Abstract: Red blood cells (RBC) are the most common types of
blood cells and are the most intensively studied in cell biology. The
lack of RBCs is a condition in which the amount of hemoglobin level
is lower than normal and is referred to as “anemia”. Abnormalities in
RBCs will affect the exchange of oxygen. This paper presents a
comparative study for various techniques for classifying the RBCs as
normal or abnormal (anemic) using WEKA. WEKA is an open
source consists of different machine learning algorithms for data
mining applications. The algorithms tested are Radial Basis Function
neural network, Support vector machine, and K-Nearest Neighbors
algorithm. Two sets of combined features were utilized for
classification of blood cells images. The first set, exclusively consist
of geometrical features, was used to identify whether the tested blood
cell has a spherical shape or non-spherical cells. While the second
set, consist mainly of textural features was used to recognize the
types of the spherical cells. We have provided an evaluation based on
applying these classification methods to our RBCs image dataset
which were obtained from Serdang Hospital - Malaysia, and
measuring the accuracy of test results. The best achieved
classification rates are 97%, 98%, and 79% for Support vector
machines, Radial Basis Function neural network, and K-Nearest
Neighbors algorithm respectively.
Abstract: In general, algorithms to find continuous k-nearest neighbors have been researched on the location based services, monitoring periodically the moving objects such as vehicles and mobile phone. Those researches assume the environment that the number of query points is much less than that of moving objects and the query points are not moved but fixed. In gaming environments, this problem is when computing the next movement considering the neighbors such as flocking, crowd and robot simulations. In this case, every moving object becomes a query point so that the number of query point is same to that of moving objects and the query points are also moving. In this paper, we analyze the performance of the existing algorithms focused on location based services how they operate under gaming environments.
Abstract: This paper presents a new approach to control robots, which can quickly find their swarm while tracking a moving target through the obstacles of the environment. In this approach, an artificial potential field is generated between each free-robot and the virtual attractive point of the swarm. This artificial potential field will lead free-robots to their swarm. The swarm-finding of these free-robots dose not influence the general motion of their swarm and nor other robots. When one singular robot approaches the swarm then its swarm-search will finish, and it will further participate with its swarm to reach the position of the target. The connections between member-robots with their neighbors are controlled by the artificial attractive/repulsive force field between them to avoid collisions and keep the constant distances between them in ordered formation. The effectiveness of the proposed approach has been verified in simulations.
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: BCI (Brain Computer Interface) is a communication machine that translates brain massages to computer commands. These machines with the help of computer programs can recognize the tasks that are imagined. Feature extraction is an important stage of the process in EEG classification that can effect in accuracy and the computation time of processing the signals. In this study we process the signal in three steps of active segment selection, fractal feature extraction, and classification. One of the great challenges in BCI applications is to improve classification accuracy and computation time together. In this paper, we have used student’s 2D sample t-statistics on continuous wavelet transforms for active segment selection to reduce the computation time. In the next level, the features are extracted from some famous fractal dimension estimation of the signal. These fractal features are Katz and Higuchi. In the classification stage we used ANFIS (Adaptive Neuro-Fuzzy Inference System) classifier, FKNN (Fuzzy K-Nearest Neighbors), LDA (Linear Discriminate Analysis), and SVM (Support Vector Machines). We resulted that active segment selection method would reduce the computation time and Fractal dimension features with ANFIS analysis on selected active segments is the best among investigated methods in EEG classification.
Abstract: In Content-Based Image Retrieval systems it is
important to use an efficient indexing technique in order to perform
and accelerate the search in huge databases. The used indexing
technique should also support the high dimensions of image features.
In this paper we present the hierarchical index NOHIS-tree (Non
Overlapping Hierarchical Index Structure) when we scale up to very
large databases. We also present a study of the influence of clustering
on search time. The performance test results show that NOHIS-tree
performs better than SR-tree. Tests also show that NOHIS-tree keeps
its performances in high dimensional spaces. We include the
performance test that try to determine the number of clusters in
NOHIS-tree to have the best search time.
Abstract: Sonogram images of normal and lymphocyte thyroid tissues have considerable overlap which makes it difficult to interpret and distinguish. Classification from sonogram images of thyroid gland is tackled in semiautomatic way. While making manual diagnosis from images, some relevant information need not to be recognized by human visual system. Quantitative image analysis could be helpful to manual diagnostic process so far done by physician. Two classes are considered: normal tissue and chronic lymphocyte thyroid (Hashimoto's Thyroid). Data structure is analyzed using K-nearest-neighbors classification. This paper is mentioned that unlike the wavelet sub bands' energy, histograms and Haralick features are not appropriate to distinguish between normal tissue and Hashimoto's thyroid.
