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Pandemic Analysis and Prediction of COVID-19 Using Gaussian Doubling Times

Computers, Materials & Continua 2022年第7期72卷 833-849页

作者： Saleh Albahli Farman Hassan Ali Javed aun irtaza Department of Information Technology College of ComputerQassim UniversityBuraydahSaudi Arabia Department of Computer Science University of Engineering and TechnologyTaxilaPakistan Department of Computer Science and Engineering Oakland UniversityMIUSA Department of Computer and Electrical Engineering University of MichiganDearbornUSA

COVID-19 has become a pandemic,with cases all over the world,with widespread disruption in some countries,such as Italy,US,India,South Korea,and Japan.Early and reliable detection of COVID-19 is mandatory to control the spread of infection.Moreover,prediction of COVID-19 spread in near future is also crucial to better plan for the disease control.For this purpose,we proposed a robust framework for the analysis,prediction,and detection of COVID-19.We make reliable estimates on key pandemic parameters and make predictions on the point of inflection and possible washout time for various countries around the world.The estimates,analysis and predictions are based on the data gathered fromJohns Hopkins Center during the time span of April 21 to June 27,2020.We use the normal distribution for simple and quick predictions of the coronavirus pandemic model and estimate the parameters of Gaussian curves using the least square parameter curve fitting for several countries in different continents.The predictions rely on the possible outcomes of Gaussian time evolution with the central limit theorem of statistics the predictions to be well justified.The parameters of Gaussian distribution,i.e.,maximumtime and width,are determined through a statisticalχ^(2)-fit for the purpose of doubling times after April 21,2020.For COVID-19 detection,we proposed a novel method based on the Histogram of Oriented Gradients(HOG)and CNN in multi-class classification scenario i.e.,Normal,COVID-19,viral pneumonia etc.Experimental results show the effectiveness of our framework for reliable prediction and detection of COVID-19.

关键词： Prediction coronavirus time-series prediction gaussian mathematical model pandemic spreading

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Hybrid Evolutionary Algorithm Based Relevance Feedback Approach for Image Retrieval

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Computers, Materials & Continua 2022年第1期70卷 963-979页

作者： Awais Mahmood Muhammad Imran aun irtaza Qammar Abbas Habib Dhahri Esam Mohammed Asem Othman Arif Jamal Malik Aaqif Afzaal Abbasi College of Applied Computer Science King Saud University(Almuzahmiyah Campus)Riyadh11543Saudi Arabia Shaheed Zulfikar Ali Bhutto Institute of Science and Technology(Islamabad Campus) 44000Pakistan University of Engineering and Technology Taxila Pakistan International Islamic University Islamabad44000Pakistan Faculty of Sciences and Technology University of KairouanSidi Bouzid4352Tunisia Department of Software Engineering Foundation University IslamabadIslamabad44000Pakistan

Searching images fromthe large image databases is one of the potential research areas of multimedia research.The most challenging task for nay CBIR system is to capture the high level semantic of user.The researchers of multimedia domain are trying to fix this issue with the help of Relevance Feedback(RF).However existing RF based approaches needs a number of iteration to fulfill user’s requirements.This paper proposed a novel methodology to achieve better results in early iteration to reduce the user interaction with the system.In previous research work it is reported that SVM based RF approach generating better results for CBIR.Therefore,this paper focused on SVM based RF approach.To enhance the performance of SVM based RF approach this research work applied Particle Swarm Optimization(PSO)and Genetic Algorithm(GA)before applying SVM on user feedback.The main objective of using thesemeta-heuristic was to increase the positive image sample size from SVM.Firstly steps PSO is applied by incorporating the user feedback and secondly GA is applied on the result generated through PSO,finally SVM is applied using the positive sample generated through GA.The proposed technique is named as Particle Swarm Optimization Genetic Algorithm-Support Vector Machine Relevance Feedback(PSO-G A-SVMRF).Precisions,recall and F-score are used as performance metrics for the assessment and validation of PSO-GA-SVM-RF approach and experiments are conducted on coral image dataset having 10908 images.From experimental results it is proved that PSO-GA-SVM-RF approach outperformed then various well known CBIR approaches.

