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Evaluation of machine learning-based regression techniques for prediction of diabetes levels fluctuations

Heliyon 2025年第1期11卷 e41199页

作者： alkalifah, Badriah Shaheen, Muhammad Tariq alotibi, Johrah alsubait, Tahani alhakami, Hosam Department of Computer Science and Artificial Intelligence College of Computing Umm Al-Qura University Makkah Saudi Arabia School of Electrical Engineering and Computer Science NUST Islamabad Pakistan Department of Software Engineering College of Computing Umm Al-Qura University Makkah Saudi Arabia

Middle-Aged and Elderly people today face a variety of health problems as a result of their modern lifestyle, which includes increased work stress, less physical activity, and altered food habits. Because of Complications arising, diabetes has become one of the most frequent, severe, and fatal illnesses around the world. Therefore, inaccurate measurements of blood glucose levels can seriously damage vital organs. Several strategies for long-term glucose prediction have been proposed in the literature. Unfortunately, these methods require the patient to identify their daily activities, which can be error-prone, such as meal intake, insulin injection, and emotional aspects. This paper suggests using continuous glucose monitoring (CGM) of 14733 patients, with three assistance factors to predict blood glucose levels independently of other parameters, hence reducing the burden on the patients. To support this an Artificial Neural Network (ANN), Binary Decision Tree (BDT), Linear Regression (LR), Boosting Regression Tree Ensemble (BSTE), Linear Regression with Stochastic Gradient Descent (LRSGD), Stepwise (SW), Support Vector Machine (SVM), and Gaussian process regression (GPR) were investigated. The result indicated that The highest classification accuracy of (92.58%) has been achieved by BDT followed by BSTE (92.04%) and GPR (88.59%). The obtained average of root means square error (MSE) was 1.64, 1.67, 1.69, mg/dL for prediction horizon (PH) respectively to GPR, BSTE, and ANN. © 2024 The Author(s)

关键词： Continuous glucose monitoring (CGM) Diabetic Hyperglycemia Hypoglycemia Machine learning (ML) Regression

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Modelling distillate output of a solar distiller with eco-friendly wood-based steam generation basin using machine learning model and starling murmuration optimizer

International Journal of Thermofluids

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International Journal of Thermofluids 2025年 27卷

作者： al-Betar, Mohammed Azmi Muskens, Otto L. Hammoodi, Karrar A. Abd Elaziz, Mohamed Shambour, Qusai Yousef Fraihat, Salam Elsheikh, Ammar H. College of Engineering and Information Technology Ajman University Ajman United Arab Emirates Faculty of Engineering and Physical Sciences University of Southampton Southampton SO17 1BJ United Kingdom College of Engineering University of Al Maarif Al Anbar31001 Iraq Department of Mathematics Faculty of Science Zagazig University Zagazig44519 Egypt Faculty of Computer Science and Engineering Galala University Suez435611 Egypt Software Engineering Department Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman Jordan Department of Production Engineering and Mechanical Design Faculty of Engineering Tanta University Tanta31527 Egypt Faculty of Engineering Pharos University in Alexandria Egypt

Yield augmentation of solar distiller (SD) using low-cost modifications with minimum environmental impacts is highly desirable. In this study, we developed an eco-friendly steam generation device made of carbonized Ficus sycomorus wood to boost the yield of the conventional single-basin solar distiller. Moreover, a hybrid model composed of a long short-term memory (LSTM) network and a Starling murmuration optimizer (SMO) was developed to predict the yield of the established SD. To verify the accuracy of the model, three other models were employed and the distillate yield was predicted. The predicted data of all models was evaluated using different error measures. These models were standalone LSTM and LSTM optimized by an artificial hummingbird algorithm (AHA) or manta ray foraging optimizer (MRFO). Real field data were employed during the training and testing of all models. The thermal performance of the distiller with the steam generation device was compared with that of a traditional distiller based on exergy output, exergy efficiency, and energy efficiency. The distillate yield, exergy efficiency, and energy efficiency of the modified SD were enhanced by 34 %, 49 %, and 40 %, respectively, compared with that of the traditional distiller. Moreover, the total amount of produced drinkable water using the modified SD could reach a high value of 6.1 L/day with a low cost of 0.014 $/L. © 2025

关键词： Exergy

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Improving Multi-Factor Authentication Security with Deep Learning-Based User Behaviour Analysis 3

Improving Multi-Factor Authentication Security with Deep Lea...

