Vehicle Type Classification using Hybrid Features and a Deep Neural Network

Currently, considerable research has been done in vehicle type classification, especially due to the success of deep learning in many image classification problems. In this research, a system incorporating hybrid features is proposed to improve the performance of vehicle type classification. The feature vectors are extracted from the pre-processed images using Gabor features, a histogram of oriented gradients and a local optimal oriented pattern. The hybrid set of features contains complementary information that could help discriminate between the classes better, further, an ant colony optimizer is utilized to reduce the dimension of the extracted feature vectors. Finally, a deep neural network is used to classify the types of vehicles in the images. The proposed approach was tested on the MIO vision traffic camera dataset and another more challenging real-world dataset consisting of videos of multiple lanes of a toll plaza. The proposed model showed an improvement in accuracy ranging from 0.28% to 8.68% in the MIO TCD dataset when compared to well-known neural network architectures.

Automatic Heart Disease Classification Using Ensemble Features Extraction Mechanism from ECG Signals

The heart disease detection and classification using the cost-effective tool electrocardiogram (ECG) becomes interesting research considering smart healthcare applications. Automation, accuracy, and robustness are vital demands for an ECG-based heart disease prediction system. Deep learning brings automation to the applications like Computer-Aided Diagnosis (CAD) systems with accuracy improvement compromising robustness. We propose the novel ECG-based heart disease prediction system using the hybrid mechanism to satisfy the automation, accuracy, and robustness requirements. We design the model via the steps of pre-processing, hybrid features formation, and classification. The ECG pre-processing is aiming at suppressing the baseline and powerline interference without loss of heartbeats. We propose a hybrid mechanism that consists of handcrafted and automatic Convolutional Neural Network (CNN) lightweight features for efficient classification. The hybrid feature vector is fed to the deep learning classifier Long Term Short Memory (LSTM) sequentially to predict the disease. The simulation results show that the proposed model reduces the diagnosis errors and time compare to state-of-art methods.

A Novel Hybrid Approach Based on Deep CNN to Detect Glaucoma Using Fundus Imaging

Glaucoma is one of the eye diseases stimulated by the fluid pressure that increases in the eyes, damaging the optic nerves and causing partial or complete vision loss. As Glaucoma appears in later stages and it is a slow disease, detailed screening and detection of the retinal images is required to avoid vision forfeiture. This study aims to detect glaucoma at early stages with the help of deep learning-based feature extraction. Retinal fundus images are utilized for the training and testing of our proposed model. In the first step, images are pre-processed, before the region of interest (ROI) is extracted employing segmentation. Then, features of the optic disc (OD) are extracted from the images containing optic cup (OC) utilizing the hybrid features descriptors, i.e., convolutional neural network (CNN), local binary patterns (LBP), histogram of oriented gradients (HOG), and speeded up robust features (SURF). Moreover, low-level features are extracted using HOG, whereas texture features are extracted using the LBP and SURF descriptors. Furthermore, high-level features are computed using CNN. Additionally, we have employed a feature selection and ranking technique, i.e., the MR-MR method, to select the most representative features. In the end, multi-class classifiers, i.e., support vector machine (SVM), random forest (RF), and K-nearest neighbor (KNN), are employed for the classification of fundus images as healthy or diseased. To assess the performance of the proposed system, various experiments have been performed using combinations of the aforementioned algorithms that show the proposed model based on the RF algorithm with HOG, CNN, LBP, and SURF feature descriptors, providing <=99% accuracy on benchmark datasets and 98.8% on k-fold cross-validation for the early detection of glaucoma.

Semi-Natural and Spontaneous Speech Recognition Using Deep Neural Networks with Hybrid Features Unification

Recently, identifying speech emotions in a spontaneous database has been a complex and demanding study area. This research presents an entirely new approach for recognizing semi-natural and spontaneous speech emotions with multiple feature fusion and deep neural networks (DNN). A proposed framework extracts the most discriminative features from hybrid acoustic feature sets. However, these feature sets may contain duplicate and irrelevant information, leading to inadequate emotional identification. Therefore, an support vector machine (SVM) algorithm is utilized to identify the most discriminative audio feature map after obtaining the relevant features learned by the fusion approach. We investigated our approach utilizing the eNTERFACE05 and BAUM-1s benchmark databases and observed a significant identification accuracy of 76% for a speaker-independent experiment with SVM and 59% accuracy with, respectively. Furthermore, experiments on the eNTERFACE05 and BAUM-1s dataset indicate that the suggested framework outperformed current state-of-the-art techniques on the semi-natural and spontaneous datasets.

