State Of Art Algorithms Research Articles

Surface area estimation for application of wound care

Estimation of surface for application of wound care is one of the important prognostic factors for wound healing. The objective of this study is to measure a surface area estimation for application of wound care. Non-invasive, 3D surface reconstruction methodology using two monocular cameras was implemented and verified. The measurement environment was setup on 10 patients with different kind of wounds. Phantom measurements, with known dimensions, show accuracy of implemented method below 11 percent The patient’s measurements has the same accuracy. The implemented method use state of art algorithms and the cheap equipment and can be treated as an auxiliary, more objective method to quantify the wound healing process.

Evolutionary algorithm and modularity for detecting communities in networks

Evolutionary algorithms are very used today to resolve problems in many fields. There are few community detection methods in networks based on evolutionary algorithms. In our paper, we develop a new approach of community detection in networks based on evolutionary algorithm. In this approach we use an evolutionary algorithm to find the first community structure that maximizes the modularity. After that we improve the community structure through merging communities to find the final community structure that has the high value of modularity. We provide a general framework for implementing our approach. Compared with the state of art algorithms, simulation results on computer-generated and real world networks reflect the effectiveness of our approach.

AIDCOR: artificial immunity inspired density based clustering with outlier removal

AIDCOR is an artificial immunity inspired density based clustering algorithm which is able to identify crisp clusters with high degree of accuracy where the input dataset presented can have varied shaped clusters, varied density distribution, low inter cluster separation and with noise/outliers also. The algorithm works out into two phases, a data preprocessing module and a clustering module. The initial data processing part of AIDCOR is artificial immune system inspired and uses a novel approach of somatic hypermutation and affinity maturation with selective antigenic binding to reduce data redundancy while preserving the original data patterns. The actual data clustering part pursues a density based approach which forms clusters with the compressed data set and doing so it inherently identifies outliers also. We have thoroughly analyzed both theoretical aspects and experimental results of the proposed algorithm with wide variety of real and synthetic data set. The results of AIDCOR are compared with several current state of art algorithms where we found that it is giving much higher clustering accuracy for nearly all type of dataset. The time complexity of AIDCOR is coming to be sub quadratic when some indexing data structure is used for nearest neighbor search and quadratic otherwise. AIDCOR needs 3 user defined parameters for its operation. A heuristic method is also proposed to automatically determine those parameters.

Detection of overlapping protein complexes in gene expression, phenotype and pathways of Saccharomyces cerevisiae using Prorank based Fuzzy algorithm

Proteins show their functional activity by interacting with other proteins and forms protein complexes since it is playing an important role in cellular organization and function. To understand the higher order protein organization, overlapping is an important step towards unveiling functional and evolutionary mechanisms behind biological networks. Most of the clustering algorithms do not consider the weighted as well as overlapping complexes. In this research, Prorank based Fuzzy algorithm has been proposed to find the overlapping protein complexes. The Fuzzy detection algorithm is incorporated in the Prorank algorithm after ranking step to find the overlapping community. The proposed algorithm executes in an iterative manner to compute the probability of robust clusters. The proposed and the existing algorithms were tested on different datasets such as PPI-D1, PPI-D2, Collins, DIP, Krogan Core and Krogan-Extended, gene expression such as GSE7645, GSE22269, GSE26923, pathways such as Meiosis, MAPK, Cell Cycle, phenotypes such as Yeast Heterogeneous and Yeast Homogeneous datasets. The experimental results show that the proposed algorithm predicts protein complexes with better accuracy compared to other state of art algorithms.

Review on High Utility Itemset Mining Algorithms

Finding interesting patterns in the database is an important research area in the field of data mining. Association Rule Mining (ARM) finds the items that go together. It finds out the association between items. Frequent Itemset Mining (FIM) finds out the itemset that occur frequently in the database. But this approach misses out the profit and the quantity of item purchased. This is addressed in High Utility Itemset Mining (HUIM). HUIM find the profit generating itemset in the database. Many algorithms have been proposed in this field in the recent years. This paper focuses on reviewing the existing state of art algorithms to create a path for the future research in the area of high utility itemset mining.

A quick artificial bee colony (qABC) algorithm and its performance on optimization problems

Artificial bee colony (ABC) algorithm inspired by the foraging behaviour of the honey bees is one of the most popular swarm intelligence based optimization techniques. Quick artificial bee colony (qABC) is a new version of ABC algorithm which models the behaviour of onlooker bees more accurately and improves the performance of standard ABC in terms of local search ability. In this study, the qABC method is described and its performance is analysed depending on the neighbourhood radius, on a set of benchmark problems. And also some analyses about the effect of the parameter limit and colony size on qABC optimization are carried out. Moreover, the performance of qABC is compared with the state of art algorithms’ performances.

EGYPTIAN LICENSE PLATE RECOGNITION USING ENHANCED STROKE WIDTH TRANSFORMATION AND FUZZY ARTMAP

License Plate Recognition (LPR) is the most importa nt type of Intelligent Transportation System (ITS). LPR is used in many different types of ITS like ele ctronic payment systems, toll station, parking fees , freeway and arterial management systems for traffic surveillance. Few years ago, Egyptians government changed the car license plate to include letters an d numbers. So the needs for efficient LPR System fo r the new license plate are increased in different ITS fi elds. This study presents an enhanced LPR detection algorithm for the new Egyptian licenses plate. The detection enhancement is done using Stroke Width Transform algorithm to extract letters from candida te areas combined with Fuzzy ARTMAP classifier. Stroke Width Transform (SWT) is a state of art algo rithm developed by Microsoft Research Lab for detecting text in natural scene, it seeks to find t he value of stroke width for each image pixel and demonstrate its use on the task of text detection i n natural images. This study is focusing on detecti ng Arabic letters in the candidate license plate area using SWT image map instead of binary image map where not all Arabic letters have uniformly stroke width and some letters have a dot above and below it. The proposed model shows 26% detection accuracy enhancement than conventional LPR systems (Sobel Edge detection with binary image map using template matching technique).

