scholarly journals Identifikasi Penyakit Diabetes Mellitus Melalui Nafas Berbasis Sensor Gas Dengan Metode Fast Fourier Transform dan Backpropagation

2018 ◽  
Vol 2 (02) ◽  
pp. 85-91
Author(s):  
Mohammad Hafiz Hersyah ◽  
Andrizal Andrizal ◽  
Revinessia Revinessia
Keyword(s):  
Neural Network ◽  
Diabetes Mellitus ◽  
Fourier Transform ◽  
Hydrogen Sulfide ◽  
Fast Fourier Transform ◽  
Success Rate ◽  
The Neural Network ◽  
Random Samples ◽  
Backpropagation Method ◽  
Bad Breath

The purpose of this research is to detect whether a person has diabetes mellitus or not. In people with diabetes mellitus uncontrolled will result in a decline in the rate of saliva that results in bad breath. The system uses the sensor TGS 2602 and MQ 4. It's function is to detect the levels of Hydrogen Sulfide and Methan in a person’s breath. The decision is made by using the neural network with a backpropagation method. The result for 5 (five) tests of diabetes mellitus samples can be detected with a success rate of 80%, whereas using random samples, the test detected with detected with a success rate of 80% samples that didn’t contain diabetes mellitus. This system could provide a solution for testing if a person is suffering from diabetes mellitus

scholarly journals A Frequency Pattern Mining Model Based on Deep Neural Network for Real-Time Classification of Heart Conditions

Healthcare ◽  
2020 ◽  
Vol 8 (3) ◽  
pp. 234 ◽  
Author(s):  
Hyun Yoo ◽  
Soyoung Han ◽  
Kyungyong Chung
Keyword(s):  
Neural Network ◽  
Big Data ◽  
Fourier Transform ◽  
Fast Fourier Transform ◽  
Real Time ◽  
Normal Control ◽  
Input Data ◽  
Deep Neural Network ◽  
Pattern Mining ◽  
F Measure

Recently, a massive amount of big data of bioinformation is collected by sensor-based IoT devices. The collected data are also classified into different types of health big data in various techniques. A personalized analysis technique is a basis for judging the risk factors of personal cardiovascular disorders in real-time. The objective of this paper is to provide the model for the personalized heart condition classification in combination with the fast and effective preprocessing technique and deep neural network in order to process the real-time accumulated biosensor input data. The model can be useful to learn input data and develop an approximation function, and it can help users recognize risk situations. For the analysis of the pulse frequency, a fast Fourier transform is applied in preprocessing work. With the use of the frequency-by-frequency ratio data of the extracted power spectrum, data reduction is performed. To analyze the meanings of preprocessed data, a neural network algorithm is applied. In particular, a deep neural network is used to analyze and evaluate linear data. A deep neural network can make multiple layers and can establish an operation model of nodes with the use of gradient descent. The completed model was trained by classifying the ECG signals collected in advance into normal, control, and noise groups. Thereafter, the ECG signal input in real time through the trained deep neural network system was classified into normal, control, and noise. To evaluate the performance of the proposed model, this study utilized a ratio of data operation cost reduction and F-measure. As a result, with the use of fast Fourier transform and cumulative frequency percentage, the size of ECG reduced to 1:32. According to the analysis on the F-measure of the deep neural network, the model had 83.83% accuracy. Given the results, the modified deep neural network technique can reduce the size of big data in terms of computing work, and it is an effective system to reduce operation time.

scholarly journals Diagnosing Diabetes Using Artificial Neural Networks

2020 ◽  
Vol 5 (2) ◽  
pp. 221-224
Author(s):  
Joy Oyinye Orukwo ◽  
Ledisi Giok Kabari
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Success Rate ◽  
Medical Diagnosis ◽  
Learning Algorithm ◽  
Diabetes Diagnosis ◽  
Feed Forward Neural Network ◽  
The Neural Network ◽  
Artificial Neural

Diabetes has always been a silent killer and the number of people suffering from it has increased tremendously in the last few decades. More often than not, people continue with their normal lifestyle, unaware that their health is at severe risk and with each passing day diabetes goes undetected. Artificial Neural Networks have become extensively useful in medical diagnosis as it provides a powerful tool to help analyze, model and make sense of complex clinical data. This study developed a diabetes diagnosis system using feed-forward neural network with supervised learning algorithm. The neural network is systematically trained and tested and a success rate of 90% was achieved.

scholarly journals Identifikasi Penyakit Diabetes Mellitus Melalui Nafas Berbasis Sensor Gas Dengan Metode Fast Fourier Transform dan Backpropagation

