Skin-disease diagnosis from dermoscopic images is obstructed by small datasets and large intra-class variability. This paper proposes a classification method that integrates a bilinear Convolutional Neural Network (CNN) with a constrained triplet network using a matrix-similarity based joint loss function to improve skin disease classification performance. The bilinear CNN captures complex spatial correlations by computing the outer product of feature maps from two distinct CNN streams, followed by bilinear pooling to obtain second-order interactions, while a constrained triplet loss encourages embeddings from the same class to group closely and enforces separation between distinct classes. A similarity matrix derived from softmax outputs guides a weighted softmax loss. Experiments on the benchmark HAM10000 and ISIC-2019 datasets show that the proposed method achieves 93.37% accuracy on HAM10000 and 93.78% on ISIC-2019, which outperform recent state-of-the-art models. This framework demonstrates the benefits of combining metric learning with second-order feature pooling for robust skin disease classification.

accurate diagnosis of skin diseases, particularly melanoma is crucial for effective treatment. Traditional approaches for skin disease classification face challenges due to the diverse and subjective nature of skin lesion, therefore, it is difficult to distinguish between situations that have visual similarities. This paper presents a novel deep learning based model to address these challenges. The model integrates dual attention mechanisms with a bilinear Convolution Neural Network (CNN) to extract discriminative features from dermoscopic images. Generate an Attention Heat Maps (AHMs) for the crop regions of the input images to focus on significant regions and a novel loss function, which combined complementary entropy and cross-entropy for mitigating class imbalance issues and improve model performance. The proposed model achieving an accuracy of 94.24% on ISIC2019 and 92.86% on HAM10000.

Deep learning has been used extensively for medical image analysis applications, assuming the training and test data adhere to the same probability distributions. However, a common challenge arises when dealing with medical images generated by different systems or even the same system with varying parameter settings. Such images often contain diverse textures and noise patterns, violating the assumption. Consequently, models trained on data from one machine or setting usually struggle to perform effectively on data from another. To address this issue in ultrasound images, we proposed a Generative Adversarial Network (GAN) based model in this paper. We formulated image harmonization and denoising tasks as an image-to-image translation task, wherein we adapt the texture pattern and reduced noise in Carotid ultrasound images while keeping the image content (the anatomy) unchanged. The performance was evaluated using feature distribution and pixel-space similarity metrics. In addition, blood-to-tissue contrast and influence on computed risk markers (Grey scale median, GSM) were evaluated. The results showed that domain adaptation was achieved in both tasks (histogram correlation 0.920 (0.043) and 0.844 (0.062)), as compared to no adaptation (0.890 (0.077) and 0.707 (0.098)), and that the anatomy of the images was retained (structure similarity index measure e.g. the arterial wall 0.71 (0.09) and 0.80 (0.08)). In addition, the image noise level (contrast) did not change in the image harmonization task (-34.1 (3.8) vs -35.2 (4.1) dB) but was improved in the noise reduction task (-23.5 (3.2) vs -46.7 (18.1) dB). To validate the performance of the proposed model, we compare its results with CycleGAN, the current state-of-the-art model. Our model outperformed CycleGAN in both tasks. Finally, the risk marker GSM was significantly changed in the noise reduction but not in the image harmonization task. We conclude that domain translation models are powerful tools for improving ultrasound image while retaining the underlying anatomy, but downstream calculations of risk markers may be affected.

The author Dr. Min Chen is added to author list. The authors would like to apologize for any inconvenience caused.

With the fast usage of internet services, the Internet of Things (IoT) and edge machines are also connecting to the World Wide Web (WWW). The frequent utilization of these devices poses a significant amount of challenge to network security researchers to deal with advanced or sophisticated cyber threats. Traditional based antiviruses and Intrusion detection system (IDS) mechanisms failed to detect advanced cyber threats like minority and majority attacks. Therefore, an efficient mechanism is required to counter this type of cyber-attacks. This paper introduces an efficient IDS mechanism using several supervised machine learning algorithms. The machine learning classifiers are performed on the CIC-IDS dataset 2017 to analyze and inspect the performance of the presented model. The presented method displayed the average accuracy (99%), recall (100%) for four classifiers, namely, Random Forest (RF), Decision Tree (DT), Extra Tree (ET), and K-Nearest Neighbor (K-NN).

