Fuzzy clustering using RSIO-FCM ppt
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Fuzzy c-means clustering using Random Sampling with Iterative Optimization using Apache Spark and comparision among rseFCM , RSIO-FCM and LFCM..and data analytics using R.
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질문이 있으면 언제든지 연락주세요!
공부한 것을 바탕으로 작성한 ppt입니다.
출처는 슬라이드 마지막에 있습니다!
Please, feel free to contact me, if you have any questions!
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email: kimnanhee97@gmail.com
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The document provides an introduction and overview of the fuzzy c-means clustering algorithm. It discusses key aspects of the algorithm including:
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질문이 있으면 언제든지 연락주세요!
공부한 것을 바탕으로 작성한 ppt입니다.
출처는 슬라이드 마지막에 있습니다!
Please, feel free to contact me, if you have any questions!
github: https://github.com/nh9k
email: kimnanhee97@gmail.com
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http://www.embedded-vision.com
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The document discusses anomaly detection techniques using deep one-class classifiers and generative adversarial networks (GANs). It proposes using an autoencoder to extract features from normal images, training a GAN on those features to model the distribution, and using a one-class support vector machine (SVM) to determine if new images are within the normal distribution. The method detects and localizes anomalies by generating a binary mask for abnormal regions. It also discusses Gaussian mixture models and the expectation-maximization algorithm for modeling multiple distributions in data.
5.MLP(Multi-Layer Perceptron)
This document discusses the basics of artificial neural networks including multi-layer perceptrons (MLPs). It explains that MLPs use multiple hidden layers between the input and output layers to extract meaningful features from the data. The document also covers topics like training neural networks using backpropagation and stochastic gradient descent, the use of mini-batches to speed up training, and common activation and loss functions.
Chapter 11 cluster advanced : web and text mining
This document provides an overview of advanced clustering analysis techniques discussed in Chapter 11 of the textbook "Data Mining: Concepts and Techniques". It begins with an introduction to probability model-based clustering and fuzzy clustering. It then discusses using the EM algorithm for fuzzy clustering and fitting univariate Gaussian mixture models. Next, it covers challenges with clustering high-dimensional data and methods for subspace clustering. It also briefly introduces clustering graphs and network data as well as clustering with constraints. The document concludes with an outline of the chapter.
Chapter 11 cluster advanced, Han & Kamber
This document summarizes various clustering algorithms including:
- K-means clustering which partitions objects into k groups by iteratively updating cluster centroids.
- Hierarchical clustering which uses distance metrics to iteratively merge or split clusters in a dendrogram without needing k as input.
- Density-based methods like DBSCAN which group together densely clustered points.
- Probabilistic and model-based clustering which represent clusters as probability distributions like Gaussian mixtures fitted using EM.
B.tech_project_ppt.pptx
Here, we have implemented CNN network in FPGA by incorporating a novel technique of convolution which includes pipelining technique as well as parallelism (by optimizing) between the two.
Trackster Pruning at the CMS High-Granularity Calorimeter
The document discusses approaches for assigning weights to layer clusters in Tracksters to indicate the likelihood of belonging to the same particle or being contaminated. The goal is to develop reproducible code, port a trained model to C, and provide a final report and presentation. Various data representations and machine learning methods are explored, including layer-cluster level, extended layer-cluster level, sequence representations using LSTM and CNN, and graph representations using GCN and adaptive sampling. Performance is evaluated on classification of purity levels. Extended layer-cluster and sequence representations showed improved performance over the basic layer-cluster approach. Notebooks containing the code are described in an appendix.
Cdm anetworkdesign
1) The document discusses CDMA network design and optimization to maximize capacity. It analyzes factors like inter-cell interference, soft handoff, power control, and their effects on capacity.
2) It presents methods to optimize the network using power compensation factors, base station location, and pilot signal power. The optimizations are evaluated on sample networks including a 27-cell network with uniform user distribution and hot spots.
3) The combined optimization of location, pilot power, and mobile transmission power is shown to significantly increase network capacity over individual optimizations, balancing load across all cells.
[Paper] learning video representations from correspondence proposals
Video Understanding, Learning Video Representations from Correspondence Proposals, Action Recognition
Optimal pilot symbol power allocation in lte
This document presents research on optimizing pilot symbol power allocation in LTE systems. The key points are:
1) The researchers derive analytical expressions for optimal pilot symbol power allocation based on maximizing post-equalization signal-to-interference-and-noise ratio (SINR) under imperfect channel knowledge.
