Sasha Shelke, Dr. Milind Peshave – May 2017 Page No.: 01-05
Stress is the difference between expectations and acceptance. Rising stress levels among students is an alarming signal as more and more youth going through this phenomenon .One of the very basic reason for rising stress among student is predominant notion about educational qualification that it fetches more respect and gives the bright career opportunities. A culture of competitive world is killing the talent of Problem solver, lifelong learners and healthy young adult. The Fear of failing in studies is causing extreme depression and anxiety among students which has substantial negative effects on their academic, and social success. The individual worth and capabilities of student is determined by the academic success and grades, and not on the basis of individual qualities Credibility s and confidence they already do possess.
This study gives an outlook on how the education system creates the social impact causing more stress among students. The study attempted to analyze the factors determining stress amongst students. The findings suggest that “Relationship with teachers and friends” is the major determinant of students’ stress in education and “External factor such as Family conflicts and Social Influences” are the least influential factors for students’ stress levels. .
- Page(s): 01-05
- Date of Publication: 31 May 2017
- Sasha Shelke
Assistant Professor,
AISSMS’s College of HMCT, Pune, India - Dr. Milind Peshave
Associate Professor,
AISSMS’s College of HMCT, Pune, India
References
[1]. Shadiya Mohamed S Baqutayan, Tile –Studio stress, International Journal of Innovation, Management and Technology, Volume-2, Issue-4, Year – 08/11, Page no-295.
[2]. Skead Natalia and Rogers, Stress, Title-Anxiety and depression in law student, Journal-Monash University Law Review, volume 40, Issue 2, Year; 2014, Page no 564-587.
[3]. Maria Amelia Dias; Barbosa, Maria Alves, Title-Teaching strategies for coping with stress, Journal-BMC medical education, Volume 13, issue1, year 2013, page no 50.
[4]. Anne Millett, Tiltel Dealing With College Students’ Stress, Anxiety, and Depression, The Journal for Quality and Participation, volume 39, issue 4, Year 01/2017, Page no 24
[5]. Coccia, Catherine, Darling, Carol A, Title -Having the Time of Their Life, Journal -Stress and Health, Volume 32, Issue 1,Year 02/2016 , Page 28-35
[6]. Darling, Carol Anderson; Mc Wey, Lenore M; Howard, Stacy N; Olmstead, Spence, Title- College student stress-The influence of interpersonal relationships on sense of coherence, Journal- Stress and Health, Volume 23, Issue 4, year 10/2007, Pages: 215 – 229
[7]. Kao, Po-Chi; Craigie, and Philip, Title-Evaluating student interpreters’ stress and coping strategies, Journal-Social Behavior and Personality: an international journal , Volume: 41, Issue:6, Date: 07/2013 Page:1035
Sasha Shelke, Dr. Milind Peshave “Students’ Stress Levels – An Emerging Concern For Academia” International Journal of Research and Innovation in Applied Science -IJRIAS vol.2 issue 5, pp.01-05 2017
For Full Text Click here
K. Devi Prasanna, K. Bhuvaneswari, G. Jai Sankar – May 2017 Page No.: 06-09
Feature Extraction is an important component of every Image Classification and Object Recognition System. An approach was developed in e-Cognition 9.0 software to perform image segmentation and classification for feature extraction. Classification algorithms formulated on pixel-based analysis often do not give the expected results when applied to high-spatial resolution remote-sensing data. To overcome this problem, the concept of Object Based Image Analysis is developed as it gives the desired result when compared to pixel based analysis. The study area is a part of the IWMP watersheds which focuses on Gandarvakottai watershed in Pudukkottai District, Tamil Nadu. The study involves the extraction of watershed developmental activity works like check dams and farm ponds.
