Adaptive PAPR Reduction Scheme for OFDM Using SLM with the Fusion of Proposed Clipping and Filtering Technique in Order to Diminish PAPR and Signal Distortion

One of the main disadvantages of multicarrier transmission is the high peak-to-average power ratio (PAPR) of the transmitted signal. If the highest transmitted power is confined by the application restrictions or regulatory, the result is to decrease the average power permitted under multicarrier transmission. Selected mapping (SLM) is a standard PAPR reduction scheme that is appropriate for orthogonal frequency division multiplexing (OFDM) scheme as it realizes the performance of PAPR reduction without signal distortion. This paper proposes a method in order to handle the difficulties of high PAPR in OFDM scheme. The offered system is an arrangement of two distinguished methods, such as clipping and SLM. Compared to other hybrid methods, where the individual methods are implemented sequentially, this paper integrates the clipping method in the SLM procedure. This produces a supplementary PAPR reduction associated to the serial arrangement. Simulation results specify that the offered scheme acquires the performance of appropriate PAPR reduction with low computational complexity. The PAPR reduction at different number of subcarriers is analyzed and compared with the existing research work. The performance of relative energy efficiency has also been focused on this paper.

     

Dementia classification using MR imaging and clinical data with voting based machine learning models

Dementia is one of the leading causes of severe cognitive decline, it induces memory loss and impairs the daily life of millions of people worldwide. In this work, we consider the classification of dementia using magnetic resonance (MR) imaging and clinical data with machine learning models. We adapt univariate feature selection in the MR data pre-processing step as a filter-based feature selection. Bagged decision trees are also implemented to estimate the important features for achieving good classification accuracy. Several ensemble learning-based machine learning approaches, namely gradient boosting (GB), extreme gradient boost (XGB), voting-based, and random forest (RF) classifiers, are considered for the diagnosis of dementia. Moreover, we propose voting-based classifiers that train on an ensemble of numerous basic machine learning models, such as the extra trees classifier, RF, GB, and XGB. The implementation of a voting-based approach is one of the important contributions, and the performance of different classifiers are evaluated in terms of precision, accuracy, recall, and F1 score. Moreover, the receiver operating characteristic curve (ROC) and area under the ROC curve (AUC) are used as metrics for comparing these classifiers. Experimental results show that the voting-based classifiers often perform better compared to the RF, GB, and XGB in terms of precision, recall, and accuracy, thereby indicating the promise of differentiating dementia from imaging and clinical data.

     

Diagnosis of breast cancer based on modern mammography using hybrid transfer learning

Breast cancer is a common cancer in women. Early detection of breast cancer in particular and cancer, in general, can considerably increase the survival rate of women, and it can be much more effective. This paper mainly focuses on the transfer learning process to detect breast cancer. Modified VGG (MVGG) is proposed and implemented on datasets of 2D and 3D images of mammograms. Experimental results showed that the proposed hybrid transfer learning model (a fusion of MVGG and ImageNet) provides an accuracy of 94.3%. On the other hand, only the proposed MVGG architecture provides an accuracy of 89.8%. So, it is precisely stated that the proposed hybrid pre-trained network outperforms other compared Convolutional Neural Networks. The proposed architecture can be considered as an effective tool for radiologists to decrease the false negative and false positive rates. Therefore, the efficiency of mammography analysis will be improved.