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A Novel Convolutional Neural Network Model for Malaria Cell Images Classification

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Computers, Materials & Continua 2022年第9期72卷 5889-5907页

作者： Esraa Hassan mahmoud y.shams Noha A.Hikal Samir Elmougy Faculty of Artificial Intelligence Kafrelsheikh UniversityKafrelsheikh33511Egypt Department of Information Technology Faculty of Computers and InformationMansoura UniversityMansoura35516Egypt Department of Computer Science Faculty of Computers and InformationMansoura UniversityMansoura35516Egypt

Infectious diseases are an imminent danger that faces human beings around the *** is considered a highly contagious *** diagnosis of various diseases,including malaria,was performed manually,but it required a lot of time and had some human ***,there is a need to investigate an efficient and fast automatic diagnosis *** deep learning algorithms can provide a solution in which they can learn complex image patterns and have a rapid improvement in medical image *** study proposed a Convolutional Neural Network(CNN)model to detect malaria automatically.A Malaria Convolutional Neural Network(MCNN)model is proposed in this work to classify the infected *** focuses on detecting infected cells,which aids in the computation of parasitemia,or infection *** proposed model achieved 0.9929,0.9848,0.9859,0.9924,0.0152,0.0141,0.0071,0.9890,0.9894,and 0.9780 in terms of specificity,sensitivity,precision,accuracy,F1-score,and Matthews Correlation Coefficient,respectively.A comparison was carried out between the proposed model and some recent works in the *** comparison demonstrates that the proposed model outperforms the compared works in terms of evaluation metrics.

关键词： Deep learning malaria dataset diagnostic medical approach

Unlocking the power of blockchain in education:An overview of innovations and outcomes

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Blockchain(Research and Applications) 2023年第4期4卷 99-117页

作者： Amr El Koshiry Entesar Eliwa Tarek Abd El-Hafeez mahmoud y.shams Department of Curricula and Teaching Methods College of EducationKing Faisal UniversityAl-Ahsa31982Saudi Arabia Faculty of Specific Education Minia UniversityMinia61519Egypt Department of Mathematics and Statistics College of ScienceKing Faisal UniversityAl-Ahsa31982Saudi Arabia Department of Computer Science Faculty of ScienceMinia UniversityMinia61519Egypt Computer Science Unit Deraya UniversityMinia UniversityMinia61519Egypt Faculty of Artificial Intelligence Kafrelsheikh UniversityKafrelsheikh33516Egypt

Blockchain is a revolutionary technology that has the potential to revolutionize various industries,including finance,supply chain management,healthcare,and *** decentralized,secure,and transparent nature makes it ideal for use in industries where trust,security,and efficiency are of paramount *** integration of blockchain technology into the education system has the potential to greatly improve the efficiency,security,and credibility of the educational *** creating secure and transparent platforms for tracking and verifying students’academic achievements,blockchain technology can help to create a more accessible and trustworthy education system,making it easier for students to showcase their skills and knowledge to potential *** the potential benefits of blockchain in education are significant,there are also several challenges that must be addressed in order to fully realize the potential of this technology in the educational *** of the major challenges include adoption,technical knowledge,interoperability,regulation,cost,data privacy and security,scalability,and *** necessary equipment for the implementation of blockchain technology in education is diverse and critical to the success of this innovative *** should carefully consider this equipment when planning their implementation of blockchain technology in education to ensure the efficient and secure transfer of educational data and transactions within the blockchain *** technology has the potential to play a significant role in promoting sustainability education and advancing the sustainability goals of both individuals and *** should consider incorporating blockchain technology into their sustainability education programs,in order to enhance the transparency,verifiability,and efficiency of their sustainability-related *** the use of blockchain technology in education is still i

关键词： Blockchain Education Sustainability Internet of things Machine learning Credential verification

A Quantum Algorithm for Evaluating the Hamming Distance

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Computers, Materials & Continua 2022年第4期71卷 1065-1078页

作者： Mohammed Zidan Manal G.Eldin mahmoud y.shams Mohamed Tolan Ayman Abd-Elhamed mahmoud Abdel-Aty Department of Artificial Intelligence Hurghada Faculty of Computers and Artificial IntelligenceSouth Valley UniversityEgypt Faculty of Engineering King Salman International UniversitySouth SinaiEgypt Department of Mathematics and Computer Science Faculty of ScienceBeni-Suef UniversityBeni-SuefEgypt Department of Machine Learning and Information Retrieval Faculty of Artificial IntelligenceKafrelsheikh UniversityEgypt Mechanical Department Faculty of Technology and EducationSuez UniversitySuezEgypt Faculty of Technological Industries King Salman International UniversitySouth SinaiEgypt Faculty of Engineering at Mataria Helwan UniversityCairoEgypt Department of Mathematics Faculty of SciencesSohag University82524 SohagEgypt

