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Ripeness classification is crucial in ensuring the quality and marketability of avocados. This paper aims to develop the Multi-Channel Hybrid Deep Neural Networks (MCHDNN) model between Visual Geometry Group 16 (VGG16) and EfficientNetB0 architectures, tailored explicitly for avocado ripeness classification in five classes: firm, breaking, ripe, overripe, and rotten. Each feature extracted is concatenated in an early fusion-based to classify the ripeness. The image dataset used for each avocado fruit was captured from six sides: front, back, left, right, bottom, and pedicel to provide a multi-channel input image in of a Convolution Neural Network (CNN). The results showed that the developed fine-tuned MCHDNN had an accuracy of 94.10% in training, 90.13% in validation, and 90.18% in testing. In addition, when considering individual class classification in the confusion matrix of the training set, it was found that the 'ripe' class had the highest accuracy of 94.58%, followed by the 'firm' and 'rotten' classes with 94.50% and 93.75% accuracy, respectively. Moreover, compared with the single-channel model, the fine-tuned MCHDNN model performs 7.70% more accurately than the fine-tuned VGG16 model and 7.77% more accurately than the fine-tuned EfficientNetB0 model. [ABSTRACT FROM AUTHOR]

White blood cells (WBCs) constitute an essential part of the human immune system. The correct identification of WBC subtypes is critical in the diagnosis of leukemia, a kind of blood cancer defined by the aberrant proliferation of malignant leukocytes in the bone marrow. The traditional approach of classifying WBCs, which involves the visual analysis of blood smear images, is labor-intensive and error-prone. Modern approaches based on deep convolutional neural networks provide significant results for this type of image categorization, but have high processing and implementation costs owing to very large feature sets. This paper presents an improved hybrid approach for efficient WBC subtype classification. First, optimum deep features are extracted from enhanced and segmented WBC images using transfer learning on pre-trained deep neural networks, i.e., DenseNet201 and Darknet53. The serially fused feature vector is then filtered using an entropy-controlled marine predator algorithm (ECMPA). This nature-inspired meta-heuristic optimization algorithm selects the most dominant features while discarding the weak ones. The reduced feature vector is classified with multiple baseline classifiers with various kernel settings. The proposed methodology is validated on a public dataset of 5000 synthetic images that correspond to five different subtypes of WBCs. The system achieves an overall average accuracy of 99.9 % with more than 95 % reduction in the size of the feature vector. The feature selection algorithm also demonstrates better convergence performance as compared to classical meta-heuristic algorithms. The proposed method also demonstrates a comparable performance with several existing works on WBC classification. [ABSTRACT FROM AUTHOR]

This paper proposes a novel intelligent fault diagnosis method to automatically identify different health conditions of wind turbine (WT) gearbox. Unlike traditional approaches, where feature extraction and classification are separately designed and performed, this paper aims to automatically learn effective fault features directly from raw vibration signals while classify the type of faults in a single framework, thus providing an end-to-end learning-based fault diagnosis system for WT gearbox without additional signal processing and diagnostic expertise. Considering the multiscale characteristics inherent in vibration signals of a gearbox, a new multiscale convolutional neural network (MSCNN) architecture is proposed to perform multiscale feature extraction and classification simultaneously. The proposed MSCNN incorporates multiscale learning into the traditional CNN architecture, which has two merits: 1) high-level fault features can be effectively learned by the hierarchical learning structure with multiple pairs of convolutional and pooling layers; and 2) multiscale learning scheme can capture complementary and rich diagnosis information at different scales. This greatly improves the feature learning ability and enables better diagnosis performance. The proposed MSCNN approach is evaluated through experiments on a WT gearbox test rig. Experimental results and comprehensive comparison analysis with respect to the traditional CNN and traditional multiscale feature extractors have demonstrated the superiority of the proposed method. [ABSTRACT FROM AUTHOR]

Recognition of finger vein patterns is essential technique that analyses the finger vein patterns to enable accurate authentication of an individual. A proper, accurate and quick learning of patterns is essentially required for improving the classification pattern. It is essential in developing an intelligent algorithm to effectively study and classify the patterns. In this paper, we develop an improved deep learning hybrid model for feature extraction and classification. A dimensional reduction deep neural network (DR-DNN) model has included a dimensional reduction model for extracting the essential features by reducing the dimensionality of feature datasets. A convolutional neural network (CNN) helps in classifying the benign vein patterns from the malignant vein patterns. The effectiveness is compared against existing deep learning classifiers to measure how effective the deep learning model is used for classifying finger vein patterns for biometric authentication. The results shows that the proposed method achieves an accuracy rate of 97.16% for the proposed method, where the other existing methods including CNN, Recurrent Neural Network (RNN) and Deep Neural Nets (DNN) has an accuracy rate of 86%, 80.66% and 88.31%, respectively. [ABSTRACT FROM AUTHOR]

