scholarly journals Air pollution as a result of the development of motorization

2020 ◽  
Keyword(s):  
Neural Networks ◽  
Air Pollution ◽  
Artificial Neural Networks ◽  
Natural Environment ◽  
Internal Combustion Engines ◽  
Negative Impact ◽  
Negative Effects ◽  
The World ◽  
Significant Negative Impact ◽  
Artificial Neural

<p>The problem of air pollution is one of the most important problems faced by the world in the context of large urban agglomerations. Numerous activities are being undertaken all over the world to both counteract existing pollution and prevent the emergence of another one. Manufacturing is one of the activities that have a significant negative impact on the natural environment. Starting from the production itself, through the life of vehicles, and ending with the problems of storage and recycling of used means of transport, at every stage there are negative effects on the environment. There is no doubt that in all types of pro-ecological activities, it may be extremely important to predict the future state based on various determinants occurring in the analysed or preceding period of time. To this end, various types of mathematical and statistical tools are being used. Recently, you can also notice interest in all types of methods aided by artificial intelligence. Such methods include artificial neural networks, fuzzy logic, or evolutionary algorithms. The article presents the problems of environmental pollution as a result of the development of motorization. The main components of exhaust gases from car engines are discussed, which are particularly harmful to the natural environment. Methods for reducing the emission of pollutants emitted from internal combustion engines are described, and the monitoring of hazards related to air pollution is discussed. The possibilities of using artificial neural networks as a tool for modelling the state of air pollution have also been discussed.</p>

scholarly journals Using Artificial Neural Networks and SPI Measure Techniques to Forecast the Risk of Drought in Iraq and Its Impact on Environment

2018 ◽  
Vol 4 (2) ◽  
pp. 69
Author(s):  
Renas A.A. Nader ◽  
Aras J.M. Karim ◽  
Mohammad M.F. Hussien
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Agricultural Production ◽  
Water Retention ◽  
Negative Impact ◽  
The Neural Network ◽  
Normal Period ◽  
The World ◽  
Artificial Neural ◽  
Severe Shortage

The world suffers from drought, which has a negative impact on human, economic, social, cultural and tourism fields. As science progressed and developed, several ways of reducing drought were found. This phenomenon is also called (aridity and infertility, and water retention), it means a severe shortage of water resources due to low precipitation and low rainfall over a specific normal period time, which are causing heavy losses in agricultural production, and the occurrence of disasters and human calamities such as starvation, and it is forcing some population to emigrate collectively. The artificial neural networks (ANN) and the Standard Rain Index (SPI) were used in the analysis of the rainfall for all Iraqi governorates for the period 1991-2016 monthly. This study shows that the best model of the neural network is [19-3-1] according to AIC to forecast the amount of rainfall, and that the Iraqi provinces over next 10 years are exposed to a different behavior of climate between moderate dry and average humidity, and increase the area of ​​desertification.

Knowledge Discovery with Artificial Neural Networks

2011 ◽  
pp. 669-673
Author(s):  
Juan R. Rabuñal Dopico ◽  
Daniel Rivero Cebrian ◽  
Julián Dorado de la Calle ◽  
Nieves Pedreira Souto
Keyword(s):  
Data Mining ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Knowledge Discovery ◽  
Powerful Technique ◽  
Complex Problems ◽  
New Information ◽  
Wide Range ◽  
The World ◽  
Artificial Neural

The world of Data Mining (Cios, Pedrycz & Swiniarrski, 1998) is in constant expansion. New information is obtained from databases thanks to a wide range of techniques, which are all applicable to a determined set of domains and count with a series of advantages and inconveniences. The Artificial Neural Networks (ANNs) technique (Haykin, 1999; McCulloch & Pitts, 1943; Orchad, 1993) allows us to resolve complex problems in many disciplines (classification, clustering, regression, etc.), and presents a series of advantages that convert it into a very powerful technique that is easily adapted to any environment. The main inconvenience of ANNs, however, is that they can not explain what they learn and what reasoning was followed to obtain the outputs. This implies that they can not be used in many environments in which this reasoning is essential.

scholarly journals Use of neural networks for forecasting of the exposure of social network users to destructive impacts

2020 ◽  
pp. 24-33
Author(s):  
Elena Doynikova ◽  
Aleksandr Branitskiy ◽  
Igor Kotenko
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Social Networks ◽  
Artificial Neural Networks ◽  
Social Network ◽  
Negative Impact ◽  
Young Generation ◽  
The Neural Network ◽  
Internet Environment ◽  
Artificial Neural

