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X-Ray Medical Image Classification based on Multi Classifiers 4

X-Ray Medical Image Classification based on Multi Classifier...

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4th international conference on Advanced Computer Science applications and Technologies (ACSAT)

作者： Abdulrazzaq, M. M. Noah, Shahrul Azman Fadhil, Moayad A. Univ Kebangsaan Malaysia Fac Informat Sci & Technol Bangi Selangor Malaysia Philadelphia Univ Fac Informat Technol Amman Jordan

ISBN: (纸本)9781509004249

Advances in the medical imaging technology has lead to a growth in the number of digital images that needs to be classified, stored and retrieved properly. Content Based Image Retrieval (CBIR) systems represent the application of specific computer vision techniques to retrieve images from large databases based on their visual features, such as color, texture and shape. Practically, the use of these visual features only does not offer appropriate measurement performance and accuracy since those features cannot express the high-level semantics of users. therefore, image classification systems based on machine learning techniques are used as solutions for this problem of CBIR systems. In our previous works, performance of different feature types were investigated by using two techniques of machine learning which are k-Nearest Neighbor (k-NN) and Support Vector machine (SVM). In this paper, we extend that work by exploring the effect of combining these two classifiers. Our experiments show accuracy improvements based on using ImageCLEF2005 dataset.

关键词： Content Based Image Retrieval (CBIR) k-Nearest Neighbor (k-NN) Support Vector machine (SVM) ImageCLEF2005 machine learning

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UNF-IDT: Automated Irony Detection in English Tweets 22

UNF-IDT: Automated Irony Detection in English Tweets

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22nd IEEE international conference on machine learning and applications, icmla 2023

作者： Jaganathan, Guna Sekaran Kilungeja, Grentina Kahanda, Indika School of Computing University of North Florida JacksonvilleFL United States

ISBN: (纸本)9798350345346

the usage of social media platforms has increased tremendously in the past decade. Twitter is one of the most popular platforms for sharing opinions and feelings on various topics, organizations rely on Twitter data to analyze and gather insights for their businesses using Natural Language Processing (NLP) techniques. However, there are various challenges in analyzing such a huge volume of tweets that are in text format. One such challenge is to identify irony in tweets, which has a significant impact on analyzing sentiments. To overcome this challenge, we propose a solution that automatically recognizes the presence of irony in text and classifies the type of irony. the solution consists of two tasks: Task A performs binary classification to annotate whether irony is expressed or not in each tweet, while Task B performs multi-class classification to classify the type of irony expressed in the tweets. We used the dataset from the 'SemEval-2018 Task 3: Irony detection in English tweets' challenge to train and test the proposed solution. the dataset contains 3,817 English tweets for training and 784 English tweets for testing both Tasks A and B. We developed different machine learning models by leveraging traditional classifiers, neural networks, and large language models. Our UNF-IDT (Irony Detector in Text) model developed using BERT (Bidirectional Encoder Representations from Transformers) was able to achieve an F1 score of 0.757 for Task A (Binary Classification) and an F1 score of 0.449 for Task B (Multi-class Classification), retrospectively obtaining 4th and 9th ranks, in the two tasks respectively. © 2023 IEEE.

关键词： BERT Irony Detection machine learning Text Mining Twitter

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Revealing Fundamental Physics from the Daya Bay Neutrino Experiment using Deep Neural Networks 15

Revealing Fundamental Physics from the Daya Bay Neutrino Exp...

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15th IEEE international conference on machine learning and applications (icmla)

作者： Racah, Evan Ko, Seyoon Sadowski, Peter Bhimji, Wahid Tull, Craig Oh, Sang-Yun Baldi, Pierre Prabhat Lawrence Berkeley Lab Berkeley CA 94720 USA Seoul Natl Univ Seoul 151747 South Korea Univ Calif Irvine Irvine CA USA Univ Calif Santa Barbara Santa Barbara CA 93106 USA

ISBN: (纸本)9781509061679

Experiments in particle physics produce enormous quantities of data that must be analyzed and interpreted by teams of physicists. this analysis is often exploratory, where scientists are unable to enumerate the possible types of signal prior to performing the experiment. thus, tools for summarizing, clustering, visualizing and classifying high-dimensional data are essential. In this work, we show that meaningful physical content can be revealed by transforming the raw data into a learned high-level representation using deep neural networks, with measurements taken at the Daya Bay Neutrino Experiment as a case study. We further show how convolutional deep neural networks can provide an effective classification filter with greater than 97% accuracy across different classes of physics events, significantly better than other machine learning approaches.

关键词： Deep learning Unsupervised learning High-Energy Physics Autoencoders

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Modifying kernels using label information improves SVM classification performance

Modifying kernels using label information improves SVM class...

