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The set of vertices g = {1,3,4} in S all vertices are cover.
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In this study, from a tree with a quasi-spanning face, the algorithm will route Hamiltonian cycles. Goodey pioneered the idea of holding facing 4 to 6 sides of a graph concurrently. Similarly, in the three connected cubic planar graphs with two-colored faces, the vertex is incident to one blue and two red faces. As a result, all red-colored faces m...
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This paper considers the following three Roman domination graph invariants on
Kneser graphs:
Roman domination, total Roman domination, and signed Roman domination.
For Kneser graph $K_{n,k}$, we present exact values for Roman domination
number $\gamma_{R}(K_{n,k})$ and total Roman domination number
$\gamma_{tR}(K_{n,k})$ proving that for $n\geqslan...
Citations
... Compared to the general population, epilepsy seizure detection which can be used in clinical purposes. Since 2016, significant research has been conducted in the subject of detecting epilepsy with deep learning models such as convolutional neural networks (CNNs), recurrent neural networks (RNNs) [12], autoencoders (AEs), CNN-RNN and CNN-AE, etc. [13][14][15][16][17]. The hard-core feature extraction for epileptic seizure detection using machine learning techniques can be eliminated by deep learning. ...
Epilepsy is one of the most usual neurological diseases characterized by abnormal brain activity, resulting in seizures or strange behavior, sensations, and, in some cases, loss of consciousness. It is a persistent, non-communicable brain condition that can affect anyone at any age, nearly 50 million people globally, with about 80% of sufferers living in low- and middle-income countries. Electroencephalography (EEG) signals are largely used in epilepsy research to examine brain activity during seizures. The extraction of features and selection from EEG signals plays a major role in epileptic seizure detection. In traditional machine learning techniques, the hard-core feature extraction needs domain expertise, and this can be eliminated by deep learning. The benefits of deep learning techniques are they try to learn high-level features from the input signals in an incremental method. To meet the requirements of complicated feature engineering, deep learning techniques have received greater attention than conventional methods. A hybrid seizure detection-convolutional neural network and vector machine (SD-CNN and SVM) model is proposed for epileptic seizure detection with EEG signals. Transformation of signal to image is performed using continuous wavelet transform technique to generate scaleogram images and also SD-CNN works as a learnable feature extractor from the generated images and SVM works as a binary classifier. The experimental results extracted 94% with high quality of scaleogram images using hybrid SD-CNN and SVM model and removed the noise levels and time–frequency data from EEG signals.
... It provides a low signal-to-noise ratio without the channel estimation as well as the frequency and time synchronization process [T. [21]]. ...
... The MMSE recipient is utilized to diminish the average estimation error that occurs in the transmitted symbol. The capacity of the error estimation is communicated using the constraint (21). ...
LTE (Long-Term Evolution) is one of the appropriate technologies to sever the requirement of the IoT (Internet of Things) together with mobile UE (User Equipment), because of priority services, high data rate, low latency, etc. LTE has received OFDM for the transmission because it meets the necessity of LTE-like range flexibility and empowers the cost gainful responses for the wide carriers. LTE offers big-data values, allows spectrum reframing, reduces cost, etc. We proposed a 4G/5G adopted OFDM based on the multi-equalizer and BPSK (Binary-Phase Shift Keying). The BER (Bit Error Rate) of the system is reduced through the optimal wavelet coefficient at the transmitter side. This optimal wavelet coefficient is obtained by applying a DWT (Discrete Wavelet Transform) to the input signal through a BFF (Binary Firefly) algorithm. The serial to parallel process is carried out through the BPSK modulation. At last, the multi-equalizer like MMSE (Minimized Mean Square Error) and MLE (Maximum Likelihood) is applied to the OFDM model to reduce the BER. The system is analyzed using the parameter like SER (Symbol Error Rate), spectral efficiency, MSE (Mean Square Error), and BER. The proposed methodology provides a better outcome than the other existing approaches.
In today’s world storing a large amount of data, large datasets, handling data in various forms is a challenging task. Data is getting produced rapidly with major small sized files. Hadoop is the solution for the big data problem except few limitations. This method is suggested to provide a better one for small file sizes in terms of storage, access effectiveness, and time. In contrast to the current methods, such as HDFS sequence files, HAR, and NHAR, a revolutionary strategy called VFS-HDFS architecture is created with the goal of optimizing small-sized files access problems. The existing HDFS architecture has been wrapped with a virtual file system layer in the proposed development. However, the research is done without changing the HFDS architecture. Using this proposed system, better results are obtained in terms of access efficiency of small sized files in HDFS. A case study is performed on the British Library datasets on .txt and .rtf files. The proposed system can be used to enhance the library if the catalogue is categorized as per their category in a container reducing the storage, improving the access efficiency at the cost of memory.
