Parallel Convolutional Layers Research Articles

Improving Differential-Neural Cryptanalysis

Our first objective is to enhance the capabilities of differential-neural distinguishers by applying more deep-learning techniques, focusing on handling more rounds and improving accuracy. Inspired by the Inception Block in GoogLeNet, we adopted a design that uses multiple parallel convolutional layers with varying kernel sizes before the residual block to capture multi-dimensional information. Additionally, we expanded the convolutional kernels in the residual blocks, enlarging the network's receptive field. In the case of Speck32/64, our efforts yield accuracy improvements in rounds 6, 7, and 8, enabling the successful training of a 9-round differential-neural distinguisher. As for Simon32/64, we developed a differential-neural distinguisher capable of effectively handling 12 rounds while achieving noteworthy accuracy enhancements in rounds 9, 10, and 11. Additionally, we utilized neutral bits to ensure the required data distribution for launching a successful key recovery attack when using multiple-ciphertext pairs as input for the neural network. Meanwhile, we redefined the formula for time complexity based on the differences in prediction speeds of the distinguisher between a single-core CPU and a GPU. Combining these various advancements allows us to considerably reduce the time and data complexity of key recovery attacks on 13-round Speck32/64. Furthermore, we used knowledge distillation techniques to reduce the model size, accelerating the distinguisher's prediction speed and reducing the time complexity. In particular, we achieved a successful 14-round key recovery attack by exhaustively guessing a 1-round subkey. For Simon32/64, we accomplished a 17-round key recovery attack for the first time and reduced the time complexity of the 16-round key recovery attack.

TCN-Inception: Temporal Convolutional Network and Inception modules for sensor-based Human Activity Recognition

The field of Human Activity Recognition (HAR) has experienced a significant surge in interest due to its essential role across numerous areas, including human–computer interaction (HCI), healthcare, smart homes, and various Internet of Things (IoT) applications. The power of deep learning methods in performing various classification tasks, including HAR, has been well-demonstrated. In light of this, our paper presents an efficient HAR system developed using a unique deep-learning architecture called TCN-Inception, which is designed for multivariate time series tasks like HAR data, by combining Temporal Convolutional Network (TCN) and Inception modules. The network starts with an Inception module that uses parallel convolution layers with various kernel sizes for feature extraction. It then includes a TCN module with dilated convolutions to grasp extended temporal patterns. Features are merged from different channels, and the use of residual connections and batch normalization improves training and deepens the architecture. We use four public datasets, UCI-HAR, MobiAct, Daphnet, and DSADS to assess the performance of the TCN-Inception model, and it obtains an average accuracy of 96.15%, 98.86%, 92.63%, and 99.50% for each dataset, respectively. Moreover, we compare the TCN-Inception to several deep learning frameworks to verify its performance. Finally, we implement an ablation study using several architectural configurations of the TCN-Inception model.

Human Activity Recognition Based on Deep Learning and Micro-Doppler Radar Data.

Activity recognition is one of the significant technologies accompanying the development of the Internet of Things (IoT). It can help in recording daily life activities or reporting emergencies, thus improving the user's quality of life and safety, and even easing the workload of caregivers. This study proposes a human activity recognition (HAR) system based on activity data obtained via the micro-Doppler effect, combining a two-stream one-dimensional convolutional neural network (1D-CNN) with a bidirectional gated recurrent unit (BiGRU). Initially, radar sensor data are used to generate information related to time and frequency responses using short-time Fourier transform (STFT). Subsequently, the magnitudes and phase values are calculated and fed into the 1D-CNN and Bi-GRU models to extract spatial and temporal features for subsequent model training and activity recognition. Additionally, we propose a simple cross-channel operation (CCO) to facilitate the exchange of magnitude and phase features between parallel convolutional layers. An open dataset collected through radar, named Rad-HAR, is employed for model training and performance evaluation. Experimental results demonstrate that the proposed 1D-CNN+CCO-BiGRU model demonstrated superior performance, achieving an impressive accuracy rate of 98.2%. This outperformance of existing systems with the radar sensor underscores the proposed model's potential applicability in real-world scenarios, marking a significant advancement in the field of HAR within the IoT framework.

Unleashing the potential of fNIRS with machine learning: classification of fine anatomical movements to empower future brain-computer interface.

