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Plasma Science and Technology 2021年第3期23卷 76-81页

作者： muhammad KHALID Ghufran ULLAH Mohsin khan Sheraz AHMAD Sardar NABI Daud khan Depatment of Physics Govermment Post Gaduate College MardanMardan 23200Pakistan Department of Physics Islamia College Peshawar(Public Sector University)Peshawar 25120Pakistan Department of Physics Government Post Graduate College NowsheraNowshera 24100Pakistan

Theoretical investigation of nonlinear electrostatic ion-acoustic cnoidal waves(IACWs) is presented in magnetized electron–positron–ion plasma with nonextensive electrons and Maxwellian positrons. Using reductive perturbation technique, Korteweg–de Vries equation is derived and its cnoidal wave solution is analyzed. For given plasma parameters, our model supports only positive potential(compressive) IACW structures. The effect of relevant plasma parameters(viz., nonextensive parameter q, positron concentration p, temperature ratio σ,obliqueness l3) on the characteristics of IACWs is discussed in detail.

关键词： ion-acoustic cnoidal waves KdV equation q-distribution

IoMT-Based Smart Monitoring Hierarchical Fuzzy Inference System for Diagnosis of COVID-19

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Computers, Materials & Continua 2020年第12期65卷 2591-2605页

作者： Tahir Abbas khan Sagheer Abbas Allah Ditta muhammad Adnan khan Hani Alquhayz Areej Fatima muhammad Farhan khan Department of Computer Science National College of Business Administration and EconomicsLahore54000Pakistan Department of Information Sciences Division of Science&TechnologyUniversity of EducationLahore54000Pakistan Department of Computer Science Lahore Garrison UniversityLahore54000Pakistan Department of Computer Science and Information College of Science in ZulfiMajmaah UniversityAl-Majmaah11952Saudi Arabia Department of Forensic Sciences University of Health SciencesLahore54000Pakistan

The prediction of human diseases,particularly COVID-19,is an extremely challenging task not only for medical experts but also for the technologists supporting them in diagnosis and *** deal with the prediction and diagnosis of COVID-19,we propose an Internet of Medical Things-based Smart Monitoring Hierarchical Mamdani Fuzzy Inference System(IoMTSM-HMFIS).The proposed system determines the various factors like fever,cough,complete blood count,respiratory rate,Ct-chest,Erythrocyte sedimentation rate and C-reactive protein,family history,and antibody detection(lgG)that are directly involved in *** expert system has two input variables in layer 1,and seven input variables in layer *** layer 1,the initial identification for COVID-19 is considered,whereas in layer 2,the different factors involved are ***,advanced lab tests are conducted to identify the actual current status of the *** major focus of this study is to build an IoMT-based smart monitoring system that can be used by anyone exposed to COVID-19;the system would evaluate the user’s health condition and inform them if they need consultation with a specialist for ***-2019a tool is used to conduct the *** COVID-19 IoMTSM-HMFIS system has an overall accuracy of approximately 83%.Finally,to achieve improved performance,the analysis results of the system were shared with experts of the Lahore General Hospital,Lahore,Pakistan.

关键词： IoMT MERS-COV Ct-chest ESR/CRP ABD(lgG) Fuzzy logic HMFIS WHO

Data Fusion Architecture Empowered with Deep Learning for Breast Cancer Classification

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Computers, Materials & Continua 2023年第12期77卷 2813-2831页

作者： Sahar Arooj muhammad Farhan khan Tariq Shahzad muhammad Adnan khan muhammad Umar Nasir muhammad Zubair Atta-ur-Rahman Khmaies Ouahada Faculty of Computing Riphah International UniversityIslamabad45000Pakistan Department of Forensic Sciences University of Health SciencesLahore54000Pakistan Department of Electrical and Electronic Engineering Science University of JohannesburgP.O.Box 524Johannesburg2006South Africa School of Computing Skyline University CollegeUniversity City SharjahSharjah1797United Arab Emirates Riphah School of Computing and Innovation Faculty of ComputingRiphah International UniversityLahore CampusLahore54000Pakistan Department of Software Faculty of Artificial Intelligence&SoftwareGachon UniversitySeongnamGyeonggido13120Korea Department of Computer Science Bahria UniversityLahore CampusLahore54000Pakistan Department of Computer Science College of Computer Science and Information Technology(CCSIT)Imam Abdulrahman Bin Faisal University(IAU)P.O.Box 1982Dammam31441Saudi Arabia

