Extraction of Posture Silhouettes using Human Posture Feature Points and Spatial Masks for Activity Recognition in Public Place

Presently, an automated system has been required for public place security. Recognizing human postures in public places has emerged as a global solution for understanding human behavior in public places. In this work, a model to extract a human feature attribute of its posture has been presented to identify human behavior. The research work in this paper focuses on identifying the seating and standing postures of a person. The proposed methodology aims towards extraction of the human attributes from public places using spatial masks. Consequently, in this process, unwanted details from the background have been removed using the technique to focus on human postures only. The feature extraction process gives us blob vector and posture vector to evaluate human authentication and posture apprehension.

Automatic first-arrival picking method via intelligent Markov optimal decision processes

Picking the first arrival is an important step in seismic processing. The large volume of the seismic data calls for automatic and objective picking. In this paper, we formulate first-arrival picking as an intelligent Markov decision process in the multi-dimensional feature attribute space. By designing a reasonable model, the global optimization is carried out in the reward function space to obtain the path with the largest cumulative reward value, to achieve the purpose of automatically picking up the first arrival. The state-value function contains a distance-related discount factor γ, which enables the Markov decision process to pick up the first-arrival continuity to consider the lateral continuity of the seismic data and avoid the bad trace information in the seismic data. On this basis, the method of this paper further introduces the optimized model that is a fuzzy clustering-based multi-dimensional attribute reward function and structure-based Gaussian stochastic policy, thereby reducing the difficulty of model design, and making the seismic data pick up more accurately and automatically. Testing this approach in the field seismic data reveals its properties and shows it can automatically pick up more reasonable first arrivals and has a certain quality control ability, especially the first-arrival energy is weak (the signal-to-noise ratio is low) or there are adjacent complex waveforms in the shallow layer.

Robust feature collection and classification of network culture

The network provides a convenient mechanism for publishing and obtaining documents, and has now become a gathering place for all kinds of information. In the network, the amount of information increases exponentially, and how to dig useful patterns or knowledge from the massive network culture has become a hot topic for scholars. In data mining, in order to enable readers to quickly obtain the content of interest, research text classification, and automatically classify text data according to a certain classification model. Internet cultural text data has the characteristics of unstructured, subjective, high-dimensional, etc., which makes it difficult for text mining algorithms to extract effective and easy-to-understand classification rules, and the computational complexity is too high. This paper proposes a feature selection method based on robust features, using sample deviation and variance as the criteria for feature attributes to rank the importance of feature attributes, and select the best feature attribute subset. The experimental results show that the classification accuracy of the feature selection method based on sample deviation and variance proposed in this paper is higher than the traditional word frequency as the feature selection method, which proves the feasibility and superiority of the feature selection method proposed in this paper.

An Empirical Study of Predictive Model for Website Quality Analytics using Dataset of different Domains of Website

Introduction:: Web analytics is the process of examining websites to uncover patterns, correla-tions, trends, insights and other useful information which can be utilized to optimize web usage and to im-prove the quality of website. Methods:: This research proffers an approach which associates the website assessment with the user satisfac-tion and acceptance. The proposed WQA (Website Quality Analytic) Model considers websites from seven domains and using 13 UX- based quality attributes evaluates the quality of websites in each domain. The quality assessment is automated using supervised learning models to predict good, average and bad websites. Results:: The real time dataset of website domains is assessed and websites are predicted as good, average and bad websites using the algorithms. Conclusion:: The feature (attribute)-based predictive model for quality analytics is empirically analyzed for five classification algorithms. A qualitative analysis of the domain-wise classification of websites is present-ed too. Discussion:: A Website quality model essentially consists of a set of criteria used to determine if a website reaches certain levels of fineness. User experience (UX) directly measures the quality of site interactions, and is an indirect representative of site success and customer conversions. That is, a bad UX bounces away visi-tors to seek a more reliable website. Every single second a user spends on a website is directly attributable to the usability of a good UX. Hence, the evaluation of quality of websites is essential to determine user ac-ceptance, that is, the users are the parameter measured for the success of the site.

Research on Relationship Strength under Personalized Recommendation Service

Relationship of users in an online social network can be applied to promote personalized recommendation services. The measurement of relationship strength between user pairs is crucial to analyze the user relationship, which has been developed by many methods. An issue that has not been fully addressed is that the interaction behavior of individuals subjected to the activity field preference and interactive habits will affect interactive behavior. In this paper, the three-way representation of the activity field is given firstly, the contribution weight of the activity filed preferences is measured based on the interactions in the positive and boundary regions. Then, the interaction strength is calculated, integrating the contribution weight of the activity field preference and interactive habit. Finally, user relationship strength is calculated by fusing the interaction strength, common friend rate and similarity of feature attribute. The experimental results show that the proposed method can effectively improve the accuracy of relationship strength calculation.

Developing an Integrated Framework for Supplier Evaluation based on Relevant Attributes and Performance Measures

Supplier evaluation and selection is essential to any organization, and planning an effective and comprehensive approach to that end seems inevitable. Meanwhile, determining the requisite criteria for evaluating and selecting suppliers is probably one of the most important steps to be taken towards developing an evaluation and selection model in the organization. In this article, first a review of the literature on the criteria and the field of supplier evaluation and selection are provided. These criteria are then placed into proper categories. In order to formulate a supplier evaluation and selection framework for the manufacturing organization under study, the implemented categorization is applied where a list of fifteen attributes and performance criteria is created; where upon it is secured with the help of a designated panel (project team). These features are then screened using Lawshe’s method the “social attribute” is removed from the list of fifteen. The remaining 14 other criteria are configured within the SEAP (Suppliers Evaluation based on Attributes and Performances) framework. The framework follows the objective of continually evaluating suppliers, both potential and actual ones through incorporating their performances into their qualification ratings. Based on the proposed framework, suppliers are evaluated on the basis of two types of criteria, - feature (attribute) and performance.

