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Importance of Features Selection,Attributes Selection,Challenges and Future Directions for Medical Imaging Data:A Review

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Computer Modeling in Engineering & Sciences 2020年第10期125卷 315-344页

作者： Nazish Naheed muhammad Shaheen Sajid Ali khan Mohammed Alawairdhi muhammad Attique khan Faculty of Engineering&Information Technology Foundation University IslamabadIslamabadPakistan College of Computing and Informatics Saudi Electronic UniversityRayadhSaudi Arabia Department of Computer Science HITEC UniversityTaxilaPakistan

In the area of pattern recognition and machine learning,features play a key role in *** famous applications of features are medical imaging,image classification,and name a few *** the exponential growth of information investments in medical data repositories and health service provision,medical institutions are collecting large volumes of *** data repositories contain details information essential to support medical diagnostic decisions and also improve patient care *** the other hand,this growth also made it difficult to comprehend and utilize data for various *** results of imaging data can become biased because of extraneous features present in larger *** selection gives a chance to decrease the number of components in such large *** selection techniques,ousting the unimportant features and selecting a subset of components that produces prevalent characterization *** correct decision to find a good attribute produces a precise grouping model,which enhances learning pace and forecast *** paper presents a review of feature selection techniques and attributes selection measures for medical *** review is meant to describe feature selection techniques in a medical domainwith their pros and cons and to signify its application in imaging data and data mining *** review reveals the shortcomings of the existing feature and attributes selection techniques to multi-sourced ***,this review provides the importance of feature selection for correct classification of medical *** the end,critical analysis and future directions are provided.

关键词： Medical imaging imaging data feature selection data mining attribute selection medical challenges future directions

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Outage Probability Analysis of Free Space Communication System Using Diversity Combining Techniques

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Computers, Materials & Continua 2022年第12期73卷 6001-6017页

作者： Hasnain Kashif muhammad Nasir khan Department of Electrical Engineering The University of LahoreLahore54000Pakistan

Recently,free space optical(FSO)communication is gaining much attention towards the research *** reason for this attention is the promises of high data-rate,license-free deployment,and non-interfering *** can,however,give rise to major system difficulties concerning alignment and atmospheric *** is the degradation in the signal quality because of atmospheric channel impairments and *** worst effect is due to fog ***,Radio Frequency(RF)links are able to transmit the data in foggy conditions but not in *** overcome these issues related to both the FSO and RF links.A free space communication system(FSCS)is proposed,in which the hybrid technology is based on the individual FSO and RF *** is a capable solution to overcome the difficulties of the existing systems(FSO and RF)as well as to enhance the overall link reliability and *** this paper,FSCS is investigated in terms of performance throughput(i.e.,outage probability and bit-error-rate(BER))by implementing the receive diversity combining *** analysis of the outage probability of the proposed system along with the individual FSO and RF system is *** results are presented to support the *** is shown that the proposed system outperforms the individual FSO andRF system and gives a power gain of 3dB over a distinct number of receive antennas.

关键词： Atmospheric turbulence free space communication system power management signal independent noise line of sight

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Deep Learning-Based Hybrid Intelligent Intrusion Detection System

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Computers, Materials & Continua 2021年第7期68卷 671-687页

作者： muhammad Ashfaq khan Yangwoo Kim Department of Information and Communication EngineeringDongguk UniversitySeoul100-715Korea Department of Electronics Engineering IoT and Big-Data Research CenterIncheon National UniversityIncheonKorea

Machine learning(ML)algorithms are often used to design effective intrusion detection(ID)systems for appropriate mitigation and effective detection of malicious cyber threats at the host and network ***,cybersecurity attacks are still *** ID system can play a vital role in detecting such *** ID systems are unable to detect malicious threats,primarily because they adopt approaches that are based on traditional ML techniques,which are less concerned with the accurate classication and feature ***,developing an accurate and intelligent ID system is a *** main objective of this study was to develop a hybrid intelligent intrusion detection system(HIIDS)to learn crucial features representation efciently and automatically from massive unlabeled raw network trafc *** ID datasets are publicly available to the cybersecurity research *** such,we used a spark MLlib(machine learning library)-based robust classier,such as logistic regression(LR),extreme gradient boosting(XGB)was used for anomaly detection,and a state-of-the-art DL,such as a long short-term memory autoencoder(LSTMAE)for misuse attack was used to develop an efcient and HIIDS to detect and classify unpredictable *** approach utilized LSTM to detect temporal features and an AE to more efciently detect global ***,to evaluate the efcacy of our proposed approach,experiments were conducted on a publicly existing dataset,the contemporary real-life ISCX-UNB *** simulation results demonstrate that our proposed spark MLlib and LSTMAE-based HIIDS signicantly outperformed existing ID approaches,achieving a high accuracy rate of up to 97.52%for the ISCX-UNB dataset respectively 10-fold crossvalidation *** is quite promising to use our proposed HIIDS in real-world circumstances on a large-scale.

