Neural networks have seen an explosion of interest over the last few years and are being successfully applied across an extraordinary range of problem domains, in areas as diverse as finance, medicine, engineering, geology, physics and biology. The excitement stems from the fact that these networks are attempts to model the capabilities of the human brain. From a statistical perspective neural networks are interesting because of their potential use inprediction and classification problems.
Artificial neural networks (ANNs) are non-linear data driven self adaptive approach as opposed to the traditional model based methods. They are powerful tools for modelling, especially when the underlying data relationship is unknown. ANNs can identify and learn correlated patterns between input data sets and corresponding target values. After training,ANNs can be used to predict the outcome of new independent input data. ANNs imitate the learning process of the human brain and can process problems involving non-linear and complex data even if the data are imprecise and noisy. Thus they are ideally suited for the modeling of agricultural data which are known to be complex and often non-linear.
These networks are “neural” in the sense that they may have been inspired by neuroscience but not necessarily because they are faithful models of biological neural or cognitive phenomena. In fact majority of the network are more closely related to traditional mathematical and/or statistical models such as non-parametric pattern classifiers, clustering algorithms, nonlinear filters, and statistical regression models than they are to neurobiology models.
Neural networks (NNs) have been used for a wide variety of applications where statistical methods are traditionally employed. They have been used in classification problems, such as identifying underwater sonar currents, recognizing speech, and predicting the secondary structure of globular proteins. In time-series applications, NNs have been used in predicting stock market performance. As statisticians or users of statistics, these problems are normally solved through classical statistical methods, such as discriminant analysis, logistic regression, Bayes analysis, multiple regression, and ARIMA time-series models. It is, therefore, time to recognize neural networks as a powerful tool for data analysis.
The approaching 4G (fourth generation) mobile communication systems are projected to solve still-remaining problems of 3G (third generation) systems and to provide a wide variety of new services, from high-quality voice to high-definition video to high-data-rate wireless channels. The term 4G is used broadly to include several types of broadband wireless access communication systems, not only cellular telephone systems.One of the terms used to describe 4G is MAGICâ€Mobile multimedia, anytime anywhere, Global mobility support, integrated wireless solution, and customized personal service. As a promise for the future, 4G systems, that is, cellular broadband wireless access systems have been attracting much interest in the mobile communication arena. The 4G systems not only will support the next generation of mobile service, but also will support the fixed wireless networks. This paper presents an overall vision of the 4G features, framework, and integration of mobile communication.
The features of 4G systems might be summarized with one wordâ€integration. The 4G systems are about seamlessly integrating terminals, networks, and applications to satisfy increasing user demands. The continuous expansion of mobile communication and wireless networks shows evidence of exceptional growth in the areas of mobile subscriber, wireless network access, mobile services, and applications. Consumers demand more from their technology. Whether it be a television, cellular phone, or refrigerator, the latest technology purchase must have new features. With the advent of the Internet, the most-wanted feature is better, faster access to information. Cellular subscribers pay extra on top of their basic bills for such features as instant messaging, stock quotes, and even Internet access right on their phones. But that is far from the limit of features; manufacturers entice customers to buy new phones with photo and even video capability. It is no longer a quantum leap to envision a time when access to all necessary information the power of a personal computer , sits in the palm of oneâ„¢s hand. To support such a powerful system, we need pervasive, high-speed wireless connectivity.
A number of technologies currently exist to provide users with high-speed digital wireless connectivity; Bluetooth and 802.11 are examples. These two standards provide very high speed network connections over short distances, typically in the tens of meters. Meanwhile, cellular providers seek to increase speed on their long-range wireless networks. The goal is the same: long-range, high-speed wireless, which for the purposes of this report will be called 4G, for fourth-generation wireless system. Such a system does not yet exist, nor will it exist in todayâ„¢s market without standardization. Fourth-generation wireless needs to be standardized throughout the world due to its enticing advantages to both users and providers.
Artificial brain is a term commonly used in the media to describe research that aims to develop software and hardware with cognitive abilities similar to the animal or human brain. Research investigating "artificial brains" plays three important roles in science:
A thought experiment in the philosophy of artificial intelligence, demonstrating that it is possible, in theory, to create a machine that has all the capabilities of a human being.
A serious long term project to create machines capable of general intelligent action or Artificial General Intelligence. This idea has been popularised by Ray Kurzweil as strong AI (taken to mean a machine as intelligent as a human being).
An example of the first objective is the project reported by Aston University in Birmingham, England where researchers are using biological cells to create "neurospheres" (small clusters of neurons) in order to develop new treatments for diseases including Alzheimer's, Motor Neurone and Parkinson's Disease.
The third objective is generally called artificial general intelligence by researchers. However Kurzweil prefers the more memorable term Strong AI. In his book The Singularity is Near he focuses onwhole brain emulation using conventional computing machines as an approach to implementing artificial brains, and claims (on grounds of computer power continuing an exponential growth trend) that this could be done by 2025. Henry Markram, director of the Blue Brain project (which is attempting brain emulation), made a similar claim (2020) at the Oxford TED conference in 2009.
