Language Classification and Recognition From Audio Using Deep Belief Network

2021 ◽  
pp. 189-213
Author(s):  
Santhi Selvaraj ◽  
Raja Sekar J. ◽  
Amutha S.
Keyword(s):  
Natural Language ◽  
Language Processing ◽  
Markov Models ◽  
Learning Algorithm ◽  
Back Propagation ◽  
Gaussian Mixture ◽  
Deep Belief Network ◽  
Mel Frequency Cepstral Coefficients ◽  
Belief Network ◽  
Language Classification

The main objective is to recognize the chat from social media as spoken language by using deep belief network (DBN). Currently, language classification is one of the main applications of natural language processing, artificial intelligence, and deep learning. Language classification is the process of ascertaining the information being presented in which natural language and recognizing a language from the audio sound. Presently, most language recognition systems are based on hidden Markov models and Gaussian mixture models that support both acoustic and sequential modeling. This chapter presents a DBN-based recognition system in three different languages, namely English, Hindi, and Tamil. The evaluation of languages is performed on the self built recorded database, which extracts the mel-frequency cepstral coefficients features from the speeches. These features are fed into the DBN with a back propagation learning algorithm for the recognition process. Accuracy of the recognition is efficient for the chosen languages and the system performance is assessed on three different languages.

Deep Appearance Model and Crow-Sine Cosine Algorithm-Based Deep Belief Network for Age Estimation

2021 ◽  
Vol 12 (3) ◽  
pp. 185-207
Author(s):  
Anjali A. Shejul ◽  
Kinage K. S. ◽  
Eswara Reddy B.
Keyword(s):  
Feature Extraction ◽  
Age Estimation ◽  
Learning Algorithm ◽  
Directional Pattern ◽  
Input Image ◽  
Deep Belief Network ◽  
Appearance Model ◽  
Belief Network ◽  
Sine Cosine Algorithm ◽  
New Feature

Age estimation has been paid great attention in the field of intelligent surveillance, face recognition, biometrics, etc. In contrast to other facial variations, aging variation presents several unique characteristics, which make age estimation very challenging. The overall process of age estimation is performed using three important steps. In the first step, the pre-processing is performed from the input image based on Viola-Jones algorithm to detect the face region. In the second step, feature extraction is done based on three important features such as local transform directional pattern (LTDP), active appearance model (AAM), and the new feature, deep appearance model (Deep AM). After feature extraction, the classification is carried out based on the extracted features using deep belief network (DBN), where the DBN classifier is trained optimally using the proposed learning algorithm named as crow-sine cosine algorithm (CS).

scholarly journals Deep Belief Network for Predicting the Predisposition to Lung Cancer in TP53 Gene

2020 ◽  
pp. 171-177 ◽  
Author(s):  
Zahraa Naser Shahweli
Keyword(s):  
Neural Network ◽  
Lung Cancer ◽  
Smoking Habit ◽  
Back Propagation ◽  
Tp53 Gene ◽  
Back Propagation Neural Network ◽  
Biological Data ◽  
Deep Belief Network ◽  
Restricted Boltzmann Machines ◽  
Belief Network

Lung cancer, similar to other cancer types, results from genetic changes. However, it is considered as more threatening due to the spread of the smoking habit, a major risk factor of the disease. Scientists have been collecting and analyzing the biological data for a long time, in attempts to find methods to predict cancer before it occurs. Analysis of these data requires the use of artificial intelligence algorithms and neural network approaches. In this paper, one of the deep neural networks was used, that is the enhancer Deep Belief Network (DBN), which is constructed from two Restricted Boltzmann Machines (RBM). The visible nodes for the first RBM are 13 nodes and 8 nodes in each hidden layer for the two RBMs. The enhancer DBN was trained by Back Propagation Neural Network (BPNN), where the data sets were divided into 6 folds, each is split into three partitions representing the training, validation, and testing. It is worthy to note that the proposed enhancer DBN predicted lung cancer in an acceptable manner, with an average F-measure value of  0. 96 and an average Matthews Correlation Coefficient (MCC) value of 0. 47 for 6 folds.

scholarly journals Lateral and Longitudinal Driving Behavior Prediction Based on Improved Deep Belief Network

