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An unsupervised aspect detection model for sentiment analysis of reviews

18th International Conference on Application of Natural Language to Information Systems, NLDB 2013

作者： Bagheri, Ayoub Saraee, Mohamad De Jong, Franciska Intelligent Database Data Mining and Bioinformatics Lab Electrical and Computer Engineering Department Isfahan University of Technology Isfahan Iran School of Computing Science and Engineering University of Salford Manchester United Kingdom Human Media Interaction University of Twente P.O. Box 217 7500 AE Enschede Netherlands

ISBN: (纸本)9783642388231

With the rapid growth of user-generated content on the internet, sentiment analysis of online reviews has become a hot research topic recently, but due to variety and wide range of products and services, the supervised and domain-specific models are often not practical. As the number of reviews expands, it is essential to develop an efficient sentiment analysis model that is capable of extracting product aspects and determining the sentiments for aspects. In this paper, we propose an unsupervised model for detecting aspects in reviews. In this model, first a generalized method is proposed to learn multi-word aspects. Second, a set of heuristic rules is employed to take into account the influence of an opinion word on detecting the aspect. Third a new metric based on mutual information and aspect frequency is proposed to score aspects with a new bootstrapping iterative algorithm. The presented bootstrapping algorithm works with an unsupervised seed set. Finally two pruning methods based on the relations between aspects in reviews are presented to remove incorrect aspects. The proposed model does not require labeled training data and can be applicable to other languages or domains. We demonstrate the effectiveness of our model on a collection of product reviews dataset, where it outperforms other techniques. © 2013 Springer-Verlag Berlin Heidelberg.

关键词： Sentiment analysis

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Systematic Literature Review on Statistics and Machine Learning Predictive Models for Rice Phenotypes

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Procedia Computer science 2023年 227卷 1054-1061页

作者： Nicholas Dominic Tjeng Wawan Cenggoro Bens Pardamean Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta 11480 Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta 11480 Indonesia Computer Science Department BINUS Graduate Program – Master of Computer Science Bina Nusantara University Jakarta 11480 Indonesia

Predicting the best-quality of rice phenotypes is the priority among agricultural researchers to fulfill worldwide food security. Trend development of predictive models from statistics to machine learning is the subject of this review. Gathered from the Google Scholar database, 14 appropriate papers (2016-2020) related to the rice phenotypes prediction were selected through title and abstract content filtering. The outputs show that Support Vector Machine, Multi-layer Perceptron, and regression are the most used models, while yield is the priority prediction point besides tiller, panicle, and 1000-grain weight of rice. However, finding the accurate predictor is invariably challenging due to distinct rice varieties in the world and high confounding factors. Thus, developing an advanced deep learning model that accommodates these needs is worth considering further.

关键词： rice phenotypes prediction statistics machine learning

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A Convolutional Neural Network-based Ancient Sundanese Character Classifier with data Augmentation

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Procedia Computer science 2021年 179卷 195-201页

作者： Alam Ahmad Hidayat Kartika Purwandari Tjeng Wawan Cenggoro Bens Pardamean Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta Indonesia 11480

With an increasing interest in the digitization effort of ancient manuscripts, ancient character recognition becomes one of the most important areas in the automated document image analysis. In this regard, we propose a Convolutional Neural Network (CNN)-based classifier to recognize the ancient Sundanese characters obtained from a digital collection of Southeast Asian palm leaf manuscripts. In this work, we utilize two different preprocessing techniques for the dataset. The first technique involves the use of geometric transformations, noise background addition, and brightness adjustment to augment the imbalanced samples to be fed into the classifier. The second technique makes use of the Otsu’s threshold method to binarize the characters and only uses the usual geometric transformations for the data augmentation. The proposed network with different data augmentation processes is trained on the training set and tested on the testing set. Image binarization from the second technique can outperform the performance of the CNN-based classifier upon the first technique by achieving a testing accuracy of 97.74%.

关键词： ancient sundanese characters convolutional neural network data augmentation document image analysis Otsu’s threshold method

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Distributed Analytics on Sensitive Medical data:The Personal Health Train

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data Intelligence 2020年第1期2卷 96-107,305,306,307页

作者： Oya Beyan Ananya Choudhury Johan van Soest Oliver Kohlbacher Lukas Zimmermann Holger Stenzhorn Md.Rezaul Karim Michel Dumontier Stefan Decker Luiz Olavo Bonino da Silva Santos Andre Dekker Fraunhofer Institute for Applied Information Technology(FIT) 53754 Sankt AugustinGermany RWTH Aachen University 52056 AachenGermany Department of Radiation Oncology(MAASTRO) GROW School for Oncology and Developmental BiologyMaastricht University Medical Center6200 MD MaastrichtThe Netherlands Institute of Data Science Maastricht UniversityUniversiteitssingel 60Maastricht 6229 ERThe Netherlands Department of Computer Science University of TübingenTubingenBaden-Württemberg 72076Germany Quantitative Biology Center University of TübingenTubingenBaden-Württemberg 72076Germany Institute for Translational Bioinformatics University of TubingenTubingenBaden-Württemberg 72076Germany Center for Bioinformatics University of TübingenGermany GO FAIR International Support&Coordination Office(GFISCO) LeidenThe Netherlands

