KNN Classification

This paper has the objective of developing a methodology to store a great mass of semi-structured data and its later recovery, focusing on possible improvements and applications by text mining, uniting related articles from many specialties. The present work intends to contribute by way of supplying a strong methodology based on text mining algorithms, searching for a better automated analysis of documents by text classification and making it easy to store and acquire the relevant information in documents on the Web.

This paper gives a comparison of two extracted features namely pitch and formants for emotion recognition from speech. The research shows that various features namely prosodic and spectral have been used for emotion recognition from speech. The database used for recognition purpose was developed on Marathi language using 100 speakers. We have extracted features pitch and formants. Angry, stress, admiration, teasing and shocking have been recognized on the basis of features energy and formants. The classification technique used here is KNearest Neighbor (KNN). The result for formants was about 100% which is comparatively better than that of energy which was 80% of accuracy. Keywords-Database, Emotion recognition, Feature Extraction, Formants, KNN classification , Pitch, Speech signals.

Data Reduction techniques play a key role in instance-based classification to lower the amount of data to be processed. Among the different existing approaches, Prototype Selection (PS) and Prototype Generation (PG) are the most representative ones. These two families differ in the way the reduced set is obtained from the initial one: while the former aims at selecting the most representative elements from the set, the latter creates new data out of it. Although PG is considered to delimit more efficiently decision boundaries, the operations required are not so well defined in scenarios involving structural data such as strings, trees or graphs. This work studies the possibility of using Dissimilarity Space (DS) methods as an intermediate process for mapping the initial structural representation to a statistical one, thereby allowing the use of PG methods. A comparative experiment over string This work was partially supported by the Spanish Ministerio de Educación, Cultura y Deporte through a FPU fellowship (AP2012-0939),

Recent years have witnessed an astronomical growth in the amount of textual information available both on the web and institutional wise document repositories. As a result, text mining has become extremely prevalent and processing of textual information from such repositories got the focus of the current age researchers. Indeed, in the researcher front of text analysis, there are numerous cutting edge applications are available for text mining. More specifically, the classification oriented text mining has been gaining more attention as it concentrates measures like coverage and accuracy. Along with the huge volume of data, the aspirations of the user are growing far higher than the human capacity, thus, an automated and competitive intelligent systems are essential for reliable text analysis. Towards this, the authors in the present paper propose an Intelligent Text Data Classification System (ITDCS) which is designed in the light of biological nature of genetic approach and able to acquire computational intelligence accurately. Initially, ITDCS focusses on preparing structured data from the huge volume of unstructured data with its procedural steps and filter methods. Subsequently, it emphasises on classifying the text data into labelled classes using KNN classification based on the selection of best features derived by genetic algorithm. In this process, it specially concentrates on adding the power of intelligence to the classifier using together with the biological parts namely, encoding strategy, fitness function and operators of genetic algorithm. The integration of all biological components of genetic algorithm in ITDCS significantly improves the accuracy and reduces the misclassification rate in classifying the text data.

Recent years have witnessed an astronomical growth in the amount of textual information available both on the web and institutional wise document repositories. As a result, text mining has become extremely prevalent and processing of textual information from such repositories got the focus of the current age researchers. Indeed, in the researcher front of text analysis, there are numerous cutting edge applications are available for text mining. More specifically, the classification oriented text mining has been gaining more attention as it concentrates measures like coverage and accuracy. Along with the huge volume of data, the aspirations of the user are growing far higher than the human capacity, thus, an automated and competitive intelligent systems are essential for reliable text analysis. Towards this, the authors in the present paper propose an Intelligent Text Data Classification System (ITDCS) which is designed in the light of biological nature of genetic approach and able to acquire computational intelligence accurately. Initially, ITDCS focusses on preparing structured data from the huge volume of unstructured data with its procedural steps and filter methods. Subsequently, it emphasises on classifying the text data into labelled classes using KNN classification based on the selection of best features derived by genetic algorithm. In this process, it specially concentrates on adding the power of intelligence to the classifier using together with the biological parts namely, encoding strategy, fitness function and operators of genetic algorithm. The integration of all biological components of genetic algorithm in ITDCS significantly improves the accuracy and reduces the misclassification rate in classifying the text data.

