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MACROBUTTON MTEditEquationSection2 Equation Chapter 1 Section 1 SEQ MTEqn \r \h \* MERGEFORMAT SEQ MTSec \r 1 \h \* MERGEFORMAT SEQ MTChap \r 1 \h \* MERGEFORMAT Paper Title: Journals of the Asia Joint Conference on Computing Publishing (Arial 17 pt., Left justification)Please DO NOT insert author name and affiliation in the manuscriptABSTRACT (Arial 11 pt)(Time new roman 10 pt.) An abstract summarizes, usually in one paragraph of 250-300 words or less, the major aspects of the entire paper in a prescribed sequence that includes: 1) the overall purpose of the study and the research problem(s) you investigated; 2) the basic design of the study; 3) major findings or trends found as a result of your analysis; and, 4) a brief summary of your interpretations and conclusions.Keywords: first term, second term, third term, fourth term, fifth term, sixth termINTRODUCTION (Arial 14 pt)The introduction leads the reader from a general subject area to a particular topic of inquiry. It establishes the scope ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"ajavv3m2o","properties":{"formattedCitation":"(Gray & Watson, 1998)","plainCitation":"(Gray & Watson, 1998)","noteIndex":0},"citationItems":[{"id":2738,"uris":[""],"uri":[""],"itemData":{"id":2738,"type":"article-journal","abstract":"Many large organizations have developed data warehouses to support decision making. The data in a warehouse are subject oriented, integrated, time variant, and nonvolatile. A data warehouse contains five types of data: current detail data, older detail data, lightly summarized data, highly summarized data, and metadata. The architecture of a data warehouse includes a backend process (the extraction of data from source systems), the warehouse, and the front-end use (the accessing of data from the warehouse). A data mart is a smaller version of a data warehouse that supports the narrower set of requirements of a single business unit. Data marts should be developed in an integrated manner in order to avoid repeating the \"silos of information\" problem.An operational data store is a database for transaction processing systems that uses the data warehouse approach to provide clean data. Data warehousing is constantly changing, with the associated opportunities for practice and research, such as the potential for knowledge management using the warehouse.","container-title":"SIGMIS Database","DOI":"10.1145/313310.313345","ISSN":"0095-0033","issue":"3","page":"83–90","source":"ACM Digital Library","title":"Present and future directions in data warehousing","volume":"29","author":[{"family":"Gray","given":"Paul"},{"family":"Watson","given":"Hugh J."}],"issued":{"date-parts":[["1998"]]}}}],"schema":""} (Gray & Watson, 1998), context, and significance of the research being conducted by summarizing current understanding and background information about the topic, stating the purpose of the work in the form of the research problem supported by a hypothesis or a set of questions, explaining briefly the methodological approach used to examine the research problem, highlighting the potential outcomes your study can reveal, and outlining the remaining structure and organization of the paper. Think of the introduction as a mental road map that must answer for the reader these four questions: 1) What was I studying? 2) Why was this topic important to investigate? 3) What did we know about this topic before I did this study? And 4) How will this study advance new knowledge or new ways of understanding?According to Reyes, there are three overarching goals of a good introduction: 1) ensure that you summarize prior studies about the topic in a manner that lays a foundation for understanding the research problem; 2) explain how your study specifically addresses gaps in the literature, insufficient consideration of the topic, or other deficiency in the literature; and, 3) note the broader theoretical, empirical, and/or policy contributions and implications of your research.A well-written introduction is important because, quite simply, you never get a second chance to make a good first impression. The opening paragraphs of your paper will provide your readers with their initial impressions about the logic of your argument, your writing style, the overall quality of your research, and, ultimately, the validity of your findings and conclusions. A vague, disorganized, or error-filled introduction will create a negative impression, whereas, a concise, engaging, and well-written introduction will lead your readers to think highly of your analytical skills, your writing style, and your research approach. All introductions should conclude with a brief paragraph that describes the organization of the rest of the paper. ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"hs3PUtR3","properties":{"formattedCitation":"(Tshilidzi, 2009)","plainCitation":"(Tshilidzi, 2009)","noteIndex":0},"citationItems":[{"id":2607,"uris":[""],"uri":[""],"itemData":{"id":2607,"type":"book","abstract":"The issue of missing data imputation has been extensively explored in information engineering, though needing a new focus and approach in putational Intelligence for Missing Data Imputation, Estimation, and Management: Knowledge Optimization Techniques focuses on methods to estimate missing values given to observed data. Providing a defining body of research valuable to those involved in the field of study, this book presents current and new computational intelligence techniques that allow computers to learn the underlying structure of data.","ISBN":"978-1-60566-337-1","language":"en","note":"Google-Books-ID: 1_O7K9EIl2UC","number-of-pages":"326","publisher":"IGI Global","source":"Google Books","title":"Computational intelligence for missing data imputation, estimation, and management: knowledge optimization techniques: knowledge optimization techniques","title-short":"Computational Intelligence for Missing Data Imputation, Estimation, and Management","author":[{"family":"Tshilidzi","given":"Marwala"}],"issued":{"date-parts":[["2009"]]}}}],"schema":""} (Tshilidzi, 2009).LITERATURE REVIEWA literature review surveys books, scholarly articles, and any other sources relevant to a particular issue, area of research, or theory, and by so doing, provides a description, summary, and critical evaluation of these works in relation to the research problem being investigated. Literature reviews are designed to provide an overview of sources you have explored while researching a particular topic and to demonstrate to your readers how your research fits within a larger field of study. Types of Literature Reviews (Arial 13 pt)Argumentative Review: This form examines literature selectively in order to support or refute an argument, deeply imbedded assumption, or philosophical problem already established in the literature. The purpose is to develop a body of literature that establishes a contrarian viewpoint. Given the value-laden nature of some social science research [e.g., educational reform; immigration control], argumentative approaches to analyzing the literature can be a legitimate and important form of discourse. However, note that they can also introduce problems of bias when they are used to make summary claims of the sort found in systematic reviews.Integrative Review: Considered a form of research that reviews, critiques, and synthesizes representative literature on a topic in an integrated way such that new frameworks and perspectives on the topic are generated. The body of literature includes all studies that address related or identical hypotheses or research problems. A well-done integrative review meets the same standards as primary research in regard to clarity, rigor, and replication. This is the most common form of review in the social sciences.Figures and TablesPosition figures and tables at the tops and bottoms of columns. Avoid placing them in the middle of columns. Large figures and tables may span across both columns ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"zLOGUOIR","properties":{"formattedCitation":"(Lim & Haron, 2013; Szynkiewicz, 2018)","plainCitation":"(Lim & Haron, 2013; Szynkiewicz, 2018)","noteIndex":0},"citationItems":[{"id":2708,"uris":[""],"uri":[""],"itemData":{"id":2708,"type":"paper-conference","abstract":"Genetic algorithm (GA), Differential Evolution (DE) and Particle Swarm Optimization (PSO) are always implemented to solve different kinds of complex optimization problems. Each method contains its own advantages and the performance varies based on different case studies. There are many Soft Computing (SC) methods which can generate different result for the same optimization problems. However, no exact result is produced because random function is usually applied in SC methods. The performance maybe is affected by the parameter setting or operations inside each method. Therefore, the motivation of this paper is to compare the performance of GA, DE and PSO by using the same parameters setting and optimization problems. The experiments can prove that although same parameters setting are applied, but different fitness and time can be obtained. Based on the result, GA was proven to perform better compared to DE and PSO in obtaining highest number of best minimum fitness and faster than both methods.","container-title":"2013 IEEE Conference on Open Systems","DOI":"10.1109/ICOS.2013.6735045","event-place":"Kuching, Malaysia","page":"41-46","publisher":"IEEE","publisher-place":"Kuching, Malaysia","source":"IEEE Xplore","title":"Performance comparison of Genetic Algorithm, Differential Evolution and Particle Swarm Optimization towards benchmark functions","author":[{"family":"Lim","given":"S. P."