Todd Lingren (800948), Pei Chen (1233810), Joseph Bochenek (3144708) et al. · Figshare · 2016
<p>ASD–Autism Spectrum Disorder; EHR–Electronic Health Records; ICD-9 –International Classification of Diseases 9<sup>th</sup> edition; DSM IV–Diagnostic and Statistical Manual of Mental Diseases 4<sup>th</sup> edition; PDD-NOS–pervasive developmental disorder not otherwise specified; sections 3a., 3b., 3c. refer to DSM IV ASD classification for Autism, Asperger’s and PDD-NOS, respectively</p>
Cited by 0Open access
Martin Řimnáč · Digital Repository (National Repository of Grey Literature) · 2005
The data structure estimation may provide a connection between the semantic web vision and currently used web sources containing no semantic description of presented information. It enables information from web pages to be processed by semantic web tools with the best to be known about these sources, with the source estimated data structure. The paper gives a method description and presents its usage for a inductive logic programming task. The advantages of a rule-based system usage is given as well.
Cited by 0
Luigi Pugliese, Michele Guagnano, Sara Groppo et al. · 2023
Obstructive Sleep Apnea Syndrome (OSAS) is a common sleep disorder characterized by repeated episodes of breathing cessation during sleep. It affects the quality of life and can lead to severe health complications. Continuous monitoring of Heart Rate Variability (HRV) and oxygen saturation (SpO2) can provide valuable insights into the presence and severity of sleep apnea. The algorithm herein proposed aims to identify the presence of OSAS and then to highly accurately differentiate (Severe, Moderate or Low) its severity level. The algorithm was evaluated on an online dataset; at the end of the algorithm assessment, a correlation coefficient of 98.65% was reached.
Cited by 2
Tao Liu · 2007
It is very urgent for a fast and effective algorithm to mine association rule with the increasing amount of database. As an evolution method on antibodies, immune algorithm is of great random, robustness and parallelism, and can search quickly and effectively in a global way. This paper presented an immune based association rule algorithm (IARA), which could improve the efficiency to search database; the searching of rules is carried out using the artificial immune algorithm (AIA) based on the distance concentration. The advantage of this approach is that it can avoid the need for massively searching for association rules. The experimental results show the effectiveness of the algorithm proposed.
Cited by 4
Dat Tien Nguyen, Krzysztof J. Cios · Applied Soft Computing · 2015
Cited by 4
S Maheswari, K. Arthi · International Journal of Innovative Technology and Exploring Engineering · 2020
Sentiment analysis is the process of finding out whether one's opinion is positive, negative, or neutral. Now-a-days the people are telling their opinion about the fields like marketing product, political and social phenomena are mostly through the online. Their opinions are positive, negative or neutral. The machine to identify the opinion is very difficult. There are so many issues in this field. The one of the issue is sarcasm detection. Sometimes the people give their opinion sarcastically. Sarcastic means, an opinion of an object is to say positive instead of negative. The machine will take this opinion as positive. So the final polarity of the product will be wrong due to this kind of identification. The purpose of this paper is to find these types of sentences and correct the polarity value.
Cited by 0Open access
Zhenglong Wu, MeiSheng Xu, Rujing Wang et al. · 2004
Fuzzy ellipsoid theory was introduced to mining fuzzy association rule from quantitative data. The algorithm includes two steps. First, fuzzy clustering procedure is taken to get fuzzy ellipsoids of the feature space. Second, mining procedure is taken to get fuzzy association rule based on the projection regions of fuzzy ellipsoids. The algorithm was compared with a Apriori-based fuzzy association rule mining algorithm. Results show that the algorithm is considerably quicker, especially when mining from large databases. Experiments on real-life data also show that the algorithm is effective to find association rules.
Cited by 0
Ziqiang Wang, Xia Sun, Dexian Zhang · Lecture notes in computer science · 2007
Cited by 46
S Maheswari, K. Arthi MCA. · International Journal of Recent Technology and Engineering (IJRTE) · 2019
Sentiment analysis is a technique to analyze the people opinion, attitude, sentiment and emotion towards any particular object. Sentiment analysis has the following steps to predict the opinion of a review sentences. The steps are preprocessing, feature selection, classification and sentiment prediction. Preprocessing is the main important step and it consists of many techniques. They are Stop word Removal, punctuation removal, conversion of numbers to number names. Stemming is another important preprocessing technique which is used to transform the words in text into their grammatical root form and is mainly used to improve the retrieval of the information from the internet. It is applied mainly to get strengthen the retrieval of the information. Many morphological languages have immense amount of morphological deviation in the words. It triggered vast challenges. Many algorithms exist with different techniques and has several drawbacks. The aim of this paper is to propose a rule based stemmer that is a truncating stemmer. The new stemming mechanism in this paper has brought about many morphological changes. The new rule based morphological variation removable stemming algorithm is better than the existing other algorithms such as New Porter, Paice/Lovins and Lancaster stemming algorithm.
