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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,...
This paper focuses on accuracy improvement of human activities detection and classification by using single Inertia Measurement Unit sensor (IMU sensor: an acceleration sensor, a gyro sensor, a magnetometer, and an air pressure sensor) which is a type of the wearable sensors. Generally, performance of classification model is determined by these methodologies; number and type of sensors, coordinate...
Low graduation rate is a significant and growing problem in U.S. higher education systems. Although previous studies have demonstrated the usefulness of building statistical models for predicting students' graduation outcomes, advanced machine learning models promise to improve the effectiveness of these models, and hone in on the “difference that makes a difference” not only on the group level, but...
Brain-computer interface (BCI) is an emerging area of research that aims to improve the quality of human-computer applications. It has enormous scope in biomedical applications, neural rehabilitation, biometric authentication, educational programmes, and entertainment applications. A BCI system has four major components: signal acquisition, signal preprocessing, feature extraction, and classification...
Automated facial expression recognition (AFER) is a crucial technology to and a challenging task for human computer interaction. Previous methods of AFER have incorporated different features and classification methods and use basic testing approaches. In this paper, we employ the best feature descriptor for AFER by empirically evaluating the feature descriptors named the Facial Landmarks descriptor...
This paper proposes a novel method for fire and smoke detection using video images. The ViBe method is used to extract a background from the whole video and to update the exact motion areas using frame-by-frame differences. Dynamic and static features extraction are combined to recognize the fire and smoke areas. For static features, we use deep learning to detect most of fire and smoke areas based...
With the advent of new technologies in the field of medicine, there is rising awareness of biomechanisms, and we are better able to treat ailments than we could earlier. Deep learning has helped a lot in this endeavor. This paper deals with the application of deep learning in brain tumor segmentation. Brain tumors are difficult to segment automatically given the high variability in the shapes and...
We propose a method that uses kernel method-based algorithms to implement an autoencoder. Deep learning-based algorithms have two characteristics, one is the high level data abstraction, the other is the multiple level data transformations and representations. The kernel method is one of the approaches that can be used in linear and non-linear transformations. It should be one of the implementations...
This paper addresses the problem of establishing semantic correspondences between images depicting different instances of the same object or scene category. Previous approaches focus on either combining a spatial regularizer with hand-crafted features, or learning a correspondence model for appearance only. We propose instead a convolutional neural network architecture, called SCNet, for learning...
The contribution of this paper is to bridge the gap on understanding the mathematical structure and the computational implementation of a convolutional neural network (CNN) using a minimal model (Minimal CNN). The proposed minimal CNN is presented using a layering approach. This approach provides a concise and accessible understanding of the main mathematical operations of a CNN. Hence, it benefits...
In this paper, we investigate deep neural networks for blind motion deblurring. Instead of regressing for the motion blur kernel and performing non-blind deblurring outside of the network (as most methods do), we propose a compact and elegant end-to-end deblurring network. Inspired by the data-driven sparse-coding approaches that are capable of capturing linear dependencies in data, we generalize...
Ultrasound medical diagnostics is a real-time modality based on a doctor's interpretation of images. So far, automated Computer-Aided Diagnostic tools were not widely applied to ultrasound imaging. The emerging methods in Artificial Intelligence, namely deep learning, gave rise to new applications in medical imaging modalities. The work's objective was to show the feasibility of implementing deep...
The complexity of multidimensionality is one of the frequently encountered problems in the high-dimensional data space. The fact that multidimensionality in the data space increases and reaches great numbers brings about the problem that the number of non-informative ones among the features associated with the target class increases along with the dataset complexity. The fact that all features included...
In this paper, in an attempt to improve power grid resilience, a machine learning model is proposed to predictively estimate the component states in response to extreme events. The proposed model is based on a multi-dimensional Support Vector Machine (SVM) considering the associated resilience index, i.e., the infrastructure quality level and the time duration that each component can withstand the...
In recent years, researchers have shown that deep learning (DL) can be used to construct highly accurate models to solve many problems. However, training DL models requires large datasets and vast amounts of computation. With millions of malware variants being created every day, we contend that there is plenty of data to build deep learning models to classify malicious applications. However, finding...
The need for systems capable of conducting inferential analysis and predictive analytics is ubiquitous in a global information society. With the recent advances in the areas of predictive machine learning models and massive parallel computing a new set of resources is now potentially available for the computer science community in order to research and develop new truly intelligent and innovative...
Functional Resonance Imaging (fMRI) methods are used to identify brain abnormalities associated to psychiatric disorders. One of the most prevalent psychiatric disorders is depression that is marked by blunted reward sensitivity, the brain function becoming different compared to healthy individuals in dopamine irrigated fronto-striatal regions. In the current paper we approach, from a machine learning...
The aim of this work is to detect diseases that occur on plants in tomato fields or in their greenhouses. For this purpose, deep learning was used to detect the various diseases on the leaves of tomato plants. In the study, it was aimed that the deep learning algorithm should be run in real time on the robot. So the robot will be able to detect the diseases of the plants while wandering manually or...
This paper presents the design of a convolutional neural network architecture using the MatConvNet library for MATLAB in order to achieve the recognition of 2 classes of hand gestures: ”open” and ”closed”. Six architectures were implemented to which their hyperparameters and depth were varied to observe their behavior through the validation error in the training and accuracy in the estimation of each...
Denizens of the Internet are coming under a barrage of phishing attacks of increasing frequency and sophistication. Emails accompanied by authentic looking websites are ensnaring users who, unwittingly, hand over their credentials compromising both their privacy and security. Methods such as the blacklisting of these phishing websites become untenable and cannot keep pace with the explosion of fake...
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