Mahmoud Y. Shams

Mahmoud Y. Shams

Kafrelsheikh University

H-index: 18

Africa-Egypt

About Mahmoud Y. Shams

Mahmoud Y. Shams, With an exceptional h-index of 18 and a recent h-index of 18 (since 2020), a distinguished researcher at Kafrelsheikh University, specializes in the field of Computer Science, Pattern Recognition, Computer Vision, Artificial Intelligence, Biomedical.

His recent articles reflect a diverse array of research interests and contributions to the field:

Neutrosophic Encoding and Decoding Algorithm for ASCII Code System

Harnessing machine learning to find synergistic combinations for FDA-approved cancer drugs

Integration between Bioinformatics Algorithms and Neutrosophic Theory

Optimizing classification of diseases through language model analysis of symptoms

Enhancing Diagnostic Decision-Making with Image Mining Techniques: A Proposed Framework for Medical Images

Detecting COVID-19 in chest CT images based on several pre-trained models

Exploring Neutrosophic Numeral System Algorithms for Handling Uncertainty and Ambiguity in Numerical Data: An Overview and Future Directions

A Proposed Model for Measuring Neutrosophic Inference of Comparative Nucleic Acids

Mahmoud Y. Shams Information

University

Kafrelsheikh University

Position

Assistant Professor Faculty of AI

Citations(all)

909

Citations(since 2020)

862

Cited By

92

hIndex(all)

18

hIndex(since 2020)

18

i10Index(all)

28

i10Index(since 2020)

28

Email

University Profile Page

Kafrelsheikh University

Mahmoud Y. Shams Skills & Research Interests

Computer Science

Pattern Recognition

Computer Vision

Artificial Intelligence

Biomedical

Top articles of Mahmoud Y. Shams

Neutrosophic Encoding and Decoding Algorithm for ASCII Code System

Authors

AA Salama,Zahraa Tarek,Eman Yousif Darwish,Sherif Elseuof,Mahmoud Y Shams

Journal

Neutrosophic Sets and Systems

Published Date

2024/1/15

Context and Background This paper addresses the challenge of encoding and decoding numerical data by introducing innovative algorithms utilizing Neutrosophic ASCII codes and ASCII Neutrosophic codes. These codes serve to represent uncertain or imprecise character values through the incorporation of neutrosophic numbers, encompassing degrees of truth, falsity, and indeterminacy. Motivation The study stems from the necessity to effectively represent uncertain or imprecise character values in numerical data. This is crucial in diverse applications where handling uncertain or ambiguous data is a prevalent concern. Hypothesis We hypothesize that employing Neutrosophic ASCII codes and ASCII Neutrosophic codes in encoding and decoding numerical data can provide a robust solution for representing uncertain or imprecise character values. Methods The encoding algorithm in this study transforms each character in the ASCII string into its corresponding ASCII code, utilizing either 7 or 8 bits based on the code type. This algorithmcalculates the degree of truth, falsity, and indeterminacy for each bit, considering the uncertainty or ambiguity associated with the character. The resulting neutrosophic numbers are appended to create the Neutrosophic ASCII code or ASCII Neutrosophic code. The decoding algorithm partitions the code into groups of neutrosophic numbers, calculates the associated degrees of truth, falsity, and indeterminacy for each ASCII bit, and converts the neutrosophic numbers back to ASCII codes, reconstructing the original ASCII character string. Results Our study yields a novel and effective approach for encoding and …

Harnessing machine learning to find synergistic combinations for FDA-approved cancer drugs

Authors

Tarek Abd El-Hafeez,Mahmoud Y Shams,Yaseen AMM Elshaier,Heba Mamdouh Farghaly,Aboul Ella Hassanien

