Clustering and Classification Methods

Plants are fundamentally important to life. Key research areas in plant science include plant species identification, weed classification using hyper spectral images, monitoring plant health and tracing leaf growth, and the semantic interpretation of leaf information. Botanists easily identify plant species by discriminating between the shape of the leaf, tip, base, leaf margin and leaf vein, as well as the texture of the leaf and the arrangement of leaflets of compound leaves. Because of the increasing demand for experts and calls for biodiversity, there is a need for intelligent systems that recognize and characterize leaves so as to scrutinize a particular species, the diseases that affect them, the pattern of leaf growth, and so on. We review several image processing methods in the feature extraction of leaves, given that feature extraction is a crucial technique in computer vision. As computers cannot comprehend images, they are required to be converted into features by individually analyzing image shapes, colors, textures and moments. Images that look the same may deviate in terms of geometric and photometric variations. In our study, we also discuss certain machine learning classifiers for an analysis of different species of leaves.

Page 1. A Parallel Genetic Algorithm for Rule Discovery in Large Databases Dieferson Luis Alves de Araujo' , Heitor S. Lopes', Alex A. Freitas2 CEFET-PR - Centro Federal de EducaGBo Tecnol6gica do Paranh CPGEI - Curso ...

We undertake a detailed analysis of a sample of over 10,000 utterances from 18 speakers in a corpus of Bequia English and apply constrained cluster analysis to discern patterns that identify the linguistic signatures for different villages and to see how individuals pattern in relation to the rest of their village. The analysis of multiple variables provides a richer picture of both group and individual than any one variable does and holds promise for better understanding the mysterious mechanisms by which variation between individuals scales up to variation between groups.

This paper is about the wireless sensor network in environmental monitoring applications. A Wireless
Sensor Network consists of many sensor nodes and a base station. The number and type of sensor nodes
and the design protocols for any wireless sensor network is application specific. The sensor data in this
application may be light intensity, temperature, pressure, humidity and their variations .Clustering and
routing are the two areas which are given more attention in this paper

Classification is one of the most predominant tasks for wide range of applications such as Sentiment analysis in text, voice recognition, image recognition, genetic engineering, data classification etc. Though many efficient classification algorithms have been introduced in the past few decades, due to the drastic increase in the amount of data generated across industry and academia there is a demand for classification algorithms with very high accuracy and robustness. This paper presents a new approach to enhance the accuracy of the classifier by combining Support Vector Machine (Classification algorithm) with K-Means Clustering algorithm and, finally using K Nearest Neighbours to make optimal choice on the classification problem .Experiments have shown that this new methodology has increased the accuracy of the classification problem and thus serves the intended purpose.

Skin diseases are among the most common health problems worldwide. In this article we proposed a method that uses computer vision based techniques to detect various kinds of dermatological skin diseases. We have used different types of image processing algorithms for feature extraction and feed forward artificial neural network for training and testing purpose. The system works on two phases- first pre-process the colour skin images to extract significant features and later identifies the diseases. The system successfully detects 9 different types of dermatological skin diseases with an accuracy rate of 90%.

License Plate Recognition (LPR) plays an important role on the traffic monitoring and parking management. A robust and efficient method for enhancing accuracy of license plate characters recognition based on K Nearest Neighbours (K-NN) classifier is presented in this paper. The system first prepares a contour form of the extracted character, then the angle and distance feature information about the character is extracted and finally K-NN classifier is used to character recognition. Angle and distance features of a character have been computed based on distribution of points on the bitmap image of character. In K-NN method, the Euclidean distance between testing point and reference points is calculated in order to find the k-nearest neighbours. We evaluated our method on the available dataset that contain 1200 sample. Using 70% samples for training, we tested our method on whole samples and obtained 99% correct recognition rate.Further, we achieved average 99.41% accuracy using three...

Comparative network analysis is an emerging line of research that provides insights into the structure and dynamics of networks by finding similarities and discrepancies in their topologies. Unfortunately, comparing networks directly is not feasible on large scales. Existing works resort to representing networks with vectors of features extracted from their topologies and employ various distance metrics to compare between these feature vectors. In this paper, instead of relying on feature vectors to represent the studied networks, we suggest fitting a network model (such as Kronecker Graph) to encode the network structure. We present the directed fitting-distance measure, where the distance from a network A to another network B is captured by the quality of B's fit to the model derived from A. Evaluation on five classes of real networks shows that KronFit based distances perform surprisingly well.

