Enviar pesquisa
Carregar
Artificial Neural Network: A brief study
•
0 gostou
•
5 visualizações
IRJET Journal
Seguir
https://www.irjet.net/archives/V10/i2/IRJET-V10I2115.pdf
Leia menos
Leia mais
Engenharia
Denunciar
Compartilhar
Denunciar
Compartilhar
1 de 6
Baixar agora
Baixar para ler offline
Recomendados
Artificial Neural Network and its Applications
Artificial Neural Network and its Applications
shritosh kumar
B05211012
B05211012
IOSR-JEN
Project Report -Vaibhav
Project Report -Vaibhav
Vaibhav Dhattarwal
IRJET- The Essentials of Neural Networks and their Applications
IRJET- The Essentials of Neural Networks and their Applications
IRJET Journal
Artificial Neural Networks
Artificial Neural Networks
கோலப்பன் தமிழன்
P2-Artificial.pdf
P2-Artificial.pdf
கோலப்பன் தமிழன்
Fuzzy Logic Final Report
Fuzzy Logic Final Report
Shikhar Agarwal
Neural network
Neural network
Guvera Ganesh
Recomendados
Artificial Neural Network and its Applications
Artificial Neural Network and its Applications
shritosh kumar
B05211012
B05211012
IOSR-JEN
Project Report -Vaibhav
Project Report -Vaibhav
Vaibhav Dhattarwal
IRJET- The Essentials of Neural Networks and their Applications
IRJET- The Essentials of Neural Networks and their Applications
IRJET Journal
Artificial Neural Networks
Artificial Neural Networks
கோலப்பன் தமிழன்
P2-Artificial.pdf
P2-Artificial.pdf
கோலப்பன் தமிழன்
Fuzzy Logic Final Report
Fuzzy Logic Final Report
Shikhar Agarwal
Neural network
Neural network
Guvera Ganesh
40120140507007
40120140507007
IAEME Publication
40120140507007
40120140507007
IAEME Publication
Neural Network
Neural Network
Sayyed Z
An Overview On Neural Network And Its Application
An Overview On Neural Network And Its Application
Sherri Cost
Artificial Neural Networks: Applications In Management
Artificial Neural Networks: Applications In Management
IOSR Journals
M010237578
M010237578
IOSR Journals
Ann model and its application
Ann model and its application
milan107
Artificial Intelligence.docx
Artificial Intelligence.docx
ashumar
Neural networks in business forecasting
Neural networks in business forecasting
Amir Shokri
Artificial Neural networks
Artificial Neural networks
Learnbay Datascience
EXPERT SYSTEMS AND ARTIFICIAL INTELLIGENCE_ Neural Networks.pptx
EXPERT SYSTEMS AND ARTIFICIAL INTELLIGENCE_ Neural Networks.pptx
Javier Daza
Artificial neural network
Artificial neural network
Alexander Decker
Nature Inspired Reasoning Applied in Semantic Web
Nature Inspired Reasoning Applied in Semantic Web
guestecf0af
IRJET-AI Neural Network Disaster Recovery Cloud Operations Systems
IRJET-AI Neural Network Disaster Recovery Cloud Operations Systems
IRJET Journal
OSPEN: an open source platform for emulating neuromorphic hardware
OSPEN: an open source platform for emulating neuromorphic hardware
International Journal of Reconfigurable and Embedded Systems
Neural Network Implementation Control Mobile Robot
Neural Network Implementation Control Mobile Robot
IRJET Journal
DATA SCIENCE
DATA SCIENCE
HarshikaBansal1
Artificial neural network for machine learning
Artificial neural network for machine learning
grinu
Blue Brain Project
Blue Brain Project
IRJET Journal
IRJET- Significant Neural Networks for Classification of Product Images
IRJET- Significant Neural Networks for Classification of Product Images
IRJET Journal
TUNNELING IN HIMALAYAS WITH NATM METHOD: A SPECIAL REFERENCES TO SUNGAL TUNNE...
TUNNELING IN HIMALAYAS WITH NATM METHOD: A SPECIAL REFERENCES TO SUNGAL TUNNE...
