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1
Sharma, Vikrant. "Relevance Feature Discovery for Text Mining." Mathematical Statistician and Engineering Applications 70, no. 1 (January 31, 2021): 225–33. http://dx.doi.org/10.17762/msea.v70i1.2303.
Повний текст джерелаАнотація:
Due to large size words also data patterns, it is difficult to ensure the quality of relevant characteristics that are found in text documents that describe user preferences. Most widely used text mining and classification techniques now in use have embraced term-based strategies. However, polysemy and synonymy issues have affected them all. The theory that pattern-based approaches should outperform term-based ones in performance in expressing user preferences has been often held throughout the years, however text mining still struggles with how to employ large-scale patterns successfully. This research introduces a novel methodology for relevance feature discovery to address this hard problem. It finds higher level features in text texts that are both positive and negative patterns and uses them instead of low-level features (terms). Additionally, it organised terms into categories and updates term weights according to the patterns and specificity of those distributions. Significant tests employing this model on the datasets RCV1, TREC themes, and Reuters-21578 reveal that it performs noticeably better than both the most advanced term-based approaches and pattern-based methods.
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Santoro, Diego, Andrea Tonon, and Fabio Vandin. "Mining Sequential Patterns with VC-Dimension and Rademacher Complexity." Algorithms 13, no. 5 (May 18, 2020): 123. http://dx.doi.org/10.3390/a13050123.
Повний текст джерелаАнотація:
Sequential pattern mining is a fundamental data mining task with application in several domains. We study two variants of this task—the first is the extraction of frequent sequential patterns, whose frequency in a dataset of sequential transactions is higher than a user-provided threshold; the second is the mining of true frequent sequential patterns, which appear with probability above a user-defined threshold in transactions drawn from the generative process underlying the data. We present the first sampling-based algorithm to mine, with high confidence, a rigorous approximation of the frequent sequential patterns from massive datasets. We also present the first algorithms to mine approximations of the true frequent sequential patterns with rigorous guarantees on the quality of the output. Our algorithms are based on novel applications of Vapnik-Chervonenkis dimension and Rademacher complexity, advanced tools from statistical learning theory, to sequential pattern mining. Our extensive experimental evaluation shows that our algorithms provide high-quality approximations for both problems we consider.
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Ti, Zhengyi, Jiazhen Li, Meng Wang, Kang Wang, Zhupeng Jin, and Caiwang Tai. "Fracture Mechanism in Overlying Strata during Longwall Mining." Shock and Vibration 2021 (June 21, 2021): 1–15. http://dx.doi.org/10.1155/2021/4764732.
Повний текст джерелаАнотація:
We used the key stratum theory to establish a more realistic thin-plate mechanical model of elastic foundation clamped boundary and study the fracture mechanism of overlying strata during longwall mining. We analyzed the fracture characteristics and factors affecting fracture of the key stratum combined with the Mohr–Coulomb yield criterion. Besides, we used numerical simulation methods to verify the evolution pattern of the overlying strata fracture. The results show that the fracture mechanisms of the elastic foundation clamped structure’s key stratum varied depending on the position under longwall mining. The advanced coal wall area of the upper surface is a compressive-shear fracture. The center area of the lower surface is a tensile fracture. With the increase of the excavation length and the load of the key stratum, the central area and the advanced coal wall area of the long side are fractured before the advanced coal wall area of the short side. With the increase of flexural rigidity of the key stratum, the advanced coal wall area of the long side fractures before the central area and the advanced coal wall area of the short side. With the increase of the foundation modulus and the advanced load of the key stratum, the central area fractures before the surrounding advanced coal wall area. The advanced influence distance was positively correlated with the key stratum’s flexural rigidity and advanced load and negatively correlated with the foundation modulus and excavation length. The advanced influence distance was not affected by the load of the key stratum. The numerical simulation results show that, with the increase of the mining area, the fracture trace of overlying strata in goaf extended to the coal wall’s interior. The fracture range of overlying strata is larger than that of the miningd: area. This study has a practical value for water disasters, gas outbursts, and rock strata control.
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Selmaoui-Folcher, Nazha, Frédéric Flouvat, Dominique Gay, and Isabelle Rouet. "Spatial Pattern Mining for Soil Erosion Characterization." International Journal of Agricultural and Environmental Information Systems 2, no. 2 (July 2011): 73–92. http://dx.doi.org/10.4018/jaeis.2011070105.
Повний текст джерелаАнотація:
The protection and the maintenance of the exceptional environment of New Caledonia are major goals for this territory. Among environmental problems, erosion has a strong impact on terrestrial and coastal ecosystems. However, due to the volume of data and its complexity, assessment of hazard at a regional scale is time-consuming, costly and rarely updated. Therefore, understanding and predicting environmental phenomenons need advanced techniques of analysis and modelization. In order to improve the understanding of the erosion phenomenon, this paper proposes a spatial approach based on co-location mining and GIS. Considering a set of Boolean spatial features, the goal of co-location mining is to find subsets of features often located together. This system provides useful and interpretable knowledge based on a new interestingness measure for co-locations and a new visualization of the discovered knowledge. The interestingness measure better reflects the importance of a co-location for the experts, and is completely integrated in the mining process. The visualization approach is a simple, concise and intuitive representation of the co-locations that takes into consideration the spatial nature of the underlying objects and the experts practice.
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Khoshahval, S., M. Farnaghi, and M. Taleai. "SPATIO-TEMPORAL PATTERN MINING ON TRAJECTORY DATA USING ARM." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-4/W4 (September 27, 2017): 395–99. http://dx.doi.org/10.5194/isprs-archives-xlii-4-w4-395-2017.
Повний текст джерелаАнотація:
Preliminary mobile was considered to be a device to make human connections easier. But today the consumption of this device has been evolved to a platform for gaming, web surfing and GPS-enabled application capabilities. Embedding GPS in handheld devices, altered them to significant trajectory data gathering facilities. Raw GPS trajectory data is a series of points which contains hidden information. For revealing hidden information in traces, trajectory data analysis is needed. One of the most beneficial concealed information in trajectory data is user activity patterns. In each pattern, there are multiple stops and moves which identifies users visited places and tasks. This paper proposes an approach to discover user daily activity patterns from GPS trajectories using association rules. Finding user patterns needs extraction of user’s visited places from stops and moves of GPS trajectories. In order to locate stops and moves, we have implemented a place recognition algorithm. After extraction of visited points an advanced association rule mining algorithm, called Apriori was used to extract user activity patterns. This study outlined that there are useful patterns in each trajectory that can be emerged from raw GPS data using association rule mining techniques in order to find out about multiple users’ behaviour in a system and can be utilized in various location-based applications.
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Patel, Pratik C., and Upasna Singh. "A novel classification model for data theft detection using advanced pattern mining." Digital Investigation 10, no. 4 (December 2013): 385–97. http://dx.doi.org/10.1016/j.diin.2013.09.002.
Повний текст джерела
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Hsu, Kuo-Wei. "Efficiently and Effectively Mining Time-Constrained Sequential Patterns of Smartphone Application Usage." Mobile Information Systems 2017 (2017): 1–18. http://dx.doi.org/10.1155/2017/3689309.
Повний текст джерелаАнотація:
Today, we have the freedom to install and use all kinds of applications on smartphones, thanks to the development of mobile communication and computing technologies. Undoubtedly, the system and application developers are eager to know how we use the applications on our smartphones in our daily life and so are the researchers. In this paper, we present our work on developing a pattern mining algorithm and applying it to smartphone application usage log collected from tens of smartphone users for several years. Our goal is to mine the sequential patterns each of which presents a series of application uses and satisfies a constraint on the maximum time interval between two application uses. However, we cannot mine such patterns by general algorithms and will miss some patterns by using the widely used implementation of the advanced algorithm specifically designed for time-constrained sequential pattern mining. We not only present an algorithm that can efficiently and effectively mine the patterns in which we are interested but also discuss and visualize the mined patterns. Our work could potentially support the related studies.
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Kim, Hyeonmo, Heonho Kim, Sinyoung Kim, Hanju Kim, Myungha Cho, Bay Vo, Jerry Chun-Wei Lin, and Unil Yun. "An advanced approach for incremental flexible periodic pattern mining on time-series data." Expert Systems with Applications 230 (November 2023): 120697. http://dx.doi.org/10.1016/j.eswa.2023.120697.
