Digital tools for direct assessment of autism risk during early childhood: A systematic review

Current challenges in early identification of autism spectrum disorder lead to significant delays in starting interventions, thereby compromising outcomes. Digital tools can potentially address this barrier as they are accessible, can measure autism-relevant phenotypes and can be administered in children’s natural environments by non-specialists. The purpose of this systematic review is to identify and characterise potentially scalable digital tools for direct assessment of autism spectrum disorder risk in early childhood. In total, 51,953 titles, 6884 abstracts and 567 full-text articles from four databases were screened using predefined criteria. Of these, 38 met inclusion criteria. Tasks are presented on both portable and non-portable technologies, typically by researchers in laboratory or clinic settings. Gamified tasks, virtual-reality platforms and automated analysis of video or audio recordings of children’s behaviours and speech are used to assess autism spectrum disorder risk. Tasks tapping social communication/interaction and motor domains most reliably discriminate between autism spectrum disorder and typically developing groups. Digital tools employing objective data collection and analysis methods hold immense potential for early identification of autism spectrum disorder risk. Next steps should be to further validate these tools, evaluate their generalisability outside laboratory or clinic settings, and standardise derived measures across tasks. Furthermore, stakeholders from underserved communities should be involved in the research and development process

A comparison of statistical machine learning methods in heartbeat detection and classification

In health care, patients with heart problems require quick responsiveness in a clinical setting or in the operating theatre. Towards that end, automated classification of heartbeats is vital as some heartbeat irregularities are time consuming to detect. Therefore, analysis of electro-cardiogram (ECG) signals is an active area of research. The methods proposed in the literature depend on the structure of a heartbeat cycle. In this paper, we use interval and amplitude based features together with a few samples from the ECG signal as a feature vector. We studied a variety of classification algorithms focused especially on a type of arrhythmia known as the ventricular ectopic fibrillation (VEB). We compare the performance of the classifiers against algorithms proposed in the literature and make recommendations regarding features, sampling rate, and choice of the classifier to apply in a real-time clinical setting. The extensive study is based on the MIT-BIH arrhythmia database. Our main contribution is the evaluation of existing classifiers over a range sampling rates, recommendation of a detection methodology to employ in a practical setting, and extend the notion of a mixture of experts to a larger class of algorithms

Feature Papers of Drones - Volume II

[EN] The present book is divided into two volumes (Volume I: articles 1–23, and Volume II: articles 24–54) which compile the articles and communications submitted to the Topical Collection ”Feature Papers of Drones” during the years 2020 to 2022 describing novel or new cutting-edge designs, developments, and/or applications of unmanned vehicles (drones). Articles 24–41 are focused on drone applications, but emphasize two types: firstly, those related to agriculture and forestry (articles 24–35) where the number of applications of drones dominates all other possible applications. These articles review the latest research and future directions for precision agriculture, vegetation monitoring, change monitoring, forestry management, and forest fires. Secondly, articles 36–41 addresses the water and marine application of drones for ecological and conservation-related applications with emphasis on the monitoring of water resources and habitat monitoring. Finally, articles 42–54 looks at just a few of the huge variety of potential applications of civil drones from different points of view, including the following: the social acceptance of drone operations in urban areas or their influential factors; 3D reconstruction applications; sensor technologies to either improve the performance of existing applications or to open up new working areas; and machine and deep learning development

Animal Welfare Assessment

This Special Issue provides a collection of recent research and reviews that investigate many areas of welfare assessment, such as novel approaches and technologies used to evaluate the welfare of farmed, captive, or wild animals. Research in this Special Issue includes welfare assessment related to pilot whales, finishing pigs, commercial turkey flocks, and dairy goats; the use of sensors or wearable technologies, such as heart rate monitors to assess sleep in dairy cows, ear tag sensors, and machine learning to assess commercial pig behaviour; non-invasive measures, such as video monitoring of behaviour, computer vision to analyse video footage of red foxes, remote camera traps of free-roaming wild horses, infrared thermography of effort and sport recovery in sport horses; telomere length and regulatory genes as novel biomarkers of stress in broiler chickens; the effect of environment on growth physiology and behaviour of laboratory rare minnows and housing system on anxiety, stress, fear, and immune function of laying hens; and discussions of natural behaviour in farm animal welfare and maintaining health, welfare, and productivity of commercial pig herds

Classification algorithms for Big Data with applications in the urban security domain

A classification algorithm is a versatile tool, that can serve as a predictor for the future or as an analytical tool to understand the past. Several obstacles prevent classification from scaling to a large Volume, Velocity, Variety or Value. The aim of this thesis is to scale distributed classification algorithms beyond current limits, assess the state-of-practice of Big Data machine learning frameworks and validate the effectiveness of a data science process in improving urban safety. We found in massive datasets with a number of large-domain categorical features a difficult challenge for existing classification algorithms. We propose associative classification as a possible answer, and develop several novel techniques to distribute the training of an associative classifier among parallel workers and improve the final quality of the model. The experiments, run on a real large-scale dataset with more than 4 billion records, confirmed the quality of the approach. To assess the state-of-practice of Big Data machine learning frameworks and streamline the process of integration and fine-tuning of the building blocks, we developed a generic, self-tuning tool to extract knowledge from network traffic measurements. The result is a system that offers human-readable models of the data with minimal user intervention, validated by experiments on large collections of real-world passive network measurements. A good portion of this dissertation is dedicated to the study of a data science process to improve urban safety. First, we shed some light on the feasibility of a system to monitor social messages from a city for emergency relief. We then propose a methodology to mine temporal patterns in social issues, like crimes. Finally, we propose a system to integrate the findings of Data Science on the citizenry’s perception of safety and communicate its results to decision makers in a timely manner. We applied and tested the system in a real Smart City scenario, set in Turin, Italy