An Intelligent Diagnosis System for English Writing Based on Data Feature Extraction and Fusion

English writing is conducive to the online communication and communication of language; the current diagnosis system of English writing is difficult to accurately find and diagnose the wrong words, which leads to a low diagnosis rate of wrong words in English writing system. To solve this problem, this paper designs an intelligent diagnosis system for English writing based on data feature extraction and fusion. First of all, B/S architecture is introduced on the basis of the conventional intelligent diagnosis system structure of English writing, which makes up for the problem that the C/S mode is prone to diagnostic errors. Secondly, the features of English lexical data are extracted and fused to provide better input for the diagnostic model, which effectively solves the problems of complex vocabulary and feature redundancy in English writing. The simulation results show that the proposed intelligent diagnosis system for English writing has higher diagnostic accuracy and faster query speed.

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Effective diagnostic model construction based on discriminative breast ultrasound image regions using deep feature extraction

Medical Physics ◽

10.1002/mp.14832 ◽

2021 ◽

Author(s):

Hailong Yu ◽

Hang Sun ◽

Jing Li ◽

Liying Shi ◽

Nan Bao ◽

...

Keyword(s):

Feature Extraction ◽

Ultrasound Image ◽

Breast Ultrasound ◽

Model Construction ◽

Diagnostic Model ◽

Deep Feature ◽

Deep Feature Extraction

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Integrated Study of Condition Monitoring and Fault Diagnosis System for Flexible Manufacturing System

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.179-180.678 ◽

2011 ◽

Vol 179-180 ◽

pp. 678-684 ◽

Cited By ~ 1

Author(s):

Xi Qin Wen ◽

Zhong Min Zhao ◽

Zhi Yu Xie

Keyword(s):

Fault Diagnosis ◽

Flexible Manufacturing ◽

Manufacturing System ◽

Flexible Manufacturing System ◽

System Structure ◽

Diagnosis System ◽

Software Model ◽

Fault Diagnosis System ◽

Fault Mechanism ◽

Integrated Study

On the basis of analyzing multiple fault mechanism of the flexible manufacturing system (FMS), the FMS diagnosis system architecture frameworks are built. The FMS status monitoring and fault diagnosis system and software model are especially proposed. The monitoring system structure and hardware composition are discussed, and software structure is also given. Finally, the FMS monitoring and diagnosis system are developed.

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A Hybrid Crow Search and Grey Wolf Optimization Technique for Enhanced Medical Data Classification in Diabetes Diagnosis System

International Journal of Computational Intelligence Systems ◽

10.1007/s44196-021-00013-0 ◽

2021 ◽

Vol 14 (1) ◽

Author(s):

C. Mallika ◽

S. Selvamuthukumaran

Keyword(s):

Predictive Accuracy ◽

Optimization Technique ◽

Hybrid Optimization ◽

Support Vector ◽

Grey Wolf Optimizer ◽

Diagnostic Model ◽

Full Potential ◽

Diabetes Diagnosis ◽

Grey Wolf ◽

Diagnosis System

AbstractDiabetes is an extremely serious hazard to global health and its incidence is increasing vividly. In this paper, we develop an effective system to diagnose diabetes disease using a hybrid optimization-based Support Vector Machine (SVM).The proposed hybrid optimization technique integrates a Crow Search algorithm (CSA) and Binary Grey Wolf Optimizer (BGWO) for exploiting the full potential of SVM in the diabetes diagnosis system. The effectiveness of our proposed hybrid optimization-based SVM (hereafter called CS-BGWO-SVM) approach is carefully studied on the real-world databases such as UCIPima Indian standard dataset and the diabetes type dataset from the Data World repository. To evaluate the CS-BGWO-SVM technique, its performance is related to several state-of-the-arts approaches using SVM with respect to predictive accuracy, Intersection Over-Union (IoU), specificity, sensitivity, and the area under receiver operator characteristic curve (AUC). The outcomes of empirical analysis illustrate that CS-BGWO-SVM can be considered as a more efficient approach with outstanding classification accuracy. Furthermore, we perform the Wilcoxon statistical test to decide whether the proposed cohesive CS-BGWO-SVM approach offers a substantial enhancement in terms of performance measures or not. Consequently, we can conclude that CS-BGWO-SVM is the better diabetes diagnostic model as compared to modern diagnosis methods previously reported in the literature.

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