The transmission of information, ideas, and thoughts requires communication, which is a crucial component of human contact. The utilization of Internet of Things (IoT) devices is a result of the advent of enormous volumes of messages delivered over the internet. The IoT botnet assault, which attempts to perform genuine, lucrative, and effective cybercrimes, is one of the most critical IoT dangers. To identify and prevent botnet assaults on connected computers, this study uses both quantitative and qualitative approaches. This study employs three basic machine learning (ML) techniques—random forest (RF), decision tree (DT), and generalized linear model (GLM)—and a stacking ensemble model to detect botnets in computer network traffic. The results reveled that random forest attained the best performance with a coefficient of determination (R²) of 0.9977, followed by decision tree with an R² of 0.9882, while GLM was the worst among the basic machine learning models with an R² of 0.9522. Almost all ML models achieved satisfactory performance, with an R² above 0.93. Overall, the stacking ensemble model obtained the best performance, with a root mean square error (RMSE) of 0.0084 m, a mean absolute error (MAE) of 0.0641 m, and an R² of 0.9997. Regarding the stacking ensemble model as compared with the single machine learning models, the R² of the stacking ensemble machine learning increased by 0.2% compared to the RF, 1.15% compared to the DT, and 3.75% compared to the GLM, while RMSE decreased by approximately 0.15% compared to the GLM, DT, and RF single machine learning techniques. Furthermore, this paper suggests best practices for preventing botnet attacks. Businesses should make major investments to combat botnets. This work contributes to knowledge by presenting a novel method for detecting botnet assaults using an artificial-intelligence-powered solution with real-time behavioral analysis. This study can assist companies, organizations, and government bodies in making informed decisions for a safer network that will increase productivity.

This review paper explores the concepts of Human Centered AI. It basically considers the various theoretical principles, theories, and paradigms. It further explores the various works in the field of Human Centered AI and the opportunities after a structured literature survey was done on a number of online journal databases. Using the PRISMA model, we screened available literature and further categorized them into various classifications. This paper agrees to the notion that there is an intrinsic need to balance human involvement with increasing computer automation. This is relevant in achieving fair, just and reliable systems especially in the era of chatbots and other AI systems. The related works also throws more light on various applications that revolve around the concept of Human Centered AI. Among the host of research suggestions for future work, we recommend a further extension of the 2-dimensional human-computer autonomy by Shneiderman and other recommendations in the fields considered. We further recommend more commitment and attention to balance human control over contemporary intelligent systems.

The field of Artificial Intelligence (AI) in recent years, has proven critical in almost every sector of the economy. Numerous applications have been developed based on the concepts and techniques provided by this field. Every field of great potential continues to draw attention and evolve. As a result, well-defined terminology also plays a key role in the development and evolution of these important fields as AI is no exception. The PRISMA Flow Diagram is used in scanning the retrieved articles from the Scopus database. A total number of 347 articles were obtained as search results based on the research query of which 21 articles provided 34 definitions. A generic definition for Artificial Intelligence is proposed.

This paper provides an in-depth review about opinions and applications in the new area of Human Centered Artificial Intelligence