Blockchain and K-Means Algorithm for Edge AI Computing.

Aggregating multiple types of complex data in stock market prediction: A model-independent framework

A series of events generates multiple types of time series data, such as numeric and text data over time, and the variations of the data types capture the events from different angles. This paper aims to integrate the analyses on such numerical and text time-series data influenced by common events with a single model to better understand the events. Specifically, we present a topic model, called a supervised dynamic topic model (sDTM), which finds topics guided by a numerical time series. We applied sDTM to financial indexes and financial news articles. First, sDTM identifies topics associated with the characteristics of time-series data from the multiple types of data. Second, sDTM predicts numerical time-series data with a higher level of accuracy than does the iterative model, which is supported by lower mean squared errors.

The healthcare system of today is considered complex and expensive. This burden of complexity can be minimized by improving healthcare record management, utilizing insurance agencies, and leveraging blockchain technologies. Initially, blockchain implementations were developed to eliminate the dependency on centralized companies and or financial institutions for account-related money exchanges. Healthcare records, insurance billing, and smart contracts could be enhanced by using blockchain technology, which enables stable access, security of data, and the centralization of transactional information. Blockchain technology is most attractive due to its features of immutability, privacy, transparency, decentralization, and distribute ledger. The main concern is that healthcare records containing confidential patient information are very complicated because they pose a privacy risk. The medical system would be better prepared to deal with public health threats if it had direct access to patient data during deadly disease outbreaks. A concise study is provided in this paper on blockchain technology in healthcare. The paper deeply works on the security and traceability issues in healthcare and pharmaceutical industries. This paper also presents blockchain as one of the complete solutions for security and traceability in healthcare and pharmaceutical industries.

SummaryThe block chain is the emerging domain that offers the features like shared, immutable, and decentralized dataset that saves the asset's history. Security and storage are the most challenging aspect. The classical techniques' issues and challenges are an encouragement for developing a fresh block chain‐assisted IPFS model with access control policies. Hence, this research paper introduces an effective access control and authentication based on an interplanetary file system (IPFS)‐based block chain and access control system for attaining secured transactions. The developed method comprises five entities, that is smart contract, data owner, data requester, block chain and IPFS along with eight phases, to be exact setup, user registration, initialization, storage and data encryption, authentication, testing, access control, and decryption phase, to provide better access control and storage capacity. The proposed IPFS‐based block chain and access control system offered effective storage and secure transactions in the block chain network. The highest detection rate (0.870), lowest communication overhead (0.766 MB), and highest privacy rate (0.719) are all achieved by the proposed IPFS‐based block chain and access control system.

BackgroundAn estimated US $2.6 billion loss is attributed to health care fraud and abuse. With traditional health care claims verification and reimbursement, the health care provider submits a claim after rendering services to a patient, which is then verified and reimbursed by the payer. However, this process leaves out a critical stakeholder: the patient for whom the services are actually rendered. This lack of patient participation introduces a risk of fraud and abuse. Blockchain technology enables secure data management with transparency, which could mitigate this risk of health care fraud and abuse.ObjectiveThe aim of this study is to develop a framework using blockchain to record claims data and transactions in an immutable format and to enable the patient to act as a validating node to help detect and prevent health care fraud and abuse.MethodsWe developed a health care fraud and abuse blockchain technical framework and prototype using key blockchain tools and application layers including consensus algorithms, smart contracts, tokens, and governance based on digital identity on the Ethereum platform (Ethereum Foundation).ResultsOur technical framework maps to the claims adjudication process and focuses on Medicare claims, with the US Centers for Medicare and Medicaid Services (CMS) as the central authority. A prototype of the framework system was developed using the blockchain platform Ethereum (Ethereum Foundation), with its design features, workflow, smart contract functions, system architecture, and software implementation outlined. The software stack used to build the system consisted of a front-end user interface framework, a back-end processing server, and a blockchain network. React was used for the user interface framework, and NodeJS and an Express server were used for the back-end processing server; Solidity was the smart contract language used to interact with a local Ethereum blockchain network.ConclusionsThe proposed framework and the initial prototype have the potential to improve the health care claims process by using blockchain technology for secure data storage and consensus mechanisms, which make the claims adjudication process more patient-centric for the purposes of identifying and preventing health care fraud and abuse. Future work will focus on the use of synthetic or historic CMS claims data to assess the real-world viability of the framework.

