Neural networks reflect the behavior of the human brain, allowing computer programs to recognize patterns and solve problems in artificial intelligence, machine learning, and deep learning. Neural networks have a layered structure, taking input data and passing it through multiple layers of nodes that perform computations and transfer the output to the next layer. Through a process of backpropagation, neural networks can learn from experience by adjusting their weights to minimize error rates. They have applications in areas like facial recognition, machine translation, and more.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Vectra AI's foundation lies in the belief that effective use of data science and AI can empower cybersecurity efforts against cyberattacks. They emphasize that AI, combined with human intelligence, can revolutionize Security Operations Centers (SOCs) by automating routine tasks and enhancing threat detection accuracy, especially in the face of sophisticated attacks and complex attack surfaces. This paper aims to provide insights into AI technologies, differentiate their efficacy, and introduce key security-related terms, helping defenders leverage AI effectively in thwarting attacks. Vectra outlines two prominent AI methodologies for threat detection and delves into their patented Attack Signal Intelligence, which detects and correlates attacker behaviors, improving alert accuracy. Vectra AI is a leader in AI-driven threat detection and response, offering coverage across various attack vectors in hybrid and multi-cloud setups, aiding organizations globally in proactively countering cyber threats.
Neural network based numerical digits recognization using nnt in matlabijcses
Â
Artificial neural networks are models inspired by human nervous system that is capable of learning. One of
the important applications of artificial neural network is character Recognition. Character Recognition
finds its application in number of areas, such as banking, security products, hospitals, in robotics also.
This paper is based on a system that recognizes a english numeral, given by the user, which is already
trained on the features of the numbers to be recognized using NNT (Neural network toolbox) .The system
has a neural network as its core, which is first trained on a database. The training of the neural network
extracts the features of the English numbers and stores in the database. The next phase of the system is to
recognize the number given by the user. The features of the number given by the user are extracted and
compared with the feature database and the recognized number is displayed.
Pattern Recognition using Artificial Neural NetworkEditor IJCATR
Â
An artificial neural network (ANN) usually called neural network. It can be considered as a resemblance to a paradigm
which is inspired by biological nervous system. In network the signals are transmitted by the means of connections links. The links
possess an associated way which is multiplied along with the incoming signal. The output signal is obtained by applying activation to
the net input NN are one of the most exciting and challenging research areas. As ANN mature into commercial systems, they are likely
to be implemented in hardware. Their fault tolerance and reliability are therefore vital to the functioning of the system in which they
are embedded. The pattern recognition system is implemented with Back propagation network and Hopfield network to remove the
distortion from the input. The Hopfield network has high fault tolerance which supports this system to get the accurate output.
A Parallel Framework For Multilayer Perceptron For Human Face RecognitionCSCJournals
Â
Artificial neural networks have already shown their success in face recognition and similar complex pattern recognition tasks. However, a major disadvantage of the technique is that it is extremely slow during training for larger classes and hence not suitable for real-time complex problems such as pattern recognition. This is an attempt to develop a parallel framework for the training algorithm of a perceptron. In this paper, two general architectures for a Multilayer Perceptron (MLP) have been demonstrated. The first architecture is All-Class-in-One-Network (ACON) where all the classes are placed in a single network and the second one is One-Class-in-One-Network (OCON) where an individual single network is responsible for each and every class. Capabilities of these two architectures were compared and verified in solving human face recognition, which is a complex pattern recognition task where several factors affect the recognition performance like pose variations, facial expression changes, occlusions, and most importantly illumination changes. Experimental results show that the proposed OCON structure performs better than the conventional ACON in terms of network training convergence speed and which can be easily exercised in a parallel environment.
Artificial Neural Networks: Applications In ManagementIOSR Journals
Â
With the advancement of computer and communication technology, the tools used for management decisions have undergone a gigantic change. Finding the more effective solution and tools for managerial problems is one of the most important topics in the management studies today. Artificial Neural Networks (ANNs) are one of these tools that have become a critical component for business intelligence. The purpose of this article is to describe the basic behavior of neural networks as well as the works done in application of the same in management sciences and stimulate further research interests and efforts in the identified topics.