2) They analyze zero-forcing equalization and derive the post-equalization SINR expression for a MIMO system with imperfect channel estimation.
3) The researchers also derive mean square error expressions for least squares and linear minimum mean square error channel estimation methods.
4) Simulation results using an LTE simulator validate the analytical model for optimal pilot symbol power allocation.
ADAPTIVE FUZZY KERNEL CLUSTERING ALGORITHM
Fuzzy clustering algorithm can not obtain good clustering effect when the sample characteristic is not obvious and need to determine the number of clusters firstly. For thi0s reason, this paper proposes an adaptive fuzzy kernel clustering algorithm. The algorithm firstly use the adaptive function of clustering number to calculate the optimal clustering number, then the samples of input space is mapped to highdimensional feature space using gaussian kernel and clustering in the feature space. The Matlab simulation results confirmed that the algorithm's performance has greatly improvement than classical clustering algorithm and has faster convergence speed and more accurate clustering results.
ADAPTIVE FUZZY KERNEL CLUSTERING ALGORITHM
Fuzzy clustering algorithm can not obtain good clustering effect when the sample characteristic is not
obvious and need to determine the number of clusters firstly. For thi0s reason, this paper proposes an
adaptive fuzzy kernel clustering algorithm. The algorithm firstly use the adaptive function of clustering
number to calculate the optimal clustering number, then the samples of input space is mapped to highdimensional
feature space using gaussian kernel and clustering in the feature space. The Matlab simulation
results confirmed that the algorithm's performance has greatly improvement than classical clustering algorithm and has faster convergence speed and more accurate clustering results
IJCCI2023.pdf
Semi-Supervised Fuzzy C-Means for Regression
We propose a method to perform regression on partially labeled data, which is based on SSFCM (Semi-Supervised Fuzzy C-Means), an algorithm for semi-supervised classification based on fuzzy clustering. The proposed method, called SSFCM-R, precedes the application of SSFCM with a relabeling module based on target discretization. After the application of SSFCM, regression is carried out according to one out of two possible schemes: (i) the output corresponds to the label of the closest cluster; (ii) the output is a linear combination of the cluster labels weighted by the membership degree of the input. Some experiments on synthetic data are reported to compare both approaches.
IJCCI 15th International joint Conference on Computational Intelligence, 13-15 November, 2023, Rome, Italy
full paper: https://www.researchgate.net/publication/375671573_Semi-Supervised_Fuzzy_C-Means_for_Regression
ENBIS 2018 presentation on Deep k-Means
Slides for the presentation at ENBIS 2018 of "Deep k-Means: Jointly Clustering with k-Means and Learning Representations" by Thibaut Thonet. Joint work with Maziar Moradi Fard and Eric Gaussier.
RLTopics_2021_Lect1.pdf
1. AlphaZero uses self-play reinforcement learning to train a neural network to evaluate board positions and select moves. It trains offline by playing games against itself, using the results to iteratively improve its network.
2. During online play, AlphaZero uses Monte Carlo tree search with the neural network to select moves. It evaluates many random simulations of possible future games to a certain depth, using the network to approximate values beyond that depth.
3. The success of AlphaZero is due to skillfully combining known reinforcement learning techniques like self-play training, neural network function approximation, and Monte Carlo tree search with powerful computational resources.
MLHEP Lectures - day 1, basic track
Introduction to machine learning terminology.
Applications within High Energy Physics and outside HEP.
* Basic problems: classification and regression.
* Nearest neighbours approach and spacial indices
* Overfitting (intro)
* Curse of dimensionality
* ROC curve, ROC AUC
* Bayes optimal classifier
* Density estimation: KDE and histograms
* Parametric density estimation
* Mixtures for density estimation and EM algorithm
* Generative approach vs discriminative approach
* Linear decision rule, intro to logistic regression
* Linear regression
GTC Taiwan 2017 GPU 平台上導入深度學習於半導體產業之 EDA 應用
A deep learning model using convolutional neural networks is proposed for lithography hotspot detection. The model takes layout clip images as input and outputs a prediction of hotspot or non-hotspot. It uses several convolutional and pooling layers to automatically learn features from the images without manual feature engineering. Evaluation shows the deep learning model achieves higher accuracy than previous shallow learning methods that rely on manually designed features.
