In today's context of escalating environmental pressure, traditional methods of natural resource conservation face numerous challenges. The use of big data analytics to support the formulation of environmental policies has become a crucial approach for enhancing the efficiency and scientific...

Online health consultation services are crucial for mental health support, particularly in densely populated areas. However, the heavy reliance on human expertise often leads to delays, necessitating more efficient and automated solutions. This study developed a machine learning framework to...

In strategic and radical innovation, the degree of uncertainty and the amount of complexity is much higher compared to 'business as usual'. Therefore, idea management systems are often used to support such innovation processes. An interesting question is what we can learn from studying data in...

Purpose The proposed model has been aimed to predict stock market signals by designing an accurate model. In this sense, the stock market is analysed by the technical analysis of Japanese Candlestick, which is combined by the following meta heuristic algorithms: support vector machine (SVM),...

This study investigates the effect of Nigeria's population on four selected banking transaction channels. The Nigerian projected population (2022-2027) was used as an input variable for forecasting future volumes of transactions for each channel. The results show that the Support Vector...

In this study, we proposed two types of hybrid models based on the heterogeneous autoregressive (HAR) model and support vector regression (SVR) model to forecast realized volatility (RV). The first model is a residual-type model, where the RV is first predicted using the HAR model, and the...

In strategic and radical innovation, the degree of uncertainty and the amount of complexity is much higher compared to 'business as usual'. Therefore, idea management systems are often used to support such innovation processes. An interesting question is what we can learn from studying data in...

Forecasting changes in stock prices is extremely challenging given that numerous factors cause these prices to fuctuate. The random walk hypothesis and efcient market hypothesis essentially state that it is not possible to systematically, reliably predict future stock prices or forecast changes...

The new energy industry is strongly supported by the state, and accurate forecasting of stock price can lead to better understanding of its development. However, factors such as cost and ease of use of new energy, as well as economic situation and policy environment, have led to continuous...

Literature shows that both market data and fnancial media impact stock prices; however, using only one kind of data may lead to information bias. Therefore, this study uses market data and news to investigate their joint impact on stock price trends. However, combining these two types of...

In this study, we compare the performance of stochastic processes, namely, the Vasicek, Cox-Ingersoll-Ross (CIR), and geometric Brownian motion (GBM) models, with that of machine learning algorithms, such as Random Forest, Support Vector Machine (SVM), and k-Nearest Neighbors (KNN), for...

The unprecedented growth in data generation has necessitated the adoption of advanced analytical techniques. Support Vector Machine (SVM) is a powerful machine learning tool that has proven invaluable in classifying observations through optimal hyperplane in higher dimensions. Despite their...

This paper employs k-means clustering, a multidimensional pattern recognition method, to categorize countries using information from five Varieties of Democracy indices. K-means country clusters are similar but not entirely identical to both k-medians clusters and arbitrary groups formed using...

Breast cancer is the most common life-threatening cancer in women and one of the leading causes of death. Early diagnosis is one of the best defenses against the spread of breast cancer. Machine learning (ML) tools are now available for cancer detection and prediction. This study presents a...

In this paper, we introduce an unsupervised machine learning method for production frontier estimation. This new approach satisfies fundamental properties of microeconomics, such as convexity and free disposability (shape constraints). The new method generalizes Data Envelopment Analysis (DEA)...

Predicting bankruptcy within selected industries is crucial because of the potential ripple effects and unique characteristics of those industries. It serves as a risk management tool, guiding various stakeholders in making decisions. While artificial intelligence (AI) has shown high success...

Fault detection and identification are critical for the accurate maintenance and management of industrial machinery. In this regard, data-driven condition monitoring models play an important role in machinery fault diagnosis and management. This study investigates the applicability of various...

Prostate cancer (PCa) symptoms are commonly confused with benign prostate hyperplasia (BPH), particularly in the early stages due to similarities between symptoms, and in some instances, underdiagnoses. Clinical methods have been utilized to diagnose PCa; however, at the full-blown stage, clinical...

Forecasting stock market movements is a challenging task from the practitioners' point of view. We explore how model selection via the least absolute shrinkage and selection operator (LASSO) approach can be better used to forecast stock closing prices using real-world datasets of daily stock...

Purpose - In this research, the main purpose is to use a suitable structure to predict the trading signals of the stock market with high accuracy. For this purpose, two models for the analysis of technical adaptation were used in this study. Design/methodology/approach - It can be seen that...

This paper employs k-means clustering, a multidimensional pattern recognition method, to categorize countries using information from five Varieties of Democracy indices. K-means country clusters are similar but not entirely identical to both k-medians clusters and arbitrary groups formed using...

Stock market time series are inherently noisy. Although support vector machine has the noise-tolerant property, the noised data still affect the accuracy of classification. Compared with other studies only classify the movements of stock market into up-trend and down-trend which does not concern...

Introduction -- Application of Data Mining Methods for Pattern Recognition in Negotiation Support Systems -- Advanced Maintenance of Data Richness in Business Communication Data – An Evaluation of Dimensionality Reduction Techniques -- Analytical Comparison of Clustering Techniques for the...

Cryptocurrencies (CCs) have become increasingly interesting for institutional investors' strategic asset allocation and will therefore be a fixed component of professional portfolios in the future. However, this asset class differs from established assets primarily in that it has a higher...

Systematic pattern recognition as well as the corresponding description of determined patterns entail numerous challenges in the application context of high-dimensional communication data. These can cause increased effort, especially with regard to machine-based processing concerning the...

In this study, we apply several advanced machine learning techniques including extreme gradient boosting (XGBoost), support vector machine (SVM), and a deep neural network to predict bankruptcy using easily obtainable financial data of 3728 Belgian Small and Medium Enterprises (SME’s) during...