Artificial Intelligence (A.I.) is a multidisciplinary field whose goal is to automate
activities that presently require human intelligence. Recent successes in A.I. include
computerized medical diagnosticians and systems that automatically customize
hardware to particular user requirements. The major problem areas addressed in A.I. can
be summarized as Perception, Manipulation, Reasoning, Communication, and Learning.
Perception is concerned with building models of the physical world from sensory input
(visual, audio, etc.). Manipulation is concerned with articulating appendages (e.g.,
mechanical arms, locomotion devices) in order to effect a desired state in the physical
world. Reasoning is concerned with higher level cognitive functions such as planning,
drawing inferential conclusions from a world model, diagnosing, designing, etc.
Communication treats the problem understanding and conveying information through
the use of language. Finally, Learning treats the problem of automatically improving
system performance over time based on the system's experience. Many important
technical concepts have arisen from A.I. that unify these diverse problem areas and that
form the foundation of the scientific discipline. Generally, A.I. systems function based
on a Knowledge Base of facts and rules that characterize the system's domain of
proficiency. The elements of a Knowledge Base consist of independently valid (or at
least plausible) chunks of information. The system must automatically organize and
utilize this information to solve the specific problems that it encounters. This
organization process can be generally characterized as a Search directed toward specific
goals. The search is made complex because of the need to determine the relevance of
information and because of the frequent occurrence of uncertain and ambiguous data.
Heuristics provide the A.I. system with a mechanism for focusing its attention and
controlling its searching processes. The necessarily adaptive organization of A.I.
systems yields the requirement for A.I. computational Architectures. All knowledge
utilized by the system must be represented within such an architecture. The acquisition
and encoding of real-world knowledge into A.I. architecture comprises the subfield of
Knowledge Engineering.
KEYWORDS â Artificial Intelligence, Machine Learning, Deep Learning, Encoding,
Subfield, Perception, Manipulation, Reasoning, Communication, and Learning.
Artificial Neural Network and its Applicationsshritosh kumar
Â
Abstract
This report is an introduction to Artificial Neural
Networks. The various types of neural networks are
explained and demonstrated, applications of neural
networks like ANNs in medicine are described, and a
detailed historical background is provided. The
connection between the artificial and the real thing is
also investigated and explained. Finally, the
mathematical models involved are presented and
demonstrated.
Providing Globus Services to Users of JASMIN for Environmental Data AnalysisGlobus
Â
JASMIN is the UKâs high-performance data analysis platform for environmental science, operated by STFC on behalf of the UK Natural Environment Research Council (NERC). In addition to its role in hosting the CEDA Archive (NERCâs long-term repository for climate, atmospheric science & Earth observation data in the UK), JASMIN provides a collaborative platform to a community of around 2,000 scientists in the UK and beyond, providing nearly 400 environmental science projects with working space, compute resources and tools to facilitate their work. High-performance data transfer into and out of JASMIN has always been a key feature, with many scientists bringing model outputs from supercomputers elsewhere in the UK, to analyse against observational or other model data in the CEDA Archive. A growing number of JASMIN users are now realising the benefits of using the Globus service to provide reliable and efficient data movement and other tasks in this and other contexts. Further use cases involve long-distance (intercontinental) transfers to and from JASMIN, and collecting results from a mobile atmospheric radar system, pushing data to JASMIN via a lightweight Globus deployment. We provide details of how Globus fits into our current infrastructure, our experience of the recent migration to GCSv5.4, and of our interest in developing use of the wider ecosystem of Globus services for the benefit of our user community.
Globus Connect Server Deep Dive - GlobusWorld 2024Globus
Â
We explore the Globus Connect Server (GCS) architecture and experiment with advanced configuration options and use cases. This content is targeted at system administrators who are familiar with GCS and currently operateâor are planning to operateâbroader deployments at their institution.