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6th international conference on machine learning and applications

作者： Min, Renqiang Bonner, Anthony Zhang, Zhaolei Univ Toronto Dept Comp Sci Toronto ON M5S 2G4 Canada Univ Toronto Banting & Best Dept Med Res Toronto M5G1L6 ON Canada

ISBN: (纸本)9780769530697

Kernel learning methods based on kernel alignment with semidefinite programming (SDP) are often memory intensive and computationally expensive, thus often impractical for problems with large-size dataset. We propose a method using label information to modify kernels based on SVD and a linear mapping. As a result, the new kernel matrix reflects the label-dependent separability of the data in a better way than the original kernel matrix. In addition, our experimental results on USPS handwritten digits and the SCOP dataset, show that the SVM classifier based on the improved kernels has better performance than the SVM classifier based on the original kernels;moreover, SVM based on the improved Profile kernel with pull-in homologs (see experiment section for explanations) produced the best results for remote homology detection on the SCOP dataset compared to the published results.

关键词： Classification (of information)

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L1-norm Principal-Component Analysis via Bit Flipping 15

L1-norm Principal-Component Analysis via Bit Flipping

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15th IEEE international conference on machine learning and applications (icmla)

作者： Markopoulos, Panos P. Kundu, Sandipan Chamadia, Shubham Pados, Dimitris A. Rochester Inst Technol Dept Elect & Microelect Engn Rochester NY 14623 USA Qualcomm Technol Inc San Jose CA 95110 USA SUNY Buffalo Dept Elect Engn Buffalo NY 14260 USA

ISBN: (纸本)9781509061679

the K L1-norm Principal Components (L1-PCs) of a data matrix X is an element of R-DxN can be found optimally with cost O(2(NK)), in the general case, and O(N-rank(X) K (K+1)), when rank(X) is a constant with respect to N [1], [2]. Certainly, in real-world applications where N is large, even the latter polynomial cost is prohibitive. In this work, we present L1BF: a novel, near-optimal algorithm that calculates the K L1PCs of X with cost O(N Dmin{N, D} + N-2 (K-4 + DK2) + DNK3), comparable to that of standard (L2-norm) Principal-Component Analysis. Our numerical studies illustrate that the proposed algorithm attains optimality with very high frequency while, at the same time, it outperforms on the L1-PCA metric any counterpart of comparable computational cost. the outlier-resistance of the L1-PCs calculated by L1-BF is documented with experiments on dimensionality reduction and genomic data classification for disease diagnosis.

关键词： Anomaly detection data contamination genomic data L1 norm L2 norm principal-component analysis outliers singular-value decomposition subspace learning

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learning to propose amendments: Identifying patterns in the right to information query log 18

Learning to propose amendments: Identifying patterns in the ...

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18th IEEE international conference on machine learning and applications, icmla 2019

作者： Kotoky, Nayantara Saradhi, Vijaya Department of Computer Science and Engineering Indian Institute of Technology Guwahati Assam India

ISBN: (纸本)9781728145495

the Right to Information (RTI) Act 2005 provides Indian citizens with the platform to access information present in government offices. Every government office has the RTI query-log data containing citizens' interaction with the government. the objective of this work is to analyze the RTI query-log data to identify underlying latent patterns such that the identified latent patterns have the potential to act as feedback for amendments to laws. We, for the first time, collect RTI queries and reply-statistics from government educational institutions across India. three latent patterns are quantified namely (i) transparency of institutions from RTI query-reply statistics (ii) factors that influence the transparency of institutions (iii) effectiveness of implementation of the RTI act. A two-dimensional institute-query-category matrix representation of the RTI data is proposed. the above three latent parameters are simultaneously quantified via maximum likelihood estimation of the data matrix using Graded Response Model. through the extracted latent patterns we identify inconsistencies in the execution of the RTI act and indicate a feedback for potential amendment to the RTI act. © 2019 IEEE.

关键词： Transparency

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learning to Coordinate with Deep Reinforcement learning in Doubles Pong Game 16

Learning to Coordinate with Deep Reinforcement Learning in D...

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16th IEEE international conference on machine learning and applications (icmla)

作者： Diallo, Elhadji Amadou Oury Sugiyama, Ayumi Sugawara, Toshiharu Waseda Univ Dept Comp Sci & Commun Engn Shinjuku Ku 3-4-1 Okubo Tokyo 1698555 Japan

ISBN: (纸本)9781538614174

this paper discusses the emergence of cooperative and coordinated behaviors between joint and concurrent learning agents using deep Q-learning. Multi-agent systems (MAS) arise in a variety of domains. the collective effort is one of the main building blocks of many fundamental systems that exist in the world, and thus, sequential decision making under uncertainty for collaborative work is one of the important and challenging issues for intelligent cooperative multiple agents. However, the decisions for cooperation are highly sophisticated and complicated because agents may have a certain shared goal or individual goals to achieve and their behavior is inevitably influenced by each other. therefore, we attempt to explore whether agents using deep Q-networks (DQN) can learn cooperative behavior. We use doubles pong game as an example and we investigate how they learn to divide their works through iterated game executions. In our approach, agents jointly learn to divide their area of responsibility and each agent uses its own DQN to modify its behavior. We also investigate how learned behavior changes according to environmental characteristics including reward schemes and learning techniques. Our experiments indicate that effective cooperative behaviors with balanced division of work load emerge. these results help us to better understand how agents behave and interact with each other in complex environments and how they coherently choose their individual actions such that the resulting joint actions are optimal.

