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Brain MR Image Segmentation with Spatial Constrained k-mean algorithm and Dual-Tree Complex Wavelet Transform

JOURNAL OF MEDICAL SYSTEMS 2014年第9期38卷 1-6页

作者： Zhang, Jingdan Jiang, Wuhan Wang, Ruichun Wang, Le Shenzhen Inst Informat Technol Dept Elect & Commun Shenzhen 518172 Peoples R China Shenzhen Inst Informat Technol Dept Basic Publ Course Shenzhen 518172 Peoples R China

In brain MR images, the noise and low-contrast significantly deteriorate the segmentation results. In this paper, we propose an automatic unsupervised segmentation method integrating dual-tree complex wavelet transform (DT-CWT) with k-mean algorithm for brain MR image. Firstly, a multi-dimensional feature vector is constructed based on the intensity, the low-frequency subband of DT-CWT and spatial position information. Then, a spatial constrained k-mean algorithm is presented as the segmentation system. The proposed method is validated by extensive experiments using both simulated and real T1-weighted MR images, and compared with the state-of-the-art algorithms.

关键词： Medical image segmentation MR images Dual-tree complex wavelet transform k-mean algorithm

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Estimation of Model order for Electromechanical Modes using Sequential k-mean algorithm 21

Estimation of Model order for Electromechanical Modes using ...

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21st IEEE Mediterranean Electrotechnical Conference (IEEE MELECON)

作者： Sandeep, Jangam Rai, Shekha Natl Inst Technol Dept Elect Engn Rourkela India

ISBN: (数字)9781665442800

ISBN: (纸本)9781665442800

One of the most pressing priorities of smart grid adoption is monitoring and consistent recognition of power quality issues. Small signal oscillations are one of the major concerns in the present-day world due to the increase in the interconnection of multiple power sources. This paper proposes a clustering-based sequential k-mean algorithm (S-k-mean) to detect the number of low-frequency oscillatory modes in the given power system signal. It is shown that the suggested algorithm can reliably estimate the number of low-frequency modes present in a signal as it considers all the significant eigenvalues which represents the energy levels of dominant modes. The suggested algorithm works even when the signal is subjected to high noise levels. The robustness of the proposed algorithm over other techniques is validated through synthetic signals at various noise level. The efficacy of the proposed scheme is further shown using a two-area network simulated through MATLAB and real-time digital simulator (RTDS) and it is also tested with a real-time signal obtained from the Western Electricity Coordination Council (WECC).

关键词： k-mean algorithm Autocorrelation matrix (AC matrix) eigenvalues Phasor measurement unit (PMU)

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k-mean and double cross-validation algorithm for LS-SVM in Sasang typology classification

K-mean and double cross-validation algorithm for LS-SVM in S...

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IEEE International Conference on Automation and Logistics

作者： Zhang, Qian Lee, ki Jung Whangbo, Taeg keun Kyungwon Univ Dept Comp Sci Songnam South Korea

ISBN: (纸本)9781424415304

The Sasang typology is the traditional typology theory in Oriental Medicine. The medical typology distributes people into four types based on their traits: Tae-Yang, So-Yang, Tae-Eum and So-Eum type. In the paper, we design a model for Sasang typology classification based on the least squares support vector machines (LS-SVM). We use k-mean algorithm for decision the feature index from the side face, then we add the side face ratios into our feature space for improvement. The choice of resample method is very important for parameters' optimization in SVM and it will influence the system's stability. We propose a novel resampling algorithm called double cross-validation through comparing the cross-validation with bootstrap approach. The result shows that the established model under double cross-validation algorithm based on LS-SVM is more robust with high performance and suffices for the requirements of control and optimization for classification processes.

关键词： double cross-validation least squares support vector machines bootstrap Sasang typology classification k-mean algorithm

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k-mean alignment for curve clustering

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COMPUTATIONAL STATISTICS & DATA ANALYSIS 2010年第5期54卷 1219-1233页

作者： Sangalli, Laura M. Secchi, Piercesare Vantini, Simone Vitelli, Valeria Politecn Milan MOX Dipartimento Matemat I-20133 Milan Italy

The problem of curve clustering when curves are misaligned is considered. A novel algorithm is described, which jointly clusters and aligns curves. The proposed procedure efficiently decouples amplitude and phase variability;in particular, it is able to detect amplitude clusters while simultaneously disclosing clustering structures in the phase, pointing out features that can neither be captured by simple curve clustering nor by simple curve alignment. The procedure is illustrated via simulation studies and applications to real data. (C) 2010 Elsevier B.V. All rights reserved.

