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The water molecules associated with coal play a significant role in spontaneous ignition and combustion of coal. This paper deals with studies to evaluate the impact of moisture content on crossing point temperature (CPT) and ignition point temperature (IPT) of some low-rank coal and lignite after various stages of drying. Experimental data suggest (i) existence of critical moisture content value determining the propensity of a particular coal toward self-heating and (ii) content of moisture and nature of oxygen-bearing functional groups to be the most influencing factors in self-heating. The auto-oxidation cum self-heating characteristics of coal become more conducive with increase in the ratio of quantity of hydroxyl to carboxyl functional groups. The FTIR studies further corroborate that the initial stages of auto-oxidation cum self-heating occur presumably at the surface of the coal. [ABSTRACT FROM AUTHOR]

Graphite is a promising and futuristic mineral that is envisaged to cause not only technological shift but also cultural shift in the days to come. Besides several applications, it is considered one of the critical raw materials for electric vehicle battery that has accelerated recent surge in the demand. However, the beneficiation of graphite ore is pivotal as it accounts for the quality, market price and suitability for various technological applications including energy storage in lithium-ion batteries. Since graphite is spontaneously hydrophobic (like coal) in nature, flotation is the common and apt technique for beneficiation of graphite ore. In this study, the research and development on the graphite beneficiation techniques especially flotation reported by several learned research groups in the world has been discussed and presented. The study provides updated insights on the state of the art and helps to understand the gaps which needs to be addressed to devise improved flotation methodology. The extent to which suitability of some orthodox coal flotation techniques towards graphite beneficiation also come to the fore. Based on several significant reported beneficiation methods, a master flow sheet for graphite beneficiation has been developed and presented that comprises various modules capable of dealing with wide range of graphite ores. The significance of this study is not only limited to the critical discussions pertaining to the preliminary and necessary flotation step of graphite ore but also speaks about the world-wide preparedness and prescribes opportunity to join the mission mode programme of energy storage. [Display omitted] • Graphite beneficiation by flotation is pivotal for electric vehicle industry. • Worldwide flotation research carried out during last decade, discussed chronologically. • Stagewise flotation involving multistage grinding improves graphite grade. • Novel conceptual graphite beneficiation flowsheet manifested from the literature survey. • Status of the world-wide preparedness in graphite-based energy storage discussed. [ABSTRACT FROM AUTHOR]

• Three phase cascade of neural networks for multi-label classification. • Network model includes stacked autoencoders and extreme learning machines. • Stacked autoencoder reduces complex input features to appropriate representation. • Multi-label extreme learning machine used for soft classification. • Soft classification scores mapped to hard labels with an extreme learning machine. This article introduces a cascade of neural networks for classification of multi-label data. Two types of networks, namely, stacked autoencoder (SAE) and extreme learning machine (ELM) have been incorporated in the proposed system. ELM is a compact and efficient single-label classifier which seems to lose its efficiency while dealing with multi-label data. This happens due to the complex nature of the multi-label data, which makes it difficult for the smaller networks to interpret it accurately. In our proposed work, we attempt to deal with few of the bottlenecks faced while handling multi-label data. Thus, we aim to enhance the performance of a stand-alone multi-label extreme learning machine (MLELM) by collaborating it with other networks. There are three basic phases in the proposed method: feature encoding, soft classification and class score approximation. In the first step, an SAE network is employed to generate a discriminating and reduced input representation of the multi-label data. This makes the data compact and more manageable for the successive stages. This data in turn is used by an MLELM in the next phase for the prediction of soft labels. In the final step, to improve the prediction capability of the previous network, a novel approach of approximating the class score is proposed using an additional MLELM. Comprehensive experimental evaluation of the proposed approach has been performed on seven datasets against eleven relevant algorithms, and overall it displays a promising performance. [ABSTRACT FROM AUTHOR]

Highlights • Pixel based, real-time, classification history sensitive, background subtraction. • Classification record of previous frames is used to update background model. • Dynamic background can be detected using temporal classification history. • Ghost effect is detected and removed using the neighbourhood classification record. Abstract In computer vision, one of the ways to identify moving objects or foregrounds from a complex video scene is background subtraction. Two problems that usually arise in background subtraction are that of dynamic background and ghost effect. In this article, an efficient, adaptive, real-time, novel background subtraction method is proposed to handle these two problems by utilizing the classification history of previous frames to detect changes in a video scene. The proposed method, coined as Record Sensitive Background Classifier (RSBC) is an amalgamation of two models, the Adaptive Background Model (ABM) and the Neighbourhood Background Model (NBM), which make use of the foreground and background classification information of the previous frames. The novel design of the ABM considers the temporal history of transitions between foreground and background of a given pixel, while the novelty in NBM lies in considering the binary classification results of the local neighbourhood in the preceding frame. Several state-of-the-art methods are compared with the proposed RSBC method, over which the proposed method shows significant improvement. Because of its good performance at fast processing speeds the proposed method can also be used as a preliminary prioritization step for slower but high accuracy methods in an object tracking system. [ABSTRACT FROM AUTHOR]

