The Role Artificial Intelligence in Modern Banking: An Exploration of AI-Driven Approaches for Enhanced Fraud Prevention, Risk Management, and Regulatory Compliance

Banking fraud prevention and risk management are paramount in the modern financial landscape, and the integration of Artificial Intelligence (AI) offers a promising avenue for advancements in these areas. This research delves into the multifaceted applications of AI in detecting, preventing, and managing fraudulent activities within the banking sector. Traditional fraud detection systems, predominantly rule-based, often fall short in real-time detection capabilities. In contrast, AI can swiftly analyze extensive transactional data, pinpointing anomalies and potentially fraudulent activities as they transpire. One of the standout methodologies includes the use of deep learning, particularly neural networks, which, when trained on historical fraud data, can discern intricate patterns and predict fraudulent transactions with remarkable precision.  Furthermore, the enhancement of Know Your Customer (KYC) processes is achievable through Natural Language Processing (NLP), where AI scrutinizes textual data from various sources, ensuring customer authenticity. Graph analytics offers a unique perspective by visualizing transactional relationships, potentially highlighting suspicious activities such as rapid fund transfers indicative of money laundering. Predictive analytics, transcending traditional credit scoring methods, incorporates a diverse data set, offering a more comprehensive insight into a customer's creditworthiness.  The research also underscores the importance of user-friendly interfaces like AI-powered chatbots for immediate reporting of suspicious activities and the integration of advanced biometric verifications, including facial and voice recognition. Geospatial analysis and behavioral biometrics further bolster security by analyzing transaction locations and user interaction patterns, respectively.  A significant advantage of AI lies in its adaptability. Self-learning systems ensure that as fraudulent tactics evolve, the AI mechanisms remain updated, maintaining their efficacy. This adaptability extends to phishing detection, IoT integration, and cross-channel analysis, providing a comprehensive defense against multifaceted fraudulent attempts. Moreover, AI's capability to simulate economic scenarios aids in proactive risk management, while its ability to ensure regulatory compliance automates and streamlines a traditionally cumbersome process

Advancements in Medical Imaging and Diagnostics with Deep Learning Technologies

Medical imaging has long been a cornerstone in diagnostic medicine, providing clinicians with a non-invasive method to visualize internal structures and processes. However, traditional imaging techniques have faced challenges in resolution, safety concerns related to radiation exposure, and the need for invasive procedures for clearer visualization. With the advent of deep learning technologies, significant advancements have been made in the field of medical imaging, addressing many of these challenges and introducing new capabilities. This research seeks into the integration of deep learning in enhancing image resolution, leading to clearer and more detailed visualizations. Furthermore, the ability to reconstruct three-dimensional images from traditional two-dimensional scans offers a more comprehensive view of the area under examination. Automated analysis powered by deep learning algorithms not only speeds up the diagnostic process but also detects anomalies that might be overlooked by the human eye. Predictive analysis, based on these enhanced images, can forecast the likelihood of diseases, and real-time analysis during surgeries ensures immediate feedback, enhancing the precision of medical procedures. Safety in medical imaging has also seen improvements. Techniques powered by deep learning require reduced radiation, minimizing risks to patients. Additionally, the enhanced clarity and detail in images reduce the need for invasive procedures, further ensuring patient safety. The integration of imaging data with Electronic Health Records (EHR) has paved the way for personalized care recommendations, tailoring treatments based on individual patient history and current diagnostics. Lastly, the role of deep learning extends to medical education, where it aids in creating realistic simulations and models, equipping medical professionals with better training tools