- Page(s): 06-09
- Date of Publication: 31 May 2017
- K. Devi Prasanna
Andhra University College of Engineering, Visakhapatnam, Andhra Pradesh, India - K. Bhuvaneswari
DNR College of Engineering & Technology, Andhra Pradesh, India - G. Jai Sankar
Andhra University College of Engineering, Visakhapatnam, Andhra Pradesh, India
References
[1]. Araya, Y.H., Hergarten, C., 2008. A comparison of pixel and object-based land cover classification: a case study of the Asmara region, Eritrea. In: WIT Transactions on the Built Environment, Geo-Environment and Landscape Evolution III 100.
[2]. Campbell, J., 2006. Introduction to Remote Sensing. Taylor & Francis, London, 467-469.
[3]. Hanumantha Rao CH. 2000. Watershed Development in India: Recent Experience and Emerging Issues. Economic and Political Weekly, 35(45): 3943-3947.
[4]. Hay G.J. and G. Castilla, Geographic Object-based Image Analysis (GEOBIA): A new name for a new discipline. In T. Blaschke, S. Lang and G.J. Hay (Eds) Object-based Image Analysis− Spatial Concepts for Knowledge-driven remote sensing applications. Springer-Verlag, Berlin, 2008.
[5]. Joshi PK, Jha AK, Wani SP, Sreedevi TK and Shaheen FA. 2008. Impact of Watershed Program and Conditions for Success: A Meta-Analysis Approach. Global Theme on Agroecosystems, Report 46. International Crops Research Institute for the Semi-Arid Tropics and National Centre for Agricultural Economics and Policy Research.
[6]. Lillesand T.M., Kiefer R.W., Chipman J.W. John Wiley & Sons; New York: 2008. Remote sensing and image interpretation.
[7]. Molden D. 2007. Water for Food, Water for Life: A Comprehensive Assessment of Water Management in Agriculture. Molden D (ed). International Water Management Institute IWMI. Earthscan, London, UK; Colombo, Sri Lanka.
[8]. Raju KV, Aziz A, Sundaram MSS, Sekher M, Wani SP and Sreedevi TK. 2008. Guildelines for Planning and Implementation of Watershed Development Program in India: A Review. Global Theme on Agroecosystems Report 48. Andhra Pradesh, India: International Crops Research Institute for the Semi-Arid Tropics.
[9]. Rockström J, Hatibu N, Oweis T, Wani S, Barron J, Bruggeman A, Qiang Z, Farahani J, and Karlberg L. 2007. Managing Water in Rain-fed Agriculture. In: (Molden D, ed), Water for Food, Water for Life. A Comprehensive Assessment of Water Management in Agriculture. International Water Management Institute. Earthscan, London, UK.
[10]. Singh RV.2000. (Ed.) Watershed planning and management. Yash Publishing House, Bikaner, Rajasthan, India.
K. Devi Prasanna, K. Bhuvaneswari, G. Jai Sankar “Use of Bi-Temporal Satellite Data for Feature Extraction” International Journal of Research and Innovation in Applied Science -IJRIAS vol.2 issue 5, pp.06-09 2017
For Full Text Click here
Vishnu B. Patel, Bhavik J. Chapadiya, Hemang A. Aghera, Dhruv R. Pabani – May 2017 Page No.: 10-14
Rise of new energy era include a strategy to build a state of the art transmission system is evident. The most likely replacement for the conventional AC transmission is HVDC. It is proven technology with many benefits over conventional technology and has a vast array of application like subsea transmission, interconnection of asynchronous AC grid and many more possibilities. Its most likely implementation is for transmission of long distance bulk power with great efficiency. In this paper we have provided a simulation in matlab of HVDC transmission system based on 12 pulse inverter and rectifier with their controller.
- Page(s): 10-14
- Date of Publication: 31 May 2017
- Vishnu B. Patel
Assistance Professor,
U.V. Patel college of Engineering, Mehsana, Gujarat, India - Bhavik J. Chapadiya
U.G. student,
U.V. Patel college of Engineering, Mehsana, Gujarat, India - Hemang A. Aghera
U.G. student,
U.V. Patel college of Engineering, Mehsana, Gujarat, India - Dhruv R. Pabani
U.G. student,
U.V. Patel college of Engineering, Mehsana, Gujarat, India
References
[1]. Nagraj and Kothari “Power System Stability” McGraw hill publication, fifth Edison.
