Multi-scale Deep Convolutional Neural Network Research Articles

Multiscale Deep - CNN Based Target Prediction in Hyperspectral Image with Transfer Learning

Abstract: There are different types of landscape throughout the world which is not readily or directly approachable for human being but their analysis to uncover factual information has become necessary for forming important decision when developing any fresh project. The geographical and landscape scenes can be adequately represented through hyperspectral images captured using remote sensors. The data in the images can potentially be both vast and intricate to analyze and it is essential to consistently perform adequate pre-processing. In this work, we have put up the use of deep learning and transfer learning for object prediction in hyperspectral data. There are mainly two algorithms that have been implemented in this research. The first method is based on Multi-Scale Deep CNN (Convolutional Neural Network) which takes hyperspectral data with varying sizes as the input to detect pixels whose intensity spreads uniformly over many wavelengths or may vary rapidly. Secondly, hyperspectral image sources are not readily available and can be expensive and there are also possibilities for high analysis complexity in the research, so a Transfer Learning based algorithm is applied to the DCNN model. Superior performance in accuracy was noted in the evaluation with respect to the F1 score and recall values for different objects fluctuate between 0.8 to 1.0. Further, we conducted a comparative study, pitting the proposed method against other state-of-the-art target prediction methodologies

Mild Traumatic Brain Injury andCareer Stage Associate with Visible Perivascular Spaces in Special Operations Forces Soldiers.

Mild traumatic brain injury (mTBI) and occupational blast exposure in military Service Members may lead to impaired brain waste clearance which increases neurological disease risk. Perivascular spaces (PVS) are a key part of the glymphatic system which supports brain waste clearance, preferentially during sleep. Visible PVS on clinical magnetic resonance imaging have been previously observed in patients with neurodegenerative diseases and animal neurotrauma models. The purpose of this study was to determine associations between PVS morphological characteristics, military career stage, and mTBI history in Special Operations Forces (SOF)Soldiers. Participants underwent T2-weighed neuroimaging to capture three-dimensional whole brain volumes. Segmentation was performed using a previously validated, multi-scale deep convolutional encoder-decoder neural network. Only PVS clusters within the white matter mask were quantified for analyses. Due to non-normal PVS metric distribution, non-parametric Mann-Whitney U tests were used to determine group differences in PVS outcomes. In total, 223 healthy SOF combat Soldiers (age = 33.1 ± 4.3yrs) were included, 217 reported career stage. Soldiers with mTBI history had greater PVS number (z = 2.51, P = 0.013) and PVS volume (z = 2.42, P = 0.016). In-career SOF combat Soldiers had greater PVS number (z = 2.56, P = 0.01) and PVS volume (z = 2.28, P = 0.02) compared to a baseline cohort. Mild TBI history is associated with increased PVS burden in SOF combat Soldiers that are clinically recovered from mTBI. This may indicate ongoing physiological changes that could lead to impaired waste clearance via the glymphatic system. Future studies should determine if PVS number and volume are meaningful neurobiological outcomes for neurodegenerative disease risk and if clinical interventions such as improving sleep can reduce PVS burden.

Numerical approximation based on deep convolutional neural network for high‐dimensional fully nonlinear merged PDEs and 2BSDEs

This paper proposes two efficient approximation methods to solve high‐dimensional fully nonlinear partial differential equations (NPDEs) and second‐order backward stochastic differential equations (2BSDEs), where such high‐dimensional fully NPDEs are extremely difficult to solve because the computational cost of standard approximation methods grows exponentially with the number of dimensions. Therefore, we consider the following methods to overcome this difficulty. For the merged fully NPDEs and 2BSDEs system, combined with the time forward discretization and ReLU function, we use multiscale deep learning fusion and convolutional neural network (CNN) techniques to obtain two numerical approximation schemes, respectively. Finally, three practical high‐dimensional test problems involving Allen–Cahn, Black–Scholes–Barenblatt, and Hamilton–Jacobi–Bellman equations are given so that the first proposed method exhibits higher efficiency and accuracy than the existing method, while the second proposed method can extend the dimensionality of the completely NPDEs–2BSDEs system over 400 dimensions, from which the numerical results highlight the effectiveness of proposed methods.