Skin cancer is one of the rapidly growing diseases in the world. Especially, millions of cases are reported every year by all types of skin cancer in America. Early detection of skin cancer using dermoscopy, the light source, and the magnification device are used to inspect the skin lesions. A dermatologist observed hypodermic structures are normally invisible. However, accurate and effective skin disease classification by humans is not straightforward and requires a long time of practice. Furthermore, it is often inaccurate and difficult to reproduce, being unable to completely use the long-term dependence connection between specific image key features and image labels even for experienced dermatologists. Therefore, it needs to develop a computer-aided diagnostic system for reliable skin cancer diagnosis. Classical methods focus on designing and combining hand-craft features from input data and face vanishing or exploding of loss gradient problem, whereas the bidirectional long short term memory (BLSTM) network does not need any prior knowledge or pre-designing, and it is an expert in keeping the associated information in both directions. Thus, to improve the classification performance for handling these problems, we proposed a hybrid classification method based on the deep convolutional neural network and stacked BLSTM network. Firstly, deep features are extracted from input skin disease facial images. Next, the sequential features among input data are learned using a dual BLSTM network, where dual BLSTM through max-pooling, the forward and backward long short term memory (LSTM) hidden states of both the feature matrix and its transpose concatenates for inputting into a dense, fully connected (FC) layer. Finally, the softmax function is used to classify skin disease images. To improve the generalization capability, we evaluate our method on two skin disease image datasets and compare their local image descriptors. The proposed method achieved the best mean accuracy of 91.73%, which shows significant improvements in skin disease classification compared with state-of-the-art skin disease classification methods.

Nowadays, skin disease among humans has been a common disease, especially in America millions of people are suffering from various kinds of skin disease. Usually, these diseases have hidden dangers which lead to human not only lack of self-confidence and psychological depression but also a risk of skin cancer. Diagnosis of these kinds of diseases usually required medical experts with high-level instruments due to a lack of visual resolution in skin disease images. Moreover, manual diagnosis of skin disease is often subjective, time-consuming, and required more human effort. Thus, there is a need to develop a computer-aided system that automatically diagnoses the skin disease problem. Moreover, most of the existing works in skin disease used convolutional neural networks (CNN) with classical loss functions, which limit the model to learn discriminative features from skin images. Thus to address the above mention problem we proposed a new framework by fine-tuning layers of ResNet152 and InceptionResNet-V2 models with a triplet loss function. In the proposed framework, first, we learning the embedding from input images into Euclidean space by using deep CNN ResNet152 and InceptionResNet-V2 model. Second, we compute L-2 distance among corresponding images from euclidean space to learn discriminative features of skin disease images by using triplet loss function. Finally, classify the input images using these L-2 distances. Human face skin disease images used in the proposed framework are acquired from the Hospital in Wuhan China. Experiment results and their analysis shows the effectiveness of the proposed framework which achieve better accuracy than many existing works in skin disease tasks.

Magnetic Resonance Imaging (MRI) is dominant modality for infant brain analysis. Segmenting the whole infant MRI brain into number of tissues such as Cerebrospinal fluid (CSF), White matter (WM), and Gray Matter (GM) are highly desirable in the clinical environment. However, traditional methods tend to be degrading due to low contrast between GM and WM in isointense phase (about 6–8 months of early life). Recently, Convolutional Neural Network (CNN) emerged as a robust intelligent approach to examine medical image. The UNet model is among the preferred CNN models that have been widely used for medical imaging applications and achieved excellent results. The UNet architecture is a combination of convolutional, pooling, and up-sampling layers. Recently, 3D-UNet architecture used to exploit 3D-contextual information of volumetric data in many applications. However, CNN faces challenge to distinguish the similar brain tissues. In this paper, we present a variant of 3D-UNet to extract the volumetric contextual information of medical data. We propose a novel combined architecture of dense connection, residual connection, and inception module. The proposed architecture contains three stages, namely the densely connected stage, a residual inception stage, and an up-sampling stage. Our proposed approach provides state-of-art results in comparison to other existing approaches. This suggested approach achieves dice scores of 0.95, 0.905, and 0.92 in CSF, WM, and GM tissues respectively.

Background and Objective: Nowadays computer-aided disease diagnosis from medical data through deep learning methods has become a wide area of research. Existing works of analyzing clinical text data in the medical domain, which substantiate useful information related to patients with disease in large quantity, benefits early-stage disease diagnosis. However, benefits of analysis not achieved well when the traditional rule-based and classical machine learning methods used; which are unable to handle the unstructured clinical text and only a single method is not able to handle all challenges related to the analysis of the unstructured text, Moreover, the contribution of all words in clinical text is not the same in the prediction of disease. Therefore, there is a need to develop a neural model which solve the above clinical application problems, is an interesting topic which needs to be explored. Methods:Thus considering the above problems, first, this paper present self-attention based recurrent convolutional neural network (RCNN) model using real-life clinical text data collected from a hospital in Wuhan, China. This model automatically learns high-level semantic features from clinical text by using bi-direction recurrent connection within convolution. Second, to deal with other clinical text challenges, we combine the ability of RCNN with the self-attention mechanism. Thus, self-attention gets the focus of the model on essential convolve features which have effective meaning in the clinical text by calculating the probability of each convolve feature through softmax. Results:The proposed model is evaluated on real-life hospital dataset and used measurement metrics as Accuracy and recall. Experiment results exhibit that the proposed model reaches up to accuracy 95.71%, which is better than many existing methods for cerebral infarction disease. Conclusions:This article presented the self-attention based RCNN model by combining the RCNN with self-attention mechanism for prediction of cerebral infarction disease. The obtained results show that the presented model better predict the cerebral infarction disease risk compared to many existing methods. The same model can also be used for the prediction of other disease risks.

