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.

As training deep neural networks enough requires a large amount of data, there have been a lot of studies to deal with this problem. Data augmentation techniques are basic solutions to increase training data using existing data. Geometric transformations and color space augmentations are well-known augmentation techniques, but they still require some manual work and can generate limited types of data only. Therefore, there are many interests in generative-model-based augmentation lately, which can learn the distribution of data. This study proposes a set of GAN-based data augmentation methods that can generate good quality training data. The proposed networks, f-DAGAN (data augmentation generative adversarial networks), have been motivated by the DAGAN that learns data distribution from two real data. The basic f-DAGAN uses dual discriminators handling both generated data and generated feature spaces for better learning the given data. The other versions of f-DAGANs have been proposed for generating hard or easy data that have additional dual classifiers for both generated data and feature spaces to control the generator. Hard data is useful for optimized training to increase the target performance such as classification accuracy. Easy data generation can be used especially in few-shot learning. The quality of generated data has been validated in two ways: using t-SNE visualization of generated data and classification accuracy by training with generated data using the MNIST data set. The t-SNE representations show that data generated by f-DAGAN are evenly distributed for every class better than the exiting generative model-based augmentation methods. The f-DAGAN also shows the best classification accuracy by training with generated data. The f-DAGAN version for easy and hard data generation generates data well from five-shot learning and performs well in sample data generation experiments.

Over the past few years, credit card fraud has become a serious problem as more individuals rely on credit cards for purchases. The significant increase in fraudulent activities can be attributed to advancements in technology and the prevalence of online transactions, leading to significant financial losses. To address this issue, an effective fraud detection system needs to be developed and put into practice. Machine learning techniques are commonly used to automatically detect credit card fraud, but they do not consider deceptive behaviour or behavioural issues that could lead to false alarms. The objective of this research is to determine how to identify instances of credit card fraud. This paper aimed to create a model using deep learning and SMOTE oversampling technique to anticipate credit card fraud. A Recurrent Neural Network with Long Short-Term Memory (RNN-LSTM) and an attention mechanism is suggested for detecting fraud. This model is known to be effective for processing sequential data with complex relationships between vectors. The performance of RNN-LSTM is compared to XGBoost, Random Forest, Naive Bayes, SVM, and ANN classifiers, and the experiments indicate that our proposed model achieves high accuracy of 99.4% and produces strong results. The suggested model has the potential to decrease financial losses worldwide by identifying instances of credit card scams or frauds.

Background The rise of the Internet-of-Medical-Things (IoMT) and smart devices has led to a substantial increase in extensive data streams in the healthcare domain. The interconnected nature of medical devices introduces dynamic and evolving data patterns. However, this dynamism poses a significant challenge known as Concept Drift, particularly crucial in the medical field. Concept Drift reflects the inherent instability in data patterns over time. In medical applications, this challenge intensifies as sensors must seamlessly transition from general healthcare monitoring to handling critical situations like emergency ICU operations. The complexity deepens due to imbalanced data distributions inherent in e-health scenarios. Methods The study introduces an Adaptive Ensemble Framework (AEF-CDA) designed to detect and adapt to concept drift in large-scale medical data streams from IoMT. The framework incorporates adaptive data preprocessing, a novel drift-centric adaptive feature selection approach, the learning of base models, and the selection of models adapted to concept drift. Additionally, an online ensemble model is integrated to enhance concept drift adaptation. Results The proposed AEF-CDA framework is evaluated using three public IoMT and IoT datasets. The experimental results demonstrate its superiority over contemporary methods, achieving a remarkable accuracy of 99.64% with a precision of 99.39%. These metrics surpass the performance of other approaches in the simulation. Conclusion In conclusion, the research presents a robust solution in the form of the adaptive ensemble framework (AEF-CDA) to effectively address the challenges posed by concept drift in IoMT data streams. The demonstrated high accuracy and precision underscore the framework’s efficacy, highlighting its potential significance in the dynamic landscape of medical data analysis.

Purpose The surge in Internet-of-Medical-Things (IoMT) and smart devices has resulted in a substantial influx of data streams within the healthcare domain. The interlinked structure of medical devices presents a pivotal hurdle referred to as Concept Drift, particularly significant in the medical arena due to the inherent instability of data patterns over time. In medical contexts, this complexity is heightened as sensors need to seamlessly shift from routine healthcare monitoring to managing urgent scenarios such as emergency ICU operations. The intricacy deepens owing to the uneven distribution of data in e-health scenarios. The complexity is further compounded by imbalanced data distributions in e-health scenarios. Method To address this challenge, our study proposes a novel Adaptive Ensemble Framework (AEF-CDA) specifically designed for detecting and adapting to concept drift in large-scale medical data streams from IoMT. The framework integrates adaptive data preprocessing, a novel drift-oriented adaptive feature selection approach, learning of base models, and model selection adapted to concept drift. Additionally, an online ensemble model is incorporated to enhance concept drift adaptation. Results The proposed AEF-CDA framework is evaluated using three public IoMT and IoT datasets. The experimental results demonstrate its superiority over contemporary methods, achieving a remarkable accuracy of 99.64% with a precision of 99.39%. These metrics surpass the performance of other approaches in the simulation. Conclusion In conclusion, the research presents an effective solution in the form of the adaptive ensemble framework (AEF-CDA) to effectively address the challenges posed by concept drift in IoMT data streams. The demonstrated high accuracy and precision underscore the framework's efficacy, highlighting its potential significance in the dynamic landscape of medical data analysis.

Data protection and security is a big challenging portion in a modern technical world against the cyber-attacks like; ransomware, man-in-the-middle, DDoS, etc. In order to overcome this scenario, there are lot of artificial intelligence framework have been introduced to detect and classify the cyber-attacks. In particular, neural networks, with their solid speculation execution ability, are capable to address an extensive variety of cyber-attacks. This article frames the training and testing of a neural network group such a way to deal with detection of cyber-attack using cognitive spectral clustering technique based on simulated annealing search method. The optimization of individual networks can be made by using adaptive memetic algorithm with simulated annealing search. It is used to enhance the neural network weights and hidden neurons respectively. This algorithm is a combination of both local and global search enhancement method and used to get rid of the premature convergence, and used to achieve the adaptive search output. The testing outcome of the proposed framework shows a better result 99.5% of overall accuracy, and effectively adaptive in terms of detecting the cyber-attacks.