An edge-driven multi-agent optimization model for infectious disease detection

This research work introduces a new intelligent framework for infectious disease detection by exploring various emerging and intelligent paradigms. We propose new deep learning architectures such as entity embedding networks, long-short term memory, and convolution neural networks, for accurately learning heterogeneous medical data in identifying disease infection. The multi-agent system is also consolidated for increasing the autonomy behaviours of the proposed framework, where each agent can easily share the derived learning outputs with the other agents in the system. Furthermore, evolutionary computation algorithms, such as memetic algorithms, and bee swarm optimization controlled the exploration of the hyper-optimization parameter space of the proposed framework. Intensive experimentation has been established on medical data. Strong results obtained confirm the superiority of our framework against the solutions that are state of the art, in both detection rate, and runtime performance, where the detection rate reaches 98% for handling real use cases.publishedVersio

Enhancing the Fairness and Performance of Edge Cameras with Explainable AI

The rising use of Artificial Intelligence (AI) in human detection on Edge camera systems has led to accurate but complex models, challenging to interpret and debug. Our research presents a diagnostic method using Explainable AI (XAI) for model debugging, with expert-driven problem identification and solution creation. Validated on the Bytetrack model in a real-world office Edge network, we found the training dataset as the main bias source and suggested model augmentation as a solution. Our approach helps identify model biases, essential for achieving fair and trustworthy models.Comment: IEEE ICCE 202

Images and Spectra From the Interior of a Relativistic Fireball

The detection of an afterglow, following a gamma-ray burst (GRB), can be described reasonably well by synchrotron emission from a relativistic spherical expanding blast wave, driven by an expanding fireball. We perform detailed calculations considering the emission from the whole region behind the shock front. We use the Blandford McKee self similar adiabatic solution to describe the fluid behind the shock. Using this detailed model, we derive expressions for the peak flux, and the peak frequency at a given observed time. These expressions provide important numerical corrections to previous, more simplified models. We calculate the observed light curve and spectra for several magnetic field models. We show that both the light curve and the spectra are flat near the peak. This rules out the interpretation of the optical peak of GRB970508 as the peak of the light curve, predicted by the existing fireball models. We calculate the observed image of a GRB afterglow. The observed image is bright near the edge and dimmer at the center, thus creating a ring. The contrast between the edge and the center is larger at high frequencies and the width of the ring is smaller.Comment: 32 page latex file including 14 figures and 2 table

Hierarchical Object Parsing from Structured Noisy Point Clouds

Object parsing and segmentation from point clouds are challenging tasks because the relevant data is available only as thin structures along object boundaries or other features, and is corrupted by large amounts of noise. To handle this kind of data, flexible shape models are desired that can accurately follow the object boundaries. Popular models such as Active Shape and Active Appearance models lack the necessary flexibility for this task, while recent approaches such as the Recursive Compositional Models make model simplifications in order to obtain computational guarantees. This paper investigates a hierarchical Bayesian model of shape and appearance in a generative setting. The input data is explained by an object parsing layer, which is a deformation of a hidden PCA shape model with Gaussian prior. The paper also introduces a novel efficient inference algorithm that uses informed data-driven proposals to initialize local searches for the hidden variables. Applied to the problem of object parsing from structured point clouds such as edge detection images, the proposed approach obtains state of the art parsing errors on two standard datasets without using any intensity information.Comment: 13 pages, 16 figure