Vectra AI's foundation lies in the belief that effective use of data science and AI can empower cybersecurity efforts against cyberattacks. They emphasize that AI, combined with human intelligence, can revolutionize Security Operations Centers (SOCs) by automating routine tasks and enhancing threat detection accuracy, especially in the face of sophisticated attacks and complex attack surfaces. This paper aims to provide insights into AI technologies, differentiate their efficacy, and introduce key security-related terms, helping defenders leverage AI effectively in thwarting attacks. Vectra outlines two prominent AI methodologies for threat detection and delves into their patented Attack Signal Intelligence, which detects and correlates attacker behaviors, improving alert accuracy. Vectra AI is a leader in AI-driven threat detection and response, offering coverage across various attack vectors in hybrid and multi-cloud setups, aiding organizations globally in proactively countering cyber threats.
Neural network based numerical digits recognization using nnt in matlabijcses
Â
Artificial neural networks are models inspired by human nervous system that is capable of learning. One of
the important applications of artificial neural network is character Recognition. Character Recognition
finds its application in number of areas, such as banking, security products, hospitals, in robotics also.
This paper is based on a system that recognizes a english numeral, given by the user, which is already
trained on the features of the numbers to be recognized using NNT (Neural network toolbox) .The system
has a neural network as its core, which is first trained on a database. The training of the neural network
extracts the features of the English numbers and stores in the database. The next phase of the system is to
recognize the number given by the user. The features of the number given by the user are extracted and
compared with the feature database and the recognized number is displayed.
Pattern Recognition using Artificial Neural NetworkEditor IJCATR
Â
An artificial neural network (ANN) usually called neural network. It can be considered as a resemblance to a paradigm
which is inspired by biological nervous system. In network the signals are transmitted by the means of connections links. The links
possess an associated way which is multiplied along with the incoming signal. The output signal is obtained by applying activation to
the net input NN are one of the most exciting and challenging research areas. As ANN mature into commercial systems, they are likely
to be implemented in hardware. Their fault tolerance and reliability are therefore vital to the functioning of the system in which they
are embedded. The pattern recognition system is implemented with Back propagation network and Hopfield network to remove the
distortion from the input. The Hopfield network has high fault tolerance which supports this system to get the accurate output.
A Parallel Framework For Multilayer Perceptron For Human Face RecognitionCSCJournals
Â
Artificial neural networks have already shown their success in face recognition and similar complex pattern recognition tasks. However, a major disadvantage of the technique is that it is extremely slow during training for larger classes and hence not suitable for real-time complex problems such as pattern recognition. This is an attempt to develop a parallel framework for the training algorithm of a perceptron. In this paper, two general architectures for a Multilayer Perceptron (MLP) have been demonstrated. The first architecture is All-Class-in-One-Network (ACON) where all the classes are placed in a single network and the second one is One-Class-in-One-Network (OCON) where an individual single network is responsible for each and every class. Capabilities of these two architectures were compared and verified in solving human face recognition, which is a complex pattern recognition task where several factors affect the recognition performance like pose variations, facial expression changes, occlusions, and most importantly illumination changes. Experimental results show that the proposed OCON structure performs better than the conventional ACON in terms of network training convergence speed and which can be easily exercised in a parallel environment.
Artificial Neural Networks: Applications In ManagementIOSR Journals
Â
With the advancement of computer and communication technology, the tools used for management decisions have undergone a gigantic change. Finding the more effective solution and tools for managerial problems is one of the most important topics in the management studies today. Artificial Neural Networks (ANNs) are one of these tools that have become a critical component for business intelligence. The purpose of this article is to describe the basic behavior of neural networks as well as the works done in application of the same in management sciences and stimulate further research interests and efforts in the identified topics.
Artificial Intelligence (A.I.) is a multidisciplinary field whose goal is to automate
activities that presently require human intelligence. Recent successes in A.I. include
computerized medical diagnosticians and systems that automatically customize
hardware to particular user requirements. The major problem areas addressed in A.I. can
be summarized as Perception, Manipulation, Reasoning, Communication, and Learning.
