Search

In Healthcare, the Internet of Things (IoT)-enabled surveillance cameras capture thousands of images every day, where face recognition provides reliable security as well as smart treatment through patient sentiment analysis, emotion detection, automated nurse calls, and hospital traffic systems. In this article, a secure face recognition system for the IoT-enabled Healthcare system has been proposed. Here each registered person will be identified by his/her face biometric with strong template protection schemes. To protect the biometric information, three-step template protection techniques are proposed: (i) Cancelable biometrics , (ii) BioCrypto-Circuit , and (iii) BioCrypto-Protection . The performance of the proposed system has been tested on four benchmark face databases, CVL, IITK, Casia-Face-v5, and FERET. The results of the proposed system are reported in terms of the correct recognition rate and the equal error rate. These performances have also been compared with some state-of-the-art methods with respect to each employed database, which shows the novelty of the proposed system.

A sentiment analysis system has been proposed in this paper for pain detection using cutting edge techniques in a smart healthcare framework. This proposed system may be eligible for detecting pain sentiments by analyzing facial expressions on the human face. The implementation of the proposed system has been divided into four components. The first component is about detecting the face region from the input image using a tree-structured part model. Statistical and deep learning-based feature analysis has been performed in the second component to extract more valuable and distinctive patterns from the extracted facial region. In the third component, the prediction models based on statistical and deep feature analysis derive scores for the pain intensities (no-pain, low-pain, and high-pain) on the facial region. The scores due to the statistical and deep feature analysis are fused to enhance the performance of the proposed method in the fourth component. We have employed two benchmark facial pain expression databases during experimentation, such as UNBC-McMaster shoulder pain and 2D Face-set database with Pain-expression. The performance concerning these databases has been compared with some existing state-of-the-art methods. These comparisons show the superiority of the proposed system.

Similarity analysis of protein sequences can expose the evolutionary relationship among them. It is required to design effective computational algorithms that can compare the similarities among the...

Background With containing chemical bases that encode a protein, genes influence the foundations of life. Mutations are changes throughout a gene that has the potential to affect the function of a protein. Whenever a mutation causes uncontrollable cellular proliferation, cancer arises. Accordingly, the tumor progression and mutations classified as drivers provide a growth advantage, whilst passengers just don't. Methods The goal of this research is to develop an effective classification system for discriminating between driver and passenger mutations from a methodological standpoint. A new gene identification and segregation model is presented in this research article. "(a) pre-processing, (b) treatment of class imbalances, (c) feature extraction, (d) feature selection, and (e) gene classification" are the five primary steps of the proposed model. To improve the quality of the data, the obtained raw data is first pre-processed through "data cleaning and data normalization". This transforms the raw data into something usable as well as effective. In reality, the dataset is skewed, with driver mutation labels appearing in far fewer instances than passenger mutation labels. To tackle the class imbalance problem, the pre-processed data is handled using enhanced K-Means + SMOTE. The most significant characteristics, such as gene-level features and mutation level features, are then retrieved from the balanced dataset. To decrease the computational burden in terms of time, the most optimum features are picked from the retrieved features using Forensic Interpretation Customized Hunger Food Search Optimization (FIHFSO). The traditional Hunger Games Search (HGS) and Forensic-Based Investigation Optimization (FBIO) are conceptually combined in this FIHFSO. The deep learning classifier that performs the segregation process is trained using the specified optimum features (using FIHFSO). A new improved Recurrent Neural Network (I-RNN) is introduced in this study effort for making the final judgment regarding the genes (i.e., classification of driver and passenger genes). Finally, the projected mode is validated to demonstrate its dominance in terms of categorization. Results The I-RNN model has been compared over the existing classifiers like CNN, LSTM, DBN, Bi-GRU, SVM, DRIVE (Dragomir et al., 2021) and EARN (Mirsadeghi et al., 2021), respectively. I-RNN model has recorded the highest accuracy as 95.5%, which is better than the existing models. The major reason behind this performance enhancement in due to the MSE loss function introduced within it. In addition, I-RNN model has recorded the minimal FPR as well as FNR. Conclusion The projected model is said to be highly significant for gene classification owing to its comparative high accuracy. The quantitative identification and segregation of passenger and driver genes in cancer datasets will contribute to precision medicine in oncology.