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Liu, Xiaodong, Shuming Zhang, Weiwen Cui, Hong Zhang, Rui Wu, Jie Huang, Zhixin Li, Xiaohan Wang, Jianing Wu, and Junqi Yang. "A Workflow Investigating the Information behind the Time-Series Energy Consumption Condition via Data Mining." Buildings 13, no. 9 (September 10, 2023): 2303. http://dx.doi.org/10.3390/buildings13092303.
Повний текст джерелаАнотація:
The purpose of this study is to develop a framework to understand building energy usage pattern finding using data mining algorithms. Developing advanced techniques and requirements for carbon emission reduction provides higher demands for building energy efficiency. Research conducted so far has mainly focused on total energy consumption data clusters instead of time-series curve peculiarity. This research adopts the time-series cluster algorithm k-shape and the ARM Apriori method to study the simulation database generated by the official restaurant energy model. These advanced data mining techniques can discover potential information hidden in a big database that has not been identified by people. The results show that the restaurant time-series energy consumption curve can be clustered into four type patterns: Invert U, M, Invert V, and Multiple M. Each mode has its own variation characteristics. Two aspects for the solution of intensity and peak shift are proposed, achieving energy savings and focusing on different curve modes. The conclusion shows that the combination of time-series clustering and the ARM algorithm work flow can successfully discover the building operation pattern. Some solutions focusing on restaurant energy usage issues have been proposed, and future investigations should pay more attention to building area-influenced factors.
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Shen, Hai Ying, Shu Ming Wen, and Ti Zhuan Wang. "Mining Engineering Professionals Motivated to Improve their Proficiency in English." Applied Mechanics and Materials 525 (February 2014): 765–69. http://dx.doi.org/10.4028/www.scientific.net/amm.525.765.
Повний текст джерелаАнотація:
The industrialization is progressing on the unparalleled scale, bringing an ever greater impetus to the growth of mining industry. China has had its unique pattern of developing its mining engineerin: firstly importing technologies, then studying and absorbing them, and finally achieving some innovation. Until now, China has been honored with some of mining theories taking the lead in the international mining world. As the disseminators of human civilization including advanced mining technologies, the Chinese professionals concerned are duty-bound to introduce abroad Chinas new progresses in mining engineering; that they are skillfully equipped with the four kinds of English abilities in reading, writing, listening and speaking, which is prerequisite for them to work well in foreign cooperation, has become an urgent matter to for them as well as for the authorities concerned.
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Indri Sulistianingsih, Wirda Fitriani, and Darmeli Nasution. "Design Information Systems for Malnutrition Analysis Apriori Algorithm." International Journal Of Computer Sciences and Mathematics Engineering 2, no. 2 (November 30, 2023): 225–30. http://dx.doi.org/10.61306/ijecom.v2i2.45.
Повний текст джерелаАнотація:
Malnutrition drives lasting detriments across individual and community wellbeing, requiring data-informed action. Advanced analytics through information systems present pathways for revelatory pattern detection from multidimensional health data. This paper outlines a system design encompassing preprocessing, modeling, analysis and interpretation techniques for mining malnutrition dataset through Apriori algorithm. The core data mining methodology enables extraction of frequencies, associations and prediction rules linking nutritional status parameters and food intake patterns. Custom algorithms filter results to high-confidence associations via statistical measures before expert evaluation. System testing verifies accurate architecture for surfaced dietary risk factors of malnutrition down to village-level. The systemization and computational augmentation of health insight derivation provides a template for needs-based analytics platforms. By targeting analysis to community data, impactful interventions become possible. The potential of customized information systems with data mining at the core is highlighted alongside domain challenges requiring cross-disciplinary impetus. The data-to-decisions system with embedded Apriori pipelines demonstrates applied informatics transforming malnutrition strategy through unveiling actionable patterns within intricacies of public welfare data.
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ZHAI, XING, XUANCHAO FENG, JINGWEI LIU, KUO GAO, ZHENHUA JIA, HUIHUI ZHAO, JUAN WANG, WEI WANG, and JIANXIN CHEN. "NEURO-ENDOCRINE-IMMUNE BIOLOGICAL NETWORK CONSTRUCTION OF QI DEFICIENCY PATTERN AND QI STAGNATION PATTERN IN TRADITIONAL CHINESE MEDICINE." Journal of Biological Systems 23, no. 02 (May 28, 2015): 305–21. http://dx.doi.org/10.1142/s0218339015500163.
Повний текст джерелаАнотація:
Nowadays, it is quite challenging to clarify Chinese medicine's results and its internal biological foundation in the traditional Chinese medicine (TCM) field. The key to break through this problem is to make the right methodological choices. From our study, a pattern consisting of many kinds of characterizations has been found, and every characterization corresponds with its internal biological indicators. If the relationship of every characterization and its biological indicators can be structured, and of each pattern and its biological indicators, it will help us to understand TCM better. Therefore, it is a better method to construct and analyze the "pattern-characterization-biological indicator" network. The aim of this paper is to distinguish two common Chinese medicine patterns, qi deficiency pattern and qi stagnation pattern, and their characterizations in terms of internal biology by using literature-mining methods. Furthermore, the results will be validated by clinical data to examine the methodological reliability. A neuro-endocrine-immune (NEI)-related gene data dictionary and a human phenotype ontology (HPO) characterizations terminology database have been established for these two patterns. Relevant literatures about the characterizations of these two patterns can be found on PubMed. Two different literature-mining software PubMiner and GenCliP, were used on the principle of "pattern-characterization-biological indicator" co-occurrence to find the characteristic NEI gene and the chemical messenger (CM) of Qi deficiency and qi stagnation patterns and to explore the difference in the bioactive substances between the two patterns, such as Hormones, Receptors, Cytokine, Neurotransmitters, etc. Biological networks of the two patterns and their various characterizations were separately constructed by using two literature-mining methods. After integrating and analyzing all kinds of networks, we found that qi deficiency pattern genes based on the NEI network include CD4, CHAT, EPO, GCG, INS, PTH, PRL, REN, SHBG and MAOA; and the key chemical transmitters include IgA, IgM, IgG, IL6, INFα, C3, C4, IL2, T, TSH, T3, T4, NO, EPO, E2, Serotonin, Histamine, ACTH, Hydrocortisone, Insulin, Cytokine, Calcitriol, Aldosterone, Adenosine, Somatostatin, Progesterone Acetylcholine, NE and Dopamine. Qi stagnation pattern genes based on the NEI network include EGF, EGFR, INS, PRL, SHBG, SNAP25, BDNF, COMT, DRD4, CD4 and IL6; and the key chemical transmitters include T3, E2, Prolactin, Serotonin, Steroids, T, ACTH, TSH, NE, ACTH and IL6. By comparing the literature data with clinical data, we found that abnormalities of the endocrine system, especially the thyroid, adrenal gland and gonadal gland, are closely related to the occurrence of coronary heart disease (CHD). The abnormalities in the endocrine system affect the immune system and nervous system, which eventually leads to CHD. The CHD of qi deficiency pattern emphasizes the imbalance of the immune system, while that of qi stagnation patients focuses on the imbalance of the nervous system. To some extent, it is feasible to use literature-mining methods to construct and analyze the "pattern-characterization-biological indicator" network as a new method of finding syndromes for the biological indicators. It provides an advanced, effective and concise method for the objectivity and internalization of traditional medicine. However, the universality and reliability of this method will need to be further validated by other syndrome studies.
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Pritee, Kumari, and R. D. Garg. "Criticality trend analysis based on highway accident factors using improved data mining algorithms." Journal of Future Sustainability 3, no. 1 (2023): 9–22. http://dx.doi.org/10.5267/j.jfs.2022.11.002.
Повний текст джерелаАнотація:
Highway accident data analysis provides probability of occurrence of road accidents by associating different accident factors using data mining algorithms. Analysis can be improved by using advanced data mining algorithms that compute relationships with minimum processing time. As accident datasets are very heterogeneous in nature, it is difficult to identify the relationship between critical factors responsible for road accidents without data mining algorithms. In this study, K-modes for clustering and frequent pattern growth algorithms to extract relationships between critical accident factors have been used. The accomplished result concludes better relationships with better accuracy than earlier implemented data mining algorithms and has found meaningful hidden situations that would be beneficial for future work in decreasing the number of highway accidents.
