Coastal waste not only has a seriously destructive effect on human life and marine ecosystems, but it also poses a long-term economic and environmental threat. To solve the issues of a poor manual coastal waste sorting environment, such as low sorting efficiency and heavy tasks, we develop a novel deep convolutional neural network by combining several strategies to realize intelligent waste recognition and classification based on the state-of-the-art Faster R-CNN framework. Firstly, to effectively detect small objects, we consider multiple-scale fusion to get rich semantic information from the shallower feature map. Secondly, RoI Align is introduced to solve positioning deviation caused by the regions of interest pooling. Moreover, it is necessary to correct key parameters and take on data augmentation to improve model performance. Besides, we create a new waste object dataset, named IST-Waste, which is made publicly to facilitate future research in this field. As a consequence, the experiment shows that the algorithm’s mAP reaches 83%. Detection performance is significantly better than Faster R-CNN and SSD. Thus, the developed scheme achieves higher accuracy and better performance against the state-of-the-art alternative.

The increase of motor vehicles year by year has led to a number of parking difficulties and traffic congestion problems. The intelligent parking system can effectively alleviate the parking difficulties and has received wide attention. Geomagnetic sensors are widely used due to their low cost and easy deployment. However, traditional geomagnetic parking detection algorithms cannot cope with complex parking behaviors and have low vehicle detection performance. Therefore, in this paper, a new parking guidance system is proposed by integrating related technologies such as ZigBee, geomagnetic sensor, and RNN. With our limited knowledge, in the research branch of the parking guidance system, RNN is applied to geomagnetic vehicle detection for the first time to detect the status of parking spaces and obtain more accurate identification results of geomagnetic signals. The training data is obtained from real scenarios. It is experimentally demonstrated that our method receives 96.6% accuracy in the detection of vehicle status, which is 9% higher than the state-of-the-art method. Finally, a robust parking guidance system gets 97% accuracy.

Circular RNAs (circRNAs) play a significant role in cancer development and therapy resistance. There is substantial evidence indicating that the expression of circRNAs affects the sensitivity of cells to drugs. Identifying circRNAs-drug sensitivity association (CDA) is helpful for disease treatment and drug discovery. However, the identification of CDA through conventional biological experiments is both time-consuming and costly. Therefore, it is urgent to develop computational methods to predict CDA. In this study, we propose a new computational method, the subgraph-aware graph convolutional network (SAGCN), for predicting CDA. SAGCN first construct a heterogeneous network composed of circRNA similarity network, drug similarity network, and circRNA-drug bipartite network. Then, a subgraph extractor is proposed to learn the latent subgraph structure of the heterogeneous network using a graph convolutional network. The extractor can capture 1-hop and 2-hop information and then a fusing attention mechanism is designed to integrate them adaptively. Simultaneously, a novel subgraph-aware attention mechanism is proposed to detect intrinsic subgraph structure. The final node feature representation is obtained to make the CDA prediction. Experimental results demonstrate that SAGCN obtained an average AUC of 0.9120 and AUPR of 0.8693, exceeding the performance of the most advanced models under 10-fold cross-validation. Case studies have demonstrated the potential of SAGCN in identifying associations between circRNA and drug sensitivity.

It is essential to understand learner emotion for MOOC. This study aimed to develop a novel sentiment analysis model to automatically classify emotion of MOOC learners, which is beneficial to educators and instructors to improve the quality of courses and platform design in MOOCs. Firstly, a robust and interpretable model was proposed, named BB-OAM, which incorporated BERT, attention method and BiLSTM to extract features of forum reviews as accurately as possible. To capture and differentiate among various affective tendencies more effectively in sentiment analysis, we have taken a breakthrough by introducing an orthogonal attention mechanism to enhance the model’s performance for emotion-ambiguous sentences. Compared with models in previous studies, SVM, Tree-CRF, BiRNN, LSTM, BiLSTM, our method improved the accuracy value of sentiment analysis by 24%, 24%, 19%, 17%, and 9%, respectively. Ablation experiments were conducted to systematically evaluate the impact of the orthogonal attention mechanism on sentiment analysis. Through visualization analyses, the model showed higher sensitivity in capturing sentiment-related content, which further validated the reliability and effectiveness of the proposed method in sentiment classification tasks of MOOC reviews. In essence, this study has great methodological and theoretical insights to help educators and instructors gain a deeper understanding of the actual needs of learners, so as to optimize the efficiency of utilizing MOOC platforms and courses, and to promote effective interactions and collaboration between learners and educators.

Automatic and precise segmentation of the prostate is beneficial to various diagnostic and therapeutic procedures on magnetic resonance imaging. However, the work is very challenging because of the heterogeneity of prostate tissue, the lack of clearly defined boundaries, and the wide variation in prostate shape between individuals. Based on the segmentation scheme for the prostate and its lesion regions, a new deep convolutional neural network is proposed in this research. To acquire excellent segmentation performance with consistency in both appearance and space, CRF-RNN is added on top of the network. By introducing an attention mechanism, the network is made to focus more feature on the prostate zones in both channel and spatial dimensions. In addition, a new dense block is created to stabilize parameter updates and prevent gradients from disappearing as the network deepens. Finally, the model was trained and validated using the real prostate dataset of 180 patients with four cross-validations. The proposed model achieves 95% HD, 86.82%, 93.90%, 94.11%, 93.8%, 7.84% for prostate, 79.2%, 89.51%, 88.43%, 89.31%, 8.39% for lesion area in segmentation in terms of IOU, Dice score, accuracy, and sensitivity. Compared to the state-of-the-art models FCN, U-Net, U-Net++ and ResU-Net, the segmentation model shows more promising results. With an outstanding achievement in automated segmentation of prostate and lesion regions, the presented model highlights the ability of the novel deep convolutional neural network to facilitate clinical disease intervention and management.

In recent years, deep learning has been widely used in the field of coastal waste detection, with excellent results. However, there are difficulties in coastal waste detection such as, for example, detecting small objects and the low performance of the object detection model. To address these issues, we propose the Multi-Strategy Deconvolution Single Shot Multibox Detector (MS-DSSD) based on DSSD. The method combines feature fusion, dense blocks, and focal loss into a state-of-the-art feed-forward network with an end-to-end training style. In the network, we employ feature fusion to import contextual information to boost the accuracy of small object detection. The dense blocks are constructed by a complex function of three concurrent operations, which can yield better feature descriptions. Then, focal loss is applied to address the class imbalance. Due to the lack of coastal waste datasets, data augmentation is designed to increase the amount of data, prevent overfitting of the model, and speed up convergence. Experimental results show that MS-DSSD513 obtains a higher mAP, of 82.2% and 84.1%, compared to the state-of-the-art object detection algorithms on PASCAL VOC2007 and our coastal waste dataset. The proposed new model is shown to be effective for small object detection and can facilitate the automatic detection of coastal waste management.