Three-dimensional (3D) point cloud classification methods based on deep learning have good classification performance; however, they adapt poorly to diverse datasets and their classification accuracy must be improved. Therefore, FSDCNet, a neural network model based on the fusion of static and dynamic convolution, is proposed and applied for multiview 3D point cloud classification in this paper. FSDCNet devises a view selection method with fixed and random viewpoints, which effectively avoids the overfitting caused by the traditional fixed viewpoint. A local feature extraction operator of dynamic and static convolution adaptive weight fusion was designed to improve the model's adaptability to different types of datasets. To address the problems of large parameters and high computational complexity associated with the current methods of dynamic convolution, a lightweight and adaptive dynamic convolution operator was developed. In addition, FSDCNet builds a global attention pooling, integrating the most crucial information on different view features to the greatest extent. Due to these characteristics, FSDCNet is more adaptable, can extract more fine-grained detailed information, and can improve the classification accuracy of point cloud data. The proposed method was applied to the ModelNet40 and Sydney Urban Objects datasets. In these experiments, FSDCNet outperformed its counterparts, achieving state-of-the-art point cloud classification accuracy. For the ModelNet40 dataset, the overall accuracy (OA) and average accuracy (AA) of FSDCNet in a single view reached 93.8% and 91.2%, respectively, which were superior to those values for many other methods using 6 and 12 views. FSDCNet obtained the best results for 6 and 12 views, achieving 94.6%, 93.3%, 95.3%, and 93.6% in OA and AA metrics, respectively. For the Sydney Urban Objects dataset, FSDCNet achieved an OA and F1 score of 81.2% and 80.1% in a single view, respectively, which were higher than most of the compared methods. In 6 and 12 views, FSDCNet reached an OA of 85.3% and 83.6% and an F1 score of 85.5% and 83.7%, respectively. [ABSTRACT FROM AUTHOR]

COVID-19 pandemic outbreak became one of the serious threats to humans. As there is no cure yet for this virus, we have to control the spread of Coronavirus through precautions. One of the effective precautions as announced by the World Health Organization is mask wearing. Surveillance systems in crowded places can lead to detection of people wearing masks. Therefore, it is highly urgent for computerized mask detection methods that can operate in real-time. As for now, most countries demand mask-wearing in public places to avoid the spreading of this virus. In this paper, we are presenting an object detection technique using a single camera, which presents real-time mask detection in closed places. Our contributions are as follows: 1) presenting a real time feature extraction module to improve the detection computational time; 2) enhancing the extracted features learned from the deep convolutional neural network models to improve small objects detection. The proposed model is a lightweight backbone CNN which ensures real time mask detection. The accuracy is also enhanced by utilizing the feature enhancement module after some of the convolution layers in the CNN. We performed extensive experiments comparing our model to the single-shot detector (SDD) and YoloV3 neural network models, which are the state-of-the-art models in the literature. The comparison shows that the result of our proposed model achieves 95.9% accuracy which is 21% higher than SSD and 17.7% higher than YoloV3 accuracy. We also conducted experiments testing the mask detection speed. It was found that our model achieves average detection time of 0.85s for images of size 1024 × 1024 pixels, which is better than the speed achieved by SSD but slightly less than the speed of YoloV3. [ABSTRACT FROM AUTHOR]

In multiclass classification, convolutional neural network (CNN) is generally coupled with the cross-entropy (CE) loss, which only penalizes the predicted probability corresponding to a ground truth class and ignores the interclass relationship. We argue that CNN can be improved by using a better loss function. On the other hand, the Wasserstein distance (WD) is a well-known metric used to measure the distance between two distributions. Directly solving the WD problem requires a prohibitively large amount of computation time, whereas the cheaper iterative algorithms have a variety of shortcomings such as computational instability and difficulty in selecting parameters. In this paper, we address these issues by giving an analytical solution to the WD problem—for the first time, we find that for two distributions in hierarchically organized data space, WD has a closed-form solution, which we call “hierarchical WD (HWD).” We use this theory to construct novel loss functions that overcome the shortcomings of CE loss. To this end, multi-CNN information fusion that provides the basis for building category hierarchies is carried out first. Then, the semantic relationship among classes is modeled as a binary tree. Then, CNN coupled with an HWD-based loss, i.e., hierarchical Wasserstein CNN (HW-CNN), is trained to learn deep features. In this way, prior knowledge about the interclass relationship is embedded into HW-CNN, and information from several CNNs provides guidance in the process of training individual HW-CNNs. We conducted extensive experiments over two publicly available remote sensing data sets and achieved a state-of-the-art performance in scene classification tasks. [ABSTRACT FROM AUTHOR]

Image classification, which aims at assigning a semantic category to images, has been extensively studied during the past few years. More recently, convolution neural network arises and has achieved very promising achievement. Compared with traditional feature extraction techniques (e.g., SIFT, HOG, GIST), the convolutional neural network can extract features from image automatically and does not need hand designed features. However, how to further improve the classification algorithm is still challenging in academic research. The latest research on CNN shows that the features extracted from middle layers is representative, which shows a possible way to improve the classification accuracy. Based on the observation, in this paper, we propose a method to fuse the latent features extracted from the middle layers in a CNN to train a more robust classifier. First, we utilize the pretrained CNN models to extract visual features from middle layer. Then, we use supervised learning method to train classifiers for each feature respectively. Finally, we use the late fusion strategy to combine the prediction of these classifiers. We evaluate the proposal with different classification methods under some several images benchmarks, and the results demonstrate that the proposed method can improve the performance effectively. [ABSTRACT FROM AUTHOR]