关键词： Feature selection image retrieval particle swarm optimization

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Recognition and Detection of Diabetic Retinopathy Using Densenet-65 Based Faster-RCNN

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Computers, Materials & Continua 2021年第5期67卷 1333-1351页

作者： Saleh Albahli Tahira Nazir aun irtaza Ali Javed Department of Information Technology College of ComputerQassim UniversityBuraydahSaudi Arabia Department of Computer Science University of Engineering and TechnologyTaxila47050Pakistan Department of Software Engineering University of Engineering and TechnologyTaxila47050Pakistan

Diabetes is a metabolic disorder that results in a retinal complication called diabetic retinopathy(DR)which is one of the four main reasons for sightlessness all over the globe.DR usually has no clear symptoms before the onset,thus making disease identication a challenging task.The healthcare industry may face unfavorable consequences if the gap in identifying DR is not lled with effective automation.Thus,our objective is to develop an automatic and cost-effective method for classifying DR samples.In this work,we present a custom Faster-RCNN technique for the recognition and classication of DR lesions from retinal images.After pre-processing,we generate the annotations of the dataset which is required for model training.Then,introduce DenseNet-65 at the feature extraction level of Faster-RCNN to compute the representative set of key points.Finally,the Faster-RCNN localizes and classies the input sample into ve classes.Rigorous experiments performed on a Kaggle dataset comprising of 88,704 images show that the introduced methodology outperforms with an accuracy of 97.2%.We have compared our technique with state-of-the-art approaches to show its robustness in term of DR localization and classication.Additionally,we performed cross-dataset validation on the Kaggle and APTOS datasets and achieved remarkable results on both training and testing phases.

关键词： Deep learning medical informatics diabetic retinopathy healthcare computer vision

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A Genetic Based Leader Election Algorithm for IoT Cloud Data Processing

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Computers, Materials & Continua 2021年第8期68卷 2469-2486页

作者： Samira Kanwal Zeshan Iqbal aun irtaza Rashid Ali Kamran Siddique Department of Computer Science University of Engineering and TechnologyTaxila47050Pakistan School of Intelligent Mechatronics Engineering Sejong UniversitySeoulKorea School of Electrical and Computer Engineering Department of Information and Communication TechnologyXiamen University MalaysiaSepang43900Malaysia

In IoT networks,nodes communicate with each other for computational services,data processing,and resource sharing.Most of the time huge data is generated at the network edge due to extensive communication between IoT devices.So,this tidal data is transferred to the cloud data center(CDC)for efficient processing and effective data storage.In CDC,leader nodes are responsible for higher performance,reliability,deadlock handling,reduced latency,and to provide cost-effective computational services to the users.However,the optimal leader selection is a computationally hard problem as several factors like memory,CPU MIPS,and bandwidth,etc.,are needed to be considered while selecting a leader amongst the set of available nodes.The existing approaches for leader selection are monolithic,as they identify the leader nodes without taking the optimal approach for leader resources.Therefore,for optimal leader node selection,a genetic algorithm(GA)based leader election(GLEA)approach is presented in this paper.The proposed GLEA uses the available resources to evaluate the candidate nodes during the leader election process.In the first phase of the algorithm,the cost of individual nodes,and overall cluster cost is computed on the bases of available resources.In the second phase,the best computational nodes are selected as the leader nodes by applying the genetic operations against a cost function by considering the available resources.The GLEA procedure is then compared against the Bees Life Algorithm(BLA).The experimental results show that the proposed scheme outperforms BLA in terms of execution time,SLA Violation,and their utilization with state-of-the-art schemes.

关键词： IoT cloud computing datacenter leader election algorithm machine learning genetic algorithm

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Defocus blur detection using novel local directional mean patterns(LDMP)and segmentation via KNN matting

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Frontiers of Computer Science 2022年第2期16卷 110-122页

作者： Awais KHAN aun irtaza Ali JAVED Tahira NAZIR Hafiz MALIK Khalid Mahmood MALIK Muhammad Ammar KHAN Department of Computer Science University of Engineering and TechnologyTaxila 47050Pakistan Department of Electrical and Computer Engineering University of MichiganDearbornMI 48128USA Department of Software Engineering University of Engineering and TechnologyTaxila 47050Pakistan Department of Computer Science and Engineering Oakland UniversityMI 48309USA

Detection and segmentation of defocus blur is a challenging task in digital imaging applications as the blurry images comprise of blur and sharp regions that wrap significant information and require effective methods for information extraction.Existing defocus blur detection and segmentation methods have several limitations i.e.,discriminating sharp smooth and blurred smooth regions,low recognition rate in noisy images,and high computational cost without having any prior knowledge of images i.e.,blur degree and camera configuration.Hence,there exists a dire need to develop an effective method for defocus blur detection,and segmentation robust to the above-mentioned limitations.This paper presents a novel features descriptor local directional mean patterns(LDMP)for defocus blur detection and employ KNN matting over the detected LDMP-Trimap for the robust segmentation of sharp and blur regions.We argue/hypothesize that most of the image fields located in blurry regions have significantly less specific local patterns than those in the sharp regions,therefore,proposed LDMP features descriptor should reliably detect the defocus blurred regions.The fusion of LDMP features with KNN matting provides superior performance in terms of obtaining high-quality segmented regions in the image.Additionally,the proposed LDMP features descriptor is robust to noise and successfully detects defocus blur in high-dense noisy images.Experimental results on Shi and Zhao datasets demonstrate the effectiveness of the proposed method in terms of defocus blur detection.Evaluation and comparative analysis signify that our method achieves superior segmentation performance and low computational cost of 15 seconds.

