Recognition of handwritten digits using proximal support vector machine

Handwritten Digit Recognition System involves reception and interpretation of handwritten digits by a machine. Due to variation in shape and orientation of handwritten digits, it is difficult for a machine to interpret handwritten digits. Handwritten digit Recognition has a wide area of research due to its vast applications like automatic bank cheques processing, billing and automatic postal service. In this thesis, an Offline Handwritten Digit Recognition System is presented. The recognition system is broadly divided into 2 parts, first part is feature extraction from handwritten images and the second one is classification of feature vector into digits. We propose descriptors for handwritten digit recognition based on Histogram of Oriented Gradient (HOG) feature .It is one of the widely used feature vector for object detection in computer vision. For classification of features, linear Proximal Support Vector Machine (PSVM) Classifier is proposed. This is a binary class classifier which is further converted to a 10 class classifier by means of One against all algorithm. Due to small training time, PSVM classifier is preferable over standard Support Vector Machine (SVM) Classifier. The handwritten images both for training and testing are taken from MNIST database. The performance of the system is measured in terms of Sensitivity, Accuracy, Positive Predictivity and Specificity. The performance of PSVM classifier is better compared to Artificial Neural Network(ANN)

An Open Virtual World for Professional Development

The paper presents a study that focuses on the issue of sup-porting educational experts to choose the right combination of educational methodology and technology tools when designing training and learning programs. It is based on research in the field of adaptive intelligent e-learning systems. The object of study is the professional growth of teachers in technology and in particular that part of their qualification which is achieved by organizing targeted training of teachers. The article presents the process of creating and testing a system to support the decision on the design of training for teachers, leading to more effective implementation of technology in education and integration in diverse educational contexts. ACM Computing Classification System (1998): H.4.2, I.2.1, I.2, I.2.4, F.4.1.∗This article presents the principal results of the Ph.D. thesis Open Virtual Worlds for Professional Development by Eliza Stefanova (Faculty of Mathematics and Informatics at Sofia University), successfully defended at the Specialized Academic Council of FMI on 10 December, 2012

Arabic Dialect Texts Classification

This study investigates how to classify Arabic dialects in text by extracting features which show the differences between dialects. There has been a lack of research about classification of Arabic dialect texts, in comparison to English and some other languages, due to the lack of Arabic dialect text corpora in comparison with what is available for dialects of English and some other languages. What is more, there is an increasing use of Arabic dialects in social media, so this text is now considered quite appropriate as a medium of communication and as a source of a corpus. We collected tweets from Twitter, comments from Facebook and online newspapers from five groups of Arabic dialects: Gulf, Iraqi, Egyptian, Levantine, and North African. The research sought to: 1) create a dataset of Arabic dialect texts to use in training and testing the system of classification, 2) find appropriate features to classify Arabic dialects: lexical (word and multi-word-unit) and grammatical variation across dialects, 3) build a more sophisticated filter to extract features from Arabic-character written dialect text files. In this thesis, the first part describes the research motivation to show the reason for choosing the Arabic dialects as a research topic. The second part presents some background information about the Arabic language and its dialects, and the literature review shows previous research about this subject. The research methodology part shows the initial experiment to classify Arabic dialects. The results of this experiment showed the need to create an Arabic dialect text corpus, by exploring Twitter and online newspaper. The corpus used to train the ensemble classifier and to improve the accuracy of classification the corpus was extended by collecting tweets from Twitter based on the spatial coordinate points and comments from Facebook posts. The corpus was annotated with dialect labels and used in automatic dialect classification experiments. The last part of this thesis presents the results of classification, conclusions and future work

The CogALex-V Shared Task on the Corpus-Based Identification of Semantic Relations

