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13th IFIP WG 6.11 Conference on e-Business, e-Services, and e-Society (I3E)

作者： Luo, Ying Fu, Haiyan Hainan Normal Univ Sch Informat Sci Technol Haikou Peoples R China

ISBN: (纸本)9783662455265;9783662455258

k-means is a classic, the division of the clustering algorithm, apply to the classification of the globular data. According to the initial clustering center, this paper comprehensive consideration the characteristics of various Hierarchical cluster algorithms and choose the appropriate Hierarchical cluster algorithm to improve k-means, and combined with Hainan Green Tangerine Peel cluster analysis of data which is compared experiments. The results indicate that the improved algorithm have increasing the distance between classes with each others, get a stable of cluster results and better implementation data mining. Finally to summary the two algorithms and the further research direction.

关键词： clustering modified k-means algorithm initial clustering center average link

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AN ACCURATE 3D LIVER SEGMENTATION METHOD FOR SELECTIVE INTERNAL RADIATION THERAPY USING A modified k-means algorithm AND PARALLEL COMPUTING

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INTERNATIONAL JOURNAL OF INNOVATIVE COMPUTING INFORMATION AND CONTROL 2012年第10A期8卷 6515-6538页

作者： Goryawala, Mohammed Guillen, Magno R. Gulec, Seza Barot, Tushar Suthar, Rekha Bhatt, Ruchir Mcgron, Anthony Adjouadi, Malek Florida Int Univ Dept Biomed Engn Miami FL 33174 USA Florida Int Univ Dept Elect & Comp Engn Miami FL 33174 USA Florida Int Univ Herbert Wertheim Coll Med Miami FL 33174 USA

3-D liver segmentation is vital in computer-assisted surgery applications such as minimal invasive surgery, targeted drug delivery,tumor resection, and donor transplantation. This study describes development and evaluation of a novel liver segmentation paradigm in support of Selective Internal Radiation Therapy (SIRT). Since segmentation accuracy and computational simplicity are the two key features for evaluation, the proposed method couples a modified k-means based segmentation and localized-contouring algorithm to obtain segmentation with high accuracy, based on an optimal number of slices. Furthermore, parallel computing is used to reduce the high computational load required of the process. Minimal manual interaction was required in the form of initialization with no correction or adjustment done during or after the process completion. Five rounds of experiments were performed to determine the accuracy and computational performance of the segmentation algorithm. Results were assessed by comparing volumes obtained from the segmentation algorithm to those obtained by manual segmentation done by experts. Statistical analysis is also carried out to determine if the same accuracy is obtained during multiple runs of the dataset and to determine if the manual initialization has any impact on the accuracy of the results. An average accuracy of 98.27% was achieved in estimating the liver volumes with consistent results obtained in various runs and independently of the user initializing the task. A reduction of 78% in computational time was accomplished by the parallel computing techniques in support of the lengthy segmentation process. Since SIRT requires accurate calculation of the liver volume, this new method provides highly accurate and computationally efficient process required of such challenging clinical requirements.

关键词： Image segmentation 3D reconstruction High performance computing modified k-means algorithm Contouring Performance metrics

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Local field potential-based brain-machine interface to inhibit epileptic seizures by spinal cord electrical stimulation

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BIOMEDICAL PHYSICS & ENGINEERING EXPRESS 2025年第1期11卷 2024 Nov 12页

作者： Cerqueira, Erika Maria Garcia de Medeiros, Raquel Emanuela Fiorin, Fernando da Silva de Arujo e Silva, Mariane Lima, Ramon Hypolito Dantas, Andre Felipe Oliveirade Azevedo Rodrigues, Abner Cardoso Delisle-Rodriguez, Denis Edmond & Lily Safra Int Inst Neurosci St Dumont Inst BR-59288899 Macaiba Brazil