Support Vector Machine Optimal Kernel Selection
This document discusses selecting the optimal kernel for support vector machines (SVMs) based on different datasets. It provides background on SVMs and how their performance depends on the kernel function used. The document evaluates 4 kernel types (linear, polynomial, radial basis function (RBF), sigmoid) on 3 datasets: heart disease data, digit recognition data, and social network ads data. For each dataset and kernel combination, it reports accuracy, sensitivity, specificity, and kappa statistic metrics from implementing SVMs in R. The linear and RBF kernels generally performed best, with RBF working best for datasets with larger numbers of features like digit recognition data.
A0530106
1) The document discusses techniques for reducing the peak-to-average power ratio (PAPR) of orthogonal frequency division multiplexing (OFDM) signals, including repeated clipping and filtering (RCF) and nonlinear commanding transform (NCT).
2) RCF projects clipping noise into the feasible extension area while removing out-of-band interference through filtering, but suffers from problems like in-band distortion, peak re-growth, and out-of-band radiation.
3) NCT aims to reduce PAPR by compressing peak signals and expanding small signals while maintaining constant average power through optimal parameter selection. The proposed NCT technique outperforms RCF in terms of lower clipping ratio,
Similar to Fuzzy clustering using RSIO-FCM ppt (20)
Trackster Pruning at the CMS High-Granularity Calorimeter
Trackster Pruning at the CMS High-Granularity Calorimeter
[Paper] learning video representations from correspondence proposals
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Recently uploaded
Hematology Analyzer Machine - Complete Blood Count
The CBC machine is a common diagnostic tool used by doctors to measure a patient's red blood cell count, white blood cell count and platelet count. The machine uses a small sample of the patient's blood, which is then placed into special tubes and analyzed. The results of the analysis are then displayed on a screen for the doctor to review. The CBC machine is an important tool for diagnosing various conditions, such as anemia, infection and leukemia. It can also help to monitor a patient's response to treatment.
2008 BUILDING CONSTRUCTION Illustrated - Ching Chapter 02 The Building.pdf
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LLM Fine Tuning with QLoRA Cassandra Lunch 4, presented by Anant
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Null Bangalore | Pentesters Approach to AWS IAM
#Abstract:
- Learn more about the real-world methods for auditing AWS IAM (Identity and Access Management) as a pentester. So let us proceed with a brief discussion of IAM as well as some typical misconfigurations and their potential exploits in order to reinforce the understanding of IAM security best practices.
- Gain actionable insights into AWS IAM policies and roles, using hands on approach.
#Prerequisites:
- Basic understanding of AWS services and architecture
- Familiarity with cloud security concepts
- Experience using the AWS Management Console or AWS CLI.
- For hands on lab create account on [killercoda.com](https://killercoda.com/cloudsecurity-scenario/)
# Scenario Covered:
- Basics of IAM in AWS
- Implementing IAM Policies with Least Privilege to Manage S3 Bucket
- Objective: Create an S3 bucket with least privilege IAM policy and validate access.
- Steps:
- Create S3 bucket.
- Attach least privilege policy to IAM user.
- Validate access.
- Exploiting IAM PassRole Misconfiguration
-Allows a user to pass a specific IAM role to an AWS service (ec2), typically used for service access delegation. Then exploit PassRole Misconfiguration granting unauthorized access to sensitive resources.
- Objective: Demonstrate how a PassRole misconfiguration can grant unauthorized access.
- Steps:
- Allow user to pass IAM role to EC2.
- Exploit misconfiguration for unauthorized access.
- Access sensitive resources.
- Exploiting IAM AssumeRole Misconfiguration with Overly Permissive Role
- An overly permissive IAM role configuration can lead to privilege escalation by creating a role with administrative privileges and allow a user to assume this role.
- Objective: Show how overly permissive IAM roles can lead to privilege escalation.
- Steps:
- Create role with administrative privileges.
- Allow user to assume the role.
- Perform administrative actions.
