scholarly journals Recognition and Evaluation of Clinical Section Headings in Clinical Documents Using Token-Based Formulation with Conditional Random Fields

2015 ◽  
Vol 2015 ◽  
pp. 1-10 ◽  
Author(s):  
Hong-Jie Dai ◽  
Shabbir Syed-Abdul ◽  
Chih-Wei Chen ◽  
Chieh-Chen Wu
Keyword(s):  
Clinical Judgment ◽  
Conditional Random Field ◽  
Contextual Information ◽  
Recognition Task ◽  
Classification Problem ◽  
Recognition System ◽  
Digital Data ◽  
Section Heading ◽  
Sentence Classification ◽  
Electronic Health

Electronic health record (EHR) is a digital data format that collects electronic health information about an individual patient or population. To enhance the meaningful use of EHRs, information extraction techniques have been developed to recognize clinical concepts mentioned in EHRs. Nevertheless, the clinical judgment of an EHR cannot be known solely based on the recognized concepts without considering its contextual information. In order to improve the readability and accessibility of EHRs, this work developed a section heading recognition system for clinical documents. In contrast to formulating the section heading recognition task as a sentence classification problem, this work proposed a token-based formulation with the conditional random field (CRF) model. A standard section heading recognition corpus was compiled by annotators with clinical experience to evaluate the performance and compare it with sentence classification and dictionary-based approaches. The results of the experiments showed that the proposed method achieved a satisfactoryF-score of 0.942, which outperformed the sentence-based approach and the best dictionary-based system by 0.087 and 0.096, respectively. One important advantage of our formulation over the sentence-based approach is that it presented an integrated solution without the need to develop additional heuristics rules for isolating the headings from the surrounding section contents.

GESTURE RECOGNITION SYSTEM FOR NIGERIAN TRIBAL GREETING POSTURES USING SUPPORT VECTOR MACHINE

2020 ◽  
Vol 5 (2) ◽  
pp. 609
Author(s):  
Segun Aina ◽  
Kofoworola V. Sholesi ◽  
Aderonke R. Lawal ◽  
Samuel D. Okegbile ◽  
Adeniran I. Oluwaranti
Keyword(s):  
Support Vector Machine ◽  
Gesture Recognition ◽  
Recognition Rate ◽  
Recognition Task ◽  
Recognition System ◽  
Human Interaction ◽  
Support Vector ◽  
System A ◽  
Extraction Algorithm ◽  
Gaussian Blur

This paper presents the application of Gaussian blur filters and Support Vector Machine (SVM) techniques for greeting recognition among the Yoruba tribe of Nigeria. Existing efforts have considered different recognition gestures. However, tribal greeting postures or gestures recognition for the Nigerian geographical space has not been studied before. Some cultural gestures are not correctly identified by people of the same tribe, not to mention other people from different tribes, thereby posing a challenge of misinterpretation of meaning. Also, some cultural gestures are unknown to most people outside a tribe, which could also hinder human interaction; hence there is a need to automate the recognition of Nigerian tribal greeting gestures. This work hence develops a Gaussian Blur – SVM based system capable of recognizing the Yoruba tribe greeting postures for men and women. Videos of individuals performing various greeting gestures were collected and processed into image frames. The images were resized and a Gaussian blur filter was used to remove noise from them. This research used a moment-based feature extraction algorithm to extract shape features that were passed as input to SVM. SVM is exploited and trained to perform the greeting gesture recognition task to recognize two Nigerian tribe greeting postures. To confirm the robustness of the system, 20%, 25% and 30% of the dataset acquired from the preprocessed images were used to test the system. A recognition rate of 94% could be achieved when SVM is used, as shown by the result which invariably proves that the proposed method is efficient.

Age dependency of the diabetes effects on the iris recognition systems performance evaluation results

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Mohammadreza Azimi ◽  
Seyed Ahmad Rasoulinejad ◽  
Andrzej Pacut
Keyword(s):  
Iris Recognition ◽  
Recognition Task ◽  
Aging Effect ◽  
Recognition System ◽  
Health Condition ◽  
Age Dependency ◽  
Biometric Systems ◽  
Kolmogorov Smirnov ◽  
Recognition Systems ◽  
Smirnov Test

AbstractIn this paper, we attempt to answer the questions whether iris recognition task under the influence of diabetes would be more difficult and whether the effects of diabetes and individuals’ age are uncorrelated. We hypothesized that the health condition of volunteers plays an important role in the performance of the iris recognition system. To confirm the obtained results, we reported the distribution of usable area in each subgroup to have a more comprehensive analysis of diabetes effects. There is no conducted study to investigate for which age group (young or old) the diabetes effect is more acute on the biometric results. For this purpose, we created a new database containing 1,906 samples from 509 eyes. We applied the weighted adaptive Hough ellipsopolar transform technique and contrast-adjusted Hough transform for segmentation of iris texture, along with three different encoding algorithms. To test the hypothesis related to physiological aging effect, Welches’s t-test and Kolmogorov–Smirnov test have been used to study the age-dependency of diabetes mellitus influence on the reliability of our chosen iris recognition system. Our results give some general hints related to age effect on performance of biometric systems for people with diabetes.

