Interpretable Deep Learning for Discriminating Pneumonia from Lung Ultrasounds

Lung ultrasound images have shown great promise to be an operative point-of-care test for the diagnosis of COVID-19 because of the ease of procedure with negligible individual protection equipment, together with relaxed disinfection. Deep learning (DL) is a robust tool for modeling infection patterns from medical images; however, the existing COVID-19 detection models are complex and thereby are hard to deploy in frequently used mobile platforms in point-of-care testing. Moreover, most of the COVID-19 detection models in the existing literature on DL are implemented as a black box, hence, they are hard to be interpreted or trusted by the healthcare community. This paper presents a novel interpretable DL framework discriminating COVID-19 infection from other cases of pneumonia and normal cases using ultrasound data of patients. In the proposed framework, novel transformer modules are introduced to model the pathological information from ultrasound frames using an improved window-based multi-head self-attention layer. A convolutional patching module is introduced to transform input frames into latent space rather than partitioning input into patches. A weighted pooling module is presented to score the embeddings of the disease representations obtained from the transformer modules to attend to information that is most valuable for the screening decision. Experimental analysis of the public three-class lung ultrasound dataset (PCUS dataset) demonstrates the discriminative power (Accuracy: 93.4%, F1-score: 93.1%, AUC: 97.5%) of the proposed solution overcoming the competing approaches while maintaining low complexity. The proposed model obtained very promising results in comparison with the rival models. More importantly, it gives explainable outputs therefore, it can serve as a candidate tool for empowering the sustainable diagnosis of COVID-19-like diseases in smart healthcare

Opportunities of IoT in Fog Computing for High Fault Tolerance and Sustainable Energy Optimization

Today, the importance of enhanced quality of service and energy optimization has promoted research into sensor applications such as pervasive health monitoring, distributed computing, etc. In general, the resulting sensor data are stored on the cloud server for future processing. For this purpose, recently, the use of fog computing from a real-world perspective has emerged, utilizing end-user nodes and neighboring edge devices to perform computation and communication. This paper aims to develop a quality-of-service-based energy optimization (QoS-EO) scheme for the wireless sensor environments deployed in fog computing. The fog nodes deployed in specific geographical areas cover the sensor activity performed in those areas. The logical situation of the entire system is informed by the fog nodes, as portrayed. The implemented techniques enable services in a fog-collaborated WSN environment. Thus, the proposed scheme performs quality-of-service placement and optimizes the network energy. The results show a maximum turnaround time of 8 ms, a minimum turnaround time of 1 ms, and an average turnaround time of 3 ms. The costs that were calculated indicate that as the number of iterations increases, the path cost value decreases, demonstrating the efficacy of the proposed technique. The CPU execution delay was reduced to a minimum of 0.06 s. In comparison, the proposed QoS-EO scheme has a lower network usage of 611,643.3 and a lower execution cost of 83,142.2. Thus, the results show the best cost estimation, reliability, and performance of data transfer in a short time, showing a high level of network availability, throughput, and performance guarantee

Performance Evaluation of Ingenious Crow Search Optimization Algorithm for Protein Structure Prediction

Protein structure prediction is one of the important aspects while dealing with critical diseases. An early prediction of protein folding helps in clinical diagnosis. In recent years, applications of metaheuristic algorithms have been substantially increased due to the fact that this problem is computationally complex and time-consuming. Metaheuristics are proven to be an adequate tool for dealing with complex problems with higher computational efficiency than conventional tools. The work presented in this paper is the development and testing of the Ingenious Crow Search Algorithm (ICSA). First, the algorithm is tested on standard mathematical functions with known properties. Then, the application of newly developed ICSA is explored on protein structure prediction. The efficacy of this algorithm is tested on a bench of artificial proteins and real proteins of medium length. The comparative analysis of the optimization performance is carried out with some of the leading variants of the crow search algorithm (CSA). The statistical comparison of the results shows the supremacy of the ICSA for almost all protein sequences

