With the exponential growth of digital content, unstructured data has become a dominant force in today's data landscape, accounting for nearly 80% of enterprise information. Traditional relational database management systems (RDBMS), governed by rigid schemas and ACID compliance, were not originally designed to handle the dynamic and heterogeneous nature of unstructured data such as images, videos, logs, emails, and social media feeds. In contrast, NoSQL databases have emerged as a viable alternative, offering flexible schema designs, (...) high scalability, and optimized performance for various unstructured and semi-structured data workloads. (shrink)

Big data ethics involves adherence to the concepts of right and wrong behavior regarding data, especially personal data. Big Data ethics focuses on structured or unstructured data collectors and disseminators. Big data ethics is supported, at EU level, by extensive documentation, which seeks to find concrete solutions to maximize the value of big data without sacrificing fundamental human rights. The European Data Protection Supervisor (EDPS) supports the right to privacy and the (...) right to the protection of personal data in the respect of human dignity. (shrink)

Big Data ethics involves adherence to the concepts of right and wrong behavior regarding data, especially personal data. Big Data ethics focuses on structured or unstructured data collectors and disseminators. Big Data ethics is supported, at EU level, by extensive documentation, which seeks to find concrete solutions to maximize the value of Big Data without sacrificing fundamental human rights. The European Data Protection Supervisor (EDPS) supports the right to privacy and the (...) right to the protection of personal data in the respect of human dignity. DOI: 10.13140/RG.2.2.30867.43041. (shrink)

This paper addresses the critical role of automated data quality monitoring in Machine Learning Operations (MLOps) pipelines. As organizations increasingly rely on machine learning models for decision-making, ensuring the quality and reliability of input data becomes paramount. The paper explores various types of data quality issues, including missing values, outliers, data drift, and integrity violations, and their potential impact on model performance. It then examines automated detection methods, such as statistical analysis, machine learning-based anomaly detection, rule-based (...) systems, and data profiling. The integration of data quality monitoring into different stages of the MLOps pipeline is discussed, emphasizing continuous monitoring at data ingestion, pre-training validation, post- deployment drift detection, and feedback loops for model retraining. The paper also addresses key challenges in implementing automated data quality monitoring, including balancing precision and recall in anomaly detection, handling high-dimensional and unstructured data, managing false positives and alert fatigue, and adapting to evolving data distributions. By providing a comprehensive framework for automating data quality monitoring in MLOps pipelines, this paper aims to equip practitioners with the knowledge and strategies necessary to enhance the reliability and performance of machine learning systems in production environments. (shrink)

This paper investigates how large language models (LLMs) can be applied to publicly available financial data to generate automated financial summaries and provide actionable recommendations for investors. We demonstrate how LLMs can process both structured financial data (balance sheets, income statements, stock prices) and unstructured text (earnings calls, management commentary) to derive insights, predict trends, and automate financial reporting. By focusing on a specific publicly traded company, this research outlines the methodology for leveraging LLMs to analyze company (...) performance and generate investor-focused summaries and recommendations. (shrink)

Abstract: Sorting algorithms have always been the primary focus of data organization and have been the same since they were discovered. They play a vital role in reducing work and maximizing efficiency as well as accuracy. This paper aims at comparing and examining traditional sorting algorithms, usually applied by programmers as opposed to AI-based methods like Decision Trees and Neural Networks. The performance of these sorting techniques would be evaluated on time over such data executions considering memory requirement (...) and accuracy on different datasets. Most traditional methods could sort structured data even of a smaller dataset, but they cannot work well when it comes to scaling and unstructured data [4]. AI-based algorithms, however, seem to perform better in terms of accuracy, which is superior in its implementation to complex and unstructured datasets, at a great cost of and accuracy; thus, making possible insights into the practical implementations. These findings show the potentials of hybrid methods regarding modern data sorting application challenges and pave the way for further future optimization and real-world implementation [8]. (shrink)

The fields of Data Science and Artificial Intelligence (AI) have evolved rapidly over the past few decades, often intersecting in ways that have transformed industries, enhanced decision-making processes, and introduced new challenges. Data Science focuses on extracting knowledge and insights from structured and unstructured data, while AI aims to simulate human intelligence processes, including learning, reasoning, and problemsolving. This paper explores the synergies between these two domains, highlighting the key advancements, real-world applications, and challenges that arise (...) from their integration. It also examines how their intersection has led to innovations in machine learning, big data analytics, and automation. (shrink)

As organizations increasingly rely on vast volumes of structured and unstructured data, effective data governance becomes a top priority. Metadata—the data about data—plays a foundational role in ensuring transparency, compliance, accessibility, and quality within data ecosystems. However, traditional metadata management approaches often fall short in scalability, accuracy, and automation. This paper explores the transformative role of artificial intelligence (AI) in metadata management, focusing on how AI can automate metadata creation, enforce governance policies, and support (...) regulatory compliance. By analyzing current tools, frameworks, and implementation strategies, we present a forward-looking vision of AI-driven metadata management as a cornerstone of modern data governance practices. (shrink)

Customer Relationship Management has become one of the most valuable tools in business, with the help of predictive analyzes organizations can identify their customer’s needs and improve business results. This research focuses on using of predictive analytics in Salesforce, the prominent CRM system that helps to gain insights from large volumes of data with the help of deep learning approaches. Using current advanced models like Recurrent Neural Networks and the transformer connections, companies can gain deep insights into such patterns (...) and trends as customer buying behavior, sales anticipation, and customer lifetime value. It is not only effective in improving decision making but also useful in the customization of the marketing strategy involving the delivery of messages that the customer might be interested. Integrating deep learning into Salesforce’s analytics is a revolution in the CRM system function. Former approaches in data analysis could not deal with unstructured data or analyses customer paths, as mentioned before. However, thanks to deep learning, large-scale customer data and aspects of previously hidden patterns have become changer for business interpretation. This research demonstrates the potential of using deep learning for predictive analytics to inform customer-oriented initiatives and increase the proportion of customers amongst leads and stability of client base. Thus, by focusing on Salesforce as an example, this study gives a clear vision of how artificial intelligence revolutionizes the approach to CRM, serves as the reference point for data-driven businesses in the digital environment. (shrink)