The shared task of the 5th Workshop on Cognitive Aspects of the Lexicon (CogALex-V) aims at providing a common benchmark for testing current corpus-based methods for the identifica- tion of lexical semantic relations ( synonymy , antonymy , hypernymy , part-whole meronymy ) and at gaining a better understanding of their respective strengths and weaknesses. The shared task uses a challenging dataset extracted from EVALution 1.0 (Santus et al., 2015b), which contains word pairs holding the above-mentioned relations as well as semantically unrelated control items ( random ). The task is split into two subtasks: (i) identification of related word pairs vs. unre- lated ones; (ii) classification of the word pairs according to their semantic relation. This paper describes the subtasks, the dataset, the evaluation metrics, the seven participating systems and their results. The best performing system in subtask 1 is GHHH ( F 1 = 0 . 790 ), while the best system in subtask 2 is LexNet ( F 1 = 0 . 445 ). The dataset and the task description are available at https://sites.google.com/site/cogalex2016/home/shared-task

Auto-encoder based deep learning for surface electromyography signal processing

© 2018 Advances in Science, Technology and Engineering Systems. All Rights Reserved. Feature extraction is taking a very vital and essential part of bio-signal processing. We need to choose one of two paths to identify and select features in any system. The most popular track is engineering handcrafted, which mainly depends on the user experience and the field of application. While the other path is feature learning, which depends on training the system on recognising and picking the best features that match the application. The main concept of feature learning is to create a model that is expected to be able to learn the best features without any human intervention instead of recourse the traditional methods for feature extraction or reduction and avoid dealing with feature extraction that depends on researcher experience. In this paper, Auto-Encoder will be utilised as a feature learning algorithm to practice the recommended model to excerpt the useful features from the surface electromyography signal. Deep learning method will be suggested by using Auto-Encoder to learn features. Wavelet Packet, Spectrogram, and Wavelet will be employed to represent the surface electromyography signal in our recommended model. Then, the newly represented bio-signal will be fed to stacked autoencoder (2 stages) to learn features and finally, the behaviour of the proposed algorithm will be estimated by hiring different classifiers such as Extreme Learning Machine, Support Vector Machine, and SoftMax Layer. The Rectified Linear Unit (ReLU) will be created as an activation function for extreme learning machine classifier besides existing functions such as sigmoid and radial basis function. ReLU will show a better classification ability than sigmoid and Radial basis function (RBF) for wavelet, Wavelet scale 5 and wavelet packet signal representations implemented techniques. ReLU will illustrate better classification ability, as an activation function, than sigmoid and poorer than RBF for spectrogram signal representation. Both confidence interval and Analysis of Variance will be estimated for different classifiers. Classifier fusion layer will be implemented to glean the classifier which will progress the best accuracies' values for both testing and training to develop the results. Classifier fusion layer brought an encouraging value for both accuracies either training or testing ones

PORK QUALITY ASSESSMENT THROUGH IMAGE SEGMENTATION AND SUPPORT VECTOR MACHINE IMPLEMENTATION

Pork is the most consumed meat in the Philippines, and efficient quality control is essential for ensuring the safety of its consumers. Current manual procedures of meat inspection are time-consuming and laboratory-intensive considering the large amount of supply to be examined. This research aims to construct a rapid objective system of pork quality assessment with respect to meat freshness through Support Vector Machine (SVM) implementation, and to ultimately have an accuracy rate of ≥ 90%. 35 meat samples were collected, and their images were acquired. 30 of these were randomly designated as part of the training dataset while the rest were designated as part of the testing dataset. Of the 30 training samples, 6 were randomly chosen for the creation of a microbial profile. In all of the acquired image samples, image segmentation was performed and the RGB, HSV, Lab, and statistical texture features were extracted. These were inputted in 15 different SVM configurations. SVM classification yielded an accuracy rate of 93.33 %. Results from the microbial profile revealed considerable microbial activity at the 5th and 6th intervals (10th and 12th hour) with 2 and 3 colonies formed, respectively. With the ability of the SVM to distinguish between samples with respect to the hour interval and with the supplementation of the microbial profile, an objective artificial intelligence mechanism for freshness detection was successfully created.Keywords: Meat quality, Image segmentation, Support vector machine, Artificial intelligenc

