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Women in data Science at Prince Sultan University (WiDS PSU), International Conference of

作者： Fetoun AlZahrani Arwa Ghouti Tanzila Saba Artificial Intelligence and Data Analytics (AIDA) Lab CCIS Prince Sultan University Riyadh Kingdom of Saudi Arabia

ISBN: (数字)9798331520922

ISBN: (纸本)9798331520939

With the increasing prevalence of computer usage, there has been a rise in cases of repetitive strain injuries (RSIs) like Carpal Tunnel Syndrome (CTS) among individuals who spend extended hours typing. Hand pose estimation plays a vital role in improving ergonomic and safe human-computer interaction (HCI). To address this challenge, we propose ErgoType, a real-time hand pose estimation application developed for Apple WatchOS. The application delivers posture classification results directly on the Apple Watch and integrates with a companion iPhone application for detailed feedback. The system employs a Support Vector Machine (SVM) model trained on smartwatch emulator data to analyze key hand-position indicators, such as wrist and elbow angles, achieving a classification accuracy of 98%. Immediate feedback is provided via on-screen notifications, alerting users to poor typing habits such as excessive wrist flexion or prolonged typing without rest. This feedback encourages users to adopt healthier typing practices. ErgoType's combination of wearable technology and real-time feedback offers a proactive solution for reducing typing-related injuries and promoting workplace wellness. It is designed to support both professionals and frequent computer users, enhancing productivity and well-being through improved ergonomics.

关键词： Support vector machines Wrist Hands Carpal tunnel syndrome Wearable Health Monitoring Systems Ergonomics Pose estimation Real-time systems Injuries Strain

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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

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Outsmarting Android Malware with Cutting-Edge Feature Engineering and Machine Learning Techniques

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Computers, Materials & Continua 2024年第4期79卷 651-673页

作者： Ahsan Wajahat Jingsha He Nafei Zhu Tariq Mahmood Tanzila Saba Amjad Rehman Khan Faten S.A.lamri Faculty of Information Technology Beijing University of TechnologyBeijing100124China Artificial Intelligence and Data Analytics(AIDA)Lab CCIS Prince Sultan UniversityRiyadh11586Saudi Arabia Faculty of Information Sciences University of EducationVehari CampusVehari61100Pakistan Department of Mathematical Sciences College of SciencePrincess Nourah bint Abdulrahman UniversityRiyadh84428Saudi Arabia

The growing usage of Android smartphones has led to a significant rise in incidents of Android malware andprivacy *** escalating security concern necessitates the development of advanced technologies capableof automatically detecting andmitigatingmalicious activities in Android applications(apps).Such technologies arecrucial for safeguarding user data and maintaining the integrity of mobile devices in an increasingly digital *** methods employed to detect sensitive data leaks in Android apps are hampered by two major limitationsthey require substantial computational resources and are prone to a high frequency of false *** meansthat while attempting to identify security breaches,these methods often consume considerable processing powerand mistakenly flag benign activities as malicious,leading to inefficiencies and reduced reliability in *** proposed approach includes a data preprocessing step that removes duplicate samples,managesunbalanced datasets,corrects inconsistencies,and imputes missing values to ensure data *** Minimaxmethod is then used to normalize numerical data,followed by feature vector extraction using the Gain ratio andChi-squared test to identify and extract the most significant characteristics using an appropriate prediction *** study focuses on extracting a subset of attributes best suited for the task and recommending a predictivemodel based on domain expert *** proposed method is evaluated using Drebin and TUANDROMDdatasets containing 15,036 and 4,464 benign and malicious samples,*** empirical result shows thatthe RandomForest(RF)and Support VectorMachine(SVC)classifiers achieved impressive accuracy rates of 98.9%and 98.8%,respectively,in detecting unknown Androidmalware.A sensitivity analysis experiment was also carriedout on all three ML-based classifiers based on MAE,MSE,R2,and sensitivity parameters,resulting in a flawlessperformance for both *** app

关键词： Android malware detection machine learning SVC K-Nearest Neighbors(KNN) RF

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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

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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

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Customized Deep Learning Framework with Advanced Sampling Techniques for Lung Cancer Detection using CT Scans

Customized Deep Learning Framework with Advanced Sampling Te...

