SIDU:Similarity Difference And Uniqueness Method for Explainable AI

A new brand of technical artificial intelligence ( Explainable AI ) research has focused on trying to open up the 'black box' and provide some explainability. This paper presents a novel visual explanation method for deep learning networks in the form of a saliency map that can effectively localize entire object regions. In contrast to the current state-of-the art methods, the proposed method shows quite promising visual explanations that can gain greater trust of human expert. Both quantitative and qualitative evaluations are carried out on both general and clinical data sets to confirm the effectiveness of the proposed method.Comment: Accepted manuscript in IEEE International Conference on Image Processin

The development of an artificial intelligence classifier to automate assessment in large class settings:Preliminary results

This evidence based practice paper presents preliminary results in using an artificial intelligence classifier to mark student assignments in a large class setting. The assessment task consists of an approximately 2000 word reflective essay that is produced under examination conditions and submitted electronically. The marking is a simple pass/fail determination, and no explicit feedback beyond the pass/fail grade is provided to the students. Each year around 1500 students complete this assignment, which places a significant and time-constrained marking load upon the teaching faculty. This paper presents a Natural Language Process (NLP) framework/tool for developing a machine learning based binary classifier for automated assessment of these assignments. The classifier allocates each assignment a score representing the probability that the assignment would receive a passing grade from a human marker. The effectiveness and performance of the classifier is measured by investigating the accuracy of those predictions. Several iterations and statistical analyses were carried out to determine operational thresholds that balance the risks of false positives and false negatives with the required quantity of human marking to assess the assignment. The resulting classifier was able to provide accuracy levels that are potentially feasible in an operational context, and the potential for significant overall reductions in the human marking load for this assignment.</p

Image illumination enhancement with an objective no-reference measure of illumination assessment based on Gaussian distribution mapping

Illumination problems have been an important concern in many image processing applications. The pattern of the histogram on an image introduces meaningful features; hence within the process of illumination enhancement, it is important not to destroy such information. In this paper we propose a method to enhance image illumination using Gaussian distribution mapping which also keeps the information laid on the pattern of the histogram on the original image. First a Gaussian distribution based on the mean and standard deviation of the input image will be calculated. Simultaneously a Gaussian distribution with the desired mean and standard deviation will be calculated. Then a cumulative distribution function of each of the Gaussian distributions will be calculated and used in order to map the old pixel value onto the new pixel value. Another important issue in the field of illumination enhancement is absence of a quantitative measure for the assessment of the illumination of an image. In this research work, a quantitative measure indicating the illumination state, i.e. contrast level and brightness of an image, is also proposed. The measure utilizes the estimated Gaussian distribution of the input image and the Kullback-Leibler Divergence (KLD) between the estimated Gaussian and the desired Gaussian distributions to calculate the quantitative measure. The experimental results show the effectiveness and the reliability of the proposed illumination enhancement technique, as well as the proposed illumination assessment measure over conventional and state-of-the-art techniques. (C) 2015 Karabuk University. Production and hosting by Elsevier B.V

39 - Expert level evaluations for explainable AI(XAI) methods in the medical domain

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