Prediction of the nutrient deficiency range and control of it through application of an appropriate amount of fertiliser at all growth stages is critical to achieving a qualitative and quantitative *** fertiliser in op...
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Prediction of the nutrient deficiency range and control of it through application of an appropriate amount of fertiliser at all growth stages is critical to achieving a qualitative and quantitative *** fertiliser in optimum amounts will protect the environment’s condition and human health *** identification also prevents the disease’s occurrence in groundnut crops.A convo-lutional neural network is a computer vision algorithm that can be replaced in the place of human experts and laboratory methods to predict groundnut crop nitro-gen nutrient deficiency through image *** chlorophyll and nitrogen are proportionate to one another,the Smart Nutrient Deficiency Prediction System(SNDP)is proposed to detect and categorise the chlorophyll concentration range via which nitrogen concentration can be *** model’sfirst part is to per-form preprocessing using Groundnut Leaf Image Preprocessing(GLIP).Then,in the second part,feature extraction using a convolution process with Non-negative ReLU(CNNR)is done,and then,in the third part,the extracted features areflat-tened and given to the dense layer(DL)***,the Maximum Margin clas-sifier(MMC)is deployed and takes the input from DL for the classification process tofind *** dataset used in this work has no visible symptoms of a deficiency with three categories:low level(LL),beginning stage of low level(BSLL),and appropriate level(AL).This model could help to predict nitrogen deficiency before perceivable *** performance of the implemented model is analysed and compared with ImageNet pre-trained *** result shows that the CNNR-MMC model obtained the highest training and validation accuracy of 99%and 95%,respectively,compared to existing pre-trained models.
The basis of this project is to investigate whether the YOLO, an object detection algorithm where 'You Only Look Once' constitutes the name, could be applied to develop FMCG management;together with the manage...
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Innovative technology solutions have been developed in response to the growing need for effective and customized client contact on e-commerce platforms. This work introduces an intelligent chatbot system that uses mac...
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In today's competitive retail landscape, supermarkets like Big Marts meticulously track the sales data of each product to anticipate consumer demand and optimize inventory management. By analyzing this data, inclu...
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A significant portion of people have suffered from a form of Parkinson's disease (PD), widely attributed to be the second most frequently diagnosed form of neurological illness that significantly impairs motor and...
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Platoon formation focuses on effectively coordinating the speeds of vehicles within a group,with automatic speed adjustments for each vehicle to maintain a desired *** implementation of appropriate control techniques ...
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Platoon formation focuses on effectively coordinating the speeds of vehicles within a group,with automatic speed adjustments for each vehicle to maintain a desired *** implementation of appropriate control techniques in platooning is crucial to achieve efficient vehicle coordination to facilitate seamless communication and synchronization among *** platoon functions as a cluster,where vehicles within the platoon are treated as *** study presents an idea for implementing clustering strategies in a platoon with a focus on achieving string stability by decreasing disturbances and variations in vehicle speed and *** also involves an indepth analysis of clustering algorithms to identify the most suitable approach for integration into vehicle platooning,specifically for network analysis *** investigation of various control techniques and clustering algorithms aims to optimize the performance and functionality of platooning systems contributing to the advancement of wireless-connected autonomous vehicles and their transformative potential in transportation.
The goal of this project is to draw a deeper understanding of the subjective nature behind online product reviews, largely by examining a large dataset received from Amazon that contains numerous star ratings and comm...
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Deep learning has risen in popularity as a face recognition technology in recent ***,a deep convolutional neural network(DCNN)developed by Google,recognizes faces with 128 bytes per *** also claims to have achieved 99...
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Deep learning has risen in popularity as a face recognition technology in recent ***,a deep convolutional neural network(DCNN)developed by Google,recognizes faces with 128 bytes per *** also claims to have achieved 99.96%on the reputed Labelled Faces in the Wild(LFW)***-ever,the accuracy and validation rate of Facenet drops down eventually,there is a gradual decrease in the resolution of the *** research paper aims at developing a new facial recognition system that can produce a higher accuracy rate and validation rate on low-resolution face *** proposed system Extended Openface performs facial recognition by using three different features i)facial landmark ii)head pose iii)eye *** extracts facial landmark detection using Scattered Gated Expert Network Constrained Local Model(SGEN-CLM).It also detects the head pose and eye gaze using Enhanced Constrained Local Neur-alfield(ECLNF).Extended openface employs a simple Support Vector Machine(SVM)for training and testing the face *** system’s performance is assessed on low-resolution datasets like LFW,Indian Movie Face Database(IMFDB).The results demonstrated that Extended Openface has a better accuracy rate(12%)and validation rate(22%)than Facenet on low-resolution images.
Evaluation system of small arms firing has an important effect in the context of military domain. A partially automated evaluation system has been conducted and performed at the ground level. Automation of such system...
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Evaluation system of small arms firing has an important effect in the context of military domain. A partially automated evaluation system has been conducted and performed at the ground level. Automation of such system with the inclusion of artificial intelligence is a much required process. This papers puts focus on designing and developing an AI-based small arms firing evaluation systems in the context of military environment. Initially image processing techniques are used to calculate the target firing score. Additionally, firing errors during the shooting have also been detected using a machine learning algorithm. However, consistency in firing requires an abundance of practice and updated analysis of the previous results. Accuracy and precision are the basic requirements of a good shooter. To test the shooting skill of combatants, firing practices are held by the military personnel at frequent intervals that include 'grouping' and 'shoot to hit' scores. Shortage of skilled personnel and lack of personal interest leads to an inefficient evaluation of the firing standard of a firer. This paper introduces a system that will automatically be able to fetch the target data and evaluate the standard based on the fuzzy *** it will be able to predict the shooter performance based on linear regression ***, it compares with recognized patterns to analyze the individual expertise and suggest improvements based on previous values. The paper is developed on a Small Arms Firing Skill Evaluation System, which makes the whole process of firing and target evaluation faster with better accuracy. The experiment has been conducted on real-time scenarios considering the military field and shows a promising result to evaluate the system automatically.
Road traffic management requires the ability to foresee geographical congestion conditions in an urban road traffic network. The proposed investigation is aimed to envisage the presence of blockage in a specific regio...
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