Intelligent IoT-BOTNET attack detection model with optimized hybrid classification model

The botnet have developed into a severe risk to Internet of Things (IoT) systems as a result of manufacturers ‘insufficient security policies and end users' lack of security awareness. By default, several ports are open and user credentials are left unmodified. ML and DL strategies have been suggested in numerous latest research for identifying and categorising botnet assaults in the IoT context, but still, it has a few issues like high error susceptibility, working only with a large amount of data, poor quality, and data acquisition. This research provided use of a brand-new IoT botnet detector built on an improved hybrid classifier. The proposed work's main components are "pre-processing, feature extraction, feature selection, and attack detection." Following that, the improved Information Gain (IIG) model is used to choose the most reliable characteristics from the received information. To detect an attack, a hybrid classifier is utilized which can be constructed by integrating the optimized Bi-GRU with the Recurrent Neural Network (RNN). To increase the detection accuracy of IoT-BOTNETS, a novel hybrid optimization approach called SMIE (Slime Mould with Immunity Evolution) is created by conceptually integrating two conventional optimization modes: Coronavirus herd immunity optimizer (CHIO) and the Slime mould algorithm. The final output of the hybrid classifier displays the presence or absence of IoT-BOTNET attacks. The projected model's accuracy is 97%, which is 22.6%, 18.5%, 27.8%, 22.6%, and 24.8% higher than the previous models like GWO+ HC, SSO+ HC, WOA+ HC, SMA+ HC, and CHIO+ HC, respectively. © 2022

Construction and Analysis of Surrounding Travel Demanding Graph Based on Dual Contrastive Learning Text Classification and Graph Neural Network

Understanding the main information about the current situation of the tourism market has become an urgent need and new trends in the development of the tourism market. In this paper, we use natural language processing technology to analyze the development of tourism around Maoming City, Guangdong Province during the COVID-19 epidemic by means of data mining methods to build a local tourism graph, refine and design models and methods such as RoBERTa-BiGRU-Attention fusion model, dual contrastive learning, BERT-BiLSTM-CRF named entity identification technique, improved Apriori algorithm, GNNLP model based on conventional models and proved the rationality and efficiency of the improved model by comparative test, provide oriented suggestions to help government departments promote tourism and tourism enterprises product supply, optimize resource allocation and explore the market constantly during the epidemic period after scientific analysis and summary. © 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0)

Intelligent IoT-BOTNET Attack Detection Model with optimized Hybrid Classification Model

The botnet have developed into a severe risk to Internet of Things (IoT) systems as a result of manufacturers ‘insufficient security policies and end users' lack of security awareness. By default, several ports are open and user credentials are left unmodified. ML and DL strategies have been suggested in numerous latest research for identifying and categorising botnet assaults in the IoT context, but still, it has a few issues like high error susceptibility, working only with a large amount of data, poor quality, and data acquisition. This research provided use of a brand-new IoT botnet detector built on an improved hybrid classifier. The proposed work's main components are "pre-processing, feature extraction, feature selection, and attack detection." Following that, the improved Information Gain (IIG) model is used to choose the most reliable characteristics from the received information. To detect an attack, a hybrid classifier is utilized which can be constructed by integrating the optimized Bi-GRU with the Recurrent Neural Network (RNN). To increase the detection accuracy of IoT-BOTNETS, a novel hybrid optimization approach called SMIE (Slime Mould with Immunity Evolution) is created by conceptually integrating two conventional optimization modes: Coronavirus herd immunity optimizer (CHIO) and the Slime mould algorithm. The final output of the hybrid classifier displays the presence or absence of IoT-BOTNET attacks. The projected model's accuracy is 97%, which is 22.6%, 18.5%, 27.8%, 22.6%, and 24.8% higher than the previous models like GWO+ HC, SSO+ HC, WOA+ HC, SMA+ HC, and CHIO+ HC, respectively.

