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Efficient Facial Recognition Authentication Using Edge and Density Variant Sketch Generator

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),labelled 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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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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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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Finger Vein Authentication Based on Wavelet Scattering Networks

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Computers, Materials & Continua 2022年第8期72卷 3369-3383页

作者： Amjad Rehman Majid Harouni Maedeh Omidiravesh Suliman Mohamed Fati Saeed Ali Bahaj Artificial Intelligence&Data Analytics Lab CCIS Prince Sultan UniversityRiyadh11586Saudi Arabia Department of Computer Engineering Dolatabad BranchIslamic Azad UniversityIsfahanIran Faculty of Computer Engineering Najafabad BranchIslamic Azad UniversityIsfahanIran MIS Department College of Business Administration Prince Sattam bin Abdulaziz UniversityAlkharj11942Saudi Arabia

Biometric-based authentication systems have attracted more attention than traditional authentication techniques such as passwords in the last two *** biometrics such as fingerprint,palm,iris,palm vein and finger vein and other biometrics have been *** of the challenges in biometrics is physical *** of finger vein is of the biometrics least exposed to physical *** methods have been proposed for authentication with the help of this biometric that suffer from weaknesses such as high computational complexity and low identification *** paper presents a novel method of scattering wavelet-based identity *** wavelet extracts image features from Gabor wavelet filters in a structure similar to convolutional neural *** distinguishes this algorithm from other popular feature extraction methods such as deep learning methods,filter-based methods,statistical methods,etc.,is that this algorithm has very high skill and accuracy in differentiating similar images but belongs to different classes,even when the image is subject to serious damage such as noise,angle changes or pixel location,this descriptor still generates feature vectors in away thatminimizes classifier *** improves classification and *** proposed method has been evaluated using two databases Finger Vein USM(FV-USM)and Homologous Multimodal biometrics Traits(SDUMLA-HMT).In addition to having reasonable computational complexity,it has recorded excellent identification rates in noise,rotation,and transmission *** best,it has a 98.2%identification rate for the SDUMLA-HMT database and a 96.1%identification rate for the FV-USM database.

关键词： Biometrics finger veins wavelet scattering vein authentication disaster risk reduction

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IoMT Enabled Melanoma Detection Using Improved Region Growing Lesion Boundary Extraction

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Computers, Materials & Continua 2022年第6期71卷 6219-6237页

作者： Tanzila Saba Rabia Javed Mohd Shafry Mohd Rahim Amjad Rehman Saeed Ali Bahaj Artificial Intelligence&Data Analytics Lab CCIS Prince Sultan UniversityRiyadh11586Saudi Arabia School of Computing Faculty of EngineeringUniversiti Teknologi MalaysiaSkudai81310Johor BahruMalaysia Department of Computer Science Lahore College for Women UniversityLahore54000Pakistan MIS Department College of Business Administration Prince Sattam bin Abdulaziz UniversityAlkharj11942Saudi Arabia

The Internet ofMedical Things(IoMT)and cloud-based healthcare applications,services are beneficial for better decision-making in recent *** is a deadly cancer with a highermortality rate than other skin cancer types such as basal cell,squamous cell,andMerkel ***,detection and treatment at an early stage can result in a higher chance of *** classical methods of detection are expensive and ***,they rely on a trained practitioner’s level,and the availability of the needed equipment is essential for the early detection of *** current improvement in computer-aided systems is providing very encouraging results in terms of precision and *** this article,we propose an improved region growing technique for efficient extraction of the lesion *** analysis and detection ofMelanoma are helpful for the expert *** CNN features are extracted using the pre-trained VGG-19 deep learning *** the end,the selected features are classified by *** proposed technique is gauged on openly accessible two datasets ISIC 2017 and *** the evaluation of our proposed framework,qualitative and quantitative experiments are *** suggested segmentation method has provided encouraging statistical results of Jaccard index 0.94,accuracy 95.7%on ISIC 2017,and Jaccard index 0.91,accuracy 93.3%on the PH2 *** results are notably better than the results of prevalent methods available on the same *** machine learning SVMclassifier executes significantly well on the suggested feature vector,and the comparative analysis is carried out with existing methods in terms of *** proposed method detects and classifies melanoma far better than other ***,our framework gained promising results in both segmentation and classification phases.

关键词： Deep features extraction lesion segmentation melanoma detection SVM VGG-19 healthcare IoMT public health

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Predicting mortality index for ICU inpatients based on clinical data extracted from electronic health record

Predicting mortality index for ICU inpatients based on clini...

