Abilash Kasi

Individuals with ASD often face challenges in understanding and expressing emotions, which can negatively impact their social interactions. This study explores how AI technologies present a promising solution to support these individuals in developing emotional intelligence skills. By utilizing AI virtual assistants to provide tailored social cues and emotional feedback, individuals with ASD can improve their ability to navigate social situations and understand other's emotions effectively. These assistants are designed to analyze real-time social scenarios and offer immediate feedback, helping individuals with ASD understand social cues and respond appropriately. Through interactive exercises and visual prompts, individuals gradually improve their emotional comprehension, making meaningful connections. AI companions also help these individuals understand the reasons behind emotions, fostering empathy. Through interactive dialogues and scenario-based simulations, individuals practice responding to emotional situations, empowering them to navigate social interactions confidently.

Fatigue is a major contributing factor to accidents involving professional drivers. Traditional methods of detecting fatigue such as self-reported questionnaires or observation are often ineffective and impractical. Artificial intelligence offers a promising solution to this problem by providing a real-time, objective, and non-intrusive way to detect fatigue. This review aims to outline AI-based fatigue detection systems that can be implemented for long-haul drivers. It uses technology to identify and track the driver's behavior and physiological indicators to detect signs of fatigue. Integrating an AI-based fatigue detection system into the vehicle's dashboard could provide real-time alerts when the drivers are drowsy as well as provide data to fleet managers to target interventions to drivers at risk of drowsy driving. This kind of fatigue detection system is relatively inexpensive to implement and can reduce the number of accidents caused by drowsy driving significantly.

Fatigue is a major contributing factor to accidents involving professional drivers. Traditional methods of detecting fatigue such as self-reported questionnaires or observation are often ineffective and impractical. Artificial intelligence offers a promising solution to this problem by providing a real-time, objective, and non-intrusive way to detect fatigue. This review aims to outline AI-based fatigue detection systems that can be implemented for long-haul drivers. It uses technology to identify and track the driver's behavior and physiological indicators to detect signs of fatigue. Integrating an AI-based fatigue detection system into the vehicle's dashboard could provide real-time alerts when the drivers are drowsy as well as provide data to fleet managers to target interventions to drivers at risk of drowsy driving. This kind of fatigue detection system is relatively inexpensive to implement and can reduce the number of accidents caused by drowsy driving significantly.

Individuals with ASD often face challenges in understanding and expressing emotions, which can negatively impact their social interactions. This study explores how AI technologies present a promising solution to support these individuals in developing emotional intelligence skills. By utilizing AI virtual assistants to provide tailored social cues and emotional feedback, individuals with ASD can improve their ability to navigate social situations and understand other's emotions effectively. These assistants are designed to analyze real-time social scenarios and offer immediate feedback, helping individuals with ASD understand social cues and respond appropriately. Through interactive exercises and visual prompts, individuals gradually improve their emotional comprehension, making meaningful connections. AI companions also help these individuals understand the reasons behind emotions, fostering empathy. Through interactive dialogues and scenario-based simulations, individuals practice responding to emotional situations, empowering them to navigate social interactions confidently.

In the wake of the COVID-19 pandemic, the landscape of healthcare, particularly in the realm of mental health, has undergone unprecedented shifts. This chapter delves into the pivotal role of artificial intelligence (AI) in shaping post-pandemic mental healthcare delivery. This chapter comprehensively explores AI-driven solutions, including predictive modeling, sentiment analysis, and virtual assistants; this chapter underscores how AI technologies can revolutionize mental health diagnosis, treatment, digitalized psychometric assessment and support care websites, and automated consultation or recommendation of specific mental issues. Additionally, it discusses integrating AI-powered tools in remote monitoring, teletherapy, and digital interventions to address the increasing demand for mental health services and bridge existing gaps in accessibility and conversion of digital platforms to provide mental health services and appointments, case records, and easy accessibility. Moreover, ethical considerations, data privacy concerns, and the importance of maintaining human-centric approaches in AI-enabled mental healthcare are examined per the Information Act and Mental Healthcare Act. By shedding light on the transformative potential of AI, this chapter aims to empower healthcare stakeholders to leverage cutting-edge technologies in fostering resilience and recovery in the post-pandemic era.

Climate change is one of the most concerning modern-day issues. The answer to whether industrialization, urbanization, and technical progress have improved our quality of life is susceptible to prejudiced and subjective perspectives, but they have harmed nature can be established with certainty. The study was done among a varied sample of college students in Tamil Nadu, India, and investigated the subtle interaction of socio-demographic characteristics, gender, character traits, and their impact on mental health, moral character, and environmental ethics. The study employs a correlational research design that makes use of self-report measurements such as the Environmental Ethics Scale (EES) and the Global Assessment of Character Strength Scale (GACS). Semi-structured interviews are used to acquire qualitative insights into participants' thoughts on climate change and potential solutions. The findings show a strong relationship between EES and GACS. The findings also emphasize the importance of socio-demographic characteristics including area, family structure, and socioeconomic level in establishing environmental values, underlining the need of taking cultural and economic issues into account when interpreting study findings and devising solutions. Finally, research advances our understanding of the complex relationships between socio-demographic factors, gender, mental health, personality traits, and environmental ethics, emphasizing the need for tailored interventions that take into account the unique needs and characteristics of various subgroups. Request access from your librarian to read this chapter's full text.