Artificial Intelligence Frameworks Research Articles

Predictive AI Models for Manufacturing Failure Detection in Multi-Site Pharmaceutical Facilities

The role of Predictive Artificial Intelligence (AI) models is continuously emerging as critical in the pharmaceutical manufacturing industry particularly in failure detection in multiple sites. These models use artificial intelligence, evolving ML techniques, and large datasets to forecast, monitor and resolve rigorous system failures. This paper aims to analyze how research has embraced the development of effective predictive AI frameworks that are relevant to multi-site pharmaceutical facilities, given the many limitations and challenges associated with such settings. A deeper elucidation of different forms of ML, such as supervised and unsupervised learning, is provided. There is special emphasis on the usage of fleet and vehicle domain knowledge and regulatory compliance knowledge and, in general, field operational data feeds to the prediction model into the system. Our approach is fully based on the mixed physics and data approach, which provides high accuracy of results and well interpretable quantitatively. This study supports the generalized understanding that predictive AI models are capable of reducing downtime, increasing product quality, and optimizing operations. Real-world experience in a multi-site pharmaceutical firm establishes more than 95% efficacy of failure prediction along with significant cost-effectiveness and time compression for products. Finally, the paper points to the implications for Industry 4.0 in the context of the pharmaceutical sector and presents additional research avenues. Keywords: Predictive AI, Machine Learning, Pharmaceutical Manufacturing, Failure Detection.

AI-Related Disciplines: A Comparative Analysis of Regional Trade Agreements and National Regulatory Approaches

The global digital economy is witnessing the transformative impact of Artificial intelligence (AI), prompting nations to engage in AI-driven trade agreements. Under the digital trade context, these agreements aim to promote and regulate AI technologies, facilitate international AI cooperation, and touch upon data-related disciplines crucial for AI. This article delves into analysing the emerging trend in specific AI disciplines and other AI-related fields, including data provisions in regional trade agreements (RTAs). It also maps out the current landscape of AI regulatory approaches in the United States (US), the European Union (EU), China, and Singapore to analyse how these diverse domestic regulatory approaches may impact regional AI rulemaking. Findings reveal that RTAs emphasize on AI recognition, AI frameworks, and AI cooperation, albeit in a flexible and soft manner. Besides, while the US’s market-driven approach could spearhead rulemaking in AI, its recent policy shifts cast doubt on its comprehensive participation in RTAs. The EU is at the forefront of domestic AI legislation, and seeks to mirror the risk and security-based value at the regional level. China, driven by its massive market potential and concerns about data security, relies on a hybrid approach that explores regulatory strategies alongside AI development. Singapore is notable for taking proactive steps in addressing AI governance through RTAs, which demonstrates a unique form of Asian regionalism. Despite the fragmented approaches among major economies, this article emphasizes on the role of RTAs in addressing trade-related AI issues and puts forward recommendations for AIspecific rulemaking in RTAs.

Advanced AI and ML frameworks for transforming drug discovery and optimization: With innovative insights in polypharmacology, drug repurposing, combination therapy and nanomedicine.

Artificial Intelligence (AI) and Machine Learning (ML) are transforming drug discovery by overcoming traditional challenges like high costs, time-consuming, and frequent failures. AI-driven approaches streamline key phases, including target identification, lead optimization, de novo drug design, and drug repurposing. Frameworks such as deep neural networks (DNNs), convolutional neural networks (CNNs), and deep reinforcement learning (DRL) models have shown promise in identifying drug targets, optimizing delivery systems, and accelerating drug repurposing. Generative adversarial networks (GANs) and variational autoencoders (VAEs) aid de novo drug design by creating novel drug-like compounds with desired properties. Case studies, such as DDR1 kinase inhibitors designed using generative models and CDK20 inhibitors developed via structure-based methods, highlight AI's ability to produce highly specific therapeutics. Models like SNF-CVAE and DeepDR further advance drug repurposing by uncovering new therapeutic applications for existing drugs. Advanced ML algorithms enhance precision in predicting drug efficacy, toxicity, and ADME-Tox properties, reducing development costs and improving drug-target interactions. AI also supports polypharmacology by optimizing multi-target drug interactions and enhances combination therapy through predictions of drug synergies and antagonisms. In nanomedicine, AI models like CURATE.AI and the Hartung algorithm optimize personalized treatments by predicting toxicological risks and real-time dosing adjustments with high accuracy. Despite its potential, challenges like data quality, model interpretability, and ethical concerns must be addressed. High-quality datasets, transparent models, and unbiased algorithms are essential for reliable AI applications. As AI continues to evolve, it is poised to revolutionize drug discovery and personalized medicine, advancing therapeutic development and patient care.

