Search results for: laryngeal feature variation

Authors: Haiyan Wu, Ying Liu, Shaoyun Shi

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Authorship attribution is to extract features to identify authors of anonymous documents. Many previous works on authorship attribution focus on statistical style features (e.g., sentence/word length), content features (e.g., frequent words, n-grams). Modeling these features by regression or some transparent machine learning methods gives a portrait of the authors' writing style. But these methods do not capture the syntactic (e.g., dependency relationship) or semantic (e.g., topics) information. In recent years, some researchers model syntactic trees or latent semantic information by neural networks. However, few works take them together. Besides, predictions by neural networks are difficult to explain, which is vital in authorship attribution tasks. In this paper, we not only utilize the statistical style and content features but also take advantage of both syntactic and semantic features. Different from an end-to-end neural model, feature selection and prediction are two steps in our method. An attentive n-gram network is utilized to select useful features, and logistic regression is applied to give prediction and understandable representation of writing style. Experiments show that our extracted features can improve the state-of-the-art methods on three benchmark datasets.

Keywords: authorship attribution, attention mechanism, syntactic feature, feature extraction

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Authors: Sahib Dino Naseerani

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The presented scientific text discusses the concept of imaginary pressure and its role in different relative motions. It explores how imaginary pressure, which is the combined effect of external atmospheric pressure and real pressure, affects various substances and their physical properties. The study aims to understand the impact of imaginary pressure and its potential applications in different contexts, such as spaceflight. The main objective of this study is to investigate the role of imaginary pressure on different relative motions. Specifically, the researchers aim to examine how imaginary pressure affects the contraction and mass variation of a body when it is in motion at the speed of light. The study seeks to provide insights into the behavior and consequences of imaginary pressure in various scenarios. The data was collected using three research papers. This research contributes to a better understanding of the theoretical implications of imaginary pressure. It elucidates how imaginary pressure is responsible for the contraction and mass variation of a body in motion, particularly at the speed of light. The findings shed light on the behavior of substances under the influence of imaginary pressure, providing valuable insights for future scientific studies. The study addresses the question of how imaginary pressure influences various relative motions and their associated physical properties. It aims to understand the role of imaginary pressure in the contraction and mass variation of a body, particularly at high speeds. By examining different substances in liquid and solid forms, the research explores the consequences of imaginary pressure on their volume, length, and mass.

Keywords: imaginary pressure, contraction, variation, relative motion

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Authors: Lina Wu, Jia Liu, Ye Li

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The goal of this project is to investigate constant properties (called the Liouville-type Problem) for a p-stable map as a local or global minimum of a p-energy functional where the domain is a Euclidean space and the target space is a closed half-ellipsoid. The First and Second Variation Formulas for a p-energy functional has been applied in the Calculus Variation Method as computation techniques. Stokes’ Theorem, Cauchy-Schwarz Inequality, Hardy-Sobolev type Inequalities, and the Bochner Formula as estimation techniques have been used to estimate the lower bound and the upper bound of the derived p-Harmonic Stability Inequality. One challenging point in this project is to construct a family of variation maps such that the images of variation maps must be guaranteed in a closed half-ellipsoid. The other challenging point is to find a contradiction between the lower bound and the upper bound in an analysis of p-Harmonic Stability Inequality when a p-energy minimizing map is not constant. Therefore, the possibility of a non-constant p-energy minimizing map has been ruled out and the constant property for a p-energy minimizing map has been obtained. Our research finding is to explore the constant property for a p-stable map from a Euclidean space into a closed half-ellipsoid in a certain range of p. The certain range of p is determined by the dimension values of a Euclidean space (the domain) and an ellipsoid (the target space). The certain range of p is also bounded by the curvature values on an ellipsoid (that is, the ratio of the longest axis to the shortest axis). Regarding Liouville-type results for a p-stable map, our research finding on an ellipsoid is a generalization of mathematicians’ results on a sphere. Our result is also an extension of mathematicians’ Liouville-type results from a special ellipsoid with only one parameter to any ellipsoid with (n+1) parameters in the general setting.

