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Proposed Biometric security system Based on Deep Learning and Chaos Algorithms

Computers, Materials & Continua 2023年第2期74卷 3515-3537页

作者： Iman Almomani Walid El-shafai Aala AlKhayer Albandari Alsumayt sumayh s.aljameel Khalid Alissa security Engineering Lab Computer Science DepartmentPrince Sultan UniversityRiyadh11586Saudi Arabia Computer science Department King Abdullah II School of Information TechnologyThe University of Jordan11942Jordan Department of Electronics and Electrical Communications Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Computer science Department Applied CollegeImam Abdulrahman Bin Faisal UniversityP.O.Box 1982Dammam31441Saudi Arabia Computer science Department College of Computer Science and Information TechnologyImam Abdulrahman Bin Faisal UniversityP.O.Box 1982Dammam31441Saudi Arabia sAUDI ARAMCO Cybersecurity Chair Networks and Communications DepartmentCollege of Computer Science and Information TechnologyImam Abdulrahman Bin Faisal UniversityP.O.Box 1982Dammam31441Saudi Arabia

Nowadays,there is tremendous growth in biometric authentication and cybersecurity ***,the efficient way of storing and securing personal biometric patterns is mandatory in most governmental and private ***,designing and implementing robust security algorithms for users’biometrics is still a hot research area to be *** work presents a powerful biometric security system(Bss)to protect different biometric modalities such as faces,iris,and *** proposed Bssmodel is based on hybridizing auto-encoder(AE)network and a chaos-based ciphering algorithm to cipher the details of the stored biometric patterns and ensures their *** employed AE network is unsupervised deep learning(DL)structure used in the proposed Bss model to extract main biometric *** obtained features are utilized to generate two random chaos *** first random chaos matrix is used to permute the pixels of biometric *** contrast,the second random matrix is used to further cipher and confuse the resulting permuted biometric pixels using a two-dimensional(2D)chaotic logisticmap(CLM)*** assess the efficiency of the proposed Bss,(1)different standardized color and grayscale images of the examined fingerprint,faces,and iris biometrics were used(2)comprehensive security and recognition evaluation metrics were *** assessment results have proven the authentication and robustness superiority of the proposed Bssmodel compared to other existing *** example,the proposed Bss succeeds in getting a high area under the receiver operating characteristic(AROC)value that reached 99.97%and low rates of 0.00137,0.00148,and 3516 CMC,2023,vol.74,no.20.00157 for equal error rate(EER),false reject rate(FRR),and a false accept rate(FAR),respectively.

关键词： Biometric security deep learning AE network 2D CLM cybersecurity and authentication applications feature extraction unsupervised learning

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An Automated system for Early Prediction of Miscarriage in the First Trimester Using Machine Learning

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Computers, Materials & Continua 2023年第4期75卷 1291-1304页

作者： sumayh s.aljameel Malak Aljabri Nida Aslam Dorieh M.Alomari Arwa Alyahya shaykhah Alfaris Maha Balharith Hiessa Abahussain Dana Boujlea Eman s.Alsulmi Department of Computer science College of Computer Science and Information TechnologyImam Abdulrahman Bin Faisal UniversityP.O.Box 1982Dammam31441Saudi Arabia Computer science Department College of Computer and Information SystemsUmm Al-Qura UniversityMakkah21955Saudi Arabia Department of Computer Engineering College of Computer Science and Information TechnologyImam Abdulrahman Bin Faisal UniversityP.O.Box 1982Dammam31441Saudi Arabia Department of Obstetrics and Gynecology College of MedicineImam Abdulrahman Bin Faisal UniversityDammamSaudi Arabia

Currently, the risk factors of pregnancy loss are increasing andare considered a major challenge because they vary between cases. The earlyprediction of miscarriage can help pregnant ladies to take the needed careand avoid any danger. Therefore, an intelligent automated solution must bedeveloped to predict the risk factors for pregnancy loss at an early stage toassist with accurate and effective diagnosis. Machine learning (ML)-baseddecision support systems are increasingly used in the healthcare sector andhave achieved notable performance and objectiveness in disease predictionand prognosis. Thus, we developed a model to help obstetricians predictthe probability of miscarriage using ML. And support their decisions andexpectations about pregnancy status by providing an easy, automated way topredict miscarriage at early stages using ML tools and techniques. Althoughmany published papers proposed similar models, none of them used saudiclinical data. Our proposed solution used ML classification algorithms tobuild a miscarriage prediction model. Four classifiers were used in this study:decision tree (DT), random forest (RF), k-nearest neighbor (KNN), andgradient boosting (GB). Accuracy, Precision, Recall, F1-score, and receiveroperating characteristic area under the curve (ROC-AUC) were used to evaluatethe proposed model. The results showed that GB overperformed the otherclassifiers with an accuracy of 93.4% and ROC-AUC of 97%. This proposedmodel can assist in the early identification of at-risk pregnant women to avoidmiscarriage in the first trimester and will improve the healthcare sector insaudi Arabia.

关键词： Miscarriage pregnancy abortion machine learning gradient boosting

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Twitter Arabic sentiment Analysis to Detect Depression Using Machine Learning

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Computers, Materials & Continua 2022年第5期71卷 3463-3477页

作者： Dhiaa A.Musleh Taef A.Alkhales Reem A.Almakki shahad E.Alnajim shaden K.Almarshad Rana s.Alhasaniah sumayh s.aljameel Abdullah A.Almuqhim Department of Computer science College of Computer Science and Information TechnologyImam Abdulrahman Bin Faisal UniversityP.O.Box 1982Dammam31441Saudi Arabia

Depression has been a major global concern for a long time,with the disease affecting aspects of many people’s daily lives,such as their moods,eating habits,and social *** Arabic culture,there is a lack of awareness regarding the importance of facing and curing mental health ***,people all over the world,including Arab citizens,tend to express their feelings openly on social media,especially Twitter,as it is a platform designed to enable the expression of emotions through short texts,pictures,or *** are inclined to treat their Twitter accounts as diaries because the platform affords them *** published studies have detected the occurrence of depression among Twitter users on the basis of data on tweets posted in English,but research on Arabic tweets is *** aim of the present work was to develop a model for analyzing Arabic users’tweets and detecting depression among Arabic Twitter *** expand the diversity of user tweets,by adding a new label(“neutral”)so the dataset include three classes(“depressed”,“non-depressed”,“neutral”).The model was created using machine learning classifiers and natural language processing techniques,such as support Vector Machine(sVM),Random Forest(RF),Logistic Regression(LR),K-nearest Neighbors(KNN),AdaBoost,and Naïve Bayes(NB).The results showed that the RF classifier outperformed the others,registering an accuracy of 82.39%.

关键词： Depression sentiment analysis twitter supervised learning machine learning

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