Coronaviruses are responsible for various diseases ranging from the common cold to severe infections like the Middle East syndromes and the severe acute respiratory syndrome. However, a new coronavirus strain known as COVID-19 developed into a pandemic resulting in an ongoing global public health crisis. Therefore, there is a need to understand the genomic transformations that occur within this family of viruses in order to limit disease spread and develop new therapeutic targets. The nucleotide sequences of SARS-CoV-2 are consist of several bases. These bases can be classified into purines and pyrimidines according to their chemical composition. Purines include adenine (A) and guanine (G), while pyrimidines include cytosine (C) and tyrosine (T). There is a need to understand the spatial distribution of these bases on the nucleotide sequence to facilitate the development of antivirals (including neutralizing antibodies) and epitomes necessary for vaccine development. This study aimed to evaluate all the purine and pyrimidine associations within the SARS-CoV-2 genome sequence by measuring mathematical parameters including;Shannon entropy, Hurst exponent, and the nucleotide guanine-cytosine content. The Shannon entropy is used to identify closely associated sequences. Whereas Hurst exponent is used to identifying the auto-correlation of purine-pyrimidine bases even if their organization differs. Different frequency patterns can be used to determine the distribution of all four proteins and the density of each base. The GC-content is used to understand the stability of the DNA. The relevant genome sequences were extracted from the National Center for Biotechnology Information (NCBI) virus database. Furthermore, the phylogenetic properties of the COVID-19 virus were characterized to compare the closeness of the COVID-19 virus with other coronaviruses by evaluating the purine and pyrimidine distribution.

Integrated studies (multi-omics studies) comprising genetic, proteomic and epigenetic data analyses have become an emerging topic in biomedical research. Protein methylation is a posttranslational modification that plays an essential role in various cellular activities. The prediction of methylation sites (arginine and lysine) is vital to understand the molecular processes of protein methylation. However, current experimental techniques used for methylation site predictions are tedious and expensive. Hence, computational techniques for predicting methylation sites in proteins are necessary. For predicting methylation sites, various computational methods have been proposed in recent years. Most existing methods require structural and evolutionary information for retrieving features, acquiring this information is not always convenient. Thus, we proposed a novel method, called multi-factorial feature extraction and site prognosis model (MufeSPM), for the prediction of protein methylation sites based on information theory features (Renyi, Shannon, Havrda–Charvat and Arimoto entropy), amino acid composition and physicochemical properties acquired from protein methylation data. A random forest algorithm was used to predict methylation sites in protein sequences. This paper also studied the impact of different features and classifiers on arginine and lysine methylation data sets. For the R methylation data set, MufeSPM yielded 82.45%($\pm $ 3.47) accuracy, and for the K methylation data set, it provided an average accuracy of 71.94%($\pm $ 2.12). Additionally, the area under the receiver operating characteristic curve for different classifiers in predicting methylation site was provided. The experimental results signify that MufeSPM performs better than the state-of-the-art predictors.

A cosmetic product recognition system is proposed in this paper. For this recognition system, we have proposed a cosmetic product database that contains image samples of forty different cosmetic items. The purpose of this recognition system is to recognize Cosmetic products with there types, brands and retailers such that to analyze a customer experience what kind of products and brands they need. This system has various applications in such as brand recognition, product recognition and also the availability of the products to the vendors. The implementation of the proposed system is divided into three components: preprocessing, feature extraction and classification. During preprocessing we have scaled and transformed the color images into gray-scaled images to speed up the process. During feature extraction, several different feature representation schemes: transformed, structural and statistical texture analysis approaches have been employed and investigated by employing the global and local feature representation schemes. Various machine learning supervised classification methods such as Logistic Regression, Linear Support Vector Machine, Adaptive k-Nearest Neighbor, Artificial Neural Network and Decision Tree classifiers have been employed to perform the classification tasks. Apart from this, we have also performed some data analytic tasks for Brand Recognition as well as Retailer Recognition and for these experimentation, we have employed some datasets from the ‘Kaggle’ website and have obtained the performance due to the above-mentioned classifiers. Finally, the performance of the cosmetic product recognition system, Brand Recognition and Retailer Recognition have been aggregated for the customer decision process in the form of the state-of-the-art for the proposed system.

