Advanced Machine-Learning Methods for Brain-Computer Interfacing

EBi-LSTM: an enhanced bi-directional LSTM for time-series data classification by heuristic development of optimal feature integration in brain computer interface

Generally, time series data is referred to as the sequential representation of data that observes from different applications. Therefore, such expertise can use Electroencephalography (EEG) signals to fetch data regarding brain neural activities in brain-computer interface (BCI) systems. Due to massive and myriads data, the signals are appealed in a non-stationary format that ends with a poor quality resolution. To overcome this existing issue, a new framework of enhanced deep learning methods is proposed. The source signals are collected and undergo feature extraction in four ways. Hence, the features are concatenated to enhance the performance. Subsequently, the concatenated features are given to probability ratio-based Reptile Search Algorithm (PR-RSA) to select the optimal features. Finally, the classification is conducted using Enhanced Bi-directional Long Short-Term Memory (EBi-LSTM), where the hyperparameters are optimized by PR-RSA. Throughout the result analysis, it is confirmed that the offered model obtains elevated classification accuracy, and thus tends to increase the performance.

Brain-computer interface: trend, challenges, and threats

Brain-computer interface (BCI), an emerging technology that facilitates communication between brain and computer, has attracted a great deal of research in recent years. Researchers provide experimental results demonstrating that BCI can restore the capabilities of physically challenged people, hence improving the quality of their lives. BCI has revolutionized and positively impacted several industries, including entertainment and gaming, automation and control, education, neuromarketing, and neuroergonomics. Notwithstanding its broad range of applications, the global trend of BCI remains lightly discussed in the literature. Understanding the trend may inform researchers and practitioners on the direction of the field, and on where they should invest their efforts more. Noting this significance, we have analyzed 25,336 metadata of BCI publications from Scopus to determine advancement of the field. The analysis shows an exponential growth of BCI publications in China from 2019 onwards, exceeding those from the United States that started to decline during the same period. Implications and reasons for this trend are discussed. Furthermore, we have extensively discussed challenges and threats limiting exploitation of BCI capabilities. A typical BCI architecture is hypothesized to address two prominent BCI threats, privacy and security, as an attempt to make the technology commercially viable to the society.

Face Recognition Method under Adaptive Image Matching and Dictionary Learning Algorithm

In this research, a robust face recognition method based on adaptive image matching and a dictionary learning algorithm was proposed. A Fisher discriminant constraint was introduced into the dictionary learning algorithm program so that the dictionary had certain category discrimination ability. The purpose was to use this technology to reduce the influence of pollution, absence, and other factors on face recognition and improve the recognition rate. The optimization method was used to solve the loop iteration to obtain the expected specific dictionary, and the selected specific dictionary was used as the representation dictionary in adaptive sparse representation. In addition, if a specific dictionary was placed in a seed space of the original training data, the mapping matrix can be used to represent the mapping relationship between the specific dictionary and the original training sample, and the test sample could be corrected according to the mapping matrix to remove the contamination in the test sample. Moreover, the feature face method and dimension reduction method were used to process the specific dictionary and the corrected test sample, and the dimensions were reduced to 25, 50, 75, 100, 125, and 150, respectively. In this research, the recognition rate of the algorithm in 50 dimensions was lower than that of the discriminatory low-rank representation method (DLRR), and the recognition rate in other dimensions was the highest. The adaptive image matching classifier was used for classification and recognition. The experimental results showed that the proposed algorithm had a good recognition rate and good robustness against noise, pollution, and occlusion. Health condition prediction based on face recognition technology has the advantages of being noninvasive and convenient operation.

Functional Evaluation of Percutaneous Coronary Intervention Based on CT Images of Three-Dimensional Reconstructed Coronary Artery Model

In order to explore the computerized tomography (CT) based on three-dimensional reconstruction of coronary artery model, the functional evaluation was made after percutaneous coronary intervention (PCI). In this study, 90 patients with coronary heart disease who received elective PCI were selected. The blood flow reserve fraction (FFR) and SYNTAX score were calculated by three-dimensional reconstruction of CT images, followed up for 2-4 years. According to the SYNTAX score, 0-22 points were defined as the low group (28 cases), 23-32 points as the medium group (33 cases), and 33 points as the high group (29 cases). In this paper, the accuracy, sensitivity, and specificity of CT images of three-dimensional reconstructed coronary artery model are 91%, 73%, and 62%, respectively. The follow-up results showed that the incidence of major adverse cerebrovascular events in the high group was significantly higher than that in the low group and the middle group, and the difference was statistically significant (P < 0.05). Pearson correlation analysis showed that SYNTAX score was related to serum total cholesterol (r = 0.234, P=0.003), triglyceride (r = 0.237, P=0.014), low-density lipoprotein cholesterol (r = 0.285, P=0.004), and ApoB/ApoA1 (R = 0.004). In this study, FFR is calculated by CT images based on three-dimensional reconstruction of coronary artery model, which can provide support for the diagnosis and treatment of coronary heart disease. SYNTAX score can be used as a risk predictor for PCI patients with coronary heart disease.

A comparative analysis of masking empirical mode decomposition and a neural network with feed-forward and back propagation along with masking empirical mode decomposition to improve the classification performance for a reliable brain-computer interface

In general, extraction and classification are used in various fields like image processing, pattern recognition, signal processing, and so on. Extracting effective characteristics from raw electroencephalogram (EEG) signals is a crucial role of the brain-computer interface for motor imagery. Recently, there has been a great deal of focus on motor imagery in the EEG signals since they encode a person's intent to do an action. Researchers have been using MI signals to assist paralyzed people and even move them on their own with certain equipment, like wheelchairs. As a result, proper decoding is an important step required for the interconnection of the brain and the computer. EEG decoding is a challenging process because of poor SNR, complexity, and other reasons. However, choosing an appropriate method to extract the features to improve the performance of motor imagery recognition is still a research hotspot. To extract the features of the EEG signal in the classification task, this paper proposes a Masking Empirical Mode Decomposition (MEMD) based Feed Forward Back Propagation Neural Network (MEMD-FFBPNN). The dataset consists of EEG signals which are first normalized using the minimax method and given as input to the MEMD to extract the features and then given to the FFBPNN to classify the tasks. The accuracy of the proposed method MEMD-FFBPNN has been measured using the confusion matrix, mean square error and which has been recorded up to 99.9%. Thus, the proposed method gives better accuracy than the other conventional methods.

Automatic Detection and Classification of Epileptic Seizures in Patients with Liver Cirrhosis and Overlapping Hev Infection Based on Deep Multimodal Fusion Technology

Liver cirrhosis is a clinical chronic developmental liver disease, which is caused by long-term or repeated effects of liver dysfunction, and there are more and more cases of epileptic seizures in patients with liver cirrhosis and HEV infection. This article aims to study how to analyze epileptic seizures in patients with liver cirrhosis and overlapping HEV infection based on deep multimodal fusion technology. This article proposes a deep learning neural network algorithm based on deep multimodal fusion technology, and how to use this algorithm to automatically detect and classify epileptic seizures. The data in the experiment in this article show that the prevalence of epilepsy accounts for 1% of the world's population, about 56.7 million people, and 1 in 25 people may have an epileptic seizure at some time in their lives, and in each person's life, the probability of seizures due to various reasons is 10%. In 2016, the proportion of males with cirrhosis reached 16%, females reached 8%, and males were 8% higher than females, which is a full double. The test results show that with the increase in patients with cirrhosis and overlapping HEV infection, the frequency of epileptic seizures is also getting higher and higher, indicating that the frequency of epileptic seizures has been increased in patients with cirrhosis and overlapping HEV infection. Therefore, it is imperative to analyze the epileptic seizures of patients with liver cirrhosis and overlapping HEV infection based on deep multimodal fusion technology.

Research on Mental Health Monitoring Scheme of Migrant Children Based on Convolutional Neural Network Based on Deep Learning

In recent years, with the acceleration of urbanization and the implementation of compulsory education, the pressure on students' study and life has increased, and the phenomenon of psychological and behavioral problems has become increasingly prominent. Therefore, the school has regarded students' mental health education as the top priority in teaching work. Effective expression classification can assist psychology researchers to study psychology and other disciplines and analyze children's psychological activities and mental states by classifying expressions, thereby reducing the occurrence of psychological behavior problems. Most of the current mainstream methods focus on the exploration of text explicit features and the optimization of representation models, and few works pay attention to deeper language expressions. Metaphors, as language expressions often used in daily life, are closely related to an individual's emotion, cognition, and psychological state. This paper studies children's smiling face recognition based on deep neural network. In order to obtain a better identification effect of mental health problems of children, this paper attempts to use multisource data, including consumption data, access control data, network logs, and grade data, and proposes a multisource data-based mental health problem identification algorithm. The main research focus is feature extraction, trying to use one-dimensional convolutional neural network (1D-CNN) to mine students' online patterns from online behavior sequences, calculate abnormal scores based on students' consumption data in the cafeteria, and describe the dietary differences among students. At the same time, this paper uses the students' psychological state data provided by the psychological center as a label to improve the deficiencies caused by the questionnaire. This paper uses the training set to train five common classification algorithms, evaluates them through the validation set, and selects the best classifier as our algorithm and uses it to identify students with mental health problems in the test set. The experimental results show that precision reaches 0.68, recall reaches 0.56, and F1-measure reaches 0.67.

