This paper proposed a novel image-based air pollution monitor (IAPM) by incorporating local and global information in the self-adaptive multiscale transform domain, so as to achieve the timely and effective leakage detection of typical air pollutants from a single image. To be specific, this paper first developed a screen-shaped module according to two significant findings in visual neuroscience, which include the high sensitivity of human eyes to horizontal and vertical stimuli and the center-surround inhibition, by designing and fusing the square module, horizontal strip module and vertical strip module parallelly for simulating the behaviour of human eyes to extract local features. Second, the learnable weights and proportional mapping were applied to incorporate the screen-shaped module and lightweight vision transformer as backbone, towards more richly exploiting and fusing local and global information just as the way a brain perceives external stimuli. Third, a new self-adaptive multiscale transform domain method was devised based on two motivations from the visual characteristics of multiscale perception and the brain characteristics of self-adaptive domain transform to modify the backbone by using the operations of pooling and pointwise convolution. Extensive experiments implemented on the datasets of carbon particulate matters and ethylene leakage confirmed the superior monitoring performance of the proposed IAPM model beyond the state-of-the-art (SOTA) peers by an accuracy gain of about 4%. Furthermore, the proposed IAPM model only required 0.089 GFLOPs and 0.15 million model parameters, remarkably outperforming SOTA competitors in computational efficiency and storage resources.

The inherent sparsity of electronic medical record (EMR) poses difficulties for the learning of patient similarity. Graph-based modeling methods can infer missing values by learning complex relations among medical facts, which are becoming the mainstream options for patient similarity analysis. However, existing graph-based solutions mainly focus on general patterns among patients and overlook local differences, potentially losing semantic information on patient similarity. Additionally, in real medical situations, entities related to symptoms or treatment types often involve multiple diseases, which provide erroneous signals in similarity assessment. Therefore, this paper proposes a novel deep learning method called Multi-view Hierarchical Learning Network (MHLN) for patient similarity measurement. This method extracts dependency information between entities from both local and global perspectives. Additionally, it assigns different importance levels to different types of medical entities, enabling the learning of dependency features between entity representations through local and global encoders to generate representative embeddings for similarity computation. Finally, we evaluate MHLN on real-world Chinese EMR data, and the results demonstrate the effectiveness of MHLN compared to related work.

With the wide-adoption of edge computing, the functionalities of Internet of Things (IoT) devices can be encapsulated as edge services, to facilitate domain applications through edge service compositions. Considering the capacity-fluctuating and resource-varying of IoT devices, edge service monitoring is essential to guarantee the healthy of their compositions at runtime. Current techniques focus mostly on the monitoring of atomic edge services, which, however, are inadequate for that of inter-and composite services. Besides, constraints to be monitored are usually pre-specified, although certain parameters may have to be adapted online according to execution context. To address these challenges, this paper proposes a novel logic-based adaptive monitoring mechanism, to achieve the interpretation of temporal constraints and time-dependent QoS constraints upon intra-, inter-, and composite services. Leveraging our proposed Compositional Signal Temporal Logic (CSTL) with extended compositional modalities and online parameter settings, constraints can be converted to CSTL formulae, and QoS variations and temporal violations are interpreted qualitatively and quantitatively at runtime. Extensive experiments are conducted upon publicly-available datasets, and evaluation results demonstrate that our CSTL performs better than baseline techniques in terms of expressiveness, applicability, and robustness.

Over the past several years, deep learning technologies have made tremendous progress in medical image tasks including classification, segmentation, and object detection. However, there are two main limitations of indocyanine green (ICG) images which are often used in breast cancer related lymphedema (BCRL): insufficient sample numbers and low image quality. Consequently, the conventional deep learning based classification methods such as ResNet have faced challenges in achieving satisfactory results. To tackle the concern, this paper puts forward a deep learning method named Denoising Diffusion Probabilistic Model Self-supervised Knowledge Distillation Net (DDPM-SKDNet) for the ICG images classification task, by incorporating a contrastive learning based approach as the network architecture and using DDPM as the image generator in the contrastive module to expand the dataset size. Furthermore, a knowledge distillation approach is utilized to increase the effectiveness of the network. The proposed method was validated on ICG datasets and achieved a significant improvement in classification accuracy, increasing it from 66.7% in the baseline method to 82.1% in the proposed method.