},{"family":"Haron","given":"H."}],"issued":{"date-parts":[["2013",12]]}}},{"id":2710,"uris":[""],"uri":[""],"itemData":{"id":2710,"type":"article-journal","container-title":"Journal of Telecommunications and Information Technology","DOI":"","issue":"4","page":"1-13","title":"A comparative study of PSO and CMA-ES algorithms on black-box optimization benchmarks","volume":"8","author":[{"family":"Szynkiewicz","given":"P."}],"issued":{"date-parts":[["2018"]]}}}],"schema":""} (Lim & Haron, 2013; Szynkiewicz, 2018). Figure captions should be centered below the figures; table captions should be centered above. Avoid placing figures and tables before their first mention in the text. Use the abbreviation “Fig. 1,” even at the beginning of a sentence. Note how the caption is centered in the column.To figure axis labels, use words rather than symbols. Do not label axes only with units. Do not label axes with a ratio of quantities and units. Figure labels should be legible, about 9-point type.Color figures will be appearing only in online publication. All figures will be black and white graphs in print publication.Table 1: Table caption here.MethodsOperationsParametersComplexityGAcrossover, mutation, and selectionChromosome, % of crossover, % of mutationO(n) ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"YSU5s7FG","properties":{"formattedCitation":"(Nopiah et al., 2010)","plainCitation":"(Nopiah et al., 2010)","noteIndex":0},"citationItems":[{"id":"XUTEKN2B/JF2MfdnQ","uris":[""],"uri":[""],"itemData":{"id":2302,"type":"paper-conference","title":"Time Complexity Analysis of the Genetic Algorithm Clustering Method","container-title":"Proceedings of the 9th WSEAS International Conference on Signal Processing, Robotics and Automation","collection-title":"ISPRA'10","publisher":"World Scientific and Engineering Academy and Society (WSEAS)","publisher-place":"Stevens Point, Wisconsin, USA","page":"171–176","source":"ACM Digital Library","event-place":"Stevens Point, Wisconsin, USA","abstract":"This paper presents the time complexity analysis of the genetic algorithm clustering method. The tested feature in the clustering algorithm is the population limit function. For the purpose of the study, segmental kurtosis analysis was done on several segmented fatigue time series data, which are then represented in two-dimensional heteroscaled datasets. These datasets are then clustered using the genetic algorithm clustering method and at the runtime of the algorithm is measured against the number of iterations. Polynomial fitting is used on the runtime data to determine the time complexity of the algorithm. The results of the analysis will be used to determine the significance of including the population limit function in the algorithm for optimal performance.","URL":"","ISBN":"978-960-474-157-1","note":"event-place: UK","author":[{"family":"Nopiah","given":"Z. M."},{"family":"Khairir","given":"M. I."},{"family":"Abdullah","given":"S."},{"family":"Baharin","given":"M. N."},{"family":"Arifin","given":"A."}],"issued":{"date-parts":[["2010"]]},"accessed":{"date-parts":[["2019",6,4]]}}}],"schema":""} (Nopiah et al., 2010), ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"dmZIEyNC","properties":{"formattedCitation":"(Pan et al., 2014)","plainCitation":"(Pan et al., 2014)","noteIndex":0},"citationItems":[{"id":"XUTEKN2B/5QpaWnWM","uris":[""],"uri":[""],"itemData":{"id":2303,"type":"book","title":"Intelligent Data analysis and its Applications, Volume I: Proceeding of the First Euro-China Conference on Intelligent Data Analysis and Applications, June 13-15, 2014, Shenzhen, China","collection-title":"Advances in Intelligent Systems and Computing","publisher":"Springer International Publishing","source":"","abstract":"This volume presents the proceedings of the First Euro-China Conference on Intelligent Data Analysis and Applications (ECC 2014), which was