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Proposed algorithm is a face recognition algorithm from video using Generalized mean Deep Learning Neural Network. Generalized mean provides fast convergence of the feature set and Deep learning neural network is enhanced using wavelet transform as it improves the classification efficiency of the neural network. The performance of the proposed algorithm is evaluated on PaSC and Youtube dataset. The...
An automatic Speech to Text (STT) conversion technology has been developed for making a visual text layout of the Speech Input for advancement of Science and Technology. This technology enables people an alternative way to understand voice communication, and pursue instruction using their visual ability. The visual ability becomes more powerful than the listening ability some time more than even in...
In this paper, we proposed an optimized Sparse Deep Learning Network (SDLN) model for Face Recognition (FR). A key contribution of this work is to learn feature coding of human face with a SDLN based on local structured Sparse Representation (SR). In traditional sparse FR methods, different poses and expressions of training samples could have great influence on the recognition results. We consider...
Passive Millimeter Wave Images (PMMWI) can be used to detect and localize objects concealed under clothing. Unfortunately, the quality of the acquired images and the unknown position, shape, and size of the hidden objects render difficult this task. In this paper we propose a method that combines image processing and statistical machine learning techniques to solve this localization/detection problem...
Early diagnosis of Alzheimer's Disease (AD) is widely regarded as necessary to allow treatment to be started before irreversible damage to the brain occur and for patients to benefit from new therapies as they become available. Low-cost point-of-care (PoC) diagnostic tools that can be used to routinely diagnose AD in its early stage would facilitate this, but such tools require reliable and accurate...
The estimation of user position in indoor environment using WLAN technology based on Received Signal Strength (RSS) is becoming increasingly important in recent years. Various indoor positioning techniques are proposed in the literature. Fingerprint positioning technique is the most promising one that consists of radio frequency (RF) map construction and location estimation phases. Machine learning...
Facial expression has made significant progress in recent years with many commercial systems are available for real-world applications. It gains strong interest to implement a facial expression system on a portable device such as tablet and smart phone device using the camera already integrated in the devices. It is very common to see face recognition phone unlocking app in new smart phones which...
Deep Learning has becoming a popular and effective way to address a large set of issues. In particular, in computer vision, it has been exploited to get satisfying recognition performance in unconstrained conditions. However, this wild race towards even better performance in extreme conditions has overshadowed an important step i.e. the assessment of the impact of this new methodology on traditional...
In this paper, the brief survey of data mining classification by using the machine learning techniques is presented. The machine learning techniques like decision tree and support vector machine play the important role in all the applications of artificial intelligence. Decision tree works efficiently with discrete data and SVM is capable of building the nonlinear boundaries among the classes. Both...
Aiming at the shorting of the existing atrial fibrillation (AF) detection algorithms and improve the ability of intelligent recognition and extraction of AF signals. Recently, deep learning theory with massive data has been used on image, voice and other filed widely. In this paper, a method based on the stack sparse autoencoder neural network, a instance of deep learning strategy, was proposed for...
In this paper, we propose a new recommender algorithm based on Slope One algorithm and new similarity measurements. We incorporate additional sources of information about the users to relieve the cold start problem. Users generate a large number of interactions while browsing a website. These users' interactions are considered accurate enough to make recommendation. Then, we propose to take into account...
While cancer treatments are constantly advancing, there is still a real risk of relapse after potentially curative treatments. At the risk of adverse side effects, certain adjuvant treatments can be given to patients that are at high risk of recurrence. The challenge, however, is in finding the best tradeoff between these two extremes. Patients that are given more potent treatments, such as chemotherapy,...
The Metaplasticity is an inherent property of the Biological neuron connections that consists in the capacity of modifying the learning mechanism using the information present in the network itself during the training. This concept can be applied to Artificial Learning Algorithms using a technique called Artificial Metaplasticity. The idea is to improve the results in Machine Learning taking as the...
In big data era, digital information is growing rapidly. False and unlawful images influence our normal work and life, especially the exaggerated or fake propaganda of electronic commerce merchants. In this article, our purpose is to help people find out fake qualification certificate information automatically. Base on collecting and classifying web images, we apply Convolutional Neural Network (CNN)...
In recent years, complex operating conditions have greatly reduced the predictability of electric grid operations and hence, there is an urgent need to improve grid security more than ever before. The best approach would be to improve grid intelligence rather than simply hardening the grid. An implementation of dynamic security assessment (DSA) would require carrying out time-domain simulations that...
This paper initiates a discussion on the design of terms, features, and descriptors that would support machine learning for automated plan recognition of drone and drone swarms engaged in threatening activity. A few prototype aerial missions for drones are discussed and semantic markers, such as distance and line of sight to potential targets, mirrored motion, path and position optimality, coordination,...
We develop a practical technique in this paper to classify the scholars in different disciplines, organizations according to their research interests. The scholar classification is important to the scholars, research organizations,, government for research, evaluation, education, research resource allocation. It becomes really difficult because name abbreviation, interdisciplinary, especially tautonym...
Stereoscopic image retargeting techniques aim to flexibly display 3D images with different aspect ratios and simultaneously preserve salient regions and comfortable depth perception. Various stereoscopic image retargeting techniques have been proposed recently. However, there is still no effective objective metric for visual quality assessment of retargeted stereoscopic images. In this paper, we build...
Automatic 2D-to-3D conversion aims to reduce the existing gap between the scarce 3D content and the incremental amount of displays that can reproduce this 3D content. Here, we present an automatic 2D-to-3D conversion algorithm that extends the functionality of the most of the existing machine learning based conversion approaches to deal with moving objects in the scene, and not only with static backgrounds...
Advances have been made continuously in detection networks such as SPPnet and Fast R-CNN. Recently the novel region proposal method RPN shares full-image convolutional features with the detection network and enables a state-of-the-art object detection network Faster R-CNN. In this work we apply Faster R-CNN to train a detection network on our digital image database of books and implement automatic...
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