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Comparative study of data augmentation approaches for improving medical image classification

Comparative study of data augmentation approaches for improv...

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international conference on Computational Science and Computational Intelligence (CSCI)

作者： Rais, Khadija Amroune, Mohamed Haouam, Mohamed Yassine Bendib, Issam Echahid Cheikh Larbi Tebessi Univ Lab Math Informat & Syst LAMIS Tebessa 12002 Algeria

ISBN: (纸本)9798350361513;9798350372304

In recent years, data augmentation has advanced to the point where it no longer relies on traditional photometric or geometric image processing techniques, such as rotation, scale, and filtering. Instead, it has turned to deep learning-based approaches like variational autoencoders (VAEs) and generative adversarial networks (GANs). This shift has made data augmentation a crucial discipline for improving artificial intelligence models, especially in fields like medicine where collecting labeled data is difficult and expensive. This study focuses on using data augmentation approaches to enhance convolutional neural networks (CNNs) classification accuracy. These approaches include geometric modifications, color space transformations, and generative techniques, and they are employed to augment the BraTS20 (Brain Tumor Segmentation 2020) dataset. After comparing the accuracy of CNNs with and without the augmented dataset, we found that DCGANs and AttnGAN achieved the highest accuracy among all techniques. They generated images that closely resembled those in BraTS20 compared to other methods.

关键词： data augmentation classic techniques generative techniques Medical image classification Brain tumor

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A digital multi-informational stochastic dynamic medical decision-making model and the application

A digital multi-informational stochastic dynamic medical dec...

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2020 international conference on data processing algorithms and models, icdpam 2020

作者： Xiao, Xiaonan Xiamen University Tan Kah Kee College Zhangzhou363105 China

As we all know, big data and intelligent computing have been widely used in information science, life science, computer science and intelligent control, intelligent robots, vehicle networking, space technology, marine development and other fields, especially in the future life science and medical field. The results of the discussion show that the intersection and fusion research of big data analysis, mathematics, computational science and life science in the 21st century is getting more tightly. We can understand life science from a new perspective by using big data and Intelligent Computing technology. Thus a new research model is being used to shift the application of mathematics from non-life to life. Furthermore, it greatly strengthens the mutual penetration and connection of big data analysis, mathematics, computational science and life science. It also greatly accelerates the research process of modern life science. © Published under licence by IOP Publishing Ltd.

关键词： Big data

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Studying the Impact of Sampling in Highly Frequent Time Series 22nd

Studying the Impact of Sampling in Highly Frequent Time Seri...

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22nd EPIA conference on Artificial Intelligence (EPIA)

作者： Ferreira, Paulo J. S. Mendes-Moreira, Joao Rodrigues, Arlete Univ Porto Fac Engn INESC TEC R Dr Roberto Frias S-N P-4200465 Porto Portugal Sistemas Seguranca SA Bosch Secur Syst Estr Nacl 109-IC 1 P-3880728 Aveiro Portugal

ISBN: (纸本)9783031490071;9783031490088

Nowadays, all kinds of sensors generate data, and more metrics are being measured. These large quantities of data are stored in large data centers and used to create datasets to train Machine Learning algorithms for most different areas. However, processing that data and training the Machine Learning algorithms require more time, and storing all the data requires more space, creating a Big data problem. In this paper, we propose simple techniques for reducing large time series datasets into smaller versions without compromising the forecasting capability of the generated model and, simultaneously, reducing the time needed to train the models and the space required to store the reduced sets. We tested the proposed approach in three public and one private dataset containing time series with different characteristics. The results show, for the datasets studied that it is possible to use reduced sets to train the algorithms without affecting the forecasting capability of their models. This approach is more efficient for datasets with higher frequencies and larger seasonalities. With the reduced sets, we obtain decreases in the training time between 40 and 94% and between 46 and 65% for the memory needed to store the reduced sets.

关键词： Time series Forecasting data reduction Numerosity reduction Big data Machine learning Holt-winters

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Can Lexicon-Based Sentiment Analysis Boost Performances of Transformer-Based models? 7

Can Lexicon-Based Sentiment Analysis Boost Performances of T...

