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Investigation of the complex data distributions for their efficient generation 1

Investigation of the complex data distributions for their ef...

引用

1st Masters Symposium on Advances in data mining, machine learning, and Computer Vision, MS-AMLV 2019

作者： Kofman, Philipp Dobosevych, Oles Hryniv, Rostyslav Ukrainian Catholic University Lviv Ukraine University of Rzeszw Rzeszw Poland

Currently, the active development of image processing methods requires large amounts of correctly labeled data. The lack of quality data makes it impossible to use various machine learning methods. In case of limited possibilities for collecting real data, used methods for their synthetic generation. In practice, we can formulate the task of the high-quality generation of synthetic images as an efficient generation of complex data distributions, which is the object of study of this work. Generating high-quality synthetic data is an expensive and complicated process in terms of existing methods. We can distinguish two main approaches that are used to generate synthetic data: image generation based on rendered 3-D scenes and the use of GANs for simple images. These methods have some drawbacks, such as a narrow range of applicability and insufficient distribution complexity of the obtained data. When using GANs to generate complex distributions, in practice, we face a visible increase in the complexity of the model architecture and training procedure. A deep understanding of the real data complex distributions can be used to improve the quality of synthetic generation. Minimizing the differences in the real and synthetic data distributions can improve not only the generation process but also develop tools for solving the problem of data lack in the field of image processing. Copyright © 2019 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 international (CC BY 4.0).

关键词： Generative adversarial networks

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Spatial co-location pattern mining using delaunay triangulation 1

引用

1st international conference on Latest Advances in machine learning and data Science, LAMDA 2017

作者： Kiran Kumar, G. Kavati, Ilaiah Srinivas Rao, Koppula Cheruku, Ramalingaswamy Department of CSE MLR Institute of Technology Hyderabad500043 India Department of CSE Anurag Group of Institutions Hyderabad500038 India Department of Computer Science and Engineering National Institute of Technology Goa PondaGoa403401 India

ISBN: (数字)9789811085697

ISBN: (纸本)9789811085680

Spatial data mining is the process of finding interesting patterns that may implicitly exist in spatial database. The process of finding the subsets of features that are frequently found together in a same location is called co-location pattern discovery. Earlier methods to find co-location patterns focuses on converting neighbourhood relations to item sets. Once item sets are obtained then can apply any method for finding patterns. The criteria to know the strength of co-location patterns is participation ratio and participation index. In this paper, Delaunay triangulation approach is proposed for mining co-location patterns. Delaunay triangulation represents the closest neighbourhood structure of the features exactly which is a major concern in finding the co-location patterns. The results show that this approach achieves good performance when compared to earlier methodologies. © Springer Nature Singapore Pte Ltd. 2018.

关键词： Location

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Overview of the First workshop on Medical Content-Based Retrieval for Clinical Decision Support at MICCAI 2009

Overview of the First Workshop on Medical Content-Based Retr...

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1st MICCAI international workshop on Medical Content-Based Retrieval for Clinical Decision Support

作者： Mueller, Henning Kalpathy-Cramer, Jayashree Caputo, Barbara Syeda-Mahmood, Tanveer Wang, Fei Univ Hosp Geneva Geneva Switzerland Univ Geneva CH-1211 Geneva 4 Switzerland Univ Appl Sci Western Switzerland HES SO Sierre Switzerland Oregon Hlth & Sci Univ Portland OR 97201 USA IDIAP Res Ctr Martigny Switzerland IBM Corp Almaden Res Ctr San Jose CA 95120 USA

ISBN: (纸本)9783642117688

In this paper, we provide an overview of the first workshop on Medical Content Based Retrieval for Clinical Decision Support (MCBR-CDS), which was held in conjunction with the Medical Image Computing and Computer Assisted Intervention (MICCAI) conference in 2009 in London, UK. The goal of the workshop was to bring together researchers from diverse communities including medical image analyses, text and image retrieval, data mining, and machine learning to discuss new techniques for multimodal image retrieval and the use of images in clinical decision support. We discuss the motivation for this workshop, provide details about the organization and participation, discuss the current state of the art in clinical image retrieval and the use of images for clinical decision support. We conclude with open issues and challenges that lie ahead for the domain of medical content based retrieval.

关键词： Content based retrieval

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An Efficient pattern recognition Based Method for Drug-Drug Interaction Diagnosis 1

An Efficient Pattern Recognition Based Method for Drug-Drug ...

