Early prediction of grape disease attack using a hybrid classifier in association with IoT sensors.

Machine learning with IoT practices in the agriculture sector has the potential to address numerous challenges encountered by farmers, including disease prediction and estimation of soil profile. This paper extensively explores the classification of diseases in grape plants and provides detailed information about the conducted experiments. It is important to keep track of each crop's current environmental conditions because different environmental conditions, such as humidity, temperature, moisture, leaf wetness, light intensity, wind speed, and wind direction, can affect or sustain the quality of a crop.

Effect of size variation of fibre-shaped waste tyre rubber as fine aggregate on the ductility of self-compacting concrete.

Abandoning shredded waste tyre rubber (WTR) in cement-based mixes facilitates safe waste tyre disposal and conserves the natural resources used in construction materials. The engineering properties of such environment-friendly materials needed to be evaluated for field applications. This study examined integrating WTR fibre on microstructural, static load, and ductility properties of self-compacting concrete (SCC).

Optimization assisted framework for thyroid detection and classification: A new ensemble technique.

Ultrasound (US) is an inexpensive and non-invasive technique for capturing the image of the thyroid gland and nearby tissue. The classification and detection of thyroid disorders is still in its infant stage. This study aims to present a new thyroid diagnosis approach, which consists of three phases like "(i) feature extraction, (ii) feature dimensionality reduction, and (iii) classification".