A novel method for person identification based on the fusion of iris and periocular biometrics has been proposed in this paper. The challenges for image acquisition for Near-Infrared or Visual Wavelength lights under constrained and unconstrained environments have been considered here. The proposed system is divided into image preprocessing data augmentation followed by feature learning for classification components. In image preprocessing an annular iris, the portion is segmented out from an eyeball image and then transformed into a fixed-sized image region. The parameters of iris localization have been used to extract the local periocular region. Due to different imaging environments, the images suffer from various noise artifacts which create data insufficiency and complicate the recognition task. To overcome this situation, a novel method for data augmentation technique has been introduced here. For features extraction and classification tasks well-known, VGG16, ResNet50, and Inception-v3 CNN architectures have been employed. The performance due to iris and periocular are fused together to increase the performance of the recognition system. The extensive experimental results have been demonstrated in four benchmark iris databases namely: MMU1, UPOL, CASIA-Iris-distance, and UBIRIS.v2. The comparison with the state-of-the-art methods with respect to these databases shows the robustness and effectiveness of the proposed approach.

Essential genes are considered to be the genes required to sustain life of different organisms. These genes encode proteins that maintain central metabolism, DNA replications, translation of genes, and basic cellular structure, and mediate the transport process within and out of the cell. The identification of essential genes is one of the essential problems in computational genomics. In this present study, to discriminate essential genes from other genes from a non-biologists perspective, the purine and pyrimidine distribution over the essential genes of four exemplary species, namely Homo sapiens , Arabidopsis thaliana , Drosophila melanogaster , and Danio rerio are thoroughly experimented using some quantitative methods. Moreover, the Indigent classification method has also been deployed for classification on the essential genes of the said species. Based on Shannon entropy, fractal dimension, Hurst exponent, and purine and pyrimidine bases distribution, 10 different clusters have been generated for the essential genes of the four species. Some proximity results are also reported herewith for the clusters of the essential genes.

A novel facial expression recognition system has been proposed in this paper. The objective of this paper is to recognize the types of expressions in the human face region. The implementation of the proposed system has been divided into four components. In the first component, a region of interest as face detection has been performed from the captured input image. For extracting more distinctive and discriminant features, in the second component, a deep learning-based convolutional neural network architecture has been proposed to perform feature learning tasks for classification purposes to recognize the types of expressions. To enhance the performance of the proposed system, in the third component, some novel data augmentation techniques have been applied to the facial image to enrich the learning parameters of the proposed CNN model. In the fourth component, a trade-off between data augmentation and deep learning features have been performed for fine-tuning the trained CNN model. Extensive experimental results have been demonstrated using three benchmark databases: KDEF (seven expression classes), GENKI-4k (two expression classes), and CK+ (seven expression classes). The performance of the proposed system respect for each database has been well presented and described and finally, these performances have been compared with the existing state-of-the-art methods. The comparison with competing methods shows the superiority of the proposed system.

Background and objective The world is currently facing a global emergency due to COVID-19, which requires immediate strategies to strengthen healthcare facilities and prevent further deaths. To achieve effective remedies and solutions, research on different aspects, including the genomic and proteomic level characterizations of SARS-CoV-2, are critical. In this work, the spatial representation/composition and distribution frequency of 20 amino acids across the primary protein sequences of SARS-CoV-2 were examined according to different parameters. Method To identify the spatial distribution of amino acids over the primary protein sequences of SARS-CoV-2, the Hurst exponent and Shannon entropy were applied as parameters to fetch the autocorrelation and amount of information over the spatial representations. The frequency distribution of each amino acid over the protein sequences was also evaluated. In the case of a one-dimensional sequence, the Hurst exponent (HE) was utilized due to its linear relationship with the fractal dimension (D), i.e. D + H E = 2 , to characterize fractality. Moreover, binary Shannon entropy was considered to measure the uncertainty in a binary sequence then further applied to calculate amino acid conservation in the primary protein sequences. Results and conclusion Fourteen (14) SARS-CoV protein sequences were evaluated and compared with 105 SARS-CoV-2 proteins. The simulation results demonstrate the differences in the collected information about the amino acid spatial distribution in the SARS-CoV-2 and SARS-CoV proteins, enabling researchers to distinguish between the two types of CoV. The spatial arrangement of amino acids also reveals similarities and dissimilarities among the important structural proteins, E, M, N and S, which is pivotal to establish an evolutionary tree with other CoV strains.

