Crop Yield Predication using Random Forest Regression Algorithm

This research presents an automated system for predicting crop yield using the Random Forest Regression algorithm. The model leverages agricultural parameters such as soil composition, rainfall, temperature, and fertilizer usage to provide real-time and accurate yield predictions. A user-friendly web application developed with Python Flask allows for easy interaction, enabling farmers and agricultural professionals to input data and receive yield estimates. The results demonstrate the model’s reliability, achieving over 99% accuracy on test data. The system's modularity supports future expansions, including real-time data integration and mobile deployment for widespread agricultural use. This project presents an AI-driven crop yield prediction system utilizing the Random Forest Regression algorithm. The system is designed to forecast agricultural yield based on various factors such as soil quality, rainfall, temperature, and historical crop data. Implemented using Python and deployed via a user-friendly Flask web interface, the application enables real-time yield prediction, empowering farmers and policymakers to make informed decisions. The model, trained on a diverse dataset, demonstrates high accuracy and robustness, handling non-linear data patterns effectively. By combining machine learning and intuitive design, this solution enhances agricultural planning, resource management, and overall productivit

Pyridinic N anchored Ag and Au hybrids for detoxification of organic pollutants

AbstractThe development of a highly reusable, non-toxic catalyst for the effective removal of 4-nitrophenol (4-NP) from marine water is a key challenge due to its diverse effects on living organisms. Herein, in situ green fabricated pyridinic N anchored Ag2O/Au decorated porous silica (CG-Ag2O/Au-SiO2) using spent coffee grounds (CG) powder is introduced as a reusable catalyst to mitigate 4-NP, Methylene blue (MB), Rhodamine 6G (R6G) and their mixture in the spiked deionized, river and marine water samples. Exceptionally high structural activity and excellent reusability of catalysts up to 15 cycles have been demonstrated due to the accessibility of a large number of active sites from pyridinic N-anchored Ag2O and Au particle surfaces. CG-Ag2O/Au-SiO2 with the lower Ag and Au contents of 0.59 and 0.11 wt% (ICP-MS) of active catalytic sites exhibit a superior activity parameter of 6000 s−1 g−1 (4-NP), 6357 s−1 g−1 (MB), and 2892 s−1 g−1 (R6G) than chemically synthesized and reported, bare and hybrid structures. Furthermore, the CG-Ag2O/Au-SiO2 shows excellent stability in marine water with promising reusability performance (≈93% after 15 successive cycles). Density functional theory (DFT) studies reveal that the observed high catalytic efficiency originates from the pyridinic N presence on the Ag2O/Au structures.</jats:p

Machine learning based car data prediction for network simulation using PETSC library

A project presented to the Department of Electrical and Computer Engineering and the faculty of the Graduate School of of Wichita State University in partial fulfillment of the requirements for the degree of Master of Science.In city areas, the profit of a business entity (i.e., shop) depends on the vehicular traffic at the nearest intersections. Argonne National Laboratory (ANL) and UChicago Argonne have developed Portable Extensible Toolkit for Scientific Computation (PETSc) and Data Management Network (DMNetwork) libraries to conduct network simulation. However, PETSc and DMNetwork do not provide a way to obtain and/or predict the number of vehicles to a network node. In this work, we develop a methodology to obtain real-time traffic data and predict future car data of the network nodes for profit analysis through network simulation. Open-Source Computer Vision Library (OpenCV) with the Background Subtractor algorithm and OpenCV with pre-trained You Only Look Once (YOLO) Version 3 datasets for vehicle detection are used for collecting car data using live video streaming file. The experimental results indicate that both methods yielded highly accurate traffic data, achieving a 90% accuracy rate, as cross-verified through manual counting for validation. We also integrate machine learning models, namely, Deep Neural Network (DNN) and Recurrent Neural Network (RNN), to forecast future car data for effective decision making. The DNN and RNN models are evaluated using three diverse datasets: Dataset-1 (represents a typical day car traffic), Dataset-2 (represents a morning high car traffic, and Dataset-3 (represents an evening high car traffic). Simulation results show that for both DNN and RNN models, Dataset-1 outperforms Dataset-2 and Dataset-3, achieving an R-squared value of 0.8 (indicating a high accuracy). Future work includes developing parallel tools for real-time car data retrieval, parallelizing file reading into PETSc DMNetwork, and incorporating additional features such as holidays, festivals, weekdays/weekends, special occasions, and weather forecasts for more robust future predictions

<i>Persea americana</i> seed extract mediated gold nanoparticles for mercury(<scp>ii</scp>)/iron(<scp>iii</scp>) sensing, 4-nitrophenol reduction, and organic dye degradation

Gold nanoparticles for highly sensitive detection of Hg(ii)/Fe(iii) and catalytic efficiency towards the reduction/degradation of 4-nitrophenol and organic dyes.</p

Reduction of 4-nitrophenol using green-fabricated metal nanoparticles

Noble metal (silver (Ag), gold (Au), platinum (Pt), and palladium (Pd)) nanoparticles have gained increasing attention due to their importance in several research fields such as environmental and medical research.</jats:p