Potato leaf disease prediction using RMSProp, Adam and SGD optimizers

Prasher, Shikha; Nelson, Leema; Jagdish, Mukta

doi:10.1109/incacct57535.2023.10141714

Cited by 10 publications

(2 citation statements)

References 10 publications

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“…Our final training is conducted in Graham using 4 GPUs simultaneously, in line with a distributed training strategy called with tf.distribute.MirroredStrategy(). 10 We have also performed extensive DeepSC training using SGD with momentum [50] and RMSprop [51]. Although we have obtained much better training results with SGD with Momentum than with RMSprop, none of these optimizers has led to better training results than the ones we have obtained with Adam.…”

Section: Training Results Of Deepscmentioning

confidence: 84%

Making Sense of Meaning: A Survey on Metrics for Semantic and Goal-Oriented Communication

2023

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Semantic communication (SemCom) aims to convey the meaning behind a transmitted message by transmitting only semantically-relevant information. This semantic-centric design helps to minimize power usage, bandwidth consumption, and transmission delay. SemCom and goal-oriented SemCom (or effectiveness-level SemCom) are therefore promising enablers of 6G and developing rapidly. Despite the surge in their swift development, the design, analysis, optimization, and realization of robust and intelligent SemCom as well as goal-oriented SemCom are fraught with many fundamental challenges. One of the challenges is that the lack of unified/universal metrics of SemCom and goal-oriented SemCom can stifle research progress on their respective algorithmic, theoretical, and implementation frontiers. Consequently, this survey paper documents the existing metrics -scattered in many references -of wireless SemCom, optical SemCom, quantum SemCom, and goal-oriented wireless SemCom. By doing so, this paper aims to inspire the design, analysis, and optimization of a wide variety of SemCom and goal-oriented SemCom systems. This article also stimulates the development of unified/universal performance assessment metrics of SemCom and goal-oriented SemCom, as the existing metrics are purely statistical and hardly applicable to reasoning-type tasks that constitute the heart of 6G and beyond.

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Section: Training Results Of Deepscmentioning

confidence: 84%

Making Sense of Meaning: A Survey on Metrics for Semantic and Goal-Oriented Communication

2023

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“…VGG is a series of CNN architectures with increasing depth and complexity, demonstrating the power of deeper networks for image classification. Some of the Optimization algorithms include RMSProp helps to adaptively adjust learning rates for individual parameters based on squared gradients, effective for non-stationary data, ADAM [15] combines features of RMSProp and AdaGrad, addressing their limitations and achieving efficient learning with momentum, Jaya [27] is a nature-inspired optimizer based on the foraging behaviour of jays, demonstrating robustness and performance across various tasks.…”

Section: Introductionmentioning

confidence: 99%

Classification and Prediction of Crop Diseases: A Review

2023

MRIJET

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Agriculture is the foundation of civilization which faces several problems in the 21st century. Crop diseases threaten global food security by reducing yields. Visual inspection to detect diseases can be time-consuming, subjective, and error-prone. Recent advances in various machine learning (ML) and deep learning (DL) techniques have led to ease in the identification of crop diseases. ML and DL demonstrate their versatility in image recognition, segmentation, and anomaly detection. In this paper, some of the recent works based on crop-disease detection using various ML and DL techniques are reviewed. It includes early illness detection and appropriate interventions to reduce yield loss. The review emphasizes the relevance of crop disease detection for food security and sustainable agriculture. ML and DL techniques can help farmers monitor crop health and optimize resource allocation.

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Enhancing Intercropping Yield Predictability Using Optimally Driven Feedback Neural Network and Loss Functions

Ikram,

Aslam

2024

IEEE Access

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Potato leaf disease prediction using RMSProp, Adam and SGD optimizers

Cited by 10 publications

References 10 publications

Making Sense of Meaning: A Survey on Metrics for Semantic and Goal-Oriented Communication

Making Sense of Meaning: A Survey on Metrics for Semantic and Goal-Oriented Communication

Classification and Prediction of Crop Diseases: A Review

Enhancing Intercropping Yield Predictability Using Optimally Driven Feedback Neural Network and Loss Functions

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