Discrimination between parkinsonian tremor and essential tremor using artificial neural network with hybrid features

BACKGROUND: Essential tremor (ET) and the tremor in Parkinson’s disease (PD) are the two most common pathological tremors with a certain overlap in the clinical presentation. OBJECTIVE: The main purpose of this work is to use an artificial neural network to select the best features and to discriminate between the two types of tremors. The features used are of hybrid type obtained from two different algorithms: the statistical signal characterization (SSC) of the signal describing its morphology, and the soft-decision wavelet-decomposition (SDWD) features extracted from the accelerometer and surface EMG signals. METHODS: The SSC method is used to obtain morphology-based features of the spectrum of the accelerometer and two surface EMG signals. The SDWD technique is used in this work to obtain the approximate spectral representation of both accelerometer and the two surface EMG signals. Two sets of data (training and test) are used in this paper. The training set consists of 21 ET subjects and 19 PD subjects, while the test set consists of 20 ET and 20 PD subjects. A neural network of the type feed forward back propagation has been used to combine best SSC features and best SDWD features of the accelerometer and EMG signals. RESULTS: Efficiency result of 92.5% was obtained using best hybrid features. CONCLUSIONS: The artificial neural network has been used successfully to combine two types of features in an automatic discrimination system between PD and ET.

Deep Learning and Machine Learning Techniques of Diagnosis Dermoscopy Images for Early Detection of Skin Diseases

With the increasing incidence of severe skin diseases, such as skin cancer, endoscopic medical imaging has become urgent for revealing the internal and hidden tissues under the skin. Diagnostic information to help doctors make an accurate diagnosis is provided by endoscopy devices. Nonetheless, most skin diseases have similar features, which make it challenging for dermatologists to diagnose patients accurately. Therefore, machine and deep learning techniques can have a critical role in diagnosing dermatoscopy images and in the accurate early detection of skin diseases. In this study, systems for the early detection of skin lesions were developed. The performance of the machine learning and deep learning was evaluated on two datasets (e.g., the International Skin Imaging Collaboration (ISIC 2018) and Pedro Hispano (PH2)). First, the proposed system was based on hybrid features that were extracted by three algorithms: local binary pattern (LBP), gray level co-occurrence matrix (GLCM), and wavelet transform (DWT). Such features were then integrated into a feature vector and classified using artificial neural network (ANN) and feedforward neural network (FFNN) classifiers. The FFNN and ANN classifiers achieved superior results compared to the other methods. Accuracy rates of 95.24% for diagnosing the ISIC 2018 dataset and 97.91% for diagnosing the PH2 dataset were achieved using the FFNN algorithm. Second, convolutional neural networks (CNNs) (e.g., ResNet-50 and AlexNet models) were applied to diagnose skin diseases using the transfer learning method. It was found that the ResNet-50 model fared better than AlexNet. Accuracy rates of 90% for diagnosing the ISIC 2018 dataset and 95.8% for the PH2 dataset were reached using the ResNet-50 model.

DrugHybrid_BS: Using Hybrid Feature Combined With Bagging-SVM to Predict Potentially Druggable Proteins

Drug targets are biological macromolecules or biomolecule structures capable of specifically binding a therapeutic effect with a particular drug or regulating physiological functions. Due to the important value and role of drug targets in recent years, the prediction of potential drug targets has become a research hotspot. The key to the research and development of modern new drugs is first to identify potential drug targets. In this paper, a new predictor, DrugHybrid_BS, is developed based on hybrid features and Bagging-SVM to identify potentially druggable proteins. This method combines the three features of monoDiKGap (k = 2), cross-covariance, and grouped amino acid composition. It removes redundant features and analyses key features through MRMD and MRMD2.0. The cross-validation results show that 96.9944% of the potentially druggable proteins can be accurately identified, and the accuracy of the independent test set has reached 96.5665%. This all means that DrugHybrid_BS has the potential to become a useful predictive tool for druggable proteins. In addition, the hybrid key features can identify 80.0343% of the potentially druggable proteins combined with Bagging-SVM, which indicates the significance of this part of the features for research.

Role-Aware Information Spread in Online Social Networks

Understanding the complex process of information spread in online social networks (OSNs) enables the efficient maximization/minimization of the spread of useful/harmful information. Users assume various roles based on their behaviors while engaging with information in these OSNs. Recent reviews on information spread in OSNs have focused on algorithms and challenges for modeling the local node-to-node cascading paths of viral information. However, they neglected to analyze non-viral information with low reach size that can also spread globally beyond OSN edges (links) via non-neighbors through, for example, pushed information via content recommendation algorithms. Previous reviews have also not fully considered user roles in the spread of information. To address these gaps, we: (i) provide a comprehensive survey of the latest studies on role-aware information spread in OSNs, also addressing the different temporal spreading patterns of viral and non-viral information; (ii) survey modeling approaches that consider structural, non-structural, and hybrid features, and provide a taxonomy of these approaches; (iii) review software platforms for the analysis and visualization of role-aware information spread in OSNs; and (iv) describe how information spread models enable useful applications in OSNs such as detecting influential users. We conclude by highlighting future research directions for studying information spread in OSNs, accounting for dynamic user roles.