In this research, nature inspired metaheuristic optimization algorithms: Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) Techniques are formulated to tune optimal combinations of PID controller parameters...
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One of the indispensable assets of the electrical systems and its most expensive components are power transformers. Condition assessment of power transformers is crucial and must be performed precisely to detect any a...
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The widespread adoption of solar photovoltaic (PV) technology as a prominent renewable energy source has significant implications for the economy of households and distribution system operators (DSOs). It is crucial t...
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This article is concerned with sampled-data synchronization problem of heterogeneous delays inertial neural networks (INNs) with generally uncertain semi-Markovian (GUSM) jumping. Different from traditional Markovian ...
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Occupancy detection is one of the key elements in improving the energy performance of buildings. Due to their nature, occupancy detection models could be trained on old building data and adapted to new buildings for f...
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The distance to obstacles is crucial for safe driving and parking. Currently, cars are equipped with sensors that measure the distance to obstacles, but the distance is only displayed using different colors and sounds...
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The received signal strength (RSS) fingerprint-based technique is extensively utilized for indoor localization, as it does not require time synchronization. However, conventional RSS fingerprint localization schemes r...
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Propensity score matching (PSM) is a method which is recommended to use at the case that data sets are not comparable. There exists several approaches at the definition of propensity score. We focused on two of them: ...
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The Internet of Things (IoT) has transformed device communication, necessitating reliable information security, especially physical layer security (PLS). Protecting data confidentiality in wireless transfers is critic...
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Recently,deep learning(DL)became one of the essential tools in bioinformatics.A modified convolutional neural network(CNN)is employed in this paper for building an integratedmodel for deoxyribonucleic acid(DNA)*** any...
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Recently,deep learning(DL)became one of the essential tools in bioinformatics.A modified convolutional neural network(CNN)is employed in this paper for building an integratedmodel for deoxyribonucleic acid(DNA)*** any CNN model,convolutional layers are used to extract features followed by max-pooling layers to reduce the dimensionality of features.A novel method based on downsampling and CNNs is introduced for feature *** downsampling is an improved form of the existing pooling layer to obtain better classification *** two-dimensional discrete transform(2D DT)and two-dimensional random projection(2D RP)methods are applied for *** convert the high-dimensional data to low-dimensional data and transform the data to the most significant feature ***,there are parameters which directly affect how a CNN model is *** this paper,some issues concerned with the training of CNNs have been *** CNNs are examined by changing some hyperparameters such as the learning rate,size of minibatch,and the number of *** and assessment of the performance of CNNs are carried out on 16S rRNA bacterial *** results indicate that the utilization of a CNN based on wavelet subsampling yields the best trade-off between processing time and accuracy with a learning rate equal to 0.0001,a size of minibatch equal to 64,and a number of epochs equal to 20.
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