We present a novel quantum algorithm to evaluate the hamming distance between two unknown oracles via measuring the degree of entanglement between two ancillary *** particular,we use the power of the entanglement degree based quantum computing model that preserves at most the locality of interactions within the quantum model *** model uses one of two techniques to retrieve the solution of a quantum computing problem at *** the first technique,the solution of the problem is obtained based on whether there is an entanglement between the two ancillary qubits or *** the second,the solution of the quantum computing problem is obtained as a function in the concurrence value,and the number of states that can be generated from the Boolean *** proposed algorithm receives two oracles,each oracle represents an unknown Boolean function,then it measures the hamming distance between these two *** hamming distance is evaluated based on the second *** is shown that the proposed algorithm provides exponential speedup compared with the classical counterpart for Boolean functions that have large numbers of Boolean *** proposed algorithm is explained via a case ***,employing recently developed experimental techniques,the proposed algorithm has been verified using IBM’s quantum computer simulator.

关键词： Quantum computing quantum algorithm quantum circuit

Machine Learning Techniques for Detecting Phishing URL Attacks

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Computers, Materials & Continua 2023年第4期75卷 1271-1290页

作者： Diana T.Mosa mahmoud y.shams Amr AAbohany El-Sayed M.El-kenawy M.Thabet Department of Cyber Security College of Engineering and Information TechnologyBuraydah Private CollegesBuraydah51418Kingdom of Saudi Arabia Faculty of Computers and Information Kafrelsheikh UniversityKafrelsheikh33516Egypt Faculty of Artificial Intelligence Kafrelsheikh UniversityKafrelsheikh33516Egypt Delta Higher Institute of Engineering and Technology Mansoura35111Egypt Faculty of Computers and Information Fayoum UniversityFayoumEgypt

Cyber Attacks are critical and destructive to all industry *** affect social engineering by allowing unapproved access to a Personal Computer(PC)that breaks the corrupted system and threatens *** defense of security requires understanding the nature of Cyber Attacks,so prevention becomes easy and accurate by acquiring sufficient knowledge about various features of Cyber ***-Security proposes appropriate actions that can handle and block attacks.A phishing attack is one of the cybercrimes in which users follow a link to illegal websites that will persuade them to divulge their private *** of the online security challenges is the enormous number of daily transactions done via phishing *** Cyber-Security have a priority for all organizations,Cyber-Security risks are considered part of an organization’s risk management *** paper presents a survey of different modern machine-learning approaches that handle phishing problems and detect with high-quality accuracy different phishing attacks.A dataset consisting of more than 11000 websites from the Kaggle dataset was utilized and studying the effect of 30 website features and the resulting class label indicating whether or not it is a phishing website(1 or−1).Furthermore,we determined the confusion matrices of Machine Learning models:Neural Networks(NN),Na飗e Bayes,and Adaboost,and the results indicated that the accuracies achieved were 90.23%,92.97%,and 95.43%,respectively.

关键词： Cyber security phishing attack URL phishing online social networks machine learning

Eye Detection-Based Deep Belief Neural Networks and Speeded-Up Robust Feature Algorithm

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Computer Systems Science & Engineering 2023年第6期45卷 3195-3213页

作者： Zahraa Tarek Samaa M.Shohieb Abdelghafar M.Elhady El-Sayed M.El-kenawy mahmoud y.shams Faculty of Computers and Information Mansoura UniversityMansoura35516Egypt Deanship of Scientific Research Umm Al-Qura UniversityMakkah21955KSA Delta Higher Institute of Engineering and Technology Mansoura35111Egypt Faculty of Artificial Intelligence Kafrelsheikh UniversityKafrelsheikh33511Egypt

The ability to detect and localize the human eye is critical for use in security applications and human identification and verification *** is because eye recognition algorithms have multiple challenges,such as multi-pose variations,ocular parts,and ***,the modern security applica-tions fail to detect facial expressions from eye *** this paper,a Speeded-Up Roust Feature(SURF)Algorithm was utilized to localize the face images of the enrolled *** highlighted on eye and pupil parts to be detected based on SURF,Hough Circle Transform(HCT),and Local Binary Pattern(LBP).Afterward,Deep Belief Neural Networks(DBNN)were used to classify the input features results from the SURF *** further determined the correctly and wrongly classified subjects using a confusion matrix with two class labels to classify people whose eye images are correctly *** apply Stochastic Gradient Descent(SGD)optimizer to address the overfitting problem,and the hyper-parameters arefine-tuned based on the applied *** accuracy of the proposed system is determined based on SURF,LBP,and DBNN classifier achieved 95.54%for the ORL dataset,94.07%for the BioID,and 96.20%for the CASIA-V5 *** proposed approach is more reliable and more advanced when compared with state-of-the-art algorithms.

