Two-layer Convolutional Neural Network Research Articles

MAHyNet: Parallel Hybrid Network for RNA-Protein Binding Sites Prediction Based on Multi-Head Attention and Expectation Pooling.

RNA-binding proteins (RBPs) can regulate biological functions by interacting with specific RNAs, and play an important role in many life activities. Therefore, the rapid identification of RNA-protein binding sites is crucial for functional annotation and site-directed mutagenesis. In this work, a new parallel network that integrates the multi-head attention mechanism and the expectation pooling is proposed, named MAHyNet. The left-branch network of MAHyNet hybrids convolutional neural networks (CNNs) and gated recurrent neural network (GRU) to extract the features of one-hot. The right-branch network is a two-layer CNN network to analyze physicochemical properties of RNA base. Specifically, the multi-head attention mechanism is a computational collection of multiple independent layers of attention, which can extract feature information from multiple dimensions. The expectation pooling combines probabilistic thinking with global pooling. This approach helps to reduce model parameters and enhance the model performance. The combination of CNN and GRU enables further extraction of high-level features in sequences. In addition, the study shows that appropriate hyperparameters have a positive impact on the model performance. Physicochemical properties can be used to supplement characterization information to improving model performance. The experimental results show that MAHyNet has better performance than other models.

TBCA: Prediction of transcription factor binding sites using a deep neural network with lightweight attention mechanism.

The identification of transcription factor binding sites (TFBSs) is crucial for understanding the regulatory mechanisms of gene expression, which contributes to unraveling cellular functions and disease development. Currently, the most common approach involves the use of deep learning techniques to predict TFBSs by combining sequence and shape features. Although significant progress has been made with these methods, the integration of local features extracted from DNA sequences and shapes with global features has not yet reached a sufficient level, and there is still significant room for improvement in the accuracy of prediction results. In this paper, we propose a novel framework based on convolution and attention mechanisms, referred to as TBCA, which combines DNA sequence information and shape information for predicting transcription factor binding sites. In this work, we employ a two-layer convolutional neural network (CNNs) and self-attention mechanism to extract complex sequence features from DNA. What's more, we utilize a Fourier-transform-enhanced multi-head attention along with channel attention to extract high-order shape features of DNA. Finally, these high-order sequence and shape features are integrated into the channel dimension to achieve accurate TFBSs prediction. Our research results demonstrate that TBCA exhibits superior predictive performance in 165 validated ChIP-seq datasets. Furthermore, the employed attention mechanisms can automatically learn important features at different positions and scales, enhancing the accuracy and robustness of feature representation. We also conduct an in-depth analysis of the contributions of five different shapes to site prediction, revealing that shape features can enhance the prediction of transcription factor DNA binding.

Convex Dual Theory Analysis of Two-Layer Convolutional Neural Networks With Soft-Thresholding.

Soft-thresholding has been widely used in neural networks. Its basic network structure is a two-layer convolution neural network with soft-thresholding. Due to the network's nature of nonlinear and nonconvex, the training process heavily depends on an appropriate initialization of network parameters, resulting in the difficulty of obtaining a globally optimal solution. To address this issue, a convex dual network is designed here. We theoretically analyze the network convexity and prove that the strong duality holds. Extensive results on both simulation and real-world datasets show that strong duality holds, the dual network does not depend on initialization and optimizer, and enables faster convergence than the state-of-the-art two-layer network. This work provides a new way to convexify soft-thresholding neural networks. Furthermore, the convex dual network model of a deep soft-thresholding network with a parallel structure is deduced.

Template Design for Complex Block Copolymer Patterns Using a Machine Learning Method.

