Rajalakshmi Institutions

Artificial Neural Networks (ANNs) have brought a great advance in biomedical engineering with their enhanced features in data analysis, pattern recognition, and data modeling systems. Taking advantage of the computational capabilities of ANNs, scientists and engineers have designed new approaches to address some of the most difficult tasks in medicine, such as the diagnosis of diseases, the selection of appropriate treatment regimes, and the establishment of personalized medicine. Advanced numerical neural networks effectively manage diverse, high-dimensional biomedical research data like imaging data or sequences and physiological signals and can provide timely and accurate decisions. This chapter aims to cover the background, the opportunity, and the advancement of the ANNs in biomedical engineering and specifically how this field has and will continue to impact the future of healthcare technology.

Artificial intelligence (AI) is swiftly reshaping diverse industries, and the pharmaceutical sector is no different. This study delves into AI's capacity to revolutionize pharmacy, exploring its current and future applications in drug discovery, personalized medicine, safety, quality control, inventory management, and patient counselling. Despite notable advancements, challenges like data privacy, ethics, and regulations are crucial considerations. AI's transformative impact is evident in faster drug discovery, enhanced patient outcomes, cost reduction, and improved operational efficiency in pharmacies. The shift from manual processes to automated AI systems ensures precision, personalization, and cost-effectiveness in patient care. Ethical and responsible AI use, along with careful societal and workforce considerations, is imperative. This chapter provides insights into the future of pharmacy, highlighting AI's transformative potential in the field.

In contemporary healthcare, artificial intelligence (AI) and humanoid robotics are transformative forces, revolutionizing patient care and medical practices. AI algorithms analyze vast datasets to enhance diagnostic accuracy, enabling early disease detection and personalized treatment plans. Humanoid robots, equipped with AI, assist in repetitive tasks, patient monitoring, and even surgery, augmenting healthcare professionals' capabilities. This synergy between AI and robotics not only improves efficiency but also fosters patient engagement and empowers healthcare providers. These technologies streamline administrative processes, reduce errors, and facilitate remote patient monitoring. However, ethical considerations and the need for responsible AI deployment must be addressed. Despite challenges, the integration of AI and humanoid robotics marks a paradigm shift in healthcare, promising more precise diagnoses, efficient treatments, and ultimately, improved patient outcomes in the ever-evolving landscape of medical science.

Carbon-based nanomaterials, such as carbon nanotubes, graphene, and fullerene, have gained prominence due to their exceptional thermal conductivity and stability, making them ideal for enhancing heat transfer in various fluids. This study explores the synthesis methods, thermal properties, and potential benefits of these nanofluids in practical applications. Additionally, it addresses the challenges associated with their dispersion stability and the need for cost-effective, scalable production techniques. The study also covers recent advancements in experimental and theoretical studies, providing insights into the mechanisms behind improved thermal performance. By enhancing heat transfer efficiency, rheological characteristics and these nanofluids hold promise for applications in cooling systems, energy storage, and electronic devices, contributing to the advancement of thermal management technologies.

The presented flexible array shaped wearable antenna is simulated on a jeans textile substrate. This array shaped microstrip antenna (proposed) has been energized by linefeed technique. It gives the wider range of frequencies from 2.914GHz to 22.518 GHz that is super high frequency band (SHF) range. The proposed antenna has -10dB simulated bandwidth is 154.168%. The presented antenna's size is 43.6×49 mm2that it resonates at multiple frequencies i.e.7.2455 GHz, 10.588 GHz, and 14.456 GHz. The proposed design is applicable for various applications such as super high frequency, satellite communication, missile guidance, airborne intercept, long range tracking, police radar, etc. The proposed antenna is better suitable for microwave imaging and satellite communications. But the prime focus is on microwave imaging, which is used to locate the body tissues that are very difficult to evaluate. It can be constructed with a simple manufacturing process. The proposed antenna offers maximum reflection coefficient of -31.668dB, adequate VSWR <2 over operating band, peak gain of 4.915 dBi.