Статті в журналах: "LDA MODEL"

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Pang, Xiongwen, Benshuai Wan, Huifang Li, and Weiwei Lin. "MR-LDA." International Journal of Grid and High Performance Computing 8, no. 4 (October 2016): 100–113. http://dx.doi.org/10.4018/ijghpc.2016100106.

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Latent Dirichlet Allocation(LDA) is an efficient method of text mining,but applying LDA directly to Chinese micro-blog texts will not work well because micro-blogs are more social, brief, and closely related with each other. Based on LDA, this paper proposes a Micro-blog Relation LDA model (MR-LDA), which takes the relations between Chinese micro-blog documents and other Chinese micro-blog documents into consideration to help topic mining in micro-blog. The authors extend LDA in the following two points. First, they aggregate several Chinese micro-blogs as a single micro-blog document to solve the problem of short texts. Second, they model the generation process of Chinese micro-blogs more accurately by taking relationship between micro-blog documents into consideration. MR-LDA is more suitable to model Chinese micro-blog data. Gibbs sampling method is borrowed to inference the model. Experimental results on actual datasets show that MR-LDA model can offer an effective solution to text mining for Chinese micro-blog.

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Aslanyan, Tatev Karen, and Flavius Frasincar. "LDA-LFM." ACM SIGAPP Applied Computing Review 21, no. 2 (June 2021): 33–47. http://dx.doi.org/10.1145/3477127.3477130.

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Most of the existing recommender systems are based only on the rating data, and they ignore other sources of information that might increase the quality of recommendations, such as textual reviews, or user and item characteristics. Moreover, the majority of those systems are applicable only on small datasets (with thousands of observations) and are unable to handle large datasets (with millions of observations). We propose a recommender algorithm that combines a rating modeling technique (i.e., Latent Factor Model) with a topic modeling method based on textual reviews (i.e., Latent Dirichlet Allocation), and we extend the algorithm such that it allows adding extra user- and item-specific information to the system. We evaluate the performance of the algorithm using Amazon.com datasets with different sizes, corresponding to 23 product categories. After comparing the built model to four other models, we found that combining textual reviews with ratings leads to better recommendations. Moreover, we found that adding extra user and item features to the model increases its prediction accuracy, which is especially true for medium and large datasets.

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Zhang, Yongjun, Zijian Wang, Yongtao Yu, Bolun Chen, Jialin Ma, and Liang Shi. "LF-LDA." International Journal of Data Warehousing and Mining 14, no. 2 (April 2018): 18–36. http://dx.doi.org/10.4018/ijdwm.2018040102.

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This article describes how text documents are a major data structure in the era of big data. With the explosive growth of data, the number of documents with multi-labels has increased dramatically. The popular multi-label classification technology, which is usually employed to handle multinomial text documents, is sensitive to the noise terms of text documents. Therefore, there still exists a huge room for multi-label classification of text documents. This article introduces a supervised topic model, named labeled LDA with function terms (LF-LDA), to filter out the noisy function terms from text documents, which can help to improve the performance of multi-label classification of text documents. The article also shows the derivation of the Gibbs Sampling formulas in detail, which can be generalized to other similar topic models. Based on the textual data set RCV1-v2, the article compared the proposed model with other two state-of-the-art multi-label classifiers, Tuned SVM and labeled LDA, on both Macro-F1 and Micro-F1 metrics. The result shows that LF-LDA outperforms them and has the lowest variance, which indicates the robustness of the LF-LDA classifier.

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Ekinci, Ekin, and Sevinç İlhan Omurca. "Concept-LDA: Incorporating Babelfy into LDA for aspect extraction." Journal of Information Science 46, no. 3 (April 29, 2019): 406–18. http://dx.doi.org/10.1177/0165551519845854.

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Latent Dirichlet allocation (LDA) is one of the probabilistic topic models; it discovers the latent topic structure in a document collection. The basic assumption under LDA is that documents are viewed as a probabilistic mixture of latent topics; a topic has a probability distribution over words and each document is modelled on the basis of a bag-of-words model. The topic models such as LDA are sufficient in learning hidden topics but they do not take into account the deeper semantic knowledge of a document. In this article, we propose a novel method based on topic modelling to determine the latent aspects of online review documents. In the proposed model, which is called Concept-LDA, the feature space of reviews is enriched with the concepts and named entities, which are extracted from Babelfy to obtain topics that contain not only co-occurred words but also semantically related words. The performance in terms of topic coherence and topic quality is reported over 10 publicly available datasets, and it is demonstrated that Concept-LDA achieves better topic representations than an LDA model alone, as measured by topic coherence and F-measure. The learned topic representation by Concept-LDA leads to accurate and an easy aspect extraction task in an aspect-based sentiment analysis system.

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SHI, Jing, Ming HU, Xin SHI, and Guo-Zhong DAI. "Text Segmentation Based on Model LDA." Chinese Journal of Computers 31, no. 10 (October 16, 2009): 1865–73. http://dx.doi.org/10.3724/sp.j.1016.2008.01865.

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Peng, Qing Xi. "Classifying Sentiment Based on LDA Model." Applied Mechanics and Materials 427-429 (September 2013): 2614–17. http://dx.doi.org/10.4028/www.scientific.net/amm.427-429.2614.

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Online reviews as a new textual domain offer a unique proposition for sentiment analysis. Their short document length suggests any sentiment they contain is compact and explicit. Although supersized methods have obtained good results, a large amount of corpus should be trained beforehand. Recently, topic models have been introduced for the simultaneous analysis for sentiment in the document. However, the LDA model makes the assumption that, given the parameters the words in the document are all independent. It obviously isnt the case. The words in the document express the sentiment of the author. This paper proposes a model to solve the problem. We assume that the sentiments are related to the topic in the documents. A sentiment layer is added to the LDA model to improve it. Experimental result in the dataset demonstrates the advantage of the proposed model.

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SHI, Jing, Meng FAN, and Wan-Long LI. "Topic Analysis Based on LDA Model." Acta Automatica Sinica 35, no. 12 (March 12, 2010): 1586–92. http://dx.doi.org/10.3724/sp.j.1004.2009.01586.

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Cho, Taemin, and Jee-Hyong Lee. "Latent Keyphrase Extraction Using LDA Model." Journal of Korean Institute of Intelligent Systems 25, no. 2 (April 25, 2015): 180–85. http://dx.doi.org/10.5391/jkiis.2015.25.2.180.

