2,063 research outputs found

    Undergraduate Catalog of Studies, 2023-2024

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    Graduate Catalog of Studies, 2023-2024

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    Graduate Catalog of Studies, 2023-2024

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    Fairness-aware Machine Learning in Educational Data Mining

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    Fairness is an essential requirement of every educational system, which is reflected in a variety of educational activities. With the extensive use of Artificial Intelligence (AI) and Machine Learning (ML) techniques in education, researchers and educators can analyze educational (big) data and propose new (technical) methods in order to support teachers, students, or administrators of (online) learning systems in the organization of teaching and learning. Educational data mining (EDM) is the result of the application and development of data mining (DM), and ML techniques to deal with educational problems, such as student performance prediction and student grouping. However, ML-based decisions in education can be based on protected attributes, such as race or gender, leading to discrimination of individual students or subgroups of students. Therefore, ensuring fairness in ML models also contributes to equity in educational systems. On the other hand, bias can also appear in the data obtained from learning environments. Hence, bias-aware exploratory educational data analysis is important to support unbiased decision-making in EDM. In this thesis, we address the aforementioned issues and propose methods that mitigate discriminatory outcomes of ML algorithms in EDM tasks. Specifically, we make the following contributions: We perform bias-aware exploratory analysis of educational datasets using Bayesian networks to identify the relationships among attributes in order to understand bias in the datasets. We focus the exploratory data analysis on features having a direct or indirect relationship with the protected attributes w.r.t. prediction outcomes. We perform a comprehensive evaluation of the sufficiency of various group fairness measures in predictive models for student performance prediction problems. A variety of experiments on various educational datasets with different fairness measures are performed to provide users with a broad view of unfairness from diverse aspects. We deal with the student grouping problem in collaborative learning. We introduce the fair-capacitated clustering problem that takes into account cluster fairness and cluster cardinalities. We propose two approaches, namely hierarchical clustering