Intrusion Detection Using Federated Learning for Computing

The integration of clusters,grids,clouds,edges and other computing platforms result in contemporary technology of jungle computing.This novel technique has the aptitude to tackle high performance computation systems and it manages the usage of all computing platforms at a time.Federated learning is a collaborative machine learning approach without centralized training data.The proposed system effectively detects the intrusion attack without human intervention and subsequently detects anomalous deviations in device communication behavior,potentially caused by malicious adversaries and it can emerge with new and unknown attacks.The main objective is to learn overall behavior of an intruder while performing attacks to the assumed target service.Moreover,the updated system model is send to the centralized server in jungle computing,to detect their pattern.Federated learning greatly helps the machine to study the type of attack from each device and this technique paves a way to complete dominion over all malicious behaviors.In our proposed work,we have implemented an intrusion detection system that has high accuracy,low False Positive Rate(FPR)scalable,and versatile for the jungle computing environment.The execution time taken to complete a round is less than two seconds,with an accuracy rate of 96%.

A Hybrid Intrusion Detection Method Based on Convolutional Neural Network and AdaBoost

To solve the problem of poor detection and limited application range of current intrusion detection methods,this paper attempts to use deep learning neural network technology to study a new type of intrusion detection method.Hence,we proposed an intrusion detection algorithm based on convolutional neural network(CNN)and AdaBoost algorithm.This algorithm uses CNN to extract the characteristics of network traffic data,which is particularly suitable for the analysis of continuous and classified attack data.The AdaBoost algorithm is used to classify network attack data that improved the detection effect of unbalanced data classification.We adopt the UNSW-NB15 dataset to test of this algorithm in the PyCharm environment.The results show that the detection rate of algorithm is99.27%and the false positive rate is lower than 0.98%.Comparative analysis shows that this algorithm has advantages over existing methods in terms of detection rate and false positive rate for small proportion of attack data.

Deep Learning-Based ECG Classification for Arterial Fibrillation Detection

The application of deep learning techniques in the medical field,specifically for Atrial Fibrillation(AFib)detection through Electrocardiogram(ECG)signals,has witnessed significant interest.Accurate and timely diagnosis increases the patient’s chances of recovery.However,issues like overfitting and inconsistent accuracy across datasets remain challenges.In a quest to address these challenges,a study presents two prominent deep learning architectures,ResNet-50 and DenseNet-121,to evaluate their effectiveness in AFib detection.The aim was to create a robust detection mechanism that consistently performs well.Metrics such as loss,accuracy,precision,sensitivity,and Area Under the Curve(AUC)were utilized for evaluation.The findings revealed that ResNet-50 surpassed DenseNet-121 in all evaluated categories.It demonstrated lower loss rate 0.0315 and 0.0305 superior accuracy of 98.77%and 98.88%,precision of 98.78%and 98.89%and sensitivity of 98.76%and 98.86%for training and validation,hinting at its advanced capability for AFib detection.These insights offer a substantial contribution to the existing literature on deep learning applications for AFib detection from ECG signals.The comparative performance data assists future researchers in selecting suitable deep-learning architectures for AFib detection.Moreover,the outcomes of this study are anticipated to stimulate the development of more advanced and efficient ECG-based AFib detection methodologies,for more accurate and early detection of AFib,thereby fostering improved patient care and outcomes.

The relationship between the distribution of HBcAg in liver cells and the pathological manifestations of the liver in 429 HBeAg-positive chronic HBV-infected individuals

