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A novel medical steganography technique based on Adversarial Neural Cryptography and digital signature using least significant bit replacement

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International Journal of Cognitive computing in Engineering 2024年第1期5卷 379-397页

作者： Hameed, Mohamed Abdel Hassaballah, M. Abdelazim, Riem Sahu, Aditya Kumar Department of Computer Science Faculty of Computers and Information Luxor University Luxor85951 Egypt Department of Computer Science College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz University AlKharj16278 Saudi Arabia Department of Computer Science Faculty of Computers and Information South Valley University Qena Egypt Department of Information Systems College of Information Technology Misr University for Science and Technology Giza Egypt Amrita School of Computing Amrita Vishwa Vidyapeetham Andhra Pradesh Amaravati522503 India

With recent advances in technology protecting sensitive healthcare data is challenging. Particularly, one of the most serious issues with medical information security is protecting of medical content, such as the privacy of patients. As medical information becomes more widely available, security measures must be established to protect confidentiality, integrity, and availability. Image steganography was recently proposed as an extra data protection mechanism for medical records. This paper describes a data-hiding approach for DICOM medical pictures. To ensure secrecy, we use Adversarial Neural Cryptography with SHA-256 (ANC-SHA-256) to encrypt and conceal the RGB patient picture within the medical image's Region of Non-Interest (RONI). To ensure anonymity, we use ANC-SHA-256 to encrypt the RGB patient image before embedding. We employ a secure hash method with 256bit (SHA-256) to produce a digital signature from the information linked to the DICOM file to validate the authenticity and integrity of medical pictures. Many tests were conducted to assess visual quality using diverse medical datasets, including MRI, CT, X-ray, and ultrasound cover pictures. The LFW dataset was chosen as a patient hidden picture. The proposed method performs well in visual quality measures including the PSNR average of 67.55, the NCC average of 0.9959, the SSIM average of 0.9887, the UQI average of 0.9859, and the APE average of 3.83. It outperforms the most current techniques in these visual quality measures (PSNR, MSE, and SSIM) across six medical assessment categories. Furthermore, the proposed method offers great visual quality while being resilient to physical adjustments, histogram analysis, and other geometrical threats such as cropping, rotation, and scaling. Finally, it is particularly efficient in telemedicine applications with high achieving security with a ratio of 99% during remote transmission of Electronic Patient Records (EPR) over the Internet, which safeguards the patien

关键词： Steganography

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Multiple Sclerosis Biomarkers Detection by a BiLSTM Deep Learning Model for miRNA Data Analysis 23

Multiple Sclerosis Biomarkers Detection by a BiLSTM Deep Lea...

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23rd International Arab Conference on Information technology, ACIT 2022

作者： Ali, Nehal M. Shaheen, Mohamed Mabrouk, Mai S. Rizka, Mohamed Abo College of Computing and Information Technology Arab Academy for Science Technology and Maritime Transport Cairo Egypt Misr University for Science and Technology Department of Biomedical Engineering Cairo Egypt

ISBN: (纸本)9798350320244

High-Throughput data technology has enabled studies on microRNA analysis to evolve in the field of early disease diagnosis. Multiple Sclerosis is one of the most known chronic autoimmune diseases that can cause severe disabilities, including partial blindness, motor disabilities, and considerable psychological impact. This work introduces a complete BiLSTM deep learning model for analyzing miRNA data of Multiple Sclerosis patients to provide early detection for this disease. The introduced model is based on a preprocessing flow published earlier by the authors. The experiments were conducted on a dataset of 215 transcriptomic miRNA samples of treated and untreated Multiple Sclerosis patients. The implicated results were quite promising, as the produced sensitivity, specificity, precision, accuracy, and F1 scores of (0.785,0.789,0.788, 0.8, and 0.79) respectively, were achieved. To ensure model robustness, the obtained accuracy of the introduced model was compared to two other state-of-Art models, and the proposed BiLSTM has relatively outperformed the other literature models. © 2022 IEEE.

关键词： Patient treatment

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A Deep Learning Solution Framework for Awareness, Diagnosing and Predicting COVID-19 6th

A Deep Learning Solution Framework for Awareness, Diagnosing...