- Differentiation between PassRole vs AssumeRole
Try at [killercoda.com](https://killercoda.com/cloudsecurity-scenario/)
CHINA’S GEO-ECONOMIC OUTREACH IN CENTRAL ASIAN COUNTRIES AND FUTURE PROSPECT
The rivalry between prominent international actors for dominance over Central Asia's hydrocarbon
reserves and the ancient silk trade route, along with China's diplomatic endeavours in the area, has been
referred to as the "New Great Game." This research centres on the power struggle, considering
geopolitical, geostrategic, and geoeconomic variables. Topics including trade, political hegemony, oil
politics, and conventional and nontraditional security are all explored and explained by the researcher.
Using Mackinder's Heartland, Spykman Rimland, and Hegemonic Stability theories, examines China's role
in Central Asia. This study adheres to the empirical epistemological method and has taken care of
objectivity. This study analyze primary and secondary research documents critically to elaborate role of
china’s geo economic outreach in central Asian countries and its future prospect. China is thriving in trade,
pipeline politics, and winning states, according to this study, thanks to important instruments like the
Shanghai Cooperation Organisation and the Belt and Road Economic Initiative. According to this study,
China is seeing significant success in commerce, pipeline politics, and gaining influence on other
governments. This success may be attributed to the effective utilisation of key tools such as the Shanghai
Cooperation Organisation and the Belt and Road Economic Initiative.
Software Engineering and Project Management - Introduction, Modeling Concepts...
Introduction, Modeling Concepts and Class Modeling: What is Object orientation? What is OO development? OO Themes; Evidence for usefulness of OO development; OO modeling history. Modeling
as Design technique: Modeling, abstraction, The Three models. Class Modeling: Object and Class Concept, Link and associations concepts, Generalization and Inheritance, A sample class model, Navigation of class models, and UML diagrams
Building the Analysis Models: Requirement Analysis, Analysis Model Approaches, Data modeling Concepts, Object Oriented Analysis, Scenario-Based Modeling, Flow-Oriented Modeling, class Based Modeling, Creating a Behavioral Model.
哪里办理(csu毕业证书)查尔斯特大学毕业证硕士学历原版一模一样
原版一模一样【微信：741003700 】【(csu毕业证书)查尔斯特大学毕业证硕士学历】【微信：741003700 】学位证，留信认证（真实可查，永久存档）offer、雅思、外壳等材料/诚信可靠,可直接看成品样本，帮您解决无法毕业带来的各种难题！外壳，原版制作，诚信可靠，可直接看成品样本。行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备。十五年致力于帮助留学生解决难题，包您满意。
本公司拥有海外各大学样板无数，能完美还原海外各大学 Bachelor Diploma degree, Master Degree Diploma
1:1完美还原海外各大学毕业材料上的工艺：水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠。文字图案浮雕、激光镭射、紫外荧光、温感、复印防伪等防伪工艺。材料咨询办理、认证咨询办理请加学历顾问Q/微741003700
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
官方认证美国密歇根州立大学毕业证学位证书原版一模一样
原版一模一样【微信：741003700 】【美国密歇根州立大学毕业证学位证书】【微信：741003700 】学位证，留信认证（真实可查，永久存档）offer、雅思、外壳等材料/诚信可靠,可直接看成品样本，帮您解决无法毕业带来的各种难题！外壳，原版制作，诚信可靠，可直接看成品样本。行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备。十五年致力于帮助留学生解决难题，包您满意。
本公司拥有海外各大学样板无数，能完美还原海外各大学 Bachelor Diploma degree, Master Degree Diploma
1:1完美还原海外各大学毕业材料上的工艺：水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠。文字图案浮雕、激光镭射、紫外荧光、温感、复印防伪等防伪工艺。材料咨询办理、认证咨询办理请加学历顾问Q/微741003700
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
22CYT12-Unit-V-E Waste and its Management.ppt
Introduction- e - waste – definition - sources of e-waste– hazardous substances in e-waste - effects of e-waste on environment and human health- need for e-waste management– e-waste handling rules - waste minimization techniques for managing e-waste – recycling of e-waste - disposal treatment methods of e- waste – mechanism of extraction of precious metal from leaching solution-global Scenario of E-waste – E-waste in India- case studies.
