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Artificial neural network has its unique advantages in forecasting.
It helps to deal with the uncertainty issues and assists the Artificial Neural Network- (ANN-) based model to achieve better performance.
Findings: Artificial neural network has a significant role in medical area.
Journal ArticleDOI
S. P. Trasatti, F. Mazza 
20 Citations
Among these, the artificial neural network (NN) system appears to be a powerful tool to tackle situations in w...
An artificial neural network has three advantages: it is unsupervised, can tolerate missing data, and produces topographical maps.
Having a young but growing interest in the use of artificial neural networks, we hoped that the publication would be an excuse for us to learn about areas in neural network research that seemed
Proceedings ArticleDOI
Manish Mishra, Monika Srivastava 
01 Aug 2014
115 Citations
In this paper also show that “what are neural networks” and “Why they are so important in today's Artificial intelligence?” Because various advances have been made in developing intelligent system, some inspired by biological neural networks.
The artificial neural network techniques are rather easy to develop and to perform.
It is observed that the inclusion of some more biological phenomenon in an artificial neural network can make it more powerful.
In such event, artificial neural network (ANN) model can be a potential alternative to the conventional models.

Related Questions

What is Artificial Neural Networks?5 answersArtificial Neural Networks (ANNs) are computational methods that mimic the working principles of the human brain. ANNs consist of interconnected neurons that communicate with each other through synapses. The aim of ANNs is to learn tasks by imitating the behavior of the brain. They can be trained to approximate any computable function and have been used in various fields such as computer vision, robotics, speech recognition, and natural language processing. ANNs are statistical methods that can model complex relationships between input and output data without prior assumptions about the data distribution. They have been successfully applied in tasks requiring attention and can simultaneously process numerous variables. ANNs use learning algorithms, such as the backpropagation algorithm, to adjust the connection weights between neurons and solve prediction and classification problems.
The significance of the study about Artificial intelligence?5 answersArtificial intelligence (AI) has significant implications in various fields. It can help in managing organizational transformation and improving employee well-being by supervising processes and computing health statistics without human intervention. AI also plays a crucial role in decision-making and real-time tasks, where human involvement is limited. Additionally, AI has the potential to challenge the legal rules governing patents, as it can innovate without human intervention, raising questions about ownership and financial rights. Moreover, AI-based technology has proven valuable in combating the COVID-19 pandemic, aiding in outbreak identification, tracking, and forecasting, as well as diagnosing and treating infected individuals. The significance of studying AI lies in its ability to enhance efficiency, improve decision-making, and address complex challenges in various domains, including organizational management, legal frameworks, and healthcare systems.
Whats 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. They learn by receiving stimuli and emitting signals, or "firing". ANNs have evolved since their inception in 1986 and have become a powerful tool in various fields such as computer vision, robotics, speech recognition, and natural language processing. They can approximate any computable function and are trained using labeled training data through the backpropagation algorithm, which is a form of gradient descent. In the context of lung cancer research, ANNs are used as efficient models for nonlinear regression or classification, providing valuable support for diagnosis and treatment decisions. ANNs have also been successful in applications such as system identification and control, face recognition, and solving nonlinear phenomena.
What is the significance of the study about Artificial intelligence?5 answersThe significance of the study about Artificial Intelligence is multifaceted. Firstly, it aims to understand the work of the human brain and create machines with a certain level of human intelligence. Secondly, it provides an overview of the scope of artificial intelligence, including its background, drivers, technologies, and applications, which can be valuable for practitioners in the real world. Thirdly, it explores the impact of artificial intelligence on project management, highlighting the benefits of AI adoption while emphasizing the need for human involvement. Additionally, the study examines how artificial intelligence makes education more accessible and cost-effective, improving the quality of education offered in various universities and benefiting disabled individuals. Lastly, it assesses the impact of artificial intelligence on education, particularly in administration, instruction, and learning, showcasing how AI has been adopted and used in different forms to enhance teaching activities and personalize learning experiences.
