Search results for: Multinomial dirichlet classification model

Authors: Thomas Bryan, Veton Kepuska, Ivica Kostanic

Abstract:

Speaker recognition is performed in high Additive White Gaussian Noise (AWGN) environments using principals of Computational Auditory Scene Analysis (CASA). CASA methods often classify sounds from images in the time-frequency (T-F) plane using spectrograms or cochleargrams as the image. In this paper atomic decomposition implemented by matching pursuit performs a transform from time series speech signals to the T-F plane. The atomic decomposition creates a sparsely populated T-F vector in “weight space” where each populated T-F position contains an amplitude weight. The weight space vector along with the atomic dictionary represents a denoised, compressed version of the original signal. The arraignment or of the atomic indices in the T-F vector are used for classification. Unsupervised feature learning implemented by a sparse autoencoder learns a single dictionary of basis features from a collection of envelope samples from all speakers. The approach is demonstrated using pairs of speakers from the TIMIT data set. Pairs of speakers are selected randomly from a single district. Each speak has 10 sentences. Two are used for training and 8 for testing. Atomic index probabilities are created for each training sentence and also for each test sentence. Classification is performed by finding the lowest Euclidean distance between then probabilities from the training sentences and the test sentences. Training is done at a 30dB Signal-to-Noise Ratio (SNR). Testing is performed at SNR’s of 0 dB, 5 dB, 10 dB and 30dB. The algorithm has a baseline classification accuracy of ~93% averaged over 10 pairs of speakers from the TIMIT data set. The baseline accuracy is attributable to short sequences of training and test data as well as the overall simplicity of the classification algorithm. The accuracy is not affected by AWGN and produces ~93% accuracy at 0dB SNR.

Keywords: Time-frequency plane, atomic decomposition, envelope sampling, Gabor atoms, matching pursuit, sparse dictionary learning, sparse autoencoder.

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Authors: Wararat Rungworawut, Twittie Senivongse

Abstract:

Business process model describes process flow of a business and can be seen as the requirement for developing a software application. This paper discusses a BPM2CD guideline which complements the Model Driven Architecture concept by suggesting how to create a platform-independent software model in the form of a UML class diagram from a business process model. An important step is the identification of UML classes from the business process model. A technique for object-oriented analysis called domain analysis is borrowed and key concepts in the business process model will be discovered and proposed as candidate classes for the class diagram. The paper enhances this step by using ontology search to help identify important classes for the business domain. As ontology is a source of knowledge for a particular domain which itself can link to ontologies of related domains, the search can give a refined set of candidate classes for the resulting class diagram.

Keywords: Business Process Model, Model DrivenArchitecture, Ontology, UML Class Diagram.

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Authors: Chee Siong Teh, Chee Peng Lim

Abstract:

This paper proposes a novel architecture for developing decision support systems. Unlike conventional decision support systems, the proposed architecture endeavors to reveal the decision-making process such that humans' subjectivity can be incorporated into a computerized system and, at the same time, to preserve the capability of the computerized system in processing information objectively. A number of techniques used in developing the decision support system are elaborated to make the decisionmarking process transparent. These include procedures for high dimensional data visualization, pattern classification, prediction, and evolutionary computational search. An artificial data set is first employed to compare the proposed approach with other methods. A simulated handwritten data set and a real data set on liver disease diagnosis are then employed to evaluate the efficacy of the proposed approach. The results are analyzed and discussed. The potentials of the proposed architecture as a useful decision support system are demonstrated.

Keywords: Interactive evolutionary computation, multivariate data projection, pattern classification, topographic map.