BRAIN TUMOR DETECTION for seminar ppt.pdf
BRAIN TUMOR DETECTION
AND CLASSIFICATION USING
ARTIFICIAL INTELLIGENCE
IEEE Aerospace and Electronic Systems Society as a Graduate Student Member
IEEE Aerospace and Electronic Systems Society as a Graduate Student Member
ISPM 15 Heat Treated Wood Stamps and why your shipping must have one
For International shipping and maritime laws all wood must contain the ISPM 15 Stamp. Here is how and why.
原版制作(Humboldt毕业证书)柏林大学毕业证学位证一模一样
原件一模一样【微信：bwp0011】《(Humboldt毕业证书)柏林大学毕业证学位证》【微信：bwp0011】学位证，留信认证（真实可查，永久存档）原件一模一样纸张工艺/offer、雅思、外壳等材料/诚信可靠,可直接看成品样本，帮您解决无法毕业带来的各种难题！外壳，原版制作，诚信可靠，可直接看成品样本。行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备。十五年致力于帮助留学生解决难题，包您满意。
本公司拥有海外各大学样板无数，能完美还原。
1:1完美还原海外各大学毕业材料上的工艺：水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠。文字图案浮雕、激光镭射、紫外荧光、温感、复印防伪等防伪工艺。材料咨询办理、认证咨询办理请加学历顾问微bwp0011
【主营项目】
一.毕业证【微bwp0011】成绩单、使馆认证、教育部认证、雅思托福成绩单、学生卡等！
二.真实使馆公证(即留学回国人员证明,不成功不收费)
三.真实教育部学历学位认证（教育部存档！教育部留服网站永久可查）
四.办理各国各大学文凭(一对一专业服务,可全程监控跟踪进度)
如果您处于以下几种情况：
◇在校期间，因各种原因未能顺利毕业……拿不到官方毕业证【微bwp0011】
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◇回国时间很长，忘记办理；
◇回国马上就要找工作，办给用人单位看；
◇企事业单位必须要求办理的
◇需要报考公务员、购买免税车、落转户口
◇申请留学生创业基金
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
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CHINA’S GEO-ECONOMIC OUTREACH IN CENTRAL ASIAN COUNTRIES AND FUTURE PROSPECT
CHINA’S GEO-ECONOMIC OUTREACH IN CENTRAL ASIAN COUNTRIES AND FUTURE PROSPECT
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Fuzzy clustering using RSIO-FCM ppt
- 1. BY : NIGAN NAYAK (3328)
- 3. An extension of k-means. Hierarchical, k-means generates partitions. › each data point can only be assigned in one cluster. Fuzzy c-means allows data points to be assigned into more than one cluster. › each data point has a degree of membership (or probability) of belonging to each cluster.
- 4. It divides the n sized data X = {x1, x2, … , xc} into c groups by optimizing the following objective function:
- 8. m = Fuzzification parameter (taken > 1) E = predefined constant (generally taken 0.001)
- 13. FEATURES OF APACHE SPARK
- 14. SCALABLE VERSION OF LITERAL FUZZY c-MEANS (SLFCM)
- 17. SCALABLE VERSION OF RANDOM SAMPLING PLUS EXTENSION FUZZY c-MEANS (sreFCM)
- 19. SCALABLE RANDOM SAMPLING WITH ITERATIVE OPTIMIZATION FUZZY c-MEANS (SRSIO-FCM)
- 21. DATA SETS Minst8m Replicated USPS Monarch-skin SUSY Replicated dim32
- 23. NORMALIZED MUTUAL INDEX (NMI) ADJUSTED RAND INDEX (ARI) RUN TIME
- 24. NMI RESULTS
- 25. ARI RESULTS
- 26. RUN TIME RESULTS
- 31. COMPLEXITY SLFCM O(ncdt/w) O(ncd) srseFCM O(ncdt/w) O(ncd/s) SRSIO-FCM O(ncd/w) O(ncd) ALGORITHM TIME COMPLEXITY SPACE COMPLEXITY
- 32. CONCLUSION Empirical evaluations on the several large datasets demonstrated that SRSIO-FCM significantly outperformed over the scalable model of previously proposed iterative algorithms. Moreover, SRSIO-FCM achieves higher or comparable clustering results compared with the scalable iterative algorithms. The merits shown in the experiments indicate that SRSIO-FCM has great potential for use in big data clustering.
- 33. THANKYOU!!