scholarly journals Artificial Neural Networks in Agriculture

Agriculture ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 497
Author(s):  
Sebastian Kujawa ◽  
Gniewko Niedbała
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Transport Processes ◽  
High Quality Research ◽  
Wide Range ◽  
Brain Structure And Function ◽  
Artificial Neural ◽  
And Function

Artificial neural networks are one of the most important elements of machine learning and artificial intelligence. They are inspired by the human brain structure and function as if they are based on interconnected nodes in which simple processing operations take place. The spectrum of neural networks application is very wide, and it also includes agriculture. Artificial neural networks are increasingly used by food producers at every stage of agricultural production and in efficient farm management. Examples of their applications include: forecasting of production effects in agriculture on the basis of a wide range of independent variables, verification of diseases and pests, intelligent weed control, and classification of the quality of harvested crops. Artificial intelligence methods support decision-making systems in agriculture, help optimize storage and transport processes, and make it possible to predict the costs incurred depending on the chosen direction of management. The inclusion of machine learning methods in the “life cycle of a farm” requires handling large amounts of data collected during the entire growing season and having the appropriate software. Currently, the visible development of precision farming and digital agriculture is causing more and more farms to turn to tools based on artificial intelligence. The purpose of this Special Issue was to publish high-quality research and review papers that cover the application of various types of artificial neural networks in solving relevant tasks and problems of widely defined agriculture.

scholarly journals SIMULATION OF SPREADING FOREST FIRE UNDER NONSTATIONARITY AND UNCERTAINTY BY MEANS OF ARTIFICIAL INTELLIGENCE AND DEEP MACHINE LEARNING

2019 ◽  
pp. 97-107
Author(s):  
Tatiana Sergeevna Stankevich
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Forest Fire ◽  
Noise Removal ◽  
Fire Dynamics ◽  
Operational Forecast ◽  
Forecasting Models ◽  
Artificial Neural

The article describes the results of increasing the efficiency of operational forecast of the forest fire dynamics under nonstationarity and uncertainty through the fire dynamics modeling based on artificial intelligence and deep machine learning. To achieve the goal there were used following methods: system analysis method, theory of neural networks, deep machine learning method, method of operational forecasting of the forest fire dynamics, method of filtering images (modified median filter), MoSCoW method, and ER-method. In the course of study there have been developed forest fire forecasting models (models of treetop and ground fires) using artificial neural networks. The developed models solve the recognition and forecasting problems in order to determine the dynamics of forest fires in successive images and generating images with a forecast of fire spread. There has been given the general logical scheme of the proposed forest fire forecasting models involving five stages: stage 1 - data input; stage 2 - preprocessing of input data (format check; size check; noise removal); stage 3 - object recognition using Convolutional Neural Networks (recognition of fire data; recognition of data on environmental factors; recognition of data on the nature of forest plantations); stage 4 - development of forest fire forecasting; stage 5 - output of the generated image with the operational forecast. To build and train artificial neural networks, a visual forest fire dynamics database was proposed to use. The developed forest fire forecasting models are based on a tree of artificial neural networks in the form of an acyclic graph and identify dependencies between the dynamics of a forest fire and the characteristics of the external and internal environment.

scholarly journals Provision of Data to Use in Artificial Intelligence Algorithms for Single Room Heating

Electronics ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 523
Author(s):  
Kristin Majetta ◽  
Christoph Clauß ◽  
Christoph Nytsch-Geusen
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Learning Algorithms ◽  
Parameter Variation ◽  
Machine Learning Algorithms ◽  
Data Basis ◽  
Single Room ◽  
Artificial Neural

This paper describes a way to generate a great amount of data and to use it to find a relation between a room controller and a certain room. Therefore, simulation scenarios are defined and developed that contain different room, location, usage and controller models. With parameter variation and optimization of the corresponding controller parameters a data basis is created with about 5300 entries. On the basis of this data, machine learning algorithms like artificial neural networks can be used to investigate the relation between rooms and their best suited controllers.

scholarly journals Мethods of Machine Learning in Ophthalmology: Review

2020 ◽  
Vol 17 (1) ◽  
pp. 20-31
Author(s):  
D. D. Garri ◽  
S. V. Saakyan ◽  
I. P. Khoroshilova-Maslova ◽  
A. Yu. Tsygankov ◽  
O. I. Nikitin ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Laboratory Data ◽  
Age Related Macular Degeneration ◽  
Digital Data ◽  
Age Related ◽  
Artificial Neural ◽  
Sensitivity Specificity

