Smart farming is an enhanced option for increasing food production, resource management and labour. Existing prediction methods need trained experts to analyse the data, which is a time-consuming process, and thus, th...
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Smart farming is an enhanced option for increasing food production, resource management and labour. Existing prediction methods need trained experts to analyse the data, which is a time-consuming process, and thus, there is a need for smart farming with the Internet of Things (IoT). Hence, a well-developed IoT-assisted smart agriculture model is proposed for managing agricultural needs to improve the economic value of farmers. The proposed framework constitutes four major aspects like Crop prediction, Crop yield prediction, Plant disease prediction and Smart irrigation. Initially, the crop images are taken as the input and fed to the Multi-scale Adaptive and Attention-based Convolution Neural Network with Atrous Spatial Pyramid Pooling (MACNN-ASPP), where the dissimilar crops are classified and obtained. In the second aspect, the crop image as well as crop-related data, are fetched from the data sources and given as input. Further, the deep features of the image are extracted that are added with soil and environment condition data. Then this feature is subjected to the Multi-scale Adaptive and Attention-based One-Dimensional Convolution Neural Network with Atrous Spatial Pyramid Pooling (MA1DCNN-ASPP) for crop yield prediction. While in the third aspect, the leaf images are assembled from the data file and fed as input to the MACNN-ASPP for detecting the variety of diseases affecting the plants. In the final aspect, smart irrigation is done by collecting field images with related data. Further, the deep features retrieved from the field images are applied to MA1DCNN-ASPP, where the different conditions of the field will be attained. In order to develop the model adaptively, the hyper-parameters in the network are optimised using the improved reptile search algorithm (IRSA). Finally, the investigation is done over the proposed methodology using multiple evaluation metrics. In contrast with other approaches, the proposed smart agriculture outperforms the performance o
This study searches the feasibility of a new hybrid extreme leaning machine tuned with improved reptile search algorithm (ELM-IRSA), in river flow modeling. The outcomes of the new method were compared with single ELM...
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This study searches the feasibility of a new hybrid extreme leaning machine tuned with improved reptile search algorithm (ELM-IRSA), in river flow modeling. The outcomes of the new method were compared with single ELM and hybrid ELM-based methods including ELM with salp swarm algorithm (SSA), ELM with equilibrium optimizer (EO) and ELM with reptilesearchalgorithm (RSA). The methods were evaluated using different lagged inputs of temperature, precipitation and river flow data obtained from Upper Indus Basin located in Pakistan. Models performance evaluation was based on common statistics such as root mean square errors (RMSE), mean absolute errors, determination coefficient and Nash-Sutcliffe Efficiency. The prediction accuracy of single ELM model with respect to RMSE was improved by 2.8%, 7.7%, 15% and 20.7% using SSA, EO, RSA and IRSA metaheuristic algorithms in the test period, respectively. The ELM-IRSA model with lagged temperature and river flow inputs provided the best predictions with the RMSE improvement of 20.7%.
The present article investigates the processing of increasing a hybrid photovoltaic-fuel cell system's performance considering an economic index. Further, by utilizing the Markov model, solutions to enhance the sy...
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The present article investigates the processing of increasing a hybrid photovoltaic-fuel cell system's performance considering an economic index. Further, by utilizing the Markov model, solutions to enhance the system's dependability are evaluated. A solar array, a fuel cell, a hydrogen storage tank, and an electrolyzer stack are embedded in the offered system. The study aims to develop an economic index that illustrates the system's excess cost in comparison to the required minimum investment to attain the highest degree of dependability. The system's dependability or the projected energy of the system is evaluated under the equipment's failure and repair rate and their likelihood of operating at the minimal ideal tank pressure. For enhancing the performance of the proposed process an improved version of reptilesearchalgorithm is utilized. The outcomes demonstrated that the management of the hydrogen contained in the tank ensures the system's capability and stability in producing energy continuously. Besides that, in the conditions of changing the capacity of the equipment (compared to the nominal capacity) in terms of reliability, the proposed system is desirable only for supplying sensitive and important loads. Moreover, the minimum pressure level of the tank had a very significant role in boosting the system's reliability. System equipment capacity's impact, such as the capacity of the solar array, fuel cell, and tank volume, on the economic index are examined. The great effectiveness of the suggested strategy is what led to the final outcomes.