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ZHAI, LIDONG, ZHAOYUN DING, YAN JIA, and BIN ZHOU. "A WORD POSITION-RELATED LDA MODEL." International Journal of Pattern Recognition and Artificial Intelligence 25, no. 06 (September 2011): 909–25. http://dx.doi.org/10.1142/s0218001411008890.

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LDA (Latent Dirichlet Allocation) proposed by Blei is a generative probabilistic model of a corpus, where documents are represented as random mixtures over latent topics, and each topic is characterized by a distribution over words, but not the attributes of word positions of every document in the corpus. In this paper, a Word Position-Related LDA Model is proposed taking into account the attributes of word positions of every document in the corpus, where each word is characterized by a distribution over word positions. At the same time, the precision of the topic-word's interpretability is improved by integrating the distribution of the word-position and the appropriate word degree, taking into account the different word degree in the different word positions. Finally, a new method, a size-aware word intrusion method is proposed to improve the ability of the topic-word's interpretability. Experimental results on the NIPS corpus show that the Word Position-Related LDA Model can improve the precision of the topic-word's interpretability. And the average improvement of the precision in the topic-word's interpretability is about 9.67%. Also, the size-aware word intrusion method can interpret the topic-word's semantic information more comprehensively and more effectively through comparing the different experimental data.

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Zhou, Xiuze, and Shunxiang Wu. "Rating LDA model for collaborative filtering." Knowledge-Based Systems 110 (October 2016): 135–43. http://dx.doi.org/10.1016/j.knosys.2016.07.020.

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Kim, Hyun-Chul, Daijin Kim, and Sung Yang Bang. "Face recognition using LDA mixture model." Pattern Recognition Letters 24, no. 15 (November 2003): 2815–21. http://dx.doi.org/10.1016/s0167-8655(03)00126-0.

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Liu, Zheng. "LDA-Based Automatic Image Annotation Model." Advanced Materials Research 108-111 (May 2010): 88–94. http://dx.doi.org/10.4028/www.scientific.net/amr.108-111.88.

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This paper presents LDA-based automatic image annotation by visual topic learning and related annotation extending. We introduce the Latent Dirichlet Allocation (LDA) model in visual application domain. Firstly, the visual topic which is most relevant to the unlabeled image is obtained. According to this visual topic, the annotations with highest likelihood serve as seed annotations. Next, seed annotations are extended by analyzing the relationship between seed annotations and related Flickr tags. Finally, we combine seed annotations and extended annotations to construct final annotation set. Experiments conducted on corel5k dataset demonstrate the effectiveness of the proposed model.

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Kim, HyangKyung, Woobin Lee, Eunhak Lee, and Seongyong Kim. "Review of evaluation and interpretation method for LDA model." Korean Data Analysis Society 25, no. 4 (August 31, 2023): 1299–310. http://dx.doi.org/10.37727/jkdas.2023.25.4.1299.

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LDA model has been widely used to investigate the subject of documents or words in various fields because it can analyze large amounts of data. Although perplexity is used to compare various LDA models, it only presents the goodness of fit, and it is not possible to consider how well each document is clustered. To resolve this problem, coherence measures have been proposed. After model selection using complexity and coherence measures, LDAvis is widely used to understand the relationship and meaning of each topic. Although LDA model has been introduced in many preceding studies, the introduction of the model evaluation method, coherence measure, and LDAvis for model interpretation is not sufficient. In this paper, we first introduce the LDA model and the mini-batch learning method, and introduce the coherence measure. We also introduce LDAvis including similarity measures and dimension reduction method to investigate the relationship between topic. Relevance is also explained to present the top words for each topic. Finally, after fitting the LDA model at various hyper-parameters to the image annotation data, the models were compared through coherence measures, and each subject was interpreted using LDAvis.

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Masood, Muhammad Ali, Rabeeh Ayaz Abbasi, Onaiza Maqbool, Mubashar Mushtaq, Naif R. Aljohani, Ali Daud, Muhammad Ahtisham Aslam, and Jalal S. Alowibdi. "MFS-LDA: a multi-feature space tag recommendation model for cold start problem." Program 51, no. 3 (September 5, 2017): 218–34. http://dx.doi.org/10.1108/prog-01-2017-0002.

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Purpose Tags are used to annotate resources on social media platforms. Most tag recommendation methods use popular tags, but in the case of new resources that are as yet untagged (the cold start problem), popularity-based tag recommendation methods fail to work. The purpose of this paper is to propose a novel model for tag recommendation called multi-feature space latent Dirichlet allocation (MFS-LDA) for cold start problem. Design/methodology/approach MFS-LDA is a novel latent Dirichlet allocation (LDA)-based model which exploits multiple feature spaces (title, contents, and tags) for recommending tags. Exploiting multiple feature spaces allows MFS-LDA to recommend tags even if data from a feature space is missing (the cold start problem). Findings Evaluation of a publicly available data set consisting of around 20,000 Wikipedia articles that are tagged on a social bookmarking website shows a significant improvement over existing LDA-based tag recommendation methods. Originality/value The originality of MFS-LDA lies in segregation of features for removing bias toward dominant features and in synchronization of multiple feature space for tag recommendation.

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Liu, Yezheng, Fei Du, Jianshan Sun, and Yuanchun Jiang. "iLDA: An interactive latent Dirichlet allocation model to improve topic quality." Journal of Information Science 46, no. 1 (January 9, 2019): 23–40. http://dx.doi.org/10.1177/0165551518822455.

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User-generated content has been an increasingly important data source for analysing user interests in both industries and academic research. Since the proposal of the basic latent Dirichlet allocation (LDA) model, plenty of LDA variants have been developed to learn knowledge from unstructured user-generated contents. An intractable limitation for LDA and its variants is that low-quality topics whose meanings are confusing may be generated. To handle this problem, this article proposes an interactive strategy to generate high-quality topics with clear meanings by integrating subjective knowledge derived from human experts and objective knowledge learned by LDA. The proposed interactive latent Dirichlet allocation (iLDA) model develops deterministic and stochastic approaches to obtain subjective topic-word distribution from human experts, combines the subjective and objective topic-word distributions by a linear weighted-sum method, and provides the inference process to draw topics and words from a comprehensive topic-word distribution. The proposed model is a significant effort to integrate human knowledge with LDA-based models by interactive strategy. The experiments on two real-world corpora show that the proposed iLDA model can draw high-quality topics with the assistance of subjective knowledge from human experts. It is robust under various conditions and offers fundamental supports for the applications of LDA-based topic modelling.

