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Identification of instances of semantic objects of a particular class, which has been heavily incorporated in people's lives through applications like autonomous driving and security monitoring, is one of the most crucial and challenging areas of computer vision. Recent developments in deep learning networks for detection have improved object detector accuracy. To provide a detailed review of the current state of object detection pipelines, we begin by analyzing the methodologies employed by classical detection models and providing the benchmark datasets used in this study. After that, we'll have a look at the one- and two-stage detectors in detail, before concluding with a summary of several object detection approaches. In addition, we provide a list of both old and new apps. It's not just a single branch of object detection that is examined. Finally, we look at how to utilize various object detection algorithms to create a system that is both efficient and effective. and identify a number of emerging patterns in order to better understand the using the most recent algorithms and doing more study.

Due to the rapid spread of coronavirus 2019(Covid 19) in the present situation, early detection of covid 19 is very important. As per WHO [1] as of 5:26pm CEST, 5 August 2022, there have been 579,092,623 confirmed cases of COVID-19, including 6,407,556 deaths, reported globally. Lot of works have been reported to investigate covid19 using Deep learning algorithm over chest X-ray(CXR) images but as per our knowledge all have taken image data only as input to classify .In our work we have processed CNN model which can take CXR images along with corresponding the metadata(non imaging data) available with the dataset to classify Covid 19.CNN model based on Resnet 50,Dense net 121,Mobile Net,VGG16,Inception-V3 and custom CNN have been developed to accept the multimodal data,i.e metadata along with CXR image.All the mentioned CNN have been used as feature extractor of CXR image and extracted feature have been fused with the features extracted from metadata and 1 passed through the classifier for final classification.Some state of art Deep learning models have been run to classify the covid 19 on the same data set and compared with our best model .Experiments have been done in two phases.In the 1st phase we used CNN models on CXR image only and in the 2nd phase we ran all modified CNN models over the same CXR images with their matadata.The experimental results shows that the output of 2nd phase out performs the output of 1st phase.After that we compared our best model with other state of art models.Modified custom CNN model provides best results of 99.07 % overall accuracy

The availability of a tremendous amount of online information bringing about a broad interest in extracting relevant information in a compact and meaningful way, prompted the need for automatic text summarization. Hence, in the proposed system, the automated text summarization has been considered as an extractive single-document summarization problem, and a Cat Swarm Optimization (CSO) algorithm-based approach is proposed to solve it, whose objective is to generate good summaries in terms of content coverage, informative, anti-redundancy, and readability. In this work, input documents are pre-processed first. Then the cat population is initialized, where each individual (cat) in a binary vector is randomly initialized in the search space, considering the constraint. The objective function is then formulated considering different sentence quality measures. The Best Cat Memory Pool (BCMP) is initialized based on the objective function score. After that, individuals are randomly distributed for position updating to perform seeking/tracing mode operations based on the mixture ratio in each iteration. BCMP is also updated accordingly. Finally, an optimal individual is chosen to generate the summary after the last iteration. DUC-2001 and DUC-2002 data sets and ROUGE measures are used for system evaluation, and the obtained results are compared with the various state-of-the-art methods. We have achieved approximately 25% and 5% improvement on ROUGE-1 and ROUGE-2 scores on the datasets over the best existing method mentioned in this paper, revealing the proposed method's superiority. The proposed system is also evaluated considering the generational distance, CPU processing time, cohesion, and readability factor, reflecting that the system-generated summaries are readable, concise, relevant, and fast. We have also conducted a two-sample t-test, and one-way ANOVA test showing the proposed approach is statistically significant.

