Abhishek Gupta

It also discusses the regulators approach to curb financial crimes and how syndication among financial institutions can create a robust ecosystem for monitoring and managing financial crimes. It opens with an introduction to financial crimes for a financial institution, the context of financial crimes, and its various participants.

This book discusses all aspects of money laundering, starting from traditional approach to financial crimes to artificial intelligence-enabled solutions. It also discusses the regulators approach to curb financial crimes and how syndication among financial institutions can create a robust ecosystem for monitoring and managing financial crimes. It opens with an introduction to financial crimes for a financial institution, the context of financial crimes, and its various participants. Various types of money laundering, terrorist financing, and dealing with watch list entities are also part of the discussion. Through its twelve chapters, the book provides an overview of ways in which financial institutions deal with financial crimes; various IT solutions for monitoring and managing financial crimes; data organization and governance in the financial crimes context; machine learning and artificial intelligence (AI) in financial crimes; customer-level transaction monitoring system; machine learning-driven alert optimization; AML investigation; bias and ethical pitfalls in machine learning; and enterprise-level AI-driven Financial Crime Investigation (FCI) unit. There is also an Appendix which contains a detailed review of various data sciences approaches that are popular among practitioners. The book discusses each topic through real-life experiences. It also leverages the experience of Chief Compliance Officers of some large organizations to showcase real challenges that heads of large organizations face while dealing with this sensitive topic. It thus delivers a hands-on guide for setting up, managing, and transforming into a best-in-class financial crimes management unit. It is thus an invaluable resource for researchers, students, corporates, and industry watchers alike.

A remarkable facet of the human brain is its ability to manage multiple tasks with apparent simultaneity. Knowledge learned from one task can then be used to enhance problem-solving in other related tasks. In machine learning, the idea of leveraging relevant information across related tasks as inductive biases to enhance learning performance has attracted significant interest. In contrast, attempts to emulate the human brain’s ability to generalize in optimization – particularly in population-based evolutionary algorithms – have received little attention to date. Recently, a novel evolutionary search paradigm, Evolutionary Multi-Task (EMT) optimization, has been proposed in the realm of evolutionary computation. In contrast to traditional evolutionary searches, which solve a single task in a single run, evolutionary multi-tasking algorithm conducts searches concurrently on multiple search spaces corresponding to different tasks or optimization problems,each possessing a unique function landscape. By exploiting the latent synergies among distinct problems, the superior search performance of EMT optimization in terms of solution quality and convergence speed has been demonstrated in a variety of continuous, discrete, and hybrid (mixture of continuous and discrete) tasks. This book discusses the foundations and methodologies of developing evolutionary multi-tasking algorithms for complex optimization, including in domains characterized by factors such as multiple objectives of interest, high-dimensional search spaces and NP-hardness.

Convenzionalmente, la diagnosi di fasciolosi tropicale nei ruminanti si basa sulla dimostrazione coprologica delle uova di Fasciola gigantica. L'esame coprologico non pu essere utilizzato durante il periodo pre-patente della malattia, che corrisponde ai principali effetti patogeni. La biochimica del siero degli animali infetti pu essere utilizzata come indicatore dell'entit del danno tissutale e della gravit dell'infezione. Tuttavia, nessun test attualmente disponibile pu essere considerato sensibile e specifico al 100%. Questo libro descrive la standardizzazione di un test dipstick-ELISA semplice e rapido per la diagnosi della fasciolosi naturale caprina e bubalina utilizzando la cisteina proteinasi 28 kDa dei parassiti adulti. Il dipstick-ELISA stato valutato utilizzando come gold standard il reale stato di infezione degli animali sottoposti ad autopsia. Anche l'aspartato aminotransferasi (AST) e la fosfatasi alcalina (ALP) sono state utilizzate come enzimi marcatori in base agli stadi della fasciolosi. Questo libro quindi molto utile per ricercatori, tecnici di laboratorio e veterinari che si occupano di diagnosi e prevenzione della fasciolosi.

