Variations in lighting conditions significantly impact the accuracy of object detection in computer vision applications, particularly when relying on edge detection techniques. This paper presents a comprehensive review of the complexities and challenges associated with edge detection with respect to varying illumination. We analyze the effects of lighting fluctuations on edge detection and explore a wide range of applications where a thorough understanding of light changes is crucial for accurate object localization. Furthermore, this work offers an extensive survey of traditional and deep-learning-based edge detection methods, with a particular focus on techniques that effectively address illumination variations. We also explore commonly used datasets, metrics, and measures for evaluating edge detection performance. Additionally, we delve into current challenges in edge detection, proposing potential future research directions to bridge the gap between automated methods and human visual perception. This comprehensive review aims to contribute to the advancement of edge detection by providing valuable insights and guidance for researchers and practitioners in the field.

Classification is a core supervised learning task in data analysis, and six classical classifier families (k-Nearest Neighbors, Support Vector Machine, Decision Tree, Random Forest, Logistic Regression, and Naïve Bayes) remain widely used in practice and underpin many subsequent variants. Although both single-family and multi-classifier surveys exist, there is still a gap for a method-centered study that, within a coherent framework, combines algorithmic representations for training and prediction, methodological characteristics, an explicit methodological comparison of the foundational variants within each family, and method-oriented advances published between 2020 and 2025. The survey is organized around a fixed set of performance-related perspectives, including accuracy, hyperparameter tuning, scalability, class imbalance, behavior in high-dimensional settings, decision-boundary complexity, interpretability, computational efficiency, and multiclass handling. It highlights strengths, weaknesses, and trade-offs across the six families and their variants, helping researchers and practitioners select or extend classification approaches. It also outlines future research directions arising from the limitations across the examined methods.

Mankind has always pursued knowledge. Over the philosophy questions by Paul Gauguin (“where do we come from, what are we, where are we going”), the science may answer these questions. In every scientific field, empirical and theoretical researches are making to describe natural processes, to better understand the universe. The questions are modified by the scientists: are these two variables correlated, do two or more independent dataset show some connection to each other, does a selected parameter have effect on the second parameter, which prediction can we state from independent variables for the dependent variable. But the effort for seeking the knowledge is the same. In this paper, our Universal Decision Support System concept is presented to help to answer questions related to different scientific field.

Analyzing processes are important to make a good prediction. Based on the results, right decisions can be made. However, there is some kind of results anyway and therefore it can be hard to differ the real result from the results which occurred randomly, despite of the validations which were used during the process. In this work, we analyzed the environment, where these random results can be appeared. After presenting a framework to deal with these random results, simulation techniques are also introduced.