关键词： Machine learning intrusion detection system deep learning spark MLlib LSTM big data

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Concept of Yager operators with the picture fuzzy set environment and its application to emergency program selection

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International Journal of Intelligent Computing and Cybernetics 2020年第4期13卷 455-483页

作者： muhammad Qiyas muhammad Ali khan Saifullah khan Saleem Abdullah Mathematics Abdul Wali Khan UniversityMardanPakistan Abdul Wali khan University MardanPakistan

Purpose-The aim of this study as to find out an approach for emergency program ***/methodology/approach-The authors have generated six aggregation operators(AOs),namely picture fuzzy Yager weighted average(PFYWA),picture fuzzy Yager ordered weighted average,picture fuzzy Yager hybrid weighted average,picture fuzzy Yager weighted geometric(PFYWG),picture fuzzy Yager ordered weighted geometric and picture fuzzy Yager hybrid weighted geometric aggregations ***-First of all,the authors defined the score and accuracy function for picture fuzzy set(FS),and some fundamental operational laws for picture FS using the Yager aggregation *** that,using the developed operational laws,developed some AOs,namely PFYWA,picture fuzzy Yager ordered weighted average,picture fuzzy Yager hybrid weighted average,PFYWG,picture fuzzy Yager ordered weighted geometric and picture fuzzy Yager hybrid weighted geometric aggregations operators,have been proposed along with their desirable properties.A decision-making(DM)approach based on these operators has also been *** illustrative example has been given for demonstrating the ***,discussed the comparison of the proposed method with the other existing methods and write the conclusion of the ***/value-To find the best alternative for emergency program selection.

关键词： Yager operations Arithmetic aggregation operators Geometric aggregation operators Emergency program selection Decision-making

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Analysis of Factors that Affect Government Digitization:A Pilot Case Study of Pakistan

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Computers, Materials & Continua 2021年第1期66卷 291-301页

作者： Salman Ahmed muhammad Ali khan Department of Computer Systems Engineering University of Engineering&TechnologyPeshawarPakistan

One of the greatest factors that affects the economic condition of a country is its *** the model of good governance,the primary elements for stronger institution include efficiency,transparency,and accountability;and technology plays a major role in improving these ***,there are myriad of challenges when it comes to practical integration of technology in these institutions for *** is more challenging when a country is developing and one that is already weak *** is also important to mention that the challenges of digitization in public sector is not limited to developing countries *** is equally challenging,even today,in already developed countries to digitally transform their public institutions for improved policymaking and for responsive service *** factors contribute to the failure of such digitization initiatives,more so within developing *** the purpose of this paper is to identify those factors,to measure the significance of each of those factors,and to realize and overcome *** research considered the case study of Pakistan;however,the results are very likely to match the conditions of other developing regions around the *** questionnaires and interviews,valuable feedback was gathered from up to 25 senior government officers that are closely associated with digitization initiatives in public *** feedback to the questions were overall *** results indicate the most significant of factors that affect government digitization in this developing region,including some factors that were not expected.

关键词： E-governance government digitization information management sustainability

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Cardiac Arrhythmia Disease Classication Using LSTM Deep Learning Approach

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Computers, Materials & Continua 2021年第4期67卷 427-443页

作者： muhammad Ashfaq khan Yangwoo Kim Department of Information and Communication Engineering Dongguk UniversitySeoul100-715Kore

Many approaches have been tried for the classication of *** to the dynamic nature of electrocardiogram(ECG)signals,it is challenging to use traditional handcrafted techniques,making a machine learning(ML)implementation *** monitoring of cardiac arrhythmia patients can save *** arrhythmia prediction and classication has improved signicantly during the last few *** are a group of conditions in which the electrical activity of the heart is abnormal,either faster or slower than *** is the most frequent cause of death for both men and women every year in the *** paper presents a deep learning(DL)technique for the classication of *** proposed technique makes use of the University of California,Irvine(UCI)repository,which consists of a high-dimensional cardiac arrhythmia dataset of 279 *** this research,our goal was to classify cardiac arrhythmia patients into 16 classes depending on the characteristics of the electrocardiography *** DL approach in the form of long short-term memory(LSTM)is an efcient technique to deal with reduced accuracy due to vanishing and exploding gradients in traditional DL frameworks for big data *** goal of this research was to categorize cardiac arrhythmia patients by developing an efcient intelligent system using the LSTM DL *** approach to arrhythmia classication includes classication algorithms along with noise removal ***,we utilized principal components analysis(PCA)for noise removal,and LSTM for *** hybrid comprehensive arrhythmia classication approach performs better than previous approaches to arrhythmia *** attained a highest classication accuracy of 93.5%with the DL based disease classication system,and outperformed the earlier approaches used for cardiac arrhythmia classication.

