Fuzzy Logic Based Web Application for Gynaecology Disease Diagnosis

2017 ◽  
pp. 141-164 ◽  
Author(s):  
A. S. Sardesai ◽  
P. W. Sambarey ◽  
V. V. Tekale Kulkarni ◽  
A. W. Deshpande ◽  
V. S. Kharat
Keyword(s):  
Fuzzy Logic ◽  
Web Application ◽  
Disease Diagnosis

Heart Disease Diagnosis System Using Fuzzy Logic

2018 ◽  
Author(s):  
Hasan Kahtan ◽  
Kamal Z. Zamli ◽  
Wan Nor Ashikin Wan Ahmad Fatthi ◽  
Azma Abdullah ◽  
Mansoor Abdulleteef ◽  
...  
Keyword(s):  
Fuzzy Logic ◽  
Heart Disease ◽  
Disease Diagnosis ◽  
Diagnosis System ◽  
Heart Disease Diagnosis

scholarly journals Fuzzy logic approach for infectious disease diagnosis: A methodical evaluation, literature and classification

2019 ◽  
Vol 39 (4) ◽  
pp. 937-955 ◽  
Author(s):  
Goli Arji ◽  
Hossein Ahmadi ◽  
Mehrbakhsh Nilashi ◽  
Tarik A. Rashid ◽  
Omed Hassan Ahmed ◽  
...  
Keyword(s):  
Infectious Disease ◽  
Fuzzy Logic ◽  
Disease Diagnosis ◽  
Fuzzy Logic Approach ◽  
Logic Approach

scholarly journals Hybrid Intrusion Detection System using Fuzzy Logic Inference Engine for SQL Injection Attack

Kursor ◽  
2018 ◽  
Vol 9 (3) ◽  
Author(s):  
Rajif Agung Yunmar
Keyword(s):  
Fuzzy Logic ◽  
Web Application ◽  
Detection System ◽  
False Negative ◽  
Inference Engine ◽  
Hybrid Architecture ◽  
Sql Injection ◽  
Fuzzy Logic Inference ◽  
Sql Injection Attack ◽  
Logic Inference

SQL injection attacks toward web application increasingly prevalent. Testing to the web that will published is the one of preventive measures. However, this method sometimes ineffective because constrained by various things. Instrusion detection system (IDS) is able to help protect the website from various attacks. This study proposed an IDS for web applications from SQL injection-based attacks. The IDS is based on hybrid architecture with a signature-based detection method, type of data to analyzed is network packet and error log. The fuzzy logic inference engine used to be drawn the conclusion based on analyzed data. Proposed hybrid IDS has good result on detecting the various type of SQL injection attack and significantly reduce or even remove the false positive and false negative.

scholarly journals A New Semantic Resource Description Framework for Medical Diagnosis based on Gene Ontology

2020 ◽  
Vol 9 (3) ◽  
pp. 1388-1396
Keyword(s):  
Gene Ontology ◽  
Semantic Web ◽  
Medical Informatics ◽  
Medical Diagnosis ◽  
Web Application ◽  
Drug Prescription ◽  
Medical Practitioner ◽  
Disease Diagnosis ◽  
Medical Diagnostics ◽  
Resource Description

Semantic-Based medical informatics system is an interdisciplinary deflect of bioinformatics research, where a dealing to knowledge discovery of proper diagnosis of disease leads to more attention about health control before health complications. To do this work is in the aspects of various ways like preserving and accessing patient’s medical records for further information retrieval from pre-recorded datasets. Now some research has mostly used to medical metrics method combined with statistical information and social network analysis citation approach to analyze. Due to this limited citation information force to develop a new dimension of the semantic web-based medical diagnosis system. In this study the approach of implementing autonomic gene computing in medical informatics, in this framework a new semantic resource description network for medical diagnostics based on Gene Ontology, this way the diagnosis context capture its levels of symptoms. Every patient has their unique symptoms which are kept and fetched by semantic web applications, In this framework we used gene-based symptom indication for disease diagnosis, and merge with drug prescription leads to more suitable results that can be useful for physicians and medical practitioner, as to achieve efficient solutions in the new direction to make life saving analysis from the database. Being a very effective and enhanced web application tool can be applied in the medical field to make it useful for the society. The web interface for drugs prescription accepts the diseases and symptoms from the domain updated in the database and successfully displays prescribed drugs for each disease