Breast cancer(BC)is the most widespread tumor in females worldwide and is a severe public health *** is the leading reason of death affecting females between the ages of 20 to 59 around the *** detection and therapy can help women receive effective treatment and,as a result,decrease the rate of breast cancer *** cancer tumor develops when cells grow improperly and attack the healthy tissue in the human *** are classified as benign or malignant,and the absence of cancer in the breast is considered *** learning,machine learning,and transfer learning models are applied to detect and identify cancerous tissue like *** research assists in the identification and classification of *** implemented the pre-trained model AlexNet and proposed model Breast cancer identification and classification(BCIC),which are machine learning-based models,by evaluating them in the form of comparative *** used 3 datasets,A,B,and *** fuzzed these datasets and got 2 datasets,A2C and *** A2C is the fusion of A,B,and C with 2 classes categorized as benign and *** B3C is the fusion of datasets A,B,and C with 3 classes classified as benign,malignant,and *** used customized AlexNet according to our datasets and BCIC in our proposed *** achieved an accuracy of 86.5%on Dataset B3C and 76.8%on Dataset A2C by using AlexNet,and we achieved the optimum accuracy of 94.5%on Dataset B3C and 94.9%on Dataset A2C by using proposed model BCIC at 40 epochs with 0.00008 learning *** proposed fuzzed dataset model using transfer *** fuzzed three datasets to get more accurate results and the proposed model achieved the highest prediction accuracy using fuzzed dataset transfer learning technique.

关键词： Breast cancer classification deep learning machine learning transfer learning learning rate

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

Severity Recognition of Aloe vera Diseases Using AI in Tensor Flow Domain

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Computers, Materials & Continua 2021年第2期66卷 2199-2216页

作者： Nazeer muhammad Rubab Nargis Bibi Oh-Young Song muhammad Attique khan Sajid Ali khan Department of IT&CS Pak-Austria Fachhochschule Institute of Applied Sciences and TechnologyHaripur22620Pakistan COMSATS University Islamabad Wah CampusWah Cantt47040Pakistan Fatima Jinnah Women University Rawalpindi44000Pakistan Software Department Sejong UniversitySeoulSouth Korea Department of Computer Science HITEC UniversityTaxila47080Pakistan Department of Software Engineering Foundation University IslamabadIslamabadPakistan

Agriculture plays an important role in the economy of all ***,plant diseases may badly affect the quality of food,production,and ultimately the *** plant disease detection and management,agriculturalists spend a huge amount of ***,the manual detection method of plant diseases is complicated and ***,automated systems for plant disease detection using machine learning(ML)approaches are ***,most of the existing ML techniques of plants diseases recognition are based on handcrafted features and they rarely deal with huge amount of input *** address the issue,this article proposes a fully automated method for plant disease detection and recognition using deep neural *** the proposed method,AlexNet and VGG19 CNNs are considered as pre-trained *** is capable to obtain the feature extraction of the given data with fine-tuning *** convolutional neural network feature extraction,it selects the best subset of features through the correlation coefficient and feeds them to the number of classifiers including K-Nearest Neighbor,Support Vector Machine,Probabilistic Neural Network,Fuzzy logic,and Artificial Neural *** validation of the proposed method is carried out on a self-collected dataset generated through the augmentation *** achieved average accuracy of our method is more than 96%and outperforms the recent techniques.

关键词： Plants diseases wavelet transform fast algorithm deep learning feature extraction classification

Satisfactory Model Predictive Control for a Hybrid Single-phase Seven-level Converter

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CSEE Journal of Power and Energy Systems 2021年第5期7卷 1102-1112页

作者： Mohamed Tamim Touati Shaoyuan Li Jing Wu muhammad Ali muhammad Mansoor khan Department of Automation SEIEEShanghai Jiao Tong UniversityShanghai 200240China Department of Electrical Engineering.SEIEE Shanghai Jiao Tong UniversityShanghai 200240China.

Today,many hybrid multilevel converters with flying capacitors are being *** the practicality of these converters in reducing the switching devices count and the cost,it is challenging to balance their capacitor voltage *** unbalanced capacitors restrain the converters’performance and lead to failure in delivering the required *** paper proposes a model predictive control(MPC)for a seven-level converter based on an active neutral point clamped converter cascaded with an H-bridge.A conventional MPC uses a single cost function with two terms,one for current tracking and the other for capacitor balancing,which needs an accurate weighting factor to balance *** this work,the suggested MPC adopts a satisfactory optimization *** evaluates the capacitor charge membership to a nominal voltage range to define the degree of freedom in which to optimize the current tracking *** transforms the relationship between the two cost terms into a more collaborative *** proposed MPC improves the output current quality and balances the capacitor charge with the least number of *** and simulation results have validated the controller^effectiveness.