Background:The natural history of chronic HBV infection is typically characterized by four stages:the immune tolerance period,the immune clearance period,the immune control period,and the immune escape period.These stages are associated with the distribution of HBcAg in liver cells;however,this relationship remains a topic of broad debate within the field of liver disease.To objectively and quantitatively measure the intracellular distribution of HBcAg,this paper aims to design a method referred to as the“layered evaluation method”and to examine its validation.Methods:The distribution of HBcAg in liver cells is assessed using Image Pro Plus image processing software,along with calculations of cytoplasmic and nuclear positive staining rates.Results:The findings indicate that the highest proportion of patients exhibited a positive cytoplasmic expression rate ranging from 0-2.5%.More than 40% of the total sample was categorized within the 0-2.5% positive nuclear expression range.The HBcAg cytoplasmic positive staining rates were classified into five levels:a cytoplasmic HBcAg positive staining rate of less than 0.05% is designated as level 0,indicating negative expression;a staining rate between 0.05% and 5% is classified as level 1;a rate from 5% to less than 10% is classified as level 2;a rate from 10% to less than 20% is classified as level 3;and a nuclear positivity rate exceeding 20% is classified as level 4.Conclusion:The inflammatory activity grade in these patients was positively correlated with the cytoplasmic distribution of HBcAg.Furthermore,the nuclear distribution rate of HBcAg was significantly higher in the G3 group compared to the other groups.

Positive Rate of Different Hepatitis B Virus Serological Markers in Peking Union Medical College Hospital,a General Tertiary Hospital in Beijing

Objectives To investigate the positive rate of different hepatitis B virus （HBV） serological markers, and the demographic factors related to HBV infection. Methods We enrolled all patients tested for HBV serological markers, such as HBV surface antigen （HBsAg）, HBV surface antibody （HBsAb）, hepatitis B e antigen （HBeAg）, hepatitis B e antibody （HBeAb）, HBV core antibody （HBcAb）, and HBV-DNA from July 2008 to July 2009 in Peking Union Medical College Hospital. The positive rate of each HBV serological marker was calculated according to gender, age, and department, respectively. The positive rates of HBV-DNA among patients with positive HBsAg were also analyzed. Results Among 27 409 samples included, 2681 （9.8%） were HBsAg positive. When patients were divided into 9 age groups, the age-specific positive rate of HBsAg was 1.2%, 9.6%, 12.3%, 10.9%, 10.3%, 9.7%, 8.0%, 5.8%, and 4.3%, respectively. The positive rate of HBsAg in non-surgical department, surgical department, and health examination center was 16.2%, 5.8%, and 4.7%, respectively. The positive rate of HBsAg of males （13.3%） was higher than that of females （7.3%, P=0.000）. Among the 2681 HBsAg （＋） patients, 1230 （45.9%） had HBV-DNA test, of whom 564 （45.9%） were positive. Patients with HBsAg （＋）, HBeAg （＋）, and HBcAg （＋） result usually had high positive rate of HBV-DNA results （71.8%, P=0.000）. Conclusions Among this group of patients in our hospital, the positive rate of HBsAg was relatively high. Age group of 20-29, males, and patients in non-surgical departments were factors associated with high positive rate of HBsAg.

SARS-CoV-2 infection rates after different vaccination schemes:An online survey in Turkey

Objective:To identify effects of various nationwide vaccination protocols on the evolution of new SARS-CoV-2 infections among adult population and to evaluate the safety of mRNA(BioNTech/Pfizer)vaccine.Methods:Totally 10735 adult volunteers that received at least one dose of BioNTech/Pfizer or triple doses of CoronaVac participated in this cross-sectional-online survey between 1 and 10 September 2021.The information was collected covering a 5-month period from April 2021 to September 2021.Information about people who were vaccinated with only single and double dose CoronaVac were not included in this study.Results:At least one side effect after single and double dose of BioNTech/Pfizer and triple doses of CoronaVac were observed in 42.1%,42.5%and 10.9%,respectively.The most common side effects were shoulder/arm pain,weakness/fatigue,muscle/joint pain and headache.The side effects were the most frequent in single BioNTech/Pfizer,while it was the least in triple CoronaVac.The rate of positive PCR tests before vaccination was 17.6%,and decreased to 3.0%after vaccination.The rates of positive SARS CoV-2-PCR were 18.8%,3.5%,3.1%,0.5%and 4.6%in single BioNTech/Pfizer,double BioNTech/Pfizer,double CoronaVac+single BioNTech/Pfizer,double CoronaVac+double BioNTech/Pfizer and triple CoronaVac,respectively.While 1.8%of PCR positive COVID-19 cases needed intensive unit care in the pre-vaccination period,intensive care unit was required in 0%,1.5%,2.4%,0%and 4.2%after single BioNTech/Pfizer,double BioNTech/Pfizer,double CoronaVac+single BioNTech/Pfizer,double CoronaVac+double BioNTech/Pfizer and triple CoronaVac,respectively.Reinfection rate after vaccination was 0.4%.Conclusions:The rarity of COVID-19 infection after vaccination suggests that efficacy of vaccines is maintained.On the other hand,the data underscore the critical importance of continued public health mitigation.