Cited by 5Open access
Elbren Antonio, Arnel C. Fajardo, Ruji P. Medina · 2020
Browsers collects information for better user experience by allowing JavaScript's and other extensions. Advertiser and other trackers take advantage on this useful information to tracked users across the web from remote devices on the purpose of individual unique identifications the so-called browser fingerprinting. Our work explores the diversity and stability of browser fingerprint by modifying the rule-based algorithm. Browser fingerprint rely only from the gathered data through browser, it is hard to tell that this piece of information still the same when upgrades and or downgrades are happening to any browsers and software's without user consent, which is stability and diversity are the most important usage of generating browser fingerprint. We implemented device fingerprint to identify consenting visitors in our website and evaluate individual devices attributes by calculating entropy of each selected attributes. In this research, it is noted that we emphasize only on data collected through a web browser by employing twenty (20) attributes to identify promising high value information to track how device information evolve and consistent in a period of time, likewise, we manually selected device information for evaluation where we apply the modified rules. Finally, this research is conducted and focused on the devices having the closest configuration and device information to test how devices differ from each other after several days of using on the basis of individual user configurations, this will prove in our study that every device is unique.
Cited by 9
ZHANG Wen1, ZHANG Gui-xu1 · SHILAP Revista de lepidopterología · 2008
An improved rule-based backward fuzzy reasoning algorithm was pointed. The algorithm is based on the fuzzy Petri net （FPN） which represents fuzzy production rules. The algorithm is also suit for a class of rule-based systems, i.e. it can deal with the fuzzy Petri net model which results from the real system. Via using the algorithm, one can calculate the tokens of any appointed places which correspond to the true fuzzy values of the relevant propositions. Through practical applications, the advantages of the proposed algorithm compared with those present results obtained by the existed algo- rithm.
Cited by 0Open access
Qing Zhang · 2012
Granular computing ( GrC ) is a method for simulating human thinking and solving complicated problems. It is a powerful tool for solving complicated problems,mining massive data sets,and dealing with fuzzy information. In this paper,the shortcoming of the traditional rule extraction methods is presented,and then the granularity principle of rule extraction is analyzed based on granular computing method. The essence of attribute reduction is to choose a maximum approximation partition space of decision-making knowledge space,and the rules acquired from maximum approximation partition space may not be the simplest. Therefore,a rule extraction algorithm based on granular computing is proposed. In the proposed algorithm,the rules based on maximal granule can be acquired from information system in a hierarchical knowledge space in top-down manner,and the results of the simulation experiments illustrate that the generalization ability of rough set method is improved.
Cited by 2
Eghbal G. Mansoori · IEEE Transactions on Fuzzy Systems · 2011
Fuzzy clustering is superior to crisp clustering when the boundaries among the clusters are vague and ambiguous. However, the main limitation of both fuzzy and crisp clustering algorithms is their sensitivity to the number of potential clusters and/or their initial positions. Moreover, the comprehensibility of obtained clusters is not expertized, whereupon in data-mining applications, the discovered knowledge is not understandable for human users. To overcome these restrictions, a novel fuzzy rule-based clustering algorithm (FRBC) is proposed in this paper. Like fuzzy rule-based classifiers, the FRBC employs a supervised classification approach to do the unsupervised cluster analysis. It tries to automatically explore the potential clusters in the data patterns and identify them with some interpretable fuzzy rules. Simultaneous classification of data patterns with these fuzzy rules can reveal the actual boundaries of the clusters. To illustrate the capability of FRBC to explore the clusters in data, the experimental results on some benchmark datasets are obtained and compared with other fuzzy clustering algorithms. The clusters specified by fuzzy rules are human understandable with acceptable accuracy.
