The Meaning of Life Essay Example | Topics and Well Written Essays - 1250 words

The Meaning of Life - Essay Example I find this as the most convincing as regarding the question to the real meaning of life. This is surely the greatest way to perceive life-one’s own life, the life of a nation, a species, the world, and all living things. The meaning of life comes to us when we do things that our heart and mind desires, things that seem of importance to us. Otherwise, doing things that draw boredom since they seem not of much interest ton us does not bring the meaning of living into us. We can realize the idea of meaningless existence and the meaningless of life if we keep on doing things that bear no fruits to us and brings nothing (Taylor 433). It surely has no meaning to perform an activity that has been drawn repeatedly but, have no direction and purpose of it. With this, the objective of existence is meaningless. Richard Taylor draws this idea from our lives, and that of the animals, the endless activities that get nowhere. However, he also says that if this is what one is willing to per sue then the idea of meaning of life comes in. At one point, we ask ourselves why we do things repeatedly without getting anything out of it, a perfect image of meaningless existence. ... He also points out something important that we realize in our day to day life, we go through life doing things that which begin to fade into time as the next time is instigated yet it would be no redemption to rest from all this (Taylor 437). It would be a denunciation that would not be redeemed if we gaze at things we have done no matter how beautiful and permanent they are. The meaning of life requires us to create new ideas and perform new tasks each and every new day. All these should be encouraged by our willingness to perform this tasks for living, and it carries on to our children and the generation to come. Epicurus has also created a clear meaning of life as life is driven by the choices, actions, and for us to make correct choices we have to be wise in our decisions. He says that we have to be wise, so as to live pleasantly. It is from this that we gain honor and just. He says that some things happen because of necessity; some happen by chance, others by agency. We should b e wise and take good actions because we are responsible for our own actions. It is from them that we take praise or blames. Epicurus calls for us to believe in God, and affirm to his blessings. He creates the idea that there are gods, but the respect given unto them does not prevail. Living also requires us to believe that death is nothing to us, for both evil and good imply the capacity for sensation, and that death is the privation of all sentience. The meaning of life requires believing that death is a part of life and that it adds the yearning of immortality. We are advised to believe that life does not end after death for not the living or the dead exists no longer. The meaning of life

Gay Marriage Essay -- Same-Sex Marriage Essays

Gay Marriage There seem to be Specific time in history where certain issues define the culture tension in a society. They become representative of large worldview and clearly emphasize the battle between divergent moral and spiritual perspectives. Homosexual marriage in the United States is one of these issues. In the past five years, there have been various threats and debates about the possibility of legalizing homosexual union. The issue took centre stage in February largely. The U.S Senate quashes a proposed constitutional amendment to ban gays from marrying. President Bush and other same-sex marriage opponents say they will try again. Most of Americans are against same-sex marriage because how the nation would change if gay were allowed to marry. Gay marriage could affects on American society in many different ways. 1.  Ã‚  Ã‚  Ã‚  Ã‚  Polygamy. 2.  Ã‚  Ã‚  Ã‚  Ã‚  Federal spending. 3.  Ã‚  Ã‚  Ã‚  Ã‚  Religious speech. 4.  Ã‚  Ã‚  Ã‚  Ã‚  Children. 5.  Ã‚  Ã‚  Ã‚  Ã‚  Civil Rights. First, it is more likely to lead to polygamy. For instance, says legalizing same-sex marriage will open the floodgates to polygamy. The more government moves towards sanctioning homosexual relationships, the less right it has to prevent plural marriage. The idea that same-sex marriage could lead to polygamy is â€Å"Not beyond the bounds of reality, although incredibly unlikely,† said Michael Allen who teaches constitutional law at Stetson university college of law in Gulf port. We do not see that is as a realistic view. For instance, it is highly doubtful that solemnizing the union of accepting adults, equal in power in their relationships, would lead to acceptance of polygamous unions, which have, throughout most of history in those societies that have accepted them. â€Å"If one man can marry anther man, you need to ask then, what is it that you doing by prohibiting one man from marrying two women?