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Theses

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2015 - 2019362020 - 202215
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Subject: Computer Engineering × Start date: 2007 ×
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Now showing 1 - 9 of 51
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    ThesisItemOpen Access
    Application of machine learning algorithms in hostel mess attendance system using face recognition
    (G. B. Pant University of Agriculture and Technology, Pantnagar, 2022-10) Pokhariya, Jyoti; Mishra, P.K.
    How to accurately and effectively identify people has always been an exciting topic in research and industry. With the rapid development of machine learning in recent years, facial recognition is gaining more and more attention. The face is the representation of one's identity. Hence, we have proposed an automated hostel mess attendance system based on face recognition for students. Face recognition is mainly used in biometric applications, surveillance systems, and computer vision. The major problem while recognizing faces arises due to pose variations, background illumination invariants and facial expressions. The proposed model utilizes two machine learning algorithms, HOG and SVM. HOG is used in preprocessing face images, and SVM is the classification algorithm used for image classification. The use of two separate machine learning algorithms improves the system recognition accuracy. In many of the hostel mess, managing the attendance of students/candidates is a tedious task, as there would be many students in the hostel mess and keeping track of all is onerous. Based on face detection and recognition algorithms, this system spontaneously detects the student when they enter the hostel mess and mark their attendance by recognizing them. The database is then modified or updated automatically. This reduces the time and effort of manually updating the attendance. The act of proxies will also be avoided using a face recognition attendance system. Face recognition system, compared to traditional card recognition, biometrics like fingerprint recognition and iris recognition, has many advantages. It is limited to non-contact, high concurrency, and user-friendly models, which are highly required after the covid-19 pandemic.
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    ThesisItemOpen Access
    Design and implementation of drone spraying module using raspberry pi
    (G. B. Pant University of Agriculture and Technology, Pantnagar, 2022-10) Jyoti Ratna; Singh, Rajeev
    AI and IoT have made significant contributions to resolving people's issues in the present day technological world. The emergence of IoT and the broad implementation of cloud and internet technologies have made it possible to integrate traditional agricultural techniques with smart devices to automate some or all associated activities. Designing an agriculture drone spraying module to spray pesticides to minimize the adverse effects on people is the primary goal of this thesis. In this study, we describe a structure-based on drone. Pesticides must be used in agriculture to maintain the standard of large-scale production. By substituting intelligent machines like drones for human labourers using the most recent technologies; agriculture can be more productive and efficient. The use of pesticides in agriculture plays a significant part in raising the production of various crops per acre. The drones will carry pesticides to spray all around the farm, minimizing farmer’s effort and improving task completion. Spraying pesticides and fertilizer is a crucial step in agriculture for excellent crop production. The research's goal is to develop a drone spraying module that can do a variety of agricultural tasks including application of fertilizers and pesticides. The proposed procedure includes creating a prototype using basic and affordable technology like a Raspberry Pi and various motors and terminal equipment’s to assist farmers in various crop field operations. The obtained results suggest that a high spraying accuracy can be obtained using the proposed developed low cost system. Also, this system can be fitted on micro and small drones for spraying in the farms. The spraying module consists of following hardware parts i.e. water pump, two 5-way splitting nozzles, water pipes, battery and L298N motor driver and the total cost of designing the spraying module is approximately Rs.2000.
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    ThesisItemOpen Access
    Multi-label classification of news titles using bidirectional long short-term memory model
    (G.B. Pant University of Agriculture and Technology, Pantnagar - 263145 (Uttarakhand), 2021-12) Goel, Yash Kumar; Samantaray, S.D.
    Multi-Label Text Classification can be used when there are two or more classes as well as the information to be classified may relate to neither of the classifications or all of them at the very same time. With the rapid advancement of devices and digital telecommunications, online news has become one of the most important attributes for people's daily lives, studies, and jobs. Online news, in comparison to other conventional media, is extensive, diversified in form, and could be updated in real-time. The lack of classification makes it hard for a person to interpret or obtain data relevant to particularly preferred classifications. Text classification, as one of the key technologies of information resource organization & management, could allow users to narrow the scope of feature extraction as well as make it more convenient as well as effective to filter via massive digital information to fulfil the needs. The technique of text classification, which in the classification stage is capable of classifying instantly against several classifications on unstructured text with natural language, is used. In the proposed work, WordNet and word sense database is used to improve the efficiency of the classifier. To handle a huge amount of data the classification deep learning approach i.e. Bidirectional Long/Short-Term Memory (Bi- LSTM) is proposed. As News Titles is a short text that could lead to ambiguity in classification class and the title of the news item could be linked to a number of different sources that could lead to ambiguity in classification class, the introduction of the phrase seeks to optimize the classification method. The challenge of news classification begins with web scraping to gather real-time news Titles from news websites, which are then instantly classified using different classification methodologies and introduce the Wordnet and WordSense database for multi-label news titles classification. The acquired accuracy of (Bi-LSTM) was 97.91 per cent, which exceeded the approximate accuracy of each individual plan. This technique could be very helpful for academicians who want to investigate headlines in order to support their instruction.
