Effect of Oxygen Vacancy on the Conduction Modulation Linearity and Classification Accuracy of Pr0.7Ca0.3MnO3 Memristor

An amorphous Pr0.7Ca0.3MnO3 (PCMO) film was grown on a TiN/SiO2/Si (TiN–Si) substrate at 300 °C and at an oxygen pressure (OP) of 100 mTorr. This PCMO memristor showed typical bipolar switching characteristics, which were attributed to the generation and disruption of oxygen vacancy (OV) filaments. Fabrication of the PCMO memristor at a high OP resulted in nonlinear conduction modulation with the application of equivalent pulses. However, the memristor fabricated at a low OP of 100 mTorr exhibited linear conduction modulation. The linearity of this memristor improved because the growth and disruption of the OV filaments were mostly determined by the redox reaction of OV owing to the presence of numerous OVs in this PCMO film. Furthermore, simulation using a convolutional neural network revealed that this PCMO memristor has enhanced classification performance owing to its linear conduction modulation. This memristor also exhibited several biological synaptic characteristics, indicating that an amorphous PCMO thin film fabricated at a low OP would be a suitable candidate for artificial synapses.

Download Full-text

Memristive and Synaptic Characteristics of Nitride-Based Heterostructures on Si Substrate

Nanomaterials ◽

10.3390/nano10050994 ◽

2020 ◽

Vol 10 (5) ◽

pp. 994 ◽

Cited By ~ 5

Author(s):

Mehr Khalid Rahmani ◽

Min-Hwi Kim ◽

Fayyaz Hussain ◽

Yawar Abbas ◽

Muhammad Ismail ◽

...

Keyword(s):

Neural Network ◽

Classification Accuracy ◽

Spike Timing ◽

Low Energy ◽

Si Substrate ◽

Artificial Synapse ◽

Switching Characteristics ◽

Switching Mode ◽

Interface Type ◽

Conductance Modulation

Brain-inspired artificial synaptic devices and neurons have the potential for application in future neuromorphic computing as they consume low energy. In this study, the memristive switching characteristics of a nitride-based device with two amorphous layers (SiN/BN) is investigated. We demonstrate the coexistence of filamentary (abrupt) and interface (homogeneous) switching of Ni/SiN/BN/n++-Si devices. A better gradual conductance modulation is achieved for interface-type switching as compared with filamentary switching for an artificial synaptic device using appropriate voltage pulse stimulations. The improved classification accuracy for the interface switching (85.6%) is confirmed and compared to the accuracy of the filamentary switching mode (75.1%) by a three-layer neural network (784 × 128 × 10). Furthermore, the spike-timing-dependent plasticity characteristics of the synaptic device are also demonstrated. The results indicate the possibility of achieving an artificial synapse with a bilayer SiN/BN structure.

Download Full-text

Decoding Three Different Preference Levels of Consumers Using Convolutional Neural Network: A Functional Near-Infrared Spectroscopy Study

Frontiers in Human Neuroscience ◽

10.3389/fnhum.2020.597864 ◽

2021 ◽

Vol 14 ◽

Author(s):

Kunqiang Qing ◽

Ruisen Huang ◽

Keum-Shik Hong

Keyword(s):

