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ABSTRACTBrain tumour is a severe disease that may lead to death if unrecognized and untreated. Brain tumor identification and segmentation is a complex and task in medical image processing. For radiologists, diagnosing and classifying tumor from various images is a challenging process. When the data size is large, deep learning methods outperform conventional learning algorithms. Convolutional Neural Networks are found to be one of the popular deep learning architectures. We propose a deep network with an Efficient Regularized CNN with Dimensionality Reduction Module (ERCNN-DRM), which works with less training data and produces more precise classification with minimal processing time and regularisation. The images are pre-processed, segmented and then the dimension reduced features are extracted using the proposed algorithms and then the proposed regularized classification takes place. The experiment is conducted on TCIA dataset which contains a total of 696 MRI, 224 of which are benign and 472 of which are malignant. The proposed scheme produces accuracy rate of 96.7% and reduces the complexity by working on dimensional reduced data. Performance measures such as accuracy, recall, precision, F-measures are analysed and the system is found to be significant than other state-of-the art.

An equalization scheme employing convolutional neural networks (CNN) is proposed in generalized frequency division multiplexing (GFDM) for propagation in a hybrid microwave-optical system. Batch Gradient Descent is used to train the CNN equalizer quickly. The approach suggested eliminates nonlinearity and produces improved performance. By incorporating the GFDM network with successive interference cancellation (SIC)-based receiver, this solution greatly increases the storage ability of consumers and also offers a larger coverage range. The suggested CNN equalizer is correlated with other approaches, including statistical models and estimator techniques. The experimental finding shows that CNN produces the highest value that is marginally better than the other equalizers with a numerical difficulty reduced to zero. The simulation findings suggest that QAM-GFDM can attain the same bit error rate (BER) as cyclic-prefix (CP) OFDM without compromising spectrum economy. The tests obtained have shown that the new approach works much better than traditional techniques. [ABSTRACT FROM AUTHOR]

Tumor is the second major cause of death in women worldwide. Breast cancer diagnosis and treatment can be difficult for radiologists. As a result, primary care helps to avoid disease and mortality. The study's main goal is to improve treatment choices and to save lives by detecting breast cancer earlier. For classification problems, we propose a DNN-ASCC architecture in this study. The Fast Non-Local Means Filter completes the initial preprocessing stage. The binary grasshopper optimization algorithm (BGOA) and the grey-level run length matrix are utilized to choose the best features for the feature extraction operation. The suggested hybrid classifier (DNN-ASCCS) is critical for identifying normal and malignant tumors. Breast cancer is accurately detected by the suggested hybrid classifier. The recommended (DNN-ASCCS) was developed using MATLAB and datasets from the BIDCIDRI. The results of the simulation showed that the proposed technique has an accurate results in classification (99.17 percent) and robustness analysis is also done. When compared to alternative approaches, experimental results show that the suggested method is efficient. [ABSTRACT FROM AUTHOR]

High data rates, extremely low power consumption, and minimal end-to-end latency are considered to be mandatory requirements for 5G wireless networks. Rapid improvements in design and performance of 5G physical layer waveforms have become necessary. The drawback of Orthogonal Frequency Division Multiplexing (OFDM) is high PAPR, that causes signal distortion, which reduces system efficiency. Generalized frequency division multiplexing (GFDM) is a promising non-orthogonal multicarrier transmission scheme, which has recently received a great deal of attention towards future fifth generation (5G) wireless networks. It overcomes the limitations of orthogonal frequency division multiplexing (OFDM), while preserving most of the advantages of it. Selective Level mapping (SLM) is one of the PAPR reduction techniques, that uses the phase shift technology. In this paper, SLM based on Chaotic Biogeography Based Optimization (CBBO) algorithm is proposed to offer an efficient solution to the problem of high PAPR, existing in the GFDM waveforms. Experimental results prove that, the proposed CBBO–SLM technique provides significant improvement in terms of PAPR reduction, as compared to the conventional SLM methods, such as conventional GFDM and OFDM-SLM. The proposed novel scheme is most suitable for QAM and QPSK applications, as it provides good PAPR reduction performance, at lower computational complexity. [ABSTRACT FROM AUTHOR]

Electricity is the most critical facility for humans. All traditional energy supplies are rapidly depleting. As a result, the energy resources are moved from traditional to non-conventional. In this research, mixture of two energy tools, namely wind and solar energy are used. Using a Hybrid Energy Storage System (HESS), continuous power can be provided. Electricity can be produced at a cost that is affordable. The integration of solar and wind in a hybrid system cause an increase in the system's stability, which is the key benefit of this research. The system's power transmission efficiency and reliability can be greatly enhanced by integrating these two intermittent sources. When one of the energy source is unavailable or inadequate to meet load demands, the other energy source will supply the power. The major contribution in this research is that, the proposed bidirectional single-inductor multiple-port (BSIMP) converter significantly lowers the component count, smaller circuit size and lower cost, allowing HESS to be integrated into DC microgrid. Minimum number of components are used for the same number of ESs in HESS in the proposed BSIMP converter. The hybridization of battery and supercapacitor (SC) for storage purpose is more cost effective, as compared to the battery energy storage system, thus improving the battery stress and hence used for large scale grid energy storage. SC's are accepted as backup and found very useful in delivering high power, not possible with batteries. The use of SC in addition to batteries can be one solution for achieving the low life cycle economy. The Single Objective Adaptive Firefly Algorithm (SOAFA) is introduced for optimising the Proportional-Integral (PI) controller parameters. The system cost is reduced by about 32%, with the constraints on wind turbine swept area, PV area, total battery and SC capacity with the proposed optimisation algorithm. [ABSTRACT FROM AUTHOR]