Classifiers

Remote sensing is collecting information about an object without any direct physical contact with the
particular object. It is widely used in many fields such as oceanography, geology, ecology. Remote sensing
uses the Satellite to detect and classify the particular object or area. They also classify the object on the
earth surfaces which includes Vegetation, Building, Soil, Forest and Water. The approach uses the
classifiers of previous images to decrease the required number of training samples for the classifier
training of an incoming image. For each incoming image, a rough classifier is predicted first based on the
temporal trend of a set of previous classifiers. The predicted classifier is then fine-tuned into a more
accurate manner with current training samples. This approach can be further applied as sequential image
data, with only a small number of training samples, which are being required from each image. This
method uses LANSAT 8 images for Training and Testing processes. First, using the Classifier Prediction
technique the Signatures are being generated for the input images. The generated Signatures are used for
the Training purposes. SVM Classification is used for classifying the images. The final results describes
that the leverage of a priori information from previous images will provide advantageous improvement for
future images in multi temporal image classification.

Magnetic Resonance Imaging is a powerful technique that helps in the diagnosis of various medical conditions. MRI Image pre-processing followed by detection of brain abnormalities, such as brain tumors, are considered in this work. These images are often corrupted by noise from various sources. The Discrete Wavelet Transforms (DWT) with details thresholding is used for efficient noise removal followed by edge detection and threshold segmentation of the denoised images. Segmented image features are then extracted using morphological operations. These features are finally used to train an improved Support Vector Machine classifier that uses a Gausssian radial basis function kernel. The performance of the classifier is evaluated and the results of the classification show that the proposed scheme accurately distinguishes normal brain images from the abnormal ones and benign lesions from malignant tumours. The accuracy of the classification is shown to be 100% which is superior to the results reported in the literature. Keyword: Discrete wavelet transform support vector machine Classifier Feature extraction MRI brain image processing Image segmentation

Most coreference resolution models determine
if two mentions are coreferent using a single
function over a set of constraints or features.
This approach can lead to incorrect decisions
as lower precision features often overwhelm
the smaller number of high precision ones. To
overcome this problem, we propose a simple
coreference architecture based on a sieve that
applies tiers of deterministic coreference models
one at a time from highest to lowest precision.
Each tier builds on the previous tier’s entity cluster output. Further, our model propagates global information by sharing attributes (e.g., gender and number) across mentions in the same cluster. This cautious sieve guarantees that stronger features are given precedence over weaker ones and that each decision is made using all of the information available
at the time. The framework is highly modular: new coreference modules can be plugged in without any change to the other modules. In spite of its simplicity, our approach outperforms many state-of-the-art supervised and unsupervised models on several
standard corpora. This suggests that sieve based approaches could be applied to other NLP tasks.

These Tourism systems can be considered as an important motivation for tourists to visit the underlined places. These system employs the technologies, such as Internet of Tings(IoT), to manage the provided program and tourists by providing useful information. In this work, we state a survey of previous work on tourism system combined with IoT in terms of managing and analyzing of big data using data mining techniques. This is to extract important information from the huge data generated by the use of Internet of things in smart tourism system. By improving an algorithm for data classification algorithm, the proposed solution is formed to produce a classifier that can deals with different types of data with accepted complexity instead of using different classifiers for numeric data types. Moreover, it introduces an enhanced evaluation method, in which most of classifier can be included with high efficiency. The android based mobile application and web site can be produced to support the using of the proposed system.

(Research Paper Review ) The detection performance of radars is usually limited by clutters. Clutter models are necessary factors in the simulation and modeling of radar systems. Therefore, Knowledge of clutters is important to identify targets and the impact of clutter cannot be ignored. Statistical distributions are established models which are widely used in radar clutters performance calculations. Gaussian, Weibull, Rayleigh and K-distribution are four common clutter models. The design of radar detectors and clutter classification are really complicated issues. For clutter classification, the different methods such as Bayesian classifier are used. But in this paper we propose a new classifier for radar clutters. We use Artificial Neural Network to classify four common radar clutter models. First we simulate clutter models then extract their important features like skewness and kurtosis. Finally, we we make neural network with two hidden layers which has twenty and fifteen neurons in each layer and we separate clutter models with high accuracy. The results prove the validity of proposed method. Keywords-Classifier, feature extraction, radar clutter, Neural Network make neural network with two hidden layers which has twenty and fifteen neurons in each layer and we separate clutter models with high accuracy. The results prove the validity of proposed method.

