Bio-Inspired optimization algorithms are inspired from principles of natural biological evolution and distributed collective of a living organism such as (insects, animal, …. etc.) for obtaining the optimal possible solutions for hard and complex optimization problems. In computer science Bio-Inspired optimization algorithms have been broadly used because of their exhibits extremely diverse, robust, dynamic, complex and fascinating phenomenon as compared to other existing classical techniques.This paper presents an overview study on the taxonomy of bio-inspired optimization algorithms according to the biological field that are inspired from and the areas where these algorithms have been successfully applied

In recent years, it has become increasingly common for individuals to connect with their relatives and friends, read the most recent news, and discuss various social topics by using online social platforms such as Twitter and Facebook. As a consequence of this, anything that is considered spam can quickly spread among them. The identification of spam is widely regarded as one of the most significant problems involved in text analysis. Previous studies on the detection of spam concentrated primarily on English-language content and paid little attention to other languages. The information gathered by the University of California; Irvine served as the basis for the development of our spam detection technology (UCI). In this research study, the effectiveness of various supervised machine learning algorithms, such as the J48, KNN, and DT, in identifying spam and ham communications is investigated. SMS spam is becoming more widespread as the number of individuals who use the internet continues to rise and an increasing number of businesses disclose their customers' personal information. E-mail spam filtering is the progenitor of SMS spam filtering, which inherits a significant number of its features. We evaluate the proposed method based on accuracy, recall, and precision. Experiments showed that DT has obtained higher accuracy compared to other machine learning classifiers.

Recognition of facial expressions has been an important topic of study over the last several decades, and despite the advancements that have been made, it is still difficult to do because of the significant intra-class diversity. The handcrafted feature is used in traditional methods to address this issue. This feature is then preceded by a classifier that is trained using a database of pictures or videos. The majority of these works do quite well on datasets of photographs that were recorded in a controlled environment. However, they do not perform as well on datasets that are more difficult to work with since they include greater image variance and partial faces. The Histogram of Oriented Gradient (HOG) descriptor is the foundation for the strategy that is suggested in this study. During the initial step of the procedure, the input picture is pre-processed in order to locate the datum region, which assists in the extraction of the most relevant characteristics. After that, the Random Forest (RF) algorithm was employed as a classifier for facial expressions. The Japanese Female Facial Emotions Database (JAFFE) is used to assess our technique. The experimental findings demonstrated that the suggested method is accurate and effective in identifying facial expressions.