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Optimizing aspect-based sentiment analysis using sentence embedding transformer, bayesian search clustering, and sparse attention mechanism

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Journal of Open Innovation: Technology, Market, and Complexity 2024年第1期10卷

作者： Marutho, Dhendra Muljono Rustad, Supriadi Purwanto Faculty of Computer Science Universitas Dian Nuswantoro Central Java Semarang Indonesia Departement of Informatics Universitas Muhammadiyah Semarang Semarang Indonesia

The aspect-based sentiment analysis of hotel reviews represents a critical yet underexplored area of research. Such reviews encompass a range of expressions that extend beyond simple positive, negative, or neutral sentiments, delving into specific aspects discussed within the review. For instance, a statement like 'the location is comfortable, the food is good, but unfortunately, the service is bad' encapsulates multiple aspects, including location, food, and service. Existing research in Aspect-Based Sentiment Analysis (ABSA) frequently employs word embedding techniques for feature extraction, utilizing both Term and Transformer models. This study introduces an innovative approach by transitioning from word to sentence embedding, employing the SBERT transformer model for this transformation. This study further innovates label creation and topic modeling by integrating Bayesian search clustering with Inverse Document Frequency (IDF) calculations. This approach enables the identification of relevant topics, categorizing IDF scores based on occurrence frequencies. Scores ranging between 4–6 are marked as having low value but high informative potential, making them suitable candidates for label-specific datasets, particularly those without pre-existing labels. Additionally, this study incorporates the BigBird model to enhance this classification methodology. Applying this comprehensive framework to hotel review datasets, the result of the ABSA evaluation achieved an impressive accuracy score of 99%, indicating the efficacy of the proposed methodology in this nuanced field. © 2024 The Authors

关键词： Aspect-Based Sentiment Analysis Bayesian Search Clustering BIGBIRD machine learning algorithm Semantic Similarity Sentence BERT Sentiment Analysis

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Prediction of reservoir saturation field in high water cut stage by bore-ground electromagnetic method based on machine learning

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JOURNAL OF PETROLEUM SCIENCE AND ENGINEERING 2021年 204卷

作者： Guo, Qi Zhuang, Tianlin Li, Zhen He, Shumei Shengli Oilfield Explorat & Dev Res Inst Dongying 257000 Shandong Peoples R China Petro China Da Gang Oilfield Explorat & Dev Res I Tianjin 300280 Peoples R China Shengli Petr Engn Co Logging Co Dongying 257000 Shandong Peoples R China

The prediction of oil saturation by bore-ground electromagnetic method provides a new expression for reservoir fluid. However, due to the complexity of the internal environment of reservoir and the influence of construction cost, the signal excitation point often cannot cover the whole vertical reservoir range. In this paper, based on the results of single-layer saturation field distribution collected and processed by bore-ground electromagnetic method, six machine learning algorithms, decision-tree, random-forest, k-nearest neighbor, AdaBoost-decision tree, AdaBoost-random forest and AdaBoost-k-nearest neighbor, are established by using machine learning algorithm to introduce inter layer reservoir flow correlation parameters. The fitting of prediction model is detected by learning curve in machine learning algorithm, and the hyper-parameters of the model are optimized by grid search, finally a machine learning algorithm suitable for predicting the saturation distribution by bore-ground electromagnetic method is evaluated and the remaining oil saturation of other layers in the vertical direction is predicted. The results show that the new method can effectively overcome the defects of the bore-ground electromagnetic method, the vertical saturation field of each layer can be reasonably predicted, and the prediction accuracy is more than 90% when it is applied to the prediction of saturation field in block 2 of Gangdong oilfield. The oil saturation distribution results are more similar to the actual reservoir.

