Double Processing Information Extraction for User Queries

Data extraction (IE) is the task of removing sorted out information from unstructured semi composed machine clear reports. In a huge part of cases this development concerns taking care of human vernacular messages by system for trademark tongue planning. Information extraction has not got as much thought as information retrieval (IR) and is routinely baffled with the later. The task of IR is to look over a get-together of artistic reports, a subset which is pertinent to a particular request, considering essential word chase and possibly extended by the use of a thesaurus. The IR gets ready conventionally and gives back a situated once-over of documents, where the rank contrasts with the relevance score that the structure assigned to the chronicle in light of the request. This paper proposes double handling data extraction technique (DPIE), where backward classification is used with normal pre-processing technique. These are data processing and query processing. This system gives better results when contrasted with existing rules.

Computational Approach of Molar Refraction, Molecular Radius and Internal Pressure of a Binary Mixture - Molecular Interaction Studies

Refractive indices and molar volume of binary liquid mixture of 1, 4-dioxane with 1-butanol were measured over the entire composition range at T= (298.15, 303.15, 308.15, 313.15 & 318) K using Anton Paar and Abbemat Refractometer. Basing empirical formulae and the measured data were utilized to evaluate the molar refraction (Rm), molecular radii (r), internal pressure (pi) along with their excess parameters. The computed results of RmE, rE and piE were fitted to the Redlich–Kister polynomial equation and focused on the molecular interactions present in the mixture.

Smart IT Technologies of the Current Century

As we know that the emerging IT technologies are essential for our day to day live. During the last few decades we have seen a great leaps in IT technologies which are all innovations that revolution the way we live and work. In this paper we cover those emerging, smart and revolutionary technologies. The paper also cover a broad range of IT areas, from software development to network management.

The Extraction of Expert Weights from Pair Wise Comparisons in Delphi Method

In the second step of Delphi, it has often seen that experts play their roles with same weights of importance. Meanwhile, some experts clearly wiser and more powerful in such matters than others. There is no specific guidance to find the weight of importance of experts in Delphi process. Therefore, this paper intends to introduce a simple method (based on Eigenvector method - by using the number of iteration to reach convergence) to find the weight of importance of experts in Delphi process. The findings in this paper confirm the effectiveness of proposed method. So that, inconsistent experts get less weight and vice-versa. A numerical example demonstrates the application of the proposed method.

Granular Region-oriented Fuzzy-Rough based kNN Improvization for Activity Recognition Modeling

Activity recognition is a complex task of the Human Computer Interaction (HCI) domain with ever-increasing research interest. Human activity recognition has been specially addressed by the advances in pattern recognition. k-Nearest Neighbors(kNN) is a non-parametric classifier from pattern recognition theory, that mimics human decision making by taking previous experiences into consideration for segregating unknown objects. A novel fuzzy-rough model, based on granular computing for improvisation of the kNN classifier is proposed herewith. In this model, feature-wise fuzzy memberships are generated to fuzzify the feature space of the nearest neighbors of the test object. These neighbors fuzzified feature space are then aggregated into granules, based on their class-belongingness. From these, lower and upper approximation granules are generated using rough set theory to classify the test object. It is shown experimentally that this model outperforms the traditional kNN by 16.43% and Fuzzy-kNN by 10.25%, in the human activity recognition domain. Another novelty is in the efficient use of the fuzzy similarity relations in class-dependent granulated feature space, and, fuzzy-rough lower/upper approximations in the hybridization of the kNN classifier.

A Control Framework of Lower Extremity Rehabilitation Exoskeleton based on Neuro-Muscular-Skeletal Model

A control system framework of lower extremity rehabilitation exoskeleton robot is presented. It is based on the Neuro-Musculo-Skeletal biological model. Its core composition moudle, the motion intent parser part, mainly comprises of three distinct parts. The first part is signal acquisition of surface electromyography (sEMG) that is the summation of motor unit action potential (MUAP) starting from central nervous system (CNS). sEMG can be used to decode action intent of operator to make the patient actively participate in specific training. As another composition part, a muscle dynamics model that is comprised of activation and contraction dynamic model is developed. It is mainly used to calculate muscle force. The last part is the skeletal dynamic model that is simplified as a linked segment mechanics. Combined with muscle dynamic model, the joint torque exerted by internal muscles can be exported, which can be ued to do a exoskeleton controller design. The developed control framework can make exoskeleton offer assistance to operators during rehabilitation by guiding motions on correct training rehabilitation trajectories, or give force support to be able to perform certain motions. Though the presentation is orientated towards the lower extremity exoskeleton, it is generic and can be applied to almost any part of the human body.

