ARITIFICIAL NEURAL NETWORKS BEGIINER TOPIC

ANN

1. Unit I – Introduction to Neural Networks Introduction Humans and Computers Organization of the Brain Biological and Artificial Neuron Models Characteristics of ANN McCulloch-Pitts Model Potential Applications of ANN .

2. Neural Networks What is Neural Net ? A neural net is an artificial representation of the human brain that tries to simulate its learning process. An artificial neural network (ANN) is often called a "Neural Network" or simply Neural Net (NN). Traditionally, the word neural network is referred to a network of biological neurons in the nervous system that process and transmit information. Artificial neural network is an interconnected group of artificial neurons that uses a mathematical model or computational model for information processing based on a connectionist approach to computation.

3. The artificial neural networks are made of interconnecting artificial neurons which may share some properties of biological neural networks. Artificial Neural network is a network of simple processing elements (neurons) which can exhibit complex global behavior, determined by the connections between the processing elements and element parameters

4. conventional computers The conventional computers are good for - fast arithmetic and does what programmer programs, ask them to do The conventional computers are not so good for - interacting with noisy data or data from the environment, massive parallelism, fault tolerance, and adapting to circumstances. The neural network systems help where we cannot formulate an algorithmic solution The von Neumann machines are based on the processing/memory abstraction of human information processing

5. conventional computers The human nervous system may be viewed as a three-stage system, as depicted in the block diagram Central to the system is the brain, represented by the neural (nerve) net, which continually receives information, perceives it, and makes appropriate decisions . . The arrows pointing from left to right indicate the forward transmission of information-bearing signals through the system. The arrows pointing from right to left signify the presence of feedback in the system The receptors convert stimuli from the human body or the external environment into electrical impulses that convey information to the neural net (brain). The effectors convert electrical impulses generated by the neural net into d responses as system outputs .

6. Human brain Ramon y Cajal (1911), introduced the idea of neurons as structural constituents of the brain. Typically, neurons are five to six orders of magnitude slower than silicon logic gates; events in a silicon chip happen in the nanosecond (10-9 s) range, whereas neural events happen in the millisecond (10-3 s) range. It is estimated that there are approximately 10 billion neurons in the human cortex, and 60 trillion synapses or connection. The net result is that the brain is an enormously efficient structure. Synapses are elementary structural and functional units that mediate the interactions between neurons In the brain there are both small-scale and large-scale anatomical organizations, and different functions take place at lower and higher levels. Figure shows a hierarchy of interwoven levels of organization that has emerged from the extensive work done on the analysis of local regions in the brain.

7. conventional computers .

8. Biological Neuron . The human brain consists of a large number, more than a billion of neural cells that process information. Each cell works like a simple processor. The massive interaction between all cells and their parallel processing only makes the brain's abilities possible Biological neuron consists of a cell nucleus which receives input from other neurons through a web of input terminals, or branches called dendrites Dendrites are branching fibers that extend from the cell body or soma. Soma or cell body of a neuron contains the nucleus and other structures, support chemical processing and production of neurotransmitters The combination of dendrites is often referred to as a dendritic tree, which receives excitatory or inhibitory signals from other neurons via an electrochemical exchange of neurotransmitters 5. Axon is a singular fiber carries information away from the soma to the synaptic sites of other neurons (dendrites and somas), muscles, or glands

9. . Axon hillock is the site of summation for incoming information. At any moment, the collective influence of all neurons that conduct impulses to a given neuron will determine whether or not an action potential will be initiated at the axon hillock and propagated along the axon Myelin Sheath consists of fat-containing cells that insulate the axon from electrical activity. This insulation acts to increase the rate of transmission of signals Nodes of Ranvier are the gaps (about 1 μ m) between myelin sheath cells long axons are Since fat serves as a good insulator, the myelin sheaths speed the rate of transmission of an electrical impulse along the axon. Synapse is the point of connection between two neurons or a neuron and a muscle or a gland. Electrochemical communication between neurons takes place at these Junctions. Terminal Buttons of a neuron are the small knobs at the end of an axon that release chemicals called neurotransmitters