In this study, we explore the potential of using functional near-infrared spectroscopy (fNIRS) signals in conjunction with modern machine-learning techniques to classify specific anatomical movements to increase the number of control commands for a possible fNIRS-based brain-computer interface (BCI) applications. The study focuses on novel individual finger-tapping, a well-known task in fNIRS and fMRI studies, but limited to left/right or few fingers. Twenty-four right-handed participants performed the individual finger-tapping task. Data were recorded by using sixteen sources and detectors placed over the motor cortex according to the 10-10 international system. The event's average oxygenated Δ HbO and deoxygenated Δ HbR hemoglobin data were utilized as features to assess the performance of diverse machine learning (ML) models in a challenging multi-class classification setting. These methods include LDA, QDA, MNLR, XGBoost, and RF. A new DL-based model named "Hemo-Net" has been proposed which consists of multiple parallel convolution layers with different filters to extract the features. This paper aims to explore the efficacy of using fNRIS along with ML/DL methods in a multi-class classification task. Complex models like RF, XGBoost, and Hemo-Net produce relatively higher test set accuracy when compared to LDA, MNLR, and QDA. Hemo-Net has depicted a superior performance achieving the highest test set accuracy of 76%, however, in this work, we do not aim at improving the accuracies of models rather we are interested in exploring if fNIRS has the neural signatures to help modern ML/DL methods in multi-class classification which can lead to applications like brain-computer interfaces. Multi-class classification of fine anatomical movements, such as individual finger movements, is difficult to classify with fNIRS data. Traditional ML models like MNLR and LDA show inferior performance compared to the ensemble-based methods of RF and XGBoost. DL-based method Hemo-Net outperforms all methods evaluated in this study and demonstrates a promising future for fNIRS-based BCI applications.

MFAM-AD: an anomaly detection model for multivariate time series using attention mechanism to fuse multi-scale features.

Multivariate time series anomaly detection has garnered significant attention in fields such as IT operations, finance, medicine, and industry. However, a key challenge lies in the fact that anomaly patterns often exhibit multi-scale temporal variations, which existing detection models often fail to capture effectively. This limitation significantly impacts detection accuracy. To address this issue, we propose the MFAM-AD model, which combines the strengths of convolutional neural networks (CNNs) and bi-directional long short-term memory (Bi-LSTM). The MFAM-AD model is designed to enhance anomaly detection accuracy by seamlessly integrating temporal dependencies and multi-scale spatial features. Specifically, it utilizes parallel convolutional layers to extract features across different scales, employing an attention mechanism for optimal feature fusion. Additionally, Bi-LSTM is leveraged to capture time-dependent information, reconstruct the time series and enable accurate anomaly detection based on reconstruction errors. In contrast to existing algorithms that struggle with inadequate feature fusion or are confined to single-scale feature analysis, MFAM-AD effectively addresses the unique challenges of multivariate time series anomaly detection. Experimental results on five publicly available datasets demonstrate the superiority of the proposed model. Specifically, on the datasets SMAP, MSL, and SMD1-1, our MFAM-AD model has the second-highest F1 score after the current state-of-the-art DCdetector model. On the datasets NIPS-TS-SWAN and NIPS-TS-GECCO, the F1 scores of MAFM-AD are 0.046 (6.2%) and 0.09 (21.3%) higher than those of DCdetector, respectively(the value ranges from 0 to 1). These findings validate the MFAMAD model's efficacy in multivariate time series anomaly detection, highlighting its potential in various real-world applications.

Speech emotion recognition based on multi-dimensional feature extraction and multi-scale feature fusion

In recent years, convolutional neural networks have been widely applied in the research of speech emotion recognition. However, most existing methods treat speech spectrogram features as images to learn the emotion features, which makes it difficult to capture rich emotion information at different scales. In this paper, a speech emotion recognition method based on multi-dimensional feature extraction and multi-scale feature fusion is proposed. Multi-dimensional features are extracted from Mel-frequency cepstral coefficients using parallel convolutional layers, emotion feature representations are learned at different scales using multi-scale residual convolutional layers, and a global feature fusion module is finally used to integrate local multi-scale emotion features to obtain key global emotion features. Experiments are conducted on the IEMOCAP, CREMA-D, and RAVDESS dataset, and the results show that our proposed network achieves a 3.5%-4% improvement in four commonly used evaluation metrics, including unweighted accuracy and weighted accuracy, compared with the state-of-the-art methods.

