Computational models for the prediction of severe thunderstorms over east indian region

Thunderstorm is one of the most spectacular weather phenomena in the atmosphere. Many parts over the Indian region experience thunderstorms at higher frequency during pre-monsoon months (March- May), when the atmosphere is highly unstable because of high temperatures prevailing at lower levels. Most dominant feature of the weather during the pre-monsoon season over the eastern Indo-Gangetic plain and northeast India is the outburst of severe local convective storms, commonly known as ‘Nor’wester’ or ‘Kalbaishakhi’. The severe thunderstorms associated with thunder, squall line, lightning and hail cause extensive losses in agriculture, damage to structure and also loss of life. The casualty due to lightning associated with thunderstorms in this region is the highest in the world. The highest numbers of aviation hazards are reported during occurrence of these thunderstorms. In India, 72% of tornadoes are associated with this thunderstorm.

Underwater Target Localization, Tracking and Classification

Underwater target localization and tracking attracts tremendous research interest due to various impediments to the estimation task caused by the noisy ocean environment. This thesis envisages the implementation of a prototype automated system for underwater target localization, tracking and classification using passive listening buoy systems and target identification techniques. An autonomous three buoy system has been developed and field trials have been conducted successfully. Inaccuracies in the localization results, due to changes in the environmental parameters, measurement errors and theoretical approximations are refined using the Kalman filter approach. Simulation studies have been conducted for the tracking of targets with different scenarios even under maneuvering situations. This system can as well be used for classifying the unknown targets by extracting the features of the noise emanations from the targets.

MicroRNA–mRNA interaction network using TSK-type recurrent neural fuzzy network

MicroRNAs are short non-coding RNAs that can regulate gene expression during various crucial cell processes such as differentiation, proliferation and apoptosis. Changes in expression profiles of miRNA play an important role in the development of many cancers, including CRC. Therefore, the identification of cancer related miRNAs and their target genes are important for cancer biology research. In this paper, we applied TSK-type recurrent neural fuzzy network (TRNFN) to infer miRNA–mRNA association network from paired miRNA, mRNA expression profiles of CRC patients. We demonstrated that the method we proposed achieved good performance in recovering known experimentally verified miRNA–mRNA associations. Moreover, our approach proved successful in identifying 17 validated cancer miRNAs which are directly involved in the CRC related pathways. Targeting such miRNAs may help not only to prevent the recurrence of disease but also to control the growth of advanced metastatic tumors. Our regulatory modules provide valuable insights into the pathogenesis of cancer

Significance Of Classification Techniques In Prediction Of Learning Disabilities

The aim of this study is to show the importance of two classification techniques, viz. decision tree and clustering, in prediction of learning disabilities (LD) of school-age children. LDs affect about 10 percent of all children enrolled in schools. The problems of children with specific learning disabilities have been a cause of concern to parents and teachers for some time. Decision trees and clustering are powerful and popular tools used for classification and prediction in Data mining. Different rules extracted from the decision tree are used for prediction of learning disabilities. Clustering is the assignment of a set of observations into subsets, called clusters, which are useful in finding the different signs and symptoms (attributes) present in the LD affected child. In this paper, J48 algorithm is used for constructing the decision tree and K-means algorithm is used for creating the clusters. By applying these classification techniques, LD in any child can be identified

Prediction of Key Symptoms of Learning Disabilities in School-Age Children Using Rough Sets

This paper highlights the prediction of learning disabilities (LD) in school-age children using rough set theory (RST) with an emphasis on application of data mining. In rough sets, data analysis start from a data table called an information system, which contains data about objects of interest, characterized in terms of attributes. These attributes consist of the properties of learning disabilities. By finding the relationship between these attributes, the redundant attributes can be eliminated and core attributes determined. Also, rule mining is performed in rough sets using the algorithm LEM1. The prediction of LD is accurately done by using Rosetta, the rough set tool kit for analysis of data. The result obtained from this study is compared with the output of a similar study conducted by us using Support Vector Machine (SVM) with Sequential Minimal Optimisation (SMO) algorithm. It is found that, using the concepts of reduct and global covering, we can easily predict the learning disabilities in children