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Women in data Science at Prince Sultan University (WiDS PSU), International Conference of

作者： Tariq Mahmmod Noor Ayesha Muhammad Mujahid Amjad Rehman Artificial Intelligence and Data Analytics (AIDA) Lab CCIS Prince Sultan University Riyadh School of Clinical Medicine Zhengzhou University Zhengzhou China Artificial Intelligence and Data Analytics (AIDA) Lab CIS Prince Sultan University Riyadh City Country

ISBN: (数字)9798350395839

ISBN: (纸本)9798350395846

Lung cancer is a devastating disease that kills around five million people annually, making it a major global threat. Timely recognition is critical to improving patient survival. With a focus on pulmonary nodules in medical imaging, CT scans are essential for the screening and identification of lung cancer. This paper introduces a customized deep-learning framework for lung cancer detection using CT Scans. To achieve an accurate and efficient early-stage lung cancer diagnosis, the model makes use of a large dataset of chest CT images for training and validation. The experimental results show that the model outperforms state-of-the-art methods for lung cancer categorization. The model provides a trustworthy and efficient means of early detection of lung cancer, which has the potential to revolutionize the field of lung cancer diagnostics. The deep framework outperforms existing approaches and highlights the need to apply transfer learning to medical image analysis. Because of the technique’s extraordinary effectiveness, lung cancer research may be significantly impacted, and improvements in early diagnosis and therapy may result.

关键词： Training Deep learning Accuracy Computed tomography Transfer learning Lung cancer Medical treatment

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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

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Image Fusion Using Wavelet Transformation and XGboost Algorithm

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Computers, Materials & Continua 2024年第4期79卷 801-817页

作者： Shahid Naseem Tariq Mahmood Amjad Rehman Khan Umer Farooq Samra Nawazish Faten S.Alamri Tanzila Saba Department of Information Sciences Division of S&TUniversity of EducationLahore54770Pakistan Artificial Intelligence and Data Analytics(AIDA)Lab CCIS Prince Sultan UniversityRiyadh11586Saudi Arabia Faculty of Information Sciences University of EducationVehari CampusVehari61100Pakistan Department of Computer Science University of Engineering and TechnologyTaxila47050Pakistan Department of Mathematical Sciences College of SciencePrincess Nourah Bint Abdulrahman UniversityRiyadh84428Saudi Arabia

Recently,there have been several uses for digital image *** fusion has become a prominent application in the domain of imaging *** create one final image that provesmore informative and helpful compared to the original input images,image fusion merges two or more initial images of the same *** fusion aims to produce,enhance,and transform significant elements of the source images into combined images for the sake of human visual *** fusion is commonly employed for feature extraction in smart robots,clinical imaging,audiovisual camera integration,manufacturing process monitoring,electronic circuit design,advanced device diagnostics,and intelligent assembly line robots,with image quality varying depending on *** research paper presents various methods for merging images in spatial and frequency domains,including a blend of stable and curvelet transformations,everageMax-Min,weighted principal component analysis(PCA),HIS(Hue,Intensity,Saturation),wavelet transform,discrete cosine transform(DCT),dual-tree Complex Wavelet Transform(CWT),and multiple wavelet *** fusion methods integrate data from several source images of an identical target,thereby enhancing information in an extremely efficient *** precisely,in imaging techniques,the depth of field constraint precludes images from focusing on every object,leading to the exclusion of certain *** tackle thess challanges,a very efficient multi-focus wavelet decomposition and recompositionmethod is *** use of these wavelet decomposition and recomposition techniques enables this method to make use of existing optimized wavelet code and filter *** simulated outcomes provide evidence that the suggested approach initially extracts particular characteristics from images in order to accurately reflect the level of clarity portrayed in the original *** study enhances the performance of the eXtreme Gradient Boosting(XGBoos