Development and performance evaluation of enhanced image dehazing method using deep learning networks

In this work, a deep learning-based high-performance image dehazing technique is proposed for image processing applications. The end-to-end network model is constructed and implemented using a dehazing network, discriminator network, and fine-tuning network models. These three methods are well-trained individually using appropriate datasets. The individual network models are integrated as an end-to-end network model to enhance the dehazing process. The applied input hazy image is processed by the dehazing network model using the estimation of transmission map and atmospheric light along with parallel convolution layers. A discriminated dehazing image was extracted from the discrimination network. Finally, fine-tuning is carried out based on the results of the discriminator network model. Various hazy images from different datasets are collected and applied to the proposed model, and performance metrics such as PSNR, SSIM, and MSE are evaluated. Qualitative, and quantitative comparison and analysis are carried out between the proposed learning-based image dehazing and existing dehazing methods. The average PSNR value of the proposed dehazing model is obtained by a maximum of 40.7 %, and a minimum of 1.34 %, when compared to the existing works. The average SSIM of the proposed work is increased by a maximum of 22.12 %, and a minimum of 3.38 % with respect to the existing works. The maximum average value of MSE for the proposed model is decreased by 72.6 % and the minimum decrease of MSE is 4.08 % when compared to the state-of-art-works.

A Novel Light U-Net Model for Left Ventricle Segmentation Using MRI

MRI segmentation and analysis are significant tasks in clinical cardiac computations. A cardiovascular MR scan with left ventricular segmentation seems necessary to diagnose and further treat the disease. The proposed method for left ventricle segmentation works as a combination of the intelligent histogram-based image enhancement technique with a Light U-Net model. This technique serves as the basis for choosing the low-contrast image subjected to the stretching technique and produces sharp object contours with good contrast settings for the segmentation process. After enhancement, the images are subjected to the encoder–decoder configuration of U-Net using a novel lightweight processing model. Encoder sampling is supported by a block of three parallel convolutional layers with supporting functions that improve the semantics for segmentation at various levels of resolutions and features. The proposed method finally increased segmentation efficiency, extracting the most relevant image resources from depth-to-depth convolutions, filtering them through each network block, and producing more precise resource maps. The dataset of MICCAI 2009 served as an assessment tool of the proposed methodology and provides a dice coefficient value of 97.7%, accuracy of 92%, and precision of 98.17%.

Evaluation of effectiveness in conversations between humans and chatbots using parallel convolutional neural networks with multiple temporal resolutions

Chatbots enable the automation of several components in customer service and allow the support of multiple users. Despite their multiple advantages, due to the large amount of conversations generated by a chatbot, it is difficult to determine whether customer requests are well-addressed. For practical reasons, chatbot’s effectiveness is evaluated manually based upon a small sample (randomly chosen) of conversations or through self-reported user satisfaction. This procedure does not guarantee the correct evaluation of the service because the sample is generally not large enough and self-reports might be influenced by different external factors not directly associated to the chatbot’s functioning. This study proposes a methodology for automatic evaluation of chatbot effectiveness in real production environments. The analysis considers convolutional neural networks adapted for natural language processing, using two parallel convolutional layers to evaluate questions and answers independently. The proposed model also incorporates filters to extract features with multiple temporal resolution. This methodology is tested upon real conversations of chatbots that provide service to two different companies. The results are compared to baseline models based on classical techniques with different pre-trained word embedding models. According to our results, the proposed approach provides accuracies between 78.95% and 80.18%, which outperforms the best result of the baseline models by 2.9%.

Human Activity Recognition by the Image Type Encoding Method of 3-Axial Sensor Data

HAR technology uses computer and machine vision to analyze human activity and gestures by processing sensor data. The 3-axis acceleration and gyro sensor data are particularly effective in measuring human activity as they can calculate movement speed, direction, and angle. Our paper emphasizes the importance of developing a method to expand the recognition range of human activity due to the many types of activities and similar movements that can result in misrecognition. The proposed method uses 3-axis acceleration and gyro sensor data to visually define human activity patterns and improve recognition accuracy, particularly for similar activities. The method involves converting the sensor data into an image format, removing noise using time series features, generating visual patterns of waveforms, and standardizing geometric patterns. The resulting data (1D, 2D, and 3D) can simultaneously process each type by extracting pattern features using parallel convolution layers and performing classification by applying two fully connected layers in parallel to the merged data from the output data of three convolution layers. The proposed neural network model achieved 98.1% accuracy and recognized 18 types of activities, three times more than previous studies, with a shallower layer structure due to the enhanced input data features.