† â€Å"What’s the evil that you are tying to prevent?† â€Å"If same sex marriage is legalized there is no natural stopping point in redefining what is or is not acceptable.† It would take much longer to legalize polygamy then gay marriage because it is more socially taboo.† That is the reason that polygamists would get away with trying to take credit on same sex marriage (Attorney John Bucher). Second, gay marriage will affect on t... ...arched alongside Martin Luther King Jr, said the proposed Marriage protection Act was a step backward in civil rights. â€Å"Those of us who came through the civil rights movement saw the federal courts as a sympathetic referee. If it had not been for the federal courts, where would we be? If it had not been for the supreme court of 1954, there would still be legalized segregation in America to vote for this legislation would be like members of congress trying to stand in the courthouse door, just like Government. Wallace stood in the schoolhouse door to stop the integration of Alabama schools today it is gay marriage, tomorrow it will be something else†. In conclusion, it seems that the basic problem facing the problem is the limits between a right and privilege. The problem with an issue like marriage is that it is neither a right nor a privilege. In its simplest form a marriage is a union between consenting souls to share assets, responsibility, and form a â€Å"Unit†. We have the right to life, liberty, and a pursuit of happiness, we do not; but it is a system that not only works well but has also been established as our system of laws. So the idea about gay marriage should be banned.

Optimism Definition Essay

Optimism is a mental attitude or world view that interprets situations and events as being best (optimized), meaning that in some way for factors that may not be fully comprehended, the present moment is in an optimum state. The concept is typically extended to include the attitude of hope for future conditions unfolding as optimal as well. The more broad concept of optimism is the understanding that all of nature, past, present and future, operates by laws of optimization along the lines of Hamilton’s principle of optimization in the realm of physics. This understanding, although criticized by counter views such as pessimism, idealism and realism, leads to a state of mind that believes everything is as it should be, and that the future will be as well. A common idiom used to illustrate optimism versus pessimism is a glass with water at the halfway point, where the optimist is said to see the glass as half full, but the pessimist sees the glass as half empty. The word is originally derived from the Latin optimum, meaning â€Å"best.† Being optimistic, in the typical sense of the word, ultimately means one expects the best possible outcome from any given situation. This is usually referred to in psychology as dispositional optimism. Researchers sometimes operationalize the term differently depending on their research, however. For example, Martin Seligman and his fellow researchers define it in terms of explanatory style, which is based on the way one explains life events. As for any trait characteristic, there are several ways to evaluate optimism, such as various forms of the Life Orientation Test, for the original definition of optimism, or the Attributional Style Questionnaire designed to test optimism in terms of explanatory style. While the heritability of optimism is largely debatable, most researchers agree that it seems to be a biological trait to some small degree, but it is also thought that optimism has more to do withenvironmental factors, making it a largely learned trait.[1] It has also been suggested that optimism could appear to be a hereditary trait because it is actually a manifestation of combined traits that are mostly heritable, like intelligence, temperament and alcoholism.[2] Optimism may also be linked to health. Explanatory style Explanatory style is different, though related to, the more traditional, narrower definition of optimism. This broader concept is based on the theory that optimism and pessimism are drawn from the particular way people explain events. There are three dimensions within typical explanations, which include internal versus external, stable versus unstable, and global versus specific. Optimistic justifications toward negative experiences are attributed to factors outside the self (external), are not likely to occur consistently (unstable), and are limited specific life domains (specific). Positive experiences would be optimistically labeled as the opposite: internal, stable, global.