scholarly journals Deteksi Level Kolesterol melalui Citra Mata Berbasis HOG dan ANN

2019 ◽  
Vol 7 (2) ◽  
pp. 284 ◽  
Author(s):  
MUHAMMAD ARSYAD SIDDIK ◽  
LEDYA NOVAMIZANTI ◽  
I NYOMAN APRAZ RAMATRYANA
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Artificial Neural Network ◽  
High Risk ◽  
Clinical Laboratory ◽  
Computing Time ◽  
Histogram Of Oriented Gradients ◽  
Cholesterol Levels ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

ABSTRAKKolesterol merupakan lemak yang berada di dalam darah yang dibutuhkan untuk pembentukan hormon dan sel baru. Kadar kolesterol normal harus kurang dari 200 mg/dL, namun jika di atas 240 mg/dL akan berisiko tinggi terkena penyakit stroke dan jantung koroner. Penelitian ini menghasilkan suatu sistem yang dapat mendeteksi kadar kolesterol seseorang melalui citra mata menggunakan metode iridologi dan image processing. Citra mata diperoleh dari pasien laboratorium klinik sebanyak 120 citra mata. Proses sistem diawali dengan mengolah citra mata dengan metode cropping, resize, dan segmentasi. Metode ekstaksi ciri menggunakan Histogram of Oriented Gradients (HOG), dan klasifikasi menggunakan Artificial Neural Network (ANN). Sistem dapat mendeteksi kadar kolesterol dengan tiga level klasifikasi, yaitu normal, berisiko kolesterol tinggi, dan kolesterol tinggi dengan tingkat akurasi sebesar 93% dan waktu komputasi 0,0862 detik.Kata kunci: citra mata, kadar kolesterol, Histogram of Oriented Gradients, Artificial Neural Network ABSTRACTCholesterol is fat in the blood that is needed for the formation of hormones and new cells. Normal cholesterol levels should be less than 200 mg / dL, but if above 240 mg / dL will be at high risk of stroke and coronary heart disease. This study produced a system that can detect a person's cholesterol levels through eye images using iridology and image processing methods. Eye images obtained from clinical laboratory patients were 120 eye images. The system process begins with processing eye images using the method of cropping, resizing, and segmentation. Feature extraction method uses Histogram of Oriented Gradients (HOG), and classification using Artificial Neural Network (ANN). The system can detect cholesterol levels with three levels of classification, namely normal, at high risk of cholesterol, and high cholesterol with an accuracy rate of 93% and computing time of 0.0862 seconds.Keywords: eye image, cholesterol level, Histogram of Oriented Gradients, Artificial Neural Network

scholarly journals PENERAPAN ARTIFICIAL NEURAL NETWORK UNTUK KLASIFIKASI FERTILITAS TELUR ITIK MENGGUNAKAN RASPBERRY PI

BUANA ILMU ◽  
2018 ◽  
Vol 3 (1) ◽  
Author(s):  
Jamaludin Indra
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Artificial Neural Network ◽  
Raspberry Pi ◽  
Detection Accuracy ◽  
Neural Network Method ◽  
Network Method ◽  
Artificial Neural Network Method ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

ABSTRAK Artificial Neural Network (ANN) telah banyak diterapkan pada berbagai bidang, salah satunya penerapan pada bidang peternakan. Penetasan menggunakan mesin penetas telur, proses pengklasifikasian embrio telur menjadi sangat penting dalam proses penetasan untuk membedakan antara yang layak, berdasarkan adanya perkembangan embrio yang dapat dilanjutkan dalam proses inkubasi atau tidak layak (fertile atau infertile), dalam penelitian ini menyajikan klasifikasi menggunakan teknik pengolahan citra digital menggunakan metode artificial neural network yang diaplikasikan pada Raspberry Pi sebagai pemroses gambar dan menampilkan hasil klasifikasi. Dengan metode artificial neural network dan penggunaan Raspberry Pi mampu mencapai akurasi pendeteksian 95%. Kata kunci: Artificial Neural Network, Pengolahan Citra Digital, Embrio , Klasifikasi, Telur . ABSTRACT Artificial Neural Network (ANN) has been widely applied in various fields, one of which is the application in the field of animal husbandry. Hatching using an egg incubator machine, the classification process of egg embryos is very important in the hatching process to distinguish between the appropriate, based on the embryonic development that can be continued in the process of incubation or inadequate (fertile or infertile), in this study presents classification using image processing techniques digital uses the artificial neural network method that is applied to the Raspberry Pi as an image processor and displays the classification results. With the artificial neural network method and the use of Raspberry Pi it is expected to be able to achieve 90% detection accuracy. Key word : Artificial Neural Network, Digital Image Processing, Embriyo, Calssification, Egg.

scholarly journals Screening of Tuberculosis Using Artificial Neural Network

2020 ◽  
Vol 3 (11) ◽  
pp. 145-149
Author(s):  
A. Anand Kumar ◽  
T. Mani ◽  
S. Gokulnath ◽  
S. K. Kabilesh ◽  
K. Dinakaran ◽  
...  
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Artificial Neural Network ◽  
Feature Extraction ◽  
Bacterial Disease ◽  
Image Processing Techniques ◽  
Neural Organization ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Processing Techniques

Tuberculosis is an infectious bacterial disease that most commonly affects the lungs. This paper reviews, screening of tuberculosis in chest radiograph images using an artificial neural network (ANN). Implementing image processing techniques having segmentation, feature extraction from chest radiographs, at that point building up a fake neural organization for programmed characterization dependent on back proliferation calculation to group tuberculosis accurately. The performance was evaluated using SVM and ANN classifiers regarding exactness, review, and precision. The trial results Confirm the effectiveness of the proposed strategy that gives great Classification proficiency.

