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Mapping favorable groundwater potential recharge zones using a GIS-based analytical hierarchical process and probability frequency ratio model:A case study from an agro-urban region of Pakistan

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Geoscience Frontiers 2020年第5期11卷 1805-1819页

作者： arfan Arshad Zhijie Zhang Wanchang Zhang Adil Dilawar Key Laboratory of Digital Earth Science Institute of Remote Sensing and Digital EarthChinese Academy of SciencesBeijing 100049China University of Chinese Academy of Sciences Beijing 100049China Department of Geography Center for Environmental Sciences and Engineering(CESE)University of ConnecticutStorrsCT06269-4148USA LREIS Institute of Geographic Sciences and Natural Resources ResearchChinese Academy of Sciences11ADatun RoadChaoyang DistrictBeijing 100101China

In Punjab(Pakistan),the increasing population and expansion of land use for agriculture have severely exploited the regional groundwater *** pumping has resulted in a rapid decline in the level of the water table as well as its *** management practices and artificial recharge are needed for the development of sustainable groundwater *** study proposes a methodology to delineate favorable groundwater potential recharge zones(FPRI)by integrating maps of groundwater potential recharge index(PRI)with the DRASTIC-based groundwater vulnerability index(VI).In order to evaluate both indexes,different thematic layers corresponding to each index were overlaid in *** the overlay analysis,the weights(for various thematic layers)and rating values(for sub-classes)were allocated based on a review of published *** were then normalized and modified using the analytical hierarchical process(AHP)and a frequency ratio model *** evaluating PRI and FPRI,these maps were validated using the area under the curve(AUC)*** PRI map indicates that 53%of the area assessed exists in very low to low recharge zones,22%in moderate,and 25%in high to excellent potential recharge *** VI map indicates that 38%of the area assessed exists in very low to low vulnerability,33%in moderate,and 29%in high to very high vulnerability *** FPRI map shows that the central region of Punjab is moderately-to-highly favorable for recharge due to its low vulnerability and high recharge *** the validation process,it was found that the AUC estimated with modified weights and rating values was 79%and 67%,for PRI and VI indexes,*** AUC was less when evaluated using original weights and rating values taken from published *** of favorable groundwater potential recharge zones are helpful for planning and implementation of wells and hydraulic structures in this region.

关键词： Weightage overlay Analytical hierarchical process(AHP)analysis Frequency ratio(FR) Vulnerability Groundwater recharge zones Area under curve(AUC)

Numerical Simulation of Reiner–Rivlin Nanofluid Flow under the Influence of Thermal Radiation and Activation Energy over a Rotating Disk

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Computer Modeling in Engineering & Sciences 2022年第6期131卷 1669-1692页

作者： arfan Shahzad muhammad Imran muhammad Nawaz Naeem Mohsan Raza Department of Mathematics Government College University FaisalabadFaisalabad38000Pakistan Department of Sciences&Humanities National University of Computer and Emerging SciencesChiniot-Faisalabad CampusFaisalabad38000Pakistan

In current study,the numerical computations of Reiner–Rivlin nanofluid flow through a rotational disk under the influence of thermal radiation and Arrhenius activation energy is *** innovative physical situations,the motile microorganisms are incorporated *** multiple slip effects are considered in the boundary *** bioconvection of motile microorganism is utilized alongside nanofluids to provide stability to enhanced thermal *** Bioconvection pattern in various nanoparticles accredits novel applications of biotechnology like the synthesis of biological polymers,biosensors,fuel cells,petroleum engineering,and the natural *** deploying some suitable similarity transformation functions,the governing partial differential equations(PDEs)of the flow problem are rehabilitated into dimensionless *** accomplished ordinary differential equations(ODEs)are solved numerically through the bvp4c scheme via a built-in function in computational MATLAB *** upshots of some prominent physical and bioconvection parameters including wall slip parameters,thermophoresis parameter,Brownian motion parameter,Reiner–Revlin nanofluid parameter,Prandtl number,Peclet number,Lewis number,bioconvection Lewis number,and the mixed convection parameter against velocity,temperature,nanoparticles concentration,and density of motile microorganism profiles are dichotomized and pondered through graphs and *** presented computations show that the velocity profiles are de-escalated by the wall slip parameters while the thermal and solutal fields are upgraded with augmentation in thermophoresis number and wall slip *** presence of thermal radiation enhances the temperature profile of *** concentration profile of nanoparticles is boosted by intensification in activation ***,the increasing values of bioconvection Lewis number and Peclet number decay the motile microorganisms’field.