IMAGE RECOGNITION BY INTEGRATION OF CONNECTIONIST AND SYMBOLIC APPROACHES

1995 ◽  
Vol 09 (03) ◽  
pp. 485-515 ◽  
Author(s):  
F. ROLI ◽  
S. B. SERPICO ◽  
G. VERNAZZA
Keyword(s):  
Image Recognition ◽  
Magnetic Resonance Image ◽  
Contextual Information ◽  
Recognition System ◽  
Recognition Problem ◽  
Training Process ◽  
Neural Architecture ◽  
Recognition Phase ◽  
Abstraction Levels

This paper presents a methodology for integrating connectionist and symbolic approaches to 2D image recognition. The proposed integration paradigm exploits the synergy of the two approaches for both the training and the recognition phases of an image recognition system. In the training phase, a symbolic module provides an approximate solution to a given image-recognition problem in terms of symbolic models. Such models are hierarchically organized into different abstraction levels, and include contextual descriptions. After mapping such models into a complex neural architecture, a neural training process is carried out to optimize the solution of the recognition problem. The so-obtained neural networks are used during the recognition phase for pattern classification. In this phase, the role of symbolic modules consists of managing complex aspects of information processing: abstraction levels, contextual information, and global recognition hypotheses. A hybrid system implementing the proposed integration paradigm is presented, and its advantages over single approaches are assessed. Results on Magnetic Resonance image recognition are reported, and comparisons with some well-known classifiers are made.

scholarly journals Recognition of Symbolic Gestures Using Depth Information

2018 ◽  
Vol 2018 ◽  
pp. 1-13 ◽  
Author(s):  
Hasan Mahmud ◽  
Md. Kamrul Hasan ◽  
Abdullah-Al-Tariq ◽  
Md. Hasanul Kabir ◽  
M. A. Mottalib
Keyword(s):  
Contextual Information ◽  
Depth Map ◽  
Recognition System ◽  
Support Vector ◽  
Svm Classifier ◽  
Depth Information ◽  
Scale Invariant ◽  
Binary Images ◽  
Depth Images ◽  
Symbolic Gestures

Symbolic gestures are the hand postures with some conventionalized meanings. They are static gestures that one can perform in a very complex environment containing variations in rotation and scale without using voice. The gestures may be produced in different illumination conditions or occluding background scenarios. Any hand gesture recognition system should find enough discriminative features, such as hand-finger contextual information. However, in existing approaches, depth information of hand fingers that represents finger shapes is utilized in limited capacity to extract discriminative features of fingers. Nevertheless, if we consider finger bending information (i.e., a finger that overlaps palm), extracted from depth map, and use them as local features, static gestures varying ever so slightly can become distinguishable. Our work here corroborated this idea and we have generated depth silhouettes with variation in contrast to achieve more discriminative keypoints. This approach, in turn, improved the recognition accuracy up to 96.84%. We have applied Scale-Invariant Feature Transform (SIFT) algorithm which takes the generated depth silhouettes as input and produces robust feature descriptors as output. These features (after converting into unified dimensional feature vectors) are fed into a multiclass Support Vector Machine (SVM) classifier to measure the accuracy. We have tested our results with a standard dataset containing 10 symbolic gesture representing 10 numeric symbols (0-9). After that we have verified and compared our results among depth images, binary images, and images consisting of the hand-finger edge information generated from the same dataset. Our results show higher accuracy while applying SIFT features on depth images. Recognizing numeric symbols accurately performed through hand gestures has a huge impact on different Human-Computer Interaction (HCI) applications including augmented reality, virtual reality, and other fields.

