Abstract: In this paper, we present an algorithm for reconstruction from incomplete 2D scans for tifinagh characters. This algorithm is based on using correlation between the lost block and its neighbors. This system proposed contains three main parts: pre-processing, features extraction and recognition. In the first step, we construct a database of tifinagh characters. In the second step, we will apply “shape analysis algorithm”. In classification part, we will use Neural Network. The simulation results demonstrate that the proposed method give good results.
Abstract: Accurate software reliability prediction not only enables developers to improve the quality of software but also provides useful information to help them for planning valuable resources. This paper examines the performance of three well-known data mining techniques (CART, TreeNet and Random Forest) for predicting software reliability. We evaluate and compare the performance of proposed models with Cascade Correlation Neural Network (CCNN) using sixteen empirical databases from the Data and Analysis Center for Software. The goal of our study is to help project managers to concentrate their testing efforts to minimize the software failures in order to improve the reliability of the software systems. Two performance measures, Normalized Root Mean Squared Error (NRMSE) and Mean Absolute Errors (MAE), illustrate that CART model is accurate than the models predicted using Random Forest, TreeNet and CCNN in all datasets used in our study. Finally, we conclude that such methods can help in reliability prediction using real-life failure datasets.
Abstract: The current trends in affect recognition research are
to consider continuous observations from spontaneous natural
interactions in people using multiple feature modalities, and to
represent affect in terms of continuous dimensions, incorporate
spatio-temporal correlation among affect dimensions, and provide
fast affect predictions. These research efforts have been propelled
by a growing effort to develop affect recognition system that
can be implemented to enable seamless real-time human-computer
interaction in a wide variety of applications. Motivated by these
desired attributes of an affect recognition system, in this work
a multi-dimensional affect prediction approach is proposed by
integrating multivariate Relevance Vector Machine (MVRVM) with
a recently developed Output-associative Relevance Vector Machine
(OARVM) approach. The resulting approach can provide fast
continuous affect predictions by jointly modeling the multiple affect
dimensions and their correlations. Experiments on the RECOLA
database show that the proposed approach performs competitively
with the OARVM while providing faster predictions during testing.
Abstract: Energy is a fundamental component in sustainability, the access and use of this resource is related with economic growth, social improvements, and environmental impacts. In this sense, energy efficiency has been studied as a factor that enhances the positive impacts of energy in communities; however, the implementation of efficiency requires strong policy and strategies that usually rely on individual measures focused in independent dimensions. In this paper, the problem of energy efficiency as a multi-objective problem is studied, using scientometric analysis to discover trends and patterns that allow to identify the main variables and study approximations related with a further development of models to integrate energy efficiency and MCA into policy making for small communities.
Abstract: Effective statistical feature extraction and classification are important in image-based automatic inspection and analysis. An automatic wood species recognition system is designed to perform wood inspection at custom checkpoints to avoid mislabeling of timber which will results to loss of income to the timber industry. The system focuses on analyzing the statistical pores properties of the wood images. This paper proposed a fuzzy-based feature extractor which mimics the experts’ knowledge on wood texture to extract the properties of pores distribution from the wood surface texture. The proposed feature extractor consists of two steps namely pores extraction and fuzzy pores management. The total number of statistical features extracted from each wood image is 38 features. Then, a backpropagation neural network is used to classify the wood species based on the statistical features. A comprehensive set of experiments on a database composed of 5200 macroscopic images from 52 tropical wood species was used to evaluate the performance of the proposed feature extractor. The advantage of the proposed feature extraction technique is that it mimics the experts’ interpretation on wood texture which allows human involvement when analyzing the wood texture. Experimental results show the efficiency of the proposed method.
Abstract: In this paper we presented a new method for tracking
flying targets in color video sequences based on contour and kernel.
The aim of this work is to overcome the problem of losing target in
changing light, large displacement, changing speed, and occlusion.
The proposed method is made in three steps, estimate the target
location by particle filter, segmentation target region using neural
network and find the exact contours by greedy snake algorithm. In
the proposed method we have used both region and contour
information to create target candidate model and this model is
dynamically updated during tracking. To avoid the accumulation of
errors when updating, target region given to a perceptron neural
network to separate the target from background. Then its output used
for exact calculation of size and center of the target. Also it is used as
the initial contour for the greedy snake algorithm to find the exact
target's edge. The proposed algorithm has been tested on a database
which contains a lot of challenges such as high speed and agility of
aircrafts, background clutter, occlusions, camera movement, and so
on. The experimental results show that the use of neural network
increases the accuracy of tracking and segmentation.
Abstract: This paper presents an analytical study on the
behavior of reinforced concrete walls with rectangular cross section.
Several experiments on such walls have been selected to be studied.