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6th International Conference on Advanced Machine Learning Technologies and Applications, AMLTA 2021

作者： ElGohary, Rania Hisham, Ahmed Salama, Mohamed Selim, Yousef A. Yousef Abdelwahab, M.S. Artificial Intelligence Technology Center Misr University for Science and Technology 6th of October City Egypt Souad Kafafi Hospiatl Misr University for Science and Technology 6th of October City Egypt Computing Misr University for Science and Technology 6th of October City Egypt

ISBN: (纸本)9783030697167

Covid-19 pandemic changed our world as many people lost their jobs. technology’s role is helping and facilitating our life, this role highlights situations like covid-19 virus pandemic. In this paper, we propose an innovative framework integrated with the latest machine learning techniques to assist of the covid-19 virus outbreak. The proposed framework provides three modules awareness and guidance for individuals through Chabot, diagnosis and prediction of COVID-19. The initial diagnosis for covid-19 using chest X-ray and predictions for covid-19 new cases. Moreover, the proposed framework has the potential to help citizens and national healthcare systems in curtailing the COVID-19 pandemic. The Chabot represent all the following: the symptoms, precautions and safety measures, early detection for COVID-19 cases using chest X-ray, predictions for covid-19 new cases to in light governments with the future of the pandemic. The proposed framework assist the decision makers to make improved decisions in quarantine and lock-down. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： COVID-19

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Hyper-Parameters Tuning Using Meta-Heuristic Algorithms for Nurses Stress Detection

Hyper-Parameters Tuning Using Meta-Heuristic Algorithms for ...

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Cyber Resilience (ICCR), International Conference on

作者： Ayat Alrosan Waleed Alomoush Merna Youssef Abdelrhman W. Nile Mohanad A. Deif Rania EL Gohary School of Computing Skyline University College Sharjah United Arab Emirates Biomedical Engineering Cairo University Giza Egypt Department of Artificial intelligence College of Information Technology Misr University for Science & Technology (MUST) 6th of October City Egypt

ISBN: (数字)9798350394962

ISBN: (纸本)9798350394979

This research utilizes machine learning techniques, particularly Random Forest, to examine the impact of mental stress on nurses. Stress, arising from challenging events or demanding conditions (stressors), poses significant risks to mental and physical well-being. Real-time data collection through wearable devices includes physiological measurements, behavioral patterns, and environmental factors, creating a comprehensive method for stress monitoring. Personalized and adaptive models, crucial for accurate stress detection due to its subjective nature, are explored. The study integrates data science and machine learning to enhance efficiency in various stress detection methods, employing meta-heuristics search algorithms. Comparative analysis yielded promising results with a 96.63% accuracy and a 95.76% f1 score.

关键词： Analytical models Machine learning algorithms Computational modeling Metaheuristics Human factors Medical services Real-time systems

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Multiple Sclerosis Biomarkers Detection by a BiLSTM Deep Learning Model for miRNA Data Analysis

Multiple Sclerosis Biomarkers Detection by a BiLSTM Deep Lea...

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Information technology (ACIT)

作者： Nehal M. Ali Mohamed Shaheen Mai S. Mabrouk Mohamed Abo Rizka College of Computing and Information Technology Arab Academy for Science Technology and Maritime Transport Cairo Egypt Department of Biomedical Engineering Misr University for Science and Technology Cairo Egypt

ISBN: (数字)9798350320244

ISBN: (纸本)9798350320251

High-throughput data technology has enabled studies on microRNA analysis to evolve in the field of early disease diagnosis. Multiple Sclerosis is one of the most known chronic autoimmune diseases that can cause severe disabilities, including partial blindness, motor disabilities, and considerable psychological impact. This work introduces a complete BiLSTM deep learning model for analyzing miRNA data of Multiple Sclerosis patients to provide early detection for this disease. The introduced model is based on a preprocessing flow published earlier by the authors. The experiments were conducted on a dataset of 215 transcriptomic miRNA samples of treated and untreated Multiple Sclerosis patients. The implicated results were quite promising, as the produced sensitivity, specificity, precision, accuracy, and F1 scores of (0.785,0.789,0.788, 0.8, and 0.79) respectively, were achieved. To ensure model robustness, the obtained accuracy of the introduced model was compared to two other state-of-art models, and the proposed BiLSTM has relatively outperformed the other literature models.

关键词： Deep learning Analytical models Multiple sclerosis Sensitivity Biological system modeling Psychology Data models

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Parkinson's Disease Detection Based on Vocal Biomarkers and Machine Learning Approach

Parkinson's Disease Detection Based on Vocal Biomarkers and ...

