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Optimization algorithms of PERT/CPM Network Diagrams in Linear Diophantine Fuzzy Environment

Computer Modeling in Engineering & Sciences 2024年第4期139卷 1095-1118页

作者： Mani Parimala Karthikeyan Prakash Ashraf al-Quran muhammad Riaz Saeid Jafari Department of Mathematics Bannari Amman Institute of TechnologySathyamangalamTamil Nadu638401India Basic Sciences Department Preparatory Year DeanshipKing Faisal UniversityAl-Ahsa31982Saudi Arabia Department of Mathematics University of the PunjabLahorePakistan College of Vestsjaelland South Herrestraede 11Slagelse4200Denmark

The idea of linear Diophantine fuzzy set(LDFS)theory with its control parameters is a strong model for machine learning and optimization under *** activity times in the critical path method(CPM)representation procedures approach are initially static,but in the Project Evaluation and Review Technique(PERT)approach,they are *** study proposes a novel way of project review and assessment methodology for a project network in a linear Diophantine fuzzy(LDF)*** LDF expected task time,LDF variance,LDF critical path,and LDF total expected time for determining the project network are all computed using LDF numbers as the time of each activity in the project *** primary premise of the LDF-PERT approach is to address ambiguities in project network activity timesmore simply than other approaches such as conventional PERT,Fuzzy PERT,and so *** LDF-PERT is an efficient approach to analyzing symmetries in fuzzy control systems to seek an optimal *** also present a new approach for locating LDF-CPM in a project network with uncertain and erroneous activity *** the available resources and activity times are imprecise and unpredictable,this strategy can help decision-makers make better judgments in a project.A comparison analysis of the proposed technique with the existing techniques has also been *** suggested techniques are demonstrated with two suitable numerical examples.

关键词： Linear Diophantine fuzzy graphs project management PERT CPM linear Diophantine fuzzy numbers score function accuracy function

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Hydro-Geochemical Characteristics of Groundwater Beneath the Gaza Strip

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Journal of Water Resource and Protection 2011年第5期3卷 341-348页

作者： muhammad al-khatib Husam al-Najar 不详

The main objective of the current research is to investigate the deterioration of groundwater quality due to the over-pumping in the coastal area of the Gaza Strip. One hundred and two (102) samples from 44 municipal water wells in the Gaza Strip were collected and analyzed for major cations like, Ca2+ and Mg2+ by Titration, Na+ and K+ by Flame photometer;anions like, Cl- and HCO3- by Titration, NO3- by spectrophotometer and SO42- by turbidity meter. The groundwater in the region mainly consists of NaCl, CaHCO3-and NaHCO3-. The saline load of groundwater is in first place controlled by chloride, sodium and calcium concentrations. The spatial changes of ionic ratios of rCa2+/(rHCO3- +? rSO42-) and the relationship between sodium and chloride in the coastal area indicate that the aquifer experienced sea-water intrusion.

关键词： Groundwater Salinity Piper Diagrams Durov Diagram Sea Intrusion

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A new fractional order 6D chaotic model:Study of model dynamics,system structure graph,electronic circuit realization and fractional control

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Journal of Ocean Engineering and Science 2024年第2期9卷 112-125页

作者： M.Higazy Norah almalki Shabbir muhammad A.al-Ghamdi Department of Mathematics and Statistics College of ScienceTaif UniversityP.O.Box 11099Taif 21944Saudi Arabia Department of Physics and Engineering Mathematics Faculty of Electronic EngineeringMenoufia UniversityMenouf 32952Egypt Research Center for Advanced Materials Science(RCAMS) King Khalid UniversityP.O.Box 9004Abha 61413Saudi Arabia Department of Chemistry College of ScienceKing Khalid UniversityP.O.Box 9004Abha 61413Saudi Arabia Department of Physics Faculty of ScienceKing Abdulaziz UniversityP.O.Box 80203Jeddah 21589Saudi Arabia

A new fractional 6D chaotic model is constructed in this *** new fractional 6D chaotic model has six positive parameters plus the fractional order with eight nonlinear *** complicated chaotic dy-namics of the new fractional 6D model is presented and *** basic properties of this model are studied and its chaotic attractors,dissipative feature,symmetry,equilibrium points,Lyapunov Exponents are *** new dynamics of the 6D fractional model is numerically simulated using Matlab *** addition,utilizing the graph theory tools certain structural characteristics are *** electrical circuit is built to implement the new 5.4 fractional order 6D ***,an active fractional order controller is proposed to control the new model at different fractional *** chaos of the new model is very useful and can be used to produce random keys for data encryption.