A Cotton Disease Diagnosis Method Using a Combined Algorithm of Case-Based Reasoning and Fuzzy Logic

2020 ◽  
Author(s):  
Yuhong Dong ◽  
Zetian Fu ◽  
Stevan Stankovski ◽  
Yaoqi Peng ◽  
Xinxing Li
Keyword(s):  
Fuzzy Logic ◽  
Disease Diagnosis ◽  
Case Based Reasoning ◽  
Cotton Production ◽  
Scoring Method ◽  
Disease Symptoms ◽  
Diagnosis Method ◽  
And Control ◽  
Combined Algorithm ◽  
Case Based

Abstract In this study, a cotton disease diagnosis method that uses a combined algorithm of case-based reasoning (CBR) and fuzzy logic was designed and implemented. It focuses on the prevention, diagnosis and control of diseases affecting cotton production in China. Conventional methods of disease diagnosis are primarily based on CBR with reference to user-provided symptoms; however, in most cases, user-provided symptoms do not fully meet the requirements of CBR. To address this problem, fuzzy logic is incorporated into CBR to allow for more flexible and accurate models. With the help of CBR and fuzzy reasoning, three diagnostic results can be obtained by the cotton disease diagnosis system (CDDS) constructed in this study: success, success but not exact and failure. To verify the reliability of the CDDS and its ability to diagnose cotton diseases, its diagnostic accuracy and stability were analyzed and compared with the results obtained by the traditional expert scoring method. The analysis results reveal that the CDDS can achieve a high diagnostic success rate (above 90%) and better diagnostic stability than the traditional expert scoring method when at least four disease symptoms are input. The CDDS provides an independent and objective source of information to assist farmers in the diagnosis and prevention of cotton diseases.

An Efficient System for Heart Disease Prediction Using Hybrid OFBAT with Rule-Based Fuzzy Logic Model

2016 ◽  
Vol 26 (04) ◽  
pp. 1750061 ◽  
Author(s):  
G. Thippa Reddy ◽  
Neelu Khare
Keyword(s):  
Fuzzy Logic ◽  
Heart Disease ◽  
Fuzzy System ◽  
Disease Diagnosis ◽  
Fuzzy Logic System ◽  
Classification Model ◽  
Rule Based ◽  
Diagnosis System ◽  
Heart Disease Diagnosis ◽  
Logic System

The objective of the work is to predict heart disease using computing techniques like an oppositional firefly with BAT and rule-based fuzzy logic (RBFL). The system would help the doctors to automate heart disease diagnosis and to enhance the medical care. In this paper, a hybrid OFBAT-RBFL heart disease diagnosis system is designed. Here, at first, the relevant features are selected from the dataset using locality preserving projection (LPP) algorithm which helps the diagnosis system to develop a classification model using the fuzzy logic system. After that, the rules for the fuzzy system are created from the sample data. Among the entire rules, the important and relevant group of rules are selected using OFBAT algorithm. Here, the opposition based learning (OBL) is hybrid to the firefly with BAT algorithm to improve the performance of the FAT algorithm while optimizing the rules of the fuzzy logic system. Next, the fuzzy system is designed with the help of designed fuzzy rules and membership functions so that classification can be carried out within the fuzzy system designed. At last, the experimentation is performed by means of publicly available UCI datasets, i.e., Cleveland, Hungarian and Switzerland datasets. The experimentation result proves that the RBFL prediction algorithm outperformed the existing approach by attaining the accuracy of 78%.

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