Detecting quality in large unstructured datasets requires capacities far beyond the limits of human perception and communicability and, as a result, there is an emerging trend towards increasingly complex analytic solutions in data science to cope with this problem. This new trend towards analytic complexity represents a severe challenge for the principle of parsimony (Occam’s razor) in science. This review article combines insight from various domains such as physics, computational science, data engineering, and cognitive science to review (...) the specific properties of big data. Problems for detecting data quality without losing the principle of parsimony are then highlighted on the basis of specific examples. Computational building block approaches for data clustering can help to deal with large unstructured datasets in minimized computation time, and meaning can be extracted rapidly from large sets of unstructured image or video data parsimoniously through relatively simple unsupervised machine learning algorithms. Why we still massively lack in expertise for exploiting big data wisely to extract relevant information for specific tasks, recognize patterns and generate new information, or simply store and further process large amounts of sensor data is then reviewed, and examples illustrating why we need subjective views and pragmatic methods to analyze big data contents are brought forward. The review concludes on how cultural differences between East and West are likely to affect the course of big data analytics, and the development of increasingly autonomous artificial intelligence (AI) aimed at coping with the big data deluge in the near future. Keywords: big data; non-dimensionality; applied data science; paradigm shift; artificial intelligence; principle of parsimony (Occam’s razor). (shrink)

According to the PubMed resource from the U.S. National Library of Medicine, over 750,000 scientific articles have been published in the ~5000 biomedical journals worldwide in the year 2007 alone. The vast majority of these publications include results from hypothesis-driven experimentation in overlapping biomedical research domains. Unfortunately, the sheer volume of information being generated by the biomedical research enterprise has made it virtually impossible for investigators to stay aware of the latest findings in their domain of interest, let alone to (...) be able to assimilate and mine data from related investigations for purposes of meta-analysis. While computers have the potential for assisting investigators in the extraction, management and analysis of these data, information contained in the traditional journal publication is still largely unstructured, free-text descriptions of study design, experimental application and results interpretation, making it difficult for computers to gain access to the content of what is being conveyed without significant manual intervention. In order to circumvent these roadblocks and make the most of the output from the biomedical research enterprise, a variety of related standards in knowledge representation are being developed, proposed and adopted in the biomedical community. In this chapter, we will explore the current status of efforts to develop minimum information standards for the representation of a biomedical experiment, ontologies composed of shared vocabularies assembled into subsumption hierarchical structures, and extensible relational data models that link the information components together in a machine-readable and human-useable framework for data mining purposes. (shrink)

The integration of information resources in the life sciences is one of the most challenging problems facing bioinformatics today. We describe how Language and Computing nv, originally a developer of ontology-based natural language understanding systems for the healthcare domain, is developing a framework for the integration of structured data with unstructured information contained in natural language texts. L&C’s LinkSuite™ combines the flexibility of a modular software architecture with an ontology based on rigorous philosophical and logical principles that is (...) designed to comprehend the basic formal relationships that structure both reality and the ways humans perceive and communicate about reality. (shrink)

In the era of big data and distributed ecosystems, understanding the origin, flow, and transformation of data across complex infrastructures is critical for ensuring transparency, accountability, and informed decision-making. As data-driven enterprises increasingly rely on hybrid cloud architectures, data lakes, and real-time pipelines, the complexity of tracking data movement and transformations grows exponentially. Traditional data lineage solutions, often based on static metadata extraction or rule-based approaches, are insufficient in dynamically evolving environments and fail to (...) provide granular, context-aware insights. This research introduces an AI-augmented, cognitive graph-based framework for autonomous data lineage, designed to enhance data traceability in large-scale and heterogeneous data ecosystems. The framework leverages cutting-edge machine learning (ML), natural language processing (NLP), and graph-based reasoning techniques to enable intelligent discovery, semantic interpretation, and continuous monitoring of data assets throughout their lifecycle. The core of the solution lies in the construction of a dynamic cognitive graph that represents relationships between datasets, processes, systems, and users, enriched with contextual annotations and temporal dimensions. Our architecture incorporates self-learning mechanisms, enabling adaptive lineage discovery and automated anomaly detection. By applying reinforcement learning and stream analytics, the framework not only maps data flows in real time but also evolves with system changes, schema variations, and business logic updates. It provides both forward and backward traceability, supports impact analysis, and enhances compliance auditing capabilities. Furthermore, our system is capable of processing both structured and unstructured metadata, employing advanced NLP models to extract implicit lineage information from data dictionaries, SQL queries, and documentation. The result is a holistic, intelligent, and scalable data lineage solution that reduces manual intervention, mitigates operational risks, and supports regulatory compliance frameworks such as GDPR, HIPAA, and SOX. Experimental evaluations conducted in hybrid cloud environments using tools such as Apache Kafka, Neo4j, Spark, and Python-based ML libraries demonstrate a significant improvement in lineage coverage, anomaly detection accuracy, and system scalability. Compared to conventional lineage tools, our AI-augmented framework delivers a 30% increase in traceability precision and a 40% reduction in manual effort required for lineage tracking and governance. This research lays the foundation for a new paradigm in data governance, where AI not only enhances observability but enables autonomous cognition within data infrastructure. The framework is poised to play a critical role in enabling data democratization, operational agility, and enterprise-wide data literacy. (shrink)

