MCGAN-a cutting edge approach to real time investigate of multimedia deepfake multi collaboration of deep generative adversarial networks with transfer learning.

The proliferation of multimedia-based deepfake content in recent years has posed significant challenges to information security and authenticity, necessitating the use of methods beyond dependable dynamic detection. In this paper, we utilize the powerful combination of Deep Generative Adversarial Networks (GANs) and Transfer Learning (TL) to introduce a new technique for identifying deepfakes in multimedia systems. Each of the GAN architectures may be customized to detect subtle changes in different multimedia formats by combining their advantages.

Security risk models against attacks in smart grid using big data and artificial intelligence.

The need to update the electrical infrastructure led directly to the idea of smart grids (SG). Modern security technologies are almost perfect for detecting and preventing numerous attacks on the smart grid. They are unable to meet the challenging cyber security standards, nevertheless.

Blockchain-enabled infrastructural security solution for serverless consortium fog and edge computing.

The robust development of the blockchain distributed ledger, the Internet of Things (IoT), and fog computing-enabled connected devices and nodes has changed our lifestyle nowadays. Due to this, the increased rate of device sales and utilization increases the demand for edge computing technology with collaborative procedures. However, there is a well-established paradigm designed to optimize various distinct quality-of-service requirements, including bandwidth, latency, transmission power, delay, duty cycle, throughput, response, and edge sense, and bring computation and data storage closer to the devices and edges, along with ledger security and privacy during transmission.

Deep residual-dense network based on bidirectional recurrent neural network for atrial fibrillation detection.

Atrial fibrillation easily leads to stroke, cerebral infarction and other complications, which will seriously harm the life and health of patients. Traditional deep learning methods have weak anti-interference and generalization ability. Therefore, we propose a new-fashioned deep residual-dense network via bidirectional recurrent neural network (RNN) model for atrial fibrillation detection.

The collaborative role of blockchain, artificial intelligence, and industrial internet of things in digitalization of small and medium-size enterprises.

Due to digitalization, small and medium-sized enterprises (SMEs) have significantly enhanced their efficiency and productivity in the past few years. The process to automate SME transaction execution is getting highly multifaceted as the number of stakeholders of SMEs is connecting, accessing, exchanging, adding, and changing the transactional executions. The balanced lifecycle of SMEs requires partnership exchanges, financial management, manufacturing, and productivity stabilities, along with privacy and security.

How to Collect and Interpret Medical Pictures Captured in Highly Challenging Environments that Range from Nanoscale to Hyperspectral Imaging.

Digital well-being records are multimodal and high-dimensional (HD). Better theradiagnostics stem from new computationally thorough and edgy technologies, i.e.

Hyperspectral Imaging: A Review and Trends towards Medical Imaging.

Hyperspectral Imaging (HSI) is a pertinent technique to provide meaningful information about unique objects in the medical field. This paper discusses the basic principles, imaging methods, comparisons, and advances in the medical applications of HSI to accentuate the importance of HSI in the medical field. To date, there are numerous tools and methods to fix the problems, but reliable medical HSI tools and methods need to be studied.