关键词： Eye localization classification DBNN detection feature extraction LBP feature extraction SURF

Wind Power Prediction Based on Machine Learning and Deep Learning Models

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Computers, Materials & Continua 2023年第1期74卷 715-732页

作者： Zahraa Tarek mahmoud y.shams Ahmed M.Elshewey El-Sayed M.El-kenawy Abdelhameed Ibrahim Abdelaziz A.Abdelhamid Mohamed A.El-dosuky Faculty of Computers and Information Computer Science DepartmentMansoura UniversityMansoura35561Egypt Faculty of Artificial Intelligence Kafrelsheikh UniversityKafrelsheikh33511Egypt Faculty of Computers and Information Computer Science DepartmentSuez UniversitySuezEgypt Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura35712Egypt Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Department of Computer Science Faculty of Computer and Information SciencesAin Shams UniversityCairo11566Egypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia Department of Computer Science Arab East CollegesRiyadh13544Saudi Arabia

Wind power is one of the sustainable ways to generate renewable *** recent years,some countries have set renewables to meet future energy needs,with the primary goal of reducing emissions and promoting sustainable growth,primarily the use of wind and solar *** achieve the prediction of wind power generation,several deep and machine learning models are constructed in this article as base *** regression models are Deep neural network(DNN),k-nearest neighbor(KNN)regressor,long short-term memory(LSTM),averaging model,random forest(RF)regressor,bagging regressor,and gradient boosting(GB)*** addition,data cleaning and data preprocessing were performed to the *** dataset used in this study includes 4 features and 50530 *** accurately predict the wind power values,we propose in this paper a new optimization technique based on stochastic fractal search and particle swarm optimization(SFSPSO)to optimize the parameters of LSTM *** evaluation criteria were utilized to estimate the efficiency of the regression models,namely,mean absolute error(MAE),Nash Sutcliffe Efficiency(NSE),mean square error(MSE),coefficient of determination(R2),root mean squared error(RMSE).The experimental results illustrated that the proposed optimization of LSTM using SFS-PSO model achieved the best results with R2 equals 99.99%in predicting the wind power values.

关键词： Prediction of wind power data preprocessing performance evaluation

Weight Prediction Using the Hybrid Stacked-LSTM Food Selection Model

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Computer Systems Science & Engineering 2023年第7期46卷 765-781页

作者： Ahmed M.Elshewey mahmoud y.shams Zahraa Tarek Mohamed Megahed El-Sayed M.El-kenawy Mohamed A.El-dosuky Faculty of Computers and Information Computer Science DepartmentSuez UniversitySuezEgypt Faculty of Artificial Intelligence Kafrelsheikh UniversityKafrelsheikh33511Egypt Faculty of Computers and Information Computer Science DepartmentMansoura University35561Egypt Faculty of Physical Education Track and Field Competitions DepartmentArish UniversityEgypt Delta Higher Institute of Engineering and Technology Mansoura35111Egypt Department of Computer Science Arab East CollegesRiyadh13544Saudi Arabia

Food choice motives(i.e.,mood,health,natural content,convenience,sensory appeal,price,familiarities,ethical concerns,and weight control)have an important role in transforming the current food system to ensure the healthiness of people and the sustainability of the *** from several domains have presented several models addressing issues influencing food choice over the ***,a multidisciplinary approach is required to better understand how various aspects interact with one another during the decision-making *** this paper,four Deep Learning(DL)models and one Machine Learning(ML)model are utilized to predict the weight in pounds based on food *** Long Short-Term Memory(LSTM)model,stacked-LSTM model,Conventional Neural Network(CNN)model,and CNN-LSTM model are the used deep learning *** the applied ML model is the K-Nearest Neighbor(KNN)*** efficiency of the proposed model was determined based on the error rate obtained from the experimental *** findings indicated that Mean Absolute Error(MAE)is 0.0087,the Mean Square Error(MSE)is 0.00011,the Median Absolute Error(MedAE)is 0.006,the Root Mean Square Error(RMSE)is 0.011,and the Mean Absolute Percentage Error(MAPE)is ***,the results demonstrated that the stacked LSTM achieved improved results compared with the LSTM,CNN,CNN-LSTM,and KNN regressor.

