The analysis of convolutional neural model is done. The software is developed, allowing to train and test convolutional neural networks of base architecture LeNet-5. Efficiency of technique multi ...training and distortions of training images is shown. The qualifier of images of the isolated figures is constructed. The estimation of stability of its characteristics on examples of known hand-written and font databases is done.
Background: This paper addresses specific challenges in predictive modeling, namely transparency issues, susceptibility to data manipulation, and fairness concerns. To overcome these obstacles, the ...study introduces DeepRing, approach that combines Convolutional Neural Networks (CNNs) and blockchain technology. Objective: DeepRing aims to enhance prediction integrity, data security, and fairness, thereby improving the ethical considerations, reliability, and accountability of predictive models. Methods: involves iterative training of a CNN model on five diverse datasets, including CIFAR-10, Fashion-MNIST, MNIST, CIFAR-100, and a Hands dataset. The CNN architecture incorporates Conv2D layers, MaxPooling2D layers, and Dense layers. Training metrics such as accuracy and sparse categorical cross-entropy loss are monitored, with the Adam optimizer employed. While achieving high accuracy on Plam (0.5300), MNIST (0.9978) and Fashion MNIST (0.9673), DeepRing exhibits moderate performance on CIFAR-10 (0.9296) and lower accuracy on CIFAR-100 (0.5973). Results: demonstrate the effectiveness of DeepRing in improving accuracy and enhancing model performance across various datasets. However, further development and validation are essential for successful model implementation, further development and validation are essential for successful model implementation. Conclusions: Introduces DeepRing as an innovative solution to address key challenges in predictive modeling, specifically focusing on transparency issues, susceptibility to data manipulation, and fairness concerns. By combining Convolutional Neural Networks (CNNs) with blockchain technology, DeepRing aims to elevate prediction integrity, enhance data security, and promote fairness, thereby contributing to the improvement of ethical considerations, reliability, and accountability in predictive modelling.
Deep spiking neural networks (SNNs) hold the potential for improving the latency and energy efficiency of deep neural networks through data-driven event-based computation. However, training such ...networks is difficult due to the non-differentiable nature of spike events. In this paper, we introduce a novel technique, which treats the membrane potentials of spiking neurons as differentiable signals, where discontinuities at spike times are considered as noise. This enables an error backpropagation mechanism for deep SNNs that follows the same principles as in conventional deep networks, but works directly on spike signals and membrane potentials. Compared with previous methods relying on indirect training and conversion, our technique has the potential to capture the statistics of spikes more precisely. We evaluate the proposed framework on artificially generated events from the original MNIST handwritten digit benchmark, and also on the N-MNIST benchmark recorded with an event-based dynamic vision sensor, in which the proposed method reduces the error rate by a factor of more than three compared to the best previous SNN, and also achieves a higher accuracy than a conventional convolutional neural network (CNN) trained and tested on the same data. We demonstrate in the context of the MNIST task that thanks to their event-driven operation, deep SNNs (both fully connected and convolutional) trained with our method achieve accuracy equivalent with conventional neural networks. In the N-MNIST example, equivalent accuracy is achieved with about five times fewer computational operations.
Hardware neural networks with mechanical flexibility are promising next-generation computing systems for smart wearable electronics. Overcoming the challenge of developing a fully synaptic plastic ...network, we demonstrate a low-operating-voltage PET/ITO/p-MXene/Ag flexible memristor device by controlling the etching of aluminum metal ions in Ti3C2Tx MXene. The presence of a small fraction of Al ions in partially etched MXene (p-Ti3C2Tx) significantly suppresses the operating voltage to 1 V compared to 7 V from fully Al etched MXene (f-Ti3C2Tx)-based devices. Former devices exhibit excellent non-volatile data storage properties, with a robust ∼103 ON/OFF ratio, high endurance of ∼104 cycles, multilevel resistance states, and long data retention measured up to ∼106 s. High mechanical stability up to ∼73° bending angle and environmental robustness are confirmed with consistent switching characteristics under increasing temperature and humid conditions. Furthermore, a p-Ti3C2Tx MXene memristor is employed to mimic the biological synapse by measuring the learning-forgetting pattern for ∼104 cycles as potentiation and depression. Spike time-dependent plasticity (STDP) based on Hebb's Learning rules is also successfully demonstrated. Moreover, a remarkable accuracy of ∼95% in recognizing modified patterns from the National Institute of Standards and Technology (MNIST) data set with just 29 training epochs is achieved in simulation. Ultimately, our findings underscore the potential of MXene-based flexible memristor devices as versatile components for data storage and neuromorphic computing.
