A Weighted Multi-Layer Analytics Based Model for Emoji Recommendation

The developed system for eye and face detection using Convolutional Neural Networks(CNN)models,followed by eye classification and voice-based assistance,has shown promising potential in enhancing accessibility for individuals with visual impairments.The modular approach implemented in this research allows for a seamless flow of information and assistance between the different components of the system.This research significantly contributes to the field of accessibility technology by integrating computer vision,natural language processing,and voice technologies.By leveraging these advancements,the developed system offers a practical and efficient solution for assisting blind individuals.The modular design ensures flexibility,scalability,and ease of integration with existing assistive technologies.However,it is important to acknowledge that further research and improvements are necessary to enhance the system’s accuracy and usability.Fine-tuning the CNN models and expanding the training dataset can improve eye and face detection as well as eye classification capabilities.Additionally,incorporating real-time responses through sophisticated natural language understanding techniques and expanding the knowledge base of ChatGPT can enhance the system’s ability to provide comprehensive and accurate responses.Overall,this research paves the way for the development of more advanced and robust systems for assisting visually impaired individuals.By leveraging cutting-edge technologies and integrating them into amodular framework,this research contributes to creating a more inclusive and accessible society for individuals with visual impairments.Future work can focus on refining the system,addressing its limitations,and conducting user studies to evaluate its effectiveness and impact in real-world scenarios.

Mental health in organizations from a healthcare analytics framework:taxonomic model,trends,and impact of COVID-19

Mental disorders negatively affect employee well-being and organizational performance.Organizations face a challenge in terms of how to manage mental health.This paper clarifies three issues(underlying patterns,trends,and impact of COVID-19)regarding the scientific study of mental health in organizations from a healthcare analytics framework.The framework comprises eight stages considering a text-driven approach with scientific corpora assisted by linguistic/computational and statistical resources.This study discovers a new taxonomic model comprising five patterns in the scientific discourse on the topic.Trend analyses reveal imbalances and concerns regarding the interests associated with the patterns,which is reinforced by examining patterns“before”and“during”COVID-19.This paper complements psychological/epidemiological studies on mental health in organizations from a healthcare analytics perspective.

Cryptocurrency valuation and ethics:a text analytic approach

A recent and potentially profound innovation is the creation of cryptocurrencies and the underlying technology that is essential for their use in various financial transactions.Given the anonymity of a user of a cryptocurrency,such digital currencies may be used for many different types of both lawful and illicit activities.The main purpose of this paper is to examine the extent to which ethical considerations associated with the use of cryptocurrencies affect the valuations attached to such currencies.The examination is based on a text analytic approach that involves measuring the extent to which ethical and unethical words are used in a discussion related to Bitcoin on Twitter to determine if there is a connection between ethics and cryptocurrency valuations.We find the frequency of an unethical discussion about Bitcoin is negatively associated with its price.In contrast,the frequency of an ethical discussion is positively associated with its price.

A Systematic Review Towards Big Data Analytics in Social Media

The recent advancement in internet 2.0 creates a scope to connect people worldwide using society 2.0 and web 2.0 technologies.This new era allows the consumer to directly connect with other individuals,business corporations,and the government.People are open to sharing opinions,views,and ideas on any topic in different formats out loud.This creates the opportunity to make the"Big Social Data"handy by implementing machine learning approaches and social data analytics.This study offers an overview of recent works in social media,data science,and machine learning to gain a wide perspective on social media big data analytics.We explain why social media data are significant elements of the improved data-driven decision-making process.We propose and build the"Sunflower Model of Big Data"to define big data and bring it up to date with technology by combining 5 V’s and 10 Bigs.We discover the top ten social data analytics to work in the domain of social media platforms.A comprehensive list of relevant statistical/machine learning methods to implement each of these big data analytics is discussed in this work."Text Analytics"is the most used analytics in social data analysis to date.We create a taxonomy on social media analytics to meet the need and provide a clear understanding.Tools,techniques,and supporting data type are also discussed in this research work.As a result,researchers will have an easier time deciding which social data analytics would best suit their needs.