Atrial fibrillation (AF) is one of the most common sustained chronic cardiac arrhythmia in elderly population, associated with a high mortality and morbidity in stroke, heart failure, coronary artery disease, systemic thromboembolism, etc. The early detection of AF is necessary for averting the possibility of disability or mortality. However, AF detection remains problematic due to its episodic pattern. In this paper, a multiscaled fusion of deep convolutional neural network (MS-CNN) is proposed to screen out AF recordings from single lead short electrocardiogram (ECG) recordings. The MS-CNN employs the architecture of two-stream convolutional networks with different filter sizes to capture features of different scales. The experimental results show that the proposed MS-CNN achieves 96.99% of classification accuracy on ECG recordings cropped/padded to 5 s. Especially, the best classification accuracy, 98.13%, is obtained on ECG recordings of 20 s. Compared with artificial neural network, shallow single-stream CNN, and VisualGeometry group network, the MS-CNN can achieve the better classification performance. Meanwhile, visualization of the learned features from the MS-CNN demonstrates its superiority in extracting linear separable ECG features without hand-craft feature engineering. The excellent AF screening performance of the MS-CNN can satisfy the most elders for daily monitoring with wearable devices. [ABSTRACT FROM AUTHOR]

Convolutional neural networks are special types of artificial neural networks that can solve various tasks in computer vision, such as image classification, object detection, and general recognition. The paper presents the basic building blocks of convolutional neural networks and their architecture, and compares their recognition accuracy with other character recognition techniques using the example of character recognition from vehicle registration plates. The purpose of the experiments was to determine the optimal configuration of the convolutional neural network and the influence of the size and design method of the training set on the recognition rate. The study shows that although convolutional neural networks have recently gained attention, traditional recognition methods are still relevant, and the choice of the right classifier and its configuration depends on the type of recognition task. [ABSTRACT FROM AUTHOR]

This paper proposes a generic method to learn interpretable convolutional filters in a deep convolutional neural network (CNN) for object classification, where each interpretable filter encodes features of a specific object part. Our method does not require additional annotations of object parts or textures for supervision. Instead, we use the same training data as traditional CNNs. Our method automatically assigns each interpretable filter in a high conv-layer with an object part of a certain category during the learning process. Such explicit knowledge representations in conv-layers of the CNN help people clarify the logic encoded in the CNN, i.e., answering what patterns the CNN extracts from an input image and uses for prediction. We have tested our method using different benchmark CNNs with various architectures to demonstrate the broad applicability of our method. Experiments have shown that our interpretable filters are much more semantically meaningful than traditional filters. [ABSTRACT FROM AUTHOR]

In semiconductor manufacturing, a wafer bin map (WBM) represents the results of wafer testing for dies using a binary pass or fail value. For WBMs, defective dies are often clustered into groups of local systematic defects. Determining their specific patterns is important, because different patterns are related to different root causes of failure. Recently, because wafer sizes have increased and the process technology has become more complicated, the probability of observing mixed-type defect patterns, i.e., two or more defect patterns in a single wafer, has increased. In this paper, we propose the use of convolutional neural networks (CNNs) to classify mixed-type defect patterns in WBMs in the framework of an individual classification model for each defect pattern. Through simulated and real data examples, we show that the CNN is robust to random noise and performs effectively, even if there are many random defects in WBMs. [ABSTRACT FROM AUTHOR]

Fall detection is a major challenge for researchers. Indeed, a fall can cause injuries such as femoral neck fracture, brain hemorrhage, or skin burns, leading to significant pain. However, in some cases, trauma caused by an undetected fall can get worse with the time and conducts to painful end of life or even death. One solution is to detect falls efficiently to alert somebody (e.g., nurses) as quickly as possible. To respond to this need, we propose to detect falls in a real apartment of 40 square meters by exploiting three ultra-wideband radars and a deep neural network model. The deep neural network is composed of a convolutional neural network stacked with a long-short term memory network and a fully connected neural network to identify falls. In other words, the problem addressed in this paper is a binary classification attempting to differentiate fall and non-fall events. As it can be noticed in real cases, the falls can have different forms. Hence, the data to train and test the classification model have been generated with falls (four types) simulated by 10 participants in three locations in the apartment. Finally, the train and test stages have been achieved according to three strategies, including the leave-one-subject-out method. This latter method allows for obtaining the performances of the proposed system in a generalization context. The results are very promising since we reach almost 90% of accuracy. [ABSTRACT FROM AUTHOR]

Chinese Sign Language (CSL) offers the main means of communication for the hearing impaired in China. Sign Language Recognition (SLR) can shorten the distance between the hearing-impaired and healthy people and help them integrate into the society. Therefore, SLR has become the focus of sign language application research. Over the years, the continuous development of new technologies provides a source and motivation for SLR. This paper aims to cover the most recent approaches in Chinese Sign Language Recognition (CSLR). With a thorough review of superior methods from 2000 to 2019 in CSLR researches, various techniques and algorithms such as scale-invariant feature transform, histogram of oriented gradients, wavelet entropy, Hu moment invariant, Fourier descriptor, gray-level co-occurrence matrix, dynamic time warping, principal component analysis, autoencoder, hidden Markov model (HMM), support vector machine (SVM), random forest, skin color modeling method, k-NN, artificial neural network, convolutional neural network (CNN), and transfer learning are discussed in detail, which are based on several major stages, that is, data acquisition, preprocessing, feature extraction, and classification. CSLR was summarized from some aspect as follows: methods of classification and feature extraction, accuracy/performance evaluation, and sample size/datasets. The advantages and limitations of different CSLR approaches were compared. It was found that data acquisition is mainly through Kinect and camera, and the feature extraction focuses on hand's shape and spatiotemporal factors, but ignoring facial expressions. HMM and SVM are used most in the classification. CNN is becoming more and more popular, and a deep neural network-based recognition approach will be the future trend. However, due to the complexity of the contemporary Chinese language, CSLR generally has a lower accuracy than other SLR. It is necessary to establish an appropriate dataset to conduct comparable experiments. The issue of decreasing accuracy as the dataset increases needs to resolve. Overall, our study is hoped to give a comprehensive presentation for those people who are interested in CSLR and SLR and to further contribute to the future research. [ABSTRACT FROM AUTHOR]

