The ability to process large amounts of data and 1 to extract useful insights from data has revolutionised society. 2 This phenomenon-dubbed as Big Data-has applications for a 3 wide assortment of industries, including the construction industry. 4 The construction industry already deals with large volumes of 5 heterogeneous data; which is expected to increase exponentially 6 as technologies such as sensor networks and the Internet of 7 Things are commoditized. In this paper, we present a detailed 8 survey of the literature, investigating the application of Big Data 9 techniques in the construction industry. We reviewed related 10 works published in the databases of American Association of 11 Civil Engineers (ASCE), Institute of Electrical and Electronics 12 Engineers (IEEE), Association of Computing Machinery (ACM), 13 and Elsevier Science Direct Digital Library. While the application 14 of data analytics in the construction industry is not new, the 15 adoption of Big Data technologies in this industry remains at a 16 nascent stage and lags the broad uptake of these technologies in 17 other fields. To the best of our knowledge, there is currently no 18 comprehensive survey of Big Data techniques in the context of 19 the construction industry. This paper fills the void and presents a 20 wide-ranging interdisciplinary review of literature of fields such 21 as statistics, data mining and warehousing, machine learning, and 22 Big Data Analytics in the context of the construction industry. 23 We discuss the current state of adoption of Big Data in the 24 construction industry and discuss the future potential of such 25 technologies across the multiple domain-specific sub-areas of the 26 construction industry. We also propose open issues and directions 27 for future work along with potential pitfalls associated with Big 28 Data adoption in the industry. 29 I. INTRODUCTION 30 The world is currently inundated with data, with fast advanc-31 ing technology leading to its steady increase. Today, companies 32 deal with petabytes (10 15 bytes) of data. Google processes 33 above 24 petabytes of data per day [1], while Facebook gets 34 more than 10 million photos per hour [1]. The glut of data 35 increased in 2012 is approximately 2.5 quintillion (10 18) bytes 36 per day [2]. This data growth brings significant opportunities 37 to scientists for identifying useful insights and knowledge. 38 Arguably, the accessibility of data can improve the status 39 quo in various fields by strengthening existing statistical and 40 algorithmic methods [3], or by even making them redundant 41 [4]. 42 The construction industry is not an exception to the per-43 vasive digital revolution. The industry is dealing with sig-44 nificant data arising from diverse disciplines throughout the 45 life cycle of a facility. Building Information Modelling (BIM) 46 is envisioned to capture multi-dimensional CAD information 47 systematically for supporting multidisciplinary collaboration 48 among the stakeholders [5]. BIM data is typically 3D ge-49 ometric enc...

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Recent years have witnessed widespread adoption of machine learning (ML)/deep learning (DL) techniques due to their superior performance for a variety of healthcare applications ranging from the prediction of cardiac arrest from onedimensional heart signals to computer-aided diagnosis (CADx) using multi-dimensional medical images. Notwithstanding the impressive performance of ML/DL, there are still lingering doubts regarding the robustness of ML/DL in healthcare settings (which is traditionally considered quite challenging due to the myriad security and privacy issues involved), especially in light of recent results that have shown that ML/DL are vulnerable to adversarial attacks. In this paper, we present an overview of various application areas in healthcare that leverage such techniques from security and privacy point of view and present associated challenges. In addition, we present potential methods to ensure secure and privacy-preserving ML for healthcare applications. Finally, we provide insight into the current research challenges and promising directions for future research.

While machine learning and artificial intelligence have long been applied in networking research, the bulk of such works has focused on supervised learning. Recently there has been a rising trend of employing unsupervised machine learning using unstructured raw network data to improve network performance and provide services such as traffic engineering, anomaly detection, Internet traffic classification, and quality of service optimization. The interest in applying unsupervised learning techniques in networking emerges from their great success in other fields such as computer vision, natural language processing, speech recognition, and optimal control (e.g., for developing autonomous self-driving cars). Unsupervised learning is interesting since it can unconstrain us from the need of labeled data and manual handcrafted feature engineering thereby facilitating flexible, general, and automated methods of machine learning. The focus of this survey paper is to provide an overview of the applications of unsupervised learning in the domain of networking. We provide a comprehensive survey highlighting the recent advancements in unsupervised learning techniques and describe their applications for various learning tasks in the context of networking. We also provide a discussion on future directions and open research issues, while also identifying potential pitfalls. While a few survey papers focusing on the applications of machine learning in networking have previously been published, a survey of similar scope and breadth is missing in literature. Through this paper, we advance the state of knowledge by carefully synthesizing the insights from these survey papers while also providing contemporary coverage of recent advances.

With the advancement in artificial intelligence (AI) and machine learning (ML) techniques, researchers are striving towards employing these techniques for advancing clinical practice. One of the key objectives in healthcare is the early detection and prediction of disease to timely provide preventive interventions. This is especially the case for epilepsy, which is characterized by recurrent and unpredictable seizures. Patients can be relieved from the adverse consequences of epileptic seizures if it could somehow be predicted in advance. Despite decades of research, seizure prediction remains an unsolved problem. This is likely to remain at least partly because of the inadequate amount of data to resolve the problem. There have been exciting new developments in ML-based algorithms that have the potential to deliver a paradigm shift in the early and accurate prediction of epileptic seizures. Here we provide a comprehensive review of state-of-the-art ML techniques in early prediction of seizures using EEG signals. We will identify the gaps, challenges, and pitfalls in the current research and recommend future directions.