The proposed work is focused on establishing an interpretable Semantic Textual Similarity (iSTS) method for a pair of sentences, which can clarify why two sentences are completely or partially similar or have some variations. This proposed interpretable approach is a pipeline of five modules that begins with the pre-processing and chunking of text. Further chunks of two sentences are aligned using a one–to–multi (1:M) chunk aligner. Thereafter, support vector, Gaussian Naive Bayes and k–Nearest Neighbours classifiers are then used to create a multiclass classification algorithm, and different class labels are used to define an alignment type. At last, a multivariate regression algorithm is developed to find the semantic equivalence of an alignment with a score (that ranges from 0 to 5). The efficiency of the proposed method is verified on three different datasets and also compared to other state–of–the–art interpretable STS (iSTS) methods. The evaluated results show that the proposed method performs better than other iSTS methods. Most importantly, the modules of the proposed iSTS method are used to develop a Textual Entailment (TE) method. It is found that, when we combined chunk level, alignment, and sentence level features the entailment results significantly improves.

Coronavirus disease (COVID-19) is caused by severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) and has infected millions worldwide. SARS-CoV-2 spike protein uses Angiotensin-converting enzyme 2 (ACE2) and Transmembrane serine protease 2 (TMPRSS2) for entering and fusing the host cell membrane. However, interaction with spike protein receptors and protease processing are not the only factors determining coronaviruses' entry. Several proteases mediate the entry of SARS-CoV-2 virus into the host cell. Identifying receptor factors helps understand tropism, transmission, and pathogenesis of COVID-19 infection in humans. The paper aims to identify novel viral receptor or membrane proteins that are transcriptionally and biologically similar to ACE2 and TMPRSS2 through a fuzzy clustering technique that employs the Grey wolf optimizer (GWO) algorithm for finding the optimal cluster center. The exploratory and exploitation capability of GWO algorithm is improved by hybridizing mutation and crossover operators of the evolutionary algorithm. Also, the genetic diversity of the grey wolf population is enhanced by eliminating weak individuals from the population. The proposed clustering algorithm's effectiveness is shown by detecting novel viral receptors and membrane proteins associated with the pathogenesis of SARS-CoV-2 infection. The expression profiles of ACE2 protein and its co-receptor factor are analyzed and compared with single-cell transcriptomics profiling using the Seurat R toolkit, mass spectrometry (MS), and immunohistochemistry (IHC). Our advanced clustering method infers that cell that expresses high ACE2 level are more affected by SARS-CoV-infection. So, SARS-CoV-2 virus affects lung, intestine, testis, heart, kidney, and liver more severely than brain, bone marrow, skin, spleen, etc. We have identified 58 novel viral receptors and 816 membrane proteins, and their role in the pathogenicity mechanism of SARS-CoV-2 infection has been studied. Besides, our study confirmed that Neuropilins (NRP1), G protein-coupled receptor 78 (GPR78), C-type lectin domain family 4 member M (CLEC4M), Kringle containing transmembrane protein 1 (KREMEN1), Asialoglycoprotein receptor 1 (ASGR1), A Disintegrin and metalloprotease 17 (ADAM17), Furin, Neuregulin-1,(NRG1), Basigin or CD147 and Poliovirus receptor (PVR) are the potential co-receptors of SARS-CoV-2 virus. A significant finding is that heparin derivative glycosaminoglycans could block the replication of SARS-CoV-2 virus inside the host cytoplasm. The membrane protein N-Deacetylase/N-Sulfotransferase-2 (NDST2), Extostosin protein (EXT1, EXT2, and EXT3), Glucuronic acid epimerase (GLCE), and Xylosyltransferase I, II (XYLT1, XYLT2) could act as the therapeutic target for inhibiting the spread of SARS-CoV-2 infection. Drugs such as carboplatin and gemcitabine are effective in such situations.

