Abstract: An extensive amount of work has been done in data
clustering research under the unsupervised learning technique in Data
Mining during the past two decades. Moreover, several approaches
and methods have been emerged focusing on clustering diverse data
types, features of cluster models and similarity rates of clusters.
However, none of the single clustering algorithm exemplifies its best
nature in extracting efficient clusters. Consequently, in order to
rectify this issue, a new challenging technique called Cluster
Ensemble method was bloomed. This new approach tends to be the
alternative method for the cluster analysis problem. The main
objective of the Cluster Ensemble is to aggregate the diverse
clustering solutions in such a way to attain accuracy and also to
improve the eminence the individual clustering algorithms. Due to
the massive and rapid development of new methods in the globe of
data mining, it is highly mandatory to scrutinize a vital analysis of
existing techniques and the future novelty. This paper shows the
comparative analysis of different cluster ensemble methods along
with their methodologies and salient features. Henceforth this
unambiguous analysis will be very useful for the society of clustering
experts and also helps in deciding the most appropriate one to resolve
the problem in hand.
Abstract: Advances in the field of image processing envision a
new era of evaluation techniques and application of procedures in
various different fields. One such field being considered is the
biomedical field for prognosis as well as diagnosis of diseases. This
plethora of methods though provides a wide range of options to select
from, it also proves confusion in selecting the apt process and also in
finding which one is more suitable. Our objective is to use a series of
techniques on bone scans, so as to detect the occurrence of
rheumatoid arthritis (RA) as accurately as possible. Amongst other
techniques existing in the field our proposed system tends to be more
effective as it depends on new methodologies that have been proved
to be better and more consistent than others. Computer aided
diagnosis will provide more accurate and infallible rate of
consistency that will help to improve the efficiency of the system.
The image first undergoes histogram smoothing and specification,
morphing operation, boundary detection by edge following algorithm
and finally image subtraction to determine the presence of
rheumatoid arthritis in a more efficient and effective way. Using preprocessing
noises are removed from images and using segmentation,
region of interest is found and Histogram smoothing is applied for a
specific portion of the images. Gray level co-occurrence matrix
(GLCM) features like Mean, Median, Energy, Correlation, Bone
Mineral Density (BMD) and etc. After finding all the features it
stores in the database. This dataset is trained with inflamed and noninflamed
values and with the help of neural network all the new
images are checked properly for their status and Rough set is
implemented for further reduction.
Abstract: This paper deals with a proposal of a new
methodology for durability assessment of solidification product for
its safe further use. The new methodology is based on a review of the
current state of assessment of treated waste in Czech Republic and
abroad. The aim of the paper is to propose an optimal evaluation
methodology for verifying properties of solidification product to
ensure its safe further use in building industry.
Abstract: This paper proposes a new method to find the equations
of transformation matrix for the rotation angles of the two rotational
axes and the coordinates of the three linear axes of an orthogonal
multi-axis milling machine. This approach provides intuitive physical
meanings for rotation angles of multi-axis machines, which can be
used to evaluate the accuracy of the conversion from CL data to NC
data.
Abstract: This paper describes an identification of specific shapes within binary images using the morphological Hit-or-Miss Transform (HMT). Hit-or-Miss transform is a general binary morphological operation that can be used in searching of particular patterns of foreground and background pixels in an image. It is actually a basic operation of binary morphology since almost all other binary morphological operators are derived from it. The input of this method is a binary image and a structuring element (a template which will be searched in a binary image) while the output is another binary image. In this paper a modification of Hit-or-Miss transform has been proposed. The accuracy of algorithm is adjusted according to the similarity of the template and the sought template. The implementation of this method has been done by C language. The algorithm has been tested on several images and the results have shown that this new method can be used for similar shape detection.
Abstract: In this paper, a new method for multi criteria decision making is represented whichspecifies a trajectory satisfying desired criteria including minimization of time. A rescue robot is defined to perform certain tasks before the arrival of rescue team, including evaluation of the probability of explosion in the area, detecting human-beings, and providing preliminary aidsin case of identifying signs of life, so that the security of the surroundings will have enhanced significantly for the individuals inside the disaster zone as well as the rescue team. The main idea behind our technique is using the Program Evaluation and Review Technique analysis along with Critical Path Method and use the Multi Criteria Decision Making (MCDM) method to decidewhich set of activities must be performed first. Since the disastrous event in one area may be well contagious to others, it is one of the robot's priorities to evaluate the relative adversity of the situation, using the above methods and prioritize its mission.
Abstract: A Vehicular Ad-Hoc Network (VANET) is a mobile Ad-Hoc Network that provides connectivity moving device to fixed equipments. Such type of device is equipped with vehicle provides safety for the passengers. In the recent research areas of traffic management there observed the wide scope of design of new methodology of extension of wireless sensor networks and ad-hoc network principal for development of VANET technology. This paper provides the wide research view of the VANET and MANET concept for the researchers to contribute the better optimization technique for the development of effective and fast atomization technique for the large size of data exchange in this complex networks.
