Vehicle Classification Using Deep Feature Fusion and Genetic Algorithms

The classification of hyperspectral images (HSIs) is one of the most popular topics in the remote sensing community. Numerous feature extraction methods have been proposed to improve the classification accuracy of HSIs. Recently, deep features extracted by convolution neural network (CNN) have been introduced into the classification process of HSIs. Due to the nonlinear and invariant advantages of the features, CNN methods provide a powerful tool for representing geographic objects and classifying HSIs. However, traditional deep features only extracted at pixel-level and often neglect multiscale characteristics of geographic objects. In this study, a new deep feature extraction method is proposed, which takes advantage of multi-scale object analysis and the CNN model. Firstly, multiscale image objects are obtained by the multiscale segmentation algorithm and multiscale low-level features of objects are extracted. Secondly, the CNN is devoted to obtain deep features from low-level object features at each scale, respectively. Thirdly, the obtained deep features at all scales are stacked and fed to one fully connected layer to extract the multiscale deep learning features for classification. Finally, the logistic regression classifier is applied to hyperspectral image (HSI) classification based on object-oriented multiscale deep features. The proposed method was carried out on three widely used hyperspectral data sets: University of Pavia, Salinas, and Washington DC. The results reveal that the proposed method provides better results than other state-of-the-art methods.

Aerial scene classification is an active and challenging problem in high-resolution remote sensing imagery understanding. Deep learning models, especially convolutional neural networks (CNNs), have achieved prominent performance in this field. The extraction of deep features from the layers of a CNN model is widely used in these CNN-based methods. Although the CNN-based approaches have obtained great success, there is still plenty of room to further increase the classification accuracy. As a matter of fact, the fusion with other features has great potential for leading to the better performance of aerial scene classification. Therefore, we propose two effective architectures based on the idea of feature-level fusion. The first architecture, i.e., texture coded two-stream deep architecture, uses the raw RGB network stream and the mapped local binary patterns (LBP) coded network stream to extract two different sets of features and fuses them using a novel deep feature fusion model. In the second architecture, i.e., saliency coded two-stream deep architecture, we employ the saliency coded network stream as the second stream and fuse it with the raw RGB network stream using the same feature fusion model. For sake of validation and comparison, our proposed architectures are evaluated via comprehensive experiments with three publicly available remote sensing scene datasets. The classification accuracies of saliency coded two-stream architecture with our feature fusion model achieve 97.79%, 98.90%, 94.09%, 95.99%, 85.02%, and 87.01% on the UC-Merced dataset (50% and 80% training samples), the Aerial Image Dataset (AID) (20% and 50% training samples), and the NWPU-RESISC45 dataset (10% and 20% training samples), respectively, overwhelming state-of-the-art methods.

This paper proposed a novel approach for detecting lung sound disorders using deep learning feature fusion. The lung sound dataset are oversampled and converted into spectrogram images. Then, extracting deep features from CNN architectures, which are pre-trained on large-scale image datasets. These deep features capture rich representations of spectrogram images from the input signals, allowing for a comprehensive analysis of lung disorders. Next, a fusion technique is employed to combine the extracted features from multiple CNN architectures totlaly 8064 feature. This fusion process enhances the discriminative power of the features, facilitating more accurate and robust detection of lung disorders. To further improve the detection performance, an improved CNN Architecture is employed. To evaluate the effectiveness of the proposed approach, an experiments conducted on a large dataset of lung disorder signals. The results demonstrate that the deep feature fusion from different CNN architectures, combined with different CNN Layers, achieves superior performance in lung disorder detection. Compared to individual CNN architectures, the proposed approach achieves higher accuracy, sensitivity, and specificity, effectively reducing false negatives and false positives. The proposed model achieves 96.03% accuracy, 96.53% Sensitivity, 99.424% specificity, 96.52% precision, and 96.50% F1 Score when predicting lung diseases from sound files. This approach has the potential to assist healthcare professionals in the early detection and diagnosis of lung disorders, ultimately leading to improved patient outcomes and enhanced healthcare practices.

