A Relevance Feedback-Based System For Quickly Narrowing Biomedical Literature Search Result

The online literature is an important source that helps people find the information. The quick increase of online literature makes the manual search process for the most relevant information a very time-consuming task and leads to sifting through many results to find the relevant ones. The existing search engines and online databases return a list of results that satisfy the user\u27s search criteria. The list is often too long for the user to go through every hit if he/she does not exactly know what he/she wants or/and does not have time to review them one by one. My focus is on how to find biomedical literature in a fastest way. In this dissertation, I developed a biomedical literature search system that uses relevance feedback mechanism, fuzzy logic, text mining techniques and Unified Medical Language System. The system extracts and decodes information from the online biomedical documents and uses the extracted information to first filter unwanted documents and then ranks the related ones based on the user preferences. I used text mining techniques to extract PDF document features and used these features to filter unwanted documents with the help of fuzzy logic. The system extracts meaning and semantic relations between texts and calculates the similarity between documents using these relations. Moreover, I developed a fuzzy literature ranking method that uses fuzzy logic, text mining techniques and Unified Medical Language System. The ranking process is utilized based on fuzzy logic and Unified Medical Language System knowledge resources. The fuzzy ranking method uses semantic type and meaning concepts to map the relations between texts in documents. The relevance feedback-based biomedical literature search system is evaluated using a real biomedical data that created using dobutamine (drug name). The data set contains 1,099 original documents. To obtain coherent and reliable evaluation results, two physicians are involved in the system evaluation. Using (30-day mortality) as specific query, the retrieved result precision improves by 87.7% in three rounds, which shows the effectiveness of using relevance feedback, fuzzy logic and UMLS in the search process. Moreover, the fuzzy-based ranking method is evaluated in term of ranking the biomedical search result. Experiments show that the fuzzy-based ranking method improves the average ranking order accuracy by 3.35% and 29.55% as compared with UMLS meaning and semantic type methods respectively

FUZZY LOGIC LEARNING METHODS IN STUDENT EDUCATION

There is a rapidly growing interest in Artificial Intelligence applications in various modern areas. Students are very interested in modern data mining methods such as artificial neural networks, fuzzy logic and clustering. Teaching experience in study work shows that students perceive graphical information better than analytical relationships during learning process. Many training courses operate with models that were previously only available in mathematics disciplines. The solution would be to use the Matlab package to implement different models in Artificial Intelligence areas. Often, an analytical solution or simulation model is much simpler than a visual Matlab model, but it provides an insight into the usefulness of using such models for prospective training purposes. In previous articles, the author has provided examples of how Matlab's capabilities can be used in economic studies, artificial neural networks, and clustering. Fuzzy logic methods are often undeservedly forgotten, although the implementation of their algorithms is relatively simple and can be implemented even for students. In the research part of the study the modelling capabilities in data mining studies are demonstrated with fuzzy logic algorithms and real examples.

Fuzzy c-Means Clustering untuk Pengenalan Pola Studi kasus Data Saham

Fuzzy Clustering is one of the five roles used by data mining experts to transform large amounts of data into useful information, and one method that is often and widely used is Fuzzy c-Means (FCM) Clustering. FCM is a data clustering technique where the existence of each data point in the cluster is based on the degree of membership. This study aims to see the pattern of data samples or data categories using FCM clustering. The analyzed data is stock data on Jakarta Stock Exchange (BEJ) in the Property and Real Estate sector (issuer group). The data mining processes comply Cross Industry Standard Process Model for Data mining Process (Crisp-DM), with several stages, starting with the stage of getting to know the business process (Business Understanding) then studying the data (Data Understanding), continuing with the Data Preparation stage, Modeling stage, Evaluation stage and finally the Deployment stage. In the modeling stage, the FCM model is used. FCM clustering model data mining can analyze data in large databases with many variables and complicated, especially to get patterns from the data. Then a Fuzzy Inference System (FIS) was built based on a known pattern for simulating input data into output data based on fuzzy logic. Keywords: Fuzzy c-Means Clustering, Pattern Recognitio