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Su, Hang, Suthakar Ganapathy, Zhi-Min Yuan, and Chul S. Ha. "P53-Based Strategy for Protection of Bone Marrow From Y-90 Ibritumomab Tiuxetan." Blood 122, no. 21 (November 15, 2013): 1839. http://dx.doi.org/10.1182/blood.v122.21.1839.1839.

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Abstract Introduction Though radiolabeled anti-CD20 antibody has expanding roles in the management of B-cell lymphoma, its main drawback has been radiation-induced damage to the bone marrow leading to acute grade 3 and 4 hematological toxicity and potential contribution to the development of myelodysplastic syndrome and secondary leukemia. Arsenic trioxide is currently used to treat acute promyelocytic leukemia and known as a cytotoxic agent. However, we have recently demonstrated that arsenic trioxide can act as a cytoprotective agent at much lower dose. This is due to its ability to temporarily and reversibly suppress p53 activation caused by DNA-damaging treatments such as chemotherapy or radiotherapy. It has been also demonstrated that this protective effect is selective to normal tissues, as it requires functional p53. We have developed a preclinical model to assess the efficacy of low dose arsenic trioxide (LDA) as a cytoprotective agent against bone marrow toxicity induced by radioimmunotherapy using Y-90 ibritumomab tiuxetan as a model. Methods To test the hypothesis that LDA protects bone marrow against Y-90 ibritumomab tiuxetan induced damage, sex-matched BAL/c mice (4-6 weeks of age) were randomized into four groups: control, LAD only, Y-90 ibritumomab tiuxetan only, LAD pretreatment followed by Y-90 ibritumomab tiuxetan. LDA pretreatment was carried out by feeding mice with water containing 1 mg/L arsenic trioxide for three days. Y-90 ibritumomab tiuxetan was then injected into mice at the dose of 200uCi via tail vein. Tissue samples were collected at different time points (3 hours to 5 weeks) after treatment. Bone marrow damage was analyzed histologically with H&E staining, and DNA damage was assessed with pH2AX staining. To test the hypothesis that LDA does not protect malignant cells, a mouse xenograft model was generated using a CD20 expressing lymphoma cell line, Karpas 422. Treatments were initiated 1 week after implantation when tumors became palpable. Tumor volumes were measured with a caliper periodically. Tumor volume was calculated using the equation: volume = length × width × depth × 0.5236 mm3. Two independent experiments were done and the tumor volumes are expressed as means ± SE. Results Y-90 ibritumomab tiuxetan treatments were associated with severe damages to bone marrow cells, and such damages were significantly reduced by LDA pretreatment (Fig 1). Consistent with this observation, much more DNA damage was accumulated in mice treated with Y-90 ibritumomab tiuxetan from as early as 3 hours to a week after treatment, as compared to mice pretreated with LDA (Fig 2). Remarkably, while DNA damage was eliminated in LDA-pretreated mice by 2wk to 5wk after treatment, damage was still observed in mice without LAD pretreatment. In tumor xenograft models, the tumor volume of the control group continued to increase with time. LDA pretreatment did not have any detectable effect on the growth of the implanted tumors. As expected, treatment with a single dose of Y-90 ibritumomab tiuxetan resulted in marked tumor growth suppression. LDA pretreatment showed little effect on radiation-induced tumor growth suppression (Fig 3). Conclusion Our results demonstrate that a brief pretreatment with LDA is associated with a marked protection of bone marrow without compromising the ability of irradiation to kill lymphoma cells. A clinical trial is being developed based on our findings. Disclosures: No relevant conflicts of interest to declare.

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Fang, Chen. "Sentiment Analysis of Weibo Comments based on LDA Model." Highlights in Science, Engineering and Technology 24 (December 27, 2022): 45–48. http://dx.doi.org/10.54097/hset.v24i.3883.

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The essence of LDA (Latent Dirichlet Allocation) model is a generative Bayesian probability model that contains three layers of words, topics and corpus (sometimes called document set). Under the LDA algorithm theory, each document represents a probability distribution formed by some topics, and each topic represents a probability distribution formed by many words. Therefore, the model fitting results will present the core keywords and specific probabilities of each topic, and researchers can interpret the meaning of the document according to the model results. In this paper, we hope to use the LDA model as the basis for the emotional analysis of microblog comments.

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Hrnjaković, Olivera. "ANALIZA I OBRADA TEKSTA POMOĆU RAZLIČITIH MODELA TEMA." Zbornik radova Fakulteta tehničkih nauka u Novom Sadu 35, no. 01 (December 28, 2019): 133–36. http://dx.doi.org/10.24867/06be29hrnjakovic.

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Ovaj rad opisuje trenutne mogućnosti i ograničenja postojećih algoritama za izdvajanju tema iz teksta. Dat je teorijski prikaz popularnih modela tema uz sve neophodne korake analize i obrade teksta koji se izvršavaju pre slanja podataka na ulaz modela. Praktičan deo rada je izdvajanje tema iz pitanja sa sajta Stack overflow. Upoređeni su LSA, PLSA i LDA pristup, a evaluacija modela je izvršena određivanjem koherentnosti tema odgovarajućim merama, imenovanjem tema i analizom njihove vizuelizacije u prostoru. Kako modeli tema unapred zahtevaju navođenje broja tema koje će biti izdvojene iz teksta, deo rada posećen je optimizaciji hiperparametara. Izabrani model za modelovanje tema jeste LDA sa 6 tema. Da bi se dobila numerička procena performansi modela 30 pitanja je ručno označeno imenima dobijenih tema i simuliran je klasifikacioni model. Ova pitanja su korišćena kao test skup podataka u kreiranom LDA klasifikacionom modelu. Postignuta je uspešnost od 77% tačnosti.

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FIOLHAIS, CARLOS, and L. M. ALMEIDA. "COMPARISON OF DENSITY FUNCTIONAL APPROXIMATIONS IN THE JELLIUM MODEL FOR METAL CLUSTERS." International Journal of Modern Physics B 15, no. 10n11 (May 10, 2001): 1724–27. http://dx.doi.org/10.1142/s0217979201006239.

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We calculated the exchange, correlation and total energies of clusters of alkali metals with N=1-150 atoms in the spherical jellium model. The calculations were made using the Kohn-Sham method with exchange and correlation energies evaluated in the meta-generalized gradient approximation (MGGA), proposed by J. P. Perdew, S. Kurth, A. Zupan and P. Blaha, in the generalized gradient approximation (GGA) of J. P. Perdew, K. Burke and M. Ernzerhof, and in the Local Density Approximation (LDA). We evaluated the relative deviations of MGGA and GGA energies with respect to LDA. Exchange energies of MGGA and GGA are more negative than the LDA exchange energy and become closer to this as the cluster size increases. On the other hand, the GGA and MGGA correlation energies, which are almost identical, are less negative than LDA. The deviations of GGA and MGGA exchange-correlation energies with respect to LDA are smaller than those of the exchange and correlation energies separately. For clusters with 18 and 20 atoms we have compared our jellium results with Variational and Diffusion Monte-Carlo results. Errors of LDA for exchange and correlation tend to cancel so that the total exchange-correlation energy is close to the Monte-Carlo results. Similar cancellations occur with GGA and MGGA. We also examined the validity of the liquid drop model.

