Titlebook: Smart Systems: Innovations in Computing; Proceedings of SSIC Arun K. Somani,Ankit Mundra,Arka Prokash Mazumdar Conference proceedings 2024

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Customer Segmentation Using K-means Clustering,ience better which in return helps it produce better marketing schemes targeting certain zones which may assist in enhancing its product growth. Here we have developed customer segmentation bearing in mind the above-mentioned trinketed attempt to turn a system that can help predict the items that th

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Web Miner: Automated Web Crawling and Database System with Puppeteer and Node.js,g pages into a database. The search engine locates the specified keyword and retrieves the web pages relevant to the user‘s query. Our implementation utilizes Puppeteer, a Node.js library, as well as MySQL and JavaScript.

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Machine Learning-Based Cardiovascular Disease Prediction,robability of a cardiovascular disease in humans and classifies patient risk levels using various data mining techniques. This article brings out the analysis of various machine learning-based algorithms that are implemented to predict cardiovascular diseases.

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Efficient Identification of Waste in Water Bodies with YOLO V7-Object Detection,YOLOv7 performs faster and more accurately than any other object detectors between 5 and 160 frames per second. Large datasets of images of marine trash are used to train this model and is able to accurately identify a wide variety of objects, including plastic bags, bottles, straws, and other debri

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Improved Gastric Cancer Diagnosis with Machine Learning Technique: Addressing Imbalanced Data Distrto enhance the accuracy of stomach cancer diagnosis. The dataset that was not balanced had a higher sensitivity for Class 0, indicating a lower number of missing negative cases. Conversely, the balanced dataset demonstrated better sensitivity and positive predictive value (PPV) values for Class 1, s

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EfficientViT: An Efficient Vision Transformer for Fire and Smoke Image Classification, main goal. We show that EfficientViT beats the other developed models, obtaining an amazing accuracy of 0.92, through a thorough experimentation and evaluation. This demonstrates how well our suggested model works at correctly differentiating between incidents of fire and smoke.

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