Efficient Prediction System Using Artificial Neural Networks

Efficient Prediction System Using Artificial Neural Networks Jay Patel Abstract- Predicting is making claims about something that will happen, often based on information from past and from current state. Neural networks can be used for prediction with various levels of success. The neural network is trained from the historical data with the hope that it will discover hidden dependencies and that it will be able to use them for predicting into future. It is an approach for making prediction efficient using best features on which prediction is more dependent. Keywords: Artificial Neural Networks; Feature set; Profiles INTRODUCTION Artificial neural networks are computational models inspired by animal central nervous systems (in particular the brain) that are capable of machine learning and pattern recognition. They are usually presented as systems of interconnected neurons that can compute values from inputs by feeding information through the network. For example, in a neural network for handwriting recognition, a set of input neurons may be activated by the pixels of an input image representing a letter or digit. The activations of these neurons are then passed on, weighted and transformed by some function determined by the networks designer, to other neurons, etc., until finally an output neuron is activated that determines which character was read. Mainly three types of ANN models are present single layer feed forward network, Multilayer feed forward network and recurrent network Single layer feed forward network consist of only one input layer and one output layer. Input layer neurons receive the input sig nals and output layer receives output signals. In a feed forward network the output of the network does not affect the operation of the layer that is producing this output. In a feedback network however the output of a layer after the layer being fed back into, can affect the output of the earlier layer. Essentially the data loops through the two layers and back to start again. This is important in control circuits, because it allows the result from a previous calculation to affect the operation of the next calculation. This means that the second calculation can take into account the results of the first calculation, and be controlled by them. Weiners work on cybernetics was based on the idea that feedback loops were a useful tool for control circuits. In fact Weiner coined the termcybernetics based on the Greek kybernutos or metallic steersman of a fictional boat mentioned in the Illiad. Neural models ranged from complex mathematical models with Floating point outputs to simple state machines with a binary output. Depending on w hether the neuron incorporates the learning mechanism or not, neural learning rules can be as simple as adding weight to a synapse each time it fires, and gradually degrading those weights over time, as in the earliest learning rules, Delta rules that accelerate the learning by applying a delta value according to some error function in a back propagation network, to Pre-synaptic/Post-synaptic rules based on biochemistry of the synapse and the firing process. Signals can be calculated in binary, linear, non-linear, and spiking values for the output. Figure 1. ANN Models Multilayer feed forward network consist of input, output and one more addition than single layer feed forward is hidden layer. Computational units of hidden layer are called hidden neurons. In Multilayer Feed Forward Network there must be only one input layer and one output layer and hidden layers can be of any numbers. There is only one difference in recurrent network from feed forward networks is that there is at least one feedback loop. In neurons we can input vectors taken as input and weights are included. With the help of weights and input vectors we can calculate weighted sum and taking weighted sum as parameter we can calculate activation function. There are different activation functions available e.g. thresholding, Signum, Sigmoidal, Hyperbolic Tangent. Phase ordering of optimization techniques In optimizing compilers, it is standard practice to apply the same set of optimization phases in a fixed order on each method of a program. However, several researchers have shown that the best ordering of optimizations varies within a program, i.e., it is function-specific. Thus,we would like a technique that selects the best ordering of optimizations for individual portions of the program, rather than applying the same fixed set of optimizations for the whole program. This paper develops a new method-specific technique that automatically selects the predicted best ordering of optimizations for different methods of a program. They develop this technique within the Jikes RVM Java JIT compiler and automatically determine good phase-orderings of optimizations on a per method basis. Rather than developing a handcrafted technique to achieve this, they make use of an artificial neural network (ANN) to predict the optimization order likely to be most beneficial for a method. Our ANNs were automatically induced using Neuro-Evolution for Augmenting Topologies (NEAT). A trained ANN uses input properties (i.e., features) of each method to represent the current optimized state of the method and given this input, the ANN outputs the optimization predicted to be most beneficial to the method at that state. Each time an optimization is applied, it potentially changes the properties of the method. Therefore, after each optimization is applied, they generate new features of the method to use as input to the ANN. The ANN then predicts the next optimization to apply based on the current optimized state of the method. This technique solves the phase ordering problem by taking advantage of the Markov property of the optimization problem. That is, the current state of the method represents all the information required to choose an optimization to be most beneficial at that decision point. Most compilers apply optimizations based on a fixed order that was determined to be best when the compiler was being