Classification of Positive COVID-19 CT Scans Using Deep Learning

In medical imaging,computer vision researchers are faced with a variety of features for verifying the authenticity of classifiers for an accurate diagnosis.In response to the coronavirus 2019(COVID-19)pandemic,new testing procedures,medical treatments,and vaccines are being developed rapidly.One potential diagnostic tool is a reverse-transcription polymerase chain reaction(RT-PCR).RT-PCR,typically a time-consuming process,was less sensitive to COVID-19 recognition in the disease’s early stages.Here we introduce an optimized deep learning(DL)scheme to distinguish COVID-19-infected patients from normal patients according to computed tomography(CT)scans.In the proposed method,contrast enhancement is used to improve the quality of the original images.A pretrained DenseNet-201 DL model is then trained using transfer learning.Two fully connected layers and an average pool are used for feature extraction.The extracted deep features are then optimized with a Firefly algorithm to select the most optimal learning features.Fusing the selected features is important to improving the accuracy of the approach;however,it directly affects the computational cost of the technique.In the proposed method,a new parallel high index technique is used to fuse two optimal vectors;the outcome is then passed on to an extreme learning machine for final classification.Experiments were conducted on a collected database of patients using a 70:30 training:Testing ratio.Our results indicated an average classification accuracy of 94.76%with the proposed approach.A comparison of the outcomes to several other DL models demonstrated the effectiveness of our DL method for classifying COVID-19 based on CT scans.

Our Daily Life Dependency Driven by Renewable and Nonrenewable Source of Energy

Our dependency on energy is so vital that it makes it difficult to imagine how humans can live on our planet earth without it.The demand for electricity,for example,is directly related to the growth of the population worldwide,and presently,to meet this demand,we need both renewable and nonrenewable energy.While nonrenewable energy has its shortcomings(negative impact on climate change,for example),renewable energy is not enough to address the ever-changing demand for energy.One way to address this need is to become more innovative,use technology more effectively,and be aware of the costs associated with different sources of renewable energy.In the case of nuclear power plants,new innovative centered around small modular reactors(SMRs)of generation 4th of these plants make them safer and less costly to own them as well as to protect them via means of cyber-security against any attack by smart malware.Of course,understanding the risks and how to address them is an integral part of the study.Natural sources of energy,such as wind and solar,are suggesting other innovating technical approaches.In this article,we are studying these factors holistically,and details have been laid out in a book by the authors’second volume of series title as Knowledge Is Power in Four Dimensions under Energy subtitle.

A Blockchain Based Framework for Stomach Abnormalities Recognition

Wireless Capsule Endoscopy(WCE)is an imaging technology,widely used in medical imaging for stomach infection recognition.However,a one patient procedure takes almost seven to eight minutes and approximately 57,000 frames are captured.The privacy of patients is very important and manual inspection is time consuming and costly.Therefore,an automated system for recognition of stomach infections from WCE frames is always needed.An existing block chain-based approach is employed in a convolutional neural network model to secure the network for accurate recognition of stomach infections such as ulcer and bleeding.Initially,images are normalized in fixed dimension and passed in pre-trained deep models.These architectures are modified at each layer,to make them safer and more secure.Each layer contains an extra block,which stores certain information to avoid possible tempering,modification attacks and layer deletions.Information is stored in multiple blocks,i.e.,block attached to each layer,a ledger block attached with the network,and a cloud ledger block stored in the cloud storage.After that,features are extracted and fused using a Mode value-based approach and optimized using a Genetic Algorithm along with an entropy function.The Softmax classifier is applied at the end for final classification.Experiments are performed on a private collected dataset and achieve an accuracy of 96.8%.The statistical analysis and individual model comparison show the proposed method’s authenticity.

Data Exploration and Reconnaissance to Identify Ocean Phenomena: A Guide for In Situ Data Collection

Marine pollution is a serious geoenvironmental problem affecting the Lebanese coast. It mainly affects the coastal zone adjacent to areas of dense population. To detect the sources of pollution along this zone, as well as to identify their characteristics, remote sensing data is used. Landsat 8 Operational Land Imager (OLI) satellite images, which have medium spatial resolution, are analyzed using ENVI 5.2 and ArcGIS 10.3.1 geospatial software for the years of 2014 and 2015. Different routines are applied to reveal anomalous features with the goal being to discriminate polluted water in the marine environment. Results showed anomalies in Akkar region. This might be due to the presence of basalts rocks, and geothermal heating, or the pollution of Oustowan river that flows into the sea. The results also showed that during the dry season, there is low movement of water causing a least extension of the anomalies. In contrary, during the wet season, rivers had an intense flow into the sea which caused an intense water movement and wide extension of anomalies on the coast. Permanently polluted coastal sites are evident in Tripoli, Kalamoun, Chekka, Batroun, Amchit, Jbeil, Jounieh, Nahr Beirut and Ouzai with the most presumed polluted months being in 2014 during April and November and in 2015 in April. The least extended pollution is during July 2014 and 2015. The length and width of each anomaly at each site shows that during the year of 2015;most of the anomalies are larger than in 2014.