关键词： Deep learning machine learning LSTM disease classication arrhythmia

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Strong interfacial charge transfer between hausmannite manganese oxide and alumina for efficient photocatalysis

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Chinese Journal of Chemical Engineering 2021年第5期34卷 147-159页

作者： Aisha Kanwal Shamaila Sajjad Sajjad Ahmed khan Leghari muhammad Naeem khan International Islamic University H-10 IslamabadPakistan Pakistan Institute of Engineering and Applied Sciences Islamabad 44000Pakistan Department of Chemistry Government College of ScienceLahorePakistan

Well crystalline manganese oxide(Mn_(3)O_(4))nanoparticles anchored on gamma alumina(γ-Al_(2)O_(3))have been successfully tailored via a proficient and cost effective chemical process as an efficient material for photo *** indicated the composite formation ofγ-Al_(2)O_(3) and hausmannite structure of Mn_(3)O_(4).SEM and TEM revealed that hetero structure of Mn_(3)O_(4)/γ-Al_(2)O_(3) exhibits an amalgam of aggregated nanoparticles and *** demonstrated the chemical states of binary *** band gap tuning has been performed withγ-Al_(2)O_(3) nanoparticles by assimilating hausmannite Mn_(3)O_(4) particles into flower like microstructure of Al_(2)O_(3).The photoluminescence spectra affirmed the enhancement in charge separation in Mn_(3)O_(4)/γ-Al_(2)O_(3) binary hybrid *** band gap becomes narrow with the increase in concentrations of Mn_(3)O_(4).The narrowing of band gap is concorded with crystalline domains of primary aggregated *** elucidate the mechanism of the photocatalytic activity linear sweep voltammetry was *** results showed that Mn_(3)O_(4)/γ-Al_(2)O_(3) nanocomposite revealed the enhancement in current density as compared to pureγ-Al_(2)O_(3) which confirmed the electron transfer from Mn_(3)O_(4) toγ-Al_(2)O_(3) through the interfacial potential gradient in conduction *** optimum concentration of 6.0%Mn_(3)O_(4)/γ-Al_(2)O_(3) for hybrid structure showed an excellent photocatalytic activity under visible light due to narrow band gap *** degree distribution of Mn_(3)O_(4) nano architects overlying onγ-Al_(2)O_(3) induces a significant synergic effect betweenγ-Al_(2)O_(3) and hausmannite phase of manganese oxide(Mn_(3)O_(4)).This strong interfacial contact betweenγ-Al_(2)O_(3) and Mn_(3)O_(4) endures the quick transfer of photo generated charge carriers across interface.

关键词： Hausmannite Mn_(3)O_(4) Alumina Band gap tuning Interfacial charge transfer Nanomaterials Catalysis

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On Edge Irregular Reflexive Labeling of Categorical Product of Two Paths

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Computer Systems Science & Engineering 2021年第3期36卷 485-492页

作者： muhammad Javed Azhar khan muhammad Ibrahim Ali Ahmad Centre for Advanced Studies in Pure and Applied Mathematics Bahauddin Zakariya UniversityMultanPakistan College of Computer Science and Information Technology Jazan UniversityJazanSaudi Arabia

Among the huge diversity of ideas that show up while studying graph theory,one that has obtained a lot of popularity is the concept of labelings of *** labelings give valuable mathematical models for a wide scope of applications in high technologies(cryptography,astronomy,data security,various coding theory problems,communication networks,etc.).A labeling or a valuation of a graph is any mapping that sends a certain set of graph elements to a certain set of numbers subject to certain *** labeling is a mapping of elements of the graph,i.e.,vertex and for edges to a set of numbers(usually positive integers),called *** the domain is the vertex-set or the edge-set,the labelings are called vertex labelings or edge labelings ***,if the domain is V(G)[E(G)],then the labeling is called total labeling.A reflexive edge irregular k-labeling of graph introduced by Tanna et al.:A total labeling of graph such that for any two different edges ab and a'b'of the graph their weights has wt_(x)(ab)=x(a)+x(ab)+x(b) and wt_(x)(a'b')=x(a')+x(a'b')+x(b') are *** smallest value of k for which such labeling exist is called the reflexive edge strength of the graph and is denoted by res(G).In this paper we have found the exact value of the reflexive edge irregularity strength of the categorical product of two paths (P_(a)×P_(b))for any choice of a≥3 and b≥3.