developed and tuned. However, programs require a specific ordering of optimizations to obtain the best performance. To demonstrate our point, we use genetic algorithms (GAs), the current state-of-the-art in phase-ordering optimizations, to show that selecting the best ordering of optimizations has the potential to significantly improve the running time of dynamically compiled programs. They used GAs to construct a custom ordering of optimizations for each of the Java Grande and SPEC JVM 98 benchmarks. In this GA approach, we create a population of strings (called chromosomes), where each chromosome corresponds to an optimization sequence. Each position (or gene) in the chromosome corresponds to a specific optimization from Table 2, and each optimization can appear multiple times in a chromosome. For each of the experiments below, we configured our GAs to create 50 chro mosomes (i.e., 50 optimization sequences) per generation and to run for 20 Generations. Technique for Implementing GA We ran two different experiments using GAs. The first experiment consisted of finding the best optimization sequence across our benchmarks. Thus, we evaluated each optimization sequence (i.e., chromosome) by compiling all our benchmarks with each sequence. We recorded their execution times and calculated their speedup by normalizing their running times with the running time observed by compiling the benchmarks at the O3 level. That is, we used average speedup of our benchmarks (normalized to opt level O3) as our fitness function for each chromosome. This result corresponds to the â€Å"Best Overall Sequence† bars in Figure 1. The purpose of this experiment was to discover the optimization ordering that worked best on average for all our benchmarks. The second experiment consisted of finding the best optimization ordering for each benchmark. Here, the fitness function for each chromosome was the speedup of that optimization sequence over O3 for one specific benchmark. This resu lt corresponds to the â€Å"Best Sequence per Benchmark† bars in Figure 1. This represents the performance that we can get by customizing an optimization ordering for each benchmark individually. Results The results of these experiments confirm two hypotheses. First, significant performance improvements can be obtained by finding good optimization orders versus the well-engineered fixed order in Jikes RVM. The best order of optimizations per benchmark gave us up to a 20% speedup (FFT) and on average 8% speedup over optimization level O3. Second, as shown in previous work, each of our benchmarks requires a different optimization sequence to obtain the best performance. One ordering of optimizations for the entire set of programs achieves decent performance speedup compared to O3. Figure 2.Results of experiments using GA However, the â€Å"Best Overall Sequence† degrades the performance of three benchmarks (LUFact, Series, and Crypt) compared to O3. In contrast, searching for the best custom optimization sequence for each benchmark, â€Å"Best Sequence for Benchmark†, allows us to outperform both O3 and the best overall sequence. Motivation Predict the current best optimization: This method would use a model to predict the best single optimization (from a given set of optimizations) that should be applied based on the characteristics of code in its present state. Once an optimization is applied, we would re-evaluate characteristics of the code and again predict the best optimization to apply given this new state of the code. For this we can apply Artificial Neural Network in this method and we will also include profiles for better prediction of optimization sequence for particular program. Automatic Feature Generation Automatic Feature generation system is comprised of the following components: training data generation, feature search and machine learning [5]. The training data generation process extracts the compiler’s intermediate representation of the program plus the optimal values for the heuristic we wish to learn. Once these data have been generated, the feature search component explores features over the compiler’s intermediate representation (IR) and provides the corresponding feature values to the machine learning tool. The machine learning tool computes how good the feature is at predicting the best heuristic value in combination with the other features in the base feature set (which is initially empty). The search component finds the best such feature and, once it can no longer improve upon it, adds that feature to the base feature set and repeats. In this way, we build up a gradually improving set of features. a. Data Generation In a similar way to the existing machine learning techniques (see section II) we must gather a number of examples of inputs to the heuristic and find out what the optimal answer should be for those examples. Each program is compiled in different ways, each with a different heuristic value. We time the execution of the compiled programs to find out which heuristic value is best for each program. We also extract from the compiler the internal data structures which describe the programs. Due to the intrinsic variability of the execution times on the target architecture, we run each compiled program several times to reduce susceptibility to noise. Figure 3. Automatic Feature Generation b. Feature Search The feature search component maintains a population of feature expressions. The expressions come from a family described by a grammar derived automatically from the compiler’s IR. Evaluating a feature on a program generates a single real number; the collection of those numbers over all programs forms a vector of feature values which are later used by the machine learning tool. c. Machine Learning The machine learning tool is the part of the system that provides feedback to the search component about how good a feature is. As mentioned above, the system maintains a list of good base features. It repeatedly searches for the best next feature to add to the base features, iteratively building up the list of good features. The final output of the system will be the latest features list. Our system additionally implements parsimony. Genetic programming can quickly generate very long feature expressions. If two features have the same quality we prefer the shorter one. This selection pressure prevents expressions becoming needlessly long. E. Motivation They have developed a new technique to automatically generate good features for machine learning based optimizing compilation. By automatically deriving a feature grammar from the internal representation of the compiler, we can search a feature space using genetic programming. We have applied this generic technique to automatically learn good features. Code Optimization in Compilers using ANN For ordering of different optimization techniques using ANN we must need to implement that in 4Cast-XL as it is a dynamic compiler. 