As the world is progressing faster, to compete with the demand, the need for proficient computing technology has increased, resulting in huge volumes of data. Consequently, the extraction of relevant information from such a massive volume of data in a short time becomes challenging. Hence, automatic text summarization (TS) has emerged as an efficient solution to this problem. In the current study, the automatic TS problem is formulated as a multi-objective optimization problem, and to mitigate this problem, the modified cat swarm optimization (MCSO) strategy is employed. In this work, the population is represented as a collection of feasible individuals where the summary length limit is considered as a constraint that determines the feasibility of an individual. Here, each individual is shaped by randomly selecting some of the sentences encoded in the binary form. Furthermore, two objective functions, namely “coverage and informativeness” and “anti-redundancy,” are used to evaluate each individual’s fitness. Also, to update the position of an individual, genetic and bit manipulating operators and the best cat memory pool have been incorporated into the system. Finally, from the generated non-dominated optimal solutions, the best solution is selected based on the ROUGE score for the summary generation process. The system’s performance is evaluated using ROUGE-1 and ROUGE-2 measures on two standard summarization datasets, namely DUC-2001 and DUC-2002, which revealed that the proposed approach achieved a noticeable improvement in ROUGE scores compared to many state-of-the-art methods mentioned in this paper. The system is also evaluated using the generational distance, CPU processing time, and cohesion, reflecting that the obtained summaries are readable, concise, and relevant being fast converging.

Invention of microarray DNA technology has paved the way of clustering gene expression data. It helps to examine and study different gene expression patterns at other instances of time points. To monitor such gene expression distribution, the fuzzy clustering algorithm is used commonly. This paper proposes an assimilated form of fuzzy clustering with a Weighted Differential Evolution (WDE) optimization algorithm for clustering gene data. Moreover, WDE algorithm based fuzzy clustering can regress efficient evolutionary search procedures. Through the simulation study, it is observed that the WDE algorithm that has been proposed for solving numerical optimization problem can also cluster real-life gene expression data effectively. Different cluster validity indexes are used to validate clustering solutions. Comparative performance analysis and experiments are conducted on real-life datasets and gene expression datasets numerically and graphically.

Image captioning is to generate captions for a given image based on the content of the image. To describe an image efficiently, it requires extracting as much information from it as possible. Apart from detecting the presence of objects and their relative orientation, the respective purpose intending the topic of the image is another vital information which can be incorporated with the model to improve the efficiency of the caption generation system. The sole aim is to put extra thrust on the context of the image imitating human approach, as the mere presence of objects which may not be related to the context representing the image should not be a part of the generated caption. In this work, the focus is on detecting the topic concerning the image so as to guide a novel deep learning-based encoder–decoder framework to generate captions for the image. The method is compared with some of the earlier state-of-the-art models based on the result obtained from MSCOCO 2017 training data set. BLEU, CIDEr, ROGUE-L, METEOR scores are used to measure the efficacy of the model which show improvement in performance of the caption generation process.

With increasing popularity of math formula search, a number of Math Information Retrieval (MIR) systems have come into being. Characterized by either conventional text-based techniques or newly evo...

Enzymes like uracil DNA glycosylase (UDG) can achieve ground state destabilization, by polarizing substrates to mimic rare tautomers. On the basis of computed nucleus independent chemical shifts, NICS(1)(zz), and harmonic oscillator model of electron delocalization (HOMED) analyses, of quantum mechanics (QM) and quantum mechanics/molecular mechanics (QM/MM) models of the UDG active site, uracil is strongly polarized when bound to UDG and resembles a tautomer >12 kcal/mol higher in energy. Natural resonance theory (NRT) analyses identified a dominant O2 imidate resonance form for residue bound 1-methyluracil. This “tautomeric strain” raises the energy of uracil, making uracilate a better than expected leaving group. Computed gas-phase S(N)2 reactions of free and hydrogen bonded 1-methyl-uracil demonstrate the relationship between the degree of polarization in uracil and the leaving group ability of uracilate.