BackgroundSingle-cell RNA sequencing (scRNA-seq) provides an effective tool to investigate the transcriptomic characteristics at the single-cell resolution. Due to the low amounts of transcripts in single cells and the technical biases in experiments, the raw scRNA-seq data usually includes large noise and makes the downstream analyses complicated. Although many methods have been proposed to impute the noisy scRNA-seq data in recent years, few of them take into account the prior associations across genes in imputation and integrate multiple types of imputation data to identify cell types.ResultsWe present a new framework, NetImpute, towards the identification of cell types from scRNA-seq data by integrating multiple types of biological networks. We employ a statistic method to detect the noise data items in scRNA-seq data and develop a new imputation model to estimate the real values of data noise by integrating the PPI network and gene pathways. Meanwhile, based on the data imputed by multiple types of biological networks, we propose an integrated approach to identify cell types from scRNA-seq data. Comprehensive experiments demonstrate that the proposed network-based imputation model can estimate the real values of noise data items accurately and integrating the imputation data based on multiple types of biological networks can improve the identification of cell types from scRNA-seq data.ConclusionsIncorporating the prior gene associations in biological networks can potentially help to improve the imputation of noisy scRNA-seq data and integrating multiple types of network-based imputation data can enhance the identification of cell types. The proposed NetImpute provides an open framework for incorporating multiple types of biological network data to identify cell types from scRNA-seq data.

With quantum computing evolving very fast as we speak, the security and integrity of blockchain-based applications will become the most crucial aspect. A proposal is raised to use blockchain technology as a platform for writing and probating ‘wills’. Blockchain technology in drafting and probating wills makes them safe from manipulations, highly secure, and transparent. It also dramatically decreases the time required without catering for the challenges created by the current system. [9] This paper presents a new method for will transfer and inheritance management by implementing quantum-resistant algorithms in the security architecture of a blockchain decentralized application (DApp). The system uses IPFS Network for data storage and quantum-safe algorithms as retrieval and sending algorithms. The system includes Quantum-Resistant Dilithium Signatures and Merkle trees as the fundamental components for safeguarding the transfers of assets and claims for inheritance. Quantum-Resistant Dilithium Signatures offer an unbreakable shield against quantum attacks that are expected to happen, which in turn safeguards the privacy and authenticity of transactions. While Merkle trees are responsible for the organization of inheritance claims in an effective and tamper-proof manner, the introduced system incorporates smart contracts to address the execution of an inheritance case, adding more security and automation to the asset distribution process. The system ensures a robust security framework by integrating quantum-resistant algorithms at the very core of the blockchain DApp for instance, retrieval and sending. This research is of great significance to blockchain technology which is the emerging technology of the future because it addresses the existing threat of quantum computing by showing the feasibility of using quantum-resistant algorithms in practical applications. As established by the findings, besides Quantum-Resistant Dilithium Signatures and Merkle trees, the systems of asset transfers and inheritance management within blockchain networks are enhanced in terms of safety and reliability. Hence, paving the road to the creation of more secure and trustworthy digital asset management systems.

In these years, Blockchain technology is growing in importance thanks to cryptocurrencies. This technology offers many features such as the immutability of data stored in the data chain, the replicability of data in nodes, the management without a central repository, etc. These features are widely adopted also in other fields such as in distributed measurement systems. Although the Blockchain technology is widespread worldwide, there is still a legislative gap in the adoption of this technology in various states. This paper will examine the challenge and future trends on distributed measurement systems based on Blockchain technology in the European context. In particular, critical aspects of Blockchain technology will be examined under GDPR point of view and in particular in the management of data and privacy. The advantages and disadvantages of using Blockchain technology in applications such as the Internet of Things and smart contracts will be discussed and some aspects of legal metrology in Blockchain networks will be analyzed. At the end, future trends in Blockchain technology applied to distributed measurement systems will be shown.