[4] There is much debate about the relationship between explanatory style and optimism. Some researchers argue that there is not much difference at all; optimism is just the lay term for what scientists call explanatory style.[5] Others argue that explanatory style is exclusive to its concept and should not be interchangeable with optimism.[6][7] It is generally thought that, though they should not be used interchangeably, dispositional optimism and explanatory style are at least marginally related. Ultimately, the problem is simply that more research must be done to either define a â€Å"bridge† or further differentiate between these concepts. Philosophy Philosophers often link concept of optimism with the name of Gottfried Wilhelm Leibniz, who held that we live in the best of all possible worlds, or that God created a physical universe that applies the laws of physics, which Voltaire famously mocked in his satirical novel Candide. The philosophical pessimism of William Godwin demonstrated perhaps even more optimism than Leibniz. He hoped that society would eventually reach the state where calm reason would replace all violence and force, that mind could eventually make matter subservient to it, and that intelligence could discover the secret of immortality. Much of this philosophy is exemplified in the Houyhnhnms of Jonathan Swift’s Gulliver’s Travels. Panglossianism The term â€Å"panglossianism† describes baseless optimism of the sort exemplified by the beliefs of Pangloss from Voltaire’s Candide, which are the opposite of his fellow traveller Martin’s pessimism and emphasis on free will. The phrase â€Å"panglossian pessimism† has been used to describe the pessimistic position that, since this is the best of all possible worlds, it is impossible for anything to get any better. The panglossian paradigm is a term coined by Stephen Jay Gould and Richard Lewontin to refer to the notion that everything has specifically adapted to suit specific purposes. Instead, they argue, accidents and exaptation (the use of old features for new purposes) play an important role in the process of evolution. Some other scientists however argue the implication that many (or most) adaptionists are panglossians is a straw man. Why People Believe Weird Things: Pseudoscience, Superstition, and Other Confusions of Our Time Michael Shermer relates Frank J. Tipler to Voltaire’s character Pangloss to show how clever people deceive themselves. Shermer explores the psychology of scholars and business men who give up their careers in their pursuit to broadcast their paranormal beliefs. In his last chapter, added to the revised version, Shermer explains that â€Å"smart people† can be more susceptible to believing in weird things. Optimalism Optimalism, as defined by Nicholas Rescher, holds that this universe exists because it is better than the alternatives.[8] While this philosophy does not exclude the possibility of a deity, it also doesn’t require one, and is compatible with atheism.[9] The positive psychologist Tal Ben-Shahar uses optimalism to mean willingness to accept failure while remaining confident that success will follow, a positive attitude he contrasts with negative perfectionism.[10] Perfectionism can be defined as a persistent compulsive drive toward unattainable goals and valuation based solely in terms of accomplishment.[11] Perfectionists reject the realities and constraints of human ability. They cannot accept failures, delaying any ambitious and productive behavior in fear of failure again. [12]This neuroticism can even lead to clinical depression and low productivity.[13] As an alternative to negative perfectionism Ben-Shahar suggests the adoption of optimalism. Optimalism allows for failure in pursuit of a goal, and expects that while the trend of activity will tend towards the positive it is not necessary to always succeed while striving to attain goals. This basis in reality prevents the optimalist from being overwhelmed in the face of failure.[10] Optimalists accept failures and also learn from them, which encourages further pursuit of achievement.[14] Dr. Tal Ben-Shahar believes that Optimalists and Perfectionists show distinct different motives. Optimalists tend to have more intrinsic, inward desires, with a motivation to learn. While perfectionists are highly motivated by a need to consistently prove themselves worthy. Assessment Life Orientation Test (LOT) Designed by Scheier and Carver (1985), this is one of the more popular tests of optimism and pessimism. There are eight measurements (and an additional four filler items), with four positively (â€Å"In uncertain times, I usually expect the best†) and four negatively (â€Å"If something can go wrong for me, it will†) worded items.[15] The LOT has been revised twice–once by the original creators (LOT-R) and also by Chang, Maydeu-Olivares, and D’Zurilla as the Extended Life Orientation Test (ELOT). All three are most commonly used because they are based on dispositional optimism, which simply means expecting positive outcomes.