Some of the key design issues are listed below- 1. Every layer needs a mechanism for identifying transmitters and receivers. Because a network generally contain several computers, some form of addressing is needed in order to specify a specific computer. 2. Decision should be made about the rules for data transfer. Among the following three modes of data communication one may be selected. a) Simplex Communication:- Data only travels in one direction. For example, home television set. b) Half-duplex Communication:- Data can travel in either direction, but not simultaneously. For example, wireless. c) Full-duplex Communication:- Data travels in both directions at once. For example, telephonic conversation. The protocol must also determine about the number of logical channels per connection and about their priorities. 3. The layer should have some means for error detection and error correction. 4. The layer should have some mechanism for orderly Transfer of data, this involves the capabi

Layered Network Architecture Layering is a widely accepted structuring technique  in designing a communication system. A set of layers and protocols is called a network architecture. To reduce their design complexity, networks are organized as a series of layers, each one built on the layer below it. The number of layers, their contents and their functions varies from network to network. However, in all networks, the purpose of each layer is to offer certain services to the higher layers, protecting those layers from the implementation details of services. The communication between the two machines or parties is done by protocols. A protocols is defined as a set of rules governing the exchange of data between two separate entities or application programs. These conventions when standardized are called protocols. Violating the protocol will make communication more difficult, if not impossible. Advantages Layering reduces the design complexity of the network. In a layered architecture, e

 Back Propagation Back Propagation is the essence of neural net training. It is the practice of finetuning the weights of a neural net based on the error rate. obtained in the previous epoch. proper tuning of the weights ensures lower error rates, making the model model reliable by increasing its generalization. Advantages The computing time is minimized if the weights chosen are small at the beginning. The mathematical formula of back propagation can be applied to any network. Disadvantages It has more number of learning steps, and also the learning phase has intensive calculations. The training may sometimes cause temporal instability to  the system The network may get trapped in a local minima. Applications Data compression Image compression Face recognition Optical character recognition Control problems Non-linear simulation Fault detection problem Load forecasting problem https://rgpvnotesforcsestudents.blogspot.com/2021/03/neural-network-architecture.html https://rgpvnotesforcses

  Neural Network Architectures An artificial neural network is a data processing system consists of a large number of highly interconnected processing elements (artificial neural) in an architecture inspired by the human brain. An artificial neural network (ANN) is represented using digraph. On the basis of learning machanism, there are several classes of neural network. In general, we may identify Three fundamentaly different classes of network architectures. 1. Single Layer Feedforward Network:- There are two layers input layer and output layer, in Single Layer Feedforward Network. The input layer neurons receive the input signals and the output layer neurons receive the output signals. 2. Multilayer Feedforward Network:- the Multilayer Feedforward Network comprises of multiple layers. Thus, this type of Architecture besides processing an input and output layer also contain one or more intermediary layer known as hidden layers. 3. Recurrent Network:- A recurrent network distinguishes

 Activation Function:- An activation function is a very important feature of an artificial neural network, basically decide whether the neural should be activated or not.  In Artificial Neural Network, the activation function defines the output of that node given an input or set of inputs. Types of activation function:- Linear Function  Non-linear Function Binary Step Function The role of activation function in artificial neural  network:- The role of the activation function can be understood by an example, Suppose a person is carrying out same work. Some force or activation may be given to make the work more efficient and to get exact output. This activation aids in obtaining the exact output. In a similar manner the activation function is applied over the net input to determine the ANN output. Linear and Non-linear Function are:- Identity Function Binary Step Function Sigmoidal Function            a) Binary Sigmoidal Function            b) Bipolar Sigmoidal Function                 4

 Statistical Analysis of Data Statistical data analysis is a procedure of performing various statistical operations. it is a kind of quantitative research which seeks to quantify the data, and typically, applies some from of statistical analysis. quantitative data basically involves descriptive data, such as survey data and observational data. statistical data analysis generally involves some from of statistical tools, which a layman cannot perform without having any statistical knowledge. there are various software packages to perform statistical data analysis. this software includes statistical analysis system (SAS), statistical packages for the social science (SPSS), stat soft, etc. Data in statistical data analysis consists of variable(s). sometimes the data is univariate or multivariate. Depending upon the number of variables, the researcher performs different statistical techniques. If the data in statistical data analysis is multiple in numbers, then several multi-variates can b