Abstract: The quality of machined surfaces is an important characteristic of cutting processes and surface roughness has strong effects on the performance of sliding, moving components. The ability to forecast these values for a given process has been of great interests among researchers for a long time. Different modeling procedures and algorithms have been worked-out, and each has its own advantages and drawbacks. A new method will be introduced in this paper which will make it possible to calculate both the profile (2D) and surface (3D) parameters of theoretical roughness in the face milling of plain surfaces. This new method is based on an expediently developed CAD model, and uses a professional program for the roughness evaluation. Cutting experiments were performed on 42CrMo4 specimens in order to validate the accuracy of the model. The results have revealed that the method is able to predict surface roughness with good accuracy.
Abstract: This paper proposes a new teaching and learning approach-project and module based teaching and learning (PMBTL). The PMBTL approach incorporates the merits of project/problem based and module based learning methods, and overcomes the limitations of these methods. The correlation between teaching, learning, practice and assessment is emphasized in this approach, and new methods have been proposed accordingly. The distinct features of these new methods differentiate the PMBTL approach from conventional teaching approaches. Evaluation of this approach on practical teaching and learning activities demonstrates the effectiveness and stability of the approach in improving the performance and quality of teaching and learning. The approach proposed in this paper is also intuitive to the design of other teaching units.
Abstract: In this paper, a new method for solution of second order linear Fredholm integral equation in power series form was studied. The result is obtained by using Banach fixed point theorem.
Abstract: Cavitation is one of the most well-known process faults that may occur in different industrial equipment especially centrifugal pumps. Cavitation also may happen in water pumps and turbines. Sometimes cavitation has been severe enough to wear holes in the impeller and damage the vanes to such a degree that the impeller becomes very ineffective. More commonly, the pump efficiency will decrease significantly during cavitation and continue to decrease as damage to the impeller increases. Typically, when cavitation occurs, an audible sound similar to ‘marbles’ or ‘crackling’ is reported to be emitted from the pump. In this paper, the most effective monitoring items and techniques in detecting cavitation discussed in details. Besides, some successful solutions for solving this problem for sea water vertical Centrifugal lift Pump discussed through a case history related to Iran oil industry. Furthermore, balance line modification, strainer choking and random resonance in sea water pumps discussed. In addition, a new Method for diagnosing mechanical conditions of sea water vertical Centrifugal lift Pumps introduced. This method involves disaggregating bus current by device into disaggregated currents having correspondences with operating currents in response to measured bus current. Moreover, some new patents and innovations in mechanical sea water pumping and cooling systems discussed in this paper.
Abstract: Big data has the potential to improve the quality of services; enable infrastructure that businesses depend on to adapt continually and efficiently; improve the performance of employees; help organizations better understand customers; and reduce liability risks. Analytics and marketing models of fixed and mobile operators are falling short in combating churn and declining revenue per user. Big Data presents new method to reverse the way and improve profitability. The benefits of Big Data and next-generation network, however, are more exorbitant than improved customer relationship management. Next generation of networks are in a prime position to monetize rich supplies of customer information—while being mindful of legal and privacy issues. As data assets are transformed into new revenue streams will become integral to high performance.
Abstract: In pattern clustering, nearest neighborhood point computation is a challenging issue for many applications in the area of research such as Remote Sensing, Computer Vision, Pattern Recognition and Statistical Imaging. Nearest neighborhood
computation is an essential computation for providing sufficient classification among the volume of pixels (voxels) in order to localize the active-region-of-interests (AROI). Furthermore, it is needed to compute spatial metric relationships of diverse area of imaging based on the applications of pattern recognition. In this paper, we propose a new methodology for finding the nearest neighbor point, depending on making a virtually grid of a hexagon cells, then locate every point beneath them. An algorithm is suggested for minimizing the computation and increasing the turnaround time of the process. The nearest neighbor query points Φ are fetched by seeking fashion of hexagon holistic. Seeking will be repeated until an AROI Φ is to be expected. If any point Υ is located then searching starts in the nearest hexagons in a circular way. The First hexagon is considered be level 0 (L0) and the surrounded hexagons is level 1 (L1). If Υ is located in L1, then search starts in the next level (L2) to ensure that Υ is the nearest neighbor for Φ. Based on the result and experimental results, we found that the proposed method has an advantage over the traditional methods in terms of minimizing the time complexity required for searching the neighbors, in turn, efficiency of classification will be improved sufficiently.
