Submitted By:-

Purnima

Department of Bioinformatics

• What is ANN?

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• An ANN is a theoretical mathematical model of the human neural network. It is an information processing system based on the structure and function of the human neural network. With the development of neural network technology, the use of ANNs is becoming more and more extensive, and their application fields are also expanding.
• An ANN is a computer network system which imitates the human neural structure, and many relatively independent artificial neurons are connected to form a network, which mimics the way that biological nerves process information to solve problems. McCulloch and Pitts proposed the earliest ANN in 1943 for logic operations. In 1949, Hebb proposed a set of rules that mimic the way that the human nervous system learns

BASIC CONCEPT OF ANN

• ANN is replica of neuron system in human brain. The human brain is composed by billions neuron which are interconnected each other .
• A nerve cell neuron is a special biological cell that processes information. According to an estimation, there are huge number of neurons, approximately 10¹¹ with numerous interconnections, approximately 10^15.

SCHEMATIC DIAGRAM OF ANN�

WORKING OF A BIOLOGICAL NEURON�

• Dendrites − They are tree-like branches, responsible for receiving the information from other neurons it is connected to.
• Soma − It is the cell body of the neuron and is responsible for processing of information, they have received from dendrites.
• Axon − It is just like a cable through which neurons send the information.
• Synapses − It is the connection between the axon and other neuron dendrites.

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STRUCTURE AND PRINCIPLE OF ANNS�

• An ANN is a hierarchical network structure made up of multiple neurons connected by a specific rule, which are divided into an input layer, hidden layer, and output layer. The working performance of an ANN is directly related to the training samples.
• If the training samples are incorrect, too few, or too similar, the working range and ability of the ANN are significantly reduced. In other words, the training sample is the teacher for the ANN. Therefore, the more training samples, the more correct and stronger the ANN's ability

MODEL OF ARTIFICIAL NEURAL NETWORK�

For the above general model of artificial neural network, the net input can be calculated as follows −

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yin=x1.w1+x2.w2+x3.w3…xm.wmyin=x1.w1+x2.w2+x3.w3…xm.wm

i.e.,

Net input yin=∑mixi.wiyin=∑imxi.wi

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The output can be calculated by applying the activation function over the net input.

Y=F(yin)Y=F(yin)

Output = function netinputcalculated

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CHARACTERISTICS OF ARTIFICIAL NEURAL NETWORK

• It is neutrally implemented mathematical model.

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• Information stored in the neurons are basically the weighted linkage of neurons.

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• The input signals arrive at the processing elements through connections and connecting weights.
• The collective behavior of the neurons describes its computational power, and no single neuron carries specific information .

BACKGROUND AND SIGNIFICANCE OF ARTIFICIAL INTELLIGENCE�

• With the development of computer and communication technology, artificial intelligence (AI) has been applied in various industries in recent years. We are in the era of big data, and the effective use of these data in combination with existing AI technologies is a hot research topic at present.
• Machine learning (ML) can help people transform enormous data resources into useful knowledge and information and help in decision-making.

Applications contd….

• Artificial neural networks (ANNs) were designed to simulate the biological nervous system, where information is sent via input signals to a processor, resulting in output signals. ANNs are composed of multiple processing units that work together to learn, recognize patterns, and predict data. 
• ANNs do not require regimented experimental design and have the ability to function even with incomplete data. They can be used in multifaceted, nonlinear systems with applications in the field of pharmacokinetic modeling.
• As a form of artificial intelligence, artificial neural networks (ANNs) have the advantages of adaptability, parallel processing capabilities, and non-linear processing. They have been widely used in the early detection and diagnosis of tumors.

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• It would be easier to do proper valuation of property, buildings, automobiles, machinery etc. with the help of neural network.

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• Neural network is suitable for the research on Animal behavior, predator/prey relationships and population cycles .

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Thank you