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.

A The meaning of life is the purpose that defines a person’s existence based on a set of fundamental objectives that give meaning to life or not. Furthermore, not all individuals have a meaning in life, and it may be absent at some point or stage of life. Objective: To analyze Peruvian older adults’ socioeconomic factors and the meaning of life. Method: A descriptive, comparative, quantitative cross-sectional study was conducted. One thousand older adults were intentionally selected through quotas of 100 older adults in 10 localities in Arequipa, Peru. They were administered a survey validated with high levels of reliability on the meaning of life and socioeconomic factors. Results: A moderate level of meaning in life was found. Most older adults believe that increasing age decreases the purpose of living, and existential emptiness grows. Conclusions: Statistically significant differences (p < 0.05) were found between the meaning of life and the following socioeconomic factors: retirement, religion, educational level, cohabitation, marital status, income, and occupation. It is understood that older adults who scored higher on these factors indicate having meaning in life because they still fulfill the role of providers for the family economy, being util to their families compared to the majority who scored low, which indicates an absence of meaning of life leading to an increase existential void.

In Ghana, youth unemployment remains significant challenges, with technical and vocational education and training (TVET) emerging as a potential solution to equip young people with practical skills for the job market. However, the uptake of TVET programmes among Ghanaian youth remains low, particularly among females. This study therefore explores the determinants that influence TVET choices among Ghanaian youth, with the goal of informing policy development to enhance participation in vocational education. Applying an enhanced multinomial logistic regression (MLR) model, this research examines the influence of socio-economic, demographic, and attitudinal factors on career decisions. The enhanced model accounts for class imbalances in the dataset and improves classification accuracy, making it a robust tool for understanding the drivers behind TVET choices. A sample of 1600 Ghanaian youth engaged in vocational careers was used, ensuring diverse representation of the population. Key findings reveal that males are approximately three times more likely to choose TVET programs than females, despite females making up 50.13% of Ghana’s population. Specific determinants influencing TVET choices include financial constraints, parental influence, peer influence, teacher influence, self-motivation, and vocational limitations. In regions with limited vocational options, youth often pursue careers based on availability rather than preference, which highlights a gap in vocational opportunities. Parental and teacher influences were found to play a dominant role in steering youth towards specific careers. The study concludes with recommendations for policymakers, instructors, and stakeholders to increase the accessibility, relevance, and quality of TVET programmes to meet the socio-economic needs of Ghanaian youth.

Education is one of the basic needs that every child should have. Information communication technology has a significant influence on special needs children’s schooling. Instead of considering learning a difficult chore, the adoption of measures such as ICT can simplify it and make inclusive education a reality. Aim: This current systematic literature review aims to determine the extent of ICT adoptions in special education scenarios. Method: This paper examined pertinent literature on ICT in special education in the period 2000 to 2023. The key articles extracted through keyword search were gathered from databases indexed in Web of Science and Scopus. The collected data were then screened using a VOS viewer for the most relevant information. From the web of Science, 31 articles were found to have connections with one another while the same process when applied to the Scopus database, helped obtain 8 articles. Results: A total of 39 articles fulfilled the search inclusion criteria of minimum two keyword occurrences. These articles were all written in English and published between 2000 and 2023. The in-depth analysis of all these articles was performed along three broad themes, viz., availability of SEN based ICTs and their impact on children with disabilities, quality of available ICT integrated curriculum for SEN and the challenges in promoting ICTs for inclusive education. Conclusions: The paper concludes that ICT integration in special education would make learning easier for children with disabilities when compared to learning using traditional methods. Implications: The paper pinpoints significant limitations in ICT use found in existing literature and the lack of it to support inclusive education. The authors make recommendations for improved ICT integrated curriculum to improve inclusivity.

This study aims to develop a robust prioritization model for municipal projects in the Holy Metropolitan Municipality (Makkah) to address the challenges of aligning short-term and long-term objectives. The research explores How multi-criteria decision-making (MCDM) techniques can prioritize municipal projects effectively while ensuring alignment with strategic goals and local needs. The methodology employs the analytic hierarchy process (AHP) and exploratory factor analysis (EFA) to ensure methodological rigor and data adequacy. Data were collected from key stakeholders, including municipal planners and community representatives, to enhance transparency and reliability. The model’s validity was assessed through latent factor analysis, confirming the relevance of identified criteria and factors. Results indicate that flood prevention projects are the highest priority (0.4246), followed by road projects (0.3532), park construction (0.1026), utility projects (0.0776), and digital transformation (0.0416). The study highlights that certain factors are critical for evaluating and prioritizing municipal projects. “Capacity and Demand” emerged as the most influential factor (0.5643), followed by “Strategic Alignment” (0.2013), “Project Interdependence” (0.1088), “Increasing Investment” (0.0950), and “Risk” (0.0306). These findings are significant as they offer a structured, data-driven approach to decision-making aligned with Saudi Vision 2030. The proposed model optimizes resource allocation and project selection, representing a pioneering effort to develop the first prioritization framework specifically tailored to Makkah’s unique municipal needs. Notably, this is the first study to establish a prioritization method specifically for Makkah’s municipal projects, providing valuable contributions to the field.