Biomedical and Pharmacology Journal (BPJ) is an international, open access, peer reviewed, quarterly research journal.

Today's epoch christened as the modern epoch, is the epoch of information and technologies. No field is untouched with the emergence of technologies. In different segments of agriculture, numerous technologies have been employed extensively. Machine learning has extensively been employed in every field today. It is composed of high-performance computing and big data technologies. Machine learning (ML) and its variants have been employed in different domains of agriculture like yield prediction, weed detection, disease detection, water management, soil management, livestock production, etc. Machine learning techniques have emerged as a boon to agriculture and have increased the quality and quantity of agricultural products. In this chapter, some applications of machine learning technologies in agriculture have been explored.

Today's era is the era of technologies. Technologies have widely been employed in each and every field. The field of agriculture is not untouched with the technologies, and in several segments of agriculture; it has been employed at large. Deep learning techniques and its variants like convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial network (AGN), and their various subcategories like AlexNet, ImageNet, visual geometry group (VGG), etc. have widely been employed in many sectors of agriculture in order to increase the quality and quantity of production. In this chapter, some applications of deep learning have been explored.

In today's dynamic and complex financial landscape, accurate and reliable financial forecasting is crucial for businesses to make informed decisions, manage risks, and achieve long-term success. Artificial intelligence (AI) has emerged as a transformative force in the financial industry, offering a powerful set of tools and techniques for enhancing financial forecasting capabilities. This chapter delves into the realm of AI-driven financial forecasting, exploring the role of soft computing techniques in harnessing the power of AI for data-driven financial insights. Soft computing, a branch of computational intelligence, encompasses a suite of methodologies that mimic human reasoning and learning processes to handle complex and uncertain data. A comprehensive overview of AI-driven financial forecasting, highlighting the strengths and applications of soft computing techniques in this domain, has been envisaged beginning by introducing the fundamental concepts of AI in financial forecasting, including data pre-processing, feature selection, and model evaluation. It then delves into the specific applications of soft computing techniques in financial forecasting, exploring their use in various forecasting tasks, such as stock price prediction, exchange rate forecasting, and credit risk assessment. The transformative potential of AI-driven financial forecasting, empowered by the power of soft computing techniques, have also been underscored. The effectiveness of soft computing techniques has been shown through real-world examples and case studies, demonstrating their ability to outperform traditional forecasting methods in various financial scenarios. It also discusses the challenges and limitations of using AI in financial forecasting, emphasizing the importance of data quality, model interpretability, and ethical considerations.

Emergence of deep learning (DL) and its applicability motivated researchers and scientists to explore its applications in their fields of expertise. In medical technology, a huge amount of data is required, and dealing with huge data is a challenging task for researchers. The emergence of neural networks and its modifications like convolutional neural networks (CNN), generative adversarial network (AGN), recurrent neural networks (RNN), and their subcategories has provided a stage to flourish deep learning. DL has been a successful tool in the fields of pattern recognition, natural language processing (NLP), image processing, speech recognition, computer vision, etc. All these techniques have been employed in healthcare. Image processing has been proven to be a fruitful technique for physicians to properly diagnose patients through CT scan, MRI, PET, radiography, nuclear medicine, ultrasound, etc. In this chapter, some applications of DL in healthcare have been envisaged, and it has been concluded that this technique is very successful in healthcare. Request access from your librarian to read this chapter's full text.

Food is the most fundamental requirement of human beings, and the only way this requirement can be satisfied is through agricultural production. The agricultural sector not only fulfills the need for food but also contributes to any country's economy. It also provides employment on a large scale. Agriculture is the backbone of any developing country due to its help in employment and economic sectors. This field is broadly divided into three parts: pre-harvest, harvest, and post-harvest. Advancement in various technologies like machine learning, image processing, the internet of things, and machine vision helps to reduce risk in farming and also increase profit. Using these techniques, a farmer can gain a brief understanding of the crop throughout its life cycle with greater precision and accuracy. This chapter provides a quick overview of different technologies along with their advantages and disadvantages. These technologies make farming more productive, accurate, and cost-effective, since they require less human effort and are less expensive.