Keywords: Bochner formula, Calculus Stokes' Theorem, Cauchy-Schwarz Inequality, first and second variation formulas, Liouville-type problem, p-harmonic map

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Authors: Behnaz Sohani, Sahand Shahalinezhad, Amir Rahmani, Aliyu Aliyu

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Recently, medical imaging and specifically medical image processing is becoming one of the most dynamically developing areas of medical science. It has led to the emergence of new approaches in terms of the prevention, diagnosis, and treatment of various diseases. In the process of diagnosis of lung cancer, medical professionals rely on computed tomography (CT) scans, in which failure to correctly identify masses can lead to incorrect diagnosis or sampling of lung tissue. Identification and demarcation of masses in terms of detecting cancer within lung tissue are critical challenges in diagnosis. In this work, a segmentation system in image processing techniques has been applied for detection purposes. Particularly, the use and validation of a novel lung cancer detection algorithm have been presented through simulation. This has been performed employing CT images based on multilevel thresholding. The proposed technique consists of segmentation, feature extraction, and feature selection and classification. More in detail, the features with useful information are selected after featuring extraction. Eventually, the output image of lung cancer is obtained with 96.3% accuracy and 87.25%. The purpose of feature extraction applying the proposed approach is to transform the raw data into a more usable form for subsequent statistical processing. Future steps will involve employing the current feature extraction method to achieve more accurate resulting images, including further details available to machine vision systems to recognise objects in lung CT scan images.

Keywords: lung cancer detection, image segmentation, lung computed tomography (CT) images, medical image processing

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Authors: Nidal F. Shilbayeh, Remah W. Al-Khatib, Sameer A. Nooh

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Arabic offline handwriting recognition systems are considered as one of the most challenging topics. Arabic Handwritten Numeral Strings are used to automate systems that deal with numbers such as postal code, banking account numbers and numbers on car plates. Segmentation of connected numerals is the main bottleneck in the handwritten numeral recognition system.  This is in turn can increase the speed and efficiency of the recognition system. In this paper, we proposed algorithms for automatic segmentation and feature extraction of Arabic handwritten numeral strings based on Watershed approach. The algorithms have been designed and implemented to achieve the main goal of segmenting and extracting the string of numeral digits written by hand especially in a courtesy amount of bank checks. The segmentation algorithm partitions the string into multiple regions that can be associated with the properties of one or more criteria. The numeral extraction algorithm extracts the numeral string digits into separated individual digit. Both algorithms for segmentation and feature extraction have been tested successfully and efficiently for all types of numerals.

Keywords: handwritten numerals, segmentation, courtesy amount, feature extraction, numeral recognition

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Authors: Krishan Kumar, Mohit Mittal, Pramod Kumar

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Wireless sensor network is one of the most promising communication networks for monitoring remote environmental areas. In this network, all the sensor nodes are communicated with each other via radio signals. The sensor nodes have capability of sensing, data storage and processing. The sensor nodes collect the information through neighboring nodes to particular node. The data collection and processing is done by data aggregation techniques. For the data aggregation in sensor network, clustering technique is implemented in the sensor network by implementing self-organizing feature map (SOFM) neural network. Some of the sensor nodes are selected as cluster head nodes. The information aggregated to cluster head nodes from non-cluster head nodes and then this information is transferred to base station (or sink nodes). The aim of this paper is to manage the huge amount of data with the help of SOM neural network. Clustered data is selected to transfer to base station instead of whole information aggregated at cluster head nodes. This reduces the battery consumption over the huge data management. The network lifetime is enhanced at a greater extent.

Keywords: artificial neural network, data clustering, self organization feature map, wireless sensor network

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Authors: Hisham Mousa Mohammed Ahmed, Ahmed Wali Mohamed Salad, Yousif Hamed Dldom Gomaa

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A drip or Trickle irrigation system is designed to apply a precise amount of water near the plant with a certain degree of uniformity. This study was conducted at the Experimental Farm of the Faculty of Agricultural Sciences, University of Gezira, in March 2018. The study aimed to design and evaluate the hydraulic performance of three drip emitter types using: average discharge (Qavg), discharge variation (Qvar %), coefficient of uniformity (CU %), coefficient of manufacturer variation (CV %), distribution uniformity (DU %), statistical uniformity (Us %), clogging (%) wetted diameter (cm) and wetted depth (cm). The emitter types used are regular gauges (RG), high compensating pressure (HCP) and low compensating pressure (LCP). The treatments were laid out in a randomized complete block design (RCBD) with four replications. Results showed that there were significant differences (P≤0.05) in all tested parameters except clogging, wetted diameter and wetted depth. Discharge variation (Qvar %) values were 12.71, 15.57 and 19.17 for RG, LCP, and HCP, respectively. The variation is quite good and within the acceptable range. Results of coefficient of manufacture variation (CV %) were 10.9, 27.8 and 52.7 for RG, LCP and HCP, respectively. It is considered within the unacceptable range except for RG type, which is excellent. Statistical uniformity (Us %) values were 89.1, 72.2 and 45.7 for RG, LCP and HCP, respectively. It is considered good, acceptable and unacceptable, respectively. Results of the coefficient of uniformity (CU %) were 91.3, 77.7 and 56.7 for RG, LCP and HCP, respectively. It is considered excellent, fair and unacceptable, respectively. Distribution uniformity (DU %) was 90.2, 67.9 and 36.5 for RG, LCP and HCP, respectively. It is considered excellent, poor and poor, respectively. The study recommended regular gauges (RG) type emitters under the heavy clay soil conditions of the Gezira State, Sudan.