Speech Emotion Analysis of Different Age Groups Using Clustering Techniques

2018 ◽  
Vol 8 (1) ◽  
pp. 69-85 ◽  
Author(s):  
Hemanta Kumar Palo ◽  
Mihir Narayan Mohanty ◽  
Mahesh Chandra
Keyword(s):  
Vocal Tract ◽  
Speech Rate ◽  
Age Groups ◽  
Recognition Task ◽  
Computation Time ◽  
Vocal Folds ◽  
Recognition System ◽  
Clustering Techniques ◽  
Fcm Algorithm ◽  
Criminal Investigators

The shape, length, and size of the vocal tract and vocal folds vary with the age of the human being. The variation may be of different age or sickness or some other conditions. Arguably, the features extracted from the utterances for the recognition task may differ for different age group. It complicates further for different emotions. The recognition system demands suitable feature extraction and clustering techniques that can separate their emotional utterances. Psychologists, criminal investigators, professional counselors, law enforcement agencies and a host of other such entities may find such analysis useful. In this article, the emotion study has been evaluated for three different age groups of people using the basic age- dependent features like pitch, speech rate, and log energy. The feature sets have been clustered for different age groups by utilizing K-means and Fuzzy c-means (FCM) algorithm for the boredom, sadness, and anger states. K-means algorithm has outperformed the FCM algorithm in terms of better clustering and lower computation time as the authors' results suggest.

Lifelog and Information Retrieval from Daily Digital Data: Narrative Review. (Preprint)

2021 ◽  
Author(s):  
Ricardo Ribeiro ◽  
Alina Trifan ◽  
António J. R. Neves
Keyword(s):  
Contextual Information ◽  
Life Quality ◽  
Research Area ◽  
Research Field ◽  
Narrative Review ◽  
Digital Data ◽  
Significant Information ◽  
Memory Problems ◽  
Wide Availability ◽  
And Behaviors

BACKGROUND The wide availability and small size, together with the decrease in pricing of different types of sensors, has made it possible, over the last decade, to acquire a huge amount of data about a person's life in real time. These sensors can be incorporated into personal electronic devices available at reasonable cost, such as smartphones and small wearable devices. They allow the acquisition of images, audio, location, physical activity and physiological signals, among other data. With these data, usually denoted as lifelog data, we can then analyze and understand personal experiences and behaviors. This process is called lifelogging. OBJECTIVE The goal of this article is to review the literature in the research area of lifelogging over the past decade and provide an historical overview on this research topic. To this purpose, we analyze lifelogging applications that monitor and assist people with memory problems. METHODS We follow a narrative review methodology to conduct a comprehensive search of relevant publications in Google Scholar and Scopus databases. In order to find these relevant publication, topic-related keywords were identified and combined based on different lifelogging type of data and applications. RESULTS A total of 124 publications were selected and included in this narrative review. 411 publications were retrieved and screened from the two scholar databases. Out of these, 114 publications were fully reviewed. In addition, 32 more publications were manually included based on our bibliographical knowledge in this research field. CONCLUSIONS The use of personal lifelogs can be beneficial to improve the life quality of people suffering from memory problems, such as dementia. Through the acquisition and analysis of lifelog data, lifelogging systems can create digital memories to be used as surrogate memory. Through this narrative we understand that contextual information can be extracted from the lifelogs and it provides significant information for understanding the daily life of people suffering from memory issues based on events, experiences and behaviors.

scholarly journals Presentation Attack Face Image Generation Based on a Deep Generative Adversarial Network

Sensors ◽  
2020 ◽  
Vol 20 (7) ◽  
pp. 1810
Author(s):  
Dat Tien Nguyen ◽  
Tuyen Danh Pham ◽  
Ganbayar Batchuluun ◽  
Kyoung Jun Noh ◽  
Kang Ryoung Park
Keyword(s):  
Recognition Task ◽  
Recognition System ◽  
Attack Detection ◽  
Image Generation ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Face Images ◽  
Problem Presentation ◽  
Recognition Systems ◽  
Public Datasets

Although face-based biometric recognition systems have been widely used in many applications, this type of recognition method is still vulnerable to presentation attacks, which use fake samples to deceive the recognition system. To overcome this problem, presentation attack detection (PAD) methods for face recognition systems (face-PAD), which aim to classify real and presentation attack face images before performing a recognition task, have been developed. However, the performance of PAD systems is limited and biased due to the lack of presentation attack images for training PAD systems. In this paper, we propose a method for artificially generating presentation attack face images by learning the characteristics of real and presentation attack images using a few captured images. As a result, our proposed method helps save time in collecting presentation attack samples for training PAD systems and possibly enhance the performance of PAD systems. Our study is the first attempt to generate PA face images for PAD system based on CycleGAN network, a deep-learning-based framework for image generation. In addition, we propose a new measurement method to evaluate the quality of generated PA images based on a face-PAD system. Through experiments with two public datasets (CASIA and Replay-mobile), we show that the generated face images can capture the characteristics of presentation attack images, making them usable as captured presentation attack samples for PAD system training.

scholarly journals Supervised Detection of Façade Openings in 3D Point Clouds with Thermal Attributes

