Journal articles: 'Cross modal association mining'

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Yao, Ran Bo, An Ping Song, Xue Hai Ding, and Ming Bo Li. "Cross Sellingusing Association Rule Mining." Applied Mechanics and Materials 687-691 (November 2014): 1337–41. http://dx.doi.org/10.4028/www.scientific.net/amm.687-691.1337.

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In the retail enterprises, it is an important problem to choose goods group through their sales record.We should consider not only the direct benefits of product, but also the benefits bring by the cross selling. On the base of the mutual promotion in cross selling, in this paper we propose a new method to generate the optimal selected model. Firstly we use Apriori algorithm to obtain the frequent item sets and analyses the association rules sets between products.And then we analyses the above results to generate the optimal products mixes and recommend relationship in cross selling. The experimental result shows the proposed method has some practical value to the decisions of cross selling.

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Cheng, Yang, Ming Cheng, Tao Pang, and Sizhen Liu. "Using Clustering Analysis and Association Rule Technology in Cross-Marketing." Complexity 2021 (April 21, 2021): 1–11. http://dx.doi.org/10.1155/2021/9979874.

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In this paper, according to the perspective of customers and products, by using clustering analysis and association rule technology, this paper proposes a cross-marketing model based on an improved sequential pattern mining algorithm, where an improved algorithm AP (Apriori all PrefixSpan) is applied. The algorithm can reduce the time cost of constructing a projection database and the influence of the increase of support on the algorithm efficiency. The improved idea is that when the first partition is used to generate the projection database, the number of itemsets in the projection database is sorted from small to large, and when the second partition is used, the sequence patterns are generated directly from the mined sequence patterns, so as to reduce the construction of the database. The experimental results show that this method can quickly mine the effective information in complex data sets, improve the accuracy and efficiency of data mining, and occupy less memory consumption, which has good theoretical value and application value.

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Amseke, Robynson, and Edi Winarko. "Aplikasi Algoritma CBA untuk Klasifikasi Resiko Pemberian Kredit (Studi kasus: PT. Telkom CDC Sub Area Kupang)." IJCCS (Indonesian Journal of Computing and Cybernetics Systems) 8, no. 2 (2014): 121. http://dx.doi.org/10.22146/ijccs.6540.

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AbstrakSalah satu penyebab kredit bermasalahberasal dari pihak internal, yaitu kurang telitinya timdalam melakukan survei dan analisis, atau bisa juga karena penilaian dan analisis yang bersifat subjektif.Penyebab ini dapat diatasi dengan sistem komputer, yaitu aplikasi komputer yang menggunakan teknik data mining.Teknik data mining digunakan dalam penelitian ini untuk klasifikasi resiko pemberian kredit dengan menerapkan algoritma Classification Based On Association (CBA). Algoritma ini merupakan salah satu algoritma klasifikasi dalam data mining yang mengintegrasikan teknik asosiasi dan klasifikasi. Data kredit awal yang telah di-preprocessing, diproses menggunakan algoritma CBA untuk membangun model, lalu model tersebut digunakan untuk mengklasifikasi data pelaku usaha baru yang mengajukan kredit ke dalam kelas lancar atau macet.Teknik Pengujian akurasi model diukur menggunakan 10-fold cross validation. Hasil pengujian menunjukkan bahwa rata-rata nilai akurasi menggunakan algoritma CBA (57,86%), sedikit lebih tinggi dibandingkan rata-rata nilai akurasi menggunakan algoritma Naive Bayes dan SVM dari perangkat lunak Rapid Miner 5.3 (56,35% dan 55,03%). Kata kunci—classification based on association, CBA, data mining, klasifikasi, resiko pemberian kredit AbstractOne of the causes of non-performing loans come from the internal, that is caused by a lack of rigorous team in conducting the survey and analysis, or it could be due to subjective evaluation and analysis. The cause of this can be solved by a computer system, the computer application that uses data mining techniques. Data mining technique, was usedin this study toclassifycreditriskby applyingalgorithmsClassificationBasedonAssociation(CBA). This algorithm is an algorithm classification of data mining which integratingassociationandclassificationtechniques. Preprocessed initial-credit data, will be processed using theCBAalgorithmto create a model of which is toclassifythe newloandata into swift class or bad one. Testing techniques the accuracy of the model was measured by 10-fold cross validation. The resultshowsthatthe accuracy averagevalue using theCBAalgorithm(57,86%), was slightly higher than those using thealgorithmsofSVM andNaiveBayes from RapidMiner5.3software(56,35% and55,03%, respectively). Keywords—classification based on association, CBA, data mining, classification, credit risk

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Islam, Arif Ul, Shamim H. Ripon, and Nuruddin Qaisar Bhuiyan. "Cervical Cancer Risk Factors: Classification and Mining Associations." APTIKOM Journal on Computer Science and Information Technologies 4, no. 1 (2020): 8–18. http://dx.doi.org/10.34306/csit.v4i1.85.

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Women of all over the world suffer from a common cancer, named Cervical cancer. Cervical cancer cellsgrow slowly at the cervix. This cancer can be avoided if it is recognized and handled in its first stage. Now it is a keychallenge for Medical experts to identify such cancer before it develops extremely. Nowadays, data mining modelsare popularly used to extract hidden patterns from huge medical dataset. This paper introduces data miningtechinques for classification and finding associations in order to detect Cervical cancer at early stage. Afterpreprocessing, the dataset was tested on Decision Tree, Random Forest, Logistic Model Tree and Artificial NeuralNetwork. These methods achieve considerable success in case of both K-fold cross validations and randomly splitdataset. Association rules has been established for detecting comparatively riskier factors which are moreresponsible for cancer development. The proposed methodology can help Medical experts to conduct their researchon Cervical cancer.