Convolution and recurrent neural network have obtained remarkable performance in natural language processing(NLP). Moreover, from the attention mechanism perspective convolution neural network(CNN) is applied less than recurrent neural network(RNN). Because RNN can learn long-term dependencies and gives better results than CNN. But CNN has its own advantage, can extract high-level features by using its local fix size context at the input level. Thus, this paper proposed a new model based on RNN with CNN-based attention mechanism by using the merits of both architectures together in one model. In the proposed model, first, CNN learns the high-level features of sentence from input representation. Second, we used attention mechanism to get the attention of the model on the features which contribute much in the prediction task by calculating the attention score from features context generated from CNN filters. Finally, these features context from CNN with attention score are commonly used at the RNN to process them sequentially. To validate the model we experiment on three benchmark datasets. Experiment results and their analysis demonstrate the effectiveness of the model.

Deep learning algorithms have achieved remarkable results in natural language processing (NLP) and computer vision. Especially, deep learning methods such as convolution and recurrent neural networks have shown remarkable performance in text analytic task. Moreover, from the attention mechanism perspective convolutional neural network (CNN) is applied less than recurrent neural network (RNN). Because RNN can learn long-term dependencies and gives better results than CNN. But CNN has its own advantage, can extract high-level features invariant to the local translation by using its local fix size context at the input level. Thus, in this paper, we proposed a new model based on RNN with CNN-style self-attention mechanism by using the merits of both architectures together in one model. In the proposed model, first, CNN learns the high-level representation of words at the input level. Second, we used self-attention mechanism to get the attention of the model on the features which contribute much in the prediction task by calculating the attentive context vectors over hidden states representation generated from CNN. Finally, hidden state representations from CNN with attentive context vectors are commonly used at the RNN to process them sequentially. To validate the model we experiment on three benchmark datasets i.e. Movie review, Stanford sentiment treebank1, and treebank2. Experiment results and their analysis demonstrate the effectiveness of the proposed model.

This article has been removed: please see Elsevier Policy on Article Withdrawal (https://www.elsevier.com/about/our-business/policies/article-withdrawal). This article has been removed at the request of the Authors. The reason for removal relates to the sixth author submitting the paper (including associated Intellectual Property) without permission from the China Electronics Corporation, therefore violating its regulations and infringing its rights.

In face recognition, distinguishing identical twins faces is a challenging task because of the high level of correlation in facial appearance.Generally, facial recognition is easy to make mistakes when it comes to twins or similar faces. To deal with the high level of correlation in similar faces, we proposed a deep convolutional neural network (CNN) using triplet loss function to differentiate the identical twins. We applied a hybrid strategy by combining the deep CNN model, which learns an embedding from facial images to Euclidean space and triplet loss function to evaluate the L2 distance between facial images into Euclidean space, Obtained L2 distance shows the level of similarity between corresponding faces. We implemented two different CNN models on our raw pixel images; additionally, we used different techniques to reduce the overfitting problem such as dropout and batch normalization, additionally L2 regularization. Our method achieves the best mean validation accuracy above 87.2%.

Nowadays neural attention model doing good in many tasks of natural language processing (NLP). More specifically attention mechanism has been used widely with convolutional neural network (CNN) and recurrent neural network (RNN) for many task of NLP. But the sentiment classification of short text is a challenging task because it contains limited contextual information. Thus, we proposed a new recurrent convolutional attention neural model for sentiment classification of the short text by using attention mechanism with RCNN (recurrent convolutional neural network). In proposed model attention score is calculated by averaging hidden units (feature maps) generated from LSTM (long short-term memory). Then we combined this attention score with recurrent convolution-based encoded text features to get final sentence representation. Here attention will focus on important text features and recurrent convolution makes full use of limited contextual information by processing sentence representation through different windows sizes with specialized recurrent convolution operation. Validation of the proposed model is done through experimentation with three benchmark datasets i.e. MR, SSTl, and SST2. Achieved results exhibit that our model performs better than many existing baselines works on all three datasets.