10. When humans meet new people or see new things every day, they learn what they look like and how they evolve with time. This is achieved by making minor alterations to the neural networks residing in their brains as they evolve

11. Dendrites receive activation from other neurons. ■ Soma processes the incoming activations and converts them into output activations. ■ Axons act as transmission lines to send activation to other neurons. ■ Synapses the junctions allow signal transmission between the axons and dendrites. ■ The process of transmission is by diffusion of chemicals called neuro-transmitter s

12. Artificial Neuron Model Artificial Neural Network (ANN) tries to approximate the structure of human brain and are fed with massive amount of data to learn, all at once. Neural network architecture is arranged into layers, where each layer consists of many simple processing units called nodes, further connected to several nodes in the layers above and below it The data is fed into the lowest layer which is then relayed to the next layer. However, ANNs can also learn based on a pre-existing representation.

13. Artificial Neuron Model This process is called fine-tuning and consists of adjusting the weights from a pre-trained network topology at a relatively slow learning rate to perform well on newly supplied input training data. While artificial neural nets were initially designed to function like biological neural networks, the neural activity and capability in our brains is far more complex than suggested by artificial neurons. Unlike Human Neural Networks, ANN cannot yet be trained to work well for many heterogeneous tasks simultaneously. .

14. Artificial Neuron Model

15. Artificial Neuron Model 1- Information processing occurs at many simple elements called neurons. 2-Signals are passed between neurons over connection links. 3-Each connection link has an associated weight which, in a typical neural net, multiplies the signal transmitted. 4-Each neuron applies an action function (usually nonlinear) to its net input (sum of weighted input signals) to determine its output signal.

16. Major Differences between these two are -  Field of application: ANNs are specialized . They can perform one task. They might be perfect at playing chess, but they fail at playing go (or vice versa). Biological neural networks can learn completely new tasks.  Signal transport and processing : The human brain works asynchronously, ANNs work synchronously.  Parameter count : Humans have many billions of adjustable parameters. Even the most complicated ANNs only have several million learnable parameters.  Training algorithm : ANNs use Gradient Descent for learning. Human brains use something different (but we don't know what)  Processing speed : Single biological neurons are slow, while standard neurons in ANNs arefast.  Topology : Biological neural networks have complicated topologies, while ANNs are often ina tree structure  Power consumption : Biological neural networks use very little power compared to artificialnetworks.  Training time : Biological networks usually don't stop / start learning. ANNs have differentfitting (train) and prediction (evaluate) phase s.

17. An artificial neuron is a mathematical function conceived as a simple model of a real (biological) neuron. The McCulloch-Pitts Neuron s. This is a simplified model of real neurons, known as a Threshold Logic Unit.The first computational model of a neuron was proposed by Warren MuCulloch (neuroscientist) and Walter Pitts (logician) in 1943 .

18. A set of input connections brings in activations from other neurons. A processing unit sums the inputs, and then applies a non-linear activation function (i.e. squashing / transfer / threshold function). An output line transmits the result to other neurons Basic Elements of neuron Neuron consists of three basic components - weights, thresholds, and a single activation function. . . .

19. Threshold for a Neuron

20. ANN is central to several AI concepts and applications such as machine learning and deep learning, natural language processing characteristics of ANNs 1. It Is Central to a Machine Learning Subset Called Deep Learning Machine learning equips computer systems with the capabilities to learn from training datasets. Deep learning is one of its subsets. It has advantages over other machine learning models because it uses an artificial neural network that surpasses the capabilities of traditional networks because it can process and learn from huge amounts of data 2. It Is an Algorithm Modeled After Biological Neural Systems An ANN is an algorithm and a computational model modeled after the human brain.An algorithm is a set of rules or instructions followed in calculations, computer applications, or other problem-solving operations. A particular ANN has a set of rules that simulate the electrical activity within a biological neural system

21. 3 . It Can Be a Hardware-Based Network or a Software-Based Network There are two types of artificial neural networks based on their structure and properties: physical hardware-based and software-based neural networks. Physical neural networks depend on the hardware components used to emulate neurons. Software-based neural networks are algorithms or digitized computer models written in computer language. 4. It Has Three Major Components Connected Via Artificial Neurons Note that the simulated biological system is a multi-layer network architecture with three major components called main layers: input layer, hidden layers, and output layer. These main layers are connected by network nodes called artificial neurons or neurodes. Each neuron can process input and forward output to other neurons in the network.