Perception is concerned with building models of the physical world from sensory input
(visual, audio, etc.). Manipulation is concerned with articulating appendages (e.g.,
mechanical arms, locomotion devices) in order to effect a desired state in the physical
world. Reasoning is concerned with higher level cognitive functions such as planning,
drawing inferential conclusions from a world model, diagnosing, designing, etc.
Communication treats the problem understanding and conveying information through
the use of language. Finally, Learning treats the problem of automatically improving
system performance over time based on the system's experience. Many important
technical concepts have arisen from A.I. that unify these diverse problem areas and that
form the foundation of the scientific discipline. Generally, A.I. systems function based
on a Knowledge Base of facts and rules that characterize the system's domain of
proficiency. The elements of a Knowledge Base consist of independently valid (or at
least plausible) chunks of information. The system must automatically organize and
utilize this information to solve the specific problems that it encounters. This
organization process can be generally characterized as a Search directed toward specific
goals. The search is made complex because of the need to determine the relevance of
information and because of the frequent occurrence of uncertain and ambiguous data.
Heuristics provide the A.I. system with a mechanism for focusing its attention and
controlling its searching processes. The necessarily adaptive organization of A.I.
systems yields the requirement for A.I. computational Architectures. All knowledge
utilized by the system must be represented within such an architecture. The acquisition
and encoding of real-world knowledge into A.I. architecture comprises the subfield of
Knowledge Engineering.
KEYWORDS â Artificial Intelligence, Machine Learning, Deep Learning, Encoding,
Subfield, Perception, Manipulation, Reasoning, Communication, and Learning.
Artificial Neural Network and its Applicationsshritosh kumar
Â
Abstract
This report is an introduction to Artificial Neural
Networks. The various types of neural networks are
explained and demonstrated, applications of neural
networks like ANNs in medicine are described, and a
detailed historical background is provided. The
connection between the artificial and the real thing is
also investigated and explained. Finally, the
mathematical models involved are presented and
demonstrated.
Providing Globus Services to Users of JASMIN for Environmental Data AnalysisGlobus
Â
JASMIN is the UKâs high-performance data analysis platform for environmental science, operated by STFC on behalf of the UK Natural Environment Research Council (NERC). In addition to its role in hosting the CEDA Archive (NERCâs long-term repository for climate, atmospheric science & Earth observation data in the UK), JASMIN provides a collaborative platform to a community of around 2,000 scientists in the UK and beyond, providing nearly 400 environmental science projects with working space, compute resources and tools to facilitate their work. High-performance data transfer into and out of JASMIN has always been a key feature, with many scientists bringing model outputs from supercomputers elsewhere in the UK, to analyse against observational or other model data in the CEDA Archive. A growing number of JASMIN users are now realising the benefits of using the Globus service to provide reliable and efficient data movement and other tasks in this and other contexts. Further use cases involve long-distance (intercontinental) transfers to and from JASMIN, and collecting results from a mobile atmospheric radar system, pushing data to JASMIN via a lightweight Globus deployment. We provide details of how Globus fits into our current infrastructure, our experience of the recent migration to GCSv5.4, and of our interest in developing use of the wider ecosystem of Globus services for the benefit of our user community.
Globus Connect Server Deep Dive - GlobusWorld 2024Globus
Â
We explore the Globus Connect Server (GCS) architecture and experiment with advanced configuration options and use cases. This content is targeted at system administrators who are familiar with GCS and currently operateâor are planning to operateâbroader deployments at their institution.
Strategies for Successful Data Migration Tools.pptxvarshanayak241
Â
Data migration is a complex but essential task for organizations aiming to modernize their IT infrastructure and leverage new technologies. By understanding common challenges and implementing these strategies, businesses can achieve a successful migration with minimal disruption. Data Migration Tool like Ask On Data play a pivotal role in this journey, offering features that streamline the process, ensure data integrity, and maintain security. With the right approach and tools, organizations can turn the challenge of data migration into an opportunity for growth and innovation.
