International Journal of Pattern Recognition and Artificial Intelligence

Automatic and accurate segmentation of tumor area from rectal CT image plays an extremely key role in the treatment and diagnosis of rectal cancer. This paper proposes the MR-U-Net network model. The improvement is that a pair of encoder and decoder is added longitudinally to the U-shaped structure, which is the network structure of the fifth layer, and a residual module is added horizontally to the encoder and decoder of each layer. This model is used to conduct targeted research on the automatic segmentation method of rectal cancer. [H. Gao et al., Rectal tumor segmentation method based on U-Net improved model, J. Comput. Appl.40(8) (2020) 2392–2397] also improved U-Net and used the same dataset as this paper, but the Dice coefficient of all targets was only 83.15%, and the Dice coefficient of small targets was only 87.17%. This paper evaluates the improved MR-U-Net network model with the three indicators of precision, recall and Dice coefficient, and finds that in comparison to Ref.  the precision is 95.13%, 2.29% higher than the former work, recall is 94.28%, higher than the former work by 0.34%, Dice coefficient of all targets is 88.45%, increased by 5.3% compared with the former work, and the small targets Dice coefficient is increased by 1.28%, which is the best optimization state of this paper. Experiments show that for datasets with extremely skewed positive and negative samples, the MR-U-Net network structure after improving the hyperparameters in the optimizer can more accurately segment the rectal CT tumor lesion area.

Analysis and extraction of effective results from the medical big data is very complex due to the existence of large volume of data and it is also very difficult to classify the risk of angiographic diseases. The challenges faced by the conventional methods are complexity in classifying the data and performance degradation for a large-sized dataset. Hence, a hybrid approach named Deep Recurrent Encoder Network and Spark Model for Angiographic Disease Risk Classification (DRE-NET) is developed in this research to achieve accurate results for classifying the risk of angiographic diseases using Spark architecture. The developed Adaptive RCOA integrates Rider Optimization Algorithm (ROA) and Chicken Swarm Optimization (CSO) with the adaptive concept. The disease risk classification process is obtained by the Recurrent Neural Network (RNN) and Deep Stacked Autoencoder (DSAE) in such a way that the weights are optimally trained by the developed Adaptive RCOA. The optimal solution is obtained by evaluating the fitness function such that the fitness with minimal error value is considered the best solution. Moreover, the developed Adaptive RCOA-based RNN+DSAE attained better result with the metrics such as specificity, accuracy, and sensitivity with the values of 100%, 97.69%, and 99.19%, respectively, using the KDD Cup dataset.

Predicting three-dimensional structure of a protein in the field of computational molecular biology has received greater attention. Most of the recent research works aimed at exploring search space, however with the increasing nature and size of data, protein structure identification and prediction are still in the preliminary stage. This work is aimed at exploring search space to tackle protein structure prediction with minimum execution time and maximum accuracy by means of quantile regressive dragonfly and structural class homolog-based deep learning (QRD-SCHDL). The proposed QRD-SCHDL method consists of two distinct steps. They are protein structure identification and prediction. In the first step, protein structure identification is performed by means of QRD optimization model to identify protein structure with minimum error. Here the protein structure identification is first performed as the raw database contains sequence information and does not contain structural information. An optimization model is designed to obtain the structural information from the database. However, protein structure gives much more insight than its sequence. Therefore, to perform computational prediction of protein structure from its sequence, actual protein structure prediction is made. The second step involves the actual protein structure prediction via structural class and homolog-based deep learning. For each protein structure prediction, a scoring matrix is obtained by utilizing structural class maximum correlation coefficient. Finally, the proposed method is tested on a set of different unique numbers of protein data and compared to the state-of-the-art methods. The obtained results showed the potentiality of the proposed method in terms of metrics, error rate, protein structure prediction time, protein structure prediction accuracy, precision, specificity, recall, ROC, Kappa coefficient and $F$-measure, respectively. It also shows that the proposed QRD-SCHDL method attains comparable results and outperformed in certain cases, thereby signifying the efficiency of the proposed work.

Plant diseases are a major threat to agricultural production. Reduced yield due to plant diseases can lead to immeasurable economic losses. Therefore, the detection and classification of crop diseases are of great significance. Current existing classification methods based on the single convolutional neural network (CNN) are not satisfactory for plant disease classification performance in a large number of classes. In this case, a CNN-based approach named multiclass plant EnsembleNet (MCPE) is proposed to address these problems. MCFN firstly adopts a data augmentation strategy-based AutoAugment enhance dataset. Next, an EnsembleNet including four CNNs is employed to classify plant species, with a new activation function, concatenated dynamic ReLU, which has better performance than conventional ReLU in the multiclass plant disease dataset. Then, training a diseases classifier for each plant, which is used to identify the types and severity of plant diseases. Experimental results on 61 plant diseases from 10 different plant species, over 40 000 images, show that MCFN outperforms the state-of-the-art methods in multiclass plant disease recognition and achieves a good identification accuracy of 97.5%. We believe that the method described in this paper can further improve the identification efficiency of plant diseases, thus providing a basis for the identification of other plant leaf diseases.

