Abstract: Paper presents an comparative evaluation of features extraction algorithm for a real-time isolated word recognition system
based on FPGA. The Mel-frequency cepstral, linear frequency cepstral, linear predictive and their cepstral coefficients were
implemented in hardware/software design. The proposed system was investigated in speaker dependent mode for 100 different
Lithuanian words. The robustness of features extraction algorithms was tested recognizing the speech records at different signal to noise rates. The experiments on clean records show highest accuracy for Mel-frequency cepstral and linear frequency cepstral coefficients. For records with 15 dB signal to noise rate the linear predictive cepstral coefficients gives best result. The hard and soft part of the system is clocked on 50 MHz and 100 MHz accordingly. For the classification purpose the pipelined dynamic time warping core was implemented. The proposed word recognition system satisfy the real-time requirements and is suitable for applications in embedded systems.
Abstract: Privacy is regarded as a fundamental human right and it is clear that the study of digital privacy is an important field. Digital privacy is influenced by new and constantly evolving technologies and this continuous change makes it hard to create legislation to protect people’s privacy from being exploited by misuse of these technologies.
This study aims to benefit digital privacy legislation efforts by evaluating the awareness and perceived importance of digital privacy legislation among computer science students. The chosen fixed variables for the population are study year and gamer classification.
The use of location based services in mobile applications and games are a concern for digital privacy. For this reason the study focused on computer science students as they have a high likelihood to use and develop this type of software. Surveys were used to evaluate awareness and perceived importance of digital privacy legislation.
The results of the study show that privacy legislation and awareness of privacy legislation are important to people. The perception of the importance of privacy legislation increases with academic experience. Awareness of privacy legislation increases from non-gamers to pro gamers.
Abstract: This paper presents the early-warning lights
classification management system for industrial parks promoted by the
Taiwan Environmental Protection Administration (EPA) since 2011,
including the definition of each early-warning light, objectives, action
program and accomplishments. All of the 151 industrial parks in
Taiwan were classified into four early-warning lights, including red,
orange, yellow and green, for carrying out respective pollution
management according to the monitoring data of soil and groundwater
quality, regulatory compliance, and regulatory listing of control site or
remediation site. The Taiwan EPA set up a priority list for high
potential polluted industrial parks and investigated their soil and
groundwater qualities based on the results of the light classification
and pollution potential assessment. In 2011-2013, there were 44
industrial parks selected and carried out different investigation, such as
the early warning groundwater well networks establishment and
pollution investigation/verification for the red and orange-light
industrial parks and the environmental background survey for the
yellow-light industrial parks. Among them, 22 industrial parks were
newly or continuously confirmed that the concentrations of pollutants
exceeded those in soil or groundwater pollution control standards.
Thus, the further investigation, groundwater use restriction, listing of
pollution control site or remediation site, and pollutant isolation
measures were implemented by the local environmental protection and
industry competent authorities; the early warning lights of those
industrial parks were proposed to adjust up to orange or red-light. Up
to the present, the preliminary positive effect of the soil and
groundwater quality management system for industrial parks has been
noticed in several aspects, such as environmental background
information collection, early warning of pollution risk, pollution
investigation and control, information integration and application, and
inter-agency collaboration. Finally, the work and goal of self-initiated
quality management of industrial parks will be carried out on the basis
of the inter-agency collaboration by the classified lights system of
early warning and management as well as the regular announcement of
the status of each industrial park.
Abstract: Red blood cells (RBCs) are among the most
commonly and intensively studied type of blood cells in cell biology.
Anemia is a lack of RBCs is characterized by its level compared to
the normal hemoglobin level. In this study, a system based image
processing methodology was developed to localize and extract RBCs
from microscopic images. Also, the machine learning approach is
adopted to classify the localized anemic RBCs images. Several
textural and geometrical features are calculated for each extracted
RBCs. The training set of features was analyzed using principal
component analysis (PCA). With the proposed method, RBCs were
isolated in 4.3secondsfrom an image containing 18 to 27 cells. The
reasons behind using PCA are its low computation complexity and
suitability to find the most discriminating features which can lead to
accurate classification decisions. Our classifier algorithm yielded
accuracy rates of 100%, 99.99%, and 96.50% for K-nearest neighbor
(K-NN) algorithm, support vector machine (SVM), and neural
network RBFNN, respectively. Classification was evaluated in highly
sensitivity, specificity, and kappa statistical parameters. In
conclusion, the classification results were obtained within short time
period, and the results became better when PCA was used.
