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

Effect of add on therapy of SGLT 2 inhibitors on glycaemic parameters

Background: Glycaemic control in type 2 diabetes mellitus can be difficult to attain, even with a combination of multiple oral agents as well as Insulin. SGLT2 inhibitors are potential novel agents inhibits the sodium glucose co transporters operated in the kidney tubules independent of the action on insulin resistance or secretion. This study aimed to evaluate the effect on the mean reduction of HbA1c levels. Also, to evaluate the effect of gliflozins on the mean reduction of FBS and PPBS values at the end of 3rd and 6th months and to find out the ADR profile over 6 months. Methods: Prospective observational study conducted on the patients with type 2 diabetes mellitus with HbA1c >7% not controlled on metformin in the outpatient over a period of 15 months. An initial visit and thereafter follow up visits at 3rd and 6th month. HbA1c, FBS and PPBS was noted. ADR profile was also noted. Results: Significant mean reduction in the glycemic parameters among 90% study population with 0.5% reduction in mean HbA1c from the baseline. Also, the reduction in FBS and PPBS were statistically significant by 3rd month of the treatment. Incidence of genital itching was more compared with conventional drugs. Hypotension and polydipsia were rare. Conclusions: SGLT 2 inhibitors are found to be a promising new category of antidiabetic medications with better control of FBS, PPBS and HbA1c

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

Automated object detection of mechanical fasteners using faster region based convolutional neural networks

Mechanical fasteners are widely used in manufacturing of hardware and mechanical components such as automobiles, turbine & power generation and industries. Object detection method play a vital role to make a smart system for the society. Internet of things (IoT) leads to automation based on sensors and actuators not enough to build the systems due to limitations of sensors. Computer vision is the one which makes IoT too much smarter using deep learning techniques. Object detection is used to detect, recognize and localize the object in an image or a real time video. In industry revolution, robot arm is used to fit the fasteners to the automobile components. This system will helps the robot to detect the object of fasteners such as screw and nails accordingly to fit to the vehicle moved in the assembly line. Faster R-CNN deep learning algorithm is used to train the custom dataset and object detection is used to detect the fasteners. Region based convolutional neural networks (Faster R-CNN) uses a region proposed network (RPN) network to train the model efficiently and also with the help of Region of Interest able to localize the screw and nails objects with a mean average precision of 0.72 percent leads to accuracy of 95 percent object detectio

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

The effect of information, education and communication on knowledge and practice regarding prevention/treatment of iron deficiency anaemia among the antenatal women attending primary health centre in Puducherry, India: a randomised control study

Background: IEC (Information, Education, and Communication) strategies may help pregnant women to prevent disease and to improve and maintain health. The present study was carried out with an aim to evaluate the effectiveness of IEC in improving the knowledge and practice regarding prevention/treatment of iron deficiency anaemia among the antenatal women attending Primary Health Centre in Puducherry.Methods: The present study was carried out in Puducherry, as a randomised control trial among antenatal mothers attending antenatal clinics in Primary Health Centre from February 2016 to August 2017. Block randomization technique was used to designate study participants into intervention and non-intervention groups. The minimum required sample size was calculated to be 84 in each group. Then intervention (Information, Education and Communication) was given to these antenatal women by using interpersonal communication methods, PowerPoint presentation and audio visual aids.Results: Correct responses to the questions were compared among the intervention and non intervention group in pre test and post test. It was noted that the proportion of correct responses were significantly higher among intervention group than that of non-intervention groups.Conclusions: Well planned and tailor made IEC material, acceptable by the regional population, by using various modes of interpersonal communication, improves the knowledge and practice of the antenatal mothers. It was also found that the haemoglobin levels of mothers in the intervention group were higher than the antenatal mothers who did not receive any IEC intervention