Sketch recognition using transfer learning

This paper proposes a combined framework of CNN+RFC to brain tumor categorization/classification using MRI (Magnetic-Resonance Imaging) images, which combines both CNN (Convolution Neural Networks) and RFC (Random Forest Classification). Preprocessing, Feature bring-out, and Categorization are the three phases of the proposed framework. In the first step, we use the Gaussian Filter Method on the data set then we combine the original data set with processed data in parallel. The feature extraction of magnetic resonance imaging was performed automatically by CNN in the second step. We also called such a type of process in this paper as non-hand-crafted feature extraction. Several classification algorithms, including RFC (Random Forest Classifier), KNN (K-Nearest Neighbor Classifier), DT (Decision Tree Classifier), SVM (Support Vector Machine Classifier), and NB (Naïve Bayes Classifier), are used in the final step. The extracted features from the CNN model are then given to the classifier algorithms, which predict Glioma tumor, Pituitary tumor, Meningioma tumor, and no tumor as a result of the testing data set. Experiments are carried out on an open data set of images selected for classification from the Kaggle databases. This data set is very complex since it contains images of brain tumor with different angles and different depths. We don't alter this data set at all. We make a separate CSV file that contains testing images' name and their specification. Using this proposed approach, we were able to achieve 99.61% accuracy on the training data set, 92.16% on the validation data, and 71.2% on the CSV/testing data.

The fusion of convolutional neural networks (CNNs) and vision transformers (ViTs) is a powerful strategy for remote sensing scene classification (RSSC), leveraging their complementary strengths in local texture and global context modeling. To fully exploit these complementary strengths for addressing the inter-class similarity and high intra-class variability caused by the complex spatial arrangements of RS scenes, this work introduces the hierarchically aligned bidirectional CNN-Transformer network (HAB-Net), which fosters deeper, two-way integration through two novel components. First, Hierarchical Attention Gating (HAG) distills multi-level attention from the ViT into spatial gates, compelling the CNN to focus on semantically critical regions. In turn, CNN-driven Feedback Attention (CFA) completes the loop by feeding these enhanced, detail-rich CNN features back into the ViT, refining its tokens via cross-attention. Ultimately, a dual-stream head merges these mutually reinforced features for scene classification. Extensive experiments on the UCM, AID, and NWPU-RESISC45 benchmarks demonstrate that HAB-Net achieves state-of-the-art performance. Ablation studies confirm the indispensable contributions of both HAG and CFA, validating the effectiveness of the proposed deep bidirectional integration strategy.

In an era where COVID-19's varied radiological presentations challenge diagnostic precision, this paper introduces a novel fusion of Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) models to refine detection capabilities. The paper capitalizes on an expansive radiography database, utilizing lung segmentation and data augmentation to bolster the training set. Advanced CNN models extract salient features, which are then classified through meticulously optimized machine learning algorithms. The synergy between diverse CNN architectures and Bayesian Optimization markedly enhances hyperparameter tuning, culminating in a significant uplift in diagnostic accuracy. The integration of LSTM models further allows for nuanced severity assessments, paving the way for more personalized and effective pandemic management. This research heralds a new frontier in medical diagnostics, promising a leap in the accuracy of pandemic detection.

As cyberattacks become more advanced, conventional centralized threat intelligence models often fail to keep up with these threats’ growing complexity and frequency, highlighting the requirement for innovative approaches to strengthen cybersecurity resilience. Federated learning (FL), a decentralized machine learning (ML) model, provides a promising solution by permitting spread objects to train techniques on local data collaboratively without distributing sensitive data. The efficiency of FL in enhancing attack intelligence skills emphasizes its probability of driving a novel period of robust and privacy-protecting cybersecurity practices. Furthermore, combining FL into cybersecurity structures can strengthen attack intelligence models by permitting real upgrades and adaptive learning mechanisms. Recently, ML and Deep Learning (DL) approaches have drawn the study community to advance security solutions for cyberattack defence mechanism models. Conventional ML and DL techniques that function with data kept on a federal server increase the main privacy issues for user information. This manuscript presents a Cyberattack Defence Mechanism System for Federated Learning Framework using Attention Induced Deep Convolution Neural Networks (CDMFL-AIDCNN) technique. The CDMFL-AIDCNN model presents an improved structure incorporating self-guided FL with attack intelligence to improve defence mechanisms across varied cybersecurity applications in distributed systems. Initially, the data preprocessing stage utilizes Z-score normalization to transform input data into a beneficial format. The Dung Beetle Optimization (DBO) technique is used in the feature selection process to identify the most relevant and non-redundant features. Furthermore, the fusion of convolutional neural networks, bidirectional long short-term memory, gated recurrent units, and attention (CBLG-A) models are employed to classify cyberattack defence mechanisms. Finally, the parameter tuning of the CBLG-A approach is performed by the growth optimizer (GO) approach. The CDMFL-AIDCNN technique is extensively analyzed using the CIC-IDS-2017 and UNSW-NB15 datasets. The comparison analysis of the CDMFL-AIDCNN technique portrayed a superior accuracy value of 99.07% and 98.64% under the CIC-IDS-2017 and UNSW-NB15 datasets.

