Decreasing arable acreage and a growing world population are pushing for new farming systems. Achieving high crop production requires maintaining a balanced nutrient level in the soil. This study offers a machine lear...
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This paper, a hybrid method, is proposed for protecting the hybrid photovoltaic (PV) and wind turbine (WT) system. The proposed protecting method is the hybrid wrapper of both the multiple support vector machine (MSVM...
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Demand for nickel-based superalloys has increased significantly in the automotive industry because of their great potential to reduce the weight of components and improve efficiency. The present study aims to improve ...
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Capacitive pressure sensors (CPSs) have attracted considerable interest due to their high sensitivity, low energy consumption, and potential for miniaturization, making them suitable for applications in automotive sys...
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The Internet of Things (IoT), which enables seamless connectivity and effective data exchange between physical items and digital systems, has completely changed the way we interact with our surroundings. This study ev...
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Heartbeat detection stays central to cardiovascular an electrocardiogram(ECG)is used to help with disease diagnosis and *** Convolutional Neural Network(CNN)-based methods suffer from the less generalization problem t...
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Heartbeat detection stays central to cardiovascular an electrocardiogram(ECG)is used to help with disease diagnosis and *** Convolutional Neural Network(CNN)-based methods suffer from the less generalization problem thus;the effectiveness and robustness of the traditional heartbeat detector methods cannot be *** contrast,this work proposes a heartbeat detector Krill based Deep Neural Network Stacked Auto Encoders(KDNN-SAE)that computes the disease before the exact heart rate by combining features from multiple ECG *** are classified independently and multiple signals are fused to estimate life threatening conditions earlier without any error in classification of heart *** work contained Training and testing stages,in the preparation part at first the Adaptive Filter Enthalpy-based Empirical Mode Decomposition(EMD)is utilized to eliminate the motion artifact in the *** that point,the robotic process automation(RPA)learning part extracts the effective features are extracted,and normalized the value of the feature then estimated utilizing the RPA loss *** last KDNN-SAE prepared training for the data stored in the *** the subsequent stage,input signal compute motion artifact and RPA Learning the evaluation part determines the detection of *** early diagnosis of heart failures is an essential *** results of the experiments show that our proposed method has a high score outcome of *** to the CIF,which reaches *** CNN and Artificial Neural Network(ANN)had less score 0.95115 and 0.90147.
This research allows the secure surveillance approach for the Internet of Things (IoT) methodology to be developed by integrating wireless signalling and image encryption strategy. Since the Cloud Service Telco (CST) ...
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In recent decades,several optimization algorithms have been developed for selecting the most energy efficient clusters in order to save power during trans-mission to a shorter distance while restricting the Primary Us...
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In recent decades,several optimization algorithms have been developed for selecting the most energy efficient clusters in order to save power during trans-mission to a shorter distance while restricting the Primary Users(PUs)*** Cognitive Radio(CR)system is based on the Adaptive Swarm Distributed Intelligent based Clustering algorithm(ASDIC)that shows better spectrum sensing among group of multiusers in terms of sensing error,power sav-ing,and convergence *** this research paper,the proposed ASDIC algorithm develops better energy efficient distributed cluster based sensing with the optimal number of clusters on their *** this research,multiple random Sec-ondary Users(SUs),and PUs are considered for ***,the pro-posed ASDIC algorithm improved the convergence speed by combining the multi-users clustered communication compared to the existing optimization *** results showed that the proposed ASDIC algorithm reduced the node power of 9.646%compared to the existing ***,ASDIC algorithm reduced 24.23%of SUs average node power compared to the existing *** of detection is higher by reducing the Signal-to-Noise Ratio(SNR)to 2 dB *** proposed ASDIC delivers low false alarm rate compared to other existing optimization algorithms in the primary *** results showed that the proposed ASDIC algorithm effectively solves the multimodal optimization problems and maximizes the performance of net-work capacity.
The dynamic pricing environment offers flexibility to the consumers to reschedule their switching *** the dynamic pricing environment results in several benefits to the utilities and consumers,it also poses some *** c...
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The dynamic pricing environment offers flexibility to the consumers to reschedule their switching *** the dynamic pricing environment results in several benefits to the utilities and consumers,it also poses some *** crowding among residential customers is one of such *** scheduling of loads at low-cost intervals causes crowding among residential customers,which leads to a fall in voltage of the distribution system below its prescribed *** order to prevent crowding phenomena,this paper proposes a priority-based demand response program for local energy *** the program,past contributions made by residential houses and demand are considered as essential parameters while calculating the priority *** non-linear programming(NLP)model proposed in this study seeks to reschedule loads at low-cost intervals to alleviate crowding *** the NLP model does not guarantee global optima due to its non-convex nature,a second-order cone programming model is proposed,which captures power flow characteristics and guarantees global *** proposed formulation is solved using General Algebraic Modeling System(GAMS)software and is tested on a 12.66 kV IEEE 33-bus distribution system,which demonstrates its applicability and efficacy.
Images obtained from hyperspectral sensors provide information about the target area that extends beyond the visible portions of the electromagnetic ***,due to sensor limitations and imperfections during the image acq...
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Images obtained from hyperspectral sensors provide information about the target area that extends beyond the visible portions of the electromagnetic ***,due to sensor limitations and imperfections during the image acquisition and transmission phases,noise is introduced into the acquired image,which can have a negative impact on downstream analyses such as classification,target tracking,and spectral *** in hyperspectral images(HSI)is modelled as a combination from several sources,including Gaussian/impulse noise,stripes,and *** HSI restoration method for such a mixed noise model is ***,a joint optimisation framework is proposed for recovering hyperspectral data corrupted by mixed Gaussian-impulse noise by estimating both the clean data as well as the sparse/impulse noise ***,a hyper-Laplacian prior is used along both the spatial and spectral dimensions to express sparsity in clean image ***,to model the sparse nature of impulse noise,anℓ_(1)−norm over the impulse noise gradient is *** the proposed methodology employs two distinct priors,the authors refer to it as the hyperspectral dual prior(HySpDualP)*** the best of authors'knowledge,this joint optimisation framework is the first attempt in this *** handle the non-smooth and nonconvex nature of the generalℓ_(p)−norm-based regularisation term,a generalised shrinkage/thresholding(GST)solver is ***,an efficient split-Bregman approach is used to solve the resulting optimisation *** results on synthetic data and real HSI datacube obtained from hyperspectral sensors demonstrate that the authors’proposed model outperforms state-of-the-art methods,both visually and in terms of various image quality assessment metrics.
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