Identifikasi permukaan aluminium dengan image processing dan artificial neural network

2010 ◽  
Vol 8 (1) ◽  
pp. 717
Author(s):  
Irwin Syahri
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Artificial Neural Network ◽  
Roughness Surface ◽  
Surface Image ◽  
Pixel Image ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Penelitian ini bertujuan untuk mengidentifikasi permukaan suatu logam, khususnya Aluminium berdasarkan image processing yang ditampilkan logam dengan pendekatan komputasi menggunakan Artificial Neural Network (ANN). Specimen dikerjakan dengan menggunakan beberapa mesin dan tingkat kecepatan putaran spindle dan kecepatan pemotongan yang berbeda sehingga didapatkan kekasaran permukaan yang berbeda. Specimen diambil image-nya menggunakan kamera digital 4 mega piksel dengan sumber pencahayaan, jarak dan jumlah pixel image yang sama. Image Alumunium selanjutnya di proses untuk dapat dikenali dengan ANN. Hasil penelitian menunjukkan model ANN 11 input 5S hidden dan 1 output: (11-5-1) menunjukkan hasil terbaik untukmengidentifikasi bentuk permukaan Alumunium dengan RMSE yang terkecil: 0.0038 untuk training dan testing.Kata kunci : Roughness surface, Image Processing, ANN

Application of the Artificial Neural Network (ANN) Method as MPPT Photovoltaic for DC Source Storage

2019 ◽  
Vol 12 (3) ◽  
pp. 145 ◽  
Author(s):  
Epyk Sunarno ◽  
Ramadhan Bilal Assidiq ◽  
Syechu Dwitya Nugraha ◽  
Indhana Sudiharto ◽  
Ony Asrarul Qudsi ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Random Forest (RF) and Artificial Neural Network (ANN) Algorithms for LULC Mapping

2020 ◽  
Vol 38 (4A) ◽  
pp. 510-514
Author(s):  
Tay H. Shihab ◽  
Amjed N. Al-Hameedawi ◽  
Ammar M. Hamza
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Artificial Neural Network ◽  
Random Forest ◽  
Satellite Image ◽  
Landsat 8 ◽  
Optical Remote Sensing ◽  
Data Set ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

In this paper to make use of complementary potential in the mapping of LULC spatial data is acquired from LandSat 8 OLI sensor images are taken in 2019.  They have been rectified, enhanced and then classified according to Random forest (RF) and artificial neural network (ANN) methods. Optical remote sensing images have been used to get information on the status of LULC classification, and extraction details. The classification of both satellite image types is used to extract features and to analyse LULC of the study area. The results of the classification showed that the artificial neural network method outperforms the random forest method. The required image processing has been made for Optical Remote Sensing Data to be used in LULC mapping, include the geometric correction, Image Enhancements, The overall accuracy when using the ANN methods 0.91 and the kappa accuracy was found 0.89 for the training data set. While the overall accuracy and the kappa accuracy of the test dataset were found 0.89 and 0.87 respectively.

Detection of Biomedical Images by Using Bio-inspired Artificial Intelligent

2020 ◽  
Vol 38 (2A) ◽  
pp. 255-264
Author(s):  
Hanan A. R. Akkar ◽  
Sameem A. Salman
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Mean Square Error ◽  
Average Rate ◽  
Mean Square ◽  
Performance Measurements ◽  
Artificial Intelligent ◽  
Average Mean Square Error ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Computer vision and image processing are extremely necessary for medical pictures analysis. During this paper, a method of Bio-inspired Artificial Intelligent (AI) optimization supported by an artificial neural network (ANN) has been widely used to detect pictures of skin carcinoma. A Moth Flame Optimization (MFO) is utilized to educate the artificial neural network (ANN). A different feature is an extract to train the classifier. The comparison has been formed with the projected sample and two Artificial Intelligent optimizations, primarily based on classifier especially with, ANN-ACO (ANN training with Ant Colony Optimization (ACO)) and ANN-PSO (training ANN with Particle Swarm Optimization (PSO)). The results were assessed using a variety of overall performance measurements to measure indicators such as Average Rate of Detection (ARD), Average Mean Square error (AMSTR) obtained from training, Average Mean Square error (AMSTE) obtained for testing the trained network, the Average Effective Processing Time (AEPT) in seconds, and the Average Effective Iteration Number (AEIN). Experimental results clearly show the superiority of the proposed (ANN-MFO) model with different features.

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