Ultrasonic Image Features under the Intelligent Algorithm in the Diagnosis of Severe Sepsis Complicated with Renal Injury

This research was aimed at analyzing the diagnosis of severe sepsis complicated with acute kidney injury (AKI) by ultrasonic image information based on the artificial intelligence pulse-coupled neural network (PCNN) algorithm and at improving the diagnostic accuracy and efficiency of clinical severe sepsis complicated with AKI. In this research, 50 patients with sepsis complicated with AKI were collected as the observation group and 50 patients with sepsis as the control group. All patients underwent ultrasound examination. The clinical data of the two groups were collected, and the scores of acute physiology and chronic health assessment (APACHE II) and sequential organ failure assessment (SOFA) were compared. The ultrasonic image information enhancement algorithm based on artificial intelligence PCNN is constructed and simulated and is compared with the maximum between-class variance (OSTU) algorithm and the maximum entropy algorithm. The results showed that the PCNN algorithm was superior to the OSTU algorithm and maximum entropy algorithm in the segmentation results of severe sepsis combined with AKI in terms of regional consistency (UM), regional contrast (CM), and shape measure (SM). The acute physiology and chronic health evaluation (APACHE II) and sequential organ failure assessment (SOFA) scores in the observation group were substantially higher than those in the control group (P < 0.05). The interlobular artery resistance index (RI) in the observation group was substantially higher than that in the control group (P < 0.05). Moreover, the mean transit time (mTT) in the observation group was significantly higher than that in the control group (4.85 ± 1.27 vs. 3.42 ± 1.04), and the perfusion index (PI) was significantly lower than that in the control group (134.46 ± 17.29 vs. 168.37 ± 19.28), with statistical significance (P < 0.05). In summary, it can substantially increase ultrasonic image information based on the artificial intelligence PCNN algorithm. The RI, mTT, and PI of the renal interlobular artery level in ultrasound images can be used as indexes for the diagnosis of severe sepsis complicated with AKI.

Diagnostic Value of Image Features of Magnetic Resonance Imaging in Intracranial Hemorrhage and Cerebral Infarction

This study aimed to investigate the differential diagnosis value of routine magnetic resonance imaging (MRI) and magnetic resonance diffusion-weighted imaging (DWI) in hyperacute intracranial hemorrhage (HICH) and hyperacute cerebral infarction (HCI). Fifty-five patients with HICH were set as group A, and 55 patients with HCI were selected as group B. All the patients underwent routine MRI and DWI examinations. The morphological distribution and signal characteristics (low, high, or mixed) of the lesions in the two groups were recorded. The diagnostic accuracy, sensitivity, and specificity of routine MRI and DWI were compared for distinguishing HICH and HCI. The results suggested that the lesions in patients with HICH were mainly manifested as mixed signals (40 cases), while those in patients with HCI showed high signals (48 cases). HICH occurred in the basal ganglia in 44 cases, in the brain stem in 6 cases, in the cerebellum in 4 cases, in the cerebral cortex in 0 cases, and in the corpus callosum in 1 case. HCI occurred in the basal ganglia area, brain stem, cerebellum, cerebral cortex, and corpus callosum in 5, 3, 35, 12, and 0 cases, respectively. The diagnostic accuracy, specificity, and sensitivity of DWI for HICH and HCI were significantly higher than those of routine MRI (P < 0.05). It was indicated that compared with routine MRI, DWI was more effective in the diagnosis of HICH and HCI, with clearer and more accurate images and better diagnostic performance.

Intelligent Reconstruction Algorithm-Based Computed Tomography Images for Automatic Detection of Gastric Tumor

The aim of this study was to explore the application of computed tomography (CT) images in the diagnosis of gastric tumor under the intelligent reconstruction algorithm (IRA). 120 patients with gastric cancer were selected and all the patients underwent CT scanning, and CT images were analyzed based on the Feldkamp-Davis-Kress algorithm (FDK algorithm) to evaluate the imaging features of gastric lesions. According to biopsy or surgical pathology, the detection rate of CT images was calculated. The results showed that there were three pathological types of benign tumors (polyps, leiomyomas, and mesenchymomas) and three pathological types of malignant tumors (mesenchymomas, adenomas, and lymphomas). In addition, the detection rates of CT scans were different, reaching 94.2% on different orientations of the stomach, 90.7% of benign tumors, and 90.9% of malignant tumors, so the detection rate of different orientations was relatively high. CT images based on the FDK IRA could realize a high detection rate in diagnosis, accurately locate the lesion, and display the characteristics of the lesion and the metastasis of surrounding tissues; there were significant differences between benign and malignant gastric tumors in CT images, and the detection effect was obvious, which is worthy of clinical application and promotion.

Analysis of Apparent Diffusion Coefficient Value and Dynamic Contrast-Enhanced Magnetic Resonance Imaging Parameters of Prostate Cancer Patients after Diagnosis and Treatment with Magnetic Resonance Imaging

This research was aimed at exploring the changes in the apparent diffusion coefficient (ADC) and dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) parameters of prostate cancer (PCa) patients. Sixty PCa patients from the hospital were recruited as the research object, and dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) scans were performed to determine the shape, scope, and enhancement characteristics of prostate lesions and their relationship with surrounding tissues. The quantitative parameters of ADC and DCE-MRI were measured. There were 4 patients (6.67%) with a Gleason score of 6 and 15 patients (25%) with a 4 + 3 score. The ADC with Gleason = 6 is 0.81 ± 0.08 × 10⁻³ s/mm², the ADC with Gleason = 3 + 4 is 0.74 ± 0.07 × 10⁻³ s/mm², the ADC with Gleason = 4 + 3 is 0.73 ± 0.05 × 10⁻³ s/mm², the ADC with Gleason = 9 is 0.65 ± 0.06 × 10⁻³ s/mm², and the ADC with Gleason = 10 is 0.59 ± 0.07 × 10⁻³ s/mm². As the Gleason score increased, the ADC decreased and the permeation parameter transfer constant increased. When the ADC was combined with the permeability parameter transfer constant, the AUC of Gleason = 6 points and Gleason = 7 points was greatly different (P < 0.05). The 95% CI of the ADC combined permeability parameter transport constant when Gleason = 6 points and Gleason = 7 points was 0.898-0.934, the sensitivity was 75.4%, and the specificity was 86.2%. The ADC value was negatively correlated with Gleason score. The ADC value combined with VTC value has good diagnostic performance in evaluating the invasion of PCa, which is very important for making treatment plan and evaluating prognosis.

Deep Learning Algorithm-Based Magnetic Resonance Imaging Feature-Guided Serum Bile Acid Profile and Perinatal Outcomes in Intrahepatic Cholestasis of Pregnancy

This study was aimed to explore magnetic resonance imaging (MRI) based on deep learning belief network model in evaluating serum bile acid profile and adverse perinatal outcomes of intrahepatic cholestasis of pregnancy (ICP) patients. Fifty ICP pregnant women diagnosed in hospital were selected as the experimental group, 50 healthy pregnant women as the blank group, and 50 patients with cholelithiasis as the gallstone group. Deep learning belief network (DLBN) was built by stacking multiple restricted Boltzmann machines, which was compared with the recognition rate of convolutional neural network (CNN) and support vector machine (SVM), to determine the error rate of different recognition methods on the test set. It was found that the error rate of deep learning belief network (7.68%) was substantially lower than that of CNN (21.34%) and SVM (22.41%) (P < 0.05). The levels of glycoursodeoxycholic acid (GUDCA), glycochenodeoxycholic acid (GCDCA), and glycocholic acid (GCA) in the experimental group were dramatically superior to those in the blank group (P < 0.05). Both the experimental group and the blank group had notable clustering of serum bile acid profile, and the experimental group and the gallstone group could be better distinguished. In addition, the incidence of amniotic fluid contamination, asphyxia, and premature perinatal infants in the experimental group was dramatically superior to that in the blank group (P < 0.05). The deep learning confidence model had a low error rate, which can effectively extract the features of liver MRI images. In summary, the serum characteristic bile acid profiles of ICP were glycoursodeoxycholic acid, glycochenodeoxycholic acid, and glycocholic acid, which had a positive effect on clinical diagnosis. The toxic effects of high concentrations of serum bile acids were the main cause of adverse perinatal outcomes and sudden death.

