Comprehensive Taxonomy Research Articles

Navigating the unlearning landscape: an organizational unlearning taxonomy and an outcome-centric model

PurposeThe purpose of this paper is to explore the phenomenon of organizational unlearning with a focus on challenging path dependence and its implications on the organizational change field. By generating a taxonomy of unlearning definitions and examining the dimensions, actors and processes involved, the authors aim to offer a holistic understanding of organizational unlearning and its potential applications for organizations facing ambiguity and uncertainty.Design/methodology/approachThis conceptual paper draws the literature on organizational unlearning to map existing definitions and categorize them into a comprehensive taxonomy to propose a model focused on the outcomes.FindingsThe findings highlight that organizational unlearning involves the three main organizational dimensions (micro: individuals; meso: groups; macro: organizations) and that the macro-organizational perspective represents the best fit for the concept. Furthermore, the authors’ argue that the most appropriate process for understanding the unlearning phenomenon is through challenge, as it allows the questioning of the present and facilitates critical reflection. Finally, applying organizational unlearning to path dependence concept, the authors’ posit that organizations can overcome negative transfer effects and build new awareness to reinterpret their dependencies in light of environmental changes.Originality/valueThis study contributes to the literature on organizational unlearning by providing a comprehensive taxonomy of definitions, clarifying the dimensions, constructs and processes involved. The integration of challenging path dependence with organizational unlearning offers a novel perspective on the potential for organizational change field. The paper’s findings have practical implications for organizations striving to survive and develop in uncertain environments.

Taxonomy of Acute Stroke: Imaging, Processing, and Treatment

Stroke management employs a variety of diagnostic imaging modalities, image processing and analysis methods, and treatment procedures. This work categorizes methods for stroke imaging, image processing and analysis, and treatment, and provides their taxonomies illustrated by a state-of-the-art review. Imaging plays a critical role in stroke management, and the most frequently employed modalities are computed tomography (CT) and magnetic resonance (MR). CT includes unenhanced non-contrast CT as the first-line diagnosis, CT angiography, and CT perfusion. MR is the most complete method to examine stroke patients. MR angiography is useful to evaluate the severity of artery stenosis, vascular occlusion, and collateral flow. Diffusion-weighted imaging is the gold standard for evaluating ischemia. MR perfusion-weighted imaging assesses the penumbra. The stroke image processing methods are divided into non-atlas/template-based and atlas/template-based. The non-atlas/template-based methods are subdivided into intensity and contrast transformations, local segmentation-related, anatomy-guided, global density-guided, and artificial intelligence/deep learning-based. The atlas/template-based methods are subdivided into intensity templates and atlases with three atlas types: anatomy atlases, vascular atlases, and lesion-derived atlases. The treatment procedures for arterial and venous strokes include intravenous and intraarterial thrombolysis and mechanical thrombectomy. This work captures the stateoftheart in stroke management summarized in the form of comprehensive and straightforward taxonomy diagrams. All three introduced taxonomies in diagnostic imaging, image processing and analysis, and treatment are widely illustrated and compared against other state-of-the-art classifications.

A Survey on Data Compression Techniques for Automotive LiDAR Point Clouds.

In the evolving landscape of autonomous driving technology, Light Detection and Ranging (LiDAR) sensors have emerged as a pivotal instrument for enhancing environmental perception. They can offer precise, high-resolution, real-time 3D representations around a vehicle, and the ability for long-range measurements under low-light conditions. However, these advantages come at the cost of the large volume of data generated by the sensor, leading to several challenges in transmission, processing, and storage operations, which can be currently mitigated by employing data compression techniques to the point cloud. This article presents a survey of existing methods used to compress point cloud data for automotive LiDAR sensors. It presents a comprehensive taxonomy that categorizes these approaches into four main groups, comparing and discussing them across several important metrics.

Advancements in prokaryotic systematics and the role of Bergey's International Society for Microbial Systematicsin addressing challenges in the meta-data era.