Sensors ◽  
2021 ◽  
Vol 21 (24) ◽  
pp. 8498
Author(s):  
Lei Yang ◽  
Chunqing Zhao ◽  
Chao Lu ◽  
Lianzhen Wei ◽  
Jianwei Gong
Keyword(s):  
Neural Network ◽  
Rbf Neural Network ◽  
Back Propagation ◽  
Front Wheel ◽  
Driving Behavior ◽  
Deep Belief Network ◽  
Intelligent Vehicles ◽  
Support Vector ◽  
Safe Driving ◽  
Belief Network

Accurately predicting driving behavior can help to avoid potential improper maneuvers of human drivers, thus guaranteeing safe driving for intelligent vehicles. In this paper, we propose a novel deep belief network (DBN), called MSR-DBN, by integrating a multi-target sigmoid regression (MSR) layer with DBN to predict the front wheel angle and speed of the ego vehicle. Precisely, the MSR-DBN consists of two sub-networks: one is for the front wheel angle, and the other one is for speed. This MSR-DBN model allows ones to optimize lateral and longitudinal behavior predictions through a systematic testing method. In addition, we consider the historical states of the ego vehicle and surrounding vehicles and the driver’s operations as inputs to predict driving behaviors in a real-world environment. Comparison of the prediction results of MSR-DBN with a general DBN model, back propagation (BP) neural network, support vector regression (SVR), and radical basis function (RBF) neural network, demonstrates that the proposed MSR-DBN outperforms the others in terms of accuracy and robustness.

scholarly journals Image Spam Detection Using Machine Learning and Natural Language Processing

2020 ◽  
Vol 55 (2) ◽  
Author(s):  
Yaseen Khather Yaseen ◽  
Alaa Khudhair Abbas ◽  
Ahmed M. Sana
Keyword(s):  
Machine Learning ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Character Recognition ◽  
Optical Character Recognition ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Optical Character ◽  
Harmful Content

Today, images are a part of communication between people. However, images are being used to share information by hiding and embedding messages within it, and images that are received through social media or emails can contain harmful content that users are not able to see and therefore not aware of. This paper presents a model for detecting spam on images. The model is a combination of optical character recognition, natural language processing, and the machine learning algorithm. Optical character recognition extracts the text from images, and natural language processing uses linguistics capabilities to detect and classify the language, to distinguish between normal text and slang language. The features for selected images are then extracted using the bag-of-words model, and the machine learning algorithm is run to detect any kind of spam that may be on it. Finally, the model can predict whether or not the image contains any harmful content. The results show that the proposed method using a combination of the machine learning algorithm, optical character recognition, and natural language processing provides high detection accuracy compared to using machine learning alone.

Bus travel time prediction based on deep belief network with back-propagation

2019 ◽  
Vol 32 (14) ◽  
pp. 10435-10449 ◽  
Author(s):  
Chao Chen ◽  
Hui Wang ◽  
Fang Yuan ◽  
Huizhong Jia ◽  
Baozhen Yao
Keyword(s):  
Travel Time ◽  
Back Propagation ◽  
Deep Belief Network ◽  
Travel Time Prediction ◽  
Belief Network ◽  
Time Prediction ◽  
Bus Travel Time

PARSING-DRIVEN GENERALIZATION FOR NATURAL LANGUAGE ACQUISITION

1993 ◽  
Vol 07 (03) ◽  
pp. 621-644 ◽  
Author(s):  
REY-LONG LIU ◽  
VON-WUN SOO
Keyword(s):  
Problem Solving ◽  
Language Acquisition ◽  
Natural Language ◽  
Language Processing ◽  
Back Propagation ◽  
Problem Solver ◽  
Concept Hierarchy ◽  
Left Hand ◽  
Simple Generalization ◽  
Asking Questions

Parsing is an important step in natural language processing. It involves tasks of searching for applicable grammatical rules which can transform natural language sentences into their corresponding parse trees. Therefore parsing can be viewed as problem solving, and language acquisition can be achieved by generalizing problem solving heuristics. In this paper we investigate how machine learning methodologies can be integrated with a Wait-And-See Parser (the problem solver) to acquire parsing-related knowledge that is needed for the parser. We call this approach parsing-driven generalization since learning (acquisition of parsing rules and classification of lexicons) is basically derived from the parsing process. Three types of generalization are reported in this paper: simple generalization, generalization by asking questions, and generalization back-propagation. Simple generalization generalizes any two parsing rules whose action parts (right-hand sides) are the same but whose condition parts (left-hand sides) have a single difference. Generalization by asking questions is triggered when a “climbing-up” move on a concept hierarchy is attempted. It is necessary for avoiding over-generalization. Generalization back-propagation propagates a confirmed generalization of some later parsing rule back to its precedent rules in a parsing sequence and thus causes them to be generalized as well. It can reduce the number of questions asked by the system. With these three types of generalization and a mechanism for maintaining lexicon classification (the domain concept hierarchy), parsing and learning can interact to utilize and acquire parsing-related knowledge. To promote the practical performance of parsing after learning, a relaxation parsing mechanism is also designed to process unseen sentences.

