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Accurate price forecasts on the European Union Emissions Trading System (EU ETS) market are of interest to many production and investment entities. The article presents a new approach, which models the most significant determinants by the use of a rule-based model containing facts written in the form of ordered fuzzy numbers, as the description of trends of imprecise values of prices. This approach allows for a qualitative analysis of potential values and directions of EU allowance (EUA) price changes and their trends along with the probability of their occurrence in the future. Article presen...

This guide provides more information on how you can use ChatGPT Code Interpreter for data analysis by uploading multiple files to analyze

Breast cancer is the second utmost common cancer among women. In this research study, two feature selection methods, namely Consistency first Search-Best First search (CFS-BFS) and Consistency-Best First Search (Consistency-BFS) are used to find out the significant biomarkers for breast cancer. The feature selection methods pick a small count of important and relevant genes providing a higher degree of accuracy. The count of biomarkers selected in Consistency-BFS is less in comparison to CFS-BFS method. Three common identified biomarkers’ genes are recognised to help in diagnosis and prognos...

Activity recognition from human action data is quite a challenging task in the biomedical data science community. The main challenge in dealing with human activity recognition (HAR) datasets is their high cardinality. Therefore, reducing cardinality is a cardinal area of research in the HAR field. In this research, reducing the data dimensionality by utilizing future selection methods has been used. This research work has extracted features using wavelet packet transform (WPT) and the cardinality of the feature set has been reduced by using the Genetic Algorithm (GA) technique. The selected fe...

Multimodal classification methods using different modalities have great advantages over traditional single-modality-based ones for the diagnosis of Alzheimer's disease (AD) and its prodromal stage mild cognitive impairment (MCI). With the increasing amount of high-dimensional heterogeneous data to be processed, multi-modality feature selection has become a crucial research direction for AD classification. However, traditional methods usually depict the data structure using pre-defined similarity matrix as a priori, which is difficult to precisely measure the intrinsic relationship across ...