This study represents the first attempt to address the inverse design problem of the guiding template for directed self-assembly (DSA) patterns using solely machine learning methods. By formulating the problem as a multi-label classification task, the study shows that it is possible to predict templates without requiring any forward simulations. A series of neural network (NN) models, ranging from the basic two-layer convolutional neural network (CNN) to the large NN models (32-layer CNN with 8 residual blocks), have been trained using simulated pattern samples generated by thousands of self-consistent field theory (SCFT) calculations; a number of augmentation techniques, especially suitable for predicting morphologies, have been also proposed to enhance the performance of the NN model. The exact match accuracy of the model in predicting the template of simulated patterns was significantly improved from 59.8% for the baseline model to 97.1% for the best model of this study. The best model also demonstrates an excellent generalization ability in predicting the template for human-designed DSA patterns, while the simplest baseline model is ineffective in this task.

Diagnosis of multiple sclerosis using optical coherence tomography supported by artificial intelligence.

Current procedures for diagnosing multiple sclerosis (MS) present a series of limitations, making it critically important to identify new biomarkers. The aim of the study was to identify new biomarkers for the early diagnosis of MS using spectral-domain optical coherence tomography (OCT) and artificial intelligence. Spectral domain OCT was performed on 79 patients with relapsing-remitting multiple sclerosis (RRMS) (disease duration ≤ 2 years, no history of optic neuritis) and on 69 age-matched healthy controls using the posterior pole protocol that incorporates the anatomic Positioning System. Median retinal thickness values in both eyes and inter-eye difference in healthy controls and patients were evaluated by area under the receiver operating characteristic (AUROC) curve analysis in the foveal, parafoveal and perifoveal areas and in the overall area spanned by the three rings. The structures with the greatest discriminant capacity - retinal thickness and inter-eye difference - were used as inputs to a convolutional neural network to assess the diagnostic capability. Analysis of retinal thickness and inter-eye difference in RRMS patients revealed that greatest alteration occurred in the ganglion cell (GCL), inner plexiform (IPL), and inner retinal (IRL) layers. By using the average thickness of the GCL (AUROC=0.82) and the inter-eye difference in the IPL (AUROC=0.71) as inputs to a two-layer convolutional neural network, automatic diagnosis attained accuracy=0.87, sensitivity=0.82, and specificity=0.92. This study adds weight to the argument that neuroretinal structure analysis could be incorporated into the diagnostic criteria for MS.

An End-to-End Multi-Channel Convolutional Bi-LSTM Network for Automatic Sleep Stage Detection.

Sleep stage detection from polysomnography (PSG) recordings is a widely used method of monitoring sleep quality. Despite significant progress in the development of machine-learning (ML)-based and deep-learning (DL)-based automatic sleep stage detection schemes focusing on single-channel PSG data, such as single-channel electroencephalogram (EEG), electrooculogram (EOG), and electromyogram (EMG), developing a standard model is still an active subject of research. Often, the use of a single source of information suffers from data inefficiency and data-skewed problems. Instead, a multi-channel input-based classifier can mitigate the aforementioned challenges and achieve better performance. However, it requires extensive computational resources to train the model, and, hence, a tradeoff between performance and computational resources cannot be ignored. In this article, we aim to introduce a multi-channel, more specifically a four-channel, convolutional bidirectional long short-term memory (Bi-LSTM) network that can effectively exploit spatiotemporal features of data collected from multiple channels of the PSG recording (e.g., EEG Fpz-Cz, EEG Pz-Oz, EOG, and EMG) for automatic sleep stage detection. First, a dual-channel convolutional Bi-LSTM network module has been designed and pre-trained utilizing data from every two distinct channels of the PSG recording. Subsequently, we have leveraged the concept of transfer learning circuitously and have fused two dual-channel convolutional Bi-LSTM network modules to detect sleep stages. In the dual-channel convolutional Bi-LSTM module, a two-layer convolutional neural network has been utilized to extract spatial features from two channels of the PSG recordings. These extracted spatial features are subsequently coupled and given as input at every level of the Bi-LSTM network to extract and learn rich temporal correlated features. Both Sleep EDF-20 and Sleep EDF-78 (expanded version of Sleep EDF-20) datasets are used in this study to evaluate the result. The model that includes an EEG Fpz-Cz + EOG module and an EEG Fpz-Cz + EMG module can classify sleep stage with the highest value of accuracy (ACC), Kappa (Kp), and F1 score (e.g., 91.44%, 0.89, and 88.69%, respectively) on the Sleep EDF-20 dataset. On the other hand, the model consisting of an EEG Fpz-Cz + EMG module and an EEG Pz-Oz + EOG module shows the best performance (e.g., the value of ACC, Kp, and F1 score are 90.21%, 0.86, and 87.02%, respectively) compared to other combinations for the Sleep EDF-78 dataset. In addition, a comparative study with respect to other existing literature has been provided and discussed in order to exhibit the efficacy of our proposed model.