Abstract: Industrial robotic arms utilize multiple end-effectors, each for a specific part and for a specific task. We propose a novel algorithm which will define a single end-effector’s configuration able to grasp a given set of objects with different geometries. The algorithm will have great benefit in production lines allowing a single robot to grasp various parts. Hence, reducing the number of endeffectors needed. Moreover, the algorithm will reduce end-effector design and manufacturing time and final product cost. The algorithm searches for a common grasp over the set of objects. The search algorithm maps all possible grasps for each object which satisfy a quality criterion and takes into account possible external wrenches (forces and torques) applied to the object. The mapped grasps are- represented by high-dimensional feature vectors which describes the shape of the gripper. We generate a database of all possible grasps for each object in the feature space. Then we use a search and
classification algorithm for intersecting all possible grasps over all
parts and finding a single common grasp suitable for all objects.
We present simulations of planar and spatial objects to validate the
feasibility of the approach.
Abstract: The paper discusses the implementation of the MultiAgent classification System (MACS) and utilizing it to provide an automated and accurate classification of end users developing applications in the spreadsheet domain. However, different technologies have been brought together to build MACS. The strength of the system is the integration of the agent technology with the FIPA specifications together with other technologies, which are the .NET widows service based agents, the Windows Communication Foundation (WCF) services, the Service Oriented Architecture (SOA), and Oracle Data Mining (ODM). The Microsoft's .NET widows service based agents were utilized to develop the monitoring agents of MACS, the .NET WCF services together with SOA approach allowed the distribution and communication between agents over the WWW. The Monitoring Agents (MAs) were configured to execute automatically to monitor excel spreadsheets development activities by content. Data gathered by the Monitoring Agents from various resources over a period of time was collected and filtered by a Database Updater Agent (DUA) residing in the .NET client application of the system. This agent then transfers and stores the data in Oracle server database via Oracle stored procedures for further processing that leads to the classification of the end user developers.
Abstract: Educational data mining is a specific data mining field applied to data originating from educational environments, it relies on different approaches to discover hidden knowledge from the available data. Among these approaches are machine learning techniques which are used to build a system that acquires learning from previous data. Machine learning can be applied to solve different regression, classification, clustering and optimization problems.
In our research, we propose a “Student Advisory Framework” that utilizes classification and clustering to build an intelligent system. This system can be used to provide pieces of consultations to a first year university student to pursue a certain education track where he/she will likely succeed in, aiming to decrease the high rate of academic failure among these students. A real case study in Cairo Higher Institute for Engineering, Computer Science and Management is presented using real dataset collected from 2000−2012.The dataset has two main components: pre-higher education dataset and first year courses results dataset. Results have proved the efficiency of the suggested framework.
Abstract: This work assessed some properties of three pedons on a toposequence in Ijah-Gbagyi district in Niger State, Nigeria. The pedons were designated as JG1, JG2 and JG3 representing the upper, middle and lower slopes respectively. The surface soil was characterized by dark yellowish brown (10YR3/4) color at the JG1 and JG2 and very dark grayish brown (10YR3/2) color at JG3. Sand dominated the mineral fraction and its content in the surface horizon decreased down the slope, whereas silt content increased down the slope due to sorting by geological and pedogenic processes. Although organic carbon (OC), total nitrogen (TN) and available phosphorus (P) were rated high, TN and available P decreased down the slope. High cation exchange capacity (CEC) was an indication that the soils have high potential for plant nutrients retention. The pedons were classified as Typic Haplustepts/ Haplic Cambisols (Eutric), Plinthic Petraquepts/ Petric Plinthosols (Abruptic) and Typic Endoaquepts/ Endogleyic Cambisols (Endoclayic).