Computed Tomography Image under Artificial Intelligence Algorithm to Evaluate the Nursing and Treatment Effect of Pemetrexed Combined Platinum-Based Chemotherapy on Elderly Lung Cancer

This study was to evaluate the clinical efficacy of pemetrexed combined with platinum-based chemotherapy in the treatment of elderly lung cancer using electronic computed tomography (CT) images based on artificial intelligence algorithms. In this study, 80 elderly patients with lung cancer treated were selected and randomly divided into two groups: patients treated with pemetrexed combined with cisplatin were included in the pemetrexed group and patients treated with docetaxel combined with cisplatin were included in the docetaxel group, with 40 cases in each group. The DenseNet network was compared with the Let Net-5 and ResNet model and applied to the CT images of 80 elderly patients with lung cancer. The diagnosis accuracy of the DenseNet network (97.4%) was higher than that of the Let Net-5 network (80.1%) and ResNet model (95.5%). Carcinoembryonic antigen (CEA), cytokeratin fragment antigen 21–1 (CYFRA 21–1), and squamous cell-associated antigen (SCC) after chemotherapy in the pemetrexed group and docetaxel group were all lower than those before chemotherapy, showing statistically obvious differences (P < 0.05). The satisfaction degree of nursing care in the pemetrexed group (92.67%) was significantly higher than that in the docetaxel group (85.62%), and the difference was statistically significant (P < 0.05). Adverse reactions such as fatigue, diarrhea, and neutrophils in the pemetrexed group were lower than those in the docetaxel group, and the difference was statistically great (P < 0.05). The DenseNet convolutional neural network has high diagnostic accuracy; methotrexate combined with platinum chemotherapy can improve the chemotherapy effect in elderly patients with lung cancer, with low degree of adverse reactions and good overall tolerance, which can be used as the first-line treatment for elderly patients with lung cancer.

Artificial Intelligence Limb Rehabilitation System on Account of Virtual Reality Technology on Long-Term Health Management of Stroke Patients in the Context of the Internet

This study was aimed at discussing artificial intelligence (AI) limb rehabilitation system on account of virtual reality (VR) on long-term health management of stroke patients. In the study, AI limb rehabilitation system on account of VR technology was compared with traditional drug therapy, and effects of the two therapies on long-term health management of stroke patients were compared. Fifty patients with stroke in the hospital were randomly divided into experimental group and control group with 25 patients in each group. Patients in experimental group were treated with AI limb rehabilitation system on account of VR technology, and those in control group were treated with traditional drug therapy. To compare and judge the recovery of patients' physical ability, patients in the two groups were compared in physical movement ability and daily living activity ability after 10-week treatment. After 10-week treatment, Fugl-Meyer assessment-upper extremity (FMA-UE), Fugl-Meyer assessment-lower extremity (FMA-LE), the Hong Kong version of functional test for the hemiplegic upper extremity (FTHUE-HK), Barthel index (BI) daily living (ADL) activities, and Berg balance scale (BBS) in control group were lower than those in experimental group, but the score of MWS was higher than that of experimental group (P < 0.05). AI limb rehabilitation system on account of VR technology could effectively recover the daily health management of stroke patients, and its effect was significantly higher than that of traditional drug therapy. This method could be popularized in clinic.

Human emotion recognition from EEG-based brain–computer interface using machine learning: a comprehensive review

Affective computing, a subcategory of artificial intelligence, detects, processes, interprets, and mimics human emotions. Thanks to the continued advancement of portable non-invasive human sensor technologies, like brain–computer interfaces (BCI), emotion recognition has piqued the interest of academics from a variety of domains. Facial expressions, speech, behavior (gesture/posture), and physiological signals can all be used to identify human emotions. However, the first three may be ineffectual because people may hide their true emotions consciously or unconsciously (so-called social masking). Physiological signals can provide more accurate and objective emotion recognition. Electroencephalogram (EEG) signals respond in real time and are more sensitive to changes in affective states than peripheral neurophysiological signals. Thus, EEG signals can reveal important features of emotional states. Recently, several EEG-based BCI emotion recognition techniques have been developed. In addition, rapid advances in machine and deep learning have enabled machines or computers to understand, recognize, and analyze emotions. This study reviews emotion recognition methods that rely on multi-channel EEG signal-based BCIs and provides an overview of what has been accomplished in this area. It also provides an overview of the datasets and methods used to elicit emotional states. According to the usual emotional recognition pathway, we review various EEG feature extraction, feature selection/reduction, machine learning methods (e.g., k-nearest neighbor), support vector machine, decision tree, artificial neural network, random forest, and naive Bayes) and deep learning methods (e.g., convolutional and recurrent neural networks with long short term memory). In addition, EEG rhythms that are strongly linked to emotions as well as the relationship between distinct brain areas and emotions are discussed. We also discuss several human emotion recognition studies, published between 2015 and 2021, that use EEG data and compare different machine and deep learning algorithms. Finally, this review suggests several challenges and future research directions in the recognition and classification of human emotional states using EEG.

Efficacy Evaluation of Zoledronic Acid Combined with Chemotherapy in the Treatment of Lung Cancer Spinal Metastases on Computed Tomography Images on Intelligent Algorithms

To analyze the effectiveness and safety of zoledronic acid combined with chemotherapy for lung cancer spinal metastases, 96 patients with lung cancer spinal metastases were averagely classified into the experimental group (gemcitabine, cisplatin, and zoledronic acid) and the control group (gemcitabine and cisplatin). An optimized noise variance estimation algorithm (OMAPB) was proposed based on the maximum a posteriori Bayesian method (MAPB), and the algorithm was applied to the patient's computed tomography (CT) scan. The results indicated that in terms of curative effect, the number of complete remission (CR), partial remission (PR) cases, effective rate, and clinical benefit rate of the test group was significantly higher than those of the control group. The number of progress disease (PD) cases was significantly lower than that of the control group (P < 0.05). The disease progression time of the test group patients was 6.2 months, and the disease progression time of the control group patients was 3.7 months (P < 0.05). The test group patients had 8 cases of bone marrow suppression and gastrointestinal reactions after treatment. In the test group, there were 8 cases of bone marrow suppression, 9 cases of gastrointestinal reaction, 3 cases of fever, 4 cases of pain, and 2 cases of hair loss. The patients in the control group were complicated with bone marrow suppression in 14 cases, gastrointestinal reaction in 17 cases, fever in 5 cases, pain in 4 cases, and hair loss in 6 cases. The difference was statistically significant (P < 0.05). It showed that zoledronic acid combined with chemotherapy could effectively improve the treatment efficiency and clinical benefit rate of patients with lung cancer spinal metastases, prolong the progression of the disease, reduce the degree of bone tissue damage, and would not increase chemotherapy adverse events.

Optical Coherence Tomography Combined with Fluorescein Fundus Angiography under Intelligent Algorithm to Evaluate the Clinical Efficacy of Ranibizumab Combined with Panretinal Photocoagulation in the Treatment of Macular Edema of Diabetic Retinopathy Patients

This study aimed at investigating the clinical effect of ranibizumab combined with panretinal photocoagulation in the treatment of macular edema in diabetic retinopathy (DR) patients. A parametric deformation model was constructed, and based on this, it was evaluated using optical coherence tomography (OCT) combined with fluorescein fundus angiography (FFA). 56 DR patients (80 eyes) who needed surgery were selected for OCT and FFA scanning, and 0.5 mg ranibizumab was administered intravitreal injection before surgery. It should observe the OCT and FFA image characteristics of patients. In addition, the vision correction status before the surgery, 1 month, 3 months, and 6 months after the surgery, the thickness of the macular retina, operation time, the number of intraoperative electrocoagulation, and complications of patients were recorded. It was found that 82.85% of patients had improved visual acuity after surgery. Compared with preoperative, the average logarithm of the minimum angle of resolution (logMAR) of patients at 6 months after surgery increased significantly ( $P<0.01$ ). With the increase of the grade of fibrosis and the grade of hemorrhage, the logMAR visual acuity recovery at 6 months after the surgery became worse; the macular retinal thickness at 6 months after the surgery decreased significantly ( $P<0.01$ ). With the increase of the grade of fibrous proliferation and the grade of bleeding, the operation time, the number of electrocoagulation, and the possibility of iatrogenic holes of patient would increase. It can be known that ranibizumab combined with panretinal photocoagulation surgery could not only reduce the macular edema but also effectively reduce the intraoperative bleeding, simplify the removal of proliferative membranes, decrease the number of electrocoagulation, and shorten the operation time, enhancing the visual function of patients.

