This study aimed to determine the socio-economic poverty status of those living in rural areas using data surveys obtained from household expenditure and income. Machine learning-based classification and clustering models were proven to provide an overview of efforts to determine similarities in poverty characteristics. Efforts to address poverty classification and clustering typically involve comprehensive strategies that aim to improve socio-economic conditions in the affected areas. This research focuses on the combined application of machine learning classification and clustering techniques to analyze poverty. It aims to investigate whether the integration of classification and clustering algorithms can enhance the accuracy of poverty analysis by identifying distinct poverty classes or clusters based on multidimensional indicators. The results showed the superiority of machine learning in mapping poverty in rural areas; therefore, it can be adopted in the private sector and government domains. It is important to have access to relevant and reliable data to apply these machine learning techniques effectively. Data sources may include household surveys, census data, administrative records, satellite imagery, and other socioeconomic indicators. Machine learning classification and clustering analyses are used as a decision support tool to gain an understanding of poverty data from each village. These strategies are also used to describe the profile of poverty clusters in the community in terms of significant socio-economic indicators present in the data. Village clusters based on an analysis of existing poverty indicators are grouped into high, moderate, and low poverty levels. Machine learning can be a valuable tool for analyzing and understanding poverty by classifying individuals or households into different poverty categories and identifying patterns and clusters of poverty. These insights can inform targeted interventions, policy decisions, and resource allocation for poverty reduction programs.

Clustering technics, like k-means and its extended version, fuzzy c-means clustering (FCM) are useful tools for identifying typical behaviours based on various attitudes and responses to well-formulated questionnaires, such as among forensic populations. As more or less standard questionnaires for analyzing aggressive attitudes do exist in the literature, the application of these clustering methods seems to be rather straightforward. Especially, fuzzy clustering may lead to new recognitions, as human behaviour and communication are full of uncertainties, which often do not have a probabilistic nature. In this paper, the cluster analysis of a closed forensic (inmate) population will be presented. The goal of this study was by applying fuzzy c-means clustering to facilitate the wider possibilities of analysis of aggressive behaviour which is treated as a heterogeneous construct resulting in two main phenotypes, premeditated and impulsive aggression. Understanding motives of aggression helps reconstruct possible events, sequences of events and scenarios related to a certain crime, and ultimately, to prevent further crimes from happening.

Islamic banking is one of the fastest-growing sectors of the financial industry. Several works have been written in this field, but none attempt to learn the entire Islamic banking and financial system. Furthermore, the study could not locate any publications investigating the conceptual and intellectual foundations of this emerging field of inquiry. The current study uses bibliometric methodologies to assess the current state of Islamic banking, financial research, and the upcoming trends. For the people who choose interest-free investments, the current research examines a conceptual research context on Islamic banking and finance at various planning and decision-making stages. One thousand research studies appearing in scholarly journals between 2005 and 2023 were reviewed for the purpose. In order to examine the works on Islamic banking and finance, bibliometric techniques were used, including analysis of citation network, content, co-citation, keyword, and publishing trends. By suggesting thirteen clusters, to enhance research on Islamic banking and finance to help interest-free investors learn more, the goal of the research is to promote the body of knowledge. The field of Islamic banking and finance has grown from a young lot to a prominent teaching and research tool. Investigating and identifying current research trends in this area is crucial. As institutions and society are placing more emphasis on Islamic banking to raise individual citizens’ responsibilities in developing interest-free investing strategies, the findings are crucial to the community of interest-free financiers. Further research urges with the studies not restricted to a thousand researches only.

Shipbuilding industry is characterized by high price competition, as well as tight deadlines for product design and production. The dominant positions in the civil shipbuilding market are occupied by the countries of Southeast Asia, and for a number of reasons, participants from other countries are uncompetitive. Thus, in order to ensure the sustainable development of companies in the global civil shipbuilding market, it is necessary to identify and analyze the main factors that provided the competitive advantages of industry leaders. Assessment of further directions of shipbuilding development is a necessary condition for the formation of competitive advantages of new market participants. The article analyzes the main directions of development of the world civil shipbuilding in the period after World War II, as well as prospects for the future. As a result of the analysis of the latest organizational management concepts, the concept of modular production in shipbuilding is proposed, and directions for further research are determined.

Credit policies for clean and renewable energy businesses play a crucial role in supporting carbon neutrality efforts to combat climate change. Clustering the credit capacity of these companies to prioritize lending is essential given the limited capital available. Support Vector Machine (SVM) and Artificial Neural Network (ANN) are two robust machine learning algorithms for addressing complex clustering problems. Additionally, hyperparameter selection within these models is effectively enhanced through the support of a robust heuristic optimization algorithm, Particle Swarm Optimization (PSO). To leverage the strength of these advanced machine learning techniques, this paper aims to develop SVM and ANN models, optimized with the PSO, for the clustering problem of green credit capacity in the renewable energy industry. The results show low Mean Square Error (MSE) values for both models, indicating high clustering accuracy. The credit capabilities of wind energy, clean fuel, and biomass pellet companies are illustrated in quadrant charts, providing stakeholders with a clear view to adjust their credit strategies. This helps ensure the efficient operation of banking green credit policies.