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Guo, Chun Fen, and Li Chen Zhuang. "Research on Mining Method of Process Knowledge Based on Ontology." Applied Mechanics and Materials 401-403 (September 2013): 1470–73. http://dx.doi.org/10.4028/www.scientific.net/amm.401-403.1470.

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Making use of hierarchical structure clearly of concept model of ontology , on the basis of Apriori algorithm and introducing cross linker method, an mining algorithm of process knowledge of association rule is proposed based on ontology. To verify the feasibility of the algorithm, partial correlation process knowledge is dicovered by this algorithm.

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-Ul-Islam, Arif, Shamim H Ripon, and Nuruddin Qaisar Bhuiyan. "Cervical Cancer Risk Factors: Classification and Mining Associations." APTIKOM Journal on Computer Science and Information Technologies 4, no. 1 (2019): 8–18. http://dx.doi.org/10.11591/aptikom.j.csit.131.

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Women of all over the world suffer from a common cancer, named Cervical cancer. Cervical cancer cells grow slowly at the cervix. This cancer can be avoided if it is recognized and handled in its first stage. Now it is a key challenge for Medical experts to identify such cancer before it develops extremely. Nowadays, data mining models are popularly used to extract hidden patterns from huge medical dataset. This paper introduces data mining techinques for classification and finding associations in order to detect Cervical cancer at early stage. After preprocessing, the dataset was tested on Decision Tree, Random Forest, Logistic Model Tree and Artificial Neural Network. These methods achieve considerable success in case of both K-fold cross validations and randomly split dataset. Association rules has been established for detecting comparatively riskier factors which are more responsible for cancer development. The proposed methodology can help Medical experts to conduct their research on Cervical cancer.

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Chen, Wei, Zhengtao Yu, Yantuan Xian, Zhenhan Wang, and Yonghua Wen. "Mining Keywords from Short Text Based on LDA-Based Hierarchical Semantic Graph Model." International Journal of Information Systems in the Service Sector 12, no. 2 (2020): 76–87. http://dx.doi.org/10.4018/ijisss.2020040106.

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Extracting keywords from a text set is an important task. Most of the previous studies extract keywords from a single text. Using the key topics in the text collection, the association relationship between the topic and the topic in the cross-text, and the association relationship between the words and the words in the cross-text has not played an important role in the previous method of extracting keywords from the text collection. In order to improve the accuracy of extracting keywords from text collections, using the semantic relationship between topics and topics in texts and highlighting the semantic relationship between words and words under the key topics, this article proposes an unsupervised method for mining keywords from short text collections. In this method, a two level semantic association model is used to link the semantic relations between topics and the semantic relations between words, and extract the key words based on the combined action. First, the text is represented with LDA; the authors used word2vec to calculate the semantic association between topic and topic, and build a semantic relation graph between topics, that is the upper level graph, and use a graph ranking algorithm to calculate each topic score. In the lower layer, the semantic association between words and words is calculated by using the topic scores and the relationship between topics in the upper network allow a graph to be constructed. Using a graph sorting algorithm sorts the words in short text sets to determine the keywords. The experimental results show that the method is better for extracting keywords from the text set, especially in short articles. In the text, the important topics, the relationship between topics and the correlation between words can improve the accuracy of extracting keywords from the text set.

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Li, Chenghao, Yiding Wang, Changwei Miao, and Cheng Huang. "Cross-Site Scripting Guardian: A Static XSS Detector Based on Data Stream Input-Output Association Mining." Applied Sciences 10, no. 14 (2020): 4740. http://dx.doi.org/10.3390/app10144740.

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The largest number of cybersecurity attacks is on web applications, in which Cross-Site Scripting (XSS) is the most popular way. The code audit is the main method to avoid the damage of XSS at the source code level. However, there are numerous limits implementing manual audits and rule-based audit tools. In the age of big data, it is a new research field to assist the manual auditing through machine learning. In this paper, we propose a new way to audit the XSS vulnerability in PHP source code snippets based on a PHP code parsing tool and the machine learning algorithm. We analyzed the operation sequence of source code and built a model to acquire the information that is most closely related to the XSS attack in the data stream. The method proposed can significantly improve the recall rate of vulnerability samples. Compared with related audit methods, our method has high reusability and excellent performance. Our classification model achieved an F1 score of 0.92, a recall rate of 0.98 (vulnerable sample), and an area under curve (AUC) of 0.97 on the test dataset.

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Wang, Hao, Xin Ning Su, and San Hong Deng. "Study on Hotspots Detection Based on CSSCI Academic Resource Ontology." Advanced Materials Research 171-172 (December 2010): 19–26. http://dx.doi.org/10.4028/www.scientific.net/amr.171-172.19.

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Cross relation and its blending degree among academic resource would be revealed through association analysis of academic resource from CSSCI. So, this paper tries to change the traditional analysis mode that using association rule mining to gain the subject relationship based on single standard, and introduces the ontology mechanism with semantic description capabilities into the knowledge organization of CSSCI academic resource for organizing all kinds of academic concepts by object-oriented approach, so that to establishes CSSCI Academic Resource Networks Model based on Ontology. On this basis, knowledge mining technique will also be adopted to detect the research hotspots of disciplines from the perspectives of high-frequency themes and themes from influential entity in disciplines, to discover the interdisciplinary hotspots so as to promote the exchange of interdisciplinary and to clear the specific direction of interdisciplinary cooperation. Upon that, all kinds of analysis conclusions and scientific laws which have academic value and could generate academic influence would be obtained to support the decision for scientific evaluation.

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Yu, Chong Ho, Hyun Seo Lee, Emily Lara, and Siyan Gan. "Adult Learning Across USA, Canada, and New Zealand: A Cross-Cultural Study of PIAAC." International Education Studies 12, no. 5 (2019): 1. http://dx.doi.org/10.5539/ies.v12n5p1.

