scholarly journals Emergent Bioanalogous Properties of Blockchain-based Distributed Systems

2021 ◽  
Author(s):  
Oleg Abramov ◽  
Kirstin L. Bebell ◽  
Stephen J. Mojzsis
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Artificial Neural Network ◽  
General Intelligence ◽  
Definitions Of Life ◽  
Growth And Change ◽  
Artificial Neural Network Ann ◽  
Definition Of ◽  
Speed Accuracy ◽  
Near Future

AbstractWe apply a novel definition of biological systems to a series of reproducible observations on a blockchain-based distributed virtual machine (dVM). We find that such blockchain-based systems display a number of bioanalogous properties, such as response to the environment, growth and change, replication, and homeostasis, that fit some definitions of life. We further present a conceptual model for a simple self-sustaining, self-organizing, self-regulating distributed ‘organism’ as an operationally closed system that would fulfill all basic definitions and criteria for life, and describe developing technologies, particularly artificial neural network (ANN) based artificial intelligence (AI), that would enable it in the near future. Notably, such systems would have a number of specific advantages over biological life, such as the ability to pass acquired traits to offspring, significantly improved speed, accuracy, and redundancy of their genetic carrier, and potentially unlimited lifespans. Public blockchain-based dVMs provide an uncontained environment for the development of artificial general intelligence (AGI) with the capability to evolve by self-direction.

scholarly journals Applications of Artificial Intelligence Techniques to Enhance Sustainability of Industry 4.0: Design of an Artificial Neural Network Model as Dynamic Behavior Optimizer of Robotic Arms

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-10 ◽  
Author(s):  
Aydin Azizi
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Artificial Neural Network ◽  
Industrial Robots ◽  
Optimal Parameters ◽  
Ann Model ◽  
Robotic Arms ◽  
Revolute Joints ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Industrial robots have a great impact on increasing the productivity and reducing the time of the manufacturing process. To serve this purpose, in the past decade, many researchers have concentrated to optimize robotic models utilizing artificial intelligence (AI) techniques. Gimbal joints because of their adjustable mechanical advantages have been investigated as a replacement for traditional revolute joints, especially when they are supposed to have tiny motions. In this research, the genetic algorithm (GA), a well-known evolutionary technique, has been adopted to find optimal parameters of the gimbal joints. Since adopting the GA is a time-consuming process, an artificial neural network (ANN) architecture has been proposed to model the behavior of the GA. The result shows that the proposed ANN model can be used instead of the complex and time-consuming GA in the process of finding the optimal parameters of the gimbal joint.

ML-PASD

2021 ◽  
pp. 82-93
Author(s):  
Vishal Jagota ◽  
Vinay Bhatia ◽  
Luis Vives ◽  
Arun B. Prasad
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Machine Learning ◽  
Autism Spectrum Disorder ◽  
Artificial Neural Network ◽  
Early Stage ◽  
Age Groups ◽  
Autism Spectrum ◽  
Screening Procedures ◽  
Artificial Neural Network Ann

Autism spectrum disorder (ASD) is growing faster than ever before. Autism detection is costly and time intensive with screening procedures. Autism can be detected at an early stage by the development of artificial intelligence and machine learning (ML). While a number of experiments using many approaches were conducted, these studies provided no conclusion as to the prediction of autism characteristics in various age groups. This chapter is therefore intended to suggest an accurate MLASD predictive model based on the ML methodology to prevent ASD for people of all ages. It is a method for prediction. This survey was conducted to develop and assess ASD prediction in an artificial neural network (ANN). AQ-10 data collection was used to test the proposed pattern. The findings of the evaluation reveal that the proposed prediction model has improved results in terms of consistency, specificity, sensitivity, and dataset accuracy.

scholarly journals Neural network for crop rotation and soil analysis in a Greenhouse

2021 ◽  
pp. 29-34
Author(s):  
Eva Rafael-Pérez ◽  
Yeimi Yanet Montero-Cortés ◽  
Alan Eduardo Ruiz-Ramírez ◽  
Maricela Morales-Hernández
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Artificial Neural Network ◽  
Crop Rotation ◽  
Soil Analysis ◽  
Prototype Model ◽  
Is Implementation ◽  
Data Collection Process ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Currently, Artificial intelligence (AI) is a very important area, the way in which it has revolutionized has allowed it to be an essential part of technological evolution in different sectors of society such as agriculture, it is a fundamental activity in the development of our country, and one of the developing areas is implementation of greenhouse crop. This article describes the use of artificial intelligence for a greenhouse through an Artificial Neural Network (ANN) of the multilayer perceptron type using the BackPropagation algorithm. The main aim is obtain the most optimal type of crop to be sown by means of the crop rotation, which, supported by a data acquisition device through sensors, obtains the values of temperature and humidity of the environment and soil pH, with those data the ANN makes the soil analysis. Through the interfaces of the data analysis module and the measurement module, the data collection process, the calculation and the results produced by the artificial neural network are shown. For this project, the Prototype model was used using the Java programming language.