关键词： Bore-ground electromagnetic method learning curve Cross validation Saturation field prediction machine learning algorithm Numerical simulation

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Automatic Detection of Human Maxillofacial Tumors by Using Thermal Imaging: A Preliminary Study

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SENSORS 2022年第5期22卷 1985页

作者： Macianskyte, Diana Adaskevicius, Rimas Lithuanian Univ Hlth Sci Clin Radiol LT-50009 Kaunas Lithuania Kaunas Univ Technol Dept Elect Power Syst LT-51368 Kaunas Lithuania

Traditional computed tomography (CT) delivers a relatively high dose of radiation to the patient and cannot be used as a method for screening of pathologies. Instead, infrared thermography (IRT) might help in the detection of pathologies, but interpreting thermal imaging (TI) is difficult even for the expert. The main objective of this work is to present a new, automated IRT method capable to discern the absence or presence of tumor in the orofacial/maxillofacial region of patients. We evaluated the use of a special feature vector extracted from face and mouth cavity thermograms in classifying TIs against the absence/presence of tumor (n = 23 patients per group). Eight statistical features extracted from TI were used in a k-nearest neighbor (kNN) classifier. Classification accuracy of kNN was evaluated by CT, and by creating a vector with the true class labels for TIs. The presented algorithm, constructed from a training data set, gives good results of classification accuracy of kNN: sensitivity of 77.9%, specificity of 94.9%, and accuracy of 94.1%. The new algorithm exhibited almost the same accuracy in detecting the absence/presence of tumor as CT, and is a proof-of-principle that IRT could be useful as an additional reliable screening tool for detecting orofacial/maxillofacial tumors.

关键词： infrared thermal image CT machine learning algorithm kNN classifier orofacial maxillofacial tumor

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Variety classification of coated maize seeds based on Raman hyperspectral imaging

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SPECTROCHIMICA ACTA PART A-MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2022年 270卷 120772页

作者： Liu, Qingyun Wang, Zuchao Long, Yuan Zhang, Chi Fan, Shuxiang Huang, Wenqian China Univ Geosci Beijing Sch Sci Beijing 100083 Peoples R China Beijing Acad Agr & Forestry Sci Intelligent Equipment Res Ctr Beijing 100097 Peoples R China Natl Res Ctr Intelligent Equipment Agr Beijing 100097 Peoples R China Minist Agr Key Lab Agriinformat Beijing 100097 Peoples R China Beijing Key Lab Intelligent Equipment Technol Agr Beijing 100097 Peoples R China

As an essential factor in quality assessment of maize seeds, variety purity profoundly impacts final yield and farmers' economic benefits. In this study, a novel method based on Raman hyperspectral imaging system was applied to achieve variety classification of coated maize seeds. A total of 760 maize seeds including 4 different varieties were evaluated. Raman spectral data of 400-1800 cm(-1) were extracted and preprocessed. Variable selection methods involved were modified competitive adaptive reweighted sampling (MCARS), successive projections algorithm (SPA), and their combination. In addition, MCARS was proposed for the first time in this paper as a stable search technology. The performance of support vector machine (SVM) models optimized by genetic algorithm (GA) was analyzed and compared with models based on random forest (RF) and back-propagation neural network (BPNN). Same models based on Vis-NIR spectral data were also established for comparison. Results showed that the MCARS-GA-SVM model based on Raman spectral data obtained the best performance with calibration accuracy of 99.29% and prediction accuracy of 100%, which were stable and easily replicated. In addition, the accuracy on the independent validation set was 96.88%, which proved that the model can be applied in practice. A more simplified MCARS-SPA-GA-SVM model, which contained only 3 variables, had more than 95% accuracy on each data set. This procedure can help to develop a real-time detection system to classify coated seed varieties with high accuracy, which is of great significance for assessing variety purity and increasing crop yield. (C) 2021 Elsevier B.V. All rights reserved.

