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Liquidity connectedness in cryptocurrency market

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Financial Innovation 2022年第1期8卷 81-105页

作者： Mudassar Hasan muhammad Abubakr naeem muhammad Arif Syed Jawad Hussain shahzad Xuan Vinh Vo Lahore Business School The University of LahoreLahorePakistan Smurfit Graduate School of Business University Col-lege DublinDublinIreland Institute of Business Research University of Economics Ho Chi Minh CityHo Chi Minh CityVietnam Department of Business Administration Shaheed Benazir Bhutto UniversityShaheed BenazirabadPakistan Montpellier Business School MontpellierFrance South Ural State University ChelyabinskRussian Federation Institute of Business Research and CFVG University of Economics Ho Chi Minh CityHo Chi Minh CityVietnam

We examine the dynamics of liquidity connectedness in the cryptocurrency *** use the connectedness models of Diebold and Yilmaz(Int J Forecast 28(1):57–66,2012)and Baruník and Křehlík(J Financ Econom 16(2):271–296,2018)on a sample of six major cryptocurrencies,namely,Bitcoin(BTC),Litecoin(LTC),Ethereum(ETH),Ripple(XRP),Monero(XMR),and *** static analysis reveals a moderate liquidity connectedness among our sample cryptocurrencies,whereas BTC and LTC play a significant role in connectedness magnitude.A distinct liquidity cluster is observed for BTC,LTC,and XRP,and ETH,XMR,and Dash also form another distinct liquidity *** frequency domain analysis reveals that liquidity connectedness is more pronounced in the short-run time horizon than the medium-and long-run time *** the short run,BTC,LTC,and XRP are the leading contributor to liquidity shocks,whereas,in the long run,ETH assumes this *** with the medium term,a tight liquidity clustering is found in the short and long *** time-varying analysis indicates that liquidity connectedness in the cryptocurrency market increases over time,pointing to the possible effect of rising demand and higher acceptability for this unique ***,more pronounced liquidity connectedness patterns are observed over the short and long run,reinforcing that liquidity connectedness in the cryptocurrency market is a phenomenon dependent on the time–frequency connectedness.

关键词： Liquidity Time-frequency connectedness Cryptocurrencies

Dynamic channel estimation-aware routing protocol in mobile cognitive radio networks for smart IIoT applications

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Digital Communications and Networks 2023年第2期9卷 367-382页

作者： Qusay M.Salih Md Arafatur Rahman A.Taufiq Asyhari muhammad Kamran naeem Mohammad Patwary Ryan Alturki Mohammed Abdulaziz Ikram Faculty of Computing Universiti Malaysia Pahang26300GambangPahangMalaysia School of Engineering Computing and Mathematical SciencesUniversity of WolverhamptonUK Data Science Program Monash UniversityIndonesiaand Birmingham City UniversityB47XGUK Department of Information Science College of Computer and Information SystemsUmm Al-Qura UniversitySaudi Arabia Computer Science Department Al Jumoum University CollegeUmm Al-Qura UniversitySaudi Arabia

Cognitive Radio Networks(CRNs)have become a successful platform in recent years for a diverse range of future systems,in particularly,industrial internet of things(IIoT)*** order to provide an efficient connection among IIoT devices,CRNs enhance spectrum utilization by using licensed ***,the routing protocol in these networks is considered one of the main problems due to node mobility and time-variant channel ***,the channel selection for routing protocol is indispensable in CRNs to provide an adequate adaptation to the Primary User(PU)activity and create a robust routing *** study aims to construct a robust routing path by minimizing PU interference and routing delay to maximize throughput within the IIoT ***,a generic routing framework from a cross-layer perspective is investigated that intends to share the information resources by exploiting a recently proposed method,namely,Channel Availability ***,a novel cross-layer-oriented routing protocol is proposed by using a time-variant channel estimation *** protocol combines lower layer(Physical layer and Data Link layer)sensing that is derived from the channel estimation ***,it periodically updates and stores the routing table for optimal route ***,in order to achieve higher throughput and lower delay,a new routing metric is *** evaluate the performance of the proposed protocol,network simulations have been conducted and also compared to the widely used routing protocols,as a *** simulation results of different routing scenarios demonstrate that our proposed solution outperforms the existing protocols in terms of the standard network performance metrics involving packet delivery ratio(with an improved margin of around 5–20%approximately)under varying numbers of PUs and cognitive users in Mobile Cognitive Radio Networks(MCRNs).Moreover,the cross-layer routing protocol successfully achiev