Graphene Oxide Based Dye-Sensitised Solar Cells Using Terminalia-Chebula

The use of carbon compounds and natural pigments help to build easy and cheap dye-sensitised solar cells. Accordingly, graphene oxide is used as a layer separation on the pigment with more efficiency. Also, instead of an expensive and commercial pigment, chebula terminalia plant which is cheap, abundant and environmental friendly was used. In the presence of graphene oxide, more voltage and conversion efficiency are obtained compared to the solar cell without the graphene oxide.

A New MADM Approach in Building Consensus in Delphis Technique

There is no doubt that Delphi is a powerful technique in group decision-making context. Despite its usefulness, Delphi has some limitations too. A major drawback is the lack of transparency in reaching the consensus among the respondents. Therefore, in this paper, to resolve this limit, a new mathematical approach (based on the improved AHP models) according to Asgharpour (2003) and Azadfallah and Azizi (2015), is proposed, and can more assure the results by applying a model. A numerical example (in technologies foresight fields) demonstrates the application of the proposed method. The findings in this paper confirm the effectiveness of proposed method.

A Novel Algorithm for Predicting Valuable Items in Data Streams

A data stream is a real time, continuous, structured sequence of data items. Mining data stream is the process of extracting knowledge from continuous arrival of rapid data records. Data can arrive fast and in continuous manner. It is very difficult to perform mining process. Normally, stream mining algorithms are designed to scan the database only once, and it is a complicated task to extract the knowledge from the database by a single scan. Data streams are a computational challenge to data mining problems because of the additional algorithmic constraints created by the large volume of data. Popular data mining techniques namely clustering, classification, and frequent pattern mining are applied to data streams for extracting the knowledge. This research work mainly concentrates on how to predict the valuable items which are found in a transactional data of a data stream. In the literature, most of the researchers have discussed about how the frequent items are mined from the data streams. This research work helps to predict the valuable items in a transactional data. Frequent item mining is defined as finding the items which occur frequently, i.e. the occurrence of items above the given threshold is considered as frequent items. Valuable item mining is nothing but finding the costliest or most valuable items of a database. Predicting this information helps businesses to know about the sales details about the valuable items which guide to make crucial decisions, such as catalogue drawing, cross promotion, end user shopping, and performance scrutiny. In this research work, a new algorithm namely VIM (Valuable Item Mining) is proposed for finding the valuable items in data streams. The performance of this algorithm is analysed by using the factors, number of valuable items discovered, and execution time.

Clustering Techniques for Biological Sequence Analysis: A Review

In the present scenario there are a variety of technical tools for supporting and validating wet-lab experiments in the field of science and biotechnology. In order to analyze biological sequences it is necessary to group similar genes. Grouping of genes can be done by using various techniques like pattern matching, classification, clustering etc. In the present study clustering is used as a tool for analyzing biological data. Clustering of Biological sequences is a very interesting and fascinating area as various researchers are working on it. But simple clustering algorithms are not much suitable for sequence analysis problems. Most of the biological sequence analysis problems are NP-hard and some strong optimization algorithm are required for these types of problems. The manuscript presented here is a survey of various clustering techniques useful for analysis of biological sequences. The 3+ stage review process is adopted for the review of literature. To prepare this report 98 papers have been reviewed from year 1997 to 2014 according to the year of publish. The papers reviewed have discussed various issues related to the analysis of biological sequences. The major issues discovered in the reviewed papers were prediction, sequence alignment, motif discovery, cluster boundary prediction etc. Various solution approaches used by researchers for the biological sequence analysis are evolutionary clustering, neural networks, hierarchical clustering, k-means, Go technologies, feature selection, incremental approach, bio-inspired methods, particle swarm optimization, fuzzy techniques, rough set theory and bi-clustering etc. Researchers have applied these solution approaches on various types of datasets. In this communication we have also discussed about these datasets and the parameters used with results mentioned in papers.