Performance characteristics of anti-thyroid peroxidase and anti-thyroglobulin assays on roche cobas E411 immunoassay system

Background: Thyroid autoantibodies are measured in the evaluation of patients with thyroid diseases. The clinically important thyroid autoantibodies are anti-thyroid peroxidase (anti-TPO) and anti-thyroglobulin (anti-TG). The aim of this study was to assess the analytical performance of anti-TPO and anti-TG assays on Roche Cobas e411, an automated electrochemiluminescence immunoassay (ECLIA) system. Materials and Methods: The following parameters, imprecision, accuracy and stability were evaluated. Imprecision (within-run and between-run) and accuracy studies were performed using two levels of quality control materials. The stability of samples for anti-TPO and anti-TG measurements were evaluated by calculating the percentage recovery of serum samples stored at different temperatures (4°C, -20°C and -80°C) on day 3 and day 12 of storage. Result: The imprecisions for anti-TPO were between 2.5 to 11.3%. The percentage deviation from the true value was -4.9% and -3.6% for low and high QC, respectively. Anti-TPO showed variable recovery from 92.0% to 119.0% on day 3 and day 12 of storage. For anti-TG, the imprecisions were between 3.0 to 11.5%. The percentage deviation from the true value was 3.5% and 12.7% for low and high QC, respectively. Anti-TG showed variable recovery from 89.8% to 117.0% on day 3 and day 12 of storage. Conclusion: Roche Cobas e411 anti-TPO and anti-TG assays have demonstrated acceptable precision and accuracy. It is recommended that samples for anti-TPO and anti-TG are stored and analysed as per recommended to avoid the effects of storage

Pixel Classification of SAR ice images using ANFIS-PSO Classifier

Synthetic Aperture Radar (SAR) is playing a vital role in taking extremely high resolution radar images. It is greatly used to monitor the ice covered ocean regions. Sea monitoring is important for various purposes which includes global climate systems and ship navigation. Classification on the ice infested area gives important features which will be further useful for various monitoring process around the ice regions. Main objective of this paper is to classify the SAR ice image that helps in identifying the regions around the ice infested areas. In this paper three stages are considered in classification of SAR ice images. It starts with preprocessing in which the speckled SAR ice images are denoised using various speckle removal filters; comparison is made on all these filters to find the best filter in speckle removal. Second stage includes segmentation in which different regions are segmented using K-means and watershed segmentation algorithms; comparison is made between these two algorithms to find the best in segmenting SAR ice images. The last stage includes pixel based classification which identifies and classifies the segmented regions using various supervised learning classifiers. The algorithms includes Back propagation neural networks (BPN), Fuzzy Classifier, Adaptive Neuro Fuzzy Inference Classifier (ANFIS) classifier and proposed ANFIS with Particle Swarm Optimization (PSO) classifier; comparison is made on all these classifiers to propose which classifier is best suitable for classifying the SAR ice image. Various evaluation metrics are performed separately at all these three stages

A study on non-destructive method for detecting Toxin in pepper using Neural networks

Mycotoxin contamination in certain agricultural systems have been a serious concern for human and animal health. Mycotoxins are toxic substances produced mostly as secondary metabolites by fungi that grow on seeds and feed in the field, or in storage. The food-borne Mycotoxins likely to be of greatest significance for human health in tropical developing countries are Aflatoxins and Fumonisins. Chili pepper is also prone to Aflatoxin contamination during harvesting, production and storage periods.Various methods used for detection of Mycotoxins give accurate results, but they are slow, expensive and destructive. Destructive method is testing a material that degrades the sample under investigation. Whereas, non-destructive testing will, after testing, allow the part to be used for its intended purpose. Ultrasonic methods, Multispectral image processing methods, Terahertz methods, X-ray and Thermography have been very popular in nondestructive testing and characterization of materials and health monitoring. Image processing methods are used to improve the visual quality of the pictures and to extract useful information from them. In this proposed work, the chili pepper samples will be collected, and the X-ray, multispectral images of the samples will be processed using image processing methods. The term "Computational Intelligence" referred as simulation of human intelligence on computers. It is also called as "Artificial Intelligence" (AI) approach. The techniques used in AI approach are Neural network, Fuzzy logic and evolutionary computation. Finally, the computational intelligence method will be used in addition to image processing to provide best, high performance and accurate results for detecting the Mycotoxin level in the samples collected.Comment: 11 pages,1 figure; International Journal of Artificial Intelligence & Applications (IJAIA), Vol.3, No.4, July 201