Adoption of Two-Dimensional Ultrasound Gastrointestinal Filling Contrast on Artificial Intelligence Algorithm in Clinical Diagnosis of Gastric Cancer

This research was aimed to explore the value of gastrointestinal filling contrast-enhanced ultrasound (CEUS) and computed tomography (CT-)-enhanced scanning based on artificial intelligence (AI) algorithm in the evaluation of gastric cancer staging. 102 patients with gastric cancer were selected as the research objects. All of them underwent CEUS of gastrointestinal filling and 64-slice spiral CT before surgery. In addition, an improved mean shift algorithm was proposed based on differential optical flow and deep convolutional neural network (D-CNN), which was applied in image processing. The predicted positive rate (PPR), sensitivity, specificity, and accuracy of gastric cancer in different stages by CEUS and CT were calculated using pathological diagnosis results as the gold standard. 17 patients with T1 stage, 41 patients with T2-T3 stage, and 35 patients with T4 stage were detected by CEUS. 13 patients with T1 stage, 34 patients with T2-T3 stage, and 30 patients with T4 stage were detected by CT enhanced examination. The PPRs of CEUS for T1, T2-T3, and T4 stages of gastric cancer were higher than those of CT enhanced (P < 0.05). The PPR of CEUS for N0 staging of gastric cancer was higher than that of CT enhanced (P < 0.05), and it for N3 staging of gastric cancer was lower than that of CT enhanced (P < 0.05). From the analysis of M staging of gastric cancer, the PPRs of CEUS for M0 and M1 staging of gastric cancer were not statistically different from the PPRs of CT enhanced (P > 0.05). The sensitivity (95.6%), specificity (81.82%), and accuracy (94.12%) of CEUS in assessing resectability were significantly higher than those of CT enhancement (89.01%, 63.67%, and 86.27%, respectively), and the differences were statistically significant (P < 0.05). In summary, CEUS gastrointestinal filling based on the D-CNN algorithm could better improve the display rate of the tissue lesions around the stomach. It also helped to judge the lesion progress, the depth of infiltration, and lymph node metastasis of the lesion. In addition, it had excellent performance in evaluating the resectability of gastric cancer before surgery and had clinical promotion value.

Binary Particle Swarm Optimization Intelligent Feature Optimization Algorithm-Based Magnetic Resonance Image in the Diagnosis of Adrenal Tumor

This research was aimed to explore the application value of magnetic resonance imaging (MRI) based on binary particle swarm optimization algorithm (BPSO) in the diagnosis of adrenal tumors. 120 patients with adrenal tumors admitted to the hospital were selected and randomly divided into the control group (conventional MRI examination) and the observation group (MRI examination based on the BPSO intelligent feature optimization algorithm), with 60 cases in each group. The sensitivity, specificity, accuracy, and Kappa of the diagnostic methods were compared between the two groups. The results showed that the calculation rate of the BPSO algorithm was the best under the same processing effect (P < 0.05). Optimization algorithm-based MRI is used in the diagnosis of adrenal tumors, and the results showed that the sensitivity, specificity, accuracy, and Kappa (83.33%, 79.17%, 81.67%, and 0.69) of the observation group were higher than those of the control group (50%, 75%, 58.33%, and 0.45). The similarity of tumor location results in the observation group (89.24%) was significantly higher than that in the control group (65.9%) (P < 0.05). In conclusion, compared with SFFS and other algorithms, the BPSO algorithm has more advantages in calculation speed. MRI based on the BPSO intelligent feature optimization algorithm has a good diagnostic effect and higher accuracy in adrenal tumors, showing the good development prospects of computer intelligence technology in the field of medicine.

Exploring the Employment Quality Evaluation Model of Application-Oriented University Graduates by Deep Learning

In view of the employment difficulties of college graduates, this paper analyzes the overflow of graduates in a particular period caused by the expansion of enrollment in various colleges and universities and the social phenomenon of social positions in short supply. First, the employment status of application-oriented college students and the deficiencies of employment guidance courses are summarized. Then, deep learning technology is combined with the relevant employment concept to construct an employment training model to guide college students in employment. Besides, a questionnaire on learning effect and employment quality is designed from four perspectives: learning motivation, concentration, teaching process, and final results. The information collected through the questionnaire demonstrates that the employment quality and learning effect of male and female students are not significantly affected by gender differences. In addition, the P values of learning motivation, concentration, and teaching process are all less than 0.01, and the unstandardized coefficient of the teaching process is 0.349, which has the most significant impact on the learning effect. In short, the three factors positively affect the learning effect. Therefore, it comes to the conclusion of improving the ability and strategy of classroom employment guidance. If one wants to be successful in job hunting and career selection, it is not enough just to be competitive but also to be good at it. Being good at the competition is reflected in having good psychological quality, strength, and a good competitive state. In the job hunting and career selection competition, attention should be paid to whether the expected value is appropriate. College students should have sufficient self-awareness before preparing to submit resumes. During the interview, they should overcome emotional anxiety. If a person can treat study, work, and life in a good mood from beginning to end, he will win the competition. The research reported here can provide some reference suggestions for the employment quality of application-oriented college graduates.

Evaluation of Therapeutic Effects of Computed Tomography Imaging Classification Algorithm-Based Transcatheter Arterial Chemoembolization on Primary Hepatocellular Carcinoma

To investigate the evaluation of therapeutic effects of computerized tomography (CT) imaging machine learning classification algorithm-based transcatheter arterial chemoembolization (TACE) on primary hepatocellular carcinoma (PHC), machine learning algorithm was optimized to propose the feature extraction of soft margin, analyze CT images, and acquire relevant texture features to assess if it can predict the multistage features of PHC for the application of the therapeutic effects of TACE on PHC. Besides, PHC patients receiving surgical excision were retrospectively collected, and then 483 patients with hepatocellular carcinoma (HCC) were determined from cases. After that, a total of 162 cases meeting the standards were selected. Besides, the features of images were classified and analyzed by machine learning algorithm, and volume of interest (VOI) images of patients in each group were acquired by image segmentation layer by layer. In addition, the texture features of images were extracted. The results showed that 5 CT image-based texture features, including 2 histogram features and 3 matrix-based features, all described the specificity and heterogeneity of tumors. The analysis of the diagnostic effectiveness of the evaluation of response group by each texture parameter demonstrated that its sensitivity, specificity, and area under curve (AUC) were 83.63%, 90.91%, and 0.08%, respectively. Based on CT prediction, machine learning algorithm was fused to realize excellent classification effects on multistage and multiphase features and offer imaging support to the clinical selection of reasonable therapeutic plans. In addition, multiphase and multifeature-based medical tumor classification method was put forward.

Deep Learning-Based Ultrasound Combined with Gastroscope for the Diagnosis and Nursing of Upper Gastrointestinal Submucous Lesions

The study focused on the diagnostic value of deep learning-based ultrasound combined with gastroscope examination for upper gastrointestinal submucous lesions and nursing. A total of 104 patients with upper gastrointestinal submucous lesions diagnosed in hospital were selected as the research subjects. In this study, the feed forward denoising convulsive neural network (DnCNN) was improved, and the n-DnCNN model was designed and applied to ultrasonic image processing. The peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) of Gaussian filtering, NL-means, and DnCNN were then compared with n-DnCNN. Subsequently, the distribution and types of submucosal lesions in different parts of the upper digestive tract were analyzed by ultrasound combined with gastroscope and gastroscope examination alone, and the diagnostic performance of this method was evaluated. The results showed that the average PSNR and SSIM of the n-DnCNN model were 33.01 dB and 0.87, respectively, which were significantly higher than GF, NL-means, and DnCNN algorithms, and the difference was statistically significant ( $P<0.05$ ). Of the 116 lesions detected, 49 were located in the esophagus (42.24%), 52 in the stomach (44.83%), and 15 in the duodenum (12.93%). Of the 49 esophageal submucosal lesions, 6.12% were located in the upper esophagus, 55.1% in the middle esophagus, and 38.79% in the lower esophagus, and the difference was statistically significant ( $P<0.05$ ). Of the gastric submucosal lesions, the lesions in the gastric cardia were significantly less than in other parts, and the difference was statistically significant ( $P<0.05$ ). The accuracy of ultrasound combined with gastroscope in the diagnosis of upper gastrointestinal submucous episodes was 82.32%, higher than that of gastroscope examination, and the difference was statistically significant ( $P<0.05$ ). In conclusion, the n-DnCNN model has a good noise reduction effect, and the obtained image is of high quality. Ultrasound combined with gastroscope examination can effectively improve the accuracy of diagnosis of upper gastrointestinal submucous lesions.

Effect of Different Nursing Interventions on Discharged Patients with Cardiac Valve Replacement Evaluated by Deep Learning Algorithm-Based MRI Information

This study was aimed to explore the application of cardiac magnetic resonance imaging (MRI) image segmentation model based on U-Net in the diagnosis of a valvular heart disease. The effect of continuous nursing on the survival of discharged patients with cardiac valve replacement was analyzed in this study. In this study, the filling completion operation, cross entropy loss function, and guidance unit were introduced and optimized based on the U-Net network. The heart MRI image segmentation model ML-Net was established. We compared the Dice, Hausdorff distance (HD), and percentage of area difference (PAD) values between ML-Net and other algorithms. The MRI image features of 82 patients with valvular heart disease who underwent cardiac valve replacement were analyzed. According to different nursing methods, they were randomly divided into the control group (routine nursing) and the intervention group (continuous nursing), with 41 cases in each group. The Glasgow Outcome Scale (GOS) score and the Self-rating Anxiety Scale (SAS) were compared between the two groups to assess the degree of anxiety of patients and the survival status at 6 months, 1 year, 2 years, and 3 years after discharge. The results showed that the Dice coefficient, HD, and PAD of the ML-Net algorithm were (0.896 ± 0.071), (5.66 ± 0.45) mm, and (15.34 ± 1.22) %, respectively. The Dice, HD, and PAD values of the ML-Net algorithm were all statistically different from those of the convolutional neural networks (CNN), fully convolutional networks (FCN), SegNet, and U-Net algorithms (P < 0.05). Atrial, ventricular, and aortic abnormalities can be seen in MRI images of patients with valvular heart disease. The cardiac blood flow signal will also be abnormal. The GOS score of the intervention group was significantly higher than that of the control group (P < 0.01). The SAS score was lower than that of the control group (P < 0.05). The survival rates of patients with valvular heart disease at 6 months, 1 year, 2 years, and 3 years after discharge were significantly higher than those in the control group (P < 0.05). The abovementioned results showed that an effective segmentation model for cardiac MRI images was established in this study. Continuous nursing played an important role in the postoperative recovery of discharged patients after cardiac valve replacement. This study provided a reference value for the diagnosis and prognosis of valvular heart disease.

