Sle-CNN: a novel convolutional neural network for sleep stage classification

Automatic classification of sleep stages based on the time-frequency image of EEG signals

Sleep stage classification is a necessary fundamental method for the diagnosis of sleep diseases, which has attracted extensive attention in recent years. Traditional methods for sleep stage classification, such as manual marking methods and machine learning algorithms, have the limitations of low efficiency and defective generalization. Recently, deep neural networks have shown improved results by the capability of learning complex pattern in the sleep data. However, these models ignore the intra-temporal sequential information and the correlation among all channels in each segment of the sleep data. To solve these problems, a hybrid attention temporal sequential network model is proposed in this paper, choosing recurrent neural network to replace traditional convolutional neural network, and extracting temporal features of polysomnography from the perspective of time. Furthermore, intra-temporal attention mechanism and channel attention mechanism are adopted to achieve the fusion of the intra-temporal representation and the fusion of channel-correlated representation. And then, based on recurrent neural network and inter-temporal attention mechanism, this model further realized the fusion of inter-temporal contextual representation. Finally, the end-to-end automatic sleep stage classification is accomplished according to the above hybrid representation. This paper evaluates the proposed model based on two public benchmark sleep datasets downloaded from open-source website, which include a number of polysomnography. Experimental results show that the proposed model could achieve better performance compared with ten state-of-the-art baselines. The overall accuracy of sleep stage classification could reach 0.801, 0.801 and 0.717, respectively. Meanwhile, the macro average F1-scores of the proposed model could reach 0.752, 0.728 and 0.700. All experimental results could demonstrate the effectiveness of the proposed model.

This work aims to construct and optimize the public emotion network communication model under the artificial intelligence (AI) environment and provide a new research direction for exploring public emotion network communication with intelligent human–computer interaction (HCI). The main challenge of this research is the lack of interactivity of intelligent human–computer expression design, and the accuracy of emotion recognition is not high. First, the theoretical analysis of network public opinion is made, and the network public opinion calculation method and emotion recognition method based on deep learning are proposed. On this basis, the public emotion network communication model is established, and the model’s communication mechanism and rules are described in detail. Next, the emotion calculation of human–computer expression interaction and the emotion expression mode of the humanoid robot are proposed, which provides an experimental basis for HCI and public emotion network communication analysis. According to the data of email network and microblog network in the real network, the network degree distribution map is established. The statistical results show that email and microblog networks belong to power law distribution, and the aggregation coefficient is relatively high. Microblog network degree changes slowly, and more points are scattered in the middle area. In a practical sense, when the positive and negative emotions in the public emotion network communication model are input into the bionic robot, the robot can show happy and lost expressions. The results show that compared with the traditional convolutional neural network (CNN) model or recurrent neural network (RNN) model, the RNN–CNN structure model of RNN combined with CNN reduces the waiting time by about 20% and improves the algorithm accuracy by at least 3.1%. The public emotion network communication model based on deep learning and intelligent HCI can accurately reflect the public emotion state in the network, which provides a practical basis for the application of AI technology in network public opinion judgment.

- This paper presents a comparative analysis between a traditional convolutional neural network (CNN) model and a hybrid ResNet50 + AdaBoost architecture for non-invasive cardiovascular risk prediction using retinal fundus images. The baseline model, trained on a small dataset for binary classification, achieves reasonable accuracy but lacks scalability and granularity. In contrast, the proposed model leverages deep residual learning for feature extraction and ensemble-based classification to predict five distinct cardiovascular risk levels. Evaluated on a class-balanced dataset of over 11,500 images, the hybrid model achieved a testing accuracy of 91.48% and a macro F1-score of approximately 0.93. The system also integrates data visualization and database logging to support clinical explainability. The findings demonstrate the proposed method’s superiority in risk stratification, robustness, and deployment readiness for real-world applications. Key Words: Retinal Imaging, Cardiovascular Risk Prediction, ResNet50, AdaBoost, Deep Learning, Ensemble Learning, Fundus Images, Heart Attack Classification

This paper presents a novel automatic sleep stage classification method based on the convolutional neural network (CNN) with Electroencephalogram (EEG). To fully utilize the local frequency domain information of original EEG signals, we define the fractional discrete Fourier transform (F-DFT). For a better classification performance in the sleep stage 3 and the stage 4, wavelet transform (WT) is used to depict the low frequency structure information of local signals rather than traditional filter methods. Using the locally corresponding relation between time and frequency domains, we generate the three-dimensional signal consisting of the EEG signal, the F-DFT signal and the WT subband signal. We feed the CNN with expanded three-dimensional signals to extract features and classify. Unlike previous methods, our process of feature extracting is automatically. As far as I can see, we first introduce the CNN to sleep stage classification, and achieve state-of-the-art performance.