[2]. Mohan, Ned, Undeland, Tore and Robbinson, William, Power Electronics: converters, Application and design, 2nd Edison, chapter 17, fig17-1, new York, Jon wiley and sons.
[3]. Swisher and Joel N “cleaner energy, greener profits: fuel cells as cost- Effective distribution energy resources, rocky Mountain, institute, Snowmass” CO, 2003.
[4]. https://www.energy.ca.gov/electricity/2003-01-28_OUTLOOK.PDF
[5]. https://www.solcomhouse.com/electricity
[6]. California’s Electricity system of the future scenario analysis in support of public interest transmission system R&D planning, California energy commission https://www.energy.ca.gov/reports/2003-05-05 500-03-010F.PDF
Vishnu B. Patel, Bhavik J. Chapadiya, Hemang A. Aghera, Dhruv R. Pabani “Descriptive approach to High Voltage D.C. Transmission” International Journal of Research and Innovation in Applied Science -IJRIAS vol.2 issue 5, pp.10-14 2017
For Full Text Click here
K. Devi Prasanna, K. Bhuvaneswari, G. Jai Sankar – May 2017 Page No.: 15-18
Change detection using time series satellite images is one of the most widely used techniques. A Define Developer e-Cognition version 9.0 software is used to identify plantation changes using multi-temporal Resoursesat-2 LISS-IV multispectral images of 5.8m spatial resolution. The vegetation change detection has been developed by taking the concept of object based image analysis as it gives the desired results for high resolution remote sensing data when compared to the pixel based image analysis. The changes in plantation have been detected after implementation of developmental activities like check dams, farm ponds, and percolation tanks etc. The main aim of this paper is to study the vegetation changes in the selected watershed over a period of time due to implementation of soil and water conservation measures using multi temporal satellite data. The study involves watershed monitoring which focuses on Gandarvakottai watershed of Pudukkottai district in TamilNadu. .
- Page(s): 15-18
- Date of Publication: 31 May 2017
- K. Devi Prasanna
Andhra University College of Engineering, Visakhapatnam, Andhra Pradesh, India - K. Bhuvaneswari
DNR College of Engineering & Technology, Andhra Pradesh, India - G. Jai Sankar
Andhra University College of Engineering, Visakhapatnam, Andhra Pradesh, India
References
[1]. Campbell, J., 2006. Introdution to Remote Sensing. Taylor & Francis, London, 467-469.
[2]. Hay G.J. and G. Castilla, Geographic Object-based Image Analysis (GEOBIA): A new name for a new discipline. In T. Blaschke, S. Lang and G.J. Hay (Eds) Object-based Image Analysis− Spatial Concepts for Knowledge-driven remote sensing applications. Springer-Verlag, Berlin, 2008.
[3]. Im, J., Rhee, J., Jensen, J.R., Hodgson, M.E., 2007. An automated binary change detection model using a calibration approach. Remote Sensing of Environment 106, 89–105.
[4]. Joshi PK, Jha AK, Wani SP, Sreedevi TK and Shaheen FA. 2008. Impact of Watershed Program and Conditions for Success: A Meta-Analysis Approach. Global Theme on Agro ecosystems, Report 46. International Crops Research Institute for the Semi-Arid Tropics and National Centre for Agricultural Economics and Policy Research.
[5]. Jensen, 2005 J.R. Jensen Digital change detection, Introductory Digital Image Processing, A Remote Sensing perspective, Pearson Prentice Hall, New York (2005), pp. 467–494.
[6]. Kennedy, R.E., Townsend, P.A., Gross, J.G., Cohen, W.G., Bolstad, P., Wang, Y.Q.and Adams, P. (2008) – Remote sensing change detection tools for natural resource managers: Understanding concepts and trade-offs in design of landscape monitoring projects, Remote Sensing of Environment, 113, 1382 -1396.
[7]. Lillesand T.M., Kiefer R.W., Chipman J.W. John Wiley & Sons; New York: 2008. Remote sensing and image interpretation.
