In machine learning, the data imbalance imposes challenges to perform data analytics in almost all areas of real-world research. The raw primary data often suffers from the skewed perspective of data distribution of one class over the other as in the case of computer vision, information security, marketing, and medical science. The goal of this article is to present a comparative analysis of the approaches from the reference of data pre-processing, algorithmic and hybrid paradigms for contemporary imbalance data analysis techniques, and their comparative study in lieu of different data distribution and their application areas.

In this paper, we present the potential of Explainable Artificial Intelligence methods for decision support in medical image analysis scenarios. Using three types of explainable methods applied to the same medical image data set, we aimed to improve the comprehensibility of the decisions provided by the Convolutional Neural Network (CNN). In vivo gastral images obtained by a video capsule endoscopy (VCE) were the subject of visual explanations, with the goal of increasing health professionals’ trust in black-box predictions. We implemented two post hoc interpretable machine learning methods, called Local Interpretable Model-Agnostic Explanations (LIME) and SHapley Additive exPlanations (SHAP), and an alternative explanation approach, the Contextual Importance and Utility (CIU) method. The produced explanations were assessed by human evaluation. We conducted three user studies based on explanations provided by LIME, SHAP and CIU. Users from different non-medical backgrounds carried out a series of tests in a web-based survey setting and stated their experience and understanding of the given explanations. Three user groups (n = 20, 20, 20) with three distinct forms of explanations were quantitatively analyzed. We found that, as hypothesized, the CIU-explainable method performed better than both LIME and SHAP methods in terms of improving support for human decision-making and being more transparent and thus understandable to users. Additionally, CIU outperformed LIME and SHAP by generating explanations more rapidly. Our findings suggest that there are notable differences in human decision-making between various explanation support settings. In line with that, we present three potential explainable methods that, with future improvements in implementation, can be generalized to different medical data sets and can provide effective decision support to medical experts.

The Internet of Things (IoT) is envisioned as a ubiquitous computing infrastructure in which everything becomes connected, enabling gigantic information exchange among Things and people. These connected smart Things generate an enormous amount of data which need to be efficiently managed to form a unified global IoT. Unfortunately, due to the lack of acceptable open standards, communication protocols, and support for device/service discovery, the recent IoT deployments in smart environments (e.g., smart home, smart building, smart city) are posing imperative challenges related to interoperability, discovery, and the configuration of deployed objects, since the number of objects is expected to grow over time. Therefore, it is of utmost importance to provide open and scalable solutions for the discovery of devices (i.e., Things), their configuration, and data management. This paper introduces an open and scalable IoT platform by adopting the modular characteristics of edge computing for smart environments. This paper: (i) performs a systematic literature review of IoT-based infrastructures and analyzes the scalability requirements; (ii) proposes a layered IoT platform for smart environments that fosters heterogeneity, interoperability, discovery, and scalability; and (iii) demonstrates the applicability of the proposed solution by relying on a comprehensive study of a Väre smart building use case at Aalto University.

Diabetic Retinopathy is an eye disorder that affects people suffering from diabetes. Higher sugar levels in blood leads to damage of blood vessels in eyes and may even cause blindness. Diabetic retinopathy is identified by red spots known as microanuerysms and bright yellow lesions called exudates. It has been observed that early detection of exudates and microaneurysms may save the patient’s vision and this paper proposes a simple and effective technique for diabetic retinopathy. Both publicly available and real time datasets of colored images captured by fundus camera have been used for the empirical analysis. In the proposed work, grading has been done to know the severity of diabetic retinopathy i.e. whether it is mild, moderate or severe using exudates and micro aneurysms in the fundus images. An automated approach that uses image processing, features extraction and machine learning models to predict accurately the presence of the exudates and micro aneurysms which can be used for grading has been proposed. The research is carried out in two segments; one for exudates and another for micro aneurysms. The grading via exudates is done based upon their distance from macula whereas grading via micro aneurysms is done by calculating their count. For grading using exudates, support vector machine and K-Nearest neighbor show the highest accuracy of 92.1% and for grading using micro aneurysms, decision tree shows the highest accuracy of 99.9% in prediction of severity levels of the disease.

Convolutional neural networks (CNN) have become a popular choice for image segmentation and classification. Internal body images are obscure in nature with involvement of noise, luminance variation, rotation and blur. Thus optimal choice of features for machine learning model to classify bleeding is still an open problem. CNN is efficient for attribute selection and ensemble learning makes a generalized robust system. Capsule endoscopy is a new technology which enables a gastroenterologist to visualize the entire digestive tract including small bowel to diagnose bleeding, ulcer and polyp. This paper presents a supervised learning ensemble to detect the bleeding in the images of Wireless Capsule Endoscopy. It accurately finds out the best possible combination of attributes required to classify bleeding symptoms in endoscopy images. A careful setting for CNN layer options and optimizer for back propagation after reducing the color palette using minimum variance quantization has shown promising results. Results of testing on public and real dataset has been analyzed. Proposed ensemble is able to achieve 0.95 on the public endoscopy dataset and 0.93 accuracy on the real video dataset. A detailed data analysis has also been incorporated in the study including RGB pixel intensities, distributions of binary classes and various class ratios for training.