Keywords: drip irrigation, uniformity, clogging, coefficient, performance

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Authors: Hamisu Idi

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The present study focuses on studying the impact of influencer recommendation in the quality of cement production. Exploratory research was done on monthly basis, where data were obtained from secondary source i.e. the record kept by an automated recompilation machine. The machine keeps all the records of the mills downtime which the process manager checks for validation and refer the fault (if any) to the department responsible for maintenance or measurement taking so as to prevent future occurrence. The findings indicated that the product of the Ashaka Cement Plc. were considered as qualitative, since all the production processes were found to be in control (preset specifications) with the exception of the natural cause of variation which is normal in the production process as it will not affect the outcome of the product. It is reduced to the bearest minimum since it cannot be totally eliminated. It is also hopeful that the findings of this study would be of great assistance to the management of Ashaka cement factory and the process manager in particular at various levels in the monitoring and implementation of statistical process control. This study is therefore of great contribution to the knowledge in this regard and it is hopeful that it would open more research in that direction.

Keywords: cement, quality, variation, assignable cause, common cause

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Authors: Diriba T. Balcha, Boris Kulig, Oliver Hensel, Eyassu Woldesenbet

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Enset fiber is agricultural waste and available in a surplus amount in Ethiopia. However, the hypothesized variation in properties of this fiber due to diversity of its plant source breed, fiber position within plant stem and chemical treatment duration had not proven that its application for the development of composite products is problematic. Currently, limited data are known on the functional properties of the fiber as a potential functional fiber. Thus, an effort is made in this study to narrow the knowledge gaps by characterizing it. The experimental design was conducted using Design-Expert software and the tensile test was conducted on Enset fiber from 10 breeds: Dego, Dirbo, Gishera, Itine, Siskela, Neciho, Yesherkinke, Tuzuma, Ankogena, and Kucharkia. The effects of 5% Na-OH surface treatment duration and fiber location along and across the plant pseudostem was also investigated. The test result shows that the rupture stress variation is not significant among the fibers from 10 Enset breeds. However, strain variation is significant among the fibers from 10 Enset breeds that breed Dego fiber has the highest strain before failure. Surface treated fibers showed improved rupture strength and elastic modulus per 24 hours of treatment duration. Also, the result showed that chemical treatment can deteriorate the load-bearing capacity of the fiber. The raw fiber has the higher load-bearing capacity than the treated fiber. And, it was noted that both the rupture stress and strain increase in the top to bottom gradient, whereas there is no significant variation across the stem. Elastic modulus variation both along and across the stem was insignificant. The rupture stress, elastic modulus, and strain result of Enset fiber are 360.11 ± 181.86 MPa, 12.80 ± 6.85 GPa and 0.04 ± 0.02 mm/mm, respectively. These results show that Enset fiber is comparable to other natural fibers such as abaca, banana, and sisal fibers and can be used as alternatives natural fiber for composites application. Besides, the insignificant variation of properties among breeds and across stem is essential for all breeds and all leaf sheath of the Enset fiber plant for fiber extraction. The use of short natural fiber over the long is preferable to reduce the significant variation of properties along the stem or fiber direction. In conclusion, Enset fiber application for composite product design and development is mechanically feasible.