Obtaining discriminative features is one of the key problems in three-dimensional (3D) shape retrieval. Recently, deep metric learning-based 3D shape retrieval methods have attracted the researchers’ attention and have achieved better performance. The triplet-center loss can learn more discriminative features than traditional classification loss, and it has been successfully used in deep metric learning-based 3D shape retrieval task. However, it has a hard margin parameter that only leverages part of the training data in each mini-batch. Moreover, the margin parameter is often determined by experience and remains unchanged during the training process. To overcome the above limitations, we propose the soft margin triplet-center loss, which replaces the margin with the nonparametric soft margin. Furthermore, we combined the proposed soft margin triplet-center loss with the softmax loss to improve the training efficiency and the retrieval performance. Extensive experimental results on two popular 3D shape retrieval datasets have validated the effectiveness of the soft margin triplet-center loss, and our proposed 3D shape retrieval method has achieved better performance than other state-of-the-art method.

A brain tumor is a collection of irregular and needless cell development in the brain region, and it is considered a life-threatening disease. Therefore, early level segmentation and brain tumor detection with Magnetic Resonance Imaging (MRI) is more important to save the patient’s life. Moreover, MRI is more effective in identifying patients with brain tumors since the recognition of this modality is moderately larger than considering other imaging modalities. The classification of brain tumors is the most important, difficult task in medical imaging systems because of size, appearance and shape variations. In this paper, Competitive Poor and Rich Optimization (CPRO)-based Deep Quantum Neural Network (Deep QNN) is proposed for brain tumor classification. Additionally, the pre-processing process assists in eradicating noises and uses image intensity to eliminate the artifacts. The significant features are extracted from pre-processed image to perform a productive classification process. The Deep QNN classifier is employed for classifying the brain tumor regions. Besides, the Deep QNN classifier is trained by the developed CPRO approach, which is newly designed by integrating Poor and Rich Optimization (PRO) and Competitive Swarm Optimizer (CSO). The developed brain tumor detection model outperformed other existing models with accuracy, sensitivity and specificity of 94.44%, 97.60% and 93.78%.

This paper proposes an innovative image compression scheme by utilizing the Adaptive Discrete Wavelet Transform-based Lifting Scheme (ADWT-LS). The most important feature of the proposed DWT lifting method is splitting the low-pass and high-pass filters into upper and lower triangular matrices. It also converts the filter execution into banded matrix multiplications with an innovative lifting factorization presented with fine-tuned parameters. Further, optimal tuning is the most important contribution that is achieved via a new hybrid algorithm known as Lioness-Integrated Whale Optimization Algorithm (LI-WOA). The proposed algorithm hybridizes the concepts of both the Lion Algorithm (LA) and Whale Optimization Algorithm (WOA). In addition, innovative cosine evaluation is initiated in this work under the CORDIC algorithm. Also, this paper defines a single objective function that relates multi-constraints like the Peak Signal-to-Noise Ratio (PSNR) as well as Compression Ratio (CR). Finally, the performance of the proposed work is compared over other conventional models regarding certain performance measures.

Human facial expressions are an essential and fundamental component for expressing the state of the human mind. The automatic analysis of these nonverbal facial expressions has become a fascinating and quite challenging problem in computer vision, with its application in different areas, such as psychology, human–machine interaction, health, and augmented reality. Recently, deep learning (DL) has become a widespread technique for studying human nonverbal facial sentiment expressions, and some research attempts have been made to propose a certain model on this topic. The purpose of this paper is to apply the appropriate convolutional neural network (CNN) approach by adding several layers of different dimensions, which allows the CNN approach to efficiently classify human facial sentiment expressions with data augmentation capable of recognizing seven basic human facial expressions: anger, sadness, fear, disgust, happiness, surprise, and neutral. In particular, this study mainly proposes a convolution neural network architecture, as well as learning factors that minimize the memory space and total training time of the proposed network due to the shallow architecture of the model. Following that, we demonstrated our proposed model’s network complexity, computational cost, and classification accuracy on the three benchmark datasets: FER2013, KDEF, and JAFFE. As a result, our proposed approach achieves accuracy of $67.5\%$, $79.5\%$, $90.0\%$ in the FER2013, KDEF, and JAFFE, respectively, which is better compared to other state-of-the-art approaches.