关键词： Raman hyperspectral imaging Coated maize seed Variety classification Variable selection machine learning algorithm

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A new diagnostic method for chronic obstructive pulmonary disease using the photoplethysmography signal and hybrid artificial intelligence

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PEERJ COMPUTER SCIENCE 2022年 8卷 e1188-e1188页

作者： Melekoglu, Engin Kocabicak, Umit Ucar, Muhammed Kursad Bilgin, Cahit Bozkurt, Mehmet Recep Cunkas, Mehmet Sakarya Univ Comp Engn Sakarya Turkey Sakarya Univ Elect & Elect Engn Sakarya Turkey Sakarya Univ Fac Med Sakarya Turkey Selcuk Univ Elect & Elect Engn Konya Turkey

Background and Purpose: Chronic obstructive pulmonary disease (COPD), is a primary public health issue globally and in our country, which continues to increase due to poor awareness of the disease and lack of necessary preventive measures. COPD is the result of a blockage of the air sacs known as alveoli within the lungs;it is a persistent sickness that causes difficulty in breathing, cough, and shortness of breath. COPD is characterized by breathing signs and symptoms and airflow challenge because of anomalies in the airways and alveoli that occurs as the result of significant exposure to harmful particles and gases. The spirometry test (breath measurement test), used for diagnosing COPD, is creating difficulties in reaching hospitals, especially in patients with disabilities or advanced disease and in children. To facilitate the diagnostic treatment and prevent these problems, it is far evaluated that using photoplethysmography (PPG) signal in the diagnosis of COPD disease would be beneficial in order to simplify and speed up the diagnosis process and make it more convenient for monitoring. A PPG signal includes numerous components, including volumetric changes in arterial blood that are related to heart activity, fluctuations in venous blood volume that modify the PPG signal, a direct current (DC) component that shows the optical properties of the tissues, and modest energy changes in the body. PPG has typically received the usage of a pulse oximeter, which illuminates the pores and skin and measures adjustments in mild absorption. PPG occurring with every heart rate is an easy signal to measure. PPG signal is modeled by machine learning to predict COPD. Methods: During the studies, the PPG signal was cleaned of noise, and a brand-new PPG signal having three low-frequency bands of the PPG was obtained. Each of the four signals extracted 25 features. An aggregate of 100 features have been extracted. Additionally, weight, height, and age were also used as character

关键词： Signal processing in biomedical Photoplethysmography signal machine learning algorithm Chronic obstructive pulmonary disease

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Leveraging Google Earth Engine platform to characterize and map small seasonal wetlands in the semi-arid environments of South Africa

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SCIENCE OF THE TOTAL ENVIRONMENT 2022年 803卷 150139页

作者： Gxokwe, Siyamthanda Dube, Timothy Mazvimavi, Dominic Univ Western Cape Inst Water Studies Dept Earth Sci Private Bag X17 ZA-7535 Cape Town South Africa

Although significant scientific research strides have been made in mapping the spatial extents and ecohydrological dynamics of wetlands in semi-arid environments, the focus on small wetlands remains a challenge. This is due to the sensing characteristics of remote sensing platforms and lack of robust data processing techniques. Advancements in data analytic tools, such as the introduction of Google Earth Engine (GEE) platform provides unique opportunities for improved assessment of small and scattered wetlands. This study thus assessed the capabilities of GEE cloud-computing platform in characterising small seasonal flooded wetlands, using the new generation Sentinel 2 data from 2016 to 2020. Specifically, the study assessed the spectral separability of different land cover classes for two different wetlands detected, using Sentinel-2 multi-year composite water and vegetation indices and to identify the most suitable GEE machine learning algorithm for accurately detecting and mapping semi-arid seasonal wetlands. This was achieved using the object based Random Forest (RF), Support Vector machine (SVM), Classification and Regression Tree (CART) and Naive Bayes (NB) advanced algorithms in GEE. The results demonstrated the capabilities of using the GEE platform to characterize wetlands with acceptable accuracy. All algorithms showed superiority, in mapping the two wetlands except for the NB method, which had lowest overall classification accuracy. These findings underscore the relevance of the GEE platform, Sentinel-2 data and advanced algorithms in characterizing small and seasonal semi-arid wetlands. (c) 2021 Elsevier B.V. All rights reserved.