Prediction of Learning Disabilities in School Age Children using SVM and Decision Tree

This paper highlights the prediction of Learning Disabilities (LD) in school-age children using two classification methods, Support Vector Machine (SVM) and Decision Tree (DT), with an emphasis on applications of data mining. About 10% of children enrolled in school have a learning disability. Learning disability prediction in school age children is a very complicated task because it tends to be identified in elementary school where there is no one sign to be identified. By using any of the two classification methods, SVM and DT, we can easily and accurately predict LD in any child. Also, we can determine the merits and demerits of these two classifiers and the best one can be selected for the use in the relevant field. In this study, Sequential Minimal Optimization (SMO) algorithm is used in performing SVM and J48 algorithm is used in constructing decision trees.

Attribute reduction and missing value imputing with ANN: prediction of learning disabilities

Learning disability (LD) is a neurological condition that affects a child’s brain and impairs his ability to carry out one or many specific tasks. LD affects about 10% of children enrolled in schools. There is no cure for learning disabilities and they are lifelong. The problems of children with specific learning disabilities have been a cause of concern to parents and teachers for some time. Just as there are many different types of LDs, there are a variety of tests that may be done to pinpoint the problem The information gained from an evaluation is crucial for finding out how the parents and the school authorities can provide the best possible learning environment for child. This paper proposes a new approach in artificial neural network (ANN) for identifying LD in children at early stages so as to solve the problems faced by them and to get the benefits to the students, their parents and school authorities. In this study, we propose a closest fit algorithm data preprocessing with ANN classification to handle missing attribute values. This algorithm imputes the missing values in the preprocessing stage. Ignoring of missing attribute values is a common trend in all classifying algorithms. But, in this paper, we use an algorithm in a systematic approach for classification, which gives a satisfactory result in the prediction of LD. It acts as a tool for predicting the LD accurately, and good information of the child is made available to the concerned

Performance Improvement of Fuzzy and Neuro Fuzzy Systems: Prediction of Learning Disabilities in School-age Children

Learning Disability (LD) is a classification including several disorders in which a child has difficulty in learning in a typical manner, usually caused by an unknown factor or factors. LD affects about 15% of children enrolled in schools. The prediction of learning disability is a complicated task since the identification of LD from diverse features or signs is a complicated problem. There is no cure for learning disabilities and they are life-long. The problems of children with specific learning disabilities have been a cause of concern to parents and teachers for some time. The aim of this paper is to develop a new algorithm for imputing missing values and to determine the significance of the missing value imputation method and dimensionality reduction method in the performance of fuzzy and neuro fuzzy classifiers with specific emphasis on prediction of learning disabilities in school age children. In the basic assessment method for prediction of LD, checklists are generally used and the data cases thus collected fully depends on the mood of children and may have also contain redundant as well as missing values. Therefore, in this study, we are proposing a new algorithm, viz. the correlation based new algorithm for imputing the missing values and Principal Component Analysis (PCA) for reducing the irrelevant attributes. After the study, it is found that, the preprocessing methods applied by us improves the quality of data and thereby increases the accuracy of the classifiers. The system is implemented in Math works Software Mat Lab 7.10. The results obtained from this study have illustrated that the developed missing value imputation method is very good contribution in prediction system and is capable of improving the performance of a classifier.

