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A deep learning technique has emerged as a successful approach for diagnostic imaging. Along with the increasing demands for dental healthcare, the automation of diagnostic imaging is increasingly desired in the field of orthodontics for many reasons (e.g., remote assessment, cost reduction, etc.). However, orthodontic diagnoses generally require dental and medical scientists to diagnose a patient...
Feature extraction and matching are two crucial components in person Re-Identification (ReID). The large pose deformations and the complex view variations exhibited by the captured person images significantly increase the difficulty of learning and matching of the features from person images. To overcome these difficulties, in this work we propose a Pose-driven Deep Convolutional (PDC) model to learn...
In this paper, we propose an attention-aware deep reinforcement learning (ADRL) method for video face recognition, which aims to discard the misleading and confounding frames and find the focuses of attentions in face videos for person recognition. We formulate the process of finding the attentions of videos as a Markov decision process and train the attention model through a deep reinforcement learning...
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...
Person Re-identification (re-id) aims to match people across non-overlapping camera views in a public space. It is a challenging problem because many people captured in surveillance videos wear similar clothes. Consequently, the differences in their appearance are often subtle and only detectable at the right location and scales. Existing re-id models, particularly the recently proposed deep learning...
This paper introduces an ensemble model that solves the binary classification problem by incorporating the basic Logistic Regression with the two recent advanced paradigms: extreme gradient boosted decision trees (xgboost) and deep learning. To obtain the best result when integrating sub-models, we introduce a solution to split and select sets of features for the sub-model training. In addition to...
In recent years, Convolutional Neural Networks (ConvNets) have become the quintessential component of several state-of-the-art Artificial Intelligence tasks. Across the spectrum of applications, the performance needs vary significantly, from high-throughput image recognition to the very low-latency requirements of autonomous cars. In this context, FPGAs can provide a potential platform that can be...
Demand is mounting in the industry for scalable GPU-based deep learning systems. Unfortunately, existing training applications built atop popular deep learning frameworks, including Caffe, Theano, and Torch, etc, are incapable of conducting distributed GPU training over large-scale clusters.To remedy such a situation, this paper presents Nexus, a platform that allows existing deep learning frameworks...
Saliency detection aims to highlight the most relevant objects in an image. Methods using conventional models struggle whenever salient objects are pictured on top of a cluttered background while deep neural nets suffer from excess complexity and slow evaluation speeds. In this paper, we propose a simplified convolutional neural network which combines local and global information through a multi-resolution...
In this paper, a big-data-driven based intelligent prognostics strategy is proposed to deal with industrial big data generated in the process of intelligent manufacturing, which is an inevitable trend in the industry 4.0 environment. The developed scheme demonstrated the important issues for the intelligent prognostics methodology, including pre-processing methods for industrial big data, association...
The open nature of Android allows application developers to take full advantage of the system. While the flexibility is brought to developers and users, it may raise significant issues related to malicious applications. Traditional malware detection approaches based on signatures or abnormal behaviors are invalid when dealing with novel malware. To solve the problem, machine learning algorithms are...
A great amount of data is usually needed for a recommender system to learn the associations between users and items. However, in practical applications, new users and new items emerge everyday, and the system has to react to them promptly. The ability to recommend proper items to new users affects the users' first impression and accordingly the retention rate, whereas recommending new items to proper...
The ability to proactively monitor business processes is one of the main differentiators for firms to remain competitive. Process execution logs generated by Process Aware Information Systems (PAIS) help to make various business process specific predictions. This enables a proactive situational awareness related to the execution of business processes. The goal of the approach proposed in the current...
Scene text detection is an important research problem in computer vision community. It has great application value in many fields. Inspired by Faster-RCNN which is a popular method for object detection, we consider to apply the Regional Proposal Network (RPN) method for scene text detection because text can be regarded as the common object. The core of RPN is to detect different sizes of objects with...
This paper presents a deep learning based time series model to predict the traffic flow of transportation systems, DeepTFP, which exploits the effectiveness of time series function in analyzing sequence data and deep learning in extracting traffic flow features. Accurate and timely prediction on the future traffic flow is strongly needed by individual travelers, public transport, and transport planning...
This work proposes multiclass deep learning classification of Alzheimer's disease (AD) using novel texture and other associated features extracted from structural MRI. Two distinct learning models (Model 1 and 2) are presented where both include subcortical area specific feature extraction, feature selection and stacked auto-encoder (SAE) deep neural network (DNN). The models learn highly complex...
Convolution neural networks (CNNs) eliminate the need for feature extraction which is one of the most important and time-consuming part of traditional machine learning (ML) methods. However, the challenge of training a deep CNN model with a limited amount of training data still remains. Transfer learning and parameter fine-tuning have emerged as solutions to this problem. Following the recent trends,...
Illegal dumping has been a chronicle problem in many cities in the world. The odors and contaminants caused by abandoned household items and dumped garbage, and construction leftovers not only ruin the city view but also threaten citizens health. To reduce the illegal dumping, a few cities have designed community-based voluntary reporting systems and surveillancecamera-based monitoring systems. However,...
Nowadays' road network infrastructure failing to cope up with the exponential increase in vehicular population, there is a constant strive to find smarter ways to deal with it using existing infrastructure. Intelligent Transport System is at the forefront of this, one of the aims is accurate and sophisticated traffic predictions that ensure smooth and hassle free commuting and administrative experience...
The botnet, which mainly consists of bots that are remotely controlled that provide the platform for most of the cyber threats. The effective countermeasure against such botnet is provided by IDS (Intrusion detection system). IDS regularly observes and identify the presence of active attack by inspecting the vulnerabilities in network traffic. A payload-inspection-based IDS (PI-IDS) recognizes active...
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