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Optical character recognition is gaining immense importance in the domain of deep learning. With each passing day, handwritten digits (0–9) data are increasing rapidly, and plenty of research has been conducted thus far. However, there is still a need to develop a robust model that can fetch useful information and investigate self-build handwritten digit data efficiently and effectively. The convolutional neural network (CNN) models incorporating a sigmoid activation function with a large number of derivatives have low efficiency in terms of feature extraction. Here, we designed a novel CNN model integrated with the extreme learning machine (ELM) algorithm. In this model, the sigmoid activation function is upgraded as the rectified linear unit (ReLU) activation function, and the CNN unit along with the ReLU activation function are used as a feature extractor. The ELM unit works as the image classifier, which makes the perfect symmetry for handwritten digit recognition. A deeplearning4j (DL4J) framework-based CNN-ELM model was developed and trained using the Modified National Institute of Standards and Technology (MNIST) database. Validation of the model was performed through self-build handwritten digits and USPS test datasets. Furthermore, we observed the variation of accuracies by adding various hidden layers in the architecture. Results reveal that the CNN-ELM-DL4J approach outperforms the conventional CNN models in terms of accuracy and computational time.

Handwritten digits recognition is an area of machine learning, in which a machine is trained to identify handwritten digits. One method of achieving this is with decision tree classification model. A decision tree classification is a machine learning approach that uses the predefined labels from the past known sets to determine or predict the classes of the future data sets where the class labels are unknown. In this paper we have used the standard kaggle digits dataset for recognition of handwritten digits using a decision tree classification approach. And we have evaluated the accuracy of the model against each digit from 0 to 9.

Young healthy adults can hear tones up to at least 20 kHz. However, clinical audiometry, by which hearing loss is diagnosed, is limited at high frequencies to 8 kHz. Evidence suggests there is salient information at extended high frequencies (EHFs; 8 to 20 kHz) that may influence speech intelligibility, but whether that information is used in challenging listening conditions remains unknown. Difficulty understanding speech in noisy environments is the most common concern people have about their hearing and usually the first sign of age-related hearing loss. Digits-in-noise (DIN), a widely used test of speech-in-noise perception, can be sensitized for detection of high-frequency hearing loss by low-pass filtering the broadband masking noise. Here, we used standard and EHF audiometry, self-report, and successively higher cutoff frequency filters (2 to 8 kHz) in a DIN test to investigate contributions of higher-frequency hearing to speech-in-noise perception. Three surprising results were found. First, 74 of 116 “normally hearing,” mostly younger adults had some hearing loss at frequencies above 8 kHz. Early EHF hearing loss may thus be an easily measured, preventive warning to protect hearing. Second, EHF hearing loss correlated with self-reported difficulty hearing in noise. Finally, even with the broadest filtered noise (≤8 kHz), DIN hearing thresholds were significantly better (P < 0.0001) than those using broadband noise. Sound energy above 8 kHz thus contributes to speech perception in noise. People with “normal hearing” frequently report difficulty hearing in challenging environments. Our results suggest that one contribution to this difficulty is EHF hearing loss.

Ellipsephic or Kempner-like harmonic series are series of inverses of integers whose expansion in base B , for some B ≥ 2 , contains no occurrence of some fixed digit or some fixed block of digits. A prototypical example was proposed by Kempner in 1914, namely the sum inverses of integers whose expansion in base 10 contains no occurrence of a nonzero given digit. Results about such series address their convergence as well as closed expressions for their sums (or approximations thereof). Another direction of research is the study of sums of inverses of integers that contain only a given finite number, say k , of some digit or some block of digits, and the limits of such sums when k goes to infinity. Generalizing partial results in the literature, we give a complete result for any digit or block of digits in any base.

In view of the increasing demand for handwritten digit recognition, a handwritten digit recognition model based on convolutional neural network is proposed. The model includes 1 input layer and 2 convolutional layers (5*5 convolution Core), 2 pooling layers (2*2 pooling core), 1 fully connected layer, 1 output layer, and use the mnist data set for model training and prediction. After a lot of training and participation, the accuracy rate of the training set was finally reached to 100%, and the accuracy rate of 99.25% was also achieved on the test set, which can meet the requirements of recognizing handwritten digits.

