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Lung or heart sound classification is challenging due to the complex nature of audio data, its dynamic properties of time, and frequency domains. It is also very difficult to detect lung or heart conditions with small amounts of data or unbalanced and high noise in data. Furthermore, the quality of data is a considerable pitfall for improving the performance of deep learning. In this paper, we propose a novel feature-based fusion network called FDC-FS for classifying heart and lung sounds. The FDC-FS framework aims to effectively transfer learning from three different deep neural network models built from audio datasets. The innovation of the proposed transfer learning relies on the transformation from audio data to image vectors and from three specific models to one fused model that would be more suitable for deep learning. We used two publicly available datasets for this study, i.e., lung sound data from ICHBI 2017 challenge and heart challenge data. We applied data augmentation techniques, such as noise distortion, pitch shift, and time stretching, dealing with some data issues in these datasets. Importantly, we extracted three unique features from the audio samples, i.e., Spectrogram, MFCC, and Chromagram. Finally, we built a fusion of three optimal convolutional neural network models by feeding the image feature vectors transformed from audio features. We confirmed the superiority of the proposed fusion model compared to the state-of-the-art works. The highest accuracy we achieved with FDC-FS is 99.1% with Spectrogram-based lung sound classification while 97% for Spectrogram and Chromagram based heart sound classification.

With the increasing use of open-source libraries and secondary development, software projects face security vulnerabilities. Existing studies on source code vulnerability detection rely on natural language processing techniques, but they overlook the intricate dependencies in programming languages. To address this, we propose a framework called Context and Multi-Features-based Vulnerability Detection (CMFVD). CMFVD integrates source code graphs and textual sequences, using a novel slicing method called Context Slicing to capture contextual information. The framework combines graph convolutional networks (GCNs) and bidirectional gated recurrent units (BGRUs) with attention mechanisms to extract local semantic and syntactic information. Experimental results on Software Assurance Reference Datasets (SARDs) demonstrate CMFVD’s effectiveness, achieving the highest F1-score of 0.986 and outperforming other models. CMFVD offers a promising approach to identifying and rectifying security flaws in large-scale codebases.

A new approach to achieve fault diagnosis and prognosis of bearing based on hidden Markov model (HMM) with multi-features is proposed. Firstly, the time domain, frequency domain, and wavelet packet decomposition are utilized to extract the condition features of bearing vibration signals, and the PCA method is merged into multi-features to reduce their dimensionality. Then the low-dimensional features are processed to obtain the scalar probabilities of each bearing condition, which are multiplied to generate the observed values of HMM. The results reveal that the established approach can well diagnose fault conditions and achieve the remaining life estimation of bearing.

This study aims to highlight the effectiveness of computer vision (CV) techniques in classifying brain tumors using a comprehensive dataset consisting of computed tomography (CT) scans. The proposed framework comprises six types of brain tumors, including benign tumors (Meningioma, Schwannoma, and Neurofibromatosis) and malignant tumors (Glioma, Chondrosarcoma, and Chordoma). The acquired images underwent pre-processing steps to enhance the dataset’s quality, including noise reduction through median and Gaussian filters and region of interest (ROIs) extraction using an automated binary threshold-based fuzzy c-means segmentation (ABTFCS) approach. A total of 900 CT-scan images were utilized, 150 images per tumor class, each with a size of 512 × 512 pixels, and 4 ROIs taken per image, so the total dataset size is 3600 (900 × 4) attributes. After pre-processing, the dataset was further analysed to extract 135 statistical multi-features for each ROI. An optimized set of 12 statistical multi-features was selected to identify the most relevant features using a feature selection technique based on correlation. For the classification stage, the optimized statistical multi-feature dataset was evaluated using five computer vision classifiers: multilayer perceptron (MLP), BayesNet, PART, random tree, and randomizable filtered classifier, employing a 10-fold cross-validation method. Among these classifiers, MLP with fine-tuned hyperparameters achieved a promising accuracy rate of 97.83%.

Drought stress (DS) is one of the most frequently occurring stresses in tomato plants. Detecting tomato plant DS is vital for optimizing irrigation and improving fruit quality. In this study, a DS identification method using the multi-features of hyperspectral imaging (HSI) and subsample fusion was proposed. First, the HSI images were measured under imaging condition with supplemental blue lights, and the reflectance spectra were extracted from the HSI images of young and mature leaves at different DS levels (well-watered, reduced-watered, and deficient-watered treatment). The effective wavelengths (EWs) were screened by the genetic algorithm. Second, the reference image was determined by ReliefF, and the first four reflectance images of EWs that are weakly correlated with the reference image and mutually irrelevant were obtained using Pearson’s correlation analysis. The reflectance image set (RIS) was determined by evaluating the superposition effect of reflectance images on identification. The spectra of EWs and the image features extracted from the RIS by LeNet-5 were adopted to construct DS identification models based on support vector machine (SVM), random forest, and dense convolutional network. Third, the subsample fusion integrating the spectra and image features of young and mature leaves was used to improve the identification further. The results showed that supplemental blue lights can effectively remove the high-frequency noise and obtain high-quality HSI images. The positive effect of the combination of spectra of EWs and image features for DS identification proved that RIS contains feature information pointing to DS. Global optimal classification performance was achieved by SVM and subsample fusion, with a classification accuracy of 95.90% and 95.78% for calibration and prediction sets, respectively. Overall, the proposed method can provide an accurate and reliable analysis for tomato plant DS and is hoped to be applied to other crop stresses