This research paper presents an in-depth analysis of advanced artificial intelligence (AI) algorithms designed to automate data preprocessing in the healthcare sector. The automation of data preprocessing is crucial due to the overwhelming volume, diversity, and complexity of healthcare data, which includes medical records, diagnostic imaging, sensor data from medical devices, genomic data, and other heterogeneous sources. These datasets often exhibit various inconsistencies such as missing values, noise, outliers, and redundant or irrelevant information that necessitate (...) extensive preprocessing before being analyzed by machine learning or statistical models. Traditional data preprocessing methods, which are largely manual and time-consuming, can result in errors that affect the quality of the data and, subsequently, the performance of predictive and diagnostic models. Thus, there is a growing need for intelligent, automated systems that can enhance data quality, streamline the preprocessing pipeline, and reduce the time and effort required by healthcare professionals and data scientists. The study begins by outlining the specific challenges associated with healthcare data, including its high dimensionality, incompleteness, and variability across different data sources and formats. These issues not only complicate the preprocessing stage but also hinder the ability to develop robust models capable of making accurate predictions or diagnoses. The paper then explores how AI algorithms—particularly those based on machine learning (ML), deep learning (DL), and reinforcement learning (RL)—can automate key data preprocessing tasks such as data cleaning, feature selection, normalization, and transformation. These algorithms are designed to identify patterns in data, detect anomalies, and automatically apply corrections or transformations based on predefined rules or learned behaviors, thereby minimizing human intervention. The paper also delves into specific AI techniques that have been successfully applied to healthcare data preprocessing. For instance, supervised learning models, such as decision trees and support vector machines (SVMs), have been utilized to perform imputation of missing data by predicting the most likely values based on the available information. Similarly, unsupervised learning methods, such as clustering algorithms, have been employed to group similar data points and remove outliers that could distort the performance of analytical models. Moreover, deep learning techniques, particularly autoencoders and generative adversarial networks (GANs), have demonstrated remarkable effectiveness in transforming high-dimensional medical data into lower-dimensional representations, enabling more efficient and accurate model training. In addition to the discussion of these algorithms, the paper emphasizes the role of natural language processing (NLP) in automating the preprocessing of unstructured healthcare data, such as clinical notes and diagnostic reports. NLP techniques, including named entity recognition (NER) and word embeddings, are instrumental in extracting relevant information from unstructured text, standardizing terminologies, and converting textual data into structured formats suitable for downstream analysis. Furthermore, AI-based feature selection algorithms are explored, which aim to identify the most relevant features in the dataset, thereby reducing its dimensionality and improving the computational efficiency of predictive models. The study goes on to highlight the significant reduction in processing time achieved by AIdriven automation of preprocessing tasks. In conventional settings, data preprocessing accounts for a substantial portion of the time spent on building healthcare models, often requiring expert intervention to manually inspect and clean the data. By employing AI algorithms, not only can this process be expedited, but the accuracy of the resulting data is also enhanced, which translates into better model performance. The paper provides a detailed comparative analysis of manual preprocessing methods versus automated AI-driven approaches, demonstrating the substantial time savings and improvements in data quality brought about by automation. (shrink)

The advent of generative artificial intelligence (AI) in the financial technology (FinTech) sector has created unprecedented opportunities for automating and enhancing financial advisory systems. This research focuses on the application of generative AI to develop automated financial advisory platforms, integrating natural language processing (NLP) and reinforcement learning (RL) for the formulation of personalized investment strategies. Traditional financial advisory models, often characterized by manual processes, human bias, and limited scalability, are increasingly unable to meet the demands of a fast-paced and diverse (...) investment landscape. In response, AI-driven systems present a transformative approach, leveraging the power of generative models to process vast amounts of data and provide real-time, tailored financial recommendations to both retail and institutional investors. This study delves into the technical mechanisms underpinning the integration of generative models with NLP and RL frameworks. Generative models, including variational autoencoders (VAEs) and generative adversarial networks (GANs), play a critical role in simulating complex financial scenarios and generating investment strategies that reflect dynamic market conditions. By synthesizing vast amounts of historical market data, these models create high-dimensional representations of financial environments, which are then used to train reinforcement learning agents. The RL agents learn optimal investment strategies through continuous interaction with these simulated environments, dynamically adjusting to new market information and user preferences. This ability to simulate and optimize investment decisions allows for more sophisticated, personalized strategies, as compared to conventional rule-based systems. Natural language processing enhances the system by enabling it to process unstructured data from various sources, including financial news, reports, and social media, which can significantly impact market trends. NLP models, particularly transformer-based architectures like BERT and GPT, are employed to extract, interpret, and summarize relevant textual information in real-time, feeding it into the generative and RL models. This integration allows the financial advisory system to understand and respond to both quantitative and qualitative factors affecting financial markets. Moreover, the NLP component supports direct interaction between the AI-driven system and users, facilitating personalized communication and user-specific strategy recommendations. This two-way communication is pivotal in enhancing customer engagement, as users can input preferences, risk tolerance, and financial goals, which the system continuously refines and incorporates into its investment strategy formulation. Reinforcement learning plays a pivotal role in the adaptive learning process, allowing the system to improve its decision-making over time by receiving feedback from the environment, such as market performance and user satisfaction. Specifically, model-free RL approaches like Q-learning and policy gradient methods are applied to optimize investment strategies. These approaches enable the system to evaluate multiple potential actions in real-time and select those with the highest expected return, given the current market state and individual user profile. Over time, the RL agent learns to maximize cumulative returns by balancing exploration of new strategies with the exploitation of known profitable actions. By leveraging RL in tandem with generative models, the system can autonomously adjust its strategy in response to changing market conditions and user requirements, thereby delivering a highly customized investment plan that evolves with the investor’s financial landscape. The potential of these AI-driven advisory systems lies not only in their technical sophistication but also in their ability to democratize financial planning. Traditionally, high-quality financial advisory services have been accessible primarily to affluent individuals or large institutions due to the high cost of personalized financial advice. By automating the advisory process through AI, these systems can provide personalized financial planning at scale, making high-quality investment strategies accessible to a broader range of users, including those with limited financial literacy or smaller investment portfolios. This democratization of financial services is particularly significant in the context of retail investors, who can now access sophisticated financial insights and recommendations that were previously reserved for institutional clients. Furthermore, this paper explores the broader implications of AI-driven financial advisory systems on investor behavior and decision-making. By providing real-time, data-driven insights and personalized investment strategies, these systems have the potential to mitigate common cognitive biases in financial decision-making, such as overconfidence, loss aversion, and herd behavior. Through continuous learning and adaptation, AI-driven systems can guide users towards more rational, objective investment decisions, potentially improving overall financial outcomes for both retail and institutional investors. However, the paper also addresses the challenges associated with the deployment of AI in financial advisory systems, including issues of data privacy, algorithmic transparency, and the need for robust regulatory frameworks to ensure the ethical and responsible use of AI in financial decision-making. (shrink)