Determining potential applications for personalized college English instruction through deep feature extraction and scene feature mining is essential for enabling Deep Learning assisted AI systems to generate effective educational outcomes. Traditional instructional approaches often struggle within dynamic digital environments, as they overlook variations in students’ learning styles, engagement levels, and behavioral patterns. This research proposes a hybrid methodology that integrates Convolutional Neural Networks for deep audio feature extraction with Random Forest algorithms for contextual and scene feature mining. The objective is to construct a personalized learning environment capable of adapting dynamically in real time to individual learner behaviors and preferences. A hybrid deep learning model is introduced to predict teaching effectiveness using speech related features such as clarity, rhythm, and engagement level. Feature fusion is employed by combining audio based deep features with contextual scene features to classify teaching effectiveness into three categories: low, medium, and high. Experimental results demonstrate strong predictive performance, achieving an accuracy of 0.9900, precision of 0.9906, recall of 0.9907, and an F1 measure of 0.9905, with minimal performance fluctuations. This AI assisted instructional framework enables college English teachers to enhance teaching quality by receiving immediate, data driven feedback, ultimately fostering improved student centered learning experiences.

e20611 Background: Predicting immunotherapy response in advanced non-small cell lung cancer (NSCLC) through non-invasive means could be a groundbreaking advancement. Deep features extracted from medical images by deep Convolutional Neural Networks (CNNs) along with radiomics have demonstrated promising results in different oncologic diseases. However, the method for extracting these deep features influences the predictive capabilities of the model. Methods: A retrospective analysis of a multicenter study was conducted in advanced NSCLC patients with high PD-L1 expression with the aim of developing an imaging features-based model for the prediction of best overall response (BOR) to a checkpoint inhibitor immunotherapy. All visible lung lesions, beyond RECIST 1.1, were manually segmented in baseline CT scans. Radiomics features were extracted at lesion-level, and various preprocessing methods and CNN architectures were tested for deep features extraction. Radiomics and deep features were harmonized using ComBat methodology. A distinctive feature signature was created for each patient by calculating the mean across all lesions. Using this data, machine learning models were trained to predict treatment response, specifically distinguishing between complete response (CR) and partial response (PR) from stable disease (SD) and progressive disease (PD). The study compared the performance of different deep features extraction methods and assessed shapley additive explanations (SHAP) values of radiomics for interpretability. Results: In the cohort of 116 patients (mean age: 67, 93% ever-smokers, 73% male), 1024 to 5475 quantitative imaging features per lesion were extracted. Among these patients, 67 patients were CR+PR, and 49 were SD+PD. The top-performing predictive model, trained with radiomics and deep features from a deep convolutional autoencoder, achieved an average AUC of 85%. SHAP values showed that impactful radiomics features were correlated with intensity changes in adjacent pixels describing the tumor heterogeneity, along with shape features potentially associated with malignancy. Ultimately, combining both features demonstrated statistical significance, leading to a performance improvement of ~2-5% compared to models exclusively using deep features. Conclusions: Diverse techniques for extracting deep features were evaluated, revealing autoencoder training as the most effective strategy for predicting immunotherapy response. Consequently, the findings suggest that employing deep learning to extract imaging features from baseline CT scans, combined with radiomics, represents a promising non-invasive approach for the predicting immunotherapy outcomes in advanced NSCLC patients.

In recent years video surveillance has gained significant attention due to its versatile applications in security and traffic flow management. Vehicle Re-Identification (Re-ID) is a complicated task in Intelligent Transportation Systems (ITS) where vehicles are tracked by multiple cameras in non-overlapping views. In re-identification the targeted vehicle is matched with the query image from the gallery of vehicle images. There are many uncertainties involved in Re-ID due to inter- class and intra-class similarity because of the viewpoint changes. Large number of studies have explored the deep learning techniques for vehicle detection but classification of vehicle images and re-identifying them is still a field of exploration. In this paper we have analyzed the issue of fine grained vehicle classification and thereby re-identifying them using different CNN models. Vehicle images are pre-processed by different data augmentation techniques and then transfer learning is applied for testing the Stanford cars dataset on three models namely ResNet152, ResNet50 and DenseNet201. The accuracy is obtained for all the models and ResNet152 produces a highest accuracy of 89% among the three models.