This paper proposes an efficient real-time background subtraction algorithm, which is essential in many computer vision applications. Initially, histograms of the intensity values for each channel of a pixel position in a set of training framesare constructed. A background model, histogram min-max bucket, is constructed from the minimum and maximum values of contiguous non-zero frequencies of the temporal intensity histogram. A novel feature of this algorithm is the use of a single sliding window to update the system adaptively, capturing the proper background even under sudden and/or gradual illumination changes in the scene. We incorporate both local and global information with the help of the current and the previously visited pixel values for binary classification of a pixel into foreground and background. This algorithm is compared with several state-of-the-art techniques and experimental studies show that the proposed method outperforms all these methods in terms of accurate binary classification. [ABSTRACT FROM AUTHOR]

Dimensionality Reduction (DR) is very useful and popular in many application areas of expert and intelligent systems, such as machine learning, finance, data and text mining, multimedia mining, image processing, anomaly detection, defense applications, bioinformatics and natural language processing. DR is widely applied for better data visualization and improving learning in all the above fields. In this manuscript, we propose a novel DR approach namely, Noisy-free Length Discriminant Analysis (NLDA) by developing Noisy-free Relevant Pattern Selection (NRPS). Traditional pattern selection methods discriminate boundary and non-boundary patterns with the help of class information and nearest neighbors. And these methods completely ignore noisy patterns which may degrade the performance of subsequent subspace learning. To overcome this, we develop Noisy-free Relevant Pattern Selection (NRPS), in which data instances are partitioned into boundary, non-boundary and noisy patterns. With the help of noisy-free boundary and non-boundary patterns, Noisy-free Length Discriminant Analysis (NLDA) has been proposed by developing new within and between-class scatters. These scatters model discriminations between lengths ( L 2 -norms) of different class instances by considering only boundary and non-boundary patterns, while ignoring noisy patterns. A cosine hyperbolic frame work has been developed to formulate the objective of NLDA. Moreover, NLDA can also model the discrimination of multimodal data as different class data may consist of different lengths. Experimental study conducted on the synthesized data, UCI, and leeds butterfly databases. Moreover, an experimental study over human and computer interaction, i.e., face recognition (one of the application areas of expert and intelligent systems), has been performed. And, these studies prove that the proposed method can produce better discriminated subspace compare to the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Most of the well-known supervised dimensionality reduction methods assume unimodal or Gaussian likelihoods, which may not be appropriate in the real life applications. In this manuscript, we introduce a novel supervised dimensionality reduction approach, moments discriminant analysis, which models linear relationships between the high-dimensional input space and a low-dimensional space by maximizing the discrimination between second order raw moments of different classes to improve the generalization capability of a classifier. Unlike the state-of-the-art methods, moments discriminant analysis is intended to accommodate data distributions that may be multimodal and non-Gaussian. Initially, experiments using synthetic random data (generated from different probability distributions) are performed to prove the efficiency of the proposed method for multimodal and non-Gaussian data with the help of five separability measures. Also, extensive experimental results on UCI machine learning repository and image retrieval on WANG and MIT (Oliva and Torralba) databases are carried out in order to exhibit the effectiveness of moments discriminant analysis over the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Both petroleum and coal derived asphaltene finds important place on the present research areas of hydrocarbon based liquid and solid fuels. It has earned a name of ‘unwanted’ fame for its deleterious impacts arising mainly due to its self aggregational phenomenon. Over the period of time the asphaltene aggregation has been seriously investigated worldwide even at the molecular scale. Besides several methods, the spectroscopic findings (UV Vis and Fluorescence), in this regard, contribute to substantial meaningful data. The major spectroscopic researches on the aggregation of asphaltene over the last ten years have been reviewed in this present study. A brief discussion on the asphaltene molecular structure and molecular weight also comes as a prelude. Asphaltenes derived from both petroleum and coal have been considered. [ABSTRACT FROM AUTHOR]

Steady state fluorescence spectroscopic technique has been used to investigate the aggregation of coal derived asphaltene (CDA) sourced from Lodna Coke Plant situated at Dhanbad, India. The emission spectra of CDA provide information regarding the various stages of onset of aggregation in carbon tetrachloride medium. Almost similar results have also been inferred from the analysis of corresponding excitation spectra. It has been observed that the onset of aggregation of CDA occurs at concentration beyond a concentration of 10 mg L −1 . Studies indicate the formation of a trimer at this concentration range. The association constant ( K ) for the trimer formation has also been evaluated from the spectroscopic data. [ABSTRACT FROM AUTHOR]

