30,564 research outputs found
Enhancing Fingerprint Liveness Detection Accuracy Using Deep Learning: A Comprehensive Study and Novel Approach
No full textLiveness detection for fingerprint impressions plays a role in the meaningful prevention of any unauthorized activity or phishing attempt. The accessibility of unique individual identification has increased the popularity of biometrics. Deep learning with computer vision has proven remarkable results in image classification, detection, and many others. The proposed methodology relies on an attention model and ResNet convolutions. Spatial attention (SA) and channel attention (CA) models were used sequentially to enhance feature learning. A three-fold sequential attention model is used along with five convolution learning layers. The method’s performances have been tested across different pooling strategies, such as Max, Average, and Stochastic, over the LivDet-2021 dataset. Comparisons against different state-of-the-art variants of Convolutional Neural Networks, such as DenseNet121, VGG19, InceptionV3, and conventional ResNet50, have been carried out. In particular, tests have been aimed at assessing ResNet34 and ResNet50 models on feature extraction by further enhancing the sequential attention model. A Multilayer Perceptron (MLP) classifier used alongside a fully connected layer returns the ultimate prediction of the entire stack. Finally, the proposed method is also evaluated on feature extraction with and without attention models for ResNet and considering different pooling strategies
Application and Use of Multivariate Control Charts In a BTA Deep Hole Drilling Process
No full textDeep hole drilling methods are used for producing holes with a high length-to-diameter ratio, good surface finish and straightness. The process is subject to dynamic disturbances usually classified as either chatter vibration or spiralling. In this paper, we will focus on the application and use of multivariate control charts to monitor the process in order to detect chatter vibrations. The results showed that chatter is detected and some alarm signals occurs at time points which can be connected to physical changes of the process. --
Data envelopment analysis of clinics with sparse data: fuzzy clustering approach
No full textThis paper presents a method for utilizing Data Envelopment Analysis (DEA) with sparse input and output data using fuzzy clustering concepts. DEA, a methodology to assess relative technical efficiency of production units is susceptible to missing data, thus, creating a need to supplement sparse data in a reliable and accurate manner. The approach presented is based on a modified fuzzy c-means clustering using Optimal Completion Strategy (OCS) algorithm. This particular algorithm is sensitive to the initial values chosen to substitute missing values and also to the selected number of clusters. Therefore, this paper proposes an approach to estimate the missing values using the OCS algorithm, while considering the issue of initial values and cluster size. This approach is demonstrated on a real and complete dataset of 22 rural clinics in the State of Kansas, assuming varying levels of missing data. Results show the effect of the clustering based approach on the data recovered considering the amount and type of missing data. Moreover, the paper shows the effect that the recovered data has on the DEA scores
Image Search Engine for Digital History: A deep learning approach
No full textThis research investigates and describes an image search engine for digital history using deep learning technologies. It is part of the Engineering Historical Memory research, contributing to a multilingual and transcultural approach to decode-encode the treasure of human experience and transmit it to the next generation of world citizens. The engine provides a new way to search in online (historical) digital libraries using content-based image retrieval and makes linguistic metadata redundant. State-of-the-art deep learning methodologies in computer vision have been investigated and tested. These methodologies include both template-based matching and feature-based matching. A VGG16 Convolutional Neural Network based approach, called D2-Net, is concluded to provide the best basis. D2-Net is then further analyzed, improved, and optimized to run on a large dataset of more than 12k image combinations related to history, heritage, and art. The final implementation shows promising results with a precision of 0.96 and a recall of 0.44 on a challenging testing dataset. Future improvements include speed improvement and model training.Authors are listed in alphabetical order (Hardy-Littlewood Rule). https://github.com/EHM-Search-Engines/ISEDH-Deep-Learning Github repository containing the source code and documentation for this thesis.Engineering Historical MemoryElectrical Engineerin
Learning Deep Belief Networks from Non-Stationary Streams
No full text18.10.13 KB. Ok to add author version to spiral from LNCS; embargo period expired. SpringerDeep learning has proven to be beneficial for complex tasks such as classifying images. However, this approach has been mostly applied to static datasets. The analysis of non-stationary (e.g., concept drift) streams of data involves specific issues connected with the temporal and changing nature of the data. In this paper, we propose a proof-of-concept method, called Adaptive Deep Belief Networks, of how deep learning can be generalized to learn online from changing streams of data. We do so by exploiting the generative properties of the model to incrementally re-train the Deep Belief Network whenever new data are collected. This approach eliminates the need to store past observations and, therefore, requires only constant memory consumption. Hence, our approach can be valuable for life-long learning from non-stationary data streams. © 2012 Springer-Verlag
Deepsign: Sign Language Detection and Recognition Using Deep Learning
No full textThe predominant means of communication is speech; however, there are persons whose speaking or hearing abilities are impaired. Communication presents a significant barrier for persons with such disabilities. The use of deep learning methods can help to reduce communication barriers. This paper proposes a deep learning-based model that detects and recognizes the words from a person’s gestures. Deep learning models, namely, LSTM and GRU (feedback-based learning models), are used to recognize signs from isolated Indian Sign Language (ISL) video frames. The four different sequential combinations of LSTM and GRU (as there are two layers of LSTM and two layers of GRU) were used with our own dataset, IISL2020. The proposed model, consisting of a single layer of LSTM followed by GRU, achieves around 97% accuracy over 11 different signs. This method may help persons who are unaware of sign language to communicate with persons whose speech or hearing is impaired
Deep End Teacher Guide : Orange
No full textDr Mills is the invited author of the Deep End Series Teacher Guides by ERA publications. This 3-volume series for teachers is used in more than 200 schools in Australia, the USA, Canada, New Zealand, Sweden, Norway, and South America
Deep End Teacher Guide : Green
No full textDr Mills is also the invited author of the Deep End Series Teacher Guides by ERA publications. This 3-volume series for teachers is used in more than 200 schools in Australia, the USA, Canada, New Zealand, Sweden, Norway, and South America
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Miss Ella of the Deep South of Texas
No full textHistorical narrative of the life of Ellen Talbot and the region of Texas the author calls the "Deep South of Texas," meaning that area near the Gulf that combines elements of the West with those of the Deep South
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