Career management in a global company with employees from various countries is a strategic process that manages, develops, and facilitates the professional growth of employees in a multinational environment. This process involves career planning, competency development, and adjustment to cultural and regulatory diversity in various jurisdictions. The diversity of employees from several different countries impacts the competition to fill specific positions, so transparency about employee career paths in the company is needed. Currently, no system supports career management in global companies that provides transparency of employee career paths, so employees do not know the certainty of their careers in the company. The purpose of this research is to develop a blockchain sharding model for a global company that ensures transparency in the career paths of employees from various countries. By implementing blockchain technology, every transaction related to the career paths of employees from several countries will be validated by each node on the blockchain network. Blockchain technology applies peer-to-peer validation to each transaction before storing it on the blockchain network. This research selects blockchain sharding to enhance the efficiency of the validation process, which spans multiple countries. The study employs a qualitative approach that includes a literature review and software simulation using Hyperledger Fabric. Research results show that blockchain technology can increase the transparency of information about employee career levels.

The blockchain network architecture is a promising technology for constructing highly secure Internet of Things (IoT) networks. IoT networks typically consist of many sensors and actuators. Blockchain network technology can be applied to secure control robots in smart factories or for reliable drone delivery in smart cities. The distributed ledger and data block validation across blockchain networks guarantee the ultimate data security. However, the current blockchain technology is restricted in terms of its overall deployment across IoT networks. A general permissionless blockchain technology typically targets high-performance network nodes with sufficient computing power and memory space. A blockchain node with less computing power and memory, such as an IoT sensor or actuator, cannot employ blockchain technology as a fully functional node. A lightweight blockchain provides practical blockchain availability to IoT networks. We propose an operational advance to develop a lightweight blockchain for IoT networks. The opportunistic block validation optimizes the block validation process. It measures the network vulnerability based on the node reputation, block validation degree, number of fraudulent messages, and network stability. The reinforcement learning mechanism employed in the opportunistic block validation determines whether block validation is to be performed. Two separately developed reinforcement learning methods can increase the processing performance while maintain the transaction data integrity. In addition, the proposed blockchain technology is easily implementable because it adopts a Hyperledger development environment. Directly embedding the proposed blockchain middleware platform in small computing devices proves the practicability of the proposed block validation mechanism.

Approaches to Adopt Blockchain Technology for Internet of Things

Ever increasing adoption of cloud technology scales up the activities like creation, exchange, and alteration of cloud data objects, which create challenges to track malicious activities and security violations. Addressing this issue requires implementation of data provenance framework so that each data object in the federated cloud environment can be tracked and recorded but cannot be modified. The blockchain technology gives a promising decentralized platform to build tamper-proof systems. Its incorruptible distributed ledger/blockchain complements the need of maintaining cloud data provenance. In this paper, we present a cloud based data provenance framework using block chain which traces data record operations and generates provenance data. We anchor provenance data records into block chain transactions, which provide validation on provenance data and preserve user privacy at the same time. Once the provenance data is uploaded to the global block chain network, it is extremely challenging to tamper the provenance data. Besides, the provenance data uses hashed user identifiers prior to uploading so the blockchain nodes cannot link the operations to a particular user. The framework ensures that the privacy is preserved. We implemented the architecture on ownCloud, uploaded records to blockchain network, stored records in a provenance database and developed a prototype in form of a web service.

Blockchain (BC) is a new technology that has been utilized to create innovative approaches in a variety of industries inclusive of healthcare. The Blockchain Network (BCN) is utilized in the HealthCare Sector (HCS) to protect and communicate patient data across hospitals, pharmacies, clinicians and diagnostic laboratories. The BC applications are capable of identifying serious as well as deadly errors in the healthcare sector. As a result, it is potential to revolutionize the performance, transparency and security of medical details sharing in the HCS. This kind of technology allows medical organizations to acquire knowledge and improve the medical record analysis. Scalability is a significant challenge to Blockchain Technology (BCT) that restricts its capacity for widespread adoption in large-scale transactions applications. This research provides a novel solution to this problem by incorporating the Snake Optimization Algorithm (SOA) into a BCT to improve the rate of transactions and reduce delay. A detailed literature study situates our approach within the present state of BC scalability initiatives which offer a mechanism for incorporating SOA towards the transaction authentication process in BCN. The findings reveal a significant reduction of latency using optimized system has attained faster average transaction rates through a range of transaction amount. The results of this research show that SOA is extremely effective in batch transactions events that exhibit a reverse scalability trend than traditional BCN and substantial contribution to BC scalability with consequences for the creation of improved and scalable BCN ideal for high-volume business applications.

