A data orchestrator acts to integrate and fine tune its instruments. It is agile, scalable and evolutive to adapt quickly. Data orchestration delivers the core from which people, processes and systems and data unite to provide one source of the truth. It is not a matter of if and when – but how – to regain valuable time while increasing profit. Up until now, the visionary leaders who have understood the importance of integrating reliable data to validate decisions have incrementally introduced various tools from vendors offering singular solutions for individual functions of the business. It is through the narrowed focus of these early disruptors we now know that addressing bit parts of an organization, as unique products come to market, is not delivering the gains they expected and often becomes a binding decision to commit to one provider.

The global Artificial Intelligence (AI) in Fintech market focuses on encompassing major statistical evidence for the Artificial Intelligence (AI) in Fintech industry as it offers our readers a value addition on guiding them in encountering the obstacles surrounding the market. A comprehensive addition of several factors such as global distribution, manufacturers, market size, and market factors that affect the global contributions are reported in the study. In addition the Artificial Intelligence (AI) in Fintech study also shifts its attention with an in-depth competitive landscape, defined growth opportunities, market share coupled with product type and applications, key companies responsible for the production, and utilized strategies are also marked. This intelligence and 2026 forecasts Artificial Intelligence (AI) in Fintech industry report further exhibits a pattern of analyzing previous data sources gathered from reliable sources and sets a precedented growth trajectory for the Artificial Intelligence (AI) in Fintech market. The report also focuses on a comprehensive market revenue streams along with growth patterns, analytics focused on market trends, and the overall volume of the market. Moreover, the Artificial Intelligence (AI) in Fintech report describes the market division based on various parameters and attributes that are based on geographical distribution, product types, applications, etc.

In this paper we develop a concept aware multi-preferential semantics for dealing with typicality in description logics, where preferences are associated with concepts, starting from a collection of ranked TBoxes containing defeasible concept inclusions. Preferences are combined to define a preferential interpretation in which defeasible inclusions can be evaluated. The construction of the concept-aware multipreference semantics is related to Brewka's framework for qualitative preferences. We exploit Answer Set Programming (in particular, asprin) to achieve defeasible reasoning under the multipreference approach for the lightweight description logic EL+bot. The paper is under consideration for acceptance in TPLP.

Much has been said about the fusion of bio-inspired optimization algorithms and Deep Learning models for several purposes: from the discovery of network topologies and hyper-parametric configurations with improved performance for a given task, to the optimization of the model's parameters as a replacement for gradient-based solvers. Indeed, the literature is rich in proposals showcasing the application of assorted nature-inspired approaches for these tasks. In this work we comprehensively review and critically examine contributions made so far based on three axes, each addressing a fundamental question in this research avenue: a) optimization and taxonomy (Why?), including a historical perspective, definitions of optimization problems in Deep Learning, and a taxonomy associated with an in-depth analysis of the literature, b) critical methodological analysis (How?), which together with two case studies, allows us to address learned lessons and recommendations for good practices following the analysis of the literature, and c) challenges and new directions of research (What can be done, and what for?). In summary, three axes - optimization and taxonomy, critical analysis, and challenges - which outline a complete vision of a merger of two technologies drawing up an exciting future for this area of fusion research.

The selection of features is an essential data preprocessing stage in data mining. The core principle of feature selection seems to be to pick a subset of possible features by excluding features with almost no predictive information as well as highly associated redundant features. In the past several years, a variety of meta-heuristic methods were introduced to eliminate redundant and irrelevant features as much as possible from high-dimensional datasets. Among the main disadvantages of present meta-heuristic based approaches is that they are often neglecting the correlation between a set of selected features. In this article, for the purpose of feature selection, the authors propose a genetic algorithm based on community detection, which functions in three steps. The feature similarities are calculated in the first step. The features are classified by community detection algorithms into clusters throughout the second step. In the third step, features are picked by a genetic algorithm with a new community-based repair operation. Nine benchmark classification problems were analyzed in terms of the performance of the presented approach. Also, the authors have compared the efficiency of the proposed approach with the findings from four available algorithms for feature selection. The findings indicate that the new approach continuously yields improved classification accuracy.

COVID-19 has dealt the world a twin crisis. We face not only our greatest global health shock but also our greatest economic shock in a century. With these dual crises comes a twin watershed moment. First, whether for school, work, health or keeping in touch with family and friends, we have realized the deep value of digital technologies. Second, the appetite for change (arguably a more challenging shift to achieve) has grown significantly.

BGL Corporate Solutions, Australia's leading supplier of SMSF administration and ASIC corporate compliance solutions, have released BGL SmartDocs, a new AI-powered document reader. SmartDocs is an AI-powered document reader that extracts data from images and PDFs (including scanned documents and photos) and converts the data into digital information. BGL SmartDocs helps fill data feed gaps, driving zero-touch processing and adding new efficiencies by linking source documents to BGL's Accounting Workpapers. "We have been running BGL SmartDocs in BETA for over 6 months," said BGL's Managing Director, Ron Lesh. "Turning paper into data has been a long term problem for accountants and advisors – BGL SmartDocs solves this problem."

In the 20 years of the "war on terror" Australia has led from the front in expanding powers for law enforcement and ramping up surveillance at the expense of public rights and freedoms. Among the seemingly endless barrage of national security legislation and surveillance that creeps into every aspect of our personal lives, more and more of our public spaces have been smothered by surveillance cameras and facial recognition technology. Corporations large and small, towns and cities, federal and state government departments and agencies have deployed these systems, snooping on us all wherever we go without any of us getting a say. State and federal law enforcement officers are accessing these technologies without any oversight. As anti-police protests spread around the world, tools and processes that exacerbate racist bias – and the wasteful spending and abuses of power that comes with it –within law enforcement and judicial systems have fallen under renewed scrutiny. Once again, Australia is lagging behind the debate.

Ruth is a mathematician and data scientist specialising in operations research, machine learning and statistics. She holds a doctorate in mathematics for her research on dynamic resource allocation. She has nearly 20 years of project management, machine learning, programming, and solution development experience in the health, education, and private sectors. At Fireball, she leads the development team building an early bushfire notification platform that uses deep learning to detect fires within minutes of ignition. We're putting the people of Queensland front and centre to support Queensland AI Hub's mission - connecting Queensland's AI ecosystem.

Features mined from knowledge graphs are widely used within multiple knowledge discovery tasks such as classification or fact-checking. Here, we consider a given set of vertices, called seed vertices, and focus on mining their associated neighboring vertices, paths, and, more generally, path patterns that involve classes of ontologies linked with knowledge graphs. Due to the combinatorial nature and the increasing size of real-world knowledge graphs, the task of mining these patterns immediately entails scalability issues. In this paper, we address these issues by proposing a pattern mining approach that relies on a set of constraints (e.g., support or degree thresholds) and the monotonicity property. As our motivation comes from the mining of real-world knowledge graphs, we illustrate our approach with PGxLOD, a biomedical knowledge graph.