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Shao, Dangguo, Chengyao Li, Chusheng Huang, Qing An, Yan Xiang, Junjun Guo, and Jianfeng He. "The short texts classification based on neural network topic model." Journal of Intelligent & Fuzzy Systems 42, no. 3 (February 2, 2022): 2143–55. http://dx.doi.org/10.3233/jifs-211471.

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Aiming at the low effectiveness of short texts feature extraction, this paper proposes a short texts classification model based on the improved Wasserstein-Latent Dirichlet Allocation (W-LDA), which is a neural network topic model based on the Wasserstein Auto-Encoder (WAE) framework. The improvements of W-LDA are as follows: Firstly, the Bag of Words (BOW) input in the W-LDA is preprocessed by Term Frequency–Inverse Document Frequency (TF-IDF); Subsequently, the prior distribution of potential topics in W-LDA is replaced from the Dirichlet distribution to the Gaussian mixture distribution, which is based on the Variational Bayesian inference; And then the sparsemax function layer is introduced after the hidden layer inferred by the encoder network to generate a sparse document-topic distribution with better topic relevance, the improved W-LDA is named the Sparse Wasserstein-Variational Bayesian Gaussian mixture model (SW-VBGMM); Finally, the document-topic distribution generated by SW-VBGMM is input to BiGRU (Bidirectional Gating Recurrent Unit) for the deep feature extraction and the short texts classification. Experiments on three Chinese short texts datasets and one English dataset represent that our model is better than some common topic models and neural network models in the four evaluation indexes (accuracy, precision, recall, F1 value) of text classification.

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Ling, Xiaoming, and Feng Zhang. "Construction of Dirichlet Mixture Allocation total probability model based on multiple class text analysis." Journal of Physics: Conference Series 2031, no. 1 (September 1, 2021): 012055. http://dx.doi.org/10.1088/1742-6596/2031/1/012055.

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Abstract The LDA model is a total probability generation model for analyzing a large number of documents. It extends PLSA, another text analysis model. In this model, each document is treated as a topic hybrid model, and the topic’s proportional prior distribution is a Dirichlet distribution. The LDA model does not reflect complex dependencies between underlying topics. Based on LDA, this paper introduces a new topic generation model, DMA (Dirichlet Mixture Allocation), which models document collections more accurately than LDA when documents are obtained in multiple classes. In this paper, we build a probabilistic topic model of DMA, use the method of variational inference to approximate each parameter of the model, study the model, and finally solve the estimation of each parameter.

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Hussain, Jamil, Zahra Azhar, Hafiz Farooq Ahmad, Muhammad Afzal, Mukhlis Raza, and Sungyoung Lee. "User Experience Quantification Model from Online User Reviews." Applied Sciences 12, no. 13 (July 1, 2022): 6700. http://dx.doi.org/10.3390/app12136700.

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Due to the advancement in information technology and the boom of micro-blogging platforms, a growing number of online reviews are posted daily on product distributed platforms in the form of spontaneous and insightful user feedback, and these can be used as a significant data source to understand user experience (UX) and satisfaction. However, despite the vast amount of online reviews, the existing literature focuses on online ratings and ignores the real textual context in reviews. We proposed a three-step UX quantification model from online reviews to understand customer satisfaction using the effect-based Kano model. First, the relevant online reviews are selected using various filter mechanisms. Second, UX dimensions (UXDs) are extracted using a proposed method called UX word embedding Latent Dirichlet allocation (UXWE-LDA) and sentiment orientation using a transformer-based pipeline. Then, the casual relationships are identified for the extracted UXDs. Third, the UXDs are mapped on the customer satisfaction model (effect-based Kano) to understand the user perspective about the system, product, or services. Finally, the different parts of the proposed quantification model are evaluated to examine the performance of this method. We present different results of the proposed method in terms of accuracy, topic coherence (TC), Topic-wise performance, and expert-based evaluation for the proposed framework validation. For review quality filters, we achieved 98.49% accuracy for the spam detection classifier and 95% accuracy for the relatedness detection classifier. The results show that the proposed method for the topic extractor module always gives a higher TC value than other models such as WE-LDA and LDA. Regarding topic-wise performance measures, UXWE-LDA achieves a 3% improvement on average compared to LDA due to the incorporation of semantic domain knowledge. We also compute the Jaccard coefficient similarity between the extracted dimensions using UXWE-LDA and UX experts-based analysis for checking the mutual agreement, which is 0.3, 0.5, and 0.4, respectively. Based on the Kano model, the presented study has potential implications concerning issues and knowing the product’s strengths and weaknesses in product design.

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LI, Wen-Bo, Le SUN, and Da-Kun ZHANG. "Text Classification Based on Labeled-LDA Model." Chinese Journal of Computers 31, no. 4 (September 28, 2009): 620–27. http://dx.doi.org/10.3724/sp.j.1016.2008.00620.

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LIU, Pei-qi, and Jie-han SUN. "Label propagation algorithm based on LDA model." Journal of Computer Applications 32, no. 2 (March 13, 2013): 403–6. http://dx.doi.org/10.3724/sp.j.1087.2012.00403.

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Yadav, Nilesh, and Narendra Shekokar. "SQLI Detection Based on LDA Topic Model." International Journal of Engineering Trends and Technology 69, no. 11 (November 25, 2021): 47–52. http://dx.doi.org/10.14445/22315381/ijett-v69i11p206.

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Liang, Jiguang, Ping Liu, Jianlong Tan, and Shuo Bai. "Sentiment Classification Based on AS-LDA Model." Procedia Computer Science 31 (2014): 511–16. http://dx.doi.org/10.1016/j.procs.2014.05.296.

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Ping, Deng Li, Guo Bing, and Zheng Wen. "Web Service Clustering Approach Based on Network and Fused Document-Based and Tag-Based Topics Similarity." International Journal of Web Services Research 18, no. 3 (July 2021): 63–81. http://dx.doi.org/10.4018/ijwsr.2021070104.

