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Steganography-Based Transmission of Medical Images Over Unsecure Network for Telemedicine Applications

Computers, Materials & Continua 2021年第9期68卷 4069-4085页

作者： romany F.Mansour moheb r.girgis Department of Mathematics Faculty of ScienceNew Valley UniversityEl-Kharga72511Egypt Department of Computer Science Faculty of ScienceMinia UniversityEl-MiniaEgypt

Steganography is one of the best techniques to hide secret *** steganography methods are available that use an image as a cover object,which is called image *** image steganography,the major features are the cover object quality and hiding data *** to poor image quality,attackers could easily hack the secret ***,the hidden data quantity should be improved,while keeping stego-image quality *** main aim of this study is combining several steganography techniques,for secure transmission of data without leakage and unauthorized *** this paper,a technique,which combines various steganographybased techniques,is proposed for secure transmission of secret *** the pre-processing step,resizing of cover image is performed with Pixel repetition Method(PrM).Then DES(Data Encryption Standard)algorithm is used to encrypt secret data before embedding it into cover *** encrypted data is then converted to hexadecimal *** is followed by embedding using Least Signification Bit(LSB)in order to hide secret data inside the cover ***,image de-noising using Convolutional Neural Network(CNN)is used to enhance the cover image with hidden encrypted *** Zerotrees of Wavelet Transform is used to compress the image in order to reduce its *** are conducted to evaluate the performance of proposed combined steganography technique and results indicate that the proposed technique outperforms all existing *** achieves better PSNr,and encryption/decryption times,than existing methods for medical and other types of images.

关键词： Steganography secure data transmission CNN encryption telemedicine

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Ensemble Machine Learning to Enhance Q8 Protein Secondary Structure Prediction

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Computers, Materials & Continua 2022年第11期73卷 3951-3967页

作者： moheb r.girgis rofida M.Gamal Enas Elgeldawi Computer Science Department Faculty of ScienceMinia University61519MiniaEgypt

Protein structure prediction is one of the most essential objectives practiced by theoretical chemistry and bioinformatics as it is of a vital importance in medicine,biotechnology and *** secondary structure prediction(PSSP)has a significant role in the prediction of protein tertiary structure,as it bridges the gap between the protein primary sequences and tertiary structure *** secondary structures are classified into two categories:3-state category and 8-state *** the 3 states and the 8 states of secondary structures from protein sequences are called the Q3 prediction and the Q8 prediction problems,*** 8 classes of secondary structures reveal more precise structural information for a variety of applications than the 3 classes of secondary structures,however,Q8 prediction has been found to be very challenging,that is why all previous work done in PSSP have focused on Q3 *** this paper,we develop an ensemble Machine Learning(ML)approach for Q8 PSSP to explore the performance of ensemble learning algorithms compared to that of individual ML algorithms in Q8 *** ensemble members considered for constructing the ensemble models are well known classifiers,namely SVM(Support Vector Machines),KNN(K-Nearest Neighbor),DT(Decision Tree),rF(random Forest),and NB(Naïve Bayes),with two feature extraction techniques,namely LDA(Linear Discriminate Analysis)and PCA(Principal Component Analysis).Experiments have been conducted for evaluating the performance of single models and ensemble models,with PCA and LDA,in Q8 *** novelty of this paper lies in the introduction of ensemble learning in Q8 PSSP *** experimental results confirmed that ensemble ML models are more accurate than individual ML *** also indicated that features extracted by LDA are more effective than those extracted by PCA.

关键词： Protein secondary structure prediction(PSSP) Q3 prediction Q8 prediction ensemble machine leaning boosting bagging

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Enhanced Self-Organizing Map Neural Network for DNA Sequence Classification

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Intelligent Information Management 2013年第1期5卷 25-33页

作者： Marghny Mohamed Abeer A. Al-Mehdhar Mohamed Bamatraf moheb r. girgis Faculty of Information and Computers Assiut University Assiut Egypt Faculty of Science Hadhramout University of Science and Technology Hadhramout Yemen Faculty of Science Minia University Minia Egypt

The artificial neural networks (ANNs), among different soft computing methodologies are widely used to meet the challenges thrown by the main objectives of data mining classification techniques, due to their robust, powerful, distributed, fault tolerant computing and capability to learn in a data-rich environment. ANNs has been used in several fields, showing high performance as classifiers. The problem of dealing with non numerical data is one major obstacle prevents using them with various data sets and several domains. Another problem is their complex structure and how hands to interprets. Self-Organizing Map (SOM) is type of neural systems that can be easily interpreted, but still can’t be used with non numerical data directly. This paper presents an enhanced SOM structure to cope with non numerical data. It used DNA sequences as the training dataset. results show very good performance compared to other classifiers. For better evaluation both micro-array structure and their sequential representation as proteins were targeted as dataset accuracy is measured accordingly.

关键词： Bioinformatics Artificial Neural Networks Self-Organizing Map Classification Sequence Alignment

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