Security Monitoring and Management for the Network Services in the Orchestration of SDN-NFV Environment Using Machine Learning Techniques

Software Defined Network(SDN)and Network Function Virtualization(NFV)technology promote several benefits to network operators,including reduced maintenance costs,increased network operational performance,simplified network lifecycle,and policies management.Network vulnerabilities try to modify services provided by Network Function Virtualization MANagement and Orchestration(NFV MANO),and malicious attacks in different scenarios disrupt the NFV Orchestrator(NFVO)and Virtualized Infrastructure Manager(VIM)lifecycle management related to network services or individual Virtualized Network Function(VNF).This paper proposes an anomaly detection mechanism that monitors threats in NFV MANO and manages promptly and adaptively to implement and handle security functions in order to enhance the quality of experience for end users.An anomaly detector investigates these identified risks and provides secure network services.It enables virtual network security functions and identifies anomalies in Kubernetes(a cloud-based platform).For training and testing purpose of the proposed approach,an intrusion-containing dataset is used that hold multiple malicious activities like a Smurf,Neptune,Teardrop,Pod,Land,IPsweep,etc.,categorized as Probing(Prob),Denial of Service(DoS),User to Root(U2R),and Remote to User(R2L)attacks.An anomaly detector is anticipated with the capabilities of a Machine Learning(ML)technique,making use of supervised learning techniques like Logistic Regression(LR),Support Vector Machine(SVM),Random Forest(RF),Naïve Bayes(NB),and Extreme Gradient Boosting(XGBoost).The proposed framework has been evaluated by deploying the identified ML algorithm on a Jupyter notebook in Kubeflow to simulate Kubernetes for validation purposes.RF classifier has shown better outcomes(99.90%accuracy)than other classifiers in detecting anomalies/intrusions in the containerized environment.

Indirect Vector Control of Linear Induction Motors Using Space Vector Pulse Width Modulation

Vector control schemes have recently been used to drive linear induction motors(LIM)in high-performance applications.This trend promotes the development of precise and efficient control schemes for individual motors.This research aims to present a novel framework for speed and thrust force control of LIM using space vector pulse width modulation(SVPWM)inverters.The framework under consideration is developed in four stages.To begin,MATLAB Simulink was used to develop a detailed mathematical and electromechanical dynamicmodel.The research presents a modified SVPWM inverter control scheme.By tuning the proportional-integral(PI)controller with a transfer function,optimized values for the PI controller are derived.All the subsystems mentioned above are integrated to create a robust simulation of the LIM’s precise speed and thrust force control scheme.The reference speed values were chosen to evaluate the performance of the respective system,and the developed system’s response was verified using various data sets.For the low-speed range,a reference value of 10m/s is used,while a reference value of 100 m/s is used for the high-speed range.The speed output response indicates that themotor reached reference speed in amatter of seconds,as the delay time is between 8 and 10 s.The maximum amplitude of thrust achieved is less than 400N,demonstrating the controller’s capability to control a high-speed LIM with minimal thrust ripple.Due to the controlled speed range,the developed system is highly recommended for low-speed and high-speed and heavy-duty traction applications.

中国水仙NtMYB7基因的克隆及功能初步研究

为研究中国水仙类黄酮代谢调控网络,从中国水仙(Narcissus tazetta var.chinensis)中克隆得到一个R2R3-MYB基因,命名为NtMYB7(GenBank登录号:MF522208)。序列分析表明,NtMYB7基因cDNA开放阅读框(ORF)为753bp,编码250个氨基酸。氨基酸多重序列比对分析发现,NtMYB7含有R2和R3保守结构域,属于R2R3-MYB家族;系统进化树分析结果显示,NtMYB7与花青素合成抑制因子聚为一类。实时荧光定量PCR分析发现,NtMYB7基因在中国水仙不同时期花瓣和副冠以及不同器官中均有表达,且NtMYB7基因在鳞茎盘中表达量最高。瞬时表达分析发现,NtMYB7使花青素合成激活因子StMYB诱导产生的红色变浅;定量PCR分析表明,NtMYB7基因显著抑制烟草黄酮醇代谢分支FLS基因的表达,同时抑制StMYB激活的花青素和原花青素合成结构基因的表达。研究结果初步判断,NtMYB7基因是中国水仙类黄酮代谢途径的抑制因子。