The authors of this paper
M. A. Darzikolaei,
A. Ebrahimzade
E. Gholami,

In this paper I address a typological puzzle concerning complementary distribution of classifiers and plural markers. I discuss a possible interpretation of a particular type of the Polish word 'para' ('pair of') as a classifier. The analysis uses formal semantic plurality framework of Landman (2000).

Nowadays Hand written Character Recognition (HCR) is major remarkable and difficult research domain in the area of Image processing. Recognition of Handwritten English alphabets have been broadly studied in the previous years. Presently various recognition methodologies are in well-known utilized for recognition of handwritten English alphabets (character). Application domain of HCR is digital document processing such as mining information from data entry, cheque, applications for loans, credit cards, tax, health insurance forms etc. During this survey we present an outline of current research work conducted for recognition of handwritten English alphabets. In Handwritten manuscript there is no restriction on the writing technique. Handwritten alphabets are complicated to recognize because of miscellaneous human handwriting technique, difference in size and shape of letters, angle. A variety of recognition methodologies for handwritten English alphabets are conferred here alongside ...

In order to create an effective article, having great content is essential. However, to achieve this, the writer needs to target a specific audience. A target audience refers to a group of readers that a writer intends to reach with his content. Defining a target audience is substantial because it has a direct effect on adjusting writing style and content of the article. Nowadays, writers rely solely on annotated attributes of articles, such as location and language to understand his/her audience. The aim of this work is to identify the audience attributes of articles , especially not-annotated attributes. Among others, this work focuses on the detection of three key audience attributes of related articles: age, gender, and personality. We compare between multiple machine learning classifiers to detect these attributes. Finally, we demonstrate a prototypical application that enables writers to run existing algorithms such as trend detection and showing related articles that are specific to a defined target audience based on the newly detected attributes.

This research is aimed mainly at positing an analysis and inventory of the classifiers in Peruvian Sign Language (Lengua de Señas Peruana, LSP) from a set of narratives and the consultation of LSP users. The study has been guided by Zwitserlood (2003)’s analysis proposal, which posits that it is necessary to restrict the set of elements that can be considered classifiers, analyzes classificatory hand configurations from the Distributed Morphology (DM) framework, and argues that they have a double function: a grammatical one, as agreement markers, when they appear in verbs of motion, existence and location (VELM), in which case they can properly be called classifiers; and a lexical one, as roots, in all the other contexts in which they can be found. The inventory posited in this study is centered in the first of these functions.

In this paper we extend the problem of classification using Fuzzy Association Rule Mining and propose the concept of Fuzzy Weighted Associative Classifier (FWAC). Classification based on Association rules is considered to be effective and advantageous in many cases. Associative classifiers are especially fit to applications where the model may assist the domain experts in their decisions. Weighted Associative
Classifiers that takes advantage of weighted Association Rule Mining is already being proposed. However, there is a so-called "sharp boundary" problem in association rules mining with quantitative attribute domains. This paper proposes a new Fuzzy Weighted Associative Classifier (FWAC) that generates classification rules using Fuzzy Weighted Support and Confidence framework. The naïve approach can be used to generating strong rules instead of weak irrelevant rules. where fuzzy logic is used in partitioning the domains. The problem of Invalidation of Downward Closure property is solved and the concept of Fuzzy Weighted Support and Fuzzy Weighted Confidence frame work for Boolean and quantitative item with weighted setting is generalized. We propose a theoretical model to introduce new associative classifier that takes advantage of Fuzzy Weighted Association rule mining

Opinion analysis is by a long shot most basic zone of characteristic language handling. It manages the portrayal of information to choose the motivation behind the wellspring of the content. The reason might be of a type of gratefulness (positive) or study (negative). This paper offers a correlation between the outcomes accomplished by applying the calculation arrangement using various classifiers for instance K-nearest neighbor and multinomial naive Bayes. These techniques are utilized to assess a significant assessment with either a positive remark or negative remark. The gathered information considered on the grounds of the extremity film datasets and an association with the results accessible proof has been created for a careful assessment. This paper investigates the word level count vectorizer and term frequency inverse document frequency (TF-IDF) influence on film sentiment analysis. We concluded that multinomial Naive Bayes (MNB) classier generate more accurate result using ...

