Planning nationwide sustainable logistics for grain exports at a tactical level assumes flexibility in handling uncertainties. Despite the dynamic operations, the solution's approach focuses mainly on optimisation, leaving out the queuing requirements. This paper presents a...

The suitability of agents for tasks and ranked preference of tasks for agents play a significant role in problems where several agents are available to perform various tasks. The agents should be allocated to various tasks so that the corresponding assignment schedules must provide solution...

Clinical Named Entity Recognition (NER) within Electronic Medical Records (EMRs) has seen substantial research attention. Since much clinical information resides in unstructured text, NER technology is pivotal in extracting vital patient data from such sources. The ubiquity of EMRs has fueled...

This paper proposes a Linear Programming-based Bi-Objective time series Forecasting Algorithm that helps forecast sub-annual short univariate time-series data. The proposed algorithm generates forecasts optimized for a pair of accuracy measures instead of just one. The algorithm is based on the...

This study presents a novel approach to arithmetic operations and ranking of Generalized Fuzzy Numbers (GFNs) for decision-making under uncertainty. Traditional methods often yield irrational outputs when dealing with GFNs, especially in complex situations where precision and clarity are...

This study presents a hybrid fuzzy Multi-Attribute Group Decision Making (MAGDM) model with application to commercial insurance selection. The proposed hybrid model uses Spherical Fuzzy (SF) sets using Yager t-norm and t-conorm operations. The Logarithmic Decomposition of Criteria Importance...

Selecting the requirements for an information system based on their ranking values is one of the critical steps in software development. Various methods for computing the ranking order of the requirements have been developed using multi-criteria decision-making methods. This study shows...

Humor identification in text is essential for natural language understanding, impacting human-computer interactions and emotion recognition. This study introduces a novel approach to automatic humor identification in text using ensemble learning techniques. The objective is to classify text into...

It is essential to innovate and improve service levels by predicting possible process outcomes due to the growth of online service providers. This study estimates the customer satisfaction level based on customer experience analysis. In doing so, we answer recent calls for research about a more...

Disaster tweet classification has gained significant attention in natural language processing (NLP) due to its potential to aid disaster response and emergency management. The goal of disaster tweet classification is to automate the identification of informative tweets containing information...