Keywords: Agricultural waste, Chemical treatment, Fiber characteristics, Natural fiber

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Authors: K. Suresh, R. Arunkumar

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The objective of this study is to investigate the preparation of gramine nano suspension and protective effect of Gramine on the apoptosis of laryngeal cancer cells cell line (HEp-2 and KB). The growth inhibition rate of Hep-2 and KB cells in vitro were measured by MTT assay and apoptosis by, levels of reactive oxygen species, mitochondrial membrane potential, morphological changes and flowcytometry. Based on the results, we determined the effective doses of gramine as 127.23µm/ml for 24 hr and 119.81 µm/ml for 48hr in hep-2 cell line and 147.58 µm ml for 24 hr and 123.74µm µm/ml for 48hr in KB cell line. cytotoxicity effects of gramine were confirmed by treatment of HEp-2 cell and KB cell with IC50 concentration of gramine resulted in sequences of events marked by the enhance the apoptosis accompanied by loss of cell viability, modulation of reactive oxygen species and cell cycle arrest through the induction of G0/G1 phase arrest on HEp-2 cells. Our study suggests that the nanosuspension of gramine possesses the more cytotoxic effect of cancer cells and a novel candidate for cancer chemoprevention.

Keywords: apoptosis, HEp-2 cell line, KB cell line mitochondria, gramine, nanosuspension

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Authors: WooYoung Jung, V. Sim

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This paper presents numerical analysis in terms of buckling resistance strength of polymer matrix composite (PMC) infill panels system under the influence of temperature on the foam core. Failure mode under in-plane compression is investigated by means of numerical analysis with ABAQUS platform. Parameters considered in this study are contact length and both the type of foam for core and the variation of its Young's Modulus under the thermal influence. Variation of temperature is considered in static cases and only applied to core. Indeed, it is shown that the effect of temperature on the panel system mechanical properties is significance. Moreover, the variations of temperature result in the decrements of the system strength. This is due to the polymeric nature of this material. Additionally, the contact length also displays the effect on performance of infill panel. Their significance factors are based on type of polymer for core. Hence, by comparing difference type of core material, the variation can be reducing.

Keywords: buckling, contact length, foam core, temperature dependent

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Authors: Muhammad Husnat Khalid, Stephan Bihn, Dirk Uwe Sauer, Nisai Fuengwarodsakul

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To combat the effects of climate change, lithium-ion batteries are getting a lot of attention for energy storage. However, due to its diverse range of applications extending from small electronics equipment to energy storage systems, its output requirements, as well as limitations, vary significantly. Many efforts are underway to increase the power and energy output of the cells without any significant compromise on their size and weight. In this paper, different active materials are explored for an existing cell Kokam that initially has graphite as anode and NCO as cathode material. The Pareto front optimization tool is then utilized to pick a cell that gives the optimum results in terms of energy, power, or both. The parameter variation of the cells is done in the MATLAB application ISEA Cell and Pack Database (ICPD) created by the Institute of Power Electronics and Electrical Drives (ISEA) RWTH Aachen, University that creates the physical-chemical model of the existing cells.

Keywords: battery storage system, lithium-ion battery, active material variation, cell design optimization

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Authors: Jigg L. Pelayo, Ricardo G. Villar, Einstine M. Opiso

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The advances of aerial laser scanning in the Philippines has open-up entire fields of research in remote sensing and machine vision aspire to provide accurate timely information for the government and the public. Rapid mapping of polygonal roads and roof boundaries is one of its utilization offering application to disaster risk reduction, mitigation and development. The study uses low density LiDAR data and high resolution aerial imagery through object-oriented approach considering the theoretical concept of data analysis subjected to machine learning algorithm in minimizing the constraints of feature extraction. Since separating one class from another in distinct regions of a multi-dimensional feature-space, non-trivial computing for fitting distribution were implemented to formulate the learned ideal hyperplane. Generating customized hybrid feature which were then used in improving the classifier findings. Supplemental algorithms for filtering and reshaping object features are develop in the rule set for enhancing the final product. Several advantages in terms of simplicity, applicability, and process transferability is noticeable in the methodology. The algorithm was tested in the different random locations of Misamis Oriental province in the Philippines demonstrating robust performance in the overall accuracy with greater than 89% and potential to semi-automation. The extracted results will become a vital requirement for decision makers, urban planners and even the commercial sector in various assessment processes.

Keywords: feature extraction, machine learning, OBIA, remote sensing

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Authors: Jiahao Cui

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In the past centuries, due to climate change and intense whaling, the global whale population has dramatically declined. Among the various whale species, the fin whale experienced the most drastic drop in number due to its popularity in whaling. Under this background, identifying fin whale calls could be immensely beneficial to the preservation of the species. This paper uses feature extraction to process the input audio signal, then a network based on AlexNet and three networks based on the ResNet model was constructed to classify fin whale calls. A mixture of the DOSITS database and the Watkins database was used during training. The results demonstrate that a modified ResNet network has the best performance considering precision and network complexity.

