Tag: Advanced Review

The early detection of dementia poses many quandaries for clinicians and potential harm for patients, as well as possible benefits.

Abstract

Dementia poses a growing challenge for health services but remains stigmatized and under-recognized. Digital technologies to aid the earlier detection of dementia are approaching market. These include traditional cognitive screening tools presented on mobile devices, smartphone native applications, passive data collection from wearable, in-home and in-car sensors, as well as machine learning techniques applied to clinic and imaging data. It has been suggested that earlier detection and diagnosis may help patients plan for their future, achieve a better quality of life, and access clinical trials and possible future disease modifying treatments. In this review, we explore whether digital tools for the early detection of dementia can or should be deployed, by assessing them against the principles of ethical screening programs. We conclude that while the importance of dementia as a health problem is unquestionable, significant challenges remain. There is no available treatment which improves the prognosis of diagnosed disease. Progression from early-stage disease to dementia is neither given nor currently predictable. Available technologies are generally not both minimally invasive and highly accurate. Digital deployment risks exacerbating health inequalities due to biased training data and inequity in digital access. Finally, the acceptability of early dementia detection is not established, and resources would be needed to ensure follow-up and support for those flagged by any new system. We conclude that early dementia detection deployed at scale via digital technologies does not meet standards for a screening program and we offer recommendations for moving toward an ethical mode of implementation.

This article is categorized under: Application Areas > Health Care Commercial, Legal, and Ethical Issues > Ethical Considerations Technologies > Artificial Intelligence

Data mining (DM) and machine learning (ML) applications in medical diagnostic systems are budding. Data privacy is extremely essential in these systems as healthcare data are highly sensitive. The proposed work first discusses various privacy and security challenges in these systems. To address these next, we discussed different privacy-preserving (PP) computation techniques in the context of DM and ML for secure data evaluation and processing. The state-of-the-art applications of these systems in healthcare are analyzed at various stages such as data collection, data publication, data distribution, and output phases regarding PPDM, and input, model, training, and output phases in the context of PPML. Furthermore, PP federated learning is also discussed.

Abstract

Data mining (DM) and machine learning (ML) applications in medical diagnostic systems are budding. Data privacy is essential in these systems as healthcare data are highly sensitive. The proposed work first discusses various privacy and security challenges in these systems. To address these next, we discuss different privacy-preserving (PP) computation techniques in the context of DM and ML for secure data evaluation and processing. The state-of-the-art applications of these systems in healthcare are analyzed at various stages such as data collection, data publication, data distribution, and output phases regarding PPDM and input, model, training, and output phases in the context of PPML. Furthermore, PP federated learning is also discussed. Finally, we present open challenges in these systems and future research directions.

This article is categorized under: Application Areas > Health Care Technologies > Machine Learning Commercial, Legal, and Ethical Issues > Security and Privacy

Abstract

A review of the deep learning based image steganography techniques is presented in this paper. For completeness, the recent traditional steganography techniques are also discussed briefly. The three key parameters (security, embedding capacity, and invisibility) for measuring the quality of an image steganographic technique are described. Various steganography techniques, with emphasis on the above three key parameters, are reviewed. The steganography techniques are classified here into three main categories: Traditional, Hybrid, and fully Deep Learning. The hybrid techniques are further divided into three sub-categories: Cover Generation, Distortion Learning, and Adversarial Embedding. The fully Deep Learning techniques, based on the nature of the input, are further divided into three sub-categories: GAN Embedding, Embedding Less, and Category Label. The main ideas of the important deep learning based steganography techniques are described. The strong and weak features of these techniques are outlined. The results reported by researchers on benchmark data sets CelebA, Bossbase, PASCAL-VOC12, CIFAR-100, ImageNet, and USC-SIPI are used to evaluate the performance of various steganography techniques. Analysis of the results shows that there is scope for new suitable deep learning architectures that can improve the capacity and invisibility of image steganography.

This article is categorized under: Technologies > Computational Intelligence Technologies > Machine Learning Technologies > Artificial Intelligence

Predictive Maintenance from a Data Mining perspective: this review analyzes the most significant predictive maintenance (PdM) contributions in recent years from Data Mining (DM) perspective. An exhaustive study is carried out to determine the most used DM techniques for solving each specific PdM problem. A specific taxonomy is proposed that summarizes the main techniques in three main steps of the DM process: data acquisition, data preprocessing and model building. Moreover,DM tasks are related with the different PdM problems. Finally, the future trends considering the growth of the last few years in PdM from a DM perspective are also analyzed.