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Authors: Yongsuk Jang Kim, Sun-Tae Chung, Boogyun Kim, Seongwon Cho

Abstract:

Realistic 3D face model is more precise in representing pose, illumination, and expression of face than 2D face model so that it can be utilized usefully in various applications such as face recognition, games, avatars, animations, and etc. In this paper, we propose a 3D face modeling method based on 3D dense morphable shape model. The proposed 3D modeling method first constructs a 3D dense morphable shape model from 3D face scan data obtained using a 3D scanner. Next, the proposed method extracts and matches facial landmarks from 2D image sequence containing a face to be modeled, and then reconstructs 3D vertices coordinates of the landmarks using a factorization-based SfM technique. Then, the proposed method obtains a 3D dense shape model of the face to be modeled by fitting the constructed 3D dense morphable shape model into the reconstructed 3D vertices. Also, the proposed method makes a cylindrical texture map using 2D face image sequence. Finally, the proposed method generates a 3D face model by rendering the 3D dense face shape model using the cylindrical texture map. Through building processes of 3D face model by the proposed method, it is shown that the proposed method is relatively easy, fast and precise.

Keywords: 3D Face Modeling, 3D Morphable Shape Model, 3DReconstruction, 3D Correspondence.

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Authors: Zahra Banakar, Farzad Tahriri

Abstract:

It has often been said that the strength of any country resides in the strength of its industrial sector, and Progress in industrial society has been accomplished by the creation of new technologies. Developments have been facilitated by the increasing availability of advanced manufacturing technology (AMT), in addition the implementation of advanced manufacturing technology (AMT) requires careful planning at all levels of the organization to ensure that the implementation will achieve the intended goals. Justification and implementation of advanced manufacturing technology (AMT) involves decisions that are crucial for the practitioners regarding the survival of business in the present days of uncertain manufacturing world. This paper assists the industrial managers to consider all the important criteria for success AMT implementation, when purchasing new technology. Concurrently, this paper classifies the tangible benefits of a technology that are evaluated by addressing both cost and time dimensions, and the intangible benefits are evaluated by addressing technological, strategic, social and human issues to identify and create awareness of the essential elements in the AMT implementation process and identify the necessary actions before implementing AMT.

Keywords: Advanced Manufacturing Technology (AMT), Justification and Classification.

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Authors: A. B. Adehor, P. R. Burrell

Abstract:

The Integrated Management of Child illnesses (IMCI) and the surveillance Health Information Systems (HIS) are related strategies that are designed to manage child illnesses and community practices of diseases. However, both strategies do not function well together because of classification incompatibilities and, as such, are difficult to use by health care personnel in rural areas where a majority of people lack the basic knowledge of interpreting disease classification from these methods. This paper discusses a single approach on how a stand-alone expert system can be used as a prompt diagnostic tool for all cases of illnesses presented. The system combines the action-oriented IMCI and the disease-oriented HIS approaches to diagnose malaria and typhoid fever in the rural areas of the Niger-delta region.

Keywords: Differential diagnosis, Health Information System(HIS), Integrated Management of Child Illnesses (IMCI), Malaria andTyphoid fever.

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Authors: Bhekisipho Twala, Mantepu Masetshaba, Ramapulana Nkoana

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Multiple classifier systems combine several individual classifiers to deliver a final classification decision. However, an increasingly controversial question is whether such systems can outperform the single best classifier, and if so, what form of multiple classifiers system yields the most significant benefit. Also, multi-target tracking detection using multiple sensors is an important research field in mobile techniques and military applications. In this paper, several multiple classifiers systems are evaluated in terms of their ability to predict a system’s failure or success for multi-sensor target tracking tasks. The Bristol Eden project dataset is utilised for this task. Experimental and simulation results show that the human activity identification system can fulfil requirements of target tracking due to improved sensors classification performances with multiple classifier systems constructed using boosting achieving higher accuracy rates.

Keywords: Single classifier, machine learning, ensemble learning, multi-sensor target tracking.

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Authors: Ahmad F. Subahi, Anthony J. H. Simons

Abstract:

This Paper presents an on-going research in the area of Model-Driven Engineering (MDE). The premise is that UML is too unwieldy to serve as the basis for model-driven engineering. We need a smaller, simpler notation with a cleaner semantics. We propose some ideas for a simpler notation with a clean semantics. The result is known as μML, or the Micro-Modelling Language.

