Identification of soil type in Pakistan using remote sensing and machine learning.

Soil study plays a significant role in the cultivation of crops. To increase the productivity of any crop, one must know the soil type and properties of that soil. The conventional soil type identification, grid sampling and hydrometer method require expert intervention, more time and extensive laboratory experimentation. Digital soil mapping, while applying remote sensing, offers soil type information and has rapidity, low cost, and spatial resolution advantages. This study proposes a model to identify the soil type using remote sensing data. Spectral data of the Upper Indus Plain of Pakistan Pothwar region and Doabs were acquired using fifteen Landsat eight images dated between June 2020 to August 2020. Bare soil images were obtained to identify the soil type classes Silt Loam, Loam, Sandy Loam, Silty Clay Loam and Clay Loam. Spectral data of band values, reflectance band values, corrective reflectance band values and vegetation indices are practiced studying the reflectance factor of soil type. Regarding multi-class classification, Random Forest and Support Vector Machine are two popular techniques used in the research community. In the present work, we used these two techniques aided with Logistic Model Tree with 10-fold cross-validation. The classification with the best performance is achieved using the spectral data, with an overall accuracy of 86.61% and 84.41% for the Random Forest and Logistic Model Tree classification, respectively. These results may be applied for crop cultivation in specific areas and assist decision-makers in better agricultural planning.

Impact of workflow interruptions on baseline activities of the doctors working in the emergency department.

BACKGROUND: Workflow interruptions are common in the emergency department (ED) of the hospitals for physicians, leading to an increased risk of errors. PURPOSE: This study aims to understand the baseline activities of the ED doctors and how these are affected by workflow interruptions. METHODS: The study was conducted in two phases to collect the doctor's perspective (through questionnaire survey) and observer's perspective (through workflow observation study) about ED doctors' baseline activities and workflow interruptions. Two different perspectives were obtained to make the insights clearer and more valuable. The point of view of the 223 doctors working in ED of the hospitals was recorded through a questionnaire survey. In the second phase, the observer's point of view (authors) was obtained through a workflow observation study, and 13 doctors were observed for 160 hours. RESULTS: Direct communication with patients (37.1%) and 'documentation and prescription' (22.7%) were found to be the most frequent activities. The most common interruptions were visual and auditory distractions, rumination (mind-wandering) and intrusion (by co-workers). Also, the time consumed on indirect patient care (6.6%) was higher than direct patient care (4. 2%). Interruptions increase the chances of errors by making it hard for a doctor to resume a primary task after facing interruptions. CONCLUSION: Interruptions increase the chances of errors and make it difficult for the doctors to resume primary tasks (after facing such incidents).

Assessing English language sentences readability using machine learning models.

Readability is an active field of research in the late nineteenth century and vigorously persuaded to date. The recent boom in data-driven machine learning has created a viable path forward for readability classification and ranking. The evaluation of text readability is a time-honoured issue with even more relevance in today's information-rich world. This paper addresses the task of readability assessment for the English language. Given the input sentences, the objective is to predict its level of readability, which corresponds to the level of literacy anticipated from the target readers. This readability aspect plays a crucial role in drafting and comprehending processes of English language learning. Selecting and presenting a suitable collection of sentences for English Language Learners may play a vital role in enhancing their learning curve. In this research, we have used 30,000 English sentences for experimentation. Additionally, they have been annotated into seven different readability levels using Flesch Kincaid. Later, various experiments were conducted using five Machine Learning algorithms, i.e., KNN, SVM, LR, NB, and ANN. The classification models render excellent and stable results. The ANN model obtained an F-score of 0.95% on the test set. The developed model may be used in education setup for tasks such as language learning, assessing the reading and writing abilities of a learner.