The integration of Big Earth Data and Artificial Intelligence (AI) has revolutionized geological and mineral mapping by delivering enhanced accuracy, efficiency, and scalability in analyzing large-scale remote sensing datasets. This study appraisals the application of advanced AI techniques, including machine learning and deep learning models such as Convolutional Neural Networks (CNNs), to multispectral and hyperspectral data for the identification and classification of geological formations and mineral deposits. The manuscript provides a critical analysis of AI’s capabilities, emphasizing its current significance and potential as demonstrated by organizations like NASA in managing complex geospatial datasets. A detailed examination of selected AI methodologies, criteria for case selection, and ethical and social impacts enriches the discussion, addressing gaps in the responsible application of AI in geosciences. The findings highlight notable improvements in detecting complex spatial patterns and subtle spectral signatures, advancing the generation of precise geological maps. Quantitative analyses compare AI-driven approaches with traditional techniques, underscoring their superiority in performance metrics such as accuracy and computational efficiency. The study also proposes solutions to challenges such as data quality, model transparency, and computational demands. By integrating enhanced visual aids and practical case studies, the research underscores its innovations in algorithmic breakthroughs and geospatial data integration. These contributions advance the growing body of knowledge in Big Earth Data and geosciences, setting a foundation for responsible, equitable, and impactful future applications of AI in geological and mineral mapping.

This research aims to analyze the contribution of the Industrial World Business program in improving the work skills and work readiness of people with disabilities through the Systematic Literature Review method. The involvement of businesses and industries in developing inclusive programs for people with disabilities is an important key to bridging the skills gap and employment opportunities. This research identifies various programs, best practices, and challenges in implementing these programs. Based on the results of the literature reviewed, it was found that inclusive job training programs significantly improve the technical and non-technical skills of people with disabilities while strengthening their readiness to face a competitive job market. On the other hand, there are still obstacles in the accessibility and adaptation of training programs that must continue to be optimized. However, to achieve greater inclusivity, improvements are still needed in terms of accessibility, program adaptation, and efforts to reduce discrimination in the world of work. It is hoped that the results of this research can become a basis for policymakers, industry players, and educational institutions to continue to develop inclusive programs and empower people with disabilities in the world of work. Collaboration between industry and vocational service providers is critical to improving employment outcomes and facilitating a successful transition from education to employment for people with disabilities.

The COVID-19 pandemic has fundamentally transformed the global education landscape, compelling institutions to adopt e-learning as an essential tool to sustain academic activities. This research examines the critical impact of e-learning on arts and science college students in Coimbatore, with an emphasis on its influence on their readiness for campus recruitment. Using a survey of 300 students, this study investigates their perceptions of online education, highlighting both its advantages, such as flexibility and accessibility, and its challenges, including engagement barriers and technical limitations. Data was collected through structured questionnaires and analyzed using statistical methods to draw meaningful insights. The research also explores the efficacy of online assessments in recruitment processes and assesses students’ awareness of available e-learning platforms and courses. The urgency of this study lies in addressing the pressing need to optimize digital education models as institutions globally transition toward blended learning post-pandemic. The findings underline the dual potential and limitations of e-learning, concluding with actionable recommendations to enhance its effectiveness, particularly in preparing students for competitive employment opportunities.

Credit risk assessment is one of the most important aspects of financial decision-making processes. This study presents a systematic review of the literature on the application of Artificial Intelligence (AI) and Machine Learning (ML) techniques in credit risk assessment, offering insights into methodologies, outcomes, and prevalent analysis techniques. Covering studies from diverse regions and countries, the review focuses on AI/ML-based credit risk assessment from consumer and corporate perspectives. Employing the PRISMA framework, Antecedents, Decisions, and Outcomes (ADO) framework and stringent inclusion criteria, the review analyses geographic focus, methodologies, results, and analytical techniques. It examines a wide array of datasets and approaches, from traditional statistical methods to advanced AI/ML and deep learning techniques, emphasizing their impact on improving lending practices and ensuring fairness for borrowers. The discussion section critically evaluates the contributions and limitations of existing research papers, providing novel insights and comprehensive coverage. This review highlights the international scope of research in this field, with contributions from various countries providing diverse perspectives. This systematic review enhances understanding of the evolving landscape of credit risk assessment and offers valuable insights into the application, challenges, and opportunities of AI and ML in this critical financial domain. By comparing findings with existing survey papers, this review identifies novel insights and contributions, making it a valuable resource for researchers, practitioners, and policymakers in the financial industry.

This study examines how Artificial Intelligence (AI) enhances Sharia compliance within Islamic Financial Institutions (IFIs) by improving operational efficiency, ensuring transparency, and addressing ethical and technical challenges. A quantitative survey across five Saudi regions resulted in 450 validated responses, analyzed using descriptive statistics, ANOVA, and regression models. The findings reveal that while AI significantly enhances transparency and compliance processes, its impact on operational efficiency is limited. Key barriers include high implementation costs, insufficient structured Sharia datasets, and integration complexities. Regional and professional differences further underscore the need for tailored adoption strategies. It introduces a novel framework integrating ethical governance, Sharia compliance, and operational scalability, addressing critical gaps in the literature. It offers actionable recommendations for AI adoption in Islamic finance and contributes to the global discourse on ethical AI practices. However, the Saudi-specific focus highlights regional dynamics that may limit broader applicability. Future research could extend these findings through cross-regional comparisons to validate and refine the proposed framework. By fostering transparency and ethical governance, AI integration aligns Islamic finance with socio-economic goals, enhancing stakeholder trust and financial inclusivity. The study emphasizes the need for targeted AI training, the development of structured Sharia datasets, and scalable solutions to overcome adoption challenges.

This contribution questions young people’s access to digital networks at the scale of intermediate cities in Saint-Louis. Thus, it analyzes the prescriptions of digital actors responsible for the development of digital economy in relation with the orientations of the Senegal Digital 2025 strategy. This is a pretex to highlight the gaps between official political discourses and the level of deployment of digital infrastructures. The study highlights the need to repoliticize the needs of populations for broadband and very high-speed connections to promote local initiatives for youth participation in Saint-Louis. Indeed, datas relating to access and use of the Internet by young people reveal inequalities linked to household income, the disparity of infrastructure and digital equipment, and the discontinuity in neighborhood development, but also to the adaptability of the internet service marketed. Through urban and explanatory sociology mobilized through the approach of young people’s real access to the Internet, our analyzes have shown at the scale of urban neighborhoods the impact of the actions recommended by those involved in the development of populations’ access to Internet. The result is that the majority of young people are forced to access the Internet through medium-speed mobile networks.