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Clustering Structured Data with the SPARE Library

Clustering Structured Data with the SPARE Library

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2011 4th IEEE international conference on Computer Science and Information Technology(ICCSIT 2011)

作者： Guido Del Vescovo Lorenzo Livi Antonello Rizzi Fabio Massimo Frattale Mascioli Department of Information Engineering Electronics and Telecommunications SAPIENZA University of Rome Via Eudossiana 1800184 RomeItaly

ISBN: (纸本)9781612848334

In this paper we propose the SPARE C++ Library as a flexible tool for solving data driven modelling problems,where the domain space is not necessary constrained to be the set of real valued *** possibility to face pattern recognition problems directly on structured domains (multimedia data,strings,graphs) is fundamental to the effective solution of many interesting applications in the field of content based retrieval and Knowledge *** an instance of this particular characteristic of SPARE,we considered a clustering problem defined in the string domain,focusing on the problem of cluster representation in data domains where only a dissimilarity measure can be *** this aim we propose to adopt the MinSOD (Minimum Sum of Distances) defined as the element of a cluster minimizing the sum of dissimilarities from all the other elements in the considered *** the precise computation of the MinSOD have a high computational cost,we propose a suboptimal procedure consisting in computing the representative of the cluster considering only a reduced pool of samples,instead of the whole set of objects in the *** have carried out some tests in order to ascertain the sensitivity of the clustering procedure with respect to the number of samples in the pool used to compute the *** show a good robustness of the proposed *** implementation is available as part of the SPARE library,published as an open source project.

关键词： pattern recognition SPARE Library Template Meta-programming Clustering Strings k-means MinSOD.

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Real-Time Texture Extraction Based on the Improved Median Robust Extended Local Binary pattern 2020

Real-Time Texture Extraction Based on the Improved Median Ro...

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Proceedings of the 2020 9th international conference on computing and pattern recognition

作者： Mengnan Wang Yanjun Zhang School of Information and Electronics Beijing Institute of Technology Beijing China CN

ISBN: (纸本)9781450387835

Local binary patterns (LBP) are considered as the most computation efficient and high-performance texture features. Among all variants of the LBPs, Median Robust Extended Local Binary pattern (MRELBP) [1] is considered as one of the highest-performance one. Based on the MRELBP, this paper proposes an improved real-time texture extraction architecture implemented on a field-programmable gate array (FPGA) device. the fixed-point fraction is used to replace the double float-point fraction, only results in less than 1% difference to the accuracy. the proposed system is implemented on Xilinx Zynq-7045, and the extraction time is linear with the size of the input image. the extraction time of the 128x128 image in the dataset Outex-TC is merely 85.64μs, which is more than 4800 times faster than Liu et al. implemented on MATLAB [2].

关键词： real-time MRELBP feature extraction Field-Programmable Gate Array (FPGA)

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Integrating EEG and NIRS improves BCI performance during motor imagery**Research supported by the National Key Research and Development Program of China under grant 2017YFB1300302, National Natural Science Foundation of China (No. 81630051, 81925020, 62006171), and Tianjin Key Technology R&D Program (No. 17ZXRGGX00020).

Integrating EEG and NIRS improves BCI performance during mot...

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international IEEE/EMBS conference on Neural Engineering, CNE

作者： Zhongpeng Wang Cong Cao Yijie Zhou Long Chen Bin Gu Shuang Liu Minpeng Xu Feng He Dong Ming College of Precision Instruments & Optoelectronics Engineering Tianjin University Tianjin China Academy of Medical Engineering and Translational Medicine Tianjin University Tianjin China

ISBN: (纸本)9781728143378;9781728143385

Hybrid brain-signal could theoretically achieve a more accurate decoding in brain-computer interfaces (BCIs). this study aims to improve the BCI performance by integrating electroencephalographic and cerebral hemodynamic patterns. Sixteen volunteers participated in a seven-session motor imagery (MI) based visual-haptic neurofeedback training (NFT) experiment. Electroencephalogram (EEG) and near infrared spectroscopy (NIRS) signals were synchronously recorded during the transient NFT. Time-frequency and topology analysis demonstrated that repetitive and continuous visual-haptic NFT conditions could induce an enhancement on cortical activations. Additionally, a classifier calibration strategy was proposed by integrating both the EEG and NIRS information into a strong classifier. the results revealed that the classifier constructed on integrating EEG and NIRS patterns was significantly superior to that only with independent information, achieving ~14% and 5% improvement respectively and reaching 84.5% in mean classification accuracy. therefore, integrating EEG and NIRS patterns is an effective classifier calibration strategy to improve the MI-BCI performance, and could also be applied to other BCI paradigms.