A bearing fault diagnosis method based on an improved convolutional neural network(CNN) is proposed to address the problem of low accuracy of traditional convolutional neural network for rolling bearing fault identification. First, the convolutional kernels of the first convolutional layer of the original network are widened to accommodate the bearing fault signal with one-dimensional input. Then, the number of convolutional kernels is reduced while maintaining the feature extraction capability of the network to ensure the deepening of the network while strongly suppressing the overfitting phenomenon of the network. Finally, the batch normalization(BN) layer is added after the convolutional and fully connected layers of the convolutional neural network to accelerate the convergence of the network and prevent the network from gradient disappearance or gradient explosion. Experimental results demonstrate that the proposed method achieves higher diagnostic accuracy and stability than traditional convolutional neural network.

<p class="Normal1">Medicinal plants have been found and utilized in traditional medical practices from ancient times. Many medicinal plants play a vital role in curating many life threatening diseases. Very few of the medicinal herbs are commercially cultivated. Many plant diseases are there which destroys these medicinal plants. Early detection of plant diseases can prevent the huge loss of these medicinal plants. Here, we presented a hybrid model that makes use of SVM along with the traditional convolutional neural network (CNN) for predicting <em>Basil</em> plants leaves diseases. We transformed the conventional CNN model by adding a classification layer Support Vector Machine (SVM) and K-Nearest Neighbor (KNN) after feature extraction and this approach tends to perform better than traditional CNN as we make the dataset balanced by data augmentation and SVN and KNN tend to perform better in case of balanced samples. CNN is used for training, SVM/KNN is used for classification. The advantages of CNN and SVM are used in proposed the CNN and SVM and KNN model. It is assumed that such a combined model would incorporate the benefits of CNN and SVM. Here, we identified the four types of diseases that affect <em>basil</em> plant leaves as Leaf spot, Downy mildew, Fusarium wilt, Fungal, and Healthy. Since there isn’t a standard dataset for <em>basil</em> leaves, we created our own 803 picture data set and used various machine learning techniques to train and evaluate the model. However, over other existing algorithms, our hybrid model i.e., CNN+SVM has produced more accurate results. For five classes of <em>basil</em> plant leaves, the proposed model produced 95.02% accuracy of for leaf diseases.</p>

The non-invasive pre-operative grading of gliomas is one of the most important issues in clinical neuro-oncology where the distinction between low-grade and high-grade tumors is the basic determinant of therapeutic strategies as well as patients prognosis. Existing modes of diagnosis are usually hampered by invasiveness of the biopsies and variability of interpretation of radiological analysis. Although deep learning has become a potent instrument of medical image analysis, the current models often use 2D image slices or traditional Convolutional Neural Network (CNN) architectures. These methods are limited in nature because they do not make the best use of the rich, three-dimensional spatial and contextual data that is available in volumetric multi-modal Magnetic Resonance Imaging (MRI). The paper suggests a new and all-inclusive deep learning model to meet this urgent clinical requirement. Our proposal is a 3D hybrid CNN- Vision Transformer architecture, which is purposefully aimed at combining the local feature extraction properties of CNNs and the global context modeling properties of Transformers. Our model is trained and tested on the Multimodal Brain Tumor Segmentation (BraTS) benchmark dataset that is internationally recognized and offers high-quality, pre-processed, multi-parametric 3D MRI scans. To fight the widespread problem of disparities in the classes of medical data, we utilize an advanced compound loss that consists of Focal Loss and Generalized Dice Loss strengths. Moreover, an effective 3D-specific data augmentation pipeline, such as elastic deformations is introduced to improve model generalization and stop overfitting. One of the main pillars of our design is the incorporation of Explainable AI (XAI) via 3D Gradient-Weighted Class Activation Mapping (Grad-Cam). This method offers essential model interpretability through the production of visual heatmaps that indicate the most important areas of the anatomy to the model to make a grading decision, which would build clinical confidence and validation. This study will create a more precise, dependable and open-minded approach to pre-operative grading of glioma, which will lead to a clinically-integrated decision support system.

