Abstract: Smartphone users are increasing rapidly. Accordingly, many companies are running BYOD (Bring Your Own Device: Policies to bring private-smartphones to the company) policy to increase work efficiency. However, smartphones are always under the threat of malware, thus the company network that is connected smartphone is exposed to serious risks. Most smartphone malware detection techniques are to perform an independent detection (perform the detection of a single target application). In this paper, we analyzed a variety of intrusion detection techniques. Based on the results of analysis propose an agent using the network IDS.
Abstract: In the present case study we examined the development and testing methods of systems that contain safety-critical elements in different industrial fields. Consequentially, we observed the classical object-oriented development and testing environment, as both medical technology and automobile industry approaches the development of safety critical elements that way. Subsequently, we examined model-based development. We introduce the quality parameters that define development and testing. While taking modern agile methodology (scrum) into consideration, we examined whether and to what extent the methodologies we found fit into this environment.
Abstract: This paper focused on application support for technical control of artillery units – PVNPG-14M, especially on interpolation issue. Artillery units of the Army of the Czech Republic, reflecting the current global security neighborhood, can be used outside the Czech Republic. The paper presents principles, evolution and calculation in the process of complete preparation. The paper presents expertise using of application of current artillery communication and information system and suggests the perspective future system. The paper also presents problems in process of complete preparing of fire especially problems in permanently information (firing table) and calculated values. The paper presents problems of current artillery communication and information system and suggests requirements of the future system.
Abstract: Human action is recognized directly from the video sequences. The objective of this work is to recognize various human actions like run, jump, walk etc. Human action recognition requires some prior knowledge about actions namely, the motion estimation, foreground and background estimation. Region of interest (ROI) is extracted to identify the human in the frame. Then, optical flow technique is used to extract the motion vectors. Using the extracted features similarity measure based classification is done to recognize the action. From experimentations upon the Weizmann database, it is found that the proposed method offers a high accuracy.
Abstract: Data mining is the procedure of determining interesting patterns from the huge amount of data. With the intention of accessing the data faster the most supporting processes needed is clustering. Clustering is the process of identifying similarity between data according to the individuality present in the data and grouping associated data objects into clusters. Cluster ensemble is the technique to combine various runs of different clustering algorithms to obtain a general partition of the original dataset, aiming for consolidation of outcomes from a collection of individual clustering outcomes. The performances of clustering ensembles are mainly affecting by two principal factors such as diversity and quality. This paper presents the overview about the different cluster ensemble algorithm along with their methods used in cluster ensemble to improve the diversity and quality in the several cluster ensemble related papers and shows the comparative analysis of different cluster ensemble also summarize various cluster ensemble methods. Henceforth this clear analysis will be very useful for the world of clustering experts and also helps in deciding the most appropriate one to determine the problem in hand.
Abstract: Liver cancer is one of the common diseases that cause the death. Early detection is important to diagnose and reduce the incidence of death. Improvements in medical imaging and image processing techniques have significantly enhanced interpretation of medical images. Computer-Aided Diagnosis (CAD) systems based on these techniques play a vital role in the early detection of liver disease and hence reduce liver cancer death rate. This paper presents an automated CAD system consists of three stages; firstly, automatic liver segmentation and lesion’s detection. Secondly, extracting features. Finally, classifying liver lesions into benign and malignant by using the novel contrasting feature-difference approach. Several types of intensity, texture features are extracted from both; the lesion area and its surrounding normal liver tissue. The difference between the features of both areas is then used as the new lesion descriptors. Machine learning classifiers are then trained on the new descriptors to automatically classify liver lesions into benign or malignant. The experimental results show promising improvements. Moreover, the proposed approach can overcome the problems of varying ranges of intensity and textures between patients, demographics, and imaging devices and settings.
Abstract: In this paper, the regression dependence of dancing
intensity from wind speed and length of span was established due to
the statistic data obtained from multi-year observations on line wires
dancing accumulated by power systems of Kazakhstan and the
Russian Federation. The lower and upper limitations of the equations
parameters were estimated, as well as the adequacy of the regression
model. The constructed model will be used in research of dancing
phenomena for the development of methods and means of protection
against dancing and for zoning plan of the territories of line wire
dancing.
Abstract: Fractal based digital image compression is a specific
technique in the field of color image. The method is best suited for
irregular shape of image like snow bobs, clouds, flame of fire; tree
leaves images, depending on the fact that parts of an image often
resemble with other parts of the same image. This technique has
drawn much attention in recent years because of very high
compression ratio that can be achieved. Hybrid scheme incorporating
fractal compression and speedup techniques have achieved high
compression ratio compared to pure fractal compression. Fractal
image compression is a lossy compression method in which selfsimilarity
nature of an image is used. This technique provides high
compression ratio, less encoding time and fart decoding process. In
this paper, fractal compression with quad tree and DCT is proposed
to compress the color image. The proposed hybrid schemes require
four phases to compress the color image. First: the image is
segmented and Discrete Cosine Transform is applied to each block of
the segmented image. Second: the block values are scanned in a
zigzag manner to prevent zero co-efficient. Third: the resulting image
is partitioned as fractals by quadtree approach. Fourth: the image is
compressed using Run length encoding technique.
