Agricultural productivity plays a vital role in India’s economy, contributing approximately 17–18% to the nation’s GDP and serving as a primary livelihood for a significant portion of the population. With India pos...
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ISBN:
(数字)9798331521691
ISBN:
(纸本)9798331521707
Agricultural productivity plays a vital role in India’s economy, contributing approximately 17–18% to the nation’s GDP and serving as a primary livelihood for a significant portion of the population. With India possessing the largest net cropped area globally, effective pest and disease management is crucial to ensuring stable agricultural yields. This study utilizes advanced technologies, including machine learning, computer vision, and deep learning, to detect and identify common diseases such as early blight and late blight in potato plant leaves. Using a Kaggle-sourced dataset comprising roughly 2,000 images, rigorous data augmentation techniques were applied to enhance the robustness of the model. A customized convolutional neural network (CNN) was deployed, achieving an impressive accuracy of 97.12% after just 32 epochs. This performance surpasses many existing Kaggle models, which typically reach around 95% accuracy.
作者：
Jeff S.ShammaComputer
Electrical and Mathematical Science and Engineering Division King Abdullah University of Science and Technology
Game theory is the study of interacting decision makers, whereas control systems involve the design of intelligent decision-making *** many control systems are interconnected, the result can be viewed through the lens...
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Game theory is the study of interacting decision makers, whereas control systems involve the design of intelligent decision-making *** many control systems are interconnected, the result can be viewed through the lens of game theory. This article discusses both long standing connections between these fields as well as new connections stemming from emerging applications.
The current study presents a detailed numerical investigation of buoyancy-driven three-dimensional heat transfer and fluid flow within a cubic cavity filled with Carbon nanotube (CNT)-Gallium nanoliquid and equipped w...
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This paper explores the band structure effect to elucidate the feasibility of an ultra-scaled GaAs Schottky MOSFET （SBFET） in a nanoscale regime. We have employed a 20-band sp3dSs＊ tight-binding （TB） approach to ...
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This paper explores the band structure effect to elucidate the feasibility of an ultra-scaled GaAs Schottky MOSFET （SBFET） in a nanoscale regime. We have employed a 20-band sp3dSs＊ tight-binding （TB） approach to compute E - K dis- persion. The considerable difference between the extracted effective masses from the TB approach and bulk values implies that quantum confinement affects the device performance. Beside high injection velocity, the ultra-scaled GaAs SBFET suffers from a low conduction band DOS in the F valley that results in serious degradation of the gate capacitance. Quan- tum confinement also results in an increment of the effective Schottky barrier height （SBH）. Enhanced Schottky barriers form a double barrier potential well along the channel that leads to resonant tunneling and alters the normal operation of the SBFET. Major factors that may lead to resonant tunneling are investigated. Resonant tunneling occurs at low temperatures and low drain voltages, and gradually diminishes as the channel thickness and the gate length scale down. Accordingly, the GaAs （100） SBFET has poor ballistic performance in nanoscale regime.
This research aims at improving the identification of Alzheimer's dementia by using the optimized attribute selection method and preliminary diagnosis data-driven learning algorithm, namely Random Forest Classifie...
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Investors struggle with the unpredictable, nonlinear nature of stock price volatility. Econometric models based on machine learning algorithms have improved prediction accuracy but remain limited in dynamic and highly...
Investors struggle with the unpredictable, nonlinear nature of stock price volatility. Econometric models based on machine learning algorithms have improved prediction accuracy but remain limited in dynamic and highly correlated markets. This paper builds upon the proximal policy optimization (PPO) algorithm, the well-established deep reinforcement learning (DRL) method, and proposes an enhanced variant called correlation graph-based PPO (CGPPO), which incorporates spatio-temporal stock correlations for more realistic and robust predictions. The reward function, designed based on trading frequency and portfolio value, enhances experimental sophistication by reflecting practical investment objectives. The experiment is conducted in the simulated market environment using four major Korean stocks while explicitly considering the correlations among them. Experimental results show that the proposed CGPPO algorithm outperforms baseline methods, achieving 64.60 % reward convergence value during training and 69.04 % prediction value during inference.
Optoelectronic devices are advantageous in in-memory light sensing for visual information processing,recognition,and storage in an energy-efficient ***,in-memory light sensors have been proposed to improve the energy,...
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Optoelectronic devices are advantageous in in-memory light sensing for visual information processing,recognition,and storage in an energy-efficient ***,in-memory light sensors have been proposed to improve the energy,area,and time efficiencies of neuromorphic computing *** study is primarily focused on the development of a single sensing-storage-processing node based on a two-terminal solution-processable MoS2 metal-oxide-semiconductor(MOS)charge-trapping memory structure—the basic structure for charge-coupled devices(CCD)—and showing its suitability for in-memory light sensing and artificial visual *** memory window of the device increased from 2.8 V to more than 6V when the device was irradiated with optical lights of different wavelengths during the program ***,the charge retention capability of the device at a high temperature(100 ℃)was enhanced from 36 to 64%when exposed to a light wavelength of 400 *** larger shift in the threshold voltage with an increasing operating voltage confirmed that more charges were trapped at the Al_(2)O_(3)/MoS_(2) interface and in the MoS_(2) layer.A small convolutional neural network was proposed to measure the optical sensing and electrical programming abilities of the *** array simulation received optical images transmitted using a blue light wavelength and performed inference computation to process and recognize the images with 91%*** study is a significant step toward the development of optoelectronic MOS memory devices for neuromorphic visual perception,adaptive parallel processing networks for in-memory light sensing,and smart CCD cameras with artificial visual perception capabilities.
This paper documents an approach to sea ice classification through a combination of methods, both algorithmic and heuristic, The resulting system is a comprehensive technique, which uses dynamic local thresholding as ...
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This paper documents an approach to sea ice classification through a combination of methods, both algorithmic and heuristic, The resulting system is a comprehensive technique, which uses dynamic local thresholding as a classification basis and then supplements that initial classification using heuristic geophysical knowledge organized in expert systems, The dynamic local thresholding method allows separation of the ice into thickness classes based on local intensity distributions, Because it utilizes the data within each image, it can adapt to varying ice thickness intensities to regional and seasonal charges and is not subject to limitations caused by using predefined parameters,
This paper considers distributed optimization for minimizing the average of local nonconvex cost functions, by using local information exchange over undirected communication networks. To reduce the required communicat...
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A multi-valued algebraic system by using the notion of M-algebra is introduced. The system, called D-algebra, is represented as an isomorphic image of the direct product of two M-algebras. Next this D-algebra is used ...
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A multi-valued algebraic system by using the notion of M-algebra is introduced. The system, called D-algebra, is represented as an isomorphic image of the direct product of two M-algebras. Next this D-algebra is used to generate tests for m-logic combinational circuits (i.e. circuits which realize m-valued logic functions, m greater than or equal to 2). The test generation is a fault oriented process (tests are derived for specific faults). This process is illustrated by means of an informal modification of the classical Roth D-algorithm (a more formal treatment is omitted). For simplicity, only the s-a-fault model is considered and several examples are given.
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