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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.

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20222
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Subject: Mathematics × End date: 2024 × Start date: 2022 × Type: Thesis × Thesis Type: M.Sc ×
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    ThesisItemOpen Access
    Some fixed point theorems in intuitionistic fuzzy metric space with certain contractive conditions
    (G.B. Pant University of Agriculture and Technology, Pantnagar, District Udham Singh Nagar, Uttarakhand. PIN - 263145, 2022-09) Dimri, Ritikesh; Sanjay Kumar
    In the present research work fixed point theorems in complete fuzzy metric space due to Wairojjana et al. (2015) and Patir et al. (2018) are extended to complete intuitionistic fuzzy metric space with certain contractive conditions using altering distance functions. The work is presented in five chapters. Chapter one focuses on the basic preliminaries and definitions related to metric spaces, fuzzy metric spaces, intuitionistic fuzzy metric spaces, contractive mapping, altering distance function and fixed point. Chapter two presents a review of the literature which is studied during the proposed work. In chapter three definition of increasing and decreasing altering distance function is given.Fixed point theorems due to Wairojjana et al. (2015) and Patir et al. (2018) for complete fuzzy metric space extented to complete intuitionistic fuzzy metric spaceare proved in this chapter. In chapter four, the theorems proved are supported by few examples. Chapter five summarizes the work in brief along with the conclusions. The literature used in the course of the study has been referred under the section of literature cited
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    ThesisItemOpen Access
    Binary classification of Pneumothorax in chest X-Ray images using deep neural network
    (G.B. Pant University of Agriculture and Technology, Pantnagar, District Udham Singh Nagar, Uttarakhand. PIN - 263145, 2022-08) Kaur, Manjeet; Arun Kumar
    Currently, lung diseases are extremely common throughout the globe and few of which include chronic obstructive pulmonary disease, pneumonia, pneumothorax, tuberculosis etc. Air in the pleural cavity is thought to be as “pneumothorax". This is a state when the lung surface or chest wall is breached, allowing air to enter the pleural space and causing the lung to collapse, it is a serious situation and might be life-threatening. Chest radiographs are the foremost significant and extensively used diagnostic techniques to detect thoracic or lung diseases among various medical imaging technologies. Pneumothorax can be diagnosed by chest X-rays, CT scans or ultrasound techniques but chest X-ray is the most often used accessible radiological method for screening and diagnosing thoracic diseases. The diagnosis from the chest X-Ray can be of great efficiency and time saving if done through automated systems instead of manual image reading. Tremendous research works are being conducted to develop reliable automatic diagnostic systems for detecting diseases within the chest radiographs. Artificial Intelligence (AI) tools have proven to be effective in optimizing the medical industry. Several framework-supported computing techniques have been proposed for the automated identification of pneumothorax from chest radiographs. Numerous models for pneumothorax automated diagnosis are certainly available. In this study, we have taken chest X-ray images and then used the Deep Transfer Learning technique along with Machine Learning to detect the presence or absence of pneumothorax in chest X-ray images. We have aggregated the unique and computational attributes of Deep Learning and Machine Learning. We have used the Deep Transfer Learning technique which is a pre-trained model that is a Residual Network for image feature extraction and the Support Vector Machine (SVM) algorithm which creates the best decision boundary known as “Hyperplane” to classify data points, is a Machine Learning model used for pneumothorax binary classification. To achieve effective outcomes, a balancing data technique with augmentation is used to create a balance between the training and validation dataset, as well as an automatic adjusting learning rate technique called "ReduceLROnPlateau" to monitor validation loss and obtain optimal learning rates. As an implementation tool, “Google Colab” is used. In our proposed method model, it outperforms in terms of metrics that include accuracy, recall (Sensitivity), f-1 score, precision, loss, and AUC(Area Under The Curve). The performance of the framework is evaluated on a dataset that is available on “Kaggle” which is chest X-ray images. This research work has set a new record with a good performance by achieving state-of-the-art results as 0.8831 in terms of AUC and 0.4375 in terms of loss. The Precision, Recall, and f-1 score is obtained as 0.78, 0.81, and 0.794 respectively.