This paper offers a new methodology for the detection of casting defects through the fusion of Convolutional Neural Networks (CNNs) and Auto-Encoders to extract features and detect anomalies. Through Utilization of a heterogeneous dataset, the model outperforms conventional methods of defect detection, with a 91% accuracy. Contrary to visual inspection and simple algorithms, this deep learning technique driven by data enhances detection accuracy and minimizes false positives. The model is versatile in various casting situations and can be used as a scalable quality control tool. Future work will involve improving model performance, solving data imbalance issues, and applying it to more general manufacturing processes.

This study presents a novel approach using Wasserstein Generative Adversarial Networks with Gradient Penalty (WGAN-GP) to generate synthetic electroencephalography (EEG) and electrocardiogram (ECG) waveforms. The synthetic EEG data represent concentration and relaxation mental states, while the synthetic ECG data correspond to normal and abnormal states. By addressing the challenges of limited biophysical data, including privacy concerns and restricted volunteer availability, our model generates realistic synthetic waveforms learned from real data. Combining real and synthetic datasets improved classification accuracy from 92% to 98.45%, highlighting the benefits of dataset augmentation for machine learning performance. The WGAN-GP model achieved 96.84% classification accuracy for synthetic EEG data representing relaxation states and optimal accuracy for concentration states when classified using a fusion of convolutional neural networks (CNNs). A 50% combination of synthetic and real EEG data yielded the highest accuracy of 98.48%. For EEG signals, the real dataset consisted of 60-s recordings across four channels (TP9, AF7, AF8, and TP10) from four individuals, providing approximately 15,000 data points per subject per state. For ECG signals, the dataset contained 1200 real samples, each comprising 140 data points, representing normal and abnormal states. WGAN-GP outperformed a basic generative adversarial network (GAN) in generating reliable synthetic data. For ECG data, a support vector machine (SVM) classifier achieved an accuracy of 98% with real data and 95.8% with synthetic data. Synthetic ECG data improved the random forest (RF) classifier’s accuracy from 97% with real data alone to 98.40% when combined with synthetic data. Statistical significance was assessed using the Wilcoxon signed-rank test, demonstrating the robustness of the WGAN-GP model. Techniques such as discrete wavelet transform, downsampling, and upsampling were employed to enhance data quality. This method shows significant potential in addressing biophysical data scarcity and advancing applications in assistive technologies, human-robot interaction, and mental health monitoring, among other medical applications.

This study aim was to explore the application effect of computed tomography (CT) image segmentation based on deep learning algorithm in the diagnosis of lung cancer. In this study, a two-dimensional (2D) convolutional neural network (CNN) and three-dimensional (3D) CNN fusion model was constructed firstly. Subsequently, 60 patients with lung cancer were randomly divided into a control group and an intervention group to receive perioperative routine nursing and rehabilitation nursing, respectively. The results revealed that the Dice value (0.876), sensitivity (0.849), and positive predictive value (PPV) (0.875) of the hybrid feature fusion model (HFFM) constructed in this study for lung cancer CT image segmentation were higher than those of other models, and the accuracy rate for lung cancer diagnosis was 96.7%. After nursing intervention, the partial arterial oxygen pressure (PaO2) and partial arterial carbon dioxide pressure (PaCO2) in the control group were 80.54 mmHg and 39.81 mmHg, respectively, while those in the intervention group were 83.09 mmHg and 36.75 mmHg, respectively. After intervention, the maximal voluntary ventilation (MVV) %, forced vital capacity (FVC) %, and forced expiratory volume in 1 sec (FEV1) %) in the intervention group were 76.03%, 82.14%, and 89.76%, respectively. It suggested that compared with the control group, the pulmonary function indexes of the intervention group improved significantly after nursing intervention ( P < 0.05). In summary, the HFFM constructed in this study can be used for segmentation and classification of CT images of lung cancer patients, which can improve the accuracy of diagnosis and help improve the lung function and quality of life of patients.