Three-Dimensional Reconstruction Algorithm-Based Magnetic Resonance Imaging Evaluation of Biomechanical Changes in Articular Cartilage in Patients after Anterior Cruciate Ligament Reconstruction

This study aimed to investigate the evaluation of biomechanical changes in articular cartilage in patients after anterior cruciate ligament (ACL) reconstruction by magnetic resonance imaging (MRI) based on a three-dimensional (3D) finite element model. The data of 90 patients undergoing arthroscopic ACL reconstruction in the hospital were collected and divided into the stable group (54 cases) and the unstable group (36 cases). A load of up to 134N was applied to the 3D finite element model, and the kinematics of knee flexion at 0°, 30°, 60°, and 90° were examined. The tibial anteversion, tibial rotation, and ACL/graft tension were recorded in the 3D finite element model, which was randomly divided into the normal group (intact group, n = 30), the ACL rupture group (deficient group, n = 30), and the anatomical reconstruction group (anatomical group, n = 30). When the graft was fixed at 0°, the anterior tibial translation at 30°, 60°, and 90° in the anatomic group was 8-19% higher than the normal value under 134 N anterior load. The tibial internal rotation in the anatomic group was 18% and 28% higher than the normal value at 30° and 90°. When the graft was fixed at 30°, the anterior tibial translation at 60° and 90° of the anatomic group was 15% higher than the normal value. The tibial internal rotation at 90° of the anatomic group was 16% higher than the normal value, and the above differences had statistical significance (P < 0.05). MRI images were used to assess the bone tunnel angle, and the statistical analysis by the independent-samples t-test showed that there were significant differences in the bone tunnel angle between the stable group and the unstable group (P < 0.05). Currently, based on the 3D finite element model, MRI can accurately evaluate the postoperative effect of anatomical ACL reconstruction in the position, diameter, and angle of tibial and femoral bone tunnels, which can be applied to clinical promotion.

Ultrasound Lung Image under Artificial Intelligence Algorithm in Diagnosis of Neonatal Respiratory Distress Syndrome

In order to analyze the application of ultrasonic lung imaging diagnosis model based on artificial intelligence algorithm in neonatal respiratory distress syndrome (NRDS), an ultrasonic lung imaging diagnosis model based on a deep residual network (DRN) was proposed. In this study, 90 premature infants in the hospital were selected as the research object and divided into the experimental group (45 cases) and control group (45 cases) according to whether or not they have NRDS. DRN was compared with the deep residual network (DRWSR) based on wavelet domain, deep residual network detection with normalization framework (Fisher-DRN), and distorted image edge detection preprocessor (DIEDP). Then, it was applied to the diagnosis of NRDS. The clinical data and ultrasound imaging results of infants with NRDS and ordinary premature infants were compared. The results showed that the gestational age, birth weight, and Apgar scores of the NRDS group were remarkably lower than those of ordinary children (P < 0.05). In addition, the segmentation accuracy, image feature extraction accuracy, algorithm convergence, and time loss of the DRN algorithm were better than the other three algorithms, and the differences were considerable (P < 0.05). In children with NRDS, the positive rate of abnormal pleural line, disappearance of A line, appearance of B line, and alveolar interstitial syndrome (AIS) test in the results of lung ultrasound examination in children with NRDS were all 100%. The lung consolidation became 70.8%, and the white lung-like change was 50.1%, both of which were higher than those of ordinary preterm infants, and the differences were considerable (P < 0.05). The diagnostic model of this study predicted that the AUC area of grade 1-2, grade 2-3, and grade 3-4 NRDS were 0.962, 0.881, and 0.902, respectively. To sum up, the ultrasound lung imaging diagnosis model based on the DRN algorithm had good diagnostic performance in children with NRDS and can provide useful information for clinical NRDS diagnosis and treatment.

Artificial Intelligence-Based MRI in Diagnosis of Injury of Cranial Nerves of Premature Infant and Its Correlation with Inflammation of Placenta

The study focused on the effects of artificial intelligence algorithms in magnetic resonance imaging (MRI) for diagnosing cranial nerve inflammation of placenta and the correlation between cranial nerve injury with placental inflammation was explored. The subjects were selected from 132 premature infants in the hospital. According to the pathological examination of placenta, 81 cases with chorioamnionitis were taken as the experimental group and 51 cases without chorioamnionitis were taken as the control group. The incidence of cranial nerve injury in different groups of premature infants was analyzed by MRI diagnosis based on the principal component analysis (PCA) artificial intelligence algorithm, so as to analyze the correlation between cranial nerve injury and placental inflammation in premature infants. It was found that when the PCA artificial intelligence algorithm was incorporated into MRI examination of cranial nerve injury of premature infant, the A (accuracy), P (precision), R (recall), and F1 values under the PCA algorithm were 92%, 93.75%, 90%, and 92.87%, respectively. The A, P, R, and F1 of the control group were 54%, 54.1%, 52%, and 53.03%, respectively; there were statistically significant differences between the two groups, P < 0.05. As for the correlation of placental inflammation and cranial nerve injury, the positive detection rate of the experimental group was 53.09%, and the positive detection rate of the control group was 15.69%, and the difference was statistically significant, P < 0.05. In conclusion, the PCA artificial intelligence algorithm has high effectiveness and high accuracy in auxiliary diagnosis of premature brain nerve injury, and placental inflammation greatly increases the chance of premature infant suffering from brain nerve injury.

Fuzzy C-Means Algorithm-Based ARM-Linux-Embedded System Combined with Magnetic Resonance Imaging for Progression Prediction of Brain Tumors

The aim of this research was to analyze the application of fuzzy C-means (FCM) algorithm-based ARM-Linux-embedded system in magnetic resonance imaging (MRI) images for prediction of brain tumors. The optimized FCM (OFCM) algorithm was proposed based on kernel function, and the ARM-Linux-embedded imaging system was designed under ARM9 chip and Linux recorder, which were applied in MRI images of brain tumor patients. It was found that the sensitivity, specificity, and accuracy of the OFCM algorithm (90.46%, 88.97%, and 97.46%) were greater obviously than those of the deterministic C-means clustering algorithm (80.38%, 77.98%, and 85.24%) and the traditional FCM algorithm (83.26%, 79.56%, and 86.45%), and the difference was statistically substantial (P < 0.05). The ME and running time of the OFCM algorithm decreased sharply in contrast to those of the deterministic C-means clustering algorithm and the traditional FCM algorithm (P < 0.05). There were great differences in fraction anisotropy (FA) and mean diffusion (MD) of tumor parenchymal area, surrounding edema area, and normal white matter area (P < 0.05). FA of stage III+IV was smaller than those of stage I and II (P < 0.05), while the apparent diffusion coefficient (ADC) of stage III+IV was greater than that of stage I and II (P < 0.05). In conclusion, the poor update data processing and low data clustering efficiency of FCM were solved by OFCM. Moreover, computational efficiency of ARM-Linux-embedded imaging system was improved, so as to better realize the prediction of brain tumor patients through ARM-Linux-embedded system based on adaptive FCM incremental clustering algorithm.

Intelligent Algorithm-Based Electrocardiography to Predict Atrial Fibrillation after Coronary Artery Bypass Grafting in the Elderly

The objective of this study was to explore the predictive value of electrocardiogram (ECG) based on intelligent analysis algorithm for atrial fibrillation (AF) in elderly patients undergoing coronary artery bypass grafting (CABG). Specifically, 106 elderly patients with coronary heart disease who underwent CABG in the hospital were selected, including 52 patients with postoperative AF (AF group) and 54 patients without arrhythmia (control group). Within 1-3 weeks after operation, the dynamic ECG monitoring system based on Gentle AdaBoost algorithm constructed in this study was adopted. After the measurement of the 12-lead P wave duration, the maximum P wave duration (Pmax) and minimum P wave duration (Pmin) were recorded. As for simulation experiments, the same data was used as the back-propagation algorithm. The results showed that for the detection accuracy of the test samples, the Gentle AdaBoost algorithm showed 93.7% accuracy after the first iteration, and the Gentle AdaBoost algorithm was 16.1% higher than the back-propagation algorithm. Compared with the control group, the detection rate of arrhythmia in patients after CABG was significantly lower ( $P<0.05$ ). Bivariate logistic regression analysis on Pmax and Pmin showed as follows: Pmax: 95% confidential interval (CI): 1.024-1.081, $P<0.05$ ; Pmin: 95% CI: 1.036-1.117, $P<0.05$ . The sensitivity of Pmax and Pmin in predicting paroxysmal AF was 78.2% and 73.4%, respectively; the specificity of them was 80.1% and 85.6%, respectively; the positive predictive value was 81.2% and 83.4%, respectively; and the negative predictive value was 79.5% and 75.3%, respectively. In conclusion, the generalization ability of Gentle AdaBoost algorithm was better than that of back-propagation algorithm, and it can identify arrhythmia better. Pmax and Pmin were important indicators of AF after CABG.