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Melati, IGA Sri, Linawati Linawati, and I. A. D. Giriantari. "Knowledge Discovery Data Akademik Untuk Prediksi Pengunduran Diri Calon Mahasiswa." Majalah Ilmiah Teknologi Elektro 17, no. 3 (December 5, 2018): 325. http://dx.doi.org/10.24843/mite.2018.v17i03.p04.
Повний текст джерелаАнотація:
Admission of new students to an educational institution such as STMIK STIKOM Bali was an activity which is routinely implemented every new academic year. The registration of new student candidates was always increasing from year to year, but not all prospective students continued registration step of a number of prospective students who had passed. It would be too late to take action if a new student enrolled very little. By not knowing the number of registration students, institution cannot measure the time and the number of new admissions target which had been achieved.
In this case the use of data mining techniques was expected to provide knowledge or information that was previously hidden in the data warehouse, thus becoming valuable information for the organization or institution. In this study, the classification model and the frequent pattern are made to identify the data pattern and its appearance for the "advanced" or "backward registration" status class.
Some task mining was used to predict the prospective student was by classification techniques and techniques Frequent Pattern which extracted model and describe important data classes. The algorithm used is Decision Tree. The software which was used for implementation is WEKA.
Index Term : Data Mining, Classification, Decision Tree, Frequent Pattern
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Mousavi, Hamid, Shi Gao, Deirdre Kerr, Markus Iseli, and Carlo Zaniolo. "Mining Semantics Structures from Syntactic Structures in Web Document Corpora." International Journal of Semantic Computing 08, no. 04 (December 2014): 461–89. http://dx.doi.org/10.1142/s1793351x14400157.
Повний текст джерелаАнотація:
The Web is making possible many advanced text-mining applications, such as news summarization, essay grading, question answering, semantic search and structured queries on corpora of Web documents. For many of such applications, statistical text-mining techniques are of limited effectiveness since they do not utilize the morphological structure of the text. On the other hand, many approaches use NLP-based techniques that parse the text into parse trees, and then use patterns to mine and analyze parse trees which are often unnecessarily complex. To reduce this complexity and ease the entire process of text mining, we propose a weighted-graph representation of text, called TextGraphs, which captures the grammatical and semantic relations between words and terms in the text. TextGraphs are generated using a new text mining framework which is the main focus of this paper. Our framework, SemScape, uses a statistical parser to generate few of the most probable parse trees for each sentence and employs a novel two-step pattern-based technique to extract from parse trees candidate terms and their grammatical relations. Moreover, SemScape resolves coreferences by a novel technique, generates domain-specific TextGraphs by consulting ontologies, and provides a SPARQL-like query language and an optimized engine for semantically querying and mining TextGraphs.
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Huang, Shian-Chang, Chei-Chang Chiou, Jui-Te Chiang, and Cheng-Feng Wu. "Online sequential pattern mining and association discovery by advanced artificial intelligence and machine learning techniques." Soft Computing 24, no. 11 (May 30, 2019): 8021–39. http://dx.doi.org/10.1007/s00500-019-04100-5.
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Yun, Unil, Gangin Lee, and Eunchul Yoon. "Advanced approach of sliding window based erasable pattern mining with list structure of industrial fields." Information Sciences 494 (August 2019): 37–59. http://dx.doi.org/10.1016/j.ins.2019.04.050.
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Guy, Travis N., and Scott B. Nokleby. "Development of an end-effector system for autonomous spraying applications and radiation surveying." Transactions of the Canadian Society for Mechanical Engineering 44, no. 4 (December 1, 2020): 541–57. http://dx.doi.org/10.1139/tcsme-2019-0184.
Повний текст джерелаАнотація:
This paper presents the design and testing of a scale proof-of-concept prototype robotic end-effector system for autonomous robotic shotcrete application and radiation surveying in underground uranium mining environments. The system presented consists of two functionally distinct prototype tools that achieve the independent tasks of autonomous robotic spray pattern control and surface radiation surveying. The first prototype tool presented is a novel, robotic shotcrete spraying tool that is capable of autonomously maintaining and adjusting its circular spray pattern diameter on target surfaces in response to changes in target surface distance. Control algorithms are presented that give the robotic shotcrete spraying tool the capability to produce advanced figure eight and spiral spraying patterns for surface preparation applications that involve spot filling deep surface cracks and pockets. Physical testing of the prototype tool empirically verified its ability to maintain circular spray pattern diameters at various target distances and demonstrated the application potential of the advanced figure eight and spiral spraying patterns. The second prototype tool presented is a Geiger–Müller tube-based radiation detection tool that uses lead shielding and a single-hole collimator in combination with precise robotic positioning to capture localized radiation measurements of surfaces within radiation-rich environments. Physical testing of the prototype tool demonstrated its ability to create radiation survey profiles that distinctly characterized the radiological profile of test target surfaces embedded with various radioactive sources.
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Zhang, Haiqing, Tao Wang, Daiwei Li, Abdelaziz Bouras, Xi Xiong, and Shaojie Qiao. "Maximal fuzzy supplement frequent pattern mining based on advanced pattern-aware dynamic search strategy and an effective FSFP-array technique." Journal of Intelligent & Fuzzy Systems 34, no. 1 (January 12, 2018): 141–52. http://dx.doi.org/10.3233/jifs-17092.
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Peng, Zhen Long, and You Lan Huang. "Research on E-Commerce Intelligence Based on IOT and Big Data." Applied Mechanics and Materials 496-500 (January 2014): 1889–94. http://dx.doi.org/10.4028/www.scientific.net/amm.496-500.1889.
Повний текст джерелаАнотація:
The computer technology together with network technology, communication technology have built a complex basic platform of computer network and a middle platform network which relate human to human, human to machine and machine to machine. Hundreds of millions of GB data generated from these platforms is stored in Cloud Computing Center. Based on this background, the paper analyzes the historical inevitability of IOT and big data, expounds the concept, process and methods of big data mining, and analyzes the natural relationship between big data mining and business intelligence. Through the deep mining of big data, its an unchangeable trend for us to grasp the user or personal behavior pattern and make marketing decision and overall consumption prediction, and then to achieve a comprehensive and advanced business intelligence.
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Ma, Yuaner, Juhaini Jabar, and Nor Azah Abdul Aziz. "Feature Selection with Integrated Gaussian Seahorse Optimization Data Mining for Cross-border Business Cooperation between the Malaysian Medical Industry and Tourism Industry." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 6 (July 30, 2023): 380–87. http://dx.doi.org/10.17762/ijritcc.v11i6.7727.
Повний текст джерелаАнотація:
The cross-border collaboration between the medical industry and the tourism industry has gained significant attention as a promising avenue for economic growth and development. Data mining techniques are employed to extract valuable patterns and insights from large-scale datasets, shedding light on the opportunities and challenges associated with this collaborative effort. This study proposes an integrated approach that combines feature selection with Gaussian Seahorse Optimization Data Mining (GSH-DM) to identify the most relevant features and optimize the data mining process. The GSH-DM assembling comprehensive datasets encompassing information from both the Malaysian medical industry and tourism industry. The integrated GSH-DM model then applies the Gaussian Seahorse Optimization algorithm to optimize the data mining process, enhancing the accuracy and efficiency of pattern discovery. the GSH-DM model, this study aims to uncover hidden patterns, relationships, and predictive models that can guide decision-making and strategy development for cross-border business cooperation. The findings of this study contribute to a deeper understanding of the factors that influence cross-border business cooperation between the Malaysian medical industry and the tourism industry. The integrated GSH-DM approach showcases the potential of combining feature selection techniques with advanced optimization algorithms in data mining applications. The results of GSH-DM provide actionable insights for stakeholders, enabling them to make informed decisions and foster successful cross-border collaborations between the Malaysian medical industry and the tourism industry. The analysis of the results demonstrated that GSH-DM exhibits improved performance for feature selection and classification.
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Zeng, Xuhui, Shu Wang, Yunqiang Zhu, Mengfei Xu, and Zhiqiang Zou. "A Knowledge Graph Convolutional Networks Method for Countryside Ecological Patterns Recommendation by Mining Geographical Features." ISPRS International Journal of Geo-Information 11, no. 12 (December 15, 2022): 625. http://dx.doi.org/10.3390/ijgi11120625.
