This document describes a proposed IoT-based artificial neural network system to analyze soil and environmental elements. Multiple IoT nodes would be deployed to collect data on soil composition, which would be sent to a server for processing and storage in the cloud. The system aims to use the collected data and AI algorithms to predict crop yields and categorize soils for different uses like farming, construction, and industry. It provides background on current soil testing methods and issues with analysis. The proposed system architecture and requirements are then outlined, with the goal of centralizing land information and usability analysis through cloud storage.
TERN Ecosystem Surveillance Plots South Australian Murray Darling Basin NRM R...TERN Australia
A summary of TERN ecosystem observing plots in the South Australian Murray Darling Basin NRM Region. The report also contains a list of the data and soil and plant samples openly available via TERN.
Remediation of heavy metals lead, cobalt and copper from industrail wastewate...EditorIJAERD
To fulfil human beings requirement number of industries increases day by day which play important role in
development of country but also causes environment pollution. Effluent of many industries contain heavy metals and
other contaminants. Industrial effluent usually used for agriculture purposes without treatment. Plants take these heavy
metals from industrial water and accumulate it in roots and Arial parts which become the part of animal and human
body through food chain causes various diseases. In this research work plants were grown using wastewater of industrial
effluents. Three sample of wastewater were made of various concentration level of lead, copper and cobalt. Typha
latifoliate was grown in controlled environment. Three sample of wastewater were used. Soil used in pots was of known
concentration of heavy metals. Using x-ray fluorescence spectrometry was used to find concentration of contaminants in
soil before and after plantation and atomic absorption spectrometry was used to find concentration of heavy metals in
industrial effluent. Wastewater of various concentration level was obtained by adding domestic water having no heavy
metals. Extraction percentage performed by plants in various lawn was found by analysis of soil before and after the
maturity of plants. soil which was irrigated by fully contaminated wastewater was remediated by 10%. Soil in which
plants were irrigated by diluted wastewater was remediated by 15 and 21% respectively for copper and cobalt. Plants
matured in seventy-five days in winter season.
Brief informations on technologies available for high throughput field based phenomics for plant breeding experiments. The instrumentations and technologies presented here are based on the year 2015. Phenomics is expanding area of plant science as more technogies and latest instruments were introduced to the scientific community
TERN Ecosystem Surveillance Plots South Australian Murray Darling Basin NRM R...TERN Australia
A summary of TERN ecosystem observing plots in the South Australian Murray Darling Basin NRM Region. The report also contains a list of the data and soil and plant samples openly available via TERN.
Remediation of heavy metals lead, cobalt and copper from industrail wastewate...EditorIJAERD
To fulfil human beings requirement number of industries increases day by day which play important role in
development of country but also causes environment pollution. Effluent of many industries contain heavy metals and
other contaminants. Industrial effluent usually used for agriculture purposes without treatment. Plants take these heavy
metals from industrial water and accumulate it in roots and Arial parts which become the part of animal and human
body through food chain causes various diseases. In this research work plants were grown using wastewater of industrial
effluents. Three sample of wastewater were made of various concentration level of lead, copper and cobalt. Typha
latifoliate was grown in controlled environment. Three sample of wastewater were used. Soil used in pots was of known
concentration of heavy metals. Using x-ray fluorescence spectrometry was used to find concentration of contaminants in
soil before and after plantation and atomic absorption spectrometry was used to find concentration of heavy metals in
industrial effluent. Wastewater of various concentration level was obtained by adding domestic water having no heavy
metals. Extraction percentage performed by plants in various lawn was found by analysis of soil before and after the
maturity of plants. soil which was irrigated by fully contaminated wastewater was remediated by 10%. Soil in which
plants were irrigated by diluted wastewater was remediated by 15 and 21% respectively for copper and cobalt. Plants
matured in seventy-five days in winter season.
Brief informations on technologies available for high throughput field based phenomics for plant breeding experiments. The instrumentations and technologies presented here are based on the year 2015. Phenomics is expanding area of plant science as more technogies and latest instruments were introduced to the scientific community
Implemented various classification models using R language to identify which one performs best for prediction of soil fertility and which properties are important in defining the fertility of soil.