IRJET Journal
STUDY THE EFFECT OF RESPONSE REDUCTION FACTOR ON RC FRAMED STRUCTURE
STUDY THE EFFECT OF RESPONSE REDUCTION FACTOR ON RC FRAMED STRUCTURE
IRJET Journal
Mais conteúdo relacionado
Semelhante a Artificial Neural Network: A brief study
40120140507007
40120140507007
IAEME Publication
40120140507007
40120140507007
IAEME Publication
Neural Network
Neural Network
Sayyed Z
An Overview On Neural Network And Its Application
An Overview On Neural Network And Its Application
Sherri Cost
Artificial Neural Networks: Applications In Management
Artificial Neural Networks: Applications In Management
IOSR Journals
M010237578
M010237578
IOSR Journals
Ann model and its application
Ann model and its application
milan107
Artificial Intelligence.docx
Artificial Intelligence.docx
ashumar
Neural networks in business forecasting
Neural networks in business forecasting
Amir Shokri
Artificial Neural networks
Artificial Neural networks
Learnbay Datascience
EXPERT SYSTEMS AND ARTIFICIAL INTELLIGENCE_ Neural Networks.pptx
EXPERT SYSTEMS AND ARTIFICIAL INTELLIGENCE_ Neural Networks.pptx
Javier Daza
Artificial neural network
Artificial neural network
Alexander Decker
Nature Inspired Reasoning Applied in Semantic Web
Nature Inspired Reasoning Applied in Semantic Web
guestecf0af
IRJET-AI Neural Network Disaster Recovery Cloud Operations Systems
IRJET-AI Neural Network Disaster Recovery Cloud Operations Systems
IRJET Journal
OSPEN: an open source platform for emulating neuromorphic hardware
OSPEN: an open source platform for emulating neuromorphic hardware
International Journal of Reconfigurable and Embedded Systems
Neural Network Implementation Control Mobile Robot
Neural Network Implementation Control Mobile Robot
IRJET Journal
DATA SCIENCE
DATA SCIENCE
HarshikaBansal1
Artificial neural network for machine learning
Artificial neural network for machine learning
grinu
Blue Brain Project
Blue Brain Project
IRJET Journal
IRJET- Significant Neural Networks for Classification of Product Images
IRJET- Significant Neural Networks for Classification of Product Images
IRJET Journal
Semelhante a Artificial Neural Network: A brief study
(20)
40120140507007
40120140507007
40120140507007
40120140507007
Neural Network
Neural Network
An Overview On Neural Network And Its Application
An Overview On Neural Network And Its Application
Artificial Neural Networks: Applications In Management
Artificial Neural Networks: Applications In Management
M010237578
M010237578
Ann model and its application
Ann model and its application
Artificial Intelligence.docx
Artificial Intelligence.docx
Neural networks in business forecasting
Neural networks in business forecasting
Artificial Neural networks
Artificial Neural networks
EXPERT SYSTEMS AND ARTIFICIAL INTELLIGENCE_ Neural Networks.pptx
EXPERT SYSTEMS AND ARTIFICIAL INTELLIGENCE_ Neural Networks.pptx
Artificial neural network
Artificial neural network
Nature Inspired Reasoning Applied in Semantic Web
Nature Inspired Reasoning Applied in Semantic Web
IRJET-AI Neural Network Disaster Recovery Cloud Operations Systems
IRJET-AI Neural Network Disaster Recovery Cloud Operations Systems
OSPEN: an open source platform for emulating neuromorphic hardware
OSPEN: an open source platform for emulating neuromorphic hardware
Neural Network Implementation Control Mobile Robot
Neural Network Implementation Control Mobile Robot
DATA SCIENCE
DATA SCIENCE
Artificial neural network for machine learning
Artificial neural network for machine learning
Blue Brain Project
Blue Brain Project
IRJET- Significant Neural Networks for Classification of Product Images
IRJET- Significant Neural Networks for Classification of Product Images
Mais de IRJET Journal
TUNNELING IN HIMALAYAS WITH NATM METHOD: A SPECIAL REFERENCES TO SUNGAL TUNNE...
TUNNELING IN HIMALAYAS WITH NATM METHOD: A SPECIAL REFERENCES TO SUNGAL TUNNE...
IRJET Journal
STUDY THE EFFECT OF RESPONSE REDUCTION FACTOR ON RC FRAMED STRUCTURE
STUDY THE EFFECT OF RESPONSE REDUCTION FACTOR ON RC FRAMED STRUCTURE
IRJET Journal
A COMPARATIVE ANALYSIS OF RCC ELEMENT OF SLAB WITH STARK STEEL (HYSD STEEL) A...
A COMPARATIVE ANALYSIS OF RCC ELEMENT OF SLAB WITH STARK STEEL (HYSD STEEL) A...
IRJET Journal
Effect of Camber and Angles of Attack on Airfoil Characteristics
Effect of Camber and Angles of Attack on Airfoil Characteristics
IRJET Journal
A Review on the Progress and Challenges of Aluminum-Based Metal Matrix Compos...
A Review on the Progress and Challenges of Aluminum-Based Metal Matrix Compos...
IRJET Journal
Dynamic Urban Transit Optimization: A Graph Neural Network Approach for Real-...
Dynamic Urban Transit Optimization: A Graph Neural Network Approach for Real-...
IRJET Journal
Structural Analysis and Design of Multi-Storey Symmetric and Asymmetric Shape...
Structural Analysis and Design of Multi-Storey Symmetric and Asymmetric Shape...
IRJET Journal
A Review of “Seismic Response of RC Structures Having Plan and Vertical Irreg...
A Review of “Seismic Response of RC Structures Having Plan and Vertical Irreg...
IRJET Journal
A REVIEW ON MACHINE LEARNING IN ADAS
A REVIEW ON MACHINE LEARNING IN ADAS
IRJET Journal
Long Term Trend Analysis of Precipitation and Temperature for Asosa district,...
Long Term Trend Analysis of Precipitation and Temperature for Asosa district,...
