A Bibliometric Analysis of Scientific Research Publications Related to Pesticide Poisoning in the South Asian Countries.

Pesticide self-intoxication leading to suicide is a widespread phenomenon in India. Implementing regulations prohibiting the use of highly toxic pesticides in agriculture has proven effective in reducing the overall suicide rate in various South Asian countries without compromising agricultural production. In this study, we conducted a bibliometric analysis of scientific publications on pesticide poisoning in South Asian countries using various databases, including PubMed, Scopus, and Web of Science, using relevant Medical Subject Heading (MeSH) terms. To analyze the data, we employed R Studio and Microsoft Excel 2019, which enabled us to collect information on the number of scientific publications, citation frequency, and keyword trends. Our analysis involved 417 articles, and the results indicated a crucial need for greater awareness and improved management of pesticide poisoning in South Asian countries. Our findings provide valuable insights for policymakers and offer guidelines for pesticide control.

A Transformer-Based Model for Evaluation of Information Relevance in Online Social-Media: A Case Study of Covid-19 Media Posts.

Online social media has become a major source of information gathering for a huge section of society. As the amount of information flows in online social media is enormous but on the other hand, the fact-checking sources are limited. This shortfall of fact-checking gives birth to the problem of misinformation and disinformation in the case of the truthfulness of facts on online social media which can have serious effects on the wellbeing of society. This problem of misconception becomes more rapid and critical when some events like the recent outbreak of Covid-19 happen when there is no or very little information is available anywhere. In this scenario, the identification of the content available online which is mostly propagated from person to person and not by any governing authority is very needed at the hour. To solve this problem, the information available online should be verified properly before being conceived by any individual. We propose a scheme to classify the online social media posts (Tweets) with the help of the BERT (Bidirectional Encoder Representations from Transformers)-based model. Also, we compared the performance of the proposed approach with the other machine learning techniques and other State of the art techniques available. The proposed model not only classifies the tweets as relevant or irrelevant, but also creates a set of topics by which one can identify a text as relevant or irrelevant to his/her need just by just matching the keywords of the topic. To accomplish this task, after the classification of the tweets, we apply a possible topic modelling approach based on latent semantic analysis and latent Dirichlet allocation methods to identify which of the topics are mostly propagated as false information.

Ensuring Elderly Well-Being During COVID-19 by Using the Internet of Things.

Coronavirus disease (COVID-19) cases continue to surge, and the world must learn from this disaster. Most of the world economies are shattered due to this pandemic. The development of infrastructure to counter such deadly viral attacks in the future is the wisest investment that can be made. The elderly population is the most vulnerable age group affected by the pandemic, and the threat to their lives becomes manifold if they are living alone. Thus, a well-formed elderly support framework is required to safeguard this vulnerable population from COVID-like disasters in the future. We report here on the research findings we conducted by laying out a mitigation system for elderly well-being during disastrous times. The proposed system demands a sound collaboration between software, hardware devices, the state, and social agencies.

Promoting Trustless Computation Through Blockchain Technology.

Records, irrespective of their nature (whether electronic or paper-based), are vulnerable to fraud. People's hard-earned money, their personal information, identity, and health are at a higher risk than ever due to the misuse of technology in doing forgery. However, the technology can also be used as an answer to counteracting against fraudulence prevalent in affairs from every walk of life. This short paper attempts to present the blockchain technology as a solution to overcome the menace of forgery by promoting trustless computing in business transactions. The paper explains the blockchain technology and a variety of its implementation through five different use cases in the field of drug supply chain, health insurance, land record management, courier services, and immigration records. The immigration blockchain is also proposed as a solution to check pandemic like the coronavirus (COVID-19) effectively. The implementation of the Blockchain is performed using a locally built IBM's hyper-ledger fabric-based platform, and Ethereum public platform. The results are encouraging enough to substitute existing business operations using Blockchain-based solutions.

Stock Market Prediction Using Optimized Deep-ConvLSTM Model.

Stock market prediction acts as a challenging area for the investors for obtaining the profits in the financial markets. A greater number of models used in stock market forecasting is not capable of providing an accurate prediction. This article proposes a stock market prediction system that effectively predicts the state of the stock market. The deep convolutional long short-term memory (Deep-ConvLSTM) model acts as the prediction module, which is trained by using the proposed Rider-based monarch butterfly optimization (Rider-MBO) algorithm. The proposed Rider-MBO algorithm is the integration of rider optimization algorithm (ROA) and MBO. Initially, the data from the live stock market are subjected to the computation of the technical indicators, representing the features from which the necessary features are obtained through clustering by using the Sparse-Fuzzy C-Means (Sparse-FCM) followed with feature selection. The robust features are given to the Deep-ConvLSTM model to perform an accurate prediction. The evaluation is based on the evaluation metrics, such as mean squared error (MSE) and root mean squared error (RMSE), by using six forms of live stock market data. The proposed stock market prediction model acquired a minimal MSE and RMSE of 7.2487 and 2.6923 that shows the effectiveness of the proposed method in stock market prediction.