• Asian Pacific Journal of Cancer Prevention
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• v.16 no.1
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• pp.227-232
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• 2015

Background: Breast cancer is the commonest cancer among Sri Lankan females, accounting for 26% of the cancer incidence in women. Early detection of breast cancer is conducted by public health midwives (PHMs) in the Well Woman Clinics. The aim of the present study was to determine the effectiveness of an educational intervention on improving knowledge, attitudes and practices (KAP) on breast cancer screening among PHMs in the district of Gampaha. Materials and Methods: Two Medical Officer of Health (MOH) areas in Gampaha district were selected using random sampling as intervention (IG) and control (CG) groups. All the PHMs in the two MOH areas participated in the study, with totals of 38 in IG and 47 in CG. They were exposed to an educational intervention with the objective of using them to subsequently conduct the same among 35-59 year women in the community. Following the intervention, post-intervention assessments were conducted at one month and six months to assess the effectiveness of the intervention. Results: The overall median scores for KAP among PHMs respectively were as follows. Pre-intervention: IG:58%(IQR: 53-69%), 90%(IQR: 70-100%) and 62%(IQR: 57-70%). CG: 64%(IQR: 56-69%), 90%(IQR: 70-90%) and 62%( IQR: 50-77%). Post-intervention: one month, IG:96%(IQR: 93-96%), 100%(IQR: 100-100%), and 85%(IQR: 81-89%). CG:67%(IQR: 60- 73%), 90%(IQR: 80-100%) and 65%(IQR: 50-73%). Post-intervention: six months, IG: 93% (IQR: 91-93%), 100%(IQR: 90-100%), and 81%(IQR: 77-89%). CG: 67%(IQR: 58- 71%), 90%(IQR: 90-100%), and 62%( IQR: 58-73%). All the above post-intervention scores of PHMs in the IG were significantly higher in comparison to CG (p<0.001). Conclusions: This planned educational intervention had a significant impact on improving KAP of PHMs for early detection of breast cancer in the Gampaha district.

• Journal of the Korean Society for Precision Engineering
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• v.22 no.10 s.175
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• pp.80-89
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• 2005

In this paper, time-frequency analysis using wavelet packet transform and advanced-MDSA (Multiple Dimensional Spectral Analysis) which based on wavelet packet transform is applied fur fault source identification and diagnosis of early detection of fault non-stationary sound/vibration signals. This method is analyzing the signal in the plane of instantaneous time and instantaneous frequency. The results of ordinary coherence function, which obtained by wavelet packet analysis, showed the possibility of early fault detection by analysis at the instantaneous time. So, by checking the coherence function trend, it is possible to detect which signal contains the major fault signal and to know how much the system is damaged. Finally, It is impossible to monitor the system is damaged or undamaged by using conventional method, because crest factor is almost constant under the range of magnitude of fault signal as its approach to normal signal. However instantaneous coherence function showed that a little change of fault signal is possible to monitor the system condition. And it is possible to predict the maintenance time by condition based maintenance for any stationary or non-stationary signals.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.29B no.9
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• pp.37-49
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• 1992

The standard definition of computational vision is a set of inverse problems of recovering surfaces from images. Thus the common characteristics of the most early vision problems are ill-posed. The main idea for solving ill-posed problems is to restrict the class of admissible solutions by introducing suitable a priori knowledge. Standard regurarization methods lead to satisfactory solutions of early vision problems but cannot deal effectively and directly with a few general problems, such as discontinuity and fusion of information from multiple modules. In this paper, we discuss limitations of standard regularization theory and present new stochastic method. We will outline a rigorous approach to overcome part of ill-posedness of image restoration, edge detection, and stereo vision problems, based on Bayes estimation and MRF(Markov random field) model, that effectively deals with the problems. This result makes one hope that this framework could be useful in the solution of other vision problems.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.9
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• pp.2904-2926
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• 2022

Currently, diabetes is the most common chronic disease in the world, affecting 23.7% of the population in the Kingdom of Saudi Arabia. Diabetes may be the cause of lower-limb amputations, kidney failure and blindness among adults. Therefore, diagnosing the disease in its early stages is essential in order to save human lives. With the revolution in technology, Artificial Intelligence (AI) could play a central role in the early prediction of diabetes by employing Machine Learning (ML) technology. In this paper, we developed a diagnosis system using machine learning models for the detection of type 2 diabetes among adults, through the adoption of two different diabetes datasets: one for training and the other for the testing, to analyze and enhance the prediction accuracy. This work offers an enhanced classification accuracy as a result of employing several pre-processing methods before applying the ML models. According to the obtained results, the implemented Random Forest (RF) classifier offers the best classification accuracy with a classification score of 98.95%.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.49 no.8
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• pp.390-398
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• 2000

Leakage impulse current of the contaminated insulators by using experiment data were studied. The impulse current in phase-time relationship was analyzed on line post insulators. Also, frequency components and crest factor of the leakage current were investigated to provide a scheme for an early detection of insulator incipient failure. The study shows that the phase-time characteristic is non-stationary and random and, non-harmonic component and crest factor can be promising parameters for detecting insulator leakage currents.

