A new model based on the extended COPRAS method for improving performance during the accreditation process of Indian Higher Educational Institutions

Agarwal, Nidhi; Tayal, Devendra K.

doi:10.1002/cae.22602

Cited by 11 publications

(5 citation statements)

References 37 publications

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“…•Unveiling Voice-Based Biomarkers: The first objective is to delve into the rich dataset of voice recordings, meticulously examining attributes such as fundamental frequency variations, amplitude nuances, and nonlinear complexity measures. By dissecting these attributes, the aim is to uncover subtle yet distinctive vocal markers that could serve as diagnostic indicators of PD [25][26][27]. This involves understanding how the voice transforms as PD progresses, potentially offering early signs of the condition before overt motor symptoms manifest.…”

Section: Objectivesmentioning

confidence: 99%

Predictive Modelling for Parkinson's Disease Diagnosis using Biomedical Voice Measurements

Dahiya,

Deepakshi

et al. 2024

EAI Endorsed Trans Perv Health Tech

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INTRODUCTION: Parkinson's Disease (PD), a progressively debilitating neurological disorder impacting a substantial global population, stands as a significant challenge in modern healthcare. The gradual onset of motor and non-motor symptoms underscores the criticality of early detection for optimal treatment outcomes. In response to this urgency, novel avenues for early diagnosis are being explored, where the amalgamation of biomedical voice analysis and advanced machine learning techniques holds immense promise. Individuals afflicted by PD experience a nuanced deterioration of bodily functions, necessitating interventions that are most effective when initiated at an early stage. The potential of biomedical voice measurements to encode subtle health indicators presents an enticing opportunity. The human voice, an intricate interplay of frequencies and patterns, might offer insights into the underlying health condition. OBJECTIVES: This research embarks on a comprehensive journey to delve into the intricate connections between voice attributes and the presence of PD, with the aim of expediting its detection and treatment. METHODS: At the heart of this exploration is the Support Vector Machine (SVM) model, a versatile machine learning tool [1-2]. Functioning as a virtual detective, the SVM model learns from historical data to decipher the intricate patterns that differentiate healthy individuals from those with PD [3-4]. RESULTS: Through the power of pattern recognition, the SVM becomes a predictive instrument, a potential catalyst in unravelling the latent manifestations of PD using the unique patterns harbored within the human voice. Embedded within this research are the practical demonstrations showcased through code snippets [5-7]. By synergizing the intricate voice measurements with the SVM model, we envision the emergence of a diagnostic paradigm where early PD detection becomes both accessible and efficient. This study not only epitomizes the synergy of voice and machine interactions but also attests to the transformative potential of technology within the domain of healthcare. . CONCLUSION: Ultimately, this research strives to harness the intricate layers of voice data, as exemplified through the provided model code [8-11], to contribute to the evolution of an advanced tool for PD prediction. By amalgamating the principles of machine learning and biomedical analysis, we aspire to expedite early PD diagnosis, thereby catalyzing more efficacious treatment strategies. In traversing this multidimensional exploration, we aspire to pave the path toward a future where technology plays an instrumental role in enhancing healthcare outcomes for individuals navigating the challenges of PD, ultimately advancing the pursuit of early diagnosis and intervention.

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Section: Objectivesmentioning

confidence: 99%

Predictive Modelling for Parkinson's Disease Diagnosis using Biomedical Voice Measurements

Dahiya,

Deepakshi

et al. 2024

EAI Endorsed Trans Perv Health Tech

Self Cite

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“…By doing so, we aim to identify the most effective approach for heart disease prediction, ultimately enhancing the accuracy of cardiovascular diagnostics. Moreover, beyond heart disease prediction, the diverse research contributions of Nidhi Agarwal and her collaborators in various fields of machine learning and data analysis showcase a wealth of expertise in the application of advanced techniques, which informs the design and methodology of our study [8,13,14,16].…”

Section: Literature Reviewmentioning

confidence: 99%

Predictive Modelling for Heart Disease Diagnosis: A Comparative Study of Classifiers

