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Smart-City-based Data Fusion Algorithm for Internet of Things

Computers, Materials & Continua 2022年第11期73卷 2407-2421页

作者： Jawad khan muhammad amir khan N.Z.Jhanjhi Mamoona Humayun Abdullah Alourani Department of Computer Science IIC University of TechnologyPhnom PenhCambodia Department of Computer Science COMSATS University IslamabadAbbottabad Campus22060Pakistan School of Computer Science and Engineering SCETaylor’s UniversitySubang JayaSelangorMalaysia Department of Information Systems College of Computer and Information SciencesJouf Universityal-JoufKSA Department of Computer Science and Information College of Science in ZulfiMajmaah UniversityAl-Majmaah11952Saudi Arabia

Increasingly,Wireless Sensor Networks(WSNs)are contributing enormous amounts of *** the recent deployments of wireless sensor networks in Smart City infrastructures,significant volumes of data have been produced every day in several domains ranging from the environment to the healthcare system to *** wireless sensor nodes,a Smart City environment may now be shown for the benefit of *** Smart City delivers intelligent infrastructure and a stimulating environment to citizens of the Smart Society,including the elderly and ***,Quality of Service(QoS)and poor data performance are common problems in WSNs,caused by the data fusion method,where a small amount of bad data can significantly impact the total fusion *** our proposed research,a WSN multisensor data fusion technique employing fuzzy logic for event *** the new proposed Algorithm,sensor nodes will collect less repeated data,and redundant data will be used to increase the data’s overall *** network’s fusion delay problem is investigated,and a minimum fusion delay approach is provided based on the nodes’fusion waiting *** proposed algorithm performs well in fusion,according to the results of the *** a result of these discoveries,It is concluded that the algorithm describe here is effective and dependable instrument with a wide range of applications.

关键词： Internet of things data fusion wireless sensor network fuzzy inference system

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A Compact Rhombus Shaped Antenna with Extended Stubs for Ultra-Wideband Applications

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Computers, Materials & Continua 2022年第11期73卷 2637-2650页

作者： Syed Misbah un Noor muhammad amir khan Shahid khan NZ Jhanjhi Mamoona Humayun Hesham A.Alhumyan Department of Electrical Engineering IIC University of TechnologyPhnom PenhCambodia Department of Computer Science COMSATS University IslamabadAbbottabad Campus22060Pakistan Department of Electrical and Computer Engineering COMSATS University IslamabadAbbottabad Campus22060Pakistan School of Computer Science SCSTaylor’s UniversitySubang Jaya47500SelangorMalaysia 5Center for Smart Society 0 CSS5 Faculty of Innovation and TechnologyTaylor’s UniversitySubang Jaya47500Malaysia Department of Information Systems College of Computer and Information SciencesJouf UniversityAl-JoufKSA

Ultra-wideband(UWB)is highly preferred for short distance *** a result of this significance,this project targets the design of a compact UWB *** paper describes a printed UWB rhombusshaped antenna with a partial ground *** achieve wideband response,two stubs and a notch are incorporated at both sides of the rhombus design and ground plane *** excite the antenna,a simple microstrip feed line is *** suggested antenna is built on a 1.6 mm thick FR4 *** proposed design is very compact with overall electrical size of 0.18λ×0.25λ(14×18 mm2).The rhombus shaped antenna covers frequency ranging from 3.5 to 11 GHz with 7.5 GHz impedance *** proposed design simulated and measured bandwidths are 83.33%and 80%,*** pattern in terms of E-field and H-field are discussed at 4,5.5 and 10 GHz *** proposed design has 65%radiation efficiency and 1.5 dBi peak *** proposed design is simulated in CST(Computer Simulation Technology)simulator and the simulated design is fabricated for the measured *** simulated and measured findings are in close *** obtained results confirm the application of the proposed design for the ultra-wide band applications.

