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It is the intention of this paper to demonstrate that environmental technology must be supplemented by other tools to be able to solve environmental problems properly. Five cases are used to illustrate the possibilities of ecological engineering, a new engineering field based on ecology, as chemical engineering is based on chemistry. It encompasses restoration of ecosystems, utilization of ecosystems to the benefit of both mankind and nature, construction of ecosystems, and ecologically sound planning of ecosystems from a holistic point of view. Ecological engineering requires a good knowledge of the system properties of ecosystems to be able to fully utilize the possibilities that ecosystem management offers. Models reflecting the ecosystem properties are furthermore needed to be able to quantify the effects of the ecological engineering solutions to the environmental problems. This is clearly demonstrated in two of the five case studies presented in the paper.

Unobtrusive Authentication Using ACTIvity-Related and Soft BIOmetrics (ACTIBIO) is an EU Specific Targeted Research Project (STREP) where new types of biometrics are combined with state-of-the-art unobtrusive technologies in order to enhance security in a wide spectrum of applications. The project aims to develop a modular, robust, multimodal biometrics security authentication and monitoring system, which uses a biodynamic physiological profile, unique for each individual, and advancements of the state of the art in unobtrusive behavioral and other biometrics, such as face, gait recognition, and seat-based anthropometrics. Several shortcomings of existing biometric recognition systems are addressed within this project, which have helped in improving existing sensors, in developing new algorithms, and in designing applications, towards creating new, unobtrusive, biometric authentication procedures in security-sensitive, Ambient Intelligence environments. This paper presents the concept of the ACTIBIO project and describes its unobtrusive authentication demonstrator in a real scenario by focusing on the vision-based biometric recognition modalities.

The electrocardiogram (ECG) is one of the most widely used diagnostic instruments in medicine and healthcare. Deep learning methods have shown promise in healthcare prediction challenges involving ECG data. This paper aims to apply deep learning techniques on the publicly available dataset to classify arrhythmia. We have used two kinds of the dataset in our research paper. One dataset is the MIT-BIH arrhythmia database, with a sampling frequency of 125 Hz with 1,09,446 ECG beats. The classes included in this first dataset are N, S, V, F, and Q. The second database is PTB Diagnostic ECG Database. The second database has two classes. The techniques used in these two datasets are the CNN model, CNN + LSTM, and CNN + LSTM + Attention Model. 80% of the data is used for the training, and the remaining 20% is used for testing. The result achieved by using these three techniques shows the accuracy of 99.12% for the CNN model, 99.3% for CNN + LSTM, and 99.29% for CNN + LSTM + Attention Model.

At present, there are some problems in the process of human motion recognition, such as poor timeliness and low fault tolerance rate. How to effectively identify the motion process accurately has become a hot spot in the optimization system. In the existing research studies, the recognition accuracy is not very good and the response time is long. To end this issue, the paper proposed an information processing system and optimization method of human motion recognition based on the GA-BP neural network algorithm. Firstly, a human motion recognition system based on dynamic capture recognition technology is designed, which realizes the recognition of motion information from common postures such as action span, speed change, motion trajectory, and other aspects in the process of human motion. Secondly, the proposed algorithm is used to comprehensively analyse and evaluate the motion state. Finally, experiments are designed to verify and analyse the results. Compared to some baseline methods in human motion recognition information systems, the system in this paper based on the GA-BP neural network algorithm has the advantages of higher data accuracy and response speed, which can quickly and accurately identify the muscle group change in the process of human motion, and it can also provide customized motion suggestions based on the results.

Realizing accurate recognition of Chinese and English information is a major difficulty in English feature recognition. Based on this difficulty, this paper studies the English feature recognition model based on deep belief network classification algorithm and Big Data analysis. First, the basic framework based on deep belief network classification algorithm and Big Data analysis is proposed. Combined with the Big Data analysis training model, the English feature information is processed. Through the recognition of different English text features, the recognition and matching of English features are realized. Then the errors of deep belief network classification algorithm and Big Data analysis are evaluated. Second, this paper describes the quantitative evaluation of deep belief network classification algorithm and Big Data analysis in this system. In the evaluation, the language feature evaluation method is used to improve the evaluation function. At the same time, the deep belief network classification algorithm and Big Data analysis are used to self-study the model, and the English feature recognition method with strong applicability is established. Finally, the effectiveness of the recognition system is verified by the experiment.

The use of Internet technology has led to the availability of different multimedia data in various formats. The unapproved customers misuse multimedia information by conveying them on various web objections to acquire cash deceptively without the first copyright holder’s intervention. Due to the rise in cases of COVID-19, lots of patient information are leaked without their knowledge, so an intelligent technique is required to protect the integrity of patient data by placing an invisible signal known as a watermark on the medical images. In this paper, a new method of watermarking is proposed on both standard and medical images. The paper addresses the use of digital rights management in medical field applications such as embedding the watermark in medical images related to neurodegenerative disorders, lung disorders, and heart issues. The various quality parameters are used to figure out the evaluation of the developed method. In addition, the testing of the watermarking scheme is done by applying various signal processing attacks.

