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.

scholarly journals AN ALGORITHM TO ANALYZE STRENGTH OF CAPTCHA

2012 ◽  
pp. 140-145
Author(s):  
AJINKYA KASHINATH PARBHANE ◽  
ANAJALI .A. CHANDAVALE ◽  
A.M. SAPKAL
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Artificial Neural Network ◽  
Character Recognition ◽  
Experimental Result ◽  
The Internet ◽  
Image Processing Techniques ◽  
Artificial Neural ◽  
Processing Techniques

CAPTCHA stands for Completely Automated Public Turing Tests to Tell Computers and Humans Apart. The CAPTCHAs have been widely used across the Internet to defend against undesirable and malicious bot programs. It was observed that an alarming number of CAPTCHAs could be broken by the technique of Image Processing and Artificial Neural Network. Many Researchers have tried to break a CAPTCHA so as to design robust CAPTCHA , but it is essential to generate a strong CAPTCHA that will resist bot attack. This paper has proposed algorithm to analyze the strength of CAPTCHAs using simple image processing techniques such as Preprocessing, Segmentation and Character recognition which in turn helps to improve the robustness and usability of CAPTCHA in Internet System. The experimental result shows the proposed algorithm gives 75 % accuracy to analyze the strength of CAPTCHA.

Signature Verification Using Artificial Neural Network

2018 ◽  
Vol 7 (12) ◽  
pp. 40
Author(s):  
Palak Patel
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Signature Verification ◽  
Dynamic Information ◽  
Signature Recognition ◽  
Scanned Image ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Processing Techniques ◽  
Image Format

The human signature is most important for access. Signature of the person is important biometric attribute of a human being which is used to authenticate human identity. There are many biometric characteristics by which one can have own identity like face recognition, fingerprint detection, iris inspection and retina scanning. In non-vision based techniques voice recognition and signature verification are most widely used. Verification can be performed either Online or Offline. Online system of signature verification uses dynamic information of a signature captured at the time the signature is made. Offline system uses scanned image of signature. In this paper, I present a method for Offline Verification of signatures using a set of simple shape based geometric features. As signatures play an important role in financial, commercial and legal transactions, truly secured authentication becomes more and more crucial. This paper presents the off-line signature recognition & verification using neural network in which the human signature is captured and presented in the image format. Various image processing techniques are used to recognize and verify the signature. Preprocessing of a scanned image is necessary to isolate the signature part and to remove any spurious noise present. Initially system use database of signatures obtained from those individuals whose signatures have to be authenticated by the system. Then artificial neural network (ANN) is used to verify and classify the signatures. The implementation details and results are discussed in the paper.

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.

Feature Extraction and Optimum Part Deposition Orientation for FDM

2015 ◽  
Vol 793 ◽  
pp. 642-646 ◽  
Author(s):  
Khairul Fauzi Karim ◽  
D. Hazry ◽  
A.H. Zulkifli ◽  
S. Faiz Ahmed ◽  
Zuradzman Mohamad Razlan ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Feature Extraction ◽  
Fused Deposition Modeling ◽  
Support Structure ◽  
Fused Deposition ◽  
Deposition Modeling ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Selection Of

Support generation is an essential for Fused Deposition Modeling (FDM) process which is dependent on part deposition orientation. Various part deposition orientation result in formation of different support and non-support features. Present work focuses on extracting the support features containing Externally-Supported Features (ESF) which are able to determine the volume and number of support structure. The methodology proposed in this work uses these information as an input for Artificial Neural Network (ANN) in order to automate the selection of optimum part deposition orientation. The results produced in present methodology can be predicted and are in agreement with the results published earlier.

Development of Quran Reciter Identification System Using MFCC and Neural Network

2016 ◽  
Vol 1 (1) ◽  
pp. 168 ◽  
Author(s):  
Tayseer Mohammed Hasan Asda ◽  
Teddy Surya Gunawan
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Feature Extraction ◽  
Learning Algorithm ◽  
Back Propagation ◽  
Identification System ◽  
Successful Match ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Mel Frequency Cepstral Coefficient

Currently, the Quran is recited by so many reciters with different ways and voices.  Some people like to listen to this reciter and others like to listen to other reciters. Sometimes we hear a very nice recitation of al-Quran and want to know who the reciter is. Therefore, this paper is about  the development of Quran reciter recognition and identification system based on Mel Frequency Cepstral Coefficient (MFCC) feature extraction and artificial neural network (ANN). From every speech, characteristics from the utterances will be extracted through neural network model. In this paper a database of five Quran reciters is created and used in training and testing. The feature vector will be fed into Neural Network back propagation learning algorithm for training and identification processes of different speakers. Consequently,  91.2%  of the successful match between targets and input occurred with certain number of hidden layers  which shows how efficient are Mel Frequency Cepstral Coefficient (MFCC) feature extraction  and artificial neural network (ANN) in identifying the reciter voice perfectly.

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

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