Journal

Scientific Reports

Published Date

2024/1/29

Combination therapy is a fundamental strategy in cancer chemotherapy. It involves administering two or more anti-cancer agents to increase efficacy and overcome multidrug resistance compared to monotherapy. However, drug combinations can exhibit synergy, additivity, or antagonism. This study presents a machine learning framework to classify and predict cancer drug combinations. The framework utilizes several key steps including data collection and annotation from the O’Neil drug interaction dataset, data preprocessing, stratified splitting into training and test sets, construction and evaluation of classification models to categorize combinations as synergistic, additive, or antagonistic, application of regression models to predict combination sensitivity scores for enhanced predictions compared to prior work, and the last step is examination of drug features and mechanisms of action to understand synergy …

Integration between Bioinformatics Algorithms and Neutrosophic Theory

Authors

Romany M Farag,Mahmoud Y Shams,Dalia A Aldawody,Huda E Khalid,Hazem M El-Bakry,Ahmed A Salama

Journal

Neutrosophic Sets and Systems

Published Date

2024/4/1

This paper presents a neutrosophic inference model for bioinformatics. The model is used to develop a system for accurate comparisons of human nucleic acids, where the new nucleic acid is compared to a database of old nucleic acids. The comparisons are analyzed in terms of accuracy, certainty, uncertainty, neutrality, and bias. The proposed system achieves good results and provides a reliable standard for future comparisons. It highlights the potential of neutrosophic inference models in bioinformatics applications. Data mining and bioinformatics play a crucial role in computational biology, with applications in scientific research and industrial development. Biological analysts rely on specialized tools and algorithms to collect, store, categorize, and analyze large volumes of unstructured data. Data mining techniques are used to extract valuable information from this data, aiding in the development of new therapies and understanding genetic relationships between organisms. Recent advancements in bioinformatics include gene expression tools, Bio sequencing, and Bio databases, which facilitate the extraction and analysis of vital biological information. These technologies contribute to the analysis of big data, identification of key bioinformatics insights, and generation of new biological knowledge. Data collection, analysis, and interpretation in this field involves the use of modern technologies such as cloud computing, machine learning, and artificial intelligence, enabling more efficient and accurate results. Ultimately, data mining and bioinformatics enhance our understanding of genetic relationships, aid in developing new therapies, and …

Optimizing classification of diseases through language model analysis of symptoms

Authors

Esraa Hassan,Tarek Abd El-Hafeez,Mahmoud Y Shams

Journal

Scientific Reports

Published Date

2024/1/17

This paper investigated the use of language models and deep learning techniques for automating disease prediction from symptoms. Specifically, we explored the use of two Medical Concept Normalization—Bidirectional Encoder Representations from Transformers (MCN-BERT) models and a Bidirectional Long Short-Term Memory (BiLSTM) model, each optimized with a different hyperparameter optimization method, to predict diseases from symptom descriptions. In this paper, we utilized two distinct dataset called Dataset-1, and Dataset-2. Dataset-1 consists of 1,200 data points, with each point representing a unique combination of disease labels and symptom descriptions. While, Dataset-2 is designed to identify Adverse Drug Reactions (ADRs) from Twitter data, comprising 23,516 rows categorized as ADR (1) or Non-ADR (0) tweets. The results indicate that the MCN-BERT model optimized with AdamP …

Enhancing Diagnostic Decision-Making with Image Mining Techniques: A Proposed Framework for Medical Images