[16] Attributional Style Questionnaire (ASQ) This questionnaire created by Peterson et al. (1982) is based on the explanatory style definition of optimism. It lists six positive and negative events (â€Å"you have been looking for a job unsuccessfully for some time†), and asks the respondents to record a possible cause for the event and rate the internality, stability, and globality of the event.[17] An optimistic person is one who perceives good things happening to them as internal, stable, and global. There are several modified versions of the ASQ including the Expanded Attributional Style Questionnaire (EASQ), theContent Analysis of Verbatim Explanations (CAVE), and the ASQ designed for testing the optimism for children.[16] Health Research has emerged showing the relationships between several psychological constructs and health. Optimism is one of these concepts and has been shown to explain between 5–10% of the variation in the likelihood of developing some health conditions (correlation coefficients between .20 and .30),[18] notably including cardiovascular disease,[19][20][21][22][23] stroke,[24]depression,[25][26] and cancer.[21][27][28] Furthermore, optimists have been shown to live healthier lifestyles which may influence disease. For example, optimists smoke less, are more physically active, consume more fruit, vegetables and whole-grain bread, and consume more moderate amounts of alcohol.[29] The relationship between optimism and health has also been studied with regards to physical symptoms, coping strategies and negative affect for those suffering from rheumatoid arthritis, asthma, and fibromyalgia. It has been found that among individuals with these diseases, optimists are not more likely than pessimists to report pain alleviation due to coping strategies, despite differences in psychological well-being between the two groups.[30] A meta-analysis has confirmed the assumption that optimism is related to psychological well-being: â€Å"Put simply, optimists emerge from difficult circumstances with less distress than do pessimists.†[31] Furthermore, the correlation appears to be attributable to coping style: â€Å"That is, optimists seem intent on facing problems head-on, taking active and constructive steps to solve their problems; pessimists are more likely to abandon their effort to attain their goals.†[31] It should be noted that research to date has demonstrated that optimists are less likely to have certain diseases or develop certain diseases over time. By comparison, research has not yet been able to demonstrate the ability to change an individual’s level of optimism through psychological intervention, and thereby alter the course of disease or likelihood for development of disease.

Future Depositors Rate Of Return Applying Neural Network - Free Essay Example

Sample details Pages: 10 Words: 3125 Downloads: 6 Date added: 2017/06/26 Category Statistics Essay Did you like this example? Islamic bank has to perform good strategy which still confirm with the Islamic law in order to deliver better return to compensate depositor`s money. This paper is embarked on to identify the relative significance assigned to selection variables for depositor in maximizing their opportunity. In such case, it becomes very necessary to have a prediction of future rate of return in order to get a clear picture in making precise decision. Don’t waste time! Our writers will create an original "Future Depositors Rate Of Return Applying Neural Network" essay for you Create order This research used some key macroeconomic variables such as Jakarta Stock Indices (JSI), inflation rate (INFR), central bank`s interest rate certificate (INTR), exchange rate (ER), and money in circulation (MIC). Since these variables are characterized as nonlinearities time series data, Artificial Neural networks (ANN) will be employed using back propagation algorithm as learning algorithm. From observation resulted that central bankà ¢Ã¢â€š ¬Ã¢â€ž ¢s interest rate certificate (INTR) and Money In Circulation (MIC) could be used as leading indicators to face the problem with 94.95% accuracy. Rate of return in Islamic bank defines as how much money will be received by depositor from their deposit in Islamic bank for one year which is that equivalent with conventional bank`s interest rate. Using profit and loss sharing principle, the Islamic bank must share the profit or loss to depositors based on predetermined profit and loss sharing ratio. So, the amount of return will be received by depositor depends on Islamic bank`s profitability which performance is affected by macroeconomic turmoil. Figure 1 shows comparison between interest rate and rate of return of an Islamic bank in Indonesia, PT Bank Syariah Mandiri for period 2007 and 2008.