and TNF-?, along with vaginal pH have emerged as potential indicators of microbial dysbiosis. This study aimed to statistically evaluate the ability of these specific inflammatory cytokines and vaginal pH to identify infection status. Cytokine concentrations and vaginal pH were measured in clinically characterized samples. The group differences were analyzed using Mann-Whitney U tests and Cliff's Delta for effect size. ROC-AUC analysis was also performed to assess the discriminative power, and correlation heatmaps explored marker synergy. The infected individuals showed increased levels of all cytokines (p < 0.001), with large effect size (? > 0.9 for IL-6, IL-1?, TNF-?). Vaginal pH also differed significantly (? = 0.60). In addition, the combination of IL-6 and vaginal pH achieved excellent discriminative performance (AUC = 0.98). These findings suggest that IL-6, IL-1?, and TNF-?, when combined with vaginal pH, can function as reliable non-invasive biomarkers for the early detection and improved diagnostic triaging of vaginal microbial infections. 2024 -
Predictors of behavioral and emotional issues in children involved in custody disputes: A cross sectional study in urban Bengaluru
Background: The increasing rates of divorce in urban India has led to the subsequent parental battle for the child's custody. This paper discusses the behavioral and emotional issues of these children in relation to their psychosocial environmental factors and other relevant socio-demographic variables. Methods: We used samples from parent interviews concerning 52 children aged 717-years-old, involved in child custody cases in the Family court of urban Bengaluru. The Strengths and Difficulties Questionnaire was used to measure response variables of behavioral and emotional issues in these children. Predictor models of quantile and multiple linear regression were used to assess the influence of psychosocial environmental factors and socio-demographic variables on the response variables. Results: The predictor models revealed that risk of child suffering emotional and behavioral issues increased with factors such as excessive parental control, change of academic environment, general unrest at school, frequency of child's court visit, child's visitation of non-custodian parent on occasions and vacations, and negatively altered family relationship. The model however intriguingly showed that residing in nuclear household rather than with their grandparents in a non-nuclear household, decreased the risk of mental health issues in these children. Conclusions: This study is a novel attempt to understand the influence of the psychosocial issues on the child's mental health in the context of custody cases in India. Despite the minimum sample size, the findings imply that family-based intervention is the need of the hour in these cases. The implications for clinical practice and research are discussed. 2021 Elsevier B.V. -
Predictors of compassion competence among nurses working in the non-profit healthcare sector in India
Objectives: For many years, the non-profit healthcare sector in India has been able to instil a sense of goodwill in the society through the provision of healthcare services, which are not only affordable and accessible, but also deliver compassionate care. This study was an attempt to evaluate the compassionate care and competence of the nurses working in India's non-profit healthcare sector, and to identify the predictive factors associated with their work environment and engagement. Methods: A cross-sectional survey of nurses working in the medical college hospitals managed by private trusts in the non-profit sector in India was conducted using an online questionnaire. The study was conducted in April 2021 after the second wave of the Covid-19 pandemic. Socio-demographic factors, compassion competence, nurse practice environment, and nurse engagement were assessed. Linear regression analysis was conducted to identify the variance and the predictors of compassion competence among Indian nurses. Results: We found that nurses practice environment (?=0.982, p=< .001) and engagement (?=0.842, p=< .001) predicted compassion competence during the Covid-19 pandemic. Moreover, nurse practice environment and engagement positively influenced compassion competence. Conclusion: There was a considerably high level of compassion competence among nurses working in the non-profit healthcare sector during the Covid-19 pandemic. The compassion phenomenon was statistically significantly impacted by the nurses practice environment and their level of engagement. Consequently, not only does competent compassion behaviour require positive work environments and engaged nurses, but also nurses compassion competence and its relationship with practice environment factors and engagement are critical in the non-profit healthcare sector in India. These findings support previous reviews that a high degree of compassion competence increases healthcare quality. 2024 Jismon, M. G., Rofin T. M., Thekkekkara, J. V., Asha K. C., & Vijesh P. V. -
Predictors of emotional and behavioral problems among Indian adolescents: A clinic-based study
Background: Emotional and behavioral problems place a heavy burden on the adolescents and their families. Many factors are known to influence adolescent mental health. The current study was designed to determine the predictors of emotional and behavioral problems among Indian adolescents. Methods: The parents of adolescents in the age group of 10 to 18 (N = 81) were recruited from the National Institute of Mental Health and Neuro Sciences, Bangalore, Karnataka, India. Alabama Parenting Questionnaire (Parent version), Strength and Difficulty Questionnaire (Parent version) and, the Parent Interview Schedule (PIS) were used to assess the parental practices, emotional and behavioral problems and abnormal psychosocial environment of the adolescents. The data were analyzed with stepwise multiple linear and Quantile regression to find out significant predictors of emotional and behavioral problems among adolescents. Results: Adolescent's age, parental involvement, and paternal age are the significant predictors of emotional problems. Parental mental disorder or deviance, gender, and inconsistent discipline are the significant predictors for conduct problems. Poor monitoring and supervision, paternal and maternal age are statistically significant predictors of prosocial skills among the adolescents. Inadequate or distorted intrafamilial communication and parental mental disorder are the significant predictors of total difficulties among the adolescents. Conclusion: The study validates the role of abnormal psychosocial environments and negative parenting practices as risk factors for emotional and behavioral problems among the adolescents. A comprehensive analysis which covers all possible variables related to adolescent mental health is mandatory for the health professionals before planning the intervention. 2018 Elsevier B.V. -
