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Why we study artificial neural network? 

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Answers from top 10 papers

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Papers (10)Insight
Open access•Journal Article
Forecasting Safety Stock of Supply Chain Using Artificial Neural Network
Lin Hong-ri
01 Jan 2004-Journal of Fuzhou Teachers College
9 Citations
Artificial neural network has its unique advantages in forecasting.
Open access•Journal Article•DOI
A Hybrid Neuro-Fuzzy and Feature Reduction Model for Classification
Himansu Das, Bighnaraj Naik, Himansu Sekhar Behera 
01 Mar 2020-Advances in Fuzzy Systems
18 Citations
It helps to deal with the uncertainty issues and assists the Artificial Neural Network- (ANN-) based model to achieve better performance.
Open access•Journal Article•DOI
Applications of Artificial Neural Networks: A Review
Rajan Dharwal, Loveneet Kaur 
28 Dec 2016-Indian journal of science and technology
24 Citations
Findings: Artificial neural network has a significant role in medical area.
Journal Article•DOI
Crevice corrosion : a neural network approach
S. P. Trasatti, F. Mazza 
01 Jan 1996-British Corrosion Journal
20 Citations
Among these, the artificial neural network (NN) system appears to be a powerful tool to tackle situations in w...
Journal Article•DOI
An Artificial Intelligence Approach for the Detection of Cervical Abnormalities: Application of the Self Organizing Map
Evangelos Salamalekis, Abraham Pouliakis, Niki Margari, Christine Kottaridi, Aris Spathis, Effrosyni Karakitsou, Alina-Roxani Gouloumi, Danai Leventakou, George Chrelias, George Valasoulis, Maria Nasioutziki, Maria Kyrgiou, Konstantinos Dinas, Ioannis Panayiotides, Evangelos Paraskevaidis, Charalampos Chrelias 
01 Apr 2019
11 Citations
An artificial neural network has three advantages: it is unsupervised, can tolerate missing data, and produces topographical maps.
Open access•Journal Article•DOI
Introduction. The use of artificial neural networks to study perception in animals.
Colin R. Tosh, Graeme D. Ruxton 
29 Mar 2007-Philosophical Transactions of the Royal Society B
7 Citations
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 Article•DOI
A view of Artificial Neural Network
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.
Proceedings Article•DOI
Identification of three phase induction motor incipient faults using neural network
A. Siddique, G.S. Yadava, Bhim Singh 
19 Sep 2004
13 Citations
The artificial neural network techniques are rather easy to develop and to perform.
Journal Article•DOI
Time-series prediction with single integrate-and-fire neuron
Abhishek Yadav, Deepak Mishra, Ram Narayan Yadav, Sudipta Ray, Prem Kalra 
01 Jun 2007
17 Citations
It is observed that the inclusion of some more biological phenomenon in an artificial neural network can make it more powerful.
Journal Article•DOI
Artificial neural network model for synthetic streamflow generation
Juran Ali Ahmed, Arup Kumar Sarma 
01 May 2007-Water Resources Management
74 Citations
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?
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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?
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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?
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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?
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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?
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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.