Refine your search
Collections
Co-Authors
A B C D E F G H I J K L M N O P Q R S T U V W X Y Z All
Kaur, Sandeep
- A Brief Review of Cognitive Radio and SEAMCAT Software Tool
Abstract Views :256 |
PDF Views:0
Authors
Affiliations
1 Department of Electronics and Communication, Punjabi University, Patiala, IN
2 Department of Electronics and Communication, BFCET, Bathinda, IN
1 Department of Electronics and Communication, Punjabi University, Patiala, IN
2 Department of Electronics and Communication, BFCET, Bathinda, IN
Source
Research Cell: An International Journal of Engineering Sciences, Vol 20 (2016), Pagination: 163-167Abstract
In today times, the use of wireless communication increases rapidly. For a wireless communication, the radio spectrum remains constant but the user's increases very fast. According to Federal communication commission (FCC) research report, seventy percent of radio spectrum allocated has been already underutilized. So we want to overcome this problem by using some new technique. Cognitive Radio is a one of the method that will solve this problem. The rapid development in wireless services caused spectrum shortage. Cognitive Radio is a technique which uses licensed spectrum in a very efficient way.Keywords
Cognitive Radio, Spectrum Sensing, Primary Users, Secondary Users, SEAMCAT.- A Review on Image Cryptography
Abstract Views :159 |
PDF Views:3
Authors
Affiliations
1 C.S.E. Deptt., SLIET University, Longowal, IN
2 Computer Deptt., Bhai Behlo Khalsa Girls College, Phaphre Bhaike, Mansa, IN
3 C.S.E. Deptt., GZS PTU Campus, Bathinda, IN
1 C.S.E. Deptt., SLIET University, Longowal, IN
2 Computer Deptt., Bhai Behlo Khalsa Girls College, Phaphre Bhaike, Mansa, IN
3 C.S.E. Deptt., GZS PTU Campus, Bathinda, IN
Source
Research Cell: An International Journal of Engineering Sciences, Vol 12 (2014), Pagination: 31-33Abstract
In this paper, we are discussing about those techniques which are basically works for images cryptography. As a result of currently day's communication through the web becomes one amongst the first wants of human. Once the communication is completed with the assistance of pictures (multimedia contents) then those ancient algorithms won't work owing to dynamic behaviour of the photographs. In follow multimedia system knowledge is common in numerous forms like audio, video, graphic and pictures; therefore info security becomes main concern in knowledge storage and transmission. Cryptography is a technique to make sure sensible security from unauthorized access in several fields like military communication and medical sciences. we've got studied varied cryptography techniques on existing work; all has its own deserves and demerits for pictures then tries to counsel the longer term scope in cryptography method with some modification keys.Keywords
Cryptography, Encryption, Key, Algorithm, Secutiry.- Various Techniques of Regression Testing
Abstract Views :101 |
PDF Views:0
Authors
Affiliations
1 Sant Baba Bhag Singh Institute of Engg & Technology, Jalandhar, IN
1 Sant Baba Bhag Singh Institute of Engg & Technology, Jalandhar, IN
Source
Research Cell: An International Journal of Engineering Sciences, Vol 4 (2011), Pagination: 391-397Abstract
Regression Testing is one of the most important and expensive activities of Software Maintenance. It involves testing the modified program to reveal faults introduced during maintenance and keeping the software's level of reliability, as cheaply as possible. Two techniques of regression testing are discussed. The first is a selective technique which identifies the modified structural attributes of a program (required elements) and selects a resettable test case set to exercise them. This technique is based on the Potential-Uses criteria family and is implemented by a regression testing tool working in conjunction with a testing tool. The second technique comprises procedures for testing and regression testing activities.. In this paper we have presented the various types of regression testing techniques their classifications.Keywords
Software Maintenance, Regression Testing, Test Case Prioritization.- Region of Interest Based Lossless and near Lossless Image Compression
Abstract Views :115 |
PDF Views:2
Authors
Affiliations
1 Ycoe, Talwandi Sabo, IN
1 Ycoe, Talwandi Sabo, IN
Source
Research Cell: An International Journal of Engineering Sciences, Vol 22 (2016), Pagination: 349-356Abstract