Abstract: Skin color can provide a useful and robust cue
for human-related image analysis, such as face detection,
pornographic image filtering, hand detection and tracking,
people retrieval in databases and Internet, etc. The major
problem of such kinds of skin color detection algorithms is
that it is time consuming and hence cannot be applied to a real
time system. To overcome this problem, we introduce a new
fast technique for skin detection which can be applied in a real
time system. In this technique, instead of testing each image
pixel to label it as skin or non-skin (as in classic techniques),
we skip a set of pixels. The reason of the skipping process is
the high probability that neighbors of the skin color pixels are
also skin pixels, especially in adult images and vise versa. The
proposed method can rapidly detect skin and non-skin color
pixels, which in turn dramatically reduce the CPU time
required for the protection process. Since many fast detection
techniques are based on image resizing, we apply our
proposed pixel skipping technique with image resizing to
obtain better results. The performance evaluation of the
proposed skipping and hybrid techniques in terms of the
measured CPU time is presented. Experimental results
demonstrate that the proposed methods achieve better result
than the relevant classic method.
Abstract: Qk
n has been shown as an alternative to the hypercube
family. For any even integer k ≥ 4 and any integer n ≥ 2, Qk
n is
a bipartite graph. In this paper, we will prove that given any pair of
vertices, w and b, from different partite sets of Qk
n, there exist 2n
internally disjoint paths between w and b, denoted by {Pi | 0 ≤ i ≤ 2n-1}, such that 2n-1
i=0 Pi covers all vertices of Qk
n. The result is
optimal since each vertex of Qk
n has exactly 2n neighbors.
Abstract: The existence of many biological systems,
especially human societies, is based on cooperative behavior
[1, 2]. If natural selection favors selfish individuals, then what
mechanism is at work that we see so many cooperative
behaviors? One answer is the effect of network structure. On a
graph, cooperators can evolve by forming network bunches
[2, 3, 4]. In a research, Ohtsuki et al used the idea of iterated
prisoners- dilemma on a graph to model an evolutionary
game. They showed that the average number of neighbors
plays an important role in determining whether cooperation is
the ESS of the system or not [3]. In this paper, we are going to
study the dynamics of evolution of cooperation in a social
network. We show that during evolution, the ratio of
cooperators among individuals with fewer neighbors to
cooperators among other individuals is greater than unity. The
extent to which the fitness function depends on the payoff of
the game determines this ratio.
Abstract: The heart tissue is an excitable media. A Cellular
Automata is a type of model that can be used to model cardiac action
potential propagation. One of the advantages of this approach against
the methods based on differential equations is its high speed in large
scale simulations. Recent cellular automata models are not able to
avoid flat edges in the result patterns or have large neighborhoods. In
this paper, we present a new model to eliminate flat edges by
minimum number of neighbors.
Abstract: Wireless sensor networks (WSNs) have gained
tremendous attention in recent years due to their numerous
applications. Due to the limited energy resource, energy efficient
operation of sensor nodes is a key issue in wireless sensor networks.
Cooperative caching which ensures sharing of data among various
nodes reduces the number of communications over the wireless
channels and thus enhances the overall lifetime of a wireless sensor
network. In this paper, we propose a cooperative caching scheme
called ZCS (Zone Cooperation at Sensors) for wireless sensor
networks. In ZCS scheme, one-hop neighbors of a sensor node form a
cooperative cache zone and share the cached data with each other.
Simulation experiments show that the ZCS caching scheme achieves
significant improvements in byte hit ratio and average query latency
in comparison with other caching strategies.
Abstract: Computer modeling has played a unique role in
understanding electrocardiography. Modeling and simulating cardiac
action potential propagation is suitable for studying normal and
pathological cardiac activation. This paper presents a 2-D Cellular
Automata model for simulating action potential propagation in
cardiac tissue. We demonstrate a novel algorithm in order to use
minimum neighbors. This algorithm uses the summation of the
excitability attributes of excited neighboring cells. We try to
eliminate flat edges in the result patterns by inserting probability to
the model. We also preserve the real shape of action potential by
using linear curve fitting of one well known electrophysiological
model.
Abstract: The lack of any centralized infrastructure in mobile ad
hoc networks (MANET) is one of the greatest security concerns in
the deployment of wireless networks. Thus communication in
MANET functions properly only if the participating nodes cooperate
in routing without any malicious intention. However, some of the
nodes may be malicious in their behavior, by indulging in flooding
attacks on their neighbors. Some others may act malicious by
launching active security attacks like denial of service. This paper
addresses few related works done on trust evaluation and
establishment in ad hoc networks. Related works on flooding attack
prevention are reviewed. A new trust approach based on the extent of
friendship between the nodes is proposed which makes the nodes to
co-operate and prevent flooding attacks in an ad hoc environment.
The performance of the trust algorithm is tested in an ad hoc network
implementing the Ad hoc On-demand Distance Vector (AODV)
protocol.