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3rd International Conference on Integrated Circuits and Communication Systems, ICICACS 2025

作者： alnafea, Fahad Sulaiman Mohammed Sengar, alok Singh Hamid, Noor Kaylan Selladurai, Kuttimarks Muthu Hassan, Sharmeen Izzat Saravanakumar, R. College of Business and Economics Qassim University Department of Accounting Buraydah Saudi Arabia Haridwar University Department of Computer Science & Engineering Roorkee India Al Kitab University Department of Computer Education Kirkuk Iraq Shivajirao S. Jondhle College of Engineering and Technology Asangaon India College of Engineering Knowledge University Department of Computer Engineering Erbil Iraq Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Ece Chennai India

ISBN: (纸本)9798331508456

With growing frequency and sophistication of cyber threats, there is an urgent need for more adaptive, secure multi-factor authentication systems. Traditional approaches often tend to make static factor-based authentication systems that just ignore the dynamic nature of user behavior and are constantly at risk from evolved attacks against authentication mechanisms. This paper presents a novel hybrid deep learning approach - that of long short-term memory networks and convolutional neural networks combined in an attempt to address these disadvantages and enhance the security of an MFA system. The approach uses LSTM to analyze sequential data on user behavior, hence recognizing long-term dependencies like login pattern and session durations. On the other hand, CNN layers will classify the attribute by learning the spatial hierarchy thus identifying small variations that can mark emerging threats to security. This method, in contrast to others, analyzes statically or all at once, providing a dynamic check into the temporal as well as spatial aspects of user interactions;thus, there is more force in its frame for anomaly detection. Experimental tests further verify the excellent performance of the system, with results indicating an accuracy of 98.2%, precision of 99.9%, recall of 99.5%, and an F1 score of 99%. The new strategy exploits MFA frameworks more effectively in minimizing their vulnerabilities against authentication attacks, while developing a dynamic and robust solution that caters to the shift in the ever-changing landscape within cybersecurity, thus advancing the possibility of practice securely. © 2025 IEEE.

关键词： Deep learning

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Improving Multi-Factor Authentication Security with Deep Learning-Based User Behaviour Analysis

Improving Multi-Factor Authentication Security with Deep Lea...

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Integrated Circuits and Communication Systems (ICICACS), IEEE International Conference on

作者： Fahad Sulaiman Mohammed alnafea alok Singh Sengar Noor Kaylan Hamid Kuttimarks Muthu Selladurai Sharmeen Izzat Hassan R Saravanakumar Department of Accounting College of Business and Economics in Qassim University Buraida Saudi Arabia Department of Computer Science & Engineering Haridwar University Roorkee India Department of Computer Education Al Kitab University Kirkuk Iraq Shivajirao S. Jondhle College of Engineering and Technology Asangaon India Department of Computer Engineering College of Engineering Knowledge University Erbil Iraq Department of ECE Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai India

