Aortic valve repair in patients with ventricular septal defect or subaortic membrane

Background: The delay in the surgical intervention of subaortic ventricular septal defect (VSD) and subaortic membrane leads to significant damage in the aortic valve, and multiple surgical interventions may be needed. We aimed to describe the pathology of the aortic valve in patients with subaortic membrane or VSD and different surgical strategies to manage the aortic regurgitation in those patients. Methods: The study included patients who had surgery for subaortic membrane or VSD from 2017 to 2021. We reviewed strategies and surgical techniques to deal with aortic regurgitation in patients with subaortic membrane or VSD and the short and midterm outcomes. Results: Twelve cases were included in the study; 5 cases had subaortic membrane, and 7 cases had subaortic VSD. The age ranged from 1.5 to 10 years old. Postoperative follow-up ranged from 1 to 3.5 years. We performed sub-commissural stitches and peeling of the leaflets to correct residual regurgitation. Four patients with subaortic membrane achieved satisfactory outcomes, and one patient had severe aortic regurgitation. Two patients with VSD had progression of the aortic regurgitation. Patients with failed repair had severe prolapse and thickening of the valve. Conclusion: Severe prolapse and dense thickening of the valve were difficult pathologies to repair. The sub-commissural stitches could be mandatory to achieve good midterm results. Complete freeing and peeling of the leaflets till restoring the natural appearance is crucial

A New Approach using Deep Learning and Reinforcement Learning in HealthCare: Skin Cancer Classification

Nowadays, skin cancer is one of the most important problems faced by the world, due especially to the rapid development of skin cells and excessive exposure to UV rays. Therefore, early detection at an early stage employing advanced automated systems based on AI algorithms plays a major job in order to effectively identifying and detecting the disease, reducing patient health and financial burdens, and stopping its spread in the skin. In this context, several early skin cancer detection approaches and models have been presented throughout the last few decades to improve the rate of skin cancer detection using dermoscopic images. This work proposed a model that can help dermatologists to know and detect skin cancer in just a few seconds. This model combined the merits of two major artificial intelligence algorithms: Deep Learning and Reinforcement Learning following the great success we achieved in the classification and recognition of images and especially in the medical sector. This research included four main steps. Firstly, the pre-processing techniques were applied to improve the accuracy, quality, and consistency of a dataset. The input dermoscopic images were obtained from the HAM10000 database. Then, the watershed algorithm was used for the segmentation process performed to extract the affected area. After that, the deep convolutional neural network (CNN) was utilized to classify the skin cancer into seven types: actinic keratosis, basal cell carcinoma, benign keratosis, dermatofibroma melanocytic nevi, melanoma vascular skin lesions. Finally, in regards to the reinforcement learning part, the Deep Q_Learning algorithm was utilized to train and retrain our model until we found the best result. The accuracy metric was utilized to evaluate the efficacy and performance of the proposed method, which achieved a high accuracy of 80%. Furthermore, the experimental results demonstrate how reinforcement learning can be effectively combined with deep learning for skin cancer classification tasks

Image malware detection using deep learning

We are currently living in an area where artificial intelligence is making out every day to day life much easier to manage. Some researchers are continuously developing the codes of artificial intelligence to utilize the benefits of the human being. And there is the process called data mining, which is used in many domains, including finance, engineering, biomedicine, and cyber security. The utilization of data mining, artificial intelligence algorithms like deep learning is so vast that we can't even name them all. This technology has almost touched every industry and cyber security is the most beneficial. The process of enhancing cyber security with the help of deep learning methods has come out of the theory books and many organizations are utilizing them rather than using a traditional piece of software to defend against online threats. Especially in the field of recognizing and classifying codes or malware. And this is essential, because, with the advent of cloud computing and the Internet of Things, expand potential malware infection sites from PCs to any electronic device. This makes our day to day life very unsafe. In this post, first, we will describe in brief how deep learning can be the most useful and promising techniques to detect malware. Besides this we will go through a deep neural network,ResNet for malware dynamic behavior classification jobs