Agarwal,

Deepakshi,

Harikiran

et al. 2024

EAI Endorsed Trans Perv Health Tech

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INTRODUCTION: Cardiovascular diseases, including heart disease, remain a significant cause of morbidity and mortality worldwide. Timely and accurate diagnosis of heart disease is crucial for effective intervention and patient care. With the emergence of machine learning techniques, there is a growing interest in leveraging these methods to enhance diagnostic accuracy and predict disease outcomes. OBJECTIVES: This study evaluates the performance of three machine learning classifiers—Naive Bayes, Logistic Regression, and k-Nearest Neighbors in predicting heart disease based on patient attributes. METHODS: In this study, we explore the application of three prominent machine learning classifiers—Naive Bayes, Logistic Regression, and k-Nearest Neighbors (kNN)—to predict the presence of heart disease based on a set of patient attributes. RESULTS: Using a dataset of 303 patient records with 14 attributes, including age, sex, and cholesterol levels, the data is pre-processed, scaled, and split into training and test sets. Each classifier is trained on the training set and evaluated on the test set. Results reveal that Naive Bayes and k-Nearest Neighbors classifiers outperform Logistic Regression in terms of accuracy, precision, recall, and area under the ROC curve (AUC). CONCLUSION: This study underscores the promising role of machine learning in medical diagnosis, showcasing the potential of Naive Bayes and k-Nearest Neighbors classifiers in improving heart disease prediction accuracy. Future work could explore advanced classifiers and feature selection techniques to enhance predictive accuracy and generalize findings to larger datasets.

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“…In recent years, COPRAS has found effective applications in various domains, such as selecting Low-E windows for enhancing public buildings [11], supervisor selection [12] and material selection of sustainable composites [13]. Agarwal and Tayal [14] proposed an enhanced model of the fuzzy COPRAS technique, which serves as an improved multicriteria decision-making method for identifying and prioritizing relevant indicators, thereby aiding institutions in enhancing accreditation data. Le et al [15] developed a MCDM method utilizing Interval-Valued Fuzzy Numbers (IVFNs), extended DEMATEL and entropy methods with IVFNs for uncertain indicator weights, and employed the extended COPRAS approach with IVFNs to assess the rock burst hazard level in mining engineering.…”

Section: A Literature Reviewmentioning

confidence: 99%

Improved COPRAS Method With Unknown Weights Under p, q-Quasirung Orthopair Fuzzy Environment: Application to Green Supplier Selection

Rahim,

Akhtar,

Yang

et al. 2024

IEEE Access

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In the last few decades, there has been a significant increase in the importance of assessing social and ecological implications within industrial product supply chains. This tendency has given rise to the notion of supplier sustainability, which entails meeting the economic, environmental, and social demands of all suppliers. The supplier selections are typically based on experts' opinion, which are then reflected in supplier ratings. When examining sustainability indicators, experts may not be fully aware of all aspects of suppliers' economic, social, and environmental qualities. In the Decision Making (DM) approaches such as Pythagorean Fuzzy (PF) and q-Rung Orthopair Fuzzy (q-ROF) sets, experts are unable to use different powers for membership and non-membership grades simultaneously. In real-life DM problems, experts may require different power levels for membership and non-membership grades. In this paper, we propose a new extension of the COPRAS method under p, q-Quasirung orthopair fuzzy (𝑝, 𝑞 −QOF) sets, which allow decision-makers to use different power levels for membership and non-membership grades by incorporating parameters p and q. In the proposed approach, it is assumed that in addition to compiling the expert scores for suppliers, the issue analyst evaluates each expert's degree of expertise for each criterion. The best choice is then determined by combining the data using the COPRAS method. The Inter-Criteria Correlation (CRITIC) method is employed to determine the unknown criteria weights. To demonstrate the effectiveness of our proposed approach, we apply it to a Multi-Criteria Group Decision-Making (MCGDM) problem that is focused on green supplier selection. Finally, we conduct a comparative study with existing approaches to demonstrate the practicality and applicability of the proposed DM method.INDEX TERMS 𝑝, 𝑞 −quasirung orthopair fuzzy sets, COPRAS method, MCGDM, green supplier selection.

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A new model based on the extended COPRAS method for improving performance during the accreditation process of Indian Higher Educational Institutions

Cited by 11 publications

References 37 publications

Predictive Modelling for Parkinson's Disease Diagnosis using Biomedical Voice Measurements

Predictive Modelling for Parkinson's Disease Diagnosis using Biomedical Voice Measurements

Predictive Modelling for Heart Disease Diagnosis: A Comparative Study of Classifiers

Improved COPRAS Method With Unknown Weights Under p, q-Quasirung Orthopair Fuzzy Environment: Application to Green Supplier Selection

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