关键词： defocus blur detection local directional mean patterns image matting sharpness metrics blur segmentation

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An automated framework for advertisement detection and removal from sports videos using audio-visual cues

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Frontiers of Computer Science 2021年第2期15卷 185-188页

作者： Abeer TOHEED Ali JAVED aun irtaza Hassan DAWOOD Hussain DAWOOD Ahmed S.ALFAKEEH Department of Software Engineering University of Engineering and Technology-TaxilaTaxila47050Pakistan Department of Computer&Network Engineering University of JeddahJeddah21577Saudi Arabia Information System Department King Abdulaziz UniversityJeddah21577Saudi Arabia

1 Introduction Advertisements detection and replacement with different ads based on the user preferences is employed during sports rebroadcasts that offers more value to both the distributor and viewer.Manual advertisements detection is a laborious activity and demands an urgent need to develop automated advertisement detection techniques to save the time,storage space,and transmission bandwidth.

关键词： automated visual framework

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Infectious causes of fever of unknown origin in developing countries: An international ID-IRI study

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Journal of Intensive Medicine 2024年第1期4卷 94-100页

作者： Hakan Erdem Jaffar AAl-Tawfiq Maha Abid Wissal Ben Yahia George Akafity Manar Ezzelarab Ramadan Fatma Amer Amani El-Kholy Atousa Hakamifard Bilal Ahmad Rahimi Farouq Dayyab Hulya Caskurlu Reham Khedr Muhammad Tahir Lysien Zambrano Mumtaz Ali Khan aun Raza Nagwa Mostafa El-Sayed Magdalena Baymakova Aysun Yalci Yasemin Cag Umran Elbahr Aamer Ikram Department of Infectious Diseases&Clinical Microbiology Gulhane School of MedicineTurkish Health Sciences UniversityAnkaraTurkey Specialty Internal Medicine and Quality and Patient Safety Department Johns Hopkins Aramco HealthcareDhahranSaudi Arabia Division of Infectious Diseases Indiana University School of MedicineIndianapolisINUSA Department of Infectious Diseases Farhat Hached University HospitalIbn El Jazzar Medical SchoolUniversity of SousseSousseTunisia Internal Medicine Department Farhat Hached University HospitalIbn El Jazzar Medical SchoolUniversity of SousseSousseTunisia Cape Coast Teaching Hospital Cape CoastGhana Medical Parasitology Faculty of MedicineSuez UniversitySuezEgypt Department of Medical Microbiology and Immunology Faculty of MedicineZagazig UniversityZagazigEgypt Department of Clinical Pathology Faculty of MedicineCairo UniversityGizaEgypt Department of Infectious Diseases School of MedicineIsfahan University of Medical SciencesIsfahanIran Department of Infectious Diseases Kandahar University Medical FacultyTeaching HospitalKandaharAfghanistan Department of Infectious Diseases Mohammed Bn Khalifa Bn Salman Alkhalifa Specialist Cardiac CenterAwaliBahrain Department of Infectious Diseases and Clinical Microbiology Faculty of MedicineIstanbul Medeniyet UniversityIstanbulTurkey Department of Pediatric Oncology National Cancer Institute–Cairo UniversityChildren Cancer Hospital EgyptCairoEgypt Department of Medicine Federal General HospitalIslamabadPakistan Institute for Research in Medical Sciences and Right to Health(ICIMEDES) Scientific Research Unit(UIC)Faculty of Medical Sciences(FCM)National Autonomous University of Honduras(UNAH)TegucigalpaHonduras National Institute of Health IslamabadPakistan Shaukat Khanum Memorial Cancer Hospital and Research Center LahorePakistan Medical Parasitology Department Research Institute of OphthalmologyGizaEgypt Department of Infectious Diseases Military Medical AcademySofiaBulgaria Department of Infecti