By fluorescence spectroscopy it has been established that coal derived asphaltene (CDA) forms aggregates in CCl4medium. The new feature of this compound is that even before aggregate formation it can incorporate water molecules and the critical aggregation concentration (CAC) in nonaqueous medium increases with increase in ω (= water:CDA mole ratio). The fluorescence spectroscopic determination of the equilibrium constant (K) of the molecular complex of [60] fullerenes with CDA in carbon tetrachloride medium has revealed thatKincreases on addition of microquantities of water. This observation has been rationalized by assuming that the nitrogenatoms at one end of CDA molecules attach themselves to the water micropool and thus producing a different conformation with a wider distance at the other end. This pre-organization further facilitates inclusion. By utilizing fluorescence variation of [60]fullerene/CDA complex in CCl4as continuous medium with change in ω a critical ω (=5) has been found. The formation of CDA/[60]fullerene complex has also been established by NMR spectroscopy. [ABSTRACT FROM PUBLISHER]

Coal-derived asphaltene (CDA) has been shown to form electron donor–acceptor-type molecular complexes with electron acceptor such as 2,5-cyclohexadiene-1,4-diylidene also known as tetracyanoquinodimethane (TCNQ) in homogeneous medium of carbon tetrachloride and heterogeneous medium of aqueous SDS. This has been investigated both by UV-Vis and fluorescence spectrophotometric method. The Charge transfer (CT) absorption peaks have been detected by difference spectral method and the wavelengths of the CT peaks are close in both the medium. The formation constants (K) have been determined using the Benesi–Hildebrand equation. The magnitudes ofKare in the order of 103 L mol−1that are comparable to some supramolecular or inclusion complexes. The study reveals the presence of π-donor (i.e., aromatic) type of compounds in CDA molecule. It also supports the view that CDA molecules exist in solution forming micellar aggregate, in the cavities of which the TCNQ molecules are included. The remarkable increase (three fold) in the formation constant in the SDS medium compared to CCl4further indicates that the CDA aggregate in SDS undergoes some preorganization to further facilitate inclusion. [ABSTRACT FROM PUBLISHER]

Highlights: [•] Coal derived asphaltene (CDA) forms molecular complex with [60]-, [70]fullerene in CCl4 medium. [•] The order of complexation for CDA:[60]fullerene is 2:1 and for CDA:[70]fullerene is 3:1. [•] Respective charge transfer absorption peaks have been detected by difference spectral method. [•] The values of Δ calculated indicate that the complexation process in both the cases is predominantly entropy-driven. [ABSTRACT FROM AUTHOR]

Highlights: [•] Coal derived asphaltene studied in aqueous micellar media of CTAB and TX-100. [•] Asphaltene forms 1:1 complex with o-chloranil. [•] The formation constant determined by fluorescence and absorbance are in good agreement. [•] The wavelength of maximum absorption of the complexes are in the order of SDS>CTAB>TX-100. [Copyright &y& Elsevier]

In this paper, a spatiocontextual unsupervised change detection technique for multitemporal, multispectral remote sensing images is proposed. The technique uses a Gibbs Markov random field (GMRF) to model the spatial regularity between the neighboring pixels of the multitemporal difference image. The difference image is generated by change vector analysis applied to images acquired on the same geographical area at different times. The change detection problem is solved using the maximum a posteriori probability (MAP) estimation principle. The MAP estimator of the GMRF used to model the difference image is exponential in nature, thus a modified Hopfield type neural network (HTNN) is exploited for estimating the MAP. In the considered Hopfield type network, a single neuron is assigned to each pixel of the difference image and is assumed to be connected only to its neighbors. Initial values of the neurons are set by histogram thresholding. An expectation–maximization algorithm is used to estimate the GMRF model parameters. Experiments are carried out on three-multispectral and multitemporal remote sensing images. Results of the proposed change detection scheme are compared with those of the manual-trial-and-error technique, automatic change detection scheme based on GMRF model and iterated conditional mode algorithm, a context sensitive change detection scheme based on HTNN, the GMRF model, and a graph-cut algorithm. A comparison points out that the proposed method provides more accurate change detection maps than other methods. [ABSTRACT FROM PUBLISHER]

This paper discusses the relevance and possible applications of evolutionary algorithms, particularly genetic algorithms, in the domain of knowledge discovery in databases. Knowledge discovery in databases is a process of discovering knowledge along with its validity, novelty, and potentiality. Various genetic-based feature selection algorithms with their pros and cons are discussed in this article. Rule (a kind of high-level representation of knowledge) discovery from databases, posed as single and multiobjective problems is a difficult optimization problem. Here, we present a review of some of the genetic-based classification rule discovery methods based on fidelity criterion. The intractable nature of fuzzy rule mining using single and multiobjective genetic algorithms reported in the literatures is reviewed. An extensive list of relevant and useful references are given for further research. © 2013 Wiley Periodicals, Inc. [ABSTRACT FROM AUTHOR]

