Conference Papers

Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506

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    An Ergonomics Assessment in India Post Manual Sorting Centre Using EDAS – A MCDM Approach
    (Springer Science and Business Media Deutschland GmbH, 2023) Vadivel, S.M.; Sequeira, A.H.; Umoh, U.; Chandana, V.
    Since workplace circumstances have improved over the past ten years, machining workloads have dramatically decreased in several industries. Due to the operators’ increased responsibilities, there are also increased cognitive demands on the individual. Any diversion or loss of focus results in subpar work or harm to people. The goal of the current study is to evaluate ergonomic concerns regarding the physical and cognitive demands placed on postal employee operators, such as scanners, sorters, managers, and stampers, who operate in post office environments. The study uses a survey and a pain scale to evaluate physical discomfort on a subjective basis. Utilizing questionnaires, cognitive demand as reported by the employee was assessed in a multitasking work environment that calls on a variety of skills. In the body, the lower leg, shoulder, neck, and back were named as key areas that cause discomfort. More people become aware of the higher cognitive demand they are experiencing as a result of the different skill needs. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
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    Interval Type-2 Fuzzy-Support Vector Regression in Representation of Uncertainty in a Non-linear System
    (Springer Science and Business Media Deutschland GmbH, 2025) Umoh, U.; Eyoh, I.; Asuquo, D.; Vadivel, S.M.; Alimot, O.
    Machine learning algorithms such as Support Vector Machine (SVM and Support Vector Regression (SVR) are faced with challenges when confronted with imprecise and noisy data, which can lead to less meaningful outcomes. This paper introduces Interval Type-2 Fuzzy Support Vector Regression (IT2FSVR) as a solution to address uncertainty in non-linear systems. By combining Interval Type-2 Fuzzy Sets (IT2FS) and SVR, the proposed method enhances performance in systems with high levels of noise and non-linearity. The integration of IT2F membership in SVR directly tackles uncertainty in prediction problems, enabling adaptive learning to varying inputs and improving generalization performance. To demonstrate the effectiveness of this approach, the authors tested the performance of IT2F-SVR using a dataset of cardiovascular disease patients. Experimental results demonstrate that IT2F-SVR effectively eliminates uncertainty and significantly improves the learning process, outperforming individual approaches when applied to the same dataset and achieving faster execution times compared to some alternatives, albeit taking more time than SVR. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.