Learning disability prediction tool using ANN and ANFIS

Learning Disability (LD) is a neurological condition that affects a child’s brain and impairs his ability to carry out one or many specific tasks. LD affects about 15 % of children enrolled in schools. The prediction of LD is a vital and intricate job. The aim of this paper is to design an effective and powerful tool, using the two intelligent methods viz., Artificial Neural Network and Adaptive Neuro-Fuzzy Inference System, for measuring the percentage of LD that affected in school-age children. In this study, we are proposing some soft computing methods in data preprocessing for improving the accuracy of the tool as well as the classifier. The data preprocessing is performed through Principal Component Analysis for attribute reduction and closest fit algorithm is used for imputing missing values. The main idea in developing the LD prediction tool is not only to predict the LD present in children but also to measure its percentage along with its class like low or minor or major. The system is implemented in Mathworks Software MatLab 7.10. The results obtained from this study have illustrated that the designed prediction system or tool is capable of measuring the LD effectively

Using Neural Network Classifier Support Vector Machine Regression for the prediction of Melting Point of Drug – like compounds

In our study we use a kernel based classification technique, Support Vector Machine Regression for predicting the Melting Point of Drug – like compounds in terms of Topological Descriptors, Topological Charge Indices, Connectivity Indices and 2D Auto Correlations. The Machine Learning model was designed, trained and tested using a dataset of 100 compounds and it was found that an SVMReg model with RBF Kernel could predict the Melting Point with a mean absolute error 15.5854 and Root Mean Squared Error 19.7576

Artificial neural network modeling for surface roughness prediction in cylindrical grinding of Al‐SiCp metal matrix composites and ANOVA analysis

Metal matrix composites (MMC) having aluminium (Al) in the matrix phase and silicon carbide particles (SiCp) in reinforcement phase, ie Al‐SiCp type MMC, have gained popularity in the re‐cent past. In this competitive age, manufacturing industries strive to produce superior quality products at reasonable price. This is possible by achieving higher productivity while performing machining at optimum combinations of process variables. The low weight and high strength MMC are found suitable for variety of components

Genetic algorithm based indicator sequence method for exon prediction

Considerable research effort has been devoted in predicting the exon regions of genes. The binary indicator (BI), Electron ion interaction pseudo potential (EIIP), Filter method are some of the methods. All these methods make use of the period three behavior of the exon region. Even though the method suggested in this paper is similar to above mentioned methods , it introduces a set of sequences for mapping the nucleotides selected by applying genetic algorithm and found to be more promising

The role of Ken&Barbie in JAK/STAT signalling during development of Drosophila melanogaster