A Deep Learning approach for detection and analysis of respiratory infections in covid-19 patients using RGB and infrared images

Within a short period, the severe acute respiratory syndrome Coronavirus Disease 2019 (COVID-19) has become a devastating global pandemic, causing enormous losses to human civilization worldwide. A significant feature of COVID-19, according to recent investigations, is an altered respiratory state induced by viral infections. In this paper, we present a non-contact method for screening the respiratory health of COVID-19 patients using RGB-infrared sensors to analyze their breathing patterns. The block diagram the proposed method is shown in Fig. 1. First, we use facial recognition to obtain breathing data from the individuals. The respiratory data is applied to multiple neural networks, including LSTM, BiLSTM, GRU, and BiGRU. An attention mechanism is then used in the neural network to obtain a health screening result from the respiration dataset. With an accuracy of 70.83 percent, our BiGRU model accurately identifies the respiratory health condition whether it is normal or abnormal. © 2022 ACM.

A Deep Learning Approach for Public Sentiment Analysis in COVID-19 Pandemic

Sentiment analysis is a process of extracting opinions into the positive, negative, or neutral categories from a pool of text using Natural Language Processing (NLP). In the recent era, our society is swiftly moving towards virtual platforms by joining virtual communities. Social media such as Facebook, Twitter, WhatsApp, etc are playing a very vital role in developing virtual communities. A pandemic situation like COVID-19 accelerated people's involvement in social sites to express their concerns or views regarding crucial issues. Mining public sentiment from these social sites especially from Twitter will help various organizations to understand the people's thoughts about the COVID-19 pandemic and to take necessary steps as well. To analyze the public sentiment from COVID-19 tweets is the main objective of our study. We proposed a deep learning architecture based on Bidirectional Gated Recurrent Unit (BiGRU) to accomplish our objective. We developed two different corpora from unlabelled and labeled COVID-19 tweets and use the unlabelled corpus to build an improved labeled corpus. Our proposed architecture draws a better accuracy of 87% on the improved labeled corpus for mining public sentiment from COVID-19 tweets. © 2022 IEEE.

Deep Learning for COVID-19 Prediction based on Blood Test

The COVID-19 pandemic is highly infectious and has caused many deaths. The COVID-19 infection diagnosis based on blood test is facing the problems of long waiting time for results and shortage of medical staff. Although several machine learning methods have been proposed to address this issue, the research of COVID-19 prediction based on deep learning is still in its preliminary stage. In this paper, we propose four hybrid deep learning models, namely CNN+GRU, CNN+Bi-RNN, CNN+Bi-LSTM and CNN+Bi-GRU, and apply them to the blood test data from Israelta Albert Einstein Hospital. We implement the four proposed models as well as other existing models CNN, CNN+LSTM, and compare them in terms of accuracy, precision, recall, F1-score and AUC. The experiment results show that CNN+Bi-GRU achieves the best performance in terms of all the five metrics (accuracy of 0.9415, F1-score of 0.9417, precision of 0.9417, recall of 0.9417, and AUC of 0.91).

Lightweight Skip Connections With Efficient Feature Stacking for Respiratory Sound Classification

As the number of deaths from respiratory diseases due to COVID-19 and infectious diseases increases, early diagnosis is necessary. In general, the diagnosis of diseases is based on imaging devices (e.g., computed tomography and magnetic resonance imaging) as well as the patient's underlying disease information. However, these examinations are time-consuming, incur considerable costs, and in a situation like the ongoing pandemic, face-to-face examinations are difficult to conduct. Therefore, we propose a lung disease classification model based on deep learning using non-contact auscultation. In this study, two respiratory specialists collected normal respiratory sounds and five types of abnormal sounds associated with lung disease, including those associated with four lung lesions in the left and right anterior chest and left and right posterior chest. For preprocessing and feature extraction, the noise was removed using three pass filters (low, band, and high), and respiratory sound features were extracted using the Log-Mel Spectrogram-Mel Frequency Cepstral Coefficient followed by feature stacking. Then, we propose a lung disease classification model of dense lightweight convolutional neural network-bidirectional gated recurrent unit skip connections using depthwise separable convolution based on the extracted respiratory sound information. The performance of the classification model was compared with both the baseline and the lightweight models. The results indicate that the proposed model achieves high performance and has an accuracy of 92.3%, sensitivity of 92.1%, specificity of 98.5%, and f1-score of 91.9%. Using the proposed model, we aim to contribute to the early detection of diseases during the COVID-19 pandemic.

Predicting Drug-Target Affinity Based on Recurrent Neural Networks and Graph Convolutional Neural Networks.