关键词： Chaotic systems Fractional calculus Lyapunov exponents Chaotic attractors Adjacency matrix Hermitian matrix Electronic circuit realization

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Deep Learning Based Efficient Crowd Counting System

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Computers, Materials & Continua 2024年第6期79卷 4001-4020页

作者： Waleed Khalid al-Ghanem Emad Ul Haq Qazi muhammad Hamza Faheem Syed Shah Amanullah Quadri Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityRiyadh12372Saudi Arabia Centre of Excellence in Cybercrime and Digital Forensics Naif Arab University for Security SciencesRiyadh14812Saudi Arabia Center of Excellence for Information Assurance(COEIA) King Saud UniversityRiyadh12372Saudi Arabia

Estimation of crowd count is becoming crucial nowadays,as it can help in security surveillance,crowd monitoring,and management for different *** is challenging to determine the approximate crowd size from an image of the crowd’s *** in this research study,we proposed a multi-headed convolutional neural network architecture-based model for crowd counting,where we divided our proposed model into two main components:(i)the convolutional neural network,which extracts the feature across the whole image that is given to it as an input,and(ii)the multi-headed layers,which make it easier to evaluate density maps to estimate the number of people in the input image and determine their number in the *** employed the available public benchmark crowd-counting datasets UCF CC 50 and ShanghaiTech parts A and B for model training and testing to validate the model’s *** analyze the results,we used two metrics Mean Absolute Error(MAE)and Mean Square Error(MSE),and compared the results of the proposed systems with the state-of-art models of crowd *** results show the superiority of the proposed system.

关键词： Crowd counting EfficientNet multi-head attention convolutional neural network transfer learning

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Improving Thyroid Disorder Diagnosis via Ensemble Stacking and Bidirectional Feature Selection

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Computers, Materials & Continua 2024年第3期78卷 4225-4241页

作者： muhammad Armghan Latif Zohaib Mushtaq Saad Arif Sara Rehman muhammad Farrukh Qureshi Nagwan Abdel Samee Maali alabdulhafith Yeong Hyeon Gu Mohammed A.al-masni Department of Computer and Information System Cleveland State UniversityOhio44115USA Department of Electrical Electronics and Computer SystemsCollege of Engineering and TechnologyUniversity of SargodhaSargodha40100Pakistan Department of Mechanical Engineering College of EngineeringKing Faisal UniversityAl-Ahsa31982Saudi Arabia Department of Biomedical Engineering Riphah International UniversityIslamabad44000Pakistan Department of Electrical Engineering Riphah International UniversityIslamabad44000Pakistan Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Artificial Intelligence College of Software and Convergence TechnologySejong UniversitySeoul05006Korea

Thyroid disorders represent a significant global health challenge with hypothyroidism and hyperthyroidism as two common conditions arising from dysfunction in the thyroid *** and timely diagnosis of these disorders is crucial for effective treatment and patient *** research introduces a comprehensive approach to improve the accuracy of thyroid disorder diagnosis through the integration of ensemble stacking and advanced feature selection *** forward feature selection,sequential backward feature elimination,and bidirectional feature elimination are investigated in this *** ensemble learning,random forest,adaptive boosting,and bagging classifiers are *** effectiveness of these techniques is evaluated using two different datasets obtained from the University of California Irvine-Machine Learning Repository,both of which undergo preprocessing steps,including outlier removal,addressing missing data,data cleansing,and feature *** experimentation demonstrates the remarkable success of proposed ensemble stacking and bidirectional feature elimination achieving 100%and 99.86%accuracy in identifying hyperthyroidism and hypothyroidism,*** enhancing detection accuracy,the ensemble stacking model also demonstrated a streamlined computational complexity which is pivotal for practical medical *** significantly outperformed existing studies with similar objectives underscoring the viability and effectiveness of the proposed *** research offers an innovative perspective and sets the platform for improved thyroid disorder diagnosis with broader implications for healthcare and patient well-being.