Energy Aware Routing using AODV Protocol for Low Energy Consumption in WSN

Wireless Sensor Network (WSN) is a self-configured and infrastructure-less network that is used to monitor the environmental conditions and transfer sensor data to the desired destination in a particular region. Energy consumption is the most important concern in WSN, which is considered as an active research area. Routing selection is one method that is used to optimize energy in WSN. There are many protocols for discovering a route between two nodes. However the performance of the Ad hoc On-Demand Distance Vector Protocol (AODV) routing protocol is a more suitable one. It is a generic reactive protocol for routing mostly used in MANET (Mobile Ad Hoc Networks) and WSNs (Wireless Sensor Networks). This protocol supports unicasting and multicasting and will also identify the shortest path. The aim of this paper is discuses about energy-aware routing,  is implemented in the AODV protocol which is derived from nodes remaining energy. The remaining energy of node is computed by Max-Min energy algorithm in order to extend the network's life span and facilitates to keep the network lively. The performance of AODV is compared with Modified AODV protocols. The comparison is done by various performance metrics such as PDR (Packet Delivery Ratio), throughput, delay time, loss rate, and energy consumption. Analysis on the experimental results showed that MAODV protocol gives better results than traditional AODV protocol and it is also inferred that MAODV avoids too much energy consumption of nodes in the network

Optimized connected Median filter using Particle Swarm Optimization

In the image processing Median filter were used to remove the impulse noise. It preserves the edges for the next level operations such as segmentation and object recognition. The present paper deals with the preprocessing of chili x-ray images. The researcher has already preprocessed the chili x-ray images by adopting the Average filter, Median filter, Wiener filter, Gamma intensity correction, CLAHE, 4-connected Median filter and weighted 4-connected median filter. The result of the above stated preprocess methods to contain noise in the pixels, hence it is considered as unsuitable for next level operations. To remove such noise from the image, this paper contributes a precise and well-organized algorithm. The proposed noise removal algorithm replaces the noisy pixels by using ‘4-connected median value’ and replaces the remaining pixels by using ‘weighted 4-connected median value’ in the selected window. The replacement of middle pixel value in 4-connected median filter is done through particle swarm optimization algorithm. Peak Signal to Noise Ratio used as the fitness function in the particle swarm optimization algorithm. The performance measures were taken for all the noise removal algorithm. Among the various results obtained, the proposed algorithm works better than others

Investigation of mall atmosphere in experiential shopping during holiday season: A case of Malaysian Shopping Malls

This study examines the influence of environmental factors in a shopping mall on consumer behaviour especially during holiday season. Within a shopping mall there needs to be a balance of external environment elements that serves to elicit human stimuli to help induce different sensations and psychological effects on a person, ultimately leading to positive consumer behaviours and increased purchase intentions. However the wrong balance of these elements can hinder the positive reactions of consumers especially during the holiday season shopping where potentially sales are at the highest. This study employed a convenience sampling data collection method from 300 respondents in a selected shopping mall during the month of December 2013 to capture the consumer’s reaction to the mall’s atmosphere which were enhanced with festive decorations to help induce potential buying behaviour. Results were collated based on the analysis of four significant environmental variables which are interior settings, music, employee engagement and consumer density. The findings indicated that the effects of interior settings, music and employee engagement were found to be evident. Conversely, consumer density was found to elicit no effect on consumer behaviour. It was also noted that gender moderates the relationship between music and consumer behaviour. The implications of the results obtained are discussed along with the managerial implications, limitations and future research

Improved Canny Edges Using Cellular Based Particle Swarm Optimization Technique for Tamil Sign Digital Images

The development of computer based sign language recognition system, for enabling communication with hearing impaired people, is an important research area that faces different challenges in the pre-processing stage of image processing, particularly in boundary detection stage. In edge detection, the possibility of achieving high quality images significantly depends on the fitting threshold values, which are generally selected using canny method, and these threshold values may vary, based on the type of images and the applications chosen. This research work presents a novel idea of establishing a hybrid particle swarm optimization algorithm, which is a combination of PSO with the behavioural pattern of cellular organism in canny method, that defines an objective to find optimal threshold values for the implementation of double thresholding hysteresis method, which is viewed as a non-linear complex problem. The attempt to incorporate the model has minimized the problem of quick convergence of PSO algorithm which has improved the detection of broken edges. The efficiency of the proposed algorithm is proved through the experimental observation, done in Tamil sign images to indicate the better performance of canny operator by introducing new variant based PSO

CELLULAR ORGANISM BASED PARTICLE SWARM OPTIMIZATION ALGORITHM FOR COMPLEX NON-LINEAR PROBLEMS

Particle Swarm Optimization (PSO) is the global optimization technique that inspires many researchers to solve large scale of non-linear optimization problems. For certain complex scenarios, the premature convergence problem of PSO algorithm cannot find global optimum in dynamic environments. In this paper, a new variant motility factor based Cellular Particle Swarm Optimization (m-CPSO) algorithm is proposed which is developed by the migration behavior observed from fibroblast cellular organism to overcome this problem. The proposed m-CPSO algorithm is modeled in two different social best and individual best models. The performance of m-CPSO is tested in the benchmark and real-time data instances and compared with classical PSO. The outcome of experimental results has demonstrated that m-CPSO algorithm produces promising results than classical PSO on all evaluated environments