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| 1 | Security Monitoring and Management for the Network Services in the Orchestration of SDN-NFV Environment Using Machine Learning Techniques显示文摘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. | Nasser Alshammari Shumaila Shahzadi Saad Awadh Alanazi Shahid Naseem Muhammad Anwar Madallah Alruwaili Muhammad Rizwan Abid Omar Alruwaili Ahmed Alsayat Fahad Ahmad | 2024 | Computer Systems Science & Engineering2024，48，2： | 0 |
| 2 | Indirect Vector Control of Linear Induction Motors Using Space Vector Pulse Width Modulation显示文摘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. | Arjmand Khaliq Syed Abdul Rahman Kashif Fahad Ahmad Muhammad Anwar Qaisar Shaheen Rizwan Akhtar Muhammad Arif Shah Abdelzahir Abdelmaboud | 2023 | Computers, Materials & Continua2023，，3： | 0 |
| 3 | Classification of Electrocardiogram Signals for Arrhythmia Detection Using Convolutional Neural Network显示文摘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%. | Muhammad Aleem Raza Muhammad Anwar Kashif Nisar Ag.Asri Ag.Ibrahim Usman Ahmed Raza Sadiq Ali Khan Fahad Ahmad | 2023 | Computers, Materials & Continua2023，77，12： | 0 |
| 4 | Novel Coronavirus and Emerging Mental Health Issues—A Timely Analysis of Potential Consequences and Legal Policies Perspective显示文摘The present outbreak of coronavirus disease 2019(COVID-19)has swiftly crossed borders,and inflicted the global mental health issues.It is also affecting peoples’daily behaviours,economics,prevention strategies and decision-making among policymakers,healthcare organisations and medical centres that may unintentionally weaken COVID-19 control strategies and lead to increased morbidity,as well as mental health care needs globally.Ultimately,this outbreak is leading to further health complications worldwide,such as stress,fear of the unknown,anger,anxiety,denial,depression symptoms,and insomnia.Notwithstanding all the resources used to counter the spread of the virus,further universal strategies are desirable to address the associated mental health problems.The present study uses the qualitative means to investigate the potential impact of COVID-19,the consequences and legal aspects,then recommend policy implications,in an attempt to cover any apparent loopholes.It presents a unique analysis of its kind on the policy and legal aspects of the ongoing pandemic,as regards mental health.It concludes that there is an acute need to prioritising the health care and curative issues,strengthen awareness and address the psychological syndromes or similar complications afflicting members of the general public during this pandemic. | Mehran Idris Khan Hafiz Abdul Rehman Saleem Muhammad Fahad Anwar Yen-Chiang Chang | 2021 | Fudan Journal of the Humanities and Social Sciences2021，14，1： | 0 |
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