Drug discovery is the process by which new drug candidates are discovered and drug development takes place. To enhance the efficiency, accuracy, and speed of the drug discovery process, machine learning (ML) could play a transformative role. For this research study, antidiabetic natural compounds from C. intybus, which is commonly known as chicory, were selected, as they have promising antidiabetic properties that can complement conventional diabetes treatments. A bioactive natural compound dataset was retrieved on the chicory plant using Indian Medicinal Plants, Phytochemistry, and Therapeutics (IMPPAT) public source information. This collected dataset was analyzed for its absorption, distribution, metabolism, and excretion (ADME) properties using the SwissADME online tool. Principal component analysis (PCA) and correlation analysis were performed using trial-version XLSTAT software 2014.5.03 and Python. The obtained dataset from SwissADME was subjected to cleaning, after that, it was used to develop machine learning models, such as support vacuum (SVM) ML, random forest (RF), Naive Bayes (NB), and decision tree (DT). The Lipinski rule of violation was chosen as the target variable. To improve the vitality of the created ADME dataset, PCA, a biplot graph, and correlation analysis were carried out. A large dataset of naturally occurring antidiabetic compounds was used to predict the drug-likeness of ML models that were effectively deployed on heterogeneous ADME datasets. Among all these ML models, DT performed better than the rest of the models.

Different organizations in today's scenario are fully dependent on information technology for their survival, suffer from various security challenges like unauthorized access, physical damages etc. To avoid various security breaches and concerns, robust mechanism for user access need to be adopted that not only secure of valuable data but can also be utilized for developing various other security applications. "Biometric" secured technology is gaining attention for over traditional security mechanism like password, smart card etc. because information related to biometric are difficult to steal as compared to other mechanisms. In this research analysis "strong biometric approach" is proposed to overcome security apprehensions of various organizations& society through iris recognition system. Iris recognition system is a mechanism to identify a person through analyzing his or her iris pattern. This recognition system includes iris image acquisition, segmentation, normalization, encoding, matching and finally validation of iris templates. The iris recognition system developed and simulated in this research study has taken IIT database iris images as inputs and utilized hamming distance as the matching parameter. The simulated results depict an efficient and novel secured approach that will overcome various unauthorized accesses across the internet. The most novel approach of this iris based recognition system as compared to other traditional systems is that, if selected images are matched with trained iris images present in database then the resultant hamming distance as most of the iris recognition systems directly accept or reject images and causes huge congestion and execution prorogations.