Unveiling the frontiers of deep learning: innovations shaping diverse domains

Deep learning (DL) enables the development of computer models that are capable of learning, visualizing, optimizing, refining, and predicting data. In recent years, DL has been applied in a range of fields, including audio-visual data processing, agriculture, transportation prediction, natural language, biomedicine, disaster management, bioinformatics, drug design, genomics, face recognition, and ecology. To explore the current state of deep learning, it is necessary to investigate the latest developments and applications of deep learning in these disciplines. However, the literature is lacking in exploring the applications of deep learning in all potential sectors. This paper thus extensively investigates the potential applications of deep learning across all major fields of study as well as the associated benefits and challenges. As evidenced in the literature, DL exhibits accuracy in prediction and analysis, makes it a powerful computational tool, and has the ability to articulate itself and optimize, making it effective in processing data with no prior training. Given its independence from training data, deep learning necessitates massive amounts of data for effective analysis and processing, much like data volume. To handle the challenge of compiling huge amounts of medical, scientific, healthcare, and environmental data for use in deep learning, gated architectures like LSTMs and GRUs can be utilized. For multimodal learning, shared neurons in the neural network for all activities and specialized neurons for particular tasks are necessary.Comment: 64 pages, 3 figures, 3 table

Bio-oil from microalgae: Materials, production, technique, and future

Because of its low environmental impact and high production, microalgae bio-oil has quickly become a popular renewable fuel option. The process utilizes microalgae which are readily available in nature to produce an alternative to fossil fuel. Although microalgal bio-oil production mechanisms have been previously reviewed in recent studies, comparatively few of them emphasize the significance of algal bio-oil production through all available bio-oil conversion mechanisms from microalgae. Here we review the available and common bio-oil conversion processes from microalgae, bio-oil upgrading, and the commercial aspects of its utilization. The most efficient route to bio-oil production can be identified by analysing both the biomass feedstock and the final product. For example, pyrolysis can produce high-energy bio-oil, but it also produces large amounts of char and gas. Although hydrothermal liquefaction and gasification are more complex and costly, they have the potential to produce bio-oil with greater consistency. However, the expense of using bio-oil in a commercial context is a major concern. The cost of producing bio-oil from microalgae is typically higher than that of producing conventional fossil fuels. Several factors, including cost, availability, and necessary infrastructure, contribute to the uncertainty of bio-oil’s commercial feasibility. With the constant improvements in technology and government support, however, bio-oil has the potential to emerge as a viable alternative to conventional fossil fuels