Illumination invariance remains the most researched, yet the most challenging aspect of automatic face recognition. In this paper we investigate the discriminative power of colour-based invariants in the presence of large illumination changes between training and test data, when appearance changes due to cast shadows and non-Lambertian effects are significant. Specifically, there are three main contributions: (i) we employ a more sophisticated photometric model of the camera and show how its parameters can be estimated, (ii) we derive several novel colour-based face invariants, and (iii) on a large database of video sequences we examine and evaluate the largest number of colour-based representations in the literature. Our results suggest that colour invariants do have a substantial discriminative power which may increase the robustness and accuracy of recognition from low resolution images.

A highly comparative, feature-based approach to time series classification is introduced that uses an extensive database of algorithms to extract thousands of interpretable features from time series. These features are derived from across the scientific time-series analysis literature, and include summaries of time series in terms of their correlation structure, distribution, entropy, stationarity, scaling properties, and fits to a range of time-series models. After computing thousands of features for each time series in a training set, those that are most informative of the class structure are selected using greedy forward feature selection with a linear classifier. The resulting feature-based classifiers automatically learn the differences between classes using a reduced number of time-series properties, and circumvent the need to calculate distances between time series. Representing time series in this way results in orders of magnitude of dimensionality reduction, allowing the method to perform well on very large datasets containing long time series or time series of different lengths. For many of the datasets studied, classification performance exceeded that of conventional instance-based classifiers, including one nearest neighbor classifiers using Euclidean distances and dynamic time warping and, most importantly, the features selected provide an understanding of the properties of the dataset, insight that can guide further scientific investigation.

Advancement in information and technology has made a major impact on medical science where the researchers come up with new ideas for improving the classification rate of various diseases. Breast cancer is one such disease killing large number of people around the world. Diagnosing the disease at its earliest instance makes a huge impact on its treatment. The authors propose a Binary Bat Algorithm (BBA) based Feedforward Neural Network (FNN) hybrid model, where the advantages of BBA and efficiency of FNN is exploited for the classification of three benchmark breast cancer datasets into malignant and benign cases. Here BBA is used to generate a V-shaped hyperbolic tangent function for training the network and a fitness function is used for error minimization. FNNBBA based classification produces 92.61% accuracy for training data and 89.95% for testing data.

Internet is the boon in modern era as every organization uses it for dissemination of information and e-commerce related applications. Sometimes people of organization feel delay while accessing internet in spite of proper bandwidth. Prediction model of web caching and prefetching is an ideal solution of this delay problem. Prediction model analysing history of internet user from server raw log files and determine future sequence of web objects and placed all web objects to nearer to the user so access latency could be reduced to some extent and problem of delay is to be solved. To determine sequence of future web objects, it is necessary to determine proximity of one web object with other by identifying proper distance metric technique related to web caching and prefetching. This paper studies different distance metric techniques and concludes that bio informatics based distance metric techniques are ideal in context to Web Caching and Web Prefetching.

Overlapping is one of the topics in wireless sensor networks that is considered by researchers in the last decades. An appropriate overlapping management system can prolong network lifetime and decrease network recovery time. This paper proposes an intelligent and knowledge‐based overlapping clustering protocol for wireless sensor networks, called IKOCP. This protocol uses some of the intelligent and knowledge‐based systems to construct a robust overlapping strategy for sensor networks. The overall network is partitioned to several regions by a proposed multicriteria decision‐making controller to monitor both small‐scale and large‐scale areas. Each region is managed by a sink, where the whole network is managed by a base station. The sensor nodes are categorized by various clusters using the low‐energy adaptive clustering hierarchy (LEACH)‐improved protocol in a way that the value of p is defined by a proposed support vector machine–based mechanism. A proposed fuzzy system determines that noncluster heads associate with several clusters in order to manage overlapping conditions over the network. Cluster heads are changed into clusters in a period by a suggested utility function. Since network lifetime should be prolonged and network traffic should be alleviated, a data aggregation mechanism is proposed to transmit only crucial data packets from cluster heads to sinks. Cluster heads apply a weighted criteria matrix to perform an inner‐cluster routing for transmitting data packets to sinks. Simulation results demonstrate that the proposed protocol surpasses the existing methods in terms of the number of alive nodes, network lifetime, average time to recover, dead time of first node, and dead time of last node.