With the evolution of convolutional neural networks, extraction of deep features for accurate classification of Remote Sensed (RS) images have gained lot of momentum. However, due to variation in the scale of high resolution remote sensed images, accurate classification still remains a challenging task. Moreover, along with the scale, variation in the angle also decreases the accuracy of extraction of deep features using convolutional neural network. In this paper, a Multiscale and Multiangle convolutional neural network (MSMA-CNN) is proposed which extracts deep features of the RS images by employing several convolutional, pooling and fully connected layers which are discriminant, nonlinear and invariant. In MSMA-CNN, along with the spatial features, spectral features are also considered for classification of remote sensing scenes thus, making the entire system robust. The RS images are scaled at different levels using Gaussian Pyramid Decomposition and rotated at different angles and further features are derived using maximally stable extremal regions (MSER) at spectral and spatial level which are further concatenated and fed to the MSMA-CNN. A regularization parameter is added to get the results for test images as close as the trained images. A hybrid MSMA-CNN structure is designed by altering various parameters of the CNN structure to get improved optimized performance. To demonstrate the effectiveness of the proposed method, we compared the results on six challenging high-resolution remote sensing datasets and achieve a classification accuracy of 92.25% which shows significant improvement compared to the other state-of-the-art scene classification methods in terms classificational accuracy and computational cost. [ABSTRACT FROM AUTHOR]

Determining the correct mushroom species with the necessary ecological characteristics is critical to continue mushroom production, which is essential in gastronomy. The mushroom farmers and collectors technique may help identify toxic mushrooms by detecting poisonous mushrooms using images of different mushroom species with distinctive morphological features. However, it can be not easy to distinguish between species. This paper used a dataset of 6714 mushroom images obtained from nine different mushroom species to classify the mushroom species. For a more straightforward comprehension of mushroom images and feature extraction by reanalysis of data sets, data visualization was performed using Grad-CAM, LIME, and Heatmap methods. Residual block-based Convolutional Neural Network (CNN) architectures are trained to automatically classify the concatenated feature map obtained from the Grad-CAM, LIME, and Heatmap methods. After extracting the deep features of the images from each architecture, the Atom Search Optimization (ASO) algorithm has been used to select the most distinctive features. The 6714×9000 size of the concatenated feature map was reduced to 6714×600 using the ASO algorithm. Classification results were evaluated using six different classifiers based on the feature map obtained to determine the mushroom species. The nine classes of mushroom species were classified successfully with 95.45 % accuracy using the proposed model with the ASO algorithm and KNN classifier. The methodology introduces novel visualization techniques for interpreting CNN-based models' decisions in mushroom species classification tasks. Using metaheuristics-based CNN models with multi-feature fusion techniques allows the model to leverage diverse sources of information, potentially enhancing its ability to discriminate between mushroom species and achieve higher classification accuracy than existing methods. This study can advance the mushroom species classification field by introducing new methodologies, improving classification accuracy, providing insights into model interpretability, and facilitating knowledge transfer to related fields. • The proposed model automatically detects mushroom species. • Grad-CAM, LIME, and Heatmap methods based on CNN architectures are used to visualize mushroom images. • The ASO algorithm has been successfully applied to residual block-based CNN models for feature selection. • The hybrid model classification accuracy for different species of mushrooms is 95.45 %. [ABSTRACT FROM AUTHOR]

The shortage of training samples remains one of the main obstacles in applying the neural networks to the hyperspectral images classification. To fuse the spatial and spectral information, pixel patches are often utilized to train a model, which may further aggregate this problem. In the existing works, an artificial neural network (ANN) model supervised by centerloss (ANNC) was introduced. Training merely with spectral information, the ANNC yields discriminative spectral features suitable for the subsequent classification tasks. In this paper, we propose a novel convolutional neural network (CNN)-based spatial feature fusion (CSFF) algorithm, which allows a smart integration of spatial information to the spectral features extracted by ANNC. As a critical part of CSFF, a CNN-based discriminant model is introduced to estimate whether two pixels belong to the same class. At the testing stage, by applying the discriminant model to the pixel pairs generated by a test pixel and each of its neighbors, the local structure is estimated and represented as a customized convolutional kernel. The spectral–spatial feature is generated by a convolutional operation between the estimated kernel and the corresponding spectral features within a local region. The final label is determined by classifying the resulting spectral–spatial feature. Without increasing the number of training samples or involving pixel patches at the training stage, the CSFF framework achieves the state of the art by declining 20%–50% classification failures in experiments on three well-known hyperspectral images. [ABSTRACT FROM AUTHOR]