scholarly journals Deep Belief Network for Prediction of Rician Fading Channel

2019 ◽  
Vol 9 (2S) ◽  
pp. 195-199
Keyword(s):  
Fading Channel ◽  
Learning Algorithm ◽  
Deep Belief Network ◽  
Rician Fading ◽  
Restricted Boltzmann Machines ◽  
Belief Network ◽  
Channel Prediction ◽  
Echo State Networks ◽  
Rician Fading Channel ◽  
Predictive Approaches

In this paper a novel channel prediction scheme is presented for rician fading channel. The channel prediction is done by using a Deep Belief Network (DBN) which is composed of two Restricted Boltzmann Machines (RBMs), this deep learning algorithm can produce fewer predictive errors than echo state networks and other predictive approaches.. Simulation results shows that the DBN channel prediction system has a lower NMSE than the prediction of the echo state network and other conventional prediction methods and the obtained SER gap between the actual CSI and predicted CSI is small.

scholarly journals Disease Prediction Chatbot

2021 ◽  
pp. 632-636
Author(s):  
Shraddha A. S ◽  
Shreepada Bhat ◽  
Shubhashri V. K ◽  
Sinchana Karnik ◽  
Narender M
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Learning Algorithm ◽  
Recent Decade ◽  
Standard Of Living ◽  
Disease Prediction ◽  
Machine Learning Algorithm

Applications in the field of machine learning and artificial intelligence have been in great demand over the recent decade. Now it has various applications in the field of health industry. With the help of machine learning algorithm prediction of diseases has been made easier. Now doctors can concentrate only on treatment with the help of technology. Technology is accelerating innovations in the healthcare domain which has increased people’s standard of living over the years. Here in our project we are making a healthcare chatbot with help of Natural language processing and machine learning algorithm to predict disease. User interacts with the chatbot just like one interacts with his doctor and based on the symptoms provided by users and the chatbot will identify the symptom and predict the disease.

scholarly journals NPCEditor: Creating Virtual Human Dialogue Using Information Retrieval Techniques

AI Magazine ◽  
2011 ◽  
Vol 32 (2) ◽  
pp. 42 ◽  
Author(s):  
Anton Leuski ◽  
David Traum
Keyword(s):  
Information Retrieval ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Virtual Human ◽  
Virtual Humans ◽  
Text Input ◽  
Processing Component ◽  
User Friendly ◽  
Language Classification

NPCEditor is a system for building a natural language processing component for virtual humans capable of engaging a user in spoken dialog on a limited domain. It uses statistical language classification technology for mapping from a user’s text input to system responses. NPCEditor provides a user-friendly editor for creating effective virtual humans quickly. It has been deployed as a part of various virtual human systems in several applications.

scholarly journals Natural Language Processing Model for Managing Maintenance Requests in Buildings

Buildings ◽  
2020 ◽  
Vol 10 (9) ◽  
pp. 160 ◽  
Author(s):  
Yassine Bouabdallaoui ◽  
Zoubeir Lafhaj ◽  
Pascal Yim ◽  
Laure Ducoulombier ◽  
Belkacem Bennadji
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Learning Algorithm ◽  
Healthcare Facility ◽  
Facility Management ◽  
Building Maintenance ◽  
Average Accuracy ◽  
Building Information ◽  
Human Management

In recent years, facility management (FM) has adopted many computer technology solutions for building maintenance, such as building information modelling (BIM) and computerized maintenance management systems (CMMS). However, maintenance requests management in buildings remains a manual and a time-consuming process that depends on human management. In this paper, a machine-learning algorithm based on natural language processing (NLP) is proposed to classify maintenance requests. This algorithm aims to assist the FM teams in managing day-to-day maintenance activities. A healthcare facility is addressed as a case study in this work. Ten-year maintenance records from the facility contributed to the design and development of the algorithm. Multiple NLP methods were used in this study, and the results reveal that the NLP model can classify work requests with an average accuracy of 78%. Furthermore, NLP methods have proven to be effective for managing unstructured text data.

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