Weight normalization optimization movie recommendation algorithm based on three-way neural interaction networks

Heterogeneous information networks are increasingly used in recommendation algorithms. However, they lack an explicit representation of meta-paths. In using bidirectional neural interaction models for recommendation models, interaction between users and items is often ignored, with an integral impact on the accuracy of the recommendations. To better apply the interaction information, this study proposes a weight-normalized movie recommendation model (SCLW_MCRec) based on a three-way neural interaction network. The model constructs a three-way neural interaction network langle user, meta-path, itemrangle from meta-path contextual information, introducing meta-paths on top of the user-item representation to represent the user-item interaction information. Introduction of a two-layer, one-dimensional convolutional neural network helps capture higher-order interaction features between the user and the item, making the model more powerful in terms of interaction. Adding a dropout layer to the interaction model and using a two-layer convolutional neural network can prevent overfitting and discard irrelevant information features to improve the recommendation. In addition, an extreme cross-entropy loss (argmaxminloss) that incorporates the properties of the argmin and argmax functions is designed to reduce the model loss. A weight-normalization optimization approach is used to better optimize the model and accelerate convergence of the stochastic gradient descent optimization. Compared to current state-of-the-art recommendation models, the SCLW_MCRec model improves the Prec evaluation index by 2.94–35.8%, Recall by 1.15–53.51%, and NDCG by 6.7–49.37% on the MovieLens dataset. The framework provides a significant improvement in recommendation accuracy and also solves the cold-start problem with application of interaction information.

A multiple long short-term model for product sales forecasting based on stage future vision with prior knowledge

Deep neural network (DNN) based multivariate time series (MTS) forecasting has been widely studied in many domains. The approach has also been successfully applied to product sales forecasting, which is invaluable in the strategic development of enterprises. However, one key challenge is to efficiently combine all influential factors into a unified framework by considering their long short-term correlations. This is particularly challenging for real time sales predictions with many important features unknown from the perspective of future vision, because of the complex and dynamic changing environment of sales time series. Besides, DNN based methods could not effectively capture stable linear correlations between multivariate sales time series. To address these challenges, a novel Stage future-vision-based multiple Long Short-term model with prior knowledge (SLST-PKNet) is proposed. The model constructs sub-models according to the types of different influential factors, and a stage future vision mechanism is used to model dependent correlations between future influential factors and product sales by combining a two-layer convolutional neural network (TLCNN) and a two-stage LSTM (TSLSTM) into a unified framework. The combination of TLCNN and TSLSTM is the basic component for long short-term modeling, and is an effective solution for capturing dynamic patterns by fusing the outputs from different layers and stages. A dynamic co-integration mechanism (DCI) is introduced to capture strong correlations between time series, which DNN is not good at. In order to further improve the capability of the model to capture long short-term patterns from complex environment, domain prior knowledge (PK) is integrated as supervision information. Extensive experiments are conducted on two sales datasets: Galanz and Cainiao. SLST-PKNet achieves significant performance improvements over 11 state-of-the-art baselines. The proposed model is also evaluated on two new datasets: Traffic and Exchange-Rate, to further verify its generalization capability. The data and code could be visited at: https://github.com/lixt47/SLST-PKNet.

