Internet-of-Things (IoT) has reshaped the smart agriculture. It has not only given a boost to the productivity and optimized the resources, but has also increased the efficiency and has minimized the cost of production. This article highlights the potential of sensors and IoT in the field of greenhouse farming and presents the future of automation. The different parameters such as humidity, water nutrients solution level, pH and EC value, temperature, UV light intensity, $CO_2$ level, mist, and amount of insecticides or pesticides are monitored through various sensors so that significant knowledge can be captured and early fault detection and diagnosis can be done. A Decision Support System (DSS) presented in this article acts as the central operating system which governs and coordinates all the activities. Furthermore, the article also accounts for the different challenges of greenhouse Rose farming and highlights a new IoT based solution which is smart and sustainable. The model presented in this article is well adapted to the changing environment, thereby redefining the terms of sustainability.

Relapse and metastasis occur in 30–40% of breast cancer patients, even after targeted treatments like trastuzumab for HER2-positive breast cancer. Accurate individual prognosis is essential for determining appropriate adjuvant treatment and early intervention. This study aims to enhance relapse and metastasis prediction using an innovative framework with machine learning (ML) and ensemble learning (EL) techniques. The developed framework is analyzed using The Cancer Genome Atlas (TCGA) data, which has 123 HER2-positive breast cancer patients. Our two-stage experimental approach first applied six basic ML models (support vector machine, logistic regression, decision tree, random forest, adaptive boosting, and extreme gradient boosting) and then ensembled these models using weighted averaging, soft voting, and hard voting techniques. The weighted averaging ensemble approach achieved enhanced performances of 88.46% accuracy, 89.74% precision, 94.59% sensitivity, 73.33% specificity, 92.11% F-Value, 71.07% Mathew’s correlation coefficient, and an AUC of 0.903. This framework enables the accurate prediction of relapse and metastasis in HER2-positive breast cancer patients using H&E images and clinical data, thereby assisting in better treatment decision-making.