444 research outputs found
Handwriting-Based Gender Classification Using End-to-End Deep Neural Networks
Handwriting-based gender classification is a well-researched problem that has
been approached mainly by traditional machine learning techniques. In this
paper, we propose a novel deep learning-based approach for this task.
Specifically, we present a convolutional neural network (CNN), which performs
automatic feature extraction from a given handwritten image, followed by
classification of the writer's gender. Also, we introduce a new dataset of
labeled handwritten samples, in Hebrew and English, of 405 participants.
Comparing the gender classification accuracy on this dataset against human
examiners, our results show that the proposed deep learning-based approach is
substantially more accurate than that of humans
Analysis Of Failure In Offline English Alphabet Recognition With Data Mining Approach
Offline handwriting recognition is a long existing approach to identify the handwritten
phrase, letters or digits. Earlier studies in the handwriting recognition field were mostly
focused on recognizing characters using Neural Network Language Model (NNLM)
classifier, Hidden Markov Model (HMM), and Support Vector Machine (SVM) with
segmentation technique, Hough Transform method, and structural features. However,
these approaches involve complex algorithms and require voluminous dataset as the
training model. Therefore, this study attempts a data mining approach to the analysis
of failure in offline English alphabet recognition. The objectives of the study are to
improve the pattern recognition approach for classifying English alphabets and to
determine the root of classification failure in handwritten English alphabets.
Handwritten data of capital letters of the English alphabet by 50 Universiti Sains
Malaysia student experimented. The data was pre-processed to remove the outliers
prior to classification analysis with the aid of the Waikato Environment for Knowledge
Analysis (WEKA) tool. Classification analysis was initially performed on all seven
classifier’s algorithms at 10-fold dross validation mode. At phase one, Stroke and
Curve are added into the dataset and classified respectively. At phase two, Sharp
Vertex, Closed Region, and Points are added in the dataset. The top three classification
algorithms were selected: IBk, LMT and Random Committee for further classification.
The classified result was further analyzed to identify the root of classification errors.
At the raw dataset classification, the classification accuracy is low with 25%. As the
attributes are added to raw dataset respectively, the accuracy of classification was
successfully increased to 89%. Conclusively, the accuracy of the classification
depends on the added attributes to distinguish characteristics of the alphabets
Objective evaluation of Parkinson's disease bradykinesia
Bradykinesia is the fundamental motor feature of Parkinson’s disease - obligatory for diagnosis and central to monitoring. It is a complex clinicalsign that describes movements with slow speed, small amplitude, irregular rhythm, brief pauses and progressive decrements. Clinical ascertainment of the presence and severity of bradykinesia relies on subjective interpretation of these components, with considerable variability amongst clinicians, and this may contribute to diagnostic error and inaccurate monitoring in Parkinson’s disease. The primary aim of this thesis was to assess whether a novel non-invasive device could objectively measure bradykinesia and predict diagnostic classification of movement data from Parkinson’s disease patients and healthy controls. The second aim was to evaluate how objective measures of bradykinesia correlate with clinical measures of bradykinesia severity. The third aim was to investigate the characteristic kinematic features of bradykinesia. Forty-nine patients with Parkinson’s disease and 41 healthy controls were recruited in Leeds. They performed a repetitive finger-tapping task for 30 seconds whilst wearing small electromagnetic tracking sensors on their finger and thumb. Movement data was analysed using two different methods - statistical measures of the separable components of bradykinesia and a computer science technique called evolutionary algorithms. Validation data collected independently from 13 patients and nine healthy controls in San Francisco was used to assess whether the results generalised. The evolutionary algorithm technique was slightly superior at classifying the movement data into the correct diagnostic groups, especially for the mildest clinical grades of bradykinesia, and they generalised to the independent group data. The objective measures of finger tapping correlated well with clinical grades of bradykinesia severity. Detailed analysis of the data suggests that a defining feature of Parkinson’s disease bradykinesia called the sequence effect may be a physiological rather than a pathological phenomenon. The results inform the development of a device that may support clinical diagnosis and monitoring of Parkinson’s disease and also be used to investigate bradykinesia
DS-Prox : dataset proximity mining for governing the data lake
With the arrival of Data Lakes (DL) there is an increasing need for efficient dataset classification to support data analysis and information retrieval. Our goal is to use meta-features describing datasets to detect whether they are similar. We utilise a novel proximity mining approach to assess the similarity of datasets. The proximity scores are used as an efficient first step, where pairs of datasets with high proximity are selected for further time-consuming schema matching and deduplication. The proposed approach helps in early-pruning unnecessary computations, thus improving the efficiency of similar-schema search. We evaluate our approach in experiments using the OpenML online DL, which shows significant efficiency gains above 25% compared to matching without early-pruning, and recall rates reaching higher than 90% under certain scenarios.Peer ReviewedPostprint (author's final draft
Analysis of a high-resolution hand-written digits data set with writer characteristics
The contributions in this article are two-fold. First, we introduce a new
hand-written digit data set that we collected. It contains high-resolution
images of hand-written digits together with various writer characteristics
which are not available in the well-known MNIST database. The data set is
publicly available and is designed to create new research opportunities.
