Titlebook: Data Science and Security; Proceedings of IDSCS Samiksha Shukla,Hiroki Sayama,Durgesh Kumar Mishra Conference proceedings 2024 The Editor(s

Encapsulate

Brain Tumor Localization Using Deep Ensemble Classification and Fast Marching Segmentation,ng the tumor through segmentation. We use a combined model of MobileNet-V3 and EfficientNetV2 for image classification. To segment the tumor in the image, we use a fast marching method. The combined model‘s classification accuracy is 98%, and the segmentation accuracy is 99.6%.

glucagon

BI-RADS Score Prediction Using AI for Breast Cancer Screening,mammography pictures from 414 subjects. The radiologist can follow this model when analyzing mammogram images. The prediction model provides results with a precision of 0.75 for Bi-rad 0, 0.96 for Bi-rad 1, and 0.6 for Bi-rad 2.

condone

Conference proceedings 2024urrent research works in the areas of data science, data security, data analytics, artificial intelligence, machine learning, computer vision, algorithms design, computer networking, data mining, big data, text mining, knowledge representation, soft computing, and cloud computing.

Insatiable

A Multidistortion Database for Image Qualityose of Classification. Evaluation metrics such as Precision, Recall, and F1 Score have been used to measure the efficiency of Classification. Logistic Regression and Gradient Boosting Algorithms have outperformed other Multiclass Classification techniques.

发怨言

Analysis of Multinomial Classification for Legal Document Categorization,ose of Classification. Evaluation metrics such as Precision, Recall, and F1 Score have been used to measure the efficiency of Classification. Logistic Regression and Gradient Boosting Algorithms have outperformed other Multiclass Classification techniques.

MUTE

仇恨

虚情假意

OPINE

Lecture Notes in Computer Scienceggregated to simplify the weighted directed graph. Pagerank is used to determine the most influential and the most influenced node over the time period. Results were reasonably robust within each network, but they were heavily dependent on the choice of metric.

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An Early Lumpy Skin Disease Detection System Using Machine Learning,rediction. Here, a Random Forest-based machine learning model is used to detect lumpy skin disease. The data used for training and validation of lumpy skin disease available on Kaggle is used. The proposed model has shown an accuracy of 98%.