In this paper, we put forward a novel region matching-based motion estimation scheme to detect objects with accurate boundaries from videos captured by moving camera. Here, a fuzzy edge incorporated Markov random field (MRF) model is considered for spatial segmentation. The algorithm is able to identify even the blurred boundaries of objects in a scene. Expectation Maximization algorithm is used to estimate the MRF model parameters. To reduce the complexity of searching, a new scheme is proposed to get a rough idea of maximum possible shift of objects from one frame to another by finding the amount of shift in positions of the centroid. We propose a \chi^2-test-based local histogram matching scheme for detecting moving objects from complex scenes from low illumination environment and objects that change size from one frame to another. The proposed scheme is successfully applied for detecting moving objects from video sequences captured in both real-life and controlled environments. It is also noticed that the proposed scheme provides better results with less object background misclassification as compared to existing techniques. [ABSTRACT FROM AUTHOR]

The study of ant colonies behavior and their self-organizing capabilities is of interest to machine learning community, because it provides models of distributed adaptive organization which are useful to solve difficult optimization and classification problems among others. Social insects like ants, bees deposit pheromone (a type of chemical) in order to communicate between the members of their community. Pheromone, that causes clumping behavior in a species and brings individuals into a closer proximity, is called aggregation pheromone. This article presents a new algorithm (called, APC) for pattern classification based on this property of aggregation pheromone found in natural behavior of real ants. Here each data pattern is considered as an ant, and the training patterns (ants) form several groups or colonies depending on the number of classes present in the data set. A new test pattern (ant) will move along the direction where average aggregation pheromone density (at the location of the new ant) formed due to each colony of ants is higher and hence eventually it will join that colony. Thus each individual test pattern (ant) will finally join a particular colony. The proposed algorithm is evaluated with a number of benchmark data sets as well as various kinds of artificially generated data sets using three evaluationmeasures. Results are compared with four other well known conventional classification techniques. Experimental results show the potentiality of the proposed algorithm in terms of all the evaluation measures compared to other algorithms. [ABSTRACT FROM AUTHOR]

Abstract: A new model for neuro-fuzzy (NF) classification systems is proposed. The motivation is to utilize the feature-wise degree of belonging of patterns to all classes that are obtained through a fuzzification process. A fuzzification process generates a membership matrix having total number of elements equal to the product of the number of features and classes present in the data set. These matrix elements are the input to neural networks. The effectiveness of the proposed model is established with four benchmark data sets (completely labeled) and two remote sensing images (partially labeled). Different performance measures such as misclassification, classification accuracy and kappa index of agreement for completely labeled data sets, and index of homogeneity and Davies–Bouldin (DB) index of compactness for remotely sensed images are used for quantitative analysis of results. All these measures supported the superiority of the proposed NF classification model. The proposed model learns well even with a lower percentage of training data that makes the system fast. [Copyright &y& Elsevier]

In this paper a new concept of ranking among the solutions of the same front, along with elite preservation mechanism and ensuring diversity through the nearest neighbor method is proposed for multi-objective genetic algorithms. This algorithm is applied on a set of benchmark multi-objective test problems and the results are compared with that of NSGA-II (a similar algorithm). The proposed algorithm is seen to over perform the existing algorithm. More specifically, the new approach has been used to solve the deceptive multi-objective optimization problems in a better way. [ABSTRACT FROM AUTHOR]

Abstract: The present article proposes a fuzzy set-based classifier with a better learning and generalization capability. The proposed classifier exploits the feature-wise degree of belonging of a pattern to all classes, generalization in the fuzzification process and the combined class-wise contribution of features effectively. The classifier uses a -type membership function and product aggregation reasoning rule (operator). Its effectiveness is verified with two conventional (completely labeled) data sets and two remote sensing images (partially labeled data sets). The proposed classifier is compared with similar fuzzy methods. Different performance measures are used for quantitative evaluation of the proposed classifier. [Copyright &y& Elsevier]

The present paper attempts to employ hybrid genetic algorithms (GAs) to solve the flexible-ligand docking problem i.e. predicting the binding conformation of a flexible ligand molecule into a rigid protein. Our hybrid GA scheme uses the concept of Lamarckian genetics to perform a local search about an individual, followed by replacing it with a better solution found in its neighborhood. Two local search schemes have been investigated and their performance relative to the standard GA have been compared. Preliminary results obtained on a set of three protein-ligand complexes are encouraging. [ABSTRACT FROM AUTHOR]

Abstract: Fluorescence spectra of coal derived asphaltenes sourced from Barari coke plant situated at Dhanbad, India, have been studied as a function of concentration in benzene, toluene and carbon tetrachloride with an eye to study the self-aggregation of the solute. Aggregation has been found to be a gradual process and the concentration characterizing onset of aggregation process coincide for the solvents. The critical aggregation constants (CAC) appear to be ca 90–100 and 180–200 for all the solvents. A small red shift of fluorescence band is observed when traces of water (∼0.05%, v/v) are present in carbon tetrachloride solution. [Copyright &y& Elsevier]