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Telecommunications Conference (ITC-Egypt), International

作者： Ayat Alrosan M. Abdel-Aty Mohamed Hafez Shawkat Alkhazaleh Mohanad A.Deif Rania ELGohary School of Computing Skyline University College Sharjah United Arab Emirates Deanship of Research and Graduate Studies Ahlia University Kingdom of Bahrain Faculty of Engineering FEQS INTI-IU-University Nilai Malaysia Department of Mathematics Faculty of Science and Information Technology Jadara University Department of Artificial intelligence College of Information Technology Misr University for Science & Technology (MUST) Egypt

ISBN: (数字)9798350351408

ISBN: (纸本)9798350351415

Parkinson's disease (PD) must be identified early to provide prompt treatment and better patient outcomes. This study looks into using machine learning methods in conjunction with vocal biomarkers to identify Parkinson's disease early. Vocal data was analyzed using four techniques: Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Random Forest, and Logistic Regression algorithms. The third strategy, which used the Synthetic Minority Over-sampling Technique (SMOTE) for data balance, performed exceptionally well and stood out among the others. Utilizing SMOTE for dataset balancing, the Random Forest algorithm produced an exceptional 98.3 % accuracy. The study highlights the effectiveness of voice biomarkers, examines the various approaches used, and draws comparisons between them.

关键词： Support vector machines Machine learning algorithms Parkinson's disease Computational modeling Biomarkers Predictive models Nearest neighbor methods

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Brain Stroke Prediction Using ANN

Brain Stroke Prediction Using ANN

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2022 International Conference on Cyber Resilience, ICCR 2022

作者： Issa, Ghassan F. Shaalan, Khaled Shaalan, Yara Saeed, Hafiza Afia Fatima, Noor Rehman, Abd Ur Skyline University College University City Sharjah School of Information Technology Sharjah1797 United Arab Emirates The British University in Dubai Faculty of Engineering and It United Arab Emirates Misr University for Science and Technology Egypt Agriculture University Department of Computer Science Faisalabad Pakistan Fatima Memorial Hospital College of Medicine and Dentistry Lahore Lahore54000 Pakistan Riphah International University Riphah School of Computing Lahore Pakistan

ISBN: (纸本)9781665461221

Brain stroke is an intense health condition that happens when a blood clot restricts the normal flow of blood and different nutrients withinside the brain. It can cause neurologic damage, headaches and often death if not cured at a certain stage. According to the WHO, brain stroke has turned out to be the maximum rising disorder that is inflicting death because of late prognosis in both adults and elder one's. Like a 'coronary heart attack' it damages or paralyzes the brain. There have been numerous predictions about cerebrovascular, however this disorder is the one that receives less consideration. Some foremost elements that contribute to the hazard of stroke are age, gender, race, and genetic history of the patient. Artificial intelligence based cognitive modeling simulates properties of biological neural networks. Artificial Neural Network offers the basis for the effectiveness of the studies. Different machine learning algorithms offer a sturdy base to discover the accuracy of the disorder. This study implements this model for higher instruction to combat pandemics in future. © 2022 IEEE.

关键词： Blood

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PID Controller Tuning Using Multi-Objective Ant Colony Optimization for Blood Glucose Level of a Diabetic Patient

PID Controller Tuning Using Multi-Objective Ant Colony Optim...

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Cyber Resilience (ICCR), International Conference on

作者： Mohanad A. Dief Waleed Alomoush Hani Atta Osama A. Khashan Ahmed Solyman Rania ELGohary Department of Artificial intelligence College of Information Technology Misr University for Science & Technology (MUST) 6th of October City Egypt School of Computing Skyline University College Sharjah United Arab Emirates Faculty of Engineering Zarqa University Zarqa Jordan Research and Innovation Centres Rabdan Academy Abu Dhabi United Arab Emirates School of Computing Engineering and Built Environment Glasgow Caledonian University Glasgow UK

ISBN: (数字)9798350394962

ISBN: (纸本)9798350394979

In this research, a nonlinear suboptimal controller was provided for type 1 diabetes patients' blood glucose level regulation based on a Proportional-Integral-Derivative (PID) controller with Ant Colony Optimization (ACO). The ACO approach is used to determine the ideal PID controller gain settings within a stability area. This study simulates three cases of a physiological model of glucose regulation in people with type 1 diabetes (No insulin treatment, fixed insulin injection, and Controlled blood glucose concentration using a PID controller) by using the Bergman Model in the context of meal-induced disturbances. In terms of dynamic performance, such as a reduction in maximum overshoot, settling time, and rising time, simulation findings suggest that the proposed technique for PID tuning using the ACO algorithm is more adaptable and cost-effective.

