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Computer Modeling in Engineering & Sciences 2025年第1期142卷 501-530页

作者： Khuram Ali Khan Saba Mubeen Ishfaq Atiqe Ur Rahman Salwa El-Morsy Department of Mathematics University of SargodhaSargodha40100Pakistan Department of Mathematics University of Management and TechnologyLahore54000Pakistan Department of Mathematics College of ScienceQassim UniversityBuraydah51452Saudi Arabia

Due to the numerous variables to take into account as well as the inherent ambiguity and uncertainty,evaluating educational institutions can be *** concept of a possibility Pythagorean fuzzy hypersoft set(pPyFHSS)is more flexible in this regard than other theoretical fuzzy set-like models,even though some attempts have been made in the literature to address such *** study investigates the elementary notions of pPyFHSS including its set-theoretic operations union,intersection,complement,OR-and *** results related to these operations are also modified for ***,the similarity measures between pPyFHSSs are formulated with the assistance of numerical examples and ***,an intelligent decision-assisted mechanism is developed with the proposal of a robust algorithm based on similarity measures for solving multi-attribute decision-making(MADM)problems.A case study that helps the decision-makers assess the best educational institution is discussed to validate the suggested *** algorithmic results are compared with the most pertinent model to evaluate the adaptability of pPyFHSS,as it generalizes the classical possibility fuzzy set-like theoretical ***,while considering significant evaluating factors,the flexibility of pPyFHSS is observed through structural comparison.

关键词： Hypersoft set Pythagorean fuzzy hypersoft set computational complexity multi-attribute decision-making optimization similarity measures uncertainty

Solid Waste Management:A MADM Approach Using Fuzzy Parameterized Possibility Single-Valued Neutrosophic Hypersoft Expert Settings

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Computer Modeling in Engineering & Sciences 2025年第1期142卷 531-553页

作者： Tmader Alballa Muhammad Ihsan Atiqe Ur Rahman Noorah Ayed Alsorayea Hamiden Abd El-Wahed Khalifa Department of Mathematics College of SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia 2 Department of Mathematics University of Management and TechnologyLahore54000Pakistan Department of Mathematics College of ScienceQassim UniversityBuraydah51452Saudi Arabia

The dramatic rise in the number of people living in cities has made many environmental and social problems *** search for a productive method for disposing of solid waste is the most notable of these *** scholars have referred to it as a fuzzy multi-attribute or multi-criteria decision-making problem using various fuzzy set-like approaches because of the inclusion of criteria and anticipated *** goal of the current study is to use an innovative methodology to address the expected uncertainties in the problem of solid waste site *** characteristics(or sub-attributes)that decision-makers select and the degree of approximation they accept for various options can both be indicators of these *** tackle these problems,a novel mathematical structure known as the fuzzy parameterized possibility single valued neutrosophic hypersoft expert set(ρˆ-set),which is initially described,is integrated with a modified version of Sanchez’s *** this,an intelligent algorithm is *** steps of the suggested algorithm are explained with an example that explains *** compatibility of solid waste management sites and systems is discussed,and rankings are established along with detailed justifications for their *** study’s strengths lie in its application of fuzzy parameterization and possibility grading to effectively handle the uncertainties embodied in the parameters’nature and alternative approximations,*** uses specific mathematical formulations to compute the fuzzy parameterized degrees and possibility grades that are missing from the prior *** is simpler for the decisionmakers to look at each option separately because the decision is *** the computed results,it is discovered that they are consistent and dependable because of their preferred properties.

关键词： Hypersoft expert set Sanchez’s method decision making optimization solid waste management possibility grade fuzzy parameterization

Analysis of the Smart Player’s Impact on the Success of a Team Empowered with Machine LeAnalysis of the Smart Player’s Impact on the Success of a Team Empowered with Machine Learningarning

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Computers, Materials & Continua 2021年第1期66卷 691-706页

作者： Muhammad Adnan Khan Mubashar Habib Shazia Saqib Tahir Alyas Khalid Masood Khan Mohammed A.Al Ghamdi Sultan H.Almotiri lahore garrison university Lahore54792Pakistan Computer Science Department Umm Al-Qura UniversityMakkah City715Saudi Arabia

