Current Studies and Applications of Krill Herd and Gravitational Search Algorithms in Healthcare

Nature-Inspired Computing or NIC for short is a relatively young field that tries to discover fresh methods of computing by researching how natural phenomena function to find solutions to complicated issues in many contexts. As a consequence of this, ground-breaking research has been conducted in a variety of domains, including synthetic immune functions, neural networks, the intelligence of swarm, as well as computing of evolutionary. In the domains of biology, physics, engineering, economics, and management, NIC techniques are used. In real-world classification, optimization, forecasting, and clustering, as well as engineering and science issues, meta-heuristics algorithms are successful, efficient, and resilient. There are two active NIC patterns: the gravitational search algorithm and the Krill herd algorithm. The study on using the Krill Herd Algorithm (KH) and the Gravitational Search Algorithm (GSA) in medicine and healthcare is given a worldwide and historical review in this publication. Comprehensive surveys have been conducted on some other nature-inspired algorithms, including KH and GSA. The various versions of the KH and GSA algorithms and their applications in healthcare are thoroughly reviewed in the present article. Nonetheless, no survey research on KH and GSA in the healthcare field has been undertaken. As a result, this work conducts a thorough review of KH and GSA to assist researchers in using them in diverse domains or hybridizing them with other popular algorithms. It also provides an in-depth examination of the KH and GSA in terms of application, modification, and hybridization. It is important to note that the goal of the study is to offer a viewpoint on GSA with KH, particularly for academics interested in investigating the capabilities and performance of the algorithm in the healthcare and medical domains.Comment: 35 page

Cloud Service Selection System Approach based on QoS Model: A Systematic Review

The Internet of Things (IoT) has received a lot of interest from researchers recently. IoT is seen as a component of the Internet of Things, which will include billions of intelligent, talkative "things" in the coming decades. IoT is a diverse, multi-layer, wide-area network composed of a number of network links. The detection of services and on-demand supply are difficult in such networks, which are comprised of a variety of resource-limited devices. The growth of service computing-related fields will be aided by the development of new IoT services. Therefore, Cloud service composition provides significant services by integrating the single services. Because of the fast spread of cloud services and their different Quality of Service (QoS), identifying necessary tasks and putting together a service model that includes specific performance assurances has become a major technological problem that has caused widespread concern. Various strategies are used in the composition of services i.e., Clustering, Fuzzy, Deep Learning, Particle Swarm Optimization, Cuckoo Search Algorithm and so on. Researchers have made significant efforts in this field, and computational intelligence approaches are thought to be useful in tackling such challenges. Even though, no systematic research on this topic has been done with specific attention to computational intelligence. Therefore, this publication provides a thorough overview of QoS-aware web service composition, with QoS models and approaches to finding future aspects

SkyDOT (Sky Database for Objects in the Time Domain): A Virtual Observatory for Variability Studies at LANL

The mining of Virtual Observatories (VOs) is becoming a powerful new method for discovery in astronomy. Here we report on the development of SkyDOT (Sky Database for Objects in the Time domain), a new Virtual Observatory, which is dedicated to the study of sky variability. The site will confederate a number of massive variability surveys and enable exploration of the time domain in astronomy. We discuss the architecture of the database and the functionality of the user interface. An important aspect of SkyDOT is that it is continuously updated in near real time so that users can access new observations in a timely manner. The site will also utilize high level machine learning tools that will allow sophisticated mining of the archive. Another key feature is the real time data stream provided by RAPTOR (RAPid Telescopes for Optical Response), a new sky monitoring experiment under construction at Los Alamos National Laboratory (LANL).Comment: to appear in SPIE proceedings vol. 4846, 11 pages, 5 figure

Direct Observation of Cosmic Strings via their Strong Gravitational Lensing Effect: II. Results from the HST/ACS Image Archive

We have searched 4.5 square degrees of archival HST/ACS images for cosmic strings, identifying close pairs of similar, faint galaxies and selecting groups whose alignment is consistent with gravitational lensing by a long, straight string. We find no evidence for cosmic strings in five large-area HST treasury surveys (covering a total of 2.22 square degrees), or in any of 346 multi-filter guest observer images (1.18 square degrees). Assuming that simulations ccurately predict the number of cosmic strings in the universe, this non-detection allows us to place upper limits on the unitless Universal cosmic string tension of G mu/c^2 < 2.3 x 10^-6, and cosmic string density of Omega_s < 2.1 x 10^-5 at the 95% confidence level (marginalising over the other parameter in each case). We find four dubious cosmic string candidates in 318 single filter guest observer images (1.08 square degrees), which we are unable to conclusively eliminate with existing data. The confirmation of any one of these candidates as cosmic strings would imply G mu/c^2 ~ 10^-6 and Omega_s ~ 10^-5. However, we estimate that there is at least a 92% chance that these string candidates are random alignments of galaxies. If we assume that these candidates are indeed false detections, our final limits on G mu/c^2 and Omega_s fall to 6.5 x 10^-7 and 7.3 x 10^-6. Due to the extensive sky coverage of the HST/ACS image archive, the above limits are universal. They are quite sensitive to the number of fields being searched, and could be further reduced by more than a factor of two using forthcoming HST data.Comment: 21 pages, 18 figure