*** study proposes a closed-loop brain-machine interface(BMI)based on spinal cordstimulation to inhibit epileptic seizures, applying a semi-supervised machine learning approach thatlearns from Local Field Potential(LFP)patterns acquired on the pre-ictal(preceding the seizure)*** epochs from the hippocampus and motor cortex are band-passfiltered from1 to 13 Hz, to obtain the time-frequency representation using the continuous Wavelet transform, andsuccessively calculate the phase lock values(PLV). As a novelty, theZ-score-based PLV normalizationusing both modifiedk-means and Davies-Bouldin's measure for clustering is proposed ***, a generic seizure's detector is calibrated for detecting seizures on the normalized PLV,and enables the spinal cord stimulation for periods of 30 s in a closed-loop, while the BMI systemdetects seizure events. To calibrate the proposed BMI, a dataset with LFP signals recorded onfiveWistar rats during basal state and epileptic crisis was used. The epileptic crisis was induced by injectingpentylenetetrazol(PTZ). Afterwards, two experiments without/with our BMI were carried out,inducing epileptic crisis by PTZ in Wistar *** *** seizure events of high LFPamplitudes and long time periods were observed in the rat, when the BMI system was not used. Incontrast, short-time seizure events of relative low intensity were observed in the rat, using theproposed BMI. The proposed system detected on unseen data the synchronized seizure activity in thehippocampus and motor cortex, provided stimulation appropriately, and consequently decreasedseizure ***-frequency LFP signals from the hippocampus and motor cortex,and cord spinal stimulation can be used to develop accurate closed-loop BMIs for early epilepticseizures inhibition, as an alternative treatment.

关键词： brain-machine interface clustering epilepsy ictal local field potential seizure detection modified k-means algorithm

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A modified k-means-based cluster head selection and Philippine eagle optimization-based secure routing for MANET

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JOURNAL OF SUPERCOMPUTING 2023年第9期79卷 10481-10504页

作者： Saravanan, R. Suresh, k. Arumugam, S. S. Rajalakshmi Inst Technol Dept Artificial Intelligence & Data Sci Chennai Tamilnadu India SRM Inst Sci & Technol Sch Comp Dept Computat Intelligence Kattankulathur India Saveetha Inst Med & Tech Sci Inst Comp Sci & Engn Saveetha Sch Engn Chennai Tamilnadu India

Decentralized mobile ad hoc networks (MANET) is a self-organizing and adaptive method comprised of multiple mobile nodes. Due to the dynamic and flexible nature of network topology, wireless communication links face major issues based on trust and security. The researchers came to know that MANET suffers greatly from the quality of service (QoS). So, MANET recommends the employment of an intrusion detection system to identify intrusions, namely attacks, message eavesdropping, and untrustworthy routing. Intrusion detection is considered a significant task to enhance network security from unauthorized access. The main issue in MANET is the limited battery source of sensor nodes which causes communication blockage after a certain transmission range. Therefore, to offer continuous data transmission without any intrusion, in this article, we introduced a secure high-yielding carry-out approach "modified k-means Philippine eagle (MkMPE) approach." Initially, for the determination of the ideal cluster head (CH) from the cluster group, the modified k-means algorithm (MkM) employs for communicating information to the destination node without any packet loss. The CH is selected according to the values of credits namely indirect, recent trust, and direct trust. Then, with the support of Philippine eagle (PE) optimization, the energy-efficient and secure paths are discovered to transfer the information with minimum delay. To examine the potential ability of the proposed MkMPE approach, various state-of-the-art methods are compared with the suggested method in terms of evaluation metrics, namely energy consumption, network lifetime, end-to-end delay, latency, packet delivery ratio, throughput, and detection rate. This proposed MkMPE approach achieved a maximum detection rate of about 78%, a throughput rate of 0.86 bps, and minimum latency of 0.09 s concerning a maximum time interval of 45 ms.

