scholarly journals An Intellectual Energy Device for Household Appliances Using Artificial Neural Network

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Hariprasath Manoharan ◽  
Yuvaraja Teekaraman ◽  
Ramya Kuppusamy ◽  
Arun Radhakrishnan
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Packet Transmission ◽  
Time Data ◽  
Energy Device ◽  
Intelligent Monitoring ◽  
Artificial Neural ◽  
Monitoring Devices ◽  
Single Cluster ◽  
The Internet Of Things

This article highlights the importance of implementing intelligent monitoring devices with the internet of things (IoT) for observing the amount of charges on different appliances in each household. In India, it has been observed that 20% of power is wasted due to commercial appliances where the amount of charge flow is much excess to corresponding appliances. Therefore, to perceive information about the flow of charges, it is necessary to implement an intelligent device, and it is possible to obtain exact information on the flow of charges with the help of wireless sensor networks (WSN). Even most of the researchers have developed an intelligent device for monitoring the amount of charges but delay, energy consumption, and cost of implementation are much higher. It is always necessary to extract precise information at corresponding time periods for reducing the delay in packet transmission of a specific network. To excerpt such real-time data in the network layer, an active procedure should be followed by integrating dissimilar network areas inside a single cluster, and binary coded artificial neural network (BCANN) is introduced to acquire information about hidden layers. To prove the effect of such integration process, several tests have been prepared using online and offline analyses where simulation results prove to be much effective in case of all different scenarios to an extent of 52.4% when compared to existing methods.

scholarly journals Security-Aware Routing Protocol Based on Artificial Neural Network Algorithm and 6LoWPAN in the Internet of Things

2022 ◽  
Vol 2022 ◽  
pp. 1-8
Author(s):  
Jiangdong Lu ◽  
Dongfang Li ◽  
Penglong Wang ◽  
Fen Zheng ◽  
Meng Wang
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Internet Of Things ◽  
Routing Protocols ◽  
Intelligent Systems ◽  
Smart Devices ◽  
The Internet ◽  
Routing Mechanism ◽  
Artificial Neural ◽  
The Internet Of Things

Today, with increasing information technology such as the Internet of Things (IoT) in human life, interconnection and routing protocols need to find optimal solution for safe data transformation with various smart devices. Therefore, it is necessary to provide an enhanced solution to address routing issues with respect to new interconnection methodologies such as the 6LoWPAN protocol. The artificial neural network (ANN) is based on the structure of intelligent systems as a branch of machine interference, has shown magnificent results in previous studies to optimize security-aware routing protocols. In addition, IoT devices generate large amounts of data with variety and accuracy. Therefore, higher performance and better data handling can be achieved when this technology incorporates data for sending and receiving nodes in the environment. Therefore, this study presents a security-aware routing mechanism for IoT technologies. In addition, a comparative analysis of the relationship between previous approaches discusses with quality of service (QoS) factors such as throughput and accuracy for improving routing mechanism. Experimental results show that the use of time-division multiple access (TDMA) method to schedule the sending and receiving of data and the use of the 6LoWPAN protocol when routing the sending and receiving of data can carry out attacks with high accuracy.

scholarly journals Optimasi Parameter Artificial Neural Network Menggunakan Algoritma Genetika Untuk Prediksi Kelulusan Mahasiswa

2019 ◽  
Vol 18 (1) ◽  
pp. 54-59
Author(s):  
Irfan Ali ◽  
Lana Sularto
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Genetic Algorithms ◽  
Student Behavior ◽  
Network Models ◽  
Time Data ◽  
Neural Network Models ◽  
Student Graduation ◽  
Artificial Neural ◽  
Artificial Neural Network Models

It is difficult to predict student graduation status in a college. Higher education needs to predict student behavior from active students so that it can be seen the failure factor of students who do not graduate on time. Data mining classification techniques used to predict students are using artificial neural networks. Artificial neural network is one method to predict student graduation. This researcher tries to apply artificial neural network methods using genetic algorithms to predict student graduation. In this study using the learning rate parameter 0.1 with optimization using genetic algorithms then evaluating to get accuracy. The results of this study get an accuracy value for artificial neural network models of 71.48% and accuracy for artificial neural network models based on genetic algorithms by 99.33% with an accuracy difference of 27.85%.

scholarly journals Application of Artificial Neural Networks in Modeling Direction Wheelchairs Using Neurosky Mindset Mobile (EEG) Device

2017 ◽  
Vol 5 (1) ◽  
pp. 170-191 ◽  
Author(s):  
Agus Siswoyo ◽  
Zainal Arief ◽  
Indra Adji Sulistijono
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Social Life ◽  
Time Signal ◽  
Mobility Impairment ◽  
Time Data ◽  
Learning Program ◽  
Keywords Navigation ◽  
Signal Input ◽  
Artificial Neural