SE_SPnet: Rice leaf disease prediction using stacked parallel convolutional neural network with squeeze‐and‐excitation

AbstractRice is one of the significant crops, and the early identification and prevention of its diseases are essential to ensure adequate and healthy availability to the world's growing population. The use of image processing is an encouraging method for automatic rice leaf disease identification and detection. In particular, the recent advancements indicate the effectiveness of convolutional neural network (CNN) based deep learning approaches. In this direction, the present work proposes a novel stacked parallel convolution layers‐based network (SPnet) with the squeeze‐and‐excitation (SE) architecture, named (SE_SPnet), for classifying diseased rice leaf images. The stacked parallel network block comprises four parallel convolution layers with different kernel sizes for abstractions of the global and local features. The SE block extracts feature information automatically while removing invalid ones. We compare the SE_SPnet model with state‐of‐the‐art CNN models such as VGG16, DenseNet121, and InceptionV3 based on computational effort, accuracy, sensitivity, specificity, precision, recall, and F1‐score. The experimental results show that the SE_SPnet outperforms standard CNN models for the considered rice leaf disease image datasets. In particular, the SE_SPnet achieves the highest accuracy (99.2%), sensitivity (98.2%), specificity (98.5%), precision (98.4%), recall (98.2%), and F1‐score (98.5%) while using stochastic gradient descent (with momentum) optimizer with a 0.01 learning rate. Furthermore, the SE_SPnet also exhibits to outperform when compared with some of the most recent and relevant existing works.

ConvSegNet: Automated Polyp Segmentation From Colonoscopy Using Context Feature Refinement With Multiple Convolutional Kernel Sizes

Colorectal cancer occurs in the rectal of humans, and early detection has been proved to reduce its mortality rate. Colonoscopy is the standard used in detecting the presence of polyps in the rectal, and accurate segmentation of the polyps from colonoscopy images often provides helpful information for early diagnosis and treatment. Although existing deep learning models often achieve high segmentation performance when tested on the same dataset used in model training; still, their performance often degrades when applied to out-of-distribution datasets, leading to low model generalization or overfitting. This challenge is often associated with the quality of the features learnt from the input images. In this work, a novel Context Feature Refinement (CFR) module is proposed to address the challenge of low model generalization and segmentation performance. The CFR module is built to extract contextual information from the incoming feature map by using multiple parallel convolutional layers with progressively increasing kernel sizes. Using multiple parallel convolutions with different kernel sizes helped to extract more efficient multi-scale contextual information and thus enabled the network to effectively identify and segment small and fine details, as well as larger and more complex structures in the input images. Extensive experiments on three public benchmark datasets in CVC-ClinicDB, Kvasir-SEG, and BKAI-NeoPolyp showed that the proposed ConvSegNet model achieved jaccard, dice and F2 scores of 0.8650, 0.9177, and 0.9328 on CVC-ClinicDB, 0.7936, 0.8618, and 0.8855 on Kvasir-SEG, and 0.8045, 0.8747 and 0.8909 on BKAI-NeoPolyp datasets respectively. Also, an improved generalization performance was achieved by the ConvSegNet model, compared to the benchmark polyp segmentation models.

A comprehensive review of methods based on deep learning for diabetes-related foot ulcers.

