Bias Protected Attributes Data Balancing using Map Reduce

Pandya, Darshanaben Dipakkumar; Gupta, Namrata S.; Jadeja, Abhijeetsinh; Patel, Rasik D.; Degadwala, Sheshang; Vyas, Dhairya

doi:10.1109/iceca55336.2022.10009363

Cited by 13 publications

(3 citation statements)

References 11 publications

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“…Roy et al [45] design AIdriven tools for remote patient monitoring. Dey et al [46] leverage deep learning for enhancing virtual reality experiences. Mukherjee et al [47] focus on AI applications in autonomous drone navigation.…”

Section: Literature Studymentioning

confidence: 99%

A Review on Machine Learning and Deep Learning Methods on Medical Image Classification

Degadwala,

Degadwala

2024

Int. J. Sci. Res. Comput. Sci. Eng. Inf. Technol

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Medical image classification, a critical component in medical diagnostics, has significantly advanced through the integration of machine learning (ML) and deep learning (DL) techniques. This review comprehensively explores the evolution, methodologies, and applications of ML and DL in medical image classification. Traditional ML methods, including support vector machines and decision trees, have provided a foundation for early advancements by utilizing handcrafted features. However, the advent of DL, particularly convolutional neural networks (CNNs), has revolutionized the field by enabling automatic feature extraction and achieving superior performance. This review examines various DL architectures, such as ResNet, VGG, and Inception, highlighting their contributions to tasks like tumor detection, organ segmentation, and disease classification. Furthermore, it addresses challenges like data scarcity, interpretability, and computational demands, discussing potential solutions like data augmentation, transfer learning, and model optimization. The review also considers the ethical implications and the need for robust validation to ensure clinical applicability. Through a comparative analysis of existing studies, this review underscores the transformative impact of ML and DL on medical imaging, emphasizing the continuous need for innovation and interdisciplinary collaboration to enhance diagnostic accuracy and patient outcomes.

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Section: Literature Studymentioning

confidence: 99%

A Review on Machine Learning and Deep Learning Methods on Medical Image Classification

Degadwala,

Degadwala

2024

Int. J. Sci. Res. Comput. Sci. Eng. Inf. Technol

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“…Therefore, if the minority class does not have enough representative samples, there is no guarantee that the given data distribution reflects the (true) underlying data distribution (in other words it may not constitute a representative sample in the statistical sense). The newly generated data points would not be able to introduce much variance to the data, being only slightly different than the original points, which could potentially lead to a bias in the estimation [58]. Thus, for these cases, oversampling the whole data, without extra assumptions about the underlying distribution, is an unbiased approach in the statistical sense.…”

Section: Augmentation Techniquesmentioning

confidence: 99%

Leveraging augmentation techniques for tasks with unbalancedness within the financial domain: a two-level ensemble approach

Ranjbaran,

Reforgiato Recupero,

Lombardo

et al. 2023

EPJ Data Sci.

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Modern financial markets produce massive datasets that need to be analysed using new modelling techniques like those from (deep) Machine Learning and Artificial Intelligence. The common goal of these techniques is to forecast the behaviour of the market, which can be translated into various classification tasks, such as, for instance, predicting the likelihood of companies’ bankruptcy or in fraud detection systems. However, it is often the case that real-world financial data are unbalanced, meaning that the classes’ distribution is not equally represented in such datasets. This gives the main issue since any Machine Learning model is trained according to the majority class mainly, leading to inaccurate predictions. In this paper, we explore different data augmentation techniques to deal with very unbalanced financial data. We consider a number of publicly available datasets, then apply state-of-the-art augmentation strategies to them, and finally evaluate the results for several Machine Learning models trained on the sampled data. The performance of the various approaches is evaluated according to their accuracy, micro, and macro F1 score, and finally by analyzing the precision and recall over the minority class. We show that a consistent and accurate improvement is achieved when data augmentation is employed. The obtained classification results look promising and indicate the efficiency of augmentation strategies on financial tasks. On the basis of these results, we present an approach focused on classification tasks within the financial domain that takes a dataset as input, identifies what kind of augmentation technique to use, and then applies an ensemble of all the augmentation techniques of the identified type to the input dataset along with an ensemble of different methods to tackle the underlying classification.

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“…Pandya et al [38] balanced data using Map Reduce for bias-protected attributes, contributing to fairer machine learning models.…”

Section: Introductionmentioning

confidence: 99%

A Review on Rainfall Nowcasting Methodology using different ML and DL Models

Amit Solanki,

Dr. Sheshang Degadwala

2024

Int. J. Sci. Res. Comput. Sci. Eng. Inf. Technol

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Weather is influenced by several factors such as temperature, pressure, air movement, moisture/water vapor, and the Earth's rotation. Forecasting weather with high geographic precision is both challenging and computationally demanding. This study explores a nowcasting method for meteorological radar images. Building on the concept of an unsupervised representation problem in deep learning, next-frame prediction is an emerging area in computer vision where future images are predicted based on previous ones. This has numerous applications, including in robotics and autonomous driving. The paper reviews the latest next-frame prediction networks, categorizing them into two main approaches: machine learning and deep learning. It compares these strategies by discussing their respective advantages and disadvantages. Lastly, the paper outlines the most promising directions for future research.

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Bias Protected Attributes Data Balancing using Map Reduce

Cited by 13 publications

References 11 publications

A Review on Machine Learning and Deep Learning Methods on Medical Image Classification

A Review on Machine Learning and Deep Learning Methods on Medical Image Classification

Leveraging augmentation techniques for tasks with unbalancedness within the financial domain: a two-level ensemble approach

A Review on Rainfall Nowcasting Methodology using different ML and DL Models

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