This work proposes a facial expression recognition system for a diversified field of applications. The purpose of the proposed system is to predict the type of expressions in a human face region. The implementation of the proposed method is fragmented into three components. In the first component, from the given input image, a tree-structured part model has been applied that predicts some landmark points on the input image to detect facial regions. The detected face region was normalized to its fixed size and then down-sampled to its varying sizes such that the advantages, due to the effect of multi-resolution images, can be introduced. Then, some convolutional neural network (CNN) architectures were proposed in the second component to analyze the texture patterns in the facial regions. To enhance the proposed CNN model’s performance, some advanced techniques, such data augmentation, progressive image resizing, transfer-learning, and fine-tuning of the parameters, were employed in the third component to extract more distinctive and discriminant features for the proposed facial expression recognition system. The performance of the proposed system, due to different CNN models, is fused to achieve better performance than the existing state-of-the-art methods and for this reason, extensive experimentation has been carried out using the Karolinska-directed emotional faces (KDEF), GENKI-4k, Cohn-Kanade (CK+), and Static Facial Expressions in the Wild (SFEW) benchmark databases. The performance has been compared with some existing methods concerning these databases, which shows that the proposed facial expression recognition system outperforms other competing methods.

In wireless sensor networks (WSNs), a many-to-one traffic pattern is adopted in a way that large numbers of sensor nodes transmit data packets to the sink. Thereby nodes located near the sink suffer from early demise due to their relaying the data traffic from the far-located nodes, and such problem is termed as a hot-spot problem. The sensing technology has brought all advancements in human lives. In this chapter, we have presented a comparative evaluation to minimize the overall energy consumption of the sensor network, by separating sensor nodes into various clusters and selecting one node from every cluster as a cluster head (CH), which is responsible for receiving and aggregating information from member nodes and then transmitting data to the sink. We give an empirical analysis of the existing methods that will help readers to select the appropriate approach for their applications. The modified artificial fish swarm algorithm (MAFS) is used to group nodes optimally along with the weighted k-means clustering algorithm. There has been a great magnitude of efforts reported for acquiring the energy efficiency in WSN; these efforts vary from conventional approaches to the metaheuristic approach for enhancing network performance. The modified artificial fish swarm optimization approach benefits by obtaining energy efficiency in the WSN.

The Lorenz model is one of the most studied dynamical systems. Chaotic dynamics of several modified models of the classical Lorenz system are studied. In this article, a new chaotic model is introduced and studied computationally. By finding the fixed points, the eigenvalues of the Jacobian, and the Lyapunov exponents. Transition from convergence behavior to the periodic behavior (limit cycle) are observed by varying the degree of the system. Also transiting from periodic behavior to the chaotic behavior are seen by changing the degree of the system.

Biological regulatory network can be modeled through a set of Boolean functions. These set of functions enable graph representation of the network structure, and hence, the dynamics of the network can be seen easily. In this article, the regulations of such network have been explored in terms of interaction graph. With the help of Boolean function decomposition, this work presents an approach for construction of interaction graphs. This decomposition technique is also used to reduce the network state space of the cell cycle network of fission yeast for finding the singleton attractors. Some special classes of Boolean functions with respect to the interaction graphs have been discussed. A unique recursive procedure is devised which uses the Cartesian product of sets starting from the set of one-variable Boolean function. Interaction graphs generated with these Boolean functions have only positive/negative edges, and the corresponding state spaces have periodic attractors with length one/two.

Comparison of protein sequence similarity is a significant study. By virtue of this method, we can expose the evolutionary relationship among protein sequences. So, it is required to design effective computational algorithms that can compare the similarities among the colossal amount of sequences. The aim of this research is to develop efficient tools in the field of protein sequences comparison and phylogenetic study. The proposed method performs a feature generation process based on the physio-chemical properties of amino acids that best describes the revolutionary relationship among the species in a protein family. The protein sequences are transferred into an Eighty dimensional feature vector among the group of amino acids. Finally, four different datasets were used to validate the accuracy of the proposal and a correlation coefficient of 0.94417 of ND5 dataset using ClustalW has been found. This is much higher than some of the methods. At last the result explains the effectiveness in the similarity analysis among genome sequences.

A pandemic caused by the SARS-CoV2 is being experienced by the whole world since December, 2019. A thorough understanding beyond just sequential similarities among the protein coding genes of SARS-CoV2 is important in order to differentiate or relate to the other known CoVs of the same genus. In this study, we compare three genomes namely MT012098 (India-Kerala), MT050493 (India-Kerala), MT358637 (India-Gujrat) from India with NC_045512 (China-Wuhan) to view the spatial as well as molecular arrangements of nucleotide bases of all the genes embedded in these four genomes. Based on different features extracted for each gene embedded in these genomes, corresponding phylogenetic relationships have been built up. Differences in phylogenetic tree arrangement with individual gene suggest that three genomes of Indian origin have come from three different origins or the evolution of viral genome is very fast process. This study would also help to understand the virulence factors, disease pathogenicity, origin and transmission of the SARS-CoV2.