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Skeptics of Programme for International Student Assessment (PISA) and Trend for International Math and Science Study (TIMSS) argue that while US elementary and high school students are behind their peers in other nations, the US workforce is still excellent because of the high quality post-secondary educational institutions in the US. However, the Programme for the International Assessment of Adult Competencies (PIAAC) indicates that US adults are in fact far behind their international peers in literacy, numeracy, and technology-based problem solving. Through the use of data mining, this study explored the possible association between PIAAC scores and several constructs. Since the US, Canada, and New Zealand were considered culturally similar nations, according to cluster analysis, patterns between PIAAC scores and selected constructs were analyzed by a variety of big data analytical methods, including cluster analysis, bootstrap forest, boosted tree, and data visualization. Given that PIAAC used multiple computerized adaptive testing, the consequential plausible values were randomly selected when the ensemble approach was used. Additionally, model comparison was utilized to decide between bagging and boosting in order to select the optimal model for each sample. In these samples, cultural engagement, readiness to learn, and social trust, respectively emerged as strong predictors for learning outcomes as they were assessed by PIAAC.

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Bezzina, Aaron, Emma K. Austin, Trent Watson, Lee Ashton, and Carole L. James. "Health and wellness in the Australian coal mining industry: A cross sectional analysis of baseline findings from the RESHAPE workplace wellness program." PLOS ONE 16, no. 6 (2021): e0252802. http://dx.doi.org/10.1371/journal.pone.0252802.

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Overweight and obesity has reach pandemic levels, with two-thirds (67%) of adult Australians classified as overweight or obese. As two of the most significant behavioral risk factors for obesity are modifiable (diet and exercise), there exists an opportunity for treatment through workplace health promotion initiatives. As one of Australia’s largest industries with its own unique workplace factors, the mining industry has previously reported higher than population levels of overweight and obesity. This represented an opportune setting to test the RESHAPE workplace wellness program. RESHAPE is an eight-step framework (based on the WHO ‘Health Workplace Framework and Model’) which aims to provide a sustained approach to wellness in the workplace. This paper presents baseline findings from a pilot study that aimed to implement RESHAPE at three mine sites in NSW, Australia, and investigates the issue of overweight and obesity in the coal mining industry. Across three mine sites, 949 coal miners were examined cross-sectionally on a range of workplace, wellness, health, diet, and exercise factors using a paper-based survey. This was a predominantly male sample (90.4%) with the majority (59.2%) of participants aged 25–44 years. Self-reported height and weight measures indicated that less than 20 percent (18.9%) of participants were in a healthy BMI range, while there were effectively equal numbers of overweight (40.9%) and obese (39.1%) participants. Only 3.5% of participants met the daily recommendation for vegetables (5 serves) and shift-workers had greater association with elevated BMI compared to non-shift workers (B = 1.21, 95% CI: 0.23, 2.20, p = 0.016). Poor nutrition is likely to be a key component in elevated levels of overweight and obesity within this industry, with workplace factors compounding challenges workers face in implementing health behavior change. Future studies would benefit from assessing diet and physical activity knowledge in relation to recommendations and serving sizes.

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Fan, Xing, Wei Jiang, Hao Luo, Weijie Mao, and Hongyan Yu. "Instance Hard Triplet Loss for In-video Person Re-identification." Applied Sciences 10, no. 6 (2020): 2198. http://dx.doi.org/10.3390/app10062198.

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Traditional Person Re-identification (ReID) methods mainly focus on cross-camera scenarios, while identifying a person in the same video/camera from adjacent subsequent frames is also an important question, for example, in human tracking and pose tracking. We try to address this unexplored in-video ReID problem with a new large-scale video-based ReID dataset called PoseTrack-ReID with full images available and a new network structure called ReID-Head, which can extract multi-person features efficiently in real time and can be integrated with both one-stage and two-stage human or pose detectors. A new loss function is also required to solve this new in-video problem. Hence, a triplet-based loss function with an online hard example mining designed to distinguish persons in the same video/group is proposed, called instance hard triplet loss, which can be applied in both cross-camera ReID and in-video ReID. Compared with the widely-used batch hard triplet loss, our proposed loss achieves competitive performance and saves more than 30% of the training time. We also propose an automatic reciprocal identity association method, so we can train our model in an unsupervised way, which further extends the potential applications of in-video ReID. The PoseTrack-ReID dataset and code will be publicly released.

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Cortés, Sandra, Liliana Zúñiga-Venegas, Floria Pancetti, et al. "A Positive Relationship between Exposure to Heavy Metals and Development of Chronic Diseases: A Case Study from Chile." International Journal of Environmental Research and Public Health 18, no. 4 (2021): 1419. http://dx.doi.org/10.3390/ijerph18041419.

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Chile is a mining country, where waste mining is frequently found in the vicinity of inhabited areas. To explore the association between metal exposure and alterations in glucose metabolism, inflammatory status, and oxidative stress in individuals with chronic exposure to metals, a cross-sectional study was performed with 25 volunteers, between 45–65 years old. Inductive coupled plasma mass spectrometry (ICP-MS) was used to measure urinary levels of total arsenic (As) and its metabolites, cooper, nickel, chromium, and lead. Lipid profile, glucose, and insulin were measured in blood, as well as inflammation (interleukin-6, IL-6) and oxidative stress (8-hydroxy-2′deoxyguanosine, 8-OHdG) markers. Increased levels of Low-density lipoprotein, high-density lipoproteins, cholesterol and 8-OHdG, and the index for homeostasis model assessment—insulin resistance (HOMA-IR) were observed in 72%, 60%, and 56% of the volunteers, respectively. Blood-glucose levels were correlated with dimethylarsinic acid (DMA) (R2 = 0.47, p = 0.019), inorganic As (Asi) (R2 = 0.40, p = 0.012), and Ni (R2 = 0.56; p = 0.044). The models with these compounds explained 72% of the glycemia variability (βDMA = −6.47; βAsi = 6.68; βNi = 6.87). Ni showed a significantly influence on IL-6 variability (β = 0.85: R2 = 0.36). Changes in glycemia could be related to exposure to low levels of Asi and Ni, representing risk factors for metabolic diseases. Body mass index would confuse the relation between IL-6 and Ni levels, probably due to known chronic inflammation present in obese people.