This work is dedicated to patients specially, rural patients can get to know the early stage detection of diseases before
laboratory tests reducing the unlimited waiting time and cost expenditure. Clinical Decision Support System (CDSS) can be used
for analyzing diseases to predict almost accurate disease automatically and patient’s query. This work has been done with the
help of a doctor as a human expert. We collected 300 sample data from patients. We have made the dataset from our sample
patient’s information. Naïve Bayes classifier is used here to classify the diseases easily. The selected diseases are Malaria,
Tuberculosis, Stroke, Fever, Diabetes, Heart disease. The prediction of a disease is measured with the prediction of a doctor
before laboratory tests to get the system’s accuracy. Here we got 100% accuracy on the trained dataset containing 180 cases

As most information (over 80%) is stored as text, text mining is believed to have a high commercial potential value. knowledge may be discovered from many sources of information; yet, unstructured texts remain the largest readily available source of knowledge .Text classification which classifies the documents according to predefined categories .In this paper we are tried to give the introduction of text classification, process of text classification as well as the overview of the classifiers and tried to compare
the some existing classifier on basis of few criteria like time complexity, principal and performance.

Este artigo versa sobre o uso de classificadores em Língua de Sinais Brasileira (Libras), ao apresentar um projeto piloto de produção de classificadores, realizado com cinco sinalizadores surdos, adultos, usuários de Libras há vários anos (média 28:8 anos), todos atuantes como instrutores de Libras, tanto para adultos ouvintes como para crianças surdas. Esses sujeitos foram submetidos a um teste de produção de língua de sinais (American Sign Language Assessment Instrument – ASLAI), elaborado para produção de classificadores em ASL e adaptado para a produção de classificadores em Libras. Essa tarefa, elaborada por Robert Hoffmeister e sua equipe (Hoffmeister et al., 1990), evoca a produção de classificadores, incluindo pluralização (quantificação) e organização de objetos, partes do corpo, relações primárias e secundárias, em construções simples ou mais complexas. Como resultado, temos uma gama de configurações de mãos usadas na construção de classificadores específicos, apontando para uma regularidade na produção de classificadores, tanto entre sinalizadores nativos ou quase nativos de Libras, quanto entre sinalizadores tardios.

To evaluate the impact of the synthetic minority oversampling technique (SMOTE) on the performance of probabilistic neural network (PNN), naïve Bayes (NB), and decision tree (DT) classifiers for predicting diabetes in a prospective cohort of the Tehran Lipid and Glucose Study (TLGS). . Data of the 6647 nondiabetic participants, aged 20 years or older with more than 10 years of follow-up, were used to develop prediction models based on 21 common risk factors. The minority class in the training dataset was oversampled using the SMOTE technique, at 100%, 200%, 300%, 400%, 500%, 600%, and 700% of its original size. The original and the oversampled training datasets were used to establish the classification models. Accuracy, sensitivity, specificity, precision, F-measure, and Youden's index were used to evaluated the performance of classifiers in the test dataset. To compare the performance of the 3 classification models, we used the ROC convex hull (ROCCH). Oversampling the minority...

The purpose of this paper is to prove the Mass Noun Hypothesis wrong. The hypothesis claims that all common nouns in classifier languages like Mandarin Chinese are mass nouns. The objection against it consists in displaying its implausible deduction, where false conclusions have been drawn due to relying on the grammar of English, which is incongruent with the grammar of Chinese. Consequently, this paper defends the Count Noun Thesis, stating that in Chinese there are count as well as mass nouns. In support of this statement, first, the typology of numeral classifiers had to be established, which resulted in gathering and completing all the reasons to distinguish classifiers from measure words. After only this necessary differentiation was made, it was possible to show that the count/mass distinction exists in Mandarin Chinese. That is, count nouns by default have only one classifier, with certain disclaimers. Apart from that, count nouns, as in every language, may undergo some measurement with measure words. Mass nouns, however, in the context of quantification may appear only with measure words, but not with classifiers. These conditions naturally follow from the ontological status of the two types of nouns' referents, i.e. bounded objects denoted by count nouns, and scattered substances denoted by mass nouns.