关键词： Reiner-Rivlin nanofluid bioconvection motile swimming microorganisms rotational disk multiple slip conditions bvp4c

Fuzzy Based Hybrid Focus Value Estimation for Multi Focus Image Fusion

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Computers, Materials & Continua 2022年第4期71卷 735-752页

作者： muhammad Ahmad M.arfan Jaffar Fawad Nasim Tehreem Masood Sheeraz Akram The Superior University Lahore 54000Pakistan University of Pittsburgh 15213USA

Due to limited depth-of-field of digital single-lens reflex cameras,the scene content within a limited distance from the imaging plane remains in focus while other objects closer to or further away from the point of focus appear as blurred(out-of-focus)in the ***-Focus Image Fusion can be used to reconstruct a fully focused image from two or more partially focused images of the same *** this paper,a new Fuzzy Based Hybrid Focus Measure(FBHFM)for multi-focus image fusion has been *** block size is very critical step for multi-focus image *** Swarm Optimization(PSO)algorithm has been used to find optimal size of the block of the images for extraction of focus measure *** finding optimal blocks,three focus measures Sum of Modified Laplacian,Gray Level Variance and Contrast Visibility has been extracted and combined these focus measures by using intelligent fuzzy *** based hybrid intelligent focus values were estimated using contrast visibility measure to generate focused *** sets of multi-focus images have been used in detailed experimentation and compared the results with state-of-the-art existing techniques such as Genetic Algorithm(GA),Principal Component Analysis(PCA),Laplacian Pyramid discrete wavelet transform(DWT),and aDWT for image *** has been found that proposed method performs well as compare to existing methods.

关键词： Fuzzy logic multi-focus image fusion defocus focus contrast visibility focus measure

Facial Image-Based Autism Detection:A Comparative Study of Deep Neural Network Classifiers

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Computers, Materials & Continua 2024年第1期78卷 105-126页

作者： Tayyaba Farhat Sheeraz Akram Hatoon SAlSagri Zulfiqar Ali Awais Ahmad arfan Jaffar Faculty of Computer Science and Information Technology The Superior UniversityLahore54600Pakistan Intelligent Data Visual Computing Research(IDVCR) Faculty of Computer Science and Information TechnologyThe Superior UniversityLahore54600Pakistan Information Systems Department College of Computer and Information SciencesImam Mohammad Ibn Saud Islamic University(IMSIU)Riyadh12571Saudi Arabia School of Computer Science and Electronic Engineering(CSEE) University of EssexWivenhoe ParkColchesterCO43SQUK

Autism Spectrum Disorder(ASD)is a neurodevelopmental condition characterized by significant challenges in social interaction,communication,and repetitive *** and precise ASD detection is crucial,particularly in regions with limited diagnostic resources like *** study aims to conduct an extensive comparative analysis of various machine learning classifiers for ASD detection using facial images to identify an accurate and cost-effective solution tailored to the local *** research involves experimentation with VGG16 and MobileNet models,exploring different batch sizes,optimizers,and learning rate *** addition,the“Orange”machine learning tool is employed to evaluate classifier performance and automated image processing capabilities are utilized within the *** findings unequivocally establish VGG16 as the most effective classifier with a 5-fold cross-validation ***,VGG16,with a batch size of 2 and the Adam optimizer,trained for 100 epochs,achieves a remarkable validation accuracy of 99% and a testing accuracy of 87%.Furthermore,the model achieves an F1 score of 88%,precision of 85%,and recall of 90% on test *** validate the practical applicability of the VGG16 model with 5-fold cross-validation,the study conducts further testing on a dataset sourced fromautism centers in Pakistan,resulting in an accuracy rate of 85%.This reaffirms the model’s suitability for real-world ASD *** research offers valuable insights into classifier performance,emphasizing the potential of machine learning to deliver precise and accessible ASD diagnoses via facial image analysis.