BackgroundDiabetes mellitus (DM) is a chronic disease with hyperglycemia. If not treated in time, it may lead to lower limb amputation. At the initial stage, the detection of diabetes-related foot ulcer (DFU) is very difficult. Deep learning has demonstrated state-of-the-art performance in various fields and has been used to analyze images of DFUs.ObjectiveThis article reviewed current applications of deep learning to the early detection of DFU to avoid limb amputation or infection.MethodsRelevant literature on deep learning models, including in the classification, object detection, and semantic segmentation for images of DFU, published during the past 10 years, were analyzed.ResultsCurrently, the primary uses of deep learning in early DFU detection are related to different algorithms. For classification tasks, improved classification models were all based on convolutional neural networks (CNNs). The model with parallel convolutional layers based on GoogLeNet and the ensemble model outperformed the other models in classification accuracy. For object detection tasks, the models were based on architectures such as faster R-CNN, You-Only-Look-Once (YOLO) v3, YOLO v5, or EfficientDet. The refinements on YOLO v3 models achieved an accuracy of 91.95% and the model with an adaptive faster R-CNN architecture achieved a mean average precision (mAP) of 91.4%, which outperformed the other models. For semantic segmentation tasks, the models were based on architectures such as fully convolutional networks (FCNs), U-Net, V-Net, or SegNet. The model with U-Net outperformed the other models with an accuracy of 94.96%. Taking segmentation tasks as an example, the models were based on architectures such as mask R-CNN. The model with mask R-CNN obtained a precision value of 0.8632 and a mAP of 0.5084.ConclusionAlthough current research is promising in the ability of deep learning to improve a patient’s quality of life, further research is required to better understand the mechanisms of deep learning for DFUs.

DFU_SPNet: A stacked parallel convolution layers based CNN to improve Diabetic Foot Ulcer classification

Diabetic Foot Ulcer (DFU) is a complication of diabetes that causes lower limb amputation. In this work, a unique stacked parallel convolution layers-based network (DFU_SPNet) is proposed to perform DFU vs. normal skin classification. The main objective of this work is to design an effective CNN-based classification model, along with proper fine-tuning of optimizer settings. DFU_SPNet consists of 3 blocks of parallel convolution layers with multiple kernel sizes, for local and global feature abstractions. The proposed DFU_SPNet, trained using SGD (with momentum) optimizer with 1e−2 learning rate on the DFUNet dataset, outperformed the current state-of-the-art results with an AUC of 0.974.

Classification of Elephant Sounds Using Parallel Convolutional Neural Network

Human-elephant conflict is the most common problem across elephant habitat Zones across the world. Human elephant conflict (HEC) is due to the migration of elephants from their living habitat to the residential areas of humans in search of water and food. One of the important techniques used to track the movements of elephants is based on the detection of Elephant Voice. Our previous work [] on Elephant Voice Detection to avoid HEC was based on Feature set Extraction using Support Vector Machine (SVM). This research article is an improved continuum of the previous method using Deep learning techniques. The current article proposes a competent approach to classify Elephant voice using Vocal set features based on Convolutional Neural Network (CNN). The proposed Methodology passes the voice feature sets to the Multi input layers that are connected to parallel convolution layers. Evaluation metrics like sensitivity, accuracy, precision, specificity, execution Time and F1 score are computed for evaluation of system performance along with the baseline features such as Shimmer and Jitter. A comparison of the proposed Deep learning methodology with that of a simple CNN-based method shows that the proposed methodology provides better performance, as the deep features are learnt from each feature set through parallel Convolution layers. The accuracy 0.962 obtained by the proposed method is observed to be better compared to Simple CNN with less computation time of 11.89 seconds.

Multiscale Spatial Attention Network for Seismic Data Denoising

Seismic background noise often damages the desired signals, thereby resulting in some artifacts in the seismic imaging that follows. Since about 2016, some supervised-deep-learning methods have shown impressive performance in seismic data denoising, but they usually only consider single-scale features and neglect the multi-scale strategy. To further reinforce their denoising performance, a novel multi-scale convolutional neural network (CNN) combined with spatial attention mechanism, called multi-scale spatial attention denoising network (MSSA-Net), is proposed to tell weak reflected signals apart from strong seismic background noise. Unlike conventional single-scale CNNs, this proposed MSSA-Net can achieve the extraction of multi-scale features which is beneficial for the suppression of strong noise and the recovery of weak reflected signals. Specifically, MSSA-Net contains a principal denoising network and two auxiliary networks. The former utilizes the widen convolution composed of multiple parallel convolution layers with different kernel sizes to capture the informative multi-scale features; the latter two leverage up and down sampling to extract local fine and global coarse features, respectively. Furthermore, a spatial attention block is adopted to fuse these multi-scale features, thereby distinguishing weak reflected signals from strong seismic background noise. Multiple experiments of synthetic and real seismic records demonstrate the effectiveness of MSSA-Net. In addition, compared with two classical single-scale CNNs, MSSA-Net performs better in signal recovery, indicating the positive effect of multi-scale strategy.