Keywords: Model-driven engineering, model transformations, domain-specific languages, end-user development.

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Authors: Catherine Maware, Olufemi Adetunji

Abstract:

The paper is aimed at developing a model to measure the impact of Lean manufacturing deployment on organizational performance. The model will help industry practitioners to assess the impact of implementing Lean constructs on organizational performance. It will also harmonize the measurement models of Lean performance with the house of Lean that seems to have become the industry standard. The sheer number of measurement models for impact assessment of Lean implementation makes it difficult for new adopters to select an appropriate assessment model or deployment methodology. A literature review is conducted to classify the Lean performance model. Pareto analysis is used to select the Lean constructs for the development of the model. The model is further formalized through the use of Structural Equation Modeling (SEM) in defining the underlying latent structure of a Lean system. An impact assessment measurement model developed can be used to measure Lean performance and can be adopted by different industries.

Keywords: Impact measurement model, lean bundles, lean manufacturing, organizational performance.

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Authors: Y. Boutiche, N. Ramou, M. Ben Gharsallah

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This paper is devoted to present and discuss a model that allows a local segmentation by using statistical information of a given image. It is based on Chan-Vese model, curve evolution, partial differential equations and binary level sets method. The proposed model uses the piecewise constant approximation of Chan-Vese model to compute Signed Pressure Force (SPF) function, this one attracts the curve to the true object(s)-s boundaries. The implemented model is used to extract weld defects from weld radiographic images in the aim to calculate the perimeter and surfaces of those weld defects; encouraged resultants are obtained on synthetic and real radiographic images.

Keywords: Active contour, Chan-Vese Model, local segmentation, weld radiographic images.

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Authors: Guowei Hou, Lixin Yu, Wei Zhuang, Hui Qin, Xue Yang

Abstract:

Formal verification is proposed to ensure the correctness of the design and make functional verification more efficient. As cache plays a vital role in the design of System on Chip (SoC), and cache with Memory Management Unit (MMU) and cache memory unit makes the state space too large for simulation to verify, then a formal verification is presented for such system design. In the paper, a formal model checking verification flow is suggested and a new cache memory model which is called “exhaustive search model” is proposed. Instead of using large size ram to denote the whole cache memory, exhaustive search model employs just two cache blocks. For cache system contains data cache (Dcache) and instruction cache (Icache), Dcache memory model and Icache memory model are established separately using the same mechanism. At last, the novel model is employed to the verification of a cache which is module of a custom-built SoC system that has been applied in practical, and the result shows that the cache system is verified correctly using the exhaustive search model, and it makes the verification much more manageable and flexible.

Keywords: Cache system, formal verification, novel model, System on Chip (SoC).

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Authors: Widad Jakjoud, Mohamed Bahaj, Jamal Bakkas

Abstract:

Through this paper we present a method for automatic generation of ontological model from any data source using Model Driven Architecture (MDA), this generation is dedicated to the cooperation of the knowledge engineering and software engineering. Indeed, reverse engineering of a data source generates a software model (schema of data) that will undergo transformations to generate the ontological model. This method uses the meta-models to validate software and ontological models.

Keywords: Meta model, model, ontology, data source.

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Authors: Y. N. Phang, E. F. Loh

Abstract:

Zero inflated strict arcsine model is a newly developed model which is found to be appropriate in modeling overdispersed count data. In this study, we extend zero inflated strict arcsine model to zero inflated strict arcsine regression model by taking into consideration the extra variability caused by extra zeros and covariates in count data. Maximum likelihood estimation method is used in estimating the parameters for this zero inflated strict arcsine regression model.

Keywords: Overdispersed count data, maximum likelihood estimation, simulated annealing.

