We have used GALEX observations of the North and South Galactic poles to study the diffuse ultraviolet background at locations where the Galactic light is expected to be at a minimum. We find offsets of 230 -290 photon units in the FUV (1531 Å) and 480 -580 photon units in the NUV (2361 Å). Of this, approximately 120 photon units can be ascribed to dust scattered light and another 110 (190 in the NUV) photon units to extragalactic radiation. The remaining radiation is, as yet, unidentified and amounts to 120 -180 photon units in the FUV and 300 -400 photon units in the NUV. We find that molecular hydrogen fluorescence contributes to the FUV when the 100 µm surface brightness is greater than 1.08 MJy sr −1 .

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We have used data from the Galaxy Evolution Explorer to study the different components of the diffuse ultraviolet background in the region between the Galactic latitudes 70 • -80 • . We find an offset at zero dust column density (E(B -V) = 0) of 240 ± 18 photon units in the FUV (1539Å) and 394 ± 37 photon units in the NUV (2316 A). This is approximately half of the total observed radiation with the remainder divided between an extragalactic component of 114 ± 18 photon units in the FUV and 194 ± 37 photon units in the NUV and starlight scattered by Galactic dust at high latitudes. The optical constants of the dust grains were found to be a=0.4±0.1 and g=0.8±0.1 (FUV) and a=0.4±0.1 and g=0.5±0.1 (NUV). We cannot differentiate between a Galactic or extragalactic origin for the zero-offset but can affirm that it is not from any known source.

Objective: This paper explains the working of the linear regression and Long Short-Term Memory model in predicting the value of a Bitcoin. Due to its raising popularity, Bitcoin has become like an investment and works on the Block chain technology which also gave raise to other crypto currency. This makes it very difficult to predict its value and hence with the help of Machine Learning Algorithm and Artificial Neural Network Model this predictor is tested. Methodology: In this study, we have used data sets for Bitcoin for testing and training the ML and AI model. With the help of python libraries, the data filtration process was done. Python has provided with a best feature for data analysis and visualization. After the understanding of the data, we trim the data and use the features or attributes best suited for the model. Implementation of the model is done and the result is recorded. Finding: It was discovered that the linear regression model's accuracy rate is very high when compared to other Machine Learning models from related works; it was found to be 99.87 percent accurate. The LSTM model, on the other hand, shows a mini error rate of 0.08 percent. This, in turn, demonstrates that the neural network model is more optimized than the machine learning model. Novelty: In this work, a small GUI has been created using the tkinter library that will allow the user to input the High, Low, and Open features values and then predict the next value for the coin. This paper compares the prediction outcomes of a machine learning model and an artificial neural network model. Because linear regression provided the highest accuracy compared to the other machine learning models, we used it to compare it to the LSTM model.

A major demanding issue is developing a Service Level Agreement (SLA) based negotiation framework in the cloud. To provide personalized service access to consumers, a novel Automated Dynamic SLA Negotiation Framework (ADSLANF) is proposed using a dynamic SLA concept to negotiate on service terms and conditions. The existing frameworks exploit a direct negotiation mechanism where the provider and consumer can directly talk to each other, which may not be applicable in the future due to increasing demand on broker-based models. The proposed ADSLANF will take very less total negotiation time due to complicated negotiation mechanisms using a third-party broker agent. Also, a novel game theory decision system will suggest an optimal solution to the negotiating agent at the time of generating a proposal or counter proposal. This optimal suggestion will make the negotiating party aware of the optimal acceptance range of the proposal and avoid the negotiation break off by quickly reaching an agreement.