A Deep Real-Time Fire Prediction Parallel D-CNN Model on燯DOO燘OLT燰8

Hazardous incidences have significant influences on human life, and fire is one of the foremost causes of such hazard in most nations. Fire prediction and classification model from a set of fire images can decrease the risk of losing human lives and assets. Timely promotion of fire emergency can be of great aid. Therefore, construction of these prediction models is relevant and critical. This article proposes an operative fire prediction model that depends on a prediction unit embedded in the processor UDOO BOLT V8 hardware to predict fires in real time. A fire image database is improved to enhance the images quality prior to classify them as either fire or non-fire. Our proposed deep learning–based Very Deep Convolutional Networks Visual Geometry Group (VGG-16) model (Parallel VGG-16) is an enhanced version of the VGG-16 model, by incorporating parallel convolution layers and a fusion module for better accuracy. The experimental results validate the performance of the Parallel VGG-16 which achieves an accuracy of 97%, compared to the compared state-of-the-art models. Moreover, we integrate the prediction module into a UDOO BOLT V8 computer, which precisely controlled the fire alarm so that it can cautious people from fire in real time. In this paper we propose a complete fire prediction model using a camera and the UDOO BOLT V8 embedded system. Our experiments validate the effectiveness and applicability of the proposed fire model.

Image source identification with known post-processed based on convolutional neural network

Image source identification is important to verify the origin and authenticity of digital images. However, when images are altered by some post-processing, the performance of the existing source verification methods may degrade. In this paper, we propose a convolutional neural network (CNN) to solve the above problem. Specifically, we present a theoretical framework for different tampering operations, to confirm whether a single operation has affected photo response non-uniformity (PRNU) contained in images. Then, we divide these operations into two categories: non-influential operation and influential operation. Besides, the images altered by the combination of non-influential and influential operations are equal to images that have only undergone a single influential operation. To make our introduced CNN robust to both non-influential operation and influential operation, we define a multi-kernel noise extractor that consists of a high-pass filter and three parallel convolution filters of different sizes. The features generated by the parallel convolution layers are then fed to subsequent convolutional layers for further feature extraction. The experimental results provide the effectiveness of our method.

Inception Architecture for Brain Image Classification

A non-invasive diagnostic support system for brain cancer diagnosis is presented in this study. Recently, very deeper convolution neural networks are designed for computerized tasks such as image classification, natural language processing. One of the standard architecture designs is the Visual Geometric Group (VGG) models. It uses a large number of small convolution filters (3x3) connected serially. Before applying max pooling, convolution filters are stacked up to four layers to extract features’ abstraction. The main drawback of going deeper is over fitting, and also updating gradient weights is very hard. These limitations are overcome using the inception module, which is wider rather than deeper. Also, it has parallel convolution layers with 3x3, 5x5, and 1x1 filters that reduce the computational complexity due to stacking, and the outputs are concatenated. This study’s experimental results show the usefulness of inception architecture for aiding brain image classification on Repository of Molecular Brain Neoplasia DaTa (REMBRANDT) Magnetic Resonance Imaging (MRI) images with an average accuracy of 95.1%, sensitivity of 96.2%, and specificity of 94%.

A fault diagnosis method based on improved convolutional neural network for bearings under variable working conditions

The fault diagnosis of rolling bearing will be negatively reduced because of variable working conditions, large environmental noise interference and insufficient effective data sample. To solve the problem, this paper proposes an improved convolutional neural network (CNN) method. The method firstly constructs a new network, multi-mode CNN (MMCNN) by using multiple parallel convolutional layers to effectively extract rich and complementary fault features, then transforms the 1D time-domain signal of the rolling bearing acquired under different frequency variable conditions to the 2D time–frequency grayscale by continuous wavelet transform (CWT) and put the grayscale into the MMCNN for training. Besides, the method combines the pseudo-label learning method with MMCNN, which can expands the labeled data set by pseudo-label processing of unlabeled data. The experimental results show that the proposed method can effectively improve the fault detection accuracy of rolling bearings under variable working conditions, which is superior to the existing methods.