IRJET Journal
P.E.B. Framed Structure Design and Analysis Using STAAD Pro
P.E.B. Framed Structure Design and Analysis Using STAAD Pro
IRJET Journal
A Review on Innovative Fiber Integration for Enhanced Reinforcement of Concre...
A Review on Innovative Fiber Integration for Enhanced Reinforcement of Concre...
IRJET Journal
Survey Paper on Cloud-Based Secured Healthcare System
Survey Paper on Cloud-Based Secured Healthcare System
IRJET Journal
Review on studies and research on widening of existing concrete bridges
Review on studies and research on widening of existing concrete bridges
IRJET Journal
React based fullstack edtech web application
React based fullstack edtech web application
IRJET Journal
A Comprehensive Review of Integrating IoT and Blockchain Technologies in the ...
A Comprehensive Review of Integrating IoT and Blockchain Technologies in the ...
IRJET Journal
A REVIEW ON THE PERFORMANCE OF COCONUT FIBRE REINFORCED CONCRETE.
A REVIEW ON THE PERFORMANCE OF COCONUT FIBRE REINFORCED CONCRETE.
IRJET Journal
Optimizing Business Management Process Workflows: The Dynamic Influence of Mi...
Optimizing Business Management Process Workflows: The Dynamic Influence of Mi...
IRJET Journal
Multistoried and Multi Bay Steel Building Frame by using Seismic Design
Multistoried and Multi Bay Steel Building Frame by using Seismic Design
IRJET Journal
Cost Optimization of Construction Using Plastic Waste as a Sustainable Constr...
Cost Optimization of Construction Using Plastic Waste as a Sustainable Constr...
IRJET Journal
Mais de IRJET Journal
(20)
TUNNELING IN HIMALAYAS WITH NATM METHOD: A SPECIAL REFERENCES TO SUNGAL TUNNE...
TUNNELING IN HIMALAYAS WITH NATM METHOD: A SPECIAL REFERENCES TO SUNGAL TUNNE...
STUDY THE EFFECT OF RESPONSE REDUCTION FACTOR ON RC FRAMED STRUCTURE
STUDY THE EFFECT OF RESPONSE REDUCTION FACTOR ON RC FRAMED STRUCTURE
A COMPARATIVE ANALYSIS OF RCC ELEMENT OF SLAB WITH STARK STEEL (HYSD STEEL) A...
A COMPARATIVE ANALYSIS OF RCC ELEMENT OF SLAB WITH STARK STEEL (HYSD STEEL) A...
Effect of Camber and Angles of Attack on Airfoil Characteristics
Effect of Camber and Angles of Attack on Airfoil Characteristics
A Review on the Progress and Challenges of Aluminum-Based Metal Matrix Compos...
A Review on the Progress and Challenges of Aluminum-Based Metal Matrix Compos...
Dynamic Urban Transit Optimization: A Graph Neural Network Approach for Real-...
Dynamic Urban Transit Optimization: A Graph Neural Network Approach for Real-...
Structural Analysis and Design of Multi-Storey Symmetric and Asymmetric Shape...
Structural Analysis and Design of Multi-Storey Symmetric and Asymmetric Shape...
A Review of “Seismic Response of RC Structures Having Plan and Vertical Irreg...
A Review of “Seismic Response of RC Structures Having Plan and Vertical Irreg...
A REVIEW ON MACHINE LEARNING IN ADAS
A REVIEW ON MACHINE LEARNING IN ADAS
Long Term Trend Analysis of Precipitation and Temperature for Asosa district,...
Long Term Trend Analysis of Precipitation and Temperature for Asosa district,...
P.E.B. Framed Structure Design and Analysis Using STAAD Pro
P.E.B. Framed Structure Design and Analysis Using STAAD Pro
A Review on Innovative Fiber Integration for Enhanced Reinforcement of Concre...
A Review on Innovative Fiber Integration for Enhanced Reinforcement of Concre...
Survey Paper on Cloud-Based Secured Healthcare System
Survey Paper on Cloud-Based Secured Healthcare System
Review on studies and research on widening of existing concrete bridges
Review on studies and research on widening of existing concrete bridges
React based fullstack edtech web application
React based fullstack edtech web application
A Comprehensive Review of Integrating IoT and Blockchain Technologies in the ...
A Comprehensive Review of Integrating IoT and Blockchain Technologies in the ...
A REVIEW ON THE PERFORMANCE OF COCONUT FIBRE REINFORCED CONCRETE.
A REVIEW ON THE PERFORMANCE OF COCONUT FIBRE REINFORCED CONCRETE.
Optimizing Business Management Process Workflows: The Dynamic Influence of Mi...
Optimizing Business Management Process Workflows: The Dynamic Influence of Mi...
Multistoried and Multi Bay Steel Building Frame by using Seismic Design
Multistoried and Multi Bay Steel Building Frame by using Seismic Design
Cost Optimization of Construction Using Plastic Waste as a Sustainable Constr...
Cost Optimization of Construction Using Plastic Waste as a Sustainable Constr...
