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The artificial neural network (ANN) is a computational method based on human brain function and is efficient in recognizing previously trained patterns.
Open accessJournal ArticleDOI
29 Jun 2019
26 Citations
The artificial neural network learns by updating the network architecture and connection weights so that the network can efficiently perform a task.
The incorporation of prior knowledge into neural networks can improve neural network learning in several respects, for example, a faster learning speed and better generalization ability.
Artificial Neural Network has the ability to learn from previous data.
Artificial neural network is a versatile modelling tool capable of making predictions of values that are difficult to obtain in numerical analysis.
Artificial neural networks thrive in solving the classification problem for a particular rigid task, where the network resembles a static entity of knowledge, acquired through generalized learning behaviour from a distinct training phase.
Proceedings ArticleDOI
T. Baker, Dan Hammerstrom 
08 May 1989
26 Citations
One popular artificial neural network model, the back-propagation algorithm, promises to be a powerful and flexible learning model.
This training set can also guarantee effective learning andcomprehension of Artificial Neural Networks (ANN).
By using these tools, users can also understand and learn working mechanism of a typical artificial neural network, using features of different models and related learning algorithms.
Open accessPosted Content
09 Feb 2020
8 Citations
Thus, it lends itself for example to local learning in artificial neural networks.

Related Questions

What is learning?5 answersLearning is a process of acquiring knowledge, improving skills, developing attitudes, and producing personality through interaction with the environment and learning resources. It involves a relatively permanent change in behavior as a result of practice or experience. Learning can occur through rote memorization or meaningful understanding. It allows individuals to adapt to their environments within a lifetime and plays a crucial role in individual adaptation. Animals learn to filter out irrelevant stimuli, enhance responses to relevant stimuli, and associate behaviors with consequences. Learning can support practical activities such as finding food, migrating, and recognizing mates. Observational learning in social settings can lead to behavioral change within a group. Learning can be facilitated through interactive and enjoyable methods, such as using games and technology. Overall, learning is a dynamic process that involves acquiring knowledge, developing skills, and shaping behavior through various interactions and experiences.
What is the definition of learning?5 answersLearning can be defined as the process of acquiring knowledge, skills, and behaviors through study, experience, or teaching. It involves a relatively permanent change in behavior based on an individual's interactional experience with their environment. Learning is an active and participative process that requires individuals to engage positively with their own learning through study. It is a form of personal adaptation that occurs within the lifespan of an individual to enhance their survival and well-being. Learning is influenced by the individual's prior experiences, interactions with more experienced individuals, and the environment. It is not limited to cognitive aspects but also includes affective and psychomotor aspects. Learning is a continuous process that enables individuals to achieve independence and adapt to various environments. Utilizing technology, such as games and interactive media, can make learning more enjoyable and effective.
What is neural networks ?3 answersNeural networks are a class of artificial intelligence/machine learning tools that are inspired by the structure and functioning of the human brain. They consist of interconnected processing elements that can perform complex data processing tasks. Neural networks have gained significant attention and have been successfully applied in various fields such as quantum chemistry, handwriting recognition, robotics, and data mining. They are capable of pattern recognition and can learn from examples to perform classification and discover new trends in data. Different types of neural networks, including multi-layer perceptron (MLP), convolutional neural networks (CNN), and graph neural networks, have been developed with their own advantages and disadvantages. Neural networks have revolutionized machine learning and have achieved remarkable success in speech recognition, computer vision, industrial inspection, and national defense.
What is learning in psychology?3 answersLearning in psychology refers to the process by which behavior is modified through practice and training. It is influenced by various factors such as environment, motivation, health, emotions, maturation, and aging. Learning involves obtaining, presenting, and transforming information into knowledge, which then guides human attitudes and behavior. It is a process of active interaction with the environment that leads to relatively permanent changes in knowledge, understanding, skills, and attitudes. Learning is a cognitive-psychological process that occurs through the interaction of an animal with its environment, resulting in a modification of subsequent behavior. The ability to learn is considered one of the most important features of intelligence, and it is a process through which knowledge and abilities are acquired through experience. Learning is not limited to academic subjects but also includes skills, interests, perceptions, and habits, which are influenced by internal and external factors.
What is artificial neural networks?5 answersArtificial neural networks (ANNs) are computational methods that aim to mimic the behavior of the human brain. ANNs consist of interconnected neurons that communicate with each other through synapses. The power of ANNs lies in their ability to learn tasks by approximating any computable function. They have been successfully applied in various fields such as computer vision, robotics, speech recognition, and natural language processing. ANNs have shown excellent aptitude in learning the relationships between input and output data without prior assumptions about the statistical distribution. However, ANNs have limitations in terms of explainability and global approximation functions. Therefore, there is a need for research in novel approaches for local approximators that can outperform ANNs in terms of accuracy, computational expense, and explainability.