Cyaniclab : Software Development Agency Portfolio.pdfCyanic lab
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CyanicLab, an offshore custom software development company based in Sweden,India, Finland, is your go-to partner for startup development and innovative web design solutions. Our expert team specializes in crafting cutting-edge software tailored to meet the unique needs of startups and established enterprises alike. From conceptualization to execution, we offer comprehensive services including web and mobile app development, UI/UX design, and ongoing software maintenance. Ready to elevate your business? Contact CyanicLab today and let us propel your vision to success with our top-notch IT solutions.
Modern design is crucial in today's digital environment, and this is especially true for SharePoint intranets. The design of these digital hubs is critical to user engagement and productivity enhancement. They are the cornerstone of internal collaboration and interaction within enterprises.
Into the Box Keynote Day 2: Unveiling amazing updates and announcements for modern CFML developers! Get ready for exciting releases and updates on Ortus tools and products. Stay tuned for cutting-edge innovations designed to boost your productivity.
Code reviews are vital for ensuring good code quality. They serve as one of our last lines of defense against bugs and subpar code reaching production.
Yet, they often turn into annoying tasks riddled with frustration, hostility, unclear feedback and lack of standards. How can we improve this crucial process?
In this session we will cover:
- The Art of Effective Code Reviews
- Streamlining the Review Process
- Elevating Reviews with Automated Tools
By the end of this presentation, you'll have the knowledge on how to organize and improve your code review proces
Software Engineering, Software Consulting, Tech Lead.
Spring Boot, Spring Cloud, Spring Core, Spring JDBC, Spring Security,
Spring Transaction, Spring MVC,
Log4j, REST/SOAP WEB-SERVICES.
Designing for Privacy in Amazon Web ServicesKrzysztofKkol1
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Data privacy is one of the most critical issues that businesses face. This presentation shares insights on the principles and best practices for ensuring the resilience and security of your workload.
Drawing on a real-life project from the HR industry, the various challenges will be demonstrated: data protection, self-healing, business continuity, security, and transparency of data processing. This systematized approach allowed to create a secure AWS cloud infrastructure that not only met strict compliance rules but also exceeded the client's expectations.
Gamify Your Mind; The Secret Sauce to Delivering Success, Continuously Improv...Shahin Sheidaei
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Games are powerful teaching tools, fostering hands-on engagement and fun. But they require careful consideration to succeed. Join me to explore factors in running and selecting games, ensuring they serve as effective teaching tools. Learn to maintain focus on learning objectives while playing, and how to measure the ROI of gaming in education. Discover strategies for pitching gaming to leadership. This session offers insights, tips, and examples for coaches, team leads, and enterprise leaders seeking to teach from simple to complex concepts.
Large Language Models and the End of ProgrammingMatt Welsh
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Talk by Matt Welsh at Craft Conference 2024 on the impact that Large Language Models will have on the future of software development. In this talk, I discuss the ways in which LLMs will impact the software industry, from replacing human software developers with AI, to replacing conventional software with models that perform reasoning, computation, and problem-solving.
How Recreation Management Software Can Streamline Your Operations.pptxwottaspaceseo
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Recreation management software streamlines operations by automating key tasks such as scheduling, registration, and payment processing, reducing manual workload and errors. It provides centralized management of facilities, classes, and events, ensuring efficient resource allocation and facility usage. The software offers user-friendly online portals for easy access to bookings and program information, enhancing customer experience. Real-time reporting and data analytics deliver insights into attendance and preferences, aiding in strategic decision-making. Additionally, effective communication tools keep participants and staff informed with timely updates. Overall, recreation management software enhances efficiency, improves service delivery, and boosts customer satisfaction.
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Science gateways allow science and engineering communities to access shared data, software, computing services, and instruments. Science gateways have gained a lot of traction in the last twenty years, as evidenced by projects such as the Science Gateways Community Institute (SGCI) and the Center of Excellence on Science Gateways (SGX3) in the US, The Australian Research Data Commons (ARDC) and its platforms in Australia, and the projects around Virtual Research Environments in Europe. A few mature frameworks have evolved with their different strengths and foci and have been taken up by a larger community such as the Globus Data Portal, Hubzero, Tapis, and Galaxy. However, even when gateways are built on successful frameworks, they continue to face the challenges of ongoing maintenance costs and how to meet the ever-expanding needs of the community they serve with enhanced features. It is not uncommon that gateways with compelling use cases are nonetheless unable to get past the prototype phase and become a full production service, or if they do, they don't survive more than a couple of years. While there is no guaranteed pathway to success, it seems likely that for any gateway there is a need for a strong community and/or solid funding streams to create and sustain its success. With over twenty years of examples to draw from, this presentation goes into detail for ten factors common to successful and enduring gateways that effectively serve as best practices for any new or developing gateway.
