CNN-OSBO Encoder-Decoder Architecture for Drug-Target Interaction (DTI) Prediction of Covid-19 Targets

Drug Target Interaction (DTI) prediction is an important factor is drug discovery and repositioning (DDR) since it detects the response of a drug over a target protein. The Coronavirus disease 2019 (COVID-19) disease created groups of deadly pneumonia with clinical appearance mostly similar to SARS-CoV. The precise diagnosis of COVID-19 clinical outcome is more challenging, since the diseases has various forms with varying structures. So predicting the interactions between various drugs with the SARS-CoV target protein is very crucial need in these days, which may leads to discovery of new drugs for the deadly disease. Recently, Deep learning (DL) techniques have been applied by the researches for DTI prediction. Since CNN is one of the major DL models which has the ability to create predictive feature vectors or embeddings, CNN-OSBO encoder-decoder architecture for DTI prediction of Covid-19 targets has been designed Given the input drug and Covid-19 target pair of data, they are fed into the Convolution Neural Networks (CNN) with Opposition based Satin Bowerbird Optimizer (OSBO) encoder modules, separately. Here OSBO is utilized for regulating the hyper parameters (HPs) of CNN layers. Both the encoded data are then embedded to create a binding module. Finally the CNN Decoder module predicts the interaction of drugs over the Covid-19 targets by returning an affinity or interaction score. Experimental results state that DTI prediction using CNN+OSBO achieves better accuracy results when compared with the existing techniques. © 2022 IEEE.

Enhancing Bandwidth Utilization of IP Telephony over IPv6 Networks

The demand for the telecommunication services, such as IP telephony, has increased dramatically during the COVID-19 pandemic lockdown. IP telephony should be enhanced to provide the expected quality. One of the issues that should be investigated in IP telephony is bandwidth utilization. IP telephony produces very small speech samples attached to a large packet header. The header of the IP telephony consumes a considerable share of the bandwidth allotted to the IP telephony. This wastes the network's bandwidth and influences the IP telephony quality. This paper proposes a mechanism (called Smallerize) that reduces the bandwidth consumed by both the speech sample and the header. This is achieved by assembling numerous IP telephony packets in one header and use the header's fields to carry the speech sample. Several metrics have been used to measure the achievement Smallerize mechanism. The number of calls has been increased by 245.1% compared to the typical mechanism. The bandwidth saving has also reached 68% with the G.28 codec. Therefore, Smallerize is a possible mechanism to enhance bandwidth utilization of the IP telephony. © 2023 CRL Publishing. All rights reserved.

Prediction model of COVID-19 based on spatiotemporal attention mechanism

The continuous spread of the COVID-19 has brought profound impacts on human society. For the prevention and control of virus spreading, it is critical to predict the future trend of epidemic situation. Existing studies on COVID-19 spread prediction, based on classic SEIR models or naive time-series prediction models, are rarely considering the characteristics of complex regional correlation and strong time series dependence in the process of epidemic spread, which limits the performance of epidemic prediction. To this end, we propose a COVID-19 prediction model based on auto-encoder and spatiotemporal attention mechanism. The proposed model estimates the trend of COVID-19 by capturing the dynamic spatiotemporal dependence between the epidemic situation sequences of different regions. In particular, a spatial attention mechanism is implemented in the encoder section for every given region to capture the dynamic correlation between the epidemic situation time-series of the region and those of the related regions. Based on the leant correlation, an long short-term memory (LSTM) network is then applied to extract the epidemic sequential features for the given region by combining the recent epidemic situations of the region and the related regions. On the other hand, to better predict the dynamic of the future epidemic situation, temporal attention is introduced into an LSTM network-based decoder to capture the temporal dependence of the epidemic situation sequence. We evaluate the proposed model on several open datasets of COVID-19, and experimental results show that the proposed model outperforms the state-of-the-art models. The metrics of RMSE and MAE of the proposed model on the COVID-19 epidemic dataset of some European countries decreased 22. 3% and 25. 0%. The metrics of RMSE and MAE of the proposed model on the COVID-19 epidemic dataset of some Chinese provinces decreased 10. 1% and 10. 4%. © 2022 Beijing University of Aeronautics and Astronautics (BUAA). All rights reserved.

Development of a Network Camera with a Built-in Mask and QR Code Recognition Function

Therefore, in different environments and places, we can use the network camera of the video surveillance system to detect whether people are wearing masks, and scan the OR code to obtain their information. Based on the collection and analysis of scene data by network cameras, this paper adopts a deep learning method, that is, the detection of mask wearing conditions can be realized through training on the face data of labeled masks, and this technology is combined with QR code recognition technology. It is also built into the network camera. In the current context, the research of this technology has very important value and significance for epidemic control during this period. Due to the outbreak of covid 19 in 2019, in order to prevent the epidemic, this article designed a network camera with a built in mask and QR code recognition function for video surveillance and access control system development. © 2022 IEEE.

