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An Efficient Encryption and Compression of Sensed IoT Medical Images Using Auto-Encoder

Computer Modeling in Engineering & Sciences 2023年第2期134卷 909-926页

作者： passent el-kafrawy Maie Aboghazalah Abdelmoty M.Ahmed Hanaa Torkey Ayman el-Sayed School of Information Technology and Computer Science Nile UniversityGiza12677Egypt Math and Computer Science Department Faculty of ScienceMenoufia UniversityMenoufia32511Egypt Department of Computer Engineering College of Computer ScienceKing Khalid UniversityAbha61421Saudi Arabia Computer Science and Engineering Department Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt

Healthcare systems nowadays depend on IoT sensors for sending data over the internet as a common *** ofmedical images is very important to secure patient *** these images consumes a lot of time onedge computing;therefore,theuse of anauto-encoder for compressionbefore encodingwill solve such a *** this paper,we use an auto-encoder to compress amedical image before encryption,and an encryption output(vector)is sent out over the *** the other hand,a decoder was used to reproduce the original image back after the vector was received and *** convolutional neural networks were conducted to evaluate our proposed approach:The first one is the auto-encoder,which is utilized to compress and encrypt the images,and the other assesses the classification accuracy of the image after decryption and *** hyperparameters of the encoder were tested,followed by the classification of the image to verify that no critical information was lost,to test the encryption and encoding *** this approach,sixteen hyperparameter permutations are utilized,but this research discusses three main cases in *** first case shows that the combination of Mean Square Logarithmic Error(MSLE),ADAgrad,two layers for the auto-encoder,and RelU had the best auto-encoder results with a Mean Absolute Error(MAE)=0.221 after 50 epochs and 75%classification with the best result for the classification *** second case shows the reflection of auto-encoder results on the classification results which is a combination ofMean Square Error(MSE),RMSprop,three layers for the auto-encoder,and RelU,which had the best classification accuracy of 65%,the auto-encoder gives MAE=0.31 after 50 *** third case is the worst,which is the combination of the hinge,RMSprop,three layers for the auto-encoder,and RelU,providing accuracy of 20%and MAE=0.485.

关键词： Auto-encoder cloud image encryption IoT healthcare

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Arrhythmia Detection by Using Chaos Theory with Machine Learning Algorithms

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Computers, Materials & Continua 2024年第6期79卷 3855-3875页

作者： Maie Aboghazalah passent el-kafrawy Abdelmoty M.Ahmed Rasha elnemr Belgacem Bouallegue Ayman el-sayed Math and Computer Science Department Faculty of ScienceMenoufia UniversityShebin El-komEgypt College of Engineering Computer Science DepartmentEffat UniversityJeddahKingdom of Saudi Arabia Department of Computer Engineering College of Computer ScienceKing Khalid UniversityAbha61421Saudi Arabia Computer Science and Engineering Department Faculty of Electronic EngineeringMenoufia UniversityShebin El-komEgypt Climate Change Information Center and Expert Systems Agriculture Research CenterGizaEgypt

Heart monitoring improves life ***(ECGs or EKGs)detect heart *** learning algorithms can create a few ECG diagnosis processing *** first method uses raw ECG and time-series *** second method classifies the ECG by patient *** third technique translates ECG impulses into Q waves,R waves and S waves(QRS)features using richer *** ECG signals vary naturally between humans and activities,we will combine the three feature selection methods to improve classification accuracy and *** using all three approaches have not been examined till *** researchers found that Machine Learning(ML)techniques can improve ECG *** study will compare popular machine learning techniques to evaluate ECG *** algorithms—Support Vector Machine(SVM),Decision Tree,Naive Bayes,and Neural Network—compare categorization *** plus prior knowledge has the highest accuracy(99%)of the four ML *** characteristics failed to identify signals without chaos *** 99.8%classification accuracy,the Decision Tree technique outperformed all previous experiments.

关键词： ECG extraction ECG leads time series prior knowledge and arrhythmia chaos theory QRS complex analysis machine learning ECG classification

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Automated classification technique for edge-on galaxies based on mathematical treatment of brightness data

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Research in Astronomy and Astrophysics 2021年第10期21卷 231-238页

作者： Mohamed Eassa Ibrahim Mohamed Selim Walid Dabour passent elkafrawy Computer Science Department Integrated Thebes InstituteCairoEgypt Faculty of Computer&Artificial Intelligence Sadat City UniversitySadat CityEgypt Math and Computer Science Dept. Faculty of ScienceMenoufia UniversityEgypt Computer Science Dept. Taibah UniversityAlula BranchKingdom of Saudi Arabia School of Information Technology and Computer Science(ITCS) Nile UniversityEgypt

Classification of edge-on galaxies is important to astronomical studies due to our Milky Way galaxy being an edge-on ***-on galaxies pose a problem to classification due to their less overall brightness levels and smaller numbers of *** the current work,a novel technique for the classification of edge-on galaxies has been *** technique is based on the mathematical treatment of galaxy brightness data from their images.A special treatment for galaxies’brightness data is developed to enhance faint galaxies and eliminate adverse effects of high brightness backgrounds as well as adverse effects of background bright stars.A novel slimness weighting factor is developed to classify edge-on galaxies based on their *** technique has the capacity to be optimized for different catalogs with different brightness *** the current work,the developed technique is optimized for the EFIGI catalog and is trained using a set of 1800 galaxies from this *** classification of the full set of 4458 galaxies from the EFIGI catalog,an accuracy of 97.5% has been achieved,with an average processing time of about 0.26 seconds per galaxy on an average laptop.

关键词： techniques:image processing methods:data analysis galaxies:formation

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Virgo Cluster Membership Based on <i>K</i>-Means Algorithm

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International Journal of Astronomy and Astrophysics 2020年第1期10卷 1-10页

作者： Ibrahim Mohamed Selim passent elkafrawy Walid Dabour Mohamed Eassa National Research Institute of Astronomy and Geophysics Cairo Egypt Computer Science Department Integrated Thebes Institute Cairo Egypt Math and Computer Science Department Faculty of Science Menoufia University Shibin El Kom Egypt Computer Science Department Taibah University Alula Branch Medina KSA

The Virgo cluster of galaxies is of great importance to study the development of the universe due to its close distance from the earth as well as being the center of the local super cluster. The problem that faces Virgo cluster studies is that it shares the same right ascension (RA) and Declination (DEC) ranges with large number of background as well as foreground galaxies. This study aims to geometrically and statistically estimate Virgo cluster membership. The study employs Virgo cluster data, prepared by Harvard University. The radial velocity (RV) data of the Virgo cluster were treated and employed in exchange of missing galaxies’ third dimension, taking advantage of their proportionality. The data were treated by K-means algorithm, using Matlab 2014, and visual and logical exclusion of extremity galaxies to determine the rational center of the Virgo galaxies cluster. Results were presented, compared and discussed. Finally distances of galaxies from the Virgo cluster center were employed along with normal probability distribution characteristics to identify the most probable Virgo cluster members from the range of Virgo cluster of galaxies. The results showed that out of 17,466 objects surveyed in Virgo galaxy range, only few of galaxies were estimated to be genuine Virgo members.

关键词： Virgo Cluster K-Means Normal Distribution

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