An accurate electrocardiogram (ECG) beat classification can benefit the diagnosis of the cardiovascular disease. Deep convolutional neural networks (CNN) can automatically extract valid features from data, which is an effective way for the classification of the ECG beats. However, the fully-connected layer in CNNs requires a fixed input dimension, which limits the CNNs to receive fixed-scale inputs. Signals of different scales are generally processed into the same size by segmentation and downsampling. If information loss occurs during a uniformly-sized process, the classification accuracy will ultimately be affected. To solve this problem, this paper constructs a new CNN framework spatial pyramid pooling (SPP) method, which solves the deficiency caused by the size of input data. The Massachusetts Institute of Technology-Biotechnology (MIT-BIH) arrhythmia database is employed as the training and testing data for the classification of heartbeat signals into six categories. Compared with the traditional method, which may lose a large amount of important information and easy to be over-fitted, the robustness of the proposed method can be guaranteed by extracting data features from different sizes. Experimental results show that the proposed architecture network can extract more high-quality features and exhibits higher classification accuracy (94%) than the traditional deep CNNs (90.4%). [ABSTRACT FROM AUTHOR]

Deep-learning models play a significant role in modern software solutions, with the capabilities of handling complex tasks, improving accuracy, automating processes, and adapting to diverse domains, eventually contributing to advancements in various industries. This study provides a comparative study on deep-learning techniques that can also be deployed on resource-constrained edge devices. As a novel contribution, we analyze the performance of seven Convolutional Neural Network models in the context of data augmentation, feature extraction, and model compression using acoustic data. The results show that the best performers can achieve an optimal trade-off between model accuracy and size when compressed with weight and filter pruning followed by 8-bit quantization. In adherence to the study workflow utilizing the forest sound dataset, MobileNet-v3-small and ACDNet achieved accuracies of 87.95% and 85.64%, respectively, while maintaining compact sizes of 243 KB and 484 KB, respectively. Henceforth, this study concludes that CNNs can be optimized and compressed to be deployed in resource-constrained edge devices for classifying forest environment sounds. [ABSTRACT FROM AUTHOR]

Neural networks overcome drawbacks of vision tasks by becoming convolutional in a wide range of layers. The salient map is affected by multilevels of strong pixels (superpixels) in global images and that is dependent on the hard threshold for their dividing. Deep neural networks have been established for saliency prediction of segmentation because the feature extraction must be suited to the input data. The convolutional neural network (CNN) also endures conflict between spatial pattern and a likeness of salient objects. Semantic segmentation is one of the approaches to continue classification based on these features. Therefore, upgrading the extraction process can be of use in saliency. In this work, we optimize DeepLab based on an atrous convolutional and a conditional random field (CRF) with a bottleneck in the semantic segmentation method, which serves for classification. The backbone of deep feature extraction is atrous convolution and the bottleneck based on CRF for hybrid saliency in the encoder-decoder system. The classification results are compared with some approaches for saliency prediction of recent deeper methods in an ISIC 2017 dataset. The results give better values not only for saliency prediction for segmentation but also for training and testing for classification. [ABSTRACT FROM AUTHOR]

A computerized coconut tree detection system can help dendrologists and laypersons in identifying coconut trees based on three morphological parameters including height, inclination, and orientation. These three parameters help to determine the health and the nature of growth of coconut trees which influences the design and use of robots for harvesting coconuts. Deep learning is a powerful tool used for feature extraction as it is better in extracting deeper details (features) in an image. In this research work, a new Modified Inception Net based Hyper Tuning Support Vector Machine classification method named MIN-SVM is proposed for coconut tree classification based on three morphological parameters including height, inclination and orientation. The features from the pre-processed coconut tree images were extracted using four distinct Convolutional Neural Network models including Visual Geometry Group, Inception Net, ResNet, and MIN-SVM. These extracted features were then classified using a Machine Learning model named Support Vector Machine (SVM). The MIN-SVM have achieved a remarkable accuracy of 95.35 percent as contrasted to Visual Geometry Group (91.90%), Inception Net (81.66%), and ResNet (71.95%). The features extracted from Modified Inception Net fitted good with SVM classifier. Experimental results show that MIN-SVM can be powerful computerized automated system to identify coconut trees based on height, inclination, and orientation. [ABSTRACT FROM AUTHOR]

In recent years, Peripheral blood smear is a generic analysis to assess the person’s health status. Manual testing of Peripheral blood smear images are difficult, time-consuming and is subject to human intervention and visual error. This method encouraged for researchers to present algorithms and techniques to perform the peripheral blood smear analysis with the help of computer-assisted and decision-making techniques. Existing CAD based methods are lacks in attaining the accurate detection of abnormalities present in the images. In order to mitigate this issue Deep Convolution Neural Network (DCNN) based automatic classification technique is introduced with the classification of eight groups of peripheral blood cells such as basophil, eosinophil, lymphocyte, monocyte, neutrophil, erythroblast, platelet, myocyte, promyocyte and metamyocyte. The proposed DCNN model employs transfer learning approach and additionally it carries three stages such as pre-processing, feature extraction and classification. Initially the pre-processing steps are incorporated to eliminate noisy contents present in the image by using Histogram Equalization (HE). It is enclosed to improve an image contrast. In order to distinguish the dissimilar class and segmentation approach is carried out with the help of Fuzzy C-Means (FCM) model whereas its centroid point optimality method with Slap Swarm based optimization strategy. Moreover some specific set of Gray Level Co-occurrence Matrix (GLCM) features of the segmented images are extracted to augment the performance of proposed detection algorithm. Finally the extracted features are recorded by DCNN and the proposed classifier has the capability to extract their own features. Based on this the diverse set of classes are classified and distinguished from qualitative abnormalities found in the image. [ABSTRACT FROM AUTHOR]