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7th international conference on New Media Studies, CONMEDIA 2023

作者： Manik, Lindung Parningotan Susianto, Harry Dinakaramani, Arawinda Pramanik, Niken Suhardijanto, Totok National Research and Innovation Agency Research Center for Data and Information Sciences Bandung Indonesia University of Nusa Mandiri Department of Computer Science Jakarta Indonesia University of Indonesia Faculty of Psychology Depok Indonesia University of Indonesia Faculty of Computer Science Depok Indonesia University of Indonesia Faculty of Humanities Department of Linguistics Depok Indonesia

ISBN: (纸本)9798350307504

An essential endeavor in natural language processing, sentiment analysis entails determining the sentiment expressed in a text. Transformer-based models like BERT have attained state-of-the-art performance in sentiment analysis tasks. However, these algorithms may have difficulty distinguishing sentiment-laden words. In response, we proposed combining lexicon-based sentiment analysis and transformer-based models. This study investigates the effect of lexicon-based sentiment analysis, particularly SentiStrength, on BERT's efficacy in sentiment analysis tasks. Experimental evaluations reveal that incorporating sentiment lexicons enhances the accuracy and F1-score of classical sentiment analysis compared to the baseline BERT model. Our findings demonstrate the value of incorporating external knowledge sources into transformer-based sentiment analysis models. © 2023 IEEE.

关键词： Sentiment analysis

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Online Behaviour Indicator Extraction for Enhanced Cancer Patient Management using Real-World data 23

Online Behaviour Indicator Extraction for Enhanced Cancer Pa...

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20th international conference on Content-Based Multimedia Indexing (CBMI)

作者： Pantalona, Georgia Barachanou, Anna Loukas, Nikolaos Apostolidis, Lazaros Tsalakanidou, Filareti Papadopoulos, Symeon CERTH ITI Thessaloniki Greece

ISBN: (纸本)9798400709128

Mental health issues of breast cancer patients and survivors are detrimental to their recovery, reintegration to normal living, and quality of life. With the increasing popularity of social media platforms and online browsing, combined with the rise of Natural Language processing (NLP), there exists great potential in analyzing textual information of various online user activities to infer important indicators about a person's online behaviour and emotional well-being. This work proposes a NLP-based framework for emotion extraction from social media posts and for user interest identification from browser history data. We experimented with contextual representations by fine-tuning pre-trained models like BERT combined with a fully connected layer, stacked LSTM and Bi-LSTM layers, and external resources in the form of emotion lexicons and self-attention. We evaluated our models on curated datasets derived from the combination of open datasets and achieved stateof-the-art performance. Overall, our work provides evidence of the potential for non-obtrusive extraction of online behaviour indicators that reflect the emotional well-being of patients.

关键词： real-world data emotional well-being online behaviour emotion recognition interest identification natural language processing

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A Survey on Spark Ecosystem: Big data processing Infrastructure, Machine Learning, and Applications (Extended abstract) 39

A Survey on Spark Ecosystem: Big Data Processing Infrastruct...

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39th IEEE international conference on data Engineering, ICDE 2023

作者： Tang, Shanjiang He, Bingsheng Yu, Ce Li, Yusen Li, Kun Tianjin University College of Intelligence and Computing Tianjin China National University of Singapore School of Computing Singapore Nankai University School of Computing Tianjin300071 China

ISBN: (纸本)9798350322279

With the explosive increase of big data in industry and academic fields, it is important to apply large-scale data processing systems to analyze Big data. Arguably, Spark is the state-of-the-art in large-scale data computing systems nowadays, due to its good properties including generality, fault tolerance, high performance of in-memory data processing, and scalability. Spark adopts a flexible Resident Distributed dataset (RDD) programming model with a set of provided transformation and action operators whose operating functions can be customized by users according to their applications. It is originally positioned as a fast and general data processing system. A large body of research efforts have been made to make it more efficient (faster) and general by considering various circumstances since its introduction. In this survey, we aim to have a thorough review of various kinds of optimization techniques on the generality and performance improvement of Spark. We introduce various data management and processing systems, machine learning algorithms and applications supported by Spark. Additionally, we make a discussion on the open issues and challenges for large-scale in-memory data processing with Spark. © 2023 IEEE.

关键词： Fault tolerance

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Prediction of Covid-19 using Machine Learning and Deep Learning approaches: A data driven study 1

Prediction of Covid-19 using Machine Learning and Deep Learn...

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1st international conference on Circuits, Power, and Intelligent Systems, CCPIS 2023

作者： Akhter, Thanzina Barua, Labannya Sultana, Mahmuda Masum, Abdul Kadar Muhammad International Islamic University Chittagong Department of Computer Science and Engineering Chittagong Bangladesh Daffodil International University Department of Software Engineering Dhaka Bangladesh