Understanding gene expression processes necessitates the accurate classification and identification of transcription factors, which is supported by high-throughput sequencing technologies. However, these techniques suffer from inherent limitations such as time consumption and high costs. To address these challenges, the field of bioinformatics has increasingly turned to deep learning technologies for analyzing gene sequences. Nevertheless, the pursuit of improved experimental results has led to the inclusion of numerous complex analysis function modules, resulting in models with a growing number of parameters. To overcome these limitations, it is proposed a novel approach for analyzing DNA transcription factor sequences, which is named as DeepCAC. This method leverages deep convolutional neural networks with a multi-head self-attention mechanism. By employing convolutional neural networks, it can effectively capture local hidden features in the sequences. Simultaneously, the multi-head self-attention mechanism enhances the identification of hidden features with long-distant dependencies. This approach reduces the overall number of parameters in the model while harnessing the computational power of sequence data from multi-head self-attention. Through training with labeled data, experiments demonstrate that this approach significantly improves performance while requiring fewer parameters compared to existing methods. Additionally, the effectiveness of our approach is validated in accurately predicting DNA transcription factor sequences.

Summary Federated edge cloud (FEC) is an edge computing environment where servers in the same edge management domain could collaborate to handle latency‐sensitive services, thus better guaranteeing users' requirements on multiple quality of service (MQoS). Traditional scheduling methods only consider whether the server meets the resource requirements of the service, without paying attention to whether their resource characteristics match. In scenarios where server's resources are dynamically changing, this may reduce the resource utilization and the efficiency of service execution. To address this challenge, a dynamic resource adaptation‐multiple quality of service (DRA‐MQoS) algorithm is proposed for service scheduling in this environment. DRA‐MQoS could dynamically evaluate the resource characteristics of servers and services from the perspectives of “individual” and “overall” by combining the historical scheduling data of services and the utilization of different resources of server clusters. By scheduling the services to servers with the same resource characteristics for execution, the proposed policy fusion algorithm efficiently responds to the dynamically changing quality of service (QoS) demands of users by changing the weight parameters of policies. Simulation results in CloudSimSDN show that the energy consumption and execution time of DRA‐MQoS are reduced by 23% and 12%, respectively, compared with existing methods.

Abstract Triple‐negative breast cancer is the worst prognosis in breast cancer, accounting for 10.0–20.8% of all breast cancers. Considering that triple‐negative breast cancer has great heterogeneity and very poor prognosis, clinical medication guidance is in urgent need of a more detailed classification of breast cancer itself. Although many researchers have been dedicated to the clustering of triple‐negative breast cancer and have found possible targets based on typing, their results are not closely related to the prognosis. This paper utilizes three clustering methods to retype the patient data with triple‐negative breast cancer, and the results show that the triple‐negative breast cancer data could be classified into two categories. Eight important genes and three important clinical factors related to the prognosis of two types of triple‐negative breast cancer have been obtained. These genes have the following three characteristics: co‐expression, differential expression and interaction. In terms of breast cancer control, the prognosis can be controlled as much as possible by regulating gene levels, which provides new directions and ideas for related research on breast cancer prognosis.