Prokaryotes are ubiquitous in the biosphere, important for human health and drive diverse biological and environmental processes. Systematics of prokaryotes, whose origins can be traced to the discovery of microorganisms in the 17th century, has transitioned from a phenotype-based classification to a more comprehensive polyphasic taxonomy and eventually to the current genome-based taxonomic approach. This transition aligns with a foundational shift from studies focused on phenotypic traits that have limited comparative value to those using genome sequences. In this context, Bergey's Manual of Systematics of Archaea and Bacteria (BMSAB) and Bergey's International Society for Microbial Systematics (BISMiS) play a pivotal role in guiding prokaryotic systematics. This review focuses on the historical development of prokaryotic systematics with a focus on the roles of BMSAB and BISMiS. We also explore significant contributions and achievements by microbiologists, highlight the latest progress in the field and anticipate challenges and opportunities within prokaryotic systematics. Additionally, we outline five focal points of BISMiS that are aimed at addressing these challenges. In conclusion, our collaborative effort seeks to enhance ongoing advancements in prokaryotic systematics, ensuring its continued relevance and innovative characters in the contemporary landscape of genomics and bioinformatics.

Driving factors for the adoption of green finance in green building for sustainable development in developing countries: The case of Ghana

AbstractWhile there are many motivating factors for green finance (GF) implementation, a comprehensive taxonomy of these variables is lacking in the literature, especially for green buildings (GBs). This study aims to analyze the criticality and interdependence of GF‐in‐GB's driving factors. This study develops a valid set of factors to justify the interrelationships among the drivers. The drivers of GF‐in‐GB are qualitative in nature, and uncertainties exist among them due to linguistic preferences. This study applies the fuzzy Delphi method to validate eight drivers under uncertainties. Fuzzy Decision‐Making Trial and Evaluation Laboratory (FDEMATEL) with qualitative information is used to determine the interrelationships among the drivers. The drivers were grouped under two categories: prominent drivers and cause‐effect drivers. The findings revealed that “increased awareness of GF models in GB” and “preferential capital requirements for low‐carbon assets” are the top two most prominent/important drivers of GF‐in‐GB. In Ghana, the top three cause group drivers are “climate commitment,” “improved access to and lower cost of capital,” and “favorable macroeconomic conditions and investment returns.” Drivers with the highest prominence values have the potential to affect and/or be affected by other drivers; therefore, managers and policymakers should prioritize promoting or pursuing these drivers in the short term. On the other hand, it is important to pay more than equal attention to the drivers with the highest net cause values because they have the largest long‐term impact on the entire system. The theoretical and practical implications of the study are discussed, enhancing understanding and decision‐making in GF‐in‐GB.

Advancing the discourse: A next-generation value chain-based taxonomy for circular economy key performance indicators

The growth of interest in circular economy (CE) has been accompanied by different approaches to measurement of CE outcomes and impacts, leading to a wide portfolio of indicators with varying degrees of overlap, inconsistency, and convergence. The aim of this paper is to propose a unifying framework for CE indicators, as the next generation of CE taxonomies. We first undertake a scoping review of 59 review papers on CE indicators using manual and computational methods (i.e., topic modelling) to inform the taxonomy structure and content. As a result, we report on 11 clusters of approaches that have been attentive to different dimensions of CE (e.g. horizontal value chain, vertical scale of operation (macro, meso, micro), impact category (economic, biophysical, social), material vs product focus, etc.). Highlighting the strengths and weakness of these approaches, we identify gaps in dimensions related to horizontal and vertical scales of measurement, and propose an agnostic, integrative framework that builds on the scientific foundations of previous research, within a more systemic and comprehensive taxonomy. This taxonomy could be used as a guiding framework or heuristic for regulators, both nationally and internationally, and for practitioners to undertake a comprehensive measurement and assessment of CE related interventions and initiatives at scale.

Implementation Costs of the APPRAISE Alcohol Brief Intervention (ABI) for Male Remand Prisoners: A Micro-Costing Protocol and Preliminary Findings.

This article documents the methods used to assess the implementation costs of the APPRAISE (A two-arm parallel group individually randomized Prison Pilot study of a male Remand Alcohol Intervention for Self-efficacy Enhancement) alcohol brief intervention (ABI) delivered to male remand prisoners across two study sites in Scotland and North East England. We first developed a comprehensive taxonomy of the activities constituting the APPRAISE ABI. Next, data about the study staff and the subject time spent were collected for each activity, in addition to the other resources used and unit costs. From the pilot data collection, it was possible to construct a narrative, for both study sites, how the intervention was delivered and the time required for each activity. The ABI was delivered by Change Grow Live and Humankind intervention staff. Staff salaries were obtained from both organizations to calculate the staff delivery costs for each site. Other costs, such as the printing of materials, were estimated based on APPRAISE study records. Because of the ongoing COVID-19 restrictions and limited access to prison resources and staff, there were significant deviations from the initial study protocols. As a result, we document the costs of implementing the ABI as delivered rather than as planned. This article provides the first estimates of the implementation costs of an ABI delivered in a criminal justice setting in the United Kingdom. Although these costs are from a pilot implementation that was heavily impacted by the COVID-19 pandemic, this article nonetheless provides useful, policy-relevant information on the potential costs of providing ABI to remand prisoners. It also serves as a methodological template, guidance, and proof of concept for future micro-costing studies of ABIs in criminal justice settings.