Authors

Doaa E Mousa,Mahmoud Y Shams,Ahmed A Salama

Journal

Alfarama Journal of Basic & Applied Sciences

Published Date

2024/4/1

The field of medical image mining has garnered significant attention from researchers and professionals alike. This paper delves into the challenges and issues associated with medical images, such as low accuracy, poor quality, and false features. In response, we propose a prototype framework that utilizes image processing and data mining to enhance diagnostic decision-making through the extraction of relevant features from medical images. Firstly, the framework implements image processing algorithms to address problems related to brightness and imaging environment, thereby improving the quality of targeted medical images. Secondly, image mining techniques, such as segmentation and clustering, are employed on the processed images to identify and extract pertinent indicators. The model is trained iteratively using reference images, and classification techniques are utilized to identify features in test medical images. The prototype, developed using MATLAB, was tested on medical images of patients suspected to have leukemia. Results demonstrate that the proposed framework outperforms many comparable models using the same dataset, with a maximum accuracy of 98% achieved using K-mean segmentation and Super vector machine (SVM) clustering, compared to the 85-95% accuracy of commonly used frameworks for leukemia diagnosis. Validation of the proposed model confirms its adequacy and highlights the value added by incorporating image mining after preprocessing medical images using typical image enhancement techniques.

Detecting COVID-19 in chest CT images based on several pre-trained models

Authors

Esraa Hassan,Mahmoud Y Shams,Noha A Hikal,Samir Elmougy

Journal

Multimedia Tools and Applications

Published Date

2024/1/15

This paper explores the use of chest CT scans for early detection of COVID-19 and improved patient outcomes. The proposed method employs advanced techniques, including binary cross-entropy, transfer learning, and deep convolutional neural networks, to achieve accurate results. The COVIDx dataset, which contains 104,009 chest CT images from 1,489 patients, is used for a comprehensive analysis of the virus. A sample of 13,413 images from this dataset is categorised into two groups: 7,395 CT scans of individuals with confirmed COVID-19 and 6,018 images of normal cases. The study presents pre-trained transfer learning models such as ResNet (50), VGG (19), VGG (16), and Inception V3 to enhance the DCNN for classifying the input CT images. The binary cross-entropy metric is used to compare COVID-19 cases with normal cases based on predicted probabilities for each class. Stochastic Gradient …

Exploring Neutrosophic Numeral System Algorithms for Handling Uncertainty and Ambiguity in Numerical Data: An Overview and Future Directions

Authors

AA Salama,Mahmoud Y Shams,Sherif Elseuofi,Huda E Khalid

Published Date

2024/3/1

The Neutrosophic Numeral System Algorithms are a set of techniques designed to handle uncertainty and ambiguity in numerical data. These algorithms use Neutrosophic Set Theory, a mathematical framework that deals with incomplete, indeterminate, and inconsistent information. In this paper, we provide an overview of different approaches used in Neutrosophic Numeral System Algorithms, including Neutrosophic Binary System, Neutrosophic Decimal System, and Neutrosophic Octal System. These systems use different bases and representations to account for degrees of truth, indeterminacy, and falsity in numerical data. We also explore the relationship between Neutrosophic Numeral System Algorithms and Number Neutrosophic Systems, which are another type of Neutrosophic System used for representing numerical data. Number Neutrosophic Systems use Neutrosophic Numbers to represent degrees of truth, indeterminacy, and falsity in numerical data, and they can be used in conjunction with Neutrosophic Numeral System Algorithms to handle uncertainty and ambiguity in decision-making and artificial intelligence applications. Moreover. We discuss the advantages and disadvantages of each algorithm and their potential applications in various fields. Finally, we highlight the importance of Neutrosophic cryptography in addressing uncertainty and ambiguity in decision making and artificial intelligence and discuss future research directions. Understanding Neutrosophic Numeral System Algorithms and their relationship with Number Neutrosophic Systems is crucial for developing effective techniques for handling uncertainty and …

A Proposed Model for Measuring Neutrosophic Inference of Comparative Nucleic Acids