Artificial Intelligence Algorithm-Based Computerized Tomography Image Features Combined with Serum Tumor Markers for Diagnosis of Pancreatic Cancer

The objective of this study was to analyze the value of artificial intelligence algorithm-based computerized tomography (CT) image combined with serum tumor markers for diagnoses of pancreatic cancer. In the study, 68 hospitalized patients with pancreatic cancer were selected as the experimental group, and 68 hospitalized patients with chronic pancreatitis were selected as the control group, all underwent CT imaging. An image segmentation algorithm on account of two-dimensional (2D)-three-dimensional (3D) convolution neural network (CNN) was proposed. It also introduced full convolutional network (FCN) and UNet network algorithm. The diagnostic performance of CT, serum carbohydrate antigen-50 (CA-50), serum carbohydrate antigen-199 (CA-199), serum carbohydrate antigen-242 (CA-242), combined detection of tumor markers, and CT-combined tumor marker testing (CT-STUM) for pancreatic cancer were compared and analyzed. The results showed that the average Dice coefficient of 2D-3D training was 84.27%, which was higher than that of 2D and 3D CNNs. During the test, the maximum and average Dice coefficient of the 2D-3D CNN algorithm was 90.75% and 84.32%, respectively, which were higher than the other two algorithms, and the differences were statistically significant (P < 0.05). The penetration ratio of pancreatic duct in the experimental group was lower than that in the control group, the rest were higher than that in the control group, and the differences were statistically significant (P < 0.05). CA-50, CA-199, and CA-242 in the experimental group were 141.72 U/mL, 1548.24 U/mL, and 83.65 U/mL, respectively, which were higher than those in the control group, and the differences were statistically significant (P < 0.05). The sensitivity, specificity, positive predictive value, and authenticity of combined detection of serum tumor markers were higher than those of CA-50, CA-199, and CA-242, and the differences were statistically significant (P < 0.05). The results showed that the proposed algorithm 2D-3D CNN had good stability and image segmentation performance. CT-STUM had high sensitivity and specificity in diagnoses of pancreatic cancer.

Intelligent Algorithm-Based Quantitative Electroencephalography in Evaluating Cerebral Small Vessel Disease Complicated by Cognitive Impairment

To analyze the application value of artificial intelligence model based on Visual Geometry Group- (VGG-) 16 combined with quantitative electroencephalography (QEEG) in cerebral small vessel disease (CSVD) with cognitive impairment, 72 patients with CSVD complicated by cognitive impairment were selected as the research subjects. As per Diagnostic and Statistical Manual (5th Edition), they were divided into the vascular dementia (VD) group of 34 cases and vascular cognitive impairment with no dementia (VCIND) group of 38 cases. The two groups were analyzed for the clinical information, neuropsychological test results, and monitoring results of QEEG based on intelligent algorithms for more than 2 hours. The accuracy rate of VGG was 84.27% and Kappa value was 0.7, while that of modified VGG (nVGG) was 88.76% and Kappa value was 0.78. The improved VGG algorithm obviously had higher accuracy. The test results found that the QEEG identified 8 normal, 19 mild, 10 moderate, and 0 severe cases in the VCIND group, while in the VD group, the corresponding numbers were 4, 13, 11, and 7; in the VCIND group, 7 cases had the normal QEEG, 11 cases had background changes, 9 cases had abnormal waves, and 11 cases had in both background changes and abnormal waves, and in the VD group, the corresponding numbers were 5, 2, 5, and 22, respectively; in the VCIND group, QEEG of 18 patients had no abnormal waves, QEEG of 11 patients had a few abnormal waves, and QEEG of 9 patients had many abnormal waves, and QEEG of 0 people had a large number of abnormal waves, and in the VD group, the corresponding numbers were 7, 6, 12, and 9. The above data were statistically different between the two groups (P < 0.05). Hence, QEEG based on intelligent algorithms can make a good assessment of CSVD with cognitive impairment, which had good clinical application value.

Medical Data Acquisition and Internet of Things Technology-Based Cerebral Stroke Disease Prevention and Rehabilitation Nursing Mobile Medical Management System

This research was aimed at exploring the application value of a mobile medical management system based on Internet of Things technology and medical data collection in stroke disease prevention and rehabilitation nursing. In this study, on the basis of radio frequency identification (RFID) technology, the signals collected by the sensor were filtered by the optimized median filtering algorithm, and a rehabilitation nursing evaluation model was established based on the backpropagation (BP) neural network. The performance of the medical management system was verified in 32 rehabilitation patients with hemiplegia after stroke and 6 healthy medical staff in the rehabilitation medical center of the hospital. The results showed that the mean square error (MSE) and peak signal-to-noise ratio (PSNR) of the median filtering algorithm after optimization were significantly higher than those before optimization (P < 0.05). When the number of neurons was 23, the prediction accuracy of the test set reached a maximum of 89.83%. Using traingda as the training function, the model had the lowest training time and root mean squared error (RMSE) value of 2.5 s and 0.29, respectively, which were significantly lower than the traingd and traingdm functions (P < 0.01). The error percentage and RMSE of the model reached a minimum of 7.56% and 0.25, respectively, when the transfer functions of both the hidden and input layers were tansig. The prediction accuracy in stages III~VI was 90.63%. It indicated that the mobile medical management system established based on Internet of Things technology and medical data collection has certain application value for the prevention and rehabilitation nursing of stroke patients, which provides a new idea for the diagnosis, treatment, and rehabilitation of stroke patients.

Three-Dimensional Skin CT Based on Intelligent Algorithm in the Analysis of Skin Lesion Sites Features in Children with Psoriasis

This research was to explore the application value of three-dimensional computed tomography (CT) based on artificial intelligent algorithm in analyzing the characteristics of skin lesions in children with psoriasis. In this study, 15 children with psoriasis were selected as the observation group, and 15 children with other skin diseases were selected as the control group. The CT images were optimized, and the feature selection was carried out based on artificial intelligent algorithm. Firstly, the results were compared with the results of simple skin three-dimensional CT to determine the effectiveness. Then, the two groups of three-dimensional skin CT image features of skin psoriasis-like hyperplasia, Munro microabscess, dermal papillary vascular dilation, and squamous epithelium based on intelligent algorithms were compared. After comparison, the detection rate of psoriasis-like hyperplasia, Munro microabscess, dermal papillary vascular dilation, and squamous epithelium in the observation group was higher than that in the control group, with significant difference and statistical significance (P < 0.05). In addition, the sensitivity of psoriasis-like hyperplasia, Munro microabscess, dermal papilla vascular dilatation, and squamous epithelium in children with psoriasis was 80.0%, 86.7%, 80.0%, and 93.3%, respectively. The specificity of psoriasis-like hyperplasia, Munro microabscess, dermal papilla vascular dilatation, and squamous epithelium in children with psoriasis was 86.7%, 93.3%, 60.0%, and 73.3%, respectively. The results showed that Munro microabscess and psoriasis-like hyperplasia had high sensitivity and specificity in all diagnostic items, which could be used as important features of skin lesion sites in the diagnosis of psoriasis in children. The research provides a basis for the clinical diagnosis of psoriasis in children, which is worthy of clinical promotion.

Segnet Network Algorithm-Based Ultrasound Images in the Diagnosis of Gallbladder Stones Complicated with Gallbladder Carcinoma and the Relationship between P16 Expression with Gallbladder Carcinoma

The study focused on how to improve the diagnostic coincidence rate of patients with gallbladder stones and gallbladder cancer based on an optimized Segnet network algorithm and the relationship of gallbladder cancer with multiple tumor suppressor 1 (P16). 300 patients diagnosed with gallbladder cancer in the hospital were selected as the research subjects. The pyramid pooling operation was incorporated into the original Segnet network algorithm, and its performance was evaluated, factoring into the intersection of union (IoU), algorithm precision (Pre), and recall rate (Recall). After 8 hours of fasting, conventional ultrasound and contrast-enhanced ultrasound examinations were performed, and the images were evaluated by three experienced ultrasound diagnosticians. The positive signal of P16 immunohistochemical staining was brownish yellow, which was generally concentrated in the nucleus, and a small part was located in the cytoplasm. In each slice, ten visual fields were selected. Then, they were observed under a high-power mirror, and the number was counted. It was found that the optimized Segnet network algorithm increased the IoU by 7.3%, the precision by 8.2%, and the recall rate by 11.1%. The diagnostic coincidence rates of conventional ultrasound and contrast-enhanced ultrasound examinations for gallbladder cancer were 78.13% (25/32) and 87.5% (25/32), respectively. The positive expression rate of P16 in gallbladder adenocarcinoma (47.06%) was significantly lower than that of acute cholecystitis with gallbladder stones (84.38%) and gallbladder polyps (67.16%) (P < 0.05). The positive expression rate of P16 in patients with stage III and stage IV (33.33% and 40%) was significantly lower than that in patients with stages I and II (87.5% and 80%) (P < 0.05). The positive expression rate of P16 in high differentiation (86.67%) was significantly higher than that of moderate differentiation (40%) and poor differentiation (28.57%) (P < 0.05). In short, contrast-enhanced ultrasound can effectively improve the diagnostic coincidence rate of gallbladder cancer, and the expression of P16 in gallbladder cancer is closely related to tumor staging and differentiation.

