A Critical Review of the Intelligent Computing Methods for the Identification of the Sleeping Disorders

2021 ◽  
pp. 829-843
Author(s):  
Anandakumar Haldorai ◽  
Arulmurugan Ramu
Keyword(s):  
Critical Review ◽  
Computing Methods ◽  
Intelligent Computing ◽  
Sleeping Disorders

Intelligent Computing Methods for Manufacturing Systems

CIRP Annals ◽  
1997 ◽  
Vol 46 (2) ◽  
pp. 629-652 ◽  
Author(s):  
R. Teti ◽  
S.R.T. Kumara
Keyword(s):  
Manufacturing Systems ◽  
Computing Methods ◽  
Intelligent Computing

scholarly journals INTELLIGENT COMPUTING METHODS FOR THE INTERPRETATION OF NEUROPSYCHIATRIC DISEASES BASED ON RBR-CBR-ANN INTEGRATION

2013 ◽  
Vol 11 (5) ◽  
pp. 2490-2511 ◽  
Author(s):  
Mohit Gangwar ◽  
R. B. Mishra ◽  
R. S. Yadav
Keyword(s):  
Computing Methods ◽  
Case Based Reasoning ◽  
Intelligent Computing ◽  
Integrated Method ◽  
Confidence Factor ◽  
Matching Process ◽  
Neuropsychiatric Diseases ◽  
Brain And Behavior ◽  
Artificial Neural Network Ann ◽  
Problem Solving Strategy

Neuropsychiatry is an integrative and collaborative field that brings together brain and behavior, but its diagnosis is complex and controversial due to the conflicting, overlapping and confusing nature of the multitude of symptoms, hence the need to retain cases in a case base and reuse effective previous solutions for current cases. This paper proposes a method based on the integration of Rule based reasoning (RBR), Case based reasoning (CBR) and Artificial neural network (ANN) that utilizes solutions to previous cases in assisting neuropsychiatrist in the diagnosis of neuropsychiatric disease. The system represents five neuropsychiatric diseases with 38 symptoms grouped into six categories. Integrated method improves the computational and reasoning efficiency of the problem-solving strategy. We have hierarchically structured the five neuropsychiatric diseases in terms of their physio-psycho (muscular, cognitive and psychological), EEG and neuroimagin based parameters. Cumulative confidence factor (CCF) is computed at different node form lowest to highest level of hierarchal structure in the process of diagnosis of the neuropsychiatric diseases. The basic objective of this work is to develop integrated model of RBR-CBR and RBR-CBR-ANN in which RBR is used to hierarchically correlate the sign and symptom of the disease and also to compute CCF of the diseases. CBR is used for diagnosing the neuropsychiatric diseases for absolute and relative diagnosis. In relative diagnosis CBR is also used to find the relative importance of sign and symptoms of a disease to other disease and ANN is used for matching process in CBR.

Computational Intelligence Clustering for Dynamic Video Watermarking

2010 ◽  
pp. 298-318
Author(s):  
Daw-Tung Lin ◽  
Guan-Jhih Liao
Keyword(s):  
Computational Intelligence ◽  
Motion Vector ◽  
Computing Methods ◽  
Video Watermarking ◽  
Compressed Domain ◽  
Motion Vectors ◽  
Intelligent Computing ◽  
Fuzzy C Means ◽  
Fuzzy C Means Clustering ◽  
Motion Characteristics

Multimedia products today broadcast over networks and are typically compressed and transmitted from host to client. Adding watermarks to the compressed domain ensures content integrity, protects copyright, and can be detected without quality degradation. Hence, watermarking video data in the compressed domain is important. This work develops a novel video watermarking system with the aid of computational intelligence, in which motion vectors define watermark locations. The number of watermark bits varies dynamically among frames. The current study employs several intelligent computing methods including K-means clustering, Fuzzy C-means clustering, Swarm intelligent clustering and Swarm intelligence based Fuzzy C-means (SI-FCM) clustering to determine the motion vectors and watermark positions. This study also discusses and compares the advantages and disadvantages among various approaches. The proposed scheme has three merits. First, the proposed watermarking strategy does not involve manually setting watermark bit locations. Second, the number of embedded motion vector clusters differs according to the motion characteristics of each frame. Third, the proposed special exclusive-OR operation closely relates the watermark bit to the video context, preventing attackers from discovering the real watermark length of each frame. Therefore, the proposed approach is highly secure. The proposed watermark-extracting scheme immediately detects forgery through changes in motion vectors. Experimental results reveal that the watermarked video retains satisfactory quality with very low degradation.

Intelligent computing methods in Air Traffic Flow Management

2010 ◽  
Vol 18 (5) ◽  
pp. 781-793 ◽  
Author(s):  
Li Weigang ◽  
Marcos Vinicius Pinheiro Dib ◽  
Daniela Pereira Alves ◽  
Antonio Marcio Ferreira Crespo
Keyword(s):  
Traffic Flow ◽  
Computing Methods ◽  
Air Traffic ◽  
Flow Management ◽  
Air Traffic Flow Management ◽  
Intelligent Computing ◽  
Air Traffic Flow ◽  
Traffic Flow Management

scholarly journals Comparing Machine Learning Methods to Improve Fall Risk Detection in Elderly with Osteoporosis from Balance Data

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
German Cuaya-Simbro ◽  
Alberto-I. Perez-Sanpablo ◽  
Eduardo-F. Morales ◽  
Ivett Quiñones Uriostegui ◽  
Lidia Nuñez-Carrera
Keyword(s):  
Random Forest ◽  
Predictive Value ◽  
Model Performance ◽  
Computing Methods ◽  
Random Forest Classifier ◽  
Community Dwelling ◽  
World Health ◽  
Clinical Test ◽  
Minority Class ◽  
Intelligent Computing

Falls are a multifactorial cause of injuries for older people. Subjects with osteoporosis are particularly vulnerable to falls. We study the performance of different computational methods to identify people with osteoporosis who experience a fall by analysing balance parameters. Balance parameters, from eyes open and closed posturographic studies, and prospective registration of falls were obtained from a sample of 126 community-dwelling older women with osteoporosis (age 74.3 ± 6.3) using World Health Organization Questionnaire for the study of falls during a follow-up of 2.5 years. We analyzed model performance to determine falls of every developed model and to validate the relevance of the selected parameter sets. The principal findings of this research were (1) models built using oversampling methods with either IBk (KNN) or Random Forest classifier can be considered good options for a predictive clinical test and (2) feature selection for minority class (FSMC) method selected previously unnoticed balance parameters, which implies that intelligent computing methods can extract useful information with attributes which otherwise are disregarded by experts. Finally, the results obtained suggest that Random Forest classifier using the oversampling method to balance the data independent of the set of variables used got the best overall performance in measures of sensitivity (>0.71), specificity (>0.18), positive predictive value (PPV >0.74), and negative predictive value (NPV >0.66) independent of the set of variables used. Although the IBk classifier was built with oversampling data considering information from both eyes opened and closed, using all variables got the best performance (sensitivity >0.81, specificity >0.19, PPV = 0.97, and NPV = 0.66).

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