Engage Wider Audience or Facilitate Quality Answers? a Mixed-methods Analysis of Questioning Strategies for Research Sensemaking on a Community Q&A Site

Discussing research-sensemaking questions on Community Question and Answering (CQA) platforms has been an increasingly common practice for the public to participate in science communication. Nonetheless, how users strategically craft research-sensemaking questions to engage public participation and facilitate knowledge construction is a significant yet less understood problem. To fill this gap, we collected 837 science-related questions and 157,684 answers from Zhihu, and conducted a mixed-methods study to explore user-developed strategies in proposing research-sensemaking questions, and their potential effects on public engagement and knowledge construction. Through open coding, we captured a comprehensive taxonomy of question-crafting strategies, such as eyecatching narratives with counter-intuitive claims and rigorous descriptions with data use. Regression analysis indicated that these strategies correlated with user engagement and answer construction in different ways (e.g., emotional questions attracted more views and answers), yet there existed a general divergence between wide participation and quality knowledge establishment, when most questioning strategies could not ensure both. Based on log analysis, we further found that collaborative editing afforded unique values in refining research-sensemaking questions regarding accuracy, rigor, comprehensiveness and attractiveness. We propose design implications to facilitate accessible, accurate and engaging science communication on CQA platforms.

ADVANCED METHODS FOR MAINTAINING AND MANAGING THE LIFE CYCLE OF CLOUD ENVIRONMENTS: SURVEY

Resource management is a fundamental concept in cloud computing and virtualization, encompassing the allocation, release, coordination, and monitoring of cloud resources to optimize efficiency. The complexity arises from the virtualized, heterogeneous, and multi-user nature of these resources. Effective governance is challenging due to uncertainty, large-scale infrastructures, and unpredictable user states. This paper presents a comprehensive taxonomy of resource management technologies, offering a detailed analysis of design architecture, virtualization, and cloud deployment models, along with capabilities, objectives, methods, and mechanisms. In a cloud computing environment, deploying application-based resource management techniques necessitates understanding the system architecture and deployment model. This paper explores centralized and distributed resource management system architectures, providing a review of effective resource management techniques for both, accompanied by a comparative analysis. The evolution of cloud computing from a centralized to a distributed paradigm is examined, emphasizing the shift towards distributed cloud architectures to harness the computing power of smart connected devices at the network edge. These architectures address challenges like latency, energy consumption, and security, crucial for IoT-based applications. The literature proposes various methods for distributed resource management, aligning with the distributed nature of these architectures. Resource management in cloud computing involves discovery, provisioning, allocation, and monitoring functions, with sub-functions like mapping and scheduling. Integrated approaches to consolidation and resource management have been explored in numerous studies. This paper summarizes and analyzes existing research on resource management functions, focusing on identification, provisioning, allocation planning, and monitoring, based on their objectives and methods.

AnnoView enables large-scale analysis, comparison, and visualization of microbial gene neighborhoods.

The analysis and comparison of gene neighborhoods is a powerful approach for exploring microbial genome structure, function, and evolution. Although numerous tools exist for genome visualization and comparison, genome exploration across large genomic databases or user-generated datasets remains a challenge. Here, we introduce AnnoView, a web server designed for interactive exploration of gene neighborhoods across the bacterial and archaeal tree of life. Our server offers users the ability to identify, compare, and visualize gene neighborhoods of interest from 30 238 bacterial genomes and 1672 archaeal genomes, through integration with the comprehensive Genome Taxonomy Database and AnnoTree databases. Identified gene neighborhoods can be visualized using pre-computed functional annotations from different sources such as KEGG, Pfam and TIGRFAM, or clustered based on similarity. Alternatively, users can upload and explore their own custom genomic datasets in GBK, GFF or CSV format, or use AnnoView as a genome browser for relatively small genomes (e.g. viruses and plasmids). Ultimately, we anticipate that AnnoView will catalyze biological discovery by enabling user-friendly search, comparison, and visualization of genomic data. AnnoView is available at http://annoview.uwaterloo.ca.

