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
n
(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.
This study aimed to assess the effects of major ecotoxic heavy metals accumulated in the Buriganga and Turag River systems on the liver, kidney, intestine, and muscle of common edible fish species
...Puntius ticto
,
Heteropneustes fossilis
, and
Channa punctatus
and determine the associated health risks. K was the predominant and reported as a major element. A large concentration of Zn was detected in diverse organs of the three edible fishes compared with other metals. Overall, trace metal analysis indicated that all organs (especially the liver and kidney) were under extreme threat because the maximum permissible limit set by different international health organizations was exceeded. The target hazard quotient and target cancer risk due to the trace metal content were the largest for
P. ticto
. Thus, excessive intake of
P. ticto
from the rivers Buriganga and Turag could result in chronic risks associated with long-term exposure to contaminants. Histopathological investigations revealed the first detectable indicators of infection and findings of long-term injury in cells, tissues, and organs. Histopathological changes in various tissue structures of fish functioned as key pointers of connection to pollutants, and definite infections and lesion types were established based on biotic pointers of toxic/carcinogenic effects. The analysis of histopathological alterations is a controlling integrative device used to assess pollutants in the environment.
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.
The goal of artificial intelligence (AI), a field with a solid scientific foundation, is to enable machines to simulate human intelligence and problem-solving abilities. AI focuses on the study, ...development, and application of complex algorithms and computational models, with a particular focus on deep learning techniques. The application of artificial intelligence to potato leaf disease detection can reduce the restrictions brought on by the artificial selection of spotted disease features and improve the efficiency and speedup. It has also turned into a research hotspot in the agricultural sector. This work consists of four types of potato leaf diseases, such as early-blight disease, septoria disease, late-blight disease, and black-leg disease. It is a challenging task to identify and classify such diseases from the healthy images. As a result, this work uses a set of benchmark deep learning models to identify and categorize these four disease types in potato leaves. Furthermore, compared to existing models, our recommended models provide better accuracy and have visible results. In comparison to other cutting-edge models, the results of the proposed deep ensemble algorithm (CNN, CNN-SVM, and DNN) offers the best accuracy of 99.98%. All the sample images (healthy and unhealthy) are collected from different farms of the West Bengal state and prepare the experimented dataset. The working model has an additional benefit in terms of running time complexity (O(E
i
)(1
i
k) and 17.86 s) and statistical comparison. Finally, LIME and SHAP are used to evaluate the findings, create more trust, and improve performance by providing explanations for predictions.
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