Keywords: convolutional neural network, ResNet, AlexNet, fin whale preservation, feature extraction

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Authors: Hironori Karachi, Haruka Yamashita

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Recently, the system of quick response (QR) code is getting popular. Many companies introduce new QR code payment services and the services are competing with each other to increase the number of users. For increasing the number of users, we should grasp the difference of feature of the demographic information, usage information, and value of users between services. In this study, we conduct an analysis of real-world data provided by Nomura Research Institute including the demographic data of users and information of users’ usages of two services; LINE Pay, and PayPay. For analyzing such data and interpret the feature of them, Nonnegative Matrix Factorization (NMF) is widely used; however, in case of the target data, there is a problem of the missing data. EM-algorithm NMF (EMNMF) to complete unknown values for understanding the feature of the given data presented by matrix shape. Moreover, for comparing the result of the NMF analysis of two matrices, there is Discriminant NMF (DNMF) shows the difference of users features between two matrices. In this study, we combine EMNMF and DNMF and also analyze the target data. As the interpretation, we show the difference of the features of users between LINE Pay and Paypay.

Keywords: data science, non-negative matrix factorization, missing data, quality of services

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Authors: Niousha Bagheri Khulenjani, Mohammad Saniee Abadeh

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Learning from very big datasets is a significant problem for most present data mining and machine learning algorithms. MicroRNA (miRNA) is one of the important big genomic and non-coding datasets presenting the genome sequences. In this paper, a hybrid method for the classification of the miRNA data is proposed. Due to the variety of cancers and high number of genes, analyzing the miRNA dataset has been a challenging problem for researchers. The number of features corresponding to the number of samples is high and the data suffer from being imbalanced. The feature selection method has been used to select features having more ability to distinguish classes and eliminating obscures features. Afterward, a Convolutional Neural Network (CNN) classifier for classification of cancer types is utilized, which employs a Genetic Algorithm to highlight optimized hyper-parameters of CNN. In order to make the process of classification by CNN faster, Graphics Processing Unit (GPU) is recommended for calculating the mathematic equation in a parallel way. The proposed method is tested on a real-world dataset with 8,129 patients, 29 different types of tumors, and 1,046 miRNA biomarkers, taken from The Cancer Genome Atlas (TCGA) database.

Keywords: cancer classification, feature selection, deep learning, genetic algorithm

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Authors: Loulwah Bokhari, Jori Nazer, Hala Sultan

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Nowadays, artificial intelligence (AI) is used as a foundation for many activities in modern computing applications at home, in vehicles, and in businesses. Many modern machines are built to carry out a specific activity or purpose. This is where the Amazon Alexa application comes in, as it is used as a virtual assistant. The purpose of this paper is to explore the use of Amazon Alexa among people and how it has improved and made simple daily tasks easier for many people. We gave our participants several questions regarding Amazon Alexa and if they had recently used or heard of it, as well as the different tasks it provides and whether it successfully satisfied their needs. Overall, we found that participants who have recently used Alexa have found it to be helpful in their daily tasks.

Keywords: artificial intelligence, Echo system, machine learning, feature for feature match

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Authors: Hassan Eshkiki, Benjamin Mora

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The Neighbor Migrating Generator is a simple and efficient approach to finding the closest potential neighbor(s) with a different label for a given instance and so without the need to calibrate any kernel settings at all. This allows determining and explaining the most important features that will influence an AI model. It can be used to either migrate a specific sample to the class decision boundary of the original model within a close neighborhood of that sample or identify global features that can help localising neighbor classes. The proposed technique works by minimizing a loss function that is divided into two components which are independently weighted according to three parameters α, β, and ω, α being self-adjusting. Results show that this approach is superior to past techniques when detecting the smallest changes in the feature space and may also point out issues in models like over-fitting.

Keywords: explainable AI, EX AI, feature importance, counterfactual explanations

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Authors: Shamsundar Kulkarni

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Video capturing by non-professionals will lead to unanticipated effects. Such as image distortion, image blurring etc. Hence, many researchers study such drawbacks to enhance the quality of videos. In this paper, an algorithm is proposed to stabilize jittery videos .A stable output video will be attained without the effect of jitter which is caused due to shaking of handheld camera during video recording. Firstly, salient points from each frame from the input video are identified and processed followed by optimizing and stabilize the video. Optimization includes the quality of the video stabilization. This method has shown good result in terms of stabilization and it discarded distortion from the output videos recorded in different circumstances.

