This academic research paper provides a comprehensive review of the concepts, stages, methodologies, tools, and real-world applications of data analysis. Special emphasis is placed on the transformative integration of Artificial Intelligence (AI) and Machine Learning (ML) across vital sectors such as education, healthcare, business, and finance. Furthermore, the study addresses critical challenges including data quality, privacy, security, and algorithmic bias, while featuring practical visual analytics and a case study on predictive modeling for student academic performance.
Paper Accepetd for publication in: Transactions on Artificial Intelligence ISSN: 2982-3439 (Scilight Press) Abstract Researchers increasingly rely on Clinical Artificial Intelligence (CAI) to derive predictive insights from massive observational datasets. However, the paradigm of Big Medical Inference often conflates statistical volume with clinical representativeness. This study argues that when methodological rigor is sacrificed for data scale, AI models risk institutionalizing historical biases, thereby compromising patient safety and public health policy integrity. We propose a precautionary, multi-level auditing framework designed to assess the structural architectural integrity of large-scale clinical datasets prior to computational deployment. Rather than contesting specific clinical outcomes, our approach establishes a quantitative prerequisite for CAI: the validation of demographic representativeness and baseline case distribution against official national benchmarks. To demonstrate the validity of our approach, we applied our framework to a prominent, high-profile case study. Our check revealed a tripartite structural divergence: a 32.5% demographic deficit in high-risk elderly (aged 65 years) strata, a 26.2% aggregate cancer incidence suppression, and a 45.1% deflation of expected cases in non-exposed groups.These discrepancies demonstrate that even massive datasets can be fundamentally misaligned with clinical reality. We conclude that structural validation is not an elective procedure but an ethical imperative for Human-Centric Clinical AI. By re-establishing this hierarchy of validation, we ensure that the intelligence of automated AI systems remains subordinate to the structural truth of the data, thereby transitioning from a reliance on Big Data alone toward a more robust, ethically sound, and authentic practice of clinical knowledge.
Marco Roccetti· Zenodo (CERN European Organi...· 0 citations
Abstract Background: Artificial intelligence (AI) holds promise in reshaping healthcare by transforming educational patterns, patient care, and research opportunities. However, there are obstacles impeding the proper integration of AI into the medical field. This study was undertakento evaluate the knowledge, attitude, and awareness of medical students and resident doctors regarding AI in medicine and healthcare. Methods:A questionnaire-based survey was conducted that included a total of 16 questions specifically designed to assess the knowledge and attitude of participants towards AI. The questionnaire used in the present study was developed for this study only and content validity of the initial questionnaire was adequately assessed. The questionnaire was converted into a Google Form, and participants were provided with the link to complete it. Statistical analysis was conducted using R version 4.3.2 (R-Studio). Results: Out of 194 respondents, 113 (58.25%) were medical students, and 81 (41.75%) were resident postgraduate doctors aged 19 to 32 (average 23.91 years) and a male-to-female ratio of 3.62:1. While 63.41% rated their AI knowledge as poor to below average, with 55.15% lacking understanding of many AI terminologies, 59.28% believed AI tools could enhance their understanding of medical concepts. 83.5% expressed interest in furthering knowledge on AI in healthcare. ChatGPT was the most used AI tool, primarily for language correction (50%), literature reviews and manuscript writing (43.3%), and creating presentation outlines (37.11%). Additionally, knowledge about AI devices and apps applicable to diagnostics, therapeutics, patient care, and data analysis was evaluated, along with opinions on barriers to incorporating AI in healthcare. 81.44% of respondents were unaware of AI's ethical considerations. Conclusions: AI has immense potential across diverse healthcare sectors. Nonetheless, our study also underscores the pressing need to confront challenges and equip our future healthcare professionals with the evolving realm of AI. This is essential to ensure they can effectively apply practical AI knowledge for enhanced patient care and management.
