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

Navigating the IoT landscape: Unraveling forensics, security issues, applications, research challenges, and future

Given the exponential expansion of the internet, the possibilities of security attacks and cybercrimes have increased accordingly. However, poorly implemented security mechanisms in the Internet of Things (IoT) devices make them susceptible to cyberattacks, which can directly affect users. IoT forensics is thus needed for investigating and mitigating such attacks. While many works have examined IoT applications and challenges, only a few have focused on both the forensic and security issues in IoT. Therefore, this paper reviews forensic and security issues associated with IoT in different fields. Future prospects and challenges in IoT research and development are also highlighted. As demonstrated in the literature, most IoT devices are vulnerable to attacks due to a lack of standardized security measures. Unauthorized users could get access, compromise data, and even benefit from control of critical infrastructure. To fulfil the security-conscious needs of consumers, IoT can be used to develop a smart home system by designing a FLIP-based system that is highly scalable and adaptable. Utilizing a blockchain-based authentication mechanism with a multi-chain structure can provide additional security protection between different trust domains. Deep learning can be utilized to develop a network forensics framework with a high-performing system for detecting and tracking cyberattack incidents. Moreover, researchers should consider limiting the amount of data created and delivered when using big data to develop IoT-based smart systems. The findings of this review will stimulate academics to seek potential solutions for the identified issues, thereby advancing the IoT field.Comment: 77 pages, 5 figures, 5 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

Waste biorefinery to produce renewable energy: Bioconversion process and circular bioeconomy

Continual global energy scarcity and its future challenges, as well as environmental disasters, are causing global devastation. Additionally, a substantial quantity of food is being wasted regularly. Therefore, the adoption of circular bioeconomy principles and the bioconversion of wasted food appears to be both highly advantageous and urgently required. However, previous studies have placed limited emphasis on the technological progress and circular bioeconomy aspects associated with the bioconversion of wasted food. The present review thus investigates how mass-generated food waste can be used to produce valuable bioproducts through bioconversion techniques such as oleaginous metabolism, anaerobic fermentation, and solventogenesis. These techniques have attracted considerable interest due to their eco-friendly and resource-recycling capacities, as well as their efficiency and sustainability. The paper also discusses approaches to integrate biorefineries within existing economies to establish a circular bioeconomy and analyses the challenges as well as the techno-economic, environmental and life cycle scenarios of these approaches. Analysis of the techno-economic and environmental effects reveals that food waste biorefineries can be lucrative if certain pathways are maintained. The environmental impact of bioconversion methods that produce valuable bioproducts is also found to be substantially lower than that of conventional methods. Integrating bioconversion processes further improves the efficiency of the process and sustainably recovers resources. Developing a circular bioeconomy requires the adoption of a biorefinery strategy with an integrated approach