4Cast-XL constructs an ANN, Integrate the ANN into Jikes RVM’s optimization driver than Evaluate ANN at the task of phase-ordering optimizations. For each method dynamically compiled, repeat the following two steps Generate a feature vector of current method’s state Generate profiles of program Use ANN to predict the best optimization to apply Use ANN to predict the best optimization to apply. Run benchmarks and obtain feedback for 4Cast-XL Record execution time for each benchmark optimized using the ANN. Obtain speedup by normalizing each benchmark’s running time to running time using default optimization heuristic. Figure 4. Code Optimization in compilers using ANN with Profiles Results Research work is aimed for optimizing code using artificial neural networks. In order to make this precise, better profiles generated from given set of features using Milepost GCC compiler with ten different programs. Experimental results demonstrate that profiles of program can be used for optimization of code. Motivation This section gives a detailed overview of how Neuro-evolution machine learning is used to construct a good optimization phase-ordering heuristic for the optimizer. The first section outlines the different activities that take place when training and deploying a phase ordering heuristic. This is followed by sections describing how we use 4cast-XL to construct an ANN, how we extract features from methods, and how best features called Profiles and ANNs allow us to learn a heuristic that determines the order of optimizations to apply. It motivates us to apply this approach for different types of predictions using Artificial Neural Networks. Prediction Using Neural Networks Neural networks can be used for prediction with various levels of success. The advantage of then includes automatic learning of dependencies only from measured data without any need to add further information (such as type of dependency like with the regression). The neural network is trained from the historical data with the hope that it will discover hidden dependencies and that it will be able to use them for predicting into future. In other words, neural network is not represented by an explicitly given model. It is more a black box that is able to learn something. 1

World Wide Fund for Nature

There are well over five thousand endangered species on Earth and humans are the cause of it. Many animals are suffering and several hundred are already extinct. Many of the most beautiful creatures are now evanescent. People are also causing the destruction of the environment, which is home to millions of animals worldwide. Through poaching and obliteration of nature, humans have managed to diminish the very place they call home. There are charities such as the World Wide Fund for Nature with the motive to halt and reverse the annihilation of the environment.The international organization World Wide Fund for Nature works on issues regarding conservation, research, and restoration of the environment. The WWF was formed on April 26, 1961, when a small group of passionate individuals had the idea of building a future where humans live in harmony with nature. Their mission was to preserve the planet’s resources, reduce pollution, and conserve the world’s life diversity. Th e WWF’s original name was World Wildlife Fund and was later changed to World Wide Fund for Nature.The panda has become the logo for WWF because it was an endangered species and served as a strong recognizable symbol that was adored by many people in the world for its appealing characteristics. Originally, their aim was to protect the wildlife species and habitat. Today, organization has grown to repopulating several different species worldwide and seeks to didactically educate people on how to have a more ecologically friendly lifestyle.The World Wide Fund for Nature is the world’s leading environmental conservation organization with a global reach of one hundred different countries. They help protect endangered animals and their habitats. The WWF does this by collaborating with businesses, governments, local communities, and other organizations to secure funds and ensure the safety of wildlife. They focus on the underlying causes of environmental deterioration.The ent ire planet depends on organizations such as this, and without it, the nature living on it would continue to be whittled down by apathetic people until it is too late. Humans will soon become their own nemesis, unless they show some solicitude, and be a partisan in restoring the planet. The WWF, with over one million members in the United States and nearly five million worldwide, is one of the most impactful charitable organizations in history. The WWF gives hope to the future of the environment and fixes the problems that previous generations have caused.Because of this organization, the earth’s environment’s status has drastically improved. They are bringing back the serene environment, and shielding the forlorn animals that inhabit it. The World Wide Fund for Nature is a very impactful charitable organization that aids in restoring the planet’s environment and saves the lives of animals that are in need. The international organization World Wide Fund for Natur e works on issues regarding conservation, research, and restoration of the environment.