关键词： Ensemble learning random forests boosting dimensionality reduction machine learning smart healthcare computer aided diagnosis

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Automatic Finding of Brain-Tumour Group Using CNN Segmentation and Moth-Flame-algorithm,Selected Deep and Handcrafted Features

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Computers, Materials & Continua 2024年第5期79卷 2585-2608页

作者： Imad Saud al Naimi Syed alwee aljunid Syed Junid muhammad lmran Ahmad K.Suresh Manic Faculty of Electronic Engineering Technology University Malaysia Perils(UniMAP)Pauh Putra CampusArauPerlis02600Malaysia Department of Electrical and Communication Engineering National University of Science and TechnologyAL HailAL Seeb130Sultanate of Oman Department of Research and Innovation Saveetha School of EngineeringSaveetha Institute of Medical and Technical Sciences(SIMATS)ChennaiTamiNadu602105India

Augmentation of abnormal cells in the brain causes brain tumor(BT),and early screening and treatmentwill reduce its harshness in ***’s clinical level screening is usually performed with Magnetic Resonance Imaging(MRI)due to its multi-modality *** overall aims of the study is to introduce,test and verify an advanced image processing technique with algorithms to automatically extract tumour sections from brain MRI scans,facilitating improved *** research intends to devise a reliable framework for detecting the BT region in the twodimensional(2D)MRI slice,and identifying its class with improved *** methodology for the devised framework comprises the phases of:(i)Collection and resizing of images,(ii)Implementation and Segmentation of Convolutional Neural Network(CNN),(iii)Deep feature extraction,(iv)Handcrafted feature extraction,(v)Moth-Flame-algorithm(MFA)supported feature reduction,and(vi)Performance *** study utilized clinical-grade brain MRI of BRATS and TCIA datasets for the *** framework segments detected the glioma(low/high grade)and glioblastoma class *** work helped to get a segmentation accuracy of over 98%with VGG-UNet and a classification accuracy of over 98%with the VGG16 *** study has confirmed that the implemented framework is very efficient in detecting the BT in MRI slices with/without the skull section.

关键词： Brain tumour VGG-UNet VGG16 moth-flame-algorithm classification

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Numerical Analysis of Bacterial Meningitis Stochastic Delayed Epidemic Model through Computational Methods

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Computer Modeling in Engineering & Sciences 2024年第10期141卷 311-329页

作者： Umar Shafique Mohamed Mahyoub al-Shamiri ali Raza Emad Fadhal muhammad Rafiq Nauman Ahmed Department of Mathematics National College of Business Administration and EconomicsLahore54660Pakistan Department of Mathematics Applied CollegeMahayl AssirKing Khalid UniversityAbha62529Saudi Arabia Department of Physical Sciences The University of ChenabGujrat50700Pakistan Department of Mathematics&Statistics College of ScienceKing Faisal UniversityP.O.Box 400Al-Ahsa31982Saudi Arabia Department of Computer Science and Mathematics Lebanese American UniversityBeirut1102-2801Lebanon Department of Mathematics Faculty of Science and TechnologyUniversity of Central PunjabLahore54000Pakistan Department of Mathematics and Statistics University of LahoreLahore54000Pakistan