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    ThesisItemOpen Access
    Stock price prediction using LSTM approach
    (G.B. Pant University of Agriculture and Technology, Pantnagar - 263145 (Uttarakhand), 2021-09) Agarwal, Shweta; Singh, B.K.
    In today's economy, the stock market, often known as the equity market, has a significant impact. The rise or decline in the share price has a significant impact on the investor's profit. The proposed method used Long short-Term Memory (LSTM) Approach. Here I am considering multi-column LSTM model which takes more than one column to analyse and train the model and based on that it will predict the values for future days. More than one features helps the model to predict the values more accurately than providing the single feature. Here the dataset is taken from Yahoo Finance website which provides historical data to almost all of the companies listed in the stock market. The dataset is taken for a particular company PETRONET LNG from 2004 to 2018. Next 30 days values are being predicted based on that historical data. The values for 2019 is not being considered as this time was affected by corona virus and every sector of the industry was affected by this pandemic. So taking these values may provide wrong predictions as there was sudden fall and rise in the stock values during this time. I have also added 2 more features to the given historical data i.e. volatility and momentum. Volatility is basically used to capture fluctuation in the market. Momentum tells us what is the changes in the price as compare to past days. Result showa that adding these features helps model to predict more accurately.
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    ThesisItemOpen Access
    Cloud based real time soil moisture content monitoring using IOT and unmanned aerial vehicles
    (G.B. Pant University of Agriculture and Technology, Pantnagar - 263145 (Uttarakhand), 2021-09) Joshi, Kritika; Singh, Rajeev
    Recently, agricultural UAVs or drones have become one of the most useful agricultural instruments utilized in smart farming, especially in the ground sensing applications. The UAV is employed as a mobility hotspot in the area lacking of the communication network to collect the data of soil and crops in order to monitor and control the water management system for precision agriculture. The agricultural UAV with a combination of IoT sensors will be effected in such areas. At present, the IoT sensors detect temperature and moisture of the soil and send them to farmers by using the cloud but not by using UAV. In this work we combined and connected the UAVs and IoT network based sensor. By this connection, the identification of locations that suffer from droughts, water scarcity, and dryness of soil profile can be easily observed. It will help the farmers to take precautionary methods such as contouring, damming, draining the surface water, and curtailing for further irrigation. The objective of this work is to design a system to collect and measure the soil moisture contents remotely in real-time through drone. For a country like India whose main source of revenue for citizens is agriculture, this domain is not explored sufficiently in terms of technology. The lack of sufficient research in this domain has been a problem for the livelihood of those farmers whose sole existence depends on agriculture. An economical process could be devised that could help automate the process of knowing the exact soil moisture content of the entire farmland. Such process would be a boon for the farmers. above work is only a step forward in this direction. In this thesis work we monitor real time soil moisture by collecting the data using the soil moisture sensor and Arduino UNO. The transmitted data is transmitted to the drone who further send the resultant data for real time monitoring and analysis to the cloud.
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    ThesisItemOpen Access
    Evaluation of controller placement approaches in SDN-based 5G networks
    (G.B. Pant University of Agriculture and Technology, Pantnagar - 263145 (Uttarakhand), 2021-09) Agarwal, Lakshita; Sharma, Jalaj
    The thesis work provided the relevant information about the SDN-based 5G networks, with the controller placement problem as well as the average latency between the nodes of the networking system. Software-defined networking (SDN) is a technology that is emerging in today’s world and it is mainly used for changing the state of the network by breaking the older version of it and by separating the network’s control layer from the layer of the routers and switches. With the evolution of the fifth generation, i.e., 5G networks, there is a steady growth in the development of different business models as well as new applications are being developed. For the development of the 5G networking and for the formation of different intelligent networks and applications, SDN technology has been considered as a key enabler. The main aim of this work is about the evaluation of the already existing controller placement approaches i.e., K-Median, K-Center and Kcritical Approaches for SDN-Based 5G networking system. The proposed work is an approach for analyzing the solutions for the placement of the controller inside the SDNbased 5G network. In this work, the network topology of 250 nodes was created and the evaluation of the three different approaches was being done. It was also concluded that out of all the three approaches the K-Critical approach can be considered as one of the best approaches for the selection of the controller inside the SDN-based 5G networks because all the criterions in this approach were satisfied according to the requirements.