Nowadays Hand written Character Recognition (HCR) is major remarkable and difficult research domain in the area of Image processing. Recognition of Handwritten English alphabets have been broadly studied in the previous years. Presently various recognition methodologies are in well-known utilized for recognition of handwritten English alphabets (character). Application domain of HCR is digital document processing such as mining information from data entry, cheque, applications for loans, credit cards, tax, health insurance forms etc. During this survey we present an outline of current research work conducted for recognition of handwritten English alphabets. In Handwritten manuscript there is no restriction on the writing technique. Handwritten alphabets are complicated to recognize because of miscellaneous human handwriting technique, difference in size and shape of letters, angle. A variety of recognition methodologies for handwritten English alphabets are conferred here alongside with their performance.

Pervasive Computing is one of the latest and more advanced paradigms currently available in the computers arena. Its ability to provide the distribution of computational services within environments where people live, work or socialize leads to make issues such as privacy, trust and identity more challenging compared to traditional computing environments. In this work we review these general issues and propose a Pervasive Computing architecture based on a simple but effective trust model that is better able to cope with them. The proposed architecture combines some Artificial Intelligence techniques to achieve close resemblance with human-like decision making. Accordingly, Apriori algorithm is first used in order to extract the behavioral patterns adopted from the users during their network interactions. Na¨ıve Bayes classifier is then used for final decision making expressed in term of probability of user trustworthiness. To validate our approach we applied it to some typical ubiquitous computing scenarios. The obtained results demonstrated the usefulness of such approach and the competitiveness against other existing ones. Keywords Pervasive Computing · Trust Model · Artificial Intelligence · Apriori algorithm · Na¨ıve Bayes Classifier

The Electroencephalogram (EEG) signal is a voltage signal arising from synchronized neural activity. EEG can be used to classify different mental states and to find abnormalities in neural activity. To check the abnormality in neural activity, EEG signal is classified using classifiers. In this project k-means clustering and fuzzy c means (FCM) clustering is used to cluster the input data set to Neural network. NeuroIntelligence is a neural network tool used to classify unknown data points. The non linear time series (NLTS) data set is initially clustered into Normal or Abnormal categories using k-means or FCM clustering methods. This clustered data set is used to train neural network. When an unknown EEG signal is taken, first NLTS measurements are extracted and input to trained neural network to classify the EEG signal. This method of classification proposed is unique and is very easy to classify EEG signals.

Intrusion detection has attracted a considerable interest from researchers and industries. The community, after many years of research, still faces the problem of building reliable and efficient IDS that are capable of handling large quantities of data, with changing patterns in real time situations. The work presented in this manuscript classifies intrusion detection systems (IDS). Moreover, a taxonomy and survey of shallow and deep networks intrusion detection systems is presented based on previous and current works. This taxonomy and survey reviews machine learning techniques and their performance in detecting anomalies. Feature selection which influences the effectiveness of machine learning (ML) IDS is discussed to explain the role of feature selection in the classification and training phase of ML IDS. Finally, a discussion of the false and true positive alarm rates is presented to help researchers model reliable and efficient machine learning based intrusion detection systems.

This thesis presents the results from a study of 27 native signing deaf children, filmed twice, between the ages of 4 and 10 years old, partipating in games eliciting depicting verbs (classifiers). A comparison is made between their development of depicting verbs, and the development of both linguistic and visual representation as described in the literature. The data show that these structures can be better analysed as visual representations, than as “purely” linguistic structures.

Bagging and Voting are both types of ensemble learning, which is a type of machine learning where multiple classifiers are combined to get better classification results. This paper presents an experimental comparison of Bagging and Voting ensemble machine learning algorithms. The iris dataset which has 150 data instances and 5 attributes was used to conduct the experiment. It was observed that bagging is a better ensemble learning algorithm than voting based on the experimental data used for classification.