An enhanced multi-scale deep convolutional orchard capsule neural network for multi-modal breast cancer detection

Breast cancer is the second-leading cause of cancer death in women. Breast cells develop into malignant, cancerous lumps, the first signs of breast cancer. Breast cancer can be discovered by the automated diagnostic system when it is still too little to be found by conventional medical methods. Early breast cancers identified with automated screening and diagnosis technologies are generally treatable. This study proposes an enhanced multi-scale deep Convolutional Capsule Neural Network (CapsNet) optimized with Orchard Optimization Algorithm for breast cancer detection. The proposed system consists of preprocessing, feature extraction, segmentation, and classification process. Two input images are taken initially: the Breast Cancer Histopathology Images dataset and the Infrared Thermal Images dataset. The quality of the collected data is improved, and unwanted noises are removed. The features are extracted to segment the image to derive a Region of Interest for effectively segmenting the affected region. Finally, the images are classified as benign/malignant for histopathology images and healthy/cancer for thermal images. The proposed CapsNet is implemented in Python, run for 200 epochs, and compared with existing methods in terms of evaluation metrics. The result shows that the proposed CapsNet attained 99.74 % accuracy, 0.0482 binary entropy loss on the Breast Cancer Histopathology Image dataset and 97 % accuracy, 0.2081 binary entropy loss on the Infrared Thermal Images dataset while incrementing the epochs at each level.

MResU-Net: multi-scale residual U-Net-based brain tumor segmentation from multimodal MRI.

Brain tumor segmentation is an important direction in medical image processing, and its main goal is to accurately mark the tumor part in brain MRI. This study proposes a brand new end-to-end model for brain tumor segmentation, which is a multi-scale deep residual convolutional neural network called mResU-Net. The semantic gap between the encoder and decoder is bridged by using skip connections in the U-Net structure. The residual structure is used to alleviate the vanishing gradient problem during training and ensure sufficient information in deep networks. On this basis, multi-scale convolution kernels are used to improve the segmentation accuracy of targets of different sizes. At the same time, we also integrate channel attention modules into the network to improve its accuracy. The proposed model has an average dice score of 0.9289, 0.9277, and 0.8965 for tumor core (TC), whole tumor (WT), and enhanced tumor (ET) on the BraTS 2021 dataset, respectively. Comparing the segmentation results of this method with existing techniques shows that mResU-Net can significantly improve the segmentation performance of brain tumor subregions.

Bayesian-based uncertainty-aware tool-wear prediction model in end-milling process of titanium alloy

Tool wear negatively affects machined surfaces and causes surface cracking, therefore increasing manufacturing costs and degrading product quality. Titanium alloys, which are widely used because of their desirable mechanical properties, have problems associated with tool wear due to poor thermal properties, such as specific heat capacity and thermal conductivity. Therefore, the accurate prediction of tool wear is necessary during the titanium alloy end-milling process to improve product quality and ensure reliability for corrective decisions like tool replacement. To this end, uncertainty-aware tool-wear prediction should be performed. In this study, a deep learning-based tool-wear prediction model based on a Bayesian approach was proposed. First, a convolutional neural network (CNN)-based architecture that integrates multiscale information extracted from raw sensor measurement data, termed deep multiscale CNN (DMSCNN), was proposed. It used different-sized convolutional kernels in parallel to enable various receptive field sizes suitable for machining processes. Second, based on a Bayesian learning approach, DMSCNN was transformed into a probabilistic model that produced a predictive distribution for estimated tool wear. In particular, a variational inference was applied to DMSCNN parameters to provide uncertainty awareness. Experiments were conducted with data collected from an actual end-milling process under three different conditions. The results proved the effectiveness of the proposed DMSCNN for tool-wear prediction. Bayesian DMSCNN showed promising results, as it outperformed existing comparative deterministic methods as well as probabilistic methods for tool-wear prediction. The proposed method is expected to be effectively applied in smart manufacturing as well as other machining processes that require data-driven digital decisions.