Prediction and decoding of metaverse coin dynamics: a granular quest using MODWT-Facebook’s prophet-TBATS and XAI methodology

Abstract The growing media buzz and industry focus on the emergence and rapid development of Metaverse technology have paved the way for the escalation of multifaceted research. Specific Metaverse coins have come into existence, but they have barely seen any traction among practitioners despite their tremendous potential. The current work endeavors to deeply analyze the temporal characteristics of 6 Metaverse coins through the lens of predictive analytics and explain the forecasting process. The dearth of research imposes serious challenges in building the forecasting model. We resort to a granular prediction setup incorporating the Maximal Overlap Discrete Wavelet Transformation (MODWT) technique to disentangle the original series into subseries. Facebook's Prophet and TBATS algorithms are utilized to individually draw predictions on granular components. Aggregating components-wise forecasted figures achieve the final forecast. Facebook's Prophet is deployed in a multivariate setting, applying a set of explanatory features covering macroeconomic, technical, and social media indicators. Rigorous performance checks justify the efficiency of the integrated forecasting framework. Additionally, to interpret the black box typed prediction framework, two explainable artificial intelligence (XAI) frameworks, SHAP and LIME, are used to gauge the nature of the influence of the predictor variables, which serve several practical insights.

The Emerging Landscape of AI-Powered Leadership: Transforming Roles and Organizations

Objectives: To explore and synthesize how artificial intelligence (AI) is transforming leadership roles, organizational structures, and competencies required for effective leadership in the AI-driven era. The study aims to identify key trends, challenges, and opportunities in leadership development and organizational adaptation to AI technologies. Theoretical Framework: The study integrates insights from organizational behavior, leadership studies, and information systems, emphasizing the interplay between human and technological dynamics. The framework highlights the transition from hierarchical management to agile, collaborative approaches underpinned by AI-driven processes and human-AI partnerships. Method: This research employs a literature review methodology, analyzing and synthesizing diverse academic and industry sources to identify patterns, key findings, and emerging themes in the impact of AI on leadership and organizational structures. Results and Discussion: The review identifies a paradigm shift in leadership practices, marked by the rise of agile and collaborative approaches. AI has driven the need for leaders to adopt orchestration roles, engage in strategic thinking, and cultivate human-AI partnerships. Key competencies such as AI literacy, data fluency, and ethical decision-making are critical for navigating this transformation. The findings also highlight the necessity for adaptive organizational structures to optimize AI capabilities and foster human-AI synergy. Ethical considerations, including mitigating bias, ensuring data privacy, and developing responsible AI frameworks, are pivotal for sustainable adoption. Research Implications: The findings offer actionable insights for organizations, policymakers, and academia to redefine leadership training programs and develop adaptive organizational frameworks that integrate AI effectively. Addressing ethical challenges and fostering interdisciplinary collaboration are essential for achieving sustainable innovation and growth in the AI era. Originality/Value: This review provides a comprehensive synthesis of the transformative impact of AI on leadership and organizational structures. By addressing gaps in current research and emphasizing the need for ethical and adaptive frameworks, the study contributes valuable insights for practitioners and researchers aiming to leverage AI for enhanced leadership and organizational performance.

