Modelling of Monthly Rainfall Patterns in the North-West India Using SVM

Polisetty, Kalpana; Paidipati, Kiran Kumar; Bodapati, Jyostna Devi

doi:10.18280/isi.240405

Cited by 4 publications

(2 citation statements)

References 18 publications

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“…To handle such a situation, climate risk information must be provided locally at the village level, where farming is the people's primary occupation, especially in India. Even some significant events get spoiled due to such unexpected weather where the vast crowd is present and such weather can create lots of difficulties to handle the situations and even may lead to a significant loss in terms of money and humanity so, automatic rainfall prediction is the most important factor in many areas especially in Agriculture [2].…”

Section: Introductionmentioning

confidence: 99%

Transfer Learning Approach - An Efficient Method to Predict Rainfall Based on Ground-Based Cloud Images

Ambildhuke¹,

Banik²

2021

ISI

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Clouds play a vital role in climate prediction. Rainfall prediction also majorly depends on the status and types of clouds present in the sky. Therefore, cloud identification is the most exciting and vital topic in meteorology and attracts most researchers from other areas. This paper presents the transfer learning technique to predict the Rainfall based on ground-based Cloud images responsible for rains. It will predict the estimated Rainfall by identifying the type of cloud by taking cloud images as input. The cloud images in the dataset are divided into three categories(classes) labeled as no-rain to very low-rain, low to medium-rain, and medium to high Rain based on the associated Precipitation responsible for the appropriate Rainfall. This model will be most helpful to the farmers to manage their Irrigation by knowing the status of Rainfall before every irrigation cycle or can also be helpful to take decisions on the outdoor events by taking prior knowledge of Rain. The model is trained on three classes to predict the Rainfall and firstly experimented with CNN. To improve the performance, the experiment is carried out with some best-pretrained models VGG16, Inception-V3, and XCeption using transfer learning and, the results are compared to the regular CNN model. The transfer learning technique is outperformed to get good accuracy as the dataset is too small and presented the best possible results of the model. Google colab with GPU setting makes the task fast and efficient to get the appropriate results in time, and performance achieved by transfer learning is excellent and can fulfill real-time requirements.

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Section: Introductionmentioning

confidence: 99%

Transfer Learning Approach - An Efficient Method to Predict Rainfall Based on Ground-Based Cloud Images

Ambildhuke¹,

Banik²

2021

ISI

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“…The features extracted from VGG16 are used to learn a model that automates the process of tumor Screening. We used different conventional and simple models [13,14] like logistic regression, K-Nearest neighbor classifier (KNN), Perceptron learning, Multi-Layer feed forward neural network (MLFFNN) and Support Vector Machine (SVM) [15] for comparative studies. We observe that SVM gives better accuracy among the other models.…”

Section: Introductionmentioning

confidence: 99%

Brain Tumor Detection Using Deep Features in the Latent Space

Bodapati¹,

Vijay²,

Naralasetti³

2020

ISI

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Tumor grown in the human brains is one of the significant reasons that lead to loss of lives globally. Tumor is malignant collection of cells that grow in the human body. If these tumors grow in the brain, then they are called as brain tumors. Every year large number of human lives are lost due to this disease. Early detection of the disease might save the lives but requires experienced clinicians and diagnostic procedure that requires time and is very expensive. Therefore, there is a requirement for a robust system that automates the process of tumor identification. The idea behind this paper is to diagnose brain tumors by identifying the affected regions from the brain MRI images using machine learning approaches. In the proposed approach, prominent features of the tumor images are collected by passing them through a pre-trained Convolutional Network, VGG16. We observe that SVM gives better accuracy than other models. Though we achieve 84% accuracy, we feel the performance is not satisfactory. To make the model more robust, we obtain the most discriminant features, by applying Linear Discriminant Analysis (LDA) on the features obtained from VGG16. We use different conventional models like logistic regression, K-Nearest neighbor classifier (KNN), Perceptron learning, Multi Layered Perceptron (MLP) and Support Vector Machine (SVM) for the comparison study of the tumor image classification task. The proposed model leads to an accuracy of 100% as deep features extract important characteristics of the data and further LDA projects the data onto the most discriminant directions.

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