Abstract Background This study aims to propose a breast cancer prediction model for early diagnosis and prognosis management of breast cancer. Objective In order to explore the pathogenesis of breast cancer and develop accurate breast cancer screening and treatment methods, we have used machine‐learning technologies to conduct an in‐depth study of breast cancer genetic data to obtain new breast cancer signature and prognostic prediction models. Methods We explored an optimal cluster by unsupervised clustering methods with different expression genes (DEGs) between normal (n = 113) and tumour (n = 1,102) samples. Using least absolute shrinkage and selection operator (LASSO) regression, we selected four biomarkers to develop a predictive model by Cox regression method in the training set (n = 1,083) and validated its predictive accuracy and independence in the testing sets (n = 2,480). Then Gene Set Enrichment Analysis (GSEA) revealed enriched biological pathways in clusters. Finally, we constructed a nomogram including this signature and other significant risk factors to predict survival rates in patients. Results Four mRNAs (CD163L1, QPRT, NKAIN1 and TP53AIP1) between two clusters from 4,938 DEGs were identified, and then a four‐gene model (risk scores = 0.454*CD163L1–0.360*NKAIN1 + 0.581*QPRT + 0.788*TP53AIP1) was established to divide patients into high‐ and low‐risk group with significantly different prognosis (p < 0.0001) in the training set. Integrated analysis revealed dysregulated molecular processes including predominantly oncogenic signalling pathway, cell cycle and DNA repair in high‐risk group but enriched metabolism pathway in low‐risk group. In addition, this model had similar predictive value (HR >1.60; p < 0.05) in three independent validation sets, which could predict survival independently with more power compared with single clinical factor. In addition, the nomogram could predict the prognosis of breast cancer patients precisely in the training set and another three testing sets. Conclusion This model could predict prognosis of breast cancer patients precisely and independently, and provide evidence to make treatment decisions and design clinical trials.

Upper limb kinematic analysis that has been employed in the clinical assessment of motion functions or rehabilitation training is traditionally tested manually with a goniometer. Nowadays, it is a trend to deploy different technology and devices including low-cost but accurate RGB cameras in order to save manual efforts. Among these, a new method using deep learning-based cameras has been investigated to provide the same ease and accessibility as a manual handheld goniometer. The key to measuring upper limb Range of Motion (ROM) using a camera is to estimate upper limb joints accurately. Many existing joint estimation algorithms focus on improving the accuracy performance but put the efficiency concerns aside. It is still challenging to apply those algorithms to low-capacity and budget-friendly devices, which is highly demanding in clinical scenarios. We propose a lightweight and fast deep learning model to estimate human pose and then use predicted joints to measure the range of motion for upper limb joints. Unlike other human pose estimation methods that learn and predict all major joints of the human body, the proposed model only focuses on the upper limb, which improves the accuracy and reduces the overhead of prediction. To further reduce model size and latency, our model is based on a compact neural network architecture, and parameters in the network are quantized to 8-bit precision. As a result, our model runs 4.1 times faster and is 15.5 times smaller compared with a full sized state of the art human pose estimation model. The proposed method is further evaluated on different upper limb functional tasks. Results show that our new method achieves a satisfying accuracy in ROM measurement and a high degree of agreement with a goniometer. Compared with the goniometer to measure ROM, our presented method is easier to operate and can be performed remotely, while still retaining good accuracy.

Graph convolutional neural networks are designed to apply convolutional operations directly on non-Euclidean structure graph data, generating orderly arranged matrix representations of graphs. However, only node features are fully exploited even though edge features may also play an important role in some domains such as chemoinformatics. In this paper, we proposed two new approaches of utilizing edge features on graph convolutional neural networks, Feature embedding adjacent matrix and Reverse graph. Methodologies of basic graph convolutional neural networks only tend to propagate node features to neighbor nodes along edges by convolutional operations. By applying Feature embedding adjacent matrix, edge features are synthesized into node features and also propagated to neighbor nodes during propagation process. Reverse graph approach builds a special auxiliary graph to propagate edge features to neighbor edges. Therefore, a synthetical presentation including both edge features and node features is built. Experiments demonstrated our new approaches improve graph classification accuracies, especially on data sets with low accuracies on basic GCNs.

Face detection tasks under the current epidemic prevention situation often acquire images with partial occlusion. General face detectors ignore the challenge brought by occlusion, making it difficult to meet daily needs. In order to address this problem, this paper proposes a real-time occluded face detection network based on the improved CenterNet with information dropping strategy. First, depth separable convolution and attention mechanism are introduced into the backbone to reduce parameters and extract occlusion-robust features. Second, a feature fusion neck is designed to improve the performance of multi-scale face detection. In addition, the data augmentation method with information removal strategy enriches the diversity of occlusion samples. Experiments indicate that our model improves the fps as well as maintains the accuracy.