Data-Driven Civil Engineering: Applications of Artificial Intelligence, Machine Learning, and Deep Learning

Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) are a great advantage that is coming to civil engineering in ways that detail accuracy can be enhanced, many tasks automated, and predictive modeling improved. Across some of the significant subdomains, these technologies allow for eminent progress in structural health monitoring, geotechnical engineering, hydraulic systems, construction management. Currently, AI-powered models such as Artificial Neural Networks (ANNs), fuzzy logic, and evolution-based algorithms allow engineers to predict failure, optimize design, and better resource management of infrastructures. Yet, despite the potential, the adoption of AI, ML, and DL into civil engineering faces a host of challenges including data availability, computational complexity, model interpretability, integration with traditional systems, etc. High-quality, real-time data collection remains expensive and the resource-intensive nature of DL models limits their application to a large scale. In addition, the "black-box" nature of these models raises ethical and regulatory issues especially in decisions related to safety. Against this backdrop, this paper reviews current and potential applications of AI, ML, and DL in civil engineering within the framework of benefits and limitations of AI, ML, and DL, focusing on comparisons. Besides that, the paper outlines future directions regarding cloud computing, explainable AI, and regulatory frameworks. With all these changes within the scope of the discipline, AI-driven technologies will be major in safe, efficient, and sustainable infrastructure systems, provided that success is specifically dependent on addressing these key challenges.

AI-driven risk mitigation: Transforming project management in construction and infrastructure development

This study explores the transformative role of artificial intelligence (AI) in risk mitigation within construction and infrastructure development, addressing the growing complexities of modern project management. The purpose of the study was to evaluate how AI technologies, frameworks, and strategies can mitigate environmental, economic, and social risks while fostering sustainability and operational efficiency. Utilizing a multidisciplinary approach, the research integrates theoretical and practical perspectives to examine AI’s applications, ethical considerations, and future trends in project management. The study identifies predictive analytics, Internet of Things (IoT)-based monitoring, and blockchain technology as pivotal tools for enhancing decision-making, resource optimization, and transparency in project workflows. It underscores AI’s significant contributions to sustainability through climate-resilient design, energy-efficient systems, and environmentally compliant practices. Ethical and legal frameworks emerged as essential to ensuring the equitable and responsible deployment of AI, focusing on data privacy, accountability, and algorithmic fairness. Key findings demonstrate that AI-driven risk mitigation enhances project outcomes by reducing delays, minimizing costs, and fostering inclusivity. Applications in public health campaigns and social housing highlight AI’s capacity to address societal challenges while advancing sustainability goals. However, the study also identifies critical challenges, including organizational resistance, data quality limitations, and regulatory gaps, which hinder the full integration of AI technologies. The study concludes that AI is a transformative force in risk mitigation, offering innovative solutions to the multifaceted challenges of modern project management. To fully leverage its potential, the research recommends fostering interdisciplinary collaboration, developing adaptive ethical and legal frameworks, and advancing AI integration with emerging technologies like IoT and blockchain. Investments in education and training are also emphasized to bridge technical knowledge gaps and empower stakeholders.

Machine Learning And Artificial Intelligence in Diabetes Prediction And Management: A Comprehensive Review of Models

Diabetes mellitus is a chronic metabolic disorder with significant global prevalence and associated healthcare burdens, necessitating early detection and effective management strategies. The integration of Machine Learning (ML) and Artificial Intelligence (AI) has revolutionized diabetes care, offering innovative approaches to prediction, monitoring, and personalized management. This study conducted a systematic review of 82 high-quality peer-reviewed articles, following the PRISMA guidelines, to provide a comprehensive evaluation of ML and AI applications in diabetes prediction and management. The review highlights the widespread adoption of supervised learning models, such as Random Forest and Support Vector Machines (SVM), which consistently demonstrate high accuracy and reliability in predicting diabetes risk. Ensemble learning methods, particularly Gradient Boosting, emerged as superior techniques for predictive performance, while deep learning models, including Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), proved effective in analyzing unstructured data such as medical images and time-series glucose data. The integration of AI into wearable devices and mobile health applications has further enhanced real-time monitoring and glycemic control, bridging the gap between technological advancements and practical healthcare solutions. Despite these advancements, challenges such as data imbalance, limited external validation, and the need for explainable AI frameworks persist, underscoring the necessity for methodological rigor and standardization. This review provides critical insights into the current state, limitations, and opportunities of ML and AI in diabetes care, emphasizing their transformative potential in addressing this global health challenge.