关键词： Training Time-frequency analysis Spectroscopy pattern classification Electroencephalography Calibration Topology

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Basic Research on Machine Vision Underpinned by Image Frame Algebra (VFA) and Visual Semantic Algebra (VSA)

Basic Research on Machine Vision Underpinned by Image Frame ...

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international conference on Machine Vision and Information Technology (CMVIT)

作者： Yingxu Wang President International Institute of Cognitive Informatics and Cognitive Computing (I2CICC) Visiting Professor: Stanford Univ. (2008|2016) MIT (2012) UC Berkeley (2008) Oxford Univ. (1995|2018–2022) Distinguished Visiting Professor Tsinghua Univ. (2019–22) Schulich School of Engineering and Hotchkiss Brain Institute University of Calgary 2500 University Drive NW Calgary Alberta Canada

Computer vison [1], [2], [3], [4], [5] studies properties of machine vision, its semantic understanding, and general manipulations by Intelligent Mathematics (IM) [6], [7], [8], [9], [10] [11], [12], [13], [14], [15] [16], [17]. Computer vison has been studies from various aspects such as algorithmic methods, analysis methods, pattern recognitions, and neural-network-regression (AI) technologies [2], [3]. However, there is a lack of fundamental theories for enabling autonomous image recognition and processing by machines. Basic research on contemporary IM has revealed that formal manipulations of visual objects by intelligent machines may be rigorously implemented by Image Frame Algebra (IFA) [8], [18] in the front-end and Visual Semantic Algebra (VSA) [19] in the backend. IFA formally manipulates visual images as general 2D matrixes by a set of algebraic operators such as modeling, analyses, syntheses, feature elicitation, and pattern recognition [4], [5], [18]. then, its counterpart, VSA, transforms the geographic relations of visual objects to their semantic interpretations by algebraic analyses and compositions. the coherent theory of IFA and VSA provides a formal methodology for machine-enabled image processing and comprehension. this keynote presents a theoretical framework of machine vision underpinned by IFA and VSA for the structural denotations of visual objects and functional manipulations of visual mechanisms [3], [8], [9]. It demonstrates how the persistent challenges to machine vision may be rigorously and efficiently solved by the IFA/VSA methodology. Case studies on applying IFA/VSA for rigorous visual pattern detection, recognition, analysis, and composition in real world will be demonstrated [5], [18], [20]. As two coherent paradigms of IM, among others [21], [22], [23], [24], [25] [26], [27], [28], [29], [30], IFA and VSA have been applied not only in robot visual and spatial reasoning, but also in computational intelligence and AI for rigorously

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Protocol for Site Selection and Movement Assessment for the Myoelectric Control of a Multi-Functional Upper-Limb Prosthesis

Protocol for Site Selection and Movement Assessment for the ...

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Annual international conference of the IEEE Engineering in Medicine and Biology Society

作者： Ali H. Al-Timemy Javier Escudero Guido Bugmann Nicholas Outram Centre for Robotics and Neural Systems (CRNS) School of Computing and Mathematics Plymouth University Signal Processing and Multimedia Communication (SPMC) research group School of Computing and Mathematics Plymouth University Centre for Robotics and Neural Systems School of Computing and Mathematics Plymouth University

ISBN: (纸本)9781457702150

Although there have been many advances in electromyography (EMG) signal processing and pattern recognition (PR) for the control of multi-functional upper-limb prostheses, some the outstanding problems need to be solved before practical PR-based prostheses can be put into service. Some of these are the lack of training and deployment protocols and the provision of the tools required. therefore, we present a preliminary procedure to personalize the prosthesis deployment. In the first step, we record the demographic information of each individual amputee person and their background. In the second step of the protocol, the EMG signals are acquired. PR algorithms and parameters will be chosen in the 3rd step of the protocol. In the 4th step, the best number of EMG sensors to achieve the maximal performance with a full set of gestures is identified. the final step involves finding the best set of movements that the amputee person can produce with an accuracy > 95% as well as identifying the movements with the worst performance, which would require further training. this proposed approach is validated with 2 transradial amputees.

