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Chinese Physics B 2016年第11期25卷 360-368页

作者： tanzila hayat S Nadeem Department of Mathematics Quaid-I-Azam University

This paper presents the buoyancy effects on the magneto-hydrodynamics stagnation point flow of an incompressible,viscous,and electrically conducting nanofluid over a vertically stretching *** impacts of an induced magnetic field and viscous dissipation are taken into *** assisting and opposing flows are *** overseeing nonlinear partial differential equations with the associated boundary conditions are reduced to an arrangement of coupled nonlinear ordinary differential equations utilizing similarity transformations and are then illuminated analytically by using the optimal homotopy investigation strategy（OHAM）.Graphs are introduced and examined for different parameters of the velocity,temperature,and concentration ***,numerical estimations of the skin friction,local Nusselt number,and local Sherwood number are explored using numerical values.

关键词： stretching electrically incompressible viscous dissipation ordinary conducting sheet hydrodynamics arrangement

Novel Feature Extractor Framework in Conjunction with Supervised Three Class-XGBoost Algorithm for Osteosarcoma Detection from Whole Slide Medical Histopathology Images

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Computers, Materials & Continua 2025年第2期82卷 3337-3353页

作者： tanzila Saba Muhammad Mujahid Shaha Al-Otaibi Noor Ayesha Amjad Rehman Khan Artificial Intelligence&Data Analytics Lab College of Computer&Information SciencesPrince Sultan UniversityRiyadh11586Saudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh11671Saudi Arabia Center of Excellence in Cyber Security(CYBEX) Prince Sultan UniversityRiyadh11586Saudi Arabia

Osteosarcomas are malignant neoplasms derived from undifferentiated osteogenic mesenchymal cells. It causes severe and permanent damage to human tissue and has a high mortality rate. The condition has the capacity to occur in any bone;however, it often impacts long bones like the arms and legs. Prompt identification and prompt intervention are essential for augmenting patient longevity. However, the intricate composition and erratic placement of osteosarcoma provide difficulties for clinicians in accurately determining the scope of the afflicted area. There is a pressing requirement for developing an algorithm that can automatically detect bone tumors with tremendous accuracy. Therefore, in this study, we proposed a novel feature extractor framework associated with a supervised three-class XGBoost algorithm for the detection of osteosarcoma in whole slide histopathology images. This method allows for quicker and more effective data analysis. The first step involves preprocessing the imbalanced histopathology dataset, followed by augmentation and balancing utilizing two techniques: SMOTE and ADASYN. Next, a unique feature extraction framework is used to extract features, which are then inputted into the supervised three-class XGBoost algorithm for classification into three categories: non-tumor, viable tumor, and non-viable tumor. The experimental findings indicate that the proposed model exhibits superior efficiency, accuracy, and a more lightweight design in comparison to other current models for osteosarcoma detection.

关键词： Medical image processing deep learning healthcare image classification histopathology

Dynamic transcriptional programs define distinct mammalian cortical lineages

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Neural Regeneration Research 2024年第2期19卷 387-389页

作者： tanzila Mukhtar Verdon Taylor Department of Biomedicine University of BaselBaselSwitzerland Eli and Edythe Broad Center of Regeneration Medicine and Stem Cell Research University of CaliforniaSan FranciscoCAUSA

The cerebral cortex is composed of billions of neurons and glia that are generated sequentially during *** cells are generated in an organized fashion during *** early stages of brain development,neural stem cells(NSC... 详细信息

关键词： cortex cerebral development

Detection of Copy-Move Forgery in Digital Images Using Singular Value Decomposition

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Computers, Materials & Continua 2023年第2期74卷 4135-4147页

作者： Zaid Nidhal Khudhair Farhan Mohamed Amjad Rehman tanzila Saba Saeed Ali bahaj Faculty of Engineering School of ComputingUniversity Technology of MalaysiaJohar BahruMalaysia UTM-IRDA MaGICX Institute of Human Centered EngineeringUniversiti Teknologi MalaysiaMalaysia Artificial Intelligence&Data Analytics Lab CCIS Prince Sultan University Riyadh Saudi Arabia Computer Techniques Engineering Department Faculty of Information TechnologyImam Ja’afar Al-Sadiq UniversityBaghdadIraq

This paper presents an improved approach for detecting copy-move forgery based on singular value decomposition(SVD).It is a block-based method where the image is scanned from left to right and top to down by a sliding window with a determined *** each step,the SVD is ***,the diagonal matrix’s maximum value(norm)is selected(representing the scaling factor for SVD and a fixed value for each set of matrix elements even when rotating thematrix or scaled).Then,the similar norms are grouped,and each leading group is separated into many subgroups(elements of each subgroup are neighbors)according to 8-adjacency(the subgroups for each leading group must be far from others by a specific distance).After that,a weight is assigned for each subgroup to classify the image as forgery or ***,the F1 score of the proposed system is measured,reaching 99.1%.This approach is robust against rotation,scaling,noisy images,and illumination *** is compared with other similarmethods and presents very promised results.