Neural Network ◽

Infrared Spectroscopy ◽

Convolutional Neural Network ◽

Near Infrared Spectroscopy ◽

Classification Accuracy ◽

Near Infrared ◽

Classification Performance ◽

Hemodynamic Responses ◽

Functional Near Infrared Spectroscopy ◽

Cerebral Hemodynamic

This study decodes consumers' preference levels using a convolutional neural network (CNN) in neuromarketing. The classification accuracy in neuromarketing is a critical factor in evaluating the intentions of the consumers. Functional near-infrared spectroscopy (fNIRS) is utilized as a neuroimaging modality to measure the cerebral hemodynamic responses. In this study, a specific decoding structure, called CNN-based fNIRS-data analysis, was designed to achieve a high classification accuracy. Compared to other methods, the automated characteristics, constant training of the dataset, and learning efficiency of the proposed method are the main advantages. The experimental procedure required eight healthy participants (four female and four male) to view commercial advertisement videos of different durations (15, 30, and 60 s). The cerebral hemodynamic responses of the participants were measured. To compare the preference classification performances, CNN was utilized to extract the most common features, including the mean, peak, variance, kurtosis, and skewness. Considering three video durations, the average classification accuracies of 15, 30, and 60 s videos were 84.3, 87.9, and 86.4%, respectively. Among them, the classification accuracy of 87.9% for 30 s videos was the highest. The average classification accuracies of three preferences in females and males were 86.2 and 86.3%, respectively, showing no difference in each group. By comparing the classification performances in three different combinations (like vs. so-so, like vs. dislike, and so-so vs. dislike) between two groups, male participants were observed to have targeted preferences for commercial advertising, and the classification performance 88.4% between “like” vs. “dislike” out of three categories was the highest. Finally, pairwise classification performance are shown as follows: For female, 86.1% (like vs. so-so), 87.4% (like vs. dislike), 85.2% (so-so vs. dislike), and for male 85.7, 88.4, 85.1%, respectively.

Download Full-text

Hyperspectral Image Classification Based on a Shuffled Group Convolutional Neural Network with Transfer Learning

Remote Sensing ◽

10.3390/rs12111780 ◽

2020 ◽

Vol 12 (11) ◽

pp. 1780 ◽

Cited By ~ 2

Author(s):

Yao Liu ◽

Lianru Gao ◽

Chenchao Xiao ◽

Ying Qu ◽

Ke Zheng ◽

...

Keyword(s):

Neural Network ◽

Neural Networks ◽

Convolutional Neural Network ◽

Transfer Learning ◽

Classification Accuracy ◽

Hyperspectral Image ◽

Classification Performance ◽

Training Dataset ◽

Hyperspectral Image Classification ◽

Different Levels

Convolutional neural networks (CNNs) have been widely applied in hyperspectral imagery (HSI) classification. However, their classification performance might be limited by the scarcity of labeled data to be used for training and validation. In this paper, we propose a novel lightweight shuffled group convolutional neural network (abbreviated as SG-CNN) to achieve efficient training with a limited training dataset in HSI classification. SG-CNN consists of SG conv units that employ conventional and atrous convolution in different groups, followed by channel shuffle operation and shortcut connection. In this way, SG-CNNs have less trainable parameters, whilst they can still be accurately and efficiently trained with fewer labeled samples. Transfer learning between different HSI datasets is also applied on the SG-CNN to further improve the classification accuracy. To evaluate the effectiveness of SG-CNNs for HSI classification, experiments have been conducted on three public HSI datasets pretrained on HSIs from different sensors. SG-CNNs with different levels of complexity were tested, and their classification results were compared with fine-tuned ShuffleNet2, ResNeXt, and their original counterparts. The experimental results demonstrate that SG-CNNs can achieve competitive classification performance when the amount of labeled data for training is poor, as well as efficiently providing satisfying classification results.

Download Full-text

Acoustic traits of bat-pollinated flowers compared to flowers of other pollination syndromes and their echo-based classification using convolutional neural networks

PLoS Computational Biology ◽

10.1371/journal.pcbi.1009706 ◽

2021 ◽

Vol 17 (12) ◽

pp. e1009706

Author(s):

Ralph Simon ◽

Karol Bakunowski ◽

Angel Eduardo Reyes-Vasques ◽

Marco Tschapka ◽

Mirjam Knörnschild ◽

...