  Data Link Layer Data link layer is the second layer of the open system interconnection (OSI) reference model. The   data link layer together with physical layer and the interconnecting medium provide a data link connection for reliable transfer of data bits over an imperfect physical connection. It improves the bit transport service of the physical layer by controlling the errors. The data link layer includes error control, flow control and the associated link management functions. This layer receives the data from the next higher layer and adds some control bits to a block of data bits to transfer to the other devices. The block of data along with the control bits is called frame, which is handed over to the physical   layer. The physical layer convert the bit into an electrical signal for transmission over the interconnecting transmission medium. The electrical signal is converted back to bits form by the physical layer at the receiving side and the frame is handed over to the da

  Computer Network Topologies The term topology refers to the way a network is laid out, either physically or logically. The connection of two or more devices forms a link and two or more links forms a topology. The topology of a network is the geometric   representation of the relationship of all the links and linking devices to one another. Basically, there are five topologies – mesh, star, tree, bus, and ring. 1)       Mesh:- Every devices has a dedicated point-to-point link to each other devices in a mesh topology. Therefore a fully connected mesh network has n(n-1)/2 physical channels to link n devices.           Mesh topology has several advantages:- a)       The use of dedicated links guarantees that each connection can carry its own data load,   therefore minimizing the traffic problems which can take place if links much be shared by multiple devices. b)       It is robust. It does not incapacitate the entire system when one link damages. c)        It enforces priv

Advantages of machine learning The advantages of machine learning are as follows-        Accurate       Automated       Fast       Customizable        Scalable   1)       Accurate: - Machine learning uses data to discover the optimal decision making engine for your problem. As you collect more data, the accuracy can increase automatically. 2)        Automated: - as answers are validated or discarded, the machine learning model can learn new patterns automatically. This allows users to embed machine learning directly into an automated workflow. 3)        Fast: - Machine learning can generate answers in a matter of milliseconds as new data streams in, allowing systems to react in real time. 4)       Customizable: - Many data-driven problems can be addressed with machine learning. Machine learning models are custom built from your own data, and can be configured to optimize whatever metric drives your business. 5)       Scalable:- As your business grows, machine learn

  Application of computer network   Some of the network applications in different fields are the following-     Marketing and Sales      Manufacturing     Financial Services     Teleconferencing     Cable Television     Information Services     Electronic Messaging     Electronic Data Interchange (EDI)    Directory Services   Cellular Telephone   1.         Marketing and Sales:-   Computer networks are widely used in both marketing and sales firms. These are used by the  marketing professionals to collect, exchange, and analyzes data relating to customer  requirements and product development cycles.         2.        Manufacturing:-     Now a days, computer networks are used in several aspects of manufacturing, including the  manufacturing process itself. 3.        Financial Services:-  In present, financial services are completely dependent on computer and networks. Main applications are credit history searches, foreign exchange and investment servic

  Computer Networking goals with its classification The most important goals of computer networks are- 1)       Reliability Goals 2)       Performance Goals 3)       Security Goals 4)       Network Cast   1)        Reliability Goals:-  Network reliability can be measured by the networks robustness in a catastrophe, failure frequency    and average down time of network. These are described as follows- a)       Catastrophe b)       Failure Frequency c)        Average Down Time   a)        Catastrophe:-  Network should be protected from catastrophe events like theft or earthquake. One protection  of network against these is a reliable system to backup network software.           b)       Failure Frequency:-    A network which fails rarely is good for a user but a network which fails oftently is of little  value to a user.                 c)        Average Down Time:-   How long does it take to restore service is called down time. A network with less value of

https://drive.google.com/file/d/1h3yY8tN8Jh7xkwO_qWplvvqbbwckU-Ch/view?usp=drivesdk   In the above link you can check all the information about framing in detail.  Framing To service the network layer, data link layer uses the service provided to it by the physical layer. Physical layer accepts the raw bit stream and delivers it to destination. This bit stream may contain error i.e., number of bits received may not be equal to number of bits transmitted. various simple methods used for framing are- a)          Character count b)       Starting and ending character, with character stuffing c)        Starting and ending flags, with bit stuffing. The different types of framing protocols are as follows:- 1)       Character-oriented Protocols 2)       Bit-oriented protocols 1)        Character-oriented Protocols The format of a frame in a character-oriented protocol . Data to be carried are 8-bit characters from a coding system like ASCII in a character-oriented protocol.