708 research outputs found

    A neural network approach to audio-assisted movie dialogue detection

    Get PDF
    A novel framework for audio-assisted dialogue detection based on indicator functions and neural networks is investigated. An indicator function defines that an actor is present at a particular time instant. The cross-correlation function of a pair of indicator functions and the magnitude of the corresponding cross-power spectral density are fed as input to neural networks for dialogue detection. Several types of artificial neural networks, including multilayer perceptrons, voted perceptrons, radial basis function networks, support vector machines, and particle swarm optimization-based multilayer perceptrons are tested. Experiments are carried out to validate the feasibility of the aforementioned approach by using ground-truth indicator functions determined by human observers on 6 different movies. A total of 41 dialogue instances and another 20 non-dialogue instances is employed. The average detection accuracy achieved is high, ranging between 84.78%±5.499% and 91.43%±4.239%

    Converting Your Thoughts to Texts: Enabling Brain Typing via Deep Feature Learning of EEG Signals

    Full text link
    An electroencephalography (EEG) based Brain Computer Interface (BCI) enables people to communicate with the outside world by interpreting the EEG signals of their brains to interact with devices such as wheelchairs and intelligent robots. More specifically, motor imagery EEG (MI-EEG), which reflects a subjects active intent, is attracting increasing attention for a variety of BCI applications. Accurate classification of MI-EEG signals while essential for effective operation of BCI systems, is challenging due to the significant noise inherent in the signals and the lack of informative correlation between the signals and brain activities. In this paper, we propose a novel deep neural network based learning framework that affords perceptive insights into the relationship between the MI-EEG data and brain activities. We design a joint convolutional recurrent neural network that simultaneously learns robust high-level feature presentations through low-dimensional dense embeddings from raw MI-EEG signals. We also employ an Autoencoder layer to eliminate various artifacts such as background activities. The proposed approach has been evaluated extensively on a large- scale public MI-EEG dataset and a limited but easy-to-deploy dataset collected in our lab. The results show that our approach outperforms a series of baselines and the competitive state-of-the- art methods, yielding a classification accuracy of 95.53%. The applicability of our proposed approach is further demonstrated with a practical BCI system for typing.Comment: 10 page

    Deep Learning in Cardiology

    Full text link
    The medical field is creating large amount of data that physicians are unable to decipher and use efficiently. Moreover, rule-based expert systems are inefficient in solving complicated medical tasks or for creating insights using big data. Deep learning has emerged as a more accurate and effective technology in a wide range of medical problems such as diagnosis, prediction and intervention. Deep learning is a representation learning method that consists of layers that transform the data non-linearly, thus, revealing hierarchical relationships and structures. In this review we survey deep learning application papers that use structured data, signal and imaging modalities from cardiology. We discuss the advantages and limitations of applying deep learning in cardiology that also apply in medicine in general, while proposing certain directions as the most viable for clinical use.Comment: 27 pages, 2 figures, 10 table

    Radar and RGB-depth sensors for fall detection: a review

    Get PDF
    This paper reviews recent works in the literature on the use of systems based on radar and RGB-Depth (RGB-D) sensors for fall detection, and discusses outstanding research challenges and trends related to this research field. Systems to detect reliably fall events and promptly alert carers and first responders have gained significant interest in the past few years in order to address the societal issue of an increasing number of elderly people living alone, with the associated risk of them falling and the consequences in terms of health treatments, reduced well-being, and costs. The interest in radar and RGB-D sensors is related to their capability to enable contactless and non-intrusive monitoring, which is an advantage for practical deployment and users’ acceptance and compliance, compared with other sensor technologies, such as video-cameras, or wearables. Furthermore, the possibility of combining and fusing information from The heterogeneous types of sensors is expected to improve the overall performance of practical fall detection systems. Researchers from different fields can benefit from multidisciplinary knowledge and awareness of the latest developments in radar and RGB-D sensors that this paper is discussing

    Sensor-based activity recognition with dynamically added context

    Get PDF
    An activity recognition system essentially processes raw sensor data and maps them into latent activity classes. Most of the previous systems are built with supervised learning techniques and pre-defined data sources, and result in static models. However, in realistic and dynamic environments, original data sources may fail and new data sources become available, a robust activity recognition system should be able to perform evolution automatically with dynamic sensor availability in dynamic environments. In this paper, we propose methods that automatically incorporate dynamically available data sources to adapt and refine the recognition system at run-time. The system is built upon ensemble classifiers which can automatically choose the features with the most discriminative power. Extensive experimental results with publicly available datasets demonstrate the effectiveness of our methods