Metaheuristic design of feedforward neural networks: a review of two decades of research

Over the past two decades, the feedforward neural network (FNN) optimization has been a key interest among the researchers and practitioners of multiple disciplines. The FNN optimization is often viewed from the various perspectives: the optimization of weights, network architecture, activation nodes, learning parameters, learning environment, etc. Researchers adopted such different viewpoints mainly to improve the FNN's generalization ability. The gradient-descent algorithm such as backpropagation has been widely applied to optimize the FNNs. Its success is evident from the FNN's application to numerous real-world problems. However, due to the limitations of the gradient-based optimization methods, the metaheuristic algorithms including the evolutionary algorithms, swarm intelligence, etc., are still being widely explored by the researchers aiming to obtain generalized FNN for a given problem. This article attempts to summarize a broad spectrum of FNN optimization methodologies including conventional and metaheuristic approaches. This article also tries to connect various research directions emerged out of the FNN optimization practices, such as evolving neural network (NN), cooperative coevolution NN, complex-valued NN, deep learning, extreme learning machine, quantum NN, etc. Additionally, it provides interesting research challenges for future research to cope-up with the present information processing era

Binary Quasars in the Sloan Digital Sky Survey: Evidence for Excess Clustering on Small Scales

We present a sample of 218 new quasar pairs with proper transverse separations R_prop < 1 Mpc/h over the redshift range 0.5 < z < 3.0, discovered from an extensive follow up campaign to find companions around the Sloan Digital Sky Survey and 2dF Quasar Redshift Survey quasars. This sample includes 26 new binary quasars with separations R_prop < 50 kpc/h (theta < 10 arcseconds), more than doubling the number of such systems known. We define a statistical sample of binaries selected with homogeneous criteria and compute its selection function, taking into account sources of incompleteness. The first measurement of the quasar correlation function on scales 10 kpc/h < R_prop < 400 kpc/h is presented. For R_prop < 40 kpc/h, we detect an order of magnitude excess clustering over the expectation from the large scale R_prop > 3 Mpc/h quasar correlation function, extrapolated down as a power law to the separations probed by our binaries. The excess grows to ~ 30 at R_prop ~ 10 kpc/h, and provides compelling evidence that the quasar autocorrelation function gets progressively steeper on sub-Mpc scales. This small scale excess can likely be attributed to dissipative interaction events which trigger quasar activity in rich environments. Recent small scale measurements of galaxy clustering and quasar-galaxy clustering are reviewed and discussed in relation to our measurement of small scale quasar clustering.Comment: 25 pages, 12 figures, 9 tables. Submitted to the Astronomical Journa

Massive Datasets in Astronomy

Astronomy has a long history of acquiring, systematizing, and interpreting large quantities of data. Starting from the earliest sky atlases through the first major photographic sky surveys of the 20th century, this tradition is continuing today, and at an ever increasing rate. Like many other fields, astronomy has become a very data-rich science, driven by the advances in telescope, detector, and computer technology. Numerous large digital sky surveys and archives already exist, with information content measured in multiple Terabytes, and even larger, multi-Petabyte data sets are on the horizon. Systematic observations of the sky, over a range of wavelengths, are becoming the primary source of astronomical data. Numerical simulations are also producing comparable volumes of information. Data mining promises to both make the scientific utilization of these data sets more effective and more complete, and to open completely new avenues of astronomical research. Technological problems range from the issues of database design and federation, to data mining and advanced visualization, leading to a new toolkit for astronomical research. This is similar to challenges encountered in other data-intensive fields today. These advances are now being organized through a concept of the Virtual Observatories, federations of data archives and services representing a new information infrastructure for astronomy of the 21st century. In this article, we provide an overview of some of the major datasets in astronomy, discuss different techniques used for archiving data, and conclude with a discussion of the future of massive datasets in astronomy.Comment: 46 Pages, 21 Figures, Invited Review for the Handbook of Massive Datasets, editors J. Abello, P. Pardalos, and M. Resende. Due to space limitations this version has low resolution figures. For full resolution review see http://www.astro.caltech.edu/~rb/publications/hmds.ps.g