Since 1991, India is emerging as a key destination for marketers from across the globe. However, littlework of relevance has been undertaken to understand the Indian Consumers. It is believed that the Indian metropolitan consumers are fairly homogeneous in nature. In this paper, we report the results of a survey conducted amongst 560 respondents in the five metropolises. The objective was to segment the metropolitan consumers on behavioral aspects and to understand their onsumption pattern. The study, designed on the basis of VALS, uses cluster analysis to segment the Indian metropolitan consumers into six behavioral categories, namely Well Settled, Strugglers, Enjoyers, Conservatives, Self Concerned and Realist. The segments have been profiled in terms of their product ownership, Activities and Interests, Financial Investment avenues and Media habits. Implications for marketing and government policy have been discussed.

L'opera è un compendio allegato alla carta dal medesimo titolo, che racconta le peculiarità ambientali più importanti dell'Isola di San Pietro. Nel libro vengono inoltre raccontati i tratti salienti, della vera storia dei Tabarkini e dei Carolini, abitanti dell'Isola di San Pietro, e per questo, la toponomastica viene descritta con il bilinguismo che contraddistingue secondo la legge, la minoranza Etnico-Linguistica del Popolo Tabarkino in Sardegna e nel Mondo.

EAN: 9788892681798
ISBN: 9788892681798

Nicola Borghero (Geologo e Cartografo)

Hanno fattivamente partecipato

Prof. Fiorenzo Toso UNISS
Nicolo Capriata (Geologo storico)
Andrea Luxoro ( Antropologo)
Salvatore Borghero (esperto archivista e catalogatore)
Srar Saba (Revisione testi)

Online reviews are a feedback to the product and play a key role in improving the product to cater to
consumers. Online reviews that rely heavily on manual categorization are time consuming and labor
intensive.The recurrent neural network in deep learning can process time series data, while the long and
short term memory network can process long time sequence data well. This has good experimental
verification support in natural language processing, machine translation, speech recognition and language
model.The merits of the extracted data features affect the classification results produced by the classification
model. The LDA topic model adds a priori a posteriori knowledge to classify the data so that the
characteristics of the data can be extracted efficiently.Applied to the classifier can improve accuracy and
efficiency. Two-way long-term and short-term memory networks are variants and extensions of cyclic neural
networks.The deep learning framework Keras uses Tensorflow as the backend to build a convenient two-way
long-term and short-term memory network model, which provides a strong technical support for the
experiment.Using the LDA topic model to extract the keywords needed to train the neural network and
increase the internal relationship between words can improve the learning efficiency of the model. The
experimental results in the same experimental environment are better than the traditional word frequency
features.

Clustering is an extremely important task in a wide variety of application domains especially in management and social science research. In this paper, an iterative procedure of two-way clustering method based on multivariate outlier detection was proposed using the famous Squared Mahalanobis distance or T 2-distance and step-wise multiple discriminant analysis. At first, Squared Mahalanobis distance or T 2 –distance should be calculated for the entire sample, then using T 2 – statistic fix a UCL. Above the UCL are treated as outliers which are grouped as outlier cluster and the remaining inliers are treated as inlier cluster. Now, perform a step-wise multiple discriminant analysis by consider the cluster membership as dependent variable and utilizing the variables already used to detect multivariate outliers as independent variables. Moreover, retain the dominant subset of discriminant independent variables and leave the remaining variables. The dominant subset of variables are treated as variables cluster-1.Now, remove the outliers from the entire sample, leave the subset of dominant variables from the original set and repeat the same procedure for the remaining inliers and variables, until one of the following results will be attain i) there are no outliers in the final cluster ii) the set of predictors in the last iteration will be empty or there are no subsets emerged and iii) the variance-covariance matrix for the variables in the final cluster achieved singularity. At each iteration, multivariate test of mean used to check the discrimination between the outlier clusters and the inliers. Moreover, multivariate control charts also used to graphically visualizes the iterations and clustering process. This paper employed this procedure for clustering 275 customers of a famous four-wheeler in India based on 19 different attributes of the four wheeler and its company. The result of the proposed technique confirms there exist 6 clusters of customers, 5 variable clusters at 5% significance level and 5 clusters of customers, 5 variable clusters at 1% significance level respectively.