What is AI neural network and what is it used for?1 answersAI neural network is a subset of artificial intelligence that involves the use of biologically inspired models for information processing. It is not an exact replica of how the brain functions, but it has shown promising results in forecasting and business classification applications. Neural networks learn by updating their architecture and connection weights, allowing them to efficiently perform tasks. They can learn from available training patterns or automatically learn from examples or input-output relations. Neural networks have been used in various fields such as healthcare, where they have been employed for greater accuracy and instant results. They have also been used in game AI, specifically in the fighting game genre, to select actions based on the game state, providing a challenging and satisfying experience for players. Additionally, neural networks have been utilized in the design and evaluation of AI systems, generating and analyzing neural networks based on user interactions and articulating their behavior in natural language.
What are the features of artificial neural network?9 answers

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How the networks work?
5 answers
Networks work by utilizing infrastructure and intangible assets to achieve their goals. In the case of epilepsy care during the COVID-19 pandemic, a multi-stakeholder epilepsy Learning Network used a transparent and mundane shared infrastructure to shape their response to the changes in care. Fully-connected feedforward neural networks (FCNNs) or multi-layer perceptrons (MLPs) exhibit properties that can be explained by a pair of operations: random projection into a higher-dimensional space and sparsification. The value of networks is primarily based on intangible assets such as employee skills and IT infrastructure, and a framework consisting of success factors, results, reference measures, and failure factors can be used to assess and utilize these assets. Understanding networking concepts can be achieved through resources that provide a solid introduction and assume no prior knowledge.
Islanding using wavelet transform
5 answers
Islanding detection using wavelet transform is a commonly used method in distributed generation (DG) systems. Several papers propose different approaches to detect islanding using wavelet transform combined with machine learning techniques. Spötl and Yilmaz & Bayrak present methods that use wavelet transform and artificial neural networks (ANN) for islanding detection. Another paper by Ajith & Shereef proposes a method that uses wavelet transform decomposition and K-Nearest Neighbor (KNN) classification for islanding detection. These methods aim to reduce the non-detection zone (NDZ) and improve the detection time for islanding events. The use of wavelet transform allows for the extraction of detailed coefficients that can capture transient variations during islanding. By combining wavelet transform with machine learning techniques, these methods provide effective islanding detection in DG systems.
What is hidden layer in artificial neural network?
5 answers
The hidden layer in an artificial neural network refers to a layer of neurons that are not directly connected to the input or output layers. It is an intermediate layer that processes the input data and extracts relevant features before passing them on to the output layer. The purpose of the hidden layer is to enable the network to learn complex patterns and improve the performance of the network. The number of hidden layers and the number of neurons in each hidden layer are important factors in determining the architecture of the neural network. Different research papers propose various methods to determine the optimal number of hidden layers and neurons, such as interconnecting the hidden neurons within the same layer, using deep learning architecture with multiple feedforward layers, or removing unnecessary hidden neurons to enhance performance.
What are the best practices for improving the performance of a chatbot?
5 answers
The best practices for improving the performance of a chatbot include the following: (1) Predicting whether the best response should be a question and retrieving responses accordingly. (2) Incorporating a Neural Machine Translation (NMT) engine and a search-based engine to enhance the chatbot's performance in language learning. (3) Using deep learning techniques, such as convolutional neural networks, to improve the accuracy of chatbots. (4) Expanding the knowledge database of the chatbot through social media platforms like Twitter to provide accurate and flexible answers. These practices have been shown to significantly improve the retrieval performance, enhance language learning, increase accuracy, and provide appropriate answers in terms of context.
How does digital self-extension influence the way people comment on online content?
5 answers
Digital self-extension, the concept of extending one's self to digital possessions, has been explored in the context of consumer behavior. However, there is limited research on how digital self-extension influences the way people comment on online content. The use of digital technologies has brought changes to the extended self, challenging traditional notions of self and the relationship between objects and consumers in digital environments. Understanding the implications of self-extension to digital possessions can provide insights into maintaining digital possessions for the long term. Additionally, the affordances of digital technologies may influence behavior and potentially impact the actions associated with saving or personal information for the long term. Further research is needed to explore the specific influence of digital self-extension on online commenting behavior.
How to improve the accuracy of chord suggestion with deep learning?