NIPUNA: A Novel Optimizer Activation Function for Deep Neural Networks

In recent years, various deep neural networks with different learning paradigms have been widely employed in various applications, including medical diagnosis, image analysis, self-driving vehicles and others. The activation functions employed in deep neural networks have a huge impact on the training model and the reliability of the model. The Rectified Linear Unit (ReLU) has recently emerged as the most popular and extensively utilized activation function. ReLU has some flaws, such as the fact that it is only active when the units are positive during back-propagation and zero otherwise. This causes neurons to die (dying ReLU) and a shift in bias. However, unlike ReLU activation functions, Swish activation functions do not remain stable or move in a single direction. This research proposes a new activation function named NIPUNA for deep neural networks. We test this activation by training on customized convolutional neural networks (CCNN). On benchmark datasets (Fashion MNIST images of clothes, MNIST dataset of handwritten digits), the contributions are examined and compared to various activation functions. The proposed activation function can outperform traditional activation functions

An improved wild horse optimization algorithm for reliability based optimal DG planning of radial distribution networks

This paper introduces a novel technique for optimal distribution system (DS) planning with distributed generation (DG) systems. It is being done to see how active and reactive power injections affect the system’s voltage profile and energy losses. DG penetration in the power systems is one approach that has several advantages such as peak savings, loss lessening, voltage profile amelioration. It also intends to increase system reliability, stability, and security. The main goal of optimal distributed generation (ODG) is a guarantee to achieve the benefits mentioned previously to increase the overall system efficiency. For extremely vast and complicated systems, analytical approaches are not suitable and insufficient. Therefore, several meta-heuristic techniques are favored to obtain better performance from were convergence and accuracy for large systems. In this paper, an Improved Wild Horse Optimization algorithm (IWHO) is proposed as a novel metaheuristic method for solving optimization issues in electrical power systems. IWHO is devised with inspirations from the social life behavior of wild horses. The suggested method is based on the horse’s decency. To assess the efficacy of the IWHO, it is implemented on the 23 benchmark functions Reliability amelioration is the most things superb as a result of DGs incorporation. Thus, in this research, a customer-side reliability appraisal in the DS that having a DG unit was carried out by a Monte Carlo Simulation (MCS) approach to construct an artificial history for each ingredient across simulation duration. For load flow calculations, the backward Forward Sweep (BFS) technique has been employed as a simulation tool to assess the network performance considering the power handling restrictions. The proposed IWHO method has been measured on IEEE 33 69 and 119 buses to ascertain the network performing in the presence of the optimal DG and the potential benefits of the suggested technique for enhancing the tools used by operators and planners to maintain the system reliability and efficiency. The results proved that IWHO is an optimization method with lofty performance regarding the exploration–exploitation balance and convergence speed, as it successfully handles complicated problems

A Multi–Objective Gaining–Sharing Knowledge-Based Optimization Algorithm for Solving Engineering Problems

Metaheuristics in recent years has proven its effectiveness; however, robust algorithms that can solve real-world problems are always needed. In this paper, we suggest the first extended version of the recently introduced gaining–sharing knowledge optimization (GSK) algorithm, named multiobjective gaining–sharing knowledge optimization (MOGSK), to deal with multiobjective optimization problems (MOPs). MOGSK employs an external archive population to store the nondominated solutions generated thus far, with the aim of guiding the solutions during the exploration process. Furthermore, fast nondominated sorting with crowding distance was incorporated to sustain the diversity of the solutions and ensure the convergence towards the Pareto optimal set, while the e- dominance relation was used to update the archive population solutions. e-dominance helps provide a good boost to diversity, coverage, and convergence overall. The validation of the proposed MOGSK was conducted using five biobjective (ZDT) and seven three-objective test functions (DTLZ) problems, along with the recently introduced CEC 2021, with fifty-five test problems in total, including power electronics, process design and synthesis, mechanical design, chemical engineering, and power system optimization. The proposed MOGSK was compared with seven existing optimization algorithms, including MOEAD, eMOEA, MOPSO, NSGAII, SPEA2, KnEA, and GrEA. The experimental findings show the good behavior of our proposed MOGSK against the comparative algorithms in particular real-world optimization problems

الحضانة في القانون القطري وتوافقه مع الشريعة الإسلامية = Custody in Qatari Law and it coincides with Shariah