This works summarizes the recent developments in GPT Models applicable to Financial Institutions. Comparative analysis highlights the strengths of proprietary models like BloombergGPT for domain-specific tasks and open-source alternatives such as FinGPT for flexibility and cost-effectiveness. The study also emphasizes the role of AI-powered platforms like AlphaSense in managing unstructured data for market intelligence. Proposed future work includes exploring alternative attention mechanisms, integrating multi-modal capabilities, and enhancing model interpretability to address the challenges of computational complexity and domain adaptation. (...) The results demonstrate that GPT-based models significantly advance the capabilities of financial institutions to analyze large datasets, identify trends, and support data-driven decision-making. This paper contributes to the growing body of research on financial AI by proposing a scalable and effective approach to deploying LLMs in finance. This paper presents a comprehensive review of framework and architecture for leveraging Generative Pre-trained Transformers (GPT) in financial analysis and decision-making. Building upon the advancements in transformer-based models, the proposed approach integrates multi-layer self-attention mechanisms, fine-tuning on domain-specific data, and reinforcement learning with human feedback to enhance natural language understanding and generation tasks. Key contributions include an adapted GPT model with improved attention mechanisms and parameter scaling to handle financial texts effectively. The methodology involves a multi-phase approach encompassing data collection, pre-training, and fine-tuning, with performance evaluated using metrics such as perplexity, accuracy, and F1 score. (shrink)

Intelligent Document Processing (IDP) has revolutionized the way businesses manage unstructured data, especially in sectors such as legal and insurance. These industries, which handle vast amounts of documents daily, often struggle with slow, error-prone manual processes. Pega's AI-powered IDP solutions address these challenges by automating document processing workflows, enabling faster data extraction and analysis. Leveraging technologies like machine learning, optical character recognition (OCR), and natural language processing (NLP), Pega's IDP significantly enhances operational efficiency and reduces costs associated (...) with manual document handling. This article explores how Pega's AI-powered IDP has transformed document management in the legal and insurance sectors. By automating the extraction and validation of data from unstructured documents, Pega’s solutions reduce the time spent on manual data entry and improve accuracy. The integration of AI ensures that documents are processed swiftly and accurately, driving better decision-making and improving customer service. The paper also examines real-world examples of businesses that have adopted Pega’s IDP, focusing on the operational benefits, challenges, and limitations. With a focus on speed, efficiency, and accuracy, this article demonstrates how Pega’s IDP is setting a new standard for document processing in data-intensive industries. (shrink)

Management faces replacement by automated processes. Workflow automation in the information processing sectors of the economy is changing the way information and knowledge workers do their jobs. I consider the changing nature of the information supply chain from the creation of knowledge in firms to the supply of information to consumers. The changing nature of data and the development of data science and machine learning methods that enable the analysis of unstructured data have meant that what (...) was once viewed as tacit knowledge is now just a problem in information processing. The rise of workflow automation in the IT industries and the emergence of the reproducible research movement in the sciences is leading to increased automation of the production of information in both industry and academia and this has profoundly changed the nature of the information supply chain. (shrink)

Abstract: Idea mining is an interesting field in the area of information retrieval and it is increasingly becoming important asset for decision makers. Huge volumes of high quality data from various sources such as scanners, mobile phones, loyalty cards, the web, and social media platforms presents enormous opportunity for organization to achieve success in their businesses. It is possible to achieve this by properly analysing data to reveal feature patterns; hence decision makers can capitalize upon the resulting ideas (...) from wealth of information available. Idea mining helps managers to use their time efficiently by grasping ideas from a huge amount of unstructured data with text mining, instead of reading through page by page, it also helps to avoid the danger of overlooking data with useful ideas. The main objective of this survey article is to provide assessment on a number of successful text mining and text classification tools combined with idea mining measures to extract the idea from texts. (shrink)

Foreign language learning problems appears especially in departments of foreign language as an another problem. It has some cognitive reason but except this reason, if suitable teaching techniques couldn’t apply in education. These will cause some problems. In searching of solution for these problems lecturers and departments should take responsibility. Using some teaching methods are important in German language teaching as a secondary or third foreign language teaching. These are necessary for useful learning. In this study, it was aimed, that (...) to offer linguistic and pedagogical solution for foreign language learning problems of students of preparatory class. In this descriptive research was used unstructured observation and documentary review methods from qualitative research. -/- . (shrink)

Almost any mainstream reading about the nature of Kant's 'content of cognition' in both non-conceptualist and conceptualist camps agree that 'singular representations' (sensible intuitions) are, at least in some weak sense, objectdependent because they supervene on a manifold of sensations that are given through the disposition of our sensibility and parallel thus the real and physical components of the world (cf. McDowell 1996, Allison 1983, Ginsborg 2008, Allais 2009). The relevant class of sensible intuitions should refer, as they argue, only (...) to empirical and not to pure ones. Kant's transcendental argument creates, however, no implications as to the metaphysics of properties. Neither does he consider the world to be consisted of sensory content simpliciter nor do sensations refer to objects per se. Instead, he provides an all-encompassing pattern for sensible intuitions of any kind through the representation of space as a cognitive map in that an object can be represented if spatial properties are attributed to it. Sensible intuitions of any kind could be referring, that is to say, and can be conceptualized if they are spatial. Consequently and unlike the contemporary philosophy of perception, Kant contrasts sensible intuitions and pure concepts of understanding 'altogether' against one single experience, from which the justification of objects should proceed. In other words, what stands at the core of exposing the content of cognition concerns not the phenomenological status and actuality of sensible intuitions in the sense of 'bits of experiential intake', but the logical structure of possibility of sensible intuitions of any (external or internal) causal background. The aim of this discussion is to clarify that Kant's transcendental idealism mutatis mutandis reviews multiple and diverse kinds of 'data of mind' and cognition in the background, without reducing the content of cognition to metaphysically determined referents as 'matters of fact' and to pure concepts of understanding. Sensible intuitions in the sense of content of cognition vary, most importantly, from unstructured and unjustifiable data of mind including (physical) contents of perception and stuff of mind and also from reflective and intellectual intuitions that are the cognates of the structured complex singular representations. Put it in simple terms, Kant advocates the thesis that 'facts' are objects for concepts if they are already well-founded trough the cognitive map of the representation of space. -/- . (shrink)

