Enhanced Approach for Bug Severity Prediction: Experimentation and Scope for Improvements

Software development is an iterative process, where developers create, test, and refine their code until it is ready for release. Along the way, bugs and issues are inevitable. A bug can be any error identified in requirement specification, design or implementation of any project. These bugs need to be categorized and assigned to developers to be resolved. the number of bugs generated in any large scale project are vast in number. These bugs can have significant or no impact on the project depending on the type of bug. The aim of this study is to develop a deep learning-based bug severity prediction model that can accurately predict the severity levels of software bugs. This study aims to address the limitations of the current manual bug severity assessment process and provide an automated solution using various classifiers e.g. Naïve Bayes, Logistic regression, KNN and Support vector machine along with Mutual information as feature selection method, that can assist software development teams in giving severity code to bugs effectively. It seeks to improve the overall software development process by reducing the time and effort required for bug resolution and enhancing the quality and reliability of software

The Rise of Crypto Malware: Leveraging Machine Learning Techniques to Understand the Evolution, Impact, and Detection of Cryptocurrency-Related Threats

Crypto malware has become a major threat to the security of cryptocurrency holders and exchanges. As the popularity of cryptocurrency continues to rise, so too does the number and sophistication of crypto malware attacks. This paper leverages machine learning techniques to understand the evolution, impact, and detection of cryptocurrency-related threats. We analyse the different types of crypto malware, including ransomware, crypto jacking, and supply chain attacks, and explore the use of machine learning algorithms for detecting and preventing these threats. Our research highlights the importance of using machine learning for detecting crypto malware and compares the effectiveness of traditional methods with deep learning techniques. Through this analysis, we aim to provide insights into the growing threat of crypto malware and the potential benefits of using machine learning in combating these attacks

Curcumin nanoformulations: A review of pharmaceutical properties and preclinical studies and clinical data related to cancer treatment

Curcumin, a natural yellow phenolic compound, is present in many kinds of herbs, particularly in Curcuma longa Linn. (turmeric). It is a natural antioxidant and has shown many pharmacological activities such as anti-inflammatory, anti-microbial, anti-cancer, and anti-Alzheimer in both preclinical and clinical studies. Moreover, curcumin has hepatoprotective, nephroprotective, cardioprotective, neuroprotective, hypoglycemic, antirheumatic, and antidiabetic activities and it also suppresses thrombosis and protects against myocardial infarction. Particularly, curcumin has demonstrated efficacy as an anticancer agent, but a limiting factor is its extremely low aqueous solubility which hampers its use as therapeutic agent. Therefore, many technologies have been developed and applied to overcome this limitation. In this review, we summarize the recent works on the design and development of nano-sized delivery systems for curcumin, including liposomes, polymeric nanoparticles and micelles, conjugates, peptide carriers, cyclodextrins, solid dispersions, lipid nanoparticles and emulsions. Efficacy studies of curcumin nanoformulations using cancer cell lines and in vivo models as well as up-to-date human clinical trials are also discussed