Cell-cell interactions during embryonic development are crucial in the co-ordination of growth, differentiation and maintenance of many different cell types. To achieve this co-ordination each cell must properly translate signals received from neighbouring cells, into spatially and temporally appropriate developmental responses. A surprisingly limited number of signal pathways are responsible for the differentiation of enormous variety of cell types. As a result, pathways are frequently 'reused' during development. Thus, in mammals the JAK/STAT pathway is required during early embryogenesis, mammary gland formation, hematopoiesis and, finally, plays a pivotal role in immune response. In the canonical way, the JAK/STAT pathway is represented by a transmembrane receptor associated with a Janus kinase (JAK), which upon stimulation by an extra-cellular ligand, phosphorylates itself, the receptor and, finally, the signal transducer and activator of transcription (STAT) molecules. Phosphorylated STATs dimerise and translocate to the nucleus where they activate transcription of target genes. The JAK/STAT pathway has been conserved throughout evolution, and all known components are present in the genome of Drosophila melanogaster. Besides hematopoietic and immunity functions, the pathway is also required during development for processes including embryonic segmentation, tracheal morphogenesis, posterior spiracle formation etc. This study describes Drosophila Ken&Barbie (Ken) as a selective regulator of JAK/STAT signalling. ken mutations identified in a screen for modulators of an eye overgrowth phenotype, caused by over-expression of the pathway ligand unpaired, also interact genetically with the pathway receptor domeless (dome) and the transcription factor stat92E. Over-expression of Ken can phenocopy developmental defects known to be caused by the loss of JAK/STAT signalling. These genetic interactions suggest that Ken may function as a negative regulator of the pathway. Ken has C-terminal Zn-finger domain, presumably for DNA binding, and N-terminal BTB/POZ domain, often found in transcriptional repressors. Using EGFP-fused construct expressed in vivo revealed nuclear accumulation of Ken. Therefore, it is proposed that Ken may act as a suppresser of STAT92E target genes. An in vitro assay, termed SELEX, determined that Ken specifically binds to a DNA sequence, with the essential for DNA recognition core overlapping that of STAT92E. This interesting observation suggests that not all STAT92E sites may also allow Ken binding. Strikingly, when effects of ectopic Ken on the expression of putative JAK/STAT pathway target genes were examined, only a subset of the genes tested, namely vvl, trh and kni, were down-regulated by Ken, whereas some others, such as eve and fj, appeared to be unresponsive. Further analysis of vvl, one of the genes susceptible to ectopic Ken, was undertaken. In the developing hindgut, expression of vvl is JAK/STAT pathway dependent, but remains repressed in the posterior spiracles, despite the stimulation of STAT92E by Upd in their primordia. Importantly, ken is also expressed in the developing posterior spiracles. Strikingly, up-regulation of vvl is observed in these tissues in ken mutant embryos. These imply that while ectopic Ken is sufficient to repress the expression of vvl in the hindgut, endogenous Ken is also necessary to prevent its activation in the posterior spiracles. It is therefore conceivable that ectopic vvl expression in the posterior spiracles of the ken mutants may be the result of de-repression of endogenous STAT92E activity. Another consequence of these observations is a fine balance that must exist between STAT92E and Ken activities. Apparently, endogenous level of Ken is sufficient to repress vvl, but not other, as yet unidentified, JAK/STAT pathway targets, whose presumable activation by STAT92E is required for posterior spiracle development as the embryos mutant for dome, the receptor of the pathway, show severe spiracle defects. These defects are also observed in the embryos mis-expressing Ken. Though it is possible that the posterior spiracle phenotype caused by higher levels of Ken results from a JAK/STAT pathway independent activity, it seems to be more likely that Ken acts in a dosage dependent manner, and extra Ken is able to further antagonise JAK/STAT pathway target genes. While STAT92E binding sites required for target gene expression have been poorly characterised, the existence of genome data allows the prediction of candidate STAT92E sites present in target genes promoters to be attempted. When a 6kb region containing the putative regulatory domains flanking the vvl locus are examined, only a single potential STAT92E binding site located 825bp upstream of the translational start can be detected. Strikingly, this site also includes a perfect Ken binding sequence. Such an in silico observation, though consistent with both Ken DNA binding assay in vitro and regulation of STAT92E target genes in vivo, however, requires further analysis. The JAK/STAT pathway is implicated in a variety of processes during embryonic and larval development as well as in imago. In each case, stimulation of the same transcription factor results in different developmental outcomes. While many potential mechanisms have been proposed and demonstrated to explain such pleiotropy, the present study indicates that Ken may represent another mechanism, with which signal transduction pathways are controlled. Ken selectively down-regulates a subset of potential target genes and so modifies the transcriptional profile generated by activated STAT92E - a mechanism, which may be partially responsible for differences in the morphogenetic processes elicited by JAK/STAT signalling during development.

Relativistic prediction of the ground state of atomic Lawrencium

In contradiction to the prediction of the Periodic Table but in agreement with earlier suggestions by Brewer and Mann, the ground state configuration of atomic Lawrencium (Z = 103) will not be 7s^2 6d^2 D_3/2 but 7s^2 7p ^2p_1/2. The reason for this deviation from normal trends across the Periodic Table are strong relativistic effects on the outermost 7P_l/2 orbital. Multicontiguration Dirac-Fock calculations are reported for Lawrencium and analogous lighter atoms. These calculations include contributions from magnetic and retardation interactions and an estimation of quantum electrodynamic corrections.

Prediction of some thermodynamic properties of selected compounds of element 104

A set of parametrized equations has been published by Bratsch and Lagowski for calculating thermodynamic properties of the lanthanides, actinides, element 104, and certainrelated elements. Since these equations were applied to element 104, new values for the first four ionization energies and radii of the ions of charge +1, +2, +3, and +4 have been calculated for this element. The parametrized equations are used here with these new values to calculate some thermodynamic properties of element 104.