PurposeIn cultivation, early harvest offers farmers an opportunity to increase production while decreasing the chances of lower crop production rates, ensuring that the economy remains balanced. The significant reason is to predict the disease in plants and distinguish the type of syndrome with the help of segmentation and random forest optimization classification. In this investigation, the accurate prior phase of crop imagery has been collected from different datasets like cropscience, yesmodes and nelsonwisc . In the current study, the real-time earlier state of crop images has been gathered from numerous data sources similar to crop_science, yes_modes, nelson_wisc dataset.Design/methodology/approachIn this research work, random forest machine learning-based persuasive plants healthcare computing is provided. If proper ecological care is not applied to early harvesting, it can cause diseases in plants, decrease the cropping rate and less production. Until now different methods have been developed for crop analysis at an earlier stage, but it is necessary to implement methods to advanced techniques. So, the detection of plant diseases with the help of threshold segmentation and random forest classification has been involved in this investigation. This implemented design is verified on Python 3.7.8 software for simulation analysis.FindingsIn this work, different methods are developed for crops at an earlier stage, but more methods are needed to implement methods with prior stage crop harvesting. Because of this, a disease-finding system has been implemented. The methodologies like “Threshold segmentation” and RFO classifier lends 97.8% identification precision with 99.3% real optimistic rate, and 59.823 peak signal-to-noise (PSNR), 0.99894 structure similarity index (SSIM), 0.00812 machine squared error (MSE) values are attained.Originality/valueThe implemented machine learning design is outperformance methodology, and they are proving good application detection rate.
Objective: This paper explains the working of the linear regression and Long Short-Term Memory model in predicting the value of a Bitcoin. Due to its raising popularity, Bitcoin has become like an investment and works on the Block chain technology which also gave raise to other crypto currency. This makes it very difficult to predict its value and hence with the help of Machine Learning Algorithm and Artificial Neural Network Model this predictor is tested. Methodology: In this study, we have used data sets for Bitcoin for testing and training the ML and AI model. With the help of python libraries, the data filtration process was done. Python has provided with a best feature for data analysis and visualization. After the understanding of the data, we trim the data and use the features or attributes best suited for the model. Implementation of the model is done and the result is recorded. Finding: It was discovered that the linear regression model's accuracy rate is very high when compared to other Machine Learning models from related works; it was found to be 99.87 percent accurate. The LSTM model, on the other hand, shows a mini error rate of 0.08 percent. This, in turn, demonstrates that the neural network model is more optimized than the machine learning model. Novelty: In this work, a small GUI has been created using the tkinter library that will allow the user to input the High, Low, and Open features values and then predict the next value for the coin. This paper compares the prediction outcomes of a machine learning model and an artificial neural network model. Because linear regression provided the highest accuracy compared to the other machine learning models, we used it to compare it to the LSTM model.
A Mobile Ad-hoc network is an autonomous system of mobile hosts connected by wireless links. A MANET has no infrastructure and no centralized administration. The network topology may dynamically change in an unpredictable manner since nodes are free to move. The operation of the nodes in wireless ad-hoc networks depend on the battery power and have limited energy resources. The loss of some intermediate nodes may cause considerable topological changes, weaken the network operation, and have an effect on the lifetime of the network. This makes energy efficiency a key concern in ensuring system resilience. Energy efficient routing problems are important in MANET dynamic environment. Energy should be optimally utilized so that the nodes will perform their action adequately. Fault tolerance is a significant property of ad-hoc network, which assures the reliability of the resources. In this paper, a novel fault tolerant multi path routing protocol is proposed to reduce the packet loss due to route breakage, which uses a new route discovery and maintenance mechanism. It uses alternative route to retransmit the data whenever an intermediate node does not able to forward it, due to link failure or node failure. The proposed protocol is simulated in NS2-and performance is evaluated using packet delivery ratio, end-to-end delay, packet drop, and throughput and energy consumption by varying the pause time, number of flows and traffic rate. Simulation results show that the proposed protocol outperform the existing works in terms of the above metrics.
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