关键词： Limpopo River Basin Object-based classification machine learning algorithm Wetland mapping Wetland condition

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Instrumentation for differentiation of exhaled air 15

Instrumentation for differentiation of exhaled air

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15th International Conference on Correlation Optics

作者： Santos, Paulo H. C. Vassilenko, Valentina B. Moura, Pedro C. Conduto, Carolina Fernandes, Jorge M. Bonifacio, Paulo NOVA Sch Sci & Technol Caparica Portugal NMT SA Caparica Portugal

ISBN: (纸本)9781510651296;9781510651289

Breath analysis is an emerging research field with tremendous potential for advance personalized, non-invasive health screening and diagnostics,while new sampling instrumentation tools andanalytical detection methods are developed. Notwithstanding of the quick development of commercially and researcher-built experimental samplers, no robust and repeatable VOCs' profile technologies have been clinically validated. Such is due to lack of an optimal standard procedure for selective breath sampling which ends in a wide range of contradictory reported results. Challenges of most breath samplers are also related to the substances' concentrations that are source (oral cavity, oesophageal and alveolar) dependent and their low values (in ppbv - pptv range). Here, we present a suitable and novel technology for selectively sampling exhaled air regarding the subject's: age, gender, metabolic production of CO2, smoking habits, nutrition and health conditions. The technology was aimed to perform real time flow measurements and collect a pre-determined portion of exhaled air by synchronizing a previously modelled respiratory cycle with the breathing cycle of the user. Through real-time synchronization of breathing cycles, the system can detect optimized sampling instants by machine learning-based algorithm. A first set of tests was conducted to evaluate the robustness and efficiency of the software's sampling algorithm with two cohorts of participants (n=15 and n=30) with different age groups (2-5 years old and 18-27 years old, respectively). The ability to selectively differentiate exhaled air was also tested through collection and posterior analysis of oesophageal and alveolar air samples obtained from an independent cohort of university students (n=31). Although it requires instrumentation improvements and optimization the breath sampling technology coupled with an efficient analyser device (GC-IMS), the results herein presented suggest a promise step forward in breath sampling

关键词： Exhaled Air Selective Air Acquisition Air Sampling/Monitoring Breath Rhythm Imposition Modelled Breath algorithm Average Time of Expiration machine learning algorithm Medical Instrumentation Prototyping

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SVM and ANN Modelling Approach for the Optimization of Membrane Permeability of a Membrane Rotating Biological Contactor for Wastewater Treatment

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MEMBRANES 2022年第9期12卷 821-821页

作者： Waqas, Sharjeel Harun, Noorfidza Yub Sambudi, Nonni Soraya Arshad, Ushtar Nordin, Nik Abdul Hadi Md Bilad, Muhammad Roil Saeed, Anwar Ameen Hezam Malik, Asher Ahmed Univ Teknol Petronas Chem Engn Dept Bandar Seri Iskandar 32610 Perak Malaysia Univ Pertamina Dept Chem Engn Simprug 12220 Jakarta Selatan Indonesia Univ Brunei Darussalam Fac Integrated Technol BE-1410 Gadong Brunei

Membrane fouling significantly hinders the widespread application of membrane technology. In the current study, a support vector machine (SVM) and artificial neural networks (ANN) modelling approach was adopted to optimize the membrane permeability in a novel membrane rotating biological contactor (MRBC). The MRBC utilizes the disk rotation mechanism to generate a shear rate at the membrane surface to scour off the foulants. The effect of operational parameters (disk rotational speed, hydraulic retention time (HRT), and sludge retention time (SRT)) was studied on the membrane permeability. ANN and SVM are machine learning algorithms that aim to predict the model based on the trained data sets. The implementation and efficacy of machine learning and statistical approaches have been demonstrated through real-time experimental results. Feed-forward ANN with the back-propagation algorithm and SVN regression models for various kernel functions were trained to augment the membrane permeability. An overall comparison of predictive models for the test data sets reveals the model's significance. ANN modelling with 13 hidden layers gives the highest R-2 value of >0.99, and the SVM model with the Bayesian optimizer approach results in R-2 values higher than 0.99. The MRBC is a promising substitute for traditional suspended growth processes, which aligns with the stipulations of ecological evolution and environmentally friendly treatment.