Prediction of insulin requirements by explainable machine learning for individuals with type 1 diabetes.

Adverse events related to insulin therapy remain common in individuals with type 1 diabetes. Optimization of insulin dose can reduce the frequency of these events and help to prevent macrovascular and microvascular complications. The aim of the present study was to develop machine learning models to predict the total daily dose (TDD) of insulin on the basis of data available in routine clinical practice, to evaluate the performance of the models, and to interpret the relation between its predictions and features. This retrospective observational study conducted at a single center recruited individuals diagnosed with type 1 diabetes who visited Kobe University Hospital and used continuous glucose monitoring in combination with continuous subcutaneous insulin infusion between 1 April 2010 and 29 February 2024. We developed TDD prediction models based on machine learning and evaluated its performance on the basis of the mean absolute percentage error (MAPE). Explainable artificial intelligence frameworks were applied to the machine learning model to facilitate interpretability. A total of 110 individuals with type 1 diabetes was included in the study. The best-performing model, the Random Forest, achieved a MAPE of 19.8%. The most important feature of the model for prediction of the TDD of insulin was body weight, followed by waist circumference and carbohydrate intake. We here developed machine learning models that predict the TDD of insulin from clinical information. Such models could contribute to the treatment of many individuals undergoing insulin therapy, with further developments being warranted.

Revolutionizing Knowledge Management in Transportation Agencies: The Role of Generative AI and Scalable Frameworks

The increased interconnectedness of the globe has led to a fast-paced expansion of transportation systems and, consequently, to the creation of effective Knowledge Management Strategies. By simply put, the ways of decision making, operations management, and solving problems related to the complex infrastructure systems have all improved. Knowledge management is made easier by the use of Generative Artificial Intelligence (AI) which enables all the processes of data generation, data synthesis and even gives room for a real-time insight within a transport department. There is also the availability of Structure and Generative Theory which allows agencies to cope with needs that keep changing without losing the quality and the speed of the flow of the knowledge. In this sense, the present contribution focuses on the perspective of generative AI and structural frameworks in transportation agencies understanding knowledge management and its future prospects in terms of technology usefulness (Chen & Li, 2021). In these external environments, transportation agencies manage vast and complex datasets that change rapidly and are often dispersed. For instance, Generative AI has applications in natural language processing, predictive analytics and intelligent reporting, which assist agencies in solving hurdles posed by conventional knowledge management. When these AI systems are given, scalable frameworks guarantee that the knowledge management systems will address the challenges that come with increased complexity in operations. This research examines how generative artificial intelligence can be applied in various knowledge management situations, with particular focus on how it can be used to deal with mundane processes, enhance responses and help to mitigate problems in the field of transportation (Nguyen et al., 2023). The results underscore the possible benefits of using generative AI within efficient KM systems, such as lower costs, better accuracy, and improved accessibility to the users. On the other hand, the concerns such as data quality, costs of implementation, and other factors like the presence of easy to use interface for non-technical personnel, are also brought up by the authors. These perspectives will be useful for transportation agencies that seek to transform their KM activities. Overcoming these obstacles, generative AI and scalable frameworks can promote radical transformation and development of dynamic and sustainable knowledge systems that meet the needs of transport management in the case of Rahman and Smith, 2023.

