Human emotional facial expressions play a vital role in interpersonal relations. Automated facial expression recognition has always remained a challenging problem in real-life applications as people ...vary significantly in the way of showing their expressions. Recently various approaches have been proposed for automatically analyzing the facial expression of a person. In this paper, a novel approach to human facial expression recognition by applying a modified version of the Cat Swarm Optimization (CSO) algorithm, called Improved Cat Swarm Optimization (ICSO) algorithm is proposed. The input image given to the proposed system retrieves similar images from the dataset as well as identifies the person’s emotional state through facial expressions. Deep features present in the face image are extracted using Deep Convolution Neural Network (DCNN) approach. ICSO is proposed to select optimal features from the face image that can uniquely distinguish the facial expression of a person. Employing DCNN with ICSO improves the retrieval performance of the proposed system. Ensemble classifiers that employ Neural Network (NN) and Support Vector Machine (SVM) are implemented to classify facial expressions such as normal, happy, sad, surprise, fear and angry. The performance of the proposed system is evaluated using JAFFE, CK+, Pie datasets and some real-world images. The proposed system outperforms the existing system, thus achieving superior accuracy and reduced computation time.
Scoliosis is a medical condition in which a person's spine has an abnormal curvature and Cobb angle is a measurement used to evaluate the severity of a spinal curvature. Presently, automatic Existing ...Cobb angle measurement techniques require huge dataset, time-consuming, and needs significant effort. So, it is important to develop an unsupervised method for the measurement of Cobb angle with good accuracy. In this work, an unsupervised local center of mass (LCM) technique is proposed to segment the spine region and further novel Cobb angle measurement method is proposed for accurate measurement. Validation of the proposed method was carried out on 2D X-ray images from the Saudi Arabian population. Segmentation results were compared with GMM-Based Hidden Markov Random Field (GMM-HMRF) segmentation method based on sensitivity, specificity, and dice score. Based on the findings, it can be observed that our proposed segmentation method provides an overall accuracy of 97.3% whereas GMM-HMRF has an accuracy of 89.19%. Also, the proposed method has a higher dice score of 0.54 compared to GMM-HMRF. To further evaluate the effectiveness of the approach in the Cobb angle measurement, the results were compared with Senior Scoliosis Surgeon at Multispecialty Hospital in Saudi Arabia. The findings indicated that the segmentation of the scoliotic spine was nearly flawless, and the Cobb angle measurements obtained through manual examination by the expert and the algorithm were nearly identical, with a discrepancy of only ± 3 degrees. Our proposed method can pave the way for accurate spinal segmentation and Cobb angle measurement among scoliosis patients by reducing observers' variability.
Few studies have investigated the validity of the Athens insomnia scale (AIS) using a robust approach of both classical theory and the rating scale model. Therefore, in this study, we investigated ...psychometric validation of the AIS using both of these approaches in nurses.
Nurses (n= 563, age= 33.2±7.1 years) working in health facilities in Saudi Arabia participated in a cross-sectional study. Participants completed the AIS, socio-demographics tool, and sleep health-related questions.
Confirmatory factor analysis (CFA) favored a 2-factor structure with both comparative fit index (CFI), and incremental fit index (IFI) having values above 0.95. The 2-factor model had the lowest values of Akaike information criterion (AIC), root mean square error of approximation (RMSEA),
, and
/df. This 2-factor structure showed configural invariance (CFI more than 0.95, RMSEA less than 0.08, and Χ
/df less than 3), and metric, scalar, and strict invariance (based on Δ CFI ≤-0.01, and Δ RMSEA ≥ 0.015 criteria). No ceiling/floor effects were seen for the AIS total scores. Infit and outfit mean square values for all the items were within the acceptable range (<1.4, >0.6). The threshold estimates for each item were ordered as expected. Cronbach's α for the AIS tool, factor-1 score, factor-2 score was 0.86, 0.82, and 0.72, respectively. AIS factor scores-1/2 were significantly associated with a habitual feeling of tiredness after usual night sleep (p<0.001), Impairment of daytime socio-occupational functioning (p<0.05), and with a feeling of daytime fatigue, irritability, and restlessness (p<0.05).
The findings favor the validity of a 2-factor structure of the AIS with adequate item properties, convergent validity, and reliability in nurses.
(1) Background: This cross-sectional study aims to highlight the assessment and foot care practices in an advanced clinical setting, the clinical characteristics of the patients, and to understand ...the barriers and facilitators for effective foot care from the perspectives of healthcare practices, resources, and patients' socioeconomic and cultural practices, and other aspects in terms of new technologies for effective foot care such as infrared thermography. (2) Methods: Clinical test data from 158 diabetic patients and a questionnaire to assess the foot care education retention rate were collected at the Karnataka Institute of Endocrinology and Research (KIER) facility. (3) Results: Diabetic foot ulcers (DFUs) were found in 6% of the examined individuals. Male patients were more likely to have diabetes complications, with an odds ratio (OR) of 1.18 (CI = 0.49-2.84). Other diabetes problems raised the likelihood of DFUs by OR 5 (CI = 1.40-17.77). The constraints include socioeconomic position, employment conditions, religious customs, time and cost, and medication non-adherence. The attitude of podiatrists and nurses, diabetic foot education, and awareness protocols and amenities at the facility were all facilitators. (4) Conclusions: Most diabetic foot complications might be avoided with foot care education, regular foot assessments as the standard of treatment, and self-care as a preventive/therapeutic strategy.
Diabetic foot complications are a major cause of concern for diabetic patients as it affects mobility and quality of life. Any computer aided diagnosis system would be very useful in the early ...detection and hence treatment and cure. For such a system, the surface temperature distribution patterns in the plantar region of the foot of both healthy and diabetic subjects have to be analyzed to detect any abnormality. In this paper we have analyzed the infrared thermal images of 62 diabetic and 20 healthy subjects to identify the temperature distribution patterns capable of detecting diabetic foot complications. The images were taken using Fluke TiX560 thermal imager. Image processing and analysis was done in MATLAB.
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