What is artificial neural network ?4 answersAn artificial neural network (ANN) is an information processing system that mimics the working principles of the human brain. It is composed of interconnected processing elements called neurons, which work together to solve specific problems. ANNs learn from examples through a learning process, where the synaptic connections between neurons are changed. This allows the network to adapt and improve its performance. ANNs have been successfully used in various fields such as pattern recognition, data classification, computer vision, robotics, speech recognition, and natural language processing. They can approximate any computable function and are trained using labeled training data and algorithms like back-propagation of errors. ANNs can solve prediction and classification problems and adjust the connection weights between neurons to achieve accurate results.

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Why machine learning in bioinformatics?
5 answers
Machine learning is used in bioinformatics to analyze and understand biological data, make predictions, and solve various bioinformatics problems. It offers processing capabilities with intelligent information for managing real-life biological data. Bioinformatics datasets present unique challenges that require specialized algorithmic approaches, and machine learning algorithms, such as Artificial Neural Networks (ANNs), have been developed to address these challenges. Machine learning approaches, including supervised, unsupervised, and reinforcement learning, play a crucial role in analyzing biological data and solving bioinformatics problems. By leveraging machine learning and bioinformatics, researchers can decipher the complexity of cancer biology, understand cancer heterogeneity, and develop methods for cancer prognosis, prediction, and treatment. Overall, machine learning in bioinformatics enables the systematic analysis of biological data, reduces experimental cost and time, and contributes to advancements in cancer research and drug discovery.
What is the knowledge gap in Machine Learning in Climate Pattern Detection?
5 answers
Machine learning methods are increasingly being used in climate science for various applications, including climate pattern detection. However, there are still knowledge gaps in this area. One of the challenges is separating climate patterns from noise in the vast amount of available data. Another gap is the testing and validation of machine learning models for weather prediction, as their uncertain outcomes pose a critical problem. Additionally, there is a need for data-driven climate modeling to accelerate climate change projections and uncover early indicators of long-term climate responses. Furthermore, while machine learning algorithms have been successfully applied to isolated Earth System features, their generic application to understand the full climate system is lacking. Finally, accurate categorization of extreme weather events in climate simulations and observational data archives remains a challenge, and deep learning techniques are being explored as an alternative tool for detection.
What is relationship between heat loss and efficiency with heat load for district heating network?
3 answers
Heat losses in district heating networks have a direct impact on their efficiency. Reducing heat losses is crucial for ensuring economic and sustainable operation of the overall heating system. Various methods can be employed to reduce heat losses, such as increasing linear heat density, improving pipe insulation, and reducing pipe diameters. Inaccurate prediction of heat loads in district heating systems can hinder energy efficiency. While simple methods exist for heat load forecasting, they often prove to be ineffective. Artificial neural networks (NN) have shown promise in improving heat load forecasting in district heating systems. A hybrid model that includes different types of artificial NN, hyperparameter optimization, and deep NN has been developed and shown to provide accurate forecasts. The relationship between heat loss and efficiency with heat load in district heating networks is complex and can be influenced by factors such as distribution concepts, load densities, and network temperatures. Novel distribution concepts with lower network temperatures have been found to be more energy efficient and cost-effective. Additionally, a graph ambient intelligence (GAIN) method has been proposed for heat load prediction, which takes into account customer load profiles, associations, weather impact, and temporal inertia of consumption.
What are the advantages and disadvantages of using simulation models and machine learning for tea crop yield prediction?
5 answers
Simulation models and machine learning have both advantages and disadvantages for tea crop yield prediction. Simulation models, such as the AquaCrop model, can provide accurate predictions by calibrating weather, soil, crop, and agro-management data. However, they require extensive data and may not perform as well with limited data. On the other hand, machine learning techniques, such as support-vector-machine (SVM) models and random forest algorithms, can achieve high accuracy with fewer data. They can also incorporate various environmental variables, such as rainfall, temperature, humidity, and soil type, to enhance prediction performance. However, machine learning models may lack robustness and generalization capabilities across different time periods and geographical locations. Additionally, errors in seasonal weather forecasting can affect the performance of machine learning models. Overall, simulation models offer accuracy but require extensive data, while machine learning models can achieve high accuracy with fewer data but may lack robustness and generalization capabilities.
Why can deep learning and machine learning in bioinformatics?
5 answers
Deep learning and machine learning are used in bioinformatics because they allow for the learning of complex interactions between functionalities and the utilization of large unlabeled data that is unrelated to the problem under investigation. In bioinformatics, deep learning techniques are particularly suited to handling big-size data and have been successful in knowledge discovery. They have been applied in various areas such as computer vision, big data analytics, bio-image informatics, and natural language processing. Deep learning architectures, including shallow NNs, RNNs, GNNs, and CNNs, provide sophisticated ways of handling large complex datasets generated from the medical field and solving problems of pattern recognition and accurate diagnosis. These architectures have been extensively used in bioinformatics to provide solutions to complex problems, such as identifying enzymes, gene expression regression, and predicting protein-protein interactions.