4 answers
To improve the accuracy of chord suggestion with deep learning, several approaches have been proposed in the literature. One method is to use contrastive learning, where the model is trained with a combination of labeled and unlabeled data, and contrast loss is applied to generate a more uniform representation of features. Another approach is to incorporate Long Short-Term Memory (LSTM) networks, which consider chord information from previous time steps, into the chord recognition system. Additionally, personalizing chord labels based on annotator subjectivity has been shown to improve the performance of automatic chord estimation systems. Finally, a compact neural network called FifthNet has been proposed, which exploits the known structure of music spectra to achieve accurate chord recognition with fewer parameters and computational expense.
Artificial intelligence on food baking proceess?
3 answers
Artificial intelligence (AI) has been applied to various aspects of the food baking process. One study developed an intelligent baking device and method based on in-depth learning, which generated baking curves and controlled mechanical motion for baking different food materials. Another study proposed an intelligent food baking oven that addressed issues such as uneven baking, poor baking effect, and manual washing difficulties. The oven incorporated features like heating and rotating devices, as well as a cleaning device. Additionally, AI has been used to analyze newspaper articles and predict the taste of words, leading to the development of a chocolate that represents the mood of each year. Furthermore, an AI-based approach has been proposed to support decision-making in food chains, including the wheat to bread chain, by formalizing knowledge and addressing conflicting priorities. These studies demonstrate the potential of AI in improving the food baking process.
What is the purpose of a chaotic system in cryptography?
4 answers
A chaotic system is used in cryptography to ensure confidentiality and create secure encryption algorithms. Chaotic systems exhibit nonlinearity and a high degree of randomness, making them effective in cryptography. These systems are based on the principles of chaos theory, which states that complex systems often produce unpredictable results. By utilizing chaotic behavior, encryption keys can be generated that are difficult to reverse engineer, making it extremely challenging to crack the code. Chaotic systems can also be used for time series forecasting, which can be applied in cryptographic applications to produce encryption keys. Additionally, chaotic systems can be used to create image encryption algorithms, resulting in cipher-images with high entropy values and low correlation between adjacent pixels. Overall, chaotic systems provide a foundation for creating secure and effective cryptographic techniques.
Why AI to predict CVD why not relying on doctor diagnosis?
5 answers
AI is being used to predict cardiovascular disease (CVD) because it can improve the diagnosis process and overcome limitations of relying solely on doctor diagnosis. AI methods, such as machine learning, can analyze large amounts of data and identify patterns that may not be easily detectable by human doctors. Ultrasound imaging, for example, can be enhanced and automated using machine learning algorithms to segment arterial walls and improve the accuracy of CVD diagnosis. Additionally, AI can provide a more objective and consistent approach to diagnosis, reducing the potential for human error. It is important to note that AI is not meant to replace doctors, but rather to assist them in making more accurate and efficient diagnoses. By combining the expertise of doctors with the analytical capabilities of AI, the diagnosis of CVD can be improved, leading to better patient outcomes.
What is the fastest way to learn a language?
5 answers
The fastest way to learn a language is through a fast language learning method and system that collects character string information input by a user and outputs multiple information layers corresponding to the input method tool. This method allows for faster language learning by utilizing knowledge learning techniques and improving learning efficiency. Additionally, a fast memory method for language learning based on an automatic cycle screening system of a computer can also be used. This method follows an optimal language learning mode theory and involves issuing detection questions, verifying language information input by the user, and deleting language units that are answered correctly. By deleting memorized language units, excessive learning is avoided, learning efficiency is improved, and learning time is saved.
Document classification on neural networks using only positive examples.
4 answers
Document classification using neural networks with only positive examples has been explored in several papers. Larry M. Manevitz and Malik Yousef have shown in their papers that a simple feedforward neural network can be trained to filter documents when only positive information is available. They have found that this method is superior to more standard methods such as tf-idf retrieval based on an "average vector". J. Farkas discusses the relevance of neural networks in classifying electronic natural language documents and shows that neural networks can be taught to classify text according to predefined specifications. Rastislav Lencses presents an enriched approach to document clustering using neural networks and distributional semantics.