关键词： Ultra-wideband impedance bandwidth radiation pattern CST electrical size

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A Systematic Literature Review of Machine Learning and Deep Learning Approaches for Spectral Image Classification in Agricultural Applications Using Aerial Photography

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Computers, Materials & Continua 2024年第3期78卷 2967-3000页

作者： Usman khan muhammad Khalid khan muhammad Ayub Latif muhammad Naveed muhammad Mansoor Alam Salman A.khan Mazliham Mohd Su’ud College of Computing and Information Sciences Karachi Institute of Economics and TechnologyKarachi75190Pakistan Faculty of Computing and Informatics(FCI) Multimedia UniversityCyberjaya63100Malaysia Faculty of Computing Riphah International UniversityIslamabad46000Pakistan Faculty of Engineering and Information Technology School of Computer ScienceUniversity of Technology SydneySydneyAustralia

Recently,there has been a notable surge of interest in scientific research regarding spectral *** potential of these images to revolutionize the digital photography industry,like aerial photography through Unmanned Aerial Vehicles(UAVs),has captured considerable *** encouraging aspect is their combination with machine learning and deep learning algorithms,which have demonstrated remarkable outcomes in image *** a result of this powerful amalgamation,the adoption of spectral images has experienced exponential growth across various domains,with agriculture being one of the prominent *** paper presents an extensive survey encompassing multispectral and hyperspectral images,focusing on their applications for classification challenges in diverse agricultural areas,including plants,grains,fruits,and *** meticulously examining primary studies,we delve into the specific agricultural domains where multispectral and hyperspectral images have found practical ***,our attention is directed towards utilizing machine learning techniques for effectively classifying hyperspectral images within the agricultural *** findings of our investigation reveal that deep learning and support vector machines have emerged as widely employed methods for hyperspectral image classification in ***,we also shed light on the various issues and limitations of working with spectral *** comprehensive analysis aims to provide valuable insights into the current state of spectral imaging in agriculture and its potential for future advancements.

关键词： Machine learning deep learning unmanned aerial vehicles multi-spectral images image recognition object detection hyperspectral images aerial photography

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Depositional and Bio-Sequence Stratigraphic Framework of Late Cretaceous Kawagarh Formation,Kala-Chitta Range,Pakistan:Equivalent of Gurpi Formation(Iran)

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Journal of Earth Science 2024年第2期35卷 332-348页

作者： muhammad Awais Bilal Wadood muhammad Ishaq muhammad Bilal Zeeshan Zafar Nasar khan Aminullah khan Department of Geology University of SwabiAnbar-SwabiKhyber PakhtunkhwaSwabi23561Pakistan Department of Earth Environmental and Resources SciencesUniversity of Naples FedericoⅡNaples80126Italy State Key Laboratory of Continental Dynamics Department of GeologyNorthwest UniversityXi’an710069China National Centre of Excellence in Geology University of PeshawarPeshawarKhyber Pakhtunkhwa25130Pakistan Department of Geological Engineering School of Earth Sciences and EngineeringXi’an Shiyou UniversityXi’an610500China Department of Petroleum Geology and Engineering School of Earth ResourcesChina University of GeosciencesWuhan430074China Department of Geology University of MalakandDir Lower18800ChakdaraPakistan Department of Earth and Environmental Sciences KU LeuvenLeuven3001Belgium

The present study is focused on evaluation of deep marine pelagic sediments of Late Cretaceous Kawagarh Formation of Kala-Chitta Range in the context of microfacies analysis,paleoenvironmental interpretation,planktonic foraminiferal biostratigraphy,sequence stratigraphy and diversification of species.A total of thirty three rock samples were collected from the measured *** microfacies are interpreted,namely planktonic foraminifera wackestone,planktonic foraminifera mudstone and sandy mudstone indicating low energy depositional environment i.e.,outer *** biostratigraphic studies show plentiful planktonic foraminifera species of Globotruncana,Heterohelix and ***,no association of benthic or siliceous organisms was *** the basis of available species assemblage,a single local planktonic foraminifera biozone i.e.,Globotruncana-Heterohelix-Globotruncanita Assemblage Biozone is *** biozone information is combined with published literature and Lower Santonian to Middle Maastrichtian age has been assigned to the Kawagarh *** trend of species occurrences evinces that species number decreases over time with pulsated rise in the Kawagarh *** Kawagarh Formation carbonates show an overall Transgressive Systems Tract(TST).The Kawagarh Formation of Pakistan evinces analogous characteristics to that of the Late Cretaceous Gurpi Formation of Iran based on the geologic age,outcrop lithology,microfacies,planktonic foraminiferal assemblages,depositional setting and sequence stratigraphy.