Predictors of Hypertension among Indian Women of Reproductive Age Group: An Analysis from NFHS-5 Data
Introduction: Hypertension among women not only augments the risk of cardiovascular diseases but also leads to antenatal and intra-natal complications. Materials and Methods: A subset of data collected during National Family Health Survey-5, comprising of 7,24,115 women, 1549 years of age was analysed to identify key predictors of hypertension, using Probit Regression Model (PRM) which was run separately for rural and urban women. Results: Overall prevalence of hypertension among women of reproductive age group was 11% (10.4% and 12% in rural and urban areas respectively). 5% and 13.41% of women were obese and 1.2% and 2.6% were diabetic in rural and urban areas respectively. Obese, uneducated, rich women and those on medications showed higher prevalence, while women consuming milk, eggs, chicken, fruits, and vegetables daily showed lower prevalence. On using PRM, significant predictors of hypertension were increasing age, rural residence, pregnancy, increasing weight, diabetes, illiteracy, access to medical insurance, and indulgence in alcohol and smoking. Conclusion: Findings from the study contribute to the body of evidence favouring multifactorial causation. Hypertension awareness should be promoted especially among rural residents, older women, with emphasis on intake of balanced diet with less consumption of sodium and increased intake of fruits and vegetables. 2023 National Journal of Community Medicine. -
Predictors of online buying behaviour
This study creates a framework by looking into various research on customer acceptance of new selfservice technologies and internet purchasing behaviour systems. According to this research, customers' attitudes towards online purchasing are initially influenced by the direct impacts of relevant characteristics of online shopping. These characteristics include functional, utilitarian characteristics and usefulness, emotional and hedonic characteristics. It looks at the technology acceptance theory (TAM) established by David in 1989 and the theory of reasoned action (TRA) to understand factors determining the attitudes of users towards online shopping for users using technology. It also provides conceptual models by using the brand image of the online platform, past experiences of buyers, information related to the product, convenience of the shoppers and trust of the customers towards online shopping. 2024, IGI Global. All rights reserved. -
Predictors of Positive Psychological Capital: An Attempt Among the Teacher Communities in Rural Jharkhand, India
In the recent times, researchers have shown an increased interest in positive psychological capital (PsyCap). However, it is acknowledged that due to the limited number of studies conducted on the antecedents of psychological capital, there is a lack of sufficient data for conclusively proving the antecedents of PsyCap. Consequently, this article aims to explore the potential antecedents of PsyCap as a reliable source of data in the context of rural school teachers. The focus is to investigate both the individual differences and the contextual factors as desirable variables that constitute PsyCap among the school teachers of rural Jharkhand, India. Samples of 1,120 respondents from different rural schools were collected and analysed with Structural Equation Modeling (AMOS 20.0). The findings of the study explained that both the individual differences (proactive personality and emotional intelligence) and the contextual factors (perceived organizational support, servant leadership and meaningful work) have a positive relationship with PsyCap. The impact of PsyCap on teacher performance can form the basis for further research on the subject. JEL Codes: M12, M53 2021 XLRI Jamshedpur, School of Business Management & Human Resources. -
Predictors of Sleep Quality Among Emerging Adults in India: Exploring the Role of FoMO, Nomophobia and Evening Chronotype
Background: The increasing integration of mobile technology into daily life has raised concerns about its effects on sleep quality and mental health, particularly among emerging adults. The interplay between evening chronotype, nomophobia (no mobile phone phobia), and FoMO is crucial to understanding these impacts, especially in the digital age. The current study investigated whether nomophobia mediates the relationship between evening chronotype and sleep quality and between chronotype and FoMO with sleep quality in emerging adults. Methods: A cross-sectional survey was conducted among N = 501 emerging adults (Males = 144, Females = 356), aged 1825 (21.2 1.85 years), after approval from the Institutional Review Board. The participants completed measures of demographic information, sleep quality, FoMO, nomophobia and chronotype. Data were analysed using Jamovi and Statistical Package for the Social Sciences (SPSS). Results: Significant negative associations were found between evening chronotype, FoMO, and sleep quality, indicating that individuals with an evening chronotype and those with higher FoMO tend to experience poorer sleep. Nomophobia significantly mediated the relationships between evening chronotype and sleep quality (Indirect estimate = ?0.00896, p < .05), and between FoMO and sleep quality (Indirect estimate = 0.0185, p < .05), amplifying these negative impacts. Conclusion: The study highlights nomophobias critical role in exacerbating the effects of evening chronotype and FoMO on sleep. Interventions targeting nomophobia and digital habits could improve sleep and mental health among emerging adults. The Author(s) 2025. This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 License (https://creativecommons.org/licenses/by-nc/4.0/) which permits non-commercial use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access page (https://us.sagepub.com/en-us/nam/open-access-at-sage). -
Prefabricated Houses - A Model to Sustainable Housing Market
Effective spatial planning in an urban center is the need of the hour especially for offering affordable and sustainable houses. separate sheet. Spatial planning may help in achieving Sustainable Development goals (SDG) (09) Industry, Innovation and Infrastructure, (11) Sustainable cities and communities, and (15) Life on Land. The prefabricated house model can be used as a strategy in achieving above mentioned SDGs. It is important to study the prefabricated housing market for a country like India, considering its growing population and the necessity of access to affordable and sustainable houses. The main objective of this study is to identify the determinant factors of prefabricated houses and its impact on preference among urban consumers. The study is quantitative in nature and adopts a survey method. SEM model is used to analyze the data. A structured questionnaire is developed based on the objectives of the study. The questionnaire majorly focused on the perception of Sustainability, Affordability, Durability, Barriers, Opportunities, and Quality. The Electrochemical Society -
Preface
[No abstract available]