Predictive Technique Of Security Data Breaches In Ai Powered Mobile Cloud Application Using Deep Random Forest Algorithm

With the rapid integration of artificial intelligence (AI) in mobile cloud applications, ensuring robust security mechanisms is vital to safeguard sensitive user data. The proliferation of AI technologies in mobile cloud applications has brought unprecedented efficiency and convenience, accompanied by an escalating risk of security breaches. As the threat landscape evolves, traditional security measures fall short in providing comprehensive protection. This research recognizes the critical need for a predictive approach to security data breaches in AI-powered mobile cloud applications. Existing security frameworks often lack the adaptability to detect and pre-emptively address emerging threats specific to AI-enhanced mobile cloud environments. This study employs the Deep Random Forest Algorithm, an advanced machine learning technique known for its ability to handle complex and dynamic datasets. The algorithm combines the power of deep learning with the versatility of random forest classifiers to predict security breaches in real-time. The results demonstrate the efficacy of the proposed Deep Random Forest Algorithm in predicting and mitigating security breaches in AI-powered mobile cloud applications. The model exhibits high accuracy and sensitivity, showcasing its potential to enhance the security posture of mobile cloud ecosystems

Potential mediators of in ovo delivered double stranded (ds) RNA-induced innate response against low pathogenic avian influenza virus infection

Abstract Background Toll like receptor (TLR) 3 is a critically important innate pattern recognizing receptor that senses many viral infections. Although, it has been shown that double stranded (ds) RNA can be used for the stimulation of TLR3 signaling pathway in a number of host-viral infection models, it’s effectiveness as an antiviral agent against low pathogenic avian influenza virus (LPAIV) needs further investigation. Methods In this study, first, we delivered TLR3 ligand, dsRNA, in ovo at embryo day (ED)18 since in ovo route is routinely used for vaccination against poultry viral and parasitic infections and infected with H4N6 LPAIV 24-h post-treatment. A subset of in ovo dsRNA treated and control groups were observed for the expressions of TLR3 and type I interferon (IFN)s, mRNA expression of interleukin (IL)-1β and macrophage recruitment coinciding with the time of H4N6 LPAIV infection (24 h post-treatment). Additionally, Day 1 chickens were given dsRNA intra-tracheally along with a control group and a subset of chickens were infected with H4N6 LPAIV 24-h post-treatment whereas the rest of the animals were observed for macrophage and type 1 IFN responses coinciding with the time of viral infection. Results Our results demonstrate that the pre-hatch treatment of eggs with dsRNA reduces H4N6 replication in lungs. Further studies revealed that in ovo delivery of dsRNA increases TLR3 expression, type I IFN production and number of macrophages in addition to mRNA expression of IL-1β in lung 24-h post-treatment. The same level of induction of innate response was not evident in the spleen. Moreover, we discovered that dsRNA elicits antiviral response against LPAIV correlating with type I IFN activity in macrophages in vitro. Post-hatch, we found no difference in H4N6 LPAIV genome loads between dsRNA treated and control chickens although we observed higher macrophage recruitment and IFN-β response coinciding with the time of viral infection. Conclusions Our findings imply that the TLR3 ligand, dsRNA has antiviral activity in ovo and in vitro but not in chickens post-hatch and dsRNA-mediated innate host response is characterized by macrophage recruitment and expressions of TLR3 and type 1 IFNs