à £Ã¢â€š ¬Ã¢â€š ¬This figure shows that rate of return of PT bank Syariah Mandiri seems so stabil compared with interest rate of Indonesian commercials and states bank. Moreover, from July 2007 to june 2008, PT Bank Syariah Mandiri provides higher return to its depositor then conventional bank. The steady expansion of Islamic banks has been the hallmark of the Muslim world financial landscape in the 1980s and 1990s. With a network that spans more than 60 countries and an asset base of more than $166 billion; Islamic banks are now playing an increasingly significant role in their respective economies (Hassan and Bashir, 2003). As the biggest Muslim country in the world, the growth of Indonesian Islamic banking industry is still far away behind Malaysia and Turkey. Indonesian Islamic bankà ¢Ã¢â€š ¬Ã¢â€ž ¢s total asset accounted for USD 3.287 million compared with Malaysia and turkey, amounted to USD 34.543 million and USD 12.902 million, respectively.à £Ã¢â€š ¬Ã¢â€š ¬ Many researches about business cycle analysis in bank industry have been conducted but then most of them have focused on the implication of the changes of the macroeconomic variables to the banks profitability and delivering the result as recommendation to management or policy maker, especially in Islamic bank industry. [see for example, Meyer and Pifer (1970), al-Osaimy (1998), Cihak and Hesse (2008), Maximilian (2008). In contrast, this research, indeed, intends to helps depositor to understand which macroeconomic variables will significantly determine pricing of individual depositor`s rate of return in PT Bank Syariah Mandiri, the biggest Islamic bank in Indonesia and then use them to predict future rate of return. 1.1 Research Objectives The ability to predict future rate of return will enable depositors, especially individual depositors to take precautionary action to minimize their opportunity of loss. In this context, individual depositors might simply use some particular macroeconomic variables as indicator to predict the rate of return will be received, whether increase, decrease, or constant compared with previous period. 2. Literature review 2.1 Prior Methods used to Predict Business Cycle Many metholodologies have been developed on research related with business cycle analysis and prediction. Mayer and Pifer (1970) used Linear Probability Model (LPM) to predict bank failure. Al-Osaimy and Bamahramah (2004) and Cihak and Hesse (2008) used Multi Discriminant Analysis (MDA), Dewaelheyns and Hulle (2007), and Erdogan (2008), used Distributed Lag Model (DLM) and Logit. On the other hand, Kiani, Khurshid and Kasten (2006), and Hsieh, Liu and Hsieh (2006) used MDA assisted neural network to predict bankruptcy of Taiwan company. Although neural networks have demonstrated some success in this area, only a few studies, for example Al-Osaimy (1998) and Maximilian (2008) employed neural network in Islamic banking research. So, we believe conducting research in Islamic bank, might benefit from neural networks model. This research, therefore, employ the model that are considered to be highly flexible functional forms of nonlinear models to find possible predictability of Islamic ba nk`s business cycle, primarily on return generation ability in Indonesian Islamic bank by using a number of macroeconomic variables as independent variables since complex unstructured relationship among variables are often encountered in economics, Maximilian (2008).à £Ã¢â€š ¬Ã¢â€š ¬ 2.2 Superiority of ANN method Prudence (2002) declared at least two advantages of ANN method compared with other methods for doing prediction. Firstly, they are universal approximators of function in that they can approximate whatever functional form best characterizes the time series. That means, ANN are considered to be data-driven rather than model-driven (Argyrou, 2006) because they are best suited for problems for which data is available but the underlying theoretical model is unknown (Zhang, Patuwo Hu, 1998). It makes ANN superior than other statistical methods which ANN able to deal with non linear data and multi dimensional aspect. Secondly, ANN method have been proven to be better for long term forecast horizons, but are often as good as statistical forecasting methods over shorter forecast horizons.à £Ã¢â€š ¬Ã¢â€š ¬Atiya (2001) summarized paper of Odom and Sharda (1990) which compared forecasting power between ANN method and MDA method. ANN achieved correct classification accuracy in the range of 77.8 % to 81.5% against MDA`s accuracy were in the range of 59.3% to 70.4% for predicting bankruptcy of 128 firms using financial ratios. 