A Novel Medical Diagnosis model for COVID-19 infection detection based on Deep Features and Bayesian Optimization

Temporal lobe injury (TLI), a form of nervous system damage in the brain, is a major neurological complication after radiation therapy (RT). TLI must be highly valued because of the irreversible brain injury. This article aims to develop a predictive pipeline, called deep longitudinal feature representations (DLFR), to detect TLI at the presymptomatic stage accurately via the learning of effective deep longitudinal feature representations. DLFR characterizes high-level information and developmental changes within and across subjects The DLFR consists of four components: (i) extraction of deep features from a pretrained ResNet50 model; (ii) compression of learned highly representative features by the global max pooling; (iii) fusion of deep longitudinal features for the fully use of all follow-up data; (iv) random forest-based prediction of the diagnostic status. In total, 244 nasopharyngeal carcinoma patients before and after RT with a follow-up period of 0 ~ 9 years were included for analysis. All patients were divided into four different latency groups, and the current latency was used for training to predict the diagnostic status of the next latency. The AUCs of the predicted three different latency groups using DLFR were 0.64 ± 0.11, 0.76 ± 0.10, and 0.88 ± 0.05, while those of radiomics features were 0.56 ± 0.06, 0.63 ± 0.03, and 0.53 ± 0.04, and those of histogram of oriented gradients features were 0.60 ± 0.09, 0.52 ± 0.03, and 0.58 ± 0.06. Most importantly, the AUCs of the predicted three different latency groups for white matter regions were 0.66 ± 0.10, 0.80 ± 0.09, and 0.78 ± 0.09. Our proposed method can dynamically detect TLI at the presymptomatic stage, which can enable the administration of preventive neurological intervention.

Fusing learned representations from Riesz Filters and Deep CNN for lung tissue classification.

Single image super‐resolution (SISR) has been revolutionized by convolutional neural networks (CNN). However, existing SISR algorithms have feature extraction and adaptive adjustment limitations, leading to information duplication and unsatisfactory image reconstruction. In this paper, we propose a deep and adaptive feature extraction attention network (DAAN), which first fully extracts shallow features and then adaptively captures precise and fine‐scale features by a deep feature extraction block (DFEB). It includes multi‐dimensional feature extraction blocks (MFEBs) that combine large kernel and dynamic convolution layers to improve large‐scale information utilization effectively. Finally, an enhanced spatial attention block (ESAB) to further selectively reinforce the transmission of details. A large number of experimental results show that our proposed model reconstruction performance is superior to existing classical methods.

Wind speed forecasting is important for high‐efficiency utilisation of wind energy and management of grid‐connected power systems. Due to the noise, instability and irregularity of atmosphere system, the current models based on raw historical data have encountered many problems. In this study, a deep novel feature extraction approach is developed based on stacked denoising autoencoders and batch normalisation. Then the deep features extracted from raw historical data are fed to long short‐term memory (LSTM) neural networks for prediction. Meanwhile, density‐based spatial clustering of applications with noise is employed to process the numerical weather prediction data. By picking out the abnormal samples, the representative training samples are selected to improve the efficiency of the model. For illustration and verification purposes, the proposed model is used to predict the wind speed of Wind Atlas for South Africa (WASA). Empirical results show that deep feature extraction can improve the forecasting accuracy of LSTM 49% than feature selection, indicating that proper feature extraction is crucial to wind speed forecasting. And the proposed model outperforms other benchmark methods at least 17%. Hence, the proposed model is promising for wind speed forecasting.

Extracting disease characteristics from large-scale Electronic Medical Records and achieving disease-assisted diagnoses have significant research value. Due to the complex multi-feature items and unbalanced data distribution of Electronic Medical Records, feature representation and disease diagnosis are difficult. Our study proposes a deep feature fusion (DFF) model based on the feature partition and deep feature extraction. First, the feature partition is performed, and different feature representation algorithms are adopted for different types of data. The discrete feature items are directly mapped into real-valued vectors, and the continuous feature items are represented by GCNN-based VAE. Then, the two parts are fused. Finally, the assisted diagnosis results are output through a supervised learning classification method based on the XGBoost framework. The dataset of our study is from the 18,590 real and effective clinical Electronic Medical Record of Huangshi Central Hospital. The experimental results show that the method can perform clinical Assisted diagnosis accurately and efficiently, which are superior to some other state-of-the-art approaches, can better meet the needs of practical clinical diagnosis applications.

In the daily bidding activities, the bid price is estimated by using qualitative and quantitative predicting methods, which are usually poor in accuracy and efficiency. In this paper, we introduce a method based on the Convolutional Neural Networks (CNNs) for the bid price estimation decision. We collect the historical bidding price data from similar projects and then form a time series according to the order of the project start time to extract deep CNN features, and finally, these deep features are used to make bidding decision. We demonstrate that the learned deep models will help the decision makers to get the trend of project quotation and improve the bid winning probability and competitiveness of the bidder.

Deep manifold feature fusion for classification of breast histopathology images

Deep learning approaches for COVID-19 detection based on chest X-ray images