Innovating Inference - Remote Triggering of Large Language Models on HPC Clus...Globus
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Large Language Models (LLMs) are currently the center of attention in the tech world, particularly for their potential to advance research. In this presentation, we'll explore a straightforward and effective method for quickly initiating inference runs on supercomputers using the vLLM tool with Globus Compute, specifically on the Polaris system at ALCF. We'll begin by briefly discussing the popularity and applications of LLMs in various fields. Following this, we will introduce the vLLM tool, and explain how it integrates with Globus Compute to efficiently manage LLM operations on Polaris. Attendees will learn the practical aspects of setting up and remotely triggering LLMs from local machines, focusing on ease of use and efficiency. This talk is ideal for researchers and practitioners looking to leverage the power of LLMs in their work, offering a clear guide to harnessing supercomputing resources for quick and effective LLM inference.
Check out the webinar slides to learn more about how XfilesPro transforms Salesforce document management by leveraging its world-class applications. For more details, please connect with sales@xfilespro.com
If you want to watch the on-demand webinar, please click here: https://www.xfilespro.com/webinars/salesforce-document-management-2-0-smarter-faster-better/
3. 1
1. Introduction:
Neural networks reflect the behavior of the human brain, allowing computer programs to
recognize patterns and solve common problems in the fields of AI, machine learning, and deep
learning.
2. Artificial Intelligence:
The intelligence proved by machines is known as Artificial Intelligence. Artificial Intelligence
has grown to be very popular in todayâs world. It is the recreation of natural intelligence in
machines that are programmed to learn and impressionist the actions of humans. These
machines are able to learn with experience and perform human-like tasks. As technologies such
as AI continue to grow, they will have a great impact on our quality of life. Itâs but natural that
everyone today wants to connect with AI technology somehow, may it be as an end-user or
following a career in Artificial Intelligence.[1]
2.1 Introduction to Artificial Intelligence
The short answer to What is Artificial Intelligence is that it depends on who you ask.
A layman with a brief understanding of technology would link it to robots. Theyâd say
Artificial Intelligence is a terminator like-figure that can act and think on its own.
If you ask about artificial intelligence to an AI researcher, (s)he would say that itâs a set of
algorithms that can produce results without having to be explicitly instructed to do so.[1]
2.2 How Artificial Intelligence Works?
To understand How Artificial Intelligence actually works, one needs to deep dive into the
various sub-domains of Artificial Intelligence and understand how those domains could be
applied to the various fields of the industry.
ï· Machine Learning: ML teaches a machine how to make inferences and decisions
based on past experience. It identifies patterns, analyses past data to infer the
meaning of these data points to reach a possible conclusion without having to
involve human experience. This automation to reach conclusions by evaluating data,
saves a human time for businesses and helps them make a better decision.
ï· Neural Network: Neural Networks work on the similar principles as of Human
Neural cells. They are a series of algorithms that captures the relationship between
various underlying variables and processes the data as a human brain does.
2.3 Advantages
ï· Reduction in human error
ï· Available 24x7
ï· Faster and more accurate decisions
ï· Improves security
ï· Digital assistance
4. 2
ï· Medical applications
ï· Helps in repetitive work
3. Machine Learning
Machine learning is the study of computer algorithms that can improve automatically through
experience and by the use of data. It is seen as a part of artificial intelligence.