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To produce a web services clustering with values that satisfy many requirements is a challenging focus. In this article, the authors proposed a new approach with two models, which are helpful to the service clustering problem. Firstly, a document-tag LDA model (DTag-LDA) is proposed that considers the tag information of web services, and the tag can describe the effective information of documents accurately. Based on the first model, this article further proposes an efficient document weight and tag weight-LDA model (DTw-LDA), which fused multi-modal data network. To further improve the clustering accuracy, the model constructs the network for describing text and tag respectively and then merges the two networks to generate web service network clustered. In addition, this article also designs experiments to verify that the used auxiliary information can help to extract more accurate semantics by conducting service classification. And the proposed method has obvious advantages in precision, recall, purity, and other performance.

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Zhao, Yi, Yu Qiao, and Keqing He. "A Novel Tagging Augmented LDA Model for Clustering." International Journal of Web Services Research 16, no. 3 (July 2019): 59–77. http://dx.doi.org/10.4018/ijwsr.2019070104.

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Clustering has become an increasingly important task in the analysis of large documents. Clustering aims to organize these documents, and facilitate better search and knowledge extraction. Most existing clustering methods that use user-generated tags only consider their positive influence for improving automatic clustering performance. The authors argue that not all user-generated tags can provide useful information for clustering. In this article, the authors propose a new solution for clustering, named HRT-LDA (High Representation Tags Latent Dirichlet Allocation), which considers the effects of different tags on clustering performance. For this, the authors perform a tag filtering strategy and a tag appending strategy based on transfer learning, Word2vec, TF-IDF and semantic computing. Extensive experiments on real-world datasets demonstrate that HRT-LDA outperforms the state-of-the-art tagging augmented LDA methods for clustering.

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Ma, Haiqun, and Tao Zhang. "Research on Policy Text Clustering Algorithm Based on LDA-Gibbs Model." Journal of Advanced Computational Intelligence and Intelligent Informatics 23, no. 2 (March 20, 2019): 268–73. http://dx.doi.org/10.20965/jaciii.2019.p0268.

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Policy text contains large amount of diversified data and strictly conforms to standards and specifications, but the traditional text clustering method cannot solve the problems of high dimensionality, sparse features, and similar meanings, so this paper proposes a weighted algorithm based on the LDA-Gibbs model to improve the accuracy of policy text clustering. Firstly, it provides realistic basis for the assumptions of the LDA-Gibbs topic model and the weighted algorithm; secondly, it pre-processes the existing policy text simulated data, establishes the LDA-Gibbs model, forms a weighted algorithm, and generates training data to determine the number of optimal topics in the LDA-Gibbs model and completes the final clustering of the policy text; finally, by summarizing, classifying and deducing the conclusions of the experimental data, this paper proves the objective validity and effects of this method. Hopefully the overall design of this method can be applied in the prospective study on the formulation of new policies in the future, the retrospective evaluation and testing of the existing policies and the formation of a two-way interactive mechanism.

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Hou, Zhaolu, Jianping Li, and Bin Zuo. "Correction of Monthly SST Forecasts in CFSv2 Using the Local Dynamical Analog Method." Weather and Forecasting 36, no. 3 (June 2021): 843–58. http://dx.doi.org/10.1175/waf-d-20-0123.1.

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AbstractNumerical seasonal forecasts in Earth science always contain forecast errors that cannot be eliminated by improving the ability of the numerical model. Therefore, correction of model forecast results is required. Analog correction is an effective way to reduce model forecast errors, but the key question is how to locate analogs. In this paper, we updated the local dynamical analog (LDA) algorithm to find analogs and depicted the process of model error correction as the LDA correction scheme. The LDA correction scheme was first applied to correct the operational seasonal forecasts of sea surface temperature (SST) over the period 1982–2018 from the state-of-the-art coupled climate model named NCEP Climate Forecast System, version 2. The results demonstrated that the LDA correction scheme improves forecast skill in many regions as measured by the correlation coefficient and root-mean-square error, especially over the extratropical eastern Pacific and tropical Pacific, where the model has high simulation ability. El Niño–Southern Oscillation (ENSO) as the focused physics process is also improved. The seasonal predictability barrier of ENSO is in remission, and the forecast skill of central Pacific ENSO also increases due to the LDA correction method. The intensity of the ENSO mature phases is improved. Meanwhile, the ensemble forecast results are corrected, which proves the positive influence from this LDA correction scheme on the probability forecast of cold and warm events. Overall, the LDA correction scheme, combining statistical and model dynamical information, is demonstrated to be readily integrable with other advanced operational models and has the capability to improve forecast results.

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Hwang, Inok, HyangKyung Kim, Ji Won Lee, and Seongyong Kim. "Clustering of image annotations using online learning-based LDA model." Korean Data Analysis Society 25, no. 2 (April 30, 2023): 537–48. http://dx.doi.org/10.37727/jkdas.2023.25.2.537.

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LDA model is one of the latent topic models used to investigate latent topics by analyzing documents and words in corpus, and has been widely used in various fields. However, LDA model proposed by Blei, Ng, Jordan (2003) is based on batch learning, which estimates parameters using whole data at once, which has limitations such as large memory requirement and computation time for large data. To resolve this problems, an online learning-based LDA model has been proposed. This method has been known to consume less memory and have a faster analysis speed than batch learning. In this paper, we investigate the topic of each image by applying an online learning-based LDA model to corpus composed of annotations for more than 10,000 images provided by Visual Genome. For analysis, preprocessing was performed on image annotations. Various combinations of the number of latent topic and several hyper-parameters were set, and measures of perplexity and coherence are compared to find optimal model. As a result of comparison, a model with 10 latent groups was selected, and topics such as ‘human’, ‘animal’, ‘downtown’, ‘sea’, ‘bathroom’, and ‘kitchen’ were derived through the top words of each group.

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Liu, Yang, Honghong Wang, Yeqi Fei, Ying Liu, Luxiang Shen, Zilong Zhuang, and Xiao Zhang. "Research on the Prediction of Green Plum Acidity Based on Improved XGBoost." Sensors 21, no. 3 (January 30, 2021): 930. http://dx.doi.org/10.3390/s21030930.