Повний текст джерелаАнотація:
The recommendation system is one of the hotspots in the field of artificial intelligence that can be applied to recommend suitable ecological patterns for the countryside. Countryside ecological patterns mean advanced patterns that can be recommended to those developing areas which have similar geographical features, which provides huge benefits for countryside development. However, current recommendation methods have low recommendation accuracy due to some limitations, such as data-sparse and ‘cold start’, since they do not consider the complex geographical features. To address the above issues, we propose a geographical Knowledge Graph Convolutional Networks method for Countryside Ecological Patterns Recommendation (KGCN4CEPR). Specifically, a geographical knowledge graph of countryside ecological patterns is established first, which makes up for the sparsity of countryside ecological pattern data. Then, a convolutional network for mining the geographical similarity of ecological patterns is designed among adjacent countryside, which effectively solves the ‘cold start’ problem in the existing recommended methods. The experimental results show that our KGCN4CEPR method is suitable for recommending countryside ecological patterns. Moreover, the proposed KGCN4CEPR method achieves the best recommendation accuracy (60%), which is 9% higher than the MKR method and 6% higher than the RippleNet method.
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Chang, Jieh-Ren, You-Shyang Chen, Chien-Ku Lin, and Ming-Fu Cheng. "Advanced Data Mining of SSD Quality Based on FP-Growth Data Analysis." Applied Sciences 11, no. 4 (February 14, 2021): 1715. http://dx.doi.org/10.3390/app11041715.
Повний текст джерелаАнотація:
Storage devices in the computer industry have gradually transformed from the hard disk drive (HDD) to the solid-state drive (SSD), of which the key component is error correction in not-and (NAND) flash memory. While NAND flash memory is under development, it is still limited by the “program and erase” cycle (PE cycle). Therefore, the improvement of quality and the formulation of customer service strategy are topics worthy of discussion at this stage. This study is based on computer company A as the research object and collects more than 8000 items of SSD error data of its customers, which are then calculated with data mining and frequent pattern growth (FP-Growth) of the association rule algorithm to identify the association rule of errors by setting the minimum support degree of 90 and the minimum trust degree of 10 as the threshold. According to the rules, three improvement strategies of production control are suggested: (1) use of the association rule to speed up the judgment of the SSD error condition by customer service personnel, (2) a quality strategy, and (3) a customer service strategy.
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Kaur, Ishleen, M. N. Doja, Tanvir Ahmad, Musheer Ahmad, Amir Hussain, Ahmed Nadeem, and Ahmed A. Abd El-Latif. "An Integrated Approach for Cancer Survival Prediction Using Data Mining Techniques." Computational Intelligence and Neuroscience 2021 (December 28, 2021): 1–14. http://dx.doi.org/10.1155/2021/6342226.
Повний текст джерелаАнотація:
Ovarian cancer is the third most common gynecologic cancers worldwide. Advanced ovarian cancer patients bear a significant mortality rate. Survival estimation is essential for clinicians and patients to understand better and tolerate future outcomes. The present study intends to investigate different survival predictors available for cancer prognosis using data mining techniques. Dataset of 140 advanced ovarian cancer patients containing data from different data profiles (clinical, treatment, and overall life quality) has been collected and used to foresee cancer patients’ survival. Attributes from each data profile have been processed accordingly. Clinical data has been prepared corresponding to missing values and outliers. Treatment data including varying time periods were created using sequence mining techniques to identify the treatments given to the patients. And lastly, different comorbidities were combined into a single factor by computing Charlson Comorbidity Index for each patient. After appropriate preprocessing, the integrated dataset is classified using appropriate machine learning algorithms. The proposed integrated model approach gave the highest accuracy of 76.4% using ensemble technique with sequential pattern mining including time intervals of 2 months between treatments. Thus, the treatment sequences and, most importantly, life quality attributes significantly contribute to the survival prediction of cancer patients.
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Shmatko, Oleksandr, Oleksii Kolomiitsev, Volodymyr Fedorchenko, Iryna Mykhailenko, and Viacheslav Tretiak. "Integrating analytical statistical models, sequential pattern mining, and fuzzy set theory for advanced mobile app reliability assessment." Innovative Technologies and Scientific Solutions for Industries, no. 4(26) (December 27, 2023): 78–86. http://dx.doi.org/10.30837/itssi.2023.26.078.
Повний текст джерелаАнотація:
The study presents a new method for evaluating the reliability of mobile applications using the Corcoran model. This model includes several aspects of reliability, including performance, reliability, availability, scalability, security, usability, and testability. The Corcoran model can be applied to evaluate mobile applications by analysing key reliability metrics. Using the model significantly improves the reliability assessment of applications compared to traditional methods, which are primarily focused on desktop and server configurations. The aim of the study is to offer a more optimised approach to evaluating the reliability of mobile applications. The paper examines the problems faced by mobile app developers. This study represents a new application of the Corcoran model in evaluating the reliability of mobile applications. This model is characterised by an emphasis on the use of quantitative statistics and the ability to provide an accurate estimate of the probability of failure without any inaccuracies, which distinguishes this model from other software reliability models. The paper suggests using a combination of analytical statistical models, data extraction methods such as sequential pattern analysis, and fuzzy set theory to implement the Corcoran model. The application of the methodology is demonstrated by studying software error reports and conducting a comprehensive statistical analysis of them. To improve the results of future research, the paper suggests making more extensive use of the Corcoran model in various mobile applications and environments. It is recommended to change the model to take into account the constantly changing characteristics of mobile applications and their increasing complexity. In addition, it is advisable to conduct additional research to improve the data mining methods used in the model and explore the possibility of integrating artificial intelligence for more advanced software reliability analysis. Applying the Corcoran model to the mobile app development process to evaluate reliability can significantly improve the quality of applications, leading to increased customer satisfaction and trust in mobile apps. This model can serve as a guide for developers and companies to evaluate and improve their applications, driving innovation and continuous improvement in the competitive mobile app sector.
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Rastogi, Swati, Siddhartha Prakash Duttagupta, and Anirban Guha. "Design and Implementation of a Self-Supervised Algorithm for Vein Structural Patterns Analysis Using Advanced Unsupervised Techniques." Machine Learning and Knowledge Extraction 6, no. 2 (May 31, 2024): 1193–209. http://dx.doi.org/10.3390/make6020056.
Повний текст джерелаАнотація:
Compared to other identity verification systems applications, vein patterns have the lowest potential for being used fraudulently. The present research examines the practicability of gathering vascular data from NIR images of veins. In this study, we propose a self-supervision learning algorithm that envisions an automated process to retrieve vascular patterns computationally using unsupervised approaches. This new self-learning algorithm sorts the vascular patterns into clusters and then uses 2D image data to recuperate the extracted vascular patterns linked to NIR templates. Our work incorporates multi-scale filtering followed by multi-scale feature extraction, recognition, identification, and matching. We design the ORC, GPO, and RDM algorithms with these inclusions and finally develop the vascular pattern mining model to visualize the computational retrieval of vascular patterns from NIR imageries. As a result, the developed self-supervised learning algorithm shows a 96.7% accuracy rate utilizing appropriate image quality assessment parameters. In our work, we also contend that we provide strategies that are both theoretically sound and practically efficient for concerns such as how many clusters should be used for specific tasks, which clustering technique should be used, how to set the threshold for single linkage algorithms, and how much data should be excluded as outliers. Consequently, we aim to circumvent Kleinberg’s impossibility while attaining significant clustering to develop a self-supervised learning algorithm using unsupervised methodologies.
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Wei, Song, Zhenqing Fang, Zongxiang Li, Yu Liu, Dongjie Hu, Chuntong Miao, and Haiwen Wang. "Spatio-temporal evolution law of gas-temperature coupling field in “110 method” goaf and prevention of spontaneous combustion." PLOS ONE 18, no. 11 (November 20, 2023): e0293829. http://dx.doi.org/10.1371/journal.pone.0293829.
