Overview Of Artificial Neural Networks Research Articles

Deep learning algorithms and their relevance: A review

Nowadays, the most revolutionary area in computer science is deep learning algorithms and models. This paper discusses deep learning and various supervised, unsupervised, and reinforcement learning models. An overview of Artificial neural network(ANN), Convolutional neural network(CNN), Recurrent neural network (RNN), Long short-term memory(LSTM), Self-organizing maps(SOM), Restricted Boltzmann machine(RBM), Deep Belief Network (DBN), Generative adversarial network(GAN), autoencoders, long short-term memory(LSTM), Gated Recurrent Unit(GRU) and Bidirectional-LSTM is provided. Various deep-learning application areas are also discussed. The most trending Chat GPT, which can understand natural language and respond to needs in various ways, uses supervised and reinforcement learning techniques. Additionally, the limitations of deep learning are discussed. This paper provides a snapshot of deep learning.

Artificial intelligence literacy for the language industry – with particular emphasis on recent large language models such as GPT-4

Abstract This article explores the concept of artificial intelligence (AI) literacy in the context of the language industry, placing particular emphasis on recent large language models such as GPT-4. After a brief introduction in which the relevance of AI literacy in the language industry is highlighted, the article provides a concise overview of artificial neural networks and a brief history of neural network-based artificial intelligence. This is intended to lay the conceptual groundwork for the subsequent discussion of the basic principles and capabilities of large language models. Then, the article investigates in detail the concept of AI literacy, discussing the AI Literacy Framework proposed by Long/Magerko (2020) and illustrating the interface between AI literacy and the two adjacent digital literacies of professional machine translation literacy and data literacy. The article then zooms in on the practical applicability of AI technologies by discussing areas where workflows in the language industry (with a focus on the computer-assisted translation process) could be automated or optimised through large language models. The article concludes with some general reflections on the relevance of field-specific and societal AI literacy in the presence of powerful AI technologies.

An Introduction to Neural Networks

Neural networks ares an important type of machine learning that have generated significant interest due to their success on difficult problems.. This tutorial will provide an introduction and a high-level overview of artificial neural networks. It will include the biological basis and the inspiration for these methods, and their colorful history. An explanation of deep learning and deep belief networks, hardware acceleration, and case studies will be used to illustrate how the technology works and the direction in which it is developing.

A survey on deep learning applied to medical images: from simple artificial neural networks to generative models.

Deep learning techniques, in particular generative models, have taken on great importance in medical image analysis. This paper surveys fundamental deep learning concepts related to medical image generation. It provides concise overviews of studies which use some of the latest state-of-the-art models from last years applied to medical images of different injured body areas or organs that have a disease associated with (e.g., brain tumor and COVID-19 lungs pneumonia). The motivation for this study is to offer a comprehensive overview of artificial neural networks (NNs) and deep generative models in medical imaging, so more groups and authors that are not familiar with deep learning take into consideration its use in medicine works. We review the use of generative models, such as generative adversarial networks and variational autoencoders, as techniques to achieve semantic segmentation, data augmentation, and better classification algorithms, among other purposes. In addition, a collection of widely used public medical datasets containing magnetic resonance (MR) images, computed tomography (CT) scans, and common pictures is presented. Finally, we feature a summary of the current state of generative models in medical image including key features, current challenges, and future research paths.

Artificial Neural Networks for Microwave Computer-Aided Design: The State of the Art

This article presents an overview of artificial neural network (ANN) techniques for a microwave computer-aided design (CAD). ANN-based techniques are becoming useful for performing forward/inverse modeling for active/passive components to enhance a circuit design. With measured or simulated data of microwave devices, ANNs can be trained to learn relevant microwave relationships, which are, otherwise, computationally expensive or for which efficient analytical formulas are not available. Fundamental concepts of the ANN structure and training, such as feedforward neural networks (FFNNs), recurrent neural networks (RNNs)/dynamic neural networks (DNNs)/time-delay neural networks (TDNNs), deep neural networks, and neural network training and extrapolation, are described. Knowledge-based neural networks (KBNNs) are described for improving the accuracy and reliability of modeling and design optimization. Various advanced ANN techniques, such as neuro-transfer function (neuro-TF) modeling, neural network inverse modeling, and deep neural network modeling, are discussed. The existing and emerging applications of ANN in microwave CAD are identified, such as electromagnetic (EM)/multiphysics modeling, modeling of nonlinear circuits and transistors, filter design, very large-scale integration (VLSI) interconnects, oscillator, transmitter and receiver modeling, and CAD applications in such as gallium nitride (GaN) high electron-mobility transistor (HEMT), wireless power transfer (WPT), microelectromechanical system (MEMS), and substrate-integrated waveguide (SIW).