Authors

Romany Messhia Farag,Mahmoud Y Shams,Dalia Awad,Hazem El-Bakry,Ahmed Salama

Journal

Alfarama Journal of Basic & Applied Sciences

Published Date

2024/1/1

This paper introduces a novel neutrosophic inference model for the field of bioinformatics. The model is applied to develop a robust model for precise comparisons of human nucleic acids, where a new DNA sequence is matched against a comprehensive database of old nucleic acids. The results are analyzed in terms of accuracy, certainty, uncertainty, impartiality, and neutrality. Although the proposed model obtained an average accuracy rate of 33% in some cases, the similarities between sequences indicating its ability to accurately with a high accuracy rate of 85% for dissimilarity which highlights its effectiveness in distinguishing dissimilar sequences. However, the neutrality criterion yielding 0% in some cases may raise concerns about potential biases in the model's results towards specific samples. Further research is needed to understand the factors influencing neutrality and improve it for unbiased results. In conclusion, this study emphasizes the importance of employing neutrosophic inference models in the field of bioinformatics. It establishes a reliable benchmark for future nucleic acid comparisons, paving the way for advanced and more comprehensive applications in sequence analysis and genomic research.

Enhancing crop recommendation systems with explainable artificial intelligence: a study on agricultural decision-making

Authors

Mahmoud Y Shams,Samah A Gamel,Fatma M Talaat

Journal

Neural Computing and Applications

Published Date

2024/1/11

Crop Recommendation Systems are invaluable tools for farmers, assisting them in making informed decisions about crop selection to optimize yields. These systems leverage a wealth of data, including soil characteristics, historical crop performance, and prevailing weather patterns, to provide personalized recommendations. In response to the growing demand for transparency and interpretability in agricultural decision-making, this study introduces XAI-CROP an innovative algorithm that harnesses eXplainable artificial intelligence (XAI) principles. The fundamental objective of XAI-CROP is to empower farmers with comprehensible insights into the recommendation process, surpassing the opaque nature of conventional machine learning models. The study rigorously compares XAI-CROP with prominent machine learning models, including Gradient Boosting (GB), Decision Tree (DT), Random Forest (RF …

Enhancing Medical Image Quality using Neutrosophic Fuzzy Domain and Multi-Level Enhancement Transforms: A Comparative Study for Leukemia Detection and Classification

Authors

AA Salama,Mahmoud Y Shams,Huda E Khalid,Doaa E Mousa

Journal

Neutrosophic Sets and Systems

Published Date

2024/3/1

Medical image processing has become a critical research area due to the vast amounts of digital image data available. However, medical images often suffer from poor illumination and low visibility of significant structures, requiring image enhancement to improve image quality before processing. In this paper, we propose a technique for enhancing medical images by removing noise and improving contrast based on three different enhancing transforms. The proposed technique embeds the image into a neutrosophic fuzzy domain, where it is mapped into three different levels of trueness, falseness, and indeterminacy, and each level is processed individually using the enhancement transforms. We compare the proposed technique with four other systems for leukemia detection and classification using accuracy and T, I, and F values. The proposed system performs the best with an accuracy of 98%, outperforming the other systems in terms of accuracy, degree of indeterminacy, and falsity. The proposed system uses different algorithms and filters to process images and extract features like color and texture. The system's classification uses k-means for segmentation and SVM for classification. The paper highlights the importance of considering T, I, and F values in evaluating the performance of different systems for leukemia detection and classification, providing a more accurate representation of the uncertainty and ambiguity involved in the evaluation process.     DOI: 10.5281/zenodo.10780568

An optimized capsule neural networks for tomato leaf disease classification

Authors

Lobna M Abouelmagd,Mahmoud Y Shams,Hanaa Salem Marie,Aboul Ella Hassanien

Journal

EURASIP Journal on Image and Video Processing

Published Date

2024/1/8

Plant diseases have a significant impact on leaves, with each disease exhibiting specific spots characterized by unique colors and locations. Therefore, it is crucial to develop a method for detecting these diseases based on spot shape, color, and location within the leaves. While Convolutional Neural Networks (CNNs) have been widely used in deep learning applications, they suffer from limitations in capturing relative spatial and orientation relationships. This paper presents a computer vision methodology that utilizes an optimized capsule neural network (CapsNet) to detect and classify ten tomato leaf diseases using standard dataset images. To mitigate overfitting, data augmentation, and preprocessing techniques were employed during the training phase. CapsNet was chosen over CNNs due to its superior ability to capture spatial positioning within the image. The proposed CapsNet approach achieved an …

