M.Phil / MS
Permanent URI for this collectionhttps://repository.cuilahore.edu.pk/handle/123456789/36
This collection archives the complete set of theses produced by students of the COMSATS University Islamabad, Lahore Campus.
Browse
4 results
Search Results
Item Identification of Best Sowing Patterns of Crop using Satellite Imagery and Deep Learning(Library Information Services, COMSATS University Islamabad, Lahore Campus, 2022) Zil E Huma Shahzadi; FA19-RCS-020; LHR TP 8337; Dr. Zeeshan GillaniRapid growths in population and climate change are resulting in food insecurity. Food security is more challenged in developing countries like Pakistan where conventional practices of crop monitoring are in place. The two aspects of crop monitoring which are crop identification and yield prediction are manual which are time-consuming and resource extensive thus resulting in delayed decision making. However, the Precision Agriculture applications for crops monitoring based on Remote Sensing data can help us to effectively monitor crops and increase production and aid decision making. This study acquires remotely sensed multispectral and multitemporal satellite imagery from Sentinel-2 mission satellites to extract spectral bands over different timestamps of the rice, wheat, and sugarcane crop season. These spectral bands along with the computed vegetation indices over the growing season of wheat and rice are used with the Long Short Term Memory network for the early identification of the crops. The study also attempted to identify the best sowing dates of wheat crops with multispectral and multitemporal data. Further, the significance of the temporal data and different combinations of the spectral bands were analyzed for crop identification of the small-sized fields and a comparison was made with the existing state of the art. The best combination of the spectral bands resulted in 99.76 % accuracy for crop identification. Further, the crops are also identified with 93.77 % accuracy within the first four weeks of their seeding. Our study also identified the sowing week of wheat crop with RMSE of 0.8. The crop identification accuracy with the proposed approach is suggestive of the applicability of the study for the automatic identification of crops on large scaleItem A POI Clustered Users Recommendation Method in LBSNs Considering the Weather Forecast(Library Information Services, COMSATS University Islamabad, Lahore Campus, 2023) Khurram Shahzad; FA19-RCS-023; Dr. Hamid Turab MirzaThe Location-Based Social Network (LBSN) is one type of social web platform that allows users to register when they visit certain Points of Interest (POIs). The practice of recommending points of interest (POI) has become an important component of location-based social networks. However, due to the unique limits such as privacy concerns, accuracy, and reliability, and lack of comprehensive POI coverage of these networks, it remains a difficult challenge. In this thesis, data mining methods (i.e., model-based approach) are applied to handle the problem of the POI recommendation system. The approach is based on a particular method of analyzing geographic data. POI recommendations have a hard time (because of limited context information, credibility concerns, and lack of personalization) persuading users to visit the suggested destinations. The major purpose of this study is to categorize locations into different areas and to use the user group check-ins to train the model to forecast the weather using classification models. In this study, two datasets including Foursquare Check-Ins Tokyo Dataset, and Foursquare Check-Ins New York Dataset are used which contain the check-ins of different locations in New York and Tokyo from 2012-2016. The user’s check-in history is used in terms of timestamps, longitude, latitude, and user behavior to analyze the reliably forecast of the user’s location weather. After applying the data mining methods, the results demonstrate that for the New York check-in corpus, the decision tree method obtained the top performance with an accuracy of 0.74. Regarding the Tokyo check-in corpus, the overall performance indicates that the decision tree algorithm achieved the maximum accuracy of 0.84. The results concerning weather classification show that random forest achieved the best performance with an accuracy of 0.95Item A Lightweight Indoor Smoke Detector with Benchmark Dataset using Deep Learning(Library Information Services, COMSATS University Islamabad, Lahore Campus, 2021) Raheel Shahzad; FA19-RCS-007; LHR TP 7602; Dr. Allah Bux SarganoA fire disaster is one of the most dangerous events that may occur at any place and time. To avoid such incidents, smoke detection is crucial before getting fire. The smoke sensors are the most widely used devices to detect fires; however, smoke sensors can only detect fires if the fire is large, and smoke reaches the sensor. Therefore, vision based smoke detectors have been proposed using machine learning and deep learning based methods. Most of these methods have been proposed for outdoor smoke detection, while little attention has been paid to indoor smoke detection due to the lack of appropriate datasets for indoor scenarios. This study creates a benchmark dataset for indoor smoke detection by properly following annotation criteria outlined by Inter Annotator Agreement (IAA) and Cohen’s Kappa evaluation metrics. The proposed dataset achieved 0.91 IAA and 0.81 Cohen’s Kappa scores, which confirms the excellent quality of the dataset. In addition to this, an innovative transfer learning-based method has been proposed for indoor smoke detection and evaluated on the proposed dataset. Furthermore, a state-of-the-art smoke detection algorithm has been implemented and evaluated on the proposed dataset for comparative analysis. The results confirm that the proposed method outperforms the state-of-the-art methods.Item Multisource Remote Sensing Land Cover Classification for Crop Identification and Yield Prediction using Multispectral Temporal Imagery(Library Information Services, COMSATS University Islamabad, Lahore Campus, 2021) Haseeb Rehman Khan; FA19-RCS-017; LHR TP 7593; Dr. Zeeshan GillaniRapid growths in population and climate change are resulting in food insecurity. Food security is more challenged in developing countries like Pakistan where conventional practices of crop monitoring are in place. The two aspects of crop monitoring which are crop identification and yield prediction are manual which are time-consuming and resource extensive thus resulting in delayed decision making. However, the Precision Agriculture applications for crops monitoring based on Remote Sensing data can help us to effectively monitor crops and increase production and aid decision making. This study acquires remotely sensed multispectral and multitemporal satellite imagery from Sentinel-2 mission satellites to extract spectral bands over different timestamps of the rice, wheat and sugarcane crop season. These spectral bands along with the computed vegetation indices over the growing season of wheat and rice are used with the Long Short Term Memory network for the early identification of the crops. The study also attempted to predict the yield of wheat crops with multispectral and multitemporal data. Further, the significance of the temporal data and different combinations of the spectral bands were analyzed for crop identification of the small-sized fields and a comparison was made with the existing state of the art. The best combination of the spectral bands resulted in 99.76 % accuracy for crop identification. Further, the crops are also identified with 93.77 % accuracy within the first four weeks of their seeding. The Root Means Square Error of 2. 61 and 71.67 % R-squared is reported for the prediction of the wheat yield. The crop identification accuracy with the proposed approach is suggestive of the applicability of the study for the automatic identification of crops on large scale.Multisource Remote Sensing