关键词： Functional data analysis Curve alignment Curve clustering k-mean algorithm

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A combination of objective functions and hybrid krill herd algorithm for text document clustering analysis

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2018年 73卷 111-125页

作者： Abualigah, Laith Mohammad khader, Ahamad Tajudin Hanandeh, Essam Said Univ Sains Malaysia Sch Comp Sci George Town 11800 Malaysia Zarqa Univ Dept Comp Informat Syst POB 13132 Zarqa Jordan

krill herd (kH) algorithm is a novel swarm-based optimization algorithm that imitates krill herding behavior during the searching for foods. It has been successfully used in solving many complex optimization problems. The potency of this algorithm is very high because of its superior performance compared with other optimization algorithms. Hence, the applicability of this algorithm for text document clustering is investigated in this work. Text document clustering refers to the method of clustering an enormous amount of text documents into coherent and dense clusters, where documents in the same cluster are similar. In this paper, a combination of objective functions and hybrid kH algorithm, called, MHkHA, is proposed to solve the text document clustering problem. In this version, the initial solutions of the kH algorithm are inherited from the k-mean clustering algorithm and the clustering decision is based on two combined objective functions. Nine text standard datasets collected from the Laboratory of Computational Intelligence are used to evaluate the performance of the proposed algorithms. Five evaluation measures are employed, namely, accuracy, precision, recall, F-measure, and convergence behavior. The proposed versions of the kH algorithm are compared with other well-known clustering algorithms and other thirteen published algorithms in the literature. The MHkHA obtained the best results for all evaluation measures and datasets used among all the clustering algorithms tested.

关键词： Combination of objective functions Hybridization krill herd algorithm k-mean algorithm Text document clustering

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Enhancing the Swimmer Movement Techniques Using Cloud Computing and Artificial Intelligence

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MOBILE NETWORkS & APPLICATIONS 2024年第2期29卷 433-447页

作者： Liu, Xurui Zhang, Guobao Beijing Sport Univ China Swimming Coll Beijing Peoples R China

Nowadays, miniature sensors can communicate with intelligent tools and pervasive computing devices to analyze and assess sports and physical activities. These sensors allow us to collect data from various physical activities using the underlying framework from anywhere, at any time, and in any location. However, these devices generate a large volume of data. Thus, having as much data as possible to assess a sports person's physical activity is critical. Wearable devices with tiny sensors, edge and cloud computing, and artificial intelligence are the pillars that are capable to change the current level of analysing physical activities in sports. Based on these features, this paper aims to combine these three pillars of physical activities to enhance the athlete's profile by predicting their physique, and recommend special training. For this purpose, a novel framework is proposed that allows us to generate a dataset from the realistic ecological conditions. To ensure the efficiency of this work, we have conducted a comprehensive literature on sports and physical activities. We underlined the limitations of the data collection, sensors, and processing techniques from literature. We hypothesize that the acquisition of data, continuous measurement, and analysis of different processes will end up in a more reliable model with the help of edge and cloud computing devices that allow the data to stream without restriction. Personalized training and profile-type approaches are used for swimmer athletes in this paper. The experimental results show that the suggested integrated method provides significant data to coaches and players, enabling focused training, performance optimisation, and improved athlete healthcare.

关键词： Edge computation Cloud computing Artificial Intelligence k-mean algorithm Convolutional Neural Network (CNN)

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Machine Learning Based Blind Decoding for Space-Time Line Code (STLC) Systems

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2019年第5期68卷 5154-5158页

作者： Joung, Jingon Jung, Bang Chul Chung Ang Univ Sch Elect & Elect Engn Seoul 06974 South Korea Chungnam Natl Univ Dept Elect Engn Daejeon 34134 South Korea

In this correspondence paper, a novel machine learning (clustering) based blind decoding method is proposed for the space-time line code (STLC) systems without the information of modulation size and channels. The number of clusters, which is equivalent to the modulation size, is estimated from the received signals by using a k-mean cluster validation metrics, such as silhouette score and Davies-Bouldin index, and the cluster indices are directly mapped to the transmitted binary information bits. To improve the clustering performance, received signal normalization and the initial centroids of the clusters are designed by exploiting the combined STLC signal structure. From the numerical results, it is verified that the proposed blind decoding method can achieve near-coherent decoding performance with either small-size modulation, low noise at the receiver, or a large number of transmit antennas.

关键词： Machine learning clustering k-mean algorithm blind decoding space-time line code

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RETRACTED: Analysing the User Actions and Location for Identifying Online Scam in Internet Banking on Cloud (Retracted Article)

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WIRELESS PERSONAL COMMUNICATIONS 2022年第SUPPL 1期127卷 5-6页