Predicting Gross Domestic Product (GDP) using a PC-LSTM-RNN model in urban profiling areas

Authors

Mahmoud Y Shams,Zahraa Tarek,El-Sayed M El-kenawy,Marwa M Eid,Ahmed M Elshewey

Journal

Computational Urban Science

Published Date

2024/1/29

Gross Domestic Product (GDP) is significant for measuring the strength of national and global economies in urban profiling areas. GDP is significant because it provides information on the size and performance of an economy. The real GDP growth rate is frequently used to indicate the economy’s health. This paper proposes a new model called Pearson Correlation-Long Short-Term Memory-Recurrent Neural Network (PC-LSTM-RNN) for predicting GDP in urban profiling areas. Pearson correlation is used to select the important features strongly correlated with the target feature. This study employs two separate datasets, denoted as Dataset A and Dataset B. Dataset A comprises 227 instances and 20 features, with 70% utilized for training and 30% for testing purposes. On the other hand, Dataset B consists of 61 instances and 4 features, encompassing historical GDP growth data for India from 1961 to 2021. To …

Wind Power Prediction Based on Machine Learning and Deep Learning Models

Authors

Zahraa Tarek,Mahmoud Y Shams,Ahmed M Elshewey,El-Sayed M El-kenawy,Abdelhameed Ibrahim,Abdelaziz A Abdelhamid,A Mohamed

Journal

Computers, Materials & Continua

Published Date

2023/1/1

Wind power is one of the sustainable ways to generate renewable energy. In 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 power. To achieve the prediction of wind power generation, several deep and machine learning models are constructed in this article as base models. These 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) regressor. In addition, data cleaning and data preprocessing were performed to the data. The dataset used in this study includes 4 features and 50530 instances. To 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 network. Five 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.

Bayesian optimization with support vector machine model for parkinson disease classification

Authors

Ahmed M Elshewey,Mahmoud Y Shams,Nora El-Rashidy,Abdelghafar M Elhady,Samaa M Shohieb,Zahraa Tarek

Journal

Sensors

Published Date

2023/2/13

Parkinson’s disease (PD) has become widespread these days all over the world. PD affects the nervous system of the human and also affects a lot of human body parts that are connected via nerves. In order to make a classification for people who suffer from PD and who do not suffer from the disease, an advanced model called Bayesian Optimization-Support Vector Machine (BO-SVM) is presented in this paper for making the classification process. Bayesian Optimization (BO) is a hyperparameter tuning technique for optimizing the hyperparameters of machine learning models in order to obtain better accuracy. In this paper, BO is used to optimize the hyperparameters for six machine learning models, namely, Support Vector Machine (SVM), Random Forest (RF), Logistic Regression (LR), Naive Bayes (NB), Ridge Classifier (RC), and Decision Tree (DT). The dataset used in this study consists of 23 features and 195 instances. The class label of the target feature is 1 and 0, where 1 refers to the person suffering from PD and 0 refers to the person who does not suffer from PD. Four evaluation metrics, namely, accuracy, F1-score, recall, and precision were computed to evaluate the performance of the classification models used in this paper. The performance of the six machine learning models was tested on the dataset before and after the process of hyperparameter tuning. The experimental results demonstrated that the SVM model achieved the best results when compared with other machine learning models before and after the process of hyperparameter tuning, with an accuracy of 92.3% obtained using BO.

