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In recent years, cryptocurrencies have received substantial attention from investors, researchers and the media due to their volatile behaviour and potential for high returns. This interest has led to an expanding body of research aimed at predicting cryptocurrency prices, which are notably influenced by a wide array of technical, sentimental, and legal factors. This paper reviews scholarly content from 2014 to 2024, employing a systematic approach to explore advanced quantitative methods for cryptocurrency price prediction. It encompasses a broad spectrum of predictive models, from early statistical analyses to sophisticated machine and deep learning algorithms. Notably, this review identifies and discusses the integration of emerging technologies such as Transformers and hybrid deep learning models, which offer new avenues for enhancing prediction accuracy and practical applicability in real-world scenarios. By thoroughly investigating various methodologies and parameters influencing cryptocurrency price predictions, including market sentiment, technical indicators, and blockchain features, this review highlights the field’s complexity and rapid evolution. The analysis identifies significant research gaps and under-explored areas, providing a foundational guideline for future studies. These guidelines aim to connect theoretical advancements with practical, profit-driven applications in cryptocurrency trading, ensuring that future research is both innovative and applicable.

People provide information on their thoughts, perceptions, and activities through a wide range of channels, including social media. The wide acceptance of social media results in vast volume of valuable data, in variety of format as well as veracity. Analysis of such ‘big data’ allows organizations and analysts to make better and faster decisions. However, this data had to be quantified and information has to be extracted, which can be very challenging because of possible data ambiguity and complexity. To address information extraction, many analytic techniques, such as text mining, machine learning, predictive analytics, and diverse natural language processing, have been proposed in the literature. Recent advances in Natural Language Understanding-based techniques more specifically transformer-based architectures can solve sequence-to-sequence modeling tasks while handling long-range dependencies efficiently. In this work, we applied transformer-based sequence modeling on short texts’ topic classification and sentiment analysis from user-posted tweets. Applicability of models is investigated on posts from the Great Barrier Reef tweet dataset and obtained findings are encouraging providing insight that can be valuable for researchers working on classification of large datasets as well as large number of target classes.

ABSTRACTThree-dimensional (3D) reconstruction of a building can be facilitated by correctly segmenting different feature points (e.g. in the form of boundary, fold edge, and planar points) over the building roof, and then, establishing relationships among the constructed feature lines and planar patches using the segmented points. Present machine learning-based segmentation approaches of Light Detection and Ranging (LiDAR) point cloud data are confined only to different object classes or semantic labelling. In the context of fine-grained feature point classification over the extracted building roof, machine learning approaches have not yet been explored. In this paper, after generating the ground truth data for the extracted building roofs from three different datasets, we apply machine learning methods to segment the roof point cloud based on seven different effective geometric features. The goal is not to semantically enhance the point cloud, but rather to facilitate the application of 3D building reconstruction algorithms, making them easier to use. The calculated F1-scores for each class confirm the competitive performances over the state-of-the-art techniques, which are more than 95% almost in each area of the used datasets.

Understanding travel patterns is helpful for decision-makers to draw insights from consumers' perspectives. This work took advantage of social media and analysed tourists' travel patterns from the length of itineraries and duration of stay. Using Chinese tourists in Australia as a case study, results showed that most visitors prefer to stay in two core destinations, with an average duration of 8.5 days, while adding one destination increases the stay by around 2.5 days and caps at approximately 14 days. The travel patterns were further analysed by social network analysis and explained the network structure using core-periphery theory. The results were compared with official national survey data and demonstrated encouraging accuracy, which provides practical implications for destination planning and management.

Background and significanceIntravascular (IV) catheters are the most invasive medical device in healthcare. Localized priority-setting related to IV catheter quality surveillance is a key objective of recent healthcare reform in Australia. We sought to determine the plausibility of using electronic health record (EHR) data for catheter surveillance by mapping currently available data across state-wide platforms. This work has identified barriers and facilitators to a state-wide EHR surveillance initiative.Materials and methodsData variables were generated and mapped from routinely used EHR sources across Queensland, Australia through a systematic search of gray literature and expert consultation with clinical information specialists. EHR systems were eligible for inclusion if they collected data related to IV catheter insertion, care, or outcomes of hospitalized patients. Generated variables were mapped against international recommendations for IV catheter surveillance, with data linkage and data export capacity narratively summarized.ResultsWe identified five EHR systems, namely, iEMR, MetaVision ICU®, Multiprac, RiskMan, and the Nephrology Registry. Systems were used across jurisdictions and hospital wards. Data linkage was not evident across systems. Extraction processes for catheter data were not standardized, lacking clear and reliable extraction techniques. In combination, EHR systems collected 43/50 international variables recommended for catheter surveillance, however, individual systems collected a median of 24/50 (IQR 22, 30) variables. We did not identify integrated clinical analytic systems (incorporating machine learning) to support clinical decision making or for risk stratification (e.g., catheter-related infection).ConclusionCurrent data linkage across EHR systems limits the development of an IV catheter quality surveillance system to provide timely data related to catheter complications and harm. To facilitate reliable and timely surveillance of catheter outcomes using clinical informatics, substantial work is needed to overcome existing barriers and transform health surveillance.