Study of the Effect of Two New Types of IUDs,TCu380A and GyneFix on the Positive Rate of Chlamydia Trahmatis

To analyze the effect of two types of IUDs, TCu380A and GyneFix on the positive rate of Chlamydia Trahmatis (CT). Methods The TCu380A and GyneFix IUDs were compared in a randomized was for the one year and two year positive rate of Chlamydia Trahmatis (CT) and with the control respectively. Results The one year positive rate of CT antigens was 5.63% of TCu380A and two year was 4.92%; the one year positive rate of CT antigens was 4.62% and two year was 5.08% of GyneFix. There was no significant difference in the positive rate of CT antigen between the TCu380A IUD, and GyneFix IUDs groups, while there were significant differences between the TCu380A IUD, GyneFix IUD and the controls (15.18%) respectively. Conclusion Both IUDs provide highly effective protection against CT infection.

A Positive Analysis on Personal Saving Rate between America and China

The level of saving not only influences the life of individuals, but also plays an important role in a country＇s development. Accordingly, the studying on saving is becoming a focus problem in modem life. In recent years, the personal saving rate in the United States has fallen sharply, but the personal saving rate in China is at an astoundingly high level. This paper studies this problem with the positive analysis method from the situation; the reasons of the saving rate disparity between the U.S. and .China, and put forward some proposals about how to deal with the saving problems.

Lung Cancer Prediction from Elvira Biomedical Dataset Using Ensemble Classifier with Principal Component Analysis

Machine learning algorithms (MLs) can potentially improve disease diagnostics, leading to early detection and treatment of these diseases. As a malignant tumor whose primary focus is located in the bronchial mucosal epithelium, lung cancer has the highest mortality and morbidity among cancer types, threatening health and life of patients suffering from the disease. Machine learning algorithms such as Random Forest (RF), Support Vector Machine (SVM), K-Nearest Neighbor (KNN) and Naïve Bayes (NB) have been used for lung cancer prediction. However they still face challenges such as high dimensionality of the feature space, over-fitting, high computational complexity, noise and missing data, low accuracies, low precision and high error rates. Ensemble learning, which combines classifiers, may be helpful to boost prediction on new data. However, current ensemble ML techniques rarely consider comprehensive evaluation metrics to evaluate the performance of individual classifiers. The main purpose of this study was to develop an ensemble classifier that improves lung cancer prediction. An ensemble machine learning algorithm is developed based on RF, SVM, NB, and KNN. Feature selection is done based on Principal Component Analysis (PCA) and Analysis of Variance (ANOVA). This algorithm is then executed on lung cancer data and evaluated using execution time, true positives (TP), true negatives (TN), false positives (FP), false negatives (FN), false positive rate (FPR), recall (R), precision (P) and F-measure (FM). Experimental results show that the proposed ensemble classifier has the best classification of 0.9825% with the lowest error rate of 0.0193. This is followed by SVM in which the probability of having the best classification is 0.9652% at an error rate of 0.0206. On the other hand, NB had the worst performance of 0.8475% classification at 0.0738 error rate.

