The proliferation of IoT devices necessitates secure and efficient mechanisms for data encryption and retrieval. This paper presents an optimized framework that leverages AES encryption integrated with memory modules ...
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Emergency response systems, water treatment facilities, wastewater collection systems, Oil and gas pipelines, electrical power transmission systems, wind farms, defence networks, and large-scale communication networks...
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Time difference of arrival (TDOA) positioning results obtained using commonly adopted algebraic methods lack uncertainty information. In this letter, we propose to incorporate interval computation into TDOA-based hype...
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A state observer was designed to estimate the state variables required to control the speed, position, and current of the rotor shaft of a synchronous motor system with a permanent magnet rotor. When designing a state...
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The cleanliness situation related to garbage management is rapidly declining, and it is essential to implement proper garbage management techniques to address the widespread garbage crisis, particularly in the Urban E...
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Device-to-device communication is a noteworthy technology that can improve the efficiency and capacity of cellular networks. Efficient resource allocation is crucial in a cellular network that supports both device-to-...
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Heart disease remains a leading cause of mortality worldwide, necessitating accurate and reliable predictive models to aid early diagnosis and treatment. Traditional machine learning methods like LR and DT Classifiers...
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This project looks into the possibility of applying machine learning to optimize wireless networks for adaptive communication. Using 5G resource data, it applies preprocessing, exploratory analysis, and visualization ...
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Using groundnut shell ash (GSA) as a component in concrete mixtures is a viable approach to achieving sustainability in building practices. This particular kind of concrete has the potential to effectively mitigate th...
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Using groundnut shell ash (GSA) as a component in concrete mixtures is a viable approach to achieving sustainability in building practices. This particular kind of concrete has the potential to effectively mitigate the issues associated with high levels of CO2 emissions and embodied energy, which are primarily attributed to the excessive utilization of cement in conventional construction materials. When GSA is utilized as a partial replacement for cement, the strength characteristics of concrete are influenced not only by the quantity of GSA replacement but also by several other factors, including cement content, water-to-cement ratio, coarse aggregate content, fine aggregate content, and curing length. This work demonstrates a predictive model for the compressive strength (CS) of GSA mixed concrete using ML methods. The models were constructed with 297 datasets obtained from published literature. These datasets included various input variables such as cement content, GSA content, fine aggregate content, coarse aggregate content, water need, and curing duration. The output variable included in the models was the CS of concrete. In this study, a set of seven machine learning algorithms was utilized as statistical assessment tools to identify the most precise and reliable model for predicting the CS of GSA mixed concrete. These techniques included linear regression, full quadratic model, artificial neural network, boosted decision tree regression, random forest regression, K nearest neighbors, and support vector regression. The present study evaluated several machine learning models, and it was shown that the random forest regression model had superior performance in forecasting the CS of GSA mixed concrete. The train data’s R 2 is 0.91, with RMSE of 2.48 MPa. Similarly, for the test data, the R 2 value is 0.89, with an RMSE of 2.42 MPa. The sensitivity analysis results of the random forest regression model indicate that the cement content primarily drives the materia
Brain medical image classification is an essential procedure in computer-Aided Diagnosis(CAD)*** methods depend specifically on the local or global *** fusion methods have also been developed,most of which are problem...
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Brain medical image classification is an essential procedure in computer-Aided Diagnosis(CAD)*** methods depend specifically on the local or global *** fusion methods have also been developed,most of which are problem-distinct and have shown to be highly favorable in medical ***,intensity-specific images are not *** recent deep learning methods ensure an efficient means to design an end-to-end model that produces final classification accuracy with brain medical images,compromising *** solve these classification problems,in this paper,Histogram and Time-frequency Differential Deep(HTF-DD)method for medical image classification using Brain Magnetic Resonance Image(MRI)is *** construction of the proposed method involves the following ***,a deep Convolutional Neural Network(CNN)is trained as a pooled feature mapping in a supervised manner and the result that it obtains are standardized intensified pre-processed features for ***,a set of time-frequency features are extracted based on time signal and frequency signal of medical images to obtain time-frequency ***,an efficient model that is based on Differential Deep Learning is designed for obtaining different *** proposed model is evaluated using National Biomedical Imaging Archive(NBIA)images and validation of computational time,computational overhead and classification accuracy for varied Brain MRI has been done.
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