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Authors: Yu-Min Chang, Kuo-Sheng Tsai, Hung-Te Tsai, Chia-Hsin Li

Abstract:

This paper presents the early-warning lights classification management system for industrial parks promoted by the Taiwan Environmental Protection Administration (EPA) since 2011, including the definition of each early-warning light, objectives, action program and accomplishments. All of the 151 industrial parks in Taiwan were classified into four early-warning lights, including red, orange, yellow and green, for carrying out respective pollution management according to the monitoring data of soil and groundwater quality, regulatory compliance, and regulatory listing of control site or remediation site. The Taiwan EPA set up a priority list for high potential polluted industrial parks and investigated their soil and groundwater qualities based on the results of the light classification and pollution potential assessment. In 2011-2013, there were 44 industrial parks selected and carried out different investigation, such as the early warning groundwater well networks establishment and pollution investigation/verification for the red and orange-light industrial parks and the environmental background survey for the yellow-light industrial parks. Among them, 22 industrial parks were newly or continuously confirmed that the concentrations of pollutants exceeded those in soil or groundwater pollution control standards. Thus, the further investigation, groundwater use restriction, listing of pollution control site or remediation site, and pollutant isolation measures were implemented by the local environmental protection and industry competent authorities; the early warning lights of those industrial parks were proposed to adjust up to orange or red-light. Up to the present, the preliminary positive effect of the soil and groundwater quality management system for industrial parks has been noticed in several aspects, such as environmental background information collection, early warning of pollution risk, pollution investigation and control, information integration and application, and inter-agency collaboration. Finally, the work and goal of self-initiated quality management of industrial parks will be carried out on the basis of the inter-agency collaboration by the classified lights system of early warning and management as well as the regular announcement of the status of each industrial park.

Keywords: Industrial park, soil and groundwater quality management, early-warning lights classification, SOP for reporting and treatment of monitored abnormal events.

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Authors: Hyun-Woo Cho

Abstract:

The early diagnostic decision making in industrial processes is absolutely necessary to produce high quality final products. It helps to provide early warning for a special event in a process, and finding its assignable cause can be obtained. This work presents a hybrid diagnostic schmes for batch processes. Nonlinear representation of raw process data is combined with classification tree techniques. The nonlinear kernel-based dimension reduction is executed for nonlinear classification decision boundaries for fault classes. In order to enhance diagnosis performance for batch processes, filtering of the data is performed to get rid of the irrelevant information of the process data. For the diagnosis performance of several representation, filtering, and future observation estimation methods, four diagnostic schemes are evaluated. In this work, the performance of the presented diagnosis schemes is demonstrated using batch process data.

Keywords: Diagnostics, batch process, nonlinear representation, data filtering, multivariate statistical approach

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Authors: Mongkorn Klingajay, Tawatchai Jitson

Abstract:

The pipe inspection operation is the difficult detective performance. Almost applications are mainly relies on a manual recognition of defective areas that have carried out detection by an engineer. Therefore, an automation process task becomes a necessary in order to avoid the cost incurred in such a manual process. An automated monitoring method to obtain a complete picture of the sewer condition is proposed in this work. The focus of the research is the automated identification and classification of discontinuities in the internal surface of the pipe. The methodology consists of several processing stages including image segmentation into the potential defect regions and geometrical characteristic features. Automatic recognition and classification of pipe defects are carried out by means of using an artificial neural network technique (ANN) based on Radial Basic Function (RBF). Experiments in a realistic environment have been conducted and results are presented.

Keywords: Artificial neural network, Radial basic function, Curve fitting, CCTV, Image segmentation, Data acquisition.

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Authors: Hidehiko Okada

Abstract:

The author previously proposed an extension of differential evolution. The proposed method extends the processes of DE to handle interval numbers as genotype values so that DE can be applied to interval-valued optimization problems. The interval DE can employ either of two interval models, the lower and upper model or the center and width model, for specifying genotype values. Ability of the interval DE in searching for solutions may depend on the model. In this paper, the author compares the two models to investigate which model contributes better for the interval DE to find better solutions. Application of the interval DE is evolutionary training of interval-valued neural networks. A result of preliminary study indicates that the CW model is better than the LU model: the interval DE with the CW model could evolve better neural networks. 