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The acidity of green plum has an important influence on the fruit’s deep processing. Traditional physical and chemical analysis methods for green plum acidity detection are destructive, time-consuming, and unable to achieve online detection. In response, a rapid and non-destructive detection method based on hyperspectral imaging technology was studied in this paper. Research on prediction performance comparisons between supervised learning methods and unsupervised learning methods is currently popular. To further improve the accuracy of component prediction, a new hyperspectral imaging system was developed, and the kernel principle component analysis—linear discriminant analysis—extreme gradient boosting algorithm (KPCA-LDA-XGB) model was proposed to predict the acidity of green plum. The KPCA-LDA-XGB model is a supervised learning model combined with the extreme gradient boosting algorithm (XGBoost), kernel principal component analysis (KPCA), and linear discriminant analysis (LDA). The experimental results proved that the KPCA-LDA-XGB model offers good acidity predictions for green plum, with a correlation coefficient (R) of 0.829 and a root mean squared error (RMSE) of 0.107 for the prediction set. Compared with the basic XGBoost model, the KPCA-LDA-XGB model showed a 79.4% increase in R and a 31.2% decrease in RMSE. The use of linear, radial basis function (RBF), and polynomial (Poly) kernel functions were also compared and analyzed in this paper to further optimize the KPCA-LDA-XGB model.

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Xu, Jigang, Shujun Liu, Ming Gao, and Yonggang Zuo. "Classification of Lubricating Oil Types Using Mid-Infrared Spectroscopy Combined with Linear Discriminant Analysis–Support Vector Machine Algorithm." Lubricants 11, no. 6 (June 20, 2023): 268. http://dx.doi.org/10.3390/lubricants11060268.

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To realize the classification of lubricating oil types using mid-infrared (MIR) spectroscopy, linear discriminant analysis (LDA) was used for the dimensionality reduction of spectrum data, and the classification model was established based on the support vector machine (SVM). The spectra of the samples were pre-processed by interval selection, Savitzky–Golay smoothing, multiple scattering correction, and normalization. The Kennard–Stone algorithm (K/S) was used to construct the calibration and validation sets. The percentage of correct classification (%CC) was used to evaluate the model. This study compared the results obtained with several chemometric methods: PLS-DA, LDA, principal component analysis (PCA)-SVM, and LDA-SVM in MIR spectroscopy applications. In both calibration and verification sets, the LDA-SVM model achieved 100% favorable results. The PLS-DA analysis performed poorly. The cyclic resistance ratio (CRR) of the calibration set was classified via the LDA and PCA-SVM analysis as 100%, but the CRR of the verification set was not as good. The LDA-SVM model was superior to the other three models; it exhibited good robustness and strong generalization ability, providing a new method for the classification of lubricating oil types by MIR spectroscopy.

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Radhi, Muna Abdul Hussain. "Human Identification Model Considering Biometrics Features." Journal La Multiapp 3, no. 4 (August 26, 2022): 198–206. http://dx.doi.org/10.37899/journallamultiapp.v3i4.692.

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In the medical field, brain classification is an effective technique for identifying a person through his brain print based on the hidden biometrics of high specificity included in the magnetic resonance images(MRI) of the brain, as this privacy strongly contributes to the issue of verification and identification of the person. In this paper, the brain print is extracted from the MRI obtained from 50 healthy people, which were passed through several pre-processing techniques in order to be used in the classification stage through convolutional neural network model, among those pre-classification stages, data collection after extracting the influential features for each image, which was based on linear discrimination analysis (LDA). The experimental results showed the importance of using LDA for feature extraction and adoption as input for K-NN and CNN classifiers. The classifiers proved successful in the classification if the features extracted with the help of LDA were adopted. Where CNN had the ability to classify with an accuracy of 99%, 82% for K-NN. The final stage in identifying a person through a brain fingerprint relied mainly on the model's success in classifying and predicting the remaining data in the testing stage.

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35

Lin, Fan, Jianbing Xiahou, and Zhuxiang Xu. "TCM clinic records data mining approaches based on weighted-LDA and multi-relationship LDA model." Multimedia Tools and Applications 75, no. 22 (April 13, 2016): 14203–32. http://dx.doi.org/10.1007/s11042-016-3363-9.

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36

Burns, Nicola, Yaxin Bi, Hui Wang, and Terry Anderson. "Enhanced Twofold-LDA Model for Aspect Discovery and Sentiment Classification." International Journal of Knowledge-Based Organizations 9, no. 4 (October 2019): 1–20. http://dx.doi.org/10.4018/ijkbo.2019100101.

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There is a need to automatically classify information from online reviews. Customers want to know useful information about different aspects of a product or service and also the sentiment expressed towards each aspect. This article proposes an Enhanced Twofold-LDA model (Latent Dirichlet Allocation), in which one LDA is used for aspect assignment and another is used for sentiment classification, aiming to automatically determine aspect and sentiment. The enhanced model incorporates domain knowledge (i.e., seed words) to produce more focused topics and has the ability to handle two aspects in at the sentence level simultaneously. The experiment results show that the Enhanced Twofold-LDA model is able to produce topics more related to aspects in comparison to the state of arts method ASUM (Aspect and Sentiment Unification Model), whereas comparable with ASUM on sentiment classification performance.

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Kung, Benson, Maurice Chiang, Gayan Perera, Megan Pritchard, and Robert Stewart. "Unsupervised Machine Learning to Identify Depressive Subtypes." Healthcare Informatics Research 28, no. 3 (July 31, 2022): 256–66. http://dx.doi.org/10.4258/hir.2022.28.3.256.

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Objectives: This study evaluated an unsupervised machine learning method, latent Dirichlet allocation (LDA), as a method for identifying subtypes of depression within symptom data. Methods: Data from 18,314 depressed patients were used to create LDA models. The outcomes included future emergency presentations, crisis events, and behavioral problems. One model was chosen for further analysis based upon its potential as a clinically meaningful construct. The associations between patient groups created with the final LDA model and outcomes were tested. These steps were repeated with a commonly-used latent variable model to provide additional context to the LDA results. Results: Five subtypes were identified using the final LDA model. Prior to the outcome analysis, the subtypes were labeled based upon the symptom distributions they produced: psychotic, severe, mild, agitated, and anergic-apathetic. The patient groups largely aligned with the outcome data. For example, the psychotic and severe subgroups were more likely to have emergency presentations (odds ratio [OR] = 1.29; 95% confidence interval [CI], 1.17–1.43 and OR = 1.16; 95% CI, 1.05–1.29, respectively), whereas these outcomes were less likely in the mild subgroup (OR = 0.86; 95% CI, 0.78–0.94). We found that the LDA subtypes were characterized by clusters of unique symptoms. This contrasted with the latent variable model subtypes, which were largely stratified by severity. Conclusions: This study suggests that LDA can surface clinically meaningful, qualitative subtypes. Future work could be incorporated into studies concerning the biological bases of depression, thereby contributing to the development of new psychiatric therapeutics.

