At present,the prediction of brain tumors is performed using Machine Learning(ML)and Deep Learning(DL)*** various ML and DL algorithms are adapted to predict brain tumors to some range,some concerns still need enhance...
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At present,the prediction of brain tumors is performed using Machine Learning(ML)and Deep Learning(DL)*** various ML and DL algorithms are adapted to predict brain tumors to some range,some concerns still need enhancement,particularly accuracy,sensitivity,false positive and false negative,to improve the brain tumor prediction system ***,this work proposed an Extended Deep Learning Algorithm(EDLA)to measure performance parameters such as accuracy,sensitivity,and false positive and false negative *** addition,these iterated measures were analyzed by comparing the EDLA method with the Convolutional Neural Network(CNN)way further using the SPSS tool,and respective graphical illustrations were *** results were that the mean performance measures for the proposed EDLA algorithm were calculated,and those measured were accuracy(97.665%),sensitivity(97.939%),false positive(3.012%),and false negative(3.182%)for ten *** in the case of the CNN,the algorithm means accuracy gained was 94.287%,mean sensitivity 95.612%,mean false positive 5.328%,and mean false negative 4.756%.These results show that the proposed EDLA method has outperformed existing algorithms,including CNN,and ensures symmetrically improved *** EDLA algorithm introduces novelty concerning its performance and particular activation *** proposed method will be utilized effectively in brain tumor detection in a precise and accurate *** algorithm would apply to brain tumor diagnosis and be involved in various medical diagnoses *** the quantity of dataset records is enormous,then themethod’s computation power has to be updated.
In this paper we formulate the problem of predicting the outcome (winner) of an ongoing National Basketball Association (NBA) match as a supervised machine learning problem. In this approach, as the match progresses, ...
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CsSnI3 is widely studied as an environmentally friendly Pb-free perovskite material for optoelectronic device applications. To further improve material and device performance, it is important to understand the surface...
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CsSnI3 is widely studied as an environmentally friendly Pb-free perovskite material for optoelectronic device applications. To further improve material and device performance, it is important to understand the surface structures of CsSnI3. We generate surface structures with various stoichiometries, perform density functional theory calculations to create phase diagrams of the CsSnI3 (001), (110), and (100) surfaces, and determine the most stable surfaces under a wide range of Cs, Sn, and I chemical potentials. Under I-rich conditions, surfaces with Cs vacancies are stable, which lead to partially occupied surface states above the valence band maximum. Under I-poor conditions, we find the stoichiometric (100) surface to be stable under a wide region of the phase diagram, which does not have any surface states and can contribute to long charge-carrier lifetimes. Consequently, the I-poor (Sn-rich) conditions will be more beneficial to improve the device performance.
Adversarial actions and a rapid climate change are disrupting operations of infrastructure networks (e.g., energy, water, and transportation systems). Unaddressed disruptions lead to system-wide shutdowns, emphasizing...
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This work develops real time model and associate control for a grid-tied battery energy storage system (BESS), based on the real industrial system specifications, 362kW/1499kWh lithium-ion BESS. An active and reactive...
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In the rapidly advancing era of self-driving cars, the imperative of ensuring robust cybersecurity measures to safeguard against evolving threats becomes paramount. This paper investigates the intricate cyber-physical...
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In this study,we employ advanced data-driven techniques to investigate the complex relationships between the yields of five major crops and various geographical and spatiotemporal features in *** analyze how these fea...
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In this study,we employ advanced data-driven techniques to investigate the complex relationships between the yields of five major crops and various geographical and spatiotemporal features in *** analyze how these features influence crop yields by utilizing remotely sensed *** methodology incorporates clustering algorithms and correlation matrix analysis to identify significant patterns and dependencies,offering a comprehensive understanding of the factors affecting agricultural productivity in *** optimize the model's performance and identify the optimal hyperparameters,we implemented a comprehensive grid search across four distinct machine learning regressors:Random Forest,Extreme Gradient Boosting(XGBoost),Categorical Boosting(CatBoost),and Light Gradient-Boosting Machine(LightGBM).Each regressor offers unique functionalities,enhancing our exploration of potential model *** top-performing models were selected based on evaluating multiple performance metrics,ensuring robust and accurate predictive *** results demonstrated that XGBoost and CatBoost perform better than the other *** introduce synthetic crop data generated using a Variational Auto Encoder to address the challenges posed by limited agricultural *** achieving high similarity scores with real-world data,our synthetic samples enhance model robustness,mitigate overfitting,and provide a viable solution for small dataset issues in *** approach distinguishes itself by creating a flexible model applicable to various crops *** integrating five crop datasets and generating high-quality synthetic data,we improve model performance,reduce overfitting,and enhance *** findings provide crucial insights for productivity drivers in key cropping systems,enabling robust recommendations and strengthening the decision-making capabilities of policymakers and farmers in datascarce regions.
Renewable energy generation is encouraging nowadays due to increasing demand day by day. At the commercial level, renewable energy generation is already in use. In this project, renewable energy generation is used on ...
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Industries are embracing information technology and constructing more robust machines known as Cyber-Physical Systems(CPS) to automate processes. CPSs are envisioned to be pervasive, coordinating, and integrating comp...
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Cavity electromagnonic system,which simultaneously consists of cavities for photons,magnons(quanta of spin waves),and acoustic phonons,provides an exciting platform to achieve coherent energy transduction among differ...
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Cavity electromagnonic system,which simultaneously consists of cavities for photons,magnons(quanta of spin waves),and acoustic phonons,provides an exciting platform to achieve coherent energy transduction among different physical systems down to single quantum *** we report a dynamical phase-field model that allows simulating the coupled dynamics of the electromagnetic waves,magnetization,and strain in 3D multiphase *** examples of application,we computationally demonstrate the excitation of hybrid magnon-photon modes(magnon polaritons),Floquet-induced magnonic Aulter-Townes splitting,dynamical energy exchange(Rabi oscillation)and relative phase control(Ramsey interference)between the two magnon polariton *** simulation results are consistent with analytical calculations based on Floquet Hamiltonian *** are also performed to design a cavity electro-magno-mechanical system that enables the triple phononmagnon-photon resonance,where the resonant excitation of a chiral,fundamental(n=1)transverse acoustic phonon mode by magnon polaritons is *** the capability to predict coupling strength,dissipation rates,and temporal evolution of photon/magnon/phonon mode profiles using fundamental materials parameters as the inputs,the present dynamical phase-fieldmodel represents a valuable computational tool to guide the fabrication of the cavity electromagnonic system and the design of operating conditions for applications in quantum sensing,transduction,and communication.
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