What are Graph convolution networks?
5 answers
Graph convolution networks (GCNs) are deep learning methods used for analyzing non-Euclidean structured data, such as graphs. They have been successful in various fields, including traffic flow forecasting, trajectory prediction, point cloud classification, and node classification. GCNs utilize graph structures to enable end-to-end learning, allowing them to capture the relationships between nodes in the graph. However, there are limitations in existing GCN models, such as over-smoothing and the inability to extract high-level features. To address these issues, researchers have proposed novel approaches like graph transformer networks, non-local message passing, and deep graph convolutional neural networks (DGCNN). These advancements have shown improved performance in graph classification tasks and have the potential to enhance the capabilities of GCNs.
What are Graph convolutional networks?
5 answers
Graph convolutional networks (GCNs) are deep learning models that are designed to process and analyze graph-structured data. They enable end-to-end learning by leveraging the graph structure to extract features and make predictions. GCNs have been applied to various tasks such as link prediction, trajectory prediction, point cloud classification, and node classification. They have shown impressive performance in these fields by learning effective representations from graph data. GCNs can be used to predict the likelihood of new links forming in social networks, extract high-level features of nodes, and classify graphs. They have also been used in recommendation systems to extract features from graphs and make personalized recommendations. Overall, GCNs have proven to be powerful tools for analyzing and understanding complex graph structures.
What are some challenges with using AI in IOMT?
5 answers
The challenges with using AI in IOMT include difficulties in coping with the wide variety of protocols and software technologies used, as well as the heterogeneity of hardware resources consuming AI. The scattered IoT landscape accentuates limitations on interoperability, especially in the deployment of AI, affecting seamless AI life-cycle management. Another challenge is the security concerns, such as remote hijacking, impersonation, denial of service attacks, password guessing, and man-in-the-middle attacks, which can compromise critical data associated with IoT connectivity. Additionally, the visual similarity between normal and melanoma lesions poses a challenge for accurate segmentation using traditional, parametric, and deep learning-based methods. These challenges highlight the need for innovative deep learning techniques, such as the use of generative deep learning models and recurrent neural networks, to address the complexities and security issues in AI-enabled IOMT systems.
What are the best ways to learn and grow?
5 answers
The best ways to learn and grow include utilizing project-based learning closely linked to business challenges. This methodology, known as "Learning to grow," has been successfully applied in various countries and has shown effectiveness in developing growth capabilities for SMEs. Additionally, the implementation of this methodology has contributed to sustaining competitiveness and improving innovative capabilities in the textile sector. Another approach is task-incremental learning, which involves continually learning new tasks without forgetting past knowledge. The framework "Learn to Bind and Grow" offers a solution by learning a neural architecture for new tasks incrementally and expanding layers that are more likely to conflict between tasks. These approaches provide opportunities for measurable short-/mid-term results and enhance organizational capabilities to sustain growth.
Which traditional machine learning methods are most effective in predicting body fat levels?
3 answers
Traditional machine learning methods that have been found to be effective in predicting body fat levels include logistic regression, naive Bayes, Radial Basis Function (RBF), local k-nearest neighbors (k-NN), classification via regression (CVR), random subspace, decision table, multiobjective evolutionary fuzzy classifier, random tree, J48, and multilayer perceptron. Among these methods, the random subspace classifier algorithm achieved the highest overall accuracy and area under the receiver operating characteristic (ROC) curve (AUC). Logistic regression, on the other hand, showed middle-ranking performance in terms of overall accuracy, sensitivity, specificity, and AUC. Therefore, these traditional machine learning methods can be considered effective options for predicting body fat levels.
What are the advantages and disadvantages of using Convolutional Neural Networks for soil image classification?
5 answers
Convolutional Neural Networks (CNNs) offer several advantages for soil image classification. They are capable of learning soil features from a few original soil surface images, allowing for accurate estimation of soil properties such as water content and dry density. CNNs can also compensate for illumination variations, which is important for classifying hyperspectral images of soils obtained by scanning type hyperspectrometers. Additionally, CNNs can improve the overall classification accuracy of soil aggregate sizes, even at lower resolutions and greater imaging heights, by combining attention modules and hypercolumns to extract more discriminative features. However, there are some limitations to using CNNs for soil image classification. The effectiveness of CNNs may decrease with decreasing image resolution and increasing photography height. Furthermore, while CNNs can achieve high accuracy rates, they may require a large amount of data for training and validation.