Abstract

Predictive maintenance is a field of study whose main objective is to optimize the timing and type of maintenance to perform on various industrial systems. This aim involves maximizing the availability time of the monitored system and minimizing the number of resources used in maintenance. Predictive maintenance is currently undergoing a revolution thanks to advances in industrial systems monitoring within the Industry 4.0 paradigm. Likewise, advances in artificial intelligence and data mining allow the processing of a great amount of data to provide more accurate and advanced predictive models. In this context, many actors have become interested in predictive maintenance research, becoming one of the most active areas of research in computing, where academia and industry converge. The objective of this paper is to conduct a systematic literature review that provides an overview of the current state of research concerning predictive maintenance from a data mining perspective. The review presents a first taxonomy that implies different phases considered in any data mining process to solve a predictive maintenance problem, relating the predictive maintenance tasks with the main data mining tasks to solve them. Finally, the paper presents significant challenges and future research directions in terms of the potential of data mining applied to predictive maintenance.

This article is categorized under: Application Areas > Industry Specific Applications Technologies > Internet of Things

A LP-graph is a taxonomy of learning pradigmas by providing information about their connections.

Abstract

Machine learning is a field composed of various pillars. Traditionally, supervised learning (SL), unsupervised learning (UL), and reinforcement learning (RL) are the dominating learning paradigms that inspired the field since the 1950s. Based on these, thousands of different methods have been developed during the last seven decades used in nearly all application domains. However, recently, other learning paradigms are gaining momentum which complement and extend the above learning paradigms significantly. These are multi-label learning (MLL), semi-supervised learning (SSL), one-class classification (OCC), positive-unlabeled learning (PUL), transfer learning (TL), multi-task learning (MTL), and one-shot learning (OSL). The purpose of this article is a systematic discussion of these modern learning paradigms and their connection to the traditional ones. We discuss each of the learning paradigms formally by defining key constituents and paying particular attention to the data requirements for allowing an easy connection to applications. That means, we assume a data-driven perspective. This perspective will also allow a systematic identification of relations between the individual learning paradigms in the form of a learning-paradigm graph (LP-graph). Overall, the LP-graph establishes a taxonomy among 10 different learning paradigms.

This article is categorized under: Technologies > Machine Learning Application Areas > Science and Technology Fundamental Concepts of Data and Knowledge > Key Design Issues in Data Mining

Studies on modal verbs.

Abstract

Modal verbs express modality, and modality is concerned with the status of the proposition that describes an event, it also expresses the opinion and attitude of a speaker toward the proposition of an utterance. Since modalities are directly related to the objective world, subjective world, and language use, they have been a hot topic of philosophers, logicians and linguists. Philosophers concern with the relations between the objective world and the true/false values of the modality; logicians are interested in the relations among the possibility, necessity and the objective world; and linguists pay attention to the modality category, sense category, function, recognition, and application of modal verbs. In recent years, the linguistic studies of modal verbs have extended from general linguistic studies to computational linguistic studies. Since modal verbs are a complex semantic system and they are often indeterminate in senses, they have been a tough issue in linguistic studies and have attracted great attention. To clarify the status of the previous linguistic studies of modal verbs and reveal the characteristics of the studies will be of great significance for the further study. Therefore, this article will focus on the review of the previous linguistic studies of English modal verbs and the data mining of the characteristics of the previous studies, and based on the summary of the previous studies, give suggestions for the further study of the English modal verbs.

This article is categorized under: Algorithmic Development > Text Mining Fundamental Concepts of Data and Knowledge > Big Data Mining Technologies > Structure Discovery and Clustering

An overview of Process Mining in the healthcare domain.

Abstract

Process mining (PM) is a well-known research area that includes techniques, methodologies, and tools for analyzing processes in a variety of application domains. In the case of healthcare, processes are characterized by high variability in terms of activities, duration, and involved resources (e.g., physicians, nurses, administrators, machineries, etc.). Besides, the multitude of diseases that the patients housed in healthcare facilities suffer from makes medical contexts highly heterogeneous. As a result, understanding and analyzing healthcare processes are certainly not trivial tasks, and administrators and doctors look for tools and methods that can concretely support them in improving the healthcare services they are involved in. In this context, PM has been increasingly used for a wide range of applications as reported in some recent reviews. However, these reviews mainly focus on discussion on applications related to the clinical pathways, while a systematic review of all possible applications is absent. In this article, we selected 172 papers published in the last 10 years, that present applications of PM in the healthcare domain. The objective of this study is to help and guide researchers interested in the medical field to understand the main PM applications in the healthcare, but also to suggest new ways to develop promising and not yet fully investigated applications. Moreover, our study could be of interest for practitioners who are considering applications of PM, who can identify and choose PM algorithms, techniques, tools, methodologies, and approaches, toward what have been the experiences of success.