AIM: Tea (Camellia sinensis (L.) O. Kuntze) is an economically important caffeine‐containing beverage crop with massive plantation in the Northeast corner of the agroclimatic belt of India. The main aim of the work was to isolate, identify and characterize the native plant growth promoting endophytes associated with tea for future microbe based bioformulation. METHODS AND RESULTS: A total of 129 endophytic bacteria were isolated and characterized for plant growth promoting traits such as indole‐3‐acetic acid (IAA), phosphate solubilization, ammonia production, biocontrol traits like siderophore and extracellular enzyme production. BOX‐PCR fingerprinting was used to differentiate the various bacterial isolates obtained from six different tea species. 16S rRNA sequencing and blast analysis showed that these isolates belonged to different genera, that is, Bacillus, Brevibacterium, Paenibacillus and Lysinibacillus. Lysinibacillus sp. S24 showed the highest phosphate solubilization and IAA acid production efficiency of 268·4 ± 14·3 and 13·5 ± 0·5 µg ml⁻¹, respectively. Brevibacterium sp. S91 showed the highest ammonia production of 6·2 ± 0·5 µmol ml⁻¹. Chitinase, cellulase, protease and pectinase activities were shown by 4·6, 34·1, 27·13 and 13·14% of the total isolates, respectively. Similarly, 41% of the total isolates were positive for 1‐aminocyclopropane‐1‐carboxylic acid (ACC) deaminase activity. Further, the potent PGP isolates, S24 and S91 were able to enhance the vegetative parameters such as dry/fresh weight of root and shoot of tea plants in nursery conditions. CONCLUSION: Our findings corroborate that tea endophytic bacteria possess the potential to demonstrate multiple PGP traits both, in vivo and in vitro and have the potential for further large‐scale trials. SIGNIFICANCE AND IMPACT OF THE STUDY: The exploration of tea endophytic bacterial community is suitable for the development of bioformulations for an integrated nutrient management and thus sustainable crop production and decreasing the hazardous effects of chemical fertilizers on the environment and human health.

In the present investigation, an efficient visible light-active, graphene oxide-based zirconium oxide nanocomposite (GO–ZrO2) has been synthesized by co-precipitation method. The synthesized photocatalyst was characterized by XRD, FTIR, FE-SEM, EDS, TEM, TGA, PL, UV-DRS and BET surface area analysis. The characterization results illustrate the homogeneous dispersion of ZrO2 nanoparticles in the GO–ZrO2 nanocomposite with excellent harmony between GO and ZrO2 nanoparticles. The photocatalytic efficiency of the synthesized GO–ZrO2 nanocomposite was evaluated by photodegradation of hazardous, water-soluble rhodamine B and methylene blue dyes under visible light irradiation. The 90% photocatalytic degradation of rhodamine B dye (with initial conc. 30 mg/L) was observed in 105 min of visible light irradiation, while 99.23% of methylene blue (with initial conc. 100 mg/L) dyes degradation was observed in just 60 min using 25 mg/100 ml dose of GO–ZrO2 nanocomposite as a photocatalyst. The mechanistic investigation using scavengers suggest that the superoxide (O 2 ·− ) is the most reactive species involved in the photodegradation of organic dyes. The synthesized photocatalyst GO–ZrO2 nanocomposite also exhibits excellent thermal stability and reusability.

Owing to the exclusive properties of cellulose, it is widely used in several industries. With the discovery of nano-sized cellulose, various properties could be reformed, and several new applications could be achieved using this material. In this chapter, we discuss about how nanocellulose has gained importance in this modern era. We introduce the term advanced materials and elaborate it with respect to cellulose nanoparticles. Advanced materials are the next-generation materials with enhanced desirable properties, and nanocellulose is a prominent player in the huge industry of advanced materials. We finally focus on the in situ polymerization technique and its use in the preparation of advanced materials.

SARS-CoV-2, the main cause of COVID-19, is a new coronavirus identified as the cause of a disease outbreak in 2019 that originated in China. COVID-19 was declared a pandemic for the entire globe in March 2020 by the World Health Organization. The transmission rate of this virus is fast and spreads from person to person through discharge from the nose or droplets of saliva when an infected person sneezes or coughs. Even COVID-19 may spread from people who are not showing symptoms. An Antibody test might take 2–3 weeks to reveal the results of COVID-19. Presently, the Saliva Test for COVID-19, which takes 48 h to reveal the results, is most commonly used. In a study of more than 1000 patients published in the journal Radiology, Chest Computed Tomography surpassed lab testing in the diagnosis of COVID-19. Researchers concluded that CT scan images should be used as the primary screening tool for COVID-19. In remote village areas, we may not have any specialized doctor who can diagnose the CT scan image taken from a suspected COVID-19 patient. This proposed work can diagnose COVID-19 suspected CT scan images very fast, without the intervention of any specialized doctor. CT scan images have been taken and trained in the Residual Networks model, which gives a maximum of 95.97% accuracy.