Predictive fertilization models for potato crops using machine learning techn...IJECEIAES
Given the influence of several factors, including weather, soils, land management, genotypes, and the severity of pests and diseases, prescribing adequate nutrient levels is difficult. A potato’s performance can be predicted using machine learning techniques in cases when there is enough data. This study aimed to develop a highly precise model for determining the optimal levels of nitrogen, phosphorus, and potassium required to achieve both high-quality and high-yield potato crops, taking into account the impact of various environmental factors such as weather, soil type, and land management practices. We used 900 field experiments from Kaggle as part of a data set. We developed, evaluated, and compared prediction models of k-nearest neighbor (KNN), linear support vector machine (SVM), naive Bayes (NB) classifier, decision tree (DT) regressor, random forest (RF) regressor, and eXtreme gradient boosting (XGBoost). We used measures such as mean average error (MAE), mean squared error (MSE), R-Squared (RS), and R 2 Root mean squared error (RMSE) to describe the model’s mistakes and prediction capacity. It turned out that the XGBoost model has the greatest R 2 , MSE and MAE values. Overall, the XGBoost model outperforms the other machine learning models. In the end, we suggested a hardware implementation to help farmers in the field.
Implemented various classification models using R language to identify which one performs best for prediction of soil fertility and which properties are important in defining the fertility of soil.
Predictive fertilization models for potato crops using machine learning techn...IJECEIAES
Given the influence of several factors, including weather, soils, land management, genotypes, and the severity of pests and diseases, prescribing adequate nutrient levels is difficult. A potato’s performance can be predicted using machine learning techniques in cases when there is enough data. This study aimed to develop a highly precise model for determining the optimal levels of nitrogen, phosphorus, and potassium required to achieve both high-quality and high-yield potato crops, taking into account the impact of various environmental factors such as weather, soil type, and land management practices. We used 900 field experiments from Kaggle as part of a data set. We developed, evaluated, and compared prediction models of k-nearest neighbor (KNN), linear support vector machine (SVM), naive Bayes (NB) classifier, decision tree (DT) regressor, random forest (RF) regressor, and eXtreme gradient boosting (XGBoost). We used measures such as mean average error (MAE), mean squared error (MSE), R-Squared (RS), and R 2 Root mean squared error (RMSE) to describe the model’s mistakes and prediction capacity. It turned out that the XGBoost model has the greatest R 2 , MSE and MAE values. Overall, the XGBoost model outperforms the other machine learning models. In the end, we suggested a hardware implementation to help farmers in the field.
Final project report on grocery store management system..pdfKamal Acharya
In today’s fast-changing business environment, it’s extremely important to be able to respond to client needs in the most effective and timely manner. If your customers wish to see your business online and have instant access to your products or services.
Online Grocery Store is an e-commerce website, which retails various grocery products. This project allows viewing various products available enables registered users to purchase desired products instantly using Paytm, UPI payment processor (Instant Pay) and also can place order by using Cash on Delivery (Pay Later) option. This project provides an easy access to Administrators and Managers to view orders placed using Pay Later and Instant Pay options.
In order to develop an e-commerce website, a number of Technologies must be studied and understood. These include multi-tiered architecture, server and client-side scripting techniques, implementation technologies, programming language (such as PHP, HTML, CSS, JavaScript) and MySQL relational databases. This is a project with the objective to develop a basic website where a consumer is provided with a shopping cart website and also to know about the technologies used to develop such a website.
This document will discuss each of the underlying technologies to create and implement an e- commerce website.
Immunizing Image Classifiers Against Localized Adversary Attacksgerogepatton
This paper addresses the vulnerability of deep learning models, particularly convolutional neural networks
(CNN)s, to adversarial attacks and presents a proactive training technique designed to counter them. We
introduce a novel volumization algorithm, which transforms 2D images into 3D volumetric representations.
When combined with 3D convolution and deep curriculum learning optimization (CLO), itsignificantly improves
the immunity of models against localized universal attacks by up to 40%. We evaluate our proposed approach
using contemporary CNN architectures and the modified Canadian Institute for Advanced Research (CIFAR-10
and CIFAR-100) and ImageNet Large Scale Visual Recognition Challenge (ILSVRC12) datasets, showcasing
accuracy improvements over previous techniques. The results indicate that the combination of the volumetric
input and curriculum learning holds significant promise for mitigating adversarial attacks without necessitating
adversary training.