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Rodríguez, Luz Helena Sánchez, Laura Andrea Rodríguez-Villamizar, Oscar Flórez-Vargas, Yolanda Vargas Fiallo, Álvaro Ordoñez, and Myriam del Carmen Gutiérrez. "No effect of mercury exposure on kidney function during ongoing artisanal gold mining activities in Colombia." Toxicology and Industrial Health 33, no. 1 (2016): 67–78. http://dx.doi.org/10.1177/0748233716659031.

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This cross-sectional study examined whether people who are exposed to mercury (Hg) vapours in ongoing artisanal gold mining activities have alteration in kidney function monitoring parameters. The study enrolled 164 miners and 127 participant controls. The Hg concentrations for miners and control participants were measured in blood (B-Hg; median 7.0 vs. 2.5 µg/L), urine (U-Hg; median 3.9 vs. 1.5 µg/g creatinine) and hair (H-Hg; median 0.8 vs. 0.4 µg/g hair). The biomarkers of renal function were creatinine, albumin and excretion of β-2 microglobulin. Glomerular filtration rate (eGFR) was calculated using the chronic kidney disease epidemiology collaboration equation. Significant statistical differences were found in Hg concentrations and eGFR levels between the two study groups ( p < 0.01) but not with the other biomarkers of renal function. A multiple regression model was applied to explore the relationship of eGFR levels and Hg concentrations. However, no association was found between the prevalence of reduced eGFR (<71.96 mL/min/1.73 m2) and the B-Hg or U-Hg levels after adjustment for covariates. Nevertheless, it was observed that having B-Hg levels above 10 µg Hg/L decreased the eGFR by 1.7 mL/min/1.73 m2 (confidence interval 95% −5.1 to 1.7) compared to having levels below 2.0 µg Hg/L. Our results found no support for kidney damage associated with Hg vapour exposure in ongoing artisanal gold mining, whose population has a level of Hg exposure from low to moderate (B-Hg from 3.4 to 11.0 µg/L and U-Hg from 1.3 to 9.6 µg/g creatinine).

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Zhang, Yin, Yue Liu, Rui Song, et al. "Validating traditional Chinese syndrome features in varied stages of chronic gastritis malignant transformation: study protocol for a cross-sectional study." BMJ Open 8, no. 8 (2018): e020939. http://dx.doi.org/10.1136/bmjopen-2017-020939.

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IntroductionThe transition from chronic non-atrophic gastritis (CNAG) to chronic atrophic gastritis (CAG) and gastric carcinoma (GC) is regarded as a representative disease model of gastric mucosa malignant transformation led by uncontrolled inflammation. Traditional Chinese medicine (TCM) syndrome-targeted therapies have been applied in treating chronic gastritis (CG) malignant transformation in China with satisfying efficacy. This study aims to validate TCM syndrome features in each stage of CG malignant transformation. The findings may shed light on the TCM hypothesis of CG malignant transformation, and thus optimise syndrome-targeted treatment strategies of CNAG, CAG and GC, respectively.Methods and analysisThe present study is a cross-sectional study conducted in China. A total of 2000 eligible patients, including 500 CNAG cases, 1000 CAG cases and 500 GC cases, will be recruited from four TCM hospitals. Primary outcome measures include the prevalence of TCM syndrome patterns in varied stages of CG malignant transformation. Secondary outcome measures include prevalence and severity of all the presenting signs and symptoms collected by using TCM four diagnostic methods. Descriptive analysis, comparative analysis and correlation analysis of all the measurement data will be performed by biostatisticians. Unsupervised data mining analyses, including exploratory factor analysis, association rule analysis, hierarchical clustering analysis, complex system entropy clustering analysis, and so on, will also be performed by data scientists respectively for in-depth analyses of TCM syndrome-related indicators.Ethics and disseminationThe protocol has been approved by the Ethical Review Board of Dongzhimen Hospital Affiliated to Beijing University of Chinese Medicine (No ECPJ-BDY-2014-02). All the study outcomes will be disseminated through national conference reports and in the meantime published on peer-reviewed journals.Trial registration numberNCT03314038; Pre-results.

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KUMAKURA, Erika, Michiko ASANO, and Kazuhiko YOKOSAWA. "Anisotropic association pattern of cross-modal correspondences." Proceedings of the Annual Convention of the Japanese Psychological Association 77 (September 19, 2013): 1PM—063–1PM—063. http://dx.doi.org/10.4992/pacjpa.77.0_1pm-063.

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Yang, Fan, Zheng Wang, Jing Xiao, and Shin'ichi Satoh. "Mining on Heterogeneous Manifolds for Zero-Shot Cross-Modal Image Retrieval." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 07 (2020): 12589–96. http://dx.doi.org/10.1609/aaai.v34i07.6949.

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Most recent approaches for the zero-shot cross-modal image retrieval map images from different modalities into a uniform feature space to exploit their relevance by using a pre-trained model. Based on the observation that manifolds of zero-shot images are usually deformed and incomplete, we argue that the manifolds of unseen classes are inevitably distorted during the training of a two-stream model that simply maps images from different modalities into a uniform space. This issue directly leads to poor cross-modal retrieval performance. We propose a bi-directional random walk scheme to mining more reliable relationships between images by traversing heterogeneous manifolds in the feature space of each modality. Our proposed method benefits from intra-modal distributions to alleviate the interference caused by noisy similarities in the cross-modal feature space. As a result, we achieved great improvement in the performance of the thermal v.s. visible image retrieval task. The code of this paper: https://github.com/fyang93/cross-modal-retrieval

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Mandal, Bibhuti B., and Veena D. Manwar. "PrevaPrevalence of musculoskeletal disorders among heavy earth moving machinery operators exposed to whole-body vibration in opencast mininglence of musculoskeletal disorders among heavy earth moving machinery operators exposed to whole-body vibration in opencast mining." International Journal Of Community Medicine And Public Health 4, no. 5 (2017): 1566. http://dx.doi.org/10.18203/2394-6040.ijcmph20171765.

