Extended PROMETHEE method with (p,q)-rung linear Diophantine fuzzy sets for robot selection problem.

The introduction of (p, q)-rung linear Diophantine fuzzy set into the field of fuzzy set theory is a significant advancement, providing a broader perspective to address complicated decision-making scenarios. Alongside, the preference ranking organization method for enrichment of evaluation (PROMETHEE) emerges as a widely recognized tool for tackling multi-criteria decision-making challenges. This study contributes theoretically to the decision-making field by integrating (p, q)-rung linear Diophantine fuzzy sets into the PROMETHEE framework, improving (p, q)-rung linear Diophantine fuzzy sets adaptability and efficiency in practical aspects, the proposed framework can effectively address various intricate decision-making challenges encountered in real-world scenarios.

Enhancing decision-making with linear diophantine multi-fuzzy set: application of novel information measures in medical and engineering fields.

This study offers a comprehensive analysis of novel information for linear diophantine multi-fuzzy sets and illustrates its applications in practical scenarios. We introduce innovative similarity metrics tailored for linear diophantine multi-fuzzy sets, including Cosine similarity, Jaccard similarity, and Exponential similarity. Additionally, we propose Entropy, Inclusion, and Distance measures, providing a robust theoretical foundation supported by developed theorems that explain the interactions between these metrics.

Optimized deep learning networks for accurate identification of cancer cells in bone marrow.

Radiologists utilize pictures from X-rays, magnetic resonance imaging, or computed tomography scans to diagnose bone cancer. Manual methods are labor-intensive and may need specialized knowledge. As a result, creating an automated process for distinguishing between malignant and healthy bone is essential.

Technology adaptation in sugarcane supply chain based on a novel p, q Quasirung Orthopair Fuzzy decision making framework.

The present paper contributes to the literature in two ways. First, it develops a novel p, q Quasirung Orthopair Fuzzy (p, q QOF) based group decision making framework to modify a recently developed multi-criteria decision making (MCDM) model such as Comparisons between Ranked Criteria (COBRAC). Second, the paper ruminates on the Strength-Weakness-Opportunity-Threat (SWOT) of the sugarcane supply chain (SSC) in India vis-à-vis adaptation of the advanced technologies featuring Industry 4.

Sentiment classification for insider threat identification using metaheuristic optimized machine learning classifiers.

This study examines the formidable and complex challenge of insider threats to organizational security, addressing risks such as ransomware incidents, data breaches, and extortion attempts. The research involves six experiments utilizing email, HTTP, and file content data. To combat insider threats, emerging Natural Language Processing techniques are employed in conjunction with powerful Machine Learning classifiers, specifically XGBoost and AdaBoost.