Keywords: Evolutionary algorithms, differential evolution, neural network, neuroevolution, interval arithmetic.

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Authors: Truong Quang Dang Khoa, Masahiro Nakagawa

Abstract:

Brain Computer Interface (BCI) has been recently increased in research. Functional Near Infrared Spectroscope (fNIRs) is one the latest technologies which utilize light in the near-infrared range to determine brain activities. Because near infrared technology allows design of safe, portable, wearable, non-invasive and wireless qualities monitoring systems, fNIRs monitoring of brain hemodynamics can be value in helping to understand brain tasks. In this paper, we present results of fNIRs signal analysis indicating that there exist distinct patterns of hemodynamic responses which recognize brain tasks toward developing a BCI. We applied two different mathematics tools separately, Wavelets analysis for preprocessing as signal filters and feature extractions and Neural networks for cognition brain tasks as a classification module. We also discuss and compare with other methods while our proposals perform better with an average accuracy of 99.9% for classification.

Keywords: functional near infrared spectroscope (fNIRs), braincomputer interface (BCI), wavelets, neural networks, brain activity, neuroimaging.

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Authors: T. Kumaresan, S. Sanjushree, C. Palanisamy

Abstract:

Image spam is a kind of email spam where the spam text is embedded with an image. It is a new spamming technique being used by spammers to send their messages to bulk of internet users. Spam email has become a big problem in the lives of internet users, causing time consumption and economic losses. The main objective of this paper is to detect the image spam by using histogram properties of an image. Though there are many techniques to automatically detect and avoid this problem, spammers employing new tricks to bypass those techniques, as a result those techniques are inefficient to detect the spam mails. In this paper we have proposed a new method to detect the image spam. Here the image features are extracted by using RGB histogram, HSV histogram and combination of both RGB and HSV histogram. Based on the optimized image feature set classification is done by using k- Nearest Neighbor(k-NN) algorithm. Experimental result shows that our method has achieved better accuracy. From the result it is known that combination of RGB and HSV histogram with k-NN algorithm gives the best accuracy in spam detection.

Keywords: File Type, HSV Histogram, k-NN, RGB Histogram, Spam Detection.

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Authors: H. J. Wattimanela, U. S. Passaribu, N. T. Puspito, S. W. Indratno

Abstract:

Molluca Collision Zone is located at the junction of the Eurasian, Australian, Pacific and the Philippines plates. Between the Sangihe arc, west of the collision zone, and to the east of Halmahera arc is active collision and convex toward the Molluca Sea. This research will analyze the behavior of earthquake occurrence in Molluca Collision Zone related to the distributions of an earthquake in each partition regions, determining the type of distribution of a occurrence earthquake of partition regions, and the mean occurence of earthquakes each partition regions, and the correlation between the partitions region. We calculate number of earthquakes using partition method and its behavioral using conventional statistical methods. In this research, we used data of shallow earthquakes type and its magnitudes ≥4 SR (period 1964-2013). From the results, we can classify partitioned regions based on the correlation into two classes: strong and very strong. This classification can be used for early warning system in disaster management.

Keywords: Molluca Collision Zone, partition regions, conventional statistical methods, Earthquakes, classifications, disaster management.

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Authors: S. Oujdi, H. Belbachir

Abstract:

Existing methods of data mining cannot be applied on spatial data because they require spatial specificity consideration, as spatial relationships. This paper focuses on the classification with decision trees, which are one of the data mining techniques. We propose an extension of the C4.5 algorithm for spatial data, based on two different approaches Join materialization and Querying on the fly the different tables. Similar works have been done on these two main approaches, the first - Join materialization - favors the processing time in spite of memory space, whereas the second - Querying on the fly different tables- promotes memory space despite of the processing time. The modified C4.5 algorithm requires three entries tables: a target table, a neighbor table, and a spatial index join that contains the possible spatial relationship among the objects in the target table and those in the neighbor table. Thus, the proposed algorithms are applied to a spatial data pattern in the accidentology domain. A comparative study of our approach with other works of classification by spatial decision trees will be detailed.

