scholarly journals A Kernel Support Vector Machine Based Technique for Crohn’s Disease Classification in Human Patients

2017 ◽  
pp. 262-273 ◽  
Author(s):  
Albert Comelli ◽  
Maria Chiara Terranova ◽  
Laura Scopelliti ◽  
Sergio Salerno ◽  
Federico Midiri ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Crohn’S Disease ◽  
Crohn's Disease ◽  
Disease Classification ◽  
Support Vector ◽  
Kernel Support Vector Machine

scholarly journals Automatic Tomato Plant Leaf Disease Classification using Multi-Kernel Support Vector Machine

2020 ◽  
Vol 9 (5) ◽  
pp. 560-565
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Learning Algorithm ◽  
Early Stage ◽  
Region Of Interest ◽  
Input Image ◽  
Disease Classification ◽  
Support Vector ◽  
Leaf Disease ◽  
Kernel Support Vector Machine

In agriculture the major problem is leaf disease identifying these disease in early stage increases the yield. To reduce the loss identifying the various disease is very important. In this work , an efficient technique for identifying unhealthy tomato leaves using a machine learning algorithm is proposed. Support Vector Machines (SVM) is the methodology of machine learning , and have been successfully applied to a number of applications to identify region of interest, classify the region. The proposed algorithm has three main staggers, namely preprocessing, feature extraction and classification. In preprocessing, the images are converted to RGB and the average filter is used to eliminate the noise in the input image. After the pre-processing stage, features such as texture, color and shape are extracted from each image. Then, the extracted features are presented to the classifier to classify an input tomato leaf as a healthy or unhealthy image. For classification, in this paper, a multi-kernel support vector machine (MKSVM) is used. The performance of the proposed method is analysed on the basis of different metrics, such as accuracy, sensitivity and specificity. The images used in the test are collected from the plant village. The proposed method implemented in MATLAB.

Fog Enabled Cloud Based Intelligent Resource Management Approach Using Improved Grey Wolf Optimization Strategy and Kernel Support Vector Machine

2021 ◽  
Vol 18 (4) ◽  
pp. 1275-1281
Author(s):  
R. Sudha ◽  
G. Indirani ◽  
S. Selvamuthukumaran
Keyword(s):  
Support Vector Machine ◽  
Resource Management ◽  
Support Vector ◽  
Management Approach ◽  
Optimization Strategy ◽  
Grey Wolf ◽  
Processing Load ◽  
Grey Wolf Optimization ◽  
Kernel Support Vector Machine ◽  
Service Response Time

Resource management is a significant task of scheduling and allocating resources to applications to meet the required Quality of Service (QoS) limitations by the minimization of overhead with an effective resource utilization. This paper presents a Fog-enabled Cloud computing resource management model for smart homes by the Improved Grey Wolf Optimization Strategy. Besides, Kernel Support Vector Machine (KSVM) model is applied for series forecasting of time and also of processing load of a distributed server and determine the proper resources which should be allocated for the optimization of the service response time. The presented IGWO-KSVM model has been simulated under several aspects and the outcome exhibited the outstanding performance of the presented model.

Long-term Disease Behavior and Surgical Intervention Analysis in Hospitalized Patients with Crohn's Disease in China: A Retrospective Cohort Study

2021 ◽  
Author(s):  
Xiaoyin Bai ◽  
Huimin Zhang ◽  
Gechong Ruan ◽  
Hong Lv ◽  
Yue Li ◽  
...  
Keyword(s):  
Crohn’S Disease ◽  
Crohn's Disease ◽  
Large Scale ◽  
Hospitalized Patients ◽  
Disease Classification ◽  
Real World Data ◽  
Disease Behavior ◽  
Initial Classification

Abstract Background There is lack of real-world data for disease behavior and surgery of Crohn’s disease (CD) from large-scale Chinese cohorts. Methods Hospitalized patients diagnosed with CD in our center were consecutively included from January 2000 to December 2018. Disease behavior progression was defined as the initial classification of B1 to the progression to B2 or B3. Clinical characteristics including demographics, disease classification and activity, medical therapy, development of cancers, and death were collected. Results Overall, 504 patients were included. Two hundred and thirty one (45.8%) patients were initially classified as B1; 30 (13.0%), 71 (30.7%), and 95 (41.1%) of them had disease progression at the 1-year follow-up, 5-year follow-up, and overall, respectively. Patients without location transition before behavior transition were less likely to experience behavior progression. However, patients without previous exposure to a corticosteroid, immunomodulator, or biological agent had a greater chance of experiencing behavior progression. When the long-term prognosis was evaluated, 211 (41.9%) patients underwent at least one CD-related surgery; 108 (21.4%) and 120 (23.8%) of these patients underwent surgery before and after their diagnosis, respectively. An initial classification as B1, no behavior transition, no surgery prior to diagnosis, and previous corticosteroid exposure during follow-up were associated with a lower risk of undergoing surgery. Conclusions This study depicts the clinical features and factors associated with behavior progression and surgery among hospitalized CD patients in a Chinese center. Behavior progression is associated with a higher probability of CD-related surgery, and strengthened therapies are necessary for them in the early phase.

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