Single-cell transcriptome sequencing (scRNA-seq) technology enables to analyze the RNA expression of each cell over a different instance of time. This provides the path to identify different patterns of gene expression through gene clustering. To analyze the gene expression pattern of scRNA-seq data, soft clustering plays a crucial role. This can exploit the underlying structure of an unknown set of patterns. To assign data points to different clusters, a newly developed weighted distance measure is utilized in the paper. Here, minimization of variance within the cluster is posed as the objective function. To obtain a good partition of the data, different search operator of gray wolf optimizer (GWO) technique is explored in conjunction with fuzzy C-means (FCM) algorithm. The proposed (fuzzy C-means and GWO) is then compared with some recent scRNA-seq clustering techniques such as Seurat, SC3, CIDR, t-SNE followed by k-means and ensemble clustering using cluster validity index. Analysis of the clustering result demonstrated that the application of an evolutionary-based optimization technique jointly with the FCM algorithm can cluster and identify the transcriptomics gene effectively.

Research on automatic text summarization is growing with the rapid growth of online information services. In the current study, Extractive Single Document Summarization (ESDS) is formulated as a multi-objective optimization problem, and an Archive-based Micro Genetic-2 Algorithm (AMGA2) is employed to solve this problem. In this approach, each summary is represented in a binary form. The initial population is created using the clustering and Latin Hypercube (LH) sampling, resulting in a good overall unbiased population distribution in the genotypic space. Two objective functions were formed considering content coverage and informativeness as objective function 1, and anti-redundancy as objective function 2. The summary length limit is considered as the constraint. For generating offspring, an archive-based mating pool and modified mutation operator of differential evaluation are used. Finally, from the optimal set of solutions, the best solution is selected to generate the summary. For evaluation, DUC-2001 and DUC-2002 datasets are utilized, and the obtained results are compared with various existing methods using ROUGE measures. The obtained result illustrates the superiority of our approach in terms of convergence rate and ROUGE scores.

Spatial Relation identification is one of the integral parts of Spatial Information Retrieval. It deals with identifying the spatially related objects in view of their physical orientation or placement with respect to each other. The concept is widely used in many fields such as Robotics, Image Caption Generation and many more such areas. In this work the focus is to gather information from multiple modalities such as Image and its corresponding Text so as to strengthen the learning process for the identification of Spatial Relation pairs from a given text. Two different multimodal approaches are proposed in this work. In the first approach, information is explored as a sequential learning process where the individual Spatial Roles are identified as connected entities, which makes the Spatial Relation retrieval easy and efficient enough. To counter the small size of the dataset along with necessity to avoid overfitting, an efficient backward propagation based Neural Network was used to classify the candidate roles and the relations. The feature selection was different for all the classification tasks. Building on the selected feature from the first approach, the second approach uses a transfer learning method that utilizes an existing image caption generation model to retrieve the vital topic based information from image which is then used for the task. Thereby both approaches used information from two modalities which are further used to train the system in the respective approach. The model achieves state-of-the-art performance in terms of Precision for two of the Spatial Roles identification. This validates the advantage of using multimodal learning when compared with other partial-multimodal processes.

Computed association strengths for 43 purine and pteridine quartets (38 to 100 kcal mol-1) show excellent linear correlation with π-conjugation gain in the assembled monomers (r2 = 0.965). Even quartets having the same secondary electrostatic interactions can display very different association strengths depending on the π-conjugation patterns of the monomeric units.