hosted by Shenzhen Graduate School of Harbin Institute of Technology and was held in Shenzhen City on June 13-15, 2014. ECC 2014 was technically co-sponsored by Shenzhen Municipal People’s Government, IEEE Signal Processing Society, Machine Intelligence Research Labs, VSB-Technical University of Ostrava (Czech Republic), National Kaohsiung University of Applied Sciences (Taiwan), and Secure E-commerce Transactions (Shenzhen) Engineering Laboratory of Shenzhen Institute of Standards and Technology.","URL":"","ISBN":"978-3-319-07775-8","title-short":"Intelligent Data analysis and its Applications, Volume I","language":"en","editor":[{"family":"Pan","given":"Jeng-Shyang"},{"family":"Snasel","given":"Vaclav"},{"family":"Corchado","given":"Emilio S."},{"family":"Abraham","given":"Ajith"},{"family":"Wang","given":"Shyue-Liang"}],"issued":{"date-parts":[["2014"]]},"accessed":{"date-parts":[["2019",6,4]]}}}],"schema":""} (Pan et al., 2014)where n is Number of ChromosomesDEcrossover, mutation, recombination, and selectionagents, position, CR, FO(n2) ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"xsmPtZrq","properties":{"formattedCitation":"(Drozdik et al., 2013)","plainCitation":"(Drozdik et al., 2013)","noteIndex":0},"citationItems":[{"id":"XUTEKN2B/vz1unYXz","uris":[""],"uri":[""],"itemData":{"id":2304,"type":"paper-conference","title":"Attempt to Reduce the Computational Complexity in Multi-objective Differential Evolution Algorithms","source":"ResearchGate","abstract":"Nondominated sorting and diversity estimation procedures are an essential part of many multiobjective optimization algorithms. In many cases these procedures are the com-putational bottleneck of the entire algorithm. We present the methods to decrease the cost of these procedures for multiobjective differential evolution (DE) algorithms. Our approach is to compute domination ranks and crowding dis-tances for the population at the beginning of the algorithm and use a combination of well known data structures to effi-ciently update these attributes. Experiments show that the cost of improved nondominated sorting is sub-quadratic in the number of individuals. In practice using our methods the overall DE algorithm can run 2 to 100 times faster.","DOI":"10.1145/2463372.2463453","author":[{"family":"Drozdik","given":"Martin"},{"family":"Aguirre","given":"Hernan"},{"family":"Tanaka","given":"Kiyoshi"}],"issued":{"date-parts":[["2013",7,6]]}}}],"schema":""} (Drozdik et al., 2013) where n is number of agentsCMA-ESUpdate: isotropic evolution path, anisotropic evolution path, covariance matrix m, σ, C, pσ, pcO(n3) ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"nLwLYoxj","properties":{"formattedCitation":"(Hansen et al., 2003)","plainCitation":"(Hansen et al., 2003)","noteIndex":0},"citationItems":[{"id":"XUTEKN2B/CoG0nhet","uris":[""],"uri":[""],"itemData":{"id":2306,"type":"article-journal","title":"Reducing the Time Complexity of the Derandomized Evolution Strategy with Covariance Matrix Adaptation (CMA-ES)","container-title":"Evolutionary computation","page":"1-18","volume":"11","source":"ResearchGate","abstract":"This paper presents a novel evolutionary optimization strategy based on the derandomized evolution strategy with covariance matrix adaptation (CMA-ES). This new approach is intended to reduce the number of generations required for convergence to the optimum. Reducing the number of generations, i.e., the time complexity of the algorithm, is important if a large population size is desired: (1) to reduce the effect of noise; (2) to improve global search properties; and (3) to implement the algorithm on (highly) parallel machines. Our method results in a highly parallel algorithm which scales favorably with large numbers of processors. This is accomplished by efficiently incorporating the available information from a large population, thus significantly reducing the number of generations needed to adapt the covariance matrix. The original version of the CMA-ES was designed to reliably adapt the covariance matrix in small populations but it cannot exploit large populations efficiently. Our modifications scale up the efficiency to population sizes of up to 10n, where n is the problem dimension. This method has been applied to a large number of test problems, demonstrating that in many cases the CMA-ES can be advanced from quadratic to linear time complexity.","DOI":"10.1162/106365603321828970","journalAbbreviation":"Evolutionary computation","author":[{"family":"Hansen","given":"Nikolaus"},{"family":"D