Preparation of Purified and Inactivated HP-PRRS Vaccine

[Objective] The aim of this study was to improve the purification and protective potency of HP-PRRS inactivated vaccine. [Method] HP-PRRS virus that had been multiplied inside Marc-145 cells was collected and concentrated 50 times and then inactivated. Complete virions were separated and collected by chromatography with Sepharose 4 Fast Flow. Oil adjuvant was added to prepare purified inactivated vaccine. [Result] Viral protein was separated from other proteins by purification and the viral protein contents ranged from 76.7% to 82.4%, and 96% of the expected serum proteins were removed. Protective potency of purified vaccine was above 4/5 and positive conversion rate of antibody was over 86%, both higher than that of unpurified vaccine. The differences were significant. [Conclusion] The experiment il-lustrated that the immune efficacy of vaccine can be enhanced through concentrat- ing and purifying, while the non-viral protein can be removed, so that allergic reaction and stress response cadsed by vaccine inoculation can be avoided.

Construction of a clinical survival prognostic model for middle-aged and elderly patients with stage III rectal adenocarcinoma

BACKGROUND Nomograms for prognosis prediction in colorectal cancer patients are few,and prognostic indicators differ with age.AIM To construct a new nomogram survival prediction tool for middle-aged and elderly patients with stage III rectal adenocarcinoma.METHODS A total of 2773 eligible patients were divided into the training cohort(70%)and the validation cohort(30%).Optimal cutoff values were calculated using the X-tile software for continuous variables.Univariate and multivariate Cox proportional hazards regression analyses were used to determine overall survival(OS)and cancer-specific survival(CSS)-related prognostic factors.Two nomograms were successfully constructed.The discriminant and predictive ability and clinical usefulness of the model were also assessed by multiple methods of analysis.RESULTS The 95%CI in the training group was 0.719(0.690-0.749)and 0.733(0.702-0.74),while that in the validation group was 0.739(0.696-0.782)and 0.750(0.701-0.800)for the OS and CSS nomogram prediction models,respectively.In the validation group,the AUC of the three-year survival rate was 0.762 and 0.770,while the AUC of the five-year survival rate was 0.722 and 0.744 for the OS and CSS nomograms,respectively.The nomogram distinguishes all-cause mortality from cancer-specific mortality in patients with different risk grades.The time-dependent AUC and decision curve analysis showed that the nomogram had good clinical predictive ability and decision efficacy and was significantly better than the tumor-node-metastases staging system.CONCLUSION The survival prediction model constructed in this study is helpful in evaluating the prognosis of patients and can aid physicians in clinical diagnosis and treatment.

Investigation on HTLV Infection among Voluntary Blood Donors in Wuzhou City

Objective: To understand the infection of HTLV among voluntary blood donors in Wuzhou City, and to provide reference for the national health administrative department to formulate blood safety screening strategies. Methods: The HTLV double-antigen sandwich ELISA reagent was used to screen the blood samples of unpaid blood donors, and the reactive samples in the initial screening were subjected to a double-well retest;Specimens that were still reactive in the retest were further confirmed by viral nucleic acid amplification test (PCR) and western blotting (WB). Results: A total of 9 of 20,222 unpaid blood donation samples were screened to be reactive, and the screening response rate was 0.04%;Two samples of HTLV-1 nucleic acid and western blotting (WB) were confirmed to be positive, and the other seven samples were negative;The confirmed positive rate was 0.01%. Conclusion: There was a certain positive rate of HTLV-1 serological screening among the non remunerated blood donors in Wuzhou City, and the confirmation test confirmed that there was a certain risk of HTLV infection;In order to further understand the HTLV infection of blood donors in this city, we should further increase the number of screening samples, so as to obtain more reliable and accurate data in this region, and provide data and reference for the health administration department to formulate HTLV screening strategies for blood donors.

Methods for Increasing Creditability of Anomaly Detection System

Based on Bayes' theorem we point out that the false positive rate must be lower than the intrusion base rate in order to make the Alarm Credibility Probability of the intrusion detection system exceed 50%. We present the methods that have been used in our developing intrusion detection system AIIDS (artificial immune intrusion detection systems) to increase the creditability of anomaly detection system. These methods include increasing the regularities of the system call trace by use of Hidden Markov Model (HMM), making every antibody or detector has finite lifetime, offering the detector a co-stimulate signal to illustrate whether there is damage in the system according to the integrity, confidentiality, or availability of the system resource.