BACKGROUND: Addressing the laborious nature of traditional biological experiments by using an efficient computational approach to analyze RNA-binding proteins (RBPs) binding sites has always been a challenging task. RBPs play a vital role in post-transcriptional control. Identification of RBPs binding sites is a key step for the anatomy of the essential mechanism of gene regulation by controlling splicing, stability, localization and translation. Traditional methods for detecting RBPs binding sites are time-consuming and computationally-intensive. Recently, the computational method has been incorporated in researches of RBPs. Nevertheless, lots of them not only rely on the sequence data of RNA but also need additional data, for example the secondary structural data of RNA, to improve the performance of prediction, which needs the pre-work to prepare the learnable representation of structural data. RESULTS: To reduce the dependency of those pre-work, in this paper, we introduce DeepPN, a deep parallel neural network that is constructed with a convolutional neural network (CNN) and graph convolutional network (GCN) for detecting RBPs binding sites. It includes a two-layer CNN and GCN in parallel to extract the hidden features, followed by a fully connected layer to make the prediction. DeepPN discriminates the RBP binding sites on learnable representation of RNA sequences, which only uses the sequence data without using other data, for example the secondary or tertiary structure data of RNA. DeepPN is evaluated on 24 datasets of RBPs binding sites with other state-of-the-art methods. The results show that the performance of DeepPN is comparable to the published methods. CONCLUSION: The experimental results show that DeepPN can effectively capture potential hidden features in RBPs and use these features for effective prediction of binding sites.

In recent years, a large number of Chinese electronic texts have been produced in the process of information construction in various fields. Identifying specific entities in these electronic texts has become a major research focus. Most existing research methods use radicals to extract the glyph features of Chinese characters but have seen its limitation. This paper extracts the features of Chinese characters from three aspects: glyph features, phonetic features, and character features, and improves conventional feature extraction methods for each kind of feature. A new named entity recognition method (AIP) is proposed by transforming Chinese characters into corresponding images for glyph feature extraction, dividing pinyin into initials, vowels, and tones for phonetic feature extraction, and fine-tuning the A Lite Bert model for character feature extraction to improve the performance of the model. This paper compares the performance of the AIP model and mainstream neural network models on Chinese named entity recognition tasks on commonly used data sets and the data sets in specific domains. The results showed that AIP achieved better results than the related work. The F1 values on the two data sets are 94.4% and 80.5%, respectively, which validates the model's versatility.

With the rapid increase of text data generated by the Internet, the problem of text information overload is becoming more and more serious. Automatic text summarization provides a good method to simplify text information. Traditional methods are mainly divided into extractive and abstractive methods. However most extractive methods do not have too much context connection, which leads to unsmooth abstracts. Abstractive method is the mainstream method, but it also deviates from the text content and has the problem of poor readability. In this paper, a hybrid automatic text summarization method is proposed based on deep learning and a rapid self-attention mechanism. This mechanism is used to obtain a hybrid model DNM (Dilated Neural Random Attention with Minimal Risk Loss) by optimizing the network structure and combining it with a specific loss function. The ROUGE score of our model is compared with the extractive Neural Document Summarization (NEUSUM), the abstractive Graph-Based Attentional (GBA) and the hybrid model CopyNet on the LCSTS dataset so as to achieve more accurate and reasonable automatic text summarization.

Summary As an important method to diagnose gastric cancer, gastric pathological sections images (GPSI) are hard and time‐consuming to be recognized even by an experienced doctor. An efficient method was designed to detect gastric cancer in magnified (20×) GPSI using deep learning technology. A novel DenseNet architecture was applied, modified with a multistage attention module (MSA‐DenseNet). To develop this model focusing on gastric features, a two‐stage‐input attention module was adopted to select more semantic information of cancer. Moreover, the pretraining process was divided into two steps to improve the effect of the attention mechanism. After training, our method achieved a state‐of‐the‐art performance yielding 0.9947 F1 score and 0.9976 ROC AUC on a test dataset. In line with our expectation in clinical practice, a high recall (0.9929) was produced with high sensitivity to the positive samples. These results indicate that this new model performs better than current artificial detection approaches and its effectiveness is therefore validated in cancer pathological diagnoses.