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Jia Qi, Lim, and Norma Alias. "Effective and Efficient LDA+ELM Model for Supervised Classification of Brain Tumor Types Using 2D MRI Scans." International Annals of Science 9, no. 1 (July 2, 2020): 160–73. http://dx.doi.org/10.21467/ias.9.1.160-173.

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Application of machine learning in multiclass classification of brain tumor types has contributed to the development of computer aided diagnosis (CAD) system that can potentially enhance accuracy and speed up diagnosis of the disease. LDA+ELM model with different activation functions were investigated to achieve the optimum performances in terms of accuracy, Kappa statistic, sensitivity, precision, F-measure, training time and test time. We also proposed a user-friendly GUI in characterizing brain tumor types using MR images. First, a total of 3064 slices of CE T1-weighted brain MR images with ground truth were downloaded from a free online database. The manually segmented tumor region was augmented and then undergo several feature extraction techniques. All the feature descriptors obtained were then concatenated, followed by LDA dimensionality approach. Performance of different number of LDA features and ELM activation functions were investigated by repeated training and test. The ELM output of training data for each class was used to fit GMM and these probabilistic models used to estimate posterior probabilities of test data. LDA+ELM model with 5 LDA feature input, utilizing sigmoid function as hidden nodes activation functions achieves the best generalization performance with accuracy of 98.92% and corresponding F-scores for meningioma, glioma and pituitary tumor of 97.81%, 99.1% and 99.5% respectively. The proposed method (LDA+ELM) model performs better compared to other previous works using the same dataset and performing the same classification task.

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Anwar, Waheed, Imran Sarwar Bajwa, and Shabana Ramzan. "Design and Implementation of a Machine Learning-Based Authorship Identification Model." Scientific Programming 2019 (January 16, 2019): 1–14. http://dx.doi.org/10.1155/2019/9431073.

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In this paper, a novel approach is presented for authorship identification in English and Urdu text using the LDA model with n-grams texts of authors and cosine similarity. The proposed approach uses similarity metrics to identify various learned representations of stylometric features and uses them to identify the writing style of a particular author. The proposed LDA-based approach emphasizes instance-based and profile-based classifications of an author’s text. Here, LDA suitably handles high-dimensional and sparse data by allowing more expressive representation of text. The presented approach is an unsupervised computational methodology that can handle the heterogeneity of the dataset, diversity in writing, and the inherent ambiguity of the Urdu language. A large corpus has been used for performance testing of the presented approach. The results of experiments show superiority of the proposed approach over the state-of-the-art representations and other algorithms used for authorship identification. The contributions of the presented work are the use of cosine similarity with n-gram-based LDA topics to measure similarity in vectors of text documents. Achievement of overall 84.52% accuracy on PAN12 datasets and 93.17% accuracy on Urdu news articles without using any labels for authorship identification task is done.

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Santanello, Joseph A., Sujay V. Kumar, Christa D. Peters-Lidard, and Patricia M. Lawston. "Impact of Soil Moisture Assimilation on Land Surface Model Spinup and Coupled Land–Atmosphere Prediction." Journal of Hydrometeorology 17, no. 2 (January 26, 2016): 517–40. http://dx.doi.org/10.1175/jhm-d-15-0072.1.

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Abstract Advances in satellite monitoring of the terrestrial water cycle have led to a concerted effort to assimilate soil moisture observations from various platforms into offline land surface models (LSMs). One principal but still open question is that of the ability of land data assimilation (LDA) to improve LSM initial conditions for coupled short-term weather prediction. In this study, the impact of assimilating Advanced Microwave Scanning Radiometer for EOS (AMSR-E) soil moisture retrievals on coupled WRF Model forecasts is examined during the summers of dry (2006) and wet (2007) surface conditions in the southern Great Plains. LDA is carried out using NASA’s Land Information System (LIS) and the Noah LSM through an ensemble Kalman filter (EnKF) approach. The impacts of LDA on the 1) soil moisture and soil temperature initial conditions for WRF, 2) land–atmosphere coupling characteristics, and 3) ambient weather of the coupled LIS–WRF simulations are then assessed. Results show that impacts of soil moisture LDA during the spinup can significantly modify LSM states and fluxes, depending on regime and season. Results also indicate that the use of seasonal cumulative distribution functions (CDFs) is more advantageous compared to the traditional annual CDF bias correction strategies. LDA performs consistently regardless of atmospheric forcing applied, with greater improvements seen when using coarser, global forcing products. Downstream impacts on coupled simulations vary according to the strength of the LDA impact at the initialization, where significant modifications to the soil moisture flux–PBL–ambient weather process chain are observed. Overall, this study demonstrates potential for future, higher-resolution soil moisture assimilation applications in weather and climate research.

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Liu, Ya Shu, and Han Bing Yan. "The Development of Topic Model Based on Beta-Negative Binomial Process." Applied Mechanics and Materials 427-429 (September 2013): 1597–600. http://dx.doi.org/10.4028/www.scientific.net/amm.427-429.1597.

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. Topic Model is one of the important subfields in Data Mining, which has been developed very quickly and has been applicated in many fields in recent years. Many researchers have been engaged in this field. In this paper, we introduce the BNB process based on Beta and Negative Binomial distribution, using the hierarchical distribution instead of Dirichlet in LDA. And we give the expression of parameter estimation used by Gibbs sampling. Then, BNB process is applicated in the text topic classification. We design experiments to decide the numbers of topics and compare the BNB process with LDA. Experiment results show that the BNB process has better performance over LDA in English Dataset, but they have almost the same result in Chinese micro-blog topic classification. Finally we analyze the problem and give the idea in further research.

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Hai, Nguyen Cao Truong, Kyung-Im Kim, and Hyuk-Ro Park. "SVD-LDA: A Combined Model for Text Classification." Journal of Information Processing Systems 5, no. 1 (March 31, 2009): 5–10. http://dx.doi.org/10.3745/jips.2009.5.1.005.

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Kumar Rai, Naveen, Vikas Srivastava, and Rahul Kumar. "Churn Prediction Model Using Linear Discriminant Analysis (LDA)." IOSR Journal of Computer Engineering 18, no. 05 (May 2016): 86–93. http://dx.doi.org/10.9790/0661-1805048693.

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Bhat, Muzafar Rasool, Majid A. Kundroo, Tanveer A. Tarray, and Basant Agarwal. "Deep LDA : A new way to topic model." Journal of Information and Optimization Sciences 41, no. 3 (June 30, 2019): 823–34. http://dx.doi.org/10.1080/02522667.2019.1616911.