关键词： depositional fabric biostratigraphy sequence stratigraphy Late Cretaceous Kawagarh Formation Pakistan Gurpi Formation Iran

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Federated Machine Learning Based Fetal Health Prediction Empowered with Bio-Signal Cardiotocography

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Computers, Materials & Continua 2024年第3期78卷 3303-3321页

作者： muhammad Umar Nasir Omar Kassem Khalil Karamath Ateeq Bassam SaleemAllah Almogadwy muhammad Adnan khan muhammad Hasnain Azam khan muhammad Adnan Department of Computer Sciences Bahria University Lahore CampusLahore54000Pakistan Faculty of Information Technology Liwa CollegeAbu Dhabi00000United Arab Emirates Department of Computing Skyline University CollegeSharjah1797United Arab Emirates Department of Computer Science Taibah UniversityMedina42353Saudi Arabia Riphah School of Computing and Innovation Faculty of ComputingRiphah International UniversityLahore CampusLahore54000Pakistan Department of Engineering and Development Dubizzle LabsLahore54000Pakistan Department of Software Faculty of Artificial Intelligence and SoftwareGachon UniversitySeongnam-si13120Korea

Cardiotocography measures the fetal heart rate in the fetus during pregnancy to ensure physical health because cardiotocography gives data about fetal heart rate and uterine shrinkages which is very beneficial to detect whether the fetus is normal or suspect or *** cardiotocography measures infer wrongly and give wrong predictions because of human *** traditional way of reading the cardiotocography measures is the time taken and belongs to numerous human errors as *** condition is very important to measure at numerous stages and give proper medications to the fetus for its *** the current period Machine learning(ML)is a well-known classification strategy used in the biomedical field on various issues because ML is very fast and gives appropriate results that are better than traditional *** techniques play a pivotal role in detecting fetal disease in its early *** research article uses Federated machine learning(FML)and ML techniques to classify the condition of the *** study proposed a model for the detection of bio-signal cardiotocography that uses FML and ML techniques to train and test the ***,the proposed model of FML used numerous data preprocessing techniques to overcome data deficiency and achieves 99.06%and 0.94%of prediction accuracy and misprediction rate,respectively,and parallel the proposed model applying K-nearest neighbor(KNN)and achieves 82.93%and 17.07%of prediction accuracy and misprediction accuracy,***,by comparing both models FML outperformed the KNN technique and achieved the best and most appropriate prediction results as compared with previous studies the proposed study achieves the best and most accurate results.

关键词： Cardiotocography ML FML fetal disease bio-signal

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Predicting Age and Gender in Author Profiling: A Multi-Feature Exploration

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Computers, Materials & Continua 2024年第5期79卷 3333-3353页

作者： Aiman muhammad Arshad Bilal khan Sadique Ahmad muhammad Asim Department of Computer Science City University of Science and Information TechnologyPeshawar25000Pakistan EIAS:Data Science and Blockchain Laboratory College of Computer and Information SciencesPrince Sultan UniversityRiyadh11586Saudi Arabia School of Computer Science and Technology Guangdong University of TechnologyGuangzhou510006China

Author Profiling (AP) is a subsection of digital forensics that focuses on the detection of the author’s personalinformation, such as age, gender, occupation, and education, based on various linguistic features, e.g., stylistic,semantic, and syntactic. The importance of AP lies in various fields, including forensics, security, medicine, andmarketing. In previous studies, many works have been done using different languages, e.g., English, Arabic, French,***, the research on RomanUrdu is not up to the ***, this study focuses on detecting the author’sage and gender based on Roman Urdu text messages. The dataset used in this study is Fire’18-MaponSMS. Thisstudy proposed an ensemble model based on AdaBoostM1 and Random Forest (AMBRF) for AP using multiplelinguistic features that are stylistic, character-based, word-based, and sentence-based. The proposed model iscontrasted with several of the well-known models fromthe literature, including J48-Decision Tree (J48),Na飗e Bays(NB), K Nearest Neighbor (KNN), and Composite Hypercube on Random Projection (CHIRP), NB-Updatable,RF, and AdaboostM1. The overall outcome shows the better performance of the proposed AdaboostM1 withRandom Forest (ABMRF) with an accuracy of 54.2857% for age prediction and 71.1429% for gender predictioncalculated on stylistic features. Regarding word-based features, age and gender were considered in 50.5714% and60%, respectively. On the other hand, KNN and CHIRP show the weakest performance using all the linguisticfeatures for age and gender prediction.