With rapid industrial development, air pollution problems, especially in urban and metropolitan centers, have become a serious societal problem and require our immediate attention and comprehensive solutions to protect human and animal health and the environment. Because bad air quality brings prominent effects on our daily life, how to forecast future air quality accurately and tenuously has emerged as a priority for guaranteeing the quality of human life in many urban areas worldwide. Existing models usually neglect the influence of wind and do not consider both distance and similarity to select the most related stations, which can provide significant information in prediction. Therefore, we propose a Geographic Self-Organizing Map (GeoSOM) spatiotemporal gated recurrent unit (GRU) model, which clusters all the monitor stations into several clusters by geographical coordinates and time-series features. For each cluster, we build a GRU model and weighted different models with the Gaussian vector weights to predict the target sequence. The experimental results on real air quality data in Beijing validate the superiority of the proposed method over a number of state-of-the-art ones in metrics, such as${R} ^{2}$, mean relative error (MRE), and mean absolute error (MAE). The MAE, MRE, and${R} ^{2}$are 16.1, 0.79, and 0.35 at the Gucheng station and 19.53, 0.82, and 0.36 at the Dongsi station.

Atmospheric visibility is an indicator of atmospheric transparency and its range directly reflects the quality of the atmospheric environment. With the acceleration of industrialization and urbanization, the natural environment has suffered some damages. In recent decades, the level of atmospheric visibility shows an overall downward trend. A decrease in atmospheric visibility will lead to a higher frequency of haze, which will seriously affect people's normal life, and also have a significant negative economic impact. The causal relationship mining of atmospheric visibility can reveal the potential relation between visibility and other influencing factors, which is very important in environmental management, air pollution control and haze control. However, causality mining based on statistical methods and traditional machine learning techniques usually achieve qualitative results that are hard to measure the degree of causality accurately. This article proposed the seq2seq-LSTM Granger causality analysis method for mining the causality relationship between atmospheric visibility and its influencing factors. In the experimental part, by comparing with methods such as linear regression, random forest, gradient boosting decision tree, light gradient boosting machine, and extreme gradient boosting, it turns out that the visibility prediction accuracy based on the seq2seq-LSTM model is about 10% higher than traditional machine learning methods. Therefore, the causal relationship mining based on this method can deeply reveal the implicit relationship between them and provide theoretical support for air pollution control.

BACKGROUND: Screening of the brain computerised tomography (CT) images is a primary method currently used for initial detection of patients with brain trauma or other conditions. In recent years, deep learning technique has shown remarkable advantages in the clinical practice. Researchers have attempted to use deep learning methods to detect brain diseases from CT images. Methods often used to detect diseases choose images with visible lesions from full-slice brain CT scans, which need to be labelled by doctors. This is an inaccurate method because doctors detect brain disease from a full sequence scan of CT images and one patient may have multiple concurrent conditions in practice. The method cannot take into account the dependencies between the slices and the causal relationships among various brain diseases. Moreover, labelling images slice by slice spends much time and expense. Detecting multiple diseases from full slice brain CT images is, therefore, an important research subject with practical implications. RESULTS: In this paper, we propose a model called the slice dependencies learning model (SDLM). It learns image features from a series of variable length brain CT images and slice dependencies between different slices in a set of images to predict abnormalities. The model is necessary to only label the disease reflected in the full-slice brain scan. We use the CQ500 dataset to evaluate our proposed model, which contains 1194 full sets of CT scans from a total of 491 subjects. Each set of data from one subject contains scans with one to eight different slice thicknesses and various diseases that are captured in a range of 30 to 396 slices in a set. The evaluation results present that the precision is 67.57%, the recall is 61.04%, the F1 score is 0.6412, and the areas under the receiver operating characteristic curves (AUCs) is 0.8934. CONCLUSION: The proposed model is a new architecture that uses a full-slice brain CT scan for multi-label classification, unlike the traditional methods which only classify the brain images at the slice level. It has great potential for application to multi-label detection problems, especially with regard to the brain CT images.

Summary The motif discovery of multi‐dimensional time series datasets can reveal the underlying behavior of the data‐generating mechanism and reflect the relationship between time series in different dimensions. The study of motif discovery is of important significance in environmental management, financial analysis, healthcare, and other fields. With the growth of various information acquisition devices, the number of multi‐dimensional time series datasets is rapidly increasing. However, it is difficult to apply traditional multi‐dimensional motif discovery methods to large‐scale datasets. This paper proposes a novel method for motif discovery and analysis in large‐scale multi‐dimensional time series. It can effectively find multi‐dimensional motifs and the correlation among the motifs. The experimental results show that the proposed method achieves better performance than the related arts on synthetic and real datasets. It is further validated on practical air quality data and provides theoretical support for real air pollution control in places such as Beijing.