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Zhang, Bingjing, Bo Peng, and Judy Qiu. "High Performance LDA through Collective Model Communication Optimization." Procedia Computer Science 80 (2016): 86–97. http://dx.doi.org/10.1016/j.procs.2016.05.300.

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Nie, Weizhi, Xixi Li, Anan Liu, and Yuting Su. "3D object retrieval based on Spatial+LDA model." Multimedia Tools and Applications 76, no. 3 (August 8, 2015): 4091–104. http://dx.doi.org/10.1007/s11042-015-2840-x.

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Li, Fangtao, Minlie Huang, and Xiaoyan Zhu. "Sentiment Analysis with Global Topics and Local Dependency." Proceedings of the AAAI Conference on Artificial Intelligence 24, no. 1 (July 5, 2010): 1371–76. http://dx.doi.org/10.1609/aaai.v24i1.7523.

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With the development of Web 2.0, sentiment analysis has now become a popular research problem to tackle. Recently, topic models have been introduced for the simultaneous analysis for topics and the sentiment in a document. These studies, which jointly model topic and sentiment, take the advantage of the relationship between topics and sentiment, and are shown to be superior to traditional sentiment analysis tools. However, most of them make the assumption that, given the parameters, the sentiments of the words in the document are all independent. In our observation, in contrast, sentiments are expressed in a coherent way. The local conjunctive words, such as “and” or “but”, are often indicative of sentiment transitions. In this paper, we propose a major departure from the previous approaches by making two linked contributions. First, we assume that the sentiments are related to the topic in the document, and put forward a joint sentiment and topic model, i.e. Sentiment-LDA. Second, we observe that sentiments are dependent on local context. Thus, we further extend the Sentiment-LDA model to Dependency-Sentiment-LDA model by relaxing the sentiment independent assumption in Sentiment-LDA. The sentiments of words are viewed as a Markov chain in Dependency-Sentiment-LDA. Through experiments, we show that exploiting the sentiment dependency is clearly advantageous, and that the Dependency-Sentiment-LDA is an effective approach for sentiment analysis.

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van Montfort, Pim, Luc J. M. Smits, Ivo M. A. van Dooren, Stéphanie M. P. Lemmens, Maartje Zelis, Iris M. Zwaan, Marc E. A. Spaanderman, and Hubertina C. J. Scheepers. "Implementing a Preeclampsia Prediction Model in Obstetrics: Cutoff Determination and Health Care Professionals’ Adherence." Medical Decision Making 40, no. 1 (December 2, 2019): 81–89. http://dx.doi.org/10.1177/0272989x19889890.

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Background. Despite improved management, preeclampsia remains an important cause of maternal and neonatal mortality and morbidity. Low-dose aspirin (LDA) lowers the risk of preeclampsia. Although several guidelines recommend LDA prophylaxis in women at increased risk, they disagree about the definition of high risk. Recently, an externally validated prediction model for preeclampsia was implemented in a Dutch region combined with risk-based obstetric care paths. Objectives. To demonstrate the selection of a risk threshold and to evaluate the adherence of obstetric health care professionals to the prediction tool. Study Design. Using a survey ( n = 136) and structured meetings among health care professionals, possible cutoff values at which LDA should be discussed were proposed. The prediction model, with chosen cutoff and corresponding risk-based care paths, was embedded in an online tool. Subsequently, a prospective multicenter cohort study ( n = 850) was performed to analyze the adherence of health care professionals. Patient questionnaires, linked to the individual risk profiles calculated by the online tool, were used to evaluate adherence. Results. Health care professionals agreed upon employing a tool with a high detection rate (cutoff: 3.0%; sensitivity 75%, specificity 64%) followed by shared decision between patients and health care professionals on LDA prophylaxis. Of the 850 enrolled women, 364 women had an increased risk of preeclampsia. LDA was discussed with 273 of these women, resulting in an 81% adherence rate. Conclusion. Consensus regarding a suitable risk cutoff threshold was reached. The adherence to this recommendation was 81%, indicating adequate implementation.

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Yu, Dongjin, Yiyu Wu, Jingchao Sun, Zhiyong Ni, Youhuizi Li, Qing Wu, and Xufeng Chen. "Mining Hidden Interests from Twitter Based on Word Similarity and Social Relationship for OLAP." International Journal of Software Engineering and Knowledge Engineering 27, no. 09n10 (November 2017): 1567–78. http://dx.doi.org/10.1142/s0218194017400113.

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Online Analytical Processing, or OLAP, is an approach to answering multidimensional analytical (MDA) queries in an interactive way. However, the traditional OLAP approaches can only deal with structured data, but not unstructured textual data like tweets. To address this problem, we propose a Latent Dirichlet Allocation (LDA)-based model, called Multilayered Semantic LDA (MS-LDA), which detects the hidden layered interests from Twitter data based on LDA. The layered dimension of interests can be further used to apply OLAP techniques to Twitter data. Furthermore, MS-LDA employs the semantic similarity among words of tweets based on word2vec, and also the social relationship among twitters, to improve its effectiveness. The extensive experiments demonstrate that MS-LDA can effectively extract the dimension hierarchy of tweeters' interests for OLAP.

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Zadnik, T., and R. Lombar. "Our Experience with Left-Sided Abomasal Displacement Correction via the Roll-and-Toggle-Pin Suture Procedure according to Grymer/Sterner Model." ISRN Veterinary Science 2011 (December 27, 2011): 1–3. http://dx.doi.org/10.5402/2011/572842.

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All over the world, and also in Slovenia, left-sided displacement of the abomasum (LDA) occurs most commonly in large-sized, high-producing adult dairy cows immediately after parturition. Yearly retrospective analyses of our ambulatory records showed significantly increased prevalence of LDA (%, %), especially in cows after first parturition. Surgical replacement is now commonly practiced, and many techniques have been devised with emphasis on avoidance of recurrence of the displacement. Because of good results as recorded in the literature and encouragement of Keith E. Sterner, the author of this method, we want to try the right paramedian abomasopexy—Grymer/Sterner model. Since May 2009 till October 2011 109 cows from 46 farms were operated on because of LDA. As many as 44 (40.3%) were affected with LDA after first parturition. The analysis of successful procedure that was carried out 2 months after suture showed that 104 (95.4%) cows were cured. Only 5 (4.5%) cows died within 24 hours after surgery (4 cases of severe toxemia with hypokalemia and one case of acute abomasal hemorrhage were established). Our experience with Grymer/Sterner LDA transfixation sutures proved favorable. Because roll-and-toggle-pin suture technique is rapid and inexpensive we recommend it.

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