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Background: Low back pain (LBP) and degenerative changes in the spinal system are reportedly associated with exposure to low frequency (0.5- 80 Hz) whole-body vibration (WBV). The main objective of the study was to determine prevalence of musculoskeletal disorders among Heavy Earth Moving Machinery (HEMM) operators exposed to WBV in an opencast mine in western India.Methods: Forty six operators from an opencast mine were recruited as exposed group in a questionnaire based cross-sectional study. Twenty eight employees engaged in sedentary office work were taken as control. All subjects were asked about location and severity of body pain in the past 7 days and 12 months. A four point pain scale was used. Significance of difference of demographic / anthropometric parameters was studied using t-test of independent samples. Association between the pain type and exposure was obtained using Chi-square test. Risk of each type of pain was determined in terms of odds ratio. Adjusted estimate of ORs were obtained through logistic regression modelling, wherein the model fitness was judged using Hosmer-Lemeshow test.Results: The mean exposure duration was 11.30 ± 7.45 years. LBP was the most predominant MSD which showed highly significant association with exposure (P <0.001). Overall, the risk analysis revealed a significant increase in the likelihood of MSD due to exposure. 39% of the exposed group required medical attention.Conclusions: Based on subjective response of exposed group, it can be said that prevalence of musculoskeletal pain as manifestation of MSD due to exposure to WBV especially LBP is high among HEMM operators which affected their quality of life.

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Zhang, Jin. "Ground Disasters Visual Representation Based on Multi-Field Geospatial Infographics Spectra in Mining Area." Abstracts of the ICA 1 (July 15, 2019): 1–2. http://dx.doi.org/10.5194/ica-abs-1-430-2019.

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Abstract. Geospatial infographics spectra is a new cross research direction of cartographic theory. It combines the infographic representing geospatial information with the spectra recording spatial-temporal evolution to form geo-infographics spectra, which reflects the characteristics and dynamic changes of the object's morphological structure, mechanism, composition, and reveals the temporal and spatial variation law of regional geo-phenomena. Infographics can be a map in the form of spatial information. It also includes other graphical forms such as images, charts, curves and so on. Spectra is a system established by object characteristics or by time series. Geospatial infographics spectra has the dual characteristics of graphics and genealogy. It is a spatial-temporal composite analysis method that visually represents the spatial morphological structure and spatial-temporal changes of the earth system and its elements and phenomena through graphical thinking and abstract generalization of multi-field geo-digital information.From geospatial morphological characteristics, spectrum characteristics, texture characteristics, topographic characteristics, deformation characteristics, structural characteristics, geological environment characteristics, mining characteristics, space-time characteristics, disaster chain characteristics and other dimensions to understand and perspective in depth for geo-disasters in mining areas, to achieve the location-morphology-attribute-semantics-structure-process-relationship of ground disaster based on geo-infographic spectra coupling representation, to construct the geo-disaster environment model expression based on surface functional zoning and geographical grids for mining areas. See Figure 1.The spatial morphologies, structure, status, movement, subsidence, stress-strain, etc in the characteristic area of ground hazards is generalized as the basic field (the shape, structure, image information, topography of the hazard body). All the fields that affect the basic field are called effecting field (deformation field, geological structure field, geophysical field, stress field, surface cover field, rainfall field, temperature field, seepage field, soil chemical field, electromagnetic field, etc.). The third is coupled fields, that is scalar or vector fields formed by the coupling of the basic and effecting field (coupling of deformation field and topographic field, deformation field and seepage field, etc.). Based on the 3D mine field model and multi-field information such as basic field, effecting field and coupling field, first we can analyse the time series development stages of stable, oscillating, multi-step, catastrophic, trend-type disasters and then quantitatively segment, partition the types of deformation curves such as V-type, B-type, D-type, R-type pendulum-type and finally set index, discovery pattern and generate geospatial infographics spectra. See Figure 2 and Figure 3.A multi-dimensional features-based ground disaster data perception model integrating time, space, semantics and relationships is constructed to realize multi-field information and multi-granularity parsing and location-based information aggregation in disaster feature area. The automatic location information discovery and matching, location semantics association and spatial behaviour inference are accomplished. The spatial behaviour inference under different location semantics is supported to reveal coal mining. It provides precise information support for the impact of the surface and the mechanism of dynamic change. See Figure 3.

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Ye, Zhaoda, and Yuxin Peng. "Sequential Cross-Modal Hashing Learning via Multi-scale Correlation Mining." ACM Transactions on Multimedia Computing, Communications, and Applications 15, no. 4 (2020): 1–20. http://dx.doi.org/10.1145/3356338.

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Tawari, Ashish, and Mohan Manubhai Trivedi. "Face Expression Recognition by Cross Modal Data Association." IEEE Transactions on Multimedia 15, no. 7 (2013): 1543–52. http://dx.doi.org/10.1109/tmm.2013.2266635.

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Hwang, Jeong Hee. "Association Service Mining using Level Cross Tree." Journal of Digital Contents Society 15, no. 5 (2014): 569–77. http://dx.doi.org/10.9728/dcs.2014.15.5.569.

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Mok, Peggy P. K., Guo Li, Joanne Jingwen Li, Hezul T. Y. Ng, and Him Cheung. "Cross-modal association between vowels and colours: A cross-linguistic perspective." Journal of the Acoustical Society of America 145, no. 4 (2019): 2265–76. http://dx.doi.org/10.1121/1.5096632.

