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An optimized ensemble model based on meta-heuristic algorithms for effective detection and classification of breast tumors

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Neural Computing and Applications 2025年第6期37卷 4881-4894页

作者： Saber, Abeer Elbedwehy, Samar Awad, Wael A. Hassan, Esraa Information Technology Department Faculty of Computers and Artificial Intelligence Damietta University Damietta34517 Egypt Department of Data Science Faculty of Artificial Intelligence Kafrelsheikh University Kafrelsheikh33511 Egypt Department of Computer Science Faculty of Computers and Artificial Intelligence Damietta University Damietta34517 Egypt Faculty of Artificial Intelligence Kafrelsheikh University Kafrelsheikh33511 Egypt

One of the most common cancers among women worldwide is breast cancer (BC), and early diagnosis can save lives. Early detection of BC increases the likelihood of a successful outcome by enabling treatment to start sooner. Even in areas without access to a specialist physician, machine learning (ML) aids in early BC detection. The medical imaging community is becoming more interested in using ML, and deep learning (DL) to increase the accuracy of cancer screening. Many disease-related data are sparse. However, for DL models to perform well, a large amount of data is required. Because of this, the DL models that are currently in use on medical images are not as effective as they could be. Convolutional neural network (CNN) models have recently gained popularity in the medical industry, and they perform admirably in terms of high performance and robustness at image classification. The proposed method classifies data using ensemble pre-trained models such as the dense convolutional network (DenseNet)-121 and EfficientNet-B5 feature extractor networks, as well as the support vector machine for classification. Using a modified meta-heuristic optimizer, the selected pre-trained CNN hyperparameters were optimized to improve the performance. The experimental results for the presented model on the INbreast dataset show that the EfficientNet-B5 model is effective for BC classification, with overall accuracy, sensitivity, specificity, precision, and area under the ROC curve (AUC) values of 99.9%, 99.9%, 99.8%, 99.1%, 1.0, respectively. © The Author(s) 2024.

关键词： Adversarial machine learning

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Heart Disease Prediction Model Using Feature Selection and Ensemble Deep Learning with Optimized Weight

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Computer Modeling in Engineering & sciences 2025年第4期143卷 875-909页

作者： Iman S.Al-Mahdi Saad M.Darwish Magda M.Madbouly Department of Information Technology Institute ofGraduate Studies and ResearchAlexandriaUniversityAlexandria21526Egypt Faculty of Computers and Data Science Alexandria UniversityAlexandria5432042Egypt

Heart disease prediction is a critical issue in healthcare,where accurate early diagnosis can save lives and reduce healthcare *** problem is inherently complex due to the high dimensionality of medical data,irrelevant or redundant features,and the variability in risk factors such as age,lifestyle,andmedical *** challenges often lead to inefficient and less *** predictionmethodologies face limitations in effectively handling large feature sets and optimizing classification performance,which can result in overfitting poor generalization,and high computational *** work proposes a novel classification model for heart disease prediction that addresses these challenges by integrating feature selection through a Genetic Algorithm(GA)with an ensemble deep learning approach optimized using the Tunicate Swarm Algorithm(TSA).GA selects the most relevant features,reducing dimensionality and improvingmodel *** features are then used to train an ensemble of deep learning models,where the TSA optimizes the weight of each model in the ensemble to enhance prediction *** hybrid approach addresses key challenges in the field,such as high dimensionality,redundant features,and classification performance,by introducing an efficient feature selection mechanism and optimizing the weighting of deep learning models in the *** enhancements result in a model that achieves superior accuracy,generalization,and efficiency compared to traditional *** proposed model demonstrated notable advancements in both prediction accuracy and computational efficiency over ***,it achieved an accuracy of 97.5%,a sensitivity of 97.2%,and a specificity of 97.8%.Additionally,with a 60-40 data split and 5-fold cross-validation,the model showed a significant reduction in training time(90 s),memory consumption(950 MB),and CPU usage(80%),highlighting its effectiveness in processing large,complex medi

关键词： Heart disease prediction feature selection ensemble deep learning optimization genetic algorithm(GA) ensemble deep learning tunicate swarm algorithm(TSA) feature selection

