For changing semantics, ontological and information presentation, as well as computational linguistics for Asian social networks, are one of the most essential platforms for offering enhanced and real-time data mapping, as well as huge data access across diverse big data sources on the web architecture, information extraction mining, statistical modeling and data modeling, database control, and so on. The concept of opinion or sentiment analysis is often used to predict or classify the textual data, sentiment, affect, subjectivity, and other emotional states in online text. Recognizing the message's positive and negative thoughts or opinions by examining the author's goals will aid in a better understanding of the text's content in terms of the stock market. An intelligent ontology and knowledge Asian social network solution can improve the effectiveness of a company's decision making support procedures by deriving important information about users from a wide variety of web sources. However, ontology is concerned primarily with problem-solving knowledge discovery. The utilization of Internet-based modernizations welcomed a significant effect on the Indian stock exchange. News related to the stock market in the most recent decade plays a vital role for the brokers or users. This article focuses on predicting stock market news sentiments based on their polarity and textual information using the concept of ontological knowledge-based Convolution Neural Network (CNN) as a machine learning approach. Optimal features are essential for the sentiment classification model to predict the stock's textual reviews' exact sentiment. Therefore, the swarm-based Artificial Bee Colony (ABC) algorithm is utilized with the Lexicon feature extraction approach using a novel fitness function. The main motivation for combining ABC and CNN is to accelerate model training, which is why the suggested approach is effective in predicting emotions from stock news.

Environmental context: The association of specific organic matter (OM) compounds with clay mineral surfaces is believed to protect these compounds from degradation and thus result in long-term protection in soil. The molecular-level composition of soil OM associated with soil fractions was measured and compared using solid-state 13C nuclear magnetic resonance (NMR) and solution-state 1H NMR methods. Combining these methods allowed more detailed characterisation of OM associated with different soil fractions and will improve the understanding of OM dynamics in soil. Organic matter (OM) associated with fine soil fractions is hypothesised to be protected from complete biodegradation by soil microbes. It is therefore important to understand the structure and stage of decomposition of OM associated with various soil fractions. Solid-state 13C nuclear magnetic resonance (NMR) spectroscopy has been used extensively to investigate the OM composition of soils and soil fractions. Solution-state 1H NMR spectroscopy has not been used as much but is an emerging tool for analysing soil OM because 1H NMR spectra are often better resolved and provide information that complements the structural information obtained from solid-state 13C NMR experiments. This study compares one-dimensional solution-state 1H NMR and solid-state 13C NMR methods for assessing the degradation and composition of OM in three different soils, and their light and clay-size fractions. The alkyl/O-alkyl degradation parameter was consistent across all NMR methods and showed that OM associated with clay-size fractions were at more advanced stages of degradation as compared to that in light density soil fractions. Solution-state 1H and diffusion edited (DE) 1H NMR results showed the presence of high concentrations of microbial-derived peptidoglycan and peptide side-chains in clay-sized fractions. Lignin was also identified in clay-sized fractions using solid-state 13C and solution-state 1H NMR techniques. The combination of solid-state 13C and solution-state 1H NMR methods provides a more detailed analysis of OM composition and thereby facilitates a better understanding of the fate and preservation of OM in soil.

The technological advent of Blockchain, Bigdata, Cloud, and AI made it easier to be stored and retrieved Electronic Health Record (EHR) data and then used it by patients, doctors, and research groups for treatments and analytics. Blockchain security ensures the security of patients' records data with smart contracts due to the most reliable and secure platform for many industries such as finance, IoT, healthcare, and logistics management. The innovative use of smart contracts in the healthcare industry enables a secure system to provide privacy and complete protection with patients' record data and to be able to integrate with various entities in the healthcare sector. The criticality of acquiring, storing, and securing EHR is essential in the healthcare industry to provide faster services. Integrating EHR data with multiple healthcare providers accrues various benefits such as quick insurance approvals and dealing with emergencies and critical situations. It expedites medical records access to patients, doctors, insurance companies, primary healthcare providers, pharmaceuticals, and laboratories to reap healthcare benefits efficiently. The challenges with EHR data are security and storage, which can be addressed with Blockchain security, and integrating it with Bigdata provides huge benefits of data storage, scalability, and performance to store and maintain data efficiently. Often acquiring, storing, securing, and maintaining healthcare data involves excessive administrative and operative costs. Most of the research work is done on Blockchain technology frameworks and implementation but the most critical part of data storage and maintenance is yet to be focused area, as how to manage this data in the long term. This paper intends to research the best Blockchain based architectural framework to store EHR data in Bigdata storage systems at a lesser cost compared to other Infrastructure facilities by utilizing inexpensive hardware grouped into clusters and assigned resources to share across the network and easy to maintain even after procuring the data across the entities securely.