    Semi-supervised learning and fairness-aware learning under class imbalance

    Get PDF
    With the advent of Web 2.0 and the rapid technological advances, there is a plethora of data in every field; however, more data does not necessarily imply more information, rather the quality of data (veracity aspect) plays a key role. Data quality is a major issue, since machine learning algorithms are solely based on historical data to derive novel hypotheses. Data may contain noise, outliers, missing values and/or class labels, and skewed data distributions. The latter case, the so-called class-imbalance problem, is quite old and still affects dramatically machine learning algorithms. Class-imbalance causes classification models to learn effectively one particular class (majority) while ignoring other classes (minority). In extend to this issue, machine learning models that are applied in domains of high societal impact have become biased towards groups of people or individuals who are not well represented within the data. Direct and indirect discriminatory behavior is prohibited by international laws; thus, there is an urgency of mitigating discriminatory outcomes from machine learning algorithms. In this thesis, we address the aforementioned issues and propose methods that tackle class imbalance, and mitigate discriminatory outcomes in machine learning algorithms. As part of this thesis, we make the following contributions: • Tackling class-imbalance in semi-supervised learning – The class-imbalance problem is very often encountered in classification. There is a variety of methods that tackle this problem; however, there is a lack of methods that deal with class-imbalance in the semi-supervised learning. We address this problem by employing data augmentation in semi-supervised learning process in order to equalize class distributions. We show that semi-supervised learning coupled with data augmentation methods can overcome class-imbalance propagation and significantly outperform the standard semi-supervised annotation process. • Mitigating unfairness in supervised models – Fairness in supervised learning has received a lot of attention over the last years. A growing body of pre-, in- and postprocessing approaches has been proposed to mitigate algorithmic bias; however, these methods consider error rate as the performance measure of the machine learning algorithm, which causes high error rates on the under-represented class. To deal with this problem, we propose approaches that operate in pre-, in- and post-processing layers while accounting for all classes. Our proposed methods outperform state-of-the-art methods in terms of performance while being able to mitigate unfair outcomes

    Detection and prediction problems with applications in personalized health care

    Full text link
    The United States health-care system is considered to be unsustainable due to its unbearably high cost. Many of the resources are spent on acute conditions rather than aiming at preventing them. Preventive medicine methods, therefore, are viewed as a potential remedy since they can help reduce the occurrence of acute health episodes. The work in this dissertation tackles two distinct problems related to the prevention of acute disease. Specifically, we consider: (1) early detection of incorrect or abnormal postures of the human body and (2) the prediction of hospitalization due to heart related diseases. The solution to the former problem could be used to prevent people from unexpected injuries or alert caregivers in the event of a fall. The latter study could possibly help improve health outcomes and save considerable costs due to preventable hospitalizations. For body posture detection, we place wireless sensor nodes on different parts of the human body and use the pairwise measurements of signal strength corresponding to all sensor transmitter/receiver pairs to estimate body posture. We develop a composite hypothesis testing approach which uses a Generalized Likelihood Test (GLT) as the decision rule. The GLT distinguishes between a set of probability density function (pdf) families constructed using a custom pdf interpolation technique. The GLT is compared with the simple Likelihood Test and Multiple Support Vector Machines. The measurements from the wireless sensor nodes are highly variable and these methods have different degrees of adaptability to this variability. Besides, these methods also handle multiple observations differently. Our analysis and experimental results suggest that GLT is more accurate and suitable for the problem. For hospitalization prediction, our objective is to explore the possibility of effectively predicting heart-related hospitalizations based on the available medical history of the patients. We extensively explored the ways of extracting information from patients' Electronic Health Records (EHRs) and organizing the information in a uniform way across all patients. We applied various machine learning algorithms including Support Vector Machines, AdaBoost with Trees, and Logistic Regression adapted to the problem at hand. We also developed a new classifier based on a variant of the likelihood ratio test. The new classifier has a classification performance competitive with those more complex alternatives, but has the additional advantage of producing results that are more interpretable. Following this direction of increasing interpretability, which is important in the medical setting, we designed a new method that discovers hidden clusters and, at the same time, makes decisions. This new method introduces an alternating clustering and classification approach with guaranteed convergence and explicit performance bounds. Experimental results with actual EHRs from the Boston Medical Center demonstrate prediction rate of 82% under 30% false alarm rate, which could lead to considerable savings when used in practice

    A Combined Deep Learning and Ensemble Learning Methodology to Avoid Electricity Theft in Smart Grids

    Get PDF
    Electricity is widely used around 80% of the world. Electricity theft has dangerous effects on utilities in terms of power efficiency and costs billions of dollars per annum. The enhancement of the traditional grids gave rise to smart grids that enable one to resolve the dilemma of electricity theft detection (ETD) using an extensive amount of data formulated by smart meters. This data are used by power utilities to examine the consumption behaviors of consumers and to decide whether the consumer is an electricity thief or benign. However, the traditional data-driven methods for ETD have poor detection performances due to the high-dimensional imbalanced data and their limited ETD capability. In this paper, we present a new class balancing mechanism based on the interquartile minority oversampling technique and a combined ETD model to overcome the shortcomings of conventional approaches. The combined ETD model is composed of long short-term memory (LSTM), UNet and adaptive boosting (Adaboost), and termed LSTM–UNet–Adaboost. In this regard, LSTM–UNet–Adaboost combines the advantages of deep learning (LSTM-UNet) along with ensemble learning (Adaboost) for ETD. Moreover, the performance of the proposed LSTM–UNet–Adaboost scheme was simulated and evaluated over the real-time smart meter dataset given by the State Grid Corporation of China. The simulations were conducted using the most appropriate performance indicators, such as area under the curve, precision, recall and F1 measure. The proposed solution obtained the highest results as compared to the existing benchmark schemes in terms of selected performance measures. More specifically, it achieved the detection rate of 0.92, which was the highest among existing benchmark schemes, such as logistic regression, support vector machine and random under-sampling boosting technique. Therefore, the simulation outcomes validate that the proposed LSTM–UNet–Adaboost model surpasses other traditional methods in terms of ETD and is more acceptable for real-time practices
    • …
    corecore