Advancement of artificial intelligence techniques based lexicon emotion analysis for vaccine of COVID-19

Emotions are a vital and fundamental part of life. Everything we do, say, or do not say, somehow reflects some of our feelings, perhaps not immediately. To analyze a human's most fundamental behavior, we must examine these feelings using emotional data, also known as affect data. Text, voice, and other types of data can be used. Affective Computing, which uses this emotional data to analyze emotions, is a scientific fields. Emotion computation is a difficult task; significant progress has been made, but there is still scope for improvement. With the introduction of social networking sites, it is now possible to connect with people from all over the world. Many people are attracted to examining the text available on these various social websites. Analyzing this data through the Internet means we're exploring the entire continent, taking in all of the communities and cultures along the way. This paper analyze text emotion of Iraqi people about COVID-19 using data collected from twitter, People's opinions can be classified based on lexicon into different separate classifications of feelings (anticipation, anger, trust, fear, sadness, surprise, disgust, and joy) as well as two distinct emotions (positive and negative), which can then be visualized using charts to find the most prevalent emotion using lexicon-based analysis

An Artificial Intelligence Approach for Verifying Persons by Employing the Deoxyribonucleic Acid (DNA) Nucleotides

Deoxyribonucleic acid (DNA) can be considered as one of the most useful biometrics. It has effectively been used for recognizing persons. However, it seems that there is still a need to propose a new approach for verifying humans, especially after the recent big wars, where too many people lost and die. This approach should have the capability to provide high personal verification performance. In this paper, a personal recognition approach based on artificial intelligence is proposed. This approach is called the artificial DNA algorithm for recognition (ADAR). It utilizes a unique identity for each person acquired from DNA nucleotides, and it can verify individuals efficiently with high performance. The ADAR has been designed and applied to multiple datasets, namely, the DNA classification (DC), sample DNA sequence (SDS), human DNA sequences (HDS), and DNA sequences (DS). For all datasets, a low value of 0% is achieved for each of the false acceptance rate (FAR) and false rejection rate (FRR)