Último
Unit 2- Effective stress & Permeability.pdf
Unit 2- Effective stress & Permeability.pdf
RagavanV2
FULL ENJOY Call Girls In Mahipalpur Delhi Contact Us 8377877756
FULL ENJOY Call Girls In Mahipalpur Delhi Contact Us 8377877756
dollysharma2066
Water Industry Process Automation & Control Monthly - April 2024
Water Industry Process Automation & Control Monthly - April 2024
Water Industry Process Automation & Control
VIP Model Call Girls Kothrud ( Pune ) Call ON 8005736733 Starting From 5K to ...
VIP Model Call Girls Kothrud ( Pune ) Call ON 8005736733 Starting From 5K to ...
SUHANI PANDEY
Design For Accessibility: Getting it right from the start
Design For Accessibility: Getting it right from the start
Quintin Balsdon
XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX
XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX
ssuser89054b
UNIT - IV - Air Compressors and its Performance
UNIT - IV - Air Compressors and its Performance
sivaprakash250
Call Girls In Bangalore ☎ 7737669865 🥵 Book Your One night Stand
Call Girls In Bangalore ☎ 7737669865 🥵 Book Your One night Stand
amitlee9823
VIP Call Girls Palanpur 7001035870 Whatsapp Number, 24/07 Booking
VIP Call Girls Palanpur 7001035870 Whatsapp Number, 24/07 Booking
dharasingh5698
A Study of Urban Area Plan for Pabna Municipality
A Study of Urban Area Plan for Pabna Municipality
Morshed Ahmed Rahath
Minimum and Maximum Modes of microprocessor 8086
Minimum and Maximum Modes of microprocessor 8086
anil_gaur
Call Girls Pimpri Chinchwad Call Me 7737669865 Budget Friendly No Advance Boo...
Call Girls Pimpri Chinchwad Call Me 7737669865 Budget Friendly No Advance Boo...
roncy bisnoi
ONLINE FOOD ORDER SYSTEM PROJECT REPORT.pdf
ONLINE FOOD ORDER SYSTEM PROJECT REPORT.pdf
Kamal Acharya
FEA Based Level 3 Assessment of Deformed Tanks with Fluid Induced Loads
FEA Based Level 3 Assessment of Deformed Tanks with Fluid Induced Loads
Arindam Chakraborty, Ph.D., P.E. (CA, TX)
Call Girls in Ramesh Nagar Delhi 💯 Call Us 🔝9953056974 🔝 Escort Service
Call Girls in Ramesh Nagar Delhi 💯 Call Us 🔝9953056974 🔝 Escort Service
9953056974 Low Rate Call Girls In Saket, Delhi NCR
Cara Menggugurkan Sperma Yang Masuk Rahim Biyar Tidak Hamil
Cara Menggugurkan Sperma Yang Masuk Rahim Biyar Tidak Hamil
Cara Menggugurkan Kandungan 087776558899
Integrated Test Rig For HTFE-25 - Neometrix
Integrated Test Rig For HTFE-25 - Neometrix
Neometrix_Engineering_Pvt_Ltd
notes on Evolution Of Analytic Scalability.ppt
notes on Evolution Of Analytic Scalability.ppt
MsecMca
(INDIRA) Call Girl Aurangabad Call Now 8617697112 Aurangabad Escorts 24x7
(INDIRA) Call Girl Aurangabad Call Now 8617697112 Aurangabad Escorts 24x7
Call Girls in Nagpur High Profile Call Girls
Employee leave management system project.
Employee leave management system project.
Kamal Acharya
Último
(20)
Unit 2- Effective stress & Permeability.pdf
Unit 2- Effective stress & Permeability.pdf
FULL ENJOY Call Girls In Mahipalpur Delhi Contact Us 8377877756
FULL ENJOY Call Girls In Mahipalpur Delhi Contact Us 8377877756
Water Industry Process Automation & Control Monthly - April 2024
Water Industry Process Automation & Control Monthly - April 2024
VIP Model Call Girls Kothrud ( Pune ) Call ON 8005736733 Starting From 5K to ...
VIP Model Call Girls Kothrud ( Pune ) Call ON 8005736733 Starting From 5K to ...
Design For Accessibility: Getting it right from the start
Design For Accessibility: Getting it right from the start
XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX
XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX
UNIT - IV - Air Compressors and its Performance
UNIT - IV - Air Compressors and its Performance
Call Girls In Bangalore ☎ 7737669865 🥵 Book Your One night Stand
Call Girls In Bangalore ☎ 7737669865 🥵 Book Your One night Stand
VIP Call Girls Palanpur 7001035870 Whatsapp Number, 24/07 Booking
VIP Call Girls Palanpur 7001035870 Whatsapp Number, 24/07 Booking
A Study of Urban Area Plan for Pabna Municipality
A Study of Urban Area Plan for Pabna Municipality
Minimum and Maximum Modes of microprocessor 8086
Minimum and Maximum Modes of microprocessor 8086
Call Girls Pimpri Chinchwad Call Me 7737669865 Budget Friendly No Advance Boo...
Call Girls Pimpri Chinchwad Call Me 7737669865 Budget Friendly No Advance Boo...