Hybrid Inductive Model of Differentially and Co-Expressed Gene Expression Profile Extraction Based on the Joint Use of Clustering Technique and Convolutional Neural Network

The development of hybrid models focused on gene expression data processing for the allocation of differentially expressed and mutually correlated genes is one of the current directions in modern bioinformatics. The solution to this problem can allow us to improve the effectiveness of existing systems for complex diseases diagnosis based on gene expression data analysis on the one hand and increase the efficiency of gene regulatory network reconstruction procedures by more careful selection of genes by considering the type of disease on the other hand. In this research, we propose a stepwise procedure to form the subsets of mutually correlated and differentially expressed gene expression profiles (GEP). Firstly, we allocate an informative GEP in terms of statistical and entropy criteria using the Harrington desirability function. Then, we performed cluster analysis using SOTA and spectral clustering algorithms implemented within the framework of objective clustering inductive technology. The result of this step’s implementation is a set of clusters containing co- and differentially expressed GEPs. Validation of the model was performed using a one-dimensional two-layer convolutional neural network (CNN). The analysis of the simulation results has shown the high efficiency of the proposed model. The clusters of GEPs formed based on the clustering quality criteria values allowed us to identify the investigated objects with high accuracy. Moreover, the simulation results have also shown that the hybrid inductive model based on the spectral clustering algorithm is more effective in comparison with the use of the SOTA clustering algorithm in terms of both the complexity of the formed optimal cluster structure and the classification accuracy of the objects that contain the allocated gene expression data as attributes. The proposed hybrid inductive model contributes to increasing objectivity during the formation of the subsets of differentially and co-expressed gene expression profiles for further their application in various disease diagnosis systems and for gene regulatory network reconstruction.

REAL-TIME DETECTIONS OF OPENED-CLOSED EYES USING CONVOLUTIONAL NEURAL NETWORK

The sleepy condition can affect changing behaviors in the human body, and one part of the human body that gets this effect is the eye; eyes are narrower than in normal conditions, and the frequency of blinking eyes is going to increase when people are sleepy. In this study, we will study the behavior of eyes, opened and closed eyes that the camera can capture in real-time, and tools of image processing that can capture and track eyes. Data images from this treatment are fed into Convolutional Neural Network (CNN) as data learning, so CNN can recognize opened and closed eyes from those eyes. In this study, we will characterize tools of image processing (Haar cascade Method) combined with CNN and their performance to detect opened-closed eyes in real-time detections.
 In this study, we use two CNN models as a comparison; the first CNN model uses 1 layer with 2 nodes, and the second CNN model uses 2 layers, with the first layer with 500 nodes and the second layer with 2 nodes; the output of each CNN has two targets namely 'open-label eyes and 'close' label eyes. The image dataset contains 20000 eye images, i.e., 10000 'open' eye images and 10000 'close' eye images. The image dataset is trained into two CNNs so that we have two CNN models: the one-layer CNN model and the two-layer CNN model. Each of those models has a pre-trained network.
 Each pre-trained model CNN is tested to detect opened-eyes and closed-eyes in real time. There are ten different people. For example, in this experiment, each person was subjected to ten trials of 'opening' and 'closing' eye detection and counted successfully detecting and failing to detect; from all the sample people tested, it can be concluded that the percentage was successful in detecting and percentage failed to detect. The Two-layers CNN model has a 55 % success rate in this experiment.

Incorporating support vector machine to the classification of respiratory sounds by Convolutional Neural Network

Classification of respiratory sounds (RS) by artificial intelligence (AI) methods has been studied by many groups, and a preferred method belonging to Deep Neural Networks (DNN) is Convolutional Neural Networks (CNN), where the Softmax function is one of the most popular classifiers used in the last layer of the network. However, there have also been studies examining the use of linear support vector machine (SVM) instead of Softmax in an artificial neural network architecture. This work focuses on incorporating SVM to CNN in multi-class RSs classification. Moreover, two-layer CNN model was added to the VGG16 model using transfer learning and similarly, the classification successes were compared with Softmax and SVM. The dataset used in this work has been obtained by clinical experts from a total of 294 subjects (105 diagnosed as normal and the rest of the patients have adventitious sound namely, 116 crackle and 73 rhonchi). During the classification phase, two-layer CNN architecture and SVM were added to this architecture, then various classifiers are implemented, namely CNN-Softmax, CNN-SVM, VGG16-CNN-Softmax, VGG16-CNN-SVM with 10-fold cross validation. In addition, state-of-art models (DenseNet 201, VGG16, InceptionV3, ResNet 101) and VGG16-CNN-KNN have also been applied to the dataset. It is found that the best classification accuracy figures have been found as 83% with VGG16-CNN-SVM model.

