As a developmental impairment, Autism Spectrum Disorder (ASD) can lead to substantial difficulties in social interaction, communication, and conduct. The intellectual ability of autistic children and the severity of their symptoms can both be enhanced with earlier intervention. Several clinical studies have found that children with ASD have different facial phenotypes than those with TD. In this study, we present a Convolutional Neural Network-based Hybrid Deep Learning model for screening potential solutions to a unique ASD dataset of clinically diagnosed children using face photos. Support Vector Machine will be used for the process of categorization. The F1-score and categorization accuracy of our model were both 0.96. The Kaggle ASD face Image Dataset was used for this analysis of face pictures for ASD detection. Our findings corroborate the clinical observation that children with ASD and TD have distinct facial characteristics. Due to the high F1-score, it is possible to apply deep learning models for ASD screening in young children. We came to the conclusion that the practicality and accuracy of deep-learning oriented ASD screening with face photos depend heavily on characteristics linked to race and ethnicity.

The so-called "dark web" has emerged as the most trustworthy platform for thieves to launch their enterprises. The healthcare industry has become a haven for illegal activities such as the sale of medical gadgets, trafficking in human beings, and the purchase of organs. This is because the sector provides a high level of privacy, which makes it an ideal location for engaging in unlawful operations. In this field of research, linear regression is utilized to uncover previously unknown patterns in customer demand. A vector will be created using a time series of medical equipment purchases to do this. When we look at the data the case firm gave us, we notice that people tend to desire to purchase products in one of three ways. After that, we sort the hospitals into groups according to the course of the trend vector by employing a technique known as "hierarchical clustering," which we apply to the data. According to the research findings, the trend-based clustering method is an excellent way to partition hospitals into subgroups that share similar tendencies. According to our model evaluations, no one model can reliably produce the most accurate forecasts for each cluster when used by itself. Some models can be utilized to make accurate predictions, and these models apply to a wide variety of time series that exhibit various patterns.

The healthcare industry is facing numerous challenges in providing efficient and effective care to patients, including increased demand, limited resources, and a growing shortage of healthcare providers. To address these challenges, many healthcare organizations are turning to technology, specifically artificial intelligence (AI) and machine learning (ML), to improve patient care and outcomes. In response, the development of smart chatbots has emerged as a promising solution in the healthcare field. This chapter focuses on the design and implementation of a smart chatbot using AI and ML for healthcare applications. The main goal of the chatbot is to provide a more convenient and accessible method of delivering healthcare information and services to patients. This chapter will also explore the various components and algorithms used in the design of the chatbot, as well as its potential impact on the healthcare industry. Overall, this chapter demonstrates the value of AI and ML in healthcare and encourages further exploration and development of chatbots for healthcare applications.

The advent of Smart Industry 4.0 has brought about significant advances in automation, efficiency, and productivity in industrial environments. However, these technological advances also pose new challenges for cybersecurity. The increasing interconnectivity of devices and systems within Smart Industry 4.0 environments makes them vulnerable to cyber attacks and security breaches. To address these challenges, there is a need to integrate AI with cybersecurity in Smart Industry 4.0 applications. AI can be used to analyze large amounts of data from sensors and devices within the industrial environment to detect and prevent cyber threats. Machine learning algorithms can be trained to identify patterns of normal behavior and flag any deviations as potential security threats. There are different AI-based cybersecurity techniques that can be used, such as anomaly detection, intrusion detection, and predictive analytics. These techniques can be used in conjunction with other cybersecurity measures, such as firewalls, intrusion detection systems, and security information and event management (SIEM) tools, to provide a comprehensive approach to cybersecurity for Smart Industry 4.0 applications. Integrating AI with cybersecurity for Smart Industry 4.0 applications can help improve the security posture of industrial environments and reduce the risk of cyber attacks and data breaches. However, it is important to ensure that these technologies are properly implemented and configured to avoid unintended consequences and potential vulnerabilities. By carefully designing and implementing AI-based cybersecurity solutions, organizations can protect their industrial environments from cyber threats and ensure the reliability and safety of their operations.