Towards 3D virtual dressing room based user-friendly metaverse strategy

Authors

Mahmoud Y Shams,Omar M Elzeki,Hanaa Salem Marie

Published Date

2023/5/17

This paper applies a meta-verse strategy as an intelligent application works as a virtual reality for the dressing room. The proposed website is designed as a virtual fashion store, such that the women and/or men may foumd their fashion with a privacy and convenience characteristics based on the E-commerce and meta-verse strategy. The proposed website is a real-time virtual dressing room that eliminates a lot of the trouble from shopping by which no more long time required in the fitting room with an armful of clothing or the time-consuming process of getting dressed and undressed multiple times. Stand a few feet in front of a webcam to utilize this device, which uses motion-sensing technology. On the computer screen, a live image of you will display, along with various categories such as trousers, shirts, and dresses. By waving your hand over Translation Controls, Scale Controls, and Selection Controls, you may …

Water quality prediction using machine learning models based on grid search method

Authors

Mahmoud Y Shams,Ahmed M Elshewey,El-Sayed M El-kenawy,Abdelhameed Ibrahim,Fatma M Talaat,Zahraa Tarek

Journal

Multimedia Tools and Applications

Published Date

2023/9/29

Water quality is very dominant for humans, animals, plants, industries, and the environment. In the last decades, the quality of water has been impacted by contamination and pollution. In this paper, the challenge is to anticipate Water Quality Index (WQI) and Water Quality Classification (WQC), such that WQI is a vital indicator for water validity. In this study, parameters optimization and tuning are utilized to improve the accuracy of several machine learning models, where the machine learning techniques are utilized for the process of predicting WQI and WQC. Grid search is a vital method used for optimizing and tuning the parameters for four classification models and also, for optimizing and tuning the parameters for four regression models. Random forest (RF) model, Extreme Gradient Boosting (Xgboost) model, Gradient Boosting (GB) model, and Adaptive Boosting (AdaBoost) model are used as classification …

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

Authors

Amr El Koshiry,Entesar Eliwa,Tarek Abd El-Hafeez,Mahmoud Y Shams

Published Date

2023/11/4

Blockchain is a revolutionary technology that has the potential to revolutionize various industries, including finance, supply chain management, healthcare, and education. Its decentralized, secure, and transparent nature makes it ideal for use in industries where trust, security, and efficiency are of paramount importance. The integration of blockchain technology into the education system has the potential to greatly improve the efficiency, security, and credibility of the educational process. By 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 employers. While the potential benefits of blockchain in education are significant, there are also several challenges that must be addressed in order to …

Deep Learning Model Based on ResNet-50 for Beef Quality Classification

Authors

Said Elshahat Abdallah,Wael M Elmessery,MY Shams,NSA Al-Sattary,AA Abohany,M Thabet

Journal

Inf. Sci. Lett

Published Date

2023/6

Food quality measurement is one of the most essential topics in agriculture and industrial fields. To classify healthy food using computer visual inspection, a new architecture was proposed to classify beef images to specify the rancid and healthy ones. In traditional measurements, the specialists are not able to classify such images, due to the huge number of beef images required to build a deep learning model. In the present study, different images of beef including healthy and rancid cases were collected according to the analysis done by the Laboratory of Food Technology, Faculty of Agriculture, Kafrelsheikh University in January of 2020. The texture analysis of the beef surface of the enrolled images makes it difficult to distinguish between the rancid and healthy images. Moreover, a deep learning approach based on ResNet-50 was presented as a promising classifier to grade and classify the beef images. In this …

Weight Prediction Using the Hybrid Stacked-LSTM Food Selection Model

Authors

Ahmed M Elshewey,Mahmoud Y Shams,Zahraa Tarek,Mohamed Megahed,El-Sayed M El-kenawy,A Mohamed

Journal

Computer Systems Science and Engineering

Published Date

2023/1/20

Food choice motives (ie, 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 world. Researchers from several domains have presented several models addressing issues influencing food choice over the years. However, a multidisciplinary approach is required to better understand how various aspects interact with one another during the decision-making procedure. In this paper, four Deep Learning (DL) models and one Machine Learning (ML) model are utilized to predict the weight in pounds based on food choices. The Long Short-Term Memory (LSTM) model, stacked-LSTM model, Conventional Neural Network (CNN) model, and CNN-LSTM model are the used deep learning models. While the applied ML model is the K-Nearest Neighbor (KNN) regressor. The efficiency of the proposed model was determined based on the error rate obtained from the experimental results. The 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 21. Therefore, the results demonstrated that the stacked LSTM achieved improved results compared with the LSTM, CNN, CNN-LSTM, and KNN regressor.