关键词： Channel selection Cross-layer design Mobile cognitive radio networks Routing protocol IIoT applications

Energy Cost Minimization Using String Matching Algorithm in Geo-Distributed Data Centers

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Computers, Materials & Continua 2023年第6期75卷 6305-6322页

作者： muhammad Imran Khan Khalil Syed Adeel Ali shah Izaz Ahmad Khan Mohammad Hijji muhammad Shiraz Qaisar shaheen Department of Computer Science&Information Technology University of Engineering&TechnologyPeshawar25000Pakistan Department of Computer Science Bacha Khan UniversityCharsadda24541Pakistan Computer Science Department University of TabukTabuk47512Saudi Arabia Department of Computer Science Federal Urdu University of ArtsScience and Technology Islamabad44000Pakistan Department of Computer Science and Information Technology The Islamia University of BahawalpurRahim Yar Khan CampusPunjabPakistan

Data centers are being distributed worldwide by cloud service providers(CSPs)to save energy costs through efficient workload alloca-tion *** CSPs are challenged by the significant rise in user demands due to their extensive energy consumption during workload *** research studies have examined distinct operating cost mitigation techniques for geo-distributed data centers(DCs).However,oper-ating cost savings during workload processing,which also considers string-matching techniques in geo-distributed DCs,remains *** this research,we propose a novel string matching-based geographical load balanc-ing(SMGLB)technique to mitigate the operating cost of the geo-distributed *** primary goal of this study is to use a string-matching algorithm(i.e.,Boyer Moore)to compare the contents of incoming workloads to those of documents that have already been processed in a data center.A successful match prevents the global load balancer from sending the user’s request to a data center for processing and displaying the results of the previously processed workload to the user to save *** the contrary,if no match can be discovered,the global load balancer will allocate the incoming workload to a specific DC for processing considering variable energy prices,the number of active servers,on-site green energy,and traces of incoming *** results of numerical evaluations show that the SMGLB can minimize the operating expenses of the geo-distributed data centers more than the existing workload distribution techniques.

关键词： String matching optimization geo-distributed data centers geographical load balancing green energy

Signet Ring Cell Detection from Histological Images Using Deep Learning

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Computers, Materials & Continua 2022年第9期72卷 5985-5997页

作者： muhammad Faheem Saleem Syed muhammad Adnan shah Tahira Nazir Awais Mehmood Marriam Nawaz muhammad Attique Khan Seifedine Kadry Arnab Majumdar Orawit Thinnukool Department of Computer Science UETTaxila47050Pakistan Department of Computer Science HITEC University TaxilaTaxilaPakistan Faculty of Applied Computing and Technology Noroff University CollegeKristiansandNorway Department of Civil Engineering Imperial College LondonLondon SW72AZUK College of Arts Mediaand TechnologyChiang Mai UniversityChiang Mai 50200Thailand

Signet Ring Cell(SRC)Carcinoma is among the dangerous types of cancers,and has a major contribution towards the death ratio caused by cancerous *** and diagnosis of SRC carcinoma at earlier stages is a challenging,laborious,and costly *** detection of SRCs in a patient’s body through medical imaging by incorporating computing technologies is a hot topic of *** the presented framework,we propose a novel approach that performs the identification and segmentation of SRCs in the histological images by using a deep learning(DL)technique named Mask Region-based Convolutional Neural Network(Mask-RCNN).In the first step,the input image is fed to Resnet-101 for feature *** extracted feature maps are conveyed to Region Proposal Network(RPN)for the generation of the region of interest(RoI)proposals as well as they are directly conveyed to ***,RoIAlign combines the feature maps with RoI proposals and generates segmentation masks by using a fully connected(FC)network and performs classification along with Bounding Box(bb)generation by using FC *** annotations are developed from ground truth(GT)images to perform experimentation on our developed *** introduced approach achieves accurate SRC detection with the precision and recall values of 0.901 and 0.897 respectively which can be utilized in clinical *** aim to release the employed database soon to assist the improvement in the SRC recognition research area.