2020 ◽  
Vol 12 (3) ◽  
pp. 543 ◽  
Author(s):  
Małgorzata Jarząbek-Rychard ◽  
Dong Lin ◽  
Hans-Gerd Maas
Keyword(s):  
Large Scale ◽  
Conditional Random Field ◽  
Contextual Information ◽  
Point Clouds ◽  
Thermal Infrared ◽  
Classification Performance ◽  
Space Images ◽  
3D Point Clouds ◽  
3D Scene ◽  
And Control

Targeted energy management and control is becoming an increasing concern in the building sector. Automatic analyses of thermal data, which minimize the subjectivity of the assessment and allow for large-scale inspections, are therefore of high interest. In this study, we propose an approach for a supervised extraction of façade openings (windows and doors) from photogrammetric 3D point clouds attributed to RGB and thermal infrared (TIR) information. The novelty of the proposed approach is in the combination of thermal information with other available characteristics of data for a classification performed directly in 3D space. Images acquired in visible and thermal infrared spectra serve as input data for the camera pose estimation and the reconstruction of 3D scene geometry. To investigate the relevance of different information types to the classification performance, a Random Forest algorithm is applied to various sets of computed features. The best feature combination is then used as an input for a Conditional Random Field that enables us to incorporate contextual information and consider the interaction between the points. The evaluation executed on a per-point level shows that the fusion of all available information types together with context consideration allows us to extract objects with 90% completeness and 95% correctness. A respective assessment executed on a per-object level shows 97% completeness and 88% accuracy.

scholarly journals Identifying Emotional Expressions: Children’s Reasoning About Pretend Emotions of Sadness and Anger

2020 ◽  
Vol 11 ◽  
Author(s):  
Elisabet Serrat ◽  
Anna Amadó ◽  
Carles Rostan ◽  
Beatriz Caparrós ◽  
Francesc Sidera
Keyword(s):  
Emotional Expression ◽  
Contextual Information ◽  
Pretend Play ◽  
Recognition Task ◽  
Young Group ◽  
Emotional Expressions ◽  
Sources Of Information ◽  
Ability Test ◽  
Facial Information ◽  
Final Sample

This study aims to further understand children’s capacity to identify and reason about pretend emotions by analyzing which sources of information they take into account when interpreting emotions simulated in pretend play contexts. A total of 79 children aged 3 to 8 participated in the final sample of the study. They were divided into the young group (ages 3 to 5) and the older group (6 to 8). The children were administered a facial emotion recognition task, a pretend emotions task, and a non-verbal cognitive ability test. In the pretend emotions task, the children were asked whether the protagonist of silent videos, who was displaying pretend emotions (pretend anger and pretend sadness), was displaying a real or a pretend emotion, and to justify their answer. The results show significant differences in the children’s capacity to identify and justify pretend emotions according to age and type of emotion. The data suggest that young children recognize pretend sadness, but have more difficulty detecting pretend anger. In addition, children seem to find facial information more useful for the detection of pretend sadness than pretend anger, and they more often interpret the emotional expression of the characters in terms of pretend play. The present research presents new data about the recognition of negative emotional expressions of sadness and anger and the type of information children take into account to justify their interpretation of pretend emotions, which consists not only in emotional expression but also contextual information.

scholarly journals Information Extraction from Electronic Medical Records Using Multitask Recurrent Neural Network with Contextual Word Embedding

2019 ◽  
Vol 9 (18) ◽  
pp. 3658 ◽  
Author(s):  
Jianliang Yang ◽  
Yuenan Liu ◽  
Minghui Qian ◽  
Chenghua Guan ◽  
Xiangfei Yuan
Keyword(s):  
Electronic Medical Records ◽  
Medical Records ◽  
Large Scale ◽  
Short Term Memory ◽  
Conditional Random Field ◽  
Named Entity Recognition ◽  
Recognition Task ◽  
Entity Recognition ◽  
Language Models ◽  
Named Entity

Clinical named entity recognition is an essential task for humans to analyze large-scale electronic medical records efficiently. Traditional rule-based solutions need considerable human effort to build rules and dictionaries; machine learning-based solutions need laborious feature engineering. For the moment, deep learning solutions like Long Short-term Memory with Conditional Random Field (LSTM–CRF) achieved considerable performance in many datasets. In this paper, we developed a multitask attention-based bidirectional LSTM–CRF (Att-biLSTM–CRF) model with pretrained Embeddings from Language Models (ELMo) in order to achieve better performance. In the multitask system, an additional task named entity discovery was designed to enhance the model’s perception of unknown entities. Experiments were conducted on the 2010 Informatics for Integrating Biology & the Bedside/Veterans Affairs (I2B2/VA) dataset. Experimental results show that our model outperforms the state-of-the-art solution both on the single model and ensemble model. Our work proposes an approach to improve the recall in the clinical named entity recognition task based on the multitask mechanism.

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