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A Novel Hybrid Approach-Based on Heartbeat and Accelerometer Signals for Enhanced Security in WBSNs

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IEEE Sensors Journal 2025年第6期25卷 10295-10308页

作者： Yan, Guixiang Zhang, Guanghe Liu, Fanghua Williams Samuel, Oluwarotimi Aborokbah, Majed Guo, Jiquan Zhu, Liqing Wei, Sikang Jiangxi Normal University School of Computer and Information Engineering Nanchang330022 China University of Derby School of Computing and Data Science Research Centre DerbyDE22 3AW United Kingdom INTI International University Faculty of Data Science and Information Technology Nilai71800 Malaysia University of Tabuk Faculty of Computers and Information Technology Tabuk71491 Saudi Arabia Jiangxi Normal University School of Digital Industry Shangrao334000 China

In wireless body sensor networks (WBSNs), ensuring secure and efficient key distribution is critical, particularly given the limited computational and energy resources of the sensors. Existing methods often struggle to balance security with these resource constraints, especially in environments involving physiological data such as acceleration (ACC) and electrocardiogram (ECG) signals. For the first time, this study proposes a novel hybrid approach that integrates fuzzy commitment with ACC signal noise and ECG features for efficient key generation and distribution in WBSNs. By employing a low-pass filter to process ACC signal noise, we generated highly random binary sequences (BSs), leveraging the inherent randomness of the signal for secure key generation. Concurrently, an optimized coding scheme was built for ECG feature construction to ensure secure key distribution between devices. Extensive experiments, including entropy analysis and National Institute of Standards and Technology (NIST) statistical tests, confirm the robustness and security of our method. The proposed scheme achieves a false acceptance rate (FAR) of 5.23%, demonstrating superior performance across multiple databases in comparison to benchmark approaches. This novel dual-key generation strategy that combines the unpredictability of ACC noise with the individual-specific traits of ECG signals can significantly enhance the security, applicability, and versatility of WBSNs. © 2001-2012 IEEE.

关键词： Acceptance tests

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Leveraging Machine Learning to Assess Factors Influencing Undernourishment in Nigeria 1st

Leveraging Machine Learning to Assess Factors Influencing Un...

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1st International Conference on Intelligent Systems, Blockchain, and Communication Technologies, ISBCom 2024

作者： Mobark, Nada Elfiky, Noha Ahmedin, Ahmed Ibrahim, Ameera Abdel-Aziz, M. Hamad, Safwat Faculty of Computers and Information South Valley University Qena83523 Egypt Business Analytics and Data Science Department School of Economics and Business Administration Saint Mary’s College of California MoragaCA94575 United States Department of Artificial Intelligence Engineering Faculty of Computer Science and Engineering King Salman International University South Sinai46511 Egypt

ISBN: (纸本)9783031823763

Notwithstanding economic progress, Nigeria continues to countenance significant food security issues. This study investigates the root causes of this problem and attempts to develop predictive models to address hunger in Nigeria. The research covering data from 2000 to 2022, sourced primarily from the World Bank and other institutions. The trend analysis, along with the linear model, indicates that investment in farming is crucial for reducing undernourishment. Additionally, there is a strong relationship between agricultural expenditure, exports, access to electricity, and unemployment in relation to undernourishment. In this paper, a linear model, correlation analysis, ARIMA, and trend analysis are utilized to demonstrate how undernourishment, representing hunger and food insecurity, is influenced by other factors. As a result, it is recommended to maintain the unemployment rate between 3.5% and 3.7%, while ensuring that exceeds 5% of government spending is recommended. In other words, to combat hunger in Nigeria, it is important to focus on investing in agriculture, creating jobs, and improving infrastructure. This aligns with our findings and with another research around the world. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： ARIMA Nigeria predictive model Undernourishment

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Multi-Classification Refactoring Framework Using Hopfield Neural Network for Sustainable Software Development

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IEEE Access 2025年 13卷 31785-31808页