Google Scholar University Ranking Algorithm to Evaluate the Quality of Institutional Research

Education quality has undoubtedly become an important local and international benchmark for education,and an institute’s ranking is assessed based on the quality of education,research projects,theses,and dissertations,which has always been controversial.Hence,this research paper is influenced by the institutes ranking all over the world.The data of institutes are obtained through Google Scholar(GS),as input to investigate the United Kingdom’s Research Excellence Framework(UK-REF)process.For this purpose,the current research used a Bespoke Program to evaluate the institutes’ranking based on their source.The bespoke program requires changes to improve the results by addressing these methodological issues:Firstly,Redundant profiles,which increased their citation and rank to produce false results.Secondly,the exclusion of theses and dissertation documents to retrieve the actual publications to count for citations.Thirdly,the elimination of falsely owned articles from scholars’profiles.To accomplish this task,the experimental design referred to collecting data from 120 UK-REF institutes and GS for the present year to enhance its correlation analysis in this new evaluation.The data extracted from GS is processed into structured data,and afterward,it is utilized to generate statistical computations of citations’analysis that contribute to the ranking based on their citations.The research promoted the predictive approach of correlational research.Furthermore,experimental evaluation reported encouraging results in comparison to the previous modi-fication made by the proposed taxonomy.This paper discussed the limitations of the current evaluation and suggested the potential paths to improve the research impact algorithm.

Classification of Electrocardiogram Signals for Arrhythmia Detection Using Convolutional Neural Network

With the help of computer-aided diagnostic systems,cardiovascular diseases can be identified timely manner to minimize the mortality rate of patients suffering from cardiac disease.However,the early diagnosis of cardiac arrhythmia is one of the most challenging tasks.The manual analysis of electrocardiogram(ECG)data with the help of the Holter monitor is challenging.Currently,the Convolutional Neural Network(CNN)is receiving considerable attention from researchers for automatically identifying ECG signals.This paper proposes a 9-layer-based CNN model to classify the ECG signals into five primary categories according to the American National Standards Institute(ANSI)standards and the Association for the Advancement of Medical Instruments(AAMI).The Massachusetts Institute of Technology-Beth Israel Hospital(MIT-BIH)arrhythmia dataset is used for the experiment.The proposed model outperformed the previous model in terms of accuracy and achieved a sensitivity of 99.0%and a positivity predictively 99.2%in the detection of a Ventricular Ectopic Beat(VEB).Moreover,it also gained a sensitivity of 99.0%and positivity predictively of 99.2%for the detection of a supraventricular ectopic beat(SVEB).The overall accuracy of the proposed model is 99.68%.

Brain Tumor Segmentation in Multimodal MRI Using U-Net Layered Structure

The brain tumour is the mass where some tissues become old or damaged,but they do not die or not leave their space.Mainly brain tumour masses occur due to malignant masses.These tissues must die so that new tissues are allowed to be born and take their place.Tumour segmentation is a complex and time-taking problem due to the tumour’s size,shape,and appearance variation.Manually finding such masses in the brain by analyzing Magnetic Resonance Images(MRI)is a crucial task for experts and radiologists.Radiologists could not work for large volume images simultaneously,and many errors occurred due to overwhelming image analysis.The main objective of this research study is the segmentation of tumors in brain MRI images with the help of digital image processing and deep learning approaches.This research study proposed an automatic model for tumor segmentation in MRI images.The proposed model has a few significant steps,which first apply the pre-processing method for the whole dataset to convert Neuroimaging Informatics Technology Initiative(NIFTI)volumes into the 3D NumPy array.In the second step,the proposed model adopts U-Net deep learning segmentation algorithm with an improved layered structure and sets the updated parameters.In the third step,the proposed model uses state-of-the-art Medical Image Computing and Computer-Assisted Intervention(MICCAI)BRATS 2018 dataset withMRI modalities such as T1,T1Gd,T2,and Fluidattenuated inversion recovery(FLAIR).Tumour types in MRI images are classified according to the tumour masses.Labelling of these masses carried by state-of-the-art approaches such that the first is enhancing tumour(label 4),edema(label 2),necrotic and non-enhancing tumour core(label 1),and the remaining region is label 0 such that edema(whole tumour),necrosis and active.The proposed model is evaluated and gets the Dice Coefficient(DSC)value for High-grade glioma(HGG)volumes for their test set-a,test set-b,and test set-c 0.9795, 0.9855 and 0.9793, respectively. DSC value for the Low-gradeglioma (LGG) volumes for the test set is 0.9950, which shows the proposedmodel has achieved significant results in segmenting the tumour in MRI usingdeep learning approaches. The proposed model is fully automatic that canimplement in clinics where human experts consumemaximumtime to identifythe tumorous region of the brain MRI. The proposed model can help in a wayit can proceed rapidly by treating the tumor segmentation in MRI.