Повний текст джерелаАнотація:
To investigate the distribution characteristics of spontaneous combustion disaster (SCD) zones in the goaf of "110" mining method with U + L ventilation system and formulate corresponding fire prevention measures, enclosed coal oxidation experiments were carried out to measure the oxidation characteristics of Dongrong Coal Mine bituminous coal sample. A coupled 3DEC-CFD (3 dimensional Distinct Element Code) model was established. The 3D transient distribution characteristics of SCD zones in the “110” mining goaf under U+L ventilation condition were analyzed. Nitrogen injection in the tailgate was proposed for coal spontaneous combustion prevention. The results show that air leakage changed the distribution of oxygen and temperature fields in the “110” goaf, causing the region 20~60 m parallel to the retained roadway to remain in the oxidation zone for spontaneous combustion. As the working face advanced, the area change curve of SCD zones in the “110” goaf exhibited a “double inflection point” pattern. Eliminating the “retained roadway oxidation zone” can effectively reduce the spontaneous combustion risks in the “110” goaf and ensure mining safety. This study enriches the mechanisms of coal spontaneous combustion.
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Park, G. K. "Selected Papers from ISIS 2007." Journal of Advanced Computational Intelligence and Intelligent Informatics 12, no. 6 (November 20, 2008): 487. http://dx.doi.org/10.20965/jaciii.2008.p0487.
Повний текст джерелаАнотація:
The 8th International Symposium on Advanced Intelligent Systems (ISIS 2007), a biennial joint conference between Korea and Japan, focuses on artificial intelligence, intelligent systems, information technology, and their applications. ISIS 2007, held on September 5-8 at Soraksan National Park under the auspices of the Korea Fuzzy Logic and Intelligent Systems Society (KFIS), was attended by 265 researchers, engineers, and other professionals and featured 206 presentations. Of more than 20 papers preliminarily selected and reviewed by the ISIS 2007 International Program Committees, 7 were chosen for the special issue of the Journal of Advanced Computational Intelligence and Intelligent Informatics, centering on advanced intelligent systems including robotics, pattern recognition, data mining, and decision-making systems. The content and conclusions presented in these fine papers should prove both interesting and informative to specialists and generalists alike. I thank the authors and reviewers for their painstaking contributions to this special issue and Prof. Kaoru Hirota of the Tokyo Institute of Technology for inviting me to guest-edit this work.
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Nie, Yong. "On-line classroom visual tracking and quality evaluation by an advanced feature mining technique." Signal Processing: Image Communication 84 (May 2020): 115817. http://dx.doi.org/10.1016/j.image.2020.115817.
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Jaramillo, Carlos A., Syed H. A. Faruqui, Mary J. Pugh, and Adel Alaeddini. "Mining Major Transitions of Chronic Conditions in Patients with Multiple Chronic Conditions." Methods of Information in Medicine 56, no. 05 (2017): 391–400. http://dx.doi.org/10.3414/me16-01-0135.
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SummaryObjectives: Evolution of multiple chronic conditions (MCC) follows a complex stochastic process, influenced by several factors including the inter-relationship of existing conditions, and patient-level risk factors. Nearly 20% of citizens aged 18 years and older are burdened with two or more (multiple) chronic conditions (MCC). Treatment for people living with MCC currently accounts for an estimated 66% of the Nation’s healthcare costs. However, it is still not known precisely how MCC emerge and accumulate among individuals or in the general population. This study investigates major patterns of MCC transitions in a diverse population of patients and identifies the risk factors affecting the transition process.Methods: A Latent regression Markov clustering (LRMCL) algorithm is proposed to identify major transitions of four MCC that include hypertension (HTN), depression, Post- Traumatic Stress Disorder (PTSD), and back pain. A cohort of 601,805 individuals randomly selected from the population of Iraq and Afghanistan war Veterans (IAVs) who received VA care during three or more years between 2002-2015, is used for training the proposed LRMCL algorithm.Results: Two major clusters of MCC transition patterns with 78% and 22% probability of membership respectively were identified. The primary cluster demonstrated the possibility of improvement when the number of MCC is small and an increase in probability of MCC accumulation as the number of co- morbidities increased. The second cluster showed stability (no change) of MCC overtime as the major pattern. Age was the most significant risk factor associated with the most probable cluster for each IAV.Conclusions: These findings suggest that our proposed LRMCL algorithm can be used to describe and understand MCC transitions, which may ultimately allow healthcare systems to support optimal clinical decision- making. This method will be used to describe a broader range of MCC transitions in this and non-VA populations, and will add treatment information to see if models including treatments and MCC emergence can be used to support clinical decision-making in patient care.
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Song, Guangyuan, Kai Du, Yidong Zhang, Zexin Li, and Lei Hu. "Study of the Overlying Strata Movement Law for Paste-Filling Longwall Fully Mechanized in Gaohe Coal Mine." Applied Sciences 13, no. 14 (July 9, 2023): 8017. http://dx.doi.org/10.3390/app13148017.
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Green mining plays a vital role in achieving environmentally friendly and ecologically sound mining practices. In domestic mining areas, the coal mining method is gradually transitioning from collapse mining to filling mining. Paste filling has been proven effective in controlling surface deformation, although the understanding of its underlying control mechanisms remains incomplete. This study focuses on the E1302 paste-filling working face at Shanxi Gaohe Energy Co., Ltd. and conducts a comprehensive investigation into the movement patterns of overlying strata in longwall fully mechanized mining with paste filling. Through mathematical analysis, a mechanical model for overburden movement in paste-filling faces is established, and the movement behavior of overburden is studied through numerical simulations. Field measurements are conducted to analyze the primary influencing factors of overburden movement, while surface subsidence monitoring is employed to analyze the subsidence characteristics of paste-filling faces. The research reveals that the deflection formula for the roof behind the paste-filling face follows a unitary quartic equation. The key factors influencing significant roof subsidence in filling faces include the filling step distance, filling body strength, and filling rate. Compared to traditional caving mining, filling mining exhibits reduced stress concentration, a smaller range of stress influence, and less deformation in the surrounding rock. The coefficient of gentle subsidence for the overlying rock in filling mining is approximately one-tenth of that in caving mining. The development of cracks in filling mining can be divided into three stages: initial crack propagation, crack recompaction, and stable maintenance of cracks. Notably, the progression of advanced cracks assumes a “sail-shaped” pattern, and the area of crack recompaction is located above the rear side of the excavation. Cracks behind the working face only appear in the basal roof rock layer. When the filling rate in longwall fully mechanized mining with paste filling exceeds 94%, the top plate of the filling working face remains intact but exhibits bending and sinking. The sinking of the top plate increases exponentially with the filling step distance, and approximately 80% of the filling body’s deformation occurs within 20 m after filling. Following backfilling mining, the stability period of the overlying rock is significantly shortened compared to caving mining, resulting in a relatively gentle movement without an active surface movement phase. After six months of backfilling, the overlying rock settles steadily and consistently. The subsidence coefficient for backfilling mining is 0.065, with a maximum surface subsidence of 215 mm. These findings highlight the successful control of surface subsidence. The research outcomes provide an effective theoretical foundation and research direction for predicting overburden movement and surface subsidence in paste-filling faces.
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Kozik, Rafał, and Michał Choraś. "Pattern Extraction Algorithm for NetFlow-Based Botnet Activities Detection." Security and Communication Networks 2017 (2017): 1–10. http://dx.doi.org/10.1155/2017/6047053.
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As computer and network technologies evolve, the complexity of cybersecurity has dramatically increased. Advanced cyber threats have led to current approaches to cyber-attack detection becoming ineffective. Many currently used computer systems and applications have never been deeply tested from a cybersecurity point of view and are an easy target for cyber criminals. The paradigm of security by design is still more of a wish than a reality, especially in the context of constantly evolving systems. On the other hand, protection technologies have also improved. Recently, Big Data technologies have given network administrators a wide spectrum of tools to combat cyber threats. In this paper, we present an innovative system for network traffic analysis and anomalies detection to utilise these tools. The systems architecture is based on a Big Data processing framework, data mining, and innovative machine learning techniques. So far, the proposed system implements pattern extraction strategies that leverage batch processing methods. As a use case we consider the problem of botnet detection by means of data in the form of NetFlows. Results are promising and show that the proposed system can be a useful tool to improve cybersecurity.
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Soleymani, Ali, and Fatemeh Arabgol. "A Novel Approach for Detecting DGA-Based Botnets in DNS Queries Using Machine Learning Techniques." Journal of Computer Networks and Communications 2021 (July 5, 2021): 1–13. http://dx.doi.org/10.1155/2021/4767388.