关键词： Edge irregular reflexive labeling reflexive edge strength categorical product of two paths

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Modelling Insurance Losses with a New Family of Heavy-Tailed Distributions

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Computers, Materials & Continua 2021年第1期66卷 537-550页

作者： muhammad Arif Dost muhammad khan Saima khan Khosa muhammad Aamir Adnan Aslam Zubair Ahmad Wei Gao Department of Statistics Abdul Wali Khan UniversityMardan23200Pakistan Department of Statistics Bahauddin Zakariya UniversityMultan60800Pakistan Department of Natural Sciences and Humanities University of Engineering and TechnologyLahore54000Pakistan Department of Statistics Yazd UniversityYazd89175-741Iran School of Information Science and Technology Yunnan Normal UniversityKunming650500China

The actuaries always look for heavy-tailed distributions to model data relevant to business and actuarial risk *** this article,we introduce a new class of heavy-tailed distributions useful for modeling data in financial sciences.A specific sub-model form of our suggested family,named as a new extended heavy-tailed Weibull distribution is examined in *** basic characterizations,including quantile function and raw moments have been *** estimates of the unknown parameters of the new model are obtained via the maximum likelihood estimation *** judge the performance of the maximum likelihood estimators,a simulation analysis is performed in ***,some important actuarial measures such as value at risk and tail value at risk are also computed.A simulation study based on these actuarial measures is conducted to exhibit empirically that the proposed model is *** usefulness of the proposed family is illustrated by means of an application to a heavy-tailed insurance loss data *** practical application shows that the proposed model is more flexible and efficient than the other six competing models including(i)the two-parameter models Weibull,Lomax and Burr-XII distributions(ii)the three-parameter distributions Marshall-Olkin Weibull and exponentiated Weibull distributions,and(iii)a well-known four-parameter Kumaraswamy Weibull distribution.

关键词： Weibull distribution actuarial measures heavy-tailed distributions estimations insurance losses

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Modelling and Simulation of COVID-19 Outbreak Prediction Using Supervised Machine Learning

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Computers, Materials & Continua 2021年第3期66卷 2397-2407页

作者： Rachid Zagrouba muhammad Adnan khan Atta-ur-Rahman muhammad Aamer Saleem muhammad Faheem Mushtaq Abdur Rehman muhammad Farhan khan Department of Computer Information System College of Computer Science and Information TechnologyImam Abdulrahman Bin Faisal UniversityDammam31441Saudi Arabia Department of Computer Science Riphah International UniversityLahore CampusLahore54000Pakistan Hamdard Institute of Engineering and Technology Hamdard UniversityIslamabad44000Pakistan Department of Information Technology Khwaja Fareed University of Engineering and Information TechnologyRahim Yar Khan64200Pakistan School of Computer Science NCBA&ELahore54000Pakistan Department of Forensic Sciences University of Health SciencesLahore54000Pakistan

Novel Coronavirus-19(COVID-19)is a newer type of coronavirus that has not been formally detected in *** is established that this disease often affects people of different age groups,particularly those with body disorders,blood pressure,diabetes,heart problems,or weakened immune *** epidemic of this infection has recently had a huge impact on people around the globe with rising mortality *** levels of mortality are attributed to their transmitting behavior through physical contact between *** is extremely necessary to monitor the transmission of the infection and also to anticipate the early stages of the disease in such a way that the appropriate timing of effective precautionary measures can be *** latest global coronavirus epidemic(COVID-19)has brought new challenges to the scientific *** Intelligence(AI)-motivated methodologies may be useful in predicting the conditions,consequences,and implications of such an *** forecasts may help to monitor and prevent the spread of these *** article proposes a predictive framework incorporating Support Vector Machines(SVM)in the forecasting of a potential outbreak of *** findings indicate that the suggested system outperforms cutting-edge *** method could be used to predict the long-term spread of such an outbreak so that we can implement proactive measures in *** findings of the analyses indicate that the SVM forecasting framework outperformed the Neural Network methods in terms of accuracy and computational *** proposed SVM system model exhibits 98.88%and 96.79%result in terms of accuracy during training and validation respectively.

关键词： Coronavirus outbreak machine learning artificial intelligence

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