Probability Based Regression Analysis for the Prediction of Cardiovascular Diseases

Machine Learning(ML)has changed clinical diagnostic procedures drastically.Especially in Cardiovascular Diseases(CVD),the use of ML is indispensable to reducing human errors.Enormous studies focused on disease prediction but depending on multiple parameters,further investigations are required to upgrade the clinical procedures.Multi-layered implementation of ML also called Deep Learning(DL)has unfolded new horizons in the field of clinical diagnostics.DL formulates reliable accuracy with big datasets but the reverse is the case with small datasets.This paper proposed a novel method that deals with the issue of less data dimensionality.Inspired by the regression analysis,the proposed method classifies the data by going through three different stages.In the first stage,feature representation is converted into probabilities using multiple regression techniques,the second stage grasps the probability conclusions from the previous stage and the third stage fabricates the final classifications.Extensive experiments were carried out on the Cleveland heart disease dataset.The results show significant improvement in classification accuracy.It is evident from the comparative results of the paper that the prevailing statistical ML methods are no more stagnant disease prediction techniques in demand in the future.

Human papillomavirus in upper digestive tract tumors from three countries

AIM: To clarify human papillomavirus (HPV) involvement in carcinogenesis of the upper digestive tract of virological and pathological analyses. METHODS: The present study examined the presence of HPV in squamous cell carcinomas of the oral cavity (n = 71), and esophagus (n = 166) collected from Japan, Pakistan and Colombia, with different HPV exposure risk and genetic backgrounds. The viral load and physical status of HPV16 and HPV16-E6 variants were examined. Comparison of p53 and p16INK4a expression in HPV-positive and HPV-negative cases was also made. RESULTS: HPV16 was found in 39 (55%) oral carcinomas (OCs) and 24 (14%) esophageal carcinomas (ECs). This site-specific difference in HPV detection between OCs and ECs was statistically significant (P < 0.001). There was a significant difference in the geographical distribution of HPV16-E6 variants. Multiple infections of different HPV types were found in 13 ECs, but multiple infections were not found in OCs. This difference was statistically significant (P = 0.001). The geometric means (95% confidence interval) of HPV16 viral load in OCs and ECs were 0.06 (0.02-0.18) and 0.12 (0.05-0.27) copies per cell, respectively. The expression of p16INK4a proteins was increased by the presence of HPV in ECs (53% and 33% in HPV-positive and-negative ECs, respectively; P = 0.036), and the high-risk type of the HPV genome was not detected in surrounding normal esophageal mucosa of HPV-positive ECs. CONCLUSION: Based on our results, we cannot deny the possibility of HPV16 involvement in the carcinogenesis of the esophagus.

Traffic Priority-Aware Medical Data Dissemination Scheme for IoT Based WBASN Healthcare Applications

Wireless Body Area Sensor Network(WBASN)is an automated system for remote health monitoring of patients.WBASN under umbrella of Internet of Things(IoT)is comprised of small Biomedical Sensor Nodes(BSNs)that can communicate with each other without human involvement.These BSNs can be placed on human body or inside the skin of the patients to regularly monitor their vital signs.The BSNs generate critical data as it is related to patient’s health.The data traffic can be classified as Sensitive Data(SD)and Non-sensitive Data(ND)packets based on the value of vital signs.These data packets have different priority to deliver.The ND packets may tolerate some delay or packet loss whereas,the SD packets required to be delivered on time with minimized packet loss otherwise it can be life threating to the patients.In this research,we propose a Traffic Priority-aware Medical Data Dissemination(TPMD2)scheme forWBASN to deliver the data packets according to their priority based on the sensitivity of the data.The assessment of the proposed scheme is carried out in various experiments.The simulation results of the TPMD2 scheme indicate a significant improvement in packets delivery,transmission delay and energy efficiency in comparison with the existing schemes.