A hard problem that hinders the movement of waxy crude oil is wax deposition in oil *** ensure the safe operation of crude oil pipelines,an accurate model must be developed to predict the rate of wax deposition in cru...
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A hard problem that hinders the movement of waxy crude oil is wax deposition in oil *** ensure the safe operation of crude oil pipelines,an accurate model must be developed to predict the rate of wax deposition in crude oil *** at the shortcomings of the ENN prediction model,which easily falls into the local minimum value and weak generalization ability in the implementation process,an optimized ENN prediction model based on the IRSA is *** validity of the new model was confirmed by the accurate prediction of two sets of experimental data on wax deposition in crude oil *** two groups of crude oil wax deposition rate case prediction results showed that the average absolute percentage errors of IRSA-ENN prediction models is 0.5476% and 0.7831%,***,it shows a higher prediction accuracy compared to the ENN prediction *** fact,the new model established by using the IRSA to optimize ENN can optimize the initial weights and thresholds in the prediction process,which can overcome the shortcomings of the ENN prediction model,such as weak generalization ability and tendency to fall into the local minimum value,so that it has the advantages of strong implementation and high prediction accuracy.
Cyber defense solutions that can adapt to new threats and learn to act independently of human guidance are necessary in light of the proliferation of so-called 'next-generation' cyberattacks. Multi-granularity...
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Cyber defense solutions that can adapt to new threats and learn to act independently of human guidance are necessary in light of the proliferation of so-called 'next-generation' cyberattacks. Multi-granularity feature aggregation is a method for detecting network intrusions, but its accuracy is often low due to class imbalance and various classifications of intrusions. To address this issue, this model employs a hybrid sampling algorithm composed of ADASYN and repeated edited nearest neighbors (RENN) for sample processing. The feature-discriminative ability of various assaults is improved by employing channel self-attention at the block level during classification. Finally, an enhanced reptilesearchalgorithm (IRSA) is proposed, which uses a sine cosine algorithm and Levy flight to optimally select the weight of the proposed model. The Levy factor boosts the exploitation capabilities of the search agents, and an algorithm with improved global search capabilities prevents local minimal entrapment by undertaking a full-scale search space. To learn binary and multiclass classification, the model was trained on the CIC-IDS 2017, UNSW-NB15, and WSN-DS datasets. Accuracy and falsehood are just some of the evaluation criteria used in the confusion matrix to determine the system's efficacy. Experimental consequences demonstrate a high detection rate, good accuracy, and a relatively low false alarm rate (FAR), validating the efficacy of the suggested approach. Following that, K4 achieved an accuracy score of 81.99, the precision-recall (PR) was 82.69, the detection rate (D.R.) was 82.12, the F1-score was 80.33, and the FAR was 2.3, all in that order.
Generating videos is a novel area of computer vision research that is still far from being addressed. The reason for the same being that videos are very complex in nature where both spatial and temporal coherence need...
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Generating videos is a novel area of computer vision research that is still far from being addressed. The reason for the same being that videos are very complex in nature where both spatial and temporal coherence needs to be taken care of. Compared to the unconditional video generation, an automated video generation from the text description is an even more difficult task, in which maintaining semantic consistency and visual quality are very crucial. The video generation from the text description seems to be non-trivial owing to the intrinsic complexity that occurs in the frames and video framework. The conditional generative models are required to be implemented for this challenging task of text-to-video generation. "Generative adversarial networks (GANs)" have had a lot of success in producing images conditioned over the natural language description. But, it is yet to be employed for producing realistic videos from text that are temporally and spatially coherent and semantically consistent with the text descriptions. Thus, a new Optimised Dual Discriminator Video Generative Adversarial Network (ODD-VGAN) for text-to-video generation is suggested in this paper. The hyper-parameters of ODD-VGAN are optimised using the improved reptile search algorithm (IRSA). The efficiency of the proposed approach is demonstrated by both qualitative and quantitative experimental results.
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