Comprehensive study: machine learning approaches for COVID-19 diagnosis

Coronavirus disease 2019 (COVID-19) is caused a large number of death since has declared as an international pandemic in December 2019, and it is spreading all over the world (more than 200 countries). This situation puts the health organizations in an aberrant demand for urgent needs to develop significant early detection and monitoring smart solutions. Therefore, that new system or solution might be capable to identify COVID-19 quickly and accurately. Nowadays, the science of artificial intelligence (AI), and internet of things (IoT) techniques have an extensive range of applications, it can be initiated a possible solution for early detection and accurate decisions. We believe, combine both of the IoT revolution and machine learning (ML) methods are expected to reshape healthcare treatment strategies to provide smart (diagnosis, treatments, monitoring, and hospitals). This work aims to overview the recent solutions that have been used for early detection, and to provide the researchers a comprehensive summary that contribute to the pandemic control such AI, IoT, cloud, fog, algorithms, and all the dataset and their sources that recently published. In addition, all models, frameworks, monitoring systems, devices, and ideas (in four sections) have been sufficiently presented with all clarifications and justifications. Also, we propose a new vision for early detection based on IoT sensors data entry using 1 million patients-data to verify three proposed methods

MC-DS-CDMA System based on DWT and STBC in ITU Multipath Fading Channels Model

 في هذه الورقة، تم تحسين أداء النفوذ المتعدد بالتقسيم لرمز السلسلة المباشر متعدد الموجات (MC-DS-CDMA) في تطبيقات MC-DS-CDMA الثابتة والتطبيقات MC-DS-CDMA  المتنقلة باستعمال تعويضات التشفير الزمنية الفضائية وتحويل فورير السريعة المنفصلة (FFT) أو تحويل المويجات المنفصلة DWT. وقد تمت محاكاة أنظمة MC-DS-CDMA  باستخدام ماتلاب 2015a. من خلال محاكاة النظام المقترح، يمكن تغيير المعالم المختلفة واختبارها. ويتم الحصول على معدل خطأ البيانات (BER) لهذه الأنظمة على مدى واسع من نسبة الإشارة إلى الضوضاء. وقد قورنت جميع نتائج المحاكاة مع بعضها البعض باستخدام حجم الموجة الحاملة الفرعية المختلفة FFT أو DWT مع ل STBC 1،2،3 و 4 هوائيات في المرسل وفي مختلف قنوات الخبو في متعددة الممرات ITU ومختلف ترددات دوبلر (fd).In this paper, the performance of multicarrier direct sequence code division multiple access (MC-DS-CDMA) in fixed MC-DS-CDMA and Mobile MC-DS-CDMA applications have been improved by using the compensations of space time block coding and Discrete Fast Fourier transforms (FFT) or Discrete Wavelets transform DWT. These MC-DS-CDMA systems had been simulated using MATLAB 2015a. Through simulation of the proposed system, various parameters can be changed and tested. The Bit Error Rate (BERs) of these systems are obtained over wide range of signal to noise ratio. All simulation results had been compared with each other using different subcarrier size of FFT or DWT with STBC for 1,2,3 and 4 antennas in transmitter and under different ITU multipath fading channels and different Doppler frequencies (fd). The proposed structures of STBC-MC-DS-CDMA system based on (DWT) batter than based on (FFT) in varies Doppler frequencies and subcarrier size. Also, proposed system with STBC based on 4 transmitters better than other systems based on 1 or 2 or 3 transmitters in all Doppler frequencies and subcarrier size in all simulation results