ONLINE FOOD ORDER SYSTEM PROJECT REPORT.pdf
ONLINE FOOD ORDER SYSTEM PROJECT REPORT.pdf
FEA Based Level 3 Assessment of Deformed Tanks with Fluid Induced Loads
FEA Based Level 3 Assessment of Deformed Tanks with Fluid Induced Loads
Call Girls in Ramesh Nagar Delhi 💯 Call Us 🔝9953056974 🔝 Escort Service
Call Girls in Ramesh Nagar Delhi 💯 Call Us 🔝9953056974 🔝 Escort Service
Cara Menggugurkan Sperma Yang Masuk Rahim Biyar Tidak Hamil
Cara Menggugurkan Sperma Yang Masuk Rahim Biyar Tidak Hamil
Integrated Test Rig For HTFE-25 - Neometrix
Integrated Test Rig For HTFE-25 - Neometrix
notes on Evolution Of Analytic Scalability.ppt
notes on Evolution Of Analytic Scalability.ppt
(INDIRA) Call Girl Aurangabad Call Now 8617697112 Aurangabad Escorts 24x7
(INDIRA) Call Girl Aurangabad Call Now 8617697112 Aurangabad Escorts 24x7
Employee leave management system project.
Employee leave management system project.
Artificial Neural Network: A brief study
1.
International Research Journal
of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 10 Issue: 02 | Feb 2023 www.irjet.net p-ISSN: 2395-0072 © 2023, IRJET | Impact Factor value: 8.226 | ISO 9001:2008 Certified Journal | Page 771 Artificial Neural Network: A brief study Mayuri Thorat1, Shraddha V. Pandit2, Supriya Balote3 1Student, Dept. of Artificial Intelligence and Machine Learning, PES’s Modern College Of Engineering, Pune, Maharashtra, India 2Associate Professor, Dept. of Artificial Intelligence and Data Science, PES’s Modern College Of Engineering, Pune, Maharashtra, India 3Assistant Professor, Dept. of Artificial Intelligence and Machine Learning, PES’s Modern College Of Engineering, Pune, Maharashtra, India ---------------------------------------------------------------------***--------------------------------------------------------------------- Abstract - An Artificial Neural Network (ANN) is a data processing paradigm inspired by the way biological nervous systems, such as the brain, process data. The unique structure of the information processing system is acrucialcomponent of this paradigm. It is made up of a huge number of highly interconnected processing elements (neurons) that work together to solve issues. ANNs, like humans, learn by example, and a huge dataset results in more accuracy. Through a learning process, an ANN is trained for a specific application, such as pattern recognition or data classification. This is also true of ANNs. This paper provides an overview of Artificial Neural Networks (ANN), their working, and training. It also describes the application and benefits of ANN. Key Words: ANN (Artificial Neural Network), Neurons 1. INTRODUCTION A person who knows how to solve a problem could give the computer instructions on how to solvethatspecific problem, so we first should know the answer to that problem or we should know how to overcome the problem thatisinfrontof us. The computer follows a set of instructions to solve a problem, but without the specific steps that the computer needs to follow or know, the computer cannot assault the problem. This limits the ability of conventional computersto solve problems to those that he already knows how to understand and solve. However, what about those issues? Whose response we are unsure of, hence networks were used instead of our conventional strategy in that situation. In essence, neural networks process information like that of the human brain, and these networks learn from examples. They will learn from their experiences and from the examples they are given; you cannot program them to carry out a certain task. ANN is primarily motivated by neurons, which are nothing more than your brain cells and how the human brain functions [4]. A vast network of processing components makes up our brains. The drawback is that the network solves the problem on its own. A network of 10 billion neurons can be found in a typical brain. Axon: Output, Synaptic: Link, Cell body: Processor, Dendrites: Input (as shown in fig 1). Here is a diagram of a neuron, which essentially shows how a biological neuron receives input from other sources, combines that input and then performs a general operation. This is similartohowthe human brain learns from experience and examples. Dendrites tries will receive signals from the other neurons, transfers them to the cell body, and the cell body will perform some function; this function may be summation or multiplication. After performing the set of inputs by exam, it is transferred to the next. We aim to build a network to do that as well. A network of 10 billion neurons can be found in a typical brain. Axon: Output, Synaptic: Link, Cell body: Processor, Dendrites: Input (as shown in fig 1). Here is a diagram of a neuron, which essentially shows how a biological neuron [4] receives input from other sources, combines that input and then performs a general operation. This is similar to how the human brain learns from experience and examples. Dendritestrieswill receivesignals from the other neurons, transfers them to the cell body, and the cell body will perform some function; this function may be summation or multiplication. After performing the set of inputs by exam, it is transferred to the next. We aim to build a network to do that as well. We are interested indeveloping artificial neural networks (ANNs) for two primary reasons: Technical viewpoint: Some issues, likecharacterrecognition or predicting a system's future states, call for highly parallel and adaptive processing. Biological viewpoint: ANNs can be used to recreate and model human (or animal) brain components, providing insight into natural information processing. ANN is a problem-solving technique that involves creating software that functions similarly to our brains.
2.