The innovation and development in data science have an impact in all trades of *** commercialization of sport has encouraged players,coaches,and other concerns to use technology to be in better position than r their *** the past,the focus was on improved training techniques for better physical *** days,sports analytics identify the patterns in the performance and highlight strengths and weaknesses of potential *** analytics not only predict the performance of players in the near future but it also performs predictive modeling for a particular behavior of a player in the *** impact of a smart player on the success of a team is always a big question mark before the start of a *** fans always want to know performance analysis of these superstar players and they always are interested to get to know more about their favorite player and they always have high hopes from their favorite *** learning(ML)based techniques help in predicting the performance of an individual player as well as for the whole *** statistics are very vital and useful for management,fans,and expert *** our proposed framework,the adaptive back propagation neural network(ABPNN)model is used for the prediction of a player’s *** data is collected from football websites,and the results are stored in the cloud for fast fetching of *** can be retrieved anywhere in the world through cloud *** results are computed with 94%accuracy and the performance of the smart player is formulated for the success of a team.

关键词： Machine learning adaptive feed forwarded neural network adaptive back propagation neural network cloud computing fetching

Autonomous Parking-Lots Detection with Multi-Sensor Data Fusion Using Machine Deep Learning Techniques

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Computers, Materials & Continua 2021年第2期66卷 1595-1612页

作者： Kashif Iqbal Sagheer Abbas Muhammad Adnan Khan Atifa Ather Muhammad Saleem Khan Areej Fatima Gulzar Ahmad School of Computer Science National College of Business Administration&EconomicsLahore54000Pakistan Department of Computer Sciences GC UniversityLahore54000Pakistan Department of Computer Science Lahore Garrison UniversityLahore54000Pakistan Department of Computer Science Comsats University IslamabadLahore CampusLahore54000Pakistan

The rapid development and progress in deep machine-learning techniques have become a key factor in solving the future challenges of ***-based target detection and object classification have been improved due to the development of deep learning *** fusion in autonomous driving is a fact and a prerequisite task of data preprocessing from multi-sensors that provide a precise,well-engineered,and complete detection of objects,scene or *** target of the current study is to develop an in-vehicle information system to prevent or at least mitigate traffic issues related to parking detection and traffic congestion *** this study we examined to solve these problems described by(1)extracting region-of-interest in the images(2)vehicle detection based on instance segmentation,and(3)building deep learning model based on the key features obtained from input parking *** build a deep machine learning algorithm that enables collecting real video-camera feeds from vision sensors and predicting free parking *** augmentation techniques were performed using edge detection,cropping,refined by rotating,thresholding,resizing,or color augment to predict the region of bounding boxes.A deep convolutional neural network F-MTCNN model is proposed that simultaneously capable for compiling,training,validating and testing on parking video frames through *** results of proposed model employing on publicly available PK-Lot parking dataset and the optimized model achieved a relatively higher accuracy 97.6%than previous reported ***,this article presents mathematical and simulation results using state-of-the-art deep learning technologies for smart parking space *** results are verified using Python,TensorFlow,OpenCV computer simulation frameworks.

关键词： Smart parking-lot detection deep convolutional neural network data augmentation region-of-interest object detection

Live Migration of Virtual Machines Using a Mamdani Fuzzy Inference System

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Computers, Materials & Continua 2022年第5期71卷 3019-3033页

作者： Tahir Alyas Iqra Javed Abdallah Namoun Ali Tufail Sami Alshmrany Nadia Tabassum Department of Computer Science Lahore Garrison UniversityLahore54000Pakistan Faculty of Computer and Information Systems Islamic University of MadinahMadinah42351Saudi Arabia Department of Computer Science Virtual University of PakistanLahore54000Pakistan

Efforts were exerted to enhance the live virtual machines(VMs)migration,including performance improvements of the live migration of services to the *** VMs empower the cloud users to store relevant data and ***,the utilization of servers has increased significantly because of the virtualization of computer systems,leading to a rise in power consumption and storage requirements by data centers,and thereby the running *** center migration technologies are used to reduce risk,minimize downtime,and streamline and accelerate the data center move ***,several parameters,such as non-network overheads and downtime adjustment,may impact the live migration time and server downtime to a large *** virtualizing the network resources,the infrastructure as a service(IaaS)can be used dynamically to allocate the bandwidth to services and monitor the network flow *** to the large amount of filthy retransmission,existing live migration systems still suffer from extensive downtime and significant performance degradation in crossdata-center *** study aims to minimize the energy consumption by restricting the VMs migration and switching off the guests depending on a threshold,thereby boosting the residual network bandwidth in the data center with a minimal breach of the service level agreement(SLA).In this research,we analyzed and evaluated the findings observed through simulating different parameters,like availability,downtime,and outage of VMs in data center *** new paradigm is composed of two forms of detection strategies in the live migration approach from the source host to the destination source machine.