Keywords: video stabilization, point feature matching, salient points, image quality measurement

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Authors: Tamara Matevosyan

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In this globalized world, intercultural communication is becoming essential for understanding communication among people, for developing understanding of cultures, to appreciate the opportunities and challenges that each culture presents to people. Moreover, it plays an important role in developing an ideal personification to understand different behaviors in different cultures. Native speakers assimilate sociolinguistic knowledge in natural conditions, while it is a great problem for language learners, and in this context feature films reveal cultural peculiarities and involve students in real communication. As we know nowadays the key role of language learning is the development of intercultural competence as communicating with someone from a different cultural background can be exciting and scary, frustrating and enlightening. Intercultural competence is important in FL learning classroom and here feature films can perform as essential tools to develop this competence and overcome the intercultural gap that foreign students face. Current proposal attempts to reveal the correlation of the given culture and language through feature films. To ensure qualified, well-organized and practical classes on Intercultural Communication for language learners a number of methods connected with movie watching have been implemented. All the pre-watching, while watching and post-watching methods and techniques are aimed at developing students’ communicative competence. The application of such activities as Climax, Role-play, Interactive Language, Daily Life helps to reveal and overcome mistakes of cultural and pragmatic character. All the above-mentioned activities are directed at the assimilation of the language vocabulary with special reference to the given culture. The study dwells into the essence of culture as one of the core concepts of intercultural communication. Sometimes culture is not a priority in the process of language learning which leads to further misunderstandings in real life communication. The application of various methods and techniques with feature films aims at developing students’ cultural competence, their understanding of norms and values of individual cultures. Thus, feature film activities will enable learners to enlarge their knowledge of the particular culture and develop a fundamental insight into intercultural communication.

Keywords: climax, intercultural competence, interactive language, role-play

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Authors: Linhang Zhu, Hongyu Zhu, Jiahe Liu

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This paper investigated a method of indoor real-time positioning and mapping based on the Manhattan world assumption. In indoor environments, relying solely on feature matching techniques or other geometric algorithms for sensor pose estimation inevitably resulted in cumulative errors, posing a significant challenge to indoor positioning. To address this issue, we adopt the Manhattan world hypothesis to optimize the camera pose algorithm based on feature matching, which improves the accuracy of camera pose estimation. A special processing method was applied to image data frames that conformed to the Manhattan world assumption. When similar data frames appeared subsequently, this could be used to eliminate drift in sensor pose estimation, thereby reducing cumulative errors in estimation and optimizing mapping and positioning. Through experimental verification, it is found that our method achieves high-precision real-time positioning in indoor environments and successfully generates maps of indoor environments. This provides effective technical support for applications such as indoor navigation and robot control.

Keywords: Manhattan world hypothesis, real-time positioning and mapping, feature matching, loopback detection

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Authors: Hanadi Mohamed Shawgi Gamal, Claus Thomas Bues

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Wood density is a variable influencing many of the technological and quality properties of wood. Understanding the pattern of wood density radial variation is important for its end-use. The X-ray technique, traditionally applied to softwood species to assess the wood quality properties, due to its simple and relatively uniform wood structure. On the other hand, very limited information is available about the validation of using this technique for hardwood species. The suitability of using the X-ray technique for the determination of hardwood density has a special significance in countries like Sudan, where only a few timbers are well known. This will not only save the time consumed by using the traditional methods, but it will also enhance the investigations of the great number of the lesser known species, the thing which will fill the huge cap of lake information of hardwood species growing in Sudan. The current study aimed to evaluate the validation of using the X-ray densitometry technique to determine the radial variation of wood density of Acacia seyal var. seyal. To this, a total of thirty trees were collected randomly from four states in Sudan. The wood density radial trend was determined using the basic density as well as density obtained by the X-ray densitometry method in order to assess the validation of X-ray technique in wood density radial variation determination. The results showed that the pattern of radial trend of density obtained by X-ray technique is very similar to that achieved by basic density. These results confirmed the validation of using the X-ray technique for Acacia seyal var. seyal density radial trend determination. It also promotes the suitability of using this method in other hardwood species.