3.Methodology On constructing model to answer the problem, this research used bankruptcy theory which states that probability of default of the firm is a function of macroeconomic variables such as interest rate, foreign exchange rate, growth rate, government expenses, unemployment rate, and aggregate savings (Azis and Dar, 2004). It means that the ability of the firm in generating profit is highly influenced by changes in macroeconomic condition. Using profit and loss sharing principles, the ability of Islamic bank to generate profit and share it with depositor is also influenced by macroeconomic turmoil. Since the variables used are characterized as nonlinearities time series data, ANN model will be constructed using back propagation algorithm as learning algorithm by employing Alyuda Neuro Intelligent software version 2.2 and on a Pentium IV machine under Windows XP Professional platform. 3.1 Artificial Neural Networks Model (ANN) ANN is a branch of artificial intelligence which able to solve problem especially in pattern classification and recognition. ANN benchmark their prediction with actual results and constantly revise their predictions, improving forecasting capability (Wong, 2009). ANN modeling approach is useful for forecasters, and researchers who employ it especially in problems where data is available but the data generating process and its underlying laws are unknown. Maximilian (2008) adopted this method to modeling Islamic bank credit risk in Indonesia and concluded that ANN does overcome the problem of data sufficiency that limits many forecasting methods. ANN are treated as nonlinear, nonparametric statistical methods due to which these are independent of the distributions of the underlying data generating processes (White 1989). This research employed ANN model used by Kiani et al (2006) as can be seen below in model [1] f(x) = sig [ÃŽÂ ±0+ÃŽÂ ±jsig (ÃŽÂ ²ijxi + ÃŽÂ ²0j)] + ÃŽÂ µÃƒ £Ã¢â€š ¬Ã¢â€š ¬Ãƒ ¢Ã¢â€š ¬Ã‚ ¦[1] where, n is the number of hidden nodes in neural networks and k is the number of explanatory variables in the networks, sig (x) = 1/(1 + e-x ), ÃŽÂ ±j represents a vector of parameters or weight that link the hidden node to output layers unit. ÃŽÂ ²ij (i =1,., k); j=1,.., n) denotes a matrix of parameters from the input to the hidden layers units and ÃŽÂ µ is the error term. The error term ÃŽÂ µ can be made arbitrary small if sufficiently many explanatory variables are included and if n is chosen to be large enough. However, the model may over fit if n is too large in which in-sample errors can be made very small but out-of sample errors may be large. 3.2 Data This research attempts to use as much as possible of macroeconomic variables as input variables. However, considering availability of data and commonly used in Indonesian Islamic banking research area, this research uses some key macroeconomics variables which used by Maximilian (2008) such as JSI which issued monthly by Indonesia Stock Exchange (ISE), and INFR, INTR, ER and MIC which issued monthly by Indonesian Central Bank (BI). As output variables, the research used general (not special) rate of return for 1 month time deposit which issued by PT Bank Syariah Mandiri every month. These macroeconomic variables are incorporated in the model to be analyzed which one will be the most determinant in pricing individual depositor`s rate of return and then also to predict the future rate. For doing so, real monthly data for sixty months were collected from January 2004 to December 2008. This whole data set was then divided into three sets which 59 data used and 1 data as outlier (Septembe r 2008 was removed from the sample, table 1). The training set is a part of input dataset used for neural network training, i.e. for adjustment of network weights and the validation set is a part of data used to tune network topology or network parameters other than weights. The software will use validation set to calculate generalization loss and retain the best network (the network with the lowest error on validation set). Meanwhile, the test set is a part of input data set used only to test how well the neural network will perform on new data. The test set is used after the network is ready (trained), to test what errors will occur during future network application. 