Keywords: C4.5 Algorithm, Decision trees, S-CART, Spatial data mining.

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Authors: Entisar A. Elgmati

Abstract:

Martingale model diagnostic for assessing the fit of logistic regression model to recurrent events data are studied. One way of assessing the fit is by plotting the empirical standard deviation of the standardized martingale residual processes. Here we used another diagnostic plot based on martingale residual covariance. We investigated the plot performance under several types of model misspecification. Clearly the method has correctly picked up the wrong model. Also we present a test statistic that supplement the inspection of the two diagnostic. The test statistic power agrees with what we have seen in the plots of the estimated martingale covariance.

Keywords: Covariance, logistic model, misspecification, recurrent events.

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Authors: Naiqin Feng, Yuhui Qiu, Yingshan Zhang, Fang Wang

Abstract:

A logic model for analyzing complex systems- stability is very useful to many areas of sciences. In the real world, we are enlightened from some natural phenomena such as “biosphere", “food chain", “ecological balance" etc. By research and practice, and taking advantage of the orthogonality and symmetry defined by the theory of multilateral matrices, we put forward a logic analysis model of stability of complex systems with three relations, and prove it by means of mathematics. This logic model is usually successful in analyzing stability of a complex system. The structure of the logic model is not only clear and simple, but also can be easily used to research and solve many stability problems of complex systems. As an application, some examples are given.

Keywords: Complex system, logic model, relation, stability.

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Authors: Ke He, Wumaier Parezhati, Haruka Yamashita

Abstract:

Recently, online marketplaces in the e-commerce industry, such as Rakuten and Alibaba, have become some of the most popular online marketplaces in Asia. In these shopping websites, consumers can select purchase products from a large number of stores. Additionally, consumers of the e-commerce site have to register their name, age, gender, and other information in advance, to access their registered account. Therefore, establishing a method for analyzing consumer preferences from both the store and the product side is required. This study uses the Doc2Vec method, which has been studied in the field of natural language processing. Doc2Vec has been used in many cases to analyze the extraction of semantic relationships between documents (represented as consumers) and words (represented as products) in the field of document classification. This concept is applicable to represent the relationship between users and items; however, the problem is that one more factor (i.e., shops) needs to be considered in Doc2Vec. More precisely, a method for analyzing the relationship between consumers, stores, and products is required. The purpose of our study is to combine the analysis of the Doc2vec model for users and shops, and for users and items in the same feature space. This method enables the calculation of similar shops and items for each user. In this study, we derive the real data analysis accumulated in the online marketplace and demonstrate the efficiency of the proposal.

Keywords: Doc2Vec, marketing, online marketplace, recommendation system.

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Authors: Daniela Elena Marinescu, Ioana Manafi, Dumitru Marin

Abstract:

We consider a Principal-Agent model with the Principal being a seller who does not know perfectly how much the buyer (the Agent) is willing to pay for the good. The buyer-s preferences are hence his private information. The model corresponds to the nonlinear pricing problem of Maskin and Riley. We assume there are three types of Agents. The model is solved using “informational rents" as variables. In the last section we present the main characteristics of the optimal contracts in asymmetric information and some possible extensions of the model.

Keywords: Adverse selection, asymmetric information, informational rent, nonlinear pricing, optimal contract

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Authors: Hsu-Yung Cheng, Chih-Chang Yu

Abstract:

In this work, we present an automatic vehicle detection system for airborne videos using combined features. We propose a pixel-wise classification method for vehicle detection using Dynamic Bayesian Networks. In spite of performing pixel-wise classification, relations among neighboring pixels in a region are preserved in the feature extraction process. The main novelty of the detection scheme is that the extracted combined features comprise not only pixel-level information but also region-level information. Afterwards, tracking is performed on the detected vehicles. Tracking is performed using efficient Kalman filter with dynamic particle sampling. Experiments were conducted on a wide variety of airborne videos. We do not assume prior information of camera heights, orientation, and target object sizes in the proposed framework. The results demonstrate flexibility and good generalization abilities of the proposed method on a challenging dataset.