关键词： Autism Autism Spectrum Disorder(ASD) disease segmentation features optimization deep learning models facial images classification

Intelligent Sound-Based Early Fault Detection System for Vehicles

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Computer Systems Science & Engineering 2023年第9期46卷 3175-3190页

作者： Fawad Nasim Sohail Masood arfan Jaffar Usman Ahmad muhammad Rashid Superior University LahorePakistan Intelligent Data Visual Computing Research(IDVCR) LahorePakistan National University of Technology IslamabadPakistan

An intelligent sound-based early fault detection system has been proposed for vehicles using machine *** system is designed to detect faults in vehicles at an early stage by analyzing the sound emitted by the *** detection and correction of defects can improve the efficiency and life of the engine and other mechanical *** system uses a microphone to capture the sound emitted by the vehicle and a machine-learning algorithm to analyze the sound and detect faults.A possible fault is determined in the vehicle based on this processed *** classification is done at the first stage to differentiate between faulty and healthy *** collected noisy and normal sound samples of the car engine under normal and different abnormal conditions from multiple workshops and verified the data from *** used the time domain,frequency domain,and time-frequency domain features to detect the normal and abnormal conditions of the vehicle *** used abnormal car data to classify it into fifteen other classical vehicle *** experimented with various signal processing techniques and presented the comparison *** the detection and further problem classification,random forest showed the highest results of 97%and 92%with time-frequency features.

关键词： Sound classification signal processing random forest random tree time-frequency domain J48

Nodule Detection Using Local Binary Pattern Features to Enhance Diagnostic Decisions

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Computers, Materials & Continua 2024年第3期78卷 3377-3390页

作者： Umar Rashid arfan Jaffar muhammad Rashid Mohammed S.Alshuhri Sheeraz Akram Faculty of Computer Science and Information Technology The Superior UniversityLahore54600Pakistan Intelligent Data Visual Computing Research(IDVCR) Lahore54600Pakistan Department of Computer Science National University of TechnologyIslamabad45000Pakistan Department of Radiological Sciences and Medical Imaging College of AppliedMedical SciencesPrince Sattam Bin Abdulaziz UniversityAl-Kharj16278Saudi Arabia Information Systems Department College of Computer and Information SciencesImam Mohammad Ibn Saud Islamic University(IMSIU)Riyadh12571Saudi Arabia

Pulmonary nodules are small, round, or oval-shaped growths on the lungs. They can be benign (noncancerous) or malignant (cancerous). The size of a nodule can range from a few millimeters to a few centimeters in diameter. Nodules may be found during a chest X-ray or other imaging test for an unrelated health problem. In the proposed methodology pulmonary nodules can be classified into three stages. Firstly, a 2D histogram thresholding technique is used to identify volume segmentation. An ant colony optimization algorithm is used to determine the optimal threshold value. Secondly, geometrical features such as lines, arcs, extended arcs, and ellipses are used to detect oval shapes. Thirdly, Histogram Oriented Surface Normal Vector (HOSNV) feature descriptors can be used to identify nodules of different sizes and shapes by using a scaled and rotation-invariant texture description. Smart nodule classification was performed with the XGBoost classifier. The results are tested and validated using the Lung Image Consortium Database (LICD). The proposed method has a sensitivity of 98.49% for nodules sized 3–30 mm.