关键词： Mask RCNN deep learning SRC segmentation

Photokilling of waterborne-resistant pathogenic bacteria using cobalt-doped zinc oxide doped on reduced graphene oxide nanoparticles

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Journal of Innovative Optical Health Sciences 2022年第4期15卷 1-10页

作者： Ameer muhammad Khan Laiba Pervez Jonathan Celli Mutiullah Khattak Maqdad Ullah ZiaUllah shah muhammad Mustafa Khan muhammad Nadeem Sulaiman Faisal Akhtar Nadhman Department of Biotechnology Institute of Integrative Biosciences CECOS University Hayatabad Phase VI PeshawarPakistan Department of Physics University of Massachusetts BostonMA 02125USA Department of Microbiology Institute of Pathology and Diagnostic Medicine Khyber Medical University Hayatabad PeshawarKPPakistan Department of Pharmacy Institute of Integrative Biosciences CECOS University Hayatabad Phase VIPeshawarPakistan

This study is aimed at the chemical synthesis of light-activated cobalt-doped zinc oxide and its further doping on reduced graphene oxide(RGO)and assessment of its antibacterial activity on antibiotic-resistant waterborne pathogens including Enterococcus faecalis,Staphylococcus aureus,Klebsiella pneumonia,and Pseudomonas *** synthesized nanoparticles were characterized via UV–vis spectroscopy,scanning electron microscopy(SEM),and energy-dispersive X-ray spectroscopy(EDS).The minimal inhibitory concentration(MIC)of nanoparticles portrayed a significant killing of both Gram-positive and Gram-negative *** synthesized nanoparticles were further found as active killers of bacteria in drinking ***,these nanoparticles were found photothermally active alongside ROS *** photokilling activity makes them ideal replacement candidates for traditional water filters.

关键词： MDR Bacteria graphene oxide doping photokilling ZnO.

Burnout amongst radiologists:A bibliometric study from 1993 to 2020

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World Journal of Psychiatry 2022年第2期12卷 368-378页

作者： muhammad Fazal Hussain Qureshi Danish Mohammad Syed Mustafa Ali shah Mahira Lakhani Muzna shah muhammad Hassan Ayub Sara Sadiq Medical College Ziauddin UniversityKarachi 75000SindhPakistan Department of Physiology CMH Institute of Medical SciencesBahawalpur 75000Pakistan

BACKGROUND Burnout amongst radiologists is common in many different institutions and is increasing day by *** battle burnout,we have to address the root causes already recognized in published ***,it is crucial to examine and discern important *** To provide evidence-based data and trends related to burnout in radiologists so that researchers can work on it further and develop preventive strategies to overcome this *** Bibliometric analysis conducted by two independent reviewers separately used Scopus Library for data extraction by using medical subject heading and International Classification of Diseases ***-nine articles were selected for analysis after an extensive *** Package for the Social Sciences version 20 was used for *** correlation coefficient,Kruskal Wallis test,and Mann-Whitney U test were *** The most productive period with regards to the number of publications was between 2017 and 2019.A total of 160 authors contributed to the topic burnout among radiologists,with an average of 3.26 authors per *** 41.68%of the authors were female,whilst 35%of them were first *** co-citation analysis by author involved 188 cited authors,13 of whom were cited at least 70 *** six out of forty-nine studies were funded by various government institutions and non-governmental *** Current analysis casts a spotlight on important trends being witnessed in regard to the mental health of radiologists,including lack of funding for mental health research,narrowing of female vs male citation gap,as well as authorship and citation trends.

关键词： Bibliometric Analysis Burnout Radiologist Stress Scientometrics

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

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Computers, Materials & Continua 2023年第3期74卷 6263-6287页

作者： Arjmand Khaliq Syed Abdul Rahman Kashif Fahad Ahmad muhammad Anwar Qaisar shaheen Rizwan Akhtar muhammad Arif shah Abdelzahir Abdelmaboud Electrical Engineering Department University of Engineering and TechnologyLahore54700Pakistan Department of Basic Sciences Deanship of Common First YearJouf UniversitySakakaAljouf72341Saudi Arabia Department of Information Science Division of Science and TechnologyUniversity of EducationLahore54000Pakistan Department of Computer Science and Information Technology The Islamia University of BahawalpurRahim Yar Khan Sub CampusPakistan Department of IT and Computer Science Pak-Austria Fachhochschule Institute of Applied Sciences and TechnologyHaripurPakistan Department of Information Systems King Khalid UniversityMuhayel Aseer61913Saudi Arabia