Definition of Artificial Neural Network

2018 ◽  
pp. 32-46
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Artificial Neural Network ◽  
Learning Tools ◽  
Activation Functions ◽  
Mathematical Definition ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Definition Of

This chapter is an explanation of artificial neural network (ANN), which is one of the machine learning tools applied for medical purposes. The biological and mathematical definition of neural network is provided and the activation functions effective for processing are listed. Some figures are collected for better understanding.

Artificial Intelligence Applications on Classification of Heart Sounds

2018 ◽  
pp. 146-183 ◽  
Author(s):  
Huseyin Coskun ◽  
Tuncay Yigit
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Artificial Neural Network ◽  
Support Vector Machine ◽  
Heart Sounds ◽  
Support Vector ◽  
Mel Frequency Cepstral Coefficients ◽  
Specific Frequency ◽  
Artificial Neural Network Ann

The aim of this chapter is to classify normal and extra systole heart sounds using artificial intelligence methods. Initially, both heart sounds have been passed from Butterworth, Chebyshev, Elliptic digital filter in specific frequency values to remove noise. Afterwards, features of heart sounds have been obtained for classification. For this process, wavelet transform and Mel-frequency cepstral coefficients (MFCC) methods have been applied. Training and test data have been created for classifier by taking means and standard deviation of gained feature. Support vector machine (SVM) and artificial neural network (ANN) methods have been used for classification of these heart sounds. Using wavelet and MFCC features, classification success of SVM has been obtained as 93.33% and 100%, respectively. Using wavelet and MFCC features, classification success of ANN has been obtained as 83.33% and 90%, respectively.

Definition of Artificial Neural Network

2014 ◽  
pp. 44-57
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Control Unit ◽  
Processing Elements ◽  
Processing Information ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Definition Of ◽  
External Stimuli ◽  
The Brain

In living creatures, the brain is the control unit and it can be divided in different anatomic and functional sub-units. An artificial neural network is a computational system for processing information as a response to external stimuli, which consists of a set of highly interconnected processing elements called neurons. It is very useful to have some knowledge of the way the biological nervous system is organized, since the artificial neural network is an inspiration of the biological neural networks. This chapter is an explanation of the Artificial Neural Network (ANN). The biological and mathematical definition of a neural network is provided and the activation functions effective for processing are listed. Some figures are collected for better understanding.

Optimization of Micro Compressor Blades Using CFD Analysis and Artificial Intelligence

2003 ◽  
Author(s):  
Akay A. Islek
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Genetic Algorithm ◽  
Artificial Neural Network ◽  
Design Method ◽  
Navier Stokes ◽  
Cfd Analysis ◽  
Compressor Blades ◽  
Artificial Neural Network Ann ◽  
Robust Design Method

This paper describes a robust design method for turbo machinery components. The method which uses a Navier Stokes (NS) solver, an Artificial Neural Network (ANN) and a Genetic Algorithm (GA) is applied to optimize radial micro compressor blades. Higher efficiency, less divergence from the design mass flow and smoother Mach number distributions are considered as objectives of the optimization.

scholarly journals DSSCC: Decision Support System for Ceramic Capacitor using Fuzzy Logic

2019 ◽  
Vol 8 (9) ◽  
pp. 3281-3286
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Artificial Neural Network ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Electrolytic Capacitor ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Ceramic Capacitor ◽  
Artificial Neural Network Ann

As the technology advances, the reliability becomes the main constraint for the successful operation of the electronic product. To fully automate the system, the electronic devices become more and more complex. Reliability becomes a challenge with the regular demand of low cost and high-speed devices. Residual life estimation of passive devices such as resistor, capacitor etc. is of a great concern. Failure of one small component can lead to fully damage of whole system. In this paper, a practical approach i.e. accelerated life testing is deployed to calculate remaining useful life of the ceramic capacitor. An intelligent model is formulated using various artificial intelligence techniques. Artificial Neural Network (ANN), Fuzzy Inference System (FIS) as well as Adaptive Neuro Fuzzy Inference System (ANFIS) are deployed to predict the remaining useful lifetime of an electrolytic capacitor. An error analysis is conducted to estimate the most accurate intelligent technique. A fuzzy based decision support system is modelled, which provides an interactive GUI to users. The user can access the live health status of electrolytic capacitor at various input parameters. It will warn the user to replace or repair the upcoming fault in the component or device, before it actually degrades or shut downs the complete system. The comparative analysis of all the artificial intelligence techniques shows that Adaptive Neuro Fuzzy Inference System (ANFIS) has the highest accuracy i.e. 99.5%, as compare to Artificial Neural Network (ANN) and Fuzzy Inference System (FIS), where accuracy rate is 98.06% and 97.84% respectively. This prediction system is helpful to reduce the problem of electronic e-waste by enabling the user to reuse the component

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
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