Cited by 65
Nurul Syafidah Jamil, Ku Ruhana Ku‐Mahamud, A. Dal Din · Universiti Utara Malaysia Institutional Repository (Universiti Utara Malaysia) · 2017
Textual document is usually in unstructured form and high dimensional data.The exploration of hidden information from the unstructured text is useful to find interesting patterns and valuable knowledge.However, not all terms in the text are relevant and can lead to misclassification. Improper filtration might cause terms that have similar meaning to be removed.Thus, to reduce the high-dimensionality of text, this study proposed a filtering algorithm that is able to filter the important terms from the pre-processed text and applied term weighting scheme to solve synonym problem which will help the selection of relevant term.The proposed filtering algorithm utilizes a keyword library that contained special terms which is developed to ensure that important terms are not eliminated during filtration process.The performance of the proposed filtering algorithm is compared with rough set attribute reduction (RSAR) and information retrieval (IR) approaches.From the experiment, the proposed filtering algorithm has outperformed both RSAR and IR in terms of extracted relevant terms.
Cited by 0Open access
I. Jasmine Selvakumari Jeya, K. Ramar · i-manager’s Journal on Software Engineering · 2007
Cited by 0
Fachao Li, Lianqing Su, Haichao Ran · 2005
Fuzzy numbers are most commonly used in tackling optimization problems in many practical domains. Rationally ranking fuzzy information becomes the basis for solving uncertainty optimization problems. Due to the credibility is not embodied by the order relation derived by level cut intervals or the order relation determined by centralized quantifying, so it is difficult to use them to rank fuzzy information under certain consciousness for fuzzy rules. In view of this, we introduce the concept of location values of fuzzy numbers with rule pools, and then develop a new way of ranking fuzzy numbers. Moreover, we give further descriptions for the location values by using coincidence degrees of fuzzy numbers with rule pools, and present the technique of composite quantification for fuzzy information. Furthermore, we establish a numerical model for computing location values and coincidence degrees. Finally, by using the location values and coincidence degrees of fuzzy numbers with rules, we propose a fuzzy genetic algorithm based on rules (BR-FGA), and then illustrate it by using two examples.
Cited by 4
Fang Su, Wenzhe Yang, Chenrui Duan et al. · 2019
Standard Krill Herd (SKH) optimization algorithm is a novel heuristic optimization algorithm, and its control parameters play an important role for its performance. In this paper, an improved Krill Herd algorithm is proposed, in which the fuzzy system is utilized as the parameter tuner to adjust control parameters by observing the progress of solving the problem in each step. The innovation is that both scaling factor and inertia weight are considered, and these parameters can be adjusted automatically according to the particle situation. In order to evaluate the proposed FKH algorithm, the efficiency of FKH algorithm is verified by using 16 benchmark functions, the results indicate the superiority of proposed FKH optimization algorithm in comparison with the standard KH.
Cited by 1
Brano Markić Ph. D, Dražena Tomić Ph. D, Ivan Pavlović · 2014
Genetic algorithms may be considering from two different points of view: as global optimization procedure and learning procedure. As global optimization procedure they analyze parallel search space and are able to escape from local extreme. They are also applicable if target value is not known in advance or cannot be defined. This paper is presenting an example of genetic algorithms where the expected results are a set of efficient frontiers and decision maker chooses acceptable values among possible solutions. The outcomes of implementing genetic algorithm are inputs into knowledge base which helps decision maker to make final judgment. The paper also includes the software solution for genetic algorithms. The integration of genetic algorithms and rule based systems has shown satisfactory development and applicable power. Key words: genetic algorithms, rule based systems, risk management, portfolio. 1.
Cited by 0
Xin Luo (428648), Jijia Sun (13202211), Hong Pan (8686) et al. · Figshare · 2023
<p>(A) Grouping matrix graph of 15 association rules mined by the algorithm. (B) Network relationship diagram of 15 association rules.</p>
Cited by 0Open access
Di Liu, Tao Gu, Jiang-Ping Xue · 2010
Rete algorithm is the mainstream of the algorithm in the rules engine; it provides efficient local entities data and rules pattern matching method. But Rete algorithm applied in rules engine has defects in performance and demand aspect, this paper applies three methods to improve the Rete algorithm in the rule engine: rule decomposition, Alpha-Node-Hashing and Beta-Node-Indexing. Rules engine is enterprise applications, business logic framework for extracting rules from software application, and make it more flexible. This paper describes the principle of Rule Engine at first, secondly show the Rete algorithm, and finally we do some improvement to Rete algorithm. It makes Rule Engine widely meet the application demand and be greatly improved the efficiency.
Cited by 29