Efficient Deep Learning Approach for Detection of Brain Tumor Disease

This article presents, we describe fully autonomous brain tumor classifying as well as the classification method depending on a multiscale DCNN Deep-Convolutional-Neural-Network (multiscale Deep Convolutional Neural Network). As compared to similar efforts, ours differs in part because it processes input images at two different spatial scales using independent neural networks. The suggested neural model can interpret MRI scans comprising two kinds of tumors: the first one meningioma, and the second one glioma, without requiring input images to be preprocessed to eliminate skull and spine components. The effectiveness we've noted model available to the public MRI image dataset containing 3060 slices of 250 cases is compared to previously reported models for conventional ML (machine learning) as well as DL (deep learning). In the comparison, our approach produced an impressive 97% accuracy in classifying tumors, which was better than some other algorithms utilizing similar datasets.

White Matter Perivascular Space Burden Is Associated With Mild Traumatic Brain Injury History In Soldiers

Mild traumatic brain injury (mTBI) exposure in military Service Members may lead to impaired brain waste clearance which increases neurological disease risk including amyloidopathies. Perivascular spaces (PVS) are part of the glymphatic pathway which support brain waste clearance, especially during sleep. Greater magnetic resonance imaging (MRI)-visible PVS burden has been previously observed in patients with neurodegenerative diseases and animal TBI models. PURPOSE: To determine the association between PVS burden (number and volume) and mTBI history in Special Operations Forces (SOF) combat Soldiers. METHODS: In this cross-sectional study, participants underwent neuroimaging to capture T2-weighted (T2w) whole brain three dimensional volumes obtained on a Siemens 3T Biograph mMR or 3T MAGNETOM Prisma scanner (TR/TE = 3200/400ms, slice thickness = 1mm, FoV = 256mm x 256 mm). The PVS segmentation was performed using a previously validated, multi-scale deep convolutional encoder-decoder neural network. All T2w images were skull stripped and a custom T2w atlas with segmented cerebral white matter and subcortical gray matter labels was registered to each subject using Advanced Normalization Tools to minimize misidentifying thin tubular structures in the skull, brainstem, cerebellum, and cortex as PVS. Only PVS clusters within the white matter mask were quantified for analyses. Binary mTBI history was self-reported (“yes” or “no”). Due to non-normal PVS metric distribution, non-parametric Mann-Whitney U tests were used to determine group differences in PVS outcomes. RESULTS: In total, 223 healthy SOF combat Soldiers (age = 33.1±4.3yrs) were included for analysis and 123 (55.16%) reported mTBI history. Soldiers with mTBI history had greater PVS number (z = 2.506, P = 0.013) and PVS volume (z = 2.419, P = 0.016). CONCLUSIONS: We found mTBI history is associated with increased PVS burden in SOF combat Soldiers that are clinically recovered from mTBI. This may indicate ongoing physiological post-mTBI changes that could lead to impaired waste clearance via the glymphatic system. Future studies should determine how sleep influences PVS burden following mTBI exposure and if PVS number and volume are meaningful neurobiological outcomes for neurodegenerative disease risk. Funded by US Special Operations Command

Perivascular Space Morphology Is Affected By Career Exposure In Special Operations Forces Combat Soldiers