作者： Almogahed, Abdullah Mahdin, Hairulnizam Hyeon Gu, Yeong Al-Masni, Mohammed A. Alzaeemi, Shehab Abdulhabib Omar, Mazni Alawadhi, Abdulwadood Obaid Barraood, Samera Rehman Gilal, Abdul Ameen Bakather, Adnan Universiti Tun Hussein Onn Malaysia Faculty of Computer Science and Information Technology Parit Raja Johor86400 Malaysia Sejong University College of AI Convergence Department of Artificial Intelligence and Data Science Seoul05006 Korea Republic of King Fahd University of Petroleum and Minerals Interdisciplinary Research Center in Finance and Digital Economy Business School Dhahran31261 Saudi Arabia Universiti Utara Malaysia School of Computing Sintok06010 Malaysia Hadhramout University College of Computers and Information Technology Department of Computer Science Hadhramaut Yemen Florida International University Knight Foundation School of Computing and Information Sciences MiamiFL33199 United States

Adopting high-quality source code is the ultimate way through which software evolution can be ensured as sustainable. Continuous refactoring in complex software systems ensures longevity and increases architecture knowledge sustainability. However, decision-making about refactoring is a challenge because the benefits of refactoring are vague and very difficult for the developers to quantify, as different refactoring strategies have different effects on quality attributes. No research has developed a multi-classification refactoring framework using artificial neural networks (ANN), specifically hopfield neural networks (HNN), to classify refactoring strategies and improve external software quality and sustainability. Therefore, this study proposes a multi-classification refactoring framework using HNN that classifies refactoring strategies by their impact on external quality attributes. Five stages have been conducted to perform this study, including selecting case studies, identifying the external quality attributes, identifying the most commonly used refactoring strategies in practice, conducting the experiments, and conducting the classification process using HNN. The proposed framework categorizes the refactoring strategies into three categories (positive, negative, and ineffective). By providing clear classifications and descriptions of each strategy, the proposed framework helps developers make informed decisions about how to improve the design and structure of their code. It helps developers mitigate risks associated with code changes by providing guidance on which strategies are likely to yield positive results for specific quality attributes. The proposed multi-classification refactoring framework enhances software sustainability by enhancing critical quality attributes. It supports maintainability, adaptability, and long-term viability, helping to ensure that the software systems remain relevant, efficient, and valuable over time. © 2025 IEEE.

关键词： Hopfield neural networks

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Enhanced multi-objective scheduling for heterogeneous computing platforms using hybrid MOEAD with SA and TS-guided initialization strategies

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Journal of Supercomputing 2025年第7期81卷 1-62页

作者： Saad, Abla Abdel-Raouf, Osama Hadhoud, Mohy Kafafy, Ahmed Machine Intelligence Department Faculty of Artificial Intelligence Menoufia University Menoufia Shibīn al-Kawm Egypt Information Technology Department Faculty of Computers & Information Menoufia University Menoufia Shibīn al-Kawm Egypt Data Science Department Faculty of Artificial Intelligence Menoufia University Menoufia Shibīn al-Kawm Egypt

Rapid growth in data and computational demands has driven the widespread adoption of heterogeneous computing systems that combine different processing units, such as CPUs, GPUs, and FPGAs. Efficient scheduling of tasks in these systems is essential to optimize resource utilization, reduce power consumption, and meet performance requirements. However, the complexities of managing multiple tasks, diverse machine architectures, and conflicting objectives make scheduling in these environments an NP-hard problem. This study reformulates the traditional scheduling model as a multi-objective optimization problem focusing on minimizing makespan and maximizing parallelization across heterogeneous systems. Multi-Objective Evolutionary Algorithms based on Decomposition (MOEA/D) have effectively solved these complex scheduling challenges. MOEA/D offers a flexible and powerful approach for handling multi-objective scheduling tasks in a heterogeneous computing environment. This paper explores how MOEA/D can be combined with other metaheuristics to further improve task scheduling and balance competing performance objectives. The effectiveness of the approach is demonstrated through 28 validation scenarios. The algorithm’s performance is evaluated using four scientific workflows: structured as (FFT and Montage) and unstructured as (Molecular-A and Molecular-B). Performance evaluation is performed using appropriate metrics, specifically hypervolume and inverted generational distance plus, to assess the quality of the solutions. Experimental results demonstrate that the GRASP-MOEA/D hybrid approach outperforms all competitors in structured workflows, achieving the best schedule by minimizing makespan and maximizing resource utilization within a reasonable runtime. For unstructured workflows, GSA-MOEA/D achieves the best performance in 75% of cases, while SA-MOEA/D is the best in 37.5% of cases. However, GSA-MOEA/D and SA-MOEA/D face significant computational overhead as workflow size