The implementation of Artificial Neural Network in prediction the direction of electric wheelchair from brain signal input for physical mobility impairment.. The control of the wheelchair as an effort in improving disabled person life quality. The interaction from disabled person is helping in relation to social life with others. Because of the mobility impairment, the wheelchair with brain signal input is made. This wheel chair is purposed to help the disabled person and elderly for their daily activity. ANN helps to develop the mapping from input to target. ANN is developed in 3 level: input level, one hidden level, and output level (6-2-1). There are 6 signal from Neurosky Mindset sensor output, Alpha1, Alpha2, Raw signal, Total time signal, Attention Signal, and Meditation signal. The purpose of this research is to find out the output value from ANN: value in turning right, turning left, and forward. From those outputs, we can prove the relevance to the target. One of the main problem that interfering with success is the problem of proper neural network training. Arduino uno is chosen to implement the learning program algorithm because it is a popular microcontroller that is economic and efficient. The training of artificial neural network in this research uses 21 data package from raw data, Alpha1, Aplha2, Meditation data, Attention data, total time data. At the time of the test there is a value of Mean square Error(MSE) at the end of training amounted to 0.92495 at epoch 9958, value a correlation coefficient of 0.92804 shows that accuracy the results of the training process good.  Keywords: Navigation, Neural network, Real-time training, Arduino 

scholarly journals Pengenalan Spesies Gulma Berdasarkan Bentuk dan Tekstur Daun Menggunakan Jaringan Syaraf Tiruan

2015 ◽  
Vol 9 (2) ◽  
pp. 207
Author(s):  
Herman Herman ◽  
Agus Harjoko
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Learning Algorithm ◽  
Species Recognition ◽  
Cultivated Plants ◽  
Time Data ◽  
Moment Invariant ◽  
Invariant Moment ◽  
Artificial Neural ◽  
Hidden Layer

AbstrakGulma merupakan tanaman pengganggu yang merugikan tanaman budidaya dengan menghambat pertumbuhan tanaman budidaya. Langkah awal dalam melakukan pengendalian gulma adalah mengenali spesies gulma pada lahan tanaman budidaya. Cara tercepat dan termudah untuk mengenali tanaman, termasuk gulma adalah melalui daunnya. Dalam penelitian ini, diusulkan pengenalan spesies gulma berdasarkan citra daunnya dengan cara mengekstrak ciri bentuk dan ciri tekstur dari citra daun gulma tersebut. Untuk mendapatkan ciri bentuk, digunakan metode moment invariant, sedangkan untuk ciri tekstur digunakan metode lacunarity yang merupakan bagian dari fraktal. Untuk proses pengenalan berdasarkan ciri-ciri yang telah diekstrak, digunakan metode Jaringan Syaraf Tiruan dengan algoritma pembelajaran Backpropagation. Dari  hasil pengujian pada penelitian ini, didapatkan tingkat akurasi pengenalan tertinggi sebesar 97.22% sebelum noise dihilangkan pada citra hasil deteksi tepi Canny. Tingkat akurasi tertinggi didapatkan menggunakan 2 ciri moment invariant (moment  dan ) dan 1 ciri lacunarity (ukuran box 4 x 4 atau 16 x 16). Penggunaan 3 neuron hidden layer pada Jaringan Syaraf Tiruan (JST) memberikan waktu pelatihan data yang lebih cepat dibandingkan dengan menggunakan 1 atau 2 neuron hidden layer. Kata kunci—3-5 gulma, daun ,moment invariant, lacunarity, jaringan syaraf tiruan AbstractWeeds are plants that harm crops by inhibiting the growth of cultivated plants. The first step to take control of weeds is by identifying weed among the cultivating plant. The fastest and easiest way to identify plants, including weeds is by its leaves. This research proposing weed species recognition based on weeds leaf images by extracting its shape and texture features. Moment invariant method is used to get the shape and Lacunarity method for the texturel.  Neural Network with backpropagation learning algorithm are implements for the extracted features recognition proses. The result of this research achievement shows the highest level of recognition accuracy of 97.22% before the noise is eliminated in the image of the Canny edge detection. Highest level of accuracy is obtained using two features from moment invariant (moment  and  ) and 1 lacunarity’s feature (size box 4 x 4 or 16 x 16). The use of 3 neurons in the hidden layer of Artificial Neural Network (ANN) provide training time data more quickly than by using 1 or 2 hidden layer neurons. Keywords— weed, leaf, moment invariant, lacunarity, artificial neural network 

Autofluorescence spectroscopy of oral leukoplakia using an artificial neural network

2000 ◽  
Vol 25 (4) ◽  
pp. 325-325
Author(s):  
J.L.N. Roodenburg ◽  
H.J. Van Staveren ◽  
N.L.P. Van Veen ◽  
O.C. Speelman ◽  
J.M. Nauta ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Oral Leukoplakia ◽  
Autofluorescence Spectroscopy ◽  
Artificial Neural

1902: Is Steinstrasse After ESWL of Renal Stones Predictable? Artificial Neural Network Analysis

2004 ◽  
Vol 171 (4S) ◽  
pp. 502-503
Author(s):  
Mohamed A. Gomha ◽  
Khaled Z. Sheir ◽  
Saeed Showky ◽  
Khaled Madbouly ◽  
Emad Elsobky ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Network Analysis ◽  
Renal Stones ◽  
Neural Network Analysis ◽  
Artificial Neural Network Analysis ◽  
Artificial Neural
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