Special Operations Forces (SOF) combat Soldiers are at high risk for repetitive blast and head impact exposure. These exposures may predispose SOF combat Soldiers to acute brain injuries and subsequent chronic manifestations including sleep disorders reported by 40% of US Service Members. Studying brain morphology such as perivascular spaces (PVS) may illustrate a strong intersection between brain glymphatic system function and sleep quality. Cerebral PVS are glymphatic system passageways normally serving to clear brain metabolic waste during sleep. Increasing MRI visible PVS number and volume are a possible pathogenesis for neurodegenerative disorders. PURPOSE: To determine the association between PVS morphological characteristics and career stage in SOF combat Soldiers. METHODS: Healthy asymptomatic SOF combat Soldiers (age=33.0±4.1yrs) cross-sectionally participated in this study at two career stages: baseline (n=141) and in-career (n=76). We collected whole brain 3D T2-weighted magnetic resonance imaging (MRI) on a Siemens 3T Biograph mMR or 3T MAGNETOM Prisma with equivalent settings (TR/TE = 3200/400ms, slice thickness = 1mm, FoV = 256mm x 256 mm). We segmented the PVS using validated multi-scale deep convolutional encoder-decoder neural networks. All images were skull stripped and registered to a custom brain atlas using Advanced Normalization Tools to identify only PVS clusters within cerebral white matter. We quantified PVS number and volume. The PVS outcome variables were non-normally distributed. As such, we employed nonparametric Mann Whitney U tests to compare the two career stage groups. RESULTS: Median (IQR) data were as follows: PVS number = 37.0 (44.0); PVS volume = 647mm3 (972mm3). In-career SOF combat Soldiers had greater PVS number (z = 2.56, P = 0.01) and PVS volume (z = 2.28, P = 0.02). CONCLUSIONS: In-career SOF combat Soldiers (typically older) show higher segmented PVS number and volume compared to baseline cohort. Also, our PVS findings may indicate impairments with clearing brain wastes suggesting sleep interventions may be warranted in SOF combat Soldier populations who experience continued exposures to combat and training. Measuring the PVS may be an important outcome in determining the efficacy of sleep studies in this population. Funded by US Special Operations Command.

Multi-Scale Convolutional Neural Network Ensemble for Multi-Class Arrhythmia Classification.

The automated analysis of electrocardiogram (ECG) signals plays a crucial role in the early diagnosis and management of cardiac arrhythmias. The diverse etiology of arrhythmia and the subtle variations in the pathological ECG characteristics pose challenges in designing reliable automated methods. Existing methods mostly use single deep convolutional neural networks (DCNN) based approaches for arrhythmia classification. Such approaches may not be adequate for effectively representing diverse pathological ECG characteristics. This paper presents a novel way of using an ensemble of multiple DCNN classifiers for effective arrhythmia classification named Deep Multi-Scale Convolutional neural network Ensemble (DMSCE). Specifically, we designed multiple scale-dependent DCNN expert classifiers with different receptive fields to encode the scale-specific pathological ECG characteristics and generate the local predictions. A convolutional gating network is designed to compute the dynamic fusion weights for the experts based on their competencies. These weights are used to aggregate the local predictions and generate final diagnosis decisions. Moreover, a new error function with a correlation penalty is formulated to enable interaction and optimal diversity among experts during the training process. The model is evaluated on the PTBXL-2020 12-lead ECG and the CinC-training2017 single-lead ECG datasets and delivers state-of-the-art performance. Average F1-score of 84.5 % and 88.3 % are obtained for the PTBXL-2020 and the CinC-training2017 datasets, respectively. Impressive performance across various cardiac arrhythmias and the elegant generalization ability for different leads make the method suitable for reliable remote or in-hospital arrhythmia monitoring applications.