The present work is an attempt to show an way of applying the univariate marginal distribution algorithm to non-stationary environments. The main idea used for this purpose is to introduce mutation (to increase diversity) as and when the environment or the optimization function changes. Simulation study is done on different time dependent versions of a function having simple but difficult landscape. Empirical studies reveal that for smaller shift in position of the optimum, the algorithm can trace this change almost instantaneously. But if the position of the optimum changes by a larger amount, the present algorithm cannot trace it. We also discuss the issue of performance measure for non-stationary environment, and propose a new measure called tractability in this respect. [ABSTRACT FROM AUTHOR]

Proposes a type of genetic algorithm (GA) to be used in multimodal problems which are difficult to solve using conventional methods. Application of a multipopulation scheme; Methods for dividing the genotypic search space; Comparison of performance with the conventional GA.

The structure of coal derived asphaltene (CDA) proposed by Mullins et al has been corroborated through theoretical and experimental examination by the virtue of its electron donor property. Here, o -chloranil has been used as an electron acceptor. Investigation of o -chloranil/CDA complex in gas phase has been performed by density functional theory (DFT) methods. The Charge transfer (CT) transition energy of the complex in gas phase was calculated by the time-dependent density function theory (TD-DFT) using the ground state optimized geometry at the respective levels of theory. For this, the three semi empirical methods AM1, PM3 and PM3MM were computed under the MPW1PW91, B3PW91, PBE1PBE, BPV86 and RCIS formalism. It is found that the two formalisms MPW1PW91 and PBEPBE under PM3 and PM3MM reproduced values close to the experimental one. However, all of them provide lower charge transfer (CT) transition energy values than that has been experimentally determined. Experimentally, FTIR spectra of the donor, acceptor and the complex further supported the understanding of complex formation and demonstrated close similarity with the theoretically determined CDA frequencies. These findings support the validity of the CDA structure to an extent. [ABSTRACT FROM AUTHOR]

The evolution of thermogenic gases during coalification has not yet been theoretically attempted. Examinations on Indian Permian vitrinites of various ranks ranging from Cwt% 76 to 92 were performed. Fourier Transform Infra-red spectroscopy (FTIR) provided insight into the structural changes where the aliphatic CH increases with increase in percentage of carbon up to ~85% and then decreases. In contrast, the aromatic CH, the ratio of aromatic CH to aliphatic CH and aromaticity (fa) increase monotonically with the advancement of rank. A relationship between fa and atomic H/C ratio as fa = 1.12–0.45H/C was established to predict fa from elemental analysis. The structural data from FTIR served as an input to construct six representative hypothetical models of coal of varying Cwt% (76, 80, 83, 86, 87 and 89). Density Functional Theory (DFT) has been applied to these coal models and the formation of principal thermogenic gases, viz. CO 2 for the low-(76%C)and CH 4 for the high-rank (87%C), with simultaneous increase in the rank was demonstrated considering the smaller structural units extracted from the relevant coal models. The suggested reaction pathways are supported by the negative values of changes in free energies of the reactions computed from the Gaussian thermo-chemistry. The studies could, therefore, show that gas evolution as a natural (spontaneous) process with feasibility of rank advancement. • FTIR examination of vitrinite concentrates of Indian Permian coals of different rank for structural parameters • Variations of structural parameters with rank • Relationship between fa and atomic H/C ratio: fa = 1.12–0.45*H/C • Construction of structural models of different ranks using FTIR data • Application of DFT to structural models for gas evolution and structural changes [ABSTRACT FROM AUTHOR]

• Manifold embedding quality assessment method quantifies the quality using local and global structure preservation. • The non-discriminative property of Anisotropic scaling during transformation is handled by measuring its degree via rigid transformation. • Kolmogorov Smirnov test is used for global structure preservation by taking account of each dimension separately. • MEQA measures the quality of embeddings by different manifold learning methods efficiently and discriminates properly. Manifold learning methods unfold the manifold structures and embed them in a lower-dimensional space. The quality of such an embedding should be measured both qualitatively and quantitatively. The proposed manifold embedding quality assessment (MEQA) method does so by taking into account of local and global structure preservation as both are important traits of an embedding. To measure the local structure preservation MEQA uses two transformations. Initially anisotropic scaling, rotation and translation are incorporated to measure the closeness between the original and the embedded data points. In the next stage, rigid transformation is incorporated to quantify the previous transformation which involved anisotropic scaling. For quantifying the global structure preservation, the Kolmogorov-Smirnov test is applied in a distributed manner over each dimension and averaged over them. To establish the superiority of MEQA we conducted several studies over standard synthetic and real-life datasets across separate existing feature extraction techniques. [ABSTRACT FROM AUTHOR]