Relevance of Machine Learning Techniques and Various Protein Features in Protein Fold Classification: A Review

Background: Protein fold prediction is a fundamental step in Structural Bioinformatics. The tertiary structure of a protein determines its function and to predict its tertiary structure, fold prediction serves an important role. Protein fold is simply the arrangement of the secondary structure elements relative to each other in space. A number of studies have been carried out till date by different research groups working worldwide in this field by using the combination of different benchmark datasets, different types of descriptors, features and classification techniques. Objective: In this study, we have tried to put all these contributions together, analyze their study and to compare different techniques used by them. Methods: Different features are derived from protein sequence, its secondary structure, different physicochemical properties of amino acids, domain composition, Position Specific Scoring Matrix, profile and threading techniques. Conclusion: Combination of these different features can improve classification accuracy to a large extent. With the help of this survey, one can know the most suitable feature/attribute set and classification technique for this multi-class protein fold classification problem.

Matrix representation of ideas: stimulating creativity using matrix Algebra

Purpose The purpose of this paper is to present a fresh approach to stimulate individual creativity. It introduces a mathematical representation for creative ideas, six creativity operators and methods of matrix-algebra to evaluate, improve and stimulate creative ideas. Creativity begins with ideas to resolve a problem or tackle an opportunity. By definition, a creative idea must be simultaneously novel and useful. To inject analytic rigor into these concepts of creative ideas, the author introduces a feature-attribute matrix-construct to represent ideas, creativity operators that use ideas as operands and methods of matrix algebra. It is demonstrated that it is now possible to analytically and quantitatively evaluate the intensity of the variables that make an idea more, equal or less, creative than another. The six creativity operators are illustrated with detailed multi-disciplinary real-world examples. The mathematics and working principles of each creativity operator are discussed. Design/methodology/approach The unit of analysis is ideas, not theory. Ideas are man-made artifacts. They are represented by an original feature-attribute matrix construct. Using matrix algebra, idea matrices can be manipulated to improve their creative intensity, which are now quantitatively measurable. Unlike atoms and cute rabbits, creative ideas, do not occur in nature. Only people can conceive and develop creative ideas for embodiment in physical, non-physical forms, or in a mix of both. For example, as widgets, abstract theorems, business processes, symphonies, organization structures, and so on. The feature-attribute matrix construct is used to represent novelty and usefulness. The multiplicative product of these two matrices forms the creativity matrix. Six creativity operators and matrix algebra are introduced to stimulate and measure creative ideas. Creativity operators use idea matrices as operands. Uses of the six operators are demonstrated using multi-disciplinary real-world examples. Metrics for novelty, usefulness and creativity are in ratio scales, grounded on the Weber–Fechner Law. This law is about persons’ ability to discern differences in the intensity of stimuli. Findings Ideas are represented using feature-attribute matrices. This construct is used to represent novel, useful and creative ideas with more clarity and precision than before. Using matrices, it is shown how to unambiguously and clearly represent creative ideas endowed with novelty and usefulness. It is shown that using matrix algebra, on idea matrices, makes it possible to analyze multi-disciplinary, real-world cases of creative ideas, with clarity and discriminatory power, to uncover insights about novelty and usefulness. Idea-matrices and the methods of matrix algebra have strong explanatory and predictive power. Using of matrix algebra and eigenvalue analyses, of idea-matrices, it is demonstrated how to quantitatively rank ideas, features and attributes of creative ideas. Matrix methods operationalize and quantitatively measure creativity, novelty and usefulness. The specific elementary variables that characterize creativity, novelty and usefulness factors, can now be quantitatively ranked. Creativity, novelty and usefulness factors are not considered as monolithic, irreducible factors, vague “lumpy” qualitative factors, but as explicit sets of elementary, specific and measurable variables in ratio scales. This significantly improves the acuity and discriminatory power in the analyses of creative ideas. The feature-attribute matrix approach and its matrix operators are conceptually consistent and complementary with key extant theories engineering design and creativity. Originality/value First to define and specify ideas as feature-attribute matrices. It is demonstrated that creative ideas, novel ideas and useful ideas can be analytically and unambiguously specified and measured for creativity. It is significant that verbose qualitative narratives will no longer be the exclusive means to specify creative ideas. Rather, qualitative narratives will be used to complement the matrix specifications of creative ideas. First to specify six creativity operators enabling matrix algebra to operate on idea-matrices as operands to generate new ideas. This capability informs and guides a person’s intuition. The myth and dependency, on non-repeatable or non-reproducible serendipity, flashes of “eureka” moments or divine inspiration, can now be vacated. Though their existence cannot be ruled out. First to specify matrix algebra and eigen-value methods of quantitative analyses of feature-attribute matrices to rank the importance of elementary variables that characterize factors of novelty, usefulness and creativity. Use of verbose qualitative narratives of novelty, usefulness and creativity as monolithic “lumpy” factors can now be vacated. Such lumpy narratives risk being ambiguous, imprecise, unreliable and non-reproducible, Analytic and quantitative methods are more reliable and consistent. First to define and specify a method of “attacking the negatives” to systematically pinpoint the improvements of an idea’s novelty, usefulness and creativity. This procedure informs and methodically guides the improvements of deficient ideas.

TCAD Simulation of Nano-Crystal Floating Gate EEPROM

The present invention relates to a Nano crystal floating gate memory which is reflected as a future nonvolatile memory. The reason behind this reflection is its invulnerability in tunnel oxide to weak-point leakage and therefore its higher scalability of tunnel oxide thickness and utilization of power. The Foremost feature attribute in NCM is that it functions on a low value of voltage.