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

The PGPR mechanisms of salt stress adaptation and plant growth promotion

Worldwide crop productivity hampers severely due to the adverse effects of salinity. Global warming causes a rapid escalation of the salt-affected area, and new agricultural land is affected through saltwater intrusion. The ever-growing human population impulses to utilize the saline area for crop cultivation to ensure food security. Salinity resistance crops could be a promising substitute but with minor success because inappropriate tactics on saline soil management resulted in unsatisfactory yield. Salt-tolerant plant growth-promoting rhizobacteria (ST-PGPR) is considered an alternate way towards enhancing crop growth in saline ecosystems. It is reported that PGPR is enabled to produce exopolysaccharides which lead to biofilm formation and generate osmoprotectants and antioxidant enzymes that can significantly contribute to stimulating plant growth in the saline ecosystem. In addition, several plant growth-promoting characteristics of PGPR such as the acquisition of essential nutrients and upsurge hormone production could enhance plant growth simultaneously. In this review, we will explore the survival mechanisms of ST-PGPR and their influence on plant growth promotion in saline ecosystems

An in vivo and in silico evaluation of the hepatoprotective potential of Gynura procumbens: A promising agent for combating hepatotoxicity.

IntroductionThe liver, the most important metabolic organ of the body, performs a wide variety of vital functions. Hepatic cell injury occurs by the activation of reactive oxygen species (ROS) that are generated by carbon tetrachloride (CCl4), xenobiotics, and other toxic substances through cytochrome P450-dependent steps resulting from the covalent bond formation with lipoproteins and nucleic acids. Observing the urgent state of hepatotoxic patients worldwide, different medicinal plants and their properties can be explored to combat such free radical damage to the liver. In vivo and in silico studies were designed and conducted to evaluate the antioxidant and hepatoprotective properties of Gynura procumbens in rats.Materials and methodsGynura procumbens leaves were collected and extracted using 70% ethanol. The required chemicals CCl4, standard drug (silymarin), and blood serum analysis kits were stocked. The in vivo tests were performed in 140 healthy Wister albino rats of either sex under well-controlled parameters divided into 14 groups, strictly maintaining Institutional Animal Ethics Committee (IEAC) protocols. For the histopathology study, 10% buffered neutral formalin was used for organ preservation. Later the specimens were studied under a fluorescence microscope. In silico molecular docking and absorption, distribution, metabolism, excretion, and toxicity (ADMET) studies were performed, and the results were analyzed statistically.Results and discussionGynura procumbens partially negate the deleterious effect of carbon tetrachloride on normal weight gain in rats. The elevated level of serum glutamate pyruvate transaminase (SGPT), serum glutamate oxaloacetate transaminase (SGOT), alkaline phosphatase (ALP), creatinine, LDH, total cholesterol (TC), low-density lipoprotein (LDL), triglycerides (TG), malondialdehyde (MDA), deoxyribonucleic acid (DNA) fragmentation ranges, gamma-glutamyl transferase (γ-GT) in CCl4 treated groups were decreased by both standard drug silymarin and G. procumbens leaf extract. We have found significant & highly significant changes statistically for different doses, here p<0.05 & p<0.01, respectively. On the other hand, G. procumbens and silymarin displayed Statistically significant (p<0.05) and high significant(p<0.01) increased levels of HDL, CAT SOD (here p<0.05 & p<0.01 for different doses) when the treatment groups were compared with the disease control group. Because the therapeutic activity imparted by plants and drugs accelerates the movement of the disturbed pathophysiological state toward the healthy state. In the molecular docking analysis, G. procumbens phytoconstituents performed poorly against transforming growth factor-beta 1 (TGF-β1) compared to the control drug silymarin. In contrast, 26 phytoconstituents scored better than the control bezafibrate against peroxisome proliferator-activated receptor alpha (PPAR-α). The top scoring compounds for both macromolecules were observed to form stable complexes in the molecular dynamics simulations. Flavonoids and phenolic compounds performed better than other constituents in providing hepatoprotective activity. It can, thus, be inferred that the extract of G. procumbens showed good hepatoprotective properties in rats

Effect of various treatment specimens on the Triglyceride level of CCl4- treated rats.

Comparison of triglyceride (mg/dl) of rats, belonged to 14 groups just before sacrifice. Each group consist of 10 rodents each with equal body mass index. The data were expressed as mean±standard deviation. X-axis represents the group distribution and y-axis represents triglyceride level of different groups. All abbreviation of different groups has been mentioned in Table 1. (*indicates statistically significant change where p<0.05, correlation is significant at a 95% confidence interval and **indicates highly significant change where p<0.01, correlation is significant at a 99% confidence interval).</p

Interactions of compounds 44, 48, 50, and 51 with TGF-β type I receptor.

Interactions of compounds 44, 48, 50, and 51 with TGF-β type I receptor.</p

Effect of various treatment specimens on the HDL level of CCl4- treated rats.

Comparison of HDL level (mg/dL) of rats, belonged to 14 groups just before sacrifice. Each group consist of 10 rodents each with equal body mass index. The data were expressed as mean±standard deviation. X-axis represents the group distribution and y-axis represents HDL level of different groups. All abbreviation of different groups has been mentioned in Table 1. (*indicates statistically significant change where p<0.05, correlation is significant at a 95% confidence interval and **indicates highly significant change where p<0.01, correlation is significant at a 99% confidence interval).</p

ADME properties of the phytoconstituents of interest.

ADME properties of the phytoconstituents of interest.</p

Interactions of the top 10 binding affinity phytoconstituents with PPAR-α.

Interactions of the top 10 binding affinity phytoconstituents with PPAR-α.</p

Effect of various treatment specimens on the total cholesterol level of CCl4- treated rats.

Comparison of total cholesterol (mg/dl) level of rats, belonged to 14 groups just before sacrifice. Each group consist of 10 rodents each with equal body mass index. The data were expressed as mean±standard deviation. X-axis represents the group distribution and y-axis represents total cholesterol level of different groups. All abbreviation of different groups has been mentioned in Table 1. (*indicates statistically significant change where p<0.05, correlation is significant at a 95% confidence interval and **indicates highly significant change where p<0.01, correlation is significant at a 99% confidence interval).</p