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Fuster, Joaquín M., Mark Bodner, and James K. Kroger. "Cross-modal and cross-temporal association in neurons of frontal cortex." Nature 405, no. 6784 (2000): 347–51. http://dx.doi.org/10.1038/35012613.

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Alcantara-Zapata, Diana Elizabeth, Shrikant I. Bangdiwala, Daniel Jiménez, Manolis Kogevinas, Nella Marchetti, and Carolina Nazzal. "Effects of chronic intermittent hypobaric hypoxia on prostate-specific antigen (PSA) in Chilean miners." Occupational and Environmental Medicine 78, no. 10 (2021): 753–60. http://dx.doi.org/10.1136/oemed-2020-107232.

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ObjectiveThe aim was to determine the effects of chronic intermittent hypobaric hypoxia (CIHH) on prostate-specific antigen (PSA) levels in Chilean miners who work at different altitudes.MethodsA cross-sectional study was conducted between April and July 2019. Miners from five mines (N=338) at different altitudes were evaluated. We recorded sociodemographic, working and altitude information. Haemoglobin oxygen saturation (SaO2) and haemoglobin (Hb) were measured in situ, while PSA and testosterone were analysed at a low level. Linear mixed-effect models were used to evaluate the association between PSA level and two CIHH exposures: composite CIHH (with four descriptors) and ChileStd-CIHH (CIHH Chilean standard; based on the Chilean technical guide for occupational exposure to CIHH). All models were adjusted by age, body mass index and day of the work the samples were taken.ResultsHighest and lowest PSA levels were found in mines ≥3000 m above sea level (mine 3: median=0.75, IQR=−0.45; mine 4: median=0.46, IQR=−0.35). In the multilevel models, the wider altitude difference between mining operation and camp showed lower PSA levels (model D: βPSA=−0.93 ng/mL, βlogPSA=−0.07, p<0001), adjusted for other CIHH descriptors, SaO2, Hb and testosterone. The descriptors of composite CIHH explained better PSA variations than ChileStd-CIHH (model D: marginal R2=0.090 vs model A: marginal R2=0.016).ConclusionsOccupational health regulations and high altitude medicine should consider these results as initial evidence on the inclusion of new descriptors for CIHH and the possible effect of this exposure on PSA levels in this male-dominated occupational sector.

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Lee, G. P. "Preserved cross-modal association following bilateral amygdalotomy in man." Neurocase 6, no. 6 (2000): 447. http://dx.doi.org/10.1093/neucas/6.6.447.

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Zheng, Qibin, Xiaoguang Ren, Yi Liu, and Wei Qin. "Abstraction and Association: Cross-Modal Retrieval Based on Consistency between Semantic Structures." Mathematical Problems in Engineering 2020 (May 7, 2020): 1–17. http://dx.doi.org/10.1155/2020/2503137.

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Cross-modal retrieval aims to find relevant data of different modalities, such as images and text. In order to bridge the modality gap, most existing methods require a lot of coupled sample pairs as training data. To reduce the demands for training data, we propose a cross-modal retrieval framework that utilizes both coupled and uncoupled samples. The framework consists of two parts: Abstraction that aims to provide high-level single-modal representations with uncoupled samples; then, Association links different modalities through a few coupled training samples. Moreover, under this framework, we implement a cross-modal retrieval method based on the consistency between the semantic structure of multiple modalities. First, both images and text are represented with the semantic structure-based representation, which represents each sample as its similarity from the reference points that are generated from single-modal clustering. Then, the reference points of different modalities are aligned through an active learning strategy. Finally, the cross-modal similarity can be measured with the consistency between the semantic structures. The experiment results demonstrate that given proper abstraction of single-modal data, the relationship between different modalities can be simplified, and even limited coupled cross-modal training data are sufficient for satisfactory retrieval accuracy.

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Kawahara, J. "Cross-modal contextual cueing: Auditory andvisual association guides spatial attention." Journal of Vision 7, no. 9 (2010): 1059. http://dx.doi.org/10.1167/7.9.1059.

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Nakamura, Tomoaki, and Yukio P. Gunji. "Audio-Visual Cross-Modal Association in Color and Sound Perception." i-Perception 2, no. 8 (2011): 792. http://dx.doi.org/10.1068/ic792.

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Lee, Gregory P., Milla F. Reed, Kimford J. Meador, Joseph R. Smith, and et al. "Is the amygdala crucial for cross-modal association in humans?" Neuropsychology 9, no. 2 (1995): 236–45. http://dx.doi.org/10.1037/0894-4105.9.2.236.

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Darvishzadeh, Reza. "Detection of ISSR markers linked to seed oil biochemical characteristics in castor (Ricinus communis L.) through association analysis." Genetika 48, no. 3 (2016): 807–17. http://dx.doi.org/10.2298/gensr1603807d.

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In major crops, panels of germplasm accessions have been assembled for allele mining at targeted candidate genes and for whole-genome association mapping studies. A germplasm collection of 60 genotypes belong to 12 castor accessions from six different provinces of Iran were evaluated for seven biochemical traits as important seed oil components and tested for significant association with the allelic profile of 16 ISSR primers. Study of population structure as a prerequisite for association analysis showed that there is six possible subgroups (K=6) in the studied population that also barplot results confirmed it. Mixed linear model (MLM) were used to discover marker/trait associations. As a result, 37 marker-trait significant associations (P?0.05) were identified and a maximum number of marker-trait associations (27.03%) were identified for Stearic acid. The identified and associated markers are expected to be useful in marker-aided breeding of high oil content genotypes in castor.

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Aoyama, A., and S. Kuriki. "P33-15 Magnetoencephalographic study of rapid association of cross-modal and cross-temporal information." Clinical Neurophysiology 121 (October 2010): S302. http://dx.doi.org/10.1016/s1388-2457(10)61234-5.