This article is categorized under: Application Areas > Health Care Fundamental Concepts of Data and Knowledge > Key Design Issues in Data Mining

Validate clustering results on validation data: select a clustering on the discovery data and check whether it stays stable on the validation data with respect to cluster membership, internal properties, external associations, visual patterns, and so forth. Here, the two clusterings look somewhat similar: a smaller cluster on the top left and a larger cluster on the bottom right.

Abstract

Cluster analysis refers to a wide range of data analytic techniques for class discovery and is popular in many application fields. To assess the quality of a clustering result, different cluster validation procedures have been proposed in the literature. While there is extensive work on classical validation techniques, such as internal and external validation, less attention has been given to validating and replicating a clustering result using a validation dataset. Such a dataset may be part of the original dataset, which is separated before analysis begins, or it could be an independently collected dataset. We present a systematic, structured review of the existing literature about this topic. For this purpose, we outline a formal framework that covers most existing approaches for validating clustering results on validation data. In particular, we review classical validation techniques such as internal and external validation, stability analysis, and visual validation, and show how they can be interpreted in terms of our framework. We define and formalize different types of validation of clustering results on a validation dataset, and give examples of how clustering studies from the applied literature that used a validation dataset can be seen as instances of our framework.

This article is categorized under: Technologies > Structure Discovery and Clustering Algorithmic Development > Statistics Technologies > Machine Learning

The applications of deep learning include the generation, detection, and attribution of synthetic media. We describe deepfake attribution not just as a means of recognizing fake images, but also to identify where they come from and how they are generated.

Abstract

Synthetic media or "deepfakes" are making great advances in visual quality, diversity, and verisimilitude, empowered by large-scale publicly accessible datasets and rapid technical progress in deep generative modeling. Heralding a paradigm shift in how online content is trusted, researchers in digital image forensics have responded with different proposals to reliably detect AI-generated images in the wild. However, binary classification of image authenticity is insufficient to regulate the ethical usage of deepfake technology as new applications are developed. This article provides an overview of the major innovations in synthetic forgery detection as of 2020, while highlighting the recent shift in research towards ways to attribute AI-generated images to their generative sources with evidence. We define the various categories of deepfakes in existence, the subtle processing traces and fingerprints that distinguish AI-generated images from reality and each other, and the different degrees of attribution possible with current understanding of generative algorithms. Additionally, we describe the limitations of synthetic image recognition methods in practice, the counter-forensic attacks devised to exploit these limitations, and directions for new research to assure the long-term relevance of deepfake forensics. Reliable, explainable, and generalizable attribution methods would hold malicious users accountable for AI-enabled disinformation, grant plausible deniability to appropriate users, and facilitate intellectual property protection of deepfake technology.

This article is categorized under: Commercial, Legal, and Ethical Issues > Security and Privacy Algorithmic Development > Multimedia

Healthcare increasingly adopts advanced algorithms, and often requires explanations for the algorithmic process. Visual analytics can provide insights in algorithms through visualization, interaction, shepherding, and direct explanations. Thus, visual analytics holds important opportunities for healthcare.

Abstract

To make predictions and explore large datasets, healthcare is increasingly applying advanced algorithms of artificial intelligence. However, to make well-considered and trustworthy decisions, healthcare professionals require ways to gain insights in these algorithms' outputs. One approach is visual analytics, which integrates humans in decision-making through visualizations that facilitate interaction with algorithms. Although many visual analytics systems have been developed for healthcare, a clear overview of their explanation techniques is lacking. Therefore, we review 71 visual analytics systems for healthcare, and analyze how they explain advanced algorithms through visualization, interaction, shepherding, and direct explanation. Based on our analysis, we outline research opportunities and challenges to further guide the exciting rapprochement of visual analytics and healthcare.

This article is categorized under: Application Areas > Health Care Fundamental Concepts of Data and Knowledge > Explainable AI Technologies > Visualization