New Binary Archimedes Optimization Algorithm and its application

Optimization problem, as a hot research field, is applied to many industries in the real world. Due to the complexity of different search spaces, metaheuristic optimization algorithms are proposed to solve this problem. As a recently introduced optimization method inspired by physics, Archimedes Optimization Algorithm (AOA) is an efficient metaheuristic algorithm based on Archimedes' law. It has the advantages of fast convergence speed and balance between local and global search ability when solving continuous problems. However, discrete problems exist more in practical applications. AOA needs to be further improved in dealing with such problems. On this basis, to make Archimedes Optimization Algorithm better applied to solve discrete problems, a Binary Archimedes Optimization Algorithm (BAOA) is proposed in this paper, which incorporates a novel V-shaped transfer function. The proposed method applies the BAOA to COVID-19 classification of medical data, segmentation of real brain lesion, and the knapsack problem. The experimental results show that the proposed BAOA can solve the discrete problem well.

A Novel Method Based on Nonlinear Binary Grasshopper Whale Optimization Algorithm for Feature Selection.

Feature Selection (FS) is considered as an important preprocessing step in data mining and is used to remove redundant or unrelated features from high-dimensional data. Most optimization algorithms for FS problems are not balanced in search. A hybrid algorithm called nonlinear binary grasshopper whale optimization algorithm (NL-BGWOA) is proposed to solve the problem in this paper. In the proposed method, a new position updating strategy combining the position changes of whales and grasshoppers population is expressed, which optimizes the diversity of searching in the target domain. Ten distinct high-dimensional UCI datasets, the multi-modal Parkinson's speech datasets, and the COVID-19 symptom dataset are used to validate the proposed method. It has been demonstrated that the proposed NL-BGWOA performs well across most of high-dimensional datasets, which shows a high accuracy rate of up to 0.9895. Furthermore, the experimental results on the medical datasets also demonstrate the advantages of the proposed method in actual FS problem, including accuracy, size of feature subsets, and fitness with best values of 0.913, 5.7, and 0.0873, respectively. The results reveal that the proposed NL-BGWOA has comprehensive superiority in solving the FS problem of high-dimensional data.

COVID-RDNet: A novel coronavirus pneumonia classification model using the mixed dataset by CT and X-rays images.

Corona virus disease 2019 (COVID-19) testing relies on traditional screening methods, which require a lot of manpower and material resources. Recently, to effectively reduce the damage caused by radiation and enhance effectiveness, deep learning of classifying COVID-19 negative and positive using the mixed dataset by CT and X-rays images have achieved remarkable research results. However, the details presented on CT and X-ray images have pathological diversity and similarity features, thus increasing the difficulty for physicians to judge specific cases. On this basis, this paper proposes a novel coronavirus pneumonia classification model using the mixed dataset by CT and X-rays images. To solve the problem of feature similarity between lung diseases and COVID-19, the extracted features are enhanced by an adaptive region enhancement algorithm. Besides, the depth network based on the residual blocks and the dense blocks is trained and tested. On the one hand, the residual blocks effectively improve the accuracy of the model and the non-linear COVID-19 features are obtained by cross-layer link. On the other hand, the dense blocks effectively improve the robustness of the model by connecting local and abstract information. On mixed X-ray and CT datasets, the sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), area under curve (AUC), and accuracy can all reach 0.99. On the basis of respecting patient privacy and ethics, the proposed algorithm using the mixed dataset from real cases can effectively assist doctors in performing the accurate COVID-19 negative and positive classification to determine the infection status of patients.

Safety of inactivated SARS-CoV-2 vaccines in patients with allergic diseases.