关键词： Forgery image forensic image processing region duplication SVD transformation technological development

A New Hybrid SARFIMA-ANN Model for Tourism Forecasting

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Computers, Materials & Continua 2022年第6期71卷 4785-4801页

作者： tanzila Saba Mirza Naveed Shahzad Sonia Iqbal Amjad Rehman Ibrahim Abunadi Artificial Intelligence&Data Analytics Research Lab(AIDA) CCIS Prince Sultan UniversityRiyadh11586Saudi Arabia Department of Statistics University of GujratGujrat50700Pakistan Department of Statistics University of SialkotSialkot51310Pakistan

Many countries developed and increased greenery in their country sights to attract international *** planning is now significantly contributing to their *** next task is to facilitate the tourists by sufficient arrangements and providing a green and clean environment;it is only possible if an upcoming number of tourists’arrivals are accurately *** accurate prediction is not easy as empirical evidence shows that the tourists’arrival data often contains linear,nonlinear,and seasonal *** traditional model,like the seasonal autoregressive fractional integrated moving average(SARFIMA),handles seasonal trends with *** contrast,the artificial neural network(ANN)model deals better with nonlinear time *** get a better forecasting result,this study combines the merits of the SARFIMA and the ANN models and the purpose of the hybrid SARFIMA-ANN ***,we have used the proposed model to predict the tourists’arrival inNew Zealand,Australia,and *** results showed that the proposed hybrid model outperforms in predicting tourists’arrival compared to the traditional SARFIMA and ANN ***,these results can be generalized to predict tourists’arrival in any country or region with a complicated data pattern.

关键词： SARFIMA hybrid model tourists’arrival forecasting ANN

Densely Convolutional BU-NET Framework for Breast Multi-Organ Cancer Nuclei Segmentation through Histopathological Slides and Classification Using Optimized Features

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Computer Modeling in Engineering & Sciences 2024年第12期141卷 2375-2397页

作者： Amjad Rehman Muhammad Mujahid Robertas Damasevicius Faten S.Alamri tanzila Saba Artificial Intelligence&Data Analytics Lab College of Computer&Information Sciences(CCIS)Prince Sultan UniversityRiyadh11586Saudi Arabia Centre of Real Time Computer Systems Kaunas University of TechnologyKaunasLT-51386Lithuania Department of Mathematical Sciences College of SciencePrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia

This study aims to develop a computational pathology approach that can properly detect and distinguish histology *** is crucial for histopathological image analysis,as it involves segmenting cell ***,challenges exist,such as determining the boundary region of normal and deformed nuclei and identifying small,irregular nuclei *** learning approaches are currently dominant in digital pathology for nucleus recognition and classification,but their complex features limit their practical use in clinical *** existing studies have limited accuracy,significant processing costs,and a lack of resilience and generalizability across diverse *** proposed the densely convolutional Breast U-shaped Network(BU-NET)framework to overcome the mentioned *** study employs BU-NET’s spatial and channel attention methods to enhance segmentation *** inclusion of residual blocks and skip connections in the BU-NEt architecture enhances the process of extracting features and reconstructing the *** enhances the robustness of training and convergence processes by reducing the occurrence of vanishing *** primary objective of BU-NEt is to enhance the model’s capacity to acquire and analyze more intricate features,all the while preserving an efficient working *** BU-NET experiments demonstrate that the framework achieved 88.7%average accuracy,88.8%F1 score for Multi-Organ Nuclei Segmentation Challenge(MoNuSeg),and 91.2%average accuracy,91.8%average F1 for the triple-negative breast cancer(TNBC)*** framework also achieved 93.92 Area under the ROC Curve(AUC)for *** results demonstrated that the technology surpasses existing techniques in terms of accuracy and effectiveness in ***,it showcases the ability to withstand and recover from different tissue types and diseases,indicating possible uses in medical *** research evaluated the efficacy of the proposed method

关键词： Breast cancer histopathology BU-NET deep learning

Crowd region detection in outdoor scenes using color spaces

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International Journal of Modeling, Simulation, and Scientific Computing 2018年第2期9卷 55-69页

作者： Huma Chaudhry Mohd Shafry Mohd Rahim tanzila Saba Amjad Rehman Faculty of Computing University Technology Malaysia 81310 JohorMalaysia College of Computer and Information Sciences Prince Sultan UniversityRiyadh 11586Saudi Arabia College of Computer and Information Systems Al-Yamamah University Riyadh 11512 Saudi Arabia

In the last few decades,crowd detection has gained much interest from the research community to assist a variety of applications in surveillance *** human detection in partially crowded scenarios have achieved many reliable works,a highly dense crowdlike situation still is far from being *** crowded scenes offer patterns that could be used to tackle these *** problem is challenging due to the crowd volume,occlusions,clutter and *** region classification is a precursor to several types of *** this paper,we propose a novel approach for crowd region detection in outdoor densely crowded scenarios based on color variation context and RGB channel *** results are presented to demonstrate the effectiveness of the new color-based features for better crowd region detection.

关键词： Crowd detection color features segmentation detection.