DeepPN: a deep parallel neural network based on convolutional neural network and graph convolutional network for predicting RNA-protein binding sites

BackgroundAddressing the laborious nature of traditional biological experiments by using an efficient computational approach to analyze RNA-binding proteins (RBPs) binding sites has always been a challenging task. RBPs play a vital role in post-transcriptional control. Identification of RBPs binding sites is a key step for the anatomy of the essential mechanism of gene regulation by controlling splicing, stability, localization and translation. Traditional methods for detecting RBPs binding sites are time-consuming and computationally-intensive. Recently, the computational method has been incorporated in researches of RBPs. Nevertheless, lots of them not only rely on the sequence data of RNA but also need additional data, for example the secondary structural data of RNA, to improve the performance of prediction, which needs the pre-work to prepare the learnable representation of structural data.ResultsTo reduce the dependency of those pre-work, in this paper, we introduce DeepPN, a deep parallel neural network that is constructed with a convolutional neural network (CNN) and graph convolutional network (GCN) for detecting RBPs binding sites. It includes a two-layer CNN and GCN in parallel to extract the hidden features, followed by a fully connected layer to make the prediction. DeepPN discriminates the RBP binding sites on learnable representation of RNA sequences, which only uses the sequence data without using other data, for example the secondary or tertiary structure data of RNA. DeepPN is evaluated on 24 datasets of RBPs binding sites with other state-of-the-art methods. The results show that the performance of DeepPN is comparable to the published methods.ConclusionThe experimental results show that DeepPN can effectively capture potential hidden features in RBPs and use these features for effective prediction of binding sites.

Non-Cooperative UAV Detection with Semi-Adaptive Sampling of Control Signal and SNR Estimation

This paper proposes a non-cooperative unmanned aerial vehicle (UAV) signal detection strategy based on a multichannel control signal with an energy detector (ED), wherein the sampling point of the control signal on each subchannel is adjusted with environmental signal-to-noise (SNR) in a semi-adaptive manner. In order to estimate the SNR in the environment, not only is a convolutional neural network (CNN) applied in the proposed signal detection strategy, but a long shor-term memory network (LSTM) network is also included; in terms of features, it combines deep features and time-dimension features. The numbers of layers of the CNN and LSTM impact the performance of the algorithm. The decision on the presence or absence of a control signal is made at the fusion center (FC) based on the majority voting rule. This paper shows that the network with a two-layer CNN and a two-layer LSTM can achieve high estimation accuracy of environmental SNR. Simultaneously, the detection accuracy is improved by about 1 dB compared with the classical multichannel detection schemes.

Real-Time Inference With 2D Convolutional Neural Networks on Field Programmable Gate Arrays for High-Rate Particle Imaging Detectors.

We present a custom implementation of a 2D Convolutional Neural Network (CNN) as a viable application for real-time data selection in high-resolution and high-rate particle imaging detectors, making use of hardware acceleration in high-end Field Programmable Gate Arrays (FPGAs). To meet FPGA resource constraints, a two-layer CNN is optimized for accuracy and latency with KerasTuner, and network quantization is further used to minimize the computing resource utilization of the network. We use “High Level Synthesis for Machine Learning” (hls4ml) tools to test CNN deployment on a Xilinx UltraScale+ FPGA, which is an FPGA technology proposed for use in the front-end readout system of the future Deep Underground Neutrino Experiment (DUNE) particle detector. We evaluate network accuracy and estimate latency and hardware resource usage, and comment on the feasibility of applying CNNs for real-time data selection within the currently planned DUNE data acquisition system. This represents the first-ever exploration of employing 2D CNNs on FPGAs for DUNE.