Review of Artificial Neural Network and Its Application Research in Distillation

With the development of rectification technology, the scale of its production equipment has continued to expand, and its calculation requirements have become more complex. The use of traditional optimized control methods can no longer meet the requirements. Artificial neural networks imitate the human brain for self-learning and optimization, intelligently process various complex information, and have been widely used in various chemical processes. Because the artificial neural network has the advantages of self-learning, associative storage, and high-speed search for optimized solutions, it can perform high-precision simulation and prediction of rectification operations, and has been widely used in the optimal control of rectification towers. This article gives a basic overview of artificial neural networks, and introduces the application research of artificial neural networks in distillation at home and abroad.

A View of Artificial Neural Network Models in Different Application Areas

Neural network is a web of million numbers of inter-connected neurons which executes parallel processing. An Artificial neural network is a nonlinear mapping structure; an information processing pattern is stimulated by the approach as biological nervous system (brain) process the information. It is used as a powerful tool for modeling the data in the application domains where incomplete understanding of the data relationship to be solved with the readily available trained data. The basic element for this processing pattern is the structure of the data which is the collection of densely interconnected neurons to elucidate the problems. A prominent part of these network is their adaptive nature to “learn by example” just like human substitutes “programming” in resolving the problems. Through learning process, neural net is designed for data classification and prediction where statistical techniques and regression model have been employed. This report is an overview of artificial neural networks in different application areas and it also illustrate the architecture structure formed for the applications. It also provides information about the training algorithm used for certain application.

Overview of artificial neural network models in the biomedical domain.

The aim of this paper is to provide an overview of artificial neural network (ANN) in biomedical domain and compare it with the logistic regression model. Artificial neural network models and logistic regression models were created and compared using a sample of a modified dataset adapted to the dataset from Framingham Heart Study. R statistical software package is used to create and compare the models. The results indicated that the ANN model is more accurate in classifying the dependent variable than the logistic regression model (84.4 % vs 82.9 %). This paper has shown the effect of artificial neural network models in classifying the survival status (event or non-event) (Tab. 2, Fig. 4, Ref. 29).

An Overview of Neural Network

Neural networks represent a brain metaphor for information processing. These models are biologically inspired rather than an exact replica of how the brain actually functions. Neural networks have been shown to be very promising systems in many forecasting applications and business classification applications due to their ability to learn from the data. This article aims to provide a brief overview of artificial neural network. The artificial neural network learns by updating the network architecture and connection weights so that the network can efficiently perform a task. It can learn either from available training patterns or automatically learn from examples or input-output relations. Neural network-based models continue to achieve impressive results on longstanding machine learning problems, but establishing their capacity to reason about abstract concepts has proven difficult. Building on previous efforts to solve this important feature of general-purpose learning systems, our latest paper sets out an approach for measuring abstract reasoning in learning machines, and reveals some important insights about the nature of generalization itself. Artificial neural networks can learn by example like the way humans do. An artificial neural net is configured for a specific application like pattern recognition through a learning process. Learning in biological systems consists of adjustments to the synaptic connections that exist between neurons. This is true of artificial neural networks as well. Artificial neural networks can be applied to an increasing number of real-world problems of considerable complexity. They are used for solving problems that are too complex for conventional technologies or those types of problems that do not have an algorithmic solution.

ARTIFICIAL NEURAL NETWORK

Artificial Neural Networking or ANN is the way by which computers can mimic the biological nervous system by building a huge number of simulated neurons, which are joined together in a number of ways to form networks. A neural network has multiple processors instead of just one central processor and thus is very task efficient. They cannot be made to perform a specific task because they learn via experience, which is unlike any other computer processor. They follow certain learning laws while receiving feedback from the environment. This paper gives an overview of Artificial Neural Network, its working and training.