Second, we perform a first analysis of this new data set. We begin with simple
supervised tasks. We assess the predictability of the writer characteristics
gathered, the effect of using some of those characteristics as predictors in
classification task and the effect of higher resolution images on
classification accuracy. We also explore semi-supervised applications; we can
leverage the high quantity of hand-written digits data sets already existing
online to improve the accuracy of various classifications task with noticeable
success. Finally, we also demonstrate the generative perspective offered by
this new data set; we are able to generate images that mimics the writing style
of specific writers. The data set provides new research opportunities and our
analysis establishes benchmarks and showcases some of the new opportunities
made possible with this new data set.Comment: Data set available here :
https://drive.google.com/drive/folders/1f2o1kjXLvcxRgtmMMuDkA2PQ5Zato4Or?usp=sharin
Handwritten Digit Recognition and Classification Using Machine Learning
In this paper, multiple learning techniques based on Optical character recognition (OCR) for the handwritten digit recognition are examined, and a new accuracy level for recognition of the MNIST dataset is reported. The proposed framework involves three primary parts, image pre-processing, feature extraction and classification. This study strives to improve the recognition accuracy by more than 99% in handwritten digit recognition. As will be seen, pre-processing and feature extraction play crucial roles in this experiment to reach the highest accuracy
Multi-Network Feature Fusion Facial Emotion Recognition using Nonparametric Method with Augmentation
Facial expression emotion identification and prediction is one of the most difficult problems in computer science. Pre-processing and feature extraction are crucial components of the more conventional methods. For the purpose of emotion identification and prediction using 2D facial expressions, this study targets the Face Expression Recognition dataset and shows the real implementation or assessment of learning algorithms such as various CNNs. Due to its vast potential in areas like artificial intelligence, emotion detection from facial expressions has become an essential requirement. Many efforts have been done on the subject since it is both a challenging and fascinating challenge in computer vision. The focus of this study is on using a convolutional neural network supplemented with data to build a facial emotion recognition system. This method may use face images to identify seven fundamental emotions, including anger, contempt, fear, happiness, neutrality, sadness, and surprise. As well as improving upon the validation accuracy of current models, a convolutional neural network that takes use of data augmentation, feature fusion, and the NCA feature selection approach may assist solve some of their drawbacks. Researchers in this area are focused on improving computer predictions by creating methods to read and codify facial expressions. With deep learning's striking success, many architectures within the framework are being used to further the method's efficacy. We highlight the contributions dealt with, the architecture and databases used, and demonstrate the development by contrasting the offered approaches and the outcomes produced. The purpose of this study is to aid and direct future researchers in the subject by reviewing relevant recent studies and offering suggestions on how to further the field. An innovative feature-based transfer learning technique is created using the pre-trained networks MobileNetV2 and DenseNet-201. The suggested system's recognition rate is 75.31%, which is a significant improvement over the results of the prior feature fusion study
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