3.3 Analysis and Prediction Process The process of analysis and prediction can be seen in Figure 2. Each arrow connecting each node represents the information (in terms of weight) in one particular note that might influence the other node. The program puts an initial weight to each arrow which is updated during the iteration process (commonly called epoch) to arrive at the lowest prediction error of default probability as the output variable in the iteration process. The level of complexity and predictive accuracy on the model depends upon the number of nodes in the architecture, Maximilian (2008). The choice of the best neural network architecture is based on a criteria mentioned in the literature and adjusted to the case of neural networks for prediction. Simply put, the network with the best structure is the one that simultaneously fulfils all the following criteria: (1) It has the smallest training error; (2) It has the smallest test error; (3) It has the smallest difference between training and testing error and (4) It has the simplest structure. The background of using ANN in this research is that of allowing the network to map the relationships between macroeconomic variables affecting rate of return given to depositor. Once this relationship between inputs and outputs is mapped, it gives the model needed to create rate of return prediction using macroeconomic data that out of sample period which are January, February and March 2009. The accuracy will be evaluated on the basis of standard statistical measures like percentage errors, as following. Error = (Rate of Return Act) (Rate of Return Predict ) x 100% (Rate of Return Actual) for i=1,2,à ¢Ã¢â€š ¬Ã‚ ¦., N, where N is the number of testing data points. Rate of return actual data used were also out of sample period which are January, February and March 2009. After calculating the forecast error, forecasting accuracy will be calculated as;à £Ã¢â€š ¬Ã¢â€š ¬ Forecasting Accuracy = 100% percentage of error in forecasting 3.4 Using Alyuda Neurointelligence Argyrou, A. (2006) describes how to run Alyuda Neurointelligence to build the model`s architecture, train and then test the models. For all dataset, the input to Alyuda Neurointelligence resembles a spreadsheet. The rate of return column was set up as output or target variables and the respective variables are categorized as input variables and designated as numerical data. The data is partitioned into training, validation and testing sets (table 1). The à ¢Ã¢â€š ¬Ã…“dateà ¢Ã¢â€š ¬? column is included to facilitate the data partitioning; it is not part of the input to the models and plays no role in training or testing the neural networks. Subsequently, the input data must be pre-processed (i.e. rate of return and macroeconomic variables) to remove data anomalies, because such anomalies can degrade the neural-network performance. In this context, data anomalies fall into the following two categories: (1) missing values and (2) outliers (Alyuda Neurointelligence v. 2.2 User Manual ). In particular, missing values are values that are not known, resulting in blank cells in the input columns. Outliers are extreme values that diverge from the majority of column data. To identify outliers, the application use the following formula for every column; mean  ± standard deviation x 3.5. Consequently, for any column, a value that lies outside this range is considered to be an outlier and thereby is being removed. The next step is à ¢Ã¢â€š ¬Ã…“normalizingà ¢Ã¢â€š ¬? the input data to make it suitable for neural-network processing. The à ¢Ã¢â€š ¬Ã…“normalizationà ¢Ã¢â€š ¬? essentially transforms the input data into a new representation before a neural network is trained. Bishop (1995) offers the following three reasons for input data normalization: (1) to ensure that the size of input data reflect their relative importance in determining the required output, (2) to enable the random initialization of weights before neural network training and (3) different variables may have different units of measurements, hence their typical values may differ significantly. All the input columns are à ¢Ã¢â€š ¬Ã…“normalizedà ¢Ã¢â€š ¬? in the same way, because they all include numerical values. Alyuda provides only one method for data normalization as follows: Y= SRmin + (X à ¢Ã¢â€š ¬Ã¢â‚¬Å" Xmin) x SF Where: SF=(SRmaxà ¢Ã¢â€š ¬Ã¢â‚¬Å"SRmin)/(Xmaxà ¢Ã¢â€š ¬Ã¢â‚¬Å"Xmin); Y=Normalized value; X=actual value of a numeric column; Xmin=minimum actual value of the column; Xmax=maximum actual value of the column; SF=scaling factor; Srmin=lower scaling range limit; Srmax=upper scaling range limit; [-1..1]=scaling range for input columns. In the next step, we define the three properties before running the application. First, the logistic activation function are selected for all the neurodes regardless of the layer on which they reside. Second, the sum of squared errors is selected to minimize the output error function This is the sum of the squared differences between the actual value and the modelà ¢Ã¢â€š ¬Ã¢â€ž ¢s output. For completeness, we restate that the neural network output falls in the range from 0 to 1 or from 0 to 100%, because of the logistic activation function. Furthermore, we run the à ¢Ã¢â€š ¬Ã…“exhaustive searchà ¢Ã¢â€š ¬? to select the best possible architecture for the models. This process takes considerable time because it searches for the best network architecture among all possible