Machine learning (ML) is a type of artificial intelligence (AI) that allows software
applications to become more accurate at predicting outcomes without being explicitly
programmed to do so. Machine learning algorithms use historical data as input to predict new
output values. The process of learning begins with observations or data, such as examples,
direct experience, or instruction, in order to look for patterns in data and make better decisions
in the future based on the examples that we provide. The primary aim is to allow the computers
learn automatically without human intervention or assistance and adjust actions accordingly.
3.1 Machine Learning Methods
Machine learning algorithms are often categorized as supervised or unsupervised.
3.1.1 Supervised Learning
It can apply what has been learned in the past to new data using labeled examples to predict
future events. Starting from the analysis of a known training dataset, the learning algorithm
produces an inferred function to make predictions about the output values. The system is able
to provide targets for any new input after sufficient training. The learning algorithm can also
compare its output with the correct, intended output and find errors in order to modify the
model accordingly.
3.1.2 Unsupervised Learning
It is used when the information used to train is neither classified nor labeled. Unsupervised
learning studies how systems can infer a function to describe a hidden structure from unlabeled
data. The system doesnât figure out the right output, but it explores the data and can draw
inferences from datasets to describe hidden structures from unlabeled data.
3.1.3 Semi-supervised Learning
It falls somewhere in between supervised and unsupervised learning, since they use both
labeled and unlabeled data for training â typically a small amount of labeled data and a large
amount of unlabeled data. The systems that use this method are able to considerably improve
learning accuracy. Usually, semi-supervised learning is chosen when the acquired labeled data
requires skilled and relevant resources in order to train it / learn from it. Otherwise, acquiring
unlabeled data generally doesnât require additional resources.
5. 3
4. Neural Network
âNeuralâ word derived from âneuronsâ and âNetworkâ means âcombiningâ. So Neural Network
means combining neurons together.
Input data Meaning Neural Network implementation
Neural networks reflect the behavior of the human brain, allowing computer programs to
recognize patterns and solve common problems in the fields of AI, machine learning. Artificial
neural networks (ANN) have been developed as generalizations of mathematical models of
biological nervous systems. An Artificial Neural Network is a network of collections of very
simple processors ("Neurons")
4.1 Layering Structure
A neural network has three layers in its structure.
ï· First layer is input layer which is directly interact with external worlds
ï· Second layer is of hidden unit where computation is done according to function
provided
ï· Last layer is output layer from where we get output.[4]
Layer 2
(Hidden layer)
Layer 1 Layer 3
(Input layer) (Output layer)
4.2 Applications of Neural Network
ï· Facial Recognition
ï· Real-Time translation
6. 4
4.3 Explanation
Neural Network form the base of deep learning, a subfield of machine learning where
algorithms are inspired by the structure of human brain. Neural Networks take in data and train
themselves to recognize the patterns in this data and then predict the outputs.
4.3.1 Back propagation
Backpropagation is the essence of neural network training. It is the method of fine-tuning the
weights of a neural network based on the error rate obtained in the previous epoch (i.e.,
iteration). Proper tuning of the weights allows you to reduce error rates and make the model
reliable by increasing its generalization.
The back propagation (BP) neural network algorithm is a multi-layer feedforward network
trained according to error back propagation algorithm and is one of the most widely applied
neural network models. BP network can be used to learn and store a great deal of mapping
relations of input-output model, and no need to disclose in advance the mathematical equation
that describes these mapping relations. Its learning rule is to adopt the steepest descent
method in which the back propagation is used to regulate the weight value and threshold
value of the network to achieve the minimum error sum of square.[2]
8. 6
5. References
[1] https://www.mygreatlearning.com/blog/what-is-artificial-intelligence/
[2] https://www.ibm.com/cloud/learn/neural-networks
[3] https://www.expert.ai/blog/machine-learning-definition/
[4] Kumar, K. and Thakur, G.S.M., 2012. Advanced applications of neural networks and
artificial intelligence: A review. International journal of information technology and computer
science, 4(6), p.57.
[5] Li, J., Cheng, J.H., Shi, J.Y. and Huang, F., 2012. Brief introduction of back propagation
(BP) neural network algorithm and its improvement. In Advances in computer science and
information engineering (pp. 553-558). Springer, Berlin, Heidelberg.