Ayush Verma

In today's modern world, traditional systems frequently find it difficult to keep up with the need for early disease identification. To effectively address these issues, early diagnoses of diseases become imperative. This work aims to develop a comprehensive platform utilizing machine learning (ML) for predictive analysis and early diagnoses of diseases. By allowing early disease identification and encouraging a holistic approach to well-being, this study aims to maximize the potential of technology to improve public health outcomes. The primary focus is on predicting heart disease, diabetes, and breast cancer using machine learning algorithms. Specifically, the Naïve Bayes (NB) classifier achieves an 85% accuracy in predicting heart disease. Support vector machine (SVM) attains 75% accuracy for diabetes prediction. The logistic regression (LR) algorithm stands out by achieving an exceptional accuracy of 98% in predicting cancer. The results highlight a high level of reliability and platform's potential for eastly diagnosis of disease.

Crop prediction and fertilizer recommendation are essential for optimizing agricultural practices, a crucial concern for the agricultural sector. However, accurate crop and fertilizer prediction has long been challenging, requiring innovative solutions rooted in the vast pool of available data. This work presents a comprehensive system for predicting crops and fertilizers based on historical data, leveraging machine learning (ML) algorithms. This work involved research analysis of various ML algorithms to predict crops and recommend suitable fertilizers. It was observed that Naive Bayes and random forest models achieved an excellent accuracy of 99.54% and 99.31%, respectively, for soil classification, indicating their proficiency in distinguishing different soil types. The proposed system also suggests fertilizer recommendations tailored to each crop based on user-provided input and a comparative evaluation of the algorithms. These results highlight the potential of ML techniques in aiding farmers to make informed decisions about soil management and fertilizer selection. Request access from your librarian to read this chapter's full text.

A person's attendance is crucial in determining their presence and attentiveness. An organisation, college, or a school daily requires attendance to monitor its employees and students. When done the old-fashioned way, managing attendance can take much time. This work focuses on a user-friendly and reliable method of keeping track of attendance considering the times. This chapter proposes an Android-based online attendance application, FaceTrace, that can be used to record attendance. It facilitates paperless attendance. It is an automated and user-friendly facial recognition attendance system designed to automate attendance tracking using facial recognition. With this application, the students can mark their attendance by scanning their faces into the camera, while teachers can take manual attendance whenever necessary. The application generates detailed attendance reports in PDF format. It eliminates the need for manual attendance taking regular paper attendance saving valuable. It is accessible from any device, making it an ideal solution for educational institutions of all sizes.