Deep Learning Methods and Applications

Ahmad, Jamil; Farman, Haleem; Jan, Zahoor

doi:10.1007/978-981-13-3459-7_3

Cited by 101 publications

(59 citation statements)

References 24 publications

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“…Unfortunately, the potential of DL models have not been explored for TDC of Urdu language. As compared to ML models, DL models are capable to learn complex features implicitly from high dimensional feature space and are faster than ML methods because DL models use Graphic Processing Units (GPUs) for parallel processing of data [15], [16].…”

Section: Introductionmentioning

confidence: 99%

Document-Level Text Classification Using Single-Layer Multisize Filters Convolutional Neural Network

et al. 2020

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The rapid growth of electronic documents are causing problems like unstructured data that need more time and effort to search a relevant document. Text Document Classification (TDC) has a great significance in information processing and retrieval where unstructured documents are organized into predefined classes. Urdu is the most favorite research language in South Asian languages because of its complex morphology, unique features, and lack of linguistic resources like standard datasets. As compared to short text, like sentiment analysis, long text classification needs more time and effort because of large vocabulary, more noise, and redundant information. Machine Learning (ML) and Deep Learning (DL) models have been widely used in text processing. Despite the major limitations of ML models, like learn directed features, these are the favorite methods for Urdu TDC. To the best of our knowledge, it is the first study of Urdu TDC using DL model. In this paper, we design a large multipurpose and multi-format dataset that contain more than ten thousand documents organize into six classes. We use Single-layer Multisize Filters Convolutional Neural Network (SMFCNN) for classification and compare its performance with sixteen ML baseline models on three imbalanced datasets of various sizes. Further, we analyze the effects of preprocessing methods on SMFCNN performance. SMFCNN outperformed the baseline classifiers and achieved 95.4%, 91.8%, and 93.3% scores of accuracy on medium, large and small size dataset respectively. The designed dataset would be publically and freely available in different formats for future research in Urdu text processing. INDEX TERMS Convolutional neural network, deep learning, machine learning, natural language processing, text document classification, Urdu text classification.

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Section: Introductionmentioning

confidence: 99%

Document-Level Text Classification Using Single-Layer Multisize Filters Convolutional Neural Network

et al. 2020

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“…In recent years, deep learning has been used widely in most of the fields worldwide [28]. In fall recognition, deep learning methods are being used in the last few years effectively [29], [30] than other approaches like threshold based algorithms [31]- [36]. Machine learning approaches are also very common in this field [37]- [44].…”

Section: Introductionmentioning

confidence: 99%

Deep Learning Based Systems Developed for Fall Detection: A Review

et al. 2020

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Accidental falls are a major source of loss of autonomy, deaths, and injuries among the elderly. Accidental falls also have a remarkable impact on the costs of national health systems. Thus, extensive research and development of fall detection and rescue systems are a necessity. Technologies related to fall detection should be reliable and effective to ensure a proper response. This paper provides a comprehensive review on state-of-the-art fall detection technologies considering the most powerful deep learning methodologies. We reviewed the most recent and effective deep learning methods for fall detection and categorized them into three categories: Convolutional Neural Network (CNN) based systems, Long Short-Term Memory (LSTM) based systems, and Auto-encoder based systems. Among the reviewed systems, three dimensional (3D) CNN, CNN with 10-fold cross-validation, LSTM with CNN based systems performed the best in terms of accuracy, sensitivity, specificity, etc. The reviewed systems were compared based on their working principles, used deep learning methods, used datasets, performance metrics, etc. This review is aimed at presenting a summary and comparison of existing state-of-the-art deep learning based fall detection systems to facilitate future development in this field.

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“…The manufacturing process has changed, and the output layer previously created may no longer sufficient. In the recent years, deep learning has attracted considerable attention, using a cascade of various levels of nonlinear processing units to extract features, and each level recognizes the previously layer results of the test as an input (Ahmad et al, 2019). With this framework, the extraction and classification of features can be incorporated into one system and optimized together, where the last layer is utilized for the output of the forecasted product, and the others are used to extract the features.…”

Section: Related Workmentioning

confidence: 99%

A Framework to Develop Intelligent System for Capturing Product Features Using Open Cv Technique

Saif

Yusof

Ahmed

et al. 2020

JTOM

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This research aims to propose an innovative framework using ISO14649 standard to detect defects in manufactured shaped objects or geometric surfaces of non-linear products of the CNC machine. The significant importance in order to recognize the potential to improve industry product quality inspection and encourage the waste of timing machines and product rejection. Open Computer Vision (Open CV) offers a smart, non-contact measurement and cost-effective technique to fulfil the requirements. The framework depends on the new technique of Open CV, which includes two parts: an intelligent selection of work-piece capturing the image for a particular inspection of the planar interfaces such as the hole, rectangular, pocket one, and the symmetric lighting model comparison approach for measurement of defects in the matched images. The contribution of this study is to build a structure in the computer vision method with a convolution neural network that predicts the classification of the feature for better accuracy and emphasizes the significant characteristics of the image processing technique coupled with experimental data on demanding image datasets and quality inspection measures.

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Deep Learning Methods and Applications

Cited by 101 publications

References 24 publications

Document-Level Text Classification Using Single-Layer Multisize Filters Convolutional Neural Network

Document-Level Text Classification Using Single-Layer Multisize Filters Convolutional Neural Network

Deep Learning Based Systems Developed for Fall Detection: A Review

A Framework to Develop Intelligent System for Capturing Product Features Using Open Cv Technique

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