Pneumonia remains a threat to human health; the coronavirus disease 2019 (COVID-19) that began at the end of 2019 had a major impact on the world. It is still raging in many countries and has caused great losses to people’s lives and property. In this paper, we present a method based on DeepConv-DilatedNet of identifying and localizing pneumonia in chest X-ray (CXR) images. Two-stage detector Faster R-CNN is adopted as the structure of a network. Feature Pyramid Network (FPN) is integrated into the residual neural network of a dilated bottleneck so that the deep features are expanded to preserve the deep feature and position information of the object. In the case of DeepConv-DilatedNet, the deconvolution network is used to restore high-level feature maps into its original size, and the target information is further retained. On the other hand, DeepConv-DilatedNet uses a popular fully convolution architecture with computation shared on the entire image. Then, Soft-NMS is used to screen boxes and ensure sample quality. Also, K-Means++ is used to generate anchor boxes to improve the localization accuracy. The algorithm obtained 39.23% Mean Average Precision (mAP) on the X-ray image dataset from the Radiological Society of North America (RSNA) and got 38.02% Mean Average Precision (mAP) on the ChestX-ray14 dataset, surpassing other detection algorithms. So, in this paper, an improved algorithm that can provide doctors with location information of pneumonia lesions is proposed.

The continuous progress in modern medicine is not only the level of medical technology, but also various high-tech medical auxiliary equipment. With the rapid development of hospital information construction, medical equipment plays a very important role in the diagnosis, treatment, and prognosis observation of the disease. However, the continuous growth of the types and quantity of medical equipment has caused considerable difficulties in the management of hospital equipment. In order to improve the efficiency of medical equipment management in hospital, based on cloud computing and the Internet of Things, this paper develops a comprehensive management system of medical equipment and uses the improved particle swarm optimization algorithm and chicken swarm algorithm to help the system reasonably achieve dynamic task scheduling. The purpose of this paper is to develop a comprehensive intelligent management system to master the procurement, maintenance, and use of all medical equipment in the hospital, so as to maximize the scientific management of medical equipment in the hospital. Scientific Management. It is very necessary to develop a preventive maintenance plan for medical equipment. From the experimental data, it can be seen that when the system simultaneously accesses 100 simulated users online, the corresponding time for submitting the equipment maintenance application form is 1228 ms, and the accuracy rate is 99.8%. When there are 1000 simulated online users, the corresponding time for submitting the equipment maintenance application form is 5123 ms, and the correct rate is 99.4%. On the whole, the medical equipment management information system has excellent performance in stress testing. It not only predicts the initial performance requirements, but also provides a large amount of data support for equipment management and maintenance.

The graph neural network (GNN) based approach has been successfully applied to session-based recommendation tasks. However, in the face of complex and changing real-world situations, the existing session recommendation algorithms do not fully consider the context information in user decision-making; furthermore, the importance of context information for the behavior model has been widely recognized. Based on this, this paper presents a session recommendation model based on context-aware and gated graph neural networks (CA-GGNNs). First, this paper presents the session sequence as data of graph structure. Second, the embedding vector representation of each item in the session graph is obtained by using the gated graph neural network (GGNN). In this paper, the GRU in GGNN is expanded to replace the input matrix and the state matrix in the conventional GRU with input context captured in the session (e.g., time, location, and holiday) and interval context (representing the proportion of the total session time of each item in the session). Finally, a soft attention mechanism is used to capture users’ interests and preferences, and a recommendation list is given. The CA-GGNN model combines session sequence information with context information at each time. The results on the open Yoochoose and Diginetica datasets show that the model has significantly improved compared with the latest session recommendation methods.

Motivation. At present, the research methods for image genetics of Alzheimer’s disease based on machine learning are mainly divided into three steps: the first step is to preprocess the original image and gene information into digital signals that are easy to calculate; the second step is feature selection aiming at eliminating redundant signals and obtain representative features; and the third step is to build a learning model and predict the unknown data with regression or bivariate correlation analysis. This type of method requires manual extraction of feature single-nucleotide polymorphisms (SNPs), and the extraction process relies on empirical knowledge to a certain extent, such as linkage imbalance and gene function information in a group sparse model, which puts forward certain requirements for applicable scenarios and application personnel. To solve the problems of insufficient biological significance and large errors in the previous methods of association analysis and disease diagnosis, this paper presents a method of correlation analysis and disease diagnosis between SNP and region of interest (ROI) based on a deep learning model. It is a data-driven method, which has no obvious feature selection process. Results. The deep learning method adopted in this paper has no obvious feature extraction process relying on prior knowledge and model assumptions. From the results of correlation analysis between SNP and ROI, this method is complementary to other regression model methods in application scenarios. In order to improve the disease diagnosis performance of deep learning, we use the deep learning model to integrate SNP characteristics and ROI characteristics. The SNP feature, ROI feature, and SNP-ROI joint feature were input into the deep learning model and trained by cross-validation technique. The experimental results show that the SNP-ROI joint feature describes the information of the samples from different angles, which makes the diagnosis accuracy higher.