Improving the quality of H.264/AVC by using a new Rate-Quantization model

International audienceRate control plays a key role in video coding standards. Its goal is to achieve a good quality at a given target bit-rate. In H.264/AVC, rate control algorithm for both Intra and Inter-frames suffers from some defects. In the Intra-frame rate control, the initial quantization parameter (QP) is mainly adjusted according to a global target bit-rate and length of GOP. This determination is inappropriate and generates errors in the whole of video sequence. For Inter coding unit (Frame or Macroblock), the use of MAD (Mean Average Differences) as a complexity measure, remains inefficient, resulting in improper QP values because the MAD handles locally images characteristics. QP miscalculations may also result from the linear prediction model which assumes similar complexity from coding unit to another. To overcome these defects, we propose in this paper, a new Rate-Quantization (R-Q) model resulting from extensive experiments. This latter is divided into two models. The first one is an Intra R-Q model used to determine an optimal initial quantization parameter for Intra-frames. The second one is an Inter R-Q model that aims at determining the QP of Inter coding unit according to the statistics of the previous coded ones. It does not use any complexity measure and substitutes both linear and quadratic models used in H.264/AVC rate controller. Objective and subjective simulations have been carried out using JM15.0 reference software. Compared to this latter, the global R-Q model (Intra and Inter models combined) improves the coding efficiency in terms of PSNR, objectively (up to +2.01dB), subjectively (by psychophysical experiments) and in terms of computational complexity

Innovations in Smart Cities Applications Volume 4: The Proceedings of the 5th International Conference on Smart City Applications

This proceedings book is the fourth edition of a series of works which features emergent research trends and recent innovations related to smart city presented at the 5th International Conference on Smart City Applications SCA20 held in Safranbolu, Turkey. This book is composed of peer-reviewed chapters written by leading international scholars in the field of smart cities from around the world. This book covers all the smart city topics including Smart Citizenship, Smart Education, Smart Mobility, Smart Healthcare, Smart Mobility, Smart Security, Smart Earth Environment & Agriculture, Smart Economy, Smart Factory and Smart Recognition Systems.info:eu-repo/semantics/publishedVersio

Stability analysis and robust damping of multiresonances in distributed-generation-based islanded microgrids

International audienc

Adaptive Reference Trajectory for Power Quality Enhancement in Three-Phase Four-Wire Standalone Power Supply Systems with Nonlinear and Unbalanced Loads

International audienc

A New Combination Formula for Treatment of Fungal Keratitis: An Experimental Study

Objective. To formulate and evaluate slow release ketoconazole and ketorolac to treat fungal keratitis and associated inflammation. Methods. Experimental study with the following outcome measures. Pharmaceutical Evaluation. Mucoadhesive gels containing ketoconazole and ketorolac were used. Microbiological in vitro evaluation was performed using cup method. In vivo evaluation was performed on 24 rabbits divided into 2 groups, 12 rabbits each, group A (fast release formula; 6 times daily) and group B (slow release formula; 3 times daily). Each group was divided into two subgroups (6 rabbits each). Both eyes of rabbits were inoculated with Candida albicans. The left eye of all rabbits received the combination formulae. The right eye for one subgroup received ketoconazole as control 1 while the other subgroup received placebo as control 2. Clinical follow-up was done and, finally, the corneas were used for microbiological and pathological evaluation. Results. Gels containing high polymer concentration showed both high viscosity and mucoadhesion properties with slower drug release. The infected eyes treated with slow release formula containing both drugs showed better curing of the cornea and pathologically less inflammation than eyes treated with fast release formula. Conclusion. Slow release formula containing ketoconazole and ketorolac showed higher activity than fast release formula against fungal keratitis and associated inflammation

Editorial: Assessment of users’ satisfaction in public spaces

editorial reviewedIn the last few years, with the spread of the pandemic and the closure of public and recreational facilities, urban public spaces have played a key role in improving the quality of life and the comfort of citizens by offering many opportunities for users to overcome stress, interact, and enjoy themselves. With the rising level of citizens' expectations and needs, designing an urban space has become a critical task with various dimensions. Social cohabitation, environmental protection, health care, and the well-being of citizens are among the main considerations of urban public spaces’ stakeholders. In this context, users' satisfaction responds to the space's design and features, which hold objective and subjective aspects that can be assessed and improved.3. Good health and well-being11. Sustainable cities and communitie