Thousands of human lives are lost every year around the globe, apart from significant damage on property, animal life, etc., due to natural disasters (e.g., earthquake, flood, tsunami, hurricane and ...other storms, landslides, cloudburst, heat wave, forest fire). In this paper, we focus on reviewing the application of data mining and analytical techniques designed so far for (i) prediction, (ii) detection, and (iii) development of appropriate disaster management strategy based on the collected data from disasters. A detailed description of availability of data from geological observatories (seismological, hydrological), satellites, remote sensing and newer sources like social networking sites as twitter is presented. An extensive and in-depth literature study on current techniques for disaster prediction, detection and management has been done and the results are summarized according to various types of disasters. Finally a framework for building a disaster management database for India hosted on open source Big Data platform like Hadoop in a phased manner has been proposed. The study has special focus on India which ranks among top five counties in terms of absolute number of the loss of human life.
COVID-19 (Coronavirus Disease-19), a disease caused by the SARS-CoV-2 virus, has been declared as a pandemic by the World Health Organization on March 11, 2020. Over 15 million people have already ...been affected worldwide by COVID-19, resulting in more than 0.6 million deaths. Protein–protein interactions (PPIs) play a key role in the cellular process of SARS-CoV-2 virus infection in the human body. Recently a study has reported some SARS-CoV-2 proteins that interact with several human proteins while many potential interactions remain to be identified.
In this article, various machine learning models are built to predict the PPIs between the virus and human proteins that are further validated using biological experiments. The classification models are prepared based on different sequence-based features of human proteins like amino acid composition, pseudo amino acid composition, and conjoint triad.
We have built an ensemble voting classifier using SVMRadial, SVMPolynomial, and Random Forest technique that gives a greater accuracy, precision, specificity, recall, and F1 score compared to all other models used in the work. A total of 1326 potential human target proteins of SARS-CoV-2 have been predicted by the proposed ensemble model and validated using gene ontology and KEGG pathway enrichment analysis. Several repurposable drugs targeting the predicted interactions are also reported.
This study may encourage the identification of potential targets for more effective anti-COVID drug discovery.
The amalgamation of ‘Quantum computing’ with image processing represents the various ways of handling images for different purposes. In this paper,an image denoising scheme based on quantum wavelet ...transform is proposed.A noisy image is embedded into the wavelet coefficients of the original image. As a result,it affects the visual quality of the original image. The quantum Daubechis kernel of 4
t
h
order is used to extract wavelet coefficients from the resultant image. Then a quantum oracle is implemented with a suitable thresholding function to decompose the wavelet coefficients into a greater effect applicable for the original image and lower effect for the noisy image wavelet coefficients. However,original image wavelet coefficients are greater than the noisy wavelet coefficients.A detail computational time complexity analysis is given and compared with some state-of-art denoising techniques. The result analysis shows that the proposed quantum image denoising technique has better visual quality in terms of PSNR,MSE and QIFM values Compare to others.
Quantum machine learning bridges the gap between abstract developments in quantum computing and the applied research on machine learning. It generally exposes the synthesis of important machine ...learning algorithms in a quantum framework. Dimensionality reduction of a dataset with a suitable feature selection strategy is one of the most important tasks in knowledge discovery and data mining. The efficient feature selection strategy helps to improve the overall accuracy of a large dataset in terms of machine learning operations. In this paper, a quantum feature selection algorithm using a graph-theoretic approach has been proposed. The proposed algorithm has used the concept of correlation coefficient based graph-theoretic classical approach initially and then applied the quantum Oracle with CNOT operation to verify whether the dataset is suitable for dimensionality reduction or not. If it is suitable, then our algorithm can efficiently estimate their high correlation values by using quantum parallel amplitude estimation and amplitude amplification techniques. This paper also shows that our proposed algorithm substantially outperforms than some popular classical feature selection algorithms for supervised classification in terms of query complexity of
O
(
k
N
c
(
k
)
N
f
(
k
)
𝜖
)
, where N is the size of the feature vectors whose values are ⩾
T
H
m
i
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(minimum threshold), k is the number of iterations and where
𝜖
is the error for estimating those feature vectors. Compared with the classical counterpart, i.e. the performance of our quantum algorithm quadratically improves than others.
