This paper presents a biosensor-CMOS platform for measuring the capacitive coupling of biorecognition elements. The biosensor is designed, fabricated, and tested for the detection and quantification of a protein that reveals the presence of early-stage cancer. For the first time, the spermidine/spermine N1 acetyltransferase (SSAT) enzyme has been screened and quantified on the surface of a capacitive sensor. The sensor surface is treated to immobilize antibodies, and the baseline capacitance of the biosensor is reduced by connecting an array of capacitors in series for fixed exposure area to the analyte. A large sensing area with small baseline capacitance is implemented to achieve a high sensitivity to SSAT enzyme concentrations. The sensed capacitance value is digitized by using a 12-bit highly digital successive-approximation capacitance-to-digital converter that is implemented in a 0.18 μm CMOS technology. The readout circuit operates in the near-subthreshold regime and provides power and area efficient operation. The capacitance range is 16.137 pF with a 4.5 fF absolute resolution, which adequately covers the concentrations of 10 mg/L, 5 mg/L, 2.5 mg/L, and 1.25 mg/L of the SSAT enzyme. The concentrations were selected as a pilot study, and the platform was shown to demonstrate high sensitivity for SSAT enzymes on the surface of the capacitive sensor. The tested prototype demonstrated 42.5 μS of measurement time and a total power consumption of 2.1 μW.

The integration of Artificial Intelligence (AI) and the Internet of Things (IoT) is revolutionizing agriculture, particularly in seed harvest and biotechnology. These technologies enhance precision farming by offering data-driven solutions for crop monitoring, disease detection, and resource management. This chapter explores how AI and IoT optimize seed harvest, improve crop yields, and promote sustainable farming. AI analyzes data on soil health, weather, and crops, aiding better decisions in planting, irrigation, and fertilization, while IoT devices provide real-time environmental data. The synergy between AI and IoT improves resource allocation and crop management. Examples from the Netherlands and India demonstrate their success in boosting yields and controlling pests. Despite challenges like high costs and technical expertise, ongoing advancements will support wider adoption. In conclusion, AI and IoT significantly improve agricultural efficiency, productivity, and sustainability, contributing to global food security.

Machine learning (ML) is transforming healthcare by enhancing the accessibility, efficiency, and accuracy of medical procedures. As a branch of artificial intelligence, ML algorithms analyze data to make informed predictions and decisions. In diagnostic imaging, ML assists radiologists in interpreting CT, MRI, and X-ray images, identifying patterns and anomalies that may be missed by the human eye, improving early diagnosis of diseases like cancer and cardiovascular disorders. ML also plays a key role in personalized medicine by predicting individual responses to therapies, particularly in oncology, where genetic variations affect treatment outcomes. Additionally, ML accelerates drug discovery, reducing time and costs for new treatments. Beyond diagnosis and therapy, ML revolutionizes patient management through real-time monitoring of vital signs via wearable devices, enabling timely treatment of chronic conditions. It also helps optimize resource allocation and streamline administrative tasks, boosting healthcare system efficiency. However, ensuring accountability and transparency in ML models is crucial. Despite challenges, ML promises to revolutionize modern healthcare by improving diagnosis, tailoring treatments, and enhancing patient care.

The integration of Artificial Intelligence (AI) and the Internet of Things (IoT) is revolutionizing agriculture, particularly in seed harvest and biotechnology. These technologies enhance precision farming by offering data-driven solutions for crop monitoring, disease detection, and resource management. This chapter explores how AI and IoT optimize seed harvest, improve crop yields, and promote sustainable farming. AI analyzes data on soil health, weather, and crops, aiding better decisions in planting, irrigation, and fertilization, while IoT devices provide real-time environmental data. The synergy between AI and IoT improves resource allocation and crop management. Examples from the Netherlands and India demonstrate their success in boosting yields and controlling pests. Despite challenges like high costs and technical expertise, ongoing advancements will support wider adoption. In conclusion, AI and IoT significantly improve agricultural efficiency, productivity, and sustainability, contributing to global food security.

The merging of artificial intelligence (AI) with healthcare in recent years has signaled the beginning of a revolutionary period in patient care and medical practice. The integration of AI technologies in the context of smart healthcare is examined in this chapter, with a focus on how these technologies might improve patient outcomes. We start by describing the basic ideas of artificial intelligence (AI) and how they relate to many aspects of healthcare, such as treatment planning, diagnosis, customized medicine, and operational effectiveness. The topic also includes the latest developments in artificial intelligence (AI) technologies, including predictive analytics, natural language processing, and machine learning algorithms, as well as their useful applications in healthcare environments. A thorough analysis of case studies demonstrates how AI-driven solutions are increasing the precision of diagnoses, enhancing treatment plans, and simplifying administrative procedures. The chapter also discusses the difficulties and constraints associated with integrating AI, such as the need for strong legal frameworks and validation, algorithmic bias, and data privacy issues. We can provide more proactive and individualized patient care by incorporating AI into healthcare systems, which will eventually improve patient outcomes and streamline the delivery of healthcare. This chapter offers a nuanced perspective on the potential of artificial intelligence (AI) to alter healthcare practices through a thorough analysis of current advances and empirical data. It also highlights future prospects for research and development in the field of smart healthcare.

The integration of Internet of Things (IoT) and Artificial Intelligence (AI) technologies in healthcare has ushered in a new era of disease detection and management, significantly impacting the way viral and cardiovascular diseases are addressed. IoT devices, such as wearable sensors and environmental monitors, collect vast amounts of real-time data, which AI algorithms then analyze to detect early signs of infections or abnormalities. This synergy between IoT and AI has proven particularly effective in the early detection and monitoring of diseases like SARS-CoV-2, HIV, influenza, and various cardiovascular conditions. By leveraging AI's predictive analytics and machine learning models, healthcare providers can detect the impact of disease route, predict outbreaks, and mark treatment strategies with unprecedented accuracy. Despite the challenges of data privacy and integration into existing healthcare infrastructures, the advancements in IoT and AI have led to significant improvements in patient outcomes. These technologies are poised to play an increasingly central role in global health strategies, offering enhanced diagnostic capabilities, real-time monitoring, and personalized care solutions that can reduce the burden of disease and improve quality of life.