ISBN: (纸本)9798350315769

COVID-19 is generated by the SARS-CoV2 virus, which has caused an outbreak around the world. Since 2020, it has been labeled a pandemic. Due to the daily growth in the number of cases, this takes time for demonstrating the laboratory data, and as a result, treatment and resource limits have emerged. Because of such limitations the necessity for making decisions using clinical data with prognostic algorithms has emerged. Forecasting algorithms could positively relieve the pressure on healthcare systems by correctly identifying the disease. We employ the following machine learning models in this research: RF, SVM, hybrid RF-SVM and deep learning: ANN, CNN, LSTM, CNN-LSTM models and Laboratory data is being used to develop clinical forecasting models that will help medical professionals to figure out the patients that are most likely to develop COVID-19. Our models' prediction ability is measured using recall, F1-score, precision, accuracy and AUC scores. The models were verified using train test method and cross validation strategy. The assessment shows that most of our prediction models can reliably identify patients with COVID-19 disease, amidst the hybrid CNN-LSTM model outperforming the others with an accuracy of 89.5 percent,0.93 F1-score, 0.91 in precision,0.97 in recall, and AUC of 0.70, whereas among the machine learning models the hybrid ensemble outperforms other ML models with an accuracy of 92%. Predictive models obtained from laboratory findings have been found to be successful in predicting COVID-19 infection, which can help medical experts administer resources more effectively. © 2023 IEEE.

关键词： COVID-19

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GenFair: A Genetic Fairness-Enhancing data Generation Framework 26th

GenFair: A Genetic Fairness-Enhancing Data Generation Framew...

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26th international conference on Discovery Science, DS 2023

作者： Mazzoni, Federico Manerba, Marta Marchiori Cinquini, Martina Guidotti, Riccardo Ruggieri, Salvatore University of Pisa Pisa Italy

ISBN: (纸本)9783031452741

Bias in the training data can be inherited by Machine Learning models and then reproduced in socially-sensitive decision-making tasks leading to potentially discriminatory decisions. The state-of-the-art of pre-processing methods to mitigate unfairness in datasets mainly considers a single binary sensitive attribute. We devise GenFair, a fairness-enhancing data pre-processing method that is able to deal with two or more sensitive attributes, possibly multi-valued, at once. The core of the approach is a genetic algorithm for instance generation, which accounts for the plausibility of the synthetic instances w.r.t. the distribution of the original dataset. Results show that GenFair is on par or even better than state-of-the-art approaches. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Genetic algorithms

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GPT-SW3: An Autoregressive Language Model for the Scandinavian Languages 30

GPT-SW3: An Autoregressive Language Model for the Scandinavi...

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Joint 30th international conference on Computational Linguistics and 14th international conference on Language Resources and Evaluation, LREC-COLING 2024

作者： Ekgren, Ariel Gyllensten, Amaru Cuba Stollenwerk, Felix Öhman, Joey Isbister, Tim Gogoulou, Evangelia Carlsson, Fredrik Casademont, Judit Sahlgren, Magnus AI Sweden Sweden RISE Sweden

ISBN: (纸本)9782493814104

This paper details the process of developing the first native large generative language model for the North Germanic languages, GPT-SW3. We cover all parts of the development process, from data collection and processing, training configuration and instruction finetuning, to evaluation, applications, and considerations for release strategies. We discuss pros and cons of developing large language models for smaller languages and in relatively peripheral regions of the globe, and we hope that this paper can serve as a guide and reference for other researchers that undertake the development of large generative models for smaller languages. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： data handling

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Activity-Based Model using GPS data and Google APIs 25

Activity-Based Model using GPS Data and Google APIs

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IEEE 25th international conference on Intelligent Transportation Systems (ITSC)

作者： Tran, Yen Hashimoto, Naohisa Ando, Takafumi Sato, Toshihisa Konishi, Naoki Takeda, Yuji Akamatsu, Motoyuki Natl Inst Adv Ind Sci & Technol Digital Architecture Res Ctr Tsukuba Ibaraki 3058568 Japan Natl Inst Adv Ind Sci & Technol Human Ctr Mobil Res Ctr Tsukuba Ibaraki 3058568 Japan

ISBN: (数字)9781665468800

ISBN: (纸本)9781665468800

This study explores the potentials of utilizing GPS data for Activity-Based models, under the condition that no additional information, such as travel diaries, is required. To extract activity details, we first developed a Time-Spatial Centroid-based Clustering algorithm to identify activity locations and times. Then a Home Detection algorithm was used in combination with two APIs from Google Maps, namely Nearby Search and Place Details, to label each identified activity as either `home' or a specific activity type. Next, a Markov Chain Multinomial Logit Choice model was developed for the extracted activities that models the sequential relationship between consecutive activities. The approach was applied to a GPS dataset collected in Japan in 2020. The estimated parameters revealed how background factors, such as activity time and the person's age, and the previous activity associate with the current activity. Thus, GPS data alone can provide certain knowledge about activity-travel, which potentially benefit practices of travel demands forecasting.

关键词： Demand forecasting Clustering algorithms Markov processes data models Internet Detection algorithms Intelligent transportation systems

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