Highlights • A novel outlier detection framework using the projection principles of stacked autoencoders and probabilistic neural networks. • An efficient technique to identify outliers in both single type outlier as well as multiple outlier type datasets. • This article also highlights how the use of deep stacked autoencoders can enhance the performance of the standard outlier detection techniques. Abstract It is obvious to see that most of the datasets do not have exactly equal number of samples for each class. However, there are some tasks like detection of fraudulent transactions, for which class imbalance is overwhelming and one of the classes has very low (even less than 10% of the entire data) amount of samples. These tasks often fall under outlier detection. Moreover, there are some scenarios where there may be multiple subsets of the outlier class. In such cases, it should be treated as a multiple outlier type detection scenario. In this article, we have proposed a system that can efficiently handle all the aforementioned problems. We have used stacked autoencoders to extract features and then used an ensemble of probabilistic neural networks to do a majority voting and detect the outliers. Such a system is seen to have a better and reliable performance as compared to the other outlier detection systems in most of the datasets tested upon. It is seen that use of autoencoders clearly enhanced the outlier detection performance. [ABSTRACT FROM AUTHOR]

The authors discuss evolutionary computation in bioinformatics. They assert that bioinformatics is becoming popular in several artificial intelligence (AI)-related areas and that classification of biomedical data faces a special challenge because of the characteristics of the data. The reconstruction of phylogenetic trees is another important problem in bioinformatics.

The COVID-19 pandemic has put a huge challenge on the Indian health infrastructure. With a larger number of people getting affected during the second wave, hospitals were overburdened, running out of supplies and oxygen. Hence, predicting new COVID-19 cases, new deaths, and total active cases multiple days in advance can aid better utilization of scarce medical resources and prudent pandemic-related decision-making. The proposed method uses gated recurrent unit networks as the main predicting model. A study is conducted by building four models pre-trained on COVID-19 data from four different countries (United States of America, Brazil, Spain, and Bangladesh) and fine-tuned on India's data. Since the four countries chosen have experienced different types of infection curves, the pre-training provides a transfer learning to the models incorporating diverse situations into account. Each of the four models then gives 7-day ahead predictions using the recursive learning method for the Indian test data. The final prediction comes from an ensemble of the predictions of the different models. This method with two countries, Spain and Bangladesh, is seen to achieve the best performance amongst all the combinations as well as compared to other traditional regression models. [ABSTRACT FROM AUTHOR]

Various problems in object detection and tracking have attracted researchers to develop methodologies for solving these problems. Occurrence of camouflage is one of such challenges that makes object detection and tracking problems more complex. However, less attention has been given to detect and track camouflaged objects due to complexity of the problem. In this article, we propose a tracking-by-detection algorithm to detect and track camouflaged objects. To increase separability between the camouflaged object and the background, we propose to integrate features (CIELab, histogram of orientation gradients and locally adaptive ternary pattern) from multi-cue (color, shape and texture) to represent a camouflaged object. A probabilistic neural network (PNN) is modified to construct an efficient discriminative appearance model for detecting camouflaged objects in video sequences. A large number of training patterns (many could be redundant) are reduced based on motion of the object in the modified PNN. The modified PNN makes the detection process faster and also increases the detection accuracy. Due to high visual similarity among the camouflaged object and the background, the boundary of camouflaged object is not well defined (i.e., boundary may be smooth and/or discontinuous). In this context, a robust fuzzy energy based active contour model using both global and local information is proposed to extract contour (boundary) of the detected camouflaged object for tracking. We show a realization of the proposed method and demonstrate its performance (both quantitatively and qualitatively) with respect to state-of-the-art techniques on several challenging sequences. Analysis of results concludes that the proposed technique can track camouflaged (fully or partially) objects as well as objects in various complex environments in a better way as compare to the existing ones. [ABSTRACT FROM AUTHOR]

The 'curse of dimensionality' is a drawback for classification of hyperspectral images. Band extraction is a technique for reducing the dimensionality andmakes it computationally lesscomplex for classification. In this article, an unsupervised band extractionmethod for hyperspectral images has been proposed. In the proposed method, kernel principal component analysis (KPCA) is used for transformation of the original data, which integrates the nonlinear characteristics, as well as, the advantages of principal component analysis and extract higher order statistics of data. The KPCA is highly dependent on the number of patterns for calculating kernelmatrix. So, a proper selection of subset of patterns, which represent the original data properly, may reduce the computational cost for the proposedmethod with considerably better performance. Here, density-based spatial clustering technique is first used to group the pixels according to their similarity, and then some percentages of pixels from each cluster are selected to form the proper subset of patterns. To demonstrate the effectiveness of the proposed clustering- and KPCA-based unsupervised band extraction method, investigation is carried out on three hyperspectral data sets, namely, Indian, KSC, and Botswana. Four evaluation measures, namely classification accuracy, kappa coefficient, class separability, and entropy are calculated over the extracted bands to measure the efficiency of the proposed method. The performance of the proposed method is compared with four state-of-the-art unsupervised band extraction approaches, both qualitatively and quantitatively, and shows promising results compared to them in terms of four evaluation measures. [ABSTRACT FROM AUTHOR]