Abstract: In this paper, an approach for the liver tumor detection
in computed tomography (CT) images is represented. The detection
process is based on classifying the features of target liver cell to
either tumor or non-tumor. Fractional differential (FD) is applied for
enhancement of Liver CT images, with the aim of enhancing texture
and edge features. Later on, a fusion method is applied to merge
between the various enhanced images and produce a variety of
feature improvement, which will increase the accuracy of
classification. Each image is divided into NxN non-overlapping
blocks, to extract the desired features. Support vector machines
(SVM) classifier is trained later on a supplied dataset different from
the tested one. Finally, the block cells are identified whether they are
classified as tumor or not. Our approach is validated on a group of
patients’ CT liver tumor datasets. The experiment results
demonstrated the efficiency of detection in the proposed technique.
Abstract: This paper presents the development of a mobile
application for students at the Faculty of Information Technology,
Rangsit University (RSU), Thailand. RSU upgrades an enrollment
process by improving its information systems. Students can
download the RSU APP easily in order to access the RSU substantial
information. The reason of having a mobile application is to help
students to access the system regardless of time and place. The objectives of this paper include: 1. To develop an application
on iOS platform for those students at the Faculty of Information
Technology, Rangsit University, Thailand. 2. To obtain the students’
perception towards the new mobile app. The target group is those
from the freshman year till the senior year of the faculty of
Information Technology, Rangsit University. The new mobile application, called as RSU APP, is developed by
the department of Information Technology, Rangsit University. It
contains useful features and various functionalities particularly on
those that can give support to students. The core contents of the app
consist of RSU’s announcement, calendar, events, activities, and ebook.
The mobile app is developed on the iOS platform. The user
satisfaction is analyzed from the interview data from 81 interviewees
as well as a Google application like a Google form which 122
interviewees are involved. The result shows that users are satisfied
with the application as they score it the most satisfaction level at 4.67
SD 0.52. The score for the question if users can learn and use the
application quickly is high which is 4.82 SD 0.71. On the other hand,
the lowest satisfaction rating is in the app’s form, apps lists, with the
satisfaction level as 4.01 SD 0.45.
Abstract: The aim of this work is to detect geometrical shape
objects in an image. In this paper, the object is considered to be as a
circle shape. The identification requires find three characteristics,
which are number, size, and location of the object. To achieve the
goal of this work, this paper presents an algorithm that combines
from some of statistical approaches and image analysis techniques.
This algorithm has been implemented to arrive at the major
objectives in this paper. The algorithm has been evaluated by using
simulated data, and yields good results, and then it has been applied
to real data.
Abstract: Polymorphism is one of the main pillars of objectoriented
paradigm. It induces hidden forms of class dependencies
which may impact software quality, resulting in higher cost factor for
comprehending, debugging, testing, and maintaining the software. In
this paper, a new cognitive complexity metric called Cognitive
Weighted Polymorphism Factor (CWPF) is proposed. Apart from the
software structural complexity, it includes the cognitive complexity
on the basis of type. The cognitive weights are calibrated based on 27
empirical studies with 120 persons. A case study and experimentation
of the new software metric shows positive results. Further, a
comparative study is made and the correlation test has proved that
CWPF complexity metric is a better, more comprehensive, and more
realistic indicator of the software complexity than Abreu’s
Polymorphism Factor (PF) complexity metric.
Abstract: Maintaining factory default battery endurance rate
over time in supporting huge amount of running applications on
energy-restricted mobile devices has created a new challenge for
mobile applications developer. While delivering customers’
unlimited expectations, developers are barely aware of efficient use
of energy from the application itself. Thus, developers need a set of
valid energy consumption indicators in assisting them to develop
energy saving applications. In this paper, we present a few software
product metrics that can be used as an indicator to measure energy
consumption of Android-based mobile applications in the early of
design stage. In particular, Trepn Profiler (Power profiling tool for
Qualcomm processor) has used to collect the data of mobile
application power consumption, and then analyzed for the 23
software metrics in this preliminary study. The results show that
McCabe cyclomatic complexity, number of parameters, nested block
depth, number of methods, weighted methods per class, number of
classes, total lines of code and method lines have direct relationship
with power consumption of mobile application.
Abstract: In this paper, we present an optimization technique or
a learning algorithm using the hybrid architecture by combining the
most popular sequence recognition models such as Recurrent Neural
Networks (RNNs) and Hidden Markov models (HMMs). In order to
improve the sequence/pattern recognition/classification performance
by applying a hybrid/neural symbolic approach, a gradient descent
learning algorithm is developed using the Real Time Recurrent
Learning of Recurrent Neural Network for processing the knowledge
represented in trained Hidden Markov Models. The developed hybrid
algorithm is implemented on automata theory as a sample test beds
and the performance of the designed algorithm is demonstrated and
evaluated on learning the deterministic finite state automata.
Abstract: Modelling of building processes of a multimodal
freight transportation support information system is discussed based
on modern CASE technologies. Functional efficiencies of ports in
the eastern part of the Black Sea are analyzed taking into account
their ecological, seasonal, resource usage parameters. By resources,
we mean capacities of berths, cranes, automotive transport, as well as
work crews and neighbouring airports. For the purpose of designing
database of computer support system for Managerial (Logistics)
function, using Object-Role Modeling (ORM) tool (NORMA–Natural ORM Architecture) is proposed, after which Entity
Relationship Model (ERM) is generated in automated process.
Software is developed based on Process-Oriented and Service-Oriented architecture, in Visual Studio.NET environment.