Abstract: Real time non-invasive Brain Computer Interfaces have a significant progressive role in restoring or maintaining a quality life for medically challenged people. This manuscript provides a comprehensive review of emerging research in the field of cognitive/affective computing in context of human neural responses. The perspectives of different emotion assessment modalities like face expressions, speech, text, gestures, and human physiological responses have also been discussed. Focus has been paid to explore the ability of EEG (Electroencephalogram) signals to portray thoughts, feelings, and unspoken words. An automated workflow-based protocol to design an EEG-based real time Brain Computer Interface system for analysis and classification of human emotions elicited by external audio/visual stimuli has been proposed. The front end hardware includes a cost effective and portable Emotiv EEG Neuroheadset unit, a personal computer and a set of external stimulators. Primary signal analysis and processing of real time acquired EEG shall be performed using MATLAB based advanced brain mapping toolbox EEGLab/BCILab. This shall be followed by the development of MATLAB based self-defined algorithm to capture and characterize temporal and spectral variations in EEG under emotional stimulations. The extracted hybrid feature set shall be used to classify emotional states using artificial intelligence tools like Artificial Neural Network. The final system would result in an inexpensive, portable and more intuitive Brain Computer Interface in real time scenario to control prosthetic devices by translating different brain states into operative control signals.
Abstract: The gap between the selection of risk-reduction options in the railway industry and the task of their effective implementation results in compromised safety and substantial losses. An effective risk management must necessarily integrate the evaluation phases with the implementation phase. This paper proposes an essential categorisation of risk reduction measures that best addresses a standard railway industry portfolio. By categorising the risk reduction options into design, operational, procedural and technical options, it is guaranteed that the efforts of the implementation facilitators (people, processes and supporting systems) are systematically harmonised. The classification is based on an integration of fundamental principles of risk reduction in the railway industry with the systems engineering approach.
This paper argues that the use of a similar classification approach is an attribute of organisations possessing a superior level of risk-reduction readiness. The integration of the proposed rational classification structure provides a solid ground for effective risk reduction.
Abstract: The paper deals with the analysis of hazards and sensitivity of potential resource of emergency water supply of population in a selected region of the Czech Republic. The procedure of identification and analysis of hazards and sensitivity is carried out on the basis of a unique methodology of classifying the drinking water resources earmarked for emergency supply of population. The hazard identification is based on a general register of hazards for individual parts of hydrological structure and the elements of technological equipment. It is followed by a semi-quantitative point indexation for the activation of each identified hazard, i.e. fires of anthropogenic origin, flood and the increased radioactive background accompanied by the leak of radon. Point indexation of sensitivity has been carried out at the same time. The analysis is the basis for a risk assessment of potential resource of emergency supply of population and the subsequent classification of such resource within the system of crisis planning.
Abstract: The exponential increase in the volume of medical image database has imposed new challenges to clinical routine in maintaining patient history, diagnosis, treatment and monitoring. With the advent of data mining and machine learning techniques it is possible to automate and/or assist physicians in clinical diagnosis. In this research a medical image classification framework using data mining techniques is proposed. It involves feature extraction, feature selection, feature discretization and classification. In the classification phase, the performance of the traditional kNN k nearest neighbor classifier is improved using a feature weighting scheme and a distance weighted voting instead of simple majority voting. Feature weights are calculated using the interestingness measures used in association rule mining. Experiments on the retinal fundus images show that the proposed framework improves the classification accuracy of traditional kNN from 78.57 % to 92.85 %.