Survey: Data Mining Techniques in Medical Data Field

Now days most of the research area are working on data mining techniques in medical data. Knowledge discovery and data mining have found numerous applications in business and scientific domain. Valuable knowledge can be discovered from application of data mining techniques in healthcare system. In this study, we briefly examine the potential use of classification based data mining techniques such as Rule based, decision tree, machine learning algorithms like Support Vector Machines, Principle Component Analysis etc., Rough Set Theory and Fuzzy logic. In particular we consider a case study using classification techniques on a medical data set of diabetic patients

Clustering Students According to their Academic Achievement Using Fuzzy Logic

The software for clustering students according to their educational achievements using fuzzy logic was developed in Python using the Google Colab cloud service. In the process of analyzing educational data, the problems of Data Mining are solved, since only some characteristics of the educational process are obtained from a large sample of data. Data clustering was performed using the classic K-Means method, which is characterized by simplicity and high speed. Cluster analysis was performed in the space of two features using the machine learning library scikit-learn (Python). The obtained clusters are described by fuzzy triangular membership functions, which allowed to correctly determine the membership of each student to a certain cluster. Creation of fuzzy membership functions is done using the scikit-fuzzy library. The development of fuzzy functions of objects belonging to clusters is also useful for educational purposes, as it allows a better understanding of the principles of using fuzzy logic. As a result of processing test educational data using the developed software, correct results were obtained. It is shown that the use of fuzzy membership functions makes it possible to correctly determine the belonging of students to certain clusters, even if such clusters are not clearly separated. Due to this, it is possible to more accurately determine the recommended level of difficulty of tasks for each student, depending on his previous evaluations.Comment: 13 pages,9 figures,ijmec

Using the Fuzzy Logic to Find Optimal Centers of Clusters of K-means

Techniques of data mining that used in the medical diagnosis a number of diseases like cancer, diabetes, stroke, and heart disease. The great importance emerging fields for providing diagnosis and a profounder understanding of medical data, its coms from Data mining in medical field .researcher attempts to solve real world health problems in the prognosis and treatment of diseases, by using Healthcare data mining. In this research, the algorithm of k-means is used for grouping medical data, the problem of k-means is to find optimal centers of clusters so, and fuzzy logic is used to get optimal centers of clusters

DATA CLASSIFICATION SYSTEM WITH FUZZY NEURAL BASED APPROACH

Knowledge Discovery in Database and Data Mining use techniques derived from machine learning, visualization and statistics to investigate real world data. Their aim is to discover patterns within the data which are new, statistically valid, interesting and understandable. In recent years, there has been an explosion in computation and information technology. With it have come vast amounts of data. Lying hidden in all this data is potentially useful information that is rarely made explicit or taken advantage. New tools based both on clever applications of established algorithms and on new methodologies, empower us to do entirely new things. In this context, data mining has arisen as an important research area that helps to reveal the hidden interesting information from the rawdatacollected. The project demonstrates how data mining can address the need of business intelligence in the process of decision making. An analysis on the field of data mining is done to show how data mining can help in business such as marketing, credit card approval. The project's objective is identifying the available data mining algorithms in data classification and applying new data mining algorithm to perform classification tasks. The proposed algorithm is a hybrid system which applied fuzzy logic and artificial neural network, which applies fuzzy logic inference to generate a set of fuzzy weighted production rules and applies artificial neural network to train the weights of fuzzy weighted rules for better classification results. Theresult of this system using the iris dataset and credit card approval dataset to evaluate the proposed algorithm's accuracy, interpretability. The project has achieved the target objectives; it can gain high accuracy for data classification task, generate rules which can help to interpret the output results, reduce the training processing. But the proposed algorithm still require high computation, the processing time will be long if the dataset is huge. However the proposed algorithm offers a promising approach to building intelligent systems