Online Mixture of Univariate Linear Regression Models for Adaptive Soft Sensors

This paper proposes a mixture of univariate linear regression models (MULRM) to be applied in time-varying scenarios, and its application to soft sensor problems. Offline and online solutions of MULRM will be obtained using the Expectation-Maximization Algorithm. A forgetting factor will be introduced in the online solution to discount the information of already learned data, so that it can be applied in time varying settings. The solution of the proposed method allows its online and recursive application in any regression problem, without the necessity of storing any past value of data. The recursive solution of the MULRM will then be applied in two time-varying real-world prediction problems. The proposed method is compared with four state of art algorithms. In all the experiments, the proposed method always exhibits the best prediction performance.

OANTALG: An Orientation Based Ant Colony Algorithm for Mobile Ad Hoc Networks

Mobile ad hoc (MANET) network is collection of nodes, which establish communication among moving nodes in a decentralized way without the use of any fixed infrastructure. Due to unpredictable network topological changes, routing in MANET is a challenging task as it requires a specialized approach to handle these changes due to the random movement of nodes. The routing protocol designed for MANETs should be able to detect and maintain route(s) between the source and the destination nodes in an efficient manner to handle the above defined issues. In this direction, ant colony algorithm is an important category of meta-heuristics techniques, which can provide an efficient solution to many engineering problems. But most of the existing ant colony algorithms explore the search space without initial directions, which lead to the risk of having local optima. To address this issue, in the present paper, we have been motivated and inspired by our previous work (Kumar et al. in Simul Model Pract Theory 19(9):1933---1945, 2011) in which the orientation factor was not considered, and the ant algorithm was applied for service selection in wireless mesh networks (WMNs). But in the current proposal, we have considered the orientation factor and applied the same in MANETs. Hence keeping this point in view, we propose an orientation based ant algorithm (OANTALG) for Routing in MANETs in which the selection of destination nodes and the exchange of ants (agents) between the source and the destination is based upon the orientation factor. During the movement of ants, the pheromone tables and the data structures are created that record the ants trip time between the nodes through which ants make a move. An efficient algorithm for orientation based routing has also been designed in the proposed scheme. The results obtained show that the proposed algorithm performs better than the other state of art algorithms, which are traditional and other ant based algorithms such as AODV, DSR, and HOPNET with respect to various performance metrics such as number of data packets send, throughput, jitter and path length. Simulation results show that OANTALG can send 1.02, 1.44, 1.61 times more number of data packets than AODV, DSR, and HOPNET, respectively. The throughput in OANTALG is 1.79, 30.69, and 48 % more than AODV, DSR and HOPNET, respectively. Packet drop ratio has also been reduced in the proposed OANTALG algorithm as compared to AODV and DSR. Average Jitter is also reduced by 42, 256 and 26.3 % from AODV, DSR and HOPNET, respectively. Average path length of OANTALG is 1.021 and 1.62 times less than AODV and DSR, respectively.

Towards the online learning with Kernels in classification and regression

In this paper, optimization models and algorithms for online learning with Kernels (OLK) in classification and regression are proposed in a reproducing Kernel Hilbert Space (RKHS) by solving a constrained optimization model. The “Forgetting” factor in the model makes it possible that the memory requirement of the algorithm can be bounded as the learning process continues. The applications of the proposed OLK algorithms in classification and regression show their effectiveness in comparing with the state of art algorithms.

Linear discriminant projection embedding based on patches alignment

Dimensionality reduction is often required as a preliminary stage in many data analysis applications. In this paper, we propose a novel supervised dimensionality reduction method, called linear discriminant projection embedding (LDPE), for pattern recognition. LDPE first chooses a set of overlapping patches which cover all data points using a minimum set cover algorithm with geodesic distance constraint. Then, principal component analysis (PCA) is applied on each patch to obtain the data's local representations. Finally, patches alignment technique combined with modified maximum margin criterion (MMC) is used to yield the discriminant global embedding. LDPE takes both label information and structure of manifold into account, thus it can maximize the dissimilarities between different classes and preserve data's intrinsic structures simultaneously. The efficiency of the proposed algorithm is demonstrated by extensive experiments using three standard face databases (ORL, YALE and CMU PIE). Experimental results show that LDPE outperforms other classical and state of art algorithms.

Finding Frequent Closed Itemsets in Sliding Window in Linear Time

One of the most well-studied problems in data mining is computing the collection of frequent itemsets in large transactional databases. Since the introduction of the famous Apriori algorithm [14], many others have been proposed to find the frequent itemsets. Among such algorithms, the approach of mining closed itemsets has raised much interest in data mining community. The algorithms taking this approach include TITANIC [8], CLOSET+ [6], DCI-Closed [4], FCI-Stream [3], GC-Tree [15], TGC-Tree [16] etc. Among these algorithms, FCI-Stream, GC-Tree and TGC-Tree are online algorithms work under sliding window environments. By the performance evaluation in [16], GC-Tree [15] is the fastest one. In this paper, an improved algorithm based on GC-Tree is proposed, the computational complexity of which is proved to be a linear combination of the average transaction size and the average closed itemset size. The algorithm is based on the essential theorem presented in Sect. 4.2. Empirically, the new algorithm is several orders of magnitude faster than the state of art algorithm, GC-Tree.