Keywords: Vehicle Detection, Airborne Video, Tracking, Dynamic Bayesian Networks

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Authors: A. A. Penin, A. S. Sidorenko

Abstract:

A convenient and physically sound mathematical model of the external or I - V characteristic of solar cells generators is presented in this paper. This model is compared with the traditional model of p-n junction. The direct analytical calculation of load regime leads to a quadratic equation, which is importantly to simplify the calculations in the real time.

Keywords: A solar cell generator, I−V characteristic, activetwo-pole.

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Authors: Isaac K. E. Ampomah, Seong-Bae Park, Sang-Jo Lee

Abstract:

Over the past decade, there have been promising developments in Natural Language Processing (NLP) with several investigations of approaches focusing on Recognizing Textual Entailment (RTE). These models include models based on lexical similarities, models based on formal reasoning, and most recently deep neural models. In this paper, we present a sentence encoding model that exploits the sentence-to-sentence relation information for RTE. In terms of sentence modeling, Convolutional neural network (CNN) and recurrent neural networks (RNNs) adopt different approaches. RNNs are known to be well suited for sequence modeling, whilst CNN is suited for the extraction of n-gram features through the filters and can learn ranges of relations via the pooling mechanism. We combine the strength of RNN and CNN as stated above to present a unified model for the RTE task. Our model basically combines relation vectors computed from the phrasal representation of each sentence and final encoded sentence representations. Firstly, we pass each sentence through a convolutional layer to extract a sequence of higher-level phrase representation for each sentence from which the first relation vector is computed. Secondly, the phrasal representation of each sentence from the convolutional layer is fed into a Bidirectional Long Short Term Memory (Bi-LSTM) to obtain the final sentence representations from which a second relation vector is computed. The relations vectors are combined and then used in then used in the same fashion as attention mechanism over the Bi-LSTM outputs to yield the final sentence representations for the classification. Experiment on the Stanford Natural Language Inference (SNLI) corpus suggests that this is a promising technique for RTE.

Keywords: Deep neural models, natural language inference, recognizing textual entailment, sentence-to-sentence relation.

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Authors: E. N. Sathishkumar, K. Thangavel, T. Chandrasekhar

Abstract:

Feature selection is a process to select features which are more informative. It is one of the important steps in knowledge discovery. The problem is that all genes are not important in gene expression data. Some of the genes may be redundant, and others may be irrelevant and noisy. Here a novel approach is proposed Hybrid K-Mean-Quick Reduct (KMQR) algorithm for gene selection from gene expression data. In this study, the entire dataset is divided into clusters by applying K-Means algorithm. Each cluster contains similar genes. The high class discriminated genes has been selected based on their degree of dependence by applying Quick Reduct algorithm to all the clusters. Average Correlation Value (ACV) is calculated for the high class discriminated genes. The clusters which have the ACV value as 1 is determined as significant clusters, whose classification accuracy will be equal or high when comparing to the accuracy of the entire dataset. The proposed algorithm is evaluated using WEKA classifiers and compared. The proposed work shows that the high classification accuracy.

Keywords: Clustering, Gene Selection, K-Mean-Quick Reduct, Rough Sets.

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Authors: Xiaoshu Lu, Martti Viljanen

Abstract:

A new analytical model is developed which provides close-formed solutions for both transient indoor and envelope temperature changes in buildings. Time-dependent boundary temperature is presented as Fourier series which can approximate real weather conditions. The final close-formed solutions are simple, concise, and comprehensive. The model was compared with numerical results and good accuracy was obtained. The model can be used as design and control guidelines in engineering applications for analysing mechanical heat transfer properties for buildings.

Keywords: Analytical model, heat transfer, whole building.

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