The use of artificial neural network and multiple linear regressions for stock market forecasting

In the recent economic crises, one of the precise uniqueness that all stock markets have in common is the uncertainty. An attempt was made to forecast future index of the Malaysia Stock Exchange Market using artificial neural network (ANN) model and a traditional forecasting tool – Multiple Linear Regressions (MLR). This paper starts with a brief introduction of stock exchange of Malaysia, an overview of artificial neural network and machine learning models used for prediction. System design and data normalization using MINITAB software were described. Training algorithm, MLR Model and network parameter models were presented. Best training graphs showing the training, validation, test and all regression values were analyzed.

ANÁLISIS CONDUCTUAL y REDES NEURALES ARTIFICIALES

This article provides an overview of Artificial Neural Networks (ANNsl, including architecture and several learning algorithms. Reviewed algorithms include Hebbian, Rescorla-Wagner, Sutton-Barto (1981), and Hutchison's. In addition, the contributions Behavior Analysts have made to ANN development, as well as the effect ANN's have made on behavior analysis, is discussed. Finally, several applications of ANNs by Behavioral Analysts are reviewed.

Artificial neural networks in analytical review procedures

This article gives an overview of artificial neural network (ANN) studies conducted in the auditing field. The review pays attention to application domains, data and sample sets, ANN‐architectures and learning parameters. The article argues that these auditing ANN‐applications could serve the analytical review (AR) process. The summary of the findings pays attention to whether authors state that ANNs have potential to improve analytical review (AR) procedures. Furthermore, the article evaluates which are the most influential contributions and which are open ends in the field. The article makes some practical suggestions to motivate academics and practitioners to collaborate in further exploration of the potential of ANNs.

Quantitative structure–activity relationships by evolved neural networks for the inhibition of dihydrofolate reductase by pyrimidines

Evolutionary computation provides a useful method for training neural networks in the face of multiple local optima. This paper begins with a description of methods for quantitative structure activity relationships (QSAR). An overview of artificial neural networks for pattern recognition problems such as QSAR is presented and extended with the description of how evolutionary computation can be used to evolve neural networks. Experiments are conducted to examine QSAR for the inhibition of dihydrofolate reductase by pyrimidines using evolved neural networks. Results indicate the utility of evolutionary algorithms and neural networks for the predictive task at hand. Furthermore, results that are comparable or perhaps better than those published previously were obtained using only a small fraction of the previously required degrees of freedom.