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1st international Conference on Artificial Intelligence and data Analytics, CAIDA 2021

作者： Javed, Rabia Saba, Tanzila Humdullah, Salman Mohd Jamail, Nor Shahida Javed Awan, Mazhar Lahore College for Women University Computer Science Department Lahore54300 Pakistan Prince Sultan University Computer and Information Science Riyadh11586 Saudi Arabia Universiti Teknologi Malaysia Faculty of Computing Johor Bahru81310 Malaysia University of Management and Technology Department of Software Engineering Lahore Pakistan

ISBN: (纸本)9780738131771

The diagnosis of interactions between two drugs is an essential procedure in drug development. Many medical tool's offer inclusive records related to DDI. However, this tool's results are not very satisfactory. The main aim is to propose an efficient approach based on pattern matching that identifies the interaction between two drugs. In this study, the goal is to collect the data from the DrugBank, which is a publicly available source. The drug-related data includes drug ID, drug names, and various kinds of sentences of drug-drug interaction. Drug names will be identified by drug names dictionary defined in the corpus, and sentences will be determined according to given patterns. These sentences will treat as input data, and preprocessing steps will perform in these sentences. Various types of features are selected for machine learning classification. Then all the attributes will be classified into desired classes. The proposed method gains 95.4% accuracy from the random forest classifier. © 2021 IEEE.

关键词： Diagnosis

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Multi channel sequence processing

Multi channel sequence processing

引用

1st international workshop on Deterministic and statistical Methods in machine learning

作者： Bengio, S Bourlard, H IDIAP Res Inst CH-1920 Martigny Switzerland Swiss Fed Inst Technol EPFL CH-1015 Lausanne Switzerland

ISBN: (纸本)3540290737

This paper summarizes some of the current research challenges arising from multi-channel sequence processing. Indeed, multiple real life applications involve simultaneous recording and analysis of multiple information sources, which may be asynchronous, have different frame rates, exhibit different stationarity properties, and carry complementary (or correlated) information. Some of these problems can already be tackled by one of the many statistical approaches towards sequence modeling. However, several challenging research issues are still open, such as taking into account asynchrony and correlation between several feature streams, or handling the underlying growing complexity. In this framework, we discuss here two novel approaches, which recently started to be investigated with success in the context of large multimodal problems. These include the asynchronous HMM, providing a principled approach towards the processing of multiple feature streams, and the layered HMM approach, providing a good formalism for decomposing large and complex (multi-stream) problems into layered architectures. As briefly reported here, combination of these two approaches yielded successful results on several multi-channel tasks, ranging from audio-visual speech recognition to automatic meeting analysis.

关键词： data processing

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1st international Congress on Blockchain and Applications, BLOCKCHAIN 2019

1st International Congress on Blockchain and Applications, B...

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1st international Congress on Blockchain and Applications, BLOCKCHAIN 2019

ISBN: (纸本)9783030238124

The proceedings contain 21 papers. The special focus in this conference is on Blockchain and Applications. The topics include: Anticipatory policy as a design challenge: Experiments with stakeholders engagement in blockchain and distributed ledger technologies (bdlts);the electronic bill of lading: Challenges of paperless trade;a methodology for a probabilistic security analysis of sharding-based blockchain protocols;the “Tokenization” of the eparticipation in public governance: An opportunity to hack democracy;blockchain approach to solve collective decision making problems for swarm robotics;prediction of transaction confirmation time in ethereum blockchain using machine learning;improving event monitoring in iot network using an integrated blockchain-distributed pattern recognition scheme;on value preservation with distributed ledger technologies, intelligent agents, and digital preservation;blockchain technology: A review of the current challenges of cryptocurrency;lightning network: A comparative review of transaction fees and data analysis;a blockchain- and ai-based platform for global employability;blockchain and biometrics: A first look into opportunities and challenges;do smart contract languages need to be turing complete?;towards integration of blockchain and iot: A bibliometric analysis of state-of-the-art;clinicappchain: A low-cost blockchain hyperledger solution for healthcare;smart contracts are more than objects: Pro-activeness on the blockchain;blockchain based informed consent with reputation support;privacy centric collaborative machine learning model training via blockchain;fuzzy rules based solution for system administration security management via a blockchain.

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A Novel Architecture for k-means Clustering Algorithm 3rd

A Novel Architecture for <i>k</i>-means Clustering Algorithm

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3rd international Afro-European Conference for Industrial Advancement (AECIA)

作者： Khawaja, S. G. Khan, Asad Mansoor Akram, M. Usman Khan, Shoab A. Natl Univ Sci & Technol Coll Elect & Mech Engn Islamabad Pakistan

ISBN: (纸本)9783319608341;9783319608334

Technological advancements in todays information age has helped the researchers to capture digital footprints of humans with regards to their daily activities. These logs of information posses valuable information for the data analytics who process it to find hidden pattern and unique behavior. Among the many algorithms k-means clustering is one of the very popular and widely used algorithm in the field of data mining and machine learning. k-means provides natural segments of dataset provided for clustering. It uses proximity to assign data points to a specific cluster, here the criteria of allocation is the minimum distance from the cluster center. Unfortunately, the rate of data growth has not been met by the speed of the algorithms. A number of hardware based solutions have been proposed to increase the processing power of different algorithms. In this paper, we present a novel algorithm for k-mean clustering which exploits the data redundancy occurring in the dataset. The proposed algorithm performs computations for the available unique items in the dataset and uses its frequency to finalize the results. Furthermore, FPGA based hardware architecture for the proposed algorithm is also presented in the paper. The performance of the proposed algorithm and its hardware implementation is evaluated using execution time, speedup and throughput. The proposed architecture provides speedup of 23 times and 2600 times against sequential hardware architecture and software implementation with a very small area requirement.

