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- MIS See Management Information systems. Multimedia
- Neural Networks
Mind MapA diagram used for linking words and ideas to a central key word or idea. It is used to visualize, classify, structure, generate ideas and facilitate problem solving and decision making. Mind maps are useful for organizing individual or collective thought and representing it visually. A mind map can present complex information in an organized, easy-to-understand visual format. A mind map enables us to get the big picture through cascading connections between related topics and sub topics. It helps us to grasp the obstacles and paths so that we can quickly choose the best course of action and assign and manage tasks, resources, timelines and deliverables. A mind map is similar to a semantic network or cognitive map but there are no formal restrictions on the kinds of links used. Most often, the map involves images, words and lines. The elements are arranged intuitively according to the importance of the concepts and organized into groups, branches, or areas. The uniform graphic formulation of the semantic structure of information on the method of gathering knowledge may aid recall of existing memories. MISSee Management Information systems. MultimediaTechnology that combines information available in various formats such as text, audio and video. Multimedia facilitates seamless sharing of knowledge through audio files, pictures and video clips that can be combined with other knowledge objects, records, transactions and discussions. Mind maps and visual thinking tools make extensive use of multimedia features to capture and organize independent or collaborative thought processes. N Neural NetworksA knowledge network that seeks to mimic how human brain functions. One of the key issues in artificial intelligence has been understanding how the human brain works and how to make computers function like the human brain. The human brain is good at recognizing patterns. Human beings can relate current problems to past problems. If computers can detect patterns, they would be extremely useful in solving business problems. Thus a manager in an insurance company would find it useful to identify fraudulent patterns while his counterpart in a mutual fund might be interested in patterns that help him understand how the financial markets will move. Neural networks are used for modeling complex, poorly understood problems for which large amounts of data have been collected. They are especially useful in finding patterns and relationships in massive amounts of data that would be too complicated and difficult for a human being to analyze. Neural networks develop this knowledge by emulating the processing patterns of the biological brain. The brain is a collection of cells called neurons that have many connections to each other. A neuron can be at rest or send a message. A neuron receives input from some cells and sends the output to other cells. A neural network is nothing but a collection of such cells. Units of neural networks can be described by a single number, their “activation” values. Each unit generates an output signal based on its activation. Units are connected to each other such that each connection has an individual “weight”. Each unit sends its output value to all other units to which they have an outgoing connection. Using these connections, the output of one unit can influence the activations of other units. The unit receiving the connections calculates its activation by taking a weighted sum of the input signals. The output is determined by the activation function based on this activation. Networks learn by changing the weights of the connections. Neural networks can identify patterns within data. Indeed, a well designed neural network can identify patterns, even if some data is missing. A neural network has three layers. The input layer receives data from external sources. The processing layer, which has already learned from solving earlier problems, tries to apply more lessons to the new data sets that are fed into the neural network. The output layer transmits the outputs or guesses to the user. Unlike expert systems, which may have to be redesigned when there is a change in the business / domain, neural networks have some capability to learn on their own, as they deal with newer and newer problems. Indeed, what is most exciting about neural networks is the possibility of learning. Neural networks are useful in classifying cases into one category or another — say, whether a loan customer is likely to default or pay back the loan. As they deal with more cases and learn, the classification becomes more accurate. In medicine, neural network applications are used for screening patients for coronary artery disease, for diagnosing patients with epilepsy, and for performing pattern recognition of pathology images. Neural networks can also be used to predict the performance of equities, corporate bond ratings or corporate bankruptcies. In the field of artificial intelligence, neural networks have been applied successfully to speech recognition, image analysis and adaptive control, in order to construct software agents or autonomous robots. Neural networks require a lot of data and a high-powered computing. Considerable amount of time has to be spent in training the neural network, cleaning up the data and preprocessing for better comparison of the data being fed in. Doing the analysis and interpreting results can be very tricky. So these systems require a very knowledgeable user, at least to set up the initial model. Subsequent data may be analyzed with the same model. Neural networks are also something of a “black box”. A particular case will be classified in a particular fashion according to nodes and variable weightings, and is therefore difficult to interpret. Some new neural networking tools hide the complexity from the user and are able to explain to some degree why the system behaves the way it does. Still, many managers do not like them because of difficulties in interpretation. Directory: books books -> The English Arrive in America books -> Active Vocabulary books -> Books we finished XIV books -> The theology of the balaam oracles: a pagan diviner and the word of god books -> The rhetoric of the father books -> Sra: Imagine It!, Themes, Taking a Stand, Ancient Civilizations Ecology, Great Expectations, Earth in Action, Art and Impact, Level 6 books -> You Can If You Try Unit 7 Nonfiction books -> The tregenza family books -> Unconditional hatred Download 0.91 Mb. Share with your friends:
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