In this article, we present an algorithm that can efficiently track the contour extracted from silhouette of the moving object of a given video sequence using local neighborhood information and fuzzy k-nearest-neighbor classifier. To classify each unlabeled sample in the target frame, instead of considering the whole training set, a subset of it is considered depending on the amount of motion of the object between immediate previous two consecutive frames. This technique makes the classification process faster and may increase the classification accuracy. Classification of the unlabeled samples in the target frame provides object (silhouette of the object) and background (non-object) regions. Transition pixels from the non-object region to the object silhouette and vice versa are treated as the boundary or contour pixels of the object. Contour or boundary of the object is extracted by connecting the boundary pixels and the object is tracked with this contour in the target frame. We show a realization of the proposed method and demonstrate it on eight benchmark video sequences. The effectiveness of the proposed method is established by comparing it with six state of the art contour tracking techniques, both qualitatively and quantitatively. [ABSTRACT FROM AUTHOR]

Protein-protein interaction (PPI) networks are believed to be important sources of information related to biological processes and complex metabolic functions of the cell. Identifying protein complexes is of great importance for understanding cellular organization and functions of organisms. In this work, a method is proposed, referred to as MIPCE, to find protein complexes in a PPI network based on mutual information. MIPCE has been biologically validated by GO-based score and satisfactory results have been obtained. We have also compared our method with some well-known methods and obtained better results in terms of various parameters such as precession, recall and F-measure. [ABSTRACT FROM AUTHOR]

In this article, we propose a moving objects detection scheme using Gibbs-Markov random field(GMRF) and Hopfield-type neural network (HTNN) in expectation maximization (EM) framework for video sequences captured by static camera. In the considered technique, the background model is built by considering a running Gaussian average over few past frames. The change vector analysis (CVA) scheme is followed on the considered target frame and the constructed reference frame to generate a difference image. The moving objects in target frame are detected by segmenting the difference image into two classes: changed and unchanged, where the changed class represents moving object regions and the unchanged class the background regions. For segmentation, we have modeled the CVA generated difference image with GMRF and the segmentation problem is solved using the maximum a posteriori probability (MAP) estimation principle. The MAP estimator is found to be exponential in nature; and thus a modified HTNN is exploited for estimating the MAP. The parameters of the GMRF model are estimated using EM algorithm. Experiments are carried out on three video sequences. Results of the proposed change detection scheme are compared with those of the codebook-based background subtraction and GMRF model with graph-cut schemes. It is found that the proposed technique provides better results. [ABSTRACT FROM AUTHOR]

Abstract: This paper presents a novel spatio-contextual fuzzy clustering algorithm for unsupervised change detection from multispectral and multitemporal remote sensing images. The proposed technique uses fuzzy Gibbs Markov Random Field (GMRF) to model the spatial gray level attributes of the multispectral difference image. The change detection problem is solved using the maximum a posteriori probability (MAP) estimation principle. The MAP estimator of the fuzzy GMRF modeled difference image is found to be exponential in nature. Convergence of conventional fuzzy clustering based search criterion is more likely to lead the clustering solutions to be getting trapped in a local minimum. Hence we adhered to the variable neighborhood searching (VNS) based global convergence criterion for iterative estimation of the fuzzy GMRF parameters. Experiments are carried out on different multispectral and multitemporal remote sensing images. Results confirm the effectiveness of the proposed technique. It is also noticed that the proposed scheme provides better results with less misclassification error as compared to the existing techniques. The computational time taken by the proposed technique is comparable with that of the HTNN scheme. [Copyright &y& Elsevier]

Due to the very large number of bands in hyperspectral imagery, two major problems which arise during classification are the ‘curse of dimensionality’ and computational complexity. To overcome these, dimensionality reduction is an important task for hyperspectral image analysis. An unsupervised band elimination method is proposed which iteratively eliminates one band from the pair of most correlated neighbouring bands depending on the discriminating capability of the bands. Correlation between neighbouring bands is calculated over partitioned band images. Capacitory discrimination is used to measure the discrimination capability of a band image. Finally, four evaluation measures, namely classification accuracy, kappa coefficient, class separability, and entropy are calculated over the selected bands to measure the efficiency of the proposed method. The proposed unsupervised band elimination technique is compared to three popular state-of-the-art approaches, both qualitatively and quantitatively, and shows promising results compared to them. [ABSTRACT FROM AUTHOR]

