A novel approach based on neutrosophic Bonferroni mean operator of trapezoidal and triangular neutrosophic interval environments in multi-attribute group decision making

Neutrosophic multicriteria is a method of decision-making that uses indeterminacy to combine several criteria or elements, frequently with incomplete or ambiguous information, to find a solution. The neutrosophic multicriteria analysis enables the assessment of qualitative and subjective aspects and can assist in resolving conflicting goals and preferences. In the Neutrosophic Multi-Attribute Group Decision Making (NMAGDM) problems, all the information provided by the decision makers (DMs) is expressed as single value neutrosophic triangular and trapezoidal numbers examined in this study which can provide more flexibility and accuracy in capturing uncertainty and aggregating preferences. We offer a novel approach for determining the neutrosophic possibility degree of two and three trapezoidal and triangular neutrosophic sets and the concepts of neutrosophic possibility mean value. The trapezoidal and triangular neutrosophic Bonferroni mean (TITRNBM) operator and the trapezoidal and triangular neutrosophic weighted Bonferroni mean (TITRNWBM) operator are two aggregation methods we then create. Further, we examine the TITRNBM and TITRNWBM attributes and their uniqueness. The NMAGDM approach with trapezoidal and triangular information is suggested based on the TITRNWBM operator and possibility degree. Finally, a concrete example of manufacturing companies searching for the best supplier for assembling the critical parts is provided to validate the established strategies and show their practical applicability and efficacy.

Machine Learning Applied To Cervical Cancer Data

Cervical cancer is a type of cancer that occurs in the cells of the cervix. Cervix is the lower part of the uterus that connects to the vagina. It arises due to the abnormal growth of cells and spreads to other parts of the body. Smoking is also considered as one of the main causes for cervical cancer. Long term use of Oral contraceptive pills can also cause cancer. Also having multiple pregnancies can cause cervical cancer. Cervical cancer often has no symptoms in its early stages. It is fatal most of the time. The most common symptom is unusual vaginal bleeding. Here the machine learning algorithms are used to predict the occurrence of cervical cancer. Predicting the presence of cervical cancer can help the diagnosis process to start at an earlier stage.

Biosynthesis, characterization and cytotoxic effect of plant mediated silver nanoparticles using Morinda citrifolia root extract

Silver has been used since time to control bodily infection, prevent food spoilage and heal wounds by preventing infection. The present study aims at an environmental friendly method of synthesizing silver nanoparticles, from the root of Morinda citrifolia; without involving chemical agents associated with environmental toxicity. The obtained nanoparticles were characterized by UV-vis absorption spectroscopy with an intense surface plasmon resonance band at 413 nm clearly reveals the formation of silver nanoparticles. Fourier transmission infra red spectroscopy (FTIR) showed nanopartilces were capped with plant compounds. Field emission-scanning electron microscopy (FE-SEM) and Transmission electron microscopy (TEM) showed that the spherical nature of the silver nanoparticles with a size of 30-55 nm. The X-ray diffraction spectrum XRD pattern clearly indicates that the silver nanoparticles formed in the present synthesis were crystalline in nature. In addition these biologically synthesized nanoparticles were also proved to exhibit excellent cytotoxic effect on HeLa cell.