Müller","given":"Sibylle"},{"family":"Koumoutsakos","given":"Petros"}],"issued":{"date-parts":[["2003",2,1]]}}}],"schema":""} (Hansen et al., 2003), ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"KVZ4SJ0S","properties":{"formattedCitation":"(Krause et al., 2016)","plainCitation":"(Krause et al., 2016)","noteIndex":0},"citationItems":[{"id":"XUTEKN2B/f3OFN9P4","uris":[""],"uri":[""],"itemData":{"id":2308,"type":"paper-conference","title":"CMA-ES with Optimal Covariance Update and Storage Complexity","source":"ResearchGate","abstract":"We propose probabilistic latent variable models for multi-view anomaly detection,\nwhich is the task of finding instances that have inconsistent views given\nmulti-view data. With the proposed model, all views of a non-anomalous instance\nare assumed to be generated from a single latent vector. On the other hand, an\nanomalous instance is assumed to have multiple latent vectors, and its different\nviews are generated from different latent vectors. By inferring the number of latent\nvectors used for each instance with Dirichlet process priors, we obtain multiview\nanomaly scores. The proposed model can be seen as a robust extension of\nprobabilistic canonical correlation analysis for noisy multi-view data. We present\nBayesian inference procedures for the proposed model based on a stochastic EM\nalgorithm. The effectiveness of the proposed model is demonstrated in terms of\nperformance when detecting multi-view anomalies.","author":[{"family":"Krause","given":"Oswin"},{"family":"Arbonès","given":"Dídac"},{"family":"Igel","given":"Christian"}],"issued":{"date-parts":[["2016",11,5]]}}}],"schema":""} (Krause et al., 2016)where n is number of samples PSOUpdate: velocity, positionparticle, c1, c2, wO(MP) ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"sAB4Uhx9","properties":{"formattedCitation":"[38]","plainCitation":"[38]","dontUpdate":true,"noteIndex":0},"citationItems":[{"id":"XUTEKN2B/TzpeMfdG","uris":[""],"uri":[""],"itemData":{"id":3017,"type":"paper-conference","title":"Particle swarm optimization for traveling salesman problem","container-title":"Proceedings of the 2003 International Conference on Machine Learning and Cybernetics (IEEE Cat. No.03EX693)","page":"1583-1585 Vol.3","volume":"3","source":"IEEE Xplore","event":"Proceedings of the 2003 International Conference on Machine Learning and Cybernetics (IEEE Cat. No.03EX693)","abstract":"This paper proposes a new application of particle swarm optimization for traveling salesman problem. We have developed some special methods for solving TSP using PSO. We have also proposed the concept of swap operator and swap sequence, and redefined some operators on the basis of them, in this way the paper has designed a special PSO. The experiments show that it can achieve good results.","DOI":"10.1109/ICMLC.2003.1259748","author":[{"family":"Wang","given":"Kang-Ping"},{"family":"Huang","given":"Lan"},{"family":"Zhou","given":"Chun-Guang"},{"family":"Pang","given":"Wei"}],"issued":{"date-parts":[["2003"]]}}}],"schema":""} [37], ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"11O1BNHS","properties":{"formattedCitation":"(Pan et al., 2014)","plainCitation":"(Pan et al., 2014)","noteIndex":0},"citationItems":[{"id":"XUTEKN2B/5QpaWnWM","uris":[""],"uri":[""],"itemData":{"id":2303,"type":"book","title":"Intelligent Data analysis and its Applications, Volume I: Proceeding of the First Euro-China Conference on Intelligent Data Analysis and Applications, June 13-15, 2014, Shenzhen, China","collection-title":"Advances in Intelligent Systems and Computing","publisher":"Springer International Publishing","source":"","abstract":"This volume presents the proceedings of the First Euro-China Conference on Intelligent Data Analysis and Applications (ECC 2014), which was hosted by Shenzhen Graduate School of Harbin Institute of Technology and was held in Shenzhen City on June 13-15, 2014. ECC 2014 was technically co-sponsored by Shenzhen Municipal People’s Government, IEEE Signal Processing Society, Machine Intelligence Research Labs, VSB-Technical University of Ostrava (Czech Republic), National Kaohsiung University of Applied Sciences (Taiwan), and Secure E-commerce Transactions (Shenzhen) Engineering Laboratory of Shenzhen Institute of Standards and Technology.","URL":"","ISBN":"978-3-319-07775-8","title-short":"Intelligent Data analysis and its Applications, Volume I","language":"en","editor":[{"family":"Pan","given":"Jeng-Shyang"},{"family":"Snasel","given":"Vaclav"},{"family":"Corchado","given":"Emilio S."