Yoram Segal

This chapter presents an approach to organizational data governance through autonomous Artificial Intelligence agent-based policy enforcement systems, specifically leveraging Data Security Platform technology capabilities. Traditional access control mechanisms often fail to adequately address the dynamic and complex nature of modern enterprise data environments, leading to significant governance gaps that compromise data quality, security, and compliance across distributed organizational infrastructures. The Intelligent Policy Agent Network (IPAN) architecture is introduced as a comprehensive solution where Large Language Model-powered agents operate as autonomous policy enforcers, performing real-time database discovery, enabling conversational access control, implementing dynamic data masking, and ensuring cross-database policy enforcement. Through empirical evaluation across diverse industry sectors including finance, insurance, and healthcare, the framework demonstrates substantial improvements in policy compliance rates, significant reductions in unauthorized access incidents, and dramatic decreases in manual governance overhead.

This paper introduces a novel Dynamic Access Decision Scoring (ADS) framework that integrates cognitive computing and big data to address emerging challenges in controlling access to healthcare data systems. Traditional rule-based access control mechanisms lack the cognitive capabilities to process dynamic security requirements, creating vulnerabilities when managing large-scale electronic health records (EHRs). Our framework leverages cognitive computing by combining machine learning algorithms, behavioral pattern analysis, and real-time data analytics to create an intelligent security system that safeguards sensitive medical data while maintaining computational efficiency. The core innovation lies in developing a cognitive mathematical template that data scientists and researchers can adapt through deep learning and analytical processing. The framework introduces a modular formula as an adaptive cognitive pattern, incorporating four computational elements: machine learning predictions, historical pattern recognition, risk analytics, and temporal context processing. Each element employs cognitive algorithms that security architects can calibrate within their specific data ecosystems. The framework’s primary contribution demonstrates how cognitive probabilistic approaches can dynamically adapt to complex healthcare environments. This research advances big data security by establishing a cognitive computing foundation for making access control decisions, effectively bridging theoretical data models with practical machine intelligence implementation in healthcare information systems.

The chapter explores the transformative role of artificial intelligence in healthcare data security and management. Integrating AI-driven security platforms represents a fundamental shift from traditional manual approaches to automated, intelligent systems capable of protecting sensitive patient information while maintaining operational efficiency. Through sophisticated deep learning architectures and advanced natural language processing, modern healthcare security systems achieve unprecedented threat detection and policy enforcement accuracy, with performance metrics exceeding 99% accuracy in critical security operations. Implementing specialized transformer-based models and dynamic permission analysis frameworks enables real-time security decisions while maintaining strict regulatory compliance. The chapter examines how GenAI and specialized language models enhance healthcare security through context-aware processing and automated policy management. The chapter focuses on the segmentation of administrative access and the automation of compliance processes through AI-driven systems. The discussion covers technical implementations and strategic considerations, including leadership requirements and scaling methodologies. The chapter identifies emerging trends in autonomous security systems and cross-domain integration, emphasizing the continuing evolution of AI-driven healthcare security platforms. This comprehensive examination provides a framework for understanding and implementing advanced security measures in modern healthcare environments.

The objective of this study is to evaluate the performance of functional tests using a camera-based system and machine learning techniques. Specifically, we investigate whether OpenPose and any standard camera can be used to assess the quality of the Single Leg Squat Test and Step Down Test functional tests. We recorded these exercises performed by forty-six healthy subjects, extract motion data, and classify them to expert assessments by three independent physiotherapists using 15 binary parameters. We calculated ranges of movement in Keypoint-pair orientations, joint angles, and relative distances of the monitored segments and used machine learning algorithms to predict the physiotherapists’ assessments. Our results show that the AdaBoost classifier achieved a specificity of 0.8, a sensitivity of 0.68, and an accuracy of 0.7. Our findings suggest that a camera-based system combined with machine learning algorithms can be a simple and inexpensive tool to assess the performance quality of functional tests.