Keywords: x-ray densitometry, wood density, Acacia seyal var. seyal, radial variation

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Authors: Kolawole F. Farinloye, Samson O. Ojo

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Two Columbiformes species were recently introduced to the newly established Awba Dam Tourism Centre [ADTC], hence there is need to investigate the effect of seasonal variation on these species with respect to hematological composition. Blood samples were obtained from superficial ulna vein of the 128 apparently healthy C. livia and C. guinea into tubes containing EDTA as anticoagulant. Thin blood smears (TBS) were prepared, stained and viewed under microscope. Values of Red Blood Cell (RBC) count, White Blood Cell (WBC) count, cholesterol (CH), Uric Acid (UA), Protein (PR), Mean Corpuscular Volume (MCV), Haemoglobin Content (HB), Blood Volume (BV), Plasma Glucose (PG) and Length/Width (L/W) ratio of red blood cells were assessed. The procedure was carried out on a seasonal basis (wet and dry seasons of 2013-2014). Data was analyzed using descriptive and inferential statistics. Lymphocyte count for C. livia was F3, 161 = 13.15, while for C. guinea was F3, 178 = 13.15. Heterophil, H/L ratio and Muscle score values for both species were (rs = -0.38, rs = -0.44), (rs = 0.51, rs = 0.31) (4, 3) respectively. Analyses also demonstrated a low WBC to RBC ratio (0.004: 25.3) in both species during the wet season compared to dry season, respectively. L/W varied significantly among sampling seasons i.e. wet (19.1% of BV, 12.6% of BV, 0.1% of BV) and dry (18.9% of BV, 12.7% of BV, 0.08% of BV). The level of HB in wet season (19.20±8.46108) is lower compared to dry season (19.70±8.48762). T-test also showed (wet=15.625, 0.111), (dry=12.125, 0.146) respectively, hence there is no association between species and haematological parameters. Species introduced were found to be haematologically stable. Although there were slight differences in seasonal composition, however this can be attributed to seasonal variation; suggesting little or no effect of seasons on their blood composition.

Keywords: seasonal variation, Columbiformes, Awba Dam tourism centre, University of Ibadan, Ibadan

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Authors: Ágoston Nagy

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This paper introduces a method of using unsupervised machine learning to visualize the feature space of a dataset in 2D, in order to find most characteristic segments in the set. After dimension reduction, users can select clusters by manual drawing. Selected clusters are recorded into a data model that is used for later predictions, based on realtime data. Predictions are made with supervised learning, using Gesture Recognition Toolkit. The paper introduces two example applications: a semantic audio organizer for analyzing incoming sounds, and a gesture database organizer where gestural data (recorded by a Leap motion) is visualized for further manipulation.

Keywords: gesture recognition, machine learning, real-time interaction, visualization

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Authors: Muhammad Abdullah Arafat, Nahrin Nowrose

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Wireless charging of electric vehicles is becoming more and more attractive as large amount of power can now be transferred to a reasonable distance using magnetic resonance coupling method. However, proper tuning of the compensation network is required to achieve maximum power transmission. Due to the variation of coil distance from the nominal value as a result of change in tire condition, change in weight or uneven road condition, the tuning of the compensation network has become challenging. In this paper, a tuning method has been described to determine the optimum values of the compensation network in order to maximize the average output power. The simulation results show that 5.2 percent increase in average output power is obtained for 10 percent variation in coupling coefficient using the optimum values without the need of additional space and electro-mechanical components. The proposed method is applicable to both static and dynamic charging of electric vehicles.

Keywords: coupling coefficient, electric vehicles, magnetic resonance coupling, tuning capacitor, wireless power transfer

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Authors: Misganaw Abebe Baye, Ji-Woo Park, Beom-Soo Kang

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The product quality of multi-point dieless forming (MDF) is identified to be dependent on the process parameters. Moreover, a certain variation of friction and material properties may have a substantially worse influence on the final product quality. This study proposed on how to compensate the MDF product defects by minimizing the sensitivity of noise parameter variations. This can be attained by reliability-based robust optimization (RRO) technique to obtain the optimal process setting of the controllable parameters. Initially two MDF Finite Element (FE) simulations of AA3003-H14 saddle shape showed a substantial amount of dimpling, wrinkling, and shape error. FE analyses are consequently applied on ABAQUS commercial software to obtain the correlation between the control process setting and noise variation with regard to the product defects. The best prediction models are chosen from the family of metamodels to swap the computational expensive FE simulation. Genetic algorithm (GA) is applied to determine the optimal process settings of the control parameters. Monte Carlo Analysis (MCA) is executed to determine how the noise parameter variation affects the final product quality. Finally, the RRO FE simulation and the experimental result show that the amendment of the control parameters in the final forming process leads to a considerably better-quality product.

Keywords: dimpling, multi-point dieless forming, reliability-based robust optimization, shape error, variation, wrinkling

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Authors: Steffy Maria Joseph, Abdu Rahiman V, Abdul Hameed K. M.