Abstract The amniote digits have undergone recurrent modifications, with the diversified molecular mechanisms more studied among mammals than reptiles. Here we focus on the emu wings and ostrich feet, both of which experienced species-specific digit changes driven respectively by secondary flight loss and adaptation to running. By comparing their digit transcriptomes to those of chicken and alligator, we identified different gene networks in skeleton/muscle development responsible for the degenerated digits in archosaur ancestors and emu, but those in epidermal development for the load-bearing digit of ostrich. These results provide new clues for developmental programs of different cell types between different digits, on which natural selection can convergently operate.

In the present work, we propose a novel method utilizing only a decoder for generation of pseudo-examples, which has shown great success in image classification tasks. The proposed method is particularly constructive when the data are in a limited quantity used for semi-supervised learning (SSL) or few-shot learning (FSL). While most of the previous works have used an autoencoder to improve the classification performance for SSL, using a single autoencoder may generate confusing pseudo-examples that could degrade the classifier’s performance. On the other hand, various models that utilize encoder–decoder architecture for sample generation can significantly increase computational overhead. To address the issues mentioned above, we propose an efficient means of generating pseudo-examples by using only the generator (decoder) network separately for each class that has shown to be effective for both SSL and FSL. In our approach, the decoder is trained for each class sample using random noise, and multiple samples are generated using the trained decoder. Our generator-based approach outperforms previous state-of-the-art SSL and FSL approaches. In addition, we released the Urdu digits dataset consisting of 10,000 images, including 8000 training and 2000 test images collected through three different methods for purposes of diversity. Furthermore, we explored the effectiveness of our proposed method on the Urdu digits dataset by using both SSL and FSL, which demonstrated improvement of 3.04% and 1.50% in terms of average accuracy, respectively, illustrating the superiority of the proposed method compared to the current state-of-the-art models.

We ćonsider 3, 1, 4, 1, 5, 9, … as a time series desćribing osćillation of a partićle. The level of instability is defined using two of ten forće parameters assigned to the time series. Irrationality of the number p ćan then be seen through instability of motion, using Newton’s sećond law. If we intervene in digits, making a rational number, with termination or a repeating sequenće, level of instability slumps to zero. New rules for digits of p are found out. High instability is ćonnećted with strong damping and driving forćes. Tendenćy to a moderate instability is observed. Unrealised series of digits, ćlose to the realised series, derogate this tendenćy.

Deep learning uses multilayered artificial neural networks to learn digitally from large datasets. It then performs advanced identification and classification tasks. To date, these multilayered neural networks have been implemented on a computer. Lin et al. demonstrate all-optical machine learning that uses passive optical components that can be patterned and fabricated with 3D-printing. Their hardware approach comprises stacked layers of diffractive optical elements analogous to an artificial neural network that can be trained to execute complex functions at the speed of light. Science , this issue p. 1004 All-optical deep learning can be implemented with 3D-printed passive optical components. Deep learning has been transforming our ability to execute advanced inference tasks using computers. Here we introduce a physical mechanism to perform machine learning by demonstrating an all-optical diffractive deep neural network (D 2 NN) architecture that can implement various functions following the deep learning–based design of passive diffractive layers that work collectively. We created 3D-printed D 2 NNs that implement classification of images of handwritten digits and fashion products, as well as the function of an imaging lens at a terahertz spectrum. Our all-optical deep learning framework can perform, at the speed of light, various complex functions that computer-based neural networks can execute; will find applications in all-optical image analysis, feature detection, and object classification; and will also enable new camera designs and optical components that perform distinctive tasks using D 2 NNs.

We compute the Hausdorff dimension of sets defined by the growth of weighted products of multiple digits at arbitrary positions in \\(d\\)-decaying Gauss-like iterated function systems. We provide the complete Hausdorff dimensional result for product of more than two digits, which was an open problem even for consecutive digits in the classical Gauss map and L\"uroth map. In our approach we do not need to assume the Bounded Distortion Property (BDP).