[8]. Lu, D. et al. 2004. Change detection techniques. Int J Rem Sens 25:2365.
[9]. Rosin, P.L., 2002. Thresholding for change detection. Computer Vision and Image Understanding 86, 79–95.
[10]. Verbesselt et al., 2010 J. Verbesselt, R. Hyndman, A. Zeileis, D. Culvenor Phenological change detection while accounting for abrupt and gradual trends in satellite image time series Remote Sens. Environ., 114 (2010), pp. 2970–2980.
K. Devi Prasanna, K. Bhuvaneswari, G. Jai Sankar “Vegetation Change Detection Using Multi-Temporal Satellite Data” International Journal of Research and Innovation in Applied Science -IJRIAS vol.2 issue 5, pp.15-18 2017
For Full Text Click here
Shikha Ujjainia, Pratima Gautam, S.Veenadhari – May 2017 Page No.: 19-21
The main source livelihood of many people is agriculture. Approximately 70 % of the people directly rely on agriculture as a mean of living. Development in agriculture may also increase savings. The rich farmers we see today started saving particularly after green revolution. This surplus quantity may be invested further in the agriculture sector to develop the sector. Green revolution began in India with an objective to give greater emphasis on Agriculture. The introduction of improved methods of agriculture and high yielding varieties (HYV) seeds, mainly wheat, had resulted into remarkable improvement in agricultural outputs. And all this was just because of technology. In this paper, we propose some novel method to find the most favorable conditions based on geographic factors for growing crops which helps in maximum crop production.
- Page(s): 19-21
- Date of Publication: 31 May 2017
- Shikha Ujjainia
Research Scholar,
AISECT University, Bhopal, Madhya Pradesh, India. - Pratima Gautam
Research Scholar,
AISECT University, Bhopal, Madhya Pradesh, India. - S.Veenadhari
Research Scholar,
AISECT University, Bhopal, Madhya Pradesh, India.
References
[1]. Hua Fang, Zhaoyang Zhang, Chanpaul Jin Wang, Mahmoud Daneshmand, Chonggang Wang and Honggang Wang “ A Survey of Big Data Research ” Journal of IEEE Network Volume:29, Issue: 5, September-October 2015.
[2]. Minwoo Ryu et al. “ Design and Implementation of a Connected Farm for Smart Farming System ” Journal of IEEE, SENSORS, pages:1-4,2015.
[3]. Aalaa Abdullah et al. “ AgriSys: A Smart and Ubiquitous Controlled Environment Agriculture System ” at 3rd MEC International Conference on Big Data and Smart City, pages: 1-6, 2016.
[4]. Ankita Patil et al. “ Smart Farming using Arduino and Data Mining ” at International Conference on Computing for Sustainable Global Development (INDIACom),pages: 1913-1917,2016.
[5]. M.R. Bendre et al. “ Big Data in Precision Agriculture : Weather Forecasting For Future Farming ” at 1st International Conference on Next Generation Computing Technologies (NGCT-2015),pages: 744-750, 2015.
[6]. M.K. Gayatri et al. “Providing Smart Agriculture Solutions to Farmers for better yielding using IoT ” at IEEE International Conference on Technological Innovations in ICT for Agriculture and Rural Development (TIAR 2015), pages: 40-43, 2015.
[7]. Wu Fan1 et al. “ Prediction of crop yield using big data ” Journal of 8th International Symposium on Computational Intelligence and Design, Volume: 1, Pages: 255-260, 2015.
[8]. Laizhong Cui Laizhong Cui, F. Richard Yu, and Qiao Yan “ When Big Data Meets Software-Defined Networking : SDN for Big Data and Big Data for SDN ” Journal of IEEE, Volume: 30, Issue: 1, January-February 2016.
Shikha Ujjainia, Pratima Gautam, S.Veenadhari “Development of Smart Crop Production System using Big Data- A Review” International Journal of Research and Innovation in Applied Science -IJRIAS vol.2 issue 5, pp.19-21 2017
For Full Text Click here