Due to its potential use in evaluating mental health and enhancing patient care, emotion recognition using physiological signals, such as EEG, ECG, and EMG, has drawn a lot of attention in the healthcare industry. However, better neural network architecture and training parameter optimization is needed to achieve accurate and dependable emotion classification. This research aims to investigate the impact of various configurations of neural network architectures, proposed multimodal fusion network and training parameters on the classification performance of emotion recognition systems utilizing EEG, ECG, and EMG signals. Nine experiments were conducted, each representing a unique combination of hyperparameters, including EEG filters, kernel sizes, pooling sizes, ECG filters, EMG filters, LSTM units, dropout rates, epochs, and batch sizes. The experiments were designed to systematically explore the effects of these hyperparameters on classification accuracy, robustness, and computational efficiency. The experiments revealed that the choice of hyperparameters significantly influences the performance of emotion recognition systems. Proposed model achieved higher average accuracies, with Experiment 3 exhibiting the highest accuracy of 87.83%. However, the model also incurred higher computational costs in terms of training time and memory usage. Additionally, experiments with higher dropout rates demonstrated a slight decrease in accuracy compared to those with lower dropout rates, emphasizing the importance of balancing regularization and model complexity.The findings underscore the importance of fine-tuning hyperparameters and selecting appropriate neural network architectures to optimize the performance of emotion recognition systems. Advanced deep learning techniques and physiological signal processing, healthcare providers can develop robust and accurate emotion recognition systems to enhance patient care and support personalized interventions. Further research may focus on exploring novel architectures and refining training strategies to continue improving the efficacy and efficiency of AI-driven emotional intelligence solutions in healthcare delivery.

The growing reliance on distributed cloud technology in mobile healthcare applications has introduced critical challenges in ensuring secure and efficient access to Electronic Health Records (EHR). Traditional methods have prolonged authentication times and access delays, compromising both the efficiency and security of healthcare systems. To address these issues, this study proposes the Secure Healthcare Access Control System (SHACS), a robust framework specifically designed to enhance security and efficiency in healthcare environments. SHACS provides a sophisticated combination of role-based access control, attribute-based policies, and dynamic rules to streamline authentication processes and safeguard data access. SHACS architecture provides the central authority and system authorities, responsible for enforcing access control policies and verifying the authenticity of users requesting access to medical records. SHACS also integrates real-time anomaly detection capabilities, utilizing the MIMIC-III dataset to identify and respond to unusual access patterns, thereby mitigating potential security breaches. Following successful authentication, SHACS generates secure decryption tokens and keys, enabling swift and secure access to EHRs while continuously updating a dynamic access list to monitor and reduce access delays. Experimental results demonstrate that SHACS significantly improves system performance, reducing authentication times by 30% and access delays by 25% compared to traditional methods. For instance, SHACS decreased the average authentication time from 40 seconds to 28 seconds and enhanced system responsiveness, lowering average access delays from 15 seconds to 11 seconds. The implementation of SHACS underscores the importance of privacy-enhancing technologies in safeguarding medical records, ensuring that only authorized personnel access sensitive data. Through rigorous testing and analysis, SHACS proves its efficacy in strengthening the security posture of cloud-based healthcare systems, ultimately contributing to the quality and accessibility of remote healthcare services.

The Diabetes-Mellitus (DM) disease is considered a persistent ailment that is triggered by excessive sugar levels in the blood of a person. It gives rise to severe health complications when left untreated and can also give rise to related diseases such as cardiac attack, nervous damage, foot problems, liver and kidney damage, and eye problems. These problems are caused by a series of factors interrelated to one another such as age, gender, family history, BMI, and Blood Glucose. Various Machine-Learning (ML) algorithms are being used in order to predict and detect the disease to avoid further complications of health. The Diabetes prediction process can be further improvised by identifying the type a person is being affected by and the probability of the occurrence of the related diseases. In order to perform the mentioned task, two types of the dataset are used in the study, namely, PIMA and a clinical survey dataset. Various ML algorithms such as Random Forest, Light Gradient Boosting Machine, Gradient Boosting Machine, Support Vector Machine, Decision Tree, and XGBoost are being used. The performance metrics used are accuracy, precision, recall, specificity, and sensitivity. Techniques such as Data Augmentation and Sampling are used. In comparison with the research conducted previously, the paper focuses on improvisation of the accuracy with a percentage of 95.20 using the LGBM Classifier, and Diabetes is also classified as Prediabetes or Diabetes using many Classification mechanisms.