The present article is a primary introduction to the semi-structured interviewing method SMILE_PH, an acronym for Sense-Making Interviews Looking at Elements of Philosophical Health. Beyond grounding this new methodology theoretically (a work that is started here but will in the future necessitate several developments), the main motivation here is pragmatic: to provide the recent philosophical health movement with a testable method and show that philosophically-oriented interviews are possible in a manner that can be reproduced, compared, tested and used systematically with (...) a population that has received no training in philosophy. The SMILE_PH approach was conceived by the author during an ethically approved pilot study focused on the philosophy of life of persons living with spinal cord injury (SCI), with the intention of rectifying the epistemic obstacles generated by rationalist, Socratic or unstructured ways of in-depth interviewing. The six-step structure of the method is also inspired by hundreds of individual dialogue sessions with philosophical counselees, led by the author between 2018 and 2022: the SMILE_PH method progressively gathers phenomenological data about 1 – our bodily sense, 2 – our sense of self, 3 – our sense of belonging, 4 – our sense of the possible, 5 – our sense of purpose and 6 – our philosophical sense. (shrink)

Artificial intelligence (AI), deep learning, machine learning and neural networks represent extremely exciting and powerful machine learning-based techniques used to solve many real-world problems. Artificial intelligence is the branch of computer sciences that emphasizes the development of intelligent machines, thinking and working like humans. For example, recognition, problem-solving, learning, visual perception, decision-making and planning. Deep learning is a subset of machine learning in artificial intelligence that has networks capable of learning unsupervised from data that is unstructured or unlabeled. (...) Deep learning is a technique used to generate face detection and recognize it for real or fake by using profile images and determine the differences between them. In this study, we used deep learning techniques to generate models for Real and Fake face detection. The goal is determining a suitable way to detect real and fake faces. The model was designed and implemented, including both Dataset of images: Real and Fake faces detection through the use of Deep learning algorithms based on neural networks. We have trained dataset which consists of 9,000 images for total in 150 epochs, and got the ResNet50 model to be the best model of network architectures used with 100% training accuracy, 99.18% validation accuracy, training loss 0.0003, validation loss 0.0265, and testing accuracy 99%. (shrink)

Large language models (LLMs) often appear to vindicate a radical empiricist picture: train on vast corpora of experience-like text, and capacities emerge without explicit symbolic rules. Yet contemporary machine learning research repeatedly emphasizes that what is learned, how quickly it is learned, and how well it generalizes depend crucially on prior constraints: architectural structure, training objectives, optimization dynamics, and representational bottlenecks. These constraints constitute inductive biases in a precise, technical sense. This paper develops a philosophical argument that uses LLMs as (...) a case study to reassess the classical tabula rasa thesis associated with Locke and its descendants. I defend two claims. First, even the most ``data-driven'' generative models are saturated with structural priors that make learning possible at scale; thus, their success cannot be straightforwardly read as a triumph of unstructured empiricism. Second, once this is appreciated, the rhetorical appeal of a ``blank slate'' conception of the infant mind weakens further: if artificial systems trained on orders of magnitude more linguistic input than any child still require rich inductive biases, it is implausible that human cognition begins wholly unstructured. I then show how contemporary debates about whether LLMs ``understand'' language recapitulate the older dispute about whether experience alone can generate semantics and conceptual structure. The upshot is a more balanced, non-caricatured empiricism: experience matters, but explanation must explicitly account for the learning system's prior structure. (shrink)

In today’s digital landscape, the exponential growth of unstructured and semi-structured data has led to increasing challenges in data management and retrieval. Metadata—descriptive data that provides context to content— plays a crucial role in mitigating this complexity. However, traditional metadata systems are often fragmented, manually curated, and inconsistent, leading to inefficiencies across data workflows. Artificial Intelligence (AI) introduces a transformative approach to metadata optimization by automating generation, improving accuracy, and enabling intelligent context understanding. This paper (...) explores how AI technologies such as machine learning (ML), natural language processing (NLP), and computer vision are reengineering metadata systems across industries. We present a review of current literature, a comparative table of traditional versus AI-powered methods, a research methodology for implementation, and a conceptual model of AI in the metadata lifecycle. The study demonstrates that AI not only enhances metadata quality and usability but also empowers organizations to derive deeper insights and ensure data governance at scale. (shrink)

Abstract: The field of life extension is full of ideas but they are unstructured. Here we suggest a comprehensive strategy for reaching personal immortality based on the idea of multilevel defense, where the next life-preserving plan is implemented if the previous one fails, but all plans need to be prepared simultaneously in advance. The first plan, plan A, is the surviving until advanced AI creation via fighting aging and other causes of death and extending one’s life. Plan B is (...) cryonics, which starts if plan A fails, and assumes cryopreservation of the brain until technical capabilities to return it to life appear. Plan C is digital immortality in the sense of collecting data about the person now so future AI will be able to recreate a model of a person. Plan D is the hope based on some unlikely scenarios of infinite survival, like so-called “quantum immortality”. All these plans have personal and social perspective. The personal aspect means efforts of the increasing chances of personal survival via taking care about one’s own health, signing cryocontract or collecting digital immortality data. The social aspect means the participation in collective work towards creation and increase of the availability of life extension technologies, which includes funding scientific research, promotion of life extension value and direct performing of research and implementation, as well as preventing global catastrophic risk. All plans converge at the end, as their result is the indefinite survival as an uploaded mind inside an ecosystem, created by a superintelligent AI. (shrink)

In pedestrian detection, occlusions are typically treated as an unstructured source of noise and explicit models have lagged behind those for object appearance, which will result in degradation of detection performance. In this paper, a hierarchical co-occurrence model is proposed to enhance the semantic representation of a pedestrian. In our proposed hierarchical model, a latent SVM structure is employed to model the spatial co-occurrence relations among the parent–child pairs of nodes as hidden variables for handling the partial occlusions. Moreover, (...) the visibility statuses of the pedestrian can be generated by learning co-occurrence relations from the positive training data with large numbers of synthetically occluded instances. Finally, based on the proposed hierarchical co-occurrence model, a pedestrian detection algorithm is implemented to incorporate visibility statuses by means of a Random Forest ensemble. The experimental results on three public datasets demonstrate the log-average miss rate of the proposed algorithm has 5% improvement for pedestrians with partial occlusions compared with the state-of-the-arts. (shrink)