Python-Based Hybrid Ai Models For Real-Time Grid Stability Analysis In Solar Energy Networks

The growing reliance on renewable energy, particularly solar power, presents significant challenges to grid stability due to the variability and intermittency of energy generation. Hybrid Artificial Intelligence (AI) models have emerged as a transformative solution, integrating multiple AI techniques to address these challenges effectively. This study systematically reviewed 130 peer-reviewed articles, adhering to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines to ensure a transparent and rigorous process. The review highlights how hybrid AI models, by combining neural networks, reinforcement learning, evolutionary algorithms, and advanced architectures like transformers, achieve superior performance in predictive modeling, fault detection, load balancing, and scalability. Predictive modeling for solar power variability saw up to a 30% improvement in mean absolute error (MAE), while fault detection systems exhibited enhanced precision and recall, diagnosing multiple simultaneous faults in real time. Hybrid AI models also excel in load balancing and demand forecasting, reducing energy wastage by 20% and enabling dynamic resource allocation. Furthermore, their scalability and adaptability make them ideal for large-scale, distributed energy networks, addressing the complexities of modern smart grids. Despite these advancements, persistent challenges such as real-time data integration, standardization, and deployment barriers remain. The study underscores the need for future research to address these limitations through innovations like federated learning and decentralized AI frameworks. This comprehensive review demonstrates the critical role of hybrid AI in advancing grid stability, optimizing renewable energy integration, and paving the way for sustainable, resilient energy infrastructures.

An open annotated dataset and baseline machine learning model for segmentation of vertebrae with metastatic bone lesions from CT.

Automatic analysis of pathologic vertebrae from computed tomography (CT) scans could significantly improve the diagnostic management of patients with metastatic spine disease. We provide the first publicly available annotated imaging dataset of cancerous CT spines to help develop artificial intelligence frameworks for automatic vertebrae segmentation and classification. This collection contains a dataset of 55 CT scans collected on patients with various types of primary cancers at two different institutions. In addition to raw images, data include manual segmentations and contours, vertebral level labeling, vertebral lesion-type classifications, and patient demographic details. Our automated segmentation model uses nnU-Net, a freely available open-source framework for deep learning in healthcare imaging, and is made publicly available. This data will facilitate the development and validation of models for predicting the mechanical response to loading and the resulting risk of fractures and spinal deformity.

Artificial intelligence and machine learning at various stages and scales of process systems engineering

AbstractWe review the utility and application of artificial intelligence (AI) and machine learning (ML) at various process scales in this work, from molecules and reactions to materials to processes, plants, and supply chains; furthermore, we highlight whether the application is at the design or operational stage of the process. In particular, we focus on the distinct representational frameworks employed at the various scales and the physics (equivariance, additivity, injectivity, connectivity, hierarchy, and heterogeneity) they capture. We also review AI techniques and frameworks important in process systems, including hybrid AI modelling, human‐AI collaborations, and generative AI techniques. In hybrid AI models, we emphasize the importance of hyperparameter tuning, especially in the case of physics‐informed regularization. We highlight the importance of studying human‐AI interactions, especially in the context of automation, and distinguish the features of human‐complements‐AI systems from those of AI‐complements‐human systems. Of particular importance in the AI‐complements‐human framework are model explanations, including rule‐based explanation, explanation‐by‐example, explanation‐by‐simplification, visualization, and feature relevance. Generative AI methods are becoming increasingly relevant in process systems engineering, especially in contexts that do not belong to ‘big data’, primarily due to the lack of high quality labelled data. We highlight the use of generative AI methods including generative adversarial networks, graph neural networks, and large language models/transformers along with non‐traditional process data (images, audio, and text).

Convergence and Innovation of Artificial Intelligence in Corporate Strategic Planning: Opportunities, Challenges and Future Research Directions