In recent years,as newer technologies have evolved around the healthcare ecosystem,more and more data have been *** analytics could power the data collected from numerous sources,both from healthcare institutions,or generated by individuals themselves via apps and devices,and lead to innovations in treatment and diagnosis of diseases;improve the care given to the patient;and empower citizens to participate in the decision-making process regarding their own health and ***,the sensitive nature of the health data prohibits healthcare organizations from sharing the *** Personal Health Train(PHT)is a novel approach,aiming to establish a distributed data analytics infrastructure enabling the(re)use of distributed healthcare data,while data owners stay in control of their own *** main principle of the PHT is that data remain in their original location,and analytical tasks visit data sources and execute the *** PHT provides a distributed,flexible approach to use data in a network of participants,incorporating the FAIR *** facilitates the responsible use of sensitive and/or personal data by adopting international principles and *** paper presents the concepts and main components of the PHT and demonstrates how it complies with FAIR principles.

关键词： Distributed analytics data reuse FAIR Health data Ethics and privacy

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Enhancing Interoperability of Digital Twin in the Maintenance phase of Lifecycle

Enhancing Interoperability of Digital Twin in the Maintenanc...

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International Conference on Information Technology, Information Systems and Electrical Engineering (ICITISEE)

作者： Dedy Ariansyah Bens Pardamean Bioinformatics and Data Science Research Centre Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Graduate Program Master of Computer Science Program Bina Nusantara University Jakarta Indonesia

In today's digitalization era, asset management has evolved following the rapid adoption of information systems and digital technologies. In the maintenance phase of the asset, Digital Twin (DT) emerges as an effective approach to improve failure recognition and structural damage prediction through DT-driven Prognostic and Health Monitoring (PHM). As a result, DT allows asset manager to proactively plan maintenance strategy to improve the availability of the assets. However, to reduce asset downtime in a cost-effective and time efficient manner, DT for maintenance phase needs to be integrated with other software systems that manage the provision of maintenance service and spare part such as computerized maintenance management system (CMMS) or Enterprise Resource Planning (ERP). Nevertheless, many of the developed DT in PHM were not equipped with semantic data model and hence, lacks interoperability with other systems (e.g., other DT or non-DT system) and limits the effective use of the DT. To qualify as an effective DT, it needs a semantical-enabled interface to connect with data and models from various domain to enable DT-driven decision-making. This paper leverages a rule-based ontology reasoning model to enhance DT interoperability with other maintenance software systems. The results depicts that the developed model paves a way for an effective use of DT-driven system in asset maintenance management.

关键词： Ontologies Software systems data models Enterprise resource planning Digital twins Prognostics and health management Interoperability

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Utilizing Semi-supervised Method in Predicting BRCA1 Pathogenicity Variants

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Procedia Computer science 2023年 227卷 36-45页

作者： Alam Ahmad Hidayat Joko Pebrianto Trinugroho Rudi Nirwantono Digdo Sudigyo Bens Pardamean Department of Mathematics School of Computer Science Bina Nusantara University Indonesia 11480 Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department BINUS Graduate Program Bina Nusantara University Jakarta Indonesia 11480

Quantifying the effect of mutations in the BRCA1 gene is useful for understanding their clinical consequences on breast cancer. Machine learning models can be applied to predict the landscape of protein variant effects that might not be always accessible by experiments. In this work, we propose a simple semi-supervised learning method using a Gaussian mixture model to predict ∼90% of the unlabeled missense variants of the BRCA1 gene collected from the ClinVar database. High-quality embeddings are used as a feature of the protein sequences, extracted using the latest pre-trained transformer-based language protein model. A statistical test show that the protein embeddings are effective and robust for predicting pathogenicity. Further, the lower representations of the features are then fed into the semi-supervised model. The prediction performance of the model only for the labeled testing data achieves an AUC score and an accuracy of 79.27% and 71.58%, respectively. Using our defined pathogenic probability score, we find that ∼94% of variants in our unlabeled dataset are well-separated into either benign or pathogenic classes according to that scoring. Our scores obtain a moderate Spearman rank correlation with the results of established unsupervised variant effect models. Finally, our approach can potentially be developed for more accurate and biologically reliable predictions of the variant effects.