Social media have been a valuable data source for studying people’s opinions, intentions, and behaviours. Such a data source incorporating advanced big data analysis methods, such as machine-operated emotion and sentiment analysis, will open unprecedented opportunities for innovative data-driven destination monitoring and management. However, a big challenge any machine-operated text analysis method faces is the ambiguity of the natural languages, which may cause an expression to have different meanings in different contexts. In this work, we address the ambiguity challenge by proposing a context-aware dictionary-based target-oriented emotion and sentiment analysis method that incorporates inputs from both humans and machines to introduce an alternative approach to measuring emotions and sentiment in limited tourism-related data. The study makes a methodological contribution by creating a target dictionary specifically for tourism sentiment analysis. To demonstrate the performance of the proposed method, a case of target-oriented emotion and sentiment analysis of posts from Twitter for the Gold Coast of Australia as a tourist destination was considered. The results suggest that Twitter data cover a broad range of destination attributes and can be a valuable source for comprehensive monitoring of tourist experiences at a destination.

Electricity has become an indispensable source of energy, and power lines play a crucial role in the functioning of modern societies. It is essential to inspect power lines promptly and precisely in order to ensure the safe and secure delivery of electricity. In steep and mountainous terrain, traditional surveying methods cannot inspect power lines precisely due to their nature. Remote sensing platforms, such as satellite and aerial images, thermal images, and light detection and ranging (LiDAR) points, were utilised for the detection and inspection of power lines. Nevertheless, with the advancements of remote sensing technologies, in recent years, LiDAR surveying has been favoured for power line corridor (PLC) inspection due to active and weather-independent nature of laser scanning. Laser ranging data and the precise location of the LiDAR can be used to generate a three-dimensional (3D) image of the PLC. The resulting 3D point cloud enables accurate extraction of power lines and measurement of their distances from the forest below. In the literature, there have been many proposals for power line extraction and reconstruction for PLC modelling. This article examines the pros and cons of each domain method, providing researchers involved in three-dimensional modelling of power lines using innovative LiDAR scanning systems with useful guidelines. To achieve these objectives, research papers were analysed, focusing primarily on geoscience-related journals and conferences for the extraction and reconstruction of power lines. There has been a growing interest in examining the extraction and reconstruction of power line spans with single and multi-conductor configurations using different image and point-based techniques. Our study provides a comprehensive overview of the methodologies offered by various approaches using laser scanning data from the perspective of power line extraction applications, as well as to discuss the benefits and drawbacks of each approach. The comparison revealed that, despite the tremendous potential of aerial and mobile laser scanning systems, human intervention and post-processing actions are still required to achieve the desired results. In addition, the majority of the methods have been evaluated on the small datasets, and very few methods have been focused on multi-conductor extraction and reconstruction for power lines modelling. These barriers hinder the automated extraction and reconstruction of power line using LiDAR data and point to unexplored areas for further research and serve as useful guidelines for future research directions. Several promising directions for future LiDAR experiments using deep learning methods are outlined in the hope that they will pave the way for applications of PLC modelling and assessment at a finer scale and on a larger scale.

A sheer amount of text generated from microblogs and social media brings huge opportunities to the text mining applications. Many techniques such as sentiment analysis and opinion mining are proven effective to deliver insights from documents. However, most of these textual data are in the form of short and fragmented texts which are difficult to visually extract due to the sparsity issue and the context in the content is often unknown. Naive while widely used models, term frequency and the bag-of-words never considered the semantic relationship between the words, making the results relatively difficult to interpret. A well-known technique in text mining like topic model may provide a general ‘at glance’ understanding but can be difficult to interpret or to understand. One alternative is to aggregate words in a semantical order and generates an output of human-understandable sentences. In this paper, we address this direction by proposing the belief graph data model that joins short texts by inducing the part-of-speech tagging to maintain the order and to preserve the context of the content. Extensive experiments showed that our approach improves the overall qualitative evaluation of text understanding compared to the previous state of the art text mining techniques.

