Cognitively Inspired Feature Extraction and Speech Recognition for Automated Hearing Loss Testing

Nisar, Shibli; Tariq, Muhammad; Adeel, Ahsan; Gogate, Mandar; Hussain, Amir

doi:10.1007/s12559-018-9607-4

Cited by 20 publications

(11 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It is worth to mention that, there are lots of studies published using machine learning and deep learning for different tasks such as natural language processing [12,13,14,15,16,17,18,19,20], speech enhancement [21,22,23,24,25,26,27], cyber-security [28], fall detection [29,30,31,32,33,34] and etc.…”

Section: Related Workmentioning

confidence: 99%

Novel Ensemble Algorithm for Multiple Activity Recognition in Elderly People Exploiting Ubiquitous Sensing Devices

et al. 2021

View full text Add to dashboard Cite

Ambient assisted living is good way to look after ageing population that enables us to detect human's activities of daily living (ADLs) and postures, as number of older adults are increasing at rapid pace. Posture detection is used to provide the assessment for monitoring the activity of elderly people. Most of the existing approaches exploit dedicated sensing devices as as cameras, thermal sensors, accelerometer, gyroscope, magnetometer and so on. Traditional methods such as recording data using these sensors, training and testing machine learning classifiers to identify various human postures. This paper exploits data recorded using ubiquitous devices such as smart phones we use on daily basis and classify different human activities such as standing, sitting, laying, walking, walking downstairs and walking upstairs. Moreover, we have used machine learning and deep learning classifiers including random forest, KNN, logistic regression, multilayer perceptron, decision tree, QDA and SVM, convolutional neural network and long short-term memory as ground truth and proposed a novel ensemble classification algorithm to classify each human activity. The proposed algorithm demonstrate classification accuracy of 98% that outperforms other algorithms.

show abstract

Section: Related Workmentioning

confidence: 99%

Novel Ensemble Algorithm for Multiple Activity Recognition in Elderly People Exploiting Ubiquitous Sensing Devices

et al. 2021

View full text Add to dashboard Cite

show abstract

“…The implementation of a robust CNN model requires the sequence of layers (e.g., convolution, pooling, non-linear transformation, fully connected layers, filters parameters, and loss function formulation) to be defined and, more significantly, requires the use of optimization methods and parameterization [ 41 , 42 ] to improve efficiency. Several techniques were proposed to address overfitting of the DL networks training process, including dropout and batch normalization, and this process has been used to enhance generalization accuracy [ 43 , 44 , 45 ]. In the case of deep neural network architectures with a number of parameters, overfitting is considered a significant drawback [ 28 ].…”

Section: Introductionmentioning

confidence: 99%

Novel Deep Convolutional Neural Network-Based Contextual Recognition of Arabic Handwritten Scripts

Ahmed

Gogate

Tahir

et al. 2021

Entropy

Self Cite

View full text Add to dashboard Cite

Offline Arabic Handwriting Recognition (OAHR) has recently become instrumental in the areas of pattern recognition and image processing due to its application in several fields, such as office automation and document processing. However, OAHR continues to face several challenges, including the high variability of the Arabic script and its intrinsic characteristics such as cursiveness, ligatures, and diacritics, the unlimited variation in human handwriting, and the lack of large public databases. In this paper, we have introduced a novel context-aware model based on deep neural networks to address the challenges of recognizing offline handwritten Arabic text, including isolated digits, characters, and words. Specifically, we have proposed a supervised Convolutional Neural Network (CNN) model that contextually extracts optimal features and employs batch normalization and dropout regularization parameters to prevent overfitting and further enhance its generalization performance when compared to conventional deep learning models. We employed numerous deep stacked-convolutional layers to design the proposed Deep CNN (DCNN) architecture. The proposed model was extensively evaluated, and it was observed to achieve excellent classification accuracy when compared to the existing state-of-the-art OAHR approaches on a diverse set of six benchmark databases, including MADBase (Digits), CMATERDB (Digits), HACDB (Characters), SUST-ALT (Digits), SUST-ALT (Characters), and SUST-ALT (Names). Further comparative experiments were conducted on the respective databases using the pre-trained VGGNet-19 and Mobile-Net models; additionally, generalization capabilities experiments on another language database (i.e., MNIST English Digits) were conducted, which showed the superiority of the proposed DCNN model.

show abstract

“…Researchers have also applied it to areas such as news, politics, sport, etc. For example, in online political debates, sentiment analysis can be used to identify people’s opinions about a certain candidate or political party [ 10 , 11 , 12 , 13 , 14 , 15 , 16 ]. However, sentiment analysis has been widely used for the English language using traditional and advanced machine learning techniques, and limited research has been conducted to develop models for the Persian language [ 17 , 18 , 19 , 20 ].…”

Section: Introductionmentioning

confidence: 99%

Sentiment Analysis of Persian Movie Reviews Using Deep Learning

Dashtipour

Gogate

Adeel

et al. 2021

Entropy

Self Cite

View full text Add to dashboard Cite

Sentiment analysis aims to automatically classify the subject’s sentiment (e.g., positive, negative, or neutral) towards a particular aspect such as a topic, product, movie, news, etc. Deep learning has recently emerged as a powerful machine learning technique to tackle the growing demand for accurate sentiment analysis. However, the majority of research efforts are devoted to English-language only, while information of great importance is also available in other languages. This paper presents a novel, context-aware, deep-learning-driven, Persian sentiment analysis approach. Specifically, the proposed deep-learning-driven automated feature-engineering approach classifies Persian movie reviews as having positive or negative sentiments. Two deep learning algorithms, convolutional neural networks (CNN) and long-short-term memory (LSTM), are applied and compared with our previously proposed manual-feature-engineering-driven, SVM-based approach. Simulation results demonstrate that LSTM obtained a better performance as compared to multilayer perceptron (MLP), autoencoder, support vector machine (SVM), logistic regression and CNN algorithms.

show abstract

Cognitively Inspired Feature Extraction and Speech Recognition for Automated Hearing Loss Testing

Cited by 20 publications

References 31 publications

Novel Ensemble Algorithm for Multiple Activity Recognition in Elderly People Exploiting Ubiquitous Sensing Devices

Novel Ensemble Algorithm for Multiple Activity Recognition in Elderly People Exploiting Ubiquitous Sensing Devices

Novel Deep Convolutional Neural Network-Based Contextual Recognition of Arabic Handwritten Scripts

Sentiment Analysis of Persian Movie Reviews Using Deep Learning

Contact Info

Product

Resources

About