A CNN-transformer hybrid approach for decoding visual neural activity into text

Background and ObjectiveMost studies used neural activities evoked by linguistic stimuli such as phrases or sentences to decode the language structure. However, compared to linguistic stimuli, it is more common for the human brain to perceive the outside world through non-linguistic stimuli such as natural images, so only relying on linguistic stimuli cannot fully understand the information perceived by the human brain. To address this, an end-to-end mapping model between visual neural activities evoked by non-linguistic stimuli and visual contents is demanded. MethodsInspired by the success of the Transformer network in neural machine translation and the convolutional neural network (CNN) in computer vision, here a CNN-Transformer hybrid language decoding model is constructed in an end-to-end fashion to decode functional magnetic resonance imaging (fMRI) signals evoked by natural images into descriptive texts about the visual stimuli. Specifically, this model first encodes a semantic sequence extracted by a two-layer 1D CNN from the multi-time visual neural activity into a multi-level abstract representation, then decodes this representation, step by step, into an English sentence. ResultsExperimental results show that the decoded texts are semantically consistent with the corresponding ground truth annotations. Additionally, by varying the encoding and decoding layers and modifying the original positional encoding of the Transformer, we found that a specific architecture of the Transformer is required in this work. ConclusionsThe study results indicate that the proposed model can decode the visual neural activities evoked by natural images into descriptive text about the visual stimuli in the form of sentences. Hence, it may be considered as a potential computer-aided tool for neuroscientists to understand the neural mechanism of visual information processing in the human brain in the future.

A training strategy for improving the robustness of memristor-based binarized convolutional neural networks

Memristor-based convolutional neural networks (CNNs) have been extensively studied in the field of edge computing, owing to the efficient characteristics of memristors, such as high integration density and powerful processing capability. However, constrained by the low yield of memristor array and the memristance variation, memristor-based CNNs have failed to be widely applied. Consequently, a training strategy is proposed to improve the robustness of memristor-based binarized neural networks for prompting embedded application. Simulation results on the MNIST dataset reveal that this strategy is able to improve the performance of a memristor-based two-layer CNN with device defects. Specifically, when the yield rate of the memristor array is 60%, the recognition rate of a two-layer memristor-based binarized convolutional neural network achieves around 91.19%, and when the characteristic of device variation is 28%, it reaches about 91.53%.

Deep/Transfer Learning with Feature Space Ensemble Networks (FeatSpaceEnsNets) and Average Ensemble Networks (AvgEnsNets) for Change Detection Using DInSAR Sentinel-1 and Optical Sentinel-2 Satellite Data Fusion

Differential interferometric synthetic aperture radar (DInSAR), coherence, phase, and displacement are derived from processing SAR images to monitor geological phenomena and urban change. Previously, Sentinel-1 SAR data combined with Sentinel-2 optical imagery has improved classification accuracy in various domains. However, the fusing of Sentinel-1 DInSAR processed imagery with Sentinel-2 optical imagery has not been thoroughly investigated. Thus, we explored this fusion in urban change detection by creating a verified balanced binary classification dataset comprising 1440 blobs. Machine learning models using feature descriptors and non-deep learning classifiers, including a two-layer convolutional neural network (ConvNet2), were used as baselines. Transfer learning by feature extraction (TLFE) using various pre-trained models, deep learning from random initialization, and transfer learning by fine-tuning (TLFT) were all evaluated. We introduce a feature space ensemble family (FeatSpaceEnsNet), an average ensemble family (AvgEnsNet), and a hybrid ensemble family (HybridEnsNet) of TLFE neural networks. The FeatSpaceEnsNets combine TLFE features directly in the feature space using logistic regression. AvgEnsNets combine TLFEs at the decision level by aggregation. HybridEnsNets are a combination of FeatSpaceEnsNets and AvgEnsNets. Several FeatSpaceEnsNets, AvgEnsNets, and HybridEnsNets, comprising a heterogeneous mixture of different depth and architecture models, are defined and evaluated. We show that, in general, TLFE outperforms both TLFT and classic deep learning for the small dataset used and that larger ensembles of TLFE models do not always improve accuracy. The best performing ensemble is an AvgEnsNet (84.862%) comprised of a ResNet50, ResNeXt50, and EfficientNet B4. This was matched by a similarly composed FeatSpaceEnsNet with an F1 score of 0.001 and variance of 0.266 less. The best performing HybridEnsNet had an accuracy of 84.775%. All of the ensembles evaluated outperform the best performing single model, ResNet50 with TLFE (83.751%), except for AvgEnsNet 3, AvgEnsNet 6, and FeatSpaceEnsNet 5. Five of the seven similarly composed FeatSpaceEnsNets outperform the corresponding AvgEnsNet.