关键词： Ant colony optimization Adaptation models Heuristic algorithms Regulation Stability analysis Glucose Diabetes

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A hybrid healthy diet recommender system based on machine learning techniques

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Computers in Biology and Medicine 2025年 184卷 109389-109389页

作者： Sweidan, Sara Askar, S.S. Abouhawwash, Mohamed Badr, Elsayed Department of Artificial Intelligence Faculty of Computer and Artificial Intelligence Benha University Benha Egypt Faculty of Computer Science and Engineering New Mansoura University New Mansoura Egypt Department of Statistics and Operations Research College of Science King Saud University P.O. Box 2455 Riyadh11451 Saudi Arabia College of Engineering Michigan State University East LansingMI48824 United States Department of Mathematics Faculty of Science Mansoura University Mansoura35516 Egypt Department of Scientific Computing Faculty of Computer and Artificial Intelligence Benha University Benha Egypt Department of Information Systems College of Information Technology Misr University for Science & Technology Giza Egypt

Obesity is a chronic disease correlated with numerous risk factors that not only negatively affect all body functions but also increase the chances of developing chronic diseases and the associated morbidity and mortality rates. This study proposes a novel system that bridges the gap between healthcare providers and patients by offering both parties some tools for navigating the intricacies of dietary planning. In this system, machine learning techniques are used to determine the required calories before starting an obesity treatment. A hybrid precision model with minimal parameters is also developed to estimate the appropriate number of calories for losing weight and to formulate a healthy diet plan. A real dataset of 15 anthropometric measurements is analyzed using SVR, LR, and DTR regression models, and all the data are preprocessed before analysis to enhance model performance. Results show that the required calories can be estimated with a high correlation (R = 0.985) from independent measurements. The proposed model also calculates the healthy daily percentages of fats, proteins, and carbohydrates based on a knowledge base of medical rules and functions, thus facilitating the sequential treatment of obese patients. In sum, this study applies different models to design a practical, cost-effective approach for accurately determining the required calories and formulating valuable diet plans for obesity treatment and management. © 2024 Elsevier Ltd

关键词： Calorie Machine learning Regression Obesity treatment Expert system

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Evaluating The Mixing Performance in A Planar Passive Micromixer With T-shape and SAR Mixing Chambers: Comparative Study

Evaluating The Mixing Performance in A Planar Passive Microm...

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Electrical, Energy, and Artificial Intelligence (EICEEAI), International Engineering Conference on

作者： Jafar Ababneh Mohanad A. Deif Ahmad Solyman Rahma S. Zekry Mohamed Hafez Samer M. Sharfo The Faculty of Information Technology Cyber security Zarqa University Zarqa Jordan College of Engineering University of Business and Technology Jeddah Saudi Arabia Department of Artificial intelligence College of Information Technology Misr University for Science & Technology (MUST) Egypt School of Computing Engineering and Built Environment Glasgow Caledonian University Glasgow UK Department of Bioelectronics Modern University of Technology and Information (MTI) University Cairo Egypt Faculty of Engineering FEQS INTI-IU-University Nilai Malaysia

ISBN: (数字)9798350373363

ISBN: (纸本)9798350373370

In Microfluidic devices have gained significant interest in various fields, including biomedical diagnostics, environmental preservation, animal epidemic avoidance, and food safety regulation. Micromixing phenomena are crucial for these devices' functionality, as they accurately and efficiently manipulate fluids within microchannels. The process aims to blend samples accurately and swiftly within these scaled-down devices, governed by the promotion of dispersion among distinct fluid species or particles. Advancements in passive and active micromixers have led to innovative designs incorporating diverse processes to enhance mixing efficiency. Examples include two-dimensional impediments, controlled imbalanced collisions, and complex configurations like spiral and convergence-divergence structures. Active micromixers use external cues to initiate and regulate mixing processes, including thermal, magnetic, sound, pressure, and electrical fields. The trajectory of micromixing technologies is significantly influenced by current developments in microfluidics. One notable advancement is the incorporation of micromixers into 3D printing methodologies, facilitating the development of adaptable microfluidic systems. Additionally, the incorporation of microfluidic principles into paper-based channels creates opportunities for the development of cost-effective and portable diagnostic devices. The process of micro-mixing is critical in boosting the functionalities of these devices.

关键词： Performance evaluation Water Resistance Spirals Three-dimensional printing Software Regulation

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