Virtual-Intelligence Applications in Petroleum Engineering: Part 3—Fuzzy Logic

Distinguished Author Series articles are general, descriptive representations that summarize the state of the art in an area of technology by describing recent developments for readers who are not specialists in the topics discussed. Written by individuals recognized to be experts in the area, these articles provide key references to more definitive work and present specific details only to illustrate the technology. Purpose: to inform the general readership of recent advances in various areas of petroleum engineering. Summary Parts 1 and 2 of this series of articles presented a general overview of artificial neural networks and evolutionary computing, respectively, and their applications in the oil and gas industry.1,2 The focus of this article is fuzzy logic. The article provides overview of the subject and its potential application in solving petroleum-engineering-related problems. As the previous articles mentioned, the most successful applications of intelligent systems, especially when solving engineering problems, have been achieved by use of different intelligent tools in concert and as a hybrid system. This article reviews the application of fuzzy logic for restimulation-candidate selection in a tight-gas formation in the Rocky Mountains. We chose this particular application because it uses fuzzy logic in a hybrid manner integrated with neural networks and genetic algorithms. Background The science of today is based on Aristotle's crisp logic formed more than2,000 years ago. Aristotelian logic looks at the world in a bivalent manner, such as black and white, yes and no, and 0 and 1. The set theory developed in the late 19th Century by German mathematician Cantor was based on Aristotle'sbivalent logic and made this logic accessible to modern science. Subsequent superimposition of probability theory made the bivalent logic reasonable and workable. Cantor's theory defines sets as a collection of definite, distinguishable objects. Fig. 1 is a simple example of Cantor's set theory and its most common operations, such as complement, intersection, and union. The first work on vagueness dates back to the first decade 20th Century, when American philosopher Pierce noted that "vagueness is no more to be done away with in the world of logic than friction in mechanics." 3 In the early 1920's, Polish mathematician and logician Lukasiewicz4developed three-valued logic and talked about many-valued, or multi-valued, logic. In 1937, quantum philosopher Black5 published a paper on vague sets. These scientists built the foundation on which fuzzy logic was later developed. Zadeh,6 known as the father of fuzzy logic, published his landmark paper "Fuzzy Sets" in 1965. He developed many key concepts, including membership values, and provided a comprehensive framework to apply the theory to engineering and scientific problems. This framework included the classical operations for fuzzy sets, which comprise all the mathematical tools necessary to apply the fuzzy-set theory to real-world problems. Zadeh was the first to use the term "fuzzy," which provoked much opposition. A tireless spokesperson for the field, he was often harshly criticized. At a 1972 conference, Kalman stated that "Fuzzification is a kind of scientific permissiveness; it tends to result in socially appealing slogans unaccompanied by the discipline of hard scientific work." 7 (Note that Kalman is a former student of Zadeh'sand inventor of the famous Kalman filter, a major statistical tool in electrical engineering. The Kalman filter is the technology behind the Patriotmissiles used in the Gulf War. Claims have been made that it has been proved that use of fuzzy logic can significantly increase the accuracy of these missiles.8,9) Despite all its adversaries, fuzzy logic continued to flourish and has become a major force behind many advances in intelligent systems. The word "fuzzy" carries a negative connotation in Western culture, and" fuzzy logic" seems to misdirect the attention and to celebrate mentalfog.10 On the other hand, Eastern culture embraces the concept of coexistence of contradictions as it appears in the yin/yang symbol (Fig.2). While Aristotelian logic preaches A or Not-A, Buddhism is all about Aand Not-A. Many believe that the tolerance of Eastern culture for such ideas is the main reason behind the success of fuzzy logic in Japan. While fuzzy logic was being attacked in the U.S., Japanese industries were busy building amultibillion-dollar industry around it. Today, the Japanese hold more than2,000 fuzzy-related patents. They have used fuzzy technology to build intelligent household appliances, such as washing machines and vacuum cleaners(Matsushita and Hitachi), rice cookers (Matsushita and Sanyo), air conditioners(Mitsubishi), and microwave ovens (Sharp, Sanyo, and Toshiba), to name a few. Matsushita used fuzzy technology to develop its digital image stabilizer for camcorders. Adaptive fuzzy systems (a hybrid with neural networks) can be found in many Japanese cars. Nissan patented a fuzzy automatic transmission that is now very popular with many other manufacturers, such as Mitsubishi and Honda.10

Structural evaluation of materials by artificial neural networks

It is shown that due to the complexity of interaction of the excitation field with a material in determining its physical characteristics, as well as sophisticated correlation relationships between the physical characteristics and structure of a real material, in many cases, relization of the structural evaluation of materials on the basis of idealized mathematical models of the underlying physical processes is of limited use. As an alternative, it is proposed to use an artificial neural network for the extraction of quantitative information of interest from measurements of the physical characteristics. A general overview of artificial neural networks is given. A methodology of the use of a multilayer perceptron for determining various structural parameters from the dielectric spectra is described. As an example, determination of the moisture content and density of sawdust from the dielectric modulusis considered by the neural-network technique. The noise performance of the neural network is analyzed by applying an additive and multiplicative noise to the input data.

SOME EXPERIENCES WITH NEURAL NETWORKS

This paper gives a brief overview of artificial neural networks which may be used to model data similar to the kind where one usually considers regression models. Many practitioners believe that neural networks perform better than regression models for prediction purposes. Some simulations were performed using three different neural net programs, namely Braincel, Ripley's S+ program, and Nychka's S+ program. These simulations reveal some interesting aspects of neural net programs which should be of interest to anyone considering the use of neural net programs to model continuous data.