关键词： embeddings mutation effect language model protein sequence semi-supervised learning

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Convolutional Neural Networks for Scops Owl Sound Classification

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Procedia Computer science 2021年 179卷 81-87页

作者： Alam Ahmad Hidayat Tjeng Wawan Cenggoro Bens Pardamean Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta Indonesia 11480

Adopting a deep learning model into bird sound classification tasks becomes a common practice in order to construct a robust automated bird sound detection system. In this paper, we employ a four-layer Convolutional Neural Network (CNN) formulated to classify different species of Indonesia scops owls based on their vocal sounds. Two widely used representations of an acoustic signal: log-scaled mel-spectrogram and Mel Frequency Cepstral Coefficient (MFCC) are extracted from each sound file and fed into the network separately to compare the model performance with different inputs. A more complex CNN that can simultaneously process the two extracted acoustic representations is proposed to provide a direct comparison with the baseline model. The dual-input network is the well-performing model in our experiment that achieves 97.55% Mean Average Precision (MAP). Meanwhile, the baseline model achieves a MAP score of 94.36% for the mel-spectrogram input and 96.08% for the MFCC input.

关键词： acoustic features bird sound classification convolutional neural network mean average precision scops owl

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APPROXIMATE BAYESIAN INFERENCE OF DIRECTED ACYCLIC GRAPHS IN BIOLOGY WITH FLEXIBLE PRIORS ON EDGE STATES

arXiv

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arXiv 2019年

作者： Martin, Evan A. Fu, Audrey Qiuyan The Bioinformatics and Computational Biology Program University of Idaho United States The Bioinformatics and Computational Biology Program Department of Mathematics and Statistical Science Institute for Modeling Collaboration and Innovation Institute for Interdisciplinary Data Sciences University of Idaho United States

Graphical models or networks describe the statistical dependence among multiple variables and are widely used in biology (e.g., gene regulatory networks). Under appropriate assumptions, directed edges may represent causal relationships. A key feature of a biological network is sparsity, defined by how likely an edge is present, of which we often have some knowledge. However, most existing Bayesian methods use priors for the entire graph, making it difficult to specify the level of sparsity. The few methods that use priors on edges estimate the two directions independently;the sum of the two probabilities can exceed 1. Here, we present baycn (BAYesian Causal Network), a novel approximate Bayesian method that represents a graph in terms of three states of edges: the two directions and edge absence, and specifies priors on these edge states. We design a pseudo Bayesian sampling algorithm for efficient inference. We apply baycn to two genomic problems: i) distinguishing direct and indirect target genes of genetic variants, using these variants as instrumental variables, and ii) inferring combinatorial binding of highly-correlated transcription factors in Drosophila. In both cases and in extensive simulations, our method demonstrates much improved accuracy over existing methods for the whole graph and for individual edges. Copyright © 2019, The Authors. All rights reserved.

关键词： Inference engines

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Systematic Evaluation of Cross Population Polygenic Risk Score on Colorectal Cancer

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Procedia Computer science 2021年 179卷 344-351页

作者： Bharuno Mahesworo Arif Budiarto Bens Pardamean Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta Indonesia 11480

The number of findings in cancer genomics research has grown rapidly in the last decade due to the decline in the cost of human sequencing and genotyping. However, the majority of the reported significant marker associated with cancer traits are based on European and East Asian population. Large population such as South Asian and South-East Asian population are under-represented in genomics research. In this study, we explored the possibility of computing a Polygenic Risk Score (PRS) of colorectal cancer on our test sample based on reported significant Single Nucleotide Polymorphism (SNP). The SNPs used to compute the risk score were collected from GWAS Central and GWAS Catalog. Significant SNPs from IC3 study were used as a benchmark. The result shows that calculating colorectal cancer risk score using reported significant marker from different population group is possible. The p-value of our statistic model shows significant differences between case and control group risk score.

关键词： Polygenic Risk Score Cross Population Colorectal Cancer

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Repurposing transfer learning strategy of computer vision for owl sound classification

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Procedia Computer science 2023年 216卷 424-430页

作者： Kevin William Gunawan Alam Ahmad Hidayat Tjeng Wawan Cenggoro Bens Pardamean Computer Science Department BINUS Graduate Program – Master of Computer Science Bina Nusantara University Jakarta 11480 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta 11480 Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta 11480 Indonesia

Accurately predicting an owl species based on its sound can be helpful for owl conservation. To build an accurate model for owl sound classification, deep learning is currently the most preferred algorithm, due to its excellent performance for modeling audio data. However, deep learning is generally underperformed for a small dataset, which is the case for recognizing scops owl sound. To overcome the issue, we proposed a transfer learning strategy, which is common for computer vision tasks, that can alleviate overfitting in a deep learning model for the owl sound classification. In our approach, we propose a neural network architecture consisting of the backbone of a EfficientNet model pre-trained on the massive ImageNet database. The model takes the sound input that has been converted as two image representations: Spectrogram and Mel Frequency Cepstral Coefficients. Our strategy enables the use of a relatively small size of pre-trained image classification model, which is widely available, for transfer learning in owl sound classification. Deploying the lightweight model in an automatic sound classifier provides a fast and accurate tool for various owl conservation purposes.

关键词： acoustics deep learning bird sound species detection transfer learning

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