Image compression is a method through which we can reduce the storage space of images, videos which will helpful to increase storage and transmission process's performance. In image compression, we do not only concentrate on reducing size but also concentrate on doing it without losing quality and information of image. Image compression may be lossy and lossless. In lossless compression the exact original data to be reconstructed from the compressed data. Lossless is in contrast to lossy data compression, which only allows constructing an approximation of the original data, in exchange for better compression rates. This research is about the image compression based upon the wavelet compressions, and use both lossless and near lossless compression. In this research a Fuzzy Measure based classifiers are specified to classify image dataset for image compression.Keywords
Image Compression, Lossless Compression, Near Lossless Compression.- A Review on Crop Yield Prediction Using Learning Techniques
Abstract Views :127 |
PDF Views:0
Authors
Affiliations
1 Research Scholar, Department of Computer Science, GNDU, Amritsar, IN
2 Associate Professor, Department of Computer Science, GNDU, Amritsar, IN
3 Assistant Professor, Department of Computer Engineering and Technology, GNDU, Amritsar, IN
1 Research Scholar, Department of Computer Science, GNDU, Amritsar, IN
2 Associate Professor, Department of Computer Science, GNDU, Amritsar, IN
3 Assistant Professor, Department of Computer Engineering and Technology, GNDU, Amritsar, IN
Source
Research Cell: An International Journal of Engineering Sciences, Vol 35, No SP (2023), Pagination: 80-96Abstract
Food is regarded as a basic human requirement that is met through agriculture. Beyond meeting fundamental human requirements, agriculture is seen as a global source of employment in developing countries like India. Sustainable crop production is a persistent issue for farmers. Getting the best crop production has always been difficult for farmers since environmental circumstances are always changing. Land types, resource availability, and weather variability are the main causes of unpredictable crop yields. Therefore, scientists from all around the world are working to develop methods that can efficiently and accurately predict crop yields well in advance so that farmers may take the necessary steps to address upcoming issues. Crop production depends entirely on timely observation and advice. Farmers can reduce their losses if appropriate recommendations and information about the crop is provided. Machine learning is the prevailing technology that helps farmer to minimize agriculture losses. The study's primary goal is to explore different learning techniques used to predict crop yield. Reviews carried out in agriculture sector indicated a strong preference for deep learning methods and hybrid models for crop yield prediction.Keywords
Machine Learning, Deep Learning, Crop Yield, Prediction.References
- “Agriculture in India: Industry Overview, Market Size, Role in Development...| IBEF.” https://www.ibef.org/industry/agriculture-india (accessed Oct. 05, 2022).
- M. K. Dharani, R. Thamilselvan, P. Natesan, P. C. D. Kalaivaani, and S. Santhoshkumar, “Review on Crop Prediction Using Deep Learning Techniques,” J. Phys. Conf. Ser., vol. 1767, no. 1, 2021, doi: 10.1088/1742-6596/1767/1/012026.
- N. Bali and A. Singla, “Deep Learning Based Wheat Crop Yield Prediction Model in Punjab Region of North India,” Appl. Artif. Intell., vol. 35, no. 15, pp. 1304–1328, 2021, doi: 10.1080/08839514.2021.1976091.
- B. Sharma, J. K. P. S. Yadav, and S. Yadav, “Predict Crop Production in India Using Machine Learning Technique: A Survey,” ICRITO 2020 - IEEE 8th Int. Conf. Reliab. Infocom Technol. Optim. (Trends Futur. Dir., no. 978, pp. 993–997, 2020, doi: 10.1109/ICRITO48877.2020.9197953.
- B. M. Nayana, K. R. Kumar, and C. Chesneau, “Wheat Yield Prediction in India Using Principal Component Analysis-Multivariate Adaptive Regression Splines (PCA-MARS),” AgriEngineering, vol. 4, no. 2, pp. 461–474, 2022, doi: 10.3390/agriengineering4020030.
- B. E and G. A, “Rice Crop Yield Prediction Using Random Forest and Deep Neural Network - An Integrated Approach,” SSRN Electron. J., no. Icsmdi, 2021, doi: 10.2139/ssrn.3852547.
- N. Bali and A. Singla, “Emerging Trends in Machine Learning to Predict Crop Yield and Study Its Influential Factors: A Survey,” Arch. Comput. Methods Eng., vol. 29, no. 1, pp. 95–112, 2022, doi: 10.1007/s11831-021-09569-8.
- D. Elavarasan and P. M. Durairaj Vincent, “Crop Yield Prediction Using Deep Reinforcement Learning Model for Sustainable Agrarian Applications,” IEEE Access, vol. 8, pp. 86886–86901, 2020, doi: 10.1109/ACCESS.2020.2992480.
- J. Pant, R. P. Pant, M. Kumar Singh, D. Pratap Singh, and H. Pant, “Analysis of agricultural crop yield prediction using statistical techniques of machine learning,” Mater. Today Proc., vol. 46, no. xxxx, pp. 10922–10926, 2021, doi: 10.1016/j.matpr.2021.01.948.
- M. K. D. P, N. Malyadri, M. S. Srikanth, and A. B. J, “recommendation model for Crop and Fertilizer,” vol. 8, no. 5, pp. 10531–10539, 2021.