ملخ ص إن للحضانة أهمية كبيرة في الأسرة والمجتمع واستقراره، وبالتالي اهتمت به الشريعة الإسلامية وجميع القوانين الوضعية، وإن القانون القطري أحد هذه القوانين التي أرعت له العناية الكبيرة، وتكمن مشكلة البحث في معرفة مدى موافقة القانون القطري مع الشريعة الإسلامية من عدمه، ويهدف البحث إلى إبراز مفهوم الحضانة في القانون القطري والشريعة الإسلامية، ومدى توافق القانون القط ري مع الشريعة الإسلامية، ومن هنا تظهر أهمية هذا البحث. وقد اتبعت الدراسة المنهج الاستقرائي وذلك بتتبع وجمع المادة العلمية من مصادرها والمراجع القانونية والشرعية، والمنهج التحليلي والذي يقود إلى تحليل وتوضيح مواد القانون مع الأحكام الشرعية ومقارنتها بالآراء الفقهية، وقد خلص البحث إلى أن الحضانة في القانون القطري لم تخرج نصوصها وموادها في الغالب عن آراء وأقوال فقهاء المذاهب الأربعة إلا قليل حيث أخذ ببعض أجزاء من الأقوال، أو جمع بين الأقوال في بعض المسائل، ونادراً ما تفرد عن بقية أقوال المذاهب الأربعة، وكل ذلك كان مراعياً للمصلحة التي يراها مناسبة للحا

C-Abl inhibition; a novel therapeutic target for Parkinson’s disease

Parkinson’s disease (PD) is the most prevalent movement disorder in the world. The major pathological hallmarks of PD are death of dopaminergic neurons and the formation of Lewy bodies. At the moment, there is no cure for PD; current treatments are symptomatic. Investigators are searching for neuroprotective agents and disease modifying strategies to slow the progress of PD. However, recently, due to the ignorance of the main pathological sequence of PD, many drug targets failed to provide neuroprotective effects in human trials. Currently, a huge amount of evidence suggests the involvement of C-Abelson (c-Abl) tyrosine kinase enzyme in the pathology of PD. C-abl plays a role in PD pathology on the levels of parkin activation, alpha synuclein aggregation, and impaired autophagy of toxic elements. Experimental studies showed that (1) c-abl activation is involved in neuronal death and (2) c-abl inhibition shows neuroprotective effects and prevents dopaminergic neurons’ death. Current evidence from experimental studies and the first in-human trial shows that c-abl inhibition holds the promise for neuroprotection against PD and therefore, justifies the movement towards larger clinical trials. In this review article, we discussed the role of c-abl in PD pathology and the findings of preclinical experiments and the first in-human trial. In addition, based on the lessons of the last decade and current preclinical evidence, we provide recommendations for future research in this area

Knowledge, Prevention, and Practice of Heat Strokes Among the Public in United Arab Emirates (UAE)

Background: Heat stroke is a predictable and preventable occurrence. Public awareness of the condition and preventative practices are essential in hot and humid regions. This study aims to assess the level of awareness (knowledge, prevention, and management) of heat stroke among United Arab Emirates (UAE) residents. Methods: This is a survey-based study of knowledge and practices of heat stroke in a random sample of adults (?18 years) in four different cities in the UAE. Each correct answer was equal to one point, and total and average scores were calculated, with the average score used as the cutoff point. Multivariate logistic regression was used to identify factors associated with below-average awareness. Results: A total of 402 people participated in the study, with an average age of 33±12 years, and 48.5% were female. Only 1 person achieved a perfect score, and 0.7%, 10%, and 17.7% achieved above-average scores in knowledge, practices, and management, respectively. Seven percent of participants had never heard of a heat stroke. A third of participants (32%) were unaware that severe heat strokes could lead to death. Males are at a higher risk of having a poor level of knowledge (Odds ratio [OR]=1.65; 95% confidence interval [95%CI]=1.10-2.47). The older the population, the poorer the knowledge of heat stroke was (OR=1.39, 95%CI=1.03–1.89). Conclusion: The results of this study show that a huge proportion of the population in the UAE does not have sufficient knowledge about heat stroke, its prevention, and management. Governmental institutes should increase awareness of heat stroke