International Research Journal
of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 10 Issue: 02 | Feb 2023 www.irjet.net p-ISSN: 2395-0072 © 2023, IRJET | Impact Factor value: 8.226 | ISO 9001:2008 Certified Journal | Page 772 Fig 1:- A Simple Neuron Diagram. 1.1 What is ANN? In essence, artificial neural networks are a class of computing systems developed to analyze and process data like that of the human brain. Artificial neural networks can be tailored for a particular application and possess self- learning features that enable them to produce improved results as new data becomes available. The ability to translate websites into several languages, use virtual assistants to do online grocery orders, and identify patterns and classify data are all made possible by neural networks. Artificial neural networks (ANNs) are computational networks that are biologically inspired As shown in Fig. 3. Artificial neural networks are a technology based on brain and nervous system research. These networks mimic biological neural networks, although they use a subsetofthe principles found in biological brain systems. ANN models, in particular, imitate the electrical activity of the brain and nervous system. Processing elementscommunicatewithone another. An artificial neural network is made up of three or more interconnected layers. The first layer is made up of input neurons. Those neuronssenddata tothedeeperlayers, which deliver the final output data to the last output layer. The inner layers are all hidden and built by unitsthatchange the information received from layer to layer adaptively through a sequence of transformations. Each layer serves as both an input and output layer, allowing the ANN to comprehend more complicated things. These inner layers are referred to collectively as the neural layer. To arrive at solutions, ANN uses data samples rather than whole data sets, which saves both time and money. ANNs are simple mathematical models used to improve existingdata analysis technology. A basic neural network is composed of three main components: An input layer, a hidden layer [5], and an output layer are components of an artificial neural network. Input Layer: Also known as Input Nodes, this layer contains the inputs/information from the outside world that the model uses to learn and draw conclusions. Input nodes send data to the following layer, the Hidden layer. Hidden Layer: A hidden layer is a group of neurons that do all computations on the input data [5]. There can be any number of hidden layers in a neural network.Themost basic network has a single hidden layer Output layer: The output layer contains the model's output/conclusions produced from all calculations. The output layer could have one or more nodes. Advantages • Adapt to new circumstances • Powerful, it can simulate intricate functions • Easy to use, learn by doing, and requires relatively little domain-specific knowledge from the user Disadvantages • Forgets • Not exact • Large complexity of the network structure The model may generalize and forecast unseen data after learning from the initial inputsandtheirassociations,aswell as from unforeseen relationships inferred from unobserved data. Fig 2:- A Simple ANN Diagram. 2. ADVANTAGES 1. A linear program cannot carry out tasks that a neural network can. Due to its parallel properties, the neural network can continue functioning even when one of its components falters. 2. After training, the system can generate output without requiring complete inputs.
3.
International Research Journal
of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 10 Issue: 02 | Feb 2023 www.irjet.net p-ISSN: 2395-0072 © 2023, IRJET | Impact Factor value: 8.226 | ISO 9001:2008 Certified Journal | Page 773 3. Since Neural Networks are made to make robots function like people, they have several advantages over human replacements and a wide range of applications. agriculture, mining, engineering, and other fields. 4. ANN does not place any limitations on the input variables, in contrast to many other prediction algorithms. Learning, rather than programming, is used to construct the system. 5. ANNs can learn and model non-linear and complicated interactions, which is crucial because many of the relationships between inputs and outputs in real-life situations are non-linear and complex. 6. One of the many advantages of neural networks is their ability to multitask. The design of new, sophisticated programs allows them to multitask 7. And produce multiple effects. A task that could take humans longer hours to complete can becompleted swiftly by a machine if it is properly programmed. 8. Any application can use it to execute it. 9. The complete software backupisaccessibleonlineif the hardware is damaged or fails. 10. Neural networks make decisions and don't need to be reprogrammed [4]. 3. APPLICATIONS With countless applications being implemented every day, various real- time applications of Artificial Neural Networks are as follows. 1. Handwriting Recognition: Handwritten characters are transformed into digital characters that a computer can read using neural networks. 2. Stock Exchange prediction: The stock exchange is 3. Travelling Issues ofsalesprofessionals: Itdealswith determining the best route between cities in a specific region. 4. Image compression: The goal of neural network data compression is to replicate the original image by storing, encrypting, and compressing it once again. we can use picture compression neural networks to reduce the amount of our data. 5. Function approximation: including time series prediction and modelling. 6. Classification: including patterns and sequence recognition, novelty detection and sequential decision-making. 7. Data processing: including filtering, clustering blinds source separation and compression. (data mining, e-mail Spam finding) 8. Monitoring of the environment: ANNs can be used to forecast natural occurrences including weather, air quality, and earthquakes. 9. Marketing: ANNs can be used in consumer segmentation and tailored advertising to forecast customer behavior and optimize marketing strategies. 10. Cybersecurity : It can be utilized for tasks like virus detection, intrusion detection, and network traffic analysis in the field of cybersecurity.
4.
