Agricultural Land Classification Based on Machine Learning Algorithms

Vikas, Kummari; Goud, Korakoppula Vaishnav; Ponaganti, Srikar; Reddy, Aku Nithyavardhan; Nagasai, Kanmatha; Mohanty, Sachi Nandan

doi:10.1109/icccnt54827.2022.9984254

Cited by 3 publications

(1 citation statement)

References 14 publications

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“…They assessed various factors related to the land, calculating a classification index and achieving preliminary land classification. Furthermore, the study emphasized the role of weather and soil characteristics in agricultural decision-making, highlighting the potential for more accurate and cost-effective land classification through satellite images and machine learning algorithms [14].…”

Section: Introductionmentioning

confidence: 99%

Power Quality Improvement Using TID based DVR Controller

Srikanth Goud,

Krishna,

Hima Bindu

et al. 2023

EAI Endorsed Trans Energy Web

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The effectiveness of power utilized by electronic devices is crucial for various reasons. Firstly, high-quality power results in better efficiency, allowing devices to operate at optimal energy levels. This article aims to clarify the significance of Power Quality by highlighting the benefits of using a Dynamic Voltage Restorer (DVR) to improve power quality for electricity consumers. The proposed method employs a hysteresis voltage control system that operates with variable switching frequency to enhance DVR reliability and ease of operation. Using a Tilt Integral Derivative (TID) controller, a type of fractional order controller similar to the PID controller, voltage compensation under disturbances is achieved with satisfactory results demonstrated via simulation. The primary objective of this project is to mitigate power quality issues such as voltage swell and sag in a distribution power system by enhancing power quality with a TID controller and comparing the results with those obtained from a previously implemented PID controller, using MATLAB software. Keywords: Dynamic Voltage Restorer (DVR), Tilt Integral Derivative controller (TID), Total Harmonic Distortion, MATLAB software.

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Section: Introductionmentioning

confidence: 99%

Power Quality Improvement Using TID based DVR Controller

Srikanth Goud,

Krishna,

Hima Bindu

et al. 2023

EAI Endorsed Trans Energy Web

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Intelligent Raspberry-Pi-Based Parking Slot Identification System

Agarwal,

Sharma,

Singh

et al. 2023

EAI Endorsed Trans Ind Net Intel Syst

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A growing population necessitates more transportation, which pressures car parking spots. Parking is a problem for public places in cities, such as theatres, malls, parks, and temples. Even though several techniques have been suggested in publications, manual parking systems are still used in most places. For large locations where it is challenging to find open spaces, traditional parking arrangements need to be more archaic and convoluted. This might lead to heavy traffic, minor mishaps, and widespread accidents. In the modern era of sophisticated parking management systems, an automatic parking spot-detecting system has been introduced in an innovative format. Experts in computer vision are drawn to this emerging field to contribute. The system could tell if the automobile was fully or partially parked. Neither during the process nor afterward, human oversight is required. As parking management enters the modern era, computer vision is becoming increasingly critical. The parking system will not only make it easier for drivers to identify parking spaces but also enhance parking administration and monitoring. Vehicles will be able to observe available parking spots due to technology that monitors parking spaces. India and other emerging nations, as well as industrialized ones, have recently shown interest in smart cities. This article's smart auto parking system was conceived and implemented utilizing a Raspberry Pi and cameras placed in various parking spaces. Using a website and an Android app, this project creates and deploys a real-time system that enables vehicles to efficiently find and reclaim open parking spaces.

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Autism Spectrum Disorder Classification Using Machine Learning and Deep Learning- A Survey

S R,

Mounika

2023

EAI Endorsed Trans Perv Health Tech

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Modern, highly developed technology has impacted reputable procedures in the medical and healthcare industries. Smart healthcare prediction to the senior sick patient is not only for quick access to data but also to get dependable treatment in an accurate prediction by healthcare service provider. smart health prediction helps in the identification of numerous diseases. Based on patient experience, Deep learning technology provides a robust application space in the medical sector for health disease prediction problems by applying deep learning techniques to analyze various symptoms. In order to classify things and make precise predictions about diseases, deep learning techniques are utilized. people's health will be more secure, medical care will be of a higher caliber, and personal information will be kept more secret. As deep learning algorithms become more widely used to construct an interactive smart healthcare prediction and evaluation model on the basis of the deep learning model, CNN is upgraded. Advanced deep learning algorithms combined with multi-mode approaches and resting-state functional magnetic resonance represent an innovative approach that researchers have taken. A DL structure for the programmed ID ASD using highlights separated from the corpus callosum and cerebrum volume from the Stand dataset is proposed. Imaging is used to reveal hidden diseased brain connectome patterns to find diagnostic and prognostic indicators.

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Agricultural Land Classification Based on Machine Learning Algorithms

Cited by 3 publications

References 14 publications

Power Quality Improvement Using TID based DVR Controller

Power Quality Improvement Using TID based DVR Controller

Intelligent Raspberry-Pi-Based Parking Slot Identification System

Autism Spectrum Disorder Classification Using Machine Learning and Deep Learning- A Survey

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