Classification of Student Success in Online Courses Using Feature Selection and Convolutional Neural Network

During the Covid 19 pandemic, the number of online learning environments that were previously used but not widespread has increased. Machine learning methods and estimation and classification studies of student success on learning analytics data in these environments have gained importance in recent years. In this study, a method based on OHE (one-hot encoding) representation of course activities, feature selection, and convolutional neural network is proposed for the classification of student success. In order to demonstrate the effectiveness of the proposed method, comparative evaluations were presented with incoming machine learning algorithms (RF, MLP, k-NN) and literature. Experiments on the UK Open University online learning dataset, which is available to researchers, show that the proposed method improves current study success in the literature. © 2022 IEEE.

A Deep Learning Approach for Viral DNA Sequence Classification using Genetic Algorithm

DNA sequence classification is one of the major challenges in biological data processing. The identification and classification of novel viral genome sequences drastically help in reducing the dangers of a viral outbreak like COVID-19. The more accurate the classification of these viruses, the faster a vaccine can be produced to counter them. Thus, more accurate methods should be utilized to classify the viral DNA. This research proposes a hybrid deep learning model for efficient viral DNA sequence classification. A genetic algorithm (GA) was utilized for weight optimization with Convolutional Neural Networks (CNN) architecture. Furthermore, Long Short-Term Memory (LSTM) as well as Bidirectional CNN-LSTM model architectures are employed. Encoding methods are needed to transform the DNA into numeric format for the proposed model. Three different encoding methods to represent DNA sequences as input to the proposed model were experimented: k-mer, label encoding, and one hot vector encoding. Furthermore, an efficient oversampling method was applied to overcome the imbalanced dataset issues. The performance of the proposed GA optimized CNN hybrid model using label encoding achieved the highest classification accuracy of 94.88% compared with other encoding methods © 2022, International Journal of Advanced Computer Science and Applications.All Rights Reserved.

Ivory: Learning Network Adaptive Streaming Codes

With the growing interest in web services during the current COVID-19 outbreak, the demand for high-quality low-latency interactive applications has never been more apparent. Yet, packet losses are inevitable over the Internet, since it is based on UDP. In this paper, we propose Ivory, a new real-world system framework designed to support network adaptive error control in real-time communications, such as VoIP, using a recently proposed low-latency streaming code. We design and implement our prototype over UDP that can correct or retransmit lost packets conditional on network conditions and application requirements.To maintain the highest quality, Ivory attempts to correct as many lost packets as possible on-the-fly, yet incurring the smallest footprint in terms of coding overhead over the network. To achieve such an objective, Ivory uses a deep reinforcement learning agent to estimate the best coding parameters in real-time based on observed network states and experience learned. It learns offline the best coding parameters to use based on previously observed loss patterns and takes into account the round-trip time observed to decide on the optimum decoding delay for a low-latency application. Our extensive array of experiments shows that Ivory achieves a better trade-off between recovering packets and using lower redundancy than the state-of-the-art network adaptive streaming codes algorithms. © 2022 IEEE.

Protocol Wireless Medical Sensor Networks in IoT for the Efficiency of Healthcare

In this article, a new medical communication scheme, protocol wireless medical sensor networks for the efficiency of healthcare (PWMSN4EoCH), shorten by (PEH), which uses hasty strategy and random network coding (RNC), is proposed. The new concept improves the performance of the healthcare network. It quickly analyzes the medical network description, focusing on some basic parameters for narrowband Internet of Things (NB-IoT) systems in wireless mesh networks (WMNs). This PEH effectively meets the requirements prescribed for wireless telemedicine applications in which medical sensors (MSs) share the downlink and uplink resources to its neighborhood, including wireless health hubs (WHHs) and wireless base stations (WBSs) for controlling the health of the human body. The PEH scheme substantially accelerates the implementation devices of telemedicine for patient satisfaction. In contrast, the state-of-the-art technique (SoAT) scheme, which is currently used, misses the entirety of the proposed principle. The proposed scheme is compared with the SoAT in terms of message size (bytes), round-trip time (RTT) (ms), overall network capacity (ONC) (bytes/s, and delivery delay (DD) in ms. Our investigation has proved that the RTT, ONC, and DD of the proposed PEH are much better than the SoAT schemes, achieving 64%, 66%, and 71%, respectively. The simulation studies clearly indicate that the PEH introduces more than 64% performance enhancement over the SoAT scheme.

Mortality Rate Extrapolation Based on Symptomatic Symptoms of Novel Corona Virus

Covid-19 is a medical pandemic originated in China due to the virus, eventually affecting all parts of the world. The study deals with the mortality rate in India using various machine learning techniques. The secondary data collection is done using various websites like Kaggle. The dataset collected contains lot of noise which is pre-processed and the inputs are converted into vectors using One-Hot encoding. The results are compared using various regression techniques and various output parameters like Accuracy, Variance, Max Square etc. The results indicated that lasso regression gives the best result. © 2021 IEEE.