Traditionnellement, le diagnostic de la fasciolose tropicale chez les ruminants repose sur la copro-d monstration des oeufs de Fasciola gigantica. L'examen coprologique ne peut pas tre utilis pendant la p riode pr -patente de la maladie qui correspond aux effets pathog nes majeurs. La biochimie s rique des animaux infect s peut tre utilis e comme indicateur de l' tendue des dommages tissulaires et de la gravit de l'infection. Mais aucun test actuellement disponible ne peut tre consid r comme sensible et sp cifique 100 %. Cet ouvrage d crit la standardisation d'un test dipstick-ELISA simple et rapide pour le diagnostic de la fasciolose caprine et bubaline naturelle en utilisant la cyst ine prot inase de 28 kDa des douves adultes. Le dipstick-ELISA a t valu en utilisant le statut infectieux r el des animaux autopsi s comme talon-or. L'aspartate aminotransf rase (AST) et la phosphatase alcaline (ALP) ont galement t exploit es comme enzymes marqueurs en fonction des stades de la fasciolose. Ce livre est donc tr s utile pour les chercheurs, les techniciens de laboratoire et les v t rinaires travaillant sur le diagnostic et la pr vention de la fasciolose.

Konventionell st tzt sich die Diagnose der tropischen Fasciolose bei Wiederk uern auf den Kopro-Nachweis von Fasciola-gigantica-Eiern. Die koprologische Untersuchung kann in der Pr -Patent-Phase der Krankheit, die den wichtigsten pathogenen Auswirkungen entspricht, nicht verwendet werden. Die Serumbiochemie infizierter Tiere kann als Indikator f r das Ausma der Gewebesch den und den Schweregrad der Infektion dienen. Kein derzeit verf gbarer Test kann jedoch als 100 % empfindlich und spezifisch angesehen werden. In diesem Buch wird die Standardisierung eines einfachen und schnellen Dipstick-ELISA f r die Diagnose der nat rlichen Fasziolose der Ziegen und Bubalinen beschrieben, bei dem die 28 kDa-Cysteinproteinase adulter Egel verwendet wird. Der Dipstick-ELISA wurde anhand des tats chlichen Infektionsstatus der autopsierten Tiere als Goldstandard bewertet. Aspartat-Aminotransferase (AST) und alkalische Phosphatase (ALP) wurden ebenfalls als Markerenzyme in den verschiedenen Stadien der Fasciolose verwendet. Daher ist dieses Buch f r Forscher, Labortechniker und Tier rzte, die sich mit der Diagnose und Pr vention von Fasciolose befassen, sehr hilfreich.

This book bridges the widening gap between two crucial constituents of computational intelligence: the rapidly advancing technologies of machine learning in the digital information age, and the relatively slow-moving field of general-purpose search and optimization algorithms. With this in mind, the book serves to offer a data-driven view of optimization, through the framework of memetic computation (MC). The authors provide a summary of the complete timeline of research activities in MC – beginning with the initiation of memes as local search heuristics hybridized with evolutionary algorithms, to their modern interpretation as computationally encoded building blocks of problem-solving knowledge that can be learned from one task and adaptively transmitted to another. In the light of recent research advances, the authors emphasize the further development of MC as a simultaneous problem learning and optimization paradigm with the potential to showcase human-like problem-solvingprowess; that is, by equipping optimization engines to acquire increasing levels of intelligence over time through embedded memes learned independently or via interactions. In other words, the adaptive utilization of available knowledge memes makes it possible for optimization engines to tailor custom search behaviors on the fly – thereby paving the way to general-purpose problem-solving ability (or artificial general intelligence). In this regard, the book explores some of the latest concepts from the optimization literature, including, the sequential transfer of knowledge across problems, multitasking, and large-scale (high dimensional) search, systematically discussing associated algorithmic developments that align with the general theme of memetics. The presented ideas are intended to be accessible to a wide audience of scientific researchers, engineers, students, and optimization practitioners who are familiar with the commonly used terminologies of evolutionary computation. A full appreciation of the mathematical formalizations and algorithmic contributions requires an elementary background in probability, statistics, and the concepts of machine learning. A prior knowledge of surrogate-assisted/Bayesian optimization techniques is useful, but not essential.