Robust Electromyography Based Control of Multifunctional Prostheses of The Upper Extremity

Multifunctional, highly dexterous and complex mechanic hand prostheses are emerging and currently entering the market. However, the bottleneck to fully exploiting all capabilities of these mechatronic devices, and to making all available functions controllable reliably and intuitively by the users, remains a considerable challenge. The robustness of scientific methods proposed to overcome this barrier is a crucial factor for their future commercial success. Therefore, in this thesis the matter of robust, multifunctional and dexterous control of prostheses of the upper limb was addressed and some significant advancements in the scientific field were aspired. To this end, several investigations grouped in four studies were conducted, all with the same focus on understanding mechanisms that influence the robustness of myoelectric control and resolving their deteriorating effects. For the first study, a thorough literature review of the field was conducted and it was revealed that many non-stationarities, which could be expected to affect the reliability of surface EMG pattern recognition myoprosthesis control, had been identified and studied previously. However, one significant factor had not been addressed to a sufficient extent: the effect of long-term usage and day-to-day testing. Therefore, a dedicated study was designed and carried out, in order to address the previously unanswered question of how reliable surface electromyography pattern recognition was across days. Eleven subjects, involving both able-bodied and amputees, participated in this study over the course of 5 days, and a pattern recognition system was tested without daily retraining. As the main result of this study, it was revealed that the time between training and testing a classifier was indeed a very relevant factor influencing the classification accuracy. More estimation errors were observed as more time lay between the classifier training and testing. With the insights obtained from the first study, the need for compensating signal non-stationarities was identified. Hence, in a second study, building up on the data obtained from the first investigation, a self-correction mechanism was elaborated. The goal of this approach was to increase the systems robustness towards non-stationarities such as those identified in the first study. The system was capable of detecting and correcting its own mistakes, yielding a better estimation of movements than the uncorrected classification or other, previously proposed strategies for error removal. In the third part of this thesis, the previously investigated ideas for error suppression for increased robustness of a classification based system were extended to regression based movement estimation. While the same method as tested in the second study was not directly applicable to regression, the same underlying idea was used for developing a novel proportional estimator. It was validated in online tests, with the control of physical prostheses by able-bodied and transradial amputee subjects. The proposed method, based on common spatial patterns, outperformed two state-of-the art control methods, demonstrating the benefit of increased robustness in movement estimation during applied tasks. The results showed the superior performance of robust movement estimation in real life investigations, which would have hardly been observable in offline or abstract cursor control tests, underlining the importance of tests with physical prostheses. In the last part of this work, the limitation of sequential movements of the previously explored system was addressed and a methodology for enhancing the system with simultaneous and proportional control was developed. As a result of these efforts, a system robust, natural and fluent in its movements was conceived. Again, online control tests of physical prostheses were performed by able-bodied and amputee subjects, and the novel system proved to outperform the sequential controller of the third study of this thesis, yielding the best control technique tested. An extensive set of tests was conducted with both able-bodied and amputee subjects, in scenarios close to clinical routine. Custom prosthetic sockets were manufactured for all subjects, allowing for experimental control of multifunction prostheses with advanced machine learning based algorithms in real-life scenarios. The tests involved grasping and manipulating objects, in ways as they are often encountered in everyday living. Similar investigations had not been conducted before. One of the main conclusions of this thesis was that the suppression of wrong prosthetic motions was a key factor for robust prosthesis control and that simultaneous wrist control was a beneficial asset especially for experienced users. As a result of all investigations performed, clinically relevant conclusions were drawn from these tests, maximizing the impact of the developed systems on potential future commercialization of the newly conceived control methods. This was emphasized by the close collaboration with Otto Bock as an industrial partner of the AMYO project and hence this work.2016-02-2