The purpose of this paper is to show concisely how we can promote chatbots in the medical sector and cure infectious diseases. We can create awareness through the users and the users can get proper ...medical solutions to prevent disease. We created a preliminary training model and a study report to improve human interaction in databases in 2021. Through natural language processing, we describe the human behaviors and characteristics of the chatbot. In this paper, we propose an AI Chatbot interaction and prediction model using a deep feedforward multilayer perceptron. Our analysis discovered a gap in knowledge about theoretical guidelines and practical recommendations for creating AI chatbots for lifestyle improvement programs. A brief comparison of our proposed model concerning the time complexity and accuracy of testing is also discussed in this paper. In our work, the loss is a minimum of 0.1232 and the highest accuracy is 94.32%. This study describes the functionalities and possible applications of medical chatbots and explores the accompanying challenges posed by the use of these emerging technologies during such health crises mainly posed by pandemics. We believe that our findings will help researchers get a better understanding of the layout and applications of these revolutionary technologies, which will be required for continuous improvement in medical chatbot functionality and will be useful in avoiding COVID-19.
Microarray technology has been successfully used in many biology studies to solve the protein–protein interaction (PPI) prediction computationally. For normal tissue, the cell regulation process ...begins with transcription and ends with the translation process. However, when cell regulation activity goes wrong, cancer occurs. Microarray data can precisely give high accuracy expression levels at normal and cancer-affected cells, which can be useful for the identification of disease-related genes. First, the differentially expressed genes (DEGs) are extracted from the cancer microarray dataset in order to identify the genes that are up-regulated and down-regulated during cancer progression in the human body. Then, proteins corresponding to these genes are collected from NCBI, and then the STRING web server is used to build the PPI network of these proteins. Interestingly, up-regulated proteins have always a higher number of PPIs compared to down-regulated proteins, although, in most of the datasets, the majority of these DEGs are down-regulated. We hope this study will help to build a relevant model to analyze the process of cancer progression in the human body.
In this paper, a quantum image edge extraction technique is developed with the help of the classical Robinson operator. A novel enhanced quantum representation (NEQR) technique is used to represent ...the quantum image. A quantum methodology is proposed to implement the Robinson masks of eight directions and perform convolution operations with the quantum shifted image sets. In this paper, a quantum parallel computation is used for evaluating gradients of the image intensity of all pixels, and a threshold-based quantum black box is designed to classify the points as edge points. The computational complexity of the proposed scheme for an image of size 2
n
× 2
n
is O(
n
2
+ 2
q
+ 3
). However, we also carry out the design and simulation analysis of our proposed algorithm and finally compare our results with some state-of-art image edge extraction algorithms in terms of PSNR (peak signal to noise ratio), MSE (mean square error) and execution time.
Emotion detection is one of the popular research topics in “Brain–Computer Interfacing” where researchers are trying to find the various emotional states of people. EEG signal is widely used for ...detecting different categories of emotions. The EEG signal is captured through multiple electrode channels, very few of them are useful for emotion detection. In our paper, a “Correlation-based subset selection” technique is introduced for dimension reduction. Then we proceed with classification process using “Higher Order Statistics” features of the reduced set of channels. However, we have classified four classes of emotions (positive, negative, angry and harmony) in our paper. The execution time of our proposed algorithm is O(n2 + 2n). The classification accuracy of this model with the reduced set of channels is 82%. Finally, we compare our proposed model with some popular emotion classification models and the result shows that our model substantially outperforms all the previous models. However, the proposed model helps physically disabled people to express their feelings with minimum time and cost-effectively.
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