A safe and secure environment is an essential part of the early childhood development of any child. This study aims to investigate the safety and protection practices of early childhood centers in the Anambra state, Nigeria, and to determine if any improvements can be made to them. This study analyzed data collected from 60 Early Childhood Care Centers (ECCE Centers) and 60 Pre-Primary Schools (Preprimary School) in Anambra State using the Evaluation of ECCE Implementation Kit (KEIEP), direct observation, and (...) unstructured interviews, all of which were conducted using the evaluation tool. Using descriptive statistics, chi-squares, and student t-statistics, the data were analyzed. According to the results of this study, 28.0% of the private ECCE centers were in compliance with the requirements for enough space for children to play, fenced facilities, a standard first aid box, a splint bandage and an antiseptic. It is important to note that the majority of public early childhood education centers (62.0%) comply with the requirements for fencing, a first aid box, splint bandages, cotton wool, antiseptic, scissors, methylated spirit, liniment, analgesics, and thermometers, whereas 31% of those centers do not have the remaining seven practices. A conclusion of the study was that both private and public ECCE centers in Anambra State comply with the safety and protection provisions and practices in place. (shrink)

With advanced NLP, AI has transformed how software specifications and validation are made. Ambiguity, inconsistency, and inefficiency are the enemies of most traditional methods, causing delays and unneeded project expenses. These issues are resolved in AI by automatically extracting, validating, prioritizing, and classifying requirements from documents. NLP helps AI understand unstructured texts to simplify communication, avoid errors, and match requirements with business goals and regulatory standards. It further allows organizations to predict changes and optimize their resources. Integrating AI in (...) DevOps and agile production environments reduces the software development lifecycle by providing real-time documentation updates, offering true automation of risk assessments, and even facilitating the process of negotiation of requirements through intelligent methods. Of course, AI chatbots and virtual assistants also help developers, project managers, and clients interact without any complications. Although requirements engineering with an AI can be challenging regarding data quality, interpretability, and ethics, and there are no good quality AI tools, AI continues to evolve, as with deep learning and contextual understanding, which will result in better requirements engineering. As AI becomes more integrated, businesses can develop high quality software faster and more accurately, increasing project success. (shrink)

Notwithstanding the growing interest in business incubation programmes and the benefits derived from such programmes, the path is beset by numerous challenges. This paper investigates the challenges faced by business incubators (BIs) as they strive to support their clients. The study utilized a qualitative approach to collect data by way of interviews to gain in-depth knowledge and understanding of the concept and challenges of business incubators. The data were collected using structured and unstructured in-depth personal interviews, which (...) were carried out with the respondents of business incubators in the Western Cape. The research participants for this study were limited to the business incubators on the database of a local organization that promotes small and medium enterprises (SMEs) development strategy and programmes in the Western Cape Province. All five business incubators on the database were deemed suitable for the study. The results indicated that an average of twenty-five entrepreneurs graduated from the incubation programme in the last five years. Furthermore, lack of sponsorship, production space, advanced technological facilities (prototype) and expansion to different areas were found to be among the challenges hindering incubators. (shrink)

This paper aims at exploring the uses and practices of English as a Foreign Language (EFL) Teachers pedagogical capital in the classroom. It examines how the use and usages of EFL teachers’ pedagogical capital affects the way they deal with their students in the language classroom. The research was grounded within the interpretive research paradigm. The in-depth unstructured interview and observation were the research tools under narrative inquiry design. The data were collected from three different participants and themes (...) were substantiated by Clandinin and Connelly to replicate their voices and experiences. The research finding reveals that teachers’ profound knowledge and skills about their capital are useful to integrate various cultures and content in the classrooms. Pedagogically inbuilt potentials of teachers and their pedagogical capital maximally enhance learners’ learning opportunities and foster students’ potential. However, it contributes to teachers’ education by rationalizing the essence of teachers’ pedagogical knowledge to transform our ELT practice in Nepal. (shrink)

This paper presents the conceptual and structural foundations of the database used in Artificial Research by Deduction within the framework of Global Artificial Intelligence (GAI). As the initial and essential stage in the automated scientific research process, the database takes the form of a comprehensive global matrix, designed to integrate all quantifiable factors relevant to human and natural systems—ranging from population demographics and economic indicators to planetary dynamics and astronomical phenomena. -/- The database is initially constructed from a wide array (...) of national, continental, and international data sources, including unstructured datasets and specific matrices derived from early experiments in Specific Artificial Intelligence for Artificial Research by Deduction (SAI-D). Through a rigorous process of standardization, this information is restructured into a unified matrix organized hierarchically into factors, sub-factors, and potentially sub-sub-factors, each defined in quantitative terms and capable of providing a continuous flow of data. -/- This standardized global matrix serves as the foundational substrate for further stages in the Artificial Research by Deduction process. In the subsequent replication stage, robotic systems populate the matrix with real-time data flows, enabling deductive algorithms and GAI itself to identify potential mathematical relationships. These relationships are evaluated as empirical hypotheses and, if deemed rational, inform the construction of models that feed into GAI’s broader modelling and decisional systems. -/- Ultimately, the database is not merely a passive repository of information, but an active and evolving structure that supports rational hypothesis generation, model construction, decision-making, and systemic self-improvement. It serves as the empirical backbone for a unified artificial rationality capable of supporting the management and ethical guidance of global systems. (shrink)

The article aims to investigate and analyze the social impact of industrialization on Badli. Badli is one of the largest industrial zones in Delhi which also bears large slums neighboring to the industries. The literature available on the area is also limited to news articles and government reports, thus further research on Badli is required. The social implications were examined through naturalistic observational research and unstructured interviews of 10 individuals from Badli. Using thematic analysis and secondary data analysis (...) the data was interpreted into common themes and key ideas. The analysis showed most Industries in Badli are unauthorized and have issues such as unemployment, child labor, hazardous working conditions, nominal wages, producing illegal substances and lack of healthcare management. People living in the neighboring slum areas suffer from issues such as poor health care facilities, below par educational infrastructure, high crime rates, harmful drainage and ventilation services, congested housing structures, economic disparity, and toxic family environment. Thus Industrialization has deeper implications on the society of Badli which can be attributed to the influx of migrants, industries and poor governance by the administrative authorities to control and manage the industrial process in Badli. It can be concluded that there is a huge correlation between social change and industrialization. Further studies are required to create socio-economic policies catering to the situation in Badli for a long-lasting sustainable effect on the area. (shrink)