This paper aims to explore the influence of virtual reality technology interactive rehabilitation training system and PT and OT operation therapy on the exercise function, daily life activity ability (ADL), and the quality of life in patients with a sports injury. In this context, this paper mainly designed three experiments to test the virtual rehabilitation system: two action experiments (experiment 1), two experiments with actions in 3 different positions (experiment 2), and five different actions (experiment 3), and the motion intention recognition rate, average total time, and task completion degree of the three experiments were calculated. The virtual scene and hardware equipment were kept stable, and the human-machine interaction effect was good. The effectiveness of the proposed virtual reality rehabilitation training system is demonstrated from other aspects. The results showed that the average completion time of 5 volunteers was 57.72 seconds, with an average offline accuracy of 89.03%. In experiment 2, the five volunteers averaged 54.98 seconds, with an average offline accuracy of 91.73%. The average recognition accuracy of the training system reached 90%, demonstrating the effectiveness of the virtual reality rehabilitation training system in terms of motor intention recognition rate, average total use time, and task completion.

Music is an indispensable part of our life and study and is one of the most important forms of multimedia applications. With the development of deep learning and neural network in recent years, how to use cutting-edge technology to study and apply music has become a research hotspot. Music waveform is not only the main form of music frequency but also the basis of music feature extraction. This paper first designs a method of note extraction based on the fast Fourier transform principle of the audio signal packet route under the self-organizing map (SOM neural network) which can accurately extract the musical features of the note, such as amplitude, loudness, period, and so on. Secondly, the audio segments are divided into summary by adding window moving matching method, and the music features such as amplitude, loudness, and period of each bar are obtained according to the performance of audio signal in each bar. Finally, according to the similarity of the audio music theory of the adjacent summary of each bar, the audio segments are divided, and the music features of each segment are obtained. The traditional recurrent neural network (RNN) is improved, and the SOM neural network is used to recognize the audio emotion features. The final experimental results show that the proposed method based on SOM neural network and big data can effectively extract and analyze music waveform features. Compared with previous studies, this paper creatively proposed a new algorithm, which can more accurately and quickly extract and analyze the data sound waveform, and used SOM neural network to analyze the emotion model contained in music for the first time.

In order to study the effect of robots in the treatment of pancreatic cancer in the context of smart medical, this paper improves the robot recognition technology and data processing technology and improves the system kernel algorithm through the hash algorithm. Unlike the traditional sequencing method that directly uses the gray average value as a feature, the hash algorithm calculates the gray three-average value of each frame block and uses the difference of the three-average value of adjacent frame blocks to perform detection. Moreover, this paper proposes a detection and localization scheme based on hash local matching, which consists of two parts: coarse matching and fine matching. In addition, this paper designs a control experiment to analyze the effect of robots in the treatment of pancreatic cancer, counts multiple sets of data, and uses mathematical statistics to process and visually display the experimental data. The research shows that the robot has a good clinical effect in the treatment of pancreatic cancer.

Insulated Gate Bipolar Transistor (IGBT) is a high-power switch in the field of power electronics. Its reliability is closely related to system stability. Once failure occurs, it may cause irreparable loss. Therefore, potential fault diagnosis methods of IGBT devices are studied in this paper, and their classification accuracy is tested. Due to the shortcomings of incomplete data application in the traditional method of characterizing the device state based on point frequency parameters, a fault diagnosis method based on full frequency threshold screening was proposed in this paper, and its classification accuracy was detected by the Extreme Learning Machine (ELM) algorithm. The randomly generated input layer weight ω and hidden layer deviation lead to the change of output layer weight β and then affect the overall output result. In view of the errors and instability caused by this randomness, an improved Finite Impulse Response Filter ELM (FIR-ELM) training algorithm is proposed. The filtering technique is used to initialize the input weights of the Single Hidden Layer Feedforward Neural Network (SLFN). The hidden layer of SLFN is used as the preprocessor to achieve the minimum output error. To reduce the structural risk and empirical risk of SLFN, the simulation results of 300 groups of spectral data show that the improved FIR-ELM algorithm significantly improves the training accuracy and has good robustness compared with the traditional extreme learning machine algorithm.