22. 5. It Has Multiple Hidden Layers in its Network Architecture The defining characteristic of an artificial neural network is its multiple hidden layers in its network architecture. Note that traditional or shallow networks have one to two hidden layers. An ANN has hidden layers that can range from several to hundreds. Multiple layers can perform much more complex processing and representation of data 6. It Has Expanded to Different Types with Different Characteristics There are also different types of artificial neural networks defined by their unique characteristics and applications. These include convolution neural networks or CNNs, which are useful for implementing computer vision, and recurrent neural networks or RNNs, which are proficient in natural language processing and large language models..

23. Applications of Neural Networks Neural Networks are regulating some key sectors including finance, healthcare, and automotive. As these artificial neurons function in a way similar to the human brain. They can be used for image recognition, character recognition and stock market predictions 1.Facial Recognition Facial Recognition Systems are serving as robust systems of surveillance. Recognition Systems matches the human face and compares it with the digital images. They are used in offices for selective entries. The systems thus authenticate a human face and match it up with the list of IDs that are present in its database. Convolutional Neural Networks (CNN) are used for facial recognition and image processing. Large number of pictures are fed into the database for training a neural network. The collected images are further processed for training

24. 2. Stock Market Prediction Investments are subject to market risks. It is nearly impossible to predict the upcoming changes in the highly volatile stock market. To make a successful stock prediction in real time a Multilayer Perceptron MLP (class of feedforward artificial intelligence algorithm) is employed. MLP comprises multiple layers of nodes, each of these layers is fully connected to the succeeding nodes. Stock’s past performances, annual returns, and non profit ratios are considered for building the MLP model. 3. Social Media Artificial Neural Networks are used to study the behaviours of social media users. Data shared everyday via virtual conversations is tacked up and analyzed for competitive analysis. Neural networks duplicate the behaviours of social media users. Post analysis of individuals' behaviours via social media networks the data can be linked to people’s spending habits. Multilayer Perceptron ANN is used to mine data from social media applications.

25. Aerospace Aerospace Engineering is an expansive term that covers developments in spacecraft and aircraft. Fault diagnosis, high performance auto piloting, securing the aircraft control systems, and modeling key dynamic simulations are some of the key areas that neural networks have taken over Time Delay Neural Networks are used for position independent feature recognition. The algorithm thus built based on time delay neural networks can recognize patterns Defence Defence is the backbone of every country. Every country’s state in the international domain is assessed by its military operations. Neural Networks also shape the defence operations of technologically advanced countries.Neural networks are used in logistics, armed attack analysis, and for object location. They are also used in air patrols, maritime patrol, and for controlling automated drones

26. 6. Healthcare The age old saying goes like “Health is Wealth”. Modern day individuals are leveraging the advantages of technology in the healthcare sector. Convolutional Neural Networks are actively employed in the healthcare industry for X ray detection, CT Scan and ultrasound.As CNN is used in image processing, the medical imaging data retrieved from aforementioned tests is analyzed and assessed based on neural network models 7.Signature Verification and Handwriting Analysis Signature Verification , as the self explanatory term goes, is used for verifying an individual’s signature. Banks, and other financial institutions use signature verification to cross check the identity of an individual. Artificial Neural Networks are used for verifying the signatures. ANN are trained to recognize the difference between real and forged signatures

27. . Weather Forecasting The forecasts done by the meteorological department were never accurate before artificial intelligence came into force. Weather Forecasting is primarily undertaken to anticipate the upcoming weather conditions beforehand Multilayer Perceptron (MLP), Convolutional Neural Network (CNN) and Recurrent Neural Networks (RNN) are used for weather forecasting

28. Activation functions used in ANNs The activation function denoted by defines the output of a neuron in terms of the induced local field v. Here we identify three basic types of activation functions: 1. Threshold Function 2. Piecewise-Linear Function 3. Sigmoid Function