关键词： Cloud computing IaaS data centre storage performance analysis live migration

Forecast the Influenza Pandemic Using Machine Learning

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Computers, Materials & Continua 2021年第1期66卷 331-340页

作者： Muhammad Adnan Khan Wajhe Ul Husnain Abidi Mohammed A.Al Ghamdi Sultan H.Almotiri Shazia Saqib Tahir Alyas Khalid Masood Khan Nasir Mahmood Department of Computer Science Lahore Garrison UniversityLahore54792Pakistan Systems Limited Lahore54792Pakistan Computer Science Department Umm Al-Qura UniversityMakkah City715Saudi Arabia Department of Computer Science University of Engineering and TechnologyLahore54000Pakistan

Forecasting future outbreaks can help in minimizing their *** is a disease primarily found in animals but transferred to humans through *** 1918,influenza became a pandemic and spread rapidly all over the world becoming the cause behind killing one-third of the human population and killing one-fourth of the pig ***,that influenza became a pandemic several times on a local and global *** 2009,influenza‘A’subtype H1N1 again took many human *** disease spread like in a pandemic *** paper proposes a forecasting modeling system for the influenza pandemic using a feed-forward propagation neural network(MSDII-FFNN).This model helps us predict the outbreak,and determines which type of influenza becomes a pandemic,as well as which geographical area is *** collection for the model is done by using IoT *** model is divided into 2 phases:The training phase and the validation phase,both being connected through the *** the training phase,the model is trained using FFNN and is updated on the *** the validation phase,whenever the input is submitted through the IoT devices,the system model is updated through the cloud and predicts the pandemic *** our dataset,the data is divided into an 85%training ratio and a 15%validation *** applying the proposed model to our dataset,the predicted output precision is 90%.

关键词： Influenza pandemic machine learning prediction influenza influenza pandemic prediction forecast pandemic influenza

Understanding the Language of ISIS:An Empirical Approach to Detect Radical Content on Twitter Using Machine Learning

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Computers, Materials & Continua 2021年第2期66卷 1075-1090页

作者： Zia Ul Rehman Sagheer Abbas Muhammad Adnan Khan Ghulam Mustafa Hira Fayyaz Muhammad Hanif Muhammad Anwar Saeed School of Computer Science National College of Business Administration&EconomicsLahore54000Pakistan Department of Computer Sciences Bahria UniversityLahore54000Pakistan Department of Computer Science Lahore Garrison UniversityLahore54000Pakistan School of Systems and Technology University of Management and TechnologyLahore54000Pakistan Department of CS&IT Virtual University of PakistanLahore54000Pakistan

The internet,particularly online social networking platforms have revolutionized the way extremist groups are influencing and radicalizing *** research reveals that the process initiates by exposing vast audiences to extremist content and then migrating potential victims to confined platforms for intensive ***,social networks have evolved as a persuasive tool for extremism aiding as recruitment platform and psychological ***,recognizing potential radical text or material is vital to restrict the circulation of the extremist *** aim of this research work is to identify radical text in social *** contributions are as follows:(i)A new dataset to be employed in radicalization detection;(ii)In depth analysis of new and previous datasets so that the variation in extremist group narrative could be identified;(iii)An approach to train classifier employing religious features along with radical features to detect radicalization;(iv)Observing the use of violent and bad words in radical,neutral and random groups by employing violent,terrorism and bad words *** research results clearly indicate that incorporating religious text in model training improves the accuracy,precision,recall,and F1-score of the *** a variation in extremist narrative has been observed implying that usage of new dataset can have substantial effect on classifier *** addition to this,violence and bad words are creating a differentiating factor between radical and random users but for neutral(anti-ISIS)group it needs further investigation.