Objectives To explore the efficacy of machine learning (ML) techniques in predicting under-five mortality (U5M) in low-income and middle-income countries (LMICs) and to identify significant predictors of U5M.Design This is a cross-sectional, proof-of-concept study.Settings and participants We analysed data from the Demographic and Health Survey. The data were drawn from 34 LMICs, comprising a total of n=1 520 018 children drawn from 956 995 unique households.Primary and secondary outcome measures The primary outcome measure was U5M; secondary outcome was comparing the efficacy of deep learning algorithms: deep neural network (DNN); convolution neural network (CNN); hybrid CNN-DNN with logistic regression (LR) for the prediction of child’s survival.Results We found that duration of breast feeding, number of antenatal visits, household wealth index, postnatal care and the level of maternal education are some of the most important predictors of U5M. We found that deep learning techniques are superior to LR for the classification of child survival: LR sensitivity=0.47, specificity=0.53; DNN sensitivity=0.69, specificity=0.83; CNN sensitivity=0.68, specificity=0.83; CNN-DNN sensitivity=0.71, specificity=0.83.Conclusion Our findings provide an understanding of determinants of U5M in LMICs. It also demonstrates that deep learning models are more efficacious than traditional analytical approach.

Abstract The rising of big data brings revolutionary changes to many aspects of our lives. Huge volume of data, along with its complexity poses big challenges to data analytic applications. Techniques proposed in data warehousing and online analytical processing, such as precomputed multidimensional cubes, dramatically improve the response time of analytic queries based on relational databases. There are some recent works extending similar concepts into NoSQL such as constructing cubes from NoSQL stores and converting existing cubes into NoSQL stores. However, only limited attention in literature have been devoted to precomputing structure within the NoSQL databases. In this paper, we present an architecture for answering temporal analytic queries over big data by precomputing the results of granulated chunks of collections which are decomposed from the original large collection. In extensive experimental evaluations on drill-down and roll-up temporal queries over large amount of data we demonstrated the effectiveness and efficiency under different settings.

Recommender-systems have been a significant research direction in both literature and practice. The core of recommender systems are the recommendation mechanisms, which suggest to a user a selected set of items supposed to match user true intent, based on existing user preferences. In some scenarios, the items to be recommended are not intended for personal use but a group of users. Group recommendation is rather more since group members have wide-ranging levels of interests and often involve conflicts. However, group recommendation endures the over-specification problem, in which the presumingly relevant items do not necessarily match true user intent. In this paper, we address the problem of diversity in group recommendation by improving the chance of returning at least one piece of information that embraces group satisfaction. We proposed a bounded algorithm that finds a subset of items with maximal group utility and maximal variety of information. Experiments on real-world rating data sets show the efficiency and effectiveness of our approach.

Existing approaches that extract buildings from point cloud data do not select the appropriate neighbourhood for estimation of normals on individual points. However, the success of these approaches depends on correct estimation of the normal vector. In most cases, a fixed neighbourhood is selected without considering the geometric structure of the object and the distribution of the input point cloud. Thus, considering the object structure and the heterogeneous distribution of the point cloud, this paper proposes a new effective approach for selecting a minimal neighbourhood, which can vary for each input point. For each point, a minimal number of neighbouring points are iteratively selected. At each iteration, based on the calculated standard deviation from a fitted 3D line to the selected points, a decision is made adaptively about the neighbourhood. The selected minimal neighbouring points make the calculation of the normal vector accurate. The direction of the normal vector is then used to calculate the inside fold feature points. In addition, the Euclidean distance from a point to the calculated mean of its neighbouring points is used to make a decision about the boundary point. In the context of the accuracy evaluation, the experimental results confirm the competitive performance of the proposed approach of neighbourhood selection over the state-of-the-art methods. Based on our generated ground truth data, the proposed fold and boundary point extraction techniques show more than 90% F1-scores.

Overhead high-voltage conductors are the chief components of power lines and their safety has a strong influence on social and daily life. In the recent decade, the airborne laser scanning (ALS) technique has been widely used to capture the three-dimensional (3D) information of power lines and surrounding objects. Most of the existing methods focused on extraction of single conductors or extracted all conductors as one object class by applying machine learning techniques. Nevertheless, power line corridors (PLCs) are built with multi-loop, multi-phase structures (bundle conductors) and exist in intricate environments (e.g., mountains and forests), and thus raise challenges to process ALS data for extraction of individual conductors. This paper proposes an automated method to extract individual subconductors in bundles from complex structure of PLCs using a combined image- and point-based approach. First, the input point cloud data are grouped into 3D voxel grid and PL points and separated from pylon and tree points using the fact that pylons and trees are vertical objects while power lines are non-vertical objects. These pylons are further separated from trees by employing a statistical analysis technique and used to extract span points between two consecutive pylons; then, by using the distribution properties of power lines in each individual span, the bundles located at different height levels are extracted using image-based processing; finally, subconductors in each bundle are detected and extracted by introducing a window that slides over the individual bundle. The orthogonal plane transformation and recursive clustering procedures are exploited in each window position and a point-based processing is conducted iteratively for extraction of complete individual subconductors in each bundle. The feasibility and validity of the proposed method are verified on two Australian sites having bundle conductors in high-voltage transmission lines. Our experiments show that the proposed method achieves a reliable result by extracting the real structure of bundle conductors in power lines with correctness of 100% and 90% in the two test sites, respectively.