关键词： Hybrid converter model predictive control multilevel converter satisfactory optimization

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

Quality and Crop Yield Potential of Moderately Degraded Alfisols Under Different Nutrient Inputs and Cropping Patterns

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Pedosphere 2019年第2期29卷 235-247页

作者： Wiqar AHMAD Farmanullah khan Zahir SHAH muhammad Jamal khan Department of Soil and Environmental Sciences The University of Agriculture PeshawarAMK Campus Department of Soil and Environmental Sciences The University of AgriculturePeshawar

Crop performance on degraded soil needs special management practices to overcome soil quality limitations. In a 2-year(from summer 2006 to winter 2007–2008) field trial on a moderately degraded Alfisol in Swabi District(34°7′12′′N, 72°28′20′′E), Pakistan,the effects of three cropping patterns, cereal-cereal(CC), cereal-legume(CL), and cereal-cereal and legume intercrop(CLI), were tested in main plots under four fertilization treatments in sub-plots, including no fertilization(control), farmers' practice(FP, 60:45 kg ha^(-1) N:P_2O_5), recommended dose(RD, 120:90:60 kg ha^(-1) N:P_2O_5:K_2O), and integrated nutrient management(INM, 20 t ha^(-1) farmyard manure integrated with 50% N, 100% P, and K of recommended dose), using a split-plot randomized complete block design. The performance of CL was superior than CC in plant height, leaf area index(LAI), cob length, grain yield, biological yield, and grain protein(8%, 26%, 8%, 5%, 10%, and 8% increases, respectively), while CLI confirmed significant improvement only in LAI(25%) over CC. Response to nutrient inputs from all sources was in the order of INM > RD > FP > control, and the maximum net economic return by INM(23% and 2.5 times higher than RD and FP, respectively) indicated severe deficiency of both macro-and micro-nutrients in the soil as well as degraded physical properties. Increases in soil organic matter, total N, total mineral N, available P and K, total porosity, and available water-holding capacity by 6%, 34%, 24%, 50%, 13%, 5%, and 7%, respectively, and decrease in soil bulk density by 4% after four crop seasons indicated optimistic changes in soil quality as a result of the combined effects of fertilization from organic and inorganic sources and legumes within crop rotation. This study suggests that keeping the soil covered under cereal-legume rotation crops all year round and treatment with INM(50% N from organic source and 50% from inorganic source) are the best management practice for sustained produc

关键词： crop rotation crop performance degraded soil farmyard manure integrated nutrient management legume intercrop sustained production

Advances in wireless sensor networks under AI-5 G for augmented reality

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Virtual Reality & Intelligent Hardware 2022年第3期4卷 I0001-I0003页

作者： muhammad khan Visual Analytics for Knowledge Laboratory(VIS 2 KNOW Lab) Department of Applied Artificial IntelligenceSchool of ConvergenceCollege of Computing and InformaticsSungkyunkwan UniversitySeoul 03063Republic of Korea

Due to the recent rapid development in the 5 G technology,the usage of sensor networks especially wireless sensor networks(WSNs)has boosted advances in the augmented reality(AR),supporting decision making in AR *** decision-making needs support and consideration of artificial intelligence(AI)techniques capable of adapting to changes in AR environments for creating systems that evolve autonomously over ***,it is important to apply new information fusion techniques that allow for the processing of information at low and high levels to improve the accuracy of such systems.

关键词： networks evolve sensor

Integrated CWT-CNN for Epilepsy Detection Using Multiclass EEG Dataset

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Computers, Materials & Continua 2021年第10期69卷 471-486页

作者： Sidra Naseem Kashif Javed muhammad Jawad khan Saddaf Rubab muhammad Attique khan Yunyoung Nam Robotics and Machine Intelligence Engineering School of Mechanical and Manufacturing EngineeringNational University of Sciences and Technology(NUST)Islamabad44000Pakistan National University of Sciences and Technology(NUST) Islamabad44000Pakistan Department of Computer Science HITEC University TaxilaTaxilaPakistan Department of Computer Science and Engineering Soonchunhyang UniversityAsanKorea

Electroencephalography is a common clinical procedure to record brain signals generated by human *** are useful in Brain controlled interfaces and other intelligent Neuroscience applications,but manual analysis of these brainwaves is complicated and time-consuming even for the experts of *** EEG analysis and classification techniques have been proposed to address this problem however,the conventional classification methods require identification and learning of specific EEG characteristics *** learning models can learn features from data without having in depth knowledge of data and prior feature *** of the great implementations of deep learning is Convolutional Neural Network(CNN)which has outperformed traditional neural networks in pattern recognition and image *** Wavelet Transform(CWT)is an efficient signal analysis technique that presents the magnitude of EEG signals as timerelated Frequency *** deep learning architectures suffer from poor performance when classifying EEG signals in the Time-frequency *** improve classification accuracy,we propose an integrated CWT and CNN technique which classifies five types of EEG signals *** compared the results of proposed integrated CWT and CNN method with existing deep learning models e.g.,GoogleNet,VGG16,***,the accuracy and loss of the proposed integrated CWT and CNN method have been cross validated using Kfold cross *** average accuracy and loss of Kfold cross-validation for proposed integrated CWT and CNN method are,76.12%and 56.02%*** model produces results on a publicly available dataset:Epilepsy dataset by UCI(Machine Learning Repository).

关键词： Deep learning electroencephalography epilepsy continuous wavelet transform