Oppression Theory That Supports Horizontal Violence Process Essay

Nurses are known to be the devoted caregiver of sick patients. How can the patients get rid of their burden if their own caregivers are in conflict among each other in hospital settings? When there is conflict in such kind of environment, it is called horizontal violence, interpersonal conflict or bullying which is aggressive and destructive behavior of nurses against each other (Woelfle & McCaffrey, 2007). It is an expression of oppressed group behavior evolving from feelings of low self-esteem and lack of respect from others which is supported by the theory of oppression. According to the theory stated by Woelfle & McCaffrey (2007), in order for the horizontal violence to take place in the nursing setting, oppression exists when a powerful and dominant group controls and exploits a less influential or easy target group. As a consequence the oppressed group displays low self esteem and self hatred as evidenced by anger and frustration (Woelfle & McCaffrey, 2007). The theory of oppression helps to explain that the behaviors of horizontal violence aren’t directed at the individual but rather is a response to the specific situation where one feels fear of punishment that prevents the nurse from responding to the oppression. When people feel oppressed they feel inferior and powerless. These kinds of nurses who feel powerless behave aggressively towards peers to relieve tension because they can’t fight against their oppressor. That results to the display of emotion which victimize the colleague where the colleague or the coworker gets the feeling of vulnerability or prone to be hurt. The emotion or body language often includes rolling of the eyes, folding the arms or storming out of the room, using sarcasm, raised voice and shouting. These people manipulate the work environment while denying doing anything wrong and get satisfied from experiential difficulty and discomfort of others. These negative behaviors have obvious results in human mind leading to anxiety and stress at work. This cycle of denial maintains its own pattern of repeated action against the vulnerable group and allows the power relations to be unchallenged. Rather than fighting back and risking from the superiors/violence creators, the oppressed groups’ frustration is manifested as conflict in their own ranks with horizontal violence from coworker to coworker. Hence, people  begin to think this kind of behavior as a norm which they displace their feeling of aggression to another highly prone groups such as new grad nurse or student and even less confident coworkers. This cycle of behavior is typically described as horizontal violence (Woelfle & McCaffrey, 2007). As an example, a coworker in a unit behaves aggressively in a reaction to their own part of stress by acting aggressively and displacing their anger to another same or lower hierarchical level group or coworker. Another coworker as a victim gets devastated with this behavior especially if the superior authority or managers don’t acknowledge the behavior. Hence the victim feels angry, frustrated and vulnerable continuing the cycle of horizontal violence. Rather than fighting back against the aggressor, this group accepts this as a behavioral norm which they unconsciously displace to other lower or same hierarchical level coworker such as grad nurse or the nursing students. These nursing students or grad nurses later learn to displace their stress to other with the verbal or nonverbal expression giving the feeling of vulnerability to the prone groups. Hence this cycle of oppression continues as a horizontal violence in the work place area as part of the work stress. Consequently the oppressed group often lack autonomy, accountability and control over their profession (Woelfle & McCaffrey, 2007). Horizontal violence is a purposeful ongoing collection of often negative behaviors and actions that accumulate over time. Moreover, it includes repeated acts involving an imbalance of strength or power, in which one or more individuals engage in over time with the intention to harm other and create a hostile work environment. They displace their part of frustration to others in the form of negative verbal or nonverbal expression. The cycle of oppression continues which is supported by the theory of oppression. The result of horizontal violence affects nurses, nursing managers, other medical and administrative staff, patient and their family. It is clear that horizontal violence is everywhere in nursing today and can drastically affect the nursing area. When the tension is elevated in the patient care, nurses cannot perform their best which often lead to poor quality patient care (Woelfle & McCaffrey, 2007). Reference: Woelfle, C. Y. & McCaffrey, R. ( July-September, 2007). Nurse on nurse. Nursing Forum, Vol 42(3), p123-131

A Question Of Internet Privacy - Free Essay Example