Ishan Gupta, Ankush Garg, Ashwin Varadarajan et al.· BMC Medical Education· 0 citations
AB156 CPF-Camouflage Police Force (Enhanced) A Preventive, Rehabilitative and Adaptive Policing Framework for Safer Cities and Communities Original Idea: 01 December 2019Author: Muhammad Asim – Global Progress VolunteerIndependent Researcher ID / ORCID: 0000-0002-8575-4447Relevant United Nations Sustainable Development Goal: SDG 11 – Sustainable Cities and Communities Abstract Street crime and urban insecurity remain important challenges for cities and communities worldwide. Although crime patterns differ considerably between countries and cities, effective public safety requires approaches that combine prevention, lawful enforcement, rehabilitation, community participation and evidence-based policing. This paper introduces the Camouflage Police Force (CPF), an original policing concept first developed by the author on 01 December 2019. CPF proposes an integrated three-stage framework: (1) root-cause prevention, (2) lawful surrender, rehabilitation and reintegration, and (3) adaptive camouflage policing. The distinctive element of CPF is its proposal to complement conventional visible policing with strategically less-obvious police deployment. As an initial research hypothesis, CPF proposes testing a 70% adaptive/non-obvious and 30% visible deployment model. This ratio is not presented as an established universal standard; rather, it should be experimentally evaluated and adjusted according to local crime patterns, legal requirements, operational capacity, community expectations and empirical results. The framework also proposes responsible use of artificial intelligence and data-driven tools for crime-pattern analysis and resource allocation, while emphasizing human oversight, privacy, due process, proportionality and independent accountability. CPF is aligned with the broader objective of United Nations Sustainable Development Goal 11, which calls for cities and human settlements to become inclusive, safe, resilient and sustainable [1]. It also reflects established evidence supporting multisectoral violence prevention, attention to risk factors, social reintegration and participatory urban safety strategies [2–6]. The paper concludes that CPF represents a researchable alternative to predominantly reactive policing by integrating prevention, rehabilitation and adaptive protection into a single framework. Keywords: Camouflage Police Force, CPF, street crime, urban safety, crime prevention, adaptive policing, hidden policing, rehabilitation, reintegration, artificial intelligence, SDG 11, sustainable cities, public safety.
Muhammad Asim - Global Progress Volunteer Muhammad Asim - Global Progress Volunteer· Zenodo (CERN European Organi...· 0 citations
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Purpose This study examines factors associated with older adults' intention to use artificial intelligence (AI) for personal financial management in China and Vietnam by integrating the technology acceptance model (TAM) and the knowledge–behavior gap (KBG) model. Design/methodology/approach Using cross-sectional survey data from 713 respondents, including 407 respondents from Vietnam and 306 from China, the proposed model was examined using partial least squares structural equation modeling (PLS-SEM). Findings Information diagnosticity was positively associated with assessment perceived utility in Vietnam, whereas the corresponding association was not statistically significant in China. Social influence was positively associated with intention to use AI for personal financial management in China but showed no statistically significant association with intention in Vietnam. AI self-efficacy and AI literacy showed significant positive associations with several technology-evaluation and acceptance constructs in both countries. Neither personal innovativeness nor brand reputation significantly moderated the association between acceptance and intention in either national sample. Originality/value Research on AI adoption has predominantly focused on technologically experienced or younger populations. This study extends the literature by examining AI-supported personal financial management among middle-aged and older adults and by comparing the structural associations observed in two Asian countries with different technological and institutional environments.