Study on the Diagnostic Value of Combined Detection of Sperm Quality, Sex Hormone and Ovulation in Infertility

Objective: To analyze the application value of combined detection of sperm quality, sex hormone and ovulation in the diagnosis of infertility. Methods: The study was conducted from June 2021 to June 2022. Sixty infertile couples who received IVF cycle treatment in our hospital during this period were selected as the observation group, and 60 couples with good sperm quality and follicle number ≥ 5 who conceived naturally after gynecological disease treatment were selected as the control group during the same period. The sperm quality, sex hormones and ovulation of the two groups were observed, and the change of positive rate was detected by the combined detection method. Results: Compared with the control group, the observation group had less semen (2.82 ± 0.12) ml, lower concentration (69.17 ± 1.28) × 106/ml, normal sperm morphology rate (2.92% ± 0.11%), lower survival rate (70.25% ± 1.16%), higher deformed sperm index (1.39 ± 0.11), and significant differences between groups (P < 0.05);The levels of FSH (7.15 ± 1.33) U/L, LH (5.13 ± 0.53) mU/ml, E2 (72.34 ± 5.11) ng/L, AMH (3.87 ± 0.67) ng/ml and AFC (7.15 ± 0.76) in the control group were significantly better than those in the observation group (P < 0.05). Compared with the single detection method of the three groups, the positive detection rate of the combined diagnosis method was higher, and the difference between the groups was significant (P < 0.05). Conclusion: For the diagnosis of infertility, the combined detection method of sperm quality, sex hormone and ovulation can effectively clarify the problems existing in the couple, significantly improve the detection rate of the cause of the patient, and is more conducive to guiding clinical symptomatic treatment, which is worthy of promotion and reference.

An early recognition algorithm for BitTorrent traffic based on improved K-means

In response to the deficiencies of BitTorrent, the concept of density radius was proposed, and the distance from the maximum point of radius density to cluster center as a cluster radius was taken to solve the too large cluster radius resulted from the discrete points and to reduce the false positive rate of early recognition algorithms. Simulation results show that in the actual network environment, the improved algorithm, compared with K-means, will reduce the false positive rate of early identification algorithm from 6.3% to 0.9% and has a higher operational efficiency.

Head-to-head comparison of PSMA PET/CT and mpMRI for detecting biochemical recurrence of prostate cancer: A systematic review and meta-analysis

Objectives This study aimed to evaluate the performance of prostate-specific membrane antigen positron emission tomography/computed tomography(PSMA PET/CT)in comparison to multiparametric magnetic resonance imaging(mpMRI)for detecting biochemical recurrence of prostate cancer(PCa).Materials and methods We conducted a comprehensive search for articles published in PubMed,Web of Science,Embase,and the Cochrane Library,spanning the inception of the database until October 26,2022,which included head-to-head comparisons of PSMA PET/CT and mpMRI for assessing the biochemical recurrence of PCa.Results A total of 5 studies including 228 patients were analyzed.The overall positivity rates of PSMA PET/CT and mpMRI for detecting biochemical recurrence of PCa after final treatment were 0.68(95%confidence interval[CI],0.52–0.89)and 0.56(95%CI,0.36–0.88),respectively.The positivity rates of PSMA PET/CT and mpMRI for detecting local recurrence,lymph node metastasis,and bone metastases were 0.37(95%CI,0.30–0.47)and 0.38(95%CI,0.22–0.67),0.44(95%CI,0.35–0.56)and 0.25(95%CI,0.17–0.35),and 0.19(95%CI,0.11–0.31)and 0.12(95%CI,0.05–0.25),respectively.Compared with mpMRI,PSMA PET/CT exhibited a higher positivity rate for detecting biochemical recurrence and lymph node metastases,and no significant difference in the positivity rate of local recurrence was observed between these 2 imaging modalities.Conclusions Compared with mpMRI,PSMA PET/CT appears to have a higher positivity rate for detecting biochemical recurrence of PCa.Although both imaging methods showed similar positivity rates of detecting local recurrence,PSMA PET/CT outperformed PSMA PET/CT in detecting lymph node involvement and overall recurrence.