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GSA-based support vector neural network:a machine learning approach for crop prediction to provision sustainable farming

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International Journal of Intelligent Computing and Cybernetics 2023年第1期16卷 1-16页

作者： a.ashwitha C.A.Latha Department of ISE Ramaiah Institute of TechnologyBangaloreIndia Department of CSE AMCECBangaloreIndia and Vishveshwarya Technical UniversityBelgaumIndia Department of CSE AMC Engineering CollegeBangaloreIndia

Purpose-Automated crop prediction is needed for the following reasons:First,agricultural yields were decided by a farmer’s ability to work in a certain field and with a particular crop *** were not always able to predict the crop and its yield solely on that idea ***,seed firms frequently monitor how well new plant varieties would grow in certain ***,predicting agricultural production is critical for solving emerging food security concerns,especially in the face of global climate *** production forecasts not only assist farmers inmaking informed economic andmanagement decisions but they also aid in the prevention of *** results in farming systems’efficiency and productivity gains,as well as reduced risk from environmental ***/methodology/approach-This research paper proposes a machine learning technique for effective autonomous crop and yield prediction,which makes use of solution encoding to create solutions randomly,and then for every generated solution,fitness is evaluated to meet highest *** focus of the proposed work is to optimize the weight parameter in the input *** algorithm continues until the optimal agent or optimal weight is selected,which contributes to maximum accuracy in automated crop ***-Performance of the proposed work is compared with different existing algorithms,such as Random Forest,support vector machine(SVM)and artificial neural network(ANN).The proposed method support vector neural network(SVNN)with gravitational search agent(GSA)is analysed based on different performance metrics,such as accuracy,sensitivity,specificity,CPU memory usage and training time,and maximum performance is *** limitations/implications-Rather than real-time data collected by Internet of Things(IoT)devices,this research focuses solely on historical data;the proposed work does not impose IoT-based smart farming,which enhances the overall agriculture system by

关键词： Crop yield Support vector machine(SVM) Artificial neural network(ANN) SVNN Gravitational search algorithm

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Signal Detection and Channel Estimation in OTFS

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ZTE Communications 2021年第4期19卷 16-33页

作者： ashwitha NAIKOTI Ananthanarayanan CHOCKALINGAM Department of ECE Indian Institute of ScienceBangalore 560012India

Orthogonal time frequency space(OTFS)modulation is a recently proposed modulation scheme that exhibits robust performance in high-Doppler *** is a two-dimensional modulation scheme where information symbols are multiplexed in the de⁃lay-Doppler(DD)***,the channel is viewed in the DD domain where the chan⁃nel response is sparse and time-invariant for a long *** simplifies channel estima⁃tion in the DD *** paper presents an overview of the state-of-the-art approaches in OTFS signal detection and DD channel *** classify the signal detection ap⁃proaches into three categories,namely,low-complexity linear detection,approximate max⁃imum a posteriori(MAP)detection,and deep neural network(DNN)based ***⁃larly,we classify the DD channel estimation approaches into three categories,namely,separate pilot approach,embedded pilot approach,and superimposed pilot *** compile and present an overview of some of the key algorithms under these categories and illustrate their performance and complexity attributes.

关键词： OTFS modulation delay-Doppler domain high-Doppler channels signal de⁃tection channel estimation

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