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Theses

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2015 - 201828
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Subject: Computer Engineering × End date: 2018 × Start date: 2010 × Type: Thesis ×
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    ThesisItemOpen Access
    Image classification using convolutional neural network with tensorflow
    (G.B. Pant University of Agriculture and Technology, Pantnagar - 263145 (Uttarakhand), 2018-08) Gangwar, Krishanveer; Negi, C.S.
    The present work proposes a methodology for classifying images of cats and dogs accurately, using Convolutional Neural Network and Back-Propagation. The image recognition market is estimated to expand from US$15.95 Billion in 2016 to US$38.92 Billion by 2021, at the CAGR of 19.5% between 2016 and 2021. Facebook is the largest image sharing site on Internet. Images represent the largest source of data usage on Facebook. On an average, more than 300 million images are uploaded to its site daily. The present work is aimed to develop a model for classification of cats and dogs. The work initiate with image acquisition. Then, applying image pre-processing to bring all the images in required shape and format. Proposed work is divided into two parts features learning and classification. Feature learning is done through convolutional layer and pooling layers and classification is done by Fully connected layers. The training dataset contains 12108 images. Python 3.6 is used for the programming and system is tested for 1920 images and gained the accuracy of 85.36%. The system is also validated using holdout validation techniques.
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    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.
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    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.
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    ThesisItemOpen Access
    Adaptive MIMO-OFDM to evaluate the performance of wireless multimedia sensor network
    (G.B. Pant University of Agriculture and Technology, Pantnagar - 263145 (Uttarakhand), 2018-08) Rawat, Gaurav; Mishra, P.K.
    Wireless Multimedia Sensor Networks (WMSN) are designed to transmit audio and video streams, still images and scalar data. Multimedia transmission over wireless sensor network has many killer applications like video surveillance system, object tracking, telemedicine, theft control system and traffic monitoring. Researchers are facing many challenges such as higher data rate, lower energy consumption, reliability, signal detection and estimation, uncertainty in network topology, Quality of Service and security and privacy-related issues to accomplish various applications of WMSN. Multiple Input Multiple Output Orthogonal Frequency Division Multiplexing (MIMO-OFDM) is proposed for Wireless Multimedia Sensor Networks to improve system performance in terms of data rate and energy consumption. Efficient wireless multimedia sensor network is designed using adaptive MIMO-OFDM and compressive sensing algorithm. QoS depends on image size, the sparsity of the information and number of measurements. In MIMO-OFDM, higher order modulation techniques improve data rate but in worst channel condition it increases the error rate. There is a trade-off between data rate and error rate. An adaptive MIMO-OFDM is developed to balance data rate and error rate. Modulation scheme get selected automatically according to channel environment that helps to improve data rate with good QoS.
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    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%.
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    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.
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    ThesisItemOpen Access
    An ensemble based classification approach for credibility analysis of online news by detecting clickbait news headlines
    (G.B. Pant University of Agriculture and Technology, Pantnagar - 263145 (Uttarakhand), 2017-08) Agarwal, Parul; Samantaray, S.D.
    The present work proposes a methodology for detecting clickbait news headlines in online news media using Ensemble based classification Technique. In this era of Digitization, presenting news now became online. Everyone is accessing online news by one or other medium. When online news is so popular and easily accessible, it also makes online news vulnerable too. Anyone can write anything in the name of news and it becomes viral whether it is informative or not. Due to the high competition and thrust of clicks, clickbait headlines are manufactured just to attract readers to click. These headlines generate enough curiosity by using some tactics so that readers compelled to click on the link to fill the knowledge gap. Clickbait headlines are compromising the meaning of true journalism. The present work is aimed to develop a clickbait detection system for analyzing the credibility of online news. So that the readers become aware and do not click on these links. News headlines are a piece of text, hence the proposed task is divided into two subtasks; Text analysis and classification. Text analysis is done for the transformation of text into numerical features usable for machine learning. These numerical features are then used for training the ensemble based classifier. The training dataset contains 10000 clickbait and 10000 non-clickbait headlines. Python 2.7 is used for the programming and system is tested for 10600 news headlines which are in an even distribution of 5800 clickbait and nonclickbait headlines and gained 93.13% accuracy. This system is also validated using k-fold cross validation technique.
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    ThesisItemOpen Access
    Estimatiom of energy consumption of skype communication application for mobile phones
    (G.B. Pant University of Agriculture and Technology, Pantnagar - 263145 (Uttarakhand), 2015-08) Ashwani; Singh, Rajeev
    Recently the use of mobile phones has increased significantly. Smartphone‟s with android OS has captured majority of market now a days. However, Smartphones are of very much importance and they have undoubtedly increased the comfort level of human beings up to a great point. The major disadvantage with Smartphones is their increased amount of battery consumption while so many synchronic applications are running. In this thesis work, an estimation is made regarding the amount of energy consumed during use of a single application (Skype). A battery manager application GSAM has been used for this purpose. This Application provides the amount of battery percentage used by Skype application for a particular instance of time. To obtain the amount of energy consumed by Skype under different-different communication modes (video, voice, text, video conferencing) WLAN (Wi-Fi) and mobile data connection are considered.
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    ThesisItemOpen Access
    Study and improvement over insertion sort along with comparative analysis
    (G.B. Pant University of Agriculture and Technology, Pantnagar - 263145 (Uttarakhand), 2015-07) Saini, Arun; Singh, B.K.
    Sorting is most widely used activity in the computer world without which computer cannot complete a single task efficiently. Sorting is used in many applications like searching, real-time systems and operating systems. Although there is a large number of sorting algorithms available but there is no single sorting algorithm that fulfills all the requirements. In this work I proposed a sorting technique; a type of hybrid sort method is based on the idea similar to insertion sort and efficiently sorts the elements of a given list. This sorting approach uses an additional step and binary search is used to find the exact location at which element is to be inserted in the list. A comprehensive empirical analysis has been done on the proposed approach and it has been compared with the traditional sorting methods- bubble, insertion, and selection sort. The results showed that proposed sorting approach is fast and effective than the sorting algorithms belong to O(N2) complexity class like insertion sort, selection sort and bubble sort in most of the cases as it required very less number of comparisons and takes less time and swaps to sort the elements of a list.