Contemporary Robotics

This book book is a collection of 18 chapters written by internationally recognized experts and well-known professionals of the field. Chapters contribute to diverse facets of contemporary robotics and autonomous systems. The volume is organized in four thematic parts according to the main subjects, regarding the recent advances in the contemporary robotics. The first thematic topics of the book are devoted to the theoretical issues. This includes development of algorithms for automatic trajectory generation using redudancy resolution scheme, intelligent algorithms for robotic grasping, modelling approach for reactive mode handling of flexible manufacturing and design of an advanced controller for robot manipulators. The second part of the book deals with different aspects of robot calibration and sensing. This includes a geometric and treshold calibration of a multiple robotic line-vision system, robot-based inline 2D/3D quality monitoring using picture-giving and laser triangulation, and a study on prospective polymer composite materials for flexible tactile sensors. The third part addresses issues of mobile robots and multi-agent systems, including SLAM of mobile robots based on fusion of odometry and visual data, configuration of a localization system by a team of mobile robots, development of generic real-time motion controller for differential mobile robots, control of fuel cells of mobile robots, modelling of omni-directional wheeled-based robots, building of hunter- hybrid tracking environment, as well as design of a cooperative control in distributed population-based multi-agent approach. The fourth part presents recent approaches and results in humanoid and bioinspirative robotics. It deals with design of adaptive control of anthropomorphic biped gait, building of dynamic-based simulation for humanoid robot walking, building controller for perceptual motor control dynamics of humans and biomimetic approach to control mechatronic structure using smart materials

Sequence-Based Specification of Embedded Systems

Software has become integral to the control mechanism of modern devices. From transportation and medicine to entertainment and recreation, embedded systems integrate fundamentally with time and the physical world to impact our lives; therefore, product dependability and safety are of paramount importance. Model-based design has evolved as an effective way to prototype systems and to analyze system function through simulation. This process mitigates the problems and risks associated with embedding software into consumer and industrial products. However, the most difficult tasks remain: Getting the requirements right and reducing them to precise specifications for development, and providing compelling evidence that the product is fit for its intended use. Sequence-based specification of discrete systems, using well-chosen abstractions, has proven very effective in exposing deficiencies in requirements, and then producing precise specifications for good requirements. The process ensures completeness, consistency, and correctness by tracing each specification decision precisely to the requirements. Likewise, Markov chain based testing has proven effective in providing evidence that systems are fit for field use. Model-based designs integrate discrete and continuous behavior; models have both hybrid and switching properties. In this research, we extend sequence-based specification to explicitly include time, continuous functions, nondeterminism, and internal events for embedded real-time systems. The enumeration is transformed into an enumeration hybrid automaton that acts as the foundation for an executable model-based design and an algebraic hybrid I/O automaton with valuable theoretical properties. Enumeration is a step-wise problem solving technique that complements model-based design by converting ordinary requirements into precise specifications. The goal is a complete, consistent, and traceably correct design with a basis for automated testing

Reachability Analysis of Cyber-Physical Systems Using Symbolic-Numeric Techniques

In this thesis, we address the problem of reachability analysis in cyber-physical systems. These are systems engineered by interfacing computational components with the physical world. They provide partially or fully automated safety-critical services in the form of medical devices, autonomous vehicles, avionics and power systems. We propose techniques to reason about the reachability of such systems, and provide methods for falsifying their safety properties. We model the cyber component as a software program and the physical component as a hybrid dynamical system. Unlike model based analysis, which uses either a purely symbolic or a numerical approach, we argue in favor of using a combination of the two. We justify this by noting that the software program running on a computer is completely specified and has precise semantics. In contrast, the model of the physical system is only an approximation. Hence, we treat the former as a white box, but treat the latter as a black box. Using symbolic methods for the cyber components and numerical methods for hybrid systems, we carefully capture the complex behaviors of software programs and circumvent the difficulty in analyzing complex models developed through first principles. To combine the two techniques, we use a Counterexample Guided Abstraction Refinement (CEGAR) framework. Furthermore, we explore learning techniques like regression and piecewise affine modeling to estimate and represent black box hybrid dynamical systems for the purpose of falsification. We use prototype implementations to demonstrate the effectiveness of presented ideas. Using non-trivial benchmarks, we compare their performance against the state of the art. We also comment on their applicability and discuss ideas for further improvement

Control Theory in Engineering

The subject matter of this book ranges from new control design methods to control theory applications in electrical and mechanical engineering and computers. The book covers certain aspects of control theory, including new methodologies, techniques, and applications. It promotes control theory in practical applications of these engineering domains and shows the way to disseminate researchers’ contributions in the field. This project presents applications that improve the properties and performance of control systems in analysis and design using a higher technical level of scientific attainment. The authors have included worked examples and case studies resulting from their research in the field. Readers will benefit from new solutions and answers to questions related to the emerging realm of control theory in engineering applications and its implementation