alternatives in the specified range. Alyuda choose the best architecture for the model which is 7-17-1 that consists of one hidden layer with seventeen neurodes. In addition, the model has 5 active neurons and 2 neurons as date which plays no role in training or testing the neural networks. The output layerà ¯Ã‚ ¼Ã‹â€ Olà ¯Ã‚ ¼Ã¢â‚¬ °has a single neurode representing the modelà ¢Ã¢â€š ¬Ã¢â€ž ¢s numeric output. In the pen-ultimate step, the model is trained with specific condition. The learning law is the backpropagation algorithm and both the learning and momentum rates are set at 0.1. The training stops when the model`s mean squared error reduces by less than 0.000001 or the model completes 20,000 iterations, whichever condition occurs first. All network`s parameters can be seen in the table 2 below.à £Ã¢â€š ¬Ã¢â€š ¬Then, the model is à ¢Ã¢â€š ¬Ã…“testedà ¢Ã¢â€š ¬? against the testing set, resulting in their respective ex-ante prediction results. Alyuda Neurointelligence presents the results from the training and testing processes in the form of classification matrices. Finally, using out of sample data, we querying prediction of target variables from out of sample period which will be tested with actual rate of return of January, February and March 2009 to measure the model`s accuracy. 4. Results and Discussion The contribution factor resulted from the network shows that the INTR and MIC ranked first and second on determination of depositor`s rate of return in PT Bank Syariah Mandiri. INFR, JSI and EXR ranked third, fourth, and fifth (table 3). The significant macroeoconomics variables resulted (INTR and MIC) are similar with condition of Islamic bank in other countries such as Bahrain, Bangladesh, Iran, Jordan, Kuwait, Malaysia, Sudan, Tunisia, Turkey and United Arab Emirates. It is reported by Ahmad and Haron (1998) which explained that interest rate and money in circulation had a significant relationship with Islamic bank`s return on capital. It means that both macroeconomic variables give significant impact on delivering return to Islamic bank`s depositor. The results of applying neural network model to do prediction based on those five variables show in general very good (table 4). The trained network was applied to the training data set and showed that its quality is outperform thorough value of Absolute Error (AE) and Absolute Relative Error (ARE). The most generally used characteristics of continuous values are RMSE (root mean squared error), AE and ARE. RMSE and AE are absolute (independent of the output value module), ARE is relative. All these values define the deviation of the predicted output value from the desired one. ARE is an error value that indicates the quality of the neural network training. This index is calculated by dividing the difference between actual and desired output values by the module of the desired output value. The smaller the network error is, the better the network had been trained.à £Ã¢â€š ¬Ã¢â€š ¬Table 5 shows the actual values of out of sample period used to query prediction result from the ANN model. Network provided 99.17% accuracy in predicting RR of January 2009.à £Ã¢â€š ¬Ã¢â€š ¬The results were slightly decreasing when predicting February 2009 and March 2009 which are 92.79% and 92.88%, respectively. That prediction resulted from actual variables which out of period sample in this followi ng table 6. Nevertheless, this network give satisfactory result for RR prediction of three months upcoming period for 94.95% accuracy in average. 5. Conclusion In this paper, we have specified an empirical framework to investigate macroeconomic determinants of Pricing Individual Depositor`s Rate of Return in PT Bank Syariah Mandiri. Based on the results of the empirical analysis, interest rates certificate of central bank of Indonesia and money in circulation significantly determine pricing individual depositor`s rate of return. By implication, these variables can be used by depositor as leading indicator to predict future rate of return. Therefore, in order to maximize their profit opportunity, depositor should pay considerable attention to these macroeconomic indicators. It should be considered that this paper is limited in one particular bank even though the bank is the biggest Islamic bank in Indonesia, so this result might be different when it is implemented in other Islamic bank. Through the continuous adoption and application of neural networks, this research, at least has fulfilled its primary aim of providing an indicator based to build understanding to depositor about the nature of Islamic banking which offers profit and loss sharing principles that create investment relationship. This research also indicates that an Islamic bank is not stand alone bank since economic turmoil strongly influenced its profitability.