Prognosis Prediction in COVID-19 Patients through Deep Feature Space Reasoning.

The COVID-19 pandemic has presented a unique challenge for physicians worldwide, as they grapple with limited data and uncertainty in diagnosing and predicting disease outcomes. In such dire circumstances, the need for innovative methods that can aid in making informed decisions with limited data is more critical than ever before. To allow prediction with limited COVID-19 data as a case study, we present a complete framework for progression and prognosis prediction in chest X-rays (CXR) through reasoning in a COVID-specific deep feature space. The proposed approach relies on a pre-trained deep learning model that has been fine-tuned specifically for COVID-19 CXRs to identify infection-sensitive features from chest radiographs. Using a neuronal attention-based mechanism, the proposed method determines dominant neural activations that lead to a feature subspace where neurons are more sensitive to COVID-related abnormalities. This process allows the input CXRs to be projected into a high-dimensional feature space where age and clinical attributes like comorbidities are associated with each CXR. The proposed method can accurately retrieve relevant cases from electronic health records (EHRs) using visual similarity, age group, and comorbidity similarities. These cases are then analyzed to gather evidence for reasoning, including diagnosis and treatment. By using a two-stage reasoning process based on the Dempster-Shafer theory of evidence, the proposed method can accurately predict the severity, progression, and prognosis of a COVID-19 patient when sufficient evidence is available. Experimental results on two large datasets show that the proposed method achieves 88% precision, 79% recall, and 83.7% F-score on the test sets.

Memory Forensics-Based Malware Detection Using Computer Vision and Machine Learning

Malware has recently grown exponentially in recent years and poses a serious threat to individual users, corporations, banks, and government agencies. This can be seen from the growth of Advanced Persistent Threats (APTs) that make use of advance and sophisticated malware. With the wide availability of computer-automated tools such as constructors, email flooders, and spoofers. Thus, it is now easy for users who are not technically inclined to create variations in existing malware. Researchers have developed various defense techniques in response to these threats, such as static and dynamic malware analyses. These techniques are ineffective at detecting new malware in the main memory of the computer and otherwise require considerable effort and domain-specific expertise. Moreover, recent techniques of malware detection require a long time for training and occupy a large amount of memory due to their reliance on multiple factors. In this paper, we propose a computer vision-based technique for detecting malware that resides in the main computer memory in which our technique is faster or memory efficient. It works by taking portable executables in a virtual environment to extract memory dump files from the volatile memory and transform them into a particular image format. The computer vision-based contrast-limited adaptive histogram equalization and the wavelet transform are used to improve the contrast of neighboring pixel and to reduce the entropy. We then use the support vector machine, random forest, decision tree, and XGBOOST machine learning classifiers to train the model on the transformed images with dimensions of 112 × 112 and 56 × 56. The proposed technique was able to detect and classify malware with an accuracy rate of 97.01%. Its precision, recall, and F1-score were 97.36%, 95.65%, and 96.36%, respectively. Our finding shows that our technique in preparing dataset with more efficient features to be trained by the Machine Learning classifiers has resulted in significant performance in terms of accuracy, precision, recall, F1-score, speed and memory consumption. The performance has superseded most of the existing techniques in its unique approach.

Disease Progression Detection via Deep Sequence Learning of Successive Radiographic Scans.

The highly rapid spread of the current pandemic has quickly overwhelmed hospitals all over the world and motivated extensive research to address a wide range of emerging problems. The unforeseen influx of COVID-19 patients to hospitals has made it inevitable to deploy a rapid and accurate triage system, monitor progression, and predict patients at higher risk of deterioration in order to make informed decisions regarding hospital resource management. Disease detection in radiographic scans, severity estimation, and progression and prognosis prediction have been extensively studied with the help of end-to-end methods based on deep learning. The majority of recent works have utilized a single scan to determine severity or predict progression of the disease. In this paper, we present a method based on deep sequence learning to predict improvement or deterioration in successive chest X-ray scans and build a mathematical model to determine individual patient disease progression profile using successive scans. A deep convolutional neural network pretrained on a diverse lung disease dataset was used as a feature extractor to generate the sequences. We devised three strategies for sequence modeling in order to obtain both fine-grained and coarse-grained features and construct sequences of different lengths. We also devised a strategy to quantify positive or negative change in successive scans, which was then combined with age-related risk factors to construct disease progression profile for COVID-19 patients. The age-related risk factors allowed us to model rapid deterioration and slower recovery in older patients. Experiments conducted on two large datasets showed that the proposed method could accurately predict disease progression. With the best feature extractor, the proposed method was able to achieve AUC of 0.98 with the features obtained from radiographs. Furthermore, the proposed patient profiling method accurately estimated the health profile of patients.

