Purpose: The workflow in an office plays a significant role for an effective and efficient work output. It is vital to prepare a strategy for streamlining the work process by eliminating errors and improving workflow for increased efficiency and work output in an architectural practice. To increase operational efficiency, a firm must first understand how to streamline their workflow that add value to their daily tasks and practice time and communication management. The primary purpose of this research is to prepare a strategy for streamlining the workflow within the office and with the consultants for efficient, effective and mistake free work output. Methodology: The research is approached by identifying the current trends of workflow management prevalent in architectural offices; identifying the issues and associated problems and finally developing an organized and streamlined workflow strategy. The research is structured initially with review of existing knowledge on the stakeholder and communication management, study of seven quality tools and their application in the given situation, followed by in-depth semi- structured expert interviews with practicing architects, and survey with a target population of practicing architects. Main Findings: From the analysis, it is evident that the primary issue lies because of communication gap with consultants and subcontractors and lack of coordination within the office, followed by lack of work experience; lack of visibility and inadequate number of projects and Hiring of Team members. The research concludes with a list of recommendations that can be beneficial for effective and efficient workflow management for a startup architectural firm to reduce conflicts at the site and increase work output.

A crucial component of agriculture is soil. There are several varieties of dirt. Different properties may be found in each kind of soil, and various crops can be grown on various types of soils. To understand which crops do better in different soil types, we need to be aware of their features and traits. In this situation, machine learning approaches may be useful. It has made significant development in recent years. In the realm of agricultural data analysis, machine learning is still a young and difficult study area. In this study, we provide a model that predicts soil series with regard to land type and, in accordance with prediction, suggests appropriate crops. For soil classification, a number of machine learning techniques are utilised, including Decision Tree (CART), Multilayer Perceptron (MLP) and support vector machines (SVM) using a gaussian kernel. The suggested SVM-based technique outperforms several current methods, according to experimental data. The support vector machine technique is used to examine and compare the classification outcomes of various training sample numbers. The purpose of this work is to evaluate the viability of land cover classification using small samples using SVM and, in accordance with the machine learning algorithm, to investigate novel techniques for quick, non-destructive, and precise land cover classification.