Dendritic Growth Optimization: A Novel Nature-Inspired Algorithm for Real-World Optimization Problems.

In numerous scientific disciplines and practical applications, addressing optimization challenges is a common imperative. Nature-inspired optimization algorithms represent a highly valuable and pragmatic approach to tackling these complexities. This paper introduces Dendritic Growth Optimization (DGO), a novel algorithm inspired by natural branching patterns. DGO offers a novel solution for intricate optimization problems and demonstrates its efficiency in exploring diverse solution spaces. The algorithm has been extensively tested with a suite of machine learning algorithms, deep learning algorithms, and metaheuristic algorithms, and the results, both before and after optimization, unequivocally support the proposed algorithm's feasibility, effectiveness, and generalizability. Through empirical validation using established datasets like diabetes and breast cancer, the algorithm consistently enhances model performance across various domains. Beyond its working and experimental analysis, DGO's wide-ranging applications in machine learning, logistics, and engineering for solving real-world problems have been highlighted. The study also considers the challenges and practical implications of implementing DGO in multiple scenarios. As optimization remains crucial in research and industry, DGO emerges as a promising avenue for innovation and problem solving.

A study on the sentiments and psychology of twitter users during COVID-19 lockdown period.

The outbreak of the novel Coronavirus in late 2019 brought severe devastation to the world. The pandemic spread across the globe, infecting more than ten million people and disrupting several businesses. Although social distancing and the use of protective masks were suggested all over the world, the cases seem to rise, which led to worldwide lockdown in different phases. The rampant escalation in the number of cases, the global effects, and the lockdown may have a severe effect on the psychology of people. The emergency protocols implemented by the authorities also lead to increased use in the number of multimedia devices. Excessive use of such devices may also contribute to psychological disorders. Hence, hence it is necessary to analyze the state of mind of people during the lockdown. In this paper, we perform a sentiment analysis of Twitter data during the pandemic lockdown, i.e., two weeks and four weeks after the lockdown was imposed. Investigating the sentiments of people in the form of positive, negative, and neutral tweets would assist us in determining how people are dealing with the pandemic and its effects on a psychological level. Our study shows that the lockdown witnessed more number positive tweets globally on multiple datasets. This is indicative of the positivity and optimism based on the sentiments and psychology of Twitter users worldwide. The study will be effective in determining people's mental well-being and will also be useful in devising appropriate lockdown strategies and crisis management in the future.

A novel LSTM-CNN-grid search-based deep neural network for sentiment analysis.

As the number of users getting acquainted with the Internet is escalating rapidly, there is more user-generated content on the web. Comprehending hidden opinions, sentiments, and emotions in emails, tweets, reviews, and comments is a challenge and equally crucial for social media monitoring, brand monitoring, customer services, and market research. Sentiment analysis determines the emotional tone behind a series of words may essentially be used to understand the attitude, opinions, and emotions of users. We propose a novel long short-term memory (LSTM)-convolutional neural networks (CNN)-grid search-based deep neural network model for sentiment analysis. The study considers baseline algorithms like convolutional neural networks, K-nearest neighbor, LSTM, neural networks, LSTM-CNN, and CNN-LSTM which have been evaluated using accuracy, precision, sensitivity, specificity, and F-1 score, on multiple datasets. Our results show that the proposed model based on hyperparameter optimization outperforms other baseline models with an overall accuracy greater than 96%.

Global Forecasting Confirmed and Fatal Cases of COVID-19 Outbreak Using Autoregressive Integrated Moving Average Model.

The world health organization (WHO) formally proclaimed the novel coronavirus, called COVID-19, a worldwide pandemic on March 11 2020. In December 2019, COVID-19 was first identified in Wuhan city, China, and now coronavirus has spread across various nations infecting more than 198 countries. As the cities around China started getting contaminated, the number of cases increased exponentially. As of March 18 2020, the number of confirmed cases worldwide was more than 250,000, and Asia alone had more than 81,000 cases. The proposed model uses time series analysis to forecast the outbreak of COVID-19 around the world in the upcoming days by using an autoregressive integrated moving average (ARIMA). We analyze data from February 1 2020 to April 1 2020. The result shows that 120,000 confirmed fatal cases are forecasted using ARIMA by April 1 2020. Moreover, we have also evaluated the total confirmed cases, the total fatal cases, autocorrelation function, and white noise time-series for both confirmed cases and fatalities in the COVID-19 outbreak.

Analysis of Outbreak and Global Impacts of the COVID-19.

Corona viruses are a large family of viruses that are not only restricted to causing illness in humans but also affect animals such as camels, cattle, cats, and bats, thus affecting a large group of living species. The outbreak of Corona virus in late December 2019 (also known as COVID-19) raised major concerns when the outbreak started getting tremendous. While the first case was discovered in Wuhan, China, it did not take long for the disease to travel across the globe and infect every continent (except Antarctica), killing thousands of people. Since it has become a global concern, different countries have been working toward the treatment and generation of vaccine, leading to different speculations. While some argue that the vaccine may only be a few weeks away, others believe that it may take some time to create the vaccine. Given the increasing number of deaths, the COVID-19 has caused havoc worldwide and is a matter of serious concern. Thus, there is a need to study how the disease has been propagating across continents by numbers as well as by regions. This study incorporates a detailed description of how the COVID-19 outbreak started in China and managed to spread across the globe rapidly. We take into account the COVID-19 outbreak cases (confirmed, recovered, death) in order to make some observations regarding the pandemic. Given the detailed description of the outbreak, this study would be beneficial to certain industries that may be affected by the outbreak in order to take timely precautionary measures in the future. Further, the study lists some industries that have witnessed the impact of the COVID-19 outbreak on a global scale.