Neuroimaging research has begun adopting deep learning to model structural differences in the brain. This is a break from previous approaches that rely largely on anatomical volumetric or thickness-based features. Currently, most studies employ either convolutional deep learning based models or traditional machine learning models that use volumetric features. Because of this split, it is unclear which approach yields better predictive performance, or whether the two approaches will lead to different neuroanatomical conclusions, potentially even when applied to the same dataset. To address these questions, we present the largest single study of sex differences in the brain using 21,390 UK Biobank T1-weighted brain MRIs, which we analyzed through both traditional volumetric and 3D convolutional neural network models. Overall, we find that 3D-CNNs outperformed traditional machine learning models, with sex classification area under the ROC curve of 0.849 and 0.683, respectively. When performing sex classification using only single regions of the brain, we observed better performance from 3D-CNNs in all regions tested, indicating sex differences in the brain likely represent both structural and volumetric changes. In addition, we find little consensus in terms of brain region prioritization between the two approaches. In summary, we find that 3D-CNNs show exceptional sex classification performance, extract additional relevant structural information from brain regions beyond volume, and possibly because of this, prioritize sex differences in neuroanatomical regions differently than volume-based approaches.

A new multi-data-driven spatiotemporal PM2.5 forecasting model based on an ensemble graph reinforcement learning convolutional network

Deep Learning and Beyond: AI’s New Horizons” is an expansive monograph that delves into the dynamic and rapidly evolving field of deep learning, a key area of artificial intelligence. This comprehensive work provides an insightful exploration of the revolutionary developments, foundational concepts, and future directions of deep learning, positioning itself as a crucial resource for understanding this transformative technology. The monograph commences with an introductory chapter that lays the groundwork by elucidating the background of machine learning and chronicling the ascent of deep learning. This foundational perspective is vital for comprehending the underlying principles and methodologies that have propelled deep learning to the forefront of technological innovation. Subsequent chapters delve into the “Historical Evolution” of AI, tracing the origins of neural networks from their early inception to the Renaissance period of deep learning. This historical analysis is pivotal in understanding the field’s trajectory and significant milestones. The “Foundations of Deep Learning” chapter presents an in-depth analysis of neural networks, covering their basic architecture, activation functions, layers, and the critical process of backpropagation and gradient descent. This is followed by examining “Advanced Neural Network Architectures,” where the focus shifts to state-of-the-art models such as convolutional neural networks, recurrent neural networks, and groundbreaking transformers and attention mechanisms. In addressing the “Training Deep Models,” the monograph explores regularization techniques, optimization strategies, and the increasingly significant role of transfer learning and pre-trained models. The application-oriented chapter, “Applications of Deep Learning,” showcases real-world implementations in domains like image recognition, natural language viii processing, and autonomous systems, illustrating deep learning technologies’ vast potential and versatility. The monograph also tackles the “Challenges in Deep Learning,” including overfitting, interpretability, and the ethical considerations surrounding AI deployment. It then ventures into the “Future of Deep Learning,” contemplating emerging trends such as integrating quantum computing, neural architecture search, and the convergence of neuroscience and deep learning. “Case Studies” provide practical insights and empirical evidence of the impact of deep learning. At the same time, the concluding chapter synthesizes the current state of deep learning, offers predictions for the future, and provides reflective thoughts on this transformative field. “Deep Learning and Beyond: AI’s New Horizons” educates and informs readers about the vast potential and future possibilities of deep learning and artificial intelligence. Keywords: Deep Learning, Artificial Intelligence, Neural Networks, Machine Learning, Convolutional Neural Networks, Recurrent Neural Networks, Transformers, Quantum Computing, Ethical AI, Neural Architecture Search, Natural Language Processing, Autonomous Systems, AI in Healthcare, Future of AI, AI Applications, AI Challenges.