Cosmetic shop management system project report.pdfKamal Acharya
Buying new cosmetic products is difficult. It can even be scary for those who have sensitive skin and are prone to skin trouble. The information needed to alleviate this problem is on the back of each product, but it's thought to interpret those ingredient lists unless you have a background in chemistry.
Instead of buying and hoping for the best, we can use data science to help us predict which products may be good fits for us. It includes various function programs to do the above mentioned tasks.
Data file handling has been effectively used in the program.
The automated cosmetic shop management system should deal with the automation of general workflow and administration process of the shop. The main processes of the system focus on customer's request where the system is able to search the most appropriate products and deliver it to the customers. It should help the employees to quickly identify the list of cosmetic product that have reached the minimum quantity and also keep a track of expired date for each cosmetic product. It should help the employees to find the rack number in which the product is placed.It is also Faster and more efficient way.
Hybrid optimization of pumped hydro system and solar- Engr. Abdul-Azeez.pdffxintegritypublishin
Advancements in technology unveil a myriad of electrical and electronic breakthroughs geared towards efficiently harnessing limited resources to meet human energy demands. The optimization of hybrid solar PV panels and pumped hydro energy supply systems plays a pivotal role in utilizing natural resources effectively. This initiative not only benefits humanity but also fosters environmental sustainability. The study investigated the design optimization of these hybrid systems, focusing on understanding solar radiation patterns, identifying geographical influences on solar radiation, formulating a mathematical model for system optimization, and determining the optimal configuration of PV panels and pumped hydro storage. Through a comparative analysis approach and eight weeks of data collection, the study addressed key research questions related to solar radiation patterns and optimal system design. The findings highlighted regions with heightened solar radiation levels, showcasing substantial potential for power generation and emphasizing the system's efficiency. Optimizing system design significantly boosted power generation, promoted renewable energy utilization, and enhanced energy storage capacity. The study underscored the benefits of optimizing hybrid solar PV panels and pumped hydro energy supply systems for sustainable energy usage. Optimizing the design of solar PV panels and pumped hydro energy supply systems as examined across diverse climatic conditions in a developing country, not only enhances power generation but also improves the integration of renewable energy sources and boosts energy storage capacities, particularly beneficial for less economically prosperous regions. Additionally, the study provides valuable insights for advancing energy research in economically viable areas. Recommendations included conducting site-specific assessments, utilizing advanced modeling tools, implementing regular maintenance protocols, and enhancing communication among system components.
CFD Simulation of By-pass Flow in a HRSG module by R&R Consult.pptxR&R Consult
CFD analysis is incredibly effective at solving mysteries and improving the performance of complex systems!
Here's a great example: At a large natural gas-fired power plant, where they use waste heat to generate steam and energy, they were puzzled that their boiler wasn't producing as much steam as expected.
R&R and Tetra Engineering Group Inc. were asked to solve the issue with reduced steam production.
An inspection had shown that a significant amount of hot flue gas was bypassing the boiler tubes, where the heat was supposed to be transferred.
R&R Consult conducted a CFD analysis, which revealed that 6.3% of the flue gas was bypassing the boiler tubes without transferring heat. The analysis also showed that the flue gas was instead being directed along the sides of the boiler and between the modules that were supposed to capture the heat. This was the cause of the reduced performance.
Based on our results, Tetra Engineering installed covering plates to reduce the bypass flow. This improved the boiler's performance and increased electricity production.
It is always satisfying when we can help solve complex challenges like this. Do your systems also need a check-up or optimization? Give us a call!
Work done in cooperation with James Malloy and David Moelling from Tetra Engineering.
More examples of our work https://www.r-r-consult.dk/en/cases-en/
Overview of the fundamental roles in Hydropower generation and the components involved in wider Electrical Engineering.
This paper presents the design and construction of hydroelectric dams from the hydrologist’s survey of the valley before construction, all aspects and involved disciplines, fluid dynamics, structural engineering, generation and mains frequency regulation to the very transmission of power through the network in the United Kingdom.
Author: Robbie Edward Sayers
Collaborators and co editors: Charlie Sims and Connor Healey.
(C) 2024 Robbie E. Sayers