High‐density crowd counting in natural scenes is an extremely difficult and challenging research subject in computer vision. Although the algorithm based on the convolutional neural network has achieved significantly better results than the traditional algorithm, most of them tend to focus on the local features of images, and difficult to obtain the rich global contextual dependencies. To solve this problem, a dual attention module and a multi‐label based fully convolutional network are proposed in this study. Moreover, the authors improve the algorithm by the following multiple perspectives. Firstly, introducing the dual attention module, the global‐context and long‐range dependency are adaptively integrated into both spatial and channel dimensions, which improve the network expression ability. Then, the prediction error is effectively reduced by designing a multi‐label mechanism, so the crowd‐counting task is transformed into foreground and background segmentation tasks to assist in the regression task of the density map. Furthermore, on the basis of the traditional Euclidean distance loss and cross‐entropy loss, the structural similarity index is introduced to further improve the training effect of the model. The test results of the UCF_CC_50, ShanghaiTech, and UCF‐QNRF datasets indicate that the proposed method is superior to the current mainstream algorithm.

Gel is a post-operative cleaning material with antibacterial effect, which helps patients recover after surgery. It is more and more popular in surgery, but it is still controversial in use. This study collected the electronic medical records of patients in a hospital for nearly three years, using a combination of a variety of special selection methods to process data and using random forest, support vector machine, LightGBM and XGBoost and other machine learning methods to predict the suitability of patients. The results show that polysaccharide gel is not suitable for all people, whether to use it should consider different situations. This paper has studied the applicability of medical gels to patients, and established a predictability model to provide data support for the clinical application of this expensive medical material.

Identifying if a subsurface target is salt or not automatically and accurately is of vital importance to oil drilling. But unfortunately, obtaining the precise position of large salt deposits is very difficult. Professional seismic imaging still requires the interpretation of salt bodies by experts. This leads to very subjective, highly variable renderings. More alarmingly, it leads to potentially dangerous situations for drillers in oil and gas companies. In this paper, a Squeeze-Extraction Feature Pyramid Networks (referred to as Se-FPN) was proposed to tackle the task of image segmentation of salt deposits. Specifically, we utilized SeNet as backbone so as to implicitly learn to suppress irrelevant regions in an input image while highlighting salient features useful for the task. Considering the importance of multi-scales information, we proposed an improved FPN to integrate information of different scales. In order to further fuse the information from multiple scales, the Hypercolumns module was inserted at the end of the network. The proposed Se-FPN has been applied to the TGS Salt Identification Challenge and achieved high quality segmentation effect. The Mean Intersection over Union value can reach 0.86.

Gastric cancer is a malignant neoplasm with a high mortality rate in the world. Nearly one million new cases occur each year. The most important measure to diagnose gastric cancer is the detection and treatment of diseases early. Gastric cancer detection is currently performed by pathologists reviewing large expanses of biological tissues, but this process is labor intensive and error-prone. In this paper, a framework for automatically detection of tumors in gastric pathology image (slide) has been proposed based on deep learning. A deep residual network with 50 layers is built by identity mapping on a dataset of pathology images. The proposed method makes the training of models easier and improves the generalization performance. Finally, the experimental results show that the F-score of our method achieves 96%. The research in auto-classification of gastric pathology images has great value for gastric cancer detection in clinical medicine.

Air pollution is threatening human's health since the industrial revolution, but there are not efficient ways to solve air pollution, so forecasting air quality has become an efficient measure to prevent citizens from hurting of heavy air pollution. In this paper, we proposed an advanced Seq2Seq (Sequence to Sequence) model called attention-based air quality forecasting model (ABAFM) whose RNN encoder is replaced by pure attention mechanism with position embedding. This improvement not only reduces the training time of Seq2Seq model with attention but also enhances the robustness of Seq2Seq models. We implemented ABAFM in Olympic center and Dongsi monitoring stations in Beijing to forecast PM2.5 in future 24 hours. The experimental results showed that the proposed model outperformed the related arts, especially in sudden changes.