The current body of literature lacks studies related to organizational managers' classification of systems thinking (ST) skills based on both their overall systemic tendency and the organizational ownership structure. The purpose of this study is to assess and classify the ST skills of senior managers who currently work in a complex business environment. Initially, we clustered managers' overall systemic thinking (OST) using the Bayesian latent class analysis (BLCA) method into two distinct clusters: managers with upper OST (holistic thinker) and managers with lower OST (reductionist thinker). Further, we classified managers' ST skills into two predefined classes to understand the characteristics of each group better. A total of 51 senior managers from two different organizational structures participated in this study. Results show that the ST skills of managers in public are more towards the upper OST/holistic cluster, whereas managers from the private sector have an inclination towards the lower OST/reductionist cluster.

The report finds the prospects of the gaming community by accessing the gamer based upon age, ethnicity, occupation, annual household income and education qualifications. The data collected as part of this survey would require further investigation and remedial action by management as certain variables provided does not contribute to the overall variance percentage in factorial analysis.

Machine learning methods used for prediction and decision
support are of great concern nowadays. Methods for learning
implicit, non-symbolic knowledge provide better predictive
accuracy. But Methods for learning explicit, symbolic
knowledge produce more comprehensible models. Hybrid
machine learning models combine strengths of both
knowledge representation of model types. In this research we
compare predictive accuracy and comprehensibility of
explicit, implicit, and hybrid machine learning models. This
research based on predicting terrorist groups responsible of
attacks in Middle East and North Africa from year 2009 up to
2013 by comparing various standard, ensemble, hybrid, and
hybrid ensemble machine learning methods namely; Naïve
Bayes, K-nearest neighbours, Decision Tree, Support Vector
Machine; Hybrid Hoeffding Tree, Functional Tree, Hybrid
Naïve Bayes with Decision Table, Classification via
Clustering; Random Forests; and Stacking classifiers.
Afterwards compare the results obtained from conducting the
experiments according to four different performance
measures. Experiments were carried out using real world
data represented by Global terrorism Database (GTD) from
National Consortium for the study of terrorism and
Responses of Terrorism (START).

A curva de carga do sistema é um parâmetro-chave em qualquer estudo que subsidie decisões em
sistemas de energia elétrica, em todas as suas etapas, desde o planejamento até a operação. Esses
estudos necessitam do agrupamento de curvas de carga e/ou da determinação de perfis típicos, em
graus de complexidade variáveis, de acordo com as aplicações, inclusive quanto à representação de
correlações espaciais e temporais. Assim, este trabalho descreve uma metodologia proposta e
implementada no Programa MODCAR para o agrupamento de dados horários de carga em patamares,
utilizando os métodos Ward e K-Means, e métricas para determinação dos números adequados de
agrupamentos. A partir do pós-processamento desses resultados, propõe-se ainda um método para
definição de perfis típicos diários de carga, baseado na combinação de técnicas estatísticas de análise
de agrupamentos e na análise exploratória de dados, e na construção de loadplots, uma representação
gráfica que combina boxplots de dados de carga em cada hora do dia, em conjunto com os níveis
agregados da curva de carga. Utilizando um conjunto de dados horários compreendendo um período
de 6 anos, são calculados perfis típicos diários de carga de energia, distintos para cada mês do ano,
para os subsistemas Sudeste/Centro-Oeste, Sul, Nordeste e Norte, adequados para a sua utilização nas
atividades de planejamento da expansão, cálculo de garantia física, planejamento da operação de
médio e curto prazos, e formação do preço de liquidação de diferenças, em especial nos modelos
NEWAVE e DECOMP.

Diabetes is a serious health issue faced by the society. Classification of tissues in a diabetic wound is highly essential to monitor the healing progress of the wound. The standard manual tissue classification methods utilized by the clinicians are prone to inaccuracy and is highly dependant on the knowledge and experience of the expert. Moreover, since the wounds may form at a body part that is difficult to access, assessing of the wound becomes time-consuming and causes distress to the patient. Hence, an automatic diabetic wound classification system is required for accurate assessment of the wound. In this paper, we offer tissue classification system for diabetic ulcers that utilizes Fuzzy C-Means and colour analysis using colour features to cluster the digital images. The clusters are initially labelled by calculating the minimum Euclidean Distance of the cluster center from the reference points. Each cluster was further analyzed by using spatial relation to label the smaller blobs, and tissue relation to label larger blobs. Granulation and Epithelial were further evaluated by computing the shiny regions caused by the reflection of light on wetter regions of the wound. The proposed algorithm will segment wound tissues into granulation, slough, eschar, and epithelial tissues and display the segmented regions along with the percentage area of each type of tissue. This system can be used by the diagnostician to effectively and easily monitor the healing progress of the wound.