ISBN: (数字)9798331508456

ISBN: (纸本)9798331508463

With growing frequency and sophistication of cyber threats, there is an urgent need for more adaptive, secure multi-factor authentication systems. Traditional approaches often tend to make static factor-based authentication systems that just ignore the dynamic nature of user behavior and are constantly at risk from evolved attacks against authentication mechanisms. This paper presents a novel hybrid deep learning approach - that of long short-term memory networks and convolutional neural networks combined in an attempt to address these disadvantages and enhance the security of an MFA system. The approach uses LSTM to analyze sequential data on user behavior, hence recognizing long-term dependencies like login pattern and session durations. On the other hand, CNN layers will classify the attribute by learning the spatial hierarchy thus identifying small variations that can mark emerging threats to security. This method, in contrast to others, analyzes statically or all at once, providing a dynamic check into the temporal as well as spatial aspects of user interactions; thus, there is more force in its frame for anomaly detection. Experimental tests further verify the excellent performance of the system, with results indicating an accuracy of 98.2%, precision of 99.9%, recall of 99.5%, and an F1 score of 99%. The new strategy exploits MFA frameworks more effectively in minimizing their vulnerabilities against authentication attacks, while developing a dynamic and robust solution that caters to the shift in the ever-changing landscape within cybersecurity, thus advancing the possibility of practice securely.

关键词： Deep learning Hands Multi-factor authentication Accuracy Neural networks Reflection Pattern recognition Convolutional neural networks Integrated circuit modeling Long short term memory

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Optimization of variables for cadmium and copper removal using magnetic nanocomposite

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BMC Chemistry 2025年第1期19卷 1-12页

作者： Hsu, Chou-Yi Mustafa, Mohammed Ahmed Ghadir, Ghadir Kamil Bansal, Pooja Kaur, Harpreet Mohammed, Amjed Qasim Abdulwahid, alzahraa S. Hussein, Ahmed Remthan Namaha, Sahira Qasim Ami, Ahmed ali Radi, Usama Kadem alzubaidi, Laith H. Kazemi, ali Department of Pharmacy Chia Nan University of Pharmacy and Science Tainan City 71710 Taiwan Department of Biology College of Education University of Samarra Samarra 34010 Iraq Department of Computer Engineering Institute of Graduate Programs Al-Turath University Al Mansour Baghdad 10013 Iraq Department of Biotechnology and Genetics Jain (Deemed-to-Be) University Karnataka Bengaluru 560069 India Department of Allied Healthcare and Sciences Vivekananda Global University Rajasthan Jaipur 303012 India School of Basic & Applied Sciences Shobhit University Uttar Pradesh Gangoh 247341 India Department of Health & Allied Sciences Arka Jain University Jharkhand Jamshedpur 831001 India Department of Dentistry Al-Manara College for Medical Sciences University of Misan Maysan Amarah Iraq Department of Medical Engineering Al-Hadi University College Baghdad 10011 Iraq Faculty of Science Mazaya University College Nasiriyah Iraq College of Dentistry Al-Esraa University Baghdad Iraq Department of Medical Laboratories Technology Al-Nisour University College Baghdad Iraq College of Pharmacy National University of Science and Technology Nasiriyah Iraq College of Technical Engineering The Islamic University of Najaf Najaf Iraq College of Technical Engineering The Islamic University of Al Diwaniyah Al Diwaniyah Iraq College of Technical Engineering The Islamic University of Babylon Babylon Iraq School of Public Health Tehran University of Medical Sciences Tehran Iran

This study aims to investigate cadmium and copper ultrasound-assisted removal efficiency on a laboratory scale using a cobalt ferrite/activated carbon (COF/AC) composite as an adsorbent. For this purpose, the effect of four independent variables (i.e., composite amount, pH, heavy metal concentrations, and ultrasound radiation time) on the performance of the cadmium and copper removal was investigated. The COF/AC composite was characterized using scanning electron microscopy (SEM), energy-dispersive X-ray (EDX), Brunauer–Emmett–Teller (BET), X-ray diffraction (XRD), and vibrating-sample magnetometer (VSM). The SEM and XRD techniques showed the successful synthesis of the COF/AC composite. The COF/AC composite has a surface area of 659.4 m2 g−1, an average diameter of 3.6 nm, and a pore volume of 0.482 cm3 g−1. In this study, R2 ˃ 0.99 and Adj-R2 ˃ 0.98 for both analytes signify a high agreement between the obtained laboratory data and the model-predicted data. The analysis results for heavy metal removal revealed the following optimal conditions: the composite content of 0.22 g, ultrasound radiation time of 22 min, concentration of 19 mg L−1, and pH of 5. Under optimal conditions, the maximum removal efficiency reached 93.46% and 97.45% for cadmium and copper, respectively. The COF/AC composite reuse results showed no significant decrease in removal efficiency up to 4 times of use during the adsorption and desorption process. Analysis of real samples showed that the removal rates of cadmium and copper were 89.62% and 96.37%, respectively. © The Author(s) 2025.