In today’s busy world, people are shifting to online shopping for their basic needs like groceries, clothes, etc. As a result, modern applications are incorporating image search as part of their ...application. However, retrieving accurate fashion images is challenging due to the low inter-class separability. In this paper, we aim to propose and implement three deep learning denoising autoencoder image retrieval models trained on Fashion-MNIST dataset and MNIST dataset. Fashion-MNIST is widely used datasets to evaluate model because of its low inter-class separability. It is difficult to get a good accuracy on Fashion-MNIST dataset. The proposed models train to learn significant features of fashion images and improve the precision of image retrieval. These models are compared based on the Label Ranking Average Precision (LRAP) values, with the third model bagging the highest LRAP value. These models also trained and tested on MNIST dataset, which have similar configuration as Fashion-MNIST dataset but with high inter-class separability. Intriguingly, the second and third models trained on MNIST dataset demonstrate neck-to-neck performance, both achieving comparable accuracy. This choice of dataset ensures that the models’ accuracy is more noticeable and significant. The outcomes of this study validate the efficacy of denoising autoencoders in fashion image retrieval and emphasize their prospective in improving online shopping experiences.
•Propose a RECOS mathematical model to answer two fundamental questions in convolutional neural networks (CNNs).•The RECOS model interprets operations in CNNs using a rectified correlation ...viewpoint.•The RECOS model justifies the need of nonlinear activation in CNNs.•The RECOS model explains the advantage of two cascaded layers over a single layer in CNNs.•Use the LeNet-5 network applied to the MNIST dataset as an illustrative example.
This work attempts to address two fundamental questions about the structure of the convolutional neural networks (CNN): (1) why a nonlinear activation function is essential at the filter output of all intermediate layers? (2) what is the advantage of the two-layer cascade system over the one-layer system? A mathematical model called the “REctified-COrrelations on a Sphere” (RECOS) is proposed to answer these two questions. After the CNN training process, the converged filter weights define a set of anchor vectors in the RECOS model. Anchor vectors represent the frequently occurring patterns (or the spectral components). The necessity of rectification is explained using the RECOS model. Then, the behavior of a two-layer RECOS system is analyzed and compared with its one-layer counterpart. The LeNet-5 and the MNIST dataset are used to illustrate discussion points. Finally, the RECOS model is generalized to a multilayer system with the AlexNet as an example.
Abstract
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.
A New Neural Dynamic Classification Algorithm Rafiei, Mohammad Hossein; Adeli, Hojjat
IEEE transaction on neural networks and learning systems,
12/2017, Volume:
28, Issue:
12
Journal Article
The keys for the development of an effective classification algorithm are: 1) discovering feature spaces with large margins between clusters and close proximity of the classmates and 2) discovering ...the smallest number of the features to perform accurate classification. In this paper, a new supervised classification algorithm, called neural dynamic classification (NDC), is presented with the goal of: 1) discovering the most effective feature spaces and 2) finding the optimum number of features required for accurate classification using the patented robust neural dynamic optimization model of Adeli and Park. The new classification algorithm is compared with the probabilistic neural network (PNN), enhanced PNN (EPNN), and support vector machine using two sets of classification problems. The first set consists of five standard benchmark problems. The second set is a large benchmark problem called Mixed National Institute of Standards and Technology database of handwritten digits. In general, NDC yields the most accurate classification results followed by EPNN. A beauty of the new algorithm is the smoothness of convergence curves which is an indication of robustness and good performance of the algorithm. The main aim is to maximize the prediction accuracy.
The fashion industry has changed a lot over the years, which makes it hard for people to compare different kinds of fashion. To make it easier, different styles of clothing are tried out to find the ...exact and precise look desired. So, we opted to employ the Convolutional Neural Network (CNN) method for fashion classification. This approach represents one of the methodologies employed to utilize computers for the purpose of recognizing and categorizing items. The goal of this research is to see how well the Convolutional Neural Network method classifies the Fashion-MNIST dataset compared to other methods, models, and classification processes used in previous research. The information in this dataset is about different types of clothes and accessories. These items are divided into 10 categories, which include ankle boots, bags, coats, dresses, pullovers, sandals, shirts, sneakers, t-shirts, and trousers. The new classification method worked better than before on the test dataset. It had an accuracy value of 95. 92%, which is higher than in previous research. This research also uses a method called image data generator to make the Fashion MNIST image better. This method helps prevent too much focus on certain details and makes the results more accurate.
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