关键词： Mobile ad hoc network Cluster head Optimal routing modified k-means algorithm Philippine eagle optimization Trust

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Genetic optimization of radial basis probabilistic neural networks

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INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE 2004年第8期18卷 1473-1499页

作者： Zhao, WB Huang, DS Du, JY Wang, LM Artillery Acad People Liberat Army Hefei 230022 Anhui Peoples R China Chinese Acad Sci Inst Intelligent Machines Beijing 100864 Peoples R China

This paper discusses using genetic algorithms (CA) to optimize the structure of radial basis probabilistic neural networks (RBPNN), including how to select hidden centers of the first hidden layer and to determine the controlling parameter of Gaussian kernel functions. In the process of constructing the genetic algorithm, a novel encoding method is proposed for optimizing the RBPNN structure. This encoding method can not only make the selected hidden centers sufficiently reflect the key distribution characteristic in the space of training samples set and reduce the hidden centers number as few as possible, but also simultaneously determine the optimum controlling parameters of Gaussian kernel functions matching the selected hidden centers. Additionally, we also constructively propose a new fitness function so as to make the designed RBPNN as simple as possible in the network structure in the case of not losing the network performance. Finally, we take the two benchmark problems of discriminating two-spiral problem and classifying the iris data, for example, to test and evaluate this designed GA. The experimental results illustrate that our designed CA can significantly reduce the required hidden centers number, compared with the recursive orthogonal least square algorithm (ROLSA) and the modified k-means algorithm (MkA). In particular, by means of statistical experiments it was proved that the optimized RBPNN by our designed GA, have still a better generalization performance with respect to the ones by the ROLSA and the MkA, in spite of the network scale having been greatly reduced. Additionally, our experimental results also demonstrate that our designed CA is also suitable for optimizing the radial basis function neural networks (RBFNN).

关键词： genetic algorithms radial basis probabilistic neural networks hidden centers recursive orthogonal least square algorithm modified k-means algorithm

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k-means for shared frailty models

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BMC MEDICAL RESEARCH METHODOLOGY 2022年第1期22卷 11-11页

作者： Govindarajulu, Usha Bedi, Sandeep Icahn Sch Med Mt Sinai Dept Populat Hlth & Policy Ctr Biostat One Gustave Levy Pl New York NY 10029 USA

Background The purpose of this research was to see how the k-means algorithm can be applied to survival analysis with single events per subject for defining groups, which can then be modeled in a shared frailty model to further allow the capturing the unmeasured confounding not already explained by the covariates in the model. Methods For this purpose we developed our own k-means survival grouping algorithm to handle this approach. We compared a regular shared frailty model with a regular grouping variable and a shared frailty model with a k-means grouping variable in simulations as well as analysis on a real dataset. Results We found that in both simulations as well as real data showed that our k-means clustering is no different than the typical frailty clustering even under different situations of varied case rates and censoring. It appeared our k-means algorithm could be a trustworthy mechanism of creating groups from data when no grouping term exists for including in a frailty term in a survival model or comparing to an existing grouping variable available in the current data to use in a frailty model.

关键词： modified k-means algorithm Shared frailty Survival analysis Heterogeneity

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The Bag-of-Features algorithm for Practical Applications Using the MySQL Database 15th

The Bag-of-Features Algorithm for Practical Applications Usi...

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15th International Conference on Artificial Intelligence and Soft Computing (ICAISC)

作者： Gabryel, Marcin Czestochowa Tech Univ Inst Computat Intelligence Al Armii Krajowej 36 PL-42200 Czestochowa Poland

ISBN: (纸本)9783319393841

This article presents a modification of the Bag-of-Features method (also known as a Bag-of-Words or Bag-of-Visual-Words method) used for image recognition in practical applications using a relational database. Our approach utilises a modified k-means algorithm, owing to which the number of clusters is automatically selected, and also the majority votes method when making decisions in the classification process. The algorithm can be used both methods in an SQL Server database or a commonly-used MySQL one. The proposed approach minimises the necessity to use additional algorithms and/or classifiers in the image classification process. This makes it possible to significantly simplify computations and use the SQL language.

关键词： Bag-of-features Image classification Image database modified k-means algorithm

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A Bag-of-Features algorithm for Applications Using a NoSQL Database 2nd

A Bag-of-Features Algorithm for Applications Using a NoSQL D...