Due to the complexity of medical images, traditional medical image classification methods have been unable to meet the actual application needs. In recent years, the rapid development of deep learning theory has provided a technical approach for solving medical image classification. However, deep learning has the following problems in the application of medical image classification. First, it is impossible to construct a deep learning model with excellent performance according to the characteristics of medical images. Second, the current deep learning network structure and training strategies are less adaptable to medical images. Therefore, this paper first introduces the visual attention mechanism into the deep learning model so that the information can be extracted more effectively according to the problem of medical images, and the reasoning is realized at a finer granularity. It can increase the interpretability of the model. Additionally, to solve the problem of matching the deep learning network structure and training strategy to medical images, this paper will construct a novel multiscale convolutional neural network model that can automatically extract high-level discriminative appearance features from the original image, and the loss function uses the Mahalanobis distance optimization model to obtain a better training strategy, which can improve the robust performance of the network model. The medical image classification task is completed by the above method. Based on the above ideas, this paper proposes a medical classification algorithm based on a visual attention mechanism-multiscale convolutional neural network. The lung nodules and breast cancer images were classified by the method in this paper. The experimental results show that the accuracy of medical image classification in this paper is not only higher than that of traditional machine learning methods but also improved compared with other deep learning methods, and the method has good stability and robustness.

The deep Q-network (DQN) is one of the most successful reinforcement learning algorithms, but it has some drawbacks such as slow convergence and instability. In contrast, the traditional reinforcement learning algorithms with linear function approximation usually have faster convergence and better stability, although they easily suffer from the curse of dimensionality. In recent years, many improvements to DQN have been made, but they seldom make use of the advantage of traditional algorithms to improve DQN. In this paper, we propose a novel Q-learning algorithm with linear function approximation, called the minibatch recursive least squares Q-learning (MRLS-Q). Different from the traditional Q-learning algorithm with linear function approximation, the learning mechanism and model structure of MRLS-Q are more similar to those of DQNs with only one input layer and one linear output layer. It uses the experience replay and the minibatch training mode and uses the agent’s states rather than the agent’s state-action pairs as the inputs. As a result, it can be used alone for low-dimensional problems and can be seamlessly integrated into DQN as the last layer for high-dimensional problems as well. In addition, MRLS-Q uses our proposed average RLS optimization technique, so that it can achieve better convergence performance whether it is used alone or integrated with DQN. At the end of this paper, we demonstrate the effectiveness of MRLS-Q on the CartPole problem and four Atari games and investigate the influences of its hyperparameters experimentally.

With the rapid development of neural network technology, we have widely used this technology in various fields. In the field of language translation, the research on automatic detection technology of English verb grammatical errors is in a hot stage. The traditional manual detection cannot be applied to the current environment. Therefore, this paper proposes an automatic detection technology of English verb grammatical errors based on recurrent neural network (RNN) algorithm to solve this problem. Firstly, the accuracy and feedback speed of traditional manual detection and recurrent neural network RNN algorithm are compared. Secondly, a detection model which can be calculated according to grammatical order combined with context is designed. Finally, when the output verb result is inconsistent with the original text, it can automatically mark the error detection effect. The experimental results show that the algorithm model studied in this paper can effectively improve the detection accuracy and feedback efficiency and is more applicable and effective than the traditional manual detection method.

The COVID-19 pandemic has a significant negative effect on people’s health, as well as on the world’s economy. Polymerase chain reaction (PCR) is one of the main tests used to detect COVID-19 infection. However, it is expensive, time-consuming, and lacks sufficient accuracy. In recent years, convolutional neural networks have grabbed many researchers’ attention in the machine learning field, due to its high diagnosis accuracy, especially the medical image recognition. Many architectures such as Inception, ResNet, DenseNet, and VGG16 have been proposed and gained an excellent performance at a low computational cost. Moreover, in a way to accelerate the training of these traditional architectures, residual connections are combined with inception architecture. Therefore, many hybrid architectures such as Inception-ResNetV2 are further introduced. This paper proposes an enhanced Inception-ResNetV2 deep learning model that can diagnose chest X-ray (CXR) scans with high accuracy. Besides, a Grad-CAM algorithm is used to enhance the visualization of the infected regions of the lungs in CXR images. Compared with state-of-the-art methods, our proposed paper proves superiority in terms of accuracy, recall, precision, and F1-measure.

In this paper, we present a novel classifier based on fuzzy logic and wavelet transformation in the form of a neural network. This classifier includes a layer to predict the numerical feature corresponded to labels or classes. The presented classifier is implemented in brain tumor diagnosis. For feature extraction, a fractal model with four Gaussian functions is used. The classification is performed on 2000 MRI images. Regarding the results, the accuracy of the DT, KNN, LDA, NB, MLP, and SVM is 93.5%, 87.6%, 61.5%, 57.5%, 68.5%, and 43.6%, respectively. Based on the results, the presented FWNNet illustrates the highest accuracy of 100% with the fractal feature extraction method and brain tumor diagnosis based on MRI images. Based on the results, the best classifier for diagnosis of the brain tumor is FWNNet architecture. However, the second and third high-performance classifiers are the DT and KNN, respectively. Moreover, the presented FWNNet method is implemented for the segmentation of brain tumors. In this paper, we present a novel supervised segmentation method based on the FWNNet layer. In the training process, input images with a sweeping filter should be reshaped to vectors that correspond to reshaped ground truth images. In the training process, we performed a PSO algorithm to optimize the gradient descent algorithm. For this purpose, 80 MRI images are used to segment the brain tumor. Based on the results of the ROC curve, it can be estimated that the presented layer can segment the brain tumor with a high true-positive rate.

