Exploration of hybrid deep learning algorithms for covid-19 mRNA vaccine degradation prediction system

Coronavirus causes a global pandemic that has adversely affected public health, the economy, including every life aspect. To manage the spread, innumerable measurements are gathered. Administering vaccines is considered to be among the precautionary steps under the blueprint. Among all vaccines, the messenger ribonucleic acid (mRNA) vaccines provide notable effectiveness with minimal side effects. However, it is easily degraded and limits its application. Therefore, considering the cruciality of predicting the degradation rate of the mRNA vaccine, this prediction study is proposed. In addition, this study compared the hybridizing sequence of the hybrid model to identify its influence on prediction performance. Five models are created for exploration and prediction on the COVID-19 mRNA vaccine dataset provided by Stanford University and made accessible on the Kaggle community platform employing the two deep learning algorithms, Long Short-Term Memory (LSTM) as well as Gated Recurrent Unit (GRU). The Mean Columnwise Root Mean Square Error (MCRMSE) performance metric was utilized to assess each model's performance. Results demonstrated that both GRU and LSTM are befitting for predicting the degradation rate of COVID-19 mRNA vaccines. Moreover, performance improvement could be achieved by performing the hybridization approach. Among Hybrid_1, Hybrid_2, and Hybrid_3, when trained with Set_1 augmented data, Hybrid_3 with the lowest training error (0.1257) and validation error (0.1324) surpassed the other two models;the same for model training with Set_2 augmented data, scoring 0.0164 and 0.0175 MCRMSE for training error and validation error, respectively. The variance in results obtained by hybrid models from experimenting claimed hybridizing sequence of algorithms in hybrid modeling should be a concerned. © 2022, Universitas Ahmad Dahlan. All rights reserved.

COVID-19 mRNA Vaccine Degradation Prediction By Using Deep Learning Algorithms

The worldwide coronavirus (COVID-19) pandemic has accelerated substantially in the 2020, necessitating a global collaborative from various entities to create and speed vaccine development to prevent illnesses and deaths. Because of its fast development, high efficiently, safe administration, and low-cost production, messenger RNA (mRNA) has emerged as a significant technology in this epidemic. However, due of the inadequate in vivo distribution of mRNA, its chemical qualities make it difficult to use the vaccine. As a result, the goal of this study is to create and construct a sequence deep model that will be used to predict the degradation rate of the COVID-19 mRNA vaccine using five reactivity values for each place in the mRNA sequence. The probability degradation rate with/without magnesium at pH10 and 50°C was one of four of these values. The fifth reactivity value shows the likelihood of the RNA sample's secondary structure. The numerical and categorical properties of the deep learning model are the most important. Categorical features are referred from the structures, sequences, and predicted loop of the mRNA sequence, while numerical features are extracted via mathematical computations. 6 models of bidirectional layers models (LSTM, GRU, LSTM+GRU (L_GRU), GRU+LSTM (G_LSTM), LSTM+GRU+LSTM (L_G_LSTM), and GRU+LSTM+GRU (G_L_GRU) give trustworthy projected outcomes because it comprises five reactivity values and validate by mean columnwise root mean square error (MCRMSE). The MCRMSE results are then used to evaluate the performance. The stronger the prediction model, the smaller the values are. The best-fitting model is L_G_LSTM with the MCRMSE difference of 0.007 will be implemented into a Graphical User Interface (GUI) prediction system. © 2022 IEEE.

Deep learning in drug discovery: a futuristic modality to materialize the large datasets for cheminformatics.

Artificial intelligence (AI) development imitates the workings of the human brain to comprehend modern problems. The traditional approaches such as high throughput screening (HTS) and combinatorial chemistry are lengthy and expensive to the pharmaceutical industry as they can only handle a smaller dataset. Deep learning (DL) is a sophisticated AI method that uses a thorough comprehension of particular systems. The pharmaceutical industry is now adopting DL techniques to enhance the research and development process. Multi-oriented algorithms play a crucial role in the processing of QSAR analysis, de novo drug design, ADME evaluation, physicochemical analysis, preclinical development, followed by clinical trial data precision. In this study, we investigated the performance of several algorithms, including deep neural networks (DNN), convolutional neural networks (CNN) and multi-task learning (MTL), with the aim of generating high-quality, interpretable big and diverse databases for drug design and development. Studies have demonstrated that CNN, recurrent neural network and deep belief network are compatible, accurate and effective for the molecular description of pharmacodynamic properties. In Covid-19, existing pharmacological compounds has also been repurposed using DL models. In the absence of the Covid-19 vaccine, remdesivir and oseltamivir have been widely employed to treat severe SARS-CoV-2 infections. In conclusion, the results indicate the potential benefits of employing the DL strategies in the drug discovery process.Communicated by Ramaswamy H. Sarma.

Detection of COVID-19 from X-RAY Images using Artificial Intelligence (AI)

Corona virus (COVID-19) is an infectious disease. Several millions of people worldwide suffer from this disease. The signs of progress of virus infection are more severe damage to lungs and causes to organs failure, death. X-rays are readily available and an excellent alternative method to x-ray imaging in the diagnosis of covid-19 and very crucial role play to recognizing this disease and recovery with hospitalization. The goal of this revise is to expand a reliable method for detecting COVID-19 from digital chest X-ray pictures using well-before deep-learning algorithms while optimizing detection performance. To train and verify, the transfer learning (TL) approach was utilized with the aid of picture extension. Current would be hugely beneficial in this pandemic because the illness severity and the necessity for prevention methods are at odds with available resources. © 2022 IEEE.