Abstract: Over the past epoch a rampant amount of work has been done in the data clustering research under the unsupervised learning technique in Data mining. Furthermore several algorithms and methods have been proposed focusing on clustering different data types, representation of cluster models, and accuracy rates of the clusters. However no single clustering algorithm proves to be the most efficient in providing best results. Accordingly in order to find the solution to this issue a new technique, called Cluster ensemble method was bloomed. This cluster ensemble is a good alternative approach for facing the cluster analysis problem. The main hope of the cluster ensemble is to merge different clustering solutions in such a way to achieve accuracy and to improve the quality of individual data clustering. Due to the substantial and unremitting development of new methods in the sphere of data mining and also the incessant interest in inventing new algorithms, makes obligatory to scrutinize a critical analysis of the existing techniques and the future novelty. This paper exposes the comparative study of different cluster ensemble methods along with their features, systematic working process and the average accuracy and error rates of each ensemble methods. Consequently this speculative and comprehensive analysis will be very useful for the community of clustering practitioners and also helps in deciding the most suitable one to rectify the problem in hand.
Abstract: In this study, two kinds of nondestructive evaluation
(NDE) techniques (rebound hardness and ultrasonic pulse velocity
methods) are investigated for the effective maintenance of underwater
concrete structures. A new methodology to estimate the underwater
concrete strengths more effectively, named “artificial neural network
(ANN) – based concrete strength estimation with the combination of
rebound hardness and ultrasonic pulse velocity methods” is proposed
and verified throughout a series of experimental works.
Abstract: Service quality is the highest requirement by users,
especially for the service in electronic government. During the past
decades, it has become a major area of academic investigation.
Considering this issue, there are a lot of researches that evaluated the
dimensions and e-service contexts. This study also identified the
dimensions of service quality, but focuses on a new concept and
provides a new methodology in developing measurement scales of
e-service quality such as information quality, service quality and
organization quality. Finally, this study will suggest a key factor to
evaluate e-government service quality better.
Abstract: Construction knowledge can be referred to and reused among involved project managers and jobsite engineers to alleviate problems on a construction jobsite and reduce the time and cost of solving problems related to constructability. This paper proposes a new methodology to provide sharing of construction knowledge by using the Building Information Modeling (BIM) approach. The main characteristics of BIM include illustrating 3D CAD-based presentations and keeping information in a digital format, and facilitation of easy updating and transfer of information in the 3D BIM environment. Using the BIM approach, project managers and engineers can gain knowledge related to 3D BIM and obtain feedback provided by jobsite engineers for future reference. This study addresses the application of knowledge sharing management in the construction phase of construction projects and proposes a BIM-based Knowledge Sharing Management (BIMKSM) system for project managers and engineers. The BIMKSM system is then applied in a selected case study of a construction project in Taiwan to verify the proposed methodology and demonstrate the effectiveness of sharing knowledge in the BIM environment. The combined results demonstrate that the BIMKSM system can be used as a visual BIM-based knowledge sharing management platform by utilizing the BIM approach and web technology.
Abstract: The coherent Self-Averaging (CSA), is a new method proposed in this work; applied to simulated signals evoked potentials related to events (ERP) to find the wave P300, useful systems in the brain computer interface (BCI). The CSA method cleans signal in the time domain of white noise through of successive averaging of a single signal. The method is compared with the traditional method, coherent averaging or synchronized (CA), showing optimal results in the improvement of the signal to noise ratio (SNR). The method of CSA is easy to implement, robust and applicable to any physiological time series contaminated with white noise
Abstract: One of the most important factors for the success of e-government is training and preparing the workforce of the public sector. As changes and innovation in the public sector progress at a very slow pace and more slowly than in the private sector, issues related to human resources require special care. This is because the workforce will eventually seize the opportunities of the technological solutions used in e-Government. Thus, the central administration should provide employees with continuous and focused training not only on new technologies but also on a wide range of subjects and also improve interdepartmental interaction.
To achieve all this, new methods and training tools need to be implemented in addition to assessment of the employees. In this spirit, we propose the development of an educational platform with user personalization features. We propose the development of this platform using Moodle as the basic tool. Incorporating a personalization mechanism is very important since different employees have different backgrounds, education levels, computer skills, or different capability to develop further. Key features of the proposed platform include, besides typical e-learning tools, communities organized in order to exchange experiences and knowledge, groups of users based on certain criteria, automatic evaluation of users and potential self-education and self-assessment. In its fully developed form, this platform can be part of a more comprehensive knowledge management system for the public sector.
Abstract: In this article, a new inexact alternating direction method(ADM) is proposed for solving a class of variational inequality problems. At each iteration, the new method firstly solves the resulting subproblems of ADM approximately to generate an temporal point ˜xk, and then the multiplier yk is updated to get the new iterate yk+1. In order to get xk+1, we adopt a new descent direction which is simple compared with the existing prediction-correction type ADMs. For the inexact ADM, the resulting proximal subproblem has closedform solution when the proximal parameter and inexact term are chosen appropriately. We show the efficiency of the inexact ADM numerically by some preliminary numerical experiments.