关键词： Cadmium Copper Optimization Removal

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Fused Fuzzy Deep Learning and Information Steganography for Privacy Preservation in Medical Consumer Electronics

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IEEE Transactions on Consumer Electronics 2025年

作者： Chen, Jingxue Zhao, Pengbiao Deng, Erqiang Khan, Fazlullah alturki, Ryan alshawi, Bandar Xiong, Hu School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu610054 China the School of Computer Science Faculty of Science and Engineering University of Nottingham Ningbo China Zhejiang Ningbo315104 China the KKR and KSR institute of technology and sciences Andhra Pradesh Guntur India the Division of Research and Development Lovely Professional University Phagwara India the University Centre of Research and Development Chandigarh University Mohali India the Department of Computer and Network Engineering College of Computing Umm Al-Qura University Makkah Saudi Arabia the Department of Software Engineering College of Computing Umm Al-Qura University Makkah Saudi Arabia

—Medical Consumer Electronics (MCE) have greatly facilitated people’s lives, allowing patients to obtain personal medical information including medical images. These medical images obtained from MCE need to be processed using segmentation, diagnosis, recognition to obtain useful information. Deep learning plays an important role in medical image segmentation and recognition. However, other personal information about the patient, such as previous cases, is often required to determine the final diagnosis. However, sending personal information and medical images generated through MCE to the clinic directly exposes patient privacy. Therefore, in this paper, we propose a digital medical image privacy protection framework based on information steganography with fuzzy deep learning. The proposed framework transforms patient privacy information into invisible noise and hides it in the deep covert space of the image. This method plays a crucial role in ensuring patient privacy and data security. Furthermore, this method can ensure a high level of image segmentation accuracy that supports the development of personalized medical services. The experimental results show that the proposed framework can embed patient information in medical images without affecting the segmentation task, and effectively protect privacy. Finally, the proposed framework provides new possibilities and impetus for the popularization of MCE. © 2025 IEEE.

关键词： Medical imaging

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Automated dual CNN-based feature extraction with SMOTE for imbalanced diabetic retinopathy classification

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Image and Vision Computing 2025年 159卷

作者： Soomro, Danyal Badar ChengLiang, Wang Ashraf, Mahmood alHammadi, Dina Abdulaziz alsubai, Shtwai Medaglia, Carlo Innab, Nisreen Umer, Muhammad College of Computer Science Chongqing University China Department of Computer Science and Information Technology Times institute Multan Pakistan Department of Information Systems College of Computer and Information Sciences Princess Nourah bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia Department of Computer Science College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz University Al-Kharj Saudi Arabia Universita Telematica degli Studi IUL Italy Department of Computer Science and Information Systems College of Applied Sciences AlMaarefa University Diriyah Riyadh13713 Saudi Arabia Department of Computer Science & Information Technology The Islamia University of Bahawalpur Bahawalpur63100 Pakistan