Deep learning theory demonstrates its great power in the complicated data processing field. As a prevalent deep learning technology, convolutional neural network (CNN) has achieved some successful applications in the field of fault detection and classification in the complicated industrial systems because of its strong nonlinear feature extraction capability. However, the basic CNN views all the features equally and can not highlight these features which play a more important role in the fault classification procedure. To deal with this problem, this paper proposes an improved CNN model, called multi-head attention CNN (MACNN), to distinguish the importance of different features for better fault classification performance. In the MACNN framework, the raw training data are firstly analyzed by the multiple convolutional layers for feature extraction. Then, the features obtained by the convolution layers are used as the input of the multi-head attention (MA) layers. The introduction of the MA mechanism can help exploit more meaningful information by emphasizing the important features. Lastly, the softmax classifier is utilized to classify the fault patterns. To test the method performance, one case study on the benchmark Tennessee Eastman industrial system is performed and the results show that MACNN gives the better fault classification results than the traditional feedforward neural network (FNN) and CNN models.

Due to the Covid-19 virus millions of people lost their lives and livelihood all the over the world, so quick and accurate diagnosis ofcovid-19 patients is the emergent need of today. Deep learning (DL) techniques, nowadays widely gained much popularity as compared to machine learning methods. Furthermore, DL is well-known in the field of image processing, whereas X-ray pictures are frequently used to diagnose Covid-19 patients. In this article, HyperCNN approach introduced through which higher accuracy can be achieved as compared to other traditional Convolutional Neural Network (CNN). Normally, the tuning of hyper parameters is done manually, which are both costly and time consuming in order to identify the optimum model with the highest accuracy. As per the experiment done, the traditional CNN model provides the accuracy 96.6% whereas the proposed model which is tuned using Bayesian optimization achieves accuracy 98.33%. Hence, it is observed that there is an improvement of 2% in the performance of the proposed model as compared to the traditional CNN model. With this experiment, we claim that automatic hyper parameter optimization is a powerful method for improving the accuracy in transfer learning.KeywordsHyper parameterOptimizationConvolutional neural networkDeep learningCovid-19 diagnosis

This paper studies the classification problem of hyperspectral image (HSI). Inspired by the great success of deep neural networks in Artificial Intelligence (AI), researchers have proposed different deep learning based algorithms to improve the performance of hyperspectral classification. However, deep learning based algorithms always require a large-scale annotated dataset to provide sufficient training. To address this problem, we propose a semi-supervised deep learning framework based on the residual networks (ResNets), which use very limited labeled data supplemented by abundant unlabeled data. The core of our framework is a novel dual-strategy sample selection co-training algorithm, which can successfully guide ResNets to learn from the unlabeled data by making full use of the complementary cues of the spectral and spatial features in HSI classification. Experiments on the benchmark HSI dataset and real HSI dataset demonstrate that, with a small number of training data, our approach achieves competitive performance with maximum improvement of 41% (compare with traditional convolutional neural network (CNN) with 5 initial training samples per class on Indian Pines dataset) for HSI classification as compared with the results from those state-of-the-art supervised and semi-supervised methods.

ObjectivesPolysomnography is essential to diagnose sleep disorders. It is used to identify a patient's sleep pattern during sleep. This pattern is obtained by a doctor or health practitioner by using a scoring process, which is time consuming. To overcome this problem, we developed a system that can automatically classify sleep stages.MethodsThis paper proposes a new method for sleep stage classification, called the fast convolutional method. The proposed method was evaluated against two sleep datasets. The first dataset was obtained from physionet.org, a physiologic signals data centers. Twenty-five patients who had a sleep disorder participated in this data collection. The second dataset was collected in Mitra Keluarga Kemayoran Hospital, Indonesia. Data was recorded from ten healthy respondents.ResultsThe proposed method reached 73.50% and 56.32% of the F-measures for the PhysioNet and Mitra Keluarga Kemayoran Hospital data, respectively. Both values were the highest among all the machine learning methods considered in this study. The proposed method also had an efficient running time. The fast convolutional models of the PhysioNet and Mitra Keluarga Kemayoran Hospital data needed 42.60 and 0.06 seconds, respectively.ConclusionsThe fast convolutional method worked well on the tested datasets. It achieved a high F-measure result and an efficient running time. Thus, it can be considered a promising tool for sleep stage classification.