Detection of COVID19 from X-ray images using multiscale Deep Convolutional Neural Network

The Coronavirus disease 2019 (COVID19) pandemic has led to a dramatic loss of human life worldwide and caused a tremendous challenge to public health. Immediate detection and diagnosis of COVID19 have lifesaving importance for both patients and doctors. The availability of COVID19 tests increased significantly in many countries, thereby provisioning a limited availability of laboratory test kits Additionally, the Reverse Transcription-Polymerase Chain Reaction (RT-PCR) test for the diagnosis of COVID 19 is costly and time-consuming. X-ray imaging is widely used for the diagnosis of COVID19. The detection of COVID19 based on the manual investigation of X-ray images is a tedious process. Therefore, computer-aided diagnosis (CAD) systems are needed for the automated detection of COVID19 disease. This paper proposes a novel approach for the automated detection of COVID19 using chest X-ray images. The Fixed Boundary-based Two-Dimensional Empirical Wavelet Transform (FB2DEWT) is used to extract modes from the X-ray images. In our study, a single X-ray image is decomposed into seven modes. The evaluated modes are used as input to the multiscale deep Convolutional Neural Network (CNN) to classify X-ray images into no-finding, pneumonia, and COVID19 classes. The proposed deep learning model is evaluated using the X-ray images from two different publicly available databases, where database A consists of 1225 images and database B consists of 9000 images. The results show that the proposed approach has obtained a maximum accuracy of 96% and 100% for the multiclass and binary classification schemes using X-ray images from dataset A with 5-fold cross-validation (CV) strategy. For dataset B, the accuracy values of 97.17% and 96.06% are achieved using multiscale deep CNN for multiclass and binary classification schemes with 5-fold CV. The proposed multiscale deep learning model has demonstrated a higher classification performance than the existing approaches for detecting COVID19 using X-ray images.

Fashion Image Retrieval based on Parallel Branched Attention Network

With the increase in vision-associated applications in e-commerce, image retrieval has become an emerging application in computer vision. Matching the exact user clothes from the database images is challenging due to noisy background, wide variation in orientation and lighting conditions, shape deformations, and the variation in the quality of the images between query and refined shop images. Most existing solutions tend to miss out on either incorporating low-level features or doing it effectively within their networks. Addressing the issue, we propose an attention-based multiscale deep Convolutional Neural Network (CNN) architecture called Parallel Attention ResNet (PAResNet50). It includes other supplementary branches with attention layers to extract low-level discriminative features and uses both high-level and low-level features for the notion of visual similarity. The attention layer focuses on the local discriminative regions and ignores the noisy background. Image retrieval output shows that our approach is robust to different lighting conditions. Experimental results on two public datasets show that our approach effectively locates the important region and significantly improves retrieval accuracy over simple network architectures without attention.

Radio Frequency Spectrum Sensing by Automatic Modulation Classification in Cognitive Radio System Using Multiscale Deep CNN

Automatic modulation categorization (AMC) is used in many applications such as cognitive radio, adaptive communication, electronic reconnaissance, and non-cooperative communications. Predicting the modulation class of an unknown radio signal without having any prior information of the signal parameters is challenging. This paper proposes a novel multiscale deep-learning-based approach for the automatic modulation classification using radio signals. The approach considered the fixed boundary range-based Empirical wavelet transform (FBREWT) based multiscale analysis technique to decompose the radio signal into sub-band signals or modes. The sub-band signals computed from the radio signal combined with the deep convolutional neural network (CNN) are used to classify modulation types. The approach is tested using the radio signals of different signal-to-noise ratio (SNR) values and four different channel types such as additive white Gaussian noise (AWGN) combination of Rayleigh fading and AWGN channels, the combination of Rician flat fading and AWGN channels, and combination of Nakagami-m fading and AWGN channels for AMC. The results show that the proposed FBREWT based deep-learning approach achieves an overall classification accuracy of 97% for AMC using the radio signals with 10dB SNR for the AWGN channel. Moreover, the proposed approach has obtained the accuracy’s as 94.56%, 95%, and 97.33% using radio signals with 10 dB SNR values for Rayleigh fading, Rician flat fading, and Nakagami-m fading channels combined through AWGN link. The comparison of the proposed multiscale deep learning-based approach with existing methods is shown for AMC.

