In the last couple of years, generative models especially large language models drawing from recent advances in AI have emerged as promising for numerous applications. This work illustrates how a large language model, CodeT5, can enhance secure text-to-code generation. CodeT5 is proposed as a unified pre-trained encoder–decoder transformer model that benefits from semantic hints given by developer-assigned identifier names, improving code understanding and promoting trustworthy text-to-code transcribing. It addresses gaps by incorporating an identifier-aware pre-training task and connecting natural language to programming language abstractions through user-written code comments. To enhance code security, CodeT5 is trained on a huge CVE dataset, leveraging code snippets before and after security patches. This new hybrid paradigm helps promote secure coding as well as AI-enhanced software engineering.

Malware, malicious software designed to disrupt, damage, or gain unauthorized access to computer systems, poses a significant and evolving threat to cybersecurity. Malware detection is an essential component of modern cybersecurity, given the escalating complexity and diversity of malicious software threats. In this study, we present a novel approach to malware detection based on behavior-based datasets using a fully connected deep neural network. Our research is motivated by the need for robust and accurate malware detection models that can adapt to evolving threats. The behavior-based dataset, which captures the dynamic interactions of malware with the host environment, provides a rich source of information for training and evaluation. The model uses the hyperbolic tangent (tanh) activation function and the Nesterov optimizer, resulting in remarkable accuracy of 100%. This study offers a high-performing solution for malware detection using behavior-based datasets. As cybersecurity continues to evolve, our approach contributes to strengthening defenses against the ever-persistent threat of malware.

Telemedicine has great potential in urology as a strong medium for providing patients with continuous high-quality urological care despite the hurdles involved in its implementation. Both clinicians and patients are crucial factors in determining the success of tele-consults in terms of simplicity of use and overall satisfaction. For it to be successfully incorporated into routine urological practice, rigorous training and evidence-based recommendations are lacking. If these issues are addressed, they can provide a significant impetus for future tele-consults in urology and their successful deployment, even beyond the pandemic, to assure safer and more environment-friendly patient management.

Telemedicine is the delivery of healthcare to patients who are not in the same location as the physician. The practice of telemedicine has a large number of advantages, including cost savings, low chances of nosocomial infection, and fewer hospital visits. Teleclinics have been reported to be successful in the post-surgery and post-cancer therapy follow-up, and in offering consulting services for urolithiasis patients. This review focuses on identifying the outcomes of the recent studies related to the usage of video consulting in urology centers for hematuria referrals and follow-up appointments for a variety of illnesses, including benign prostatic hyperplasia (BPH), kidney stone disease (KSD), and urinary tract infections (UTIs) and found that they are highly acceptable and satisfied. Certain medical disorders can cause embarrassment, social exclusion, and also poor self-esteem, all of which can negatively impair health-related quality-of-life. Telemedicine has proven beneficial in such patients and is a reliable, cost-effective patient-care tool, and it has been successfully implemented in various healthcare settings and specialties.

With the inter and multi-disciplinary collaboration of the medical community with technologists in conjunction with a disproportionately alarming doctor-patient ratio, it has now become a matter of concern for researchers to enhance patient care with advanced technology along with the reduction of burden on medical professionals. Artificial Intelligence (AI) has now been accepted willingly in the healthcare sector, which has led to a tremendous increase in computational power and large data handling capabilities and is widely used in gastrointestinal endoscopy. The objective of this review is to explore the state of current literature on different AI-based methods applied in intestinal endoscopy for the detection of colonic polyps. A detailed non-systematic literature review was conducted to identify all relevant studies using PubMed/MEDLINE, Scopus, EMBASE, and Google Scholar databases. The technique of AI systems, model building steps, and diagnostic measuring techniques are also discussed. In the automated diagnosis of polyps, AI-based platforms have achieved clinically acceptable diagnostic efficiency. AI-based methods can be of clinical importance in gastroenterology, and as computing strength and algorithms enhance, the application is likely to grow and expand in the field.