Aiming at the problem that it is difficult to extract fault features from the nonlinear and non-stationary vibration signals of wind turbine rolling bearings, which leads to the low diagnosis and recognition rate, a feature extraction method based on multi-island genetic algorithm (MIGA) improved variational mode decomposition (VMD) and multi-features is proposed. The decomposition effect of the VMD method is limited by the number of decompositions and the selection of penalty factors. This paper uses MIGA to optimize the parameters. The improved VMD method is used to decompose the vibration signal into a number of intrinsic mode functions (IMF), and a group of components containing the most information is selected through the Holder coefficient. For these components, multi-features based on Renyi entropy feature, singular value feature, and Hjorth parameter feature are extracted as the final feature vector, which is input to the classifier to realize the fault diagnosis of rolling bearing. The experimental results prove that the proposed method can more effectively extract the fault characteristics of rolling bearings. The fault diagnosis model based on this method can accurately identify bearing signals of 16 different fault types, severity, and damage points.

As a vital part of the Eurasian temperate grassland, the Chinese temperate grassland is primarily distributed in the Inner Mongolia Plateau. This paper focuses on mapping temperate grassland dynamics from the 1980s to the 2010s in Inner Mongolia, which was divided into temperate meadow steppe (TMS), temperate typical steppe (TTS), temperate desert steppe (TDS), temperate steppe desert (TSD) and temperate desert (TD). Multi-source features, including multispectral reflectance, vegetation growth, topography, water bodies, meteorological data, and soil characteristics, were selected based on their distinct physical properties and remote sensing variations. Then, we applied deep neural network (DNN) models to classify them, achieving an accuracy of 79.4% in the 1980s and 81.1% in the 2000s. Additionally, validation in the 2010s through field reconnaissance demonstrated an accuracy of 72.7%, which was acceptable, confirming that DNN is an effective method for classifying temperate grasslands. The results revealed that TTS had the highest proportion in the study area (39%), while TMS and TSD had the lowest (8.2% and 8.1%, respectively). Grassland types have the distribution law of aggregation; according to statistics, 61.1% of the grassland area remained unchanged, and the transition zone between adjacent grassland classes was highly easy to change. The area variation mainly came from TTS, TDS, and TSD, but not TD. The mutual transformation of different grassland types occurred mainly in adjacent areas between them. This study demonstrates the potential of DNN for long-term grassland mapping and provides the most comprehensive classification maps of Inner Mongolia grasslands to date, which are invaluable for grassland research and conservation efforts in the area.

For effective and secure access of Internet, the Border Gateway Protocol (BGP) has to be capable to identify and stop odd concurrences in realistic time. Despite the fact that more studies were done over the precedent 10 years to find out anomalies in BGP, the issue is still demanding since attackers and network misconfigurations frequently exhibit new, peculiar behavior. The following two main parts establishes a novel BGP anomaly detection model: It reads, \"(i) Feature Extraction; (ii) Anomaly Detection.\"Extensive features, such as \"statistical features,\" \"higher-order statistical features,\" \"improved holo-entropy features,\" and \"correntropy features\" are retrieved to improve the detection's accuracy and dependability. Next, the proposed DBN is deployed to identify the existence or absence of an anomaly. Furthermore, a hybrid RHMFO Optimization is used to fine-tune the weight of DBN in order to improve classification accuracy. The DBN result lets us know whether there are network anomalies or not. Finally, analysis is done, where, accuracy of the DBN + RHMFO is ( ~) 97%, which is 12.3%, 27.83%, 48.4%, 69.07%, and 51.5% improved than MLP-NN, SVM-BGPAD, DBN + ROA, DBN + EHO, and DBN + MFO, respectively.

This study describes the potential of computer vision (CV) approaches for liver tumor classification. Two-dimensional (2D) computed Tomography (CT) images dataset of benign and malignant liver tumors that were cholangiocarcinoma, focal nodular hyperplasia, hepatic adenoma, hemangioma, hepatoblastoma, and hepatocellular carcinoma was acquired for this study. The CT dataset comprising 150 images, each sized at (512 × 512), encompassing various types of liver tumors. This dataset consisted of a total of 900 (150 × 6) CT images representing six benign and malignant liver tumor types. To enhance data quality, a Mean filter was applied for noise reduction, followed by the selection of two regions of interest (ROIs) from each liver image. Subsequently, the preprocessed data was subjected to feature extraction, resulting in 67 multi-features per image, incorporating histogram, spectral, and texture features. From these features, 21 optimized multi-features were derived through the implementation of a correlation-based feature selection (CFS) algorithm. These optimized multi-features formed the basis for analysis and were fed into six classifiers: multilayer perceptron (MLP), logistic regression, random subspace, decision tree, produce error reduction, and multiclass classifier. The performance evaluation of these classifiers was conducted using 10-fold cross-validation techniques. The MLP showed a better accuracy of 97.67% on the optimized feature dataset among all the deployed CV classifiers. The experimental findings indicated that the suggested approach was systematic and resilient, offering valuable assistance to radiologists in detecting liver tumor diseases through CT Dataset images, even amid differing imaging standards.