Ultrasonic Elastography Combined with Human Papilloma Virus Detection Based on Intelligent Denoising Algorithm in Diagnosis of Cervical Intraepithelial Neoplasia

The aim of this research was to study the application of ultrasonic elastography combined with human papilloma virus (HPV) detection based on bilateral filter intelligent denoising algorithm in the diagnosis of cervical intraepithelial neoplasia (CIN) and provide a theoretical basis for clinical diagnosis and treatment of CIN. In this study, 100 patients with cervical lesions were selected as research objects and randomly divided into control group and experimental group, with 50 cases in each group. Patients in control group and experimental group were diagnosed by ultrasonic elastography combined with HPV detection. The experimental group used the optimized image map of bilateral filter intelligent denoising algorithm for denoising and optimization, while the control group did not use optimization, and the differences between them were analyzed and compared. The diagnostic effects of the two groups were compared. As a result, the three accuracy rates of the experimental group were 95%, 95%, and 98%, respectively; the three sensitivity rates were 96%, 92%, and 94%, respectively; and the three specificity rates were 99%, 97%, and 98%, respectively. In the control group, the three accuracy rates were 84%, 86%, and 84%, respectively; the three sensitivity rates were 88%, 84%, and 86%, respectively; and the three specificity rates were 81%, 83%, and 88%, respectively. The accuracy, sensitivity, and specificity of experiment group were significantly higher than those of control group, and the difference was statistically significant (P < 0.05). In summary, the bilateral filter intelligent denoising algorithm has a good denoising effect on the ultrasonic elastography. The ultrasonic image processed by the algorithm combined with HPV detection has a better diagnosis of CIN.

Fast Independent Component Analysis Algorithm-Based Functional Magnetic Resonance Imaging in the Diagnosis of Changes in Brain Functional Areas of Cerebral Infarction

The aim of this study was to analyze the application value of functional magnetic resonance imaging (FMRI) optimized by the fast independent component correlation algorithm (ICA algorithm) in the diagnosis of brain functional areas in patients with lumbar disc herniation (LDH). An optimized fast ICA algorithm was established based on the ICA algorithm. 50 patients with cerebral infarction were selected as the research objects, and 30 healthy people were selected as the control group. The 50 patients from the observation group were examined by fMRI based on Fast ICA algorithm, while the control group was tested by fMRI based on the routine ICA algorithm. The performances of the two algorithms, the analysis results of the two groups of brain functional areas, cerebral blood flow (CBF), resting state functional connectivity (rsFC), behavioral data, and image data correlation of patients were compared. The results showed that the sensitivity, specificity, and accuracy of Fast ICA algorithm were 97.83%, 89.52%, and 96.27%, respectively, which in the experimental group were greatly better than the control group (88.73%, 72.19%, and 89.72%), showing statistically significant differences (P < 0.05). The maximum Dice coefficient of FAST ICA algorithm was 0.967, and FAST ICA algorithm was better obviously than the traditional ICA algorithm (P < 0.05). The cerebral blood flow of the healthy superior frontal gyrus (SFG) and healthy superior marginal gyrus (SMG) of the observation group with good motor function recovery were 1.02 ± 0.22 and 1.53 ± 0.61, respectively; both indicators showed an increasing trend, and those in the experimental group were much higher in contrast to the control group, showing statistically obvious differences (P < 0.05). Besides, the detection results of cerebral blood flow (CBF) in the healthy SFG and healthy SMG were negatively correlated with the results of connection test B. In summary, the fMRI based on the Fast ICA algorithm showed a good diagnostic effect in the changes of brain functional areas in patients with cerebral infarction. The experimental results showed that the cerebral blood flow in the brain area was related to motor or cognitive function. The results of this study provided a reliable reference for the examination and diagnosis of brain functional areas in patients with cerebral infarction.

Diagnostic Value of Deep Learning-Based CT Feature for Severe Pulmonary Infection

The study aimed to explore the diagnostic value of computed tomography (CT) images based on cavity convolution U-Net algorithm for patients with severe pulmonary infection. A new lung CT image segmentation algorithm (U-Net+ deep convolution (DC)) was proposed based on U-Net network and compared with convolutional neural network (CNN) algorithm. Then, it was applied to CT image diagnosis of 100 patients with severe lung infection in The Second Affiliated Hospital of Fujian Medical University hospital and compared with traditional methods, and its sensitivity, specificity, and accuracy were compared. It was found that the single training time and loss of U-Net + DC algorithm were reduced by 59.4% and 9.8%, respectively, compared with CNN algorithm, while Dice increased by 3.6%. The lung contour segmented by the proposed model was smooth, which was the closest to the gold standard. Fungal infection, bacterial infection, viral infection, tuberculosis infection, and mixed infection accounted for 28%, 18%, 7%, 7%, and 40%, respectively. 36%, 38%, 26%, 17%, and 20% of the patients had ground-glass shadow, solid shadow, nodule or mass shadow, reticular or linear shadow, and hollow shadow in CT, respectively. The incidence of various CT characteristics in patients with fungal and bacterial infections was statistically significant (P < 0.05). The specificity (94.32%) and accuracy (97.22%) of CT image diagnosis based on U-Net + DC algorithm were significantly higher than traditional diagnostic method (75.74% and 74.23%), and the differences were statistically significant (P < 0.05). The network of the algorithm in this study demonstrated excellent image segmentation effect. The CT image based on the U-Net + DC algorithm can be used for the diagnosis of patients with severe pulmonary infection, with high diagnostic value.

Magnetic Resonance Imaging Segmentation on the Basis of Boundary Tracking Algorithm in Lung Cancer Surgery

This work was to study the guiding value of magnetic resonance imaging (MRI) based on the target region boundary tracking algorithm in lung cancer surgery. In this study, the traditional boundary tracking algorithm was optimized, and the target neighborhood point boundary tracking method was proposed. The iterative method was used to binarize the lung MRI image, which was applied to the MRI images of 50 lung cancer patients in hospital. The patients were divided into two groups as the progression-free survival (PFS) and overall survival (OS) of surgical treatment group (experimental group, n = 25) and nonsurgical treatment group (control group, n = 25). The experimental group received surgical resection, while the control group received systemic chemotherapy. The results showed that the traditional boundary tracking algorithm needed to manually rejudge whether the concave and convex parts of the image were missing. The target boundary tracking algorithm can effectively avoid the leakage of concave and convex parts and accurately locate the target image contour, fast operation, without manual intervention. The PFS time of the experimental group (325 days) was significantly higher than that of the control group (186 days) (P < 0.05). The OS time of the experimental group (697 days) was significantly higher than that of the control group (428 days) (P < 0.05). Fisher exact probability method was used to test the total survival time of patients in the two groups, and the tumor classification and treatment group had significant influence on the OS time (P < 0.05). The target boundary tracking algorithm in this study can effectively locate the contour of the target image, and the operation speed was fast. Surgical resection of lung cancer can improve the PFS and OS of patients.

Image Mosaic Algorithm-Based Analysis of Pathological Characteristics of Gastric Polyp Patients Using Computed Tomography Images

The application value of image mosaic algorithm (IMA) based CT imaging technology in the analysis of pathological characteristics of gastric polyp (GP) patients was explored in this work. 588 cases of GP patients in the hospital were selected as the research objects, and CT images based on IMA were adopted for examination. The patient's basic information, image performance, and gastroscopy results were recorded. The results showed that the absolute mean bright error (AMBE) index and information entropy of the IMA are 0.0625 and 7.0385, respectively. The clinical symptoms of patients were mostly abdominal pain (21.4%), abdominal distension (15.6%), and sour regurgitation (17.8%). The common size of GP was no more than 0.5 cm, and the common type was Yamada type II. There were notable differences between single and multiple GPs of different pathological types (P < 0.05). Proliferative polyps were mostly found in the stomach and antrum, while fundus gland polyps were mostly in the stomach and fundus. There was significant difference between the growth location of the hyperplastic polyp and basal gland polyp (P < 0.05). In summary, the CT images of IMA proposed in this paper can not only realize image splicing effectively but also were superior to the traditional SIFT method in the quality of splicing image and were conducive to the analysis of the pathological characteristics of GP patients, which had significant clinical promotion value.