Machine learning is applied in every field of human activity using digital data. In recent years, many papers have been published concerning artificial intelligence use in classification, regression and segmentation purposes in medicine and in ophthalmology, in particular. Artificial intelligence is a subsection of computer science and its principles, and concepts are often incomprehensible or used and interpreted by doctors incorrectly. Diagnostics of ophthalmology patients is associated with a significant amount of medical data that can be used for further software processing. By using of machine learning methods, it’s possible to find out, identify and count almost any pathological signs of diseases by analyzing medical images, clinical and laboratory data. Machine learning includes models and algorithms that mimic the architecture of biological neural networks. The greatest interest in the field is represented by artificial neural networks, in particular, networks based on deep learning due to the ability of the latter to work effectively with complex and multidimensional databases, coupled with the increasing availability of databases and performance of graphics processors. Artificial neural networks have the potential to be used in automated screening, determining the stage of diseases, predicting the therapeutic effect of treatment and the diseases outcome in the analysis of clinical data in patients with diabetic retinopathy, age-related macular degeneration, glaucoma, cataracts, ocular tumors and concomitant pathology. The main characteristics were the size of the training and validation datasets, accuracy, sensitivity, specificity, AUROC (Area Under Receiver Operating Characteristic Curve). A number of studies investigate the comparative characteristics of algorithms. Many of the articles presented in the review have shown the results in accuracy, sensitivity, specificity, AUROC, error values that exceed the corresponding indicators of an average ophthalmologist. Their introduction into routine clinical practice will increase the diagnostic, therapeutic and professional capabilities of a clinicians, which is especially important in the field of ophthalmic oncology, where there is a patient survival matter.

scholarly journals Exploiting Artificial Neural Networks Machine Learning Errors for Attacks on AI Systems

2021 ◽  
pp. 23-32
Author(s):  
Т. В. Гавриленко ◽  
А. В. Гавриленко
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Machine Learning ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Artificial Neural ◽  
Current Paradigm ◽  
Artificial Intelligence Systems

В статье приведен обзор различных методов атак и подходов к атакам на системы искусственного интеллекта, построенных на основе искусственных нейронных сетей. Показано, что начиная с 2015 года исследователи в различных странах активно развивают методы атак и подходы к атакам на искусственные нейронные сети, при этом разработанные методы и подходы могут иметь критические последствия при эксплуатации систем искусственного интеллекта. Делается вывод о необходимости развития методологической и теоретической базы искусственных нейронных сетей и невозможности создания доверительных систем искусственного интеллекта в текущей парадигме. The paper provides an overview of methods and approaches to attacks on neural network-based artificial intelligence systems. It is shown that since 2015, global researchers have been intensively developing methods and approaches for attacks on artificial neural networks, while the existing ones may have critical consequences for artificial intelligence systems operations. We come to the conclusion that theory and methodology for artificial neural networks is to be elaborated, since trusted artificial intelligence systems cannot be created in the framework of the current paradigm.

scholarly journals What is Interpretability?

2020 ◽  
Author(s):  
Adrian Erasmus ◽  
Tyler D. P. Brunet ◽  
Eyal Fisher
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Novel Theory ◽  
Scientific Phenomenon ◽  
Artificial Neural ◽  
Artificial Intelligence Systems

AbstractWe argue that artificial networks are explainable and offer a novel theory of interpretability. Two sets of conceptual questions are prominent in theoretical engagements with artificial neural networks, especially in the context of medical artificial intelligence: (1) Are networks explainable, and if so, what does it mean to explain the output of a network? And (2) what does it mean for a network to be interpretable? We argue that accounts of “explanation” tailored specifically to neural networks have ineffectively reinvented the wheel. In response to (1), we show how four familiar accounts of explanation apply to neural networks as they would to any scientific phenomenon. We diagnose the confusion about explaining neural networks within the machine learning literature as an equivocation on “explainability,” “understandability” and “interpretability.” To remedy this, we distinguish between these notions, and answer (2) by offering a theory and typology of interpretation in machine learning. Interpretation is something one does to an explanation with the aim of producing another, more understandable, explanation. As with explanation, there are various concepts and methods involved in interpretation: Total or Partial, Global or Local, and Approximative or Isomorphic. Our account of “interpretability” is consistent with uses in the machine learning literature, in keeping with the philosophy of explanation and understanding, and pays special attention to medical artificial intelligence systems.