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22nd International Conference on Information and Software Technologies (ICIST)

作者： Gabryel, Marcin Czestochowa Tech Univ Inst Computat Intelligence Al Armii Krajowej 36 PL-42200 Czestochowa Poland

ISBN: (纸本)9783319462547;9783319462530

In this paper we present a Bag-of-Words (also known as a Bag-of-Features) method developed for the use of its implementation in NoSQL databases. When working with this algorithm special attention was brought to facilitating its implementation and reducing the number of computations to a minimum so as to use what the database engine has to offer to its maximum. The algorithm is presented using an example of image storing and retrieving. In this case it proves necessary to use an additional step of preprocessing, during which image characteristic features are retrieved and to use a clustering algorithm in order to create a dictionary. We present our own k-means algorithm which automatically selects the number of clusters. This algorithm does not comprise any computationally complicated classification algorithms, but it uses the majority vote method. This makes it possible to significantly simplify computations and use the Javascript language used in a common NoSQL database.

关键词： NoSQL database Image classification Bag-of-Features modified k-means algorithm

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A New Data Preparation Method Based on Clustering algorithms for Diagnosis Systems of Heart and Diabetes Diseases

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JOURNAL OF MEDICAL SYSTEMS 2014年第5期38卷 48-48页

作者： Yilmaz, Nihat Inan, Onur Uzer, Mustafa Serter Selcuk Univ Fac Engn Elect Elect Engn Dept Konya Turkey Selcuk Univ Fac Engn Dept Comp Engn Konya Turkey

The most important factors that prevent pattern recognition from functioning rapidly and effectively are the noisy and inconsistent data in databases. This article presents a new data preparation method based on clustering algorithms for diagnosis of heart and diabetes diseases. In this method, a new modified k-means algorithm is used for clustering based data preparation system for the elimination of noisy and inconsistent data and Support Vector Machines is used for classification. This newly developed approach was tested in the diagnosis of heart diseases and diabetes, which are prevalent within society and figure among the leading causes of death. The data sets used in the diagnosis of these diseases are the Statlog (Heart), the SPECT images and the Pima Indians Diabetes data sets obtained from the UCI database. The proposed system achieved 97.87 %, 98.18 %, 96.71 % classification success rates from these data sets. Classification accuracies for these data sets were obtained through using 10-fold cross-validation method. According to the results, the proposed method of performance is highly successful compared to other results attained, and seems very promising for pattern recognition applications.

关键词： Heart and Diabetes diseases Support Vector Machine modified k-means algorithm

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Towards an effective deep learning-based intrusion detection system in the internet of things

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TELEMATICS AND INFORMATICS REPORTS 2022年 7卷

作者： Pampapathi, B. M. Guptha, M. Nageswara Hema, M. S. Sri Venkateshwara Coll Engn Dept Comp Sci & Engn Bengaluru Karnataka India Anurag Univ Dept Informat Technol Hyderabad Telangana India

Distributed Sensor Networks play a vital role in the day-to-day world of computing applications, from the cloud to the Internet of Things (IoT). These computing applications devices are normally attached with the microcontrollers like Sensors, actuators, and Adriano network connectivity. Defensive network with an Intrusion Detection System thus serves as the need of modern networks. Despite decades of inevitable development, the Intrusion Detection System is still a challenging research area as the existing Intrusion Detection System operates using signature-based techniques rather than anomaly detection. The existing Intrusion Detection System are thus facing challenges for improvement in Intrusion Detection, Handling heterogeneous data sources is hard for discovering zero-day attacks in IoT networks. This paper presents Filtered Deep Learning Model for Intrusion Detection with a Data Communication approach. The proposed model is composed of five phases: Initialization of Sensor Networks, Cluster Formation in addition to Cluster Head Selection, Connectivity, Attack Detection, and Data Broker. The proposed Model for Intrusion Detection was found to outperform the existing Deep Learning Neural Network and Artificial Neural Network. Experimental results showed a better result of 96.12% accuracy than the dominant algorithms.

关键词： Filtered deep learning neural network modified k-means algorithm Advanced message queuing protocol Levy flight based Elephant herding optimization algorithm Message queuing telemetry transport

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