Detection of Fake News Text Classification on COVID-19 Using Deep Learning Approaches.

A vast amount of data is generated every second for microblogs, content sharing via social media sites, and social networking. Twitter is an essential popular microblog where people voice their opinions about daily issues. Recently, analyzing these opinions is the primary concern of Sentiment analysis or opinion mining. Efficiently capturing, gathering, and analyzing sentiments have been challenging for researchers. To deal with these challenges, in this research work, we propose a highly accurate approach for SA of fake news on COVID-19. The fake news dataset contains fake news on COVID-19; we started by data preprocessing (replace the missing value, noise removal, tokenization, and stemming). We applied a semantic model with term frequency and inverse document frequency weighting for data representation. In the measuring and evaluation step, we applied eight machine-learning algorithms such as Naive Bayesian, Adaboost, K-nearest neighbors, random forest, logistic regression, decision tree, neural networks, and support vector machine and four deep learning CNN, LSTM, RNN, and GRU. Afterward, based on the results, we boiled a highly efficient prediction model with python, and we trained and evaluated the classification model according to the performance measures (confusion matrix, classification rate, true positives rate...), then tested the model on a set of unclassified fake news on COVID-19, to predict the sentiment class of each fake news on COVID-19. Obtained results demonstrate a high accuracy compared to the other models. Finally, a set of recommendations is provided with future directions for this research to help researchers select an efficient sentiment analysis model on Twitter data.

Experimental Investigations of a Solar Water Treatment System for Remote Desert Areas of Pakistan

Pakistan is among the countries that have already crossed the water scarcity line, and the situation is worsened due to the recent pandemic. This is because the major budget of the country is shifted to primary healthcare activities from other development projects that included water treatment and transportation infrastructure. Consequently, water-borne diseases have increased drastically in the past few months. Therefore, there is a dire need to address this issue on a priority basis to ameliorate the worsening situation. One possible solution is to shift the focus/load from mega-projects that require a plethora of resources, money, and time to small domestic-scale systems for water treatment. For this purpose, domestic-scale solar stills are designed, fabricated, and tested in one of the harshest climatic condition areas of Pakistan, Rahim Yar Khan. A comprehensive overview of the regional climatology, including wind speed, solar potential, and ambient temperature is presented for the whole year. The analysis shows that the proposed system can adequately resolve the drinking water problems of deprived areas of Pakistan. The average water productivity of 1.5 L/d/m2 is achieved with a total investment of PKR 3000 (<$20). This real site testing data will serve as a guideline for similar system design in other arid areas globally.

The Safe Resumption of Elective Plastic Surgery in Accredited Ambulatory Surgery Facilities During the COVID-19 Pandemic.

BACKGROUND: On March 11, 2020, the World Health Organization declared the novel Coronavirus-19 (COVID-19) a worldwide pandemic, resulting in an unprecedented shift in the Canadian healthcare system, where protection of an already overloaded system became a priority; all elective surgeries and non-essential activities were ceased. With the impact being less than predicted, on May 26, 2020, elective surgeries and non-essential activities were permitted to resume. OBJECTIVES: The authors sought to examine outcomes following elective aesthetic surgery and the impact on the Canadian healthcare system with the resumption of these services during the COVID-19 worldwide pandemic. METHODS: Data were collected in a prospective manner on consecutive patients who underwent elective plastic surgery procedures in 6 accredited ambulatory surgery facilities. Data included patient demographics, procedural characteristics, COVID-19 polymerase chain reaction (PCR) test status, airway management, and postoperative outcomes. RESULTS: A total of 368 patients underwent elective surgical procedures requiring a general anesthetic. All 368 patients who underwent surgery were negative on pre-visit screening. A COVID-19 PCR test was completed by 352 patients (95.7%) and all were negative. In the postoperative period, 7 patients (1.9%) had complications, 3 patients (0.8%) required a hospital visit, and 1 patient (0.3%) required hospital admission. No patients or healthcare providers developed COVID-19 symptoms or had a positive test for COVID-19 within 30 days of surgery. CONCLUSIONS: With appropriate screening and safety precautions, elective aesthetic plastic surgery can be performed in a manner that is safe for patients and healthcare providers and with a very low risk for accelerating virus transmission within the community.