The availability of different processing levels for satellite images makes it important to measure their suitability for classification tasks. This study investigates the impact of the Landsat data processing level on the accuracy of land cover classification using a support vector machine (SVM) classifier. The classification accuracy values of Landsat 8 (LS8) and Landsat 9 (LS9) data at different processing levels vary notably. For LS9, Collection 2 Level 2 (C2L2) achieved the highest accuracy of (86.55%) with the polynomial kernel of the SVM classifier, surpassing the Fast Line-of-Sight Atmospheric Analysis of Spectral Hypercubes (FLAASH) at (85.31%) and Collection 2 Level 1 (C2L1) at (84.93%). The LS8 data exhibits similar behavior. Conversely, when using the maximum-likelihood classifier, the highest accuracy (83.06%) was achieved with FLAASH. The results demonstrate significant variations in accuracies for different land cover classes, which emphasizes the importance of per-class accuracy. The results highlight the critical role of preprocessing techniques and classifier selection in optimizing the classification processes and land cover mapping accuracy for remote sensing geospatial applications. Finally, the actual differences in classification accuracy between processing levels are larger than those given by the confusion matrix. So, the consideration of alternative evaluation methods with the absence of reference images is critical.
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YY Lazim, NAB Azizan, 2nd International Conference on Innovation and Entrepreneurship, 2014
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The recent advancements in security approaches have significantly increased the ability to identify and mitigate any type of threat or attack in any network infrastructure, such as a software-defined network (SDN), and protect the internet security architecture against a variety of threats or attacks. Machine learning (ML) and deep learning (DL) are among the most popular techniques for preventing distributed denial-of-service (DDoS) attacks on any kind of network. The objective of this systematic review is to identify, evaluate, and discuss new efforts on ML/DL-based DDoS attack detection strategies in SDN networks. To reach our objective, we conducted a systematic review in which we looked for publications that used ML/DL approach
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Machine learning has a significant advantage for many difficulties in the oil and gas industry, especially when it comes to resolving complex challenges in reservoir characterization. Permeability is one of the most difficult petrophysical parameters to predict using conventional logging techniques. Clarifications of the work flow methodology are presented alongside comprehensive models in this study. The purpose of this study is to provide a more robust technique for predicting permeability; previous studies on the Bazirgan field have attempted to do so, but their estimates have been vague, and the methods they give are obsolete and do not make any concessions to the real or rigid in order to solve the permeability computation. To
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The aim of this paper to study the effect of the implicit factors on the entrepreneurial spirit of the students of the Algerian university. Our structural model was proposed based on the model (Shapiro et Sokol, 1982) and the model (Ajzen, 1991). We tested it on a sample of 163 university students at the University of Algiers 3. The model consists of a set of variables (the intention of contracting as a dependent variable, structural and social educational support as independent variables). The results showed that educational and social support factors affect the entrepreneurial spirit of students more than structural support. The Applied Impacts are the enhancing of knowledge capacities of university stu
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When optimizing the performance of neural network-based chatbots, determining the optimizer is one of the most important aspects. Optimizers primarily control the adjustment of model parameters such as weight and bias to minimize a loss function during training. Adaptive optimizers such as ADAM have become a standard choice and are widely used for their invariant parameter updates' magnitudes concerning gradient scale variations, but often pose generalization problems. Alternatively, Stochastic Gradient Descent (SGD) with Momentum and the extension of ADAM, the ADAMW, offers several advantages. This study aims to compare and examine the effects of these optimizers on the chatbot CST dataset. The effectiveness of each optimizer is evaluat
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Iraqi economy has grown rapidly. Iraqi citizen, therefore, should be very much involved with the comprehensive development after his long patience. Such development should begin with him and his family to get the housing commodity, which is indeed not a cheap one.
In this regard, the Iraqi legislator drew attention to the necessity of issuing housing finan
... Show MoreThis research aimed to identify the level of English language knowledge among the students of scientific colleges and the preparatory year (scholarship students) at the Islamic University of Madinah. The research was limited to identifying students at the meanings of vocabulary and grammar of the English language. The researcher used a descriptive approach to achieve study goals. It applied three tools to collect information from the targeted sample; first, a test for determining the level of students in the achievement of vocabulary which applied to (69) students. Second, a test to determine the level of students in the English language grammar, (73) students took part. Third, a survey to get students’ opinions about the program of te
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