With the advent of the era of big data (BD), people’’s living standards and lifestyle have been greatly changed, and people’s requirements for the service level of the service industry are becoming higher and higher. The personalized needs of customers and private customization have become the hot issues of current research. The service industry is the core enterprise of the service industry. Optimizing the service industry supply network and reasonably allocating the tasks are the focus of the research at home and abroad. Under the background of BD, this paper takes the optimization of service industry supply network as the research object and studies the task allocation optimization of service industry supply network based on the analysis of customers’ personalized demand and user behavior. This paper optimizes the supply chain network of service industry based on genetic algorithm (GA), designs genetic operator, effectively avoids the premature of the algorithm, and improves the operation efficiency of the algorithm. The experimental results show that when m = 8 and n = 40, the average running time of the improved GA is 54.1 s. The network optimization running time of the algorithm used in this paper is very fast, and the stability is also higher.

The recommendation system in the online medical consultation website is a system to assist patients to find appropriate doctors. Based on the analysis of the current situation of the development of an online medical community (Haodf.com) in China, this paper puts forward recommendation suggestions of finding the right hospital and doctor to promote the rapid integration of Internet technology and traditional medical services. A new recommendation model called Probabilistic Matrix Factorization integrated with Convolutional Neural Network (PMF-CNN) is proposed in the paper. Doctors’ data in Haodf.com were used to evaluate the performance of our system. The model improves the performance of medical consultation recommendations by fusing review text and doctor information based on CNN (Convolutional Neural Network). Specifically, CNN is used to learn the feature representation of the review text and the doctors’ information. Furthermore, the extended matrix factorization model is exploited to fuse the review information feature and the initial value of the doctors’ information for recommendation. As is shown in the experimental results on Haodf.com datasets, the proposed PMF-CNN achieves better recommendation performances than the other state-of-the-art recommendation algorithms. And the recommendation system in an online medical website improves the utilization efficiency of doctors and the balance of public health resources allocation.

Brain tumors are one of the most deadly diseases with a high mortality rate. The shape and size of the tumor are random during the growth process. Brain tumor segmentation is a brain tumor assisted diagnosis technology that separates different brain tumor structures such as edema and active and tumor necrosis tissues from normal brain tissue. Magnetic resonance imaging (MRI) technology has the advantages of no radiation impact on the human body, good imaging effect on structural tissues, and an ability to realize tomographic imaging of any orientation. Therefore, doctors often use MRI brain tumor images to analyze and process brain tumors. In these images, the tumor structure is only characterized by grayscale changes, and the developed images obtained by different equipment and different conditions may also be different. This makes it difficult for traditional image segmentation methods to deal well with the segmentation of brain tumor images. Considering that the traditional single-mode MRI brain tumor images contain incomplete brain tumor information, it is difficult to segment the single-mode brain tumor images to meet clinical needs. In this paper, a sparse subspace clustering (SSC) algorithm is introduced to process the diagnosis of multimodal MRI brain tumor images. In the absence of added noise, the proposed algorithm has better advantages than traditional methods. Compared with the top 15 in the Brats 2015 competition, the accuracy is not much different, being basically stable between 10 and 15. In order to verify the noise resistance of the proposed algorithm, this paper adds 5%, 10%, 15%, and 20% Gaussian noise to the test image. Experimental results show that the proposed algorithm has better noise immunity than a comparable algorithm.

The majority of medical workers are eager to obtain realistic and real-time CT 3D reconstruction results. However, autonomous or involuntary motion of patients can cause blurring of CT images. For the 3D reconstruction scene of motion-blurred CT image, this paper consists of two parts: firstly, a GAN image translation network deblurring algorithm is proposed to remove blurred results. This algorithm adopts the clear image to supervise the training process of the blurred image, which creates solutions that are close to the clear image. Secondly, this paper proposes a Marching Cubes (MC) algorithm based on the fusion of golden section and isosurface direction smooth (GI-MC) for 3D reconstruction of CT images. The golden section algorithm is used to calculate the equivalent points and normal vectors, which reduces the calculation numbers from four to one. The isosurface direction smooth algorithm computes the mean value of the normal vector, so as to smooth the direction of all triangular patches in spatial arrangement. The experimental results show that for different blurred angle and blurred amplitude, comparing the results of the Shannon entropy ratio and peak signal-to-noise ratio, our GAN image translation network deblurring algorithm has better restoration than other algorithms. Furthermore, for different types of liver patients, the reconstruction accuracy of our GI-MC algorithm is 9.9%, 7.7%, and 3.9% higher than that of the traditional MC algorithm, Li’s algorithm, and Pratomo’s algorithm, respectively.