The effect of choosing optimizer algorithms to improve computer vision tasks: a comparative study

Authors

Esraa Hassan,Mahmoud Y Shams,Noha A Hikal,Samir Elmougy

Journal

Multimedia Tools and Applications

Published Date

2023/5

Optimization algorithms are used to improve model accuracy. The optimization process undergoes multiple cycles until convergence. A variety of optimization strategies have been developed to overcome the obstacles involved in the learning process. Some of these strategies have been considered in this study to learn more about their complexities. It is crucial to analyse and summarise optimization techniques methodically from a machine learning standpoint since this can provide direction for future work in both machine learning and optimization. The approaches under consideration include the Stochastic Gradient Descent (SGD), Stochastic Optimization Descent with Momentum, Rung Kutta, Adaptive Learning Rate, Root Mean Square Propagation, Adaptive Moment Estimation, Deep Ensembles, Feedback Alignment, Direct Feedback Alignment, Adfactor, AMSGrad, and Gravity. prove the ability of each …

See List of Professors in Mahmoud Y. Shams University(Kafrelsheikh University)

Mahmoud Y. Shams FAQs

What is Mahmoud Y. Shams's h-index at Kafrelsheikh University?

The h-index of Mahmoud Y. Shams has been 18 since 2020 and 18 in total.

What are Mahmoud Y. Shams's top articles?

The articles with the titles of

Neutrosophic Encoding and Decoding Algorithm for ASCII Code System

Harnessing machine learning to find synergistic combinations for FDA-approved cancer drugs

Integration between Bioinformatics Algorithms and Neutrosophic Theory

Optimizing classification of diseases through language model analysis of symptoms

Enhancing Diagnostic Decision-Making with Image Mining Techniques: A Proposed Framework for Medical Images

Detecting COVID-19 in chest CT images based on several pre-trained models

Exploring Neutrosophic Numeral System Algorithms for Handling Uncertainty and Ambiguity in Numerical Data: An Overview and Future Directions

A Proposed Model for Measuring Neutrosophic Inference of Comparative Nucleic Acids

...

are the top articles of Mahmoud Y. Shams at Kafrelsheikh University.

What are Mahmoud Y. Shams's research interests?

The research interests of Mahmoud Y. Shams are: Computer Science, Pattern Recognition, Computer Vision, Artificial Intelligence, Biomedical

What is Mahmoud Y. Shams's total number of citations?

Mahmoud Y. Shams has 909 citations in total.

What are the co-authors of Mahmoud Y. Shams?

The co-authors of Mahmoud Y. Shams are Aboul Ella Hassanien Ali, O. M. Elzeki, shahenda sarhan, Dr. Kalka dubey, Tamer Medhat Mohammed Ibrahim, Mohammed ElAraby.

    Co-Authors

    H-index: 87
    Aboul Ella Hassanien Ali

    Aboul Ella Hassanien Ali

    Cairo University

    H-index: 13
    O. M. Elzeki

    O. M. Elzeki

    Mansoura University

    H-index: 13
    shahenda sarhan

    shahenda sarhan

    Mansoura University

    H-index: 11
    Dr. Kalka dubey

    Dr. Kalka dubey

    Indian Institute of Technology Roorkee

    H-index: 9
    Tamer Medhat Mohammed Ibrahim

    Tamer Medhat Mohammed Ibrahim

    Kafrelsheikh University

    H-index: 3
    Mohammed ElAraby

    Mohammed ElAraby

    Beni-Suef University

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