This study presents a novel facial makeup detection scheme using fusion of multi-scale Local Binary Patterns (ms-LBP) texture methods and convolutional neural network (CNN) deep learning extractor. Facial makeups affect the accuracy of face recognition systems due to appearance alteration of individuals. In order to fuse the facial features, the proposed scheme first considers concatenation of extracted global histogram of a whole image using three different LBP operators. The LBP scales are used to extract and then concatenate the local histogram of overlapping and nonoverlapping blocks of images. This way utilizes the advantages of microtexton information of local primitives besides the extracted global textures. Finally, the extracted features using CNN feature extractor are combined with global and local multi-scale textures. In general, CNN deep learning extractor learns high-level discriminative characteristics of images and therefore the proposed system improves the facial makeup detection rate by involving both microtexton and discriminative information of facial images. In addition, the proposed method attempts to select the optimized subset of facial features to increase the detection performance of system by applying Particle Swarm Optimization (PSO) technique after feature level fusion. The paper uses Support Vector Machine (SVM) classifier for classifying the facial vectors into makeup or no-makeup classes. The proposed scheme is then evaluated using YMU, VMU and MIW facial makeup databases with consideration of light, medium and heavy makeups on several datasets. Experimental results analysis clarifies the effectiveness of proposed facial makeup detection framework of this study.

Due to their simplicity and efficiency, the margin-based Softmax losses are proposed to enhance feature discrimination in face recognition. Recently, the strategy of hard sample mining is incorporated to the margin-based Softmax losses for focusing the misclassified samples and achieves superior performance. However, the current mining-based Softmax losses indicate the sample difficultness only from the perspective of the negative cosine similarity, which is local and not robust. To obtain more discriminative deep face features, a novel adaptive hardness indicator Softmax (AHI-Softmax) loss is proposed in this paper to fully exploit the hardness information of samples. Our AHI-Softmax firstly defines a global sample hardness indicator function that integrates three difficultness factors to robustly indicate the level of “hardness” in numerical form. Then, a training stage indicator is incorporated to avoid the convergence issue. Finally, a novel sample-related modulation coefficient of the negative cosine similarity which combines the global and local hardness indicator will be defined to further enhance the differentiation of constraints imposed on samples. The experimental results on general face datasets, including LFW, AgeDB-30, CFP-FP, CALFW, CPLFW, MegaFace, IJB-B and IJB-C, show that our method can obtain more discriminative features and achieve superior verification and recognition results.

This paper presents a new fusion scheme for medical (CT-MRI) images which is based on the nonsubsampled shearlet transform (NSST). The various image pairs to be fused are obtained from primary and internet sources. Initially, the images are decomposed through NSST into general and detailed features. The smallest uni-value segment assimilating nucleus (SUSAN) and local sum of Gaussian weighted pixel intensities-based activity measures are proposed to fuse the detailed sub-bands and low-frequency sub-band of NSST, respectively, for faster execution of the algorithm. Visual and parametric comparison of the proposed scheme is done through five traditional fusion algorithms using nine fusion performance parameters. In addition, Wilcoxon signed ranks test is also applied to compare different methods scientifically with the proposed fusion scheme. It is observed that the presented method is better in retaining bone, calcification, cerebrospinal fluid (CSF), edema and tumor details of the source images and is faster than other classical fusion schemes. The fused images of the proposed method are suitable for locating the site of biopsy externally or incision location in the bone of the brain skull with minimum diagnostic time.

Ureteral stent tubes are important medical devices used to repair ureteral obstruction or injury. However, relevant experiments of ureteral stent tubes are usually time-consuming and expensive. This research introduces a mechanical model that can simulate the force and deformation of ureteral stents. In addition, a novel optimization algorithm called improved exploration-enhanced gray wolf optimizer (IEE-GWO) is proposed to optimize parameters of the model. In order to balance exploration and exploitation of gray wolf optimizer (GWO), a dimension learning-based hunting (DLH) search strategy and a nonlinear control parameter strategy are integrated into the IEE-GWO. The experimental results show that the proposed IEE-GWO has better performance, such as fast convergence speed and high solution quality. Furthermore, the novel approach can improve the accuracy of the mechanical modal.

In multi-round dialogue tasks, how to maintain the consistency of model answers is a major research challenge. Every answer to the model should be time dependent, causal, and logical. In order to maintain the consistency of the personality, dialogue style, and context of the model, it is necessary to retain the key information in the historical dialogue as much as possible so that the model can generate more accurate answers. Utterance rewriting is a technique that replenishes the information of the current sentence by analyzing the historical dialogue, so as to retain the key information. This paper mainly uses text augmentation, Maximum Mutual Information (MMI) method and character correction method based on Knuth–Morria–Pratt (KMP) algorithm to improve the effect of utterance rewriting generation. The number of original statement rewriting datasets is limited, and the cost of manual manufacturing is too high. By using the method of text data augmentation based on coreference resolution, the positive dataset that is missing from the statement rewriting dataset is repaired. At the same time, the existing datasets are expanded to increase the number of data. The generated results are optimized by using the MMI method, and the KMP character correction method is used to modify the wrong characters to improve the overall accuracy.