Probabilistic Principle Component Analysis based Feature Extraction of Embedded System Applications with Deep Neural Network based Implementation in FPGA

The study of hardware and software systems is of major are very important advent in new devices for communication and progress in system of security. In fast pace mobile and embedded devices application in every day’s life leads some new emerging area for research in data mining field. In this we have some technologies which have demand and error free using the principle of component of PPCA. For Embedded system the applications of PCA is basically applied initially for the lessen the having different qualities especially being to simple of the data. PPCA which have the updated version of PCA which is surveyed by similarity measure. In this work, experiments are extensively carried out, using a FPGA based light weight cryptographic data set having benchmark set to check and illustrate the viability, competence, litheness which are reconfigurable embedded system which are having data mining . Which have FPGA are reconfigurable for the computing architectures for hardware and in neural network. FPGA using the multilayer Cascaded for neural network which are forward in nature (CFFNN) and Deep Neural Network also called as DNN with a huge neuron is still a thought-provoking task. This shortcoming leads to elect the FPGA capacity for a particular application we have used the method of implementation which has two neural network have been implemented and compared , namely, CFFNN and DNN. It can be shown that for reconfigurable embedded system, PPCA based data mining and Machine learning based realization can give more speed up less iteration and more space savings when we have compared it with the static conventional version

An Artificial Neural Network Model For Estimating Paddy Crop Using Remotely Sensed Information For Davangere Region

Crop yield forecasting is a very important task for researchers in remote sensing. Problems exist with traditional statistical modelling (especially regression models) of nonlinear functions with multiple factors in the cropland ecosystem. This paper describes the successful application of an artificial neural network in developing a model for crop yield forecasting using back-propagation algorithms. The model has been adapted and calibrated using on the ground survey and statistical data, and it has proven to be stable and highly accurate. This study presents a novel approach for predicting paddy yield using artificial neural networks (ANNs) and remote sensing data. The accurate estimation of crop yield is crucial for effective agricultural planning and resource allocation. In this research, remote sensing data, including satellite imagery and climate variables, were integrated into an ANN model to forecast paddy yield. The ANN model was trained and validated using historical yield data and corresponding remote sensed inputs.The results demonstrate the effectiveness of the proposed approach in accurately predicting paddy yield. The ANN model's ability to capture complex relationships between remote sensing variables and yield parameters is highlighted. The integration of remote sensing data provides valuable insights into the spatial and temporal dynamics of the crop growth process. This enables informed decision-making for farmers and policymakers. Furthermore, the study discusses the significance of accurate yield prediction in mitigating the impacts of climate variability and ensuring food security. The application of ANN-based yield estimation using remote sensed data holds promise for enhancing agricultural productivity and resource management. This research contributes to the growing field of precision agriculture by showcasing a data-driven approach that leverages advanced techniques to improve crop yield predictions.In conclusion, the utilization of artificial neural networks in conjunction with remote sensing data presents a robust method for predicting paddy yield. The findings underscore the potential benefits of integrating cutting-edge technology into agriculture, emphasizing the need for further research and practical implementation of such models on a broader scale

Mood & Emotion Detection: Whistling Ball Movement Game

In this investigation, a modified and improved version of the ball-throwing game is presented. The goal is to boost mood and promote mental health utilizing the game system Ball movement and music therapy. The user's whistle pitch determines how the ball moves in this paper. Whistling can serve as a stimulant for the medical reduction of stress, which makes it useful for rehabilitation. Our algorithm adjusts the ball movement performance dependent on the player's whistling score after analyzing it. This article focuses on the system's development and operations