2018 ◽  
Vol 2 (02) ◽  
pp. 35-41
Author(s):  
Mohammad Hafiz Hersyah
Keyword(s):  
Diabetes Mellitus ◽  
Fourier Transform ◽  
Fast Fourier Transform

Penelitian ini bertujuan untuk mendeteksi bau mulut seseorang apakah diabetes mellitus atau tidak. Pada penderita diabetes mellitus yang tidak terkontrol akan mengakibatkan turunnya kadar air liur yang mengakibatkan bau mulut. Sistem yang dirancang menggunakan sensor gas TGS 2602 yang berfungsi untuk mendeteksi kadar gas hidrogen sulfida dan sensor gas MQ 4 yang berfungsi untuk mendeteksi kadar gas methane yang terkandung pada nafas seseorang. Untuk pengambilan keputusan, sistem ini menggunakan jaringan syaraf tiruan dengan metode pembelajaran backpropagation. Hasil yang diperoleh dengan 5 kali pengujian pada sampel diabetes mellitus dapat dideteksi dengan tingkat keberhasilan 80%, sedangkan untuk sampel acak dapat dideteksi dengan tingkat keberhasilan 80% tidak mengalami diabetes mellitus. Dengan adanya sistem ini, diharapkan dapat dijadikan sebagai alternatif untuk menguji apakah seseorang menderita diabetes mellitus atau tidak.

scholarly journals Improved Docking of Protein Models by a Combination of Alphafold2 and ClusPro

2021 ◽  
Author(s):  
Usman Ghani ◽  
Israel Desta ◽  
Akhil Jindal ◽  
Omeir Khan ◽  
George Jones ◽  
...  
Keyword(s):  
Neural Network ◽  
Success Rate ◽  
Protein Docking ◽  
Additional Step ◽  
The Neural Network ◽  
Docking Program ◽  
Protein Models ◽  
Docking Benchmark ◽  
Benchmark Sets

AbstractIt has been demonstrated earlier that the neural network based program AlphaFold2 can be used to dock proteins given the two sequences separated by a gap as the input. The protocol presented here combines AlphaFold2 with the physics based docking program ClusPro. The monomers of the model generated by AlphaFold2 are separated, re-docked using ClusPro, and the resulting 10 models are refined by AlphaFold2. Finally, the five original AlphaFold2 models are added to the 10 AlphaFold2 refined ClusPro models, and the 15 models are ranked by their predicted aligned error (PAE) values obtained by AlphaFold2. The protocol is applied to two benchmark sets of complexes, the first based on the established protein-protein docking benchmark, and the second consisting of only structures released after May 2018, the cut-off date for training AlphaFold2. It is shown that the quality of the initial AlphaFold2 models improves with each additional step of the protocol. In particular, adding the AlphaFold2 refined ClusPro models to the AlphaFold2 models increases the success rate by 23% in the top 5 predictions, whereas considering the 10 models obtained by the combined protocol increases the success rate to close to 40%. The improvement is similar for the second benchmark that includes only complexes distinct from the proteins used for training the neural network.

scholarly journals Intelligent Automatic V6 and V8 Engine Sound Detection Based on Artificial Neural Network

2019 ◽  
Vol 130 ◽  
pp. 01035 ◽  
Author(s):  
Wenny Vincent ◽  
Astuti Winda ◽  
Mahmud Iwan Solihin
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Fourier Transform ◽  
Fast Fourier Transform ◽  
Intelligent System ◽  
Recorded Sound ◽  
Sound Detection ◽  
Testing Accuracy ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

The sound of V6 or V8 engines has its own cultural appeal that cannot be replaced by the modern four-cylinder naturally aspirated or turbocharged engines. The identification of the type of engine by the sound is not an easy task, even for the professionals. An intelligent system that can identify V6 to V8 engines from various cars will give an insight of the features in the engine sounds that characterized the two different engines. In this work, an Artificial Neural Network (ANN) approach is applied for identifying cylinder of the engine based on the engine sound identification is proposed. The recorded sound of the engine is then processed in order to get some features which later be used in the proposed system. The Fast Fourir Transform (FFT) is adopted as a feature and later used as input to the Artificial Neural Network (ANN) based identifier. The Experimental results confirm the effectiveness of the proposed intelligent automatic six cylinder and eight cylinder engine based on Fast Fourier Transform (FFT) and Artificial Neural Network (ANN), since it resulting the training and testing accuracy of 100 % and 100 %, respectively.

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