In the contemporary digital era, the swift evolution of Artificial Intelligence (AI) has not only revolutionized various sectors but has also become an indispensable asset in corporate strategic planning, decision-making processes, and operational efficiency. This paper delves into the multifaceted applications of AI across a myriad of industries, providing a comprehensive analysis of its integration into corporate strategy and its profound impact on business operations. This study adopts a comprehensive research methodology that merges qualitative case studies with quantitative data analysis to scrutinize the practical use of AI in strategic planning. It delves into the interdependent relationship between AI and conventional digital frameworks, such as Customer Relationship Management (CRM) and Enterprise Resource Planning (ERP) systems. The research aims to illuminate how the fusion of these technologies can substantially enhance corporate competencies and optimize operational workflows. The study's findings indicate that while AI has the potential to offer transformative solutions, its integration into strategic planning is not without challenges. Ethical considerations, legal ramifications, and societal implications must be carefully navigated. Additionally, the study reveals a notable void in existing literature concerning the enduring strategic ramifications of AI and its effects on corporate culture and talent management practices. This gap highlights the need for further exploration into how AI shapes the long-term strategic direction and internal dynamics within organizations.

An empirical study of AI techniques in mobile applications

The integration of artificial intelligence (AI) into mobile applications has significantly transformed various domains, enhancing user experiences and providing personalized services through advanced machine learning (ML) and deep learning (DL) technologies. AI-driven mobile apps typically refer to applications that leverage ML/DL technologies to perform key tasks such as image recognition and natural language processing. Despite existing research exploring how mobile apps exploit AI techniques, they have the following main limitations: (1) Most existing studies focus on DL-based apps, with limited research on ML-based apps. (2) Existing research typically focuses on investigating the apps and the technologies utilized in the apps, lacking user-level analysis. (3) The number of apps studied is limited, with only 1,000 to 2,000 ML/DL apps identified after filtering. To fill the gap, in this paper, we conducted the most extensive empirical study on AI applications, exploring on-device ML apps, on-device DL apps, and AI service-supported (cloud-based) apps. Our study encompasses 56,682 real-world AI applications, focusing on three crucial perspectives: (1) Application analysis, where we analyze the popularity of AI apps and investigate the update states of AI apps; (2) Framework and model analysis, where we analyze AI framework usage and AI model protection; (3) User analysis, where we examine user privacy protection and user review attitudes. Our study has strong implications for AI app developers, users, and AI R&D. On one hand, our findings highlight the growing trend of AI integration in mobile applications, demonstrating the widespread adoption of various AI frameworks and models. On the other hand, our findings emphasize the need for robust model protection to enhance app security. Additionally, our study highlights the importance of user privacy and presents user attitudes towards the AI technologies utilized in current AI apps. We provide our AI app dataset (currently the most extensive AI app dataset) as an open-source resource for future research on AI technologies utilized in mobile applications.

Green knowledge management: a bibliometric analysis, research trends and future directions

Purpose Green knowledge management (GKM) has become a critical factor for modern organizations, offering a multifaceted approach to enhancing business operations while promoting environmental sustainability. The purpose of this paper is to review the literature and explore how GKM helps companies reduce their ecological footprint by digitalizing processes and minimizing physical storage needs, thus aligning with growing expectations for corporate environmental responsibility. The necessity for conducting a GKM review is underscored by the increasing recognition of sustainability, environmental performance and as a critical component of organizational corporate success in the contemporary business landscape. Design/methodology/approach This study conducted a bibliometric analysis of 994 papers related to GKM from 2005 until August 2024. Findings Over the 20 years of research on GKM, this bibliometric analysis drew the interlinked relationships between main theme of KM in the context of sustainable practices, processes, social and related theories. This study identifies green innovation, digital transformation, green and lean supply chains, leadership and technology tools. Future research can explore themes such as green and lean processes, barriers and enablers, role of leadership and ambidexterity in green supply chain integration using different theoretical lenses. Research limitations/implications Although the findings of this study are important to GKM research, there are a number of limitations to this investigation. The research database used exclusively was the Web of Science and Scopus, which contained the indexed papers. Future investigations may draw from various databases, such as EBSCO and Google Scholar. Additionally, the use of topic modelling and other artificial intelligence frameworks could enhance research analysis. Subsequently, research papers may expand their scope to encompass conference papers, books and book chapters, in addition to articles. Practical implications This review gives a holistic understanding of GKM practices across various sectors which not only enhances organizational performance but also contributes to broader environmental goals. This study offers an understanding on GKM on holistic approach that combines total quality management (TQM), absorptive capacity and collaborative frameworks to foster innovation and sustainability. Originality/value It is evident from the review that research in GKM is evolving gaining momentum. This review provides a comprehensive understanding of the GKM literature, from its earliest forms of basic concepts aligned with the sustainability and progressing more into the concepts of GKM adoption for green innovation by highlighting multiple theories, factors and gaps in the studies.

