Explainable Image Similarity: Integrating Siamese Networks and Grad-CAM.

With the proliferation of image-based applications in various domains, the need for accurate and interpretable image similarity measures has become increasingly critical. Existing image similarity models often lack transparency, making it challenging to understand the reasons why two images are considered similar. In this paper, we propose the concept of explainable image similarity, where the goal is the development of an approach, which is capable of providing similarity scores along with visual factual and counterfactual explanations.

Special Issue on Machine Learning and AI for Sensors.

This article summarizes the works published under the "" (https://www [...

A Convolutional Autoencoder Topology for Classification in High-Dimensional Noisy Image Datasets.

Deep convolutional neural networks have shown remarkable performance in the image classification domain. However, Deep Learning models are vulnerable to noise and redundant information encapsulated into the high-dimensional raw input images, leading to unstable and unreliable predictions. Autoencoders constitute an unsupervised dimensionality reduction technique, proven to filter out noise and redundant information and create robust and stable feature representations.

Automatic classification of solitary pulmonary nodules in PET/CT imaging employing transfer learning techniques.

Early and automatic diagnosis of Solitary Pulmonary Nodules (SPN) in Computed Tomography (CT) chest scans can provide early treatment for patients with lung cancer, as well as doctor liberation from time-consuming procedures. The purpose of this study is the automatic and reliable characterization of SPNs in CT scans extracted from Positron Emission Tomography and Computer Tomography (PET/CT) system. To achieve the aforementioned task, Deep Learning with Convolutional Neural Networks (CNN) is applied.

Smoothing and stationarity enforcement framework for deep learning time-series forecasting.

Time-series analysis and forecasting problems are generally considered as some of the most challenging and complicated problems in data mining. In this work, we propose a new complete framework for enhancing deep learning time-series models, which is based on a data preprocessing methodology. The proposed framework focuses on conducting a sequence of transformations on the original low-quality time-series data for generating high-quality time-series data, "" for efficiently training and fitting a deep learning model.

An Improved Self-Labeled Algorithm for Cancer Prediction.

Nowadays, cancer constitutes the second leading cause of death globally. The application of an efficient classification model is considered essential in modern diagnostic medicine in order to assist experts and physicians to make more accurate and early predictions and reduce the rate of mortality. Machine learning techniques are being broadly utilized for the development of intelligent computational systems, exploiting the recent advances in digital technologies and the significant storage capabilities of electronic media.

Explainable Machine Learning Framework for Image Classification Problems: Case Study on Glioma Cancer Prediction.

Image classification is a very popular machine learning domain in which deep convolutional neural networks have mainly emerged on such applications. These networks manage to achieve remarkable performance in terms of prediction accuracy but they are considered as black box models since they lack the ability to interpret their inner working mechanism and explain the main reasoning of their predictions. There is a variety of real world tasks, such as medical applications, in which interpretability and explainability play a significant role.