A Prediction Method for the RUL of Equipment for Missing Data

We present a prediction framework to estimate the remaining useful life (RUL) of equipment based on the generative adversarial imputation net (GAIN) and multiscale deep convolutional neural network and long short-term memory (MSDCNN-LSTM). The method we proposed addresses the problem of missing data caused by sensor failures in engineering applications. First, a binary matrix is used to adjust the proportion of “0” to simulate the number of missing data in the engineering environment. Then, the GAIN model is used to impute the missing data and approximate the true sample distribution. Finally, the MSDCNN-LSTM model is used for RUL prediction. Experiments are carried out on the commercial modular aero-propulsion system simulation (C-MAPSS) dataset to validate the proposed method. The prediction results show that the proposed method outperforms other methods when packet loss occurs, showing significant improvements in the root mean square error (RMSE) and the score function value.

Multi-scale deep coupling convolutional neural network with heterogeneous sensor data for intelligent fault diagnosis

With the innovation and development of detection technology, various types of sensors are installed to monitor the operating status of equipment in modern industry. Compared with the same type of sensors for monitoring, heterogeneous sensors can collect more comprehensive complementary fault information. Due to the large distribution differences and serious noise pollution of heterogeneous sensor data collected in industrial sites, this brings certain challenges to the development of heterogeneous data fusion strategies. In view of the large distribution difference in the feature spatial of heterogeneous data and the difficulty of effective fusion of fault information, this paper presents a multi-scale deep coupling convolutional neural network (MDCN), which is used to map the heterogeneous fault information from different feature spaces to the common spaces for full fusion. Specifically, a multi-scale convolution module (MSC) with multiple filters of different sizes is adopted to extract multi-scale fault features of heterogeneous sensor data. Then, the maximum mean discrepancy (MMD) is applied to measure the distance between different spatial features in the coupling layer, and the common failure information in the heterogeneous data is mined by minimizing MMD to fuse effectively in order to identify the failure state of the device. The validity of this method is verified by the data collected on a first-level parallel gearbox mixed fault experiment platform.

Identification of wool and cashmere fibers based on multiscale geometric analysis

The identification of wool and cashmere fibers has always been an essential topic in the field of textile. In this work, a fiber recognition algorithm based on multiscale geometric analysis and deep convolutional neural network is introduced. As a new tool for signal analysis with multiscale and multi-direction, curvelet transform not only has multiscale and multi-resolution characteristics of the wavelet transform, but also has very strong directionality in the sense that it can provide an optimally sparse representation of fiber image with a large number of fiber contours information. The proposed method was based on multiscale geometric analysis to reduce the dimension of wool/cashmere fiber images and reduce the calculation of redundant data. The deep convolutional neural network was used to classify and recognize wool/cashmere fiber images. Then 400 fiber images of two kinds of fiber samples including wool and cashmere fibers, were collected respectively and processed by the methods including random interception and rotation to obtain a total of 600 fiber images respectively for the experiment analysis. The results show that the recognition accuracy is up to 96.67%. Compared with the traditional feature extraction and classification algorithm, this method dramatically improves model performance and identification accuracy.

A RUL prediction method of equipments based on MSDCNN-LSTM

In order to solve the problems of high data dimension and insufficient consideration of time series correlation information, a multi-scale deep convolutional neural network and long-short-term memory (MSDCNN-LSTM) hybrid model is proposed for remaining useful life (RUL) of equipments. First, the sensor data is processed through normalization and sliding time window to obtain input samples; then multi-scale deep convolutional neural network (MSDCNN) is used to capture detailed spatial features, at the same time, time-dependent features are extracted for effective prediction combining with long short-term memory (LSTM). Experiments on simulation dataset of commercial modular aero-propulsion system show that, compared with other state-of-the-art methods, the prediction method proposed in this paper has achieved better RUL prediction results, especially for the prediction of the life of equipment with complex failure modes and operating conditions. The effect is obvious. It can be seen that the prediction method proposed in this paper is feasible and effective.

3D multi-scale deep convolutional neural networks for pulmonary nodule detection.