Keyword(s):

Neural Network ◽

Neural Networks ◽

Convolutional Neural Network ◽

Plant Species ◽

Classification Accuracy ◽

Classification Performance ◽

Target Strength ◽

South East Asia ◽

Pollination Syndromes ◽

Good Classification

Bat-pollinated flowers have to attract their pollinators in absence of light and therefore some species developed specialized echoic floral parts. These parts are usually concave shaped and act like acoustic retroreflectors making the flowers acoustically conspicuous to the bats. Acoustic plant specializations only have been described for two bat-pollinated species in the Neotropics and one other bat-dependent plant in South East Asia. However, it remains unclear whether other bat-pollinated plant species also show acoustic adaptations. Moreover, acoustic traits have never been compared between bat-pollinated flowers and flowers belonging to other pollination syndromes. To investigate acoustic traits of bat-pollinated flowers we recorded a dataset of 32320 flower echoes, collected from 168 individual flowers belonging to 12 different species. 6 of these species were pollinated by bats and 6 species were pollinated by insects or hummingbirds. We analyzed the spectral target strength of the flowers and trained a convolutional neural network (CNN) on the spectrograms of the flower echoes. We found that bat-pollinated flowers have a significantly higher echo target strength, independent of their size, and differ in their morphology, specifically in the lower variance of their morphological features. We found that a good classification accuracy by our CNN (up to 84%) can be achieved with only one echo/spectrogram to classify the 12 different plant species, both bat-pollinated and otherwise, with bat-pollinated flowers being easier to classify. The higher classification performance of bat-pollinated flowers can be explained by the lower variance of their morphology.

Download Full-text

An Improved Convolutional Neural Network for Text Classification

Journal of Physics Conference Series ◽

10.1088/1742-6596/2066/1/012091 ◽

2021 ◽

Vol 2066 (1) ◽

pp. 012091

Author(s):

Xiaojing Fan ◽

A Runa ◽

Zhili Pei ◽

Mingyang Jiang

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Text Classification ◽

Classification Accuracy ◽

Classification Performance ◽

Classification Model ◽

Classification Problems ◽

Training Time ◽

Accuracy And Precision ◽

And Training

Abstract This paper studies the text classification based on deep learning. Aiming at the problem of over fitting and training time consuming of CNN text classification model, a SDCNN model is constructed based on sparse dropout convolutional neural network. Experimental results show that, compared with CNN, SDCNN further improves the classification performance of the model, and its classification accuracy and precision can reach 98.96% and 85.61%, respectively, indicating that SDCNN has more advantages in text classification problems.

Download Full-text

Environmental Sound Classification Method Based on Two-Stream Lightweight Convolutional Neural Network

10.21203/rs.3.rs-860631/v1 ◽

2021 ◽

Author(s):

Jingru Fang ◽

Bo Yin ◽

Xiaopeng Ji ◽

Zehua Du

Keyword(s):

Neural Network ◽

Neural Networks ◽

Classification Accuracy ◽

Computational Cost ◽

Evidence Theory ◽

Classification Performance ◽

Environmental Sound ◽

Sound Classification ◽

Stream Model ◽

High Computational Cost

Abstract Neural networks have achieved success in the task of environmental sound classification. However, the traditional neural network model has too many parameters and high computational cost. The lightweight networks solve these problems by compressing parameters, but reduce the classification accuracy. To solve the problems in existing research, we propose a two-stream model based on two lightweight convolutional neural networks, called TSLCNN-DS, which saves memory and improves the classification performance of environmental sounds. Specifically, we first used data patching and data balancing to slightly expand the amount of experimental data. Then we designed two lightweight and efficient classification networks based on the attention mechanism and residual learning. Finally, the Dempster-Shafer evidence theory is used to fuse the output of the two networks, and the two-stream model is integrated. Experiments have shown that the model has achieved a classification accuracy of 97.44% on the UrbanSound8k dataset, using only 0.12 M parameters.