In the medical domain, 3D convolutional neural network has accomplished excellent results in analysing brain MRI. Inspired by the dense connection, dilation, types of feature fusion methods and availability of essential standards of information presented in multi-modalities and hierarchical knowledge transfers, we present a unique solution for automatically segmenting high-grade glioblastoma, which is a critical type of brain tumor leading to a large number of deaths in the world. The improved survival rates during early detection of this tumor encourage us to provide our contribution using popular machine learning techniques, such as deep learning. In such contribution, the convolutional layer automatically handles the learning feature with a vital concept of receptive field. The deep network allows increased receptive field for learning excellent information. However, this method can be halted by limited memory, especially in building a deep hierarchical architecture. Our model provides a solution towards the path of building a deep, dense architecture by implementing the dilation layer with dense pattern. The receptive field can be increased exponentially and the excessive use of convolution layers can be reduced to reduce parameters and allow the efficient utilisation of GPU memory. Effectively combining local and global contextual information in our model helps in further understanding of different tumor types. We propose to segment the entire tumor hierarchically and use its information to segment its sub-regions, thereby enhancing the tumor and tumor core. The network trained and validated with Brats 2018 data set can achieve dice scores of 0.8480, 0.8574 and 0.8219. Results verify the utilisation of dense connectivity with mixed dilated and conventional convolutional layers. To learn the global contextual information, we initially increase and then decrease the dilation rated with dense connections to minimise the possible loss of information due to the successive dilated rates. Our experimental results verify our innovative work for utilising this type of connectivity in medical image segmentation.

With the rapid growth of biomedical and healthcare data, machine learning methods are used in more and more work to predict disease risk. However, most works use single-mode data to predict disease risk and only few works use multimodal data to predict disease risk. Thus, a new multimodal data-based recurrent convolutional neural network (MD-RCNN) for disease risk prediction is proposed. This model not only can use patient's structured data and text data, but also can extract structured and unstructured features in fine-grained. Furthermore, in order to obtain the highly non-linear relationships between structured data and unstructured data, we use deep belief network (DBN)to fuse the features. Finally, we experiment with the medical big data of a Chinese two grade hospital during 2013–2015. Experimental results show that the accuracy of MD-RCNN algorithm can reaches 96% and outperforms several state-of-the-art methods.

Deep learning algorithms have achieved remarkable results in the natural language processing(NLP) and computer vision. Hence, a trend still going on to use these algorithms, such as convolution and recurrent neural networks, for text analytic task to extract useful information. Features extraction is one of the important reasons behind the success of these networks. Moreover passing features from one layer to another layer within the network and one network to another network have done. However multilevel and multitype features fusion remains unexplored in sentiment analysis. So, in this paper, we use three datasets to display the advantages of extracting and fusing multilevel as well as multitype features from different neural networks. Multilevel features are from different layers of the same network, and multitype features are from different network architectures. Experiment results demonstrate that the proposed model based on multilevel and multitype weighted features fusion outperforms than many exiting works with an accuracy of 80.2%, 48.2%, and 87.0% on MR, SST1, and SST2 datasets respectively.

This paper presents the analysis of real-life medical big data obtained from a hospital in central China from 2013 to 2015 for risk assessment of cerebral infarction disease. We propose a new recurrent convolutional neural network (RCNN)-based disease risk assessment multimodel by utilizing structured and unstructured text data from the hospital. In the proposed model, the convolutional layer becomes a bidirectional recurrent neural network by utilizing the intra-layer recurrent connection within the convolutional layer. Each neuron within convolutional layer receives feedforward and recurrent inputs from the previous unit and neighborhood, respectively. In addition to step-by-step recurrent operation, the region of context capture increases, thereby facilitating fine-grain feature extraction. Furthermore, we use a data parallelism approach over multimodel data during training and testing of the proposed model. Results show that the data parallelism approach leads to fast conversion speed. The RCNN-based model works differently from the traditional convolutional neural network and other typical methods. The proposed model exhibits a prediction accuracy of 96.02%, which is higher than those of typical existing methods.

At present, big data is very popular, because it has proved to be much successful in many fields such as social media, E-commerce transactions, etc. Big data describes the tools and technologies needed to capture, manage, store, distribute, and analyze petabyte or larger-sized datasets having different structures with high speed. Big data can be structured, unstructured, or semi structured. Hadoop is an open source framework that is used to process large amounts of data in an inexpensive and efficient way, and job scheduling is a key factor for achieving high performance in big data processing. This paper gives an overview of big data and highlights the problems and challenges in big data. It then highlights Hadoop Distributed File System (HDFS), Hadoop MapReduce, and various parameters that affect the performance of job scheduling algorithms in big data such as Job Tracker, Task Tracker, Name Node, Data Node, etc. The primary purpose of this paper is to present a comparative study of job scheduling algorithms along with their experimental results in Hadoop environment. In addition, this paper describes the advantages, disadvantages, features, and drawbacks of various Hadoop job schedulers such as FIFO, Fair, capacity, Deadline Constraints, Delay, LATE, Resource Aware, etc, and provides a comparative study among these schedulers.