Loading...
Thumbnail Image

Govind Ballabh Pant University of Agriculture and Technology, Pantnagar

After independence, development of the rural sector was considered the primary concern of the Government of India. In 1949, with the appointment of the Radhakrishnan University Education Commission, imparting of agricultural education through the setting up of rural universities became the focal point. Later, in 1954 an Indo-American team led by Dr. K.R. Damle, the Vice-President of ICAR, was constituted that arrived at the idea of establishing a Rural University on the land-grant pattern of USA. As a consequence a contract between the Government of India, the Technical Cooperation Mission and some land-grant universities of USA, was signed to promote agricultural education in the country. The US universities included the universities of Tennessee, the Ohio State University, the Kansas State University, The University of Illinois, the Pennsylvania State University and the University of Missouri. The task of assisting Uttar Pradesh in establishing an agricultural university was assigned to the University of Illinois which signed a contract in 1959 to establish an agricultural University in the State. Dean, H.W. Hannah, of the University of Illinois prepared a blueprint for a Rural University to be set up at the Tarai State Farm in the district Nainital, UP. In the initial stage the University of Illinois also offered the services of its scientists and teachers. Thus, in 1960, the first agricultural university of India, UP Agricultural University, came into being by an Act of legislation, UP Act XI-V of 1958. The Act was later amended under UP Universities Re-enactment and Amendment Act 1972 and the University was rechristened as Govind Ballabh Pant University of Agriculture and Technology keeping in view the contributions of Pt. Govind Ballabh Pant, the then Chief Minister of UP. The University was dedicated to the Nation by the first Prime Minister of India Pt Jawaharlal Nehru on 17 November 1960. The G.B. Pant University is a symbol of successful partnership between India and the United States. The establishment of this university brought about a revolution in agricultural education, research and extension. It paved the way for setting up of 31 other agricultural universities in the country.

Browse

Search Results

Now showing 1 - 4 of 4
  • ThesisItemOpen Access
    Performance evaluation of fuzzy C mean and simulated annealing based clustering in WSN
    (G.B. Pant University of Agriculture and Technology, Pantnagar - 263145 (Uttarakhand), 2018-08) Awasthi, Shivangi; Mishra, P.K.
    Cluster based routing technique is most popular routing technique in Wireless Sensor Networks (WSNs). Due to varying need of WSN applications, efficient energy utilization in routing protocols is still a potential area of research. In this research work, focus is made on the optimization of clustering and to balance the load over the routes so that energy can be used effectively. The proposed work tried to overcome the problem of random distribution of clusters in LEACH. In this study, the optimization of clusters is made by using Fuzzy C Mean Clustering that gives uniformity in the cluster due to central tendency so that a uniform density can be seen in the participating clusters. To further distribute the load of transmitting data through a planned routing scheme Simulated Annealing is used. The performance of proposed work is evaluated on MATLAB by comparing it with some existing protocols.
  • ThesisItemOpen Access
    Application of FCM, GLCM & DWT and SVM for disease identification in mango
    (G.B. Pant University of Agriculture and Technology, Pantnagar - 263145 (Uttarakhand), 2018-08) Rohan, J.; Singh, Rajeev
    India is basically known as agricultural nation where 70% of individuals rely on agribusiness, in this way plants play an influential factor in their life and furthermore assume an inescapable part in the ecological balance of the nation. With regards to plant sickness, there are numerous sorts of ailment existed in world contrasted from area to district. The plant ailments happen every now and again and contrast from each other. These sicknesses can lessen the nature of rural items and caused substantial misfortunes even undermined the sustenance security and for the most part caused irresistible creatures or different other ecological factor. At some point the plant illness taints other piece of plants like leaves or branches and prompts finish collect misfortune and even uphold sustenance shortage. A computerized acknowledgment and characterization framework for these rural items can upgrade its quality by perceiving sickness side effects prior and analyze it. Because of quick advancement in data innovation, it assumes a critical part in preparing, perceiving and characterizing the plant ailments. In this paper we proposed a picture based malady arrangement for mango natural product utilizing GLCM & DWT, FCM and SVM. GLCM and DWT are used for highlight extraction; FCM is utilized for segmenting the images and Support Vector Machine is used for classification.
  • ThesisItemOpen Access
    Fake news detection using text similarity approach
    (G.B. Pant University of Agriculture and Technology, Pantnagar - 263145 (Uttarakhand), 2018-08) Jodhani, Geetika; Samantaray, S.D.
    The present work proposes a methodology for detecting Fake News in online news media using Text Similarity Approach. In this era of digitization, most of the people now get news from internet and often it can be difficult to tell whether stories are credible or not. Information overload and a general lack of understanding about how the internet works by people has also contributed to an increase in fake news or hoax stories. Traditionally we got our news from trusted sources, journalists and media outlets that are required to follow strict codes of practice. However, the internet has enabled a completely new way to publish, share and consume information and news with very little regulation or editorial standards. The present work is aimed to develop an automatic fake news detection system for analysing the credibility of online news. So that the reader become aware about the news that is factually incorrect and optimized for sharing. News articles are nothing but a piece of text. Hence, the proposed work can be divided into two subtasks; Text Analysis and Performance Evaluation. Text analysis is done for the transformation of text into numerical features. These numerical features are then used for matching the similarity between queried article and other articles. For articles similarity I have used hybrid of three text similarity approaches, two methods from lexical similarity features (N-grams (Character Based) and Cosine Similarity method (Corpus Based) and one from semantic similarity feature (Explicit Semantic Analysis (ESA) - TF*IDF (Term Based Similarity)). Python 3.5 is used for programming. System is tested for 100 news articles and analysed that if more than three articles have matching with matching value ≥ 0.70 and < 0.80, then it will result to truthiness of the input article. Our proposed system has gained the accuracy of 91.67%.
  • ThesisItemOpen Access
    Classification of liver disorder from serum profile using extreme learning machine
    (G.B. Pant University of Agriculture and Technology, Pantnagar - 263145 (Uttarakhand), 2018-07) Kharayat, Shweta; Singh, B.K.
    A problem is said to be well posed if it fulfilled the following properties: 1) a solution exists, 2) the solution is unique, and 3) non-perturbation. There exists a special case of the well posed problem where the inverse of the major function does not exist. The prediction of liver disorder in human from serum profile is one such problem. There are several diseases that could possibly affect the liver, with ample category of symptoms. In liver along with symptoms variation, intensity and severity differ from nearly insignificant to life-threatening. The proposed work predicts liver disorder in humans by finding relationship between serum profile parameters and occurrence of liver disorder using machine learning approach. Extreme Learning Machine (ELM) has enough capabilities to solve this type of problem. Extreme Learning Machine approach is utilized for implementing the proposed work by predicting liver disorder in human using serum profile. The serum profile considered in the research work is Tb, Alkphos, Sgpt, Sgot and others. Model classifies on the basis of disease severity. The datset utilized in the thesis is compiled from the UCI machine learning repository. Model is evaluated on several samples of dataset utilizing confusion matrixes and ROC curves for different activation functions. Based on that accuracy of the model evaluation is performed and RBF found to be best activation function to be used in the liver disorder problem. Proposed model has an accuracy of 80.9% for Radial Basis Function.