Date of publication xxxx 00, 0000, date of current version xxxx 00, 0000.
Serious illnesses such as strokes and heart attacks can be triggered by high levels of cholesterol in human blood that exceeds ideal conditions, where the ideal cholesterol level is below 200 mg/dL. To find out cholesterol levels need a long process because the patient must go through a blood sugar test that requires the patient to undergo fasting for 10–12 hours first before the test. Iridology is a branch of science that studies human iris and its relation to the wellness of human internal organs. The method can be used as an alternative for medical analysis. Iridology thus can be used to assess the conditions of organs, body construction, and other psychological conditions. This paper proposes a cholesterol detection system based on the iris image processing using Gray Level Co-Occurrence Matrix (GLCM) and Support Vector Machine (SVM). GLCM is used as the feature extraction method of the image, while SVM acts as the classifier of the features. In addition to GLCM and SVM, this paper also construct a preprocessing method which consist of image resizing, segmentation, and color image to gray level conversion of the iris image. These steps are necessary before the GLCM feature extraction step can be applied. In principle, the GLCM method is a construction of a matrix containing the information about the proximity position of gray level images pixels. The output of GLCM is fed to the SVM that relies on the best hyperplane. Thus, SVM performs as a separator of two data classes of the input space. From the simulation results, the system built was able to detect excess cholesterol levels through iris image and classify into three classes, namely: non–cholesterol (< 200 ), risk of cholesterol (200–239 ) and high cholesterol (> 240 ). The accuracy rate obtained was 94.67% with an average computation time of 0.0696 . It was using each of the 75 training and test data, with the second-order parameters used are contrast–correlation–energy–homogeneity, pixel distance = 1, quantization level = 8, Polynomial kernel types and One Against One Multiclass. Iris has specific advantages which can record all organ conditions, body construction and psychological conditions. Therefore, Iridology as a science based on the arrangement of iris fibers can be an alternative for medical analysis. In this paper proposes a cholesterol detection system through the iris using Gray Level Co-occurence Matrix and Support Vector Machine. Input system is an iris image that will be processed by pre-processing and then extracted features with the Gray Level Co-Occurrence Matrix method which is a matrix containing information about position the proximity of pixels that have a certain gray level. And then classified with the Support Vector Machine method that relies on the best hyper lane which functions as a separator of two data classes in the input space. From the simulation results, the system built was able to detect excess cholesterol levels through iris image and classify into three classes are: risk of cholesterol, high cholesterol and non–cholesterol with an accuracy rate of 96.47% and average computation time was 0.0696 using each of the 75 training and test data, with the second-order parameters used are contrast–correlation–energy–homogeneity, pixel distance = 1, quantization level = 8, Polynomial kernel types and One Against One Multiclass.
Electricity is very limited in Matantimali village where it is located on the slopes of Gawalise, Marawola District, Sigi Regency, Central Sulawesi Province. In this village, the state electricity company (PLN) can only provide electricity during the day from 06:00 to 18:00. During night, the diesel generator becomes the main energy source for the village providing electrical energy from 18:00 until 06:00. However, the use of diesel generators contradicts with the government commitment to generate environmental eco-friendly power generation. This study aims to analyse and to optimize the price of off-grid solar systems which will be implemented in the village of Matantimali as a potential replacement of the existing diesel based electric energy. We find in this study that 11 PV units with a total power of 3,686 Wp, two 2400 W powered inverter, and a 16-unit battery cover are the required specification for optimal power of 1300 VA load. The Off-grid solar panel installation system creates annual cost savings of IDR 3,543,397 compared to the use of conventional electricity using diesel generators and PLN.
Climate change, greenhouse gases, and global warming are global issues today. Of course, this global issue cannot be separated from the issue of emissions. Various methods to solve generator scheduling problems by considering emissions or Economic Emission Dispatch (EED) have been published, but not to the extent of calculating the cost to reduce emissions. The main objective of this research is to determine the cost of reducing the emission of electricity generation in Indonesia through solving the EED problem. The method proposed to solve the EED problem is an annealing simulation algorithm and tested using an electrical system of eight generators, four different loads, and five combinations of cost and emission weights. This method is tested with various loads (conditions), and each condition is tested with various combinations of cost weights and emission weights. The obtained results were compared with the results of the calculation of the Cuckoo algorithm, and the whale optimization algorithm. The simulation results show that it costs US$258.81 to reduce 1 ton of emissions. This paper can be used as a material for further consideration for the government and generator providers in making policies related to the operation of power plants by considering emissions.
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