• Structural Monitoring and Maintenance
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• v.2 no.2
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• pp.165-180
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• 2015

In this paper lead zirconate titanate transducers (PZT) are employed for damage detection of four reinforced concrete (RC) column specimens retrofitted with carbon fiber reinforced polymer (CFRP) jackets. A major disadvantage of FRP jacketing in RC members is the inability to inspect visually if the concrete substrate is damaged and in such case to estimate the extent of damage. The parameter measured during uniaxial compression tests at random times for known strain values is the real part of the complex number of the Electromechanical Admittance (Conductance) of the sensors, obtained by a PXI platform. The transducers are placed in specific positions along the height of the columns for detecting the damage in different positions and carrying out conclusions for the variation of the Conductance in relation to the position the failure occurred. The quantification of the damage at the concrete substrate is achieved with the use of the root-mean-square-deviation (RMSD) index, which is evaluated for the corresponding strain values. The experimental results provide evidence that PZT transducers are sensitive to damage detection from an early stage of the experiment and that the use of PZT sensors for monitoring and detecting the damage of FRP-retrofitted reinforced concrete members, by using the Electromechanical Admittance (EMA) approach, can be a highly promising method.

• Journal of Institute of Control, Robotics and Systems
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• v.9 no.11
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• pp.874-882
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• 2003

The condition assessment of engineering systems has increased in importance because the manpower needed to operate and supervise various plants has been reduced. Especially, induction motors are at the core of most engineering processes, and there is an indispensable need to monitor their health and performance. So detection and diagnosis of motor faults is a base to improve efficiency of the industrial plant. In this paper, a model-based fault detection system is developed for induction motors, using steady state vibration signals. Early various fault detection systems using vibration signals are a trivial method and those methods are prone to have missed fault or false alarms. The suggested motor fault detection system was developed using a model-based reference value. The stationary signal had been extracted from the non-stationary signal using a data segmentation method. The signal processing method applied in this research is FFT. A reference model with spectra signal is developed and then the residuals of the vibration signal are generated. The ratio of RMS values of vibration residuals is proposed as a fault indicator for detecting faults. The developed fault detection system is tested on 800 hp motor and it is shown to be effective for detecting faults in the air-gap eccentricities and broken rotor bars. The suggested system is shown to be effective for reducing missed faults and false alarms. Moreover, the suggested system has advantages in the automation of fault detection algorithms in a random signal system, and the reference model is not complicated.

• International Journal of Computer Science & Network Security
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• v.23 no.4
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• pp.179-186
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• 2023

For the future prosperity of any society, the sound growth of children is essential. Autism Spectrum Disorder (ASD) is a neurobehavioral disorder which has an impact on social interaction of autistic child and has an undesirable effect on his learning, speaking, and responding skills. These children have over or under sensitivity issues of touching, smelling, and hearing. Its symptoms usually appear in the child of 4- to 11-year-old but parents did not pay attention to it and could not detect it at early stages. The process to diagnose in recent time is clinical sessions that are very time consuming and expensive. To complement the conventional method, machine learning techniques are being used. In this way, it improves the required time and precision for diagnosis. We have applied TFLite model on image based dataset to predict the autism based on facial features of child. Afterwards, various machine learning techniques were trained that includes Logistic Regression, KNN, Gaussian Naïve Bayes, Random Forest and Multi-Layer Perceptron using Autism Spectrum Quotient (AQ) dataset to improve the accuracy of the ASD detection. On image based dataset, TFLite model shows 80% accuracy and based on AQ dataset, we have achieved 100% accuracy from Logistic Regression and MLP models.

• ETRI Journal
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• v.44 no.4
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• pp.613-623
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• 2022

Autism spectrum disorder (ASD) is a developmental disorder associated with cognitive and neurobehavioral disorders. It affects the person's behavior and performance. Autism affects verbal and non-verbal communication in social interactions. Early screening and diagnosis of ASD are essential and helpful for early educational planning and treatment, the provision of family support, and for providing appropriate medical support for the child on time. Thus, developing automated methods for diagnosing ASD is becoming an essential need. Herein, we investigate using various machine learning methods to build predictive models for diagnosing ASD in children using facial images. To achieve this, we used an autistic children dataset containing 2936 facial images of children with autism and typical children. In application, we used classical machine learning methods, such as support vector machine and random forest. In addition to using deep-learning methods, we used a state-of-the-art method, that is, automated machine learning (AutoML). We compared the results obtained from the existing techniques. Consequently, we obtained that AutoML achieved the highest performance of approximately 96% accuracy via the Hyperpot and tree-based pipeline optimization tool optimization. Furthermore, AutoML methods enabled us to easily find the best parameter settings without any human efforts for feature engineering.

• Smart Structures and Systems
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• v.15 no.5
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• pp.1215-1232
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• 2015

In the present paper, a method for identifying damage in a multi storeyed shear building structure is presented using minimum number of modal parameters of the structure. A damage at any level of the structure may lead to a major failure if the damage is not attended at appropriate time. Hence an early detection of damage is essential. The proposed identification methodology requires experimentally determined sparse modal data of any particular mode as input to detect the location and extent of damage in the structure. Here, the first natural frequency and corresponding partial mode shape values are used as input to the model and results are compared by changing the sensor placement locations at different floors to conclude the best location of sensors for accurate damage identification. Initially experimental data are simulated numerically by solving eigen value problem of the damaged structure with inclusion of random noise on the vibration characteristics. Reliability of the procedure has been demonstrated through a few examples of multi storeyed shear structure with different damage scenarios and various noise levels. Validation of the methodology has also been done using dynamic data obtained through experiment conducted on a laboratory scale steel structure.