关键词： machine learning algorithm artificial neural networks support vector machines membrane fouling biological wastewater treatment

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A comprehensive method for improvement of water quality index (WQI) models for coastal water quality assessment

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WATER RESEARCH 2022年 219卷 118532页

作者： Uddin, Md Galal Nash, Stephen Rahman, Azizur Olbert, Agnieszka I. Natl Univ Ireland Coll Sci & Engn Sch Engn Civil Engn Galway Ireland Natl Univ Ireland Ryan Inst Galway Ireland Natl Univ Ireland MaREI Res Ctr Galway Ireland Charles Sturt Univ Sch Comp Math & Engn Wagga Wagga Australia Charles Sturt Univ Gulbali Inst Agr Water & Environm Wagga Wagga Australia

Here, we present an improved water quality index (WQI) model for assessment of coastal water quality using Cork Harbour, Ireland, as the case study. The model involves the usual four WQI components - selection of water quality indicators for inclusion, sub-indexing of indicator values, sub-index weighting and sub-index aggregation - with improvements to make the approach more objective and data-driven and less susceptible to eclipsing and ambiguity errors. The model uses the machine learning algorithm, XGBoost, to rank and select water quality indicators for inclusion based on relative importance to overall water quality status. Of the ten indicators for which data were available, transparency, dissolved inorganic nitrogen, ammoniacal nitrogen, BOD5, chlorophyll, temperature and orthophosphate were selected for summer, while total organic nitrogen, dissolved inorganic nitrogen, pH, transparency and dissolved oxygen were selected for winter. Linear interpolation functions developed using national recommended guideline values for coastal water quality are used for sub-indexing of water quality indicators and the XGBoost rankings are used in combination with the rank order centroid weighting method to determine sub-index weight values. Eight sub-index aggregation functions were tested - five from existing WQI models and three proposed by the authors. The computed indices were compared with those obtained using a multiple linear regression (MLR) approach and R2 and RMSE used as indicators of aggregation function performance. The weighted quadratic mean function (R2 = 0.91, RMSE = 4.4 for summer;R2 = 0.97, RMSE = 3.1 for winter) and the unweighted arithmetic mean function (R2 = 0.92, RMSE = 3.2 for summer;R2 = 0.97, RMSE = 3.2 for winter) proposed by the authors were identified as the best functions and showed reduced eclipsing and ambiguity problems compared to the others.

关键词： Water quality Coastal waters machine learning algorithm Rank order centroid method Aggregation function

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machine learning Classification of Endangered Tree Species in a Tropical Submontane Forest Using WorldView-2 Multispectral Satellite Imagery and Imbalanced Dataset

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REMOTE SENSING 2021年第24期13卷

作者： Jackson, Colbert M. Adam, Elhadi Univ Witwatersrand Sch Geog Archaeol & Environm Studies ZA-2050 Johannesburg South Africa

Accurate maps of the spatial distribution of tropical tree species provide valuable insights for ecologists and forest management. The discrimination of tree species for economic, ecological, and technical reasons is usually necessary for achieving promising results in tree species mapping. Most of the data used in tree species mapping normally have some degree of imbalance. This study aimed to assess the effects of imbalanced data in identifying and mapping trees species under threat in a selectively logged sub-montane heterogeneous tropical forest using random forest (RF) and support vector machine with radial basis function (RBF-SVM) kernel classifiers and WorldView-2 multispectral imagery. For comparison purposes, the original imbalanced dataset was standardized using three data sampling techniques: oversampling, undersampling, and combined oversampling and undersampling techniques in R. The combined oversampling and undersampling technique produced the best results: F1-scores of 68.56 +/- 2.6% for RF and 64.64 +/- 3.4% for SVM. The balanced dataset recorded improved classification accuracy compared to the original imbalanced dataset. This research observed that more separable classes recorded higher F1-scores. Among the species, Syzygium guineense and Zanthoxylum gilletii were the most accurately mapped whereas Newtonia buchananii was the least accurately mapped. The most important spectral bands with the ability to detect and distinguish between tree species as measured by random forest classifier, were the Red, Red Edge, Near Infrared 1, and Near Infrared 2.

关键词： tropical forests endangered tree species selective logging imbalanced data pixel-based classification machine learning algorithm

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