Download Full-text

Titania Based Nano-ionic Memristive Crossbar Arrays: Fabrication and Resistive Switching Characteristics

Nanoscience &Nanotechnology-Asia ◽

10.2174/2210681208666180628122146 ◽

2019 ◽

Vol 9 (4) ◽

pp. 486-493 ◽

Cited By ~ 1

Author(s):

S. Sahoo ◽

P. Manoravi ◽

S.R.S. Prabaharan

Keyword(s):

Resistive Switching ◽

Bottom Electrode ◽

Rf Sputtering ◽

Hysteresis Curve ◽

Bipolar Switching ◽

Post Annealing ◽

Endurance Tests ◽

Bilayer Device ◽

Switching Characteristics ◽

Pinched Hysteresis

Introduction: Intrinsic resistive switching properties of Pt/TiO2-x/TiO2/Pt crossbar memory array has been examined using the crossbar (4×4) arrays fabricated by using DC/RF sputtering under specific conditions at room temperature. Materials and Methods: The growth of filament is envisaged from bottom electrode (BE) towards the top electrode (TE) by forming conducting nano-filaments across TiO2/TiO2-x bilayer stack. Non-linear pinched hysteresis curve (a signature of memristor) is evident from I-V plot measured using Pt/TiO2-x /TiO2/Pt bilayer device (a single cell amongst the 4×4 array is used). It is found that the observed I-V profile shows two distinguishable regions of switching symmetrically in both SET and RESET cycle. Distinguishable potential profiles are evident from I-V curve; in which region-1 relates to the electroformation prior to switching and region-2 shows the switching to ON state (LRS). It is observed that upon reversing the polarity, bipolar switching (set and reset) is evident from the facile symmetric pinched hysteresis profile. Obtaining such a facile switching is attributed to the desired composition of Titania layers i.e. the rutile TiO2 (stoichiometric) as the first layer obtained via controlled post annealing (650oC/1h) process onto which TiO2-x (anatase) is formed (350oC/1h). Results: These controlled processes adapted during the fabrication step help manipulate the desired potential barrier between metal (Pt) and TiO2 interface. Interestingly, this controlled process variation is found to be crucial for measuring the switching characteristics expected in Titania based memristor. In order to ensure the formation of rutile and anatase phases, XPS, XRD and HRSEM analyses have been carried out. Conclusion: Finally, the reliability of bilayer memristive structure is investigated by monitoring the retention (104 s) and endurance tests which ensured the reproducibility over 10,000 cycles.

Download Full-text

Improving Sentiment Analysis using Hybrid Deep Learning Model

Recent Advances in Computer Science and Communications ◽

10.2174/2213275912666190328200012 ◽

2020 ◽

Vol 13 (4) ◽

pp. 627-640 ◽

Cited By ~ 1

Author(s):

Avinash Chandra Pandey ◽

Dharmveer Singh Rajpoot

Keyword(s):

Neural Network ◽

Deep Learning ◽

Sentiment Analysis ◽

Classification Accuracy ◽

Short Term Memory ◽

Computational Cost ◽

Extraction Process ◽

Learning Model ◽

Sentiment Classification ◽

Deep Learning Model

Background: Sentiment analysis is a contextual mining of text which determines viewpoint of users with respect to some sentimental topics commonly present at social networking websites. Twitter is one of the social sites where people express their opinion about any topic in the form of tweets. These tweets can be examined using various sentiment classification methods to find the opinion of users. Traditional sentiment analysis methods use manually extracted features for opinion classification. The manual feature extraction process is a complicated task since it requires predefined sentiment lexicons. On the other hand, deep learning methods automatically extract relevant features from data hence; they provide better performance and richer representation competency than the traditional methods. Objective: The main aim of this paper is to enhance the sentiment classification accuracy and to reduce the computational cost. Method: To achieve the objective, a hybrid deep learning model, based on convolution neural network and bi-directional long-short term memory neural network has been introduced. Results: The proposed sentiment classification method achieves the highest accuracy for the most of the datasets. Further, from the statistical analysis efficacy of the proposed method has been validated. Conclusion: Sentiment classification accuracy can be improved by creating veracious hybrid models. Moreover, performance can also be enhanced by tuning the hyper parameters of deep leaning models.

Download Full-text