The aim was to analyze the application of computed tomography (CT) images in the diagnosis of lesions, and the composition of traditional Chinese medicine (TCM) syndromes in children with lobar pneumonia. Lung CT image registration algorithm was constructed based on optimized mean square deviation (OMSD) algorithm, which was applied to CT images of 188 children patients with lobar pneumonia before

Damages and misfortunes caused by fire on ships have recently accelerated the creation of new approaches, development, and building the security and unchanging quality of the fire detection framework. Simultaneously, with the growing interest in better early fire detection and prevention, numerous frameworks are being created for the detection of progress, with control calculations having the task

With the enrichment of land subsidence monitoring means, data fusion of multisource land subsidence data has gradually become a research hotspot. The Interferometry Synthetic Aperture Radar (InSAR) is a potential Earth observation approach, and it has been verified to have a variety of applications in measuring ground movement, urban subsidence, and landslides but similar to Global Positioning System

This special issue delivers a platform in which researchers expose intersections between algorithm design, software platforms, and hardware architectures to deal with emerging challenges in the scientific field of management of water and water-dependent resources. Since the call for papers was announced in June 2019, this special issue has received 10 manuscripts. After a rigorous review process, 6

To explore the performance of the improved DenseNet network in diagnosing pulmonary nodules (PNs) and differentiating benign and malignant PNs, improved DenseNet network was applied to segment MRI images of 60 PN patients, which were defined as the test group, while those segmented by the traditional one were undertaken as the control group. The MRI results were compared with the pathological diagnostic

Objective. The study aimed to explore the efficacy of pulmonary surfactant (PS) combined with Mucosolvan in the diagnosis of meconium aspiration syndrome (MAS) in newborns through ultrasonic images of lung based on machine learning. Methods. 138 cases of infants with MAS were selected as the research subjects and randomly divided into PS group (n = 46), Mucosolvan group (n = 46), and combination group

Protein-protein interactions (PPIs) in plants are crucial for understanding biological processes. Although high-throughput techniques produced valuable information to identify PPIs in plants, they are usually expensive, inefficient, and extremely time-consuming. Hence, there is an urgent need to develop novel computational methods to predict PPIs in plants. In this article, we proposed a novel approach

This work was aimed at investigating image feature recognition and clinical nursing of children’s rheumatoid arthritis- (CRA-) related lung injury under maximum correlation minimum redundancy algorithm of machine learning. In this study, 18 children with CRA in the hospital were selected as the rheumatoid group to explore the nursing method, and 18 healthy children were selected as the control group

In this paper, we look into the optimal sales strategy of e-commerce platform which sells fresh-product advertising in advance-selling period and spot-selling period, respectively, when advance-selling and spot-selling integration mode is applied. The study found that, with the increase of advertising investment, the marginal benefit of e-commerce platform is declining; whether advertising during the

This paper aimed to explore the risk factors of peripherally inserted central catheter (PICC) related thrombosis (PICC-RT) in tumor patients mediated by ultrasound images under minimum variance (MV) algorithm and put forward the corresponding nursing intervention methods based on the risk factors. The smoothing algorithm, diagonal loading algorithm, and coherence factor algorithm were optimized via

Anomalies in time series, also called “discord,” are the abnormal subsequences. The occurrence of anomalies in time series may indicate that some faults or disease will occur soon. Therefore, development of novel computational approaches for anomaly detection (discord search) in time series is of great significance for state monitoring and early warning of real-time system. Previous studies show that

The study focused on segmentation effects of the improved algorithm of traditional neural network algorithm, small kernels two-path convolutional neural network (SK-TPCNN) combined with random forest (RF) algorithm on MRI images for patella, and the influencing factors of patellar dislocation during exercise. In this article, the MRI images for patellar dislocation patients were detected by virtue

This work aimed to explore the effects of image mosaic algorithm- (IMA-) based low-dose X-rays on deoxyribonucleic acid (DNA) damage of peripheral blood lymphocytes in cardiovascular patients. 84 patients with interventional cardiovascular surgeries were selected as the research objects and divided into coronary intervention surgery (A) group, coronary angiography (B) group, radiofrequency ablation

In this work, the related risk factors and perinatal outcomes of pregnant women with gestational diabetes mellitus (GDM) were analyzed based on color Doppler ultrasound (CDU) diagnosis. Backpropagation (BP) algorithm-based CDU imaging algorithm (BPC) was constructed in this study and applied in CDU images of 80 pregnant women with GDM. Besides, amplitude and phase estimation (APES) and low-complexity

Objective. It is to study the application of edge correction algorithm (ECA) in computed tomography (CT) medical image segmentation, explore its guiding significance in the analysis of clinical characteristics of children with refractory mycoplasma pneumoniae (RMPP), and discuss the therapeutic value of fiberoptic bronchoscopy bronchoalveolar lavage (BAL) for RMPP. Methods. The accuracy of ECA in CT