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In today’s security landscape, advanced threats are becoming increasingly difficult to detect as the pattern of attacks expands. Classical approaches that rely heavily on static matching, such as blacklisting or regular expression patterns, may be limited in flexibility or uncertainty in detecting malicious data in system data. This is where machine learning techniques can show their value and provide new insights and higher detection rates. The behavior of botnets that use domain-flux techniques to hide command and control channels was investigated in this research. The machine learning algorithm and text mining used to analyze the network DNS protocol and identify botnets were also described. For this purpose, extracted and labeled domain name datasets containing healthy and infected DGA botnet data were used. Data preprocessing techniques based on a text-mining approach were applied to explore domain name strings with n-gram analysis and PCA. Its performance is improved by extracting statistical features by principal component analysis. The performance of the proposed model has been evaluated using different classifiers of machine learning algorithms such as decision tree, support vector machine, random forest, and logistic regression. Experimental results show that the random forest algorithm can be used effectively in botnet detection and has the best botnet detection accuracy.
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S. Iwin Thanakumar Joseph. "Advanced Digital Image Processing Technique based Optical Character Recognition of Scanned Document." Journal of Innovative Image Processing 4, no. 3 (October 13, 2022): 195–205. http://dx.doi.org/10.36548/jiip.2022.3.007.
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For many years, the field of image processing and pattern approval has included handwriting approval among its most intriguing and rigid analytical fields. In this article, the steps necessary to convert text from a paper document to a computer-readable format has been discussed. This is the most tedious and labor-intensive task. For nearly three decades, scientists have been trying to figure out how to make a computer read like a human. In Optical Character Recognition (OCR), a scanned picture is converted mechanically or electronically into an image that may be read as handwritten, typed, or printed text. It's a way to turn paper documents into digital files that can be searched and utilised in automated procedures. To facilitate applications like machine translation, text-to-speech, and text mining, OCR encodes the pictures as machine-readable text. It's an easy and inexpensive approach to make OCR that can read any document in a standard font size and with standard handwriting.
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Matrenin, Pavel V., Vadim Z. Manusov, Alexandra I. Khalyasmaa, Dmitry V. Antonenkov, Stanislav A. Eroshenko, and Denis N. Butusov. "Improving Accuracy and Generalization Performance of Small-Size Recurrent Neural Networks Applied to Short-Term Load Forecasting." Mathematics 8, no. 12 (December 4, 2020): 2169. http://dx.doi.org/10.3390/math8122169.
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The load forecasting of a coal mining enterprise is a complicated problem due to the irregular technological process of mining. It is necessary to apply models that can distinguish both cyclic components and complex rules in the energy consumption data that reflect the highly volatile technological process. For such tasks, Artificial Neural Networks demonstrate advanced performance. In recent years, the effectiveness of Artificial Neural Networks has been significantly improved thanks to new state-of-the-art architectures, training methods and approaches to reduce overfitting. In this paper, the Recurrent Neural Network architecture with a small-size model was applied to the short-term load forecasting of a coal mining enterprise. A single recurrent model was developed and trained for the entire four-year operational period of the enterprise, with significant changes in the energy consumption pattern during the period. This task was challenging since it required high-level generalization performance from the model. It was shown that the accuracy and generalization properties of small-size recurrent models can be significantly improved by the proper selection of the hyper-parameters and training method. The effectiveness of the proposed approach was validated using a real-case dataset.
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Dou, Litong, Ke Yang, Wenjie Liu, Xiaolou Chi, and Zhijie Wen. "Mining-Induced Stress-Fissure Field Evolution and the Disaster-Causing Mechanism in the High Gas Working Face of the Deep Hard Strata." Geofluids 2020 (September 25, 2020): 1–14. http://dx.doi.org/10.1155/2020/8849666.
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The compound dynamic disaster of coal and gas outbursts and rockburst is a typical hazard jeopardizing the mining of the high gas content coal seam under a hard roof condition. In this study, the hard roof’s mechanism inducing this hazard is analyzed. Physical analog modeling experiments and in situ monitoring of mining-induced stress were performed during coal seam mining under a hard roof condition. The pattern of hard roof breakage effect on the stress-fissure field evolution was revealed. The elastic energy was released and propagated on both sides immediately after the hard roof breaking, leading to energy accumulation. Meanwhile, expansive roof collapse resulted in the intense weighting of the working face and rockburst. Thus, the coal and gas outburst occurred under the joint action of the impact energy generated by breaking the hard roof and gas expansion energy. In other words, the compound dynamic disaster happened. Synergistic stereoextraction integrating cross-seam drilling and along-seam drilling was combined with deep hole advanced presplitting blasting technology to cope with the compound dynamic disaster in the high gas coal seam under a hard roof condition.
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Rzepka, Rafal, Yali Ge, and Kenji Araki. "Common Sense from the Web? Naturalness of Everyday Knowledge Retrieved from WWW." Journal of Advanced Computational Intelligence and Intelligent Informatics 10, no. 6 (November 20, 2006): 868–75. http://dx.doi.org/10.20965/jaciii.2006.p0868.
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This paper is to suggest opportunities for advanced systems hiding in the millions of WWW pages. While usually the Internet is used for achieving knowledge for humans, we present opposite approach where a machine retrieves usual knowledge about humans, their common behaviors and feelings. We claim that in long run such capability will be necessary for every machine interacting with a human user. We will concentrate on our theories and illustrate them with the results of web-mining experiment.
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Sudirman, Sudirman, and M. Alhudhori. "Analisis Sektor Unggulan dalam Meningkatkan Perekonomian dan Pembangunan Wilayah Provinsi Jambi." J-MAS (Jurnal Manajemen dan Sains) 3, no. 1 (April 26, 2018): 94. http://dx.doi.org/10.33087/jmas.v3i1.46.
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Based on pattern classification Typologi Klassen of the growth sectors of the economy in Jambi province makes the agricultural sector and the sector of mining and excavation are on the I quadrant i.e. as a sector that developed and developing fast, water procurement sector, trash, waste treatment and recycling, and education services sectors are at a quadrant II sectors advanced but that is depressed. After dianalis the pattern of growth sectors of the economy, may be known to the classification of economic sectors in the province of Jambi, for a deeper analysis of the sector required base with LQ method to find the base of the sector can be prioritized into the flagship sector. In accordance with the results of the analysis of the economic base by the method of LQ for the level of Jambi province are known to exist in four major sectors constituting the base sector of the economy. The fourth sector is agriculture, a sector of mining and excavation of the procurement sector, garbage, water, sewage treatment and recycling, and educational services. So, from both Typologi and Klassen LQ analysis it can be concluded that the economic sector in Jambi province which should be developed and can be prioritized into a flagship sector is agriculture, a sector of mining and excavation, the sector procurement of waste, water, sewage treatment and recycling, and education services sectors. Keywords: (1) GDP Jambi province; Indonesia'S GDP and (2) the rate of growth of GDP and contribution to Indonesia and Jambi province; (3) Data on the economic potential of Jambi province
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Islam, Humayera, Abu S. M. Mosa, Hirsch K. Srivastava, Vasanthi Mandhadi, Dhinakaran Rajendran, and Laine M. Young-Walker. "Discovery of Comorbid Psychiatric Conditions among Youth Detainees in Juvenile Justice System using Clinical Data." ACI Open 04, no. 02 (July 2020): e136-e148. http://dx.doi.org/10.1055/s-0040-1718542.
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Abstract Objective The main aim was to analyze the prevalence and patterns of comorbidity in 11 identified broad categories of psychiatric conditions and 48 specific psychiatric conditions among 613 youth from the Missouri Division of Youth Services (DYS) residential sites using advanced data mining techniques on clinical assessment data. Methods This study was based on youth detainee population at DYS residential placements receiving psychiatric care through the telemedicine network established between DYS and University of Missouri Department of Psychiatry. Association Rule Mining (ARM) algorithm was used to determine the associations and the co-occurrence pattern among the comorbid psychiatric conditions. Results About 88% of the DYS youth are diagnosed with two or more psychiatric disorders. From the ARM analysis, the most commonly co-occurred disorders are obtained as substance-related or -addicted disorders (SUD) and disruptive, impulse-control, and conduct disorders (CD) (n [%] = 258 [42.1%], followed by SUD, CD, and depressive disorder (DD) (145 [23.7%]), SUD, CD, and neurodevelopmental disorder (NDD) (133 [21.7%]), and DD, CD and NDD (120 [19.6%]). Discussion The study found high prevalence of comorbidity among the youth patients of the Missouri DYS facilities receiving care through the University of Missouri telemedicine network. The ideal scenario for assessment of any of these disorders in a patient should include substantial consideration in delineating the symptoms and history before eliminating any of them. Conclusion The comorbid patterns obtained can help in determining treatment regimens for DYS youth that can be effective in reducing recidivism and delinquency.
