Predictive modeling has become increasingly important as digital transformation, sensing technologies, and large-scale data collection generate complex multivariate datasets across domains such as healthcare, finance, engineering, and industry. These datasets often contain nonlinear relationships, uncertainty, noise, and high dimensionality, making traditional statistical methods less effective. Advanced machine learning techniques, including Random Forests, Support Vector Regression, Artificial Neural Networks, and deep learning models, offer improved predictive accuracy and robustness for such complex data. This study presents a systematic review of predictive modeling approaches and proposes a framework comprising data preprocessing, feature selection, model development, training, validation, and performance evaluation. Comparative analysis of Multiple Linear Regression, Random Forest, Support Vector Regression, and Artificial Neural Networks demonstrates that ensemble and neural network-based models outperform traditional methods, particularly in handling noisy and highly correlated variables. The findings also highlight the importance of feature selection and dimensionality reduction in improving computational efficiency and model generalization. The proposed framework provides valuable guidance for developing reliable predictive systems in modern data-driven environments.
Chloe Bernard· International Journal of Mac...· 0 citations
Moral education plays a crucial role in modern society by helping develop responsible, ethical, and socially aware individuals. In a rapidly changing world influenced by technology and globalization, schools are expected to provide not only academic knowledge but also moral guidance. Moral education promotes values such as honesty, respect, empathy, responsibility, and social justice, which are essential for building responsible citizens. Many education systems focus mainly on academic achievement and technical skills, often overlooking students’ moral and ethical development. However, moral education helps address social issues such as bullying, prejudice, corruption, and ethical challenges in digital environments. It encourages critical moral thinking, emotional intelligence, and ethical decision-making. This study examines the role of moral education in school curricula and its effectiveness in shaping students’ behavior and character. Teaching approaches include value-based education, character education, ethical discussions, service learning, and experiential learning. A mixed-method research approach using questionnaires, interviews, and classroom observations was conducted with teachers, students, and administrators. Findings show that schools with structured moral education programs improve students’ empathy, social responsibility, and moral awareness. However, challenges such as limited curriculum time, lack of teacher training, cultural diversity, and digital influences remain. Overall, integrating moral education with supportive policies and teacher development is essential for fostering ethical and responsible individuals in society.
Lucas Martin, Chloe Bernard· International Journal of Inn...· 0 citations
The study concludes that long-term business success depends on redesigning traditional business models by integrating digital capabilities with organizational agility and customer-centric strategies.
Chloe Bernard· International Journal of Com...· 0 citations
This study presents a comprehensive analysis of renewable energy forecasting using deep learning techniques, focusing on short-term and medium-term prediction horizons, and shows that deep learning models provide significantly better forecasting accuracy, particularly under highly variable weather conditions.
Lucas Martin, Chloe Bernard· International Journal of Mod...· 0 citations
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