The convergence of the Internet of Things (IoT), fog computing, and block chain technology has revolutionized contemporary digital ecosystems by making data management decentralized, efficient, and scalable. Nonetheless, this integration poses serious security and privacy threats. IoT devices produce enormous amounts of sensitive data, making them easy targets for cyber attacks, unauthorized access, and data breaches. Classic cloud-based solutions tend to be challenged by latency, bandwidth limitations, and centralized attacks, making the use of fog computing inevitable to decentralize data processing for better responsiveness and decreased dependency on remote cloud servers. While fog computing offers advantages, it also poses new security threats through possible insider attacks and data integrity issues. Block chain technology solves these problems by presenting a tamper-resistant, decentralized ledger that improves trust, security, and transparency for IoT networks. Block chain, via cryptographic authentication, consensus protocols, and smart contracts, ensures the secure transaction of data while alleviating risks against unauthorized data manipulation. Scalability, computational complexity, and energy efficiency are still challenges that need to be met for widespread IoT adoption. This chapter discusses the complex security and privacy problems in IoT, fog, and blockchain networks and examines current threats and countermeasures. Advanced cryptographic methods, secure communication schemes, and access control techniques for strengthening the security of these connected systems are mentioned. It further presents future research directions and trends that will improve a safer, more efficient, and privacy-augmented digital infrastructure.

With the advent of high throughput genome-wide assays it has become possible to simultaneously measure multiple types of genomic data. Several projects like TCGA, ICGC, NCI-60 has generated comprehensive, multi-dimensional maps of the key genomic changes like MiRNA, MRNA, proteomics etc. from cancer samples[2,4]. These genomic data can be used for classifying tumour types[5]. Integrative analysis of these data from multiple sources can potentially provide additional biological insights, but methods to do any such analysis are lacking. One of the widely used solutions to handle high dimension data is by removing redundant information in the integrated sample. Most of the expressed genes are overlapped and can be projected onto lower dimension, and then be used to classify different tumor types, without the loss of any/much information. Sufficient dimension reduction (SDR) [1], a supervised dimension reduction approach, can be ideal to achieve such a goal. In this paper, we propose a novel integrative SDR method that can reduce dimensions of multiple data types simultaneously while sharing common latent structures to improve prediction and interpretation. In particular, we extend the sliced inverse regression (SIR) technique, a major SDR method, to integrate multiple omits data for simultaneous dimension reduction. SIR is a supervised dimension reduction method that assumes that the outcome variable Y depends on the predictor variable X through d unknown linear combinations of the predictor[3]. The predictor variable is replaced by its projection into a lower dimension subspace of the predictor space without the loss of information. The aim is to find the intersection of all the subspaces δ called the central susbspace (CS) of the predictor space satisfying the property Y ╨ X| Pδ X. To integrate multiple types of data, we propose and implement a new integrative sufficient dimension reduction method extending SIR[3], called integrative SIR. The main idea is that we take into account all the multi-omics data information simultaneously while finding a basis matrix for each data type with some sharing latent structures. Finally, we get d dimension data which is much smaller than the original data dimension. The reduced dimension d was achieved by cross validation. To demonstrate the integrated analysis of multi-omics data, we applied and compared conventional SIR and integrative SIR to analyze MRNA, MiRNA and proteomics expression profile of a subset of cell lines from the NCI-60 panel. The data used is taken from [6]. The outcomes we have to classify are CNS, Leukemia and Melanoma tumor types. We pre-screened 400 variables from each data type with the criteria of high variance. To find classification error, we performed random forest classification after we applied to each method with leave-one-out cross-validation. As a result, we found out that integrative SIR leads to less classification error as compared to conventional SIR. To summarize, we proposed a new integrative SIR method, a supervised dimension reduction technique for integrative analysis of multi-omics data types. Unlike conventional SDR methods, the new approach can reduce the dimensions of multiple omics data simultaneously while sharing common latent structures across data types without losing any information in prediction. By efficiently capturing the common information, our numerical study shows that integrative SIR classifies tumor types more accurately as compared to conventional SDR methods.