Teaching and Learning based 5G cognitive radio application for future application

Ramaiah, V. Subba; Singh, Bhopendra; Raju, A. Ratna; Reddy, Gade Nagi; Saikumar, K.; Ratnayake, Dulini

doi:10.1109/iccike51210.2021.9410797

Cited by 25 publications

(3 citation statements)

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“…Logistic regression (Ramaiah et al, 2021) 65.330 Support vector machine (Mohammad et al, 2021) 40.330 k-nearest neighbor (Padmini et al, 2021) 66.760 CART (Raju et al, 2020) 64.660 Random forests (Saikumar and Rajesh, 2020a) 70.140 Naive Bayes (Saikumar and Rajesh, 2020b) 57…”

Section: Discussionmentioning

confidence: 99%

“…The images selected for the pre-processing stage are pre-crop harvesting leaf images (Woods, 2007; Vijayaraghavan et al , 2013). Around 15 images were used as samples for this investigation (Ramaiah et al , 2021; Mohammad et al , 2021). During pre-processing, the primary stage is image acquisition in which images are classified into three categories (Padmini et al , 2021; Raju et al ., 2020):Image clipping;Image smoothing andImage enhancement.…”

Section: Random Forest Optimizationmentioning

confidence: 99%

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An IoT-based agriculture maintenance using pervasive computing with machine learning technique

Kailasam

Achanta

Rao³

et al. 2021

IJICC

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PurposeIn cultivation, early harvest offers farmers an opportunity to increase production while decreasing the chances of lower crop production rates, ensuring that the economy remains balanced. The significant reason is to predict the disease in plants and distinguish the type of syndrome with the help of segmentation and random forest optimization classification. In this investigation, the accurate prior phase of crop imagery has been collected from different datasets like cropscience, yesmodes and nelsonwisc . In the current study, the real-time earlier state of crop images has been gathered from numerous data sources similar to crop_science, yes_modes, nelson_wisc dataset.Design/methodology/approachIn this research work, random forest machine learning-based persuasive plants healthcare computing is provided. If proper ecological care is not applied to early harvesting, it can cause diseases in plants, decrease the cropping rate and less production. Until now different methods have been developed for crop analysis at an earlier stage, but it is necessary to implement methods to advanced techniques. So, the detection of plant diseases with the help of threshold segmentation and random forest classification has been involved in this investigation. This implemented design is verified on Python 3.7.8 software for simulation analysis.FindingsIn this work, different methods are developed for crops at an earlier stage, but more methods are needed to implement methods with prior stage crop harvesting. Because of this, a disease-finding system has been implemented. The methodologies like “Threshold segmentation” and RFO classifier lends 97.8% identification precision with 99.3% real optimistic rate, and 59.823 peak signal-to-noise (PSNR), 0.99894 structure similarity index (SSIM), 0.00812 machine squared error (MSE) values are attained.Originality/valueThe implemented machine learning design is outperformance methodology, and they are proving good application detection rate.

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Section: Discussionmentioning

confidence: 99%

Section: Random Forest Optimizationmentioning

confidence: 99%

An IoT-based agriculture maintenance using pervasive computing with machine learning technique

Kailasam

Achanta

Rao³

et al. 2021

IJICC

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“…This radiation could be emitted by a microwave or a laser. The topic of this work-the magnetically coupled WPT for any electric car charging application-has been the subject of numerous studies [19]. Two modes of WPT operation are static and dynamic.…”

Section: Introductionmentioning

confidence: 99%

V2G Nonlinear Connection in EV’s for Wireless and Sustainable Mobility with Support of Machine Learning

Sudeepthi Govathoti

2024

cana

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In order to achieve reliable, rapid, and affordable charging solutions, this study examines the several approaches and strategies used in electric vehicle wireless charging systems. Wireless charging offers an eco-friendly and user-friendly alternative to standard cable systems by doing away with the need for physical connections. In the first section of the review, presented below are the numerous modes of wireless power transfer, with an emphasis on the inductive & capacitive methods. Next, the essay delves into the several kinds of electric vehicle wireless charging systems, including mobile and static alternatives, and how they are categorised. Noteworthy to this research is its examination of dynamic charging systems' standard model and design characteristics, which enable wireless charging in moving vehicles. It also shows how important control system features are for effective power transmission and communication between charging receiver and transmitter parts. In addition to reviewing various battery models and comparing various types of batteries, the article acknowledges the crucial function that batteries play in electric vehicles and highlights how they affect the parameters of the charging system. Finally, the paper delves into a discussion of its findings and makes suggestions for further research in this area.

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Energy-Efficient Power-Controlled Resource Allocation for MIMO-Based Cognitive-Enabled B5G/6G Indoor-Flying Networks

2022

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Teaching and Learning based 5G cognitive radio application for future application

Cited by 25 publications

References 8 publications

An IoT-based agriculture maintenance using pervasive computing with machine learning technique

An IoT-based agriculture maintenance using pervasive computing with machine learning technique

V2G Nonlinear Connection in EV’s for Wireless and Sustainable Mobility with Support of Machine Learning

Energy-Efficient Power-Controlled Resource Allocation for MIMO-Based Cognitive-Enabled B5G/6G Indoor-Flying Networks

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