Swarm-LSTM: Condition Monitoring of Gearbox Fault Diagnosis Based on Hybrid LSTM Deep Neural Network Optimized by Swarm Intelligence Algorithms

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Computers, Materials & Continua 2021年第2期66卷 2041-2059页

作者： Gopi Krishna Durbhaka Barani Selvaraj Mamta Mittal tanzila Saba Amjad Rehman Lalit Mohan Goyal School of Electrical and Electronics Sathyabama Institute of Science and TechnologyChennai600119India Department of Computer Science and Engineering G.B.Pant Government Engineering CollegeNew Delhi110020India Artificial Intelligence and Data Analytics(AIDA)Laboratory CCISPrince Sultan UniversityRiyadh11586Saudi Arabia Department of Computer Engineering J.C.Bose University of Science and TechnologyFaridabad121006India

Nowadays,renewable energy has been emerging as the major source of energy and is driven by its aggressive expansion and falling *** of the renewable energy sources involve turbines and their operation and maintenance are vital and a difficult *** monitoring and fault diagnosis have seen remarkable and revolutionary up-gradation in approaches,practices and technology during the last *** mostly do use a rotating type of machinery and analysis of those signals has been challenging to localize the *** paper proposes a new hybrid model wherein multiple swarm intelligence models have been evaluated to optimize the conventional Long Short-Term Memory(LSTM)model in classifying the faults from the vibration signals data acquired from the *** helps to analyze the performance and behavioral patterns of the system more effectively and efficiently which helps to suggest for replacement of the unit with higher *** results have demonstrated that the proposed hybrid modeling approach is effective in classifying the faults of the gearbox from the time series data and achieve higher diagnostic accuracy in comparison to the conventional LSTM methods.

关键词： Gearbox long short term memory fault classification swarm intelligence optimization condition monitoring

EfficientNetB1 Deep Learning Model for Microscopic Lung Cancer Lesion Detection and Classification Using Histopathological Images

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Computers, Materials & Continua 2024年第10期81卷 809-825页

作者： Rabia Javed tanzila Saba Tahani Jaser Alahmadi Sarah Al-Otaibi Bayan AlGhofaily Amjad Rehman Department of Computer Science Lahore College for Women UniversityLahore54000Pakistan Artificial Intelligence&Data Analytics Lab. CCIS Prince Sultan UniversityRiyadh11586Saudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science College of Computer and Information SciencesKing Saud UniversityRiyadh145111Saudi Arabia

Cancer poses a significant threat due to its aggressive nature,potential for widespread metastasis,and inherent heterogeneity,which often leads to resistance to *** cancer ranks among the most prevalent forms of cancer worldwide,affecting individuals of all *** and accurate lung cancer detection is critical for improving cancer patients’treatment outcomes and survival *** examinations for lung cancer detection,however,frequently fall short of detecting small polyps and *** address these limitations,computer-aided techniques for lung cancer detection prove to be invaluable resources for both healthcare practitioners and patients *** research implements an enhanced EfficientNetB1 deep learning model for accurate detection and classification using histopathological *** proposed technique accurately classifies the histopathological images into three distinct classes:(1)no cancer(benign),(2)adenocarcinomas,and(3)squamous cell *** evaluated the performance of the proposed technique using the histopathological(LC25000)lung *** preprocessing steps,such as image resizing and augmentation,are followed by loading a pretrained model and applying transfer *** dataset is then split into training and validation sets,with fine-tuning and retraining performed on the training *** model’s performance is evaluated on the validation dataset,and the results of lung cancer detection and classification into three classes are *** study’s findings show that an enhanced model achieves exceptional classification accuracy of 99.8%.

关键词： Colon cancer EfficientNetB1 histopathological image processing transfer learning health risks

A Hybrid Deep Learning Architecture for the Classification of Superhero Fashion Products:An Application for Medical-Tech Classification

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Computer Modeling in Engineering & Sciences 2020年第9期124卷 1017-1033页

作者： Inzamam Mashood Nasir Muhammad Attique Khan Majed Alhaisoni tanzila Saba Amjad Rehman Tassawar Iqbal Department of Computer Science HITEC UniversityTaxilaPakistan College of Computer Science and Engineering University of Ha’ilHa’ilSaudi Arabia College of Computer and Information Sciences Prince Sultan UniversityRiyadhSaudi Arabia Department of Computer Science COMSATS University IslamabadWah CampusIslamabadPakistan

Comic character detection is becoming an exciting and growing research area in the domain of machine *** this regard,recently,many methods are proposed to provide adequate ***,most of these methods utilized the custom datasets,containing a few hundred images and fewer classes,to evaluate the performances of their models without comparing it,with some standard *** article takes advantage of utilizing a standard publicly dataset taken from a competition,and proposes a generic data balancing technique for imbalanced dataset to enhance and enable the in-depth training of the *** addition,to classify the superheroes efficiently,a custom 17-layer deep convolutional neural network is also *** computed results achieved overall classification accuracy of 97.9%which is significantly superior to the accuracy of competition’s winner.

关键词： Superheroes deep convolutional neural network data augmentation transfer learning machine learning