Database from various experiments were collected and nominal shear
wall strengths have been calculated using formulas, such as those of
the ACI (American), NZS (New Zealand), Mexican (NTCC), and
Wood and Barda equations. Subsequently, nominal shear wall
strengths from the formulas were compared with the ultimate shear
wall strengths from the database. These formulas vary substantially in
functional form and do not account for all variables that affect the
response of walls. There is substantial scatter in the predicted values
of ultimate shear strength. Two new semi empirical equations are
developed using data from tests of 57 walls for transitions walls and
27 for slender walls with the objective of improving the prediction of
peak strength of walls with the most possible accurate.
Abstract: In this paper, we present a novel 2.5D face recognition method based on Gabor Discrete Cosine Transform (GDCT). In the proposed method, the Gabor filter is applied to extract feature vectors from the texture and the depth information. Then, Discrete Cosine Transform (DCT) is used for dimensionality and redundancy reduction to improve computational efficiency. The system is combined texture and depth information in the decision level, which presents higher performance compared to methods, which use texture and depth information, separately. The proposed algorithm is examined on publically available Bosphorus database including models with pose variation. The experimental results show that the proposed method has a higher performance compared to the benchmark.
Abstract: Health diseases have a vital significance affecting human being's life and life quality. Sudden death events can be prevented owing to early diagnosis and treatment methods. Electrical signals, taken from the human being's body using non-invasive methods and showing the heart activity is called Electrocardiogram (ECG). The ECG signal is used for following daily activity of the heart by clinicians. Heart Rate Variability (HRV) is a physiological parameter giving the variation between the heart beats. ECG data taken from MITBIH Arrhythmia Database is used in the model employed in this study. The detection of arrhythmic heart beats is aimed utilizing the features extracted from the HRV time domain parameters. The developed model provides a satisfactory performance with ~89% accuracy, 91.7 % sensitivity and 85% specificity rates for the detection of arrhythmic beats.
Abstract: Approximately 10,000 different types of dyes and
pigments are being used in various industrial applications yearly,
which include the textile and printing industries. However, these dyes
are difficult to degrade naturally once they enter the aquatic system.
Their high persistency in natural environment poses a potential health
hazard to all form of life. Hence, there is a need for alternative dye
removal strategy in the environment via bioremediation. In this study,
fungi laccase is investigated via commercial agar dyes plates and
submerged fermentation to explore the application of fungi laccase in
textile dye wastewater treatment. Two locally isolated basidiomycetes
were screened for laccase activity using media added with commercial
dyes such as 2, 2-azino-bis (3-ethylbenzothiazoline-6-sulfonic acid
(ABTS), guaiacol and Remazol Brillant Blue R (RBBR). Isolate TBB3
(1.70±0.06) and EL2 (1.78±0.08) gave the highest results for ABTS
plates with the appearance of greenish halo on around the isolates.
Submerged fermentation performed on Isolate TBB3 with the
productivity 3.9067 U/ml/day, whereas the laccase activity for Isolate
EL2 was much lower (0.2097 U/ml/day). As isolate TBB3 showed
higher laccase production, it was subjected to molecular
characterization by DNA isolation, PCR amplification and sequencing
of ITS region of nuclear ribosomal DNA. After being compared with
other sequences in National Center for Biotechnology Information
(NCBI database), isolate TBB3 is probably from species Trametes
hirsutei. Further research work can be performed on this isolate by
upscale the production of laccase in order to meet the demands of the
requirement for higher enzyme titer for the bioremediation of textile
dyes.
Abstract: Automatic License plate recognition (ALPR) is a technology which recognizes the registration plate or number plate or License plate of a vehicle. In this paper, an Indian vehicle number plate is mined and the characters are predicted in efficient manner. ALPR involves four major technique i) Pre-processing ii) License Plate Location Identification iii) Individual Character Segmentation iv) Character Recognition. The opening phase, named pre-processing helps to remove noises and enhances the quality of the image using the conception of Morphological Operation and Image subtraction. The second phase, the most puzzling stage ascertain the location of license plate using the protocol Canny Edge detection, dilation and erosion. In the third phase, each characters characterized by Connected Component Approach (CCA) and in the ending phase, each segmented characters are conceptualized using cross correlation template matching- a scheme specifically appropriate for fixed format. Major application of ALPR is Tolling collection, Border Control, Parking, Stolen cars, Enforcement, Access Control, Traffic control. The database consists of 500 car images taken under dissimilar lighting condition is used. The efficiency of the system is 97%. Our future focus is Indian Vehicle License Plate Validation (Whether License plate of a vehicle is as per Road transport and highway standard).