Early diagnosis of cancer has focused on the use of advanced algorithms to achieve accurate diagnosis. The proposed study assesses the effectiveness of Transformer-based models and Convolutional Neural Networks (CNN) in cancer diagnosis with respect to multimodal imaging and genomic data. The performance comparisons between the two algorithmic methods with such complex datasets, which combine multi-modal imaging and genomic information, are presented. In search of the optimal neural network configuration, a series of experiments were conducted with respect to different layers, attention mechanisms in case of transformers, and convolutional architectures in case of CNNs. Besides, parameters related to training, such as learning rates, batch sizes, and optimization algorithms, have also been systematically tuned. The different models were evaluated against accuracy, precision, recall, and the F1-score. Our results show that the proposed multimodal model, with accuracy from 92.5 to 93.2, F1-scores between 91.5 and 92.2, precision of 91.5 to 92.2, and recall values of 92.5 to 93.2. In contrast, much lower accuracy, F1-scores, precision, and recall values were noticed when using baselines, especially VGG. All these findings indicate the fact that the presented techniques, especially the Multimodal and Transformer models, are more robust solutions for classification tasks with better balance between precision and recall, as well as with higher overall accuracy. This came with the cost of the expense of computational resources: CNNs are less resource-intensive but have competitive performance with better precision and recall. The results underline how algorithm selection and hyperparameter optimization play a crucial role in cancer detection tasks. This study has shown how state-of-the-art deep learning methods can be effectively combined with multi-modal data for building more accurate and efficient systems in cancer diagnosis. Two main lines of future work would be improving these algorithms and understanding their applicability in real clinical practice to obtain maximum benefits from them.

Within numerous IoT domains, recent IoT proliferation has influenced organizational activities and business procedures. The number of connected edge devices has increased dramatically, resulting in a vast stream of data that is prone to leakage and manipulation, decreasing the security level of the attacked IoT ecosystem. Anomaly detection systems based on graphs have been widely used to prevent network malfunction while considering the mergers of organizations involved, modeling their relationships, and integrating their structural features, content, and temporary features. We propose a multi-agent system that uses the collaborative environment of smart agents to find anomalies. Each agent implements a Graph-based Layered Learning-Driven (GLLD) network. To this end, we design an anomaly detection technique that attempts to efficiently monitor the entire network infrastructure to combat the spreading nature of cyber-attacks. BoT IoT dataset and Code Red worm attack dataset are used for training and testing various traffic distributions to demonstrate the outperformance of the model.

The difficulty or cost of obtaining data or labels in applications like medical imaging has progressed less quickly. If deep learning techniques can be implemented reliably, automated workflows and more sophisticated analysis may be possible in previously unexplored areas of medical imaging. In addition, numerous characteristics of medical images, such as their high resolution, three-dimensional nature, and anatomical detail across multiple size scales, can increase the complexity of their analysis. This study employs multiconvolutional transfer learning (MCTL) for applying deep learning to small medical imaging datasets in an effort to address these issues. Multiconvolutional transfer learning is a model based on transfer learning that enables deep learning with small datasets. In order to learn new features on a smaller target dataset, an initial baseline is used in the transfer learning process. In this study, 3D MRI images of brain tumors are classified using a convolutional autoencoder method. In order to use unenhanced Magnetic Resonance Imaging (MRI) for clinical diagnosis, expensive and invasive contrast-enhancing procedures must be performed. MCTL has been shown to increase accuracy by 1.5%, indicating that small targets are more easily detected with MCTL. This research can be applied to a wide range of medical imaging and diagnostic procedures, including improving the accuracy of brain tumor severity diagnosis through the use of MRI.