Personal and industrial users desire to utilize email as one of the crucial resource of communication. The volume of business-critical emails maintain to grow, the need to computerize the management of emails amplify for numerous reasons, such as spam email categorization, phishing email categorization, and multi-folder classification, in the middle of others. We propose a novel method named as " Semantic Match " which enlarges a learning replica with the carry of WordNet Tool. The proposed Technique is competent of behaviour the extracted Futures resemblance and computation description such as correctness and complication. Here we will be by means of WordNet dictionary folder which affords a semantic dictionary for English. WordNet is a great lexical database of English. And also here we use the classification method of SVM and KNN to classify the E-Mails in an appropriate manner.

This paper examines the distribution and meaning of the –nyi suffix. -nyi naturally attaches to container classifiers and induces a measure interpretation of the container noun. I show that  –nyi is not restricted to the dimension of volume but applies generally to count nouns. –nyi combines with N to create a measure head, analogous to expressions such as kilo. I maintain that being a measure head, N-nyi combines with a numeral to form complex phrasal modifiers.  I then examine the semantics of –nyi itself.  I show that it has two interpretations:  it induces a measure reading of the N pohár in két pohár-nyi bor, and it forces an approximative interpretation when suffixed to to an explicit measure expression as
in kiló-nyi.

Optical Coherence Tomography (OCT) imaging aids in retinal abnormality detection by showing the tomographic retinal layers. OCT images are a useful tool for detecting Diabetic Retinopathy (DR) disease because of their capability to capture micrometer-resolution. An automated technique was introduced to differentiate DR images from normal ones. 214 images were subjected to the experiment, of which 160 images were used for classifiers' training, and 54 images were used for testing. Different features were extracted to feed our classifiers, including statistical features and local binary pattern (LBP) features. The experimental results demonstrated that our classifiers were able to discriminate DR retina from the normal retina with Area Under the Receiver Operating Characteristic (ROC) Curve (AUC) of 100%. The retinal OCT images have common texture patterns and using a powerful tool for pattern analysis like LBP features has a significant impact on the achieved results. The result has better performance than previously proposed methods in the literature.

This book presents a preliminary sketch of Liangmai grammar. A brief outline on Liangmai phonetic and phonology, morphology and syntax in the light of scientific linguistic approach is provided. The main focus of the book is on the classifier system in Liangmai. A descriptive analysis of different types of classifiers found in Liangmai, its functions and semantic categories are highlighted. The book also discusses phrase structure in Liangmai with special reference to classifier construction.
This book throw open the potentiality of linguistic research in Liangmai in particular and the languages of the Northeast India in general. The book could serve as a source material for other linguists and researchers to pursue their studies further.

In this introduction, we provide a general overview of a variety of phenomena related to the encoding of the cognitive category of number in natural language, e.g., number-marking, collective nouns, conjunctions, numerals and other quantifiers, as well as classifiers, and show how Slavic data can contribute to our understanding of these phenomena. We also examine the main strands of the study of number in language developed within formal linguistics, linguistic typology, and psycholinguistics. Finally, we introduce the content of this collective monograph and discuss its relevance to current research.

Sentiment analysis is an opinion mining process, in which computational analysis and categorization of opinion of a
piece of text is done to obtain an unbiased understanding of the writer’s opinion towards any specific topic. In this paper,
Sentiment Analysis of the twitter user demographic towards Citizenship Amendment Act, which came into effect in India from
January 10th, 2020, has been done. CAA was considered, as it had garnered mixed opinions from different sections of the Indian
demographic, so there was no clear understanding of the overall sentiment of the public towards it. It had also led to protests and
riots in various parts of India, which the Government struggled to handle as it was unexpected