International Research Journal
of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 10 Issue: 02 | Feb 2023 www.irjet.net p-ISSN: 2395-0072 © 2023, IRJET | Impact Factor value: 8.226 | ISO 9001:2008 Certified Journal | Page 774 Fig 3:- A simple neuron and ANN comparison diagram 4. WORKING OF AN ANN Artificial neural networks are built in the same way as the human brain is, with neuron nodes connected in a web-like pattern. The human brain is made up of billions of neurons. Each neuron is made up of a cell body that transports information to and from the brain. The primary principle behind such networks is inspired by how the human neural system processes data and information to learn and develop knowledge. The fundamental component of this concept is the development of new structures for the information processing system. The system is composed of a huge number of highly interconnected processing components known as neurons that collaborate to solve problems and communicate information via synapses (electromagnetic connections). The neurons are tightly linked and grouped into layers. The data is received by the input layer, and the ultimate result is generated by the output layer. Typically, one or more secret layers are placed between the two. The number of hidden layers is determined by the application under consideration. The output of the input layer becomes the input of the hidden layer, while the output of the hidden layer becomes the input of the output layer. This configuration makes anticipating or knowing the precise flow of data challenges. In real-time applications, the layer of input neurons receives data either through input files or directly from electrical sensors. The output layer sends data to the outside world, a secondary computer operation, or other devices such as a mechanical control system. Many hidden levels can exist between these two layers. Many neurons are found in these internal layers in diverse interrelated systems. Each of these hidden neurons' inputs and outputs simply goes to other neurons. All artificial neural networks have a similar structure or topology as shown in Figure 4. In most networks, each neuron in a hidden layer gets signals from all neurons in the layer above it, which is often an input layer. After completing its role, a neuron sends its output to all neurons in the layer below it, creating a feedforward path to the output. These routes of communication between neurons are critical components of neural networks. They are the connections that supply varying levels of strength to an input. These links are classified into two sorts. One causes the following neuron's summing mechanism to add, while the other causes it to remove. In human words, one excites while the other inhibits.
5.
International Research Journal
of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 10 Issue: 02 | Feb 2023 www.irjet.net p-ISSN: 2395-0072 © 2023, IRJET | Impact Factor value: 8.226 | ISO 9001:2008 Certified Journal | Page 775 Fig 4:- An ANN’s Diagram with activation function (Y). As shown in the figure, we have various inputs (X1, X2, ... Xn) and corresponding weights [5] (W1, W2, ...Wn).Wecalculate the weighted sum of someof these inputsandthenpassthem through an activation function [5], which is nothing more than a threshold value. Threshold Value: The threshold value decides whether a neuron fires or not. Above the threshold value, the neuron fires; otherwise, it doesnotfire.Artificialneuralnetworksare made up of a large number of artificial neurons, each with its activation function [1] and a processing element. Perceptron has two modes: training mode and using mode. Training mode: The neuron can be programmedtofireornot fire in response to specific input patterns. Using mode: When a taught input pattern is detected at the input, the related output is set to the current output. We must first train our perceptron, and only then can we use it. We give more weight to more significant factors and less weight to less important factors. Weight W1 is assigned to input X1, similarly, X2 is assigned toW2,andXnisassignedto Wn [3]. Example: X1, X2 and X3 are the inputs. Weights = W1, W2, W3 and Threshold = P Calculation: O = X1 x W1 + X2 x W2 + X3 x W3 If P (Threshold Value) is greater than O, the neuron is activated. Each layer will attempt to obtain a more abstract version of the input. 5. TRAINING AN ANN When a network has been structured for a specific application, it is ready to be trained. The initial weights are picked at random to begin this process. The training, or learning, process then begins. Training methods are classified into three types: supervised, unsupervised, and Reinforcement Training. Both the inputs andtheoutputs are provided in supervised training. Unsupervised training provides the network with inputs but not the expected outputs. The system must then pick which features will be used to group the supplied data. Supervised learning, Reinforcement learning, and Unsupervised learning are the three types of ANN training. There are some drawbacks to adopting supervised learning. These constraints can be bypassed byemployingtheunsupervisedlearningtechnique. A. Supervised Training [5]. The first type of training technique is supervised training. Where we are given both inputs and outputs. Backpropagation is the most often used algorithm for the supervisedtrainingofmulti-layerperceptron’s.Toreducethe error, wepropagatebackwards and updatethe weights after the weighted sum of inputs and travel through the activation function. The output is often referredtoasthestopcondition. If our actual outcome matches the desired output, we can stop executing the algorithm( wehavereachedourgoalstate ). If there is a disparity between the target and actual output, it is due to an error factor. If the factor is discoveredusingthe formula [3], E total = We employ backpropagation [5] to reduce this error by modifying weights ( By increasing and decreasing values of weights ). This is a trial-and-error strategy for achieving a zero-error factor. It is the fastest learning mechanism with high accuracy. Many different tasks, including as classification, regression, and sequence prediction, can be accomplished by supervised learning. For instance, in image classification, an image serves as the input, and the label identifying the object or category in the image serves as the output. Regression produces continuous values, such as the price of a house depending on its characteristics.Insequence prediction, the network learns to foretell the following element in a chain, such as the word that will come next in a sentence. For training, supervised learning needs a lot of labelled data, which can be expensiveandtime-consumingto acquire. Nonetheless, it has been effective in a variety of applications, including computer vision, natural language processing, and speech recognition. B. Unsupervised Training. Unsupervised training is the second type of training. The network is given inputs but not the desired outputs during unsupervised training. The system must then pick which features will be used to group the input data. It is an independent learning process which requires no help from a supervisor and contains an unlabeled dataset. It is used to uncover hidden patterns. Autoencoders are one popular unsupervised learning method used in ANN training. Using neural networks, autoencoders can be trained to encode input data intoa lower-dimensional representationandthen decode it to restore the original space. In order to reduce the
6.