Abstract:

Online handwritten character recognition is a challenging field in Artificial Intelligence. The classification success rate of current techniques decreases when the dataset involves similarity and complexity in stroke styles, number of strokes and stroke characteristics variations. Malayalam is a complex south indian language spoken by about 35 million people especially in Kerala and Lakshadweep islands. In this paper, we consider the significant feature extraction for the similar stroke styles of Malayalam. This extracted feature set are suitable for the recognition of other handwritten south indian languages like Tamil, Telugu and Kannada. A classification scheme based on support vector machines (SVM) is proposed to improve the accuracy in classification and recognition of online malayalam handwritten characters. SVM Classifiers are the best for real world applications. The contribution of various features towards the accuracy in recognition is analysed. Performance for different kernels of SVM are also studied. A graphical user interface has developed for reading and displaying the character. Different writing styles are taken for each of the 44 alphabets. Various features are extracted and used for classification after the preprocessing of input data samples. Highest recognition accuracy of 97% is obtained experimentally at the best feature combination with polynomial kernel in SVM.

Keywords: SVM, matlab, malayalam, South Indian scripts, onlinehandwritten character recognition

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Authors: K. S. Parlak, E. Turan

Abstract:

The separation of marbles according to the pattern quality is a process made according to expert decision. The classification phase is the most critical part in terms of economic value. In this study, a self-learning system is proposed which performs the classification of marbles quickly and with high success. This system performs ten feature extraction by taking ten marble images from the camera. The marbles are classified by decision tree method using the obtained properties. The user forms the training set by training the system at the marble classification stage. The system evolves itself in every marble image that is classified. The aim of the proposed system is to minimize the error caused by the person performing the classification and achieve it quickly.

Keywords: decision tree, feature extraction, k-means clustering, marble classification

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Authors: Rosa Figueroa, Christopher Flores

Abstract:

Text categorization is the problem of assigning a new document to a set of predetermined categories, on the basis of a training set of free-text data that contains documents whose category membership is known. To train a classification model, it is necessary to extract characteristics in the form of tokens that facilitate the learning and classification process. In text categorization, the feature extraction process involves the use of word sequences also known as N-grams. In general, it is expected that documents belonging to the same category share similar features. The Smith-Waterman (SW) algorithm is a dynamic programming algorithm that performs a local sequence alignment in order to determine similar regions between two strings or protein sequences. This work explores the use of SW algorithm as an alternative to feature extraction in text categorization. The dataset used for this purpose, contains 2,610 annotated documents with the classes Obese/Non-Obese. This dataset was represented in a matrix form using the Bag of Word approach. The score selected to represent the occurrence of the tokens in each document was the term frequency-inverse document frequency (TF-IDF). In order to extract features for classification, four experiments were conducted: the first experiment used SW to extract features, the second one used unigrams (single word), the third one used bigrams (two word sequence) and the last experiment used a combination of unigrams and bigrams to extract features for classification. To test the effectiveness of the extracted feature set for the four experiments, a Support Vector Machine (SVM) classifier was tuned using 20% of the dataset. The remaining 80% of the dataset together with 5-Fold Cross Validation were used to evaluate and compare the performance of the four experiments of feature extraction. Results from the tuning process suggest that SW performs better than the N-gram based feature extraction. These results were confirmed by using the remaining 80% of the dataset, where SW performed the best (accuracy = 97.10%, weighted average F-measure = 97.07%). The second best was obtained by the combination of unigrams-bigrams (accuracy = 96.04, weighted average F-measure = 95.97) closely followed by the bigrams (accuracy = 94.56%, weighted average F-measure = 94.46%) and finally unigrams (accuracy = 92.96%, weighted average F-measure = 92.90%).

Keywords: comorbidities, machine learning, obesity, Smith-Waterman algorithm

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Authors: Lee Jeong Min, Lee Mi Hee, Eo Yang Dam

Abstract:

Geospatial feature extraction is a very important issue in the remote sensing research. In the meantime, the image classification based on statistical techniques, but, in recent years, data mining and machine learning techniques for automated image processing technology is being applied to remote sensing it has focused on improved results generated possibility. In this study, artificial neural network and decision tree technique is applied to classify the high-resolution satellite images, as compared to the MLC processing result is a statistical technique and an analysis of the pros and cons between each of the techniques.

Keywords: remote sensing, artificial neural network, decision tree, maximum likelihood classification

Procedia PDF Downloads 328