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Tallon, Erin M., Maria J. Redondo, Chi-Ren Shyu, Danlu Liu, Katrina Boles, and Mark A. Clements. "Contrast Pattern Mining With the T1D Exchange Clinic Registry Reveals Complex Phenotypic Factors and Comorbidity Patterns Associated With Familial Versus Sporadic Type 1 Diabetes." Diabetes Care 45, no. 3 (January 19, 2022): e56-e59. http://dx.doi.org/10.2337/dc21-2239.
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Zeng, Chunlin, Yuejin Zhou, Leiming Zhang, Donggui Mao, and Kexin Bai. "Study on overburden failure law and surrounding rock deformation control technology of mining through fault." PLOS ONE 17, no. 1 (January 24, 2022): e0262243. http://dx.doi.org/10.1371/journal.pone.0262243.
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In the mining process of working face, the additional stress generated by the fault changes the law of roadway deformation and failure as well as the law of overburden failure. Aiming at the influence of the fault in the mining process of working face, this study introduced the geological strength index (GSI) to analyze the stress distribution in the elastic-plastic zone of the surrounding rock of the roadway. And similar experiments under different engineering backgrounds were combined to study the characteristics of overburden movement and stress evolution. Based on the conclusions obtained, the roadway support scheme was designed. This study shows that, compared with ordinary mining, through-the-fault mining causes slippage and dislocation of the fault, the load of the overburden is transferred to both sides of the fault, and the stress near the fault accumulates abnormally. The “three zones” characteristics of the overburden movement disappear, the subsidence pattern is changed from "trapezoid" to "inverted triangle", and the influence distance of the advanced mining stress on the working face is extended from 20m to 30m. The instability range of roadway surrounding rock is exponentially correlated with the rupture degree of the surrounding rock. Through the introduction of GSI, the critical instability range of roadway surrounding rock is deduced to be 2.32m. According to the conclusion, the bolt length and roadway reinforced support length are redesigned. Engineering application shows that the deformation rate of the roadway within 60 days is controlled below 0.1~0.5mm/d, the deformation amount is controlled within 150mm, and the roadway deformation is controlled, which generally meets the requirements of use. The research results provide guidance and reference for similar roadway support.
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Wang, Yanfang, Shan Zhao, Hengtao Zuo, Xin Hu, Ying Guo, Ding Han, and Yuejia Chang. "Tracking the Vegetation Change Trajectory over Large-Surface Coal Mines in the Jungar Coalfield Using Landsat Time-Series Data." Remote Sensing 15, no. 24 (December 7, 2023): 5667. http://dx.doi.org/10.3390/rs15245667.
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Coal mining and ecological restoration activities significantly affect land surfaces, particularly vegetation. Long-term quantitative analyses of vegetation disturbance and restoration are crucial for effective mining management and ecological environmental supervision. In this study, using the Google Earth Engine and all available Landsat images from 1987 to 2020, we employed the Landsat-based Detection of Trends in Disturbance and Recovery (LandTrendr) algorithm and Support Vector Machine (SVM) to conduct a comprehensive analysis of the year, intensity, duration, and pattern of vegetation disturbance and restoration in the Heidaigou and Haerwusu open-pit coal mines (H-HOCMs) in the Jungar Coalfield of China. Our findings indicate that the overall accuracy for extractions of disturbance and restoration events in the H-HOCMs area is 83% and 84.5%, respectively, with kappa coefficients of 0.82 for both. Mining in Heidaigou has continued since its beginning in the 1990s, advancing toward the south and then eastward directions, and mining in the Haerwusu has advanced from west to east since 2010. The disturbance magnitude of the vegetation greenness in the mining area is relatively low, with a duration of about 4–5 years, and the restoration magnitude and duration vary considerably. The trajectory types show that vegetation restoration (R, 44%) occupies the largest area, followed by disturbance (D, 31%), restoration–disturbance (RD, 16%), disturbance–restoration (DR, 8%), restoration–disturbance–restoration (RDR), and no change (NC). The LandTrendr algorithm effectively detected changes in vegetation disturbance and restoration in H-HOCMs. Vegetation disturbance and restoration occurred in the study area, with a cumulative disturbance-to-restoration ratio of 61.79% since 1988. Significant restoration occurred primarily in the external dumps and continued ecological recovery occurred in the surrounding area.
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Rene, Antonio Oliveira Nzinga, Koji Okuhara, and Takeshi Matsui. "Natural Language Generation System for Knowledge Acquisition Based on Patent Database." Journal of Advanced Computational Intelligence and Intelligent Informatics 26, no. 2 (March 20, 2022): 160–68. http://dx.doi.org/10.20965/jaciii.2022.p0160.
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Privacy concerns at the individual and public or private organizational levels are a crucial. Its importance is highly evident nowadays, with the development of advanced technology. This study proposes a system for text mining that analyzes characteristics related to language. This factor makes it possible to generate a fictitious system while analyzing the patent within a bird’s-eye view and presenting keywords to support an idea. By mapping each patent’s information and relationship to an n-dimensional space, one can search for similar patents employing cosine similarity. Quantitative and qualitative evaluation verified the usefulness of the system.
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Domas, Zico Karya Saputra, Subagio Subagio, and M. Rizkiawan. "Transparency Prediction of Fraud Violations as an Anti-corruption Culture: Experiment of Decision Tree." Jurnal Bina Praja 14, no. 2 (August 2022): 289–300. http://dx.doi.org/10.21787/jbp.14.2022.289-300.
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Several prominent reports have highlighted the unsatisfactory level of anti-corruption transparency for the private sector in Indonesia. Hence, the anti-corruption vision is still an aspect that deserves to be campaigned for to form an advanced and just civilization. This study aims to obtain a pattern of knowledge in predicting the level of transparency of disclosure of fraud violations based on a data mining approach. The classification function algorithm in this study is a decision tree which is then compared with other classification function algorithms, naive Bayes, and k-nn. The sample in this study is 141 companies combined in the construction, mining, and banking sectors, which are on the IDX for the 2019 period. As a result, the decision tree algorithm provides the second-best performance in predicting the level of corporate transparency, namely an accuracy of 70.92% and an AUC level of 0.740. Then in terms of different tests, the decision tree algorithm is in the same cluster as the algorithm with the best performance because the t-test results show no significant difference. Based on the pattern generated by the decision tree algorithm, the elements of opportunity, pressure, and arrogance are considered key factors in predicting the level of transparency of disclosure of fraud violations. One of them can be interpreted that a company that is supervised by a minimum of four independent commissioners means company tends to be predicted to be more daring in disclosing anti-corruption information in its annual report to the wider public. This study also recommends that every authorized institution in Indonesia can apply a data mining algorithm approach in utilizing the advantages of each agency's internal data volume to map anti-corruption cultural socialization strategies in private sector companies.
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Chandrashaker Reddy, P., and A. Suresh Babu. "Usage of co-event pattern mining with optimal fuzzy rule-based classifier for effective web page retrieval." International Journal of Engineering & Technology 7, no. 3.29 (August 24, 2018): 275. http://dx.doi.org/10.14419/ijet.v7i3.29.18811.