A Review of the Role of Machine Learning Techniques towards Brain–Computer Interface Applications

This review article provides a deep insight into the Brain–Computer Interface (BCI) and the application of Machine Learning (ML) technology in BCIs. It investigates the various types of research undertaken in this realm and discusses the role played by ML in performing different BCI tasks. It also reviews the ML methods used for mental state detection, mental task categorization, emotion classification, electroencephalogram (EEG) signal classification, event-related potential (ERP) signal classification, motor imagery categorization, and limb movement classification. This work explores the various methods employed in BCI mechanisms for feature extraction, selection, and classification and provides a comparative study of reviewed methods. This paper assists the readers to gain information regarding the developments made in BCI and ML domains and future improvements needed for improving and designing better BCI applications.

Comprehensive Analysis of Regulatory Network for LINC00472 in Clear Cell Renal Cell Carcinoma

Renal cell carcinoma (RCC) accounts for about 2% to 3% of adult malignancies, and clear cell renal cell carcinoma (ccRCC) is the most common and aggressive type of kidney cancer. It accounts for 75% of all kidney tumors. Although new targeted drugs continue to appear, they are still not suitable for all patients. Therefore, an in-depth study of the molecular mechanism of the development of ccRCC and exploration of new targets for the treatment of ccRCC will help to achieve precise treatment for ccRCC. With the development of molecular research, the study of long noncoding RNA (LncRNA) has given us a new understanding of tumors. Although LncRNA does not encode proteins, it directly interacts with proteins in various signaling pathways and affects cell functions. Therefore, it is of great significance to study the mechanism of LncRNA in ccRCC. The expression level of Linc00472 in ccRCC tissues is significantly lower than adjacent normal tissues, and its low expression is closely related to Furman's high grade. The low expression of Linc00472 is associated with poor prognosis in patients with ccRCC. The results of protein interaction and functional enrichment analysis indicate that genes upregulated in renal clear cell carcinoma may play a major role. Analysis of target gene prediction results showed that Linc00472 may be used as ceRNA in the miR-24-3p-HLA-DPB1 pathway, miR-24-3p-CXCL9 pathway, miR-221-3p-C3aR1-VEGFR2 pathway, miR-17-5p-HLA-DQA1/HLA-DQB1 pathway, and miR-17-5p-C3aR1/C5aR1-VEGFR2 pathway which play important functions. In addition, the regulatory relationship between miR-24-3p and TNFR2 (TNFRSF1B), CD36, and COL4A1 should also be noted. The value of Linc00472 in the diagnosis and treatment of ccRCC is worthy of further study.

Method of text mining based on fuzzy logic and neural network in hotel management

With the rapid economic development, people cannot do without hotels in daily life and travel. The number of hotels is increasing rapidly, and the competitiveness between hotels is gradually increasing. A reasonable and sound hotel management system has become the key to the survival and development of an enterprise. The operation and operation of a hotel generates a large amount of information and data every day. Data text mining is an important method in current information processing technology. The purpose of this article is to explore the specific application effects of text mining methods based on fuzzy logic and neural networks in hotel management. Through the design of a hotel management system based on fuzzy logic and neural network, text mining database design is carried out according to hotel management needs, and the application effect of the hotel management system is evaluated. The evaluation indicators include customer satisfaction, operating costs, management efficiency, and hotel income. The results of the study show that the hotel management system based on fuzzy logic and neural network text mining can increase customer satisfaction by 37.4%, the hotel’s comprehensive management efficiency by 28.6%, and the hotel’s revenue level by 23.5%. At the same time, through the text mining of the key information generated in the hotel operation and management, the weak links in the management system can be strengthened and the hotel operation cost can be saved. Therefore, it is feasible to apply the text mining method based on fuzzy logic and neural network to hotel management.

Joint Detection of Tap and CEA Based on Deep Learning Medical Image Segmentation: Risk Prediction of Thyroid Cancer

In recent years, the incidence of thyroid nodules has shown an increasing trend year by year and has become one of the important diseases that endanger human health. Ultrasound medical images based on deep learning are widely used in clinical diagnosis due to their cheapness, no radiation, and low cost. The use of image processing technology to accurately segment the nodule area provides important auxiliary information for the doctor's diagnosis, which is of great value for guiding clinical treatment. The purpose of this article is to explore the application value of combined detection of abnormal sugar-chain glycoprotein (TAP) and carcinoembryonic antigen (CEA) in the risk estimation of thyroid cancer in patients with thyroid nodules of type IV and above based on deep learning medical images. In this paper, ultrasound thyroid images are used as the research content, and the active contour level set method is used as the segmentation basis, and a segmentation algorithm for thyroid nodules is proposed. This paper takes ultrasound thyroid images as the research content, uses the active contour level set method as the basis of segmentation, and proposes an image segmentation algorithm Fast-SegNet based on deep learning, which extends the network model that was mainly used for thyroid medical image segmentation to more scenarios of the segmentation task. From January 2019 to October 2020, 400 patients with thyroid nodules of type IV and above were selected for physical examination and screening at the Health Management Center of our hospital, and they were diagnosed as thyroid cancer by pathological examination of thyroid nodules under B-ultrasound positioning. The detection rates of thyroid cancer in patients with thyroid nodules of type IV and above are compared; serum TAP and CEA levels are detected; PT-PCR is used to detect TTF-1, PTEN, and NIS expression; the detection, missed diagnosis, misdiagnosis rate, and diagnostic efficiency of the three detection methods are compared. This article uses the thyroid nodule region segmented based on deep learning medical images and compares experiments with CV model, LBF model, and DRLSE model. The experimental results show that the segmentation overlap rate of this method is as high as 98.4%, indicating that the algorithm proposed in this paper can more accurately extract the thyroid nodule area.

Application research of artificial intelligence English audio translation system based on fuzzy algorithm

With the development of globalization, people’s demand for English audio interaction is increasing. In order to overcome the shortcomings of traditional translation methods in grammatical variables, such as semantic ambiguity, quantifier errors, low translation accuracy, improve the quality and speed of English translation, and get more accurate and speed guaranteed translation, this study proposes an artificial intelligence English audio translation cross language system based on fuzzy algorithm. In this experiment, the collected analog speech signal is converted into a digital speech signal, and then, the speech features are modeled and digitized, and the whole set of speech samples are integrated and modified to eliminate the interference caused by noise as far as possible. After that, the collected voice will be stored in the text format, and then the text will be translated to achieve English audio translation. The DNN-HMM speech recognition model and the traditional GMM-HMM speech recognition model are used to preprocess the original corpus, and the accuracy of the corpus processing is compared. After that, the accuracy and utilization of the fuzzy algorithm are evaluated between the first type TSK and the second type TSK. For speech synthesis in which the corpus lacks language, it is meaningful to explore the least amount of training data for the synthesis of acceptable speech. The experimental results show that the accuracy of the fuzzy algorithm is about 97.34%, and the utilization rate is about 98.14%. The accuracy rate of type 1 and type 2 algorithms are about 85.77% and 76.87% respectively, and the utilization rate is about 83.25% and 78.63% respectively. The fuzzy algorithm based artificial intelligence English audio translation cross language system is obviously better than the other two algorithms.

Clinical Study on Electronic Medical Neuroelectric Stimulation Based on the Internet of Things to Treat Epilepsy Patients with Anxiety and Depression

With the continuous development and improvement of the level of medical technology in our country in recent years, the treatment of epilepsy has been constantly updated and developed. Nerve electrical stimulation is considered to be a very effective method for treating epilepsy with anxiety and depression. There are many traditional methods for the treatment of epilepsy. For example, vagus nerve stimulation (VNS) has been applied earlier, and the therapeutic effect has been confirmed, but it will cause serious complications and is easier to be uncomfortable; deep brain stimulation for epilepsy is still in the immature stage, and there is no final conclusion. Therefore, this article proposes a clinical study on the treatment of patients with epilepsy with anxiety and depression based on the electronic medical nerve stimulation of the Internet of Things. First of all, this article uses the literature method to study the causes of epilepsy and previous treatment methods. Then, we designed an experimental study of epilepsy with depression based on the Internet of Things electronic medical neuroelectric stimulation therapy and selected the core quality of life questionnaire, SDS, and SAS as observation indicators. Finally, the comparison of epilepsy symptoms and depression and anxiety between the control group and the observation group before and after treatment was analyzed. The results of the experiment showed that, among the 50 subjects in the study, the observation group that used electrical nerve stimulation therapy had 5 people who stopped seizures after treatment, accounting for 10%, while in the control group of traditional drug treatment methods, after treatment, only one person stopped the seizure, accounting for 2%. In addition, the SAS and SDS scores of the observation group were also lower than those of the control group. Therefore, the use of nerve electrical stimulation to treat epilepsy with anxiety and depression symptoms has better performance and can help patients recover as soon as possible.