- A. Sharma, A. Jain, P. Gupta, and V. Chowdary, “Machine Learning Applications for Precision Agriculture: A Comprehensive Review,” IEEE Access, vol. 9, pp. 4843–4873, 2021, doi: 10.1109/ACCESS.2020.3048415.
- S. Vaishnavi, M. Shobana, R. Sabitha, and S. Karthik, “Agricultural Crop Recommendations based on Productivity and Season,” 2021 7th Int. Conf. Adv. Comput. Commun. Syst. ICACCS 2021, pp. 883–886, 2021, doi: 10.1109/ICACCS51430.2021.9441736.
- A. H. Tidake, Y. K. Sharma, and V. S. Deshpande, “Design Efficient Model to Increase Crop Yield Using Deep Learning,” Proceeding - 1st Int. Conf. Innov. Trends Adv. Eng. Technol. ICITAET 2019, no. Dl, pp. 221–226, 2019, doi: 10.1109/ICITAET47105.2019.9170227.
- S. M. Bharath, S. Manoj, P. Adhappa, P. L. Patagar, and R. Bhaskar, “Crop Yield Prediction with Efficient Use of Fertilizers,” Lect. Notes Electr. Eng., vol. 783, pp. 937–943, 2022, doi: 10.1007/978-981-16-3690-5_87.
- U. Shruthi, V. Nagaveni, and B. K. Raghavendra, “A Review on Machine Learning Classification Techniques for Plant Disease Detection,” 2019 5th Int. Conf. Adv. Comput. Commun. Syst. ICACCS 2019, pp. 281–284, Mar. 2019, doi: 10.1109/ICACCS.2019.8728415.
- S. Agarwal and S. Tarar, “A hybrid approach for crop yield prediction using machine learning and deep learning algorithms,” J. Phys. Conf. Ser., vol. 1714, no. 1, 2021, doi: 10.1088/1742-6596/1714/1/012012.
- P. Malik, S. Sengupta, and J. S. Jadon, “Comparative Analysis of Soil Properties to Predict Fertility and Crop Yield using Machine Learning Algorithms,” Proc. Conflu. 2021 11th Int. Conf. Cloud Comput. Data Sci. Eng., pp. 1004–1007, 2021, doi: 10.1109/Confluence51648.2021.9377147.
- R. Kumar, M. P. Singh, P. Kumar, and J. P. Singh, “(Paper 7 Tafur),” no. May, pp. 138–145, 2015.
- S. Godara and D. Toshniwal, “Deep Learning-based query-count forecasting using farmers’ helpline data,” Comput. Electron. Agric., vol. 196, no. February, 2022, doi: 10.1016/j.compag.2022.106875.
- V. Meshram, K. Patil, V. Meshram, D. Hanchate, and S. D. Ramkteke, “Machine learning in agriculture domain: A state-of-art survey,” Artif. Intell. Life Sci., vol. 1, no. September, p. 100010, 2021, doi: 10.1016/j.ailsci.2021.100010.
- S. S. Kale and P. S. Patil, “A Machine Learning Approach to Predict Crop Yield and Success Rate,” 2019 IEEE Pune Sect. Int. Conf. PuneCon 2019, pp. 1–5, 2019, doi: 10.1109/PuneCon46936.2019.9105741.
- D. J. Reddy and M. R. Kumar, “Crop yield prediction using machine learning algorithm,” Proc. - 5th Int. Conf. Intell. Comput. Control Syst. ICICCS 2021, pp. 1466–1470, May 2021, doi: 10.1109/ICICCS51141.2021.9432236.
- S. M. Pande, P. K. Ramesh, A. Anmol, B. R. Aishwarya, K. Rohilla, and K. Shaurya, “Crop Recommender System Using Machine Learning Approach,” Proc. - 5th Int. Conf. Comput. Methodol. Commun. ICCMC 2021, pp. 1066–1071, Apr. 2021, doi: 10.1109/ICCMC51019.2021.9418351.
- S. Sharma, S. Rai, and N. C. Krishnan, “Wheat Crop Yield Prediction Using Deep LSTM Model,” 2020, [Online]. Available: http://arxiv.org/abs/2011.01498.
- S. Lingwal, K. K. Bhatia, and M. Singh, “A novel machine learning approach for rice yield estimation,” Journal of Experimental and Theoretical Artificial Intelligence. 2022, doi: 10.1080/0952813X.2022.2062458.
- P. S. Nishant, P. Sai Venkat, B. L. Avinash, and B. Jabber, “Crop yield prediction based on indian agriculture using machine learning,” 2020 Int. Conf. Emerg. Technol. INCET 2020, no. October, 2020, doi: 10.1109/INCET49848.2020.9154036.