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With the coming of the World Wide Web and the rise of web-based business applications and informal organizations, associations over the web create a lot of information on a daily basis. It is becoming more complex and critical task to retrieve exact information from web expected by its users. In the recent times, the Web has extended its noteworthiness to the point of transforming into the point of convergence of our propelled lives. The search engine as an apparatus to explore the web must get the coveted outcomes for any given query. The greater part of the search engines can't totally fulfill user’s necessities and the outcomes are regularly inaccurate and irrelevant. knowledge of ontology and history is not much personalization in the existing techniques. To conquer these issues, data mining systems must be connected to the web and one advanced powerful concept is web-page recommendation which is becoming more powerful now a day. In this paper, the design of a fuzzy logic classifier algorithm is defined as a search problem in the solution space where every node represents a rule set, membership function, and the particular framework behaviour. Therefore, the hybrid optimization algorithm is applied to search for an optimal location of this solution space which hopefully represents the near optimal rule set and membership function. In this article, we reviewed various techniques proposed by different researchers for web page personalization and proposed a novel approach for finding optimal solutions to search the relevant information..
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Deng, Wenping, Ziyu Yang, Peng Xun, Peidong Zhu, and Baosheng Wang. "Advanced Bad Data Injection Attack and Its Migration in Cyber-Physical Systems." Electronics 8, no. 9 (August 26, 2019): 941. http://dx.doi.org/10.3390/electronics8090941.
Повний текст джерелаАнотація:
False data injection (FDI) attack is a hot topic in cyber-physical systems (CPSs). Attackers inject bad data into sensors or return false data to the controller to cause the inaccurate state estimation. Although there exists many detection approaches, such as bad data detector (BDD), sequence pattern mining, and machine learning methods, a smart attacker still can inject perfectly false data to go undetected. In this paper, we focus on the advanced false data injection (AFDI) attack and its detection method. An AFDI attack refers to the attack where a malicious entity accurately and successively changes sensory data, making the normal system state continuously evaluated as other legal system states, causing wrong outflow of controllers. The attack can lead to an automatic and long-term system failure/performance degradation. We first depict the AFDI attack model and analyze limitations of existing detectors for detecting AFDI. Second, we develop an approach based on machine learning, which utilizes the k-Nearest Neighbor (KNN) technique and heterogeneous data including sensory data and system commands to implement a classifier for detecting AFDI attacks. Finally, simulation experiments are given to demonstrate AFDI attack impact and the effectiveness of the proposed method for detecting AFDI attacks.
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Savelonas, Michalis A., Christos N. Veinidis, and Theodoros K. Bartsokas. "Computer Vision and Pattern Recognition for the Analysis of 2D/3D Remote Sensing Data in Geoscience: A Survey." Remote Sensing 14, no. 23 (November 27, 2022): 6017. http://dx.doi.org/10.3390/rs14236017.
Повний текст джерелаАнотація:
Historically, geoscience has been a prominent domain for applications of computer vision and pattern recognition. The numerous challenges associated with geoscience-related imaging data, which include poor imaging quality, noise, missing values, lack of precise boundaries defining various geoscience objects and processes, as well as non-stationarity in space and/or time, provide an ideal test bed for advanced computer vision techniques. On the other hand, the developments in pattern recognition, especially with the rapid evolution of powerful graphical processing units (GPUs) and the subsequent deep learning breakthrough, enable valuable computational tools, which can aid geoscientists in important problems, such as land cover mapping, target detection, pattern mining in imaging data, boundary extraction and change detection. In this landscape, classical computer vision approaches, such as active contours, superpixels, or descriptor-guided classification, provide alternatives that remain relevant when domain expert labelling of large sample collections is often not feasible. This issue persists, despite efforts for the standardization of geoscience datasets, such as Microsoft’s effort for AI on Earth, or Google Earth. This work covers developments in applications of computer vision and pattern recognition on geoscience-related imaging data, following both pre-deep learning and post-deep learning paradigms. Various imaging modalities are addressed, including: multispectral images, hyperspectral images (HSIs), synthetic aperture radar (SAR) images, point clouds obtained from light detection and ranging (LiDAR) sensors or digital elevation models (DEMs).
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Kritikakis, George, Michael Galetakis, Antonios Vafidis, George Apostolopoulos, Theodore Michalakopoulos, Miltiades Triantafyllou, Christos Roumpos, et al. "Toward the Optimization of Mining Operations Using an Automatic Unmineable Inclusions Detection System for Bucket Wheel Excavator Collision Prevention: A Synthetic Study." Sustainability 15, no. 17 (August 30, 2023): 13097. http://dx.doi.org/10.3390/su151713097.
Повний текст джерелаАнотація:
This work introduces a methodology for the automatic unmineable inclusions detection and Bucket Wheel Excavator (BWE) collision prevention, using electromagnetic (EM) inspection and a fuzzy inference system. EM data are collected continuously ahead from the bucket wheel of a BWE and subjected to processing. Two distinct methodologies for data processing were developed and integrated into the MATLAB programming environment. The first approach, named “Simple Mode”, utilizes statistical process control to generate real-time alerts in the event of a potential collision involving the excavator’s bucket and hard rock inclusions. The advanced processing flow (“Advanced Mode”) requires accurate instrument positioning and data from successive EM scans. It incorporates techniques of local resistivity maxima detection (Position Prominence Index) as well as Neural Network-based Pattern Recognition (NNPR). A decision support process based on a Fuzzy Inference System (FIS) has been developed to assist BWE operators in avoiding collision when digging hard rock inclusions. The proposed methodology was extensively tested using synthetic EM data. Limited real data, acquired with a CMD2 (GF Instruments) EM instrument equipped with GPS, were used to control its efficiency. Increased accuracy in the automatic detection of unmineable inclusions was observed using the Advanced Mode. On the other hand, the Simple Mode processing technique offers the advantage of being independent of instrument positioning as well as it provides real-time inspection of the excavated mine slope. This work introduces a methodology for hard rock inclusion detection and can contribute to the optimization of mine operations by improving resource efficiency, safety, cost savings, and environmental sustainability.
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Kapp, Vadim, Marvin Carl May, Gisela Lanza, and Thorsten Wuest. "Pattern Recognition in Multivariate Time Series: Towards an Automated Event Detection Method for Smart Manufacturing Systems." Journal of Manufacturing and Materials Processing 4, no. 3 (September 5, 2020): 88. http://dx.doi.org/10.3390/jmmp4030088.
Повний текст джерелаАнотація:
This paper presents a framework to utilize multivariate time series data to automatically identify reoccurring events, e.g., resembling failure patterns in real-world manufacturing data by combining selected data mining techniques. The use case revolves around the auxiliary polymer manufacturing process of drying and feeding plastic granulate to extrusion or injection molding machines. The overall framework presented in this paper includes a comparison of two different approaches towards the identification of unique patterns in the real-world industrial data set. The first approach uses a subsequent heuristic segmentation and clustering approach, the second branch features a collaborative method with a built-in time dependency structure at its core (TICC). Both alternatives have been facilitated by a standard principle component analysis PCA (feature fusion) and a hyperparameter optimization (TPE) approach. The performance of the corresponding approaches was evaluated through established and commonly accepted metrics in the field of (unsupervised) machine learning. The results suggest the existence of several common failure sources (patterns) for the machine. Insights such as these automatically detected events can be harnessed to develop an advanced monitoring method to predict upcoming failures, ultimately reducing unplanned machine downtime in the future.
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Saidala, Ravi Kumar. "Variant of Northern Bald Ibis Algorithm for Unmasking Outliers." International Journal of Software Science and Computational Intelligence 12, no. 1 (January 2020): 15–29. http://dx.doi.org/10.4018/ijssci.2020010102.
Повний текст джерелаАнотація:
Clustering, one of the most attractive data analysis concepts in data mining, are frequently used by many researchers for analysing data of variety of real-world applications. It is stated in the literature that traditional clustering methods are trapped in local optima and fail to obtain optimal clusters. This research work gives the design and development of an advanced optimum clustering method for unmasking abnormal entries in the clinical dataset. The basis is the NOA, a recently proposed algorithm, driven by mimicking the migration pattern of Northern Bald Ibis (Threskiornithidae) birds. First, we developed the variant of the standard NOA by replacing C1 and C2 parameters of NOA with chaotic maps turning it into the VNOA. Later, we utilized the VNOA in the design of a new and advanced clustering method. VNOA is first benchmarked on a 7 unimodal (F1–F7) and 6 multimodal (F8–F13) mathematical functions. We tested the numerical complexity of proposed VNOA-based clustering methods on a clinical dataset. We then compared the obtained graphical and statistical results with well-known algorithms. The superiority of the presented clustering method is evidenced from the simulations and comparisons.