Classification of brain hemorrhage computed tomography (CT) images provides a better diagnostic implementation for emergency patients. Attentively, each brain CT image must be examined by doctors. This situation is time‐consuming, exhausting, and sometimes leads to making errors. Hence, we aim to find the best algorithm owing to a requirement for automatic classification of CT images to detect brain hemorrhage. In this study, we developed OzNet hybrid algorithm, which is a novel convolution neural networks (CNN) algorithm. Although OzNet achieves high classification performance, we combine it with Neighborhood Component Analysis (NCA) and many classifiers: Artificial neural networks (ANN), Adaboost, Bagging, Decision Tree, K‐Nearest Neighbor (K‐NN), Linear Discriminant Analysis (LDA), Naïve Bayes and Support Vector Machines (SVM). In addition, Oznet is utilized for feature extraction, where 4096 features are extracted from the fully connected layer. These features are reduced to have significant and informative features with minimum loss by NCA. Eventually, we use these classifiers to classify these significant features. Finally, experimental results display that OzNet‐NCA‐ANN excellent classifier model and achieves 100% accuracy with created Dataset 2 from Brain Hemorrhage CT images. [ABSTRACT FROM AUTHOR]

In the recent time, enviromental sound classification has received much popularity. This area of research comes under domain of non-speech audio classification. In this work, we have proposed a dilated Convolutional Neural Network approch to classify urban sound. We have carried out feature extraction, data augmentation techniques to carry out our experimental strategy smoothly. We also found out the activation maps of each layers of dilated convolution neural network. An increamental dilation rate has exploited Overall we achieved 84.16% of accuracy from the proposed dilated convolutional method. The gradual increaments of dilation rate has exploited the worse effect of grindding and has lowered down the computational cost. Also, overall classification performance, precision, recall,overall truth and kappa value have been obtained from our proposed method. We have considered 10 fold cross validation for the implementation of the dilated CNN model. [ABSTRACT FROM AUTHOR]

Biometric identification depends on the statistical analysis of the unique physical and behavioral characteristics of individuals. However, a unimodal biometric system is susceptible to different attacks such as spoof attacks. To overcome these limitations, we propose a multimodal biometric authentication system based on deep fusion of electrocardiogram (ECG) and finger vein. The proposed system has three main components, which are biometric pre-processing, deep feature extraction, and authentication. During the pre-processing, normalization and filtering techniques are adapted for each biometric. In the feature extraction process, the features are extracted using a proposed deep Convolutional Neural Network (CNN) model. Then, the authentication process is performed on the extracted features using five well-known machine learning classifiers: Support Vector Machine (SVM), K-Nearest Neighbors (KNNs), Random Forest (RF), Naive Bayes (NB), and Artificial Neural Network (ANN). In addition, to represent the deep features in a low-dimensional feature space and speed up the authentication task, we adopt Multi-Canonical Correlation Analysis (MCCA). We combine the two biometric systems based on ECG and finger vein into a single multimodal biometric system using feature and score fusion. The performance of the proposed system is tested on two finger vein (TW finger vein and VeinPolyU finger vein) databases and two ECG (MWM-HIT and ECG-ID) databases. Experimental results reveal improvement in terms of authentication performance with Equal Error Rates (EERs) of 0.12% and 1.40% using feature fusion and score fusion, respectively. Furthermore, the authentication with the proposed multimodal system using MCCA feature fusion with a KNN classifier shows an increase of accuracy by an average of 10% compared with those of other machine learning algorithms. Therefore, the proposed biometric system is effective in performing secure authentication and assisting the stakeholders in making accurate authentication of users. [ABSTRACT FROM AUTHOR]

Since the existing music emotion classification researches focus on the single-modal analysis of audio or lyrics, the correlation among models are neglected, which lead to partial information loss. Therefore, a music emotion classification method based on deep learning and improved attention mechanism is proposed. First, the music lyrics features are extracted by Term Frequency-Inverse Document Frequency (TF-IDF) and Word2vec method, and the term frequency weight vector and word vector are obtained. Then, by using the feature extraction ability of Convolutional Neural Network (CNN) and the ability of Long Short-Term Memory (LSTM) network to process the serialized data, and integrating the matching attention mechanism, an emotion analysis model based on CNN-LSTM is constructed. Finally, the output results of the deep neural network and CNN-LSTM model are fused, and the emotion types are obtained by Softmax classifier. The experimental analysis based on the selected data sets shows that the average classification accuracy of the proposed method is 0.848, which is better than the other comparison methods, and the classification efficiency has been greatly improved. [ABSTRACT FROM AUTHOR]