关键词： Image fusion max-min average CWT XGBoost DCT inclusive innovations spatial and frequency domain

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Efficient Facial Recognition Authentication Using Edge and Density Variant Sketch Generator

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Computers, Materials & Continua 2022年第1期70卷 505-521页

作者： Summra Saleem M.Usman Ghani Khan Tanzila Saba Ibrahim Abunadi Amjad Rehman Saeed Ali Bahaj Department of Computer Science UETLahorePakistan Al-Khwarizmi Institute of Computer Science UETLahorePakistan Artificial Intelligence&Data Analytics Lab CCIS Prince Sultan University Riyadh11586Saudi Arabia MIS Department College of Business Administration Prince Sattam Bin Abdulaziz UniversityAlkharjSaudi Arabia

Image translation plays a significant role in realistic image synthesis,entertainment tasks such as editing and colorization,and security including personal *** Edge GAN,the major contribution is attribute guided vector that enables high visual quality content *** research study proposes automatic face image realism from freehand sketches based on Edge *** propose a density variant image synthesis model,allowing the input sketch to encompass face features with minute *** density level is projected into non-latent space,having a linear controlled function *** assists the user to appropriately devise the variant densities of facial sketches and image *** data set of Large Scale CelebFaces Attributes(ClebA),lab.lled Faces in theWild(LFWH),Chinese University of Hong Kong(CHUK),and self-generated Asian images are used to evaluate the proposed *** solution is validated to have the capability for generating realistic face images through quantitative and qualitative results and human evaluation.

关键词： Edge generator density variant sketch generator face translation recognition residual block

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Early Stage Kidney Chronic Diseases Diagnosis using Feature Fusion and Deep Learning Algorithms 17

Early Stage Kidney Chronic Diseases Diagnosis using Feature ...

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17th International Conference on Developments in eSystems Engineering, DeSE 2024

作者： Saba, Tanzila Mujahid, Muhamamd Ayesha, Noor Kolivand, Mahyar Artificial Intelligence & Data Analytics Lab CCIS Prince Sultan University Riyadh11586 Saudi Arabia School of Clinical Medicine Zhengzhou University Henan ZhengzhouRP 450001 China Department of Medicine University of Liverpool Liverpool United Kingdom School of Computing and Digital Technologies Staffordshire University Staffordshire United Kingdom

ISBN: (纸本)9798350368697

Chronic Kidney Disease (CKD) is a global health issue due to pre-existing conditions like anemia, diabetes, and hypertension. Early warning signals are commonly neglected, complicating diagnosis and treatment. This study uses high-performance data mining techniques to provide self-analysis and prognostic tools for medical professionals. Clinical decision support using domain experts and specialists aids CKD prognosis and rehabilitation. We use advanced data mining to evaluate demographic characteristics, test findings, and patient medical information to uncover insights and patterns. We used a modified artificial neural network to extract characteristics from a dataset and long-term and short-term memory to classify chronic kidney disease stages. LSTM algorithms improve the accuracy of CKD diagnosis and stage prediction. Initial validation tests demonstrate a 92.23% accuracy gain over traditional methods. The binary classification AUC was 99.95% and the multi-class classification was 94.13% with the proposed technique. LSTM algorithms use temporal dynamics in patient data to enhance diagnostic accuracy, early diagnosis, therapy customization, and healthcare costs. We proposed a novel CKD diagnostic and prognosis paradigm and test multiple classification algorithms for disease diagnosis and staging. Multiple methods improve reliability and account for CKD diagnostic peculiarities. © 2024 IEEE.

关键词： Chronic kidney disease (CKD) Classifier fusion Health rehabilitation Healthcare

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