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ThesisItem Open 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.ThesisItem Open Access Classification of Glaucoma and Bright Lesions in Retinal Fundus Images using SVM(G.B. Pant University of Agriculture and Technology, Pantnagar - 263145 (Uttarakhand), 2017-08) Upadhyaya, Himanshu; Negi, Chetan SinghIn terms of population, India stands at the second position in the world, and with such a huge population it is very difficult to provide medical assistance to each and every one especially to those living in the remote areas. As these retinal diseases require regular check-ups and timely intervention to control the progress of disease, an ophthalmologist with all the medical equipment is required which could be highly expansive. Therefore an automated clinical support system should be developed for the diagnosis of retinal diseases like glaucoma and diabetic retinopathy which could be used to make the screening of real time population easy and efficient and also identify those who are at risk in the early stages. This technique would minimize the cost, estimation time and also assist the ophthalmologist to perform the treatment plan. This thesis presents a classification system for the diagnosis of Glaucoma and Bright Lesions in retinal fundus images where different anatomical and statistical features are extracted and classified using SVM. It has been observed that the anatomical features proved to be a promising features as compared to the other statistical features and a good accuracy is achieved using SVM classification. In this thesis work the performance analysis of this classification system over different feature sets is reported and discussed.ThesisItem Open Access Encoder-decoder based integrity verification for video surveillance(G.B. Pant University of Agriculture and Technology, Pantnagar - 263145 (Uttarakhand), 2017-08) Panwar, Amit; Singh, RajeevSurveillance cameras are widely used anywhere to record video data. In order to counter the editing or copying by adversaries, assuring the integrity of the extracted video is one of the fundamental issues in this area. Video surveillance is increasing significance as organizations seek to safe guard physical and capital assets. At the same time, the necessity to observe more people, places, and things coupled with a desire to pull out more useful information from video data is motivating new demands for scalability, capabilities, and capacity. Two improved system are described for verifying video content integrity, one uses frame level integrity and other uses digital watermarking. Existing verification systems are unable to distinguish between attacks and regular modifications and are thus unsuitable countermeasures against actual threats. The first proposed method helps in identifying the distortion in the video data at the frame level. The second proposed method distinguishes attacks against video content from regular modifications by extracting time codes and header hash values embedded in the content itself and comparing them with the actual ones, making it well suited for content storage services. Evaluation showed that second method is more effective than the one using the digital signature scheme.ThesisItem Open Access A novel approach for mapping of a boolean function using artificial neural network(G.B. Pant University of Agriculture and Technology, Pantnagar - 263145 (Uttarakhand), 2017-07) Sharma, Jalaj; Samantaray, S.D.Boolean functions have enormous importance in the field of Computer Engineering. These functions are important not only because of the fact that computer hardware architecture is based on them but also because of ever-increasing automation using devices like programmable logical controllers that utilize such functions in ladder programming. Another aspect of the importance of Boolean functions is the ability to transform any number to binary form for the purpose of any kind of processing or analysis of the data. Owing to this fact, various methods for representation of Boolean functions have been suggested in the literature. The input-output relationship for the devices based on Boolean functions can be mapped using trained artificial neural networks. Artificial Neural Network (ANN) is an intelligent tool with parallel computational capability. Conventional ANN once designed needs to be trained using iterative training process. The proposed method for mapping of Boolean functions is advantageous because of its generality and ease of implementation. This method is based on a novel neural architecture known as Pi-Sigma neuron model. Although Pi-Sigma neuron model is complex but the proposed new Pi-Sigma neuron model named as Simplified Pi-Sigma neuron model reduces the complexity and makes the learning process simple and non-iterative. While the conventional neuron models have summation operation for aggregation, the proposed neuron model has multiplication as well as summation operations for representing aggregation of the dendritic inputs. Incorporation of the multiplication operation along with the summation operation is based on some biological evidences as observed by researchers in the field of computational neuroscience. As any Boolean function can be represented in terms of sum of products, the proposed neuron model is capable of representing any Boolean function because of its inherent nature of performing multiplication operations before performing summation operations. The advantage with this method is that it does not require a long process of iterations for training. Weights and biases are directly calculated by presenting the training data in a single stroke. The proposed model works primarily for Boolean functions, but it can be extended to any kind of functions by using the conversion of number systems along with this method of functional mapping.ThesisItem Open Access Threshold sensitive energy efficient multi-sink routing protocol for heterogeneous wireless sensor networks(G.B. Pant University of Agriculture and Technology, Pantnagar - 263145 (Uttarakhand), 2017-08) Sumit Kumar; Mishra, P. K.The prominence of Wireless Sensor Networks (WSN) has expanded massively in late time because of development in Micro-Electro-Mechanical Systems (MEMS) innovation. WSN has the possibility to associate the physical world with the virtual world by framing a system of sensor nodes. Here, sensor nodes are typically battery-driven gadgets, and consequently energy consumption of sensor nodes is a noteworthy design issue. This thesis addresses ‘WSN’s lifetime and stability period optimization problem” which is to design an energy efficient protocol in such a way that energy consumption of every node in the wireless sensor networks is minimized which results in an improved stability period and prolonged lifetime of WSNs. This thesis solves the problem by introducing a static clustering based multi-sink routing protocol for heterogeneous wireless sensor networks. The idea of threshold aware transmission is also utilized to accomplish these objectives. The results are compared with two well known traditional clustering protocols namely LEACH and SEP using stability period, network lifetime, instability period and throughput as performance metrics. The proposed work performs better than the other protocols under consideration.ThesisItem Open Access Quality assessment of pan-sharpened images by the fusion of panchromatic and multispectral images(G.B. Pant University of Agriculture and Technology, Pantnagar - 263145 (Uttarakhand), 2017-08) Simali, Priyanka; Negi, Chetan SinghThesisItem Open Access Paraphrasing detection using dependency tree recursive autoencoder(G.B. Pant University of Agriculture and Technology, Pantnagar - 263145 (Uttarakhand), 2017-08) Rana, Deepak Singh; Mishra, P.K.ThesisItem Open Access Disease manifestation prediction from weather data using extreme learning machine(G.B. Pant University of Agriculture and Technology, Pantnagar - 263145 (Uttarakhand), 2017-08) Bisen, Tejasvee; Singh, B.K.ThesisItem Open Access Energy efficient threshold sensitive cluster heads selection based on minimum distance & maximum energy for wireless sensor networks(G.B. Pant University of Agriculture and Technology, Pantnagar - 263145 (Uttarakhand), 2017-08) Rishab Kumar; Mishra, P.K.