关键词： Electromyography Protocols Accuracy Prosthetics Electrodes Muscles Training

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Deepfake Detection Using Machine Learning Algorithms

Deepfake Detection Using Machine Learning Algorithms

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IIAI international conference on Advanced Applied Informatics (IIAIAAI)

作者： Md. Shohel Rana Beddhu Murali Andrew H. Sung School of Computing Sciences and Computer Engineering The University of Southern Mississippi Hattiesburg MS USA

ISBN: (纸本)9781665424219

Deepfake, a new video manipulation technique, has drawn much attention recently. Among the unlawful or nefarious applications, Deepfake has been used for spreading misinformation, fomenting political discord, smearing opponents, or even blackmailing. As the technology becomes more sophisticated and the apps for creating them ever more available, detecting Deepfake has become a challenging task, and accordingly researchers have proposed various deep learning (DL) methods for detection. though the DL-based approach can achieve good solutions, this paper presents the results of our study indicating that traditional machine learning (ML) techniques alone can obtain superior performance in detecting Deepfake. the ML-based approach is based on the standard methods of feature development and feature selection, followed by training, tuning, and testing an ML classifier. the advantage of the ML approach is that it allows better understandability and interpretability of the model with reduced computational cost. We present results on several Deepfake datasets that are obtained relatively fast with comparable or superior performance to the state-of-the-art DL-based methods: 99.84% accuracy on FaceForecics++, 99.38% accuracy on DFDC, 99.66% accuracy on VDFD, and 99.43% on Celeb-DF datasets. Our results suggest that an effective system for detecting Deepfakes can be built using traditional ML methods.

关键词： Training Deep learning Machine learning algorithms Computational modeling Feature extraction Task analysis Informatics

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VLSI Parasitic Capacitance Extraction Based on pattern Matching Using Modified Machine Learning Methods

VLSI Parasitic Capacitance Extraction Based on Pattern Match...

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international conference on Computer and Communications (ICCC)

作者： Hanyu Shi Qing He Department of Electronic Science and Technology Tongji University Shanghai China

ISBN: (数字)9798331507077

ISBN: (纸本)9798331507084

the increasing complexity in the process of IC design and verification has made the rule-based methods widely used by layout parasitic extraction (LPE) tools meet more and more challenges in precision, due to the increasingly severe parasitic effects resulting from the rapid growth in chip integration especially for Very Large-Scale Integration (VLSI). Based on the machine learning algorithms, a new pattern segmentation and combination method is proposed and a corresponding pattern library is established, which is more suitable for the two automatic classification models developed in this research. Two kinds of machine learning networks are trained for experiments and comparisons. For the CNN model, the number of network parameters at different depths and the effect of training layer depth on classification accuracy are compared. For the KNN algorithm, fuzzy distance pre-processing is performed on samples, and a KD-Tree model is established to store and retrieve the “distance” of point pairs. the classification accuracy of models on different training datasets is analyzed. For the labeled pattern datasets, the KNN classification model achieved a better result; Performance improvement is required only in certain mirror patterns. For a set of patterns that are not explicitly classified, the model can potentially be adapted into an unsupervised method to assist in building a pattern library. this method possesses the potential benefit of simplifying the introduction of new processes, preventing the occurrence of pattern mismatch, and saving costs when the computing resource is limited.

关键词： Training Adaptation models Machine learning algorithms Accuracy Computational modeling Machine learning Nearest neighbor methods Very large scale integration Libraries Data mining

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Health CARE Prediction using Predictive Analytics

Health CARE Prediction using Predictive Analytics

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international conference on System Modeling & Advancement in Research Trends (SMART)

作者： Palwinder Kaur Mangat Kamaljit Singh Saini University Institute of Computing Chandigarh University Gharuan India