The lack of labels is one of the major challenges in hyperspectral image (HSI) classification. Widely used Deep Learning (DL) models such as convolutional neural networks (CNNs) experience serious performance degradation when training samples are limited. In contrast, graph convolutional networks (GCNs) can simultaneously exploit the insufficient labeled data and massive unlabeled data of HSI in a semisupervised learning fashion. However, in order to reduce computational cost and mitigate noise, existing GCN-based classification methods usually perform superpixel segmentation as a preprocessing step and implement feature extraction as well as node classification on the predefined superpixel graph, where one superpixel might incorporate pixels with different labels. Moreover, the local spectral–spatial information within superpixels is generally ignored. To alleviate these two issues, we propose an end-to-end mixhop superpixel-based GCN (EMS-GCN) framework for HSI classification. Specifically, we first introduce the differentiable superpixel segmentation algorithm to map the pixel representations into a superpixel feature space, which allows refining the superpixel boundary with the training of the network. After that, a superpixel graph is constructed and fed into a novel mixhop superpixel-based GCN, where both the local information within superpixels and long-range information among superpixels are extracted, while the structure of the superpixel graph is updated at the same time. Finally, the enhanced superpixel representations are mapped back into a pixel feature space to conduct pixel-wise classification. Extensive experiments demonstrate the effectiveness of the proposed EMS-GCN method compared with other state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Classification and identification of the materials lying over or beneath the earth’s surface have long been a fundamental but challenging research topic in geoscience and remote sensing (RS), and have garnered a growing concern owing to the recent advancements of deep learning techniques. Although deep networks have been successfully applied in single-modality-dominated classification tasks, yet their performance inevitably meets the bottleneck in complex scenes that need to be finely classified, due to the limitation of information diversity. In this work, we provide a baseline solution to the aforementioned difficulty by developing a general multimodal deep learning (MDL) framework. In particular, we also investigate a special case of multi-modality learning (MML)—cross-modality learning (CML) that exists widely in RS image classification applications. By focusing on “what,” “where,” and “how” to fuse, we show different fusion strategies as well as how to train deep networks and build the network architecture. Specifically, five fusion architectures are introduced and developed, further being unified in our MDL framework. More significantly, our framework is not only limited to pixel-wise classification tasks but also applicable to spatial information modeling with convolutional neural networks (CNNs). To validate the effectiveness and superiority of the MDL framework, extensive experiments related to the settings of MML and CML are conducted on two different multimodal RS data sets. Furthermore, the codes and data sets will be available at https://github.com/danfenghong/IEEE%5fTGRS%5fMDL-RS , contributing to the RS community. [ABSTRACT FROM AUTHOR]

Recently, multimodal cloud samples are utilized to learn completed feature representations for cloud classification. However, the existing methods neglect the related information from other multimodal cloud samples in the learning process, which leads to inadequate learning. In this article, we propose a novel deep model to learn heterogeneous deep features (HDFs) for multimodal ground-based remote sensing cloud classification. Specifically, we first design the convolutional neural network (CNN) extractor to combine the visual information and the multimodal information (MI) to obtain the CNN-based features of multimodal cloud samples. Afterward, we treat the CNN-based features of multimodal cloud samples as the nodes of graph, and utilize the similarity between nodes as the adjacency matrix. We feed the graph and the adjacency matrix into the graph convolutional network (GCN) extractor to obtain the GCN-based features that could capture correlations among multimodal cloud samples using graph convolutional layers. After obtaining CNN-based features and GCN-based features, we concatenate the two kinds of heterogeneous features to represent the multimodal cloud samples. As a result, the concatenated feature contains the visual information, the MI and the related information among multimodal cloud samples. We conduct a series of experiments on the multimodal ground-based cloud database (MGCD), and the experimental results verify that the proposed HDF outperforms state-of-the-art methods. [ABSTRACT FROM AUTHOR]

The convolutional neural network (CNN) is widely used for image classification because of its powerful feature extraction capability. The key challenge of CNN in remote sensing (RS) scene classification is that the size of data set is small and images in each category vary greatly in position and angle, while the spatial information will be lost in the pooling layers of CNN. Consequently, how to extract accurate and effective features is very important. To this end, we present a Siamese capsule network to address these issues. Firstly, we introduce capsules to extract the spatial information of the features so as to learn equivariant representations. Secondly, to improve the classification accuracy of the model on small data sets, the proposed model utilizes the structure of the Siamese network as embedded verification. Finally, the features learned through Capsule networks are regularized by a metric learning term to improve the robustness of our model. The effectiveness of the model on three benchmark RS data sets is verified by different experiments. Experimental results demonstrate that the comprehensive performance of the proposed method surpasses other existing methods. [ABSTRACT FROM AUTHOR]

The identification of DNA sequences as exon and intron is a common problem in genome analysis. The methods used for feature extraction and mapping techniques for the digitization of sequences affect directly the solution of this problem. The existing mapping techniques are not enough to detect coding and noncoding regions in some genomes because the digital representation of each base in a DNA sequence with an integer does not fully reflect the structure of an original DNA sequence. In the entropy-based mapping technique, we could overcome this problem because the technique deepens distinction rates of exon regions, and better reflects the complexity of DNA sequences. Moreover, in the literature, features are extracted by using various statistical techniques. The statistical features to be extracted are chosen by a system designer's experience. The other proposed approach in this study is to carry out the feature extraction using the transfer learning method. Transfer learning and feature extraction are performed automatically by convolutional neural network models as independent of the data set. In this study, we propose a new method to classify DNA sequences as exon and intron using two approaches. In the first approach, the entropy-based numerical technique was used for the numerical representation of DNA sequences. In the second approach, transfer learning was used to extract features. Then, the obtained features were classified by support vector machine and k -nearest neighbors algorithm. As a result of the classification, accurate performance with 97.8% was achieved. The performance of the current method was compared with the other numerical mapping techniques and feature extraction methods. The results showed that the developed method was much more successful than other methods. [ABSTRACT FROM AUTHOR]