作者： Lazar, Arokia Jesu Prabhu Sengan, Sudhakar Cavaliere, Luigi Pio Leonardo Nadesan, Thillaiarasu Sharma, Deepesh Gupta, Mukesh kumar Palaniswamy, Thangam Vellingiri, Mahendiran Sharma, Dilip kumar Subramani, Thirukumaran CMR Inst Technol Dept Comp Sci & Engn Hyderabad 501401 Telangana India PSN Coll Engn & Technol Dept Comp Sci & Engn Tirunelveli 627152 Tamil Nadu India Univ Foggia Dept Econ Via Sabotino 4 I-71016 San Severo FG Italy REVA Univ Sch Comp & Informat Technol Bangalore 560064 Karnataka India Deenbandhu Chhotu Ram Univ Sci & Technol Dept Elect Engn Sonepat 131039 Haryana India Swami Keshvanand Inst Technol Management & Gramothan SKIT Comp Sci & Engn Jaipur 302017 Rajasthan India King Abdulaziz Univ Dept Elect & Comp Engn Jeddah Saudi Arabia Jaypee Univ Engn & Technol Dept Math Guna 473226 Madhya Pradesh India KL Univ Dept Comp Applicat Vijayawada India

Although money laundering does seem to be a new problem, the financial crisis generally occurs during an economic recession compared with standard economic development periods. Due to user fraud transactions and tolerance, they encountered numerous issues, particularly with internet banking fraud. Modernization and an enormous impact on online shopping have caused a significant increase in card payments (like credit, debit) worldwide. Increased acceptance and affordability of i-Banking services for online ordering have benefited customers personally, but it has also increased hackers' number. Fraud entails a significant financial level of risk that can adversely affect an economic entity's profit margins and image. In-fractions of a second, the system is capable of Online Transaction Fraud Detection. This method recommends an unchecked method for dynamically profiling a customer's behavioral patterns. This method's importance is that secure authentication credentials are not exposed to banks and cloud authorization servers but enable them to authenticate their remote access. The received transactions are then evaluated by comparing to the customer ID to identify abnormalities, upon which the appropriate warnings are output. This paper aims to provide such a high-level outline of how new technologies can enhance fraud observation within a publicly or privately economic unit.

关键词： User fraud detection i-Banking k-mean algorithm Data security and privacy Public cloud

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MOMHR: A Dynamic Multi-hop Routing Protocol for WSN Using Heuristic Based Multi-objective Function

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WIRELESS PERSONAL COMMUNICATIONS 2020年第2期111卷 883-907页

作者： Vinodhini, R. Gomathy, C. SRM Inst Sci & Technol ECE Dept Chennai 603203 Tamil Nadu India

Wireless sensor networks (WSNs) are the networks which mainly focuses on the applications and are composed of considerable sensor nodes. The use of energy in a valuable way is considered as a feature for the design structure of WSNs. In the WSNs, the nodes power sources are limited. Moreover, because of this, there is a must for a different approach regarding the energy availability and this is mainly for long distance communication, for this multi-hop (MH) systems are chosen. Even though MH decreases the energy cost used by all node along the path, however, to obtain the best routing path among nodes is yet an interesting subject. In this article, we present a multi-objective multi-hop routing (MOMHR) protocol for optimal data routing to gain the network lifetime. In the first phase, the k-means algorithm is applied to split the nodes into k clusters. Next, the artificial bee colony optimisation algorithm is applied to obtain the best possible CH within each cluster then using a multi-objective functions finally the multi-hop routing protocol finds a multihop path with minimum communication cost from the node to the base station. Our proposed method is simulated in MATLAB platform and compared with two recent protocols such as low energy adaptive clustering hierarchy and energy efficient centroid-based routing protocol. The execution of the proposed MOMHR protocols using multi-objective function is evaluated using metrics such as energy efficiency and network lifetime.

关键词： Wireless sensor networks Multi-hop routing Ant bee colony optimisation k-mean algorithm Multi-objective function

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Unsupervised Learning Approach for Clustering Source Code based on Functionalities

Unsupervised Learning Approach for Clustering Source Code ba...

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International Conference on Decision Aid Sciences and Application (DASA)

作者： Ifham, Mohamed kumara, B. T. G. S. kuhaneswaran, Banujan Sabaragamuwa Univ Sri Lanka Dept Comp & Informat Syst Belihuloya Sri Lanka

ISBN: (纸本)9781665416344

The source codes of a software system are one of the most valuable resources in software development. A developer spends more time and money on development if they start coding from fresh for each similar functionality project. Topic modelling is crucial for software reuse since it can be difficult for developers to remove obsolete source code from large software systems with a lot of code. Topic modelling techniques has been used to evaluate and model source codes in several ways. Several studies on this domain have been used to extract topics from source codes using various statistical approaches and methodologies. These topic extraction methods are interconnected, and if software best practices aren't followed in older systems, it may ensue unreliability of the outcome. In response to these findings, the author conducted a study in which he extracted source code using a java parser library and predicted the source code functionality name using an unsupervised learning approach such as k-mean algorithm and Expectation Maximization (EM) clustering approach. This is the first effort to develop a clustering model to predict the semantic function name of source code using an unsupervised learning approach and compare the algorithms to get the optimal model.

关键词： k-mean algorithm Parser library Unsupervised learning Software reuse Expectation maximization clustering

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