Accurate wound assessment is a critical task for patient care and health cost reduction at hospital but even still worse in the context of clinical studies in laboratory. This task, completely devoted to nurses, still relies on manual and tedious practices. Wound shape is measured with rules, tracing papers or rarely with alginate castings and serum injection. The wound tissues proportion is also estimated by a qualitative visual assessment based on the red-yellow-black code. Further to our preceding works on wound 3D complete assessment using a simple freehanded digital camera, we explore here the adaptation of this tool to wounds artificially created for experimentation purposes. It results that tissue uniformity and flatness leads to a simplified approach but requires multispectral imaging for enhanced wound delineation. We demonstrate that, in this context, a simple active contour method can successfully replace more complex tools such as SVM supervised classification, as no training step is required and that one shot is enough to deal with perspective projection errors. Moreover, involving all the spectral response of the tissue and not only RGB components provides a higher discrimination for separating healed epithelial tissue from granulation tissue. This research work is part of a comparative preclinical study on healing wounds. It aims to compare the efficiency of specific medical honeys with classical pharmaceuticals for wound care. Results revealed that medical honey competes with more expensive pharmaceuticals.

The instrument COSIMA (COmetary Secondary Ion Mass Analyzer) onboard of the European Space Agency mission Rosetta collected and analyzed dust particles in the neighborhood of comet 67P/Churyumov-Gerasimenko. The chemical composition of the particle surfaces was characterized by time-offlight secondary ion mass spectrometry. A set of 2213 spectra has been selected, and relative abundances for CH-containing positive ions as well as positive elemental ions define a set of multivariate data with nine variables. Evaluation by complementary chemometric techniques shows different compositions of sample groups collected during two periods of the mission. The first period was August to November 2014 (far from the Sun); the second period was January 2015 to February 2016 (nearer to the Sun). The applied data evaluation methods consider the compositional nature of the mass spectral data and comprise robust principal component analysis as well as classification with discriminant partial least squares regression, k-nearest neighbor search, and random forest decision trees. The results indicate a high importance of the relative abundances of the secondary ions C + and Fe + for the group separation and demonstrate an enhanced content of carbon-containing substances in samples collected in the period with smaller distances to the Sun.

Repeated double cross validation (rdCV) has recently been suggested as a careful and conservative strategy for optimizing and evaluating empirical multivariate calibration models. This evaluation strategy is adapted in this work for k-nearest neighbor (KNN) classification. The basics of rdCV are described, including the search for an optimum k, and tests with Italian Olive Oil Data. KNN-rdCV is applied to classify 17 mineral groups, relevant for the composition of comet dust particles, characterized by the peak heights at 20 selected masses in time-of-flight secondary ion mass spectra (TOF-SIMS). Predictive abilities for 15 mineral classes are N95%, for two classes 75 and 85%.

Classification based on k-nearest neighbors (kNN classification) is one of the most widely used classification methods. The number k of nearest neighbors used for achieving a high accuracy in classification is given in advance and is highly dependent on the data set used. If the size of data set is large, the sequential or binary search of NNs is inapplicable due to the increased computational costs. Therefore, indexing schemes are frequently used to speed-up the classification process. If the required number of nearest neighbors is high, the use of an index may not be adequate to achieve high performance. In this paper, we demonstrate that the execution of the nearest neighbor search algorithm can be interrupted if some criteria are satisfied. This way, a decision can be made without the computation of all k nearest neighbors of a new object. Three different heuristics are studied towards enhancing the nearest neighbor algorithm with an early-break capability. These heuristics aim at: (i) reducing computation and I/O costs as much as possible, and (ii) maintaining classification accuracy at a high level. Experimental results based on real-life data sets illustrate the applicability of the proposed method in achieving better performance than existing methods.

This paper gives a comparison of two extracted features namely pitch and formants for emotion recognition from speech. The research shows that various features namely prosodic and spectral have been used for emotion recognition from speech. The database used for recognition purpose was developed on Marathi language using 100 speakers. We have extracted features pitch and formants. Angry, stress, admiration, teasing and shocking have been recognized on the basis of features energy and formants. The classification technique used here is KNearest Neighbor (KNN). The result for formants was about 100% which is comparatively better than that of energy which was 80% of accuracy. Keywords-Database, Emotion recognition, Feature Extraction, Formants, KNN classification , Pitch, Speech signals.

Classification based on k-nearest neighbors (kNN classification) is one of the most widely used classification methods. The number k of nearest neighbors used for achieving a high accuracy in classification is given in advance and is highly dependent on the data set used. If the size of data set is large, the sequential or binary search of NNs is inapplicable due to the increased computational costs. Therefore, indexing schemes are frequently used to speed-up the classification process. If the required number of nearest neighbors is high, the use of an index may not be adequate to achieve high performance. In this paper, we demonstrate that the execution of the nearest neighbor search algorithm can be interrupted if some criteria are satisfied. This way, a decision can be made without the computation of all k nearest neighbors of a new object. Three different heuristics are studied towards enhancing the nearest neighbor algorithm with an early-break capability. These heuristics aim at: (i) reducing computation and I/O costs as much as possible, and (ii) maintaining classification accuracy at a high level. Experimental results based on real-life data sets illustrate the applicability of the proposed method in achieving better performance than existing methods.