关键词： Multiobjective optimization

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Diabetes: Non-Invasive Blood Glucose Monitoring Using Federated Learning with Biosensor Signals

Biosensors

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Biosensors 2025年第4期15卷 255-255页

作者： Chellamani, Narmatha Albelwi, Saleh Ali Shanmuganathan, Manimurugan Amirthalingam, Palanisamy Paul, Anand Faculty of Computers and Information Technology University of Tabuk Tabuk71491 Saudi Arabia Department of Pharmacy Practice Faculty of Pharmacy University of Tabuk Tabuk71491 Saudi Arabia Biostatistics and Data Science Department The School of Public Health LSU Health Sciences Center New Orleans New OrleansLA70112 United States

Diabetes is a growing global health concern, affecting millions and leading to severe complications if not properly managed. The primary challenge in diabetes management is maintaining blood glucose levels (BGLs) within a safe range to prevent complications such as renal failure, cardiovascular disease, and neuropathy. Traditional methods, such as finger-prick testing, often result in low patient adherence due to discomfort, invasiveness, and inconvenience. Consequently, there is an increasing need for non-invasive techniques that provide accurate BGL measurements. Photoplethysmography (PPG), a photosensitive method that detects blood volume variations, has shown promise for non-invasive glucose monitoring. Deep neural networks (DNNs) applied to PPG signals can predict BGLs with high accuracy. However, training DNN models requires large and diverse datasets, which are typically distributed across multiple healthcare institutions. Privacy concerns and regulatory restrictions further limit data sharing, making conventional centralized machine learning (ML) approaches less effective. To address these challenges, this study proposes a federated learning (FL)-based solution that enables multiple healthcare organizations to collaboratively train a global model without sharing raw patient data, thereby enhancing model performance while ensuring data privacy and security. In the data preprocessing stage, continuous wavelet transform (CWT) is applied to smooth PPG signals and remove baseline drift. Adaptive cycle-based segmentation (ACBS) is then used for signal segmentation, followed by particle swarm optimization (PSO) for feature selection, optimizing classification accuracy. The proposed system was evaluated on diverse datasets, including VitalDB and MUST, under various conditions with data collected during surgery and anesthesia. The model achieved a root mean square error (RMSE) of 19.1 mg/dL, demonstrating superior predictive accuracy. Clarke error grid analysis (CEGA

关键词： Wavelet transforms

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Internet of Things-Based Smart Infant-Incubators Using Machine Learning Analysis

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IEEE Access 2025年 13卷 77673-77685页

作者： Gamal, Mahmoud Radi, Ibrahim Yousef, Amr Mohamed, Ali Gaber Alexandria University Faculty of Computers and Data Science Alexandria Egypt Higher Institute of Engineering and Technology Department of Computer Engineering El-Behira Egypt University of Business and Technology Electrical Engineering Department Jeddah23435 Saudi Arabia Alexandria University Faculty of Engineering Engineering Mathematics Department Alexandria21544 Egypt Electronics Engineering and Communications Faculty of Engineering New Alamein City Egypt Alexandria University Faculty of Engineering Department of Electrical Engineering Communications and Electronics Section Alexandria Egypt

Many international manufacturers of infant incubators use IoT technology, presenting tough competition for local manufacturers. This research examines methods to fulfill parents need to monitor their babies directly when they are lying inside an incubator. This bridges the gap present in neonatal care units. As such, this study proposes a prototype for an incubator that employs IoT along with different sensors to monitor babies occupying these incubators in real time and send the data to a remote server. With the different technologies used in this monitoring system, parents have the ability to listen to their babies remotely through a mobile application. A convolutional neural network (CNN) algorithm is used to take neonatal care a step further. It then goes beyond monitoring by interpreting the nature of a babys cries inside the specified incubator. The proposed prototype represents a step forward in the applications of Industry 4.0 in healthcare, especially those related to infant care and, more specifically, incubators for babies. This research is part of advancements in the use of IoT and handheld applications for the future development of baby incubators, fulfilling the needs of parents to constantly monitor their infants and neonatal care, specifically within modern healthcare. © 2013 IEEE.