Abstract: Thousands of masters athletes participate
quadrennially in the World Masters Games (WMG), yet this cohort
of athletes remains proportionately under-investigated. Due to a
growing global obesity pandemic in context of benefits of physical
activity across the lifespan, the prevalence of obesity in this unique
population was of particular interest. Data gathered on a sub-sample
of 535 football code athletes, aged 31-72 yrs ( =47.4, s =±7.1),
competing at the Sydney World Masters Games (2009) demonstrated
a significantly (p
Abstract: In this paper, an extreme learning machine with an automatic segmentation algorithm is applied to heart disorder classification by heart sound signals. From continuous heart sound signals, the starting points of the first (S1) and the second heart pulses (S2) are extracted and corrected by utilizing an inter-pulse histogram. From the corrected pulse positions, a single period of heart sound signals is extracted and converted to a feature vector including the mel-scaled filter bank energy coefficients and the envelope coefficients of uniform-sized sub-segments. An extreme learning machine is used to classify the feature vector. In our cardiac disorder classification and detection experiments with 9 cardiac disorder categories, the proposed method shows significantly better performance than multi-layer perceptron, support vector machine, and hidden Markov model; it achieves the classification accuracy of 81.6% and the detection accuracy of 96.9%.
Abstract: In this paper we propose a new classification method for automatic sleep scoring using an artificial neural network based decision tree. It attempts to treat sleep scoring progress as a series of two-class problems and solves them with a decision tree made up of a group of neural network classifiers, each of which uses a special feature set and is aimed at only one specific sleep stage in order to maximize the classification effect. A single electroencephalogram (EEG) signal is used for our analysis rather than depending on multiple biological signals, which makes greatly simplifies the data acquisition process. Experimental results demonstrate that the average epoch by epoch agreement between the visual and the proposed method in separating 30s wakefulness+S1, REM, S2 and SWS epochs was 88.83%. This study shows that the proposed method performed well in all the four stages, and can effectively limit error propagation at the same time. It could, therefore, be an efficient method for automatic sleep scoring. Additionally, since it requires only a small volume of data it could be suited to pervasive applications.
Abstract: Biometrics, which refers to identifying an individual
based on his or her physiological or behavioral characteristics, has
the capability to reliably distinguish between an authorized person
and an imposter. Signature verification systems can be categorized as
offline (static) and online (dynamic). This paper presents a neural
network based recognition of offline handwritten signatures system
that is trained with low-resolution scanned signature images.
Abstract: In this paper, we investigated the characteristic of a
clinical dataseton the feature selection and classification
measurements which deal with missing values problem.And also
posed the appropriated techniques to achieve the aim of the activity;
in this research aims to find features that have high effect to mortality
and mortality time frame. We quantify the complexity of a clinical
dataset. According to the complexity of the dataset, we proposed the
data mining processto cope their complexity; missing values, high
dimensionality, and the prediction problem by using the methods of
missing value replacement, feature selection, and classification.The
experimental results will extend to develop the prediction model for
cardiology.
Abstract: Text categorization is the problem of classifying text
documents into a set of predefined classes. After a preprocessing
step, the documents are typically represented as large sparse vectors.
When training classifiers on large collections of documents, both the
time and memory restrictions can be quite prohibitive. This justifies
the application of feature selection methods to reduce the
dimensionality of the document-representation vector. In this paper,
we present three feature selection methods: Information Gain,
Support Vector Machine feature selection called (SVM_FS) and
Genetic Algorithm with SVM (called GA_SVM). We show that the
best results were obtained with GA_SVM method for a relatively
small dimension of the feature vector.
Abstract: In this paper, an automatic system of diagnosis was
developed to detect and locate in real time the defects of the wound
rotor asynchronous machine associated to electronic converter. For
this purpose, we have treated the signals of the measured parameters
(current and speed) to use them firstly, as indicating variables of the
machine defects under study and, secondly, as inputs to the Artificial
Neuron Network (ANN) for their classification in order to detect the
defect type in progress. Once a defect is detected, the interpretation
system of information will give the type of the defect and its place of
appearance.
Abstract: MM-Path, an acronym for Method/Message Path, describes the dynamic interactions between methods in object-oriented systems. This paper discusses the classifications of MM-Path, based on the characteristics of object-oriented software. We categorize it according to the generation reasons, the effect scope and the composition of MM-Path. A formalized representation of MM-Path is also proposed, which has considered the influence of state on response method sequences of messages. .Moreover, an automatic MM-Path generation approach based on UML Statechart diagram has been presented, and the difficulties in identifying and generating MM-Path can be solved. . As a result, it provides a solid foundation for further research on test cases generation based on MM-Path.