The automatic assessment of the aesthetic value of an image is a task with many applications but really complex and challenging, due to the subjective component of the aesthetics for humans. The computational systems that carry out this task are usually composed of a set of ad hoc metrics proposed by the researchers and a machine learning system. We propose a new approach that fully automates the metrics creation process, its filtering and adjustment without human subjectivity. Thus, it does not depend on the authors' human aesthetic intuitions. Our proposal is therefore based on the integration of two machine learning algorithms: CNN, which works as a feature extractor, and Correlation by Genetic Search (CGS)—a novel regression method, working as a supervised learning method. CGS is based on the creation of an adjusted linear regression model using Pearson's correlation as a measure of performance in an evolutionary process. Experiments were conducted on a very well-known aesthetics database called "Photo.net" with more than a million images from over 400,000 users. The comparison of results with other approaches using the same dataset demonstrates that the fusion of CNN transfer learning features with this specific machine learning method has achieved robust and significantly better results than other state-of-the-art methods and hybrid approaches in terms of AUROC (0.93), accuracy (0.93) and Pearson's correlation value (0.94). [ABSTRACT FROM AUTHOR]

Network traffic classification has become more important with the rapid growth of Internet and online applications. Numerous studies have been done on this topic which have led to many different approaches. Most of these approaches use predefined features extracted by an expert in order to classify network traffic. In contrast, in this study, we propose a deep learning-based approach which integrates both feature extraction and classification phases into one system. Our proposed scheme, called "Deep Packet," can handle both traffic characterization in which the network traffic is categorized into major classes (e.g., FTP and P2P) and application identification in which identifying end-user applications (e.g., BitTorrent and Skype) is desired. Contrary to most of the current methods, Deep Packet can identify encrypted traffic and also distinguishes between VPN and non-VPN network traffic. The Deep Packet framework employs two deep neural network structures, namely stacked autoencoder (SAE) and convolution neural network (CNN) in order to classify network traffic. Our experiments show that the best result is achieved when Deep Packet uses CNN as its classification model where it achieves recall of 0.98 in application identification task and 0.94 in traffic categorization task. To the best of our knowledge, Deep Packet outperforms all of the proposed classification methods on UNB ISCX VPN-nonVPN dataset. [ABSTRACT FROM AUTHOR]

The challenges in hyperspectral image (HSI) classification lie in the existence of noisy spectral information and lack of contextual information among pixels. Considering the three different levels in HSIs, i.e., subpixel, pixel, and superpixel, offer complementary information, we develop a novel HSI feature learning network (HSINet) to learn consistent features by self-supervision for HSI classification. HSINet contains a three-layer deep neural network and a multifeature convolutional neural network. It automatically extracts the features such as spatial, spectral, color, and boundary as well as context information. To boost the performance of self-supervised feature learning with the likelihood maximization, the conditional random field (CRF) framework is embedded into HSINet. The potential terms of unary, pairwise, and higher order in CRF are constructed by the corresponding subpixel, pixel, and superpixel. Furthermore, the feedback information derived from these terms are also fused into the different-level feature learning process, which makes the HSINet-CRF be a trainable end-to-end deep learning model with the back-propagation algorithm. Comprehensive evaluations are performed on three widely used HSI data sets and our method outperforms the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Convolutional neural networks (CNNs) have strong feature extraction capability, which have been used to extract features from the hyperspectral image. Local binary pattern (LBP) is a simple but powerful descriptor for spatial features, which can lessen the workload of CNNs and improve the classification accuracy. In order to make full use of the feature extraction capability of CNNs and the discrimination of LBP features, a novel classification method combining dual-channel CNNs and LBP is proposed. Specifically, a one-dimensional CNN (1D-CNN) is adopted to process original hyperspectral data to extract hierarchical spectral features and another same 1D-CNN is applied to process LBP features to further extract spatial features. Then, the concatenation of two fully connected layers from the two CNNs, which fused features, is fed into a softmax classifier to complete the classification. The experimental results demonstrate that the proposed method can provide 98.52%, 99.54% and 99.54% classification accuracy on the Indian Pines, University of Pavia and Salinas data, respectively. And the proposed method can also obtain good performance even with limited training samples. [ABSTRACT FROM AUTHOR]

In recent years, the development of convolutional neural networks (CNNs) has promoted continuous progress in scene classification of remote sensing images. Compared with natural image datasets, however, the acquisition of remote sensing scene images is more difficult, and consequently the scale of remote sensing image datasets is generally small. In addition, many problems related to small objects and complex backgrounds arise in remote sensing image scenes, presenting great challenges for CNN-based recognition methods. In this article, to improve the feature extraction ability and generalization ability of such models and to enable better use of the information contained in the original remote sensing images, we introduce a multitask learning framework which combines the tasks of self-supervised learning and scene classification. Unlike previous multitask methods, we adopt a new mixup loss strategy to combine the two tasks with dynamic weight. The proposed multitask learning framework empowers a deep neural network to learn more discriminative features without increasing the amounts of parameters. Comprehensive experiments were conducted on four representative remote sensing scene classification datasets. We achieved state-of-the-art performance, with average accuracies of 94.21%, 96.89%, 99.11%, and 98.98% on the NWPU, AID, UC Merced, and WHU-RS19 datasets, respectively. The experimental results and visualizations show that our proposed method can learn more discriminative features and simultaneously encode orientation information while effectively improving the accuracy of remote sensing scene classification. [ABSTRACT FROM AUTHOR]