Based on theWorld Health Organization(WHO),Meningitis is a severe infection of the meninges,the membranes covering the brain and spinal *** is a devastating disease and remains a significant public health *** study investigates a bacterial meningitis model through deterministic and stochastic ***-compartment population dynamics explain the concept,particularly the susceptible population,carrier,infected,and *** model predicts the nonnegative equilibrium points and reproduction number,i.e.,the Meningitis-Free Equilibrium(MFE),and Meningitis-Existing Equilibrium(MEE).For the stochastic version of the existing deterministicmodel,the twomethodologies studied are transition probabilities and non-parametric ***,positivity,boundedness,extinction,and disease persistence are studiedrigorouslywiththe helpofwell-known *** and nonstandard techniques such as EulerMaruyama,stochastic Euler,stochastic Runge Kutta,and stochastic nonstandard finite difference in the sense of delay have been presented for computational analysis of the stochastic ***,standard methods fail to restore the biological properties of the model,so the stochastic nonstandard finite difference approximation is offered as an efficient,low-cost,and independent of time step *** addition,the convergence,local,and global stability around the equilibria of the nonstandard computational method is studied by assuming the perturbation effect is *** simulations and comparison of the methods are presented to support the theoretical results and for the best visualization of results.

关键词： BacterialMeningitis disease stochastic delayed model stability analysis extinction and persistence computational methods

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Complex Decision Modeling Framework with Fairly Operators and Quaternion Numbers under Intuitionistic Fuzzy Rough Context

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Computer Modeling in Engineering & Sciences 2024年第5期139卷 1893-1932页

作者： Nadeem Salamat muhammad Kamran Shahzaib Ashraf Manal Elzain Mohammed Abdulla Rashad Ismail Mohammed M.al-Shamiri Institute of Mathematics Khwaja Fareed University of Engineering&Information TechnologyRahim Yar Khan64200Pakistan Department of Mathematics Thal University BhakkarPunjab30000Pakistan Department of Mathematics Faculty of Science and ArtsKing Khalid UniversityMuhayl Assir61913Saudi Arabia

The main goal of informal computing is to overcome the limitations of hypersensitivity to defects and uncertainty while maintaining a balance between high accuracy,accessibility,and *** paper investigates the potential applications of intuitionistic fuzzy sets(IFS)with rough sets in the context of sparse *** it comes to capture uncertain information emanating fromboth upper and lower approximations,these intuitionistic fuzzy rough numbers(IFRNs)are superior to intuitionistic fuzzy sets and pythagorean fuzzy sets,*** use rough sets in conjunction with IFSs to develop several fairly aggregation operators and analyze their underlying *** present numerous impartial laws that incorporate the idea of proportionate dispersion in order to ensure that the membership and non-membership activities of IFRNs are treated equally within these *** operations lead to the development of the intuitionistic fuzzy rough weighted fairly aggregation operator(IFRWFA)and intuitionistic fuzzy rough ordered weighted fairly aggregation operator(IFRFOWA).These operators successfully adjust to membership and non-membership categories with fairness and *** highlight the unique qualities of these suggested aggregation operators and investigate their use in the multiattribute decision-making *** use the intuitionistic fuzzy rough environment’s architecture to create a novel strategy in situation involving several decision-makers and non-weighted ***,we developed a novel technique by combining the IFSs with quaternion *** establish a unique connection between alternatives and qualities by using intuitionistic fuzzy quaternion numbers(IFQNs).With the help of this framework,we can simulate uncertainty in real-world situations and address a number of decision-making *** the examples we have released,we offer a sophisticated and systematically constructed illustrative scenario that is intricately wo

关键词： Intuitionistic fuzzy set quaternion numbers fuzzy logic decision-making rough set

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Smart Energy Management System Using Machine Learning

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Computers, Materials & Continua 2024年第1期78卷 959-973页

作者： ali Sheraz Akram Sagheer Abbas muhammad Adnan Khan Atifa Athar Taher M.Ghazal Hussam al Hamadi School of Computer Science National College of Business Administration&EconomicsLahore54000Pakistan Riphah School of Computing&Innovation Faculty of ComputingRiphah International UniversityLahore CampusLahore54000Pakistan Pattern Recognition and Machine Learning Lab Department of SoftwareGachon UniversitySeongnamGyeonggido13120Korea Department of Computer Science COMSATS University IslamabadLahore CampusLahore54000Pakistan School of Information Technology Skyline University CollegeSharjah1797United Arab Emirates Network and Communication Technology Lab Center for Cyber SecurityFaculty of Information Science and TechnologyUniversiti Kebangsaan MalaysiaBangiSelangor43600Malaysia College of Engineering and IT University of DubaiDubaiUnited Arab Emirates