关键词： Engineering education

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An Adaptive Hate Speech Detection Approach Using Neutrosophic Neural Networks for Social Media Forensics

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computers, Materials & Continua 2024年第4期79卷 243-262页

作者： Yasmine M.Ibrahim Reem Essameldin Saad M.Darwish Department of Information Technology Institute of Graduate Studies and ResearchAlexandria UniversityAlexandria21526Egypt Faculty of Computers and Information Technology Egyptian E-Learning University(EELU)Giza12611Egypt Faculty of Computers and Data Science Alexandria UniversityAlexandria21554Egypt

Detecting hate speech automatically in social media forensics has emerged as a highly challenging task due tothe complex nature of language used in such platforms. Currently, several methods exist for classifying hatespeech, but they still suffer from ambiguity when differentiating between hateful and offensive content and theyalso lack accuracy. The work suggested in this paper uses a combination of the Whale Optimization Algorithm(WOA) and Particle Swarm Optimization (PSO) to adjust the weights of two Multi-Layer Perceptron (MLPs)for neutrosophic sets classification. During the training process of the MLP, the WOA is employed to exploreand determine the optimal set of weights. The PSO algorithm adjusts the weights to optimize the performanceof the MLP as fine-tuning. Additionally, in this approach, two separate MLP models are employed. One MLPis dedicated to predicting degrees of truth membership, while the other MLP focuses on predicting degrees offalse membership. The difference between these memberships quantifies uncertainty, indicating the degree ofindeterminacy in predictions. The experimental results indicate the superior performance of our model comparedto previous work when evaluated on the Davidson dataset.

关键词： Hate speech detection whale optimization neutrosophic sets social media forensics

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Integrating generative AI and machine learning classifiers for solving heterogenous MCGDM: a case of employee churn prediction

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Scientific Reports 2025年第1期15卷 1-18页

作者： Abu-Faty, Hagar G. Kafafy, Ahmed Hadhoud, Mohiy M. Abdel-Raouf, Osama Data Science Department Faculty of Artificial Intelligence Minufiya University Shebeen El-Kom Egypt Machine Intelligence Department Faculty of Artificial Intelligence Minufiya University Shebeen El-Kom Egypt Information Technology Department Faculty of Computers and Information Minufiya University Shebeen El-Kom Egypt

Employee churn is a critical issue for companies and organizations, as it directly impacts productivity, efficiency, and overall operational success. High turnover rates increase recruitment and training costs, and disrupt workflows, making it a top concern for institutions aiming to maintain stability, growth and continuity. This study presents a methodology to address the employee churn prediction problem in heterogeneous environments by framing it as a Multiple Criteria Group Decision Making (MCGDM) problem. The proposed methodology integrates generative AI, Traditional MCGDM techniques, and machine learning classifiers to handle this problem type. The proposed methodology is structured into four main stages: data collection, generative AI for creating expert profiles, MCGDM for employee ranking, and machine learning for predictive modeling. ChatGPT-4 is used as the generative AI model to simulate expert profiles from diverse fields related to churn prediction. The Analytical Hierarchy Process (AHP) is employed to calculate criteria weights, while the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is concerned with alternatives’ ranking and employee’s classification into churn likelihood categories. These rankings data are then used for different nine machine learning classifiers, reducing the computational complexity for future predictions. The results reveal that Neural Networks, Gradient Boosting, and Random Forest outperform other used models in predicting employee churn in terms of accuracy. The proposed methodology offers a scalable, data-driven solution for addressing MCGDM problems, particularly employee churn prediction, by integrating advanced AI techniques with traditional decision-making frameworks. © The Author(s) 2025.

关键词： AHP Churn prediction Generative AI GPT Machine learning (ML) MCGDM TOSIS

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