Hà Vy Nguyễn, Ngoc Kim Ngan Le, Thi Hoang Trang Nguyen et al.· China Finance Review Interna...· 0 citations
AB156 CPF-Camouflage Police Force (Enhanced) A Preventive, Rehabilitative and Adaptive Policing Framework for Safer Cities and Communities Original Idea: 01 December 2019Author: Muhammad Asim – Global Progress VolunteerIndependent Researcher ID / ORCID: 0000-0002-8575-4447Relevant United Nations Sustainable Development Goal: SDG 11 – Sustainable Cities and Communities Abstract Street crime and urban insecurity remain important challenges for cities and communities worldwide. Although crime patterns differ considerably between countries and cities, effective public safety requires approaches that combine prevention, lawful enforcement, rehabilitation, community participation and evidence-based policing. This paper introduces the Camouflage Police Force (CPF), an original policing concept first developed by the author on 01 December 2019. CPF proposes an integrated three-stage framework: (1) root-cause prevention, (2) lawful surrender, rehabilitation and reintegration, and (3) adaptive camouflage policing. The distinctive element of CPF is its proposal to complement conventional visible policing with strategically less-obvious police deployment. As an initial research hypothesis, CPF proposes testing a 70% adaptive/non-obvious and 30% visible deployment model. This ratio is not presented as an established universal standard; rather, it should be experimentally evaluated and adjusted according to local crime patterns, legal requirements, operational capacity, community expectations and empirical results. The framework also proposes responsible use of artificial intelligence and data-driven tools for crime-pattern analysis and resource allocation, while emphasizing human oversight, privacy, due process, proportionality and independent accountability. CPF is aligned with the broader objective of United Nations Sustainable Development Goal 11, which calls for cities and human settlements to become inclusive, safe, resilient and sustainable [1]. It also reflects established evidence supporting multisectoral violence prevention, attention to risk factors, social reintegration and participatory urban safety strategies [2–6]. The paper concludes that CPF represents a researchable alternative to predominantly reactive policing by integrating prevention, rehabilitation and adaptive protection into a single framework. Keywords: Camouflage Police Force, CPF, street crime, urban safety, crime prevention, adaptive policing, hidden policing, rehabilitation, reintegration, artificial intelligence, SDG 11, sustainable cities, public safety.
Muhammad Asim - Global Progress Volunteer Muhammad Asim - Global Progress Volunteer· Zenodo (CERN European Organi...· 0 citations
This article presents a narrative review of Explainability and Interpretability of Black-Box Models in the context of Artificial Intelligence. The literature on this topic has expanded substantially over recent decades, yet it remains fragmented across subfields, methods, and national research traditions. Drawing on an interpretive synthesis of representative contributions, the review reconstructs the historical development of the area, examines the conceptual foundations and definitional disputes that organize its debates, and maps the contemporary landscape of research, including the methodological shift toward data-intensive approaches and the institutional pressures that shape publication practice. Particular attention is given to the role of explainable AI and interpretability as organizing themes, and to the conditions under which findings from different research traditions can be brought into productive comparison. The review identifies three synthetic conclusions: the literature is cumulatively strong but organizationally weak; methodological pluralism is better understood as a resource than as a defect; and the growing practical salience of the topic raises the stakes of its unresolved conceptual questions. An agenda for future work is proposed, emphasizing integrative research designs, transparent synthesis practices, and the protection of definitional and infrastructural work on which cumulative progress depends. The article is intended as both a reference map for newcomers and a provocation for specialists in Artificial Intelligence.
Zen Revista, 10 IA· Zenodo (CERN European Organi...· 0 citations
This study proposes the Inclusive Didactic Mediation Model Based on Artificial Intelligence for University Mathematics Education in Latin America (MMDIAI-LAT), structured around four interdependent dimensions: pedagogical-didactic, ethical-inclusive, technological-functional, and sociocultural-Latin American contextual. The research adopts a critical integrative review (Torraco, 2005) of eighteen high-impact sources. The model positions AI as a didactic mediator supporting visualization, accessibility, and conceptual construction in university mathematics, with equity as a constitutive design condition rather than a secondary aspiration. This preprint is submitted for peer review.
Mariana Gabriela Torres· Zenodo (CERN European Organi...· 0 citations
This study proposes the Inclusive Didactic Mediation Model Based on Artificial Intelligence for University Mathematics Education in Latin America (MMDIAI-LAT), structured around four interdependent dimensions: pedagogical-didactic, ethical-inclusive, technological-functional, and sociocultural-Latin American contextual. The research adopts a critical integrative review (Torraco, 2005) of eighteen high-impact sources. The model positions AI as a didactic mediator supporting visualization, accessibility, and conceptual construction in university mathematics, with equity as a constitutive design condition rather than a secondary aspiration. This preprint is submitted for peer review.