What are developers talking about information security? A large-scale study using semantic analysis of Q&A posts.

Digitalization and rapid technological improvement in the present day bring numerous benefits, but they also raise the complexity and diversity of cyber security risks, putting critical information security issues on the agenda. Growing issues and worries about information security endanger not only the security of individuals and organizations but also global social and economic stability. This study investigates the issues and challenges regarding information security by analyzing all the postings on ISSE (Information Security Stack Exchange), a Q&A website focused on information security. In order to identify the primary topics addressed in postings shared on the ISSE platform, we employed a probabilistic topic modeling method called latent Dirichlet allocation (LDA), which is generative in nature and relies on unsupervised machine learning processes. Through this investigation, a total of 38 topics were identified, demonstrating the present state of information security issues and challenges. Considering these topics, a comprehensive taxonomy of seven categories was devised to address information security issues, taking into account their backgrounds and perspectives. Subsequently, we conducted an examination of the prevalence and complexity of the matters at hand. In addition, we have defined the prevailing technologies utilized in the realm of information security, including tasks, certifications, standards, methods, tools, threats, and defenses. We have provided a number of implications for different stakeholders, including academics, developers, educators, and practitioners, who are working towards advancing the field of information security.

A comparative analysis of blockchain attack classifications

As blockchain technology has evolved, it has introduced an array of functionalities and mechanisms. However, this advancement has also attracted a growing number of threats specifically targeting blockchains, heightening concerns regarding blockchain security. Although several researchers have attempted to categorize blockchain attacks in their respective studies, there remains a significant disparity among these taxonomies. This paper delves into three distinct classification methodologies, comparing their respective strengths and weaknesses. Additionally, it offers insights into the essential attributes that a comprehensive and effective taxonomy should possess. By breaking down each classification method, the paper provides a clearer understanding of how various researchers approach the challenge of categorizing blockchain threats. This includes looking at the criteria each method uses, such as the level of technical sophistication required for each attack, the potential damage inflicted, or the underlying motivations of the attackers. Furthermore, the paper emphasizes the importance of a universally accepted taxonomy, as this would not only facilitate more effective communication among researchers but also help in devising better defense mechanisms. In conclusion, by analyzing and comparing these classification methodologies, the study hopes to pave the way for a more unified and comprehensive approach to understanding blockchain security threats in the future.

Molecular approach for identification and validation of Eurycoma longifolia, Labisia pumila, and Orthosiphon stamineus in herbal products

BackgroundEurycoma longifolia (E. longifolia), Labisia pumila (L. pumila), and Orthosiphon stamineus (O. stamineus) are popular species known for their therapeutic properties. An increase in local demand for herbal products makes them susceptible to adulteration, which poses a risk to their safety and efficacy. Current identification methods, such as organoleptic, microscopic, and macroscopic analysis, need to be revised to identify plant species in highly processed herbal products due to their limited ability to detect morphological features and provide comprehensive plant taxonomy information.MethodsThis research objective was to develop a simple, reliable, and accurate DNA molecular identification method based on polymerase chain reaction–restriction fragment length polymorphism (PCR–RFLP) for E. longifolia, L. pumila, and O. stamineus, used to validate the species identification for herbal products. PCR–RFLP was developed for rapid identification using restriction enzymes TaqI, BamH I, HinfI, EcoRI, EcoRV, Mbol, and Mspl.ResultsThe nuclear DNA internal transcribed spacer 2 (ITS2) sequences were identified and compared between plant specimens of E. longifolia, L. pumila, and O. stamineus and 101 samples of commercial herbal products. Plant specimens of E. longifolia, L. pumila, and O. stamineus were successfully identified with high similarity of 100%, 100%, and 99.33%, respectively, based on National Center for Biotechnology Information (NCBI) GenBank. The recovery of DNA sequences from the herbal products was 60.4%, of which 81.97% were identified, and 18.03% showed no sequence through Basic Local Alignment Search Tool (BLAST) identification.ConclusionA reliable approach for identifying and validating plant species in herbal products has been created using restriction enzymes. This simple and accurate PCR–RFLP approach efficiently identifies E. longifolia, L. pumila, and O. stamineus by analysing ITS2 sequences, assuring consumer health and safety.