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Park, Hee Chang. "Proposition of Modified Balance Cross Entropy in Association Rule Mining." Korean Data Analysis Society 19, no. 4 (2017): 1733–41. http://dx.doi.org/10.37727/jkdas.2017.19.4.1733.

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Agapito, Giuseppe, Mario Cannataro, Pietro Hiram Guzzi, and Marianna Milano. "Using GO-WAR for mining cross-ontology weighted association rules." Computer Methods and Programs in Biomedicine 120, no. 2 (2015): 113–22. http://dx.doi.org/10.1016/j.cmpb.2015.03.007.

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Xia, Dongliang, Lu Miao, and Aiwan Fan. "A cross-modal multimedia retrieval method using depth correlation mining in big data environment." Multimedia Tools and Applications 79, no. 1-2 (2019): 1339–54. http://dx.doi.org/10.1007/s11042-019-08238-0.

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Jin, Hwabong, and Hoisoo Kim. "Working memory mechanism underlying cross-modal association: the verbal and cisual information." Journal of Educational Technology 19, no. 1 (2003): 51–75. http://dx.doi.org/10.17232/kset.19.1.51.

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Albertazzi, Liliana, Luisa Canal, Rocco Micciolo, and Iacopo Hachen. "Cross-Modal Perceptual Organization in Works of Art." i-Perception 11, no. 4 (2020): 204166952095075. http://dx.doi.org/10.1177/2041669520950750.

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This study investigates the existence of cross-modal correspondences between a series of paintings by Kandinsky and a series of selections from Schönberg music. The experiment was conducted in two phases. In the first phase, by means of the Osgood semantic differential, the participants evaluated the perceptual characteristics first of visual stimuli (some pictures of Kandinsky’s paintings, with varying perceptual characteristics and contents) and then of auditory stimuli (musical excerpts taken from the repertoire of Schönberg’s piano works) relative to 11 pairs of adjectives tested on a continuous bipolar scale. In the second phase, participants were required to associate pictures and musical excerpts. The results of the semantic differential test show that certain paintings and musical excerpts were evaluated as semantically more similar, while others were evaluated as semantically more different. The results of the direct association between musical excerpts and paintings showed both attractions and repulsions among the stimuli. The overall results provide significant insights into the relationship between concrete and abstract concepts and into the process of perceptual grouping in cross-modal phenomena.

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Bonetti, Leonardo, and Marco Costa. "Pitch-verticality and pitch-size cross-modal interactions." Psychology of Music 46, no. 3 (2017): 340–56. http://dx.doi.org/10.1177/0305735617710734.

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Two studies were conducted on cross-modal matching between pitch and sound source localization on the vertical axis, and pitch and size. In the first study 100 Hz, 200 Hz, 600 Hz, and 800 Hz tones were emitted by a loudspeaker positioned 60 cm above or below to the participant’s ear level. Using a speeded classification task, 30 participants had to indicate the sound source in 160 trials. Both reaction times and errors were analyzed. The results showed that in the congruent condition of high-pitched tones emitted from the upper loudspeaker, reaction times were significantly faster and the number of errors was significantly lower. Pitch was mapped on the vertical axis for sound localization. A main effect for sound source direction was also found. Tones coming from the upper loudspeaker were recognized faster and more accurately. Males were faster than females in identifying sound source direction. In the second experiment, 20 participants had to match 21 tones varying in pitch with 9 circles differing in visual angle on 42 trials. The results showed a clear inverse linear association between log-spaced tone pitch and circle diameter.

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Ramana, V. Venkata, M. V. Rathnamma, and A. Rama Mohan Reddy. "Methods for Mining Cross Level Association Rule In Taxonomy Data Structures." International Journal of Computer Applications 7, no. 3 (2010): 28–35. http://dx.doi.org/10.5120/1144-1497.

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Park, Hee-Chang. "Proposition of a Balanced Cross Entropy Measure in Association Rule Mining." Korean Data Analysis Society 19, no. 2 (2017): 699–708. http://dx.doi.org/10.37727/jkdas.2017.19.2.699.

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Manda, Prashanti, Seval Ozkan, Hui Wang, Fiona McCarthy, and Susan M. Bridges. "Cross-Ontology Multi-level Association Rule Mining in the Gene Ontology." PLoS ONE 7, no. 10 (2012): e47411. http://dx.doi.org/10.1371/journal.pone.0047411.

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Lee, Dongwon, Sung-Hyuk Park, and Songchun Moon. "Utility-based association rule mining: A marketing solution for cross-selling." Expert Systems with Applications 40, no. 7 (2013): 2715–25. http://dx.doi.org/10.1016/j.eswa.2012.11.021.

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Melati, Delila, and Titi Sri Wahyuni. "ASSOCIATION RULE DALAM MENENTUKAN CROSS-SELLING PRODUK MENGGUNAKAN ALGORITMA FP-GROWTH." Voteteknika (Vocational Teknik Elektronika dan Informatika) 7, no. 4 (2020): 102. http://dx.doi.org/10.24036/voteteknika.v7i4.106499.

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Sales transaction data at Bigmart stored in a database will be able to become new knowledge if processed using the data mining process. In addition, inventory is also a problem that is being faced by Bigmart. Data mining is able to analyze data into information in the form of transaction patterns that are useful in increasing revenue, one of which is Cross-Selling products. Association rule is one of the data mining methods included in the Market Basket Analysis method. The algorithm used is the FP-Growth algorithm because it has the virtue of shorter time processing data. The pattern obtained is determined by the value of support (support) and the value of confidence (confidence). To find the association rules the FP-Growth algorithm is used. To get more accurate association rules, use the Weka 8.3 tool. There are 11 association rules obtained using the Weka 8.3 tool which is classified as a Stong Rule that meets the Minimum support value of 10% and Minimum confidence 80%. Keywords: Database, Cross-selling, Market Basket Analysis, Association Rule, FP-Growth

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Asteria, Beta. "DETERMINAN FAKTOR STRUKTUR MODAL PERUSAHAAN TAMBANG YANG TERDAFTAR DI BURSA EFEK INDONESIA." MANAJEMEN DEWANTARA 2, no. 1 (2018): 1. http://dx.doi.org/10.26460/md.v2i1.2478.