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    ThesisItemOpen Access
    Using ensemble and TOPSIS with AHP for classification and selection of web services
    (G.B. Pant University of Agriculture and Technology, Pantnagar - 263145 (Uttarakhand), 2021-09) Pandey, Mithilesh; Jalal, Sunita
    Due to the increasing number of web services with the same functionality, selecting a web service that best serves the needs of the Web Client has become a tremendously challenging task. Present approaches use non-functional parameters of the web Services, but they do not consider any preprocessing of the set of functionally similar web services. Due to the lack of preprocessing, the web services selection method also has to process web services with a very low to no chance of satisfying the consumer's requirements. This thesis proposes an Ensemble classification method for preprocessing and a web services selection method based on the Quality of Service (QoS) parameters. Once the most eligible web services are enumerated through classification, they are ranked using the TOPSIS (Technique of Order Preference by Similarity to Ideal Solution) method with AHP (Analytic Hierarchy Process) used for weight calculation. A prototype of the method is developed, and experiments are conducted on a real world web services dataset. Results demonstrate the feasibility of the proposed method.
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    ThesisItemOpen Access
    A hybrid Artificial Bee Colony Genetic Algorithm (ABCGA) approach for energy efficiency in wireless sensor network communication
    (G.B. Pant University of Agriculture and Technology, Pantnagar - 263145 (Uttarakhand), 2021-09) Pandey, Garima; Mishra, P.K.
    Non-rechargeable batteries with restrained energy capability are used to power the wireless sensor network nodes, but in hostile conditions, replacing a node battery is a difficult task. Thus, enhancing the energy efficiency resulting in increasing the lifetime of the network is a suitable decision. Recent work suggests that clustering is an efficient mechanism for reducing energy consumption, increasing network scalability, maintain load balancing, all of which contribute to maximizing total network life. Appropriate cluster head selection in a cluster is crucial as it prominently affects the wireless sensor network life. Metaheuristic algorithms can be utilised effectively for this.This thesis main contribution is to design a bee colony optimization technique named ABCGA dependent on an artificial bee method. In the proposed algorithm ABCGA, theartificial bee method is integrated with the features of the genetic algorithm for optimal cluster head selection. Also, data compression is done before data transmission, resulting in reduced energy consumption and increased network life. This proposed method has been evaluated against the LEACH, PBC-CP, PSO, and HSAPSO techniques in terms of the following factors- the number of active nodes, the number of nodes dead, remaining energy, and throughput. In the end, the simulation findings demonstrated that the proposed approach ABCGA outperforms all these four standard algorithms.
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    ThesisItemOpen Access
    Identification of nutritionally important protein in Amaranthus genes using sequence mining
    (G.B. Pant University of Agriculture and Technology, Pantnagar - 263145 (Uttarakhand), 2021-02) Dheeraj Kumar; Samantaray, S.D.
    Various herbs have been used since ancient time to solve physical problems. It is known that a traditional discovery of Amaranthus plant can neutralize unique diseases. In addition, it can help to overcome the system of disease and increase the effect of scientific treatment and medicine. Amaranthus can be considered a safe haven for wellbeing, due to its therapeutic properties. It affects severe physical problems involving coronary disease, malignant growth, inflammation of joints, stagnation, and liver-kidney problems. As the world's population is increasing day by day and ground, water and food resources are limited, it is of the utmost importance that good sources of protein should be included in human diets, keeping in mind the quantity and quality of proteins required to meet human diets. This article gives a comprehensive idea of Amaranthus that focuses on the research reporting its use in the medical trials and all of its profit to human health. The purpose of this research is to detect the presence of nutritionally important protein sequences in Amaranthus Genes using BLAST (tBLASTn), classification techniques and sequence mining techniques. We found the high similarity searches for Ama1 protein in Amaranthus genes having the following results: maximum bit score 304, total bit score 1247.2, gap 0/146 (0%) and E-Value (min) 4e-134. The second largest similarity found for Dreb1a protein having the following values: maximum bit score 192, total bit score 435, gap 10/197(5%) and E-Value (min) 2e-56.