In this paper, a novel multimodal convolutional neural network (CNN) fusion architecture is proposed for heart sound signal classification. Instead of using features from just one domain, general frequency features as well as Mel domain features are extracted from the raw heart sound. The multimodal CNN fusion architecture is individually trained based on the feature maps resulting from various feature extraction methods. These feature maps are then merged for optimizing the diversified extracted features. The proposed method provides an opportunity to explore the optimal selection of features for heart sound classification. Extensive experimentations are carried out, showing that an outstanding accuracy of 98.5% is achieved by the multimodal CNN architecture, which outperforms the other state-of-the-art approaches.

The precipitation oxygen isotopic composition is a useful environmental tracer for climatic and hydrological studies. However, the observed precipitation oxygen is limited at both temporal and spatial scales. Isotope-equipped general circulation models (iGCMs) can compensate for the temporal and spatial discontinuity of observation networks, but they suffer from coarse spatial resolutions and systematic biases. The objective of this study is to build a high-resolution precipitation oxygen isoscape in China for a period of 148 years by integrating observed and iGCMs-simulated precipitation oxygen isotope composition (δ18Op) using data fusion and bias correction methods. The temporal and spatial resolutions are month and 50–60 km for the isoscape, respectively. Prior to building the oxygen isoscape, the performance of two bias correction methods (BCMs) and three data fusion methods (DFMs) is compared after post-processing of eight iGCM simulations. Results show that the outputs of the Convolutional Neural Networks (CNN) fusion method exhibit the strongest correlation with observations with correlation coefficient mostly larger than 0.8, and the smallest bias with root mean square error mostly smaller than 2 ‰. The other two DFMs also perform slightly better than the two BCMs, which show similar performance. Thus, precipitation oxygen isoscape is generated by using the CNN fusion method for the 1969–2007 period in which all iGCMs have output and by using the bias correction methods for the remaining years. Based on the precipitation oxygen isoscape, the spatiotemporal patterns of δ18Op across China are investigated. The generated isoscape shows similar spatial and temporal distribution characteristics to observations. In general, the distribution pattern of δ18Op is consistent with the temperature effect in northern China, and with the precipitation amount effect in southern China, and be more specific in the Qinghai-Tibet Plateau of China. The δ18Op time series mirrors a fluctuating upward trend of the temperature or precipitation in most regions of China. The temporal and spatial distribution characteristics of the generated isoscape are consistent with the characteristics of atmospheric circulation and climate change, indicating successful assimilation and extension of the observed precipitation oxygen isotopes in time and space. Overall, the built isoscape is reliable and useful for providing strong support for tracking atmospheric and hydrological processes. The dataset is available in Zenodo at https://doi.org/10.5281/zenodo.5703811 (Chen et al., 2021).

The precipitation oxygen isotopic composition is a useful environmental tracer for climatic and hydrological studies. However, the observed precipitation oxygen is limited at both temporal and spatial scales. Isotope-equipped general circulation models (iGCMs) can compensate for the temporal and spatial discontinuity of observation networks, but they suffer from coarse spatial resolutions and systematic biases. The objective of this study is to build a high-resolution precipitation oxygen isoscape in China for a period of 148 years by integrating observed and iGCMs-simulated precipitation oxygen isotope composition (δ18Op) using data fusion and bias correction methods. The temporal and spatial resolutions are month and 50–60 km for the isoscape, respectively. Prior to building the oxygen isoscape, the performance of two bias correction methods (BCMs) and three data fusion methods (DFMs) is compared after post-processing of eight iGCM simulations. Results show that the outputs of the Convolutional Neural Networks (CNN) fusion method exhibit the strongest correlation with observations with correlation coefficient mostly larger than 0.8, and the smallest bias with root mean square error mostly smaller than 2 ‰. The other two DFMs also perform slightly better than the two BCMs, which show similar performance. Thus, precipitation oxygen isoscape is generated by using the CNN fusion method for the 1969–2007 period in which all iGCMs have output and by using the bias correction methods for the remaining years. Based on the precipitation oxygen isoscape, the spatiotemporal patterns of δ18Op across China are investigated. The generated isoscape shows similar spatial and temporal distribution characteristics to observations. In general, the distribution pattern of δ18Op is consistent with the temperature effect in northern China, and with the precipitation amount effect in southern China, and be more specific in the Qinghai-Tibet Plateau of China. The δ18Op time series mirrors a fluctuating upward trend of the temperature or precipitation in most regions of China. The temporal and spatial distribution characteristics of the generated isoscape are consistent with the characteristics of atmospheric circulation and climate change, indicating successful assimilation and extension of the observed precipitation oxygen isotopes in time and space. Overall, the built isoscape is reliable and useful for providing strong support for tracking atmospheric and hydrological processes. The dataset is available in Zenodo at https://doi.org/10.5281/zenodo.5703811 (Chen et al., 2021).