scholarly journals Artificial Neural Networks in Medicine

2019 ◽  
Vol 12 (2) ◽  
pp. 117-129
Author(s):  
Zoltan Tamas Kocsis
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Neural Networks ◽  
Information Technology ◽  
Artificial Neural Networks ◽  
Face Recognition ◽  
Learning Processes ◽  
Complex Learning ◽  
Artificial Neural ◽  
The Relationship

In recent years, Information Technology has been developed in a way that applications based on Artificial Intelligence have emerged. This development has resulted in machines being able to perform increasingly complex learning processes. The use of Information Technology, including Artificial Intelligence is becoming more and more widespread in all fields of life. Some common examples are face recognition in smartphones, or the programming of washing machines. As you may think, Artificial Intelligence can also be used in medicine. In this study I am presenting the relationship between machine learning and neural networks and their possible use in medicine.

scholarly journals A Machine Learning Approach as a Surrogate for a Finite Element Analysis: Status of Research and Application to One Dimensional Systems

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1654
Author(s):  
Poojitha Vurtur Badarinath ◽  
Maria Chierichetti ◽  
Fatemeh Davoudi Kakhki
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Finite Element Analysis ◽  
Finite Element ◽  
Artificial Neural Networks ◽  
Stress Distribution ◽  
Maintenance Scheduling ◽  
Element Analysis ◽  
One Dimensional ◽  
Artificial Neural

Current maintenance intervals of mechanical systems are scheduled a priori based on the life of the system, resulting in expensive maintenance scheduling, and often undermining the safety of passengers. Going forward, the actual usage of a vehicle will be used to predict stresses in its structure, and therefore, to define a specific maintenance scheduling. Machine learning (ML) algorithms can be used to map a reduced set of data coming from real-time measurements of a structure into a detailed/high-fidelity finite element analysis (FEA) model of the same system. As a result, the FEA-based ML approach will directly estimate the stress distribution over the entire system during operations, thus improving the ability to define ad-hoc, safe, and efficient maintenance procedures. The paper initially presents a review of the current state-of-the-art of ML methods applied to finite elements. A surrogate finite element approach based on ML algorithms is also proposed to estimate the time-varying response of a one-dimensional beam. Several ML regression models, such as decision trees and artificial neural networks, have been developed, and their performance is compared for direct estimation of the stress distribution over a beam structure. The surrogate finite element models based on ML algorithms are able to estimate the response of the beam accurately, with artificial neural networks providing more accurate results.

scholarly journals Improved Effort and Cost Estimation Model Using Artificial Neural Networks and Taguchi Method with Different Activation Functions

Entropy ◽  
2021 ◽  
Vol 23 (7) ◽  
pp. 854
Author(s):  
Nevena Rankovic ◽  
Dragica Rankovic ◽  
Mirjana Ivanovic ◽  
Ljubomir Lazic
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Cost Estimation ◽  
Time Estimation ◽  
Effort Estimation ◽  
Activation Functions ◽  
Estimation Model ◽  
Wide Range ◽  
Software Product ◽  
Artificial Neural

Software estimation involves meeting a huge number of different requirements, such as resource allocation, cost estimation, effort estimation, time estimation, and the changing demands of software product customers. Numerous estimation models try to solve these problems. In our experiment, a clustering method of input values to mitigate the heterogeneous nature of selected projects was used. Additionally, homogeneity of the data was achieved with the fuzzification method, and we proposed two different activation functions inside a hidden layer, during the construction of artificial neural networks (ANNs). In this research, we present an experiment that uses two different architectures of ANNs, based on Taguchi’s orthogonal vector plans, to satisfy the set conditions, with additional methods and criteria for validation of the proposed model, in this approach. The aim of this paper is the comparative analysis of the obtained results of mean magnitude relative error (MMRE) values. At the same time, our goal is also to find a relatively simple architecture that minimizes the error value while covering a wide range of different software projects. For this purpose, six different datasets are divided into four chosen clusters. The obtained results show that the estimation of diverse projects by dividing them into clusters can contribute to an efficient, reliable, and accurate software product assessment. The contribution of this paper is in the discovered solution that enables the execution of a small number of iterations, which reduces the execution time and achieves the minimum error.

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