Background:Fever of unknown origin(FUO)in developing countries is an important dilemma and further research is needed to elucidate the infectious causes of FUO.Methods:A multi-center study for infectious causes of FUO in lower middle-income countries(LMIC)and lowincome countries(LIC)was conducted between January 1,2018 and January 1,2023.In total,15 participating centers from seven different countries provided the data,which were collected through the Infectious DiseasesInternational Research Initiative platform.Only adult patients with confirmed infection as the cause of FUO were included in the study.The severity parameters were quick Sequential Organ Failure Assessment(qSOFA)≥2,intensive care unit(ICU)admission,vasopressor use,and invasive mechanical ventilation(IMV).Results:A total of 160 patients with infectious FUO were included in the study.Overall,148(92.5%)patients had community-acquired infections and 12(7.5%)had hospital-acquired infections.The most common infectious syndromes were tuberculosis(TB)(n=27,16.9%),infective endocarditis(n=25,15.6%),malaria(n=21,13.1%),brucellosis(n=15,9.4%),and typhoid fever(n=9,5.6%).Plasmodium falciparum,Mycobacterium tuberculosis,Brucellae,Staphylococcus aureus,Salmonella typhi,and Rickettsiae were the leading infectious agents in this study.A total of 56(35.0%)cases had invasive procedures for diagnosis.The mean qSOFA score was 0.76±0.94{median(interquartile range[IQR]):0(0–1)}.ICU admission(n=26,16.2%),vasopressor use(n=14,8.8%),and IMV(n=10,6.3%)were not rare.Overall,38(23.8%)patients had at least one of the severity parameters.The mortality rate was 15(9.4%),and the mortality was attributable to the infection causing FUO in 12(7.5%)patients.Conclusions:In LMIC and LIC,tuberculosis and cardiac infections were the most severe and the leading infections causing FUO.

关键词： Developing countries Fever of unknown origin Infection

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净化河水的废锰砂和废离子交换树脂处置方法

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精细与专用化学品 2024年第5期32卷 14-16,30页

作者：张建勋盐城市大丰生态环境局经济开发区分局江苏盐城224100

在工业生产中,合法合规地取用河水,并对取用的河水进行有效净化,可以丰富工业用水的来源方式。河水净化工艺过程中通常有废锰砂、废离子交换树脂等固废产生。明确这类固废的规范处置流程既有利于提高生态环境执法效率、保护生态环境,也... 详细信息

在工业生产中,合法合规地取用河水,并对取用的河水进行有效净化,可以丰富工业用水的来源方式。河水净化工艺过程中通常有废锰砂、废离子交换树脂等固废产生。明确这类固废的规范处置流程既有利于提高生态环境执法效率、保护生态环境,也有利于降低企业的运营成本。通过模拟废锰砂、废离子交换树脂对土壤环境的影响,得出净化特定河水的废锰砂、废离子交换树脂可以作为一般固废处置的结论。

关键词：河水净化废锰砂废离子交换树脂一般固废

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Did green debt instruments aid diversification during the COVID-19 pandemic?

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Financial Innovation 2022年第1期8卷 681-695页

作者： Paresh Kumar Narayan Syed aun R.Rizvi Ali Sakti Monash University MelbourneAustralia Lahore University of Management Sciences LahorePakistan Bank Indonesia JakartaIndonesia

Faced with a persistent pandemic,investors are concerned about portfolio diversification.While the literature on COVID-19 has evolved impressively,limited work remains on diversification opportunities.We contribute to the literature by exploring the volatility and co-movement of different sovereign debt instruments,including green sukuk,sukuk,bond and Islamic and conventional equity indices for Indonesia.Our results consistently point towards increased asset co-movement and weak profitability during the pandemic.Interestingly,sukuk and green sukuk have a 14%correlation with stocks,suggesting potential diversification prospects in times of extreme shocks.

关键词： COVID-19 Green Sukuk Indonesia Capital market

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Time to forge ahead:The Internet of Things for healthcare

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Digital Communications and Networks 2023年第1期9卷 223-235页

作者： Denzil Furtado Andre F.Gygax Chien aun Chan Ashley I.Bush Melbourne Dementia Research Centre&Department of Chemical Engineering University of MelbourneVIC3010Australia Faculty of Business and Economics University of MelbourneVIC3010Australia Department of Electrical and Electronic Engineering University of MelbourneVIC3010Australia Florey Institute of Neuroscience and Mental Health University of MelbourneVIC3010Australia

Situated at the intersection of technology and medicine,the Internet of Things(IoT)holds the promise of addressing some of healthcare's most pressing challenges,from medical error,to chronic drug shortages,to overburdened hospital systems,to dealing with the COVID-19 pandemic.However,despite considerable recent technological advances,the pace of successful implementation of promising IoT healthcare initiatives has been slow.To inspire more productive collaboration,we present here a simple—but surprisingly underrated—problemoriented approach to developing healthcare technologies.To further assist in this effort,we reviewed the various commercial,regulatory,social/cultural,and technological factors in the development of the IoT.We propose that fog computing—a technological paradigm wherein the burden of computing is shifted from a centralized cloud server closer to the data source—offers the greatest promise for building a robust and scalable healthcare IoT ecosystem.To this end,we explore the key enabling technologies that underpin the fog architecture,from the sensing layer all the way up to the cloud.It is our hope that ongoing advances in sensing,communications,cryptography,storage,machine learning,and artificial intelligence will be leveraged in meaningful ways to generate unprecedented medical intelligence and thus drive improvements in the health of many people.

关键词： Internet of Things Healthcare Information Fog computing Artificial intelligence Machine learning Big data COVID-19 pandemic

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