The Covid-19 pandemic has left a trail of untold damage in many countries, and there is no foreseeable end to its spread. Besides the loss of life, the impact of the virus on the economy and small businesses, in particular, is not yet clear. Even so, the policies aimed at containing the spread of the virus have exerted further pressure and uncertainty on the survival of small businesses in general and immigrant-owned businesses in particu­lar. This study explored the pockmark of (...) C0VID-19 on immigrant retail businesses in selected outlets in West Rand, Johannesburg. A qualitative research approach was adopted as it best supports the exploratory intent of the study. The purposive sampling technique was utilised to draw participants for the study until a saturation point was attained. Data was collected through face-to-face interviews using unstructured, open-ended questions, with interviews recorded and transcribed. Atlas-ti 8 software was used to code, categorise and analyse the data to form themes and patterns, which helped to inform the outcome of the study. Findings indicate that none of the immigrant businesses was prepared for the pandemic and lockdown. Furthermore, it was found that most of the businesses may cease to exist due to cash fragility should the lockdown be extended for a longer period and the pandemic not abate soon. Despite the government having announced some palliative measures to help reduce the impact of the pandemic and lockdown on small businesses, results from this study indicate that immigrant businesses received no such support from the government. (shrink)

Different methodological approaches to empirical macroeconomics will be described and it will be explained that they represent different answers to the question from the title. Structural approaches require that macroeconometrical research should be explicitly founded on the (micro)economic theory in order to be able to measure the causal structure of the macroeconomic phenomena. Unstructural VAR approach suggest using econometric models to try to find out as much as possible about causal structure from the data, without prior restrictions from the (...) theory. Problems with both are described. (shrink)

Purity is the principle that fundamental facts only have fundamental constituents. In recent years, it has played a significant (if sometimes implicit) role in metaphysical theorizing. A philosopher will argue that a fact [p] contains a derivative entity and cite Purity as a reason to deny that [p] is fundamental. I argue that recent developments in higher order logic reveal a subtle ambiguity regarding the interpretation of Purity; there are stronger and weaker versions of that principle. Justifications for Purity support (...) only the weaker interpretation, but arguments that rely upon it only succeed if the stronger interpretation holds. Consequently, nearly every metaphysician who has invoked Purity has made a mistake, in that their inferences are not justified by their arguments. (shrink)

Economists can value things for cost-benefit analysis using either a structured or an unstructured approach. The first imposes some theoretical structure on the valuation; the second does not. This paper explains the difference between the approaches and examines the relative merits of each. Cost-benefit analysis may be aimed at finding what would be the best action, or alternatively at finding which action should be done in a democracy. The paper explains the difference, and argues that the appropriate aim is (...) the first. Given that, it comes down in favour of the structured approach to valuation. As an example, it discusses different approaches to valuing life in economics. (shrink)

Abstract-In mathematics and mathematical logic, Boolean algebra is a branch of algebra. It differs from elementary algebra in two ways. First, the values of the variables are the truth values true and false, usually denoted 1 and 0, whereas in elementary algebra the values of the variables are numbers. From Poincaré to Turing mathematics is developed at the basis of the fundamental processes.

This paper reinterprets modularity not merely as an architectural feature of complex systems but as a quantum-cognitive and ontological principle. Drawing on mathematical modeling, quantum theory and epistemic phenomenology, we introduce the anthropic modularity function M(x), which defines a topological threshold between structured differentiation and cognitive overload. Through six modular sections, we explore how modularity shapes historical empires, fractal languages, observer-centered cognition and ethical responsibility. We argue that both excessive modularization and enforced monolithic integration lead to systemic collapse—epistemically, structurally and (...) ethically. The modular golden mean, defined near M(x) ≈ σ/2, marks the zone of maximal resonance between parts and whole, suggesting a new topological ethics of differentiation anchored in the dynamics of phase coherence. Modularity, we propose, is the logic of sustained distinction within an entangled reality. This interdisciplinary study proposes the function ( ) M(x) of coherent modularity as both an epistemic measure and a practical principle for system design, including potential applications in artificial intelligence. By framing modularity as a topological function of cognitive resolution, we introduce a method for quantifying meaningful differentiation, one capable of guiding architectures of cognition, AI and self-organizing systems. The anthropic threshold c becomes a pivot between structured complexity and recursive breakdown — a criterion applicable to both biological minds and synthetic intelligences. (shrink)

Scientific data without uncertainty estimates are increasingly seen as incomplete. Recent discussions in the philosophy of data, however, have given little attention to the nature of uncertainty estimation. We begin to redress this gap by, first, discussing the concepts and practices of uncertainty estimation in metrology and showing how they can be adapted for scientific data more broadly; and second, advancing five philosophical theses about uncertainty estimates for data: they are substantive epistemic products; they are fallible; (...) they can be iteratively improved; they should be judged in terms of their adequacy-for-purpose; and these estimates, in turn, are essential for judging data adequacy. We illustrate these five theses using the example of the GISTEMP global temperature dataset. Our discussion introduces a novel adequacy-for-purpose view of uncertainty estimation, addresses a weakness in a recent philosophical account of data, and provides a new perspective on the “safety” versus “precision” debate in metrology. (shrink)

We critically engage two traditional views of scientific data and outline a novel philosophical view that we call the pragmatic-representational view of data. On the PR view, data are representations that are the product of a process of inquiry, and they should be evaluated in terms of their adequacy or fitness for particular purposes. Some important implications of the PR view for data assessment, related to misrepresentation, context-sensitivity, and complementary use, are highlighted. The PR view provides (...) insight into the common but little-discussed practices of iteratively reusing and repurposing data, which result in many datasets’ having a phylogeny—an origin and complex evolutionary history—that is relevant to their evaluation and future use. We relate these insights to the open-data and data-rescue movements, and highlight several future avenues of research that build on the PR view of data. (shrink)

The potential to collect brain data more directly, with higher resolution, and in greater amounts has heightened worries about mental and brain privacy. In order to manage the risks to individuals posed by these privacy challenges, some have suggested codifying new privacy rights, including a right to “mental privacy.” In this paper, we consider these arguments and conclude that while neurotechnologies do raise significant privacy concerns, such concerns are—at least for now—no different from those raised by other well-understood (...) class='Hi'>data collection technologies, such as gene sequencing tools and online surveillance. To better understand the privacy stakes of brain data, we suggest the use of a conceptual framework from information ethics, Helen Nissenbaum’s “contextual integrity” theory. To illustrate the importance of context, we examine neurotechnologies and the information flows they produce in three familiar contexts—healthcare and medical research, criminal justice, and consumer marketing. We argue that by emphasizing what is distinct about brain privacy issues, rather than what they share with other data privacy concerns, risks weakening broader efforts to enact more robust privacy law and policy. (shrink)