关键词： Games Multi-agent systems Markov processes machine learning learning (artificial intelligence) Neural networks

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Combining the Wavelet Transform and Forecasting Models to Predict Gas Forward Prices

Combining the Wavelet Transform and Forecasting Models to Pr...

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7th international conference on machine learning and applications

作者： Nguyen, Hang T. Nabney, Ian T. Aston Univ Sch Engn & Appl Sci Neural Comp Res Grp Birmingham B4 7ET W Midlands England

ISBN: (纸本)9780769534954

this paper presents a forecasting technique for forward energy prices, one day ahead. this technique combines a wavelet transform and forecasting models such as multilayer perceptron, linear regression or GARCH. these techniques are applied to real data from the UK gas markets to evaluate their performance. the results show that the forecasting accuracy is improved significantly by using the wavelet transform. the methodology can be also applied to forecasting market clearing prices and electricity/gas loads.

关键词： Wavelet transforms

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A Hybrid machine learning Approach for Planning Safe Trajectories in Complex Traffic-Scenarios 15

A Hybrid Machine Learning Approach for Planning Safe Traject...

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15th IEEE international conference on machine learning and applications (icmla)

作者： Chaulwar, Amit Botsch, Michael Utschick, Wolfgang Ingolstadt Univ Appl Sci Fac Elect Engn D-85049 Ingolstadt Germany Tech Univ Munich Dept Elect Engn D-80333 Munich Germany

ISBN: (纸本)9781509061679

Planning of safe trajectories with interventions in both lateral and longitudinal dynamics of vehicles has huge potential for increasing the road traffic safety. Main challenges for the development of such algorithms are the consideration of vehicle nonholonomic constraints and the efficiency in terms of implementation, so that algorithms run in real time in a vehicle. the recently introduced Augmented CL-RRT algorithm is an approach that uses analytical models for trajectory planning based on the brute force evaluation of many longitudinal acceleration profiles to find collision-free trajectories. the algorithm considers nonholonomic constraints of the vehicle in complex road traffic scenarios with multiple static and dynamic objects, but it requires a lot of computation time. this work proposes a hybrid machine learning approach for predicting suitable acceleration profiles in critical traffic scenarios, so that only few acceleration profiles are used with the Augmented CL-RRT to find a safe trajectory while reducing the computation time. this is realized using a convolutional neural network variant, introduced as 3D-ConvNet, which learns spatiotemporal features from a sequence of predicted occupancy grids generated from predictions of other road traffic participants. these learned features together with hand-designed features of the EGO vehicle are used to predict acceleration profiles. Simulations are performed to compare the brute force approach with the proposed approach in terms of efficiency and safety. the results show vast improvement in terms of efficiency without harming safety. Additionally, an extension to the Augmented CL-RRT algorithm is introduced for finding a trajectory with low severity of injury, if a collision is already unavoidable.

关键词： Trajectories

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Diagnosis of bearing defects in induction motors by fuzzy-neighborhood density-based clustering 14

Diagnosis of bearing defects in induction motors by fuzzy-ne...

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IEEE 14th international conference on machine learning and applications icmla

作者： Farajzadeh-Zanjani, M. Razavi-Far, R. Saif, M. Zarei, J. Palade, V. Univ Windsor Dept Elect & Comp Engn 401 Sunset Ave Windsor ON N9B 3P4 Canada Shiraz Univ Technol Dept Elect Engn Shiraz Iran Coventry Univ Fac Engn & Comp Coventry W Midlands England

ISBN: (纸本)9781509002870

In this paper, a supervised fuzzy-neighborhood density-based clustering approach is proposed for the fault diagnosis of induction motors' bearings. the proposed approach makes use of the labeled data regarding the actual classes of faulty and fault-free cases, in order to train the fuzzy-neighborhood density-based clustering algorithm in a supervised manner, by resorting to an invasive weed optimization algorithm that aims to minimize an error-based objective function. the proposed classifier can properly classify multi-class data with complex and variously shaped decision boundaries among the different classes of faults and the fault-free state, and is robust against noise. this is due mainly to the fact that the classifier is constructed using the fuzzy-neighborhood density based clustering method, which is not sensitive to the geometrical shape of clusters in the feature space.

关键词： Bearing defects Fault diagnosis Fuzzy-neighborhood density-based clustering Induction motors

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