Abstract: Learning outcomes of a course (CLOs) and the abilities at the time of graduation referred to as Student Outcomes (SOs) are required to be assessed for ABET accreditation. A question in an assessment must target a CLO as well as an SO and must represent a required level of competence. This paper presents the idea of an Expert System (ES) to select a proper question to satisfy ABET accreditation requirements. For ES implementation, seven attributes of a question are considered including the learning outcomes and Bloom’s Taxonomy level. A database contains all the data about a course including course content topics, course learning outcomes and the CLO-SO relationship matrix. The knowledge base of the presented ES contains a pool of questions each with tags of the specified attributes. Questions and the attributes represent expert opinions. With implicit rule base the inference engine finds the best possible question satisfying the required attributes. It is shown that the novel idea of such an ES can be implemented and applied to a course with success. An application example is presented to demonstrate the working of the proposed ES.
Abstract: In recent years, a wide variety of applications are developed with Support Vector Machines -SVM- methods and Artificial Neural Networks -ANN-. In general, these methods depend on intrusion knowledge databases such as KDD99, ISCX, and CAIDA among others. New classes of detectors are generated by machine learning techniques, trained and tested over network databases. Thereafter, detectors are employed to detect anomalies in network communication scenarios according to user’s connections behavior. The first detector based on training dataset is deployed in different real-world networks with mobile and non-mobile devices to analyze the performance and accuracy over static detection. The vulnerabilities are based on previous work in telemedicine apps that were developed on the research group. This paper presents the differences on detections results between some network scenarios by applying traditional detectors deployed with artificial neural networks and support vector machines.
Abstract: Effect of Zn addition on the microstructure and
mechanical properties of Mg-Zn alloys with Zn contents from 6 to 10
weight percent was investigated in this study. Through calculation of
phase equilibria of Mg-Zn alloys, carried out by using FactSage® and
FTLite database, solution treatment temperature was decided as
temperatures from 300 to 400oC, where supersaturated solid solution
can be obtained. Solid solution treatment of Mg-Zn alloys was
successfully conducted at 380oC and supersaturated microstructure
with all beta phase resolved into matrix was obtained. After solution
treatment, hot rolling was successfully conducted by reduction of
60%. Compression and tension tests were carried out at room
temperature on the samples as-cast, solution treated, hot-rolled and
recrystallized after rolling. After solid solution treatment, each alloy
was annealed at temperatures of 180 and 200oC for time intervals from
1 min to 48 hrs and hardness of each condition was measured by
micro-Vickers method. Peak aging conditions were deduced as at the
temperature of 200oC for 10 hrs. By addition of Zn by 10 weight
percent, hardness and strength were enhanced.
Abstract: Introduction: To update ourselves and understand the
concept of latest electronic formats available for Health care
providers and how it could be used and developed as per standards.
The idea is to correlate between the patients Manual Medical Records
keeping and maintaining patients Electronic Information in a Health
care setup in this world. Furthermore, this stands with adapting to the
right technology depending upon the organization and improve our
quality and quantity of Healthcare providing skills. Objective: The
concept and theory is to explain the terms of Electronic Medical
Record (EMR), Electronic Health Record (EHR) and Personal Health
Record (PHR) and selecting the best technical among the available
Electronic sources and software before implementing. It is to guide
and make sure the technology used by the end users without any
doubts and difficulties. The idea is to evaluate is to admire the uses
and barriers of EMR-EHR-PHR. Aim and Scope: The target is to
achieve the health care providers like Physicians, Nurses, Therapists,
Medical Bill reimbursements, Insurances and Government to assess
the patient’s information on easy and systematic manner without
diluting the confidentiality of patient’s information. Method: Health
Information Technology can be implemented with the help of
Organisations providing with legal guidelines and help to stand by
the health care provider. The main objective is to select the correct
embedded and affordable database management software and
generating large-scale data. The parallel need is to know how the
latest software available in the market. Conclusion: The question lies
here is implementing the Electronic information system with
healthcare providers and organization. The clinicians are the main
users of the technology and manage us to “go paperless”. The fact is
that day today changing technologically is very sound and up to date.
Basically, the idea is to tell how to store the data electronically safe
and secure. All three exemplifies the fact that an electronic format
has its own benefit as well as barriers.
Abstract: In order to retrieve images efficiently from a large
database, a unique method integrating color and texture features
using genetic programming has been proposed. Opponent color
histogram which gives shadow, shade, and light intensity invariant
property is employed in the proposed framework for extracting color
features. For texture feature extraction, fast discrete curvelet
transform which captures more orientation information at different
scales is incorporated to represent curved like edges. The recent
scenario in the issues of image retrieval is to reduce the semantic gap
between user’s preference and low level features. To address this
concern, genetic algorithm combined with relevance feedback is
embedded to reduce semantic gap and retrieve user’s preference
images. Extensive and comparative experiments have been conducted
to evaluate proposed framework for content based image retrieval on
two databases, i.e., COIL-100 and Corel-1000. Experimental results
clearly show that the proposed system surpassed other existing
systems in terms of precision and recall. The proposed work achieves
highest performance with average precision of 88.2% on COIL-100
and 76.3% on Corel, the average recall of 69.9% on COIL and 76.3%
on Corel. Thus, the experimental results confirm that the proposed
content based image retrieval system architecture attains better
solution for image retrieval.