The primary cause of Diabetic Retinopathy (DR) is high blood sugar due to long-term diabetes. Early and correct diagnosis of the DR is essential for timely and effective treatment. Despite high performance of recently developed models, there is still a need to overcome the problem of class imbalance issues and feature extraction to achieve accurate results. To resolve this problem, we have presented an automated model combining the customized ResNet-50 and EfficientNetB0 for detecting and classifying DR in fundus images. The proposed model addresses class imbalance using data augmentation and Synthetic Minority Oversampling Technique (SMOTE) for oversampling the training data and enhances the feature extraction process through fine-tuned ResNet50 and EfficientNetB0 models with ReLU activations and global average pooling. Combining extracted features and then passing it to four different classifiers effectively captures both local and global spatial features, thereby improving classification accuracy for diabetic retinopathy. For Experiment, The APTOS 2019 Dataset is used, and it contains of 3662 high-quality fundus images. The performance of the proposed model is assessed using several metrics, and the findings are compared with contemporary methods for diabetic retinopathy detection. The suggested methodology demonstrates substantial enhancement in diabetic retinopathy diagnosis for fundus pictures. The proposed automated model attained an accuracy of 98.5% for binary classification and 92.73% for multiclass classification. © 2025 Elsevier B.V.

关键词： Automated CNN Deep learning Diabetic retinopathy classification Hybrid feature extraction SMOTE Transfer learning

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Free convection investigation for a Casson-based Cu−H₂O nanofluid in semi parabolic enclosure with corrugated cylinder

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Heliyon 2025年第1期11.0卷 e40960页

作者： Hameed, Rafel H. Hussein, Rana ali al-Salami, Qusay H. alomari, Mohammed Azeez Hassan, Ahmed M. alyousuf, Farah Q.A. alqurashi, Faris Flayyih, Mujtaba A. University of Babylon College of Engineering Mechanical Engineering Department Iraq Power Mechanics Engineering Department Al-Musaib Technical College Al-Furat Al-Awsat Technical Babylon Hillah Iraq Department of Business Administration College of Administrative and Financial Sciences Cihan University-Erbil Iraq Department of Mechanical Engineering University of Al-Qadisiyah Ad-Diwaniyah 58001 Iraq College of Engineering University of Warith Al-Anbiyaa Karbala Iraq Department of Mechanical Engineering University of Al-Qadisiyah Al-Qadisiyah 58001 Iraq Department of Computer Network College of Engineering and Computer Science Lebanese French University Kurdistan Region Erbil Iraq Department of Mechanical Engineering College of Engineering University of Bisha P.O. Box 551 Bisha 61922 Saudi Arabia Biomedical Engineering Department College of Engineering and Technologies Al-Mustaqbal University Hillah Iraq

The optimization of heat transfer in various engineering applications, such as thermal management systems and energy storage devices, remains a crucial challenge. This study aims to investigate the potential of Casson-based Cu-H₂O nanofluids in enhancing free convection heat transfer within complex geometries. The research examines the free convection heat transfer and fluid flow characteristics of a Casson-based Cu-H₂O nanofluid within a semi-parabolic enclosure that includes a wavy corrugated cylinder. Utilizing numerical simulations based on the Galerkin Finite Element Method, the study investigates the impact of different factors, including the Rayleigh number (10³ ≤ Ra ≤ 10⁶), Casson fluid parameter (0.1 ≤ γ ≤ 1), corrugation number (3 ≤ N ≤ 10), nanoparticle volume fraction (0 ≤ ϕ ≤ 0.15), and enclosure inclination angle (0° ≤ ζ ≤ 60°), on both heat transfer efficiency and flow patterns. The results reveal that increasing the Rayleigh number and Casson fluid parameter enhances heat transfer performance, with the average Nusselt number increasing by up to 165 % as Ra increases from 10³ to 10⁶. An optimal range of corrugation numbers is identified for maximizing heat transfer at higher Rayleigh numbers. The addition of nanoparticles significantly improves heat transfer, with a 20 % increase in the average Nusselt number observed at Ra = 10⁵ when the nanoparticle volume fraction increases from 0 to 0.15. These findings provide valuable insights for designing more efficient thermal management systems in applications such as electronics cooling, solar thermal collectors, and heat exchangers, potentially leading to improved energy efficiency and performance in various industrial and technological sectors. © 2024