This paper presents an in-depth survey and performance evaluation of the Cat Swarm Optimization (CSO) Algorithm. CSO is a robust and powerful metaheuristic swarm-based optimization approach that has received very positive feedback since its emergence. It has been tackling many optimization problems and many variants of it have been introduced. However, the literature lacks a detailed survey or a performance evaluation in this regard. Therefore, this paper is an attempt to review all these works, including its developments and applications, and group them accordingly. In addition, CSO is tested on 23 classical benchmark functions and 10 modern benchmark functions (CEC 2019). The results are then compared against three novel and powerful optimization algorithms, namely Dragonfly algorithm (DA), Butterfly optimization algorithm (BOA) and Fitness Dependent Optimizer (FDO). These algorithms are then ranked according to Friedman test and the results show that CSO ranks first on the whole. Finally, statistical approaches are employed to further confirm the outperformance of CSO algorithm., 31 pages

Multifocus image fusion is the merging of images of the same scene and having multiple different foci into one all-focus image. Most existing fusion algorithms extract high-frequency information by designing local filters and then adopt different fusion rules to obtain the fused images. In this paper, a wavelet is used for multiscale decomposition of the source and fusion images to obtain high-frequency and low-frequency images. To obtain clearer and complete fusion images, this paper uses a deep convolutional neural network to learn the direct mapping between the high-frequency and low-frequency images of the source and fusion images. In this paper, high-frequency and low-frequency images are used to train two convolutional networks to encode the high-frequency and low-frequency images of the source and fusion images. The experimental results show that the method proposed in this paper can obtain a satisfactory fusion image, which is superior to that obtained by some advanced image fusion algorithms in terms of both visual and objective evaluations.

Human blood is an important medical detection index. With the development in clinical medical detection instruments and detection technology, the requirements for detection accuracy and efficiency have been gradually improved. Fluorescent immunochromatography is a new detection technique. It has the characteristics of high efficiency, convenience, no pollution, and wide detection range. Human blood can be detected quickly using fluorescent immunochromatography. At present, it has received great attention from the field of clinical testing. In this paper, a set of fluorescent immunochromatographic analyzer has been designed. It is mainly based on the principle of fluorescence immunochromatography. A new method of signal analysis and system design for fluorescent immunochromatography analyzer is proposed. By using the improved threshold function denoising algorithm, the quantitative detection of fluorescent immunochromatographic strip is realized. The concentration of pathogenic factors (cancer cells) in human serum can be measured conveniently and accurately. The system integrates many peripheral modules, including fluorescence signal acquisition, fluorescence signal processing, quantitative curve fitting, and test results. In this paper, the quantitative detection experiments of the system are carried out in three aspects: linearity, repeatability, and sensitivity. The experimental results show that the linear correlation coefficient is up to 0.9976, and the limit of detection is up to 0.05 ng/ml. The requirements of the system are satisfied. The system performance is good, and the quantitative result is accurate. Therefore, the establishment of a fluorescence analysis system is of great significance.

Different optimization techniques are used for the training and fine-tuning of feed forward neural networks, for the estimation of STATCOM voltages and reactive powers. In the first part, the paper presents the voltage regulation in IEEE buses using the Static Compensator (STATIC) and discusses efficient ways to solve the power systems featuring STATCOM by load flow equations. The load flow equations are solved using iterative algorithms such as Newton-Raphson method. In the second part, the paper focuses on the use of estimation techniques based on Artificial Neural Networks as an alternative to the iterative methods. Different training algorithms have been used for training the weights of Artificial Neural Networks; these methods include Back-Propagation, Particle Swarm Optimization, Shuffled Frog Leap Algorithm, and Genetic Algorithm. A performance analysis of each of these methods is done on the IEEE bus data to examine the efficiency of each algorithm. The results show that SFLA outperforms other techniques in training of ANN, seconded by PSO.

In order to improve the convergence and distribution of a many-objective evolutionary algorithm, this paper proposes an improved NSGA-III algorithm based on weight vector adjustment (called NSGA-III-WA). First, an adaptive weight vector adjustment strategy is proposed to decompose the objective space into several subspaces. According to different subspace densities, the weight vector is sparse or densely adjusted to ensure the uniformity of the weight vector distribution on the Pareto front surface. Secondly, the evolutionary model that combines the new differential evolution strategy and genetic evolution strategy is proposed to generate new individuals and enhance the exploration ability of the weight vector in each subspace. The proposed algorithm is tested on the optimization problem of 3–15 objectives on the DTLZ standard test set and WFG test instances, and it is compared with the five algorithms with better effect. In this paper, the Whitney–Wilcoxon rank-sum test is used to test the significance of the algorithm. The experimental results show that NSGA-III-WA has a good effect in terms of convergence and distribution.

