Background: Atherosclerotic cardiovascular disease (CVD) is severe and early-stage detection is crucial. Elevated arterial stiffness observed in childhood atherosclerosis is associated with CVD. Stiffness is an efficient marker of CVD in hypertensives.Assessment of stiffness includes waveform analysis and image-based techniques. Researchers observed several challenges: realtime application, accuracy, operator variability, image quality, scanning procedure, instrument variability and deficiency of standardized procedure in the assessment. Methods: We searched PubMed, Embase and Cochrane online library from inception up to July 2020. Multiple articles on stiffness, pulse wave velocity, assessment and deep learning (DL)-based methods were analysed. Above all, a DL-based technique for assessment of stiffness from cine-loop is proposed. The method includes region of interest (ROI) localisation in multiple frames, segmentation of lumen and parameter estimation. Results: Compared to conventional methods DL provide improved result in lumen diameter and intima-media thickness (IMT) measurements. Using convolutional neural network (CNN), IMT error was 0.08 mm. Further, error using extreme learning machine-autoencoder was 5.79±34.42 \mum. Furthermore, Jaccard index and Dice similarity in fully convolution neural network (FCN) manifested 0.94 and 0.97 for lumen segmentation respectively. Conclusion: This paper focuses on the association of stiffness and atherosclerosis leading to CVD. Success of image-based stiffness estimation depends on the visibility and orientation of arteries, operator experience, intensity variation, shadowing, artefacts, and noise. Traditional methods include transformations to compensate for these challenges. The success of DL-based techniques in segmentation and localisation inspired application in stiffness measurement. DL is used to estimate stiffness from cine-loop.

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Intracerebral haemorrhage (ICH) is defined as bleeding occurs in the brain and causes vascular abnormality, tumor, venous Infarction, therapeutic anticoagulation, trauma property, and cerebral aneurysm. It is a dangerous disease and increases high mortality rate within the age of 15 to 24. It may be cured by finding what type of ICH is affected in the brain within short period with more accuracy. The previous method did not provide adequate accuracy and increase the computational time. Therefore, in this manuscript Detection and Categorization of Acute Intracranial Hemorrhage (ICH) subtypes using a Multi-Layer DenseNet-ResNet Architecture with Improved Random Forest Classifier (IRF) is proposed to detect the subtypes of ICH with high accuracy, less computational time with maximal speed. Here, the brain CT images are collected from Physionet repository publicly dataset. Then the images are pre-processed to eliminate the noises. After that, the image features are extracted by using multi layer Densely Connected Convolutional Network (DenseNet) combined with Residual Network (ResNet) architecture with multiple Convolutional layers. The sub types of ICH (Epidural Hemorrhage (EDH), Subarachnoid Hemorrhage (SAH), Intracerebral Hemorrhage (ICH), Subdural Hemorrhage (SDH), Intraventricular Hemorrhage (IVH), normal is classified by using Improved Random Forest (IRF) Classifier with high accuracy. The simulation is activated in MATLAB platform. The proposed Multilayer-DenseNet-ResNet-IRF approach attains higher accuracy 23.44%, 31.93%, 42.83%, 41.9% compared with existing approaches, like Detection with classification of intracranial haemorrhage on CT images utilizing new deep-learning algorithm (ICH-DC-CNN), Detection with classification of intracranial haemorrhage on CT images utilizing new deep-learning algorithm (ICH-DC-CNN-ResNet-50), Shallow 3D CNN for detecting acute brain hemorrhage from medical imaging sensors (ICH-DC-S-3D-CNN), Convolutional neural network: a review of models, methods and applications to object detection (ICH-DC-CNN-AlexNet) respectively.

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Cognitive radio (CR) is a smart innovation in wireless industry, to resolve the issues of spectrum scarcity and underutilization. In this article, detection performance of majority rule is improved further by modifying the minimum number of justifiable local decisions required. A weight based improved hard fusion technique is proposed to enhance the detection performance. A weight factor is provided to the sensed results of the secondary users (SUs) based on their position from primary user (PU) and signal‐to‐noise ratio (SNR) value. A pipelined frame structure is proposed for combining sensing and reporting durations of users, nearer to PU. This increases duration of data transmission. Finally, a relay assistance method is proposed. Users with low residual energy are provided relay assistance with relay users (RUs), to prevent from running out of energy. With improved Majority fusion, 86.93% increase in detection is obtained at SNR of (−10 to −1) dB for false alarm of 0.2. There is about 24.30%, 38.10%, and 79.56% increase in detection for weight based AND, OR, and majority fusion respectively at false alarm of 0.2. In addition to detection performance, energy consumption, and throughput analysis of the proposed methods are performed to determine the achieved energy efficiency (EE).