关键词： Pulmonary nodules segmentation histogram thresholding

Lung Cancer Detection Using Modified AlexNet Architecture and Support Vector Machine

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Computers, Materials & Continua 2023年第1期74卷 2039-2054页

作者： Iftikhar Naseer Tehreem Masood Sheeraz Akram arfan Jaffar muhammad Rashid muhammad Amjad Iqbal Faculty of Computer Science and Information Technology The Superior UniversityLahore54000Pakistan Department of Computer Science National University of TechnologyIslamabad45000Pakistan Faculty of IT University of Central PunjabLahore54100Pakistan

Lung cancer is the most dangerous and death-causing disease indicated by the presence of pulmonary nodules in the *** is mostly caused by the instinctive growth of cells in the *** nodule detection has a significant role in detecting and screening lung cancer in Computed tomography(CT)scan *** detection plays an important role in the survival rate and treatment of lung cancer ***,pulmonary nodule classification techniques based on the convolutional neural network can be used for the accurate and efficient detection of lung *** work proposed an automatic nodule detection method in CT images based on modified AlexNet architecture and Support vector machine(SVM)algorithm namely *** proposed model consists of seven convolutional layers,three pooling layers,and two fully connected layers used to extract *** vector machine classifier is applied for the binary classification of nodules into benign *** experimental analysis is performed by using the publicly available benchmark dataset Lung nodule analysis 2016(LUNA16).The proposed model has achieved 97.64%of accuracy,96.37%of sensitivity,and 99.08%of specificity.A comparative analysis has been carried out between the proposed LungNet-SVM model and existing stateof-the-art approaches for the classification of lung *** experimental results indicate that the proposed LungNet-SVM model achieved remarkable performance on a LUNA16 dataset in terms of accuracy.

关键词： Lung cancer alexnet luna16 computed tomography support vector machine

A Multi-Modal Deep Learning Approach for Emotion Recognition

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Intelligent Automation & Soft Computing 2023年第5期36卷 1561-1570页

作者： H.M.Shahzad Sohail Masood Bhatti arfan Jaffar muhammad Rashid The Superior University LahorePakistan National University of Technology IslamabadPakistan Intelligent Data Visual Computing Research(IDVCR) LahorePakistan

In recent years,research on facial expression recognition(FER)under mask is *** a mask for protection from Covid 19 has become a compulsion and it hides the facial expressions that is why FER under the mask is a difﬁcult *** prevailing unimodal techniques for facial recognition are not up to the mark in terms of good results for the masked face,however,a multi-modal technique can be employed to generate better *** proposed a multi-modal methodology based on deep learning for facial recognition under a masked face using facial and vocal *** multimodal has been trained on a facial and vocal *** have used two standard datasets,M-LFW for the masked dataset and CREMA-D and TESS dataset for vocal *** vocal expressions are in the form of audio while the faces data is in image form that is why the data is *** order to make the data homogeneous,the voice data is converted into images by taking spectrogram.A spectrogram embeds important features of the voice and it converts the audio format into the ***,the dataset is passed to the multimodal for *** network and the experimental results demonstrate that the proposed multimodal algorithm outsets unimodal methods and other state-of-the-art deep neural network models.

关键词： Deep learning facial expression recognition multi-model neural network speech emotion recognition spectrogram covid-19

A facile synthesis of novel unsymmetrical N-(4-oxo-2-phenyl-3(4H)-quinazolinoyl)-N-(aryl) acetamidines

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Chinese Chemical Letters 2010年第8期21卷 905-910页

作者： Rasool Khan Mohammad arfan Javeed Mahmood Shazia Anjum M.Iqbal Choudhary Department of Chemistry Abdul Wali Khan UniversityMardan-23200NWFPPakistan Institute of Chemical Sciences University of PeshawarPeshawar-25120Pakistan University of Saskatchewan Department of ChemistrySaskatoon S7N5C9Canada HEJRIC International Center for Chemical and Biological SciencesUniversity of KarachiKarachi-75270Pakistan

A series of novel unsymmetrical N-（4-oxo-2-phenyl-3（4H）-quinazolinoyl）-N-（aryl） acetamidines was synthesized by reacting ethyl（1H）-N-（4-oxo-2-phenylquinazolin-3（4H）-y1）ethanimidoate（2） and suitable reactive aromatic ***＇ determination of the synthesized compounds was carried out using spectroscopic techniques including IR,~1H NMR,and mass spectrometry. Structural effects on reactivity were also studied.

关键词： 3-Amino-2-phenyl-4(3H)-quinazolinone Triethylorthoacetate Unsymmetrical N-(4-oxo-2-phenyl-3(4H)-quinazolinoyl)-N-(aryl) acetamidines