iosr-jmca   Volume-3 ~ Issue-3 ~ May – June 2016

ABSTRACT: Artificial neural networks (ANNs) are non-linear mapping structures based on the function of the human brain. ANNs are known to be universal function approximators and are capable of exploiting nonlinear relationships between variables. ANNs can identify and learn correlated patterns between input data sets and corresponding target values. Crop production forecasting is a very important task for researchers in agriculture........

Keywords: Artificial neural network, Back propagation, MAE, Nonlinear regression, RMSE

[1]. H. Altun, A. Bilgil, and B.C. Fidan, Treatment of multidimensional data to enhance neural network estimators in regression problems, Expert Systems with Applications, 32(2), 2007, 599–605.
[2]. Y. Bard, Nonlinear Parameter Estimation, (Academic Press: New York, 1974).
[3]. W. J. Bullied, R.C. Van Acker and P.R. Bullock, Hydrothermal modelling of seedling emergence timing across topography and soil depth. Agronomy Journal, 104, 2012,pp.423–436.
[4]. R. Cao, M. Francisco-Fernández, A. Anand, F. Bastida and J.L.González-Andújar, Computing statistical indices for hydrothermal times using weed emergence data. Journal of Agricultural Science, Cambridge, 149, 2011, pp.701–712.

ABSTRACT: Now-a-days smartphones with embedded sensor is a component of pervasive applications. Smartphone consists powerful sensors like accelerometer, orientation sensor, and proximity sensor. Mobile application based on sensor has grown rapidly over a decade. Sherlock, which is micro-environment is used for providing platform. By referring Sherlock platform, system is designing a micro-environment. Proposed system achieves energy efficiency for smartphone using sensor.......

Keywords: Context awareness, GPS (Global Position system), Microenvironment

[1]. Amrutkar Rajashri, Ingle Sushil, Sutar Vaibhav, Akshay Ugale, and Kinkar Vandana "A Survey Paper on Micro-environment Sensing Platform for Smartphone", on I2IT conference2016.
[2]. Zheng Yang, Member, IEEE, Longfei Shangguan, Student Member, IEEE, Chenshu Wu, Student Member, IEEE, and Yunhao Liu, Senior Member, IEEE "Sherlock: Micro-environment Sensing for Smartphones IEEE Transactions on Parallel and Distributed Systems, in 2013.
[3]. J. Dai, X. Bai, Z. Yang, Z. Shen, D. Xuan. PerFallD: A Pervasive Fall Detection System Using Mobile Phones. In PervasiveHealth'10, 2010.
[4]. P. Mohan, V. Padmanabhan, and R. Ramjee. Rich Monitoring of Roads and Traffic Using Mobile Smartphones. In SenSys'08, 2008.

ABSTRACT: Many universities have started to assess sustainability, and there are many tools have been developed to assess the sustainability. For universities at an early stage of sustainability implementation, choosing a tool that fits their specific context is an important step in their assessment process. Therefore, in this study, it is found Unit‐based Sustainability Assessment Tool (USAT) to assess the sustainability in Cihan University........

Keywords: Sustainability, USAT, Cihan Unvirsity, HEIs .

[1]. Cortese, A., 2003. The critical role of higher education in creating a sustainable future. Plan. High. Educ., 15e22.
[2]. Lukman, R., Glavic, P., 2007. What are the key elements of a sustainable university? Clean. Technol. Environ. Policy 9, 104e114.
[3]. Gomez, F.U, Navarrete, C. S., Lioi, S. R., & Marzuca, V. I. , 2015, Adaptable model for assessing sustainability in higher education. Journal of Cleaner Production.
[4]. Shriberg, M., 2002 , Institutional assessment tools for sustainability in higher education: strengths, weaknesses, and implications for practice and theory. Int. J. Sustain. High. Educ. 3 (3).
[5]. Kamal, A.M., &Asmuss, M. , 2012, Benchmarking tools for assessing and tracking sustainability in higher educational institutions. International Journal of Sustainability in Higher Education, Vol. 14 Iss 4 pp. 449 – 465.