关键词： data mining

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FAM-based fuzzy inference for detecting shot transitions

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2nd international workshop on machine learning and data mining in pattern recognition, MLDM 2001

作者： Jang, Seok-Woo Kim, Gye-Young Choi, Hyung-Il Soongsil University 1-1 Sangdo-5 Dong Dong-Jak Ku Seoul Korea Republic of

ISBN: (纸本)3540423591

We describe a fuzzy inference approach for detecting and classifying shot transitions in video sequences. Our approach basically extends FAM(Fuzzy Associative Memory) to detect and classify shot transitions, including cuts, fades and dissolves. We consider a set of feature values that characterize differences between two consecutive frames as input fuzzy sets, and the types of shot transitions as output fuzzy sets. An initial implementation runs at approximately 7 frames per second on PC and yields promising results. © Springer-Verlag Berlin Heidelberg 2001.

关键词： Fuzzy sets

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1st IEEE international workshop on Biologically Motivated Computer Vision, BMCV 2000

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1st IEEE international workshop on Biologically Motivated Computer Vision, BMCV 2000

ISBN: (纸本)3540675604

The proceedings contain 64 papers. The special focus in this conference is on Segmentation, Detection, Object recognition, Computational Model and Attentive Vision. The topics include: A new framework for object categorization in cortex;the perception of spatial layout in a virtual world;towards a computational model for object recognition in it cortex;straight line detection as an optimization problem;an approach motivated by the jumping spider visual system;factorial code representation of faces for recognition;moving object segmentation based on human visual sensitivity;object classification using a fragment-based representation;confrontation of retinal adaptation model with key features of psychophysical gain behavior dynamics;polarization-based orientation in a natural environment;computation model of eye movement in reading using foveated vision;top-down attention control at feature space for robust pattern recognition;development of a biologically inspired real-time visual attention system;heading perception and moving objects;curve inference and stereo correspondence;an efficient data structure for feature extraction in a foveated environment;parallel trellis based stereo matching using constraints;unsupervised learning of biologically plausible object recognition strategies;face recognition under varying views;pose-independent object representation by 2-d views;an image enhancement technique based on wavelets;face reconstruction using a small set of feature points;a neural network model for long-range contour diffusion by visual cortex;automatic generation of photo-realistic mosaic image and face and facial landmarks location based on log-polar mapping.

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New Perspectives in e-learning: EEG-Based Modelling of Human Cognition Individual Differences 13th

New Perspectives in e-Learning: EEG-Based Modelling of Hum...

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13th mining Humanistic data workshop, MHDW 2024, 9th workshop on 5G-Putting Intelligence to the Network Edge, 5G-PINE 2024 and 1st workshop on AI in Applications for Achieving the Green Deal Targets, AI4GD 2024 held as parallel events of the IFIP WG 12.5 international workshops on Artificial Intelligence Applications and Innovations, AIAI 2024

作者： Trigka, Maria Dritsas, Elias Mylonas, Phivos Athena Research and Innovation Center Patras26504 Greece Department of Informatics and Computer Engineering University of West Attica Aigaleo Greece

ISBN: (纸本)9783031632266

learning is the process of acquiring knowledge, skills and values, through lifelong education. learning abilities are affected by the subject’s cognitive skills showing how they think, process information, pay attention and remember things. The dynamic nature of a human’s mental state usually impacts the aforementioned structural characteristics of human cognition, hindering learning performance as well. Nowadays, technological advances in Brain-Computer Interface (BCI) systems, in combination with advanced processing methods, have paved the way for the highly accurate capturing of human brain activity, helping to decode cognitive and mental status to adapt the learning process. This paper aims to present the first research outcomes of a study in progress. More specifically, the main proposition lies in extending the basic functionalities of our electroencephalography (EEG)-based e-learning prototype system to deliver personalized solutions by taking into account the individual cognitive differences of the learners. Some primary findings are showcased that investigate the potential association of cognitive style with the power spectral features of brain activity in a specific context where the subjects execute a visual task. It is anticipated that the real-time recognition of the learner’s cognitive style will help educators adapt and advance the learning process. © IFIP international Federation for Information Processing 2024.

关键词： Brain computer interface

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