The title is 'Rainfall Prediction Using machinelearning'. The initiative's dataset is written in Python and stored in Microsoft Excel. A wide range of machinelearning algorithms are used to discover whic...
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Artificial Intelligence is a process that enables machines to imitate human behaviour. Both machinelearning and Deep learning are subsets of AI. The basic difference between ML(machinelearning) and DL(Deep learning)...
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The sparse reward in traditional reinforcement learning will lead to poor training effect of autonomous vehicles. This paper proposes a hierarchical automatic driving decision method based on reinforcement learning. T...
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In this paper, An improved algorithm for the extreme learningmachine is proposed and applied to SAR target *** order to solve the influence of the noise and spatial distribution of the training samples on the calcula...
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ISBN:
(数字)9781510634107
ISBN:
(纸本)9781510634107
In this paper, An improved algorithm for the extreme learningmachine is proposed and applied to SAR target *** order to solve the influence of the noise and spatial distribution of the training samples on the calculation of the classification plane, different penalty factors are given to different training samples,and according to this, the "weighted extreme learningmachine" is proposed. And then,the kernel function is introduced into the "extreme learningmachine" to improve the ability of nonlinear function approximation. Considering that the general training algorithm of the weighted extreme learningmachine is slow and consumes a lot of computer memory when the number of training samples is large, a training method based on conjugate gradient algorithm is proposed. The test on "banana benchmark data" shows that the weighted extreme learningmachine based on the conjugate gradient method can complete the convergence in the number of iterations far less than the number of samples, and the calculation speed is much faster than the traditional algorithm. Finally, this proposed algorithm is applied to SAR target recognition. The test on MSTAR data set shows that the proposed algorithm is not only extremely fast in SAR target recognition, but also has better recognition performance than support vector machine. general limit learningmachine. BP neural network and other algorithms.
Cuff-Less Blood Pressure Stratification using signalprocessing with machinelearning has gained immense attraction in the past decade among the research community. Blood Pressure, one of the most vital parameter of t...
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The aim of this paper is to measure the performance of Auto Associative Neural Network (AANN) in terms of recognition rate of spoken words with reference to Assamese language. One AANN models is built in the present s...
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The proceedings contain 245 papers. The topics discussed include: developing an advanced tool for transforming spreadsheet data into interactive dashboards with predictive insights;smart education ecosystems for enhan...
ISBN:
(纸本)9798331541217
The proceedings contain 245 papers. The topics discussed include: developing an advanced tool for transforming spreadsheet data into interactive dashboards with predictive insights;smart education ecosystems for enhancing learning experiences through deep attention learning and artificial intelligence;recent innovations in patch antenna technology for wearable biomedical devices;wine quality prediction using machinelearning techniques;data analytics and artificial intelligence for improving employee retention strategies;hybrid steganographic and encryption approach using color analysis for secure image communication;a robotic arm vehicle using voice recognition for physically challenged people;artificial intelligence based plant disease detection system;comprehensive review for video surveillance based suspicious human activity detection;edge computing in smart cities: enhancing real-time data processing;a comprehensive analysis of various federated learning techniques;cardiovascular disease prediction implementing artificial intelligence andmachinelearning approaches;andsignalprocessing techniques for enhanced wireless network performance.
Deep learning algorithms have surpassed human resolution in applications such as face recognition and object classification. However, it can only produce very blurred, lack of details of the image. Generative Adversar...
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ISBN:
(纸本)9781450360920
Deep learning algorithms have surpassed human resolution in applications such as face recognition and object classification. However, it can only produce very blurred, lack of details of the image. Generative Adversarial Network is a game training of minimax antagonism between generator G and discriminator D, and ultimately achieves Nash equilibrium. We use deep convolutional GAN that recognizes sequence numbers and without split characters. First we use convolution network to extract character features. Second we construct a convolution neural network to recognize digits of natural scene house number. DCGAN is used to improve the resolution of the number of fuzzy houses, so as to extract more abundant data features in data set training. It can better recognize the numbers in the natural street.
Video processing is a significant field of research interest in recent years. Before going into the recent advancement of video processing, an overview about the traditional video processing is a matter of interest. K...
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People in today's world are so busy with work and other commitments that they rarely have time to see doctors for illnesses that initially seem minor but eventually become life-threatening. In the above situations...
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