In this article, we introduce a new approach to human movement by defining the movement as a static super object represented by a single two-dimensional image. The described method is applicable in remote healthcare applications, such as physiotherapeutic exercises. It allows researchers to label and describe the entire exercise as a standalone object, isolated from the reference video. This approach allows us to perform various tasks, including detecting similar movements in a video, measuring and comparing movements, generating new similar movements, and defining choreography by controlling specific parameters in the human body skeleton. As a result of the presented approach, we can eliminate the need to label images manually, disregard the problem of finding the start and the end of an exercise, overcome synchronization issues between movements, and perform any deep learning network-based operation that processes super objects in images in general. As part of this article, we will demonstrate two application use cases: one illustrates how to verify and score a fitness exercise. In contrast, the other illustrates how to generate similar movements in the human skeleton space by addressing the challenge of supplying sufficient training data for deep learning applications (DL). A variational auto encoder (VAE) simulator and an EfficientNet-B7 classifier architecture embedded within a Siamese twin neural network are presented in this paper in order to demonstrate the two use cases. These use cases demonstrate the versatility of our innovative concept in measuring, categorizing, inferring human behavior, and generating gestures for other researchers.

In this article, we introduce a new approach to human movement by defining the movement as a static object or a super object in one two-dimensional image. This method can allow researchers to label and describe the total movement as an object isolated from a reference video. This ap-proach allows us to perform various tasks, including finding similar movements in a video, measuring, and comparing movements, generating new similar movements, and defining chore-ography by controlling specific parameters in the human body skeleton. As a result of the pre-sented approach, we can eliminate the need to label images manually, disregard the problem of finding the beginning and the end of a movement, overcome synchronization issues between movements, and perform any deep learning network-based operation that processes super objects in images in general. As part of this article, we will demonstrate two application use cases: one il-lustrates how to verify and score a requested movement. In contrast, the other illustrates how to generate similar movements in the human skeleton space by addressing the challenge of supply-ing sufficient training data for deep learning applications (DL). A Variational Auto Encoder (VAE) simulator and an EfficientNet-B7 classifier architecture embedded within a Siamese twin neural network are presented in this paper in order to demonstrate two use cases. These use cases demonstrated the versatility of our innovative concept in measuring, categorizing, inferring hu-man behavior, and generating gestures for other researchers.

This Ph.D. thesis develops a neural network simulator for quantifying, tagging, and inferring human gestures using an anonymized patient gesture database. Deep Learning (DL) applications require a sufficient data set for training. In this work, we propose enriching a database that contains a limited number of videos of human physiotherapy exercises by generating synthetic data. Our pose generator produces human movement in the form of skeletal vectors. We use OpenPose (OP) to convert videos and images containing multiple individuals into human skeletal. Within every video frame, OP represents each pose of the human skeleton as a vector in three-dimensional Euclidean space. We employ the Generative Adversarial Network (GAN) to generate new samples and control the motion parameters. We rearrange the joints in our skeletal model to emphasize the connections between them by using depth-first search (DFS), a tree structure search algorithm. Moreover, this research examines common challenges associated with capturing human gesture data, including synchronizing activities, temporal, and spatial relations, and how to address them. We intend to build an innovative simulator that will generate a set of human virtual choreography movements from a textual script.

Inter-Prediction is used effectively in multiple standards, including H.264 and HEVC (also known as H.265). It leverages correlation between blocks of consecutive video frames in order to perform motion compensation and thus predict block pixel values and reduce transmission bandwidth. In order to reduce the magnitude of the transmitted Motion Vector (MV) and thus reduce bandwidth, the encoder utilizes Predicted Motion Vector (PMV), which is derived by taking the median vector of the corresponding MVs of the neighboring blocks. In this research, we propose innovative methods, based on neural networks prediction, for improving the accuracy of the calculated PMV. We begin by showing a straightforward approach of calculating the best matching PMV and signaling its neighbor block index value to the decoder while reducing the number of bits required to represent the result without adding any computation complexity. Then we use a classification Fully Connected Neural Networks (FCNN) to estimate from neighbors the PMV without requiring signaling and show the advantage of the approach when employed for high motion movies. We demonstrate the advantages using fast forward movies. However, the same improvements apply to camera streams of autonomous vehicles, drone cameras, Pan-Tilt-Zoom (PTZ) cameras, and similar applications whereas the MVs magnitudes are expected to be large. We also introduce a regression FCNN to predict the PMV. We calculate Huffman coded streams and demonstrate an order of ~34% reduction in number of bits required to transmit the best matching calculated PMV without reducing the quality, for fast forward movies with high motion.