Introduction: In social networks, the users can remotely communicate, express themselves, and search for people with similarinterests. At the same time, social networks as a source of information can have a negative impact on the behavior and thinking oftheir users. Purpose: Developing a technique of forecasting the exposure of social network users to destructive influences, based onthe use of artificial neural networks. Results: A technique has been developed and experimentally evaluated for forecasting Ammon’stest results by a social network user’s profile using artificial neural networks. The technique is based on the results of Ammon’s testfor medical students. For training the neural network, a set of features was generated based on the information provided by socialnetwork users. The results of the experiments have confirmed the dependence between the data provided by social network users andtheir psychological characteristics. A mechanism has been developed aimed at prompt detection of destructive impacts or social networkusers’ profiles indicating the susceptibility to such impacts, in order to facilitate the work of psychologists. The experiments haveshown that out of the four investigated types of neural networks, the highest accuracy is provided by a multilayer neural network. Inthe future, it is planned to expand the set of features in order to achieve a better accuracy. Practical relevance: The obtained results canbe used to develop systems for monitoring the Internet environment, detecting the impacts potentially dangerous for mental health ofthe young generation and the nation as a whole.

scholarly journals A study and identification of COVID-19 viruses using N-grams with Naïve Bayes, K-Nearest Neighbors, Artificial Neural Networks, Decision tree and Support Vector Machine

2020 ◽  
Author(s):  
Mohamed El Boujnouni
Keyword(s):  
Neural Networks ◽  
Support Vector Machine ◽  
Artificial Neural Networks ◽  
Decision Tree ◽  
Naive Bayes ◽  
Nearest Neighbors ◽  
Support Vector ◽  
K Nearest Neighbors ◽  
The World ◽  
Artificial Neural

Abstract Coronavirus disease 2019 or COVID-19 is a global health crisis caused by a virus officially named as severe acute respiratory syndrome coronavirus 2 and well known with the acronym (SARS-CoV-2). This very contagious illness has severely impacted people and business all over the world and scientists are trying so far to discover all useful information about it, including its potential origin(s) and inter-host(s). This study is a part of this scientific inquiry and it aims to identify precisely the origin(s) of a large set of genomes of SARS-COV-2 collected from different geographic locations in all over the world. This research is performed through the combination of five powerful techniques of machine learning (Naïve Bayes, K-Nearest Neighbors, Artificial Neural Networks, Decision tree and Support Vector Machine) and a widely known tool of language modeling (N-grams). The experimental results have shown that the majority of techniques gave the same global results concerning the origin(s) and inter-host(s) of SARS-COV-2. These results demonstrated that this virus has one zoonotic source which is Pangolin.

scholarly journals A Developmental Model for the Evolution of Artificial Neural Networks

2000 ◽  
Vol 6 (3) ◽  
pp. 189-218 ◽  
Author(s):  
J. C. Astor ◽  
C. Adami
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Genetic Information ◽  
Developmental Model ◽  
Complex Structures ◽  
Artificial Neuron ◽  
Artificial Chemistry ◽  
The World ◽  
Artificial Neural ◽  
Distributed Genetic Algorithm

We present a model of decentralized growth and development for artificial neural networks (ANNs), inspired by developmental biology and the physiology of nervous systems. In this model, each individual artificial neuron is an autonomous unit whose behavior is determined only by the genetic information it harbors and local concentrations of substrates. The chemicals and substrates, in turn, are modeled by a simple artificial chemistry. While the system is designed to allow for the evolution of complex networks, we demonstrate the power of the artificial chemistry by analyzing engineered (handwritten) genomes that lead to the growth of simple networks with behaviors known from physiology. To evolve more complex structures, a Java-based, platform-independent, asynchronous, distributed genetic algorithm (GA) has been implemented that allows users to participate in evolutionary experiments via the World Wide Web.

scholarly journals Optimization and Modelling of the Wear in Piston Ring and Cylinder Liner Using Taguchi Method and Artificial Neural Networks

2021 ◽  
Author(s):  
Idris cesur ◽  
Aslan Çoban ◽  
Beytullah Eren
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Diesel Fuel ◽  
Alternative Energy ◽  
Internal Combustion Engines ◽  
Friction And Wear ◽  
Piston Ring ◽  
Cylinder Liner ◽  
Biodiesel Fuel ◽  
Artificial Neural

Abstract Alternative energy sources are needed to meet the energy needs of the rapidly increasing population and developing industry and to increase the efficiency of the systems. In internal combustion engines, biodiesel is used as an alternative fuel for both being an alternative energy source and having a better efficiency compared to diesel fuel. Efficiency loss in the engines is largely due to friction and wear between the piston ring (PR) and the cylinder liner (CL). Different lubrication regimes in engines have substantial effects on wear and friction. In the present study, the effects of diesel and biodiesel (chicken oil methyl ester, COME) fuels on friction and wear in different loads (40-60-80-100 N) and speeds (60-90-120-150 rpm) were examined using the Taguchi experimental design method. In addition, an artificial neural networks (ANNs) model is utilized for modeling the wear at the cylinder liner (CL) and the piston rings (PR) using different fuel types, speeds and loads. As a result of the study, biodiesel fuel has a lower friction coefficient and abrasion in all load and cycle intervals due to its high viscosity and lubrication properties compared to diesel fuel. Besides, the developed ANN model has good predictive capability for the wear at the CL and the PR according to perfect match between experimental and modeling results.

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