While the term “reactivity” has come to be associated with specific phenomena in the social sciences, having to do with subjects’ awareness of being studied, this paper takes a broader stance on this concept. I argue that reactivity is a ubiquitous feature of the psychological subject matter and that this fact is a precondition of experimental research, while also posing potential problems for the experimenter. The latter are connected to the worry about distorted data and experimental artifacts. But what (...) are experimental artifacts and what is the most productive way of dealing with them? In this paper, I approach these questions by exploring the ways in which experimenters in psychology simultaneously exploit and suppress the reactivity of their subject matter in order to produce experimental data that speak to the question or subject matter at hand. Highlighting the artificiality of experimental data, I raise the question of what distinguishes a genuine experimental result from an experimental artifact. My analysis construes experimental results as the outcomes of inferences from the data that take material background assumptions as auxiliary premises. Artifacts occur when one or more of these background assumptions are false, such that the data do not reliably serve the purposes they were generated for. I conclude by laying out the ways in which my analysis of data quality is relevant to, and informed by, recent debates about the replicability of experimental results. (shrink)

'Big Data' refers to extensive, interconnected datasets that are continuously generated and updated, encompassing a wide variety of sources, formats, and applications. It includes a significant portion of anonymized personal data as well as non-human data, such as derived datasets and by-products produced through everyday digital activities and human-machine interactions. These data points include traces from online shopping, browsing history, search queries, system logs, sensor readings, weather data, and aggregated location data. For the purposes (...) of this study, the term ‘Big Data’ specifically denotes this broad spectrum of data categories, explicitly excluding any personally identifying or legally protected data. By concentrating on these datasets, the study argues that certain forms of Big Data can be governed as shared resources or a ‘common heritage of humanity,’ supporting initiatives like data commons, open data movements, open government data, and data cooperatives, and emphasizing the potential for collaborative stewardship and equitable access for the public good. However, ethical considerations surrounding Big Data are complex due to novel dilemmas arising within the evolving cyberworld. In response, this study draws on the concept of stewardship within Catholic Social Teaching, with particular focus on “Laudato Si’,” and presents it as a guiding ethical principle for responsible data custodianship, upholding human dignity, ensuring accountability, and harnessing Big Data’s potential for the common good. Just as “Laudato Si'” highlights our interconnectedness within the biosphere, this parallels our interconnectedness within the datasphere—the digital ecosystem—underscoring the necessity of stewardship in both domains. (shrink)

Data mining possess a significant threat to ethics, privacy, and legality, especially when we consider the fact that data mining makes it difficult for an individual or consumer (in the case of a company) to control accessibility and usage of his data. Individuals should be able to control how his/ her data in the data warehouse is being access and utilize while at the same time providing enabling environment which enforces legality, privacy and ethicality on (...) data scientists, or data engineer during data mining process. This paper review issues of legality, privacy, and ethicality in data mining, review processes of Data mining, and also proposes solution to current ethical and privacy issue in data mining. It introduces a new method which enforces data mining without infringing on the privacy of individual or consumer whose data are being used. The sole aim of this paper is to propose a new method of mining data which restricts scientists within the constraints of legality, privacy, and ethicality. (shrink)

Between 2006 and 2011, humanity accumulated 1,600 EB of data. As a result of this growth, there is now more data produced than available storage. This article explores the problem of “Big Data,” arguing for an epistemological approach as a possible solution to this ever-increasing challenge.

Recently, it has been argued that the use of Big Data transforms the sciences, making data-driven research possible and studying causality redundant. In this paper, I focus on the claim on causal knowledge by examining the Big Data project EXPOsOMICS, whose research is funded by the European Commission and considered capable of improving our understanding of the relation between exposure and disease. While EXPOsOMICS may seem the perfect exemplification of the data-driven view, I show how causal (...) knowledge is necessary for the project, both as a source for handling complexity and as an output for meeting the project’s goals. Consequently, I argue that data-driven claims about causality are fundamentally flawed and causal knowledge should be considered a necessary aspect of Big Data science. In addition, I present the consequences of this result on other data-driven claims, concerning the role of theoretical considerations. I argue that the importance of causal knowledge and other kinds of theoretical engagement in EXPOsOMICS undermine theory-free accounts and suggest alternative ways of framing science based on Big Data. (shrink)

This paper addresses a relatively new mode of ecological research: data synthesis studies. Data synthesis studies involve reusing data to create a general model as well as a reusable, aggregated dataset. Using a case from movement ecology, I analyse the trade-offs and strategies involved in data synthesis. Like theoretical ecological modelling, I find that synthesis studies involve a modelling trade-off between generality, precision and realism; they deal with this trade-off by adopting a pragmatic kludging strategy. I (...) also identify an additional trade-off, the synthesis trade-off, between making data easy to synthesise for a particular project, on the one hand, and facilitating data reuse for other projects, on the other. In response to this synthesis trade-off, researchers create flexible datasets that are relatively easy to use for particular projects and can be adjusted to suit some other purposes. The flexibility compromise is also found in broader open data efforts, making it a significant element in the future of data-intensive ecology. (shrink)

The current global health situation (primarily as a result of Covid-19) has fostered a change in customer behaviour towards the use of telecommunications services, which has led to an increase in data traffic. As a result of this change, telecommunications operators have a golden opportunity to create new sources of revenue by utilising Big Data Analytics (BDA) solutions. In the process of establishing a BDA project, we encountered a number of obstacles, the most significant of which were the (...) selection of the technological solution from among the vast array of tools that were already available, as well as the selection of the governance methodology that would be used to control both the project and the data. The vast majority of study materials that pertain to the telecommunications industry have not addressed the implementation of BDA projects from the beginning to the end. The goal of this study is to concentrate on a BDA telecommunications project, specifically the governance of the project, the architecture of the project, the governance of the data, and the team working on the BDA project. Utilisable BDA use cases are presented in the final section of this study. These use cases are presented in terms of applications that enable income generating and cost optimisation. It would appear that this effort will make it easier to implement BDA projects and will provide telecoms operators with the ability to have a better understanding of the fundamental components that need to be focused on. (shrink)