A clutter rejection scheme is proposed for synthetic aperture radar (SAR) imagery based on two-stage two-dimensional principal component analysis (two-stage 2DPCA) followed by a bipolar eigenspace separation transformation (BEST) and a multilayer perceptron (MLP). For this, we have examined and analyzed four different algorithms. They are based on principal component analysis (PCA) both in one dimension (conventional PCA) and in two dimension with three different forms (2DPCA, alternative 2DPCA and two-stage 2DPCA), followed by BEST and MLP in each case. Feature extraction in different cases is carried out using respective PCA scheme. Each algorithm uses the BEST to further reduce dimensionality and enhance the generalization capability of the classifier. Classification between target chips and clutter chips is finally made through an MLP classifier. Experimental results on MSTAR public release database of SAR imagery are presented. Comparison of all the 2DPCA algorithms with an existing technique shows improvement both in performance and time. Moreover, all the 2DPCA algorithms compute eigenvectors and eigenvalues of image covariance matrix accurately and very efficiently with respect to time, and further reduces the effect of noise better than that in the case of PCA. Comparison reveals the fact that two-stage 2DPCA-based algorithm is the best (both in performance and time) between all algorithms. [ABSTRACT FROM AUTHOR]

Abstract: This article presents a novel ‘self-training’ based semi-supervised classification algorithm using the property of aggregation pheromone found in real ants. The proposed method has no assumption regarding the data distribution and is free from parameters to be set by the user. It can also capture arbitrary shapes of the classes. The proposed algorithm is evaluated with a number of synthetic as well as real life benchmark datasets in terms of accuracy, macro and micro averaged F 1 measures. Results are compared with two supervised and three semi-supervised classification techniques and are statistically validated using paired t-test. Experimental results show the potentiality of the proposed algorithm. [Copyright &y& Elsevier]

Although background subtraction techniques have been used for several years in vision systems for moving object detection, many of them fail to provide good results in presence of noise, illumination variation, non-static background, etc. A basic requirement of background subtraction scheme is the construction of a stable background model and then comparing each incoming image frame with it so as to detect moving objects. The novelty of the proposed scheme is to construct a stable background model from a given video sequence dynamically. The constructed background model is compared with different image frames of the same sequence to detect moving objects. In the proposed scheme the background model is constructed by analyzing a sequence of linearly dependent past image frames in Wronskian framework. The Wronskian based change detection model is further used to detect the changes between the constructed background scene and the considered target frame. The proposed scheme is an integration of Gaussian averaging and Wronskian change detection model. Gaussian averaging uses different modes which arise over time to capture the underlying richness of background, and it is an approach for background building by considering temporal modes. Similarly, Wronskian change detection model uses a spatial region of support in this regard. The proposed scheme relies on spatio-temporal modes arising over time to build the appropriate background model by considering both spatial and temporal modes. The results obtained by the proposed model is found to provide accurate shape of moving objects. The effectiveness of the proposed scheme is verified by comparing the results with those of some of the existing state of the art background subtraction techniques on public benchmark databases. We found that the average F-measure is significantly improved by the proposed scheme from that of the state-of-the-art techniques. [ABSTRACT FROM AUTHOR]

The change-detection problem can be viewed as an unsupervised classification problem with two classes corresponding to changed and unchanged areas. Image differencing is a widely used approach to change detection. It is based on the idea of generating a difference image that represents the modulus of the spectral change vectors associated with each pixel in the study area. To separate out the changed and unchanged classes in the difference image automatically, any unsupervised technique can be used. Thresholding is one of the cheapest techniques among them. However, in thresholding approaches, selection of the best threshold value is not a trivial task. In this work, several non-fuzzy and fuzzy histogram thresholding techniques are investigated and compared for the change-detection problem. Experimental results, carried out on different multitemporal remote sensing images (acquired before and after an event), are used to assess the effectiveness of each of the thresholding techniques. Among all the thresholding techniques investigated here, Liu's fuzzy entropy followed by Kapur's entropy are found to be the most robust techniques. [ABSTRACT FROM AUTHOR]

Abstract: This article addresses a problem of moving object detection by combining two kinds of segmentation schemes: temporal and spatial. It has been found that consideration of a global thresholding approach for temporal segmentation, where the threshold value is obtained by considering the histogram of the difference image corresponding to two frames, does not produce good result for moving object detection. This is due to the fact that the pixels in the lower end of the histogram are not identified as changed pixels (but they actually correspond to the changed regions). Hence there is an effect on object background classification. In this article, we propose a local histogram thresholding scheme to segment the difference image by dividing it into a number of small non-overlapping regions/windows and thresholding each window separately. The window/block size is determined by measuring the entropy content of it. The segmented regions from each window are combined to find the (entire) segmented image. This thresholded difference image is called the change detection mask (CDM) and represent the changed regions corresponding to the moving objects in the given image frame. The difference image is generated by considering the label information of the pixels from the spatially segmented output of two image frames. We have used a Markov Random Field (MRF) model for image modeling and the maximum a posteriori probability (MAP) estimation (for spatial segmentation) is done by a combination of simulated annealing (SA) and iterated conditional mode (ICM) algorithms. It has been observed that the entropy based adaptive window selection scheme yields better results for moving object detection with less effect on object background (mis) classification. The effectiveness of the proposed scheme is successfully tested over three video sequences. [Copyright &y& Elsevier]