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

关键词： Space vector pulse width modulation linear induction motor proportional-integral controller indirect vector control electromechanical dynamic modeling

Brain Tumor Detection and Classification Using PSO and Convolutional Neural Network

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Computers, Materials & Continua 2022年第12期73卷 4501-4518页

作者： muhammad Ali Jamal Hussain shah muhammad Attique Khan Majed Alhaisoni Usman Tariq Tallha Akram Ye Jin Kim Byoungchol Chang Department of Computer Science COMSATS University IslamabadWah CampusPakistan Departmnt of Computer Science HITEC University TaxilaPakistan Computer Sciences Department College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh11671Saudi Arabia College of Computer Engineering and Sciences Prince Sattam bin Abdulaziz UniversityAl-KharjSaudi Arabia Department of EE COMSATS University IslamabadWah CampusPakistan Department of Computer Science Hanyang UniversitySeoul04763Korea Center for Computational Social Science Hanyang UniversitySeoul04763Korea

Tumor detection has been an active research topic in recent years due to the high mortality *** vision(CV)and image processing techniques have recently become popular for detecting tumors inMRI *** automated detection process is simpler and takes less time than manual *** addition,the difference in the expanding shape of brain tumor tissues complicates and complicates tumor detection for *** proposed a newframework for tumor detection aswell as tumor classification into relevant categories in this *** tumor segmentation,the proposed framework employs the Particle Swarm Optimization(PSO)algorithm,and for classification,the convolutional neural network(CNN)*** preprocessing techniques such as noise removal,image sharpening,and skull stripping are used at the start of the segmentation ***,PSO-based segmentation is *** the classification step,two pre-trained CNN models,alexnet and inception-V3,are used and trained using transfer *** a serial approach,features are extracted from both trained models and fused features for final *** classification,a variety of machine learning classifiers are *** dice values on datasets BRATS-2018 and BRATS-2017 are 98.11 percent and 98.25 percent,respectively,whereas average jaccard values are 96.30 percent and 96.57%(Segmentation Results).The results were extended on the same datasets for classification and achieved 99.0%accuracy,sensitivity of 0.99,specificity of 0.99,and precision of ***,the proposed method is compared to state-of-the-art existingmethods and outperforms them.

关键词： Magnetic resonance imaging(MRI) tumor segmentation deep learning features extraction classification

A Three-Dimensional Real-Time Gait-Based Age Detection System Using Machine Learning

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Computers, Materials & Continua 2023年第4期75卷 165-182页

作者： muhammad Azhar Sehat Ullah Khalil Ullah Habib shah Abdallah Namoun Khaliq Ur Rahman Department of Computer Science&IT University of MalakandChakdara18800Pakistan Department of Software Engineering University of MalakandChakdara18800Pakistan College of Computer Science King Khalid UniversityAbha62529Saudi Arabia Faculty of Computer and Information Systems Islamic University of MadinahMadinah42351Saudi Arabia Department of Statistics Abdul Wali Khan UniversityMardan23200Pakistan

Human biometric analysis has gotten much attention due to itswidespread use in different research areas, such as security, surveillance,health, human identification, and classification. Human gait is one of the keyhuman traits that can identify and classify humans based on their age, gender,and ethnicity. Different approaches have been proposed for the estimation ofhuman age based on gait so far. However, challenges are there, for which anefficient, low-cost technique or algorithm is needed. In this paper, we proposea three-dimensional real-time gait-based age detection system using a machinelearning approach. The proposed system consists of training and testingphases. The proposed training phase consists of gait features extraction usingthe Microsoft Kinect (MS Kinect) controller, dataset generation based onjoints’ position, pre-processing of gait features, feature selection by calculatingthe Standard error and Standard deviation of the arithmetic mean and bestmodel selection using R2 and adjusted R2 techniques. T-test and ANOVAtechniques show that nine joints (right shoulder, right elbow, right hand, leftknee, right knee, right ankle, left ankle, left, and right foot) are statisticallysignificant at a 5% level of significance for age estimation. The proposedtesting phase correctly predicts the age of a walking person using the resultsobtained from the training phase. The proposed approach is evaluated on thedata that is experimentally recorded from the user in a real-time *** (50) volunteers of different ages participated in the experimental *** the limited features, the proposed method estimates the age with 98.0%accuracy on experimental images acquired in real-time via a classical generallinear regression model.

关键词： Age estimation gait biometrics classical linear regression model