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. In this article, we present a systematic review of blockchain Hyperledger enabling fog and edge computing, which integrates as an outsourcing computation over the serverless consortium network environment. The main objective of this article is to classify recently published articles and survey reports on the current status in the domain of edge distributed computing and outsourcing computation, such as fog and edge. In addition, we proposed a blockchain-Hyperledger Sawtooth-enabled serverless edge-based distributed outsourcing computation architecture. This theoretical architecture-based solution delivers robust data security in terms of integrity, transparency, provenance, and privacy-protected preservation in the immutable storage to store the outsourcing computational ledgers. This article also highlights the changes between the proposed taxonomy and the current system based on distinct parameters, such as system security and privacy. Finally, a few open research issues and limitations with promising future directions are listed for future research work.

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 combination of one-dimensional dense residual network and bidirectional RNN for atrial fibrillation detection simplifies the tedious feature extraction steps, and constructs the end-to-end neural network to achieve atrial fibrillation detection through data feature learning. Meanwhile, the attention mechanism is utilized to fuse the different features and extract the high-value information. The accuracy of the experimental results is 97.72%, the sensitivity and specificity are 93.09% and 98.71%, respectively compared with other methods.

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. Interoperability platform issue is another critical challenging aspect while designing and managing a secure distributed Peer-to-Peer industrial development environment for SMEs. However, till now, it is hard to maintain operations of SMEs' integrity, transparency, reliability, provenance, availability, and trustworthiness between two different enterprises due to the current nature of centralized server-based infrastructure. This paper bridges these problems and proposes a novel and secure framework with a standardized process hierarchy/lifecycle for distributed SMEs using collaborative techniques of blockchain, the internet of things (IoT), and artificial intelligence (AI) with machine learning (ML). A blockchain with IoT-enabled permissionless network structure is designed called "B-SMEs" that provides solutions to cross-chain platforms. In this, B-SMEs address the lightweight stakeholder authentication problems as well. For that purpose, three different chain codes are deployed. It handles participating SMEs' registration, day-to-day information management and exchange between nodes, and analysis of partnership exchange-related transaction details before being preserved on the blockchain immutable storage. Whereas AI-enabled ML-based artificial neural networks are utilized, the aim is to handle and optimize day-to-day numbers of SME transactions; so that the proposed B-SMEs consume fewer resources in terms of computational power, network bandwidth, and preservation-related issues during the complete process of SMEs service deliverance. The simulation results present highlight the benefits of B-SMEs, increases the rate of ledger management and optimization while exchanging information between different chains, which is up to 17.3%, and reduces the consumption of the system's computational resources down to 9.13%. Thus, only 14.11% and 7.9% of B-SME's transactions use network bandwidth and storage capabilities compared to the current mechanism of SMEs, respectively.

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 (HSI) imaging, super-resolution, and nanoimaging, but advance mess data portrayal and retrieval. A patient's state involves multiple signals, medical imaging (MI) modalities, clinical variables, dialogs between clinicians and patients, metadata, genome sequencing, and signals from wearables. Patients' high volume, personalized data amassed over time have advanced artificial intelligence (AI) models for higherprecision inferences, prognosis, and tracking. AI promises are undeniable, but with slow spreading and adoption, given partly unstable AI model performance after real-world use. The HD data is a ratelimiting factor for AI algorithms generalizing real-world scenarios. This paper studies many health data challenges to robust AI models' growth, aka the dimensionality curse (DC). This paper overviews DC in the MIs' context, tackles the negative out-of-sample influence and stresses important worries for algorithm designers. It is tricky to choose an AI platform and analyze hardships. Automating complex tasks requires more examination. Not all MI problems need automation via DL. AI developers spend most time refining algorithms, and quality data are crucial. Noisy and incomplete data limits AI, requiring time to handle control, integration, and analyses. AI demands data mixing skills absent in regular systems, requiring hardware/software speed and flexible storage. A partner or service can fulfill anomaly detection, predictive analysis, and ensemble modeling.

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. The enactment and analytical competencies of HSI for medical imaging are discussed. Specifically, the recent successes and limitations of HSI in biomedical are presented to offer the readers an insight into its current potential for medical research. Lastly, we have discussed the future challenges concerning medical applications and possible ways to overcome these limitations.