With the rapid development of big data and artificial intelligence technology, computer-aided pulmonary nodule detection based on deep learning has achieved some successes. However, the sizes of pulmonary nodules vary greatly, and the pulmonary nodules have visual similarity with structures such as blood vessels and shadows around pulmonary nodules, which make the quick and accurate detection of pulmonary nodules in CT image still a challenging task. In this paper, we propose two kinds of 3D multi-scale deep convolution neural networks for nodule candidate detection and false positive reduction respectively. Among them, the nodule candidate detection network consists of two parts: 1) the backbone network part Res2SENet, which is used to extract multi-scale feature information of pulmonary nodules, it is composed of the multi-scale Res2Net modules of multiple available receptive fields at a granular level and the squeeze-and-excitation units; 2) the detection part, which uses a region proposal network structure to determine region candidates, and introduces context enhancement module and spatial attention module to improve detection performance. The false positive reduction network, also composed of the multi-scale Res2Net modules and the squeeze-and-excitation units, can further classify the nodule candidates generated by the nodule candidate detection network and screen out the ground truth positive nodules. Finally, the prediction probability generated by the nodule candidate detection network is weighted average with the prediction probability generated by the false positive reduction network to obtain the final results. The experimental results on the publicly available LUNA16 dataset showed that the proposed method has a superior ability to detect pulmonary nodules in CT images.

A Network Intrusion Detection Method Based on Deep Multi-scale Convolutional Neural Network

Network intrusion detection (NID) is an important method for network system administrators to detect various security holes. The performance of traditional NID methods can be affected when unknown or new attacks are detected. Compared with other machine learning methods, the intrusion detection method based on convolutional neural network (CNN) can significantly improve the accuracy of classification, but the convergence speed and generalization ability of CNN are not ideal in model training process resulting in a low true rate and a high false alarm rate. To solve the above problems, this paper proposes a deep multi-scale convolutional neural network (DMCNN) for network intrusion detection. Different levels of features in a large number of high-dimensional unlabeled original data are extracted by different scales convolution kernel. And the learning rate of network structure is optimized by batch normalization method to obtain the optimal feature representation of the raw data. We use NSL-KDD dataset as the benchmark thus we can compare the performance of our proposed method with other existing works. This dataset includes two testing sets which are the first one is KDDTest+ while the second one is $$\text {KDDTest}^{-21}$$ which is more difficult to be classified. The experimental results reveal that the AC and TPR are higher through our DMCNN model. Especially, in terms of DOS, the AC appropriately reaches to 98%. DMCNN has a high intrusion detection accuracy and a low false alarm rate, which overcomes the limitations of using the traditional detection methods and makes the new approach an attractive one for practical intrusion detection.

An Improved Topology Prediction of Alpha-Helical Transmembrane Protein Based on Deep Multi-Scale Convolutional Neural Network.

Alpha-helical proteins ( αTMPs) are essential in various biological processes. Despite their tertiary structures are crucial for revealing complex functions, experimental structure determination remains challenging and costly. In the past decades, various sequence-based topology prediction methods have been developed to bridge the gap between the sequences and structures by characterizing the structural features, but significant improvements are still required. Deep learning brings a great opportunity for its powerful representation learning capability from limited original data. In this work, we improved our αTMP topology prediction method DMCTOP using deep learning, which composed of two deep convolutional blocks to simultaneously extract local and global contextual features. Consequently, the inputs were simplified to reflect the original features of the sequence, including a protein sequence feature and an evolutionary conservation feature. DMCTOP can efficiently and accurately identify all topological types and the N-terminal orientation for an αTMP sequence. To validate the effectiveness of our method, we benchmarked DMCTOP against 13 peer methods according to the whole sequence, the transmembrane segment and the traditional criterion in testing experiments. All the results reveal that our method achieved the highest prediction accuracy and outperformed all the previous methods. The method is available at https://icdtools.nenu.edu.cn/dmctop.