Ship Detection and Feature Visualization Analysis Based on Lightweight CNN in VH and VV Polarization Images

Synthetic aperture radar (SAR) is a significant application in maritime monitoring, which can provide SAR data throughout the day and in all weather conditions. With the development of artificial intelligence and big data technologies, the data-driven convolutional neural network (CNN) has become widely used in ship detection. However, the accuracy, feature visualization, and analysis of ship detection need to be improved further, when the CNN method is used. In this letter, we propose a two-stage ship detection for land-contained sea area without a traditional sea-land segmentation process. First, to decrease the possibly existing false alarms from the island, an island filter is used as the first step, and then threshold segmentation is used to quickly perform candidate detection. Second, a two-layer lightweight CNN model-based classifier is built to separate false alarms from the ship object. Finally, we discuss the CNN interpretation and visualize in detail when the ship is predicted in vertical–horizontal (VH) and vertical–vertical (VV) polarization. Experiments demonstrate that the proposed method can reach an accuracy of 99.4% and an F1 score of 0.99 based on the Sentinel-1 images for a ship with a size of less than 32 × 32.

Threshold segmentation based multi-layer analysis for detecting diabetic retinopathy using convolution neural network

Diabetic retinopathy (DR) syndrome affects the vision of the eyes by damaging the blood vessels. Fore-hand detection and prevention of this syndrome are most significant as it results in vision blindness. Diagnosis and procedural analysis of this syndrome with modern healthcare science and technology are aided through artificial intelligence and processing units. In this article, a threshold segmentation based DR detection method is introduced. This method is keen is classifying the foreground and background of the input retinal image and processing through pixel-based segmentation. The process of assessing the layers is augmented using a two-layer convolutional neural network (CNN) that mitigates the false positives during classification. This process is sequential in determining the precise detection of the infected region of the retina. Besides, the segment-based CNN (S-CNN) handles the flaw in diagnosis through two-hidden layers for differentiating the threshold and normalized conditions based on classification. The proposed method is reliable in achieving better accuracy of detection, sensitivity, and true positives.

Sequential Convolutional Recurrent Neural Networks for Fast Automatic Modulation Classification

A novel and efficient end-to-end learning model for automatic modulation classification is proposed for wireless spectrum monitoring applications, which automatically learns from the time domain in-phase and quadrature data without requiring the design of hand-crafted expert features. With the intuition of convolutional layers with pooling serving as the role of front-end feature distillation and dimensionality reduction, sequential convolutional recurrent neural networks are developed to take complementary advantage of parallel computing capability of convolutional neural networks and temporal sensitivity of recurrent neural networks. Experimental results demonstrate that the proposed architecture delivers overall superior performance in signal to noise ratio range above -10~dB, and achieves significantly improved classification accuracy from 80\% to 92.1\% at high signal to noise ratio range, while drastically reduces the average training and prediction time by approximately 74% and 67%, respectively. Response patterns learned by the proposed architecture are visualized to better understand the physics of the model. Furthermore, a comparative study is performed to investigate the impacts of various sequential convolutional recurrent neural network structure settings on classification performance. A representative sequential convolutional recurrent neural network architecture with the two-layer convolutional neural network and subsequent two-layer long short-term memory neural network is developed to suggest the option for fast automatic modulation classification.