Abstract: Advances in spatial and spectral resolution of satellite
images have led to tremendous growth in large image databases. The
data we acquire through satellites, radars, and sensors consists of
important geographical information that can be used for remote
sensing applications such as region planning, disaster management.
Spatial data classification and object recognition are important tasks
for many applications. However, classifying objects and identifying
them manually from images is a difficult task. Object recognition is
often considered as a classification problem, this task can be
performed using machine-learning techniques. Despite of many
machine-learning algorithms, the classification is done using
supervised classifiers such as Support Vector Machines (SVM) as the
area of interest is known. We proposed a classification method,
which considers neighboring pixels in a region for feature extraction
and it evaluates classifications precisely according to neighboring
classes for semantic interpretation of region of interest (ROI). A
dataset has been created for training and testing purpose; we
generated the attributes by considering pixel intensity values and
mean values of reflectance. We demonstrated the benefits of using
knowledge discovery and data-mining techniques, which can be on
image data for accurate information extraction and classification from
high spatial resolution remote sensing imagery.
Abstract: With the advancement of knowledge about the utility
and impact of sustainability, its feasibility has been explored into
different walks of life. Scientists, however; have established their
knowledge in four areas viz environmental, economic, social and
cultural, popularly termed as four pillars of sustainability. Aspects of
environmental and economic sustainability have been rigorously
researched and practiced and huge volume of strong evidence of
effectiveness has been founded for these two sub-areas. For the social
and cultural aspects of sustainability, dependable evidence of
effectiveness is still to be instituted as the researchers and
practitioners are developing and experimenting methods across the
globe. Therefore, the present research aimed to identify globally used
practices of social and cultural sustainability and through evidence
synthesis assess their outcomes to determine the effectiveness of
those practices. A PICO format steered the methodology which
included all populations, popular sustainability practices including
walkability/cycle tracks, social/recreational spaces, privacy, health &
human services and barrier free built environment, comparators
included ‘Before’ and ‘After’, ‘With’ and ‘Without’, ‘More’ and
‘Less’ and outcomes included Social well-being, cultural coexistence,
quality of life, ethics and morality, social capital, sense of
place, education, health, recreation and leisure, and holistic
development. Search of literature included major electronic
databases, search websites, organizational resources, directory of
open access journals and subscribed journals. Grey literature,
however, was not included. Inclusion criteria filtered studies on the
basis of research designs such as total randomization, quasirandomization,
cluster randomization, observational or single studies
and certain types of analysis. Studies with combined outcomes were
considered but studies focusing only on environmental and/or
economic outcomes were rejected. Data extraction, critical appraisal
and evidence synthesis was carried out using customized tabulation,
reference manager and CASP tool. Partial meta-analysis was carried
out and calculation of pooled effects and forest plotting were done.
As many as 13 studies finally included for final synthesis explained
the impact of targeted practices on health, behavioural and social
dimensions. Objectivity in the measurement of health outcomes
facilitated quantitative synthesis of studies which highlighted the
impact of sustainability methods on physical activity, Body Mass
Index, perinatal outcomes and child health. Studies synthesized
qualitatively (and also quantitatively) showed outcomes such as
routines, family relations, citizenship, trust in relationships, social
inclusion, neighbourhood social capital, wellbeing, habitability and
family’s social processes. The synthesized evidence indicates slight
effectiveness and efficacy of social and cultural sustainability on the
targeted outcomes. Further synthesis revealed that such results of this
study are due weak research designs and disintegrated implementations. If architects and other practitioners deliver their
interventions in collaboration with research bodies and policy
makers, a stronger evidence-base in this area could be generated.
Abstract: This article discusses the passage of RDB to XML
documents (schema and data) based on metadata and semantic
enrichment, which makes the RDB under flattened shape and is
enriched by the object concept. The integration and exploitation of
the object concept in the XML uses a syntax allowing for the
verification of the conformity of the document XML during the
creation. The information extracted from the RDB is therefore
analyzed and filtered in order to adjust according to the structure of
the XML files and the associated object model. Those implemented
in the XML document through a SQL query are built dynamically. A
prototype was implemented to realize automatic migration, and so
proves the effectiveness of this particular approach.
Abstract: This report examines the current state of human gait
simulator development based on the human hip joint model. This unit
will create a database of human gait types, useful for setting up and
calibrating Mechano devices, as well as the creation of new systems
of rehabilitation, exoskeletons and walking robots. The system has
many opportunities to configure the dimensions and stiffness, while
maintaining relative simplicity.