A Longitudinal Survey for Genome-based Identification of SARS-CoV-2 in Sewage Water in Selected Lockdown Areas of Lahore City, Pakistan: A Potential Approach for Future Smart Lockdown Strategy

In 2019,the newly emerged SARS-CoV-2 virus caused pneumonia-like illness.The disease rapidly spread globally,leading to a worldwide outbreak referred to as the COVID-19 pandemic.

Supportive tactics for innovative and sustainability performance in emerging SMEs

For this research,we examined the influence of access to domestic and international financing on sustainability performance with a mediating role of innovative performance and a moderating role of access to government support.Data were collected from 317 small and medium-sized enterprises(SMEs)through structured questionnaires.The results indicated that access to domestic and international financing significantly contributes to sustainability and innovative performances.Accordingly,we found a partial mediating role of innovative performance between access to domestic financing and sustainability performance as well as between access to international financing and sustainability performance.Access to government support significantly moderates the relationship between access to domestic finances and innovative performance as well as between access to international finances and innovative performance.Practitioners and policymakers should encourage national and international financial institutions and banks to facilitate SMEs by lending them funds for innovative activities and sustainability performance.Moreover,the government should support SMEs,so that they can contribute to economic growth and the gross domestic product.The implications from these matters will be further discussed in this paper.

中国水仙R2R3-MYB基因NtMYB5的克隆和功能研究

为研究中国水仙(Narcissus tazetta var.chinensis)中类黄酮代谢途径的调控网络,从其转录组中筛选出1条R2R3-MYB基因并从花瓣cDNA中克隆其编码区全长序列,命名为NtMYB5。NtMYB5开放阅读框为681 bp,编码226个氨基酸。蛋白多重序列比对分析发现NtMYB5含有R2和R3结构域以及1个pd LNLD/ELxiG/S氨基酸基序;系统进化树分析表明,NtMYB5与花青素合成抑制因子亲缘关系最近;通过对NtMYB5在中国水仙中的表达检测发现,其表达量在花器官中较高,且随花开放逐渐上升;在烟草瞬时表达中,NtMYB5显著抑制花青素合成促进因子St MYB的效果;转NtMYB5烟草花瓣颜色变浅,q PCR检测表明NtMYB5抑制类黄酮代谢途径大部分结构基因表达。NtMYB5为中国水仙中花青素合成抑制因子。

中国水仙DFR基因启动子的克隆及功能

为了解中国水仙花青素合成途径关键基因DFR(NtDFR)的表达调控以及中国水仙不能合成花青素的分子机制,采用染色体步移法从中国水仙中克隆NtDFR基因起始密码子上游962 bp的启动子序列.生物信息学分析结果表明启动子序列除包含TATA-box、CAAT-box等基本启动子元件外,还包含光调控元件、植物激素响应元件、胁迫响应元件等多个顺式作用元件.此外,该启动子序列还含有MYB转录因子结合位点.为验证启动子的表达特性,将NtDFR启动子取代植物表达载体pBI121上35S启动子,构建pBI121-pNtDFR::GUS载体,利用农杆菌转化烟草叶片瞬时表达,通过GUS组织染色法确定了克隆的启动子的活性.将中国水仙R2R3-MYB转录因子NtMYB2、NtMYB5分别和pBI121-pNtDFR::GUS共同注射烟草,定量PCR和GUS组织化学染色结果表明NtMYB2和NtMYB5都使NtDFR启动子诱导的烟草叶片GUS颜色变浅以及GUS基因表达量下降,表明NtMYB2和NtMYB5是NtDFR的抑制因子.本研究结果有助于了解中国水仙花青素合成途径的分子调控机制.