},{"family":"Abraham","given":"Ajith"},{"family":"Wang","given":"Shyue-Liang"}],"issued":{"date-parts":[["2014"]]},"accessed":{"date-parts":[["2019",6,4]]}}}],"schema":""} (Pan et al., 2014), ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"3sX0rQZ5","properties":{"formattedCitation":"(Al-Kazemi & Habib, 2006)","plainCitation":"(Al-Kazemi & Habib, 2006)","noteIndex":0},"citationItems":[{"id":"XUTEKN2B/vaTYLYj9","uris":[""],"uri":[""],"itemData":{"id":2310,"type":"paper-conference","title":"Complexity Analysis of Problem-dimension Using PSO","container-title":"Proceedings of the 7th WSEAS International Conference on Evolutionary Computing","collection-title":"EC'06","publisher":"World Scientific and Engineering Academy and Society (WSEAS)","publisher-place":"Stevens Point, Wisconsin, USA","page":"45–52","source":"ACM Digital Library","event-place":"Stevens Point, Wisconsin, USA","abstract":"This work analyzes the internal behavior of particle swarm optimization (PSO) algorithm when the complexity of the problem increased. The impact of number of dimensions for three well-known benchmark functions, DeJong, Rosenbrock and Rastrigin, were tested using PSO. A Problem-Specific Distance Function (PSDF) was defined to evaluate the fitness of individual solutions and test the diversity in neighboring individuals. The PSDF started with a large value, but converged to the optimum in few generations, irrespective of complexity of problem or objective benchmark function. The simulation illustrates that all parameters in any dimension behave in similar pattern and we can expect similar behavior for additional complexity in the problem.","URL":"","note":"event-place: Cavtat, Croatia","author":[{"family":"Al-Kazemi","given":"Buthainah S."},{"family":"Habib","given":"Sami J."}],"issued":{"date-parts":[["2006"]]},"accessed":{"date-parts":[["2019",6,4]]}}}],"schema":""} (Al-Kazemi & Habib, 2006), where M indicate iterations and P indicates ParticlesPaper LengthThe manuscript should be between 8-12 pages and NOT?normally?exceed 15 pages, including references, figures, tables, and acknowledgement. Please insert line numbers continuously from the first page to the last page and also insert page number (Arial 8 pt.) on the top-right corner of every page except the first page.EquationEquations should be centered in the column. The paragraph description of the line containing the equation should be set for 6 points ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"oKuxFAMr","properties":{"formattedCitation":"(2017)","plainCitation":"(2017)","noteIndex":0},"citationItems":[{"id":"XUTEKN2B/SbK6BN4I","uris":[""],"uri":[""],"itemData":{"id":126,"type":"paper-conference","title":"Semantic association rule mining: A new approach for stock market prediction","container-title":"Proceedings of the 2nd Conference on Swarm Intelligence and Evolutionary Computation","publisher":"IEEE","publisher-place":"Kerman, Iran","page":"106-111","source":"IEEE Xplore","event":"2017 2nd Conference on Swarm Intelligence and Evolutionary Computation (CSIEC)","event-place":"Kerman, Iran","abstract":"the amount of ontologies and semantic annotations available on the Web is constantly growing and heterogeneous data raises new challenges for the data mining community. Yet there are still many problems causing users extra problems in discovering knowledge or even failing to obtain the real and useful knowledge they need. In this paper, we survey some semantic data mining methods specifically focusing on association rules. However, there are few works that have focused in mining semantic web data itself. For extracting rules in semantic data, we present an intelligent data mining approach incorporated with domain. The paper contributes a new algorithm for discovery of new type of patterns from semantic data. This new type of patterns is appropriate for some data such as stock market. We take advantage of the knowledge encoded in the ontology and MICF measure to inference in three steps to prune the search space and generated rules to derive appropriate rules from thousands of rules. Some experiments performed on stock market data and show the usefulness and efficiency of the approach.","DOI":"10.1109/CSIEC.2017.7940158","title-short":"Semantic association rule mining","author":[{"family":"Asadifar","given":"S."},{"family":"Kahani","given":"M."