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This study aims to determine the influence of Current Ratio (CR), Asset Structure (SA), Profitability (ROE) and Company Size (SIZE) partially and simultaneously to the capital structure of mining companies listed in Indonesia Stock Exchange 2013-2016. The type of research data is secondary data in the form of financial statement data, published by Indonesia Stock Exchange. Technique is using purposive sampling. The data used is panel data. There are 14 companies as cross section data and time series for 4 years from year 2013-2016. Research samples are 56. Method analyze data use multiple regression with program of SPSS. The result of the research shows that: 1) Current Ratio (CR) has significant effect to Capital Structure (CS) of Mining Company listed in Indonesian Stock Exchange in 2013-2016, 2) Asset Structure (SA) has no significant effect to Capital Structure CS) Mining Companies listed on the Indonesia Stock Exchange in 2013-2016, 3) Company size (SIZE) significantly influence the Capital Structure (CS) of Mining Companies listed on Indonesia Stock Exchange in 2013-2016, 4) Return On Equity (ROE) has no significant effect on Capital Structure (CS) of Mining Companies listed on Indonesia Stock Exchange in 2013-2016 and 5) Current Ratio (CR), Asset Structure (SA), Profitability (ROE) and Company Size (RO) SIZE) simultaneously have a significant effect on the company's capital structure (CS). Keywords: Capital Structure, Current Ratio, Asset Structure, Return on Equity and Company Size

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Momeni, S. S., S. M. Beno, J. L. Baker, et al. "Caries-Associated Biosynthetic Gene Clusters in Streptococcus mutans." Journal of Dental Research 99, no. 8 (2020): 969–76. http://dx.doi.org/10.1177/0022034520914519.

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Early childhood caries (ECC) is a chronic disease affecting the oral health of children globally. This disease is multifactorial, but a primary factor is cariogenic microorganisms such as Streptococcus mutans. Biosynthetic gene clusters (BGCs) encode small molecules with diverse biological activities that influence the development of many microbial diseases, including caries. The purpose of this study was to identify BGCs in S. mutans from a high-caries risk study population using whole-genome sequencing and assess their association with ECC. Forty representative S. mutans isolates were selected for genome sequencing from a large-scale epidemiological study of oral microbiology and dental caries in children from a localized Alabama population. A total of 252 BGCs were identified using the antiSMASH BGC-mining tool. Three types of BGCs identified herein—butyrolactone-like, ladderane-like, and butyrolactone-ladderane-like hybrid (BL-BGC)—have not been reported in S. mutans. These 3 BGCs were cross-referenced against public transcriptomics data, and were found to be highly expressed in caries subjects. Furthermore, based on a polymerase chain reaction screening for core BL genes, 93% of children with BL-BGC had ECC. The role of BL-BGC was further investigated by examining cariogenic traits and strain fitness in a deletion mutant using in vitro biofilm models. Deletion of the BL-BGC significantly increased biofilm pH as compared to the parent strain, while other virulence and fitness properties remained unchanged. Intriguingly, BL-BGC containing strains produced more acid, a key cariogenic feature, and less biofilm than the model cariogenic strain S. mutans UA159, suggesting the importance of this BL-BGC in S. mutans–mediated cariogenesity. The structure of any BL-BGC derived metabolites, their functions, and mechanistic connection with acid production remain to be elucidated. Nevertheless, this study is the first to report the clinical significance of a BL-BGC in S. mutans. This study also highlights pangenomic diversity, which is likely to affect phenotype and virulence.

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Hannah, Beverly, Yue Wang, Allard Jongman, and Joan A. Sereno. "Cross-modal association between auditory and visual-spatial information in Mandarin tone perception." Journal of the Acoustical Society of America 140, no. 4 (2016): 3225. http://dx.doi.org/10.1121/1.4970187.

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Anjana Devi and Bhuvaneswaran. "Adaptive Association Rule Mining Based Cross Layer Intrusion Detection System for MANET." International Journal of Network Security & Its Applications 3, no. 5 (2011): 243–56. http://dx.doi.org/10.5121/ijnsa.2011.3519.

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Park, Hee-Chang. "Proposition of a Purely Balanced Cross Entropy Measure in Association Rule Mining." Korean Data Analysis Society 20, no. 5 (2018): 2343–53. http://dx.doi.org/10.37727/jkdas.2018.20.5.2343.

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Ye, Zheng, Jimmy Xiangji Huang, Ben He, and Hongfei Lin. "Mining a multilingual association dictionary from Wikipedia for cross-language information retrieval." Journal of the American Society for Information Science and Technology 63, no. 12 (2012): 2474–87. http://dx.doi.org/10.1002/asi.22696.

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Agarwal, Reshu. "Modified Ranking With Temporal Association Rule Mining in Supply Chains." International Journal of Service Science, Management, Engineering, and Technology 11, no. 4 (2020): 58–71. http://dx.doi.org/10.4018/ijssmet.2020100104.

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This article deals with data mining applications for the supply chain inventory management. ABC classification is usually used for inventory items classification because the number of inventory items is so large that it is not computationally feasible to set stock and service control guidelines for each individual item. Moreover, in ABC classification, the inter-relationship between items is not considered. But practically, the sale of one item could affect the sale of other items (cross selling effect). Hence, within time-periods, the inventories should be classified. In this article, a modified approach is proposed considering both time-periods and cross-selling effect to rank inventory items. A numerical example and an empirical study with a data set are used to evaluate the proposed approach. It is illustrated that by using this modified approach, the ranking of items may get affected resulting in higher profit.

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Journal articles on the topic 'Cross modal association mining'

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