Mariana Gabriela Torres· Zenodo (CERN European Organi...· 0 citations
The development of Artificial Intelligence (AI) has brought significant changes to the field of education, particularly in supporting students in completing their final projects. This study aims to analyze the effect of Artificial Intelligence usage on the effectiveness of undergraduate students' final project completion at Bina Adinata Institute of Technology and Business. This research employed a descriptive quantitative method by collecting data through a Likert-scale questionnaire distributed to 47 students selected using purposive sampling. The collected data were analyzed using descriptive statistical analysis based on five indicators: AI usage intensity, ease of AI use, AI utilization for information retrieval, AI utilization for academic writing, and AI output quality. The results indicate that the majority of respondents agreed and strongly agreed with all research indicators. AI was perceived as helpful in searching for information, finding references, understanding research materials, improving grammar, organizing final project content, and providing information relevant to research topics, thereby enhancing the effectiveness and efficiency of final project completion. The findings imply that Artificial Intelligence can serve as an effective supporting technology to improve students' productivity and the quality of final project writing. However, AI should be used responsibly by balancing it with critical thinking skills, verification of information through credible academic sources, and adherence to academic integrity to maintain the quality of scientific work.
Ade Budi Setiawan, M. Yasin, Yunella Yunella et al.· Jurnal Publikasi Teknik Info...· 0 citations
Recent years have witnessed considerable progress in computer-aided drug discovery, driven by the incorporation of computational technologies within both academic and pharmaceutical environments. This evolution is marked by a significant accumulation of data pertaining to detailed three-dimensional structural information, ligand properties, and their interactions with therapeutic targets. The augmentation of computational capabilities and the accessibility of extensive chemical libraries containing billions of drug-like small molecules have further facilitated this transition. To effectively utilize these resources, it is imperative to employ rapid computing methods for virtual screening, which encompass structure-driven in silico screening across vast molecular spaces, supported by efficient recurrent profiling techniques. Furthermore, advancements in deep learning methodologies are required to improve the accuracy of prediction concerning target functionalities and ligand characteristics, even when complete receptor structures are not available. Here, this review discusses the expansion of chemical space, advanced virtual screening, deep learning, molecular dynamics (MD) simulations, Absorption, Distribution, Metabolism, Excretion, and Toxicity (ADMET) modelling, and challenges for drug discovery. It examines how experimental validation integrates computational predictions with laboratory testing for effective candidate selection. Finally, it outlines future research directions for artificial intelligence-driven, ultra-high performance computing (UHPC) in drug discovery, offering new prospects for the economical creation of safer and more efficacious molecule-level therapies.
Lunna Li, Lianna D. Soriano, W. M. Kedir et al.· Molecular Biomedicine· 0 citations
Introduction: The integration of artificial intelligence (AI) into healthcare is an inevitable global trend. Healthcare students’ perceptions and attitudes are critical in shaping future AI adoption and informing educational reform. This study aimed to examine students’ perceptions and attitudes towards AI in healthcare and to assess the relationship between these constructs. Methods: A cross-sectional study was conducted among 967 healthcare students from December 2024 to January 2025. Data were collected using a structured questionnaire assessing perceptions and attitudes towards AI. Statistical analyses were performed using SPSS version 26.0. Descriptive statistics summarised the data, while Pearson’s correlation and multivariate linear regression were used to examine associations (p < .05). Results: Participants demonstrated moderate perceptions (3.43 ± .64) and attitudes (3.45 ± .64) towards AI. Most students recognised AI’s potential in preventive health (65.0%), patient documentation (65.4%), and population health surveillance (54.5%). Additionally, 64.9% of participants reported that AI makes medicine more engaging, and over 61% believed it enhances healthcare delivery and supports career development. A strong positive correlation was observed between perceptions and attitudes (r=.724; p<.001). In the regression analysis, perception remained a strong independent predictor of attitude (β = .718, p < .001) after adjustment. Conclusion: Healthcare students exhibited moderate readiness for AI adoption, with perceptions strongly influencing attitudes. These findings underscore the importance of integrating AI into healthcare curricula, with emphasis on practical exposure and discipline-specific training. Future research should incorporate perspectives from educators, and healthcare organisations to provide a more comprehensive understanding.
Thuy Thi Luu, Giang Huong Nguyen, Hoai Thi Yen Nguyen et al.· The Asia Pacific Scholar· 0 citations