Development of Contactless Individual Management System with Covid - 19 Preemptive Measures

The significance of social distancing and non-contact habits was emphasized by the Covid-19 pandemic. Even after the pandemic, everyone should adhere to the same hygiene procedures. Preventive measures must be implemented prior to the individuals' return. These include identifying people's presence and monitoring their health. This research focuses on using sensor fusion and deep learning technology to create a contactless individual management system. It is capable of carrying out the attendance routine without compromising the precautionary measures. Persons can be identified without removing the mask by employing random Quick Response (QR) code recognition. While recognising the QR, the system will double-verify the individual by identifying the Media Access Control (MAC) address of the user's mobile Bluetooth at the backend. Then the system employs a pre-trained deep learning model to detect masks. The Convolutional Neural Network (CNN) technique produces a deep learning model that can distinguish between Faces with and without masks. The system then monitors the body temperature with an Infrared (IR) temperature sensor followed by dispensing sanitizer. The response for the entire procedure will be updated in both the person's mobile application and the Management Authority. © 2022 IEEE.

Deep Neural Network for Virus Mutation Prediction: A Comprehensive Review

Artificial intelligence (AI) and Deep Learning Algorithms are potential methods for preventing the alarmingly widespread RNA viruses and ensuring pandemic safety, they have become an integrative part of the modern scientific methodology, offering automated procedures for the prediction of a phenomenon based on past observations, unraveling underlying patterns in data and providing insights about the problem. With the continuous growth in the number of RNA Virus COVID-19 patients, likely, doctors and healthcare personnel won’t be helpful in treating every case. Thus, data scientists can help in the battle against RNA Viruses Mutations by implementing more innovative solutions in order to accomplish controlling severe acute respiratory syndrome quickly RNA Viruses are viruses that are made up of strands of RNA. This work studies the induction of machine learning models and motivating their design and purpose whenever possible. In the second part of this work, we analyze and discuss the biological data in the eyes of deep learning models. The core of our contributions rests in the role of machine learning in viruses pandemics. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Deep learning offering resilience from trending cyber-attacks, a review

During the Covid-19 pandemic world has witnessed the rise of cyber-attacks, especially during the Lockdown time course announced by the countries throughout the world, when almost every aspect of life changed the routine from offline to online. Protecting and securing information resources during pandemics has been a top priority for the modern computing world, with databases, banking, E-commerce and mailing services, etc. being the eye-catching credentials to the attackers. Apart from cryptography, machine learning and deep learning can offer an enormous amount of help in testing, training, and extracting negligible information from the data sets. Deep learning and machine learning have many methods and models in the account to detect and classify the different versions of cyber-attacks occasionally, from the datasets. Some of the most common deep learning methods inspired by the neural networks are Recurrent Neural Networks, Convolutional Neural Networks, Deep Belief Networks, Deep Boltzman Networks, Autoencoders, and Stacked Auto-encoders. Also counting machine learning algorithms into the account, there is a vast variety of algorithms that are meant to perform classification and regression. The survey will provide some of the most important deep learning and machine learning architectures used for Cyber-security and can offer protective services against cyber-attacks. The paper is a survey about various categories of cyber-attacks with a timeline of different attacks that took place in India and some of the other countries in the world. The final section of the report is about what deep learning methods can offer for developing and improving the security policies and examining vulnerabilities of an information system. © 2021 IEEE.

COVID-19 Fake News Detection System

This article deals with the problem of the rapidly increasing COVID-19 infodemic in the world. Thus, there is a need for an effective framework of detecting fake information or misleading news related to COVID-19 virus/disease. To resolve this, we have used a dataset obtained from ConstraintAI'21. The dataset consists of 10,700 tweets and online posts of fake and real news concerning COVID-19. Machine Learning (ML) algorithms compared in this paper to classify the given news or tweet into real or fake are Logistic Regression (LR), K-Nearest Neighbor (KNN), Linear Support Vector Machine (LSVM), Random Forest Classifier (RFC), Decision Tree (DT), Naive Bayes (NB) and Stochastic Gradient Descent (SGD) algorithm. Two feature extraction techniques were used count vectorization and TF-IDF. Deep Learning (DL) algorithms implemented using Adam optimizer are Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU). The best testing accuracy was achieved with the LSVM model using TF-IDF feature extraction method followed by Stochastic Gradient Descent classifier with TF-IDF feature extraction technique. LR, DT, and RFC performed better with the Count vectorization feature extraction technique, whereas LSVM, KNN, NB and SGD had better accuracy with TF-IDF feature extraction technique. The LSTM model performed slightly better among the DL algorithms. © 2022 IEEE.

Digital Customer Acquisition and Onboarding during the Pandemic

A must for telecom industry in times of social distancing: Digital customer acquisition and onboarding. Digital channels gained more importance as classical sales channels could not work with the expected performance during the pandemic. In this paper, the digital sales paperless project carried out in the telecom industry is handled. The identification scanning with OCR (Optical Character Recognition), the verification with deep learning artificial intelligence algorithms, the management of remote vendors and other stakeholders in extensive software projects is told. © 2021 IEEE.