Exploring the Artificial Intelligence and Its Impact in Pharmaceutical Sciences: Insights Toward the Horizons Where Technology Meets Tradition.

The technological revolutions in computers and the advancement of high-throughput screening technologies have driven the application of artificial intelligence (AI) for faster discovery of drug molecules with more efficiency, and cost-friendly finding of hit or lead molecules. The ability of software and network frameworks to interpret molecular structures' representations and establish relationships/correlations has enabled various research teams to develop numerous AI platforms for identifying new lead molecules or discovering new targets for already established drug molecules. The prediction of biological activity, ADME properties, and toxicity parameters in early stages have reduced the chances of failure and associated costs in later clinical stages, which was observed at a high rate in the tedious, expensive, and laborious drug discovery process. This review focuses on the different AI and machine learning (ML) techniques with their applications mainly focused on the pharmaceutical industry. The applications of AI frameworks in the identification of molecular target, hit identification/hit-to-lead optimization, analyzing drug-receptor interactions, drug repurposing, polypharmacology, synthetic accessibility, clinical trial design, and pharmaceutical developments are discussed in detail. We have also compiled the details of various startups in AI in this field. This review will provide a comprehensive analysis and outline various state-of-the-art AI/ML techniques to the readers with their framework applications. This review also highlights the challenges in this field, which need to be addressed for further success in pharmaceutical applications.

Unveiling Uncertainty: Exploring the Potential of Neutrosophic Statistics for Artificial Intelligence

Artificial intelligence (AI) has revolutionized various fields, but its effectiveness can hampered by limitations in handling uncertainty inherent in real-world data. Traditional statistical methods often rely on binary logic (true/false) and may not adequately capture the nuances of uncertainty present in real-world data. This paper explores neutrosophic statistics (NS) as a novel approach to address uncertainty in AI applications. NS, introduced by Smarandache (1998), generalizes classical set theory by incorporating three truth-values: truth (T), indeterminacy (I), and falsity (F). This allows for a more nuanced representation of uncertainty compared to traditional binary methods. We discuss the limitations of classical statistics and how NS can overcome them. The paper explores the potential of NS for AI in areas like machine learning, data fusion, and Explainable AI (XAI). We propose research questions and methodology to investigate the effectiveness of NS integration with AI frameworks. The expected outcome is to contribute to the development of more robust and reliable AI systems by demonstrating the effectiveness of NS in handling uncertainty. Finally, we discuss future research directions to further explore the potential of NS for AI.

1.5 million materials narratives generated by chatbots

The advent of artificial intelligence (AI) has enabled a comprehensive exploration of materials for various applications. However, AI models often prioritize frequently encountered material examples in the scientific literature, limiting the selection of suitable candidates based on inherent physical and chemical attributes. To address this imbalance, we generated a dataset consisting of 1,453,493 natural language-material narratives from OQMD, Materials Project, JARVIS, and AFLOW2 databases based on ab initio calculation results that are more evenly distributed across the periodic table. The generated text narratives were then scored by both human experts and GPT-4, based on three rubrics: technical accuracy, language and structure, and relevance and depth of content, showing similar scores but with human-scored depth of content being the most lagging. The integration of multimodal data sources and large language models holds immense potential for AI frameworks to aid the exploration and discovery of solid-state materials for specific applications of interest.

Systematic Review of Real-Time Analytics and Artificial Intelligence Frameworks for Financial Fraud Detection

Systematic Review of Real-Time Analytics and Artificial Intelligence Frameworks for Financial Fraud Detection