The objective of this study was to investigate the diagnosis of colonic polyps (CP) through the computed tomography (CT) images combined with colonoscopy based on Fourier central slice theorem algorithm. In this study, 86 patients with CP admitted to hospital were selected as research objects. CT imaging and colonoscopy were applied to diagnose the patients based on the algorithm of Fourier central

Objective. This work aimed to study the application of iterative reconstruction algorithm-based computed tomography (CT) imaging in the diagnosis of gastric cancer (GC). Methods. 40 cases of GC patients diagnosed by gastroscopy biopsy and pathology in hospital were retrospectively analyzed. Scanning images of the upper abdomen were obtained after plain scanning and double-phase enhanced scanning. Then

In this paper, the change characteristics of sagittal parameters of the patient’s body before and after anterior cervical surgery and rehabilitation effect were analyzed. The rotation transformation and perspective projection transformation were applied to construct a positive and lateral position (PLP) matching-based three-dimensional reconstruction (PLP-3DR) algorithm, which was compared with the

Cloud computing models use virtual machine (VM) clusters for protecting resources from failure with backup capability. Cloud user tasks are scheduled by selecting suitable resources for executing the task in the VM cluster. Existing VM clustering processes suffer from issues like preconfiguration, downtime, complex backup process, and disaster management. VM infrastructure provides the high availability

Objective. To explore the use of the noninvasive high-frequency oscillatory ventilation and CPAP ventilation mode in the treatment of neonatal respiratory distress syndrome and to compare the treatment effect and the incidence of complications and whether it can reduce the time to go to the hospital and the number of hospital stays. Methods. Seventy-four children with RDS treated in hospital were selected

The importance of automatic pollen recognition has been examined in several areas ranging from paleoclimate studies to some daily practice such as pollen hypersensitivity forecasting. This paper attempts to present an automatic 3D pollen image recognition method based on convolutional neural network. To achieve this purpose, high feature dimensions and complex posture transformation should be taken

Cervical cancer is frequently a deadly disease, common in females. However, early diagnosis of cervical cancer can reduce the mortality rate and other associated complications. Cervical cancer risk factors can aid the early diagnosis. For better diagnosis accuracy, we proposed a study for early diagnosis of cervical cancer using reduced risk feature set and three ensemble-based classification techniques

In recent years, competitive aerobics has developed rapidly in my country, and the corresponding sports injury risks have gradually increased. A number of studies have shown that due to the characteristics of aerobics itself, difficult movement requirements, fast-paced music accompaniment and coherent coordinated movements, athletes will suffer sports injuries if they are not paying attention. Therefore

In the field of noncontact human-computer interaction, it is of crucial importance to distinguish different surface electromyography (sEMG) gestures accurately for intelligent prosthetic control. Gesture recognition based on low sampling frequency sEMG signal can extend the application of wearable low-cost EMG sensor (for example, MYO bracelet) in motion control. In this paper, a combination of sEMG

With the continuous development of Earth science, soil temperature has received more and more attention in Earth system research as an important parameter. The change of soil temperature (Ts) in different regions and related time series is affected by many factors, which bring certain difficulties to the accuracy of soil temperature prediction and the robustness of the algorithm. In this paper, an

In order to improve the clarity and color fidelity of traffic images under the complex environment of haze and uneven illumination and promote road traffic safety monitoring, a traffic image enhancement model based on illumination adjustment and depth of field difference is proposed. The algorithm is based on Retinex theory, uses dark channel principle to obtain image depth of the field, and uses spectral

The work proposed a denoising speech method using deep learning. The predictor and target network signals were the amplitude spectra of the wavelet-decomposition vectors of the noisy audio signal and clean audio signal, respectively. The output of the network was the amplitude spectrum of the denoised signal. Besides, the regression network used the input of the predictor to minimize the mean square

Smart government is an important part of the smart world. The use of big data analysis technology can effectively improve the government’s ability of fine management. Taking China’s bike-sharing industry as the research object, we study the relationship between public-derived big data and industrial policy. First, a feature-enhanced short text clustering method is proposed to perform topic clustering

Software firms are interested in outsourcing and developing of software globally to the virtual crowd for minimizing the product cost and for increasing the software quality. Developments in information technology (IT) have changed the organizational working environment from centralized to disperse development working practices. As a result, companies have recognized the value of virtual world networks

This study aimed to compare the role of magnetic resonance imaging (MRI) and computed tomography (CT) images based on the low-rank matrix (LRM) denoising (LRMD) algorithm in the diagnosis of cerebral aneurysms (CAs). By comparing the role of MRI and CT in the diagnosis of CA, it would be helpful to formulate more reasonable diagnosis strategies and provide a solid foundation for clinical treatment

