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A review on classification of imbalanced data for wireless sensor networks
H Patel, D Singh Rajput… - International …, 2020 - journals.sagepub.com
Classification of imbalanced data is a vastly explored issue of the last and present decade
and still keeps the same importance because data are an essential term today and it …
and still keeps the same importance because data are an essential term today and it …
An Approach Using Fuzzy Sets and Boosting Techniques to Predict Liver Disease.
P Kumar, RS Thakur - Computers, Materials & Continua, 2021 - search.ebscohost.com
The aim of this research is to develop a mechanism to help medical practitioners predict and
diagnose liver disease. Several systems have been proposed to help medical experts by …
diagnose liver disease. Several systems have been proposed to help medical experts by …
[PDF][PDF] A New Fuzzy Adaptive Algorithm to Classify Imbalanced Data.
Classification of imbalanced data is a well explored issue in the data mining and machine
learning community where one class representation is overwhelmed by other classes. The …
learning community where one class representation is overwhelmed by other classes. The …
Diagnosis of liver disorder using fuzzy adaptive and neighbor weighted k-nn method for lft imbalanced data
P Kumar, RS Thakur - … on Smart Structures and Systems (ICSSS …, 2019 - ieeexplore.ieee.org
In today's world, the liver disorder is a serious disease and the number of people with liver
disease continues to increase. The side effect of the disease is hard to identify by the …
disease continues to increase. The side effect of the disease is hard to identify by the …
[ספר][B] Applied Assistive Technologies and Informatics for Students with Disabilities
R Kaluri, M Mahmud, TR Gadekallu, DS Rajput… - 2024 - Springer
Classroom learning aims to strengthen previously learned concepts while simultaneously
teaching students new ones. In a classroom where most students are learning at a rapid …
teaching students new ones. In a classroom where most students are learning at a rapid …
Empowering Inclusive Education: Leveraging AI-ML and Innovative Tech Stacks to Support Students with Learning Disabilities in Higher Education
In this chapter, our primary focus centers on the practical applications of artificial intelligence
(AI) and machine learning (ML) technologies within the realm of higher education …
(AI) and machine learning (ML) technologies within the realm of higher education …
Stock market forecasting using ensemble learning and statistical indicators
With a volume of 2 billion+ trades per day and a market capitalization of 2.56 trillion USD the
national stock exchange (NSE), India is one of the largest stock exchanges in the world …
national stock exchange (NSE), India is one of the largest stock exchanges in the world …
Predicting Liver Disorders Using an Extreme Learning Machine
G Raja, K Reka, P Murugesan… - SN Computer …, 2024 - Springer
Liver diseases are caused by excessive alcohol intake or viral infection. The liver can fail or
develop cancer if it is not detected in its early stages. In the proposed approach, liver …
develop cancer if it is not detected in its early stages. In the proposed approach, liver …
[PDF][PDF] MiNB: Minority Sensitive Naïve Bayesian Algorithm for Multi-Class Classification of Unbalanced Data
The unbalanced nature of data makes it tough to achieve the desire performance goal for
classification algorithms. The sub-optimal prediction system isn't a viable solution due to the …
classification algorithms. The sub-optimal prediction system isn't a viable solution due to the …
FuzzyCSampling: A Hybrid fuzzy c-means clustering sampling strategy for imbalanced datasets
A MARAŞ, Ç EROL - Turkish Journal of Electrical Engineering …, 2023 - journals.tubitak.gov.tr
Classification model with imbalanced datasets is recently one of the most researched areas
in machine learning applications since they induce to the emergence of low-performing …
in machine learning applications since they induce to the emergence of low-performing …