166,883 research outputs found
Analysis of Hybrid Soft and Hard Computing Techniques for Forex Monitoring Systems
In a universe with a single currency, there would be no foreign exchange
market, no foreign exchange rates, and no foreign exchange. Over the past
twenty-five years, the way the market has performed those tasks has changed
enormously. The need for intelligent monitoring systems has become a necessity
to keep track of the complex forex market. The vast currency market is a
foreign concept to the average individual. However, once it is broken down into
simple terms, the average individual can begin to understand the foreign
exchange market and use it as a financial instrument for future investing. In
this paper, we attempt to compare the performance of hybrid soft computing and
hard computing techniques to predict the average monthly forex rates one month
ahead. The soft computing models considered are a neural network trained by the
scaled conjugate gradient algorithm and a neuro-fuzzy model implementing a
Takagi-Sugeno fuzzy inference system. We also considered Multivariate Adaptive
Regression Splines (MARS), Classification and Regression Trees (CART) and a
hybrid CART-MARS technique. We considered the exchange rates of Australian
dollar with respect to US dollar, Singapore dollar, New Zealand dollar,
Japanese yen and United Kingdom pounds. The models were trained using 70% of
the data and remaining was used for testing and validation purposes. It is
observed that the proposed hybrid models could predict the forex rates more
accurately than all the techniques when applied individually. Empirical results
also reveal that the hybrid hard computing approach also improved some of our
previous work using a neuro-fuzzy approach
Classification of Traffic Vehicle Density Using Deep Learning
The volume density of vehicles is a problem that often occurs in every city, as for the impact of vehicle density is congestion. Classification of vehicle density levels on certain roads is required because there are at least 7 vehicle density level conditions. Monitoring conducted by the police, the Department of Transportation and the organizers of the road currently using video-based surveillance such as CCTV that is still monitored by people manually. Deep Learning is an approach of synthetic neural network-based learning machines that are actively developed and researched lately because it has succeeded in delivering good results in solving various soft-computing problems, This research uses the convolutional neural network architecture. This research tries to change the supporting parameters on the convolutional neural network to further calibrate the maximum accuracy. After the experiment changed the parameters, the classification model was tested using K-fold cross-validation, confusion matrix and model exam with data testing. On the K-fold cross-validation test with an average yield of 92.83% with a value of K (fold) = 5, model testing is done by entering data testing amounting to 100 data, the model can predict or classify correctly i.e. 81 data
Tree-formed Verification Data for Trusted Platforms
The establishment of trust relationships to a computing platform relies on
validation processes. Validation allows an external entity to build trust in
the expected behaviour of the platform based on provided evidence of the
platform's configuration. In a process like remote attestation, the 'trusted'
platform submits verification data created during a start up process. These
data consist of hardware-protected values of platform configuration registers,
containing nested measurement values, e.g., hash values, of loaded or started
components. Commonly, the register values are created in linear order by a
hardware-secured operation. Fine-grained diagnosis of components, based on the
linear order of verification data and associated measurement logs, is not
optimal. We propose a method to use tree-formed verification data to validate a
platform. Component measurement values represent leaves, and protected
registers represent roots of a hash tree. We describe the basic mechanism of
validating a platform using tree-formed measurement logs and root registers and
show an logarithmic speed-up for the search of faults. Secure creation of a
tree is possible using a limited number of hardware-protected registers and a
single protected operation. In this way, the security of tree-formed
verification data is maintained.Comment: 15 pages, 11 figures, v3: Reference added, v4: Revised, accepted for
publication in Computers and Securit
Optical techniques for 3D surface reconstruction in computer-assisted laparoscopic surgery
One of the main challenges for computer-assisted surgery (CAS) is to determine the intra-opera- tive morphology and motion of soft-tissues. This information is prerequisite to the registration of multi-modal patient-specific data for enhancing the surgeon’s navigation capabilites by observ- ing beyond exposed tissue surfaces and for providing intelligent control of robotic-assisted in- struments. In minimally invasive surgery (MIS), optical techniques are an increasingly attractive approach for in vivo 3D reconstruction of the soft-tissue surface geometry. This paper reviews the state-of-the-art methods for optical intra-operative 3D reconstruction in laparoscopic surgery and discusses the technical challenges and future perspectives towards clinical translation. With the recent paradigm shift of surgical practice towards MIS and new developments in 3D opti- cal imaging, this is a timely discussion about technologies that could facilitate complex CAS procedures in dynamic and deformable anatomical regions
Use Case Point Approach Based Software Effort Estimation using Various Support Vector Regression Kernel Methods
The job of software effort estimation is a critical one in the early stages
of the software development life cycle when the details of requirements are
usually not clearly identified. Various optimization techniques help in
improving the accuracy of effort estimation. The Support Vector Regression
(SVR) is one of several different soft-computing techniques that help in
getting optimal estimated values. The idea of SVR is based upon the computation
of a linear regression function in a high dimensional feature space where the
input data are mapped via a nonlinear function. Further, the SVR kernel methods
can be applied in transforming the input data and then based on these
transformations, an optimal boundary between the possible outputs can be
obtained. The main objective of the research work carried out in this paper is
to estimate the software effort using use case point approach. The use case
point approach relies on the use case diagram to estimate the size and effort
of software projects. Then, an attempt has been made to optimize the results
obtained from use case point analysis using various SVR kernel methods to
achieve better prediction accuracy.Comment: 13 pages, 6 figures, 11 Tables, International Journal of Information
Processing (IJIP
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