Sample details Pages: 3 Words: 1006 Downloads: 10 Date added: 2019/07/01 Category Law Essay Level High school Tags: Internet Privacy Essay Did you like this example? Introduction The internet is used by thousands of people around the world to share ideas, conduct financial transactions, and keep in touch with relatives, friends, and colleagues. However if this biggest network is so helpful in our daily life, it is also low our privacy. No more day goes without someone being a victim of a cybercrime; always with the same backdrop problem our database; all the data stored on our computers. Don’t waste time! Our writers will create an original "A Question Of Internet Privacy" essay for you Create order Internet has allow the interconnection of computer and other devices all over the world. This interconnection also is also the interconnection of the huge amount of database around the world. Those information that everyone computer carry. Then, the risk to lose your privacy with internet has become the huge concern of state , companies and individuals. Then internet privacy has become the huge concern of companies: IT security has become a real concern for business leaders and IT managers because in recent years the threats have multiplied. The computer tool is an integral part of the functioning of companies, but also of public institutions. We will use it to simplify the tasks of the various collaborators, to save time, but also to store data that is confidential and that should only be handled by the concerned protagonists. However, It is not uncommon, too, in the enterprise, to suffer hacking, which allows to reach many data, to practice espionage, or even to disrupt the entire network of a company, more or less serious . It has been proven that significant financial losses are related to computer security and data protection problems, every year in business, at least when a good system of protection has not been implemented. For example in 2009, PlayStation Network breach has cost Sony $171 million. Sony suffered a massive breach in its video game online network that led to the theft of names, addresses and possibly credit card data belonging to 77 million user accounts in what is one of the largest-ever Internet security break-ins. Sony learned that user information had been stolen from its PlayStation Network seven days ago, prompting it to shut down the network immediately. But Sony did not tell the public until Tuesday. That hacker obtained peoples names, addresses, email address, birth dates, usernames, passwords, logins, security questions and more, Sony said lately on its U.S. PlayStation blog on Tuesday. (Baker Liana P. 12) Next , one of the biggest scandal happened recently in the universe o f social network. James Zuckenberg founder of Facebook, reveals to the media that Facebook just carried out its biggest purge from American accounts peddling disinformation. The company had removed 66 accounts , pages and apps linked to the Russian firm related to the Russian government. It is therefore essential for companies to find an effective way to generate good security within its structure, so as to be able to protect their database from all the risks inherent to the use of a computer station, whether on the internet or intranet. Confidential information can be unveiled or even worse, lost forever. Internet privacy has become the huge concern of individuals In 2016, the National Telecommunications and Information Administration (NTIA) in 2016,issues statistics that shows that 84% of Americans household were already really concerned about the internet security. In fact, it is not a secret to anyone that internet user in the USA have fewer privacy. First, everything became complex after the terrorist attacks of September 11. The National Security Agency greatly expand its surveillance everywhere. Furthermore in April 2016 the Congress voted to allow internet providers to collect and sell their customers ?data.( Liz Mineo P. 2) . Those are also the reason why Americans are so exposed online. All layers of the society are concerned by this issues, even the little ones are not spared. For example a huge amount of student from elementary to high school use digital devices where they type all their personal information and save in the school database which can be a cloud. In fact school always gather all those personal information that they stored in their databases. A school database carry students education record generally such as demographic information, race, ethnicity, income level; discipline records, grades and test scores, disabilities and Individual Education Plans (IEPs), mental health and medical history, counseling records and much more. Later, students could not imagine that all those information sometimes end up in the hands of companies which can sell them . In short, everything is done with the complicity of the schools which does not care that much about providing adequate privacy policies. At least, Parent have to be aware about this leak of information of their children and ask the school to take more precautions in protecting their childrens data. Valerie Strauss explains this sprain by the Family Educational Rights and Privacy Act (FERPA). she said that this act included that medical and counseling records that are included in your childs education records are unprotected by HIPAA (the Health Insurance Portability and Accountability Act passed by Congress in 1996). Thus, very sensitive mental and physical health information can be shared outside of the school without parent consent. Therefore everyone understands how it becomes unpleasant for any internet user to know that by sharing , sending and storing personal medical data, business communications, and even intimate conversations over this global network , also third person might be sharing your privacy. Conclusion Obviously there is a very real concern for a lot of people and companies that their privacy is somehow being abused. But for the Internet to continue to grow and thrive, users must be able to trust that their personal information will be secure and their privacy protected. Therefore, actions must be led by individuals, aimed to protect your internal network. as well as your internet access, so as not to be in a bad position if necessary. E-mails must be filtered properly. Next , companies need to train their staff in order to avoid the bad manipulations that could cost the company. Finally, government need to take action and vote law that will protect persons privacy instead of instead of being exposed as it is today.