A decentralized randomized coordinate descent method is proposed to solve a large-scale linearly constrained, separable resource optimization problem with selfish agent. This method has a cheap computational cost and can guarantee an improvement of selected objective function without jeopardizing the others in each iteration. The convergence rate is obtained using an alternative gap benchmark of objective

Tourist image retrieval has attracted increasing attention from researchers. Mainly, supervised deep hash methods have significantly boosted the retrieval performance, which takes hand-crafted features as inputs and maps the high-dimensional binary feature vector to reduce feature-searching complexity. However, their performance depends on the supervised labels, but few labeled temporal and discriminative

The epicardial adipose tissue volume (EATV) was quantitatively measured by deep learning-based computed tomography (CT) images, and its correlation with coronary heart disease (CHD) was investigated in this study. 150 patients who underwent coronary artery CT examination in hospital were taken as research objects. Besides, patients from the observation group (group A) suffered from vascular stenosis

In radiotherapy, the location of the target area is very important. If the target area is small, the treatment is not complete, so the location of the target area is generally larger than the actual cancerous site. However, the damage of radiotherapy to normal cells is the same. In order to reduce the damage to the body as much as possible, we need to complete the most suitable target area. This paper

Evaluating the performance assessments of solvers (e.g., for computation programs), known as the solver benchmarking problem, has become a topic of intense study, and various approaches have been discussed in the literature. Such a variety of approaches exist because a benchmark problem is essentially a multicriteria problem. In particular, the appropriate multicriteria decision-making problem can

The advancement and rising of information technology have promoted the flipped classroom in an effective way. It flips knowledge transfer and knowledge internalization from two levels of teaching structure and teaching process, reversing the traditional teaching knowledge transfer in class and knowledge deepening after class from time and space. Although the use of flipped classrooms in ideological

This study aimed to explore the application effect of magnetic resonance imaging (MRI) based on deep learning in laparoscopic surgery for colorectal carcinoma (CRC). 40 patients with CRC who were diagnosed and required laparoscopic surgery were selected in the research. The MRI scan images of all patients were processed based on the convolutional neural network algorithm. The MRI images before and

Agriculture is suffering from the problem of low fertility and climate hazards such as increased pest attacks and diseases. Early prediction of pest attacks can be very helpful in improving productivity in agriculture. Insect pest (whitefly) attack has a high influence on cotton crop yield. Internet of Things solution is proposed to predict the whitefly attack to take prevention measures. An insect

Objective. To evaluate the three-dimensional ultrasound paper cleft lip and palate deformities in applications in prenatal diagnosis. Methods. 25 cases of cleft lip and palate fetus, 20–32 weeks of gestational age, with the maternal age of 22–44 years, were examined by prenatal ultrasound in our hospital; conventional two-dimensional ultrasound examination was performed after a cleft lip, and the application

This paper aimed to explore the application values of computed tomography (CT) imaging in the treatment of patients with severe traumatic brain injury (STBI), to provide help in the treatment of STBI. In this study, 86 patients with STBI were selected as the research objects and examined by CT based on a self-adaptive particle swarm algorithm (APSO). Besides, patients were treated with hyperbaric oxygen

Construction projects require a significant amount of money and other resources in order to carry them out in an effective way. Cost control is a critical step in ensuring the success of the project and increasing its value. Nowadays, the large-scale application of BIM technology has ushered a technical change in the development of construction projects, which has greatly increased the level and efficiency

Health is considered an important foundation for students’ success. However, with the accelerated pace of life, rising pressure from various parties, weak health awareness, lack of exercise time, and other reasons, students’ physical quality is generally declining, the incidence of health diseases is increasing, and the onset age tends to be younger. With the development of the concept of “health first

Financial data as a kind of multimedia data contains rich information, which has been widely used for data analysis task. However, how to predict the stock price is still a hot research problem for investors and researchers in financial field. Forecasting stock prices becomes an extremely challenging task due to high noise, nonlinearity, and volatility of the stock price time series data. In order

Gestures recognition based on surface electromyography (sEMG) has been widely used for human-computer interaction. However, there are few research studies on overcoming the influence of physiological factors among different individuals. In this paper, a cross-individual gesture recognition method based on long short-term memory (LSTM) networks is proposed, named cross-individual LSTM (CI-LSTM). CI-LSTM

With the rapid growth of population, more diverse crowd activities, and the rapid development of socialization process, group scenes are becoming more common, so the demand for modeling, analyzing, and understanding group behavior data in video is increasing. Compared with the previous work on video content analysis, factors such as the increasing number of people in the group video and the more complex

Different types of connectivity are available on smartphones such as WiFi, infrared, Bluetooth, GPRS, GPS, and GSM. The ubiquitous computing features of smartphones make them a vital part of our lives. The boom in smartphone technology has unfortunately attracted hackers and crackers as well. Smartphones have become the ideal hub for malware, gray ware, and spyware writers to exploit smartphone vulnerabilities

