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Accurate estimation of detection/classification performance for sidescan sonar systems in Mine Counter-Measure (MCM) applications is important for informing mission tactics and adapting autonomous behaviors. The approach presented in this paper assumes that detection/classification performance can be estimated solely from historical data collected from similar surveys. This paper introduces an algorithm...
Facial micro-expression refers to split-second muscle changes in the face, indicating that a person is either consciously or unconsciously suppressing their true emotions and even mental health. Therefore, micro-expression recognition attracts increasing research efforts in both fields of psychology and computer vision. Existing research on micro-expression recognition has mainly used hand-crafted...
Despite the linear relation between the number of observed spectra and the searching time, the current protein search engines, even the parallel versions, could take several hours to search a large amount of MS/MS spectra, which can be generated in a short time. After a laborious searching process, some (and at times, majority) of the observed spectra are labeled as non-identifiable. We evaluate the...
Fingerprinting Localization Solutions (FPSs) enjoy huge popularity due to their good performance and minimal environment information requirement. Considered as a data-driven approach, many modern data analytics can be used to improve its performance. In this paper, we propose tow learning algorithms, namely a deep learning architecture for regression and Support Vector Machine (SVM) for classification,...
The diversity of the available protein search engines with respect to the utilized matching algorithms, the low overlap ratios among their results and the disparity of their coverage encourage the community of proteomics to utilize ensemble solutions of different search engines. The advancing in cloud computing technology and the availability of distributed processing clusters can also provide support...
Image geolocalization, inferring the geographic location of an image, is a challenging computer vision problem with many potential applications. The recent state-of-the-art approach to this problem is a deep image classification approach in which the world is spatially divided into cells and a deep network is trained to predict the correct cell for a given image. We propose to combine this approach...
In this study, we apply machine learning algorithms to predict technical failures that can be encountered in Oracle databases and related services. In order to train machine learning algorithms, data from log files are collected hourly from Oracle database systems and labeled with two classes; normal or abnormal. We use several data science approaches to preprocess and transform the input data from...
Positioning applications become more popular with the advancement of location aware services. Global Positioning System is a successful solution for outdoors whereas it is not suitable for indoor environments due to the lack of line of sight for radio frequency signals. Therefore, various systems have been developed to solve the indoor positioning problem. Enhancing the performance of these systems...
Aphasia is an acquired language disorder resulting from damage to language related networks of the brain, most often as a result of ischemic stroke or traumatic brain injury. Within the European Union, over 580000 people are affected each year. Both assessment and treatment of aphasia require the analysis of language, in particular of spontaneous speech. Factoring in therapy and diagnosis sessions,...
Face sketch synthesis plays an important role in both law enforcement and digital entertainment. The existing methods for sketch synthesis always suffer from noising and blurring effect. To resolve these problems, a nonsubsampled Shearlet transform (NSST) based detail enhancement strategy is proposed. The exemplar-based method is firstly adopted to synthesize the primary sketch, then the final sketch...
Recently many industries and companies are developing machine learning algorithms and services, and they are publishing them on the internet. However, because most of people who want to use the machine learning services to analyze data are familiar with sheet data rather than programming language, it is difficult to use those services written in programming language. For the reason, we developed a...
In this paper, we introduce seven emotions and positive and negative emotion recognition methods using facial images and the development of apps based on the method. In previous researches, they used the deep-learning technology to generate models with emotion-based facial expressions to recognized emotions. There are existing apps that express six emotions, but not seven emotions and positive and...
This paper proposes an accurate and generalizable deep learning framework for iris recognition. The proposed framework is based on a fully convolutional network (FCN), which generates spatially corresponding iris feature descriptors. A specially designed Extended Triplet Loss (ETL) function is introduced to incorporate the bit-shifting and non-iris masking, which are found necessary for learning discriminative...
The emerging field of materials informatics has the potential to greatly reduce time-to-market and development costs for new materials. The success of such efforts hinges on access to large, high-quality databases of material properties. However, many such data are only to be found encoded in text within esoteric scientific articles, a situation that makes automated extraction difficult and manual...
SVM (Support Vector Machine), a state of the art classifier model is implemented on a computational mobile platform and its performances are evaluated against a low complexity classifier such as SFSVC (Super Fast Vector Support Classifier) on the same platform. For a better comparison, similar implementation for the two architectures are considered, such as using the same basic linear algebra library...
In this work, we adopt the use of deep learning method for no-reference image quality assessment. With the development of deep neural networks technology, foundational and deep features of images could be captured without much prior knowledge. So a sparse autoencoder (SAE) was trained to express a 32 × 32 pixels image into a feature vector. Then the original images were cut into serial sub-images...
The SFSVC (Super Fast Support Vector Classifier) architecture is implemented to a computational mobile platform and its performances are evaluated against its implementation on a classic machine (personal computer). The aim of this article is to prove that the SFSVC architecture can have good performances on an environment with very limited resources by taking advantages of its compact structure and...
Face sketch to digital image matching is an important challenge of face recognition that involves matching across different domains. Current research efforts have primarily focused on extracting domain invariant representations or learning a mapping from one domain to the other. In this research, we propose a novel transform learning based approach termed as DeepTransformer, which learns a transformation...
The accuracy of object recognition has been greatly improved due to the rapid development of deep learning, but the deep learning generally requires a lot of training data and the training process is very slow and complex. We propose an incremental object recognition system based on deep learning techniques and speech recognition technology with high learning speed and wide applicability. The system...
Timely and robust diagnosis of plant diseases and nutrient deficiencies play a major role in management of crop yield. Automation is a low cost alternative to human experts and can help to detect early onset of crop diseases which aids faster decision making and in giving recommendations to farmers to curb yield loss. We have developed a smart-phone based participatory sensing application for agriculture...
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