To overcome the shortcomings of traditional manual counting methods, which are labor-intensive, resource-consuming, and inefficient, this study introduces a computer-based counting model. This model integrates convolutional neural networks (CNNs) with Transformer networks to efficiently recognize and count specific target objects in large-scale data scenarios. This approach leverages CNNs for local feature extraction and incorporates Transformer networks to capture long-range global information, achieving a synergistic effect. The methodology includes key steps such as “CNN for feature extraction and Transformer for global attention.” The experiment outcomes show that the model has an average absolute error of 10.13, a root mean square error of 12.08, an average counting accuracy of 98.6%, a peak signal-to-noise ratio of 23.75, a structural similarity of 0.933, a coefficient of determination of 0.901, an average counting time of about 6.58ms per image, and a parameter count of 3.21 in target counting. It can also recognize and respond well to high complexity scenes while maintaining high accuracy. Compared to the CNN model, the research model reduces the error rate by 13.4%, indicating that the fusion of CNN and Transformer networks is effective in object counting for computer vision tasks. This result indicates that the model integrating convolutional neural networks and fully self attention networks can be well applied to computer recognition and object counting.

The pervasive issue of online sexism continues to pose significant challenges, fostering environments characterized by toxicity and perpetuating harmful societal norms. In response, this paper presents an approach for the discovery of sexist statements employing convolutional neural networks (CNNs), Word Embeddings, and data augmentation techniques. Through the fusion of CNNs’ capacity for hierarchical feature extraction with the semantic representations afforded by Word Embeddings, our method achieves exemplary discrimination performance. Additionally, the incorporation of data augmentation enriches the training dataset, thereby augmenting model generalization and resilience. Empirical evaluation on a larger dataset of statements demonstrates the efficacy of our approach, surpassing many baseline approaches in terms of discovery accuracy, precision, recall and F1-score.

This study proposes an integrated framework that integrates convolutional neural network and Bayesian model to predict the life and usage of stairs, and achieves accurate inference by comprehensively analyzing wear characteristics and material degradation. The convolutional neural network extracts spatial features from the stair wear image to effectively distinguish the upward and downward usage patterns, while the Bayesian model combines observational data such as natural corrosion, artificial deformation and crack width to infer the life distribution and provide confidence intervals. The fusion framework integrates the outputs of both through a joint probability model, significantly improving the prediction performance. Experimental verification shows that the framework performs well in wear pattern classification and life prediction, provides an innovative tool for archaeological analysis and building maintenance, demonstrates the potential of combining deep learning with probability statistics, and has broad application prospects.

Fans are the important equipment in the manufacturing industry, which are easy to cause faults because of the complex working conditions and high speed. Various fault detection algorithms based on electrical, vibration and video have been proposed, but these algorithms are difficult to meet the detection requirements of enterprises. In this paper, a novel fan fault detection algorithm is presented based on sound signal detection technology. According to the different characteristics of the fan sound signal in the time and frequency domains, we combined the convolutional neural networks (CNN, which is sensitive to spatial information) and the gated recurrent unit (GRU, which is sensitive to timing information), and proposed a fan sound fault detection algorithm. Firstly, according to the variation characteristics of the acoustic signal in different states, the spectrogram of sound signal containing spatial and temporal characteristics is extracted, and the histogram equalization is used to enhance the spectrogram. On this basis, CNN and GRU are combined to train the Log-Fbank spectrogram respectively, so that it can better model the different characteristics of time domain and frequency domain information, so as to obtain a fan state model with high confidence and realize the sound fault detection of the fan. Experiments on MIMII dataset show that the algorithm has high fault recognition accuracy and fast convergence speed, which has good application value.

This paper has achieved high levels of accuracy through the combination of CNNs and RF and been highly praised, especially for medical image recognition studies. This paper concentrates on how these methodologies can be combined to take the advantages of one in feature extraction and the other in classification. CNNs are well known for learning probably hierarchical features from image data while RFs on the other hand provide stable an robust classification. Thus, through introducing these aspects, it is possible to improve the efficiency and effectiveness of vision systems to a considerable extent. In this work, several types of fusion approaches are discussed in detail and the comparative investigation of them is carried out to assess the efficiency of the approach in brain cancer detection based on MRI data. Combining analysis and semantically bottom-up and top-down approaches to image fusion we discuss advantages and disadvantages of various approaches in the aspects of classification, identification and segmentation. By performing numerous experiments and analyses on benchmarks, the authors prove that the applied CNN-RF fusion concept enhances the accuracy and robustness of the corresponding classification models and is highly scalable and easy to implement for complicated vision problems. We see our results indicating the promising nature of the presented hybrid approach as a way to improve modern deep vision systems most notably the framework of medical imaging.