Distributed Compressed Sensing (DCS) is an important research area of compressed sensing (CS). This paper aims at solving the Distributed Compressed Sensing (DCS) problem based on mixed support model. In solving this problem, the previous proposed greedy pursuit algorithms easily fall into suboptimal solutions. In this paper, an intelligent grey wolf optimizer (GWO) algorithm called DCS-GWO is proposed by combining GWO and q-thresholding algorithm. In DCS-GWO, the grey wolves’ positions are initialized by using the q-thresholding algorithm and updated by using the idea of GWO. Inheriting the global search ability of GWO, DCS-GWO is efficient in finding global optimum solution. The simulation results illustrate that DCS-GWO has better recovery performance than previous greedy pursuit algorithms at the expense of computational complexity.

Advances in the healthcare technology have positioned biomedical technology as a major driver in global knowledge-based economies. A successful healthcare intervention depends not only on the capability or experience of clinicians, but also the adequacy of medical instruments and assistive devices. In addition, the technical aids and assistive devices for elderly or people with severe motor disability are getting more attention due to our aging society all over the world, and they are widely used in daily life. As a result, medical mechatronics becomes an important emerging technology to improve healthcare. Medical mechatronics is the integration of technologies and knowledge from various domains [1], including biosignal sensing fusion, real-time clinical data analysis, electric and mechanical system design, assistive/rehabilitation robot development, and machine/deep learning algorithms. Although medical mechatronics has proven to be successful in healthcare applications, there still remain difficulties and challenges to overcome. For example, most previous assistive devices/robots were developed to provide patients with rehabilitation training in hospital. With the rapid growth of aging population, these assistive devices are required to have smaller size, cheaper production cost, and be safer in order to meet the requirement of in-house rehabilitation [2]. As a result, the medical mechatronic components in these assistive devices/robots need to be re-designed.The goal of this special issue is to bring together the researchers in these fields and present high-quality research on recent developments on medical mechatronics, and novel applications of medical mechatronics in healthcare, as well as the relevant prospect on opportunities and challenges. The selected eleven papers underwent a rigorous refereeing and revision process. Most of the studies were carried out on clinical data, which provides the results reported in this special issue a high confidence level. Moreover, most of the papers in this special issue include mechatronics and healthcare experts as coauthors, which is beneficial to open new methods and issues to medical experts in this multidisciplinary field.

Brain magnetic resonance imaging (MRI) classification into normal and abnormal is a critical and challenging task. Owing to that, several medical imaging classification techniques have been devised in which Learning Vector Quantization (LVQ) is amongst the potential. The main goal of this paper is to enhance the performance of LVQ technique in order to gain higher accuracy detection for brain tumor in MRIs. The classical way of selecting the winner code vector in LVQ is to measure the distance between the input vector and the codebook vectors using Euclidean distance function. In order to improve the winner selection technique, round off function is employed along with the Euclidean distance function. Moreover, in competitive learning classifiers, the fitting model is highly dependent on the class distribution. Therefore this paper proposed a multiresampling technique for which better class distribution can be achieved. This multiresampling is executed by using random selection via preclassification. The test data sample used are the brain tumor magnetic resonance images collected from Universiti Kebangsaan Malaysia Medical Center and UCI benchmark data sets. Comparative studies showed that the proposed methods with promising results are LVQ1, Multipass LVQ, Hierarchical LVQ, Multilayer Perceptron, and Radial Basis Function.

Relation extraction is one of the important research topics in the field of information extraction research. To solve the problem of semantic variation in traditional semisupervised relation extraction algorithm, this paper proposes a novel semisupervised relation extraction algorithm based on ensemble learning (LXRE). The new algorithm mainly uses two kinds of support vector machine classifiers based on tree kernel for integration and integrates the strategy of constrained extension seed set. The new algorithm can weaken the inaccuracy of relation extraction, which is caused by the phenomenon of semantic variation. The numerical experimental research based on two benchmark data sets (PropBank and AIMed) shows that the LXRE algorithm proposed in the paper is superior to other two common relation extraction methods in four evaluation indexes (Precision, Recall,F-measure, and Accuracy). It indicates that the new algorithm has good relation extraction ability compared with others.