BACKGROUND: Considering the considerable prevalence of allergic disease in the general population, an urgent need exists for inactivated SARS-CoV-2 vaccines that can be safely administered to those subjects. METHODS: This retrospective cohort study including 1926 participants who received inactivated SARS-CoV-2 vaccines, compared their local and systemic reactions in 7 days after each dose of inactivated SARS-CoV-2 vaccine, and anti-SARS-CoV-2 IgG after vaccination in all participants. RESULTS: Pain at the injection site within seven days after the first injection was the most commonly reported local reaction, occurring in 31.0% of the patients with allergic disease and 18.9% in the control group, respectively (P < 0.001). After the first dose, systemic events were more frequently reported in patients with allergic disease than control group (30.2% vs. 22.9%, P < 0.001). After the second dose, systemic events occurred less often, affecting 17.1% of the patients with allergic disease and 11.1% of the control group (P < 0.002). The occurrence of fatigue, vertigo, diarrhea, skin rash, sore throat were the most frequent systemic reactions. Overall, a lower incidence of local and systemic reactive events was observed after the second dose than the first dose in patients with allergic disease and control group. Nearly all participants had positive IgG antibodies, and participants with allergic disease had higher frequencies compared with control group (100.0 vs.99.4%). CONCLUSIONS: Although local and systemic reactions were more frequently reported in patients with allergic disease than control group, administration of the inactivated SARS-CoV-2 vaccine was safe and well tolerated by all participants; no participants experienced a serious adverse event, and none were hospitalized. TRIAL REGISTRATION: Chinese Clinical Trial Registry, ChiCTR2100048549. Registered Jul 10, 2021.

ISW-LM: An intensive symptom weight learning mechanism for early COVID-19 diagnosis.

The novel coronavirus disease 2019 (COVID-19) pandemic has severely impacted the world. The early diagnosis of COVID-19 and self-isolation can help curb the spread of the virus. Besides, a simple and accurate diagnostic method can help in making rapid decisions for the treatment and isolation of patients. The analysis of patient characteristics, case trajectory, comorbidities, symptoms, diagnosis, and outcomes will be performed in the model. In this paper, a symptom-based machine learning (ML) model with a new learning mechanism called Intensive Symptom Weight Learning Mechanism (ISW-LM) is proposed. The proposed model designs three new symptoms' weight functions to identify the most relevant symptoms used to diagnose and classify COVID-19. To verify the efficiency of the proposed model, multiple laboratory and clinical datasets containing epidemiological symptoms and blood tests are used. Experiments indicate that the importance of COVID-19 infection symptoms varies between countries and regions. In most datasets, the most frequent and significant predictive symptoms for diagnosing COVID-19 are fever, sore throat, and cough. The experiment also compares the state-of-the-art methods with the proposed method, which shows that the proposed model has a high accuracy rate of up to 97.1711%. The positive results indicate that the proposed learning mechanism can help clinicians quickly diagnose and screen patients for COVID-19 at an early stage.

COVID-19 deep classification network based on convolution and deconvolution local enhancement.

Computer Tomography (CT) detection can effectively overcome the problems of traditional detection of Corona Virus Disease 2019 (COVID-19), such as lagging detection results and wrong diagnosis results, which lead to the increase of disease infection rate and prevalence rate. The novel coronavirus pneumonia is a significant difference between the positive and negative patients with asymptomatic infections. To effectively improve the accuracy of doctors' manual judgment of positive and negative COVID-19, this paper proposes a deep classification network model of the novel coronavirus pneumonia based on convolution and deconvolution local enhancement. Through convolution and deconvolution operation, the contrast between the local lesion region and the abdominal cavity of COVID-19 is enhanced. Besides, the middle-level features that can effectively distinguish the image types are obtained. By transforming the novel coronavirus detection problem into the region of interest (ROI) feature classification problem, it can effectively determine whether the feature vector in each feature channel contains the image features of COVID-19. This paper uses an open-source COVID-CT dataset provided by Petuum researchers from the University of California, San Diego, which is collected from 143 novel coronavirus pneumonia patients and the corresponding features are preserved. The complete dataset (including original image and enhanced image) contains 1460 images. Among them, 1022 (70%) and 438 (30%) are used to train and test the performance of the proposed model, respectively. The proposed model verifies the classification precision in different convolution layers and learning rates. Besides, it is compared with most state-of-the-art models. It is found that the proposed algorithm has good classification performance. The corresponding sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and precision are 0.98, 0.96, 0.98, and 0.97, respectively.