ISBN: (纸本)9781665439718

throughout the years, several studies have been conducted on how to improve the health sector’s management and administration activities, as well as, most importantly, the healthcare delivered to its patients. In the health-care system, data is currently growing at an exponential rate. In this view, the deployment of technology capable of utilizing it in a constructive way for the company, assisting it in achieving its goals, is critical. Health data analytics, particularly analytics that are used for making predictions are being used as a tool for enabling more preventive treatment alternatives. Despite having access to an abundance of data, even though there is no actionable knowledge using which healthcare industry can make predictions. this is due to the fact that, despite its abundance, healthcare data is fundamentally complex and fragmented. Critical care that is also a part of healthcare areas, also facing a problem of increasing population as well as economic pressures, due to which it is difficult for most of the human beings to get required treatments. When we talk ICU patients their health frequently changes at every movement. When in a county most of the population is aged then there will be more need of ICUs due to the health problems of old people. Equally, patient expectations are increasing with the advancement of technology in healthcare sector, but it is not possible to deliver the required service due to increasing inflation. Better, more productive care is thus the big challenge. this paper we are going to examine the use of predictive analytics in healthcare sector as well as how it is different than the other sectors where the predictive analytics are being used. It also has been also discussed in this paper the various applications of predictive analytics in healthcare sector and the challenges that predictive analytics are facing in the healthcare sector.

关键词： Industries Heart Sociology Smart healthcare Medical services Predictive models Market research

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Use of Machine Learning in Application of Artificial Intelligence

Use of Machine Learning in Application of Artificial Intelli...

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international conference on System Modeling & Advancement in Research Trends (SMART)

作者： Aaditya Jain Anubhuti Singh Ishika Jain College of Computing Sciences and Information Technology Teerthanker Mahaveer University Moradabad UP India

ISBN: (纸本)9781665439718

In modern days technology machine learning is one of the best discovery is in science, and it is one of the critical inventions in intellectualize, which is made artificially. To make this type of revolution, any product the programming behind it is not so complicated and even this type of programming language are used daily. So it is quite sure that it is not so difficult to execute as the program behind it is quite familiar. According to some specific case studies when every human individual is searching some queries through searching instrument via browser, the desired result comes conveniently than ever before, and all of this revolution only can happen because in present times machine level language learning has gone so far and not only that but also very few important researches are also happening around the whole world to improve this specific section of artificial intelligence better than ever. In modern days internet surfing cannot be more convenient without machine learning and artificial intelligence. this study reflects the idea if using machine learning in the application of Artificial Intelligence (A). For the last few years one of the biggest tech giants Google and along with Microsoft are usually implementing machine learning and artificial intelligence in their search engine and as well as their software-based products and not only that but they are also investing in this type of technology to improve as much as it can be in upcoming future as well. Not only in Google and Microsoft but also present generation social media is an essential thing in daily life, and by naming social media, the first thing that comes into mind is Facebook. this particular study reflects the explanation of using machine learning features in the application of Artificial Intelligence. this study explores the classifications of software categories where machine learning can be used. As the data are being more significant every day, this study shows it's necessary to improve

关键词： Technological innovation Machine learning algorithms Systematics Social networking (online) Machine learning Learning (artificial intelligence) Search engines

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HM-QCNN: Hybrid Multi-branches Quantum-Classical Neural Network for Image Classification 9th

HM-QCNN: Hybrid Multi-branches Quantum-Classical Neural Netw...

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19th international conference on Advanced Data Mining and Applications, ADMA 2023

作者： Liu, Haowen Gao, Yufei Shi, Lei Wei, Lin Shan, Zheng Zhao, Bo School of Cyber Science and Engineering Songshan Laboratory Zhengzhou University Zhengzhou China State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China

ISBN: (纸本)9783031466632

Quantum machine learning has been developing in recent years, demonstrating great potential in various research domains and promising applications for pattern recognition. However, due to the constraints of quantum hardware, the input qubits are restricted caused by small circuit size, and the fuzziness in all dimensions caused by the features that are difficult to be effectively mined. Besides, previous studies focus on binary classification, but multi-classification received little attention. To address the difficulty in multi-classification, this paper proposed a hybrid multi-branches quantum-classical neural network (HM-QCNN) that utilizes a multi-branch strategy to construct the convolutional part. the part consists of three branches to extract the features of different scales and morphologies. Two quantum convolutional layers apply quantum CRZ gates and rotational gates to design a random quantum circuit (RQC) with 4 qubits and full qubits measurements. the experiments on three public datasets (MNIST, Fashion MNIST, and MedMNIST) demonstrate that HM-QCNN outperforms other prevalent methods with accuracy, precision, and convergence speed. Compared with the classical CNN and the hybrid neural network without multi-branches, HM-QCNN reached 97.40% and improved the accuracy of classification by 6.45% and 1.36% on the MNIST dataset, respectively. © 2023, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Qubits

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