The present paper aims at demonstrating that expressions of the type N1 N2 in Bulgarian are ambigous and have different syntax due to their different compositional semantics. The paper offers various empirical observations about such expressions, which can contain a count or a mass noun, and proposes that according to their counting or measuring function they must receive two different syntactic analyses. 1. Съчетания N1 N2 в българския език Оsenova (2014) анализира три групи съчетания с вътрешна структура N1 N2 в българския език (вж. (1)), като и в трите групи N2 може да е броимо или не: а) мярка/мерна единица-вещество; б) съдържащо-съдържание; в) група-групирани елементи: 1 (1) а. литър мляко, килограм ябълки б. чаша вода, кошница домати в.облак прах, тълпа хора, наръч дърва (вж. Буров/Burov 2004 за още примери за събирателна множественост). Езиците като българския, притежаващи категорията число и разграничението между броими от неброими съществителни, използват за измерване на неброими количества имена за мярка, които някои учени (Landman 2004, Borer 2005) наричат "класификаторни" единици (нумеративи или мензуративи). 2 Според Буров/Burov (2004: 219) функцията на нумеративите (мензуративите) е да партиципират веществения континуум, задавайки го във вид на дискретни единици, които да могат да бъдат преброени или измерени. Това твърдение по принцип е вярно, но само частично. Наистина неброимите съществителни имат нужда от единица мярка, за да се свържат с числително или друг квантификаторен израз (срв. *три брашно). Но преброяване и измерване са два различни начина за приписване на квантитативни свойства на веществата или на частите от едно цяло и не зависят от броимостта на съществителното (Rothstein 2009). 3 При преброяване функцията на числителното е да отброи 1 Към списъка на Осенова можем да добавим още две групи, чието значение условно можем да наречем "вещество-дискретна единица" (1г) и "вещество-порция" (1д). (1) г. глава лук, скилидка чесън, зърно черен пипер д. резен сирене, филия/самун хляб 2 Класификатор е категория, обикновено свързвана с езици, които не притежават категорията число или не разграничават броими от неброими съществителни. В тези езици класификаторът е необходим, за да маркира по граматически път връзката между числителното и съществителното-или защото езикът, напр. мандарин, няма разграничение между броими и неброими съществителни, или пък защото няма морфология за множествено число, така че N може да означава само вид обект, но не и конкретни представители на вида. В това се състои индивидуализиращата (нумеративната) функция на класификатора. Според Аikhenvald (2000) индоевропейските езици нямат нумерални класификатори, а само конструкции с квантификаторни изрази. 3 Според Дж. Керкия (вж. Chierchia 1989): "[с]амо атоми могат да се броят (Only atoms can be counted)." Авторът предлага тезата, че всички съществителни-броими като напр. кучета и неброими като напр. мебел представляват изражения на понятието "вид" (kind). С други думи, съществителното идва от речника с денотация на затворена единица-представител на група, която обаче може допълнително да

This dissertation presents the first cross-linguistic study of the Noun Phrase in the indigenous languages of South America. It builds upon a considerable amount of data that have recently become available for languages in this continent. Based on a sample of 55 languages, this study gives a novel account of the syntactic, morphosyntactic, and semantic properties of the NP. For example, the analysis shows that personal pronouns commonly receive the same possessive markers as nominal possessors, which implies that a fully grammaticalized category of possessive pronouns is rare in South American languages. In addition, the new South American data only partly confirm typological claims for tendencies in the NP domain. For instance, a morphologically distinct class of adjectives is found in many languages of the sample; however, this class is often small, and the dominant way to encode property concepts is with verbs. Finally, this study also includes a discussion of the geographic patterning of structural features in the NP, evaluating the assumption that there is a major typological split between so-called Andean and Amazonian languages. The analysis shows that most of the features cannot be attributed to either of these larger areas. It also demonstrates, however, that there is some evidence for a broad structural division of languages into the western part of the continent (corresponding to the Andean sphere) and the rest of the continent. One of the features that define this split is the parameter of alienability.

In this paper I deal with the semantic properties of Polish NPs headed by numerals with the suffix -e, e.g. dwoje studentów (“two students”). I present constraints on the distribution of Polish -e numerals and provide evidence that these constraints follow on from their semantics. I examine three types of NPs in which numerals with the suffix -e can appear and propose a semantic interpretation of each type of such NPs. Furthermore, I argue that Polish numerals with the suffix -e are compositional and discuss the semantic contribution of each morpheme in their morphological make-up. The analysis is based on the formal semantic theory of Landman (2000).

The notion of Duat plays a major role in ancient Egyptian funerary beliefs. Yet, a single definition of this notion is impossible to achieve, as it underwent a process of evolution over thousands of years without ever designating just one unambiguous place at a time. In this context, an approach based on cognitive linguistics and on the significance of the determinatives accompanying the word Duat can be exploited to shed more light on this elusive concept. The focus will be here on the Pyramid Texts and the Coffin Texts as well as the intermediary phase of transmission of the funerary literature during the period between the end of the Old Kingdom and the beginning of the Middle Kingdom.

DOI: https://doi.org/10.1515/zaes-2018-0018