Unveiling the dimensions of digital transformation: A comprehensive taxonomy and assessment model for business

Digital transformation (DT) is an adaptive strategy in the evolving tech and business landscape. It helps organizations enhance operations and customer experiences to remain competitive. However, defining digital transformation, creating a universal taxonomy, and developing a practical assessment model remain challenges. This study aims to establish a comprehensive DT taxonomy by deconstructing DT into various dimensions. The research goes a step further by developing sub-dimensions and indicators, offering essential tools for investigating an organization's DT status. The study emphasizes the importance of considering industry-specific approaches, with a focus on the retail sector as a practical case study. Our research employs a structured approach, including an extensive literature review, focus group interviews, and the utilization of the FAHP method and e-surveys to prioritize DT taxonomy dimensions. The study makes significant contributions, including providing a clear DT definition, bridging the gap between corporate and academic perspectives, and introducing a practical survey questionnaire for DT initiatives. We highlight the critical role of customer experience, DT training, and resource allocation, and introduce a conceptual model illustrating the dynamic relationship among organizational operations, DT technology, and process optimization. This model also proposed the moderating roles played by customer experience and resource allocation.

Categorizing digital data collection and intervention tools in health and wellbeing living lab settings: A modified Delphi study

BackgroundHealth and Wellbeing Living Labs are a valuable research infrastructure for exploring innovative solutions to tackle complex healthcare challenges and promote overall wellbeing. A knowledge gap exists in categorizing and understanding the types of ICT tools and technical devices employed by Living Labs. AimDefine a comprehensive taxonomy that effectively categorizes and organizes the digital data collection and intervention tools employed in Health and Wellbeing Living Lab research studies. MethodsA modified consensus-seeking Delphi study was conducted, starting with a pre-study involving a survey and semistructured interviews (N=30) to gather information on existing equipment. The follow-up three Delphi rounds with a panel of living lab experts (R1 N=18, R2 - 3 N=15) from 10 different countries focused on achieving consensus on the category definitions, ease of reading, and included subitems for each category. Due to the controversial results in the 2nd round of qualitative feedback, an online workshop was organized to clarify the contradictory issues. ResultsThe resulting taxonomy included 52 subitems, which were divided into three levels as follows: The first level consists of 'devices for data monitoring and collection' and 'technologies for intervention.' At the second level, the 'data monitoring and collection' category is further divided into 'environmental' and 'human' monitoring. The latter includes the following third-level categories: 'biometrics,' 'activity and behavioral monitoring,' 'cognitive ability and mental processes,' 'electrical biosignals and physiological monitoring measures,' '(primary) vital signs,' and 'body size and composition.' At the second level, 'technologies for intervention' consists of 'assistive technology,' 'extended reality – XR (VR & AR),' and 'serious games' categories. ConclusionA common language and standardized terminology are established to enable effective communication with living labs and their customers. The taxonomy opens a roadmap for further studies to map related devices based on their functionality, features, target populations, and intended outcomes, fostering collaboration and enhancing data capture and exploitation.

Pinched Hysteresis Loop with Nonlinear Electronic Components: From Memristor to Hysteristor Concepts

A memristor is an electrical two-terminal passive device that exhibits a pinched hysteresis loop that always passes through the origin in the voltage-current plane. We found a system that also exhibits pinched hysteresis loop, which, consequently following the trend in the literature, can be called memristors; however, their dynamics do not match the equation of memristor that is widely spread and used in the literature. For this reason, in this work, we proposed the name of hysteristor of order n. It is a passive system with zero-crossing hysteresis loop in the V-I plane but not necessarily governed by the conventional equations of memristors. The system is proposed to provide a comprehensive circuit taxonomy. The concept of a hysteristor encapsulates and generalizes the idea of memristive systems. To validate the theory, we present theoretical analysis and representative simulations of a novel hysteristor of order 1.

