American Express (United States)

The current paper provides an artificial intelligence-based end to end data validation and regulatory compliance framework in real time cloud native data pipelines. Current solutions tend to apply data quality assurance and compliance enforcement separately or as an after-processing activity which causes a fragmented trust, delayed anomaly detection, and a lack of flexibility to changing regulations. To overcome such restrictions, the framework presented here proposes a cascaded structure that consists of two complementary modules: the Aegis Fuzzy Recta Net, which is responsible of the adaptive detection and rectification of anomalies and the Lexi-Regula Transformer Engine, which is needed to interpret and enforce regulatory policies dynamically. Raw streaming data are consumed, regularized and verified and then finally undergo compliance classification in a single operational cycle. The framework is tested on the AWS Public NYC Taxi and Limousine Trips data to reflect scalability and application in real time. The experimental results demonstrate effective reduction of the severity of the anomaly and the high performance in the compliance classification in terms of precision, recall, and overall accuracy. The suggested solution allows making interpretable, regulation-conscious decisions and being robust and scalable, which is appropriate in the context of compliance-sensitive distributed cloud deployments.

Quantum error correction (QEC) is critical for quantum computing. However, current implementations often add significant circuit depth to quantum algorithms. Parity measurements have been shown to serve as a lightweight alternative to traditional QEC by non-destructively verifying quantum states in circuits. In turn, this enables the rejection of measurements that result from the use of incorrect mid-circuit states. We discuss preliminary work that indicates this method improves simulated QAOA optimization performance in noisy environments with significantly fewer gates than full QEC approaches. Additionally, we examine how such parity-based approaches perform as a function of noise using a simulated teleportation circuit.

10.70593/978-93-49910-62-1_10

Our goal is to articulate the transition of the core banking infrastructure from a traditional on-premise existing world into a digital first world, where both the digital and physical branches integrate and complement each other. We describe key areas for successful implementation of this digital evolution - APIs for integration of services across banking and non-banking solution providers, convergence of Digital Banking app with Digital Front-end systems, and embracing of advanced and emerging technologies – a next generation public cloud infrastructure to create new banking business models, data analytics as the operating system to offer more personalized products and services, and blockchain technology to develop a new financial services ecosystem.

Skin acts as a natural shield, protecting the body from ultraviolet rays, extreme weather, and harmful chemicals. However, it can be affected by pollution, weakened immunity, and unhealthy lifestyles, leading to various skin diseases. Early detection of these conditions is crucial for timely treatment and better outcomes. Existing research has often overlooked skin diseases with similar visual characteristics, making it challenging to distinguish between different conditions using visual inspection alone. To address this, the paper proposes an AI-enabled prediction framework for skin disease prediction using EN-QNN and Grad-CAM. Initially, the images are collected using IoMT devices of skin diseases and undergo preprocessing, which includes resizing, noise removal and contrast enhancement using AK-CLAHE, followed by color analysis and segmentation using YCbCr and DF-U-Net. Morphological operations are then applied during post-processing. The shape and structure of lesions are analyzed using CMED. Meanwhile, using Grad-CAM, Contextual Information Analysis (CIA) is performed on preprocessed data. Concurrently, disease symptom prediction data (i.e. clinical data) are collected, and features are extracted from this data, including boundary localization and CIA. Finally, skin diseases are classified using EN-QNN. The proposed model achieved a high accuracy of 98.68051%, surpassing current techniques.

This paper describes a novel shared learning-based cloud computing architecture for smart services that prioritizes privacy and resource efficiency. Flexible resource distribution and differential privacy solutions protect data and speed up numerous energy suppliers. Dynamically altering learning rates and resource utilization, depending on provider computer power, achieves scaling and decreases connection costs. Private data is protected via secure gradient-sharing mechanisms when the model is modified. The recommended strategy improves model accuracy, processing speed, and energy consumption. It speeds up the process while maintaining anonymity. This ensures the system operates properly in diverse settings. The strategy increases utility seller fairness by considering resource supply inequalities. This makes it ideal for large-scale applications. After solving the fundamental concerns of scalability, security, and efficiency, this shared learning system makes it simple for models to function in a variety of data circumstances. Thus, the provided solution protects machine learning privacy for smart utility applications.

The advent of artificial intelligence (AI) has driven an emergence in applications demanding online responses to immense amounts of data. Typically, the cloud-based deployment of these applications requires resource management to be optimized for high performance while meeting cost efficiency. However, conventional static resource allocation methods may only partially apply to the context of real-time AI applications as they are dynamic, unpredictable, and require more efficient & adaptive ways of allocating resources. To address this challenge, and as a solution in this research, we introduce an RL-based method for online resource management of cloud-based AI systems. Reinforcement learning ($\mathbf{R L}$) is a machine-learning algorithm that allows systems to learn how best to perform tasks in changing environments based on feedback from those interactions with the environment. The third way our proposed approach utilizes the weapon of RL entails allowing for dynamic resource allocation so that resources are allocated when needed rather than from a static method as set at design time.