BACKGROUND: Drug development requires a lot of money and time, and the outcome of the challenge is unknown. So, there is an urgent need for researchers to find a new approach that can reduce costs. Therefore, the identification of drug-target interactions (DTIs) has been a critical step in the early stages of drug discovery. These computational methods aim to narrow the search space for novel DTIs and to elucidate the functional background of drugs. Most of the methods developed so far use binary classification to predict the presence or absence of interactions between the drug and the target. However, it is more informative but also more challenging to predict the strength of the binding between a drug and its target. If the strength is not strong enough, such a DTI may not be useful. Hence, the development of methods to predict drug-target affinity (DTA) is of significant importance Method: We have improved the GraphDTA model from a dual-channel model to a triple-channel model. We interpreted the target/protein sequences as time series and extracted their features using the LSTM network. For the drug, we considered both the molecular structure and the local chemical background, retaining the four variant networks used in GraphDTA to extract the topological features of the drug and capturing the local chemical background of the atoms in the drug by using BiGRU. Thus, we obtained the latent features of the target and two latent features of the drug. The connection of these three feature vectors is then inputted into a 2 layer FC network, and a valuable binding affinity is the output. RESULT: We used the Davis and Kiba datasets, using 80% of the data for training and 20% of the data for validation. Our model showed better performance when compared with the experimental results of GraphDTA Conclusion: In this paper, we altered the GraphDTA model to predict drug-target affinity. It represents the drug as a graph and extracts the two-dimensional drug information using a graph convolutional neural network. Simultaneously, the drug and protein targets are represented as a word vector, and the convolutional neural network is used to extract the time-series information of the drug and the target. We demonstrate that our improved method has better performance than the original method. In particular, our model has better performance in the evaluation of benchmark databases.

Time series forecasting of new cases and new deaths rate for COVID-19 using deep learning methods.

The first known case of Coronavirus disease 2019 (COVID-19) was identified in December 2019. It has spread worldwide, leading to an ongoing pandemic, imposed restrictions and costs to many countries. Predicting the number of new cases and deaths during this period can be a useful step in predicting the costs and facilities required in the future. The purpose of this study is to predict new cases and deaths rate one, three and seven-day ahead during the next 100 days. The motivation for predicting every n days (instead of just every day) is the investigation of the possibility of computational cost reduction and still achieving reasonable performance. Such a scenario may be encountered in real-time forecasting of time series. Six different deep learning methods are examined on the data adopted from the WHO website. Three methods are LSTM, Convolutional LSTM, and GRU. The bidirectional extension is then considered for each method to forecast the rate of new cases and new deaths in Australia and Iran countries. This study is novel as it carries out a comprehensive evaluation of the aforementioned three deep learning methods and their bidirectional extensions to perform prediction on COVID-19 new cases and new death rate time series. To the best of our knowledge, this is the first time that Bi-GRU and Bi-Conv-LSTM models are used for prediction on COVID-19 new cases and new deaths time series. The evaluation of the methods is presented in the form of graphs and Friedman statistical test. The results show that the bidirectional models have lower errors than other models. A several error evaluation metrics are presented to compare all models, and finally, the superiority of bidirectional methods is determined. This research could be useful for organisations working against COVID-19 and determining their long-term plans.

A deep learning framework using CNN and stacked Bi-GRU for COVID-19 predictions in India.

The novel coronavirus infection (COVID-19) first appeared in Wuhan, China, in December 2019. COVID-19 declared as a global pandemic by the WHO was the most rapidly spreading disease all across the world. India, the second most populated nation in the world, is still fighting it, when coronavirus reached the stage where community transmission takes place at an exponential rate. Therefore, it is crucial to examine the future trends of COVID-19 in India and anticipate how it will affect economic and social growth in a short run. In this paper, a new deep learning framework using CNN and stacked Bi-GRU has been developed for predicting and analyzing the COVID-19 cases in India. The proposed model can predict the next 30 days' new positive cases, new death cases, recovery rate and containment and health index values with high accuracy. The proposed method is compared against Gaussian process regression (GPR) model on COVID-19 datasets. The experimental result shows that the proposed framework is highly reliable for COVID-19 prediction over the GPR model.