The Catalogue of Soundscape Interventions (CSI) project—A tool to bridge soundscape research and practice

The Catalogue of Soundscape Interventions (CSI) project addressed the growing interest in urban soundscapes by establishing a comprehensive taxonomy for soundscape design. The project development is described, emphasizing its role in connecting soundscape research with practical applications. The proposed concepts for soundscape design interventions and other soundscape interventions are discussed, which seek to inform the creation of “soundscape design briefs.” These briefs, intended for use by local authorities, aim to enhance communication among soundscape consultants and researchers, and other relevant stakeholders. This work also presents the intersection of the CSI project with the technical specifications outlined in the ISO/AWI TS 12913-4 Acoustics Soundscape Part 4: Design and Intervention, currently in development. By aligning with emerging standards, the CSI project seeks to contribute to a unified framework for soundscape practices, and to establish a model to describe phases and necessary action points in the life cycle of a soundscape design intervention.

Multi-Objective Reinforcement Learning Based on Decomposition: A Taxonomy and Framework

Multi-objective reinforcement learning (MORL) extends traditional RL by seeking policies making different compromises among conflicting objectives. The recent surge of interest in MORL has led to diverse studies and solving methods, often drawing from existing knowledge in multi-objective optimization based on decomposition (MOO/D). Yet, a clear categorization based on both RL and MOO/D is lacking in the existing literature. Consequently, MORL researchers face difficulties when trying to classify contributions within a broader context due to the absence of a standardized taxonomy. To tackle such an issue, this paper introduces multi-objective reinforcement learning based on decomposition (MORL/D), a novel methodology bridging the literature of RL and MOO. A comprehensive taxonomy for MORL/D is presented, providing a structured foundation for categorizing existing and potential MORL works. The introduced taxonomy is then used to scrutinize MORL research, enhancing clarity and conciseness through well-defined categorization. Moreover, a flexible framework derived from the taxonomy is introduced. This framework accommodates diverse instantiations using tools from both RL and MOO/D. Its versatility is demonstrated by implementing it in different configurations and assessing it on contrasting benchmark problems. Results indicate MORL/D instantiations achieve comparable performance to current state-of-the-art approaches on the studied problems. By presenting the taxonomy and framework, this paper offers a comprehensive perspective and a unified vocabulary for MORL. This not only facilitates the identification of algorithmic contributions but also lays the groundwork for novel research avenues in MORL.

Next generation of computer vision for plant disease monitoring in precision agriculture: A contemporary survey, taxonomy, experiments, and future direction

Efficient and rational monitoring of plant health is an essential prerequisite for ensuring optimal crop production and resource management in the field of agriculture. Computer vision techniques have revolutionized visual disease monitoring with their exceptional visual recognition performance. However, despite the outstanding results, the widespread acceptance of these methods in agriculture practice is still in its early stages. This study presents a comprehensive survey of the next generation of computer vision models applied to plant disease monitoring in precision agriculture. Our study begins by tracing the evolution of agricultural computer vision research over the past decade, encompassing legacy methods such as convolutional neural networks (CNNs), progressing to newer techniques like vision transformers (ViTs), and culminating in cutting-edge vision multi-layer perceptrons (MLPs). Next, our study embraces both qualitative and quantitative approaches, supporting a profound review of literature and classifying methodologies and experimental approaches. A significant contribution lies in our comprehensive taxonomy, offering a fine-grained categorization of current computer vision models. This taxonomy meticulously highlights the potentials and limitations of these models while explaining their roles in improving plant disease management. Moreover, extensive experimental comparisons are conducted on PlantVillage dataset to evaluate the performance of state-of-the-art computer-vision models for plant recognition data. The obtained results are then utilized to draw insightful conclusions about the behavior of these models and provide guidance for selecting the most suitable one for specific tasks at hand. Additionally, we discuss open research avenues and future directions of computer-vision models in plant disease management including challenges related to the data scarcity, the computational efficiency, need for explainability, and multi-modal analysis.

The Role of Explainable AI in Building Trust and Confidence in Automated Business Processe

Computer-based intelligence (AI) has shown significant potential in various applications, but it requires a clear understanding of the local area. This issue falls within the Reasonable simulated intelligence (XAI) field, which is crucial for the practical application of AI models. This article discusses the current work in XAI, defining explainability in machine learning and proposing a new definition. It also discusses ongoing commitments related to AI models, including Profound Learning strategies. The article suggests Dependable Computerized reasoning, a method for large-scale AI execution in real-world contexts with reliability, model logic, and responsibility at its core. The goal is to provide a comprehensive taxonomy for newcomers and professionals to embrace AI's benefits.