Cataract is defined as a lenticular opacity presenting usually with poor visual acuity. It is considered the most common cause of blindness. Early diagnosis and treatment can reduce the suffering of patients and prevent visual impairment from turning into blindness. Recently, cataract diagnosis applying pattern recognition is in a rising period. For retinal fundus images, the task is usually cataract classification. However, it needs complex manual processing, which demands dexterous people and time taking exertion. Besides, it faces the challenge of effective interpretability and dependability. In this paper, we develop a deep-learning algorithm to intuitively identify cataract attributes to solve these limitations. Our model, is a 18(50)-layer convolutional neural network that inputs retinal image in G channel and outputs the prediction with heatmap. The heatmap localizes the areas where most indicative of different levels of cataract. Furthermore, we extend the training strategy for the corresponding task, which aims at improving the performance of the network. Comparing with other methods in cataract classification, we succeeded to achieve state of the art accuracy of proposed method on detection and grading task. Most importantly, our model provides a compelling reason via localizing the areas revealing cataract in the image.

The diagnosis of breast cancer in the middle and early period is conducive to later treatment, but the current diagnosis rate is not very desirable. Using machine learning to predict the benign and malignant of breast cancer can provide some assist to doctors' treatment in clinical practice. In this paper, we have collected data from digitized images of a fine needle aspirate (FNA) of a breast mass. They describe characteristics of the cell nuclei presented in the image. This work adopts several feature selection methods to select the most related features for breast cancer diagnosis. Based on the selected features, four machine learning models, Support Vector Machine (SVM), Decision Tree (DT), AdaBoost and Random Forest (RF) are built and their performance are evaluated. The experimental results show that the accuracy of RF is higher than the other three methods.

At present, high road surface temperature (RST) is threatening the safety of expressway transportation. It can lead to accidents and damages to road, accordingly, people have paid more attention to RST forecasting. Numerical methods on RST prediction are often hard to obtain precise parameters, whereas statistical methods cannot achieve desired accuracy. To address these problems, this paper proposes GBELM-RBF method that utilizes gradient boosting to ensemble Radial Basis Function Extreme Learning Machine. To evaluate the performance of the proposed method, GBELM-RBF is compared with other ELM algorithms on the datasets of airport expressway and Badaling expressway during November 2012 and September 2014. The root mean squared error (RMSE), accuracy and Pearson Correlation Coefficient (PCC) of these methods are analyzed. The experimental results show that GBELM-RBF has the best performance. For airport expressway dataset, the RMSE is less than 3, the accuracy is 78.8% and PCC is 0.94. For Badaling expressway dataset, the RMSE is less than 3, the accuracy is 81.2% and PCC is 0.921.

The scale of the modern city has been expanding, which leads to a lot of serious environmental pollution problems. Among them, the air pollution problem is the most prominent. In order to control the air pollution in urban cities, the government has deployed a lot of air pollutant monitoring equipment which produce massive multi-dimensional time series data. Through the motif discovery and analysis of these multi-dimensional time series, we can find the relationships and the rules between air pollutants to provide support and suggestions for controlling the air pollution. In this paper, a novel method on motif discovery and analysis for large-scale multi-dimensional time series data is proposed. The new method can effectively find multi-dimensional motifs and the correlation between them as much as possible, which reveals the underlying rule of different air pollutants. It is validated on practical historical data of air pollutants in Beijing. The experimental results show that the proposed method could obtain better performance than the related work.

In recent years, air quality has become a severe environmental problem in China. Since bad air quality brought significant influences on traffic and people's daily life, how to predict the future air quality precisely and subtly, has been an urgent and important problem. In this paper, a Spatio-Temporal Extreme Learning Machine (STELM) method is proposed for air quality prediction. STELM considers temporal and spatial characteristics of air quality data and related meteorological data, constructs a prediction model based on ELM, and realizes air quality prediction with more than 80% precision. A prototype system is implemented and the experiments on practical air quality data in Beijing validate the effectiveness of our method and system.