Abstract. Classification based on k-nearest neighbors (kNN classification) is one of the most widely used classification methods. The number k of nearest neighbors used for achieving a high precision in classification is given in advance and is highly dependent on the data set used. If the size of data set is large, the sequential or binary search of NNs is inapplicable due to the increased computational costs. Therefore, indexing schemes are frequently used to speed-up the classification process. If the required number of nearest neighbors is high, ...

This paper gives a comparison of two extracted features namely pitch and formants for emotion recognition from speech. The research shows that various features namely prosodic and spectral have been used for emotion recognition from speech. The database used for recognition purpose was developed on Marathi language using 100 speakers. We have extracted features pitch and formants. Angry, stress, admiration, teasing and shocking have been recognized on the basis of features energy and formants. The classification technique used here is KNearest Neighbor (KNN). The result for formants was about 100% which is comparatively better than that of energy which was 80% of accuracy. Keywords-Database, Emotion recognition, Feature Extraction, Formants, KNN classification , Pitch, Speech signals.

This paper reviews the English language isolated words databases which are collected from native and non -native speakers of English language throughout the world. It also reports the various methods that are used in English speech recognition system. The second part of the paper discusses the design and development of English isolated speech database. The data base contains the English language isolated spoken words corpuses which were recorded through PRAAT software from 20 regional Non –native speaker of English language including males and females from Marathwada region of Maharashtra state in India.

Autism Spectrum Disorder (ASD) is a multifaceted neurodevelopmental condition. Atypical communication mostly occurs in tandem with ASD. We compared voice pitch of 16 Marathi children and adolescents with ASD of age of 7 to 18 with 27 Typically Developing (TD). Speech samples have been recorded and stored in .wav format with sampling frequency of 48000 Hz. For analysis we used PRAAT, a program for speech analysis, manipulation and synthesis. We divided the ASD and TD group into total 4 groups on basis of age and gender for comparison. We found that differences in voice pitch are present in these comparison groups, and male ASD group have more pitch variation than respective comparison groups. In future we look forward to include more ASD participants in study to increase the Marathi speech database for ASD.

This paper gives a comparison of two extracted features namely pitch and formants for emotion recognition from speech. The research shows that various features namely prosodic and spectral have been used for emotion recognition from speech. The database used for recognition purpose was developed on Marathi language using 100 speakers. We have extracted features pitch and formants. Angry, stress, admiration, teasing and shocking have been recognized on the basis of features energy and formants. The classification technique used here is KNearest Neighbor (KNN). The result for formants was about 100% which is comparatively better than that of energy which was 80% of accuracy. Keywords-Database, Emotion recognition, Feature Extraction, Formants, KNN classification , Pitch, Speech signals.

This paper gives a comparison of two extracted features namely pitch and formants for emotion recognition from speech. The research shows that various features namely prosodic and spectral have been used for emotion recognition from speech. The database used for recognition purpose was developed on Marathi language using 100 speakers. We have extracted features pitch and formants. Angry, stress, admiration, teasing and shocking have been recognized on the basis of features energy and formants. The classification technique used here is KNearest Neighbor (KNN). The result for formants was about 100% which is comparatively better than that of energy which was 80% of accuracy. Keywords-Database, Emotion recognition, Feature Extraction, Formants, KNN classification , Pitch, Speech signals.

Due to the high demands for computing, the available resources always lack. The approximate computing technique is the key to lowering hardware complexity and improving energy efficiency and performance. However, it is a challenge to properly design approximate multipliers since input data are unseen to users. This challenge can be overcome by Machine Learning (ML) classifiers. ML classifiers can predict the detailed feature of upcoming input data. Previous approximate multipliers are designed using simple adders based on ML classifiers but by using a simple adder-based approximate mul-tiplier, the level of approximation cannot change at runtime. To overcome this drawback, using an accumulator and recon-figurable adders instead of simple adders are proposed in this paper. Also, the rounding technique is applied to approximate floating-point multipliers for further improvement. Our experimental results show that when the error tolerance of our target application is less than 5%, the proposed approximate multiplier can save area by 70.98%, and when the error tolerance is less than 3%, a rounding enhanced simple adders-based approximate multiplier can save area by 65.9% and a reconfigurable adder-based approximate multiplier with rounding can reduce the average delay and energy by 54.95% and 46.67% respectively compared to an exact multiplier.

This paper gives a comparison of two extracted features namely pitch and formants for emotion recognition from speech. The research shows that various features namely prosodic and spectral have been used for emotion recognition from speech. The database used for recognition purpose was developed on Marathi language using 100 speakers. We have extracted features pitch and formants. Angry, stress, admiration, teasing and shocking have been recognized on the basis of features energy and formants. The classification technique used here is K-Nearest Neighbor (KNN). The result for formants was about 100% which is comparatively better than that of energy which was 80% of accuracy.