The physical and chemical mechanisms in a refuse ncinerator are complex. It is difficult to make a full comprehension of the system without a thorough research and long-term on-site experiments. In addition, many sensors are equipped in refuse incineration plant and much data are collected, those data were supposed to be useful since there may be some operational experience within. But to cope with the huge data that may exceed the computation capability, sequential Forward Floating Search algorithm (SFFS) is used to reduce the data dimension and find relevant features as well as to remove redundant information. In this research, data mining technique is applied toward three critical target attributes, steam production, NOx and SOx, to build decision tree models and extract operational experiences in the form of decision rules. Those models are evaluated by predicting accuracies, and rules extracted from decision tree models are also of great help to the on-site operation and prediction as well.

In the forestry industry tree trunks are currently classified manually. The object of this thesis is to answer whether it is possible to automate this using modern computer hardware and image-classification of tree-trunks using machine learning algorithms. The report concludes, based on results from controlled experiments that it is possible to achieve an accuracy above 90% across the genuses Birch, Pine and Spruce with a classification-time per tree shorter than 500 milli seconds. The report further compares these results against previous research and concludes that better results are probable.

Trusted execution environments (TEEs) are an emerging technology that provides a protected hardware environment for processing and storing sensitive information. By using TEEs, developers can bolster the security of software systems. However, incorporating TEE into existing software systems can be a costly and labor-intensive endeavor. Software maintenance—changing software after its initial release—is known to contribute the majority of the cost in the software development lifecycle. The first step of making use of a TEE requires that developers accurately identify which pieces of code would benefit from being protected in a TEE. For large code bases, this identification process can be quite tedious and time-consuming. To help reduce the software maintenance costs associated with introducing a TEE into existing software, this thesis introduces ML-TEE, a recommendation tool that uses a deep learning model to classify whether an input function handles sensitive information or sensitive code. By applying ML-TEE, developers can reduce the burden of manual code inspection and analysis. ML-TEE's model was trained and tested on functions from GitHub repositories that use Intel SGX and on an imbalanced dataset. The accuracy of the final model used in the recommendation system has an accuracy of 98.86% and an F1 score of 80.00%. In addition, we conducted a pilot study, in which participants were asked to identify functions that needed to be placed inside a TEE in a third-party project. The study found that on average, participants who had access to the recommendation system's output had a 4% higher accuracy and completed the task 21% faster. / Master of Science / Improving the security of software systems has become critically important. A trusted execution environment (TEE) is an emerging technology that can help secure software that uses or stores confidential information. To make use of this technology, developers need to identify which pieces of code handle confidential information and should thus be placed in a TEE. However, this process is costly and laborious because it requires the developers to understand the code well enough to make the appropriate changes in order to incorporate a TEE. This process can become challenging for large software that contains millions of lines of code. To help reduce the cost incurred in the process of identifying which pieces of code should be placed within a TEE, this thesis presents ML-TEE, a recommendation system that uses a deep learning model to help reduce the number of lines of code a developer needs to inspect. Our results show that the recommendation system achieves high accuracy as well as a good balance between precision and recall. In addition, we conducted a pilot study and found that participants from the intervention group who used the output from the recommendation system managed to achieve a higher average accuracy and perform the assigned task faster than the participants in the control group.

ABSTRACT DECISION TREE CLASSIFICATION OF MULTI-TEMPORAL IMAGES FOR FIELD-BASED CROP MAPPING Sencan, Se&ccedil / il M. Sc., Department of Geodetic and Geographic Information Technologies Supervisor: Assist. Prof. Dr. Mustafa T&uuml / rker August 2004, 125 pages A decision tree (DT) classification approach was used to identify summer (August) crop types in an agricultural area near Karacabey (Bursa), Turkey from multi-temporal images. For the analysis, Landsat 7 ETM+ images acquired in May, July, and August 2000 were used. In addition to the original bands, NDVI, PCA, and Tasselled Cap Transformation bands were also generated and included in the classification procedure. Initially, the images were classified on a per-pixel basis using the multi-temporal masking technique together with the DT approach. Then, the classified outputs were applied a field-based analysis and the class labels of the fields were directly entered into the Geographical Information System (GIS) database. The results were compared with the classified outputs of the three dates of imagery generated using a traditional maximum likelihood (ML) algorithm. It was observed that the proposed approach provided significantly higher overall accuracies for the May and August images, for which the number of classes were low. In May and July, the DT approach produced the classification accuracies of 91.10% and 66.15% while the ML classifier produced 84.38% and 63.55%, respectively. However, in August nearly the similar overall accuracies were obtained for the ML (70.82%) and DT (69.14%) approaches. It was also observed that the use of additional bands for the proposed technique improved the separability of the sugar beet, tomato, pea, pepper, and rice classes.