Abstract: This paper objects to extend Jon Kleinberg-s research. He introduced the structure of small-world in a grid and shows with a greedy algorithm using only local information able to find route between source and target in delivery time O(log2n). His fundamental model for distributed system uses a two-dimensional grid with longrange random links added between any two node u and v with a probability proportional to distance d(u,v)-2. We propose with an additional information of the long link nearby, we can find the shorter path. We apply the ant colony system as a messenger distributed their pheromone, the long-link details, in surrounding area. The subsequence forwarding decision has more option to move to, select among local neighbors or send to node has long link closer to its target. Our experiment results sustain our approach, the average routing time by Color Pheromone faster than greedy method.
Abstract: We present here the results for a comparative study of
some techniques, available in the literature, related to the relevance
feedback mechanism in the case of a short-term learning. Only one
method among those considered here is belonging to the data mining
field which is the K-nearest neighbors algorithm (KNN) while the
rest of the methods is related purely to the information retrieval field
and they fall under the purview of the following three major axes:
Shifting query, Feature Weighting and the optimization of the
parameters of similarity metric. As a contribution, and in addition to
the comparative purpose, we propose a new version of the KNN
algorithm referred to as an incremental KNN which is distinct from
the original version in the sense that besides the influence of the
seeds, the rate of the actual target image is influenced also by the
images already rated. The results presented here have been obtained
after experiments conducted on the Wang database for one iteration
and utilizing color moments on the RGB space. This compact
descriptor, Color Moments, is adequate for the efficiency purposes
needed in the case of interactive systems. The results obtained allow
us to claim that the proposed algorithm proves good results; it even
outperforms a wide range of techniques available in the literature.
Abstract: We report a computational study of the spreading
dynamics of a viral infection in a complex (scale-free) network. The
final epidemic size distribution (FESD) was found to be unimodal or
bimodal depending on the value of the basic reproductive
number R0 . The FESDs occurred on time-scales long enough for
intermediate-time epidemic size distributions (IESDs) to be important
for control measures. The usefulness of R0 for deciding on the
timeliness and intensity of control measures was found to be limited
by the multimodal nature of the IESDs and by its inability to inform
on the speed at which the infection spreads through the population. A
reduction of the transmission probability at the hubs of the scale-free
network decreased the occurrence of the larger-sized epidemic events
of the multimodal distributions. For effective epidemic control, an
early reduction in transmission at the index cell and its neighbors was
essential.
Abstract: We propose an enhanced key management scheme
based on Key Infection, which is lightweight scheme for tiny sensors.
The basic scheme, Key Infection, is perfectly secure against node
capture and eavesdropping if initial communications after node
deployment is secure. If, however, an attacker can eavesdrop on
the initial communications, they can take the session key. We use
common neighbors for each node to generate the session key. Each
node has own secret key and shares it with its neighbor nodes. Then
each node can establish the session key using common neighbors-
secret keys and a random number. Our scheme needs only a few
communications even if it uses neighbor nodes- information. Without
losing the lightness of basic scheme, it improves the resistance against
eavesdropping on the initial communications more than 30%.
Abstract: Computerized lip reading has been one of the most
actively researched areas of computer vision in recent past because
of its crime fighting potential and invariance to acoustic environment.
However, several factors like fast speech, bad pronunciation,
poor illumination, movement of face, moustaches and beards make
lip reading difficult. In present work, we propose a solution for
automatic lip contour tracking and recognizing letters of English
language spoken by speakers using the information available from
lip movements. Level set method is used for tracking lip contour
using a contour velocity model and a feature vector of lip movements
is then obtained. Character recognition is performed using modified
k nearest neighbor algorithm which assigns more weight to nearer
neighbors. The proposed system has been found to have accuracy
of 73.3% for character recognition with speaker lip movements as
the only input and without using any speech recognition system in
parallel. The approach used in this work is found to significantly
solve the purpose of lip reading when size of database is small.
Abstract: In order to accelerate the similarity search in highdimensional database, we propose a new hierarchical indexing method. It is composed of offline and online phases. Our contribution concerns both phases. In the offline phase, after gathering the whole of the data in clusters and constructing a hierarchical index, the main originality of our contribution consists to develop a method to construct bounding forms of clusters to avoid overlapping. For the online phase, our idea improves considerably performances of similarity search. However, for this second phase, we have also developed an adapted search algorithm. Our method baptized NOHIS (Non-Overlapping Hierarchical Index Structure) use the Principal Direction Divisive Partitioning (PDDP) as algorithm of clustering. The principle of the PDDP is to divide data recursively into two sub-clusters; division is done by using the hyper-plane orthogonal to the principal direction derived from the covariance matrix and passing through the centroid of the cluster to divide. Data of each two sub-clusters obtained are including by a minimum bounding rectangle (MBR). The two MBRs are directed according to the principal direction. Consequently, the nonoverlapping between the two forms is assured. Experiments use databases containing image descriptors. Results show that the proposed method outperforms sequential scan and SRtree in processing k-nearest neighbors.