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作者： Aisha Dawood Tanzila Saba Artificial Intelligence & Data Analytics Lab (AIDA) CCIS Prince Sultan University

Predicting ICU inpatients mortality index needs to be improved to incorporate clinical *** is also helpful to reflect the patient's recovery and hospitals *** this research machine learning model LightGBM was trained and *** study used a dataset for ICU admissions for adult patients from six *** a total of 130,000 patient records were included in the *** final model achieved AUROC(95% CI) of 0.97,an accuracy of 0.95,and an F1 score of 0.81 on the *** on results,it is observed that machine learning models with the support of conventional mortality scoring indices can provide a successful and useful model for predicting the outcome of critical and severe cases in the ICU.

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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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Cognitive Skill Enhancement System Using Neuro-Feedback for ADHD Patients

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Computers, Materials & Continua 2021年第8期68卷 2363-2376页

作者： Muhammad Usman Ghani Khan Zubaira Naz Javeria Khan Tanzila Saba Ibrahim Abunadi Amjad Rehman Usman Tariq Al-khawarizmi Institute of Computer Science University of Engineering and TechnologyLahore54890Pakistan Department of Computer Science University of Engineering and TechnologyLahore54890Pakistan Artificial Intelligence and Data Analytics Lab CCIS Prince Sultan UniversityRiyadhSaudi Arabia College of Computer Engineering and Science Prince Sattam bin Abdulaziz UniversityAlkharjSaudi Arabia

The National Health Interview Survey(NHIS)shows that there are 13.2%of children at the age of 11 to 17 who are suffering from Attention Deficit Hyperactivity Disorder(ADHD),*** treatment methods for ADHD are either psycho-stimulant medications or cognitive *** traditional methods,namely therapy,need a large number of visits to hospitals and include *** could be used for the effective treatment of *** could be a helpful tool in improving children and ADHD patients’cognitive skills by using Brain–Computer Interfaces(BCI).BCI enables the user to interact with the computer through brain activity using Electroencephalography(EEG),which can be used to control different computer applications by processing acquired brain *** paper proposes a system based on neurofeedback that can improve cognitive skills such as attention level,mediation level,and spatial *** proposed system consists of a puzzle game where its complexity increases with each *** signals were acquired using the Neurosky headset;then sent the signals to the designed gaming *** neurofeedback system was tested on 10 different subjects,and their performance was calculated using different evaluation *** results show that this game improves player overall performance from 74%to 98%by playing each game level.

关键词： Neurogaming brain-computer interface attention deficit hyperactivity disorder electroencephalography spatial memory cognitive skills attention level

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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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Empirical enhancement of EEG classification models for epileptic seizures using a fuzzy statistical perspective

Empirical enhancement of EEG classification models for epile...

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作者： Ricky Mohanty Subhendu Kumar Pani Amjad Rehman Noor Ayesha Artificial Intelligence & Data Analytics Lab CCIS Prince Sultan University School of Clinical Medicine Zhengzhou University

The signals produced by an electrocardiogram(ECG) are made up of intricate pattern sequences that have a periodic *** pattern sequences contain an initial P-wave,which denotes the beginning of an ECG wave,a QRS sequence,which denotes the intensity of the pulse,and a T segment,which denotes the conclusion of the *** such as PR interval,QRS interval,QT interval,ST interval,R to R interval,*** employed to recognize chronic,ischemic,and other cardiac *** wave patterns need the simultaneous execution of many high-complexity signal processing operations to be classified into cardiac *** pre-processing,feature extraction,feature selection,classification into epileptic and non-epileptic seizures,and post-processing are some of these *** each of these operations,researchers create a wide range of *** algorithms' performance differs significantly in terms of the quantity of leads utilized for ECG collection,filtering effectiveness,feature extraction & selection effectiveness,and classifier ***,researchers and system designers have become unclear when choosing the optimum algorithm set for an *** text offers a thorough analysis & design of fuzzy CNN model of a broad range of epileptic & non-epileptic seizure classification techniques to lessen *** neural network(CNN) based models outperform other models in terms of general-purpose performance,whereas application-specific deployments need the employment of customized fuzzy CNN models.A significant area of research & development is presented by the observation that fuzzy logic techniques are not observed while constructing ECG classification *** on these findings,a new fuzzy logic-based classification method is provided in this text that employs quantization techniques to transform input ECG signals into fuzzy *** these parameters and a specially created CNN model,it was possible to a

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