关键词： Radicalization extremism machine learning natural language processing twitter text mining

Multi-valued interval neutrosophic linguistic soft set theory and its application in knowledge management

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CAAI Transactions on Intelligence Technology 2020年第3期5卷 200-208页

作者： Nor Liyana Amalini Mohd Kamal Lazim Abdullah Ilyani Abdullah Muhammad Saqlain Faculty of Ocean Engineering and Informatics Universiti Malaysia Terengganu21030 Kuala NerusTerengganuMalaysia Department of Mathematics Lahore Garrison UniversityDHA Phase VISector-CLahorePakistan

This study proposed the concepts of a multi-valued interval neutrosophic linguistic soft set(MVINLSS)by integrating the concepts of multi-valued interval neutrosophic soft set and linguistic *** is presented as it can deal with ambiguous and uncertain information in decision making which is characterised by linguistic *** proposed set amalgamates quantitative assessment with the qualitative assessment which can solve complex problems with ambiguity,uncertainty,and *** can be an effective tool to describe the real decision-makers’opinions and reflect their ***,the authors propose the basic operational rules of ***,they define a multi-valued interval neutrosophic linguistic soft weighted arithmetic averaging *** work proposed a multi-criteria decision-making(MCDM)method which considers different priority levels of criteria and the criteria values take the form of multi-valued interval neutrosophic linguistic soft ***,the proposed MCDM method is applied to the knowledge management case,considering 11 criteria.

关键词： valued qualitative integrating

Reducing Dataset Specificity for Deepfakes Using Ensemble Learning

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Computers, Materials & Continua 2023年第2期74卷 4261-4276页

作者： Qaiser Abbas Turki Alghamdi Yazed Alsaawy Tahir Alyas Ali Alzahrani Khawar Iqbal Malik Saira Bibi Faculty of Computer and Information Systems Islamic university Madinah Madinah42351Saudi Arabia Department of Computer Science Lahore Garrison UniversityLahore54000Pakistan The university of lahore Sargodha40100Pakistan university of Sargodha Department of Computer Science&ITSargodha40100Pakistan

The emergence of deep fake videos in recent years has made image falsification a real danger.A person’s face and emotions are deep-faked in a video or speech and are substituted with a different face or voice employing deep learning to analyze speech or emotional *** of how clever these videos are frequently,Manipulation is challenging to *** media are the most frequent and dangerous targets since they are weak outlets that are open to extortion or slander a *** earlier times,it was not so easy to alter the videos,which required expertise in the domain and ***,the generation of fake videos has become easier and with a high level of realism in the *** are forgeries and altered visual data that appear in still photos or video *** automatic identification systems have been developed to solve this issue,however they are constrained to certain datasets and performpoorly when applied to different *** study aims to develop an ensemble learning model utilizing a convolutional neural network(CNN)to handle deepfakes or *** employed ensemble learning,a technique combining many classifiers to achieve higher prediction performance than a single classifier,boosting themodel’s *** performance of the generated model is evaluated on Face *** work is about building a new powerful model for automatically identifying deep fake videos with the DeepFake-Detection-Challenges(DFDC)*** test our model using the DFDC,one of the most difficult datasets and get an accuracy of 96%.

关键词： Deep machine learning deep fake CNN DFDC ensemble learning

Intelligent Dynamic Gesture Recognition Using CNN Empowered by Edit Distance

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Computers, Materials & Continua 2021年第2期66卷 2061-2076页

作者： Shazia Saqib Allah Ditta Muhammad Adnan Khan Syed Asad Raza Kazmi Hani Alquhayz Department of ComputerScience Lahore Garrison UniversityLahore54000Pakistan Department of Information Sciences Division of Science&TechnologyUniversity of EducationLahore54000Pakistan GC university Lahore54000Pakistan Department of Computer Science and Information College of Sciencein ZulfiMajmaah UniversityAl-Majmaah11952Saudi Arabia

Human activity detection and recognition is a challenging *** surveillance can benefit greatly by advances in Internet of Things(IoT)and cloud *** intelligence IoT(AIoT)based devices form the basis of a smart *** research presents Intelligent dynamic gesture recognition(IDGR)using a Convolutional neural network(CNN)empowered by edit distance for video *** proposed system has been evaluated using AIoT enabled devices for static and dynamic gestures of Pakistani sign language(PSL).However,the proposed methodology can work efficiently for any type of *** proposed research concludes that deep learning and convolutional neural networks give a most appropriate solution retaining discriminative and dynamic information of the input *** research proposes recognition of dynamic gestures using image recognition of the keyframes based on CNN extracted from the human *** distance is used to find out the label of the word to which those sets of frames belong *** simulation results have shown that at 400 videos per human action,100 epochs,234×234 image size,the accuracy of the system is 90.79%,which is a reasonable accuracy for a relatively small dataset as compared to the previously published techniques.

关键词： Sign languages keyframe edit distance misrate accuracy