Crowdsourcing is a task-solving model in which human crowd is hired to solve a particular task. During the crowdsourcing process, the crowd selection is performed in order to select appropriate crowd workers for a specific task; without appropriate selection of crowd workers, the process of crowdsourcing is aimless. The main goal of this paper was to identify the features of crowd in crowdsourcing

The Legend of the Condor Heroes (LCH) is one of the fifteen well-known Wuxia novels penned by Jin Yong. It portrays a number of characters in the background of the Southern Song Dynasty. In this research, we attempt to analyze the relationship of characters in LCH based on social network, including network feature analysis, cluster analysis, and data visualization. Moreover, the approach can be extended

This work was aimed to study the risk factors and prognostic treatment for acute ischemic stroke (AIS) patients with early neurological deterioration (END) after intravenous thrombolytic therapy via compressed sensing algorithm-based magnetic resonance imaging (CS-MRI). 231 patients who were diagnosed with AIS were selected, and the final involved number of patients was 182. Patients with AIS were

With the rapid development of the information age, the development of industrial robots is also advancing by leaps and bounds. In the scenes of automobile, medicine, aerospace, and public service, we have fully enjoyed the convenience brought by industrial robots. However, with the continuous development of industrial robot-related concepts and technologies, human-computer interaction and cooperation

This study was to analyze the diagnostic effects of computed tomography (CT) and magnetic resonance imaging (MRI) in patients with cerebrovascular diseases (CVDs) based on low-rank matrix denoising (LRMD) algorithm. The LRMD algorithm was adopted for MRI diagnosis and CT diagnosis for comparative analysis. 129 CVD patients were selected as the research objects, 43 cases were diagnosed by CT, 43 cases

To explore the impact of different image registration algorithms on the diagnosis of visual path damage in patients with primary open angle glaucoma (POAG), 60 cases of suspected POAG patients were selected as the research objects. Shape-preserving scale invariant feature transform (SP-SIFT) algorithm, scale invariant feature transform (SIFT) algorithm, and Kanade-Lucas-Tomasi (KLT) algorithm were

This study was aimed at studying the pulmonary nodule (PN) classification and diagnosis through computed tomography (CT) images based on segmentation algorithms. 120 PN patients were taken as research subjects. Linear filter fine segmentation algorithm under 3D region growth was compared with the initial segmentation algorithm and applied to images of PN patients. The results showed that the segmentation

The study drew attention to the therapeutic effects of cold snare polypectomy guided by a deep convolutional neural network- (CNN-) based abdominal CT and hot snare polypectomy (HSP) on colonic and rectal polyps. Specifically, 90 patients were enrolled into a blank group, a control (Ctrl) group, and an experimental group. The blank group accepted HSP, the Ctrl accepted cold snare polypectomy, and the

To investigate the effectiveness of shear-wave elastography (SWE) based on e-health in diagnosing liver fibrosis in patients infected with hepatitis B virus (HBV) and provide theoretical basis for early diagnosis and treatment of patients with chronic HBV infection, in this study, 73 patients with chronic HBV infection who underwent e-health SWE examination were divided into chronic hepatitis B group

As a recognized complex dynamic system, the stock market has many influencing factors, such as nonstationarity, nonlinearity, high noise, and long memory. It is difficult to explain it simply through mathematical models. Therefore, the analysis and prediction of the stock market have been a very challenging job since long time. Therefore, this paper adopts an encoder-decoder model of attention mechanism

In the context of modern people increasingly paying attention to health and promoting aerobics, the amount of data and audiences of aerobics videos has grown rapidly, and its potential application value has attracted widespread attention from scientific research and industry perspectives. This article has integrated computer vision and deep learning related knowledge to realize the intelligent recognition

Internet of Things (IoT) has been considered as one of the emerging network and information technologies that can comprehend automatic monitoring, identification, and management through a network of smart IoT devices. The effective use of IoT in different areas has improved efficiency and reduced errors. The rapid growth of smart devices such as actuators, sensors, and wearable devices has made the

The application value of the convolutional neural network (CNN) algorithm in the diagnosis of sports knee osteoarthropathy was investigated in this study. A network model was constructed in this experiment for image analysis of magnetic resonance imaging (MRI) technology. Then, 100 cases of sports knee osteoarthropathy patients and 50 healthy volunteers were selected. Digital radiography (DR) images

Text segmentation of the URL domain name is a straightforward and convenient method to analyze users’ online behaviors and is crucial to determine their areas of interest. However, the performance of popular word segmentation tools is relatively low due to the unique structure of the website domain name (such as extremely short lengths, irregular names, and no contextual relationship). To address this