}],"issued":{"date-parts":[["2017",3,7]]}},"suppress-author":true}],"schema":""} (2017) before and 6 points after. Number equations consecutively with equation numbers in parentheses flush with the right margin, as in (1). Italicize Roman symbols for quantities and variables, but not Greek symbols. Punctuate equations with commas or periods when they are part of a sentence, as in(1)Symbols in your equation should be defined before the equation appears or immediately following. Use “(1),” not “Eq. (1)” or “equation (1),” except at the beginning of a sentence: “Equation (1) is ...”ReferencesThe preference format for AJCC is American Psychological Association 7th Edition (APA) style. As shown in the following examples.More information you can look at the link Journal ArticleFigure 2. APA journal article reference style.BookFigure 3. APA book reference style.Book ChapterFigure 4. APA book chapter reference style.ConferenceLim, S. P., & Haron, H. (2013). Performance comparison of Genetic Algorithm, Differential Evolution and Particle Swarm Optimization towards benchmark functions. 2013 IEEE Conference on Open Systems, 41–46. AUTHORS’ CONTRIBUTIONS [Option]Author A conceived of and designed the study. Author B and Author C analyzed the data. Author A and Author C developed the algorithm for the framework. Author A, Author B, and Author C wrote the manuscript. Author C revised, submitted, and responded to the reviewer’s comments. All of the authors read and approved the final manuscript and declared that no competing interests exist. Author acceptable roles including: Conceptualization, Data curation, Formal analysis, Funding acquisition,Investigation, Methodology, Project administration, Resources, Software, Supervision, Validation, Visualization, Writing - original draft, Writing - review \& editing.ACKNOWLEDGMENTS [Option]This research was supported by XXXX, (Grant no: 11111) and YYYYYY (Grant no: 22222). We also acknowledge the contribution of Mr. AAAA of the BBBB faculty at CCCC University for his editing and checking of English grammar and expression in this paper. The funder had no role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. All of the authors read and approved the final manuscript and declared that no competing interests exist.References ADDIN ZOTERO_BIBL {"uncited":[],"omitted":[],"custom":[]} CSL_BIBLIOGRAPHY Al-Kazemi, B. S., & Habib, S. J. (2006). Complexity Analysis of Problem-dimension Using PSO. Proceedings of the 7th WSEAS International Conference on Evolutionary Computing, 45–52. Asadifar, S., & Kahani, M. (2017). Semantic association rule mining: A new approach for stock market prediction. Proceedings of the 2nd Conference on Swarm Intelligence and Evolutionary Computation, 106–111. Drozdik, M., Aguirre, H., & Tanaka, K. (2013). Attempt to Reduce the Computational Complexity in Multi-objective Differential Evolution Algorithms. , P., & Watson, H. J. (1998). Present and future directions in data warehousing. SIGMIS Database, 29(3), 83–90. , N., D Müller, S., & Koumoutsakos, P. (2003). Reducing the Time Complexity of the Derandomized Evolution Strategy with Covariance Matrix Adaptation (CMA-ES). Evolutionary Computation, 11, 1–18. , O., Arbonès, D., & Igel, C. (2016). CMA-ES with Optimal Covariance Update and Storage Complexity.Lim, S. P., & Haron, H. (2013). Performance comparison of Genetic Algorithm, Differential Evolution and Particle Swarm Optimization towards benchmark functions. 2013 IEEE Conference on Open Systems, 41–46. Nopiah, Z. M., Khairir, M. I., Abdullah, S., Baharin, M. N., & Arifin, A. (2010). Time Complexity Analysis of the Genetic Algorithm Clustering Method. Proceedings of the 9th WSEAS International Conference on Signal Processing, Robotics and Automation, 171–176. , J.-S., Snasel, V., Corchado, E. S., Abraham, A., & Wang, S.-L. (Eds.). (2014). Intelligent Data analysis and its Applications, Volume I: Proceeding of the First Euro-China Conference on Intelligent Data Analysis and Applications, June 13-15, 2014, Shenzhen, China. Springer International Publishing. , P. (2018). A comparative study of PSO and CMA-ES algorithms on black-box optimization benchmarks. Journal of Telecommunications and Information Technology, 8(4), 1–13. , M. (2009). Computational intelligence for missing data imputation, estimation, and management: Knowledge optimization techniques: knowledge optimization techniques. IGI Global.Wang, K.-P., Huang, L., Zhou, C.-G., & Pang, W. (2003). Particle swarm optimization for traveling salesman problem. Proceedings of the 2003 International Conference on Machine Learning and Cybernetics (IEEE Cat. No.03EX693), 3, 1583-1585 Vol.3. ................
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