关键词： Digital forensics author profiling for security AdaBoostM1 random forest ensemble learning

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Modelling of debris-flow susceptibility and propagation: a case study from Northwest Himalaya

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Journal of Mountain Science 2024年第1期21卷 200-217页

作者： Hamza DAUD Javed Iqbal TANOLI Sardar muhammad ASIF muhammad QASIM muhammad ALI Junaid khan Zahid Imran BHATTI Ishtiaq Ahmad khan JADOON Badong National Observation and Research Station of Geohazards China University of GeosciencesWuhan 430074China Department of Earth Sciences COMSATS University IslamabadAbbottabad Campus 22010Pakistan Department of Earth Sciences Sultan Qaboos UniversityAl-KhoudMascat 123Oman Faculty of Engineering China University of Geosciences(Wuhan)Wuhan 430074China Department of Earth Sciences Abbottabad University of Science and TechnologyHavelian 22500Pakistan

The geological and geographical position of the Northwest Himalayas makes it a vulnerable area for mass movements particularly landslides and debris flows. Mass movements have had a substantial impact on the study area which is extending along Karakorum Highway(KKH) from Besham to Chilas. Intense seismicity, deep gorges, steep terrain and extreme climatic events trigger multiple mountain hazards along the KKH, among which debris flow is recognized as the most destructive geohazard. This study aims to prepare a field-based debris flow inventory map at a regional scale along a 200 km stretch from Besham to Chilas. A total of 117 debris flows were identified in the field, and subsequently, a point-based debris-flow inventory and catchment delineation were performed through Arc GIS analysis. Regional scale debris flow susceptibility and propagation maps were prepared using Weighted Overlay Method(WOM) and Flow-R technique sequentially. Predisposing factors include slope, slope aspect, elevation, Topographic Roughness Index(TRI), Topographic Wetness Index(TWI), stream buffer, distance to faults, lithology rainfall, curvature, and collapsed material layer. The dataset was randomly divided into training data(75%) and validation data(25%). Results were validated through the Receiver Operator Characteristics(ROC) curve. Results show that Area Under the Curve(AUC) using WOM model is 79.2%. Flow-R propagation of debris flow shows that the 13.15%, 22.94%, and 63.91% areas are very high, high, and low susceptible to debris flow respectively. The propagation predicated by Flow-R validates the naturally occurring debris flow propagation as observed in the field surveys. The output of this research will provide valuable input to the decision makers for the site selection, designing of the prevention system, and for the protection of current infrastructure.

关键词： North Pakistan Debris flow Flow-R Propagation Susceptibility mapping Debris-flow inventory Weighted Overlay Method

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Insight into the dynamics of non-Newtonian Carreau fluid when viscous dissipation,entropy generation,convective heating and diffusion are significant

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Applied Mathematics(A Journal of Chinese Universities) 2024年第1期39卷 34-46页

作者： ZHOU Shuang-shuang muhammad Ijaz khan Sami Ullah khan Sumaira Qayyum School of Science Hunan City UniversityYiyang 413000China Department of Mechanical Engineering Prince Mohammad Bin Fahd UniversityP.O.Box1664Al-Khobar 31952Kingdom of Saudi Arabia Department of Mathematics Namal UniversityMianwali 42250Pakistan Department of Mathematics Quaid-I-Azam University 45320Islamabad 44000Pakistan

The investigation endorsed the convective flow of Carreau nanofluid over a stretched surface in presence of entropy generation *** novel dynamic of viscous dissipation is utilized to analyze the thermal mechanism of magnetized *** convective boundary assumptions are directed in order to examine the heat and mass transportation of *** thermal concept of thermophoresis and Brownian movements has been re-called with the help of Buongiorno *** problem formulated in dimensionless form is solved by NDSolve *** graphical analysis for parameters governed by the problem is performed with physical *** affiliation of entropy generation and Bejan number for different parameters is inspected in *** numerical data for illustrating skin friction,heat and mass transfer rate is also *** motion of the fluid is highest for the viscosity ratio *** temperature of the fluid rises via thermal Biot *** generation rises for greater Brinkman number and diffusion parameter.