关键词： Casson-based nanofluid Corrugated cylinder Cu-H2O nanofluid Free convection Heat transfer enhancement Semi-parabolic enclosure

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Robust Feature engineering Techniques for Designing Motor Imagery-Based Bci Systems

SSRN

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SSRN 2025年

作者： Gardezi Ahmed, Syed Saim Khan, Mahnoor Bukhari, Syeda Muneeba Khan, Rizwan Ahmed Jawed, Soyiba Department of Biomedical Engineering Faculty of Engineering Salim Habib University Karachi Pakistan Department of Computer Software Engineering College of Electrical and Mechanical Engineering NUST Islamabad Pakistan Department of Physics Faculty of Applied Sciences Riphah International University Islamabad Pakistan Department of Biomedical Engineering Faculty of Engineering NED University Karachi Pakistan Department of Computer Science Faculty of Information Technology Salim Habib University Karachi Pakistan

A multitude of individuals across the globe grapple with motor disabilities. Neural prosthetics utilizing Brain-computer Interface (BCI) technology exhibit promise for improving motor rehabilitation outcomes. The intricate nature of EEG data poses a significant hurdle for current BCI systems. Recently, a qualitative repository of EEG signals tied to both upper and lower limb execution of motor and motor imagery tasks has been unveiled. Despite this, the productivity of the Machine Learning (ML) Models that were trained on this dataset was alarmingly deficient, and the evaluation framework seemed insufficient. To enhance outcomes, robust feature engineering (signal processing) methodologies are implemented. A collection of time domain, frequency domain, and wavelet-derived features was obtained from 16-channel EEG signals, and the Maximum Relevance Minimum Redundancy (MRMR) approach was employed to identify the four most significant features. For classification K Nearest Neighbors, Support Vector Machine, Decision Tree, and Naive Bayes models were implemented with these selected features, evaluating their effectiveness through metrics such as testing accuracy, precision, recall, and F1 Score. By leveraging SVM with a Gaussian Kernel, a remarkable maximum testing accuracy of 92.50% for motor activities and 95.48% for imagery activities is achieved. These results are notably more dependable and gratifying compared to the previous study, where the peak accuracy was recorded at 74.36%. This research work provide an in-depth analysis of the MI Limb EEG dataset and it will help in designing and developing simple, cost-effective and reliable BCI systems for neuro-rehabilitation. © 2025, The Authors. all rights reserved.

关键词： Image enhancement

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Privacy Enhanced Edge-AI Healthcare Devices Authentication: A Federated Learning Approach

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IEEE Transactions on Consumer Electronics 2025年

作者： Ahmed, Syed Thouheed Kaladevi, A.C. Kumar, V. Vinod Shankar, Achyut alqahtani, Fayez REVA University School of Computing and Information Technology India Vellore Institute of Technology School of Computer Science Engineering Vellore632014 India Bennett University School of Computer Science Engineering and Technology Uttar Pradesh Greater Noida201310 India King Saud University College of Computer and Information Sciences Software Engineering Department Riyadh12372 Saudi Arabia

Medical devices connected via internet are open to cyber-attacks, hence the privacy preservation of the user and the devices plays an important role. In this paper, Medical Edge-devices privacy enhanced technique based on the federated learning principles is proposed on the distributed coordination of devices and computational system. The technique generates a dedicated token via Centralized Authentication Authority (CAA) for the active users at the local Federated Learning (FL) cum training model. Further the technique introduces dual layer validation of tokens via Authentication Authority Valuation Layer (AAVL) and Centralized Server Firewall Authentication Layer (CSFal). The purpose of these layers is to optimize the active and inactive tokens, assigning tokens and revoking the inactive tokens from the user groups via distributed federated learning model. The technique is validated on 1500 edge devices of IoT/IoMT on 5 clusters servers of global model. The technique has demonstrated an accuracy of 96% in training and optimizing the IoMT device operations via privacy policies proposed in this paper. © 1975-2011 IEEE.

关键词： Federated learning

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