International Research Journal
of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 10 Issue: 02 | Feb 2023 www.irjet.net p-ISSN: 2395-0072 © 2023, IRJET | Impact Factor value: 8.226 | ISO 9001:2008 Certified Journal | Page 776 difference between the input and the reconstructed output, the network is trained. Datacompression, featureextraction, and dimensionality reduction are all tasks that can be accomplished with autoencoders. Clustering is a typical unsupervised learning method used in ANN training. Based on a similarity metric,clustering algorithmscombine similar data points. In unsupervised learning, the network learns to map input data points to the corresponding cluster labels using the output of clustering algorithms. Tasks including anomaly detection, categorization, and recommendation systems can be accomplished using this method. In ANN training,generativeadversarialnetworks(GANs)areanother well-liked unsupervised learning method. A generator network and a discriminator network make up GANs. The discriminator learns to discriminate between actual and created data, while the generator learns to producenewdata samples that mimic the input data. The networks are trained iteratively withthegeneratortryingtotrickthediscriminator and the discriminator trying to classify the data accurately. Before ANNs are fine-tuned with supervised learning, they can be pretrained using unsupervised learning. By giving the network useful properties and lowering the amount of labelled data needed for training, pretraining can enhance network performance. C. Reinforcement Training. The third type is reinforcement training, It is also called as Markov decision process. It learns from feedback and past experiences. It is a long-term iterative process. The more feedback, the more accuracy. It is and combination of supervised andunsupervisedtraining. Actor-critic trainingis a popular technique for ANNs with RL. The performer and the reviewer make up each of the two segments of the network in this strategy. The critic learns to estimate the value function, which is the predicted cumulative reward from a particular state, and the actor learns the policy. In order to update the policy, the valuefunctionoffersfeedback on the effectiveness of the activities made. The actor-critic technique is an iterative processwherethecritic changesthe value function based on the observed rewards and the actor adjusts the policy based onfeedback fromthecritic.Gradient descent is frequently used for the updates, and thegradients are calculated with consideration for the network's parameters 6. CONCLUSIONS In this paper, we discussed how artificial neural networks (ANNs) work, and also an ANN's training stages. ANN has several benefits overthetraditional methodandapplications of ANNs. We may soon witness additional developments in the use of neural networks,givenhowquicklybusinesses are adopting AI and machine learning at the moment. Users everywhere will have access to a wide range of customized options thanks to AI and machinelearning.Neural networks, for instance, can enable the enhanced tailored experience that all mobile and web applications strive to provide you with depending on your search history. Neurology and psychology are also two fields of study that benefit from the study of neural networks. They are frequently employed to examine the inner workings of the brain and model certain aspects of living organizations.Thetransferfunctions,sizeof the training sample, network topology, and weights adjusting technique are only a few examples of the many variables that determine how well ANNs perform. REFERENCES [1] Lippmann, R.P., 1987. An introductiontocomputing with neural nets. IEEE Accost. Speech Signal Process. Mag., April: 4-22. [2]Nam Pham, Hao Yu, Bogdan M. Wilamowski, “Neural Network Trainer through Computer Networks”, 2010 24th IEEE International Conference on Advanced Information Networking and Applications. [3] K Ishthaq Ahamed, Dr. Shaheda Akthar, “Survey on Artificial Neural Network Learning Technique Algorithms”, International Journal of Imaging and Robotics, [Formerly known as the “International ResearchJournal ofEngineering and Technology (IRJET) Volume: 03 Issue: 02 | Feb-2016 [4] Roza Dastres, Mohsen Soori, “Artificial Neural Network Systems”, International Journal of Imaging and Robotics, [Formerly known as the “International Journal of Imaging” (ISSN 0974-0627)] Volume 21; Issue No. 2; Year 2021; Int. J. Imag. Robot. [5] Dr S.Maheswari, “Neural network based individual classification system”, International Research Journal of Engineering and Technology (IRJET) Volume: 03 Issue: 02 | Feb-2016. [6]Happiness Ugochi Dike, Yimin Zhou, Kranthi Kumar Deveerasetty, Qingtian Wu, “Unsupervised Learning Based On Artificial Neural Network: A Review”, 2018 IEEE International Conference on Cyborg and Bionic Systems (CBS). [7]Miguel Antonio Sovierzoski; Fernanda Isabel Marques Argoud; Fernando Mendes de Azevedo, “Evaluation of ANN ClassifiersDuringSupervisedTrainingwithROCAnalysisand Cross Validation”, 2008 International Conference on BioMedical Engineering and Informatics.
Baixar agora