关键词： heat generation surface reaction CNTs based nano uid stretching/shrinking sheet thermal radiation

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Cervical Cancer Prediction Empowered with Federated Machine Learning

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Computers, Materials & Continua 2024年第4期79卷 963-981页

作者： muhammad Umar Nasir Omar Kassem Khalil Karamath Ateeq Bassam SaleemAllah Almogadwy M.A.khan khan muhammad Adnan Department of Computer Sciences Bahria University Lahore CampusLahore54000Pakistan Faculty of Information Technology Liwa CollegeAbu Dhabi20009UAE Department of Computing Skyline University CollegeSharjah999041UAE Department of Computer Science Taibah UniversityMedina42315Saudi Arabia Riphah School of Computing and Innovation Faculty of ComputingRiphah International UniversityLahore54000Pakistan Department of Software Faculty of Artificial Intelligence and SoftwareGachon UniversitySeongnam13120Korea

Cervical cancer is an intrusive cancer that imitates various women around the world. Cervical cancer ranks in thefourth position because of the leading death cause in its premature stages. The cervix which is the lower end of thevagina that connects the uterus and vagina forms a cancerous tumor very slowly. This pre-mature cancerous tumorin the cervix is deadly if it cannot be detected in the early stages. So, in this delineated study, the proposed approachuses federated machine learning with numerous machine learning solvers for the prediction of cervical cancer totrain the weights with varying neurons empowered fuzzed techniques to align the neurons, Internet of MedicalThings (IoMT) to fetch data and blockchain technology for data privacy and models protection from hazardousattacks. The proposed approach achieves the highest cervical cancer prediction accuracy of 99.26% and a 0.74%misprediction rate. So, the proposed approach shows the best prediction results of cervical cancer in its early stageswith the help of patient clinical records, and all medical professionals will get beneficial diagnosing approachesfrom this study and detect cervical cancer in its early stages which reduce the overall death ratio of women due tocervical cancer.

关键词： Cervical cancer federated machine learning neurons blockchain technology

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Deep Convolutional Neural Networks for Accurate Classification of Gastrointestinal Tract Syndromes

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Computers, Materials & Continua 2024年第1期78卷 1207-1225页

作者： Zahid Farooq khan muhammad Ramzan Mudassar Raza muhammad Attique khan Khalid Iqbal Taerang Kim Jae-Hyuk Cha Department of Computer Science COMSATS University IslamabadWah CampusWah47040Pakistan Department of Computer Science and Mathematics Lebanese American UniversityBeirut13-5053Lebanon Department of Computer Science HITEC UniversityTaxila47080Pakistan Department of Computer Science COMSATS University IslamabadAttock CampusAttock43600Pakistan Department of Computer Science Hanyang UniversitySeoul04763Korea

Accurate detection and classification of artifacts within the gastrointestinal(GI)tract frames remain a significant challenge in medical image *** science combined with artificial intelligence is advancing to automate the diagnosis and treatment of numerous *** to this is the development of robust algorithms for image classification and detection,crucial in designing sophisticated systems for diagnosis and *** study makes a small contribution to endoscopic image *** proposed approach involves multiple operations,including extracting deep features from endoscopy images using pre-trained neural networks such as Darknet-53 and ***,feature optimization utilizes the binary dragonfly algorithm(BDA),with the fusion of the obtained feature *** fused feature set is input into the ensemble subspace k nearest neighbors(ESKNN)*** Kvasir-V2 benchmark dataset,and the COMSATS University Islamabad(CUI)Wah private dataset,featuring three classes of endoscopic stomach images were *** assessments considered various feature selection techniques,including genetic algorithm(GA),particle swarm optimization(PSO),salp swarm algorithm(SSA),sine cosine algorithm(SCA),and grey wolf optimizer(GWO).The proposed model excels,achieving an overall classification accuracy of 98.25% on the Kvasir-V2 benchmark and 99.90% on the CUI Wah private *** approach holds promise for developing an automated computer-aided system for classifying GI tract syndromes through endoscopy images.

关键词： Feature fusion Darknet-53 Xception binary dragonfly algorithm ensemble

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