Energy management is an inspiring domain in developing of renewable energy ***,the growth of decentralized energy production is revealing an increased complexity for power grid managers,inferring more quality and reliability to regulate electricity flows and less imbalance between electricity production and *** major objective of an energy management system is to achieve optimum energy procurement and utilization throughout the organization,minimize energy costs without affecting production,and minimize environmental *** energy management is an essential and complex subject because of the excessive consumption in residential buildings,which necessitates energy optimization and increased user *** address the issue of energy management,many researchers have developed various frameworks;while the objective of each framework was to sustain a balance between user comfort and energy consumption,this problem hasn’t been fully solved because of how difficult it is to solve *** inclusive and Intelligent Energy Management System(IEMS)aims to provide overall energy efficiency regarding increased power generation,increase flexibility,increase renewable generation systems,improve energy consumption,reduce carbon dioxide emissions,improve stability,and reduce energy *** Learning(ML)is an emerging approach that may be beneficial to predict energy efficiency in a better way with the assistance of the Internet of Energy(IoE)*** IoE network is playing a vital role in the energy sector for collecting effective data and usage,resulting in smart resource *** this research work,an IEMS is proposed for Smart Cities(SC)using the ML technique to better resolve the energy management *** proposed system minimized the energy consumption with its intelligent nature and provided better outcomes than the previous approaches in terms of 92.11% accuracy,and 7.89% miss-rate.

关键词： Intelligent energy management system smart cities machine learning

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Securing Cloud-Encrypted Data:Detecting Ransomware-as-a-Service(RaaS)Attacks through Deep Learning Ensemble

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Computers, Materials & Continua 2024年第4期79卷 857-873页

作者： Amardeep Singh Hamad ali Abosaq Saad Arif Zohaib Mushtaq muhammad Irfan Ghulam Abbas Arshad ali alanoud al Mazroa School of Computer Information Sciences University of CumberlandsWilliamsburgKentucky40769USA Computer Science Department College of Computer Science and Information SystemsNajran UniversityNajran61441Saudi Arabia Department of Mechanical Engineering College of EngineeringKing Faisal UniversityAl-Ahsa31982Saudi Arabia Department of Electrical Electronics and Computer SystemsCollege of Engineering and TechnologyUniversity of SargodhaSargodha40100Pakistan Electrical Engineering Department College of EngineeringNajran UniversityNajran61441Saudi Arabia Department of Electrical Engineering The University of LahoreLahore54000Pakistan Faculty of Computer and Information Systems Islamic University of MadinahMadinah42351Saudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia

Data security assurance is crucial due to the increasing prevalence of cloud computing and its widespread use across different industries,especially in light of the growing number of cybersecurity threats.A major and everpresent threat is Ransomware-as-a-Service(RaaS)assaults,which enable even individuals with minimal technical knowledge to conduct ransomware *** study provides a new approach for RaaS attack detection which uses an ensemble of deep learning *** this purpose,the network intrusion detection dataset“UNSWNB15”from the Intelligent Security Group of the University of New South Wales,Australia is *** the initial phase,the rectified linear unit-,scaled exponential linear unit-,and exponential linear unit-based three separate Multi-Layer Perceptron(MLP)models are ***,using the combined predictive power of these three MLPs,the RansoDetect Fusion ensemble model is introduced in the suggested *** proposed ensemble technique outperforms previous studieswith impressive performance metrics results,including 98.79%accuracy and recall,98.85%precision,and 98.80%*** empirical results of this study validate the ensemble model’s ability to improve cybersecurity defenses by showing that it outperforms individual *** expanding the field of cybersecurity strategy,this research highlights the significance of combined deep learning models in strengthening intrusion detection systems against sophisticated cyber threats.

关键词： Cloud encryption RaaS ensemble threat detection deep learning cybersecurity

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