关键词： Weight prediction machine learning deep learning LSTM CNN KNN

Metaheuristic Optimization Through Deep Learning Classification of COVID-19 in Chest X-Ray Images

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Computers, Materials & Continua 2022年第11期73卷 4193-4210页

作者： Nagwan Abdel Samee El-Sayed M.El-Kenawy Ghada Atteia Mona M.Jamjoom Abdelhameed Ibrahim Abdelaziz A.Abdelhamid Noha E.El-Attar Tarek Gaber Adam Slowik mahmoud y.shams Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh 11671Saudi Arabia Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura 35111Egypt Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura 35712Egypt Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh 11671Saudi Arabia Computer Engineering and Control Systems Department Faculty of EngineeringMansoura University35516MansouraEgypt Department of Computer Science Faculty of Computer and Information SciencesAin Shams University11566CairoEgypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia Faculty of Computers and Artificial Intelligence Benha UniversityEgypt School of Science Engineeringand EnvironmentUniversity of SalfordUK Faculty of Computers and Informatics Suez Canal UniversityIsmailia41522Egypt Koszalin University of Technology Poland Faculty of Artificial Intelligence Kafrelsheikh UniversityKafrelsheikh33511Egypt

As corona virus disease(COVID-19)is still an ongoing global outbreak,countries around the world continue to take precautions and measures to control the spread of the *** of the excessive number of infected patients and the resulting deficiency of testing kits in hospitals,a rapid,reliable,and automatic detection of COVID-19 is in extreme need to curb the number of *** analyzing the COVID-19 chest X-ray images,a novel metaheuristic approach is proposed based on hybrid dipper throated and particle swarm *** lung region was segmented from the original chest X-ray images and augmented using various transformation ***,the augmented images were fed into the VGG19 deep network for feature *** the other hand,a feature selection method is proposed to select the most significant features that can boost the classification ***,the selected features were input into an optimized neural network for *** neural network is optimized using the proposed hybrid *** experimental results showed that the proposed method achieved 99.88%accuracy,outperforming the existing COVID-19 detection *** addition,a deep statistical analysis is performed to study the performance and stability of the proposed *** results confirm the effectiveness and superiority of the proposed approach.

关键词： Covid-19 feature selection dipper throated optimization particle swarm optimization deep learning

Adaptive Dynamic Dipper Throated Optimization for Feature Selection in Medical Data

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Computers, Materials & Continua 2023年第4期75卷 1883-1900页

作者： Ghada Atteia El-Sayed M.El-kenawy Nagwan Abdel Samee Mona M.Jamjoom Abdelhameed Ibrahim Abdelaziz A.Abdelhamid Ahmad Taher Azar Nima Khodadadi Reham A.Ghanem mahmoud y.shams Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura35712Egypt Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Department of Computer Science Faculty of Computer and Information SciencesAin Shams UniversityCairo11566Egypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia Faculty of Computers and Artificial Intelligence Benha UniversityBenha13518Egypt College of Computer and Information Sciences Prince Sultan UniversityRiyadh11586Saudi Arabia Department of Civil Engineering University of Science and TechnologyMiami33101USA Department of Civil and Environmental Engineering Florida International UniversityMiamiUSA Oral Biology Department Faculty of Oral and Dental MedicineDelta University for Science and TechnologyGamasaEgypt Faculty of Artificial Intelligence Kafrelsheikh UniversityKafrelsheikh33511Egypt

The rapid population growth results in a crucial problem in the early detection of diseases inmedical *** all the cancers unveiled,breast cancer is considered the second most severe ***,an exponential rising in death cases incurred by breast cancer is expected due to the rapid population growth and the lack of resources required for performing medical *** recent advances in machine learning could help medical staff in diagnosing diseases as they offer effective,reliable,and rapid responses,which could help in decreasing the death *** this paper,we propose a new algorithm for feature selection based on a hybrid between powerful and recently emerged optimizers,namely,guided whale and dipper throated *** proposed algorithm is evaluated using four publicly available breast cancer *** evaluation results show the effectiveness of the proposed approach from the accuracy and speed *** prove the superiority of the proposed algorithm,a set of competing feature selection algorithms were incorporated into the conducted *** addition,a group of statistical analysis experiments was conducted to emphasize the superiority and stability of the proposed *** best-achieved breast cancer prediction average accuracy based on the proposed algorithm is 99.453%.This result is achieved in an average time of 3.6725 s,the best result among all the competing approaches utilized in the experiments.

关键词： Medical dataset breast cancer guided whale optimizer dipper throated optimizer feature selection meta-heuristics