Living systems are often maintained by information flows and, as such, they present interesting mathematical problems for example, in the modeling and analysis of spatial structures, self-organization, environmental interaction, behavior, and development. Biomedical signals extract information from the complex phenomena being measured, which are typically a time series having both a regular and random components. Solutions attempt to map general principles, which are used to model how the living systems work. Many researchers have been studying these problems because of their interesting mathematical features and because of their scientific importance. The focus of this special issue is the mathematical analysis and modeling of time series in living systems and biomedical signals. It is mostly interested in the related new development of both theoretical study and practical implementation, either with modeling, complexity, statistics, or signal transformation in living systems. The papers selected for this special issue represent a good panel in recent challenges. The topics of the research papers and review papers are connected with the living systems and biomedical signals, including modeling dynamical complexity in living systems, for example, network dynamics, mass action, dynamical systems theory, methods for analysis and characterization of dynamical complexity, biomedical signal analysis such as mathematical pattern analysis of biological signals, generative mechanisms of biological signal patterning, implementation of signal analysis algorithms, linking biological structure to biological signal generation, and intracellular signal processing, as well as related models and applications, such as systems theory, biological organization, and biomedical information processing. This special issue contains 31 papers. In the category of modeling dynamical complexity, L. Sena et al. present a fuzzy model to interpret data of drive performances from patients with sleep deprivation. S. Chen et al. review the modeling of biological intelligence for supply chain management system optimization. M. Li presents approximating ideal filters by systems of fractional order. L. T. Ko et al. present nested quantization index modulation for reversible watermarking and its application to healthcare information management systems. C. Cattani studies on the existence of wavelet symmetries in Archaea DNA. A. Ciancio and C. Cattani present separable transition density in the hybrid model for tumor-immune competition. Q. Guan et al. present solid dynamic models for analysis of stress and strain in human hearts. G. Xiong et al. present theorems and application of local activity of CNN with five state variables and one port. L. Fanea et al. present theoretical compartment modeling of DCE-MRI data based on the transport across physiological barriers in the brain. M. Crisan et al. present a multicriteria optimization model for the study of the efficacy of skin antiaging therapy. In the category of methods for analysis of dynamical complexity, L. Xu et al. present high resolution remotely sensed small target detection by imitating fly visual perception mechanism. J. Zhang et al. present target contour recovering for tracking people in complex environments. M. Li and W. Zhao present a report on CPNs for asymptotic identity in min-plus algebra. M. Xu and C. Wei present remotely sensed image classification by complex network eigenvalue and connected-degree. J. Wen et al. present a batch rival penalized expectation-maximization algorithm for Gaussian mixture clustering with automatic model selection. M. Strbac and D. Popovic show a software tool for the prosthetic foot modeling and stiffness optimization. T. Carletti and A. Filisetti present the stochastic evolution of a protocell: the Gillespie algorithm in a dynamically varying volume. In the category of biomedical signal analysis, S. Chen and X. Li review the annual progress of functional magnetic resonance imaging for imaging neural activity in the human brain. H. C. Hsin et al. present an adaptive coding pass scanning algorithm for optimal rate control in biomedical images. I. Chiorean et al. present a medicoeconomic index for photo-induced skin cancers. C. Vicas et al. present the influence of expert dependent variability over the performance of noninvasive fibrosis assessment at patients with chronic hepatitis C, by the means of texture analysis. G. Nut et al. present a finite element method applied to a problem of blood flow in vessels. S. Chen et al. summarize recent advances in morphological cell image analysis. C. Yao et al. present motion analysis of live objects by super-resolution fluorescence microscopy. H. Castillejos et al. present wavelet transform fuzzy algorithms for dermoscopic image segmentation. D. A. Mitrea et al. present abdominal tumor characterization and recognition using superior order cooccurrence matrices, based on ultrasound images. Q. Guan et al. present a method for modeling and representation of human hearts for volumetric measurement. X. Li et al. present characteristics of evoked potential multiple EEG recordings in patients with chronic pain by means of parallel factor analysis. A. I. Mitrea et al. present iterative methods for obtaining energy-minimizing parametric snakes, with application to medical imaging. K. Lu et al. present nonlocal means based denoising for medical images. K. T. Q. Dang et al. present detecting epileptic seizure from scalp EEG using Lyapunov spectrum. Of 67 submissions, 31 papers are selected in this special issue. Of course, the topics and papers are not an exhaustive representation of the area of biomedical signal processing and modeling complexity of living systems. It can be seen that although some solutions and models become available, most problems remain open and research is highly active in this field. In the near future, we expect more contributions that will address all of the key aspects raised above. Nonetheless, the special issue represents the recent concerns in the community and we have the pleasure of sharing them with the readers.

This paper presents an investigation aimed at drastically reducing the processing burden required by motor imagery brain-computer interface (BCI) systems based on electroencephalography (EEG). In this research, the focus has moved from the channel to the feature paradigm, and a 96% reduction of the number of features required in the process has been achieved maintaining and even improving the classification success rate. This way, it is possible to build cheaper, quicker, and more portable BCI systems. The data set used was provided within the framework of BCI Competition III, which allows it to compare the presented results with the classification accuracy achieved in the contest. Furthermore, a new three-step methodology has been developed which includes a feature discriminant character calculation stage; a score, order, and selection phase; and a final feature selection step. For the first stage, both statistics method and fuzzy criteria are used. The fuzzy criteria are based on the S-dFasArt classification algorithm which has shown excellent performance in previous papers undertaking the BCI multiclass motor imagery problem. The score, order, and selection stage is used to sort the features according to their discriminant nature. Finally, both order selection and Group Method Data Handling (GMDH) approaches are used to choose the most discriminant ones.