Reconfigurable Intelligent Surfaces(RIS)have emerged as a promising technology for improving the reliability of massive MIMO communication ***,conventional RIS suffer from poor Spectral Efficiency(SE)and high energy c...
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Reconfigurable Intelligent Surfaces(RIS)have emerged as a promising technology for improving the reliability of massive MIMO communication ***,conventional RIS suffer from poor Spectral Efficiency(SE)and high energy consumption,leading to complex Hybrid Precoding(HP)*** address these issues,we propose a new low-complexity HP model,named Dynamic Hybrid Relay Reflecting RIS based Hybrid Precoding(DHRR-RIS-HP).Our approach combines active and passive elements to cancel out the downsides of both conventional *** first design a DHRR-RIS and optimize the pilot and Channel State Information(CSI)estimation using an adaptive threshold method and Adaptive Back Propagation Neural Network(ABPNN)algorithm,respectively,to reduce the Bit Error Rate(BER)and energy *** optimize the data stream,we cluster them into private and public streams using Enhanced Fuzzy C-Means(EFCM)algorithm,and schedule them based on priority and emergency *** maximize the sum rate and SE,we perform digital precoder optimization at the Base Station(BS)side using deep Deterministic Policy Gradient(DDPG)algorithm and analog precoder optimization at the DHRR-RIS using Fire Hawk Optimization(FHO)*** implement our proposed work using MATLAB R2020a and compare it with existing works using several validation *** results show that our proposed work outperforms existing works in terms of SE,Weighted Sum Rate(WSR),and BER.
The characterization of drug -metabolizing enzymes is a significant problem for customized therapy. It is important to choose the right drugs for cancer victims, and the ability to forecast how those drugs will react ...
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The characterization of drug -metabolizing enzymes is a significant problem for customized therapy. It is important to choose the right drugs for cancer victims, and the ability to forecast how those drugs will react is usually based on the available information, genetic sequence, and structural properties. To the finest of our knowledge, this is the first study to evaluate optimization algorithms for selection of features and pharmaco-genetics categorization using classification methods based on a successful evolutionary algorithm using datasets from the Cancer Cell Line Encyclopaedia (CCLE) and Genomics of Drug Sensitivity in Cancer (GDSC). The study proposes the uses of Firefly and Grey Wolf Optimization techniques for feature extraction, while comparing the traditional machinelearning (ML), ensemble ML and Stacking Algorithm with the proposed Convolutional Temporal deep Neural Network or CTDN. With the potential to increase efficiency from the suggested intelligible classifier model for a suggestive chemotherapeutic drugs response prediction, our study is important in particular for selecting an acceptable feature selection method. The comparison analysis demonstrates that the proposed model not only surpasses the prior state-of-the-art methods, but also uses Grey Wolf and Fire Fly Optimization to lessen multicollinearity and overfitting.
The regional energy demand for Southern Africa has been predicted to increase by ten to fourteen times between the years 2010 and 2070. Thus, to address the proliferation of energy demand, South Africa's integrate...
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The regional energy demand for Southern Africa has been predicted to increase by ten to fourteen times between the years 2010 and 2070. Thus, to address the proliferation of energy demand, South Africa's integrated resource plan, which includes using renewable energy sources to increase the electricity supply and reduce the country's carbon footprint, has been formulated. However, integrating renewable power into the power grid brings different dynamics for the system operators, as renewable power sources are variable and uncertain. Thus, accurate demand and generation forecasting become critical to the safe operation and ensuring continuity of supply, as consumers require. Due to the complexity of the earth's atmosphere, weather forecasting uncertainty, and region-specific criteria, traditional forecasting models are limited. Thus, machinelearning, deeplearning, and other artificial intelligence techniques are attractive possibilities for improving classical forecasting models. This study comprehensively reviewed relevant works on AI-based models for generation potential and load demand forecasting toward intelligent energy resource management and planning. The approach involved searching research databases and other sources for studies, reports, and publications on location-specific energy resource management using criteria such as demography, policy, and sociotechnical information. Consequently, the review study has highlighted how AI predictive analytics can enhance long-term energy resource potential and load forecasting toward improving electricity sector performance and promoting integrated energy system management implementation in South Africa.
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