Brain tumor segmentation in medical imaging is a critical task for diagnosis and treatment while preserving patient data privacy and security. Traditional centralized approaches often encounter obstacles in data sharing due to privacy regulations and security concerns, hindering the development of advanced AI-based medical imaging applications. To overcome these challenges, this study proposes the utilization of federated learning. The proposed framework enables collaborative learning by training the segmentation model on distributed data from multiple medical institutions without sharing raw data. Leveraging the U-Net-based model architecture, renowned for its exceptional performance in semantic segmentation tasks, this study emphasizes the scalability of the proposed approach for large-scale deployment in medical imaging applications. The experimental results showcase the remarkable effectiveness of federated learning, significantly improving specificity to 0.96 and the dice coefficient to 0.89 with the increase in clients from 50 to 100. Furthermore, the proposed approach outperforms existing convolutional neural network (CNN)- and recurrent neural network (RNN)-based methods, achieving higher accuracy, enhanced performance, and increased efficiency. The findings of this research contribute to advancing the field of medical image segmentation while upholding data privacy and security.

Brain tumor segmentation from magnetic resonance imaging (MRI) scans is critical for the diagnosis, treatment planning, and monitoring of therapeutic outcomes. Thus, this research introduces a novel hybrid approach that combines handcrafted features with convolutional neural networks (CNNs) to enhance the performance of brain tumor segmentation. In this study, handcrafted features were extracted from MRI scans that included intensity-based, texture-based, and shape-based features. In parallel, a unique CNN architecture was developed and trained to detect the features from the data automatically. The proposed hybrid method was combined with the handcrafted features and the features identified by CNN in different pathways to a new CNN. In this study, the Brain Tumor Segmentation (BraTS) challenge dataset was used to measure the performance using a variety of assessment measures, for instance, segmentation accuracy, dice score, sensitivity, and specificity. The achieved results showed that our proposed approach outperformed the traditional handcrafted feature-based and individual CNN-based methods used for brain tumor segmentation. In addition, the incorporation of handcrafted features enhanced the performance of CNN, yielding a more robust and generalizable solution. This research has significant potential for real-world clinical applications where precise and efficient brain tumor segmentation is essential. Future research directions include investigating alternative feature fusion techniques and incorporating additional imaging modalities to further improve the proposed method’s performance.

Brain cancer is a bad disease and affects millions of people in worldwide. Approximately 70% of patients diagnosed with this disease do not survive. The Machine learning is a promising and recent development in this area. However, very limited research is performed in this direction. Therefore, in this research, we propose an evolutionary lightweight model aimed at detecting brain cancer and classification, starting from the analysis of magnetic resonance images. The proposed model named lightweight ensemble combines (weighted average and lightweight combines multiple XGBoost decision trees) is the modified version of the recent Multimodal Lightweight XGBoost. Herein, we provide prediction explain ability by considering the preprocessing of Magnetic Resonance Imaging (MRI) data and the feature extraction (Intensity, texture, and shape). The process in the evolutionary model involves a various step - first, prepare the data, extract important features, and finally, merge together using a special kind of classification called ensemble classification. We evaluate our proposed model using BraTS 2020 dataset. The dataset consists of 285 MRI scans of patients diagnosed with gliomas. The simulation results showed that our proposed model achieved 93.0% accuracy, 0.94 precision, 0.93 recall, 0.94 F1 score, and an area under Receiver Operating Characteristic Curve (AUC-ROC) value of 0.984. The efficient results demonstrate the effectiveness of our proposed model for brain tumor grading and classification using four grades. The efficient results show the potential of our proposed approach as a valuable tool for early diagnosis and effective treatment planning of brain tumors. Finally, the proposed model holds promise for aiding in early cancer diagnosis and treatment.

The proliferation of the Internet of Things (IoT) has brought unprecedented connectivity, introducing complex trust and reputation management challenges across vast, heterogeneous networks. This paper introduces the DeepTrust framework, a novel approach leveraging deep learning algorithms to assess and manage trust and reputation in IoT environments dynamically. We demonstrate the framework’s superiority in accurately identifying trustworthy and untrustworthy devices through extensive experiments, significantly enhancing IoT security. DeepTrust has demonstrated marked superiority over existing methods, showcasing enhanced accuracy in identifying trustworthy versus untrustworthy devices, thereby significantly bolstering IoT network security. Specifically, our results reveal an improvement in accuracy by 15%, precision by 20%, and recall rates by 18% compared to conventional models, highlighting DeepTrust’s effectiveness in real-time, adaptive trust assessments. There are several avenues for enhancing and expanding the DeepTrust framework. Future research will explore optimization techniques for reducing computational demands, enabling deployment on resource-constrained IoT devices. Additionally, incorporating incremental learning mechanisms could improve the framework’s adaptability to new and changing IoT environments. Enhancing data privacy and security measures within the framework constitutes another critical development area, ensuring the protection of sensitive information used in trust assessments. Lastly, extending the framework’s applicability across various IoT domains and applications presents a promising direction, aiming to establish a universal trust management solution adaptable to the unique requirements of different IoT ecosystems. By outlining these potential future directions, we aim to highlight the current achievements of the DeepTrust framework and chart a course for its continued development and refinement. This comprehensive approach underscores our commitment to advancing the field of IoT trust and reputation management, paving the way for more secure and reliable IoT networks.

Intrusion detection in Internet of Things (IoT)-based wireless sensor networks (WSNs) is essential due to their widespread use and inherent vulnerability to security breaches. Traditional centralized intrusion detection systems (IDS) face significant challenges in data privacy, computational efficiency, and scalability, particularly in resource-constrained IoT environments. This study aims to create and assess a federated learning (FL) framework that integrates with long short-term memory (LSTM) networks for efficient intrusion detection in IoT-based WSNs. We design the framework to enhance detection accuracy, minimize false positive rates (FPR), and ensure data privacy, while maintaining system scalability. Using an FL approach, multiple IoT nodes collaboratively train a global LSTM model without exchanging raw data, thereby addressing privacy concerns and improving detection capabilities. The proposed model was tested on three widely used datasets: WSN-DS, CIC-IDS-2017, and UNSW-NB15. The evaluation metrics for its performance included accuracy, F1 score, FPR, and root mean square error (RMSE). We evaluated the performance of the FL-based LSTM model against traditional centralized models, finding significant improvements in intrusion detection. The FL-based LSTM model achieved higher accuracy and a lower FPR across all datasets than centralized models. It effectively managed sequential data in WSNs, ensuring data privacy while maintaining competitive performance, particularly in complex attack scenarios. FL and LSTM networks work well together to make a strong way to find intrusions in IoT-based WSNs, which improves both privacy and detection. This study underscores the potential of FL-based systems to address key challenges in IoT security, including data privacy, scalability, and performance, making the proposed framework suitable for real-world IoT applications.

As the manufacturing industry advances towards Industry 5.0, which heavily integrates advanced technologies such as cyber-physical systems, artificial intelligence, and the Internet of Things (IoT), the potential for web-based attacks increases. Cybersecurity concerns remain a crucial challenge for Industry 5.0 environments, where cyber-attacks can cause devastating consequences, including production downtime, data breaches, and even physical harm. To address this challenge, this research proposes an innovative deep-learning methodology for detecting web-based attacks in Industry 5.0. Convolutional neural networks (CNNs), recurrent neural networks (RNNs), and transformer models are examples of deep learning techniques that are investigated in this study for their potential to effectively classify attacks and identify anomalous behavior. The proposed transformer-based system outperforms traditional machine learning methods and existing deep learning approaches in terms of accuracy, precision, and recall, demonstrating the effectiveness of deep learning for intrusion detection in Industry 5.0. The study’s findings showcased the superiority of the proposed transformer-based system, outperforming previous approaches in accuracy, precision, and recall. This highlights the significant contribution of deep learning in addressing cybersecurity challenges in Industry 5.0 environments. This study contributes to advancing cybersecurity in Industry 5.0, ensuring the protection of critical infrastructure and sensitive data.