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Big Data systems are often composed of information extraction, preprocessing, processing, ingestion and integration, data analysis, interface and visualization components. Different big data systems will have ...
Data visuals (scientific images) display and express various amounts and types of information, and, as the saying goes,“an image is worth 1,000 words.” Based on a review of two studies, a new estimation of how...
To reduce disruptions of processes and the cost of maintenance, predicting the onset of failure (or a similar event) of a physical system (or components of a physical system) has become important. Prediction o...
Modeling and analyses of complex systems using network theory have been an object of study for a long time. They have caught attention in many disciplines such as sociology, epidemiology, ecology, psychology, ...
As the scope of scientific questions increase and datasets grow larger, the visualization of relevant information correspondingly becomes more difficult and complex. Sharing visualizations amongst collaborator...
Genomic GC content varies both within and, substantially, between microbial genomes. While some of this variation can be explained by evolutionary divergence and environmental factors, a notable portion is not...
Data preprocessing techniques are devoted to correcting or alleviating errors in data. Discretization and feature selection are two of the most extended data preprocessing techniques. Although we can find many...
It is important to examine the cost-effectiveness of medical education. The public wants to know how government spending is being utilized to train doctors. There are very few studies that examine national dat...
Associative Classification, a combination of two important and different fields (classification and association rule mining), aims at building accurate and interpretable classifiers by means of association rul...
Emerging pattern mining is a data mining task that extracts rules describing discriminative relationships amongst variables. These rules should be understandable for the experts. Comprehensibility of a rule is...
In matrix completion fields, the traditional convex regularization may fall short of delivering reliable low-rank estimators with good prediction performance. Previous works use the alternation least squares a...
The emerging of depth-camera technology is paving the way for variety of new applications and it is believed that plane detection is one of them. In fact, planes are common in man-made living structures, thus ...
Organization of companies and their HR departments are becoming hugely affected by recent advancements in computational power and Artificial Intelligence, with this trend likely to dramatically rise in the nex...
With the prominent growth of power market, real-time electricity price has become a trend in smart grid as it enables moderation of power consumption of customers. Accurate forecast of real-time price (RTP) ha...
Attention Deficit Hyperactivity Disorder (ADHD) is one of the most common brain disorders among children and is very difficult to diagnose using current methods.
Genome Wide Analytics Studies with regard to structural variations is a key component in phenome association. Here we analyze a family trio of father, mother and children for scientific discovery purpose.
Unsupervised machine-learned analysis of cluster structures, applied using the emergent self-organizing feature maps (ESOM) combined with the unified distance matrix (U-matrix) has been shown to provide an unb...
One of the key technologies for future large-scale location-aware services covering a complex of multi-story buildings is a scalable indoor localization technique. In this paper, we report the current status of o...
Distance metric plays an important role in machine learning which is crucial to the performance of a range of algorithms. Metric learning, which refers to learning a proper distance metric for a particular tas...
In recent years, video becomes the dominant resource of information on the Web, where the text within video usually carries significant semantic
A comparative study of the use of bio-inspired optimization technologies including the Cuckoo Search (CS) algorithm, the Differential Evolution (DE) algorithm, and Quantum-behaved Particle Swarm Optimization (...
Can biomarkers be used to predict the future work ability of patients admitted to hospital? To answer this question, we use a combination of biological measurements, registry data, data from questionnaires, an...
Practitioners and researchers often found the intrinsic representations of high-dimensional problems has much fewer independent variables. However such intrinsic structure may not be easily discovered due to n...
How to build a suitable discourse topic structure is an important issue in discourse topic analysis, which is the core of natural language understanding. Not only is it the key basic unit to implement automati...
Advertisers optimization is one of the most fundamental tasks in paid search, which is a multi-billion industry as a major part of the growing online advertising market. As paid search is a three-player game (...
Hot springs harbor rich bacterial diversity that could be the source of commercially important enzymes, antibiotics and many more products. Most of the hot springs present in Northeast of India are unexplored ...
In many applications where moderate to large datasets are used, plotting relationships between pairs of variables can be problematic. A large number of observations will produce a scatter-plot which is difficu...
The large amounts of data have created a need for new frameworks for processing. The MapReduce model is a framework for processing and generating large-scale datasets with parallel and distributed algorithms. ...
Sentiment analysis becomes ubiquitous for a variety of applications used in marketing, commerce, and public sector. This has been raising a natural interest within the academic research and industry to develop...
Link prediction is one of the most fundamental tasks in statistical network analysis, for which latent feature models have been widely used. As large-scale networks are available in various application domains...
Finding orthologous genes among multiple sequenced genomes is a primary step in comparative genomics studies. With the number of sequenced genomes increasing exponentially, comparative genomics becomes more po...
In this era of data science, many software vendors are rushing towards providing better solutions for data management, analytics, validation and security. The government, being one of the most important custom...
Neuromorphic Engineering has emerged as an exciting research area, primarily owing to the paradigm shift from conventional computing architectures to data-driven, cognitive computing. There is a diversity of w...
Clustering is a key data mining task. This is the problem of partitioning a set of observations into clusters such that the intra-cluster observations are similar and the inter-cluster observations are dissimi...
Data-based modeling is becoming practical in predicting outcomes. In the era of big data, two practically conflicting challenges are eminent: (1) the prior knowledge on the subject is largely insufficient; (2)...
The massive growth in the scale of data has been observed in recent years being a key factor of the Big Data scenario. Big Data can be defined as high volume, velocity and variety of data that require a new hi...
To ensure the output quality, current crowdsourcing systems highly rely on redundancy of answers provided by multiple workers with varying expertise, however massive redundancy is very expensive and time-consu...
Mixed Order Hyper Networks (MOHNs) are a type of neural network in which the interactions between inputs are modelled explicitly by weights that can connect any number of neurons. Such networks have a human re...
Human movement such as physical work, exercise and sport activities can be analyzed to determine kinetic (force) and kinematic (motion) characteristics. In the past, proper assessment of force variables requir...
Real world data analysis problems often require nonlinear methods to get successful prediction. Kernel methods, e.g. Kernelized Principal Component Analysis, are a common way to get nonlinear properties based ...
Deep learning techniques, particularly convolutional neural networks (CNNs), are poised for widespread application in the research fields of information retrieval and natural language processing. However, ther...
Cyber security is vital to the success of today’s digital economy. The major security threats are coming from within, as opposed to outside forces. Insider threat detection and prediction are important mitigat...
The International Symposium “Advances in Systems Biology in Neurosciences” was held in February 2015 in Geneva. A hundred scientists with a variety of expertise gathered around the theme of human brain complex...
We would like to welcome you to Big Data Analytics, a pioneering multi-disciplinary open access and peer-reviewed journal, which welcomes cutting-edge articles describing biologically-inspired computational, theo...
This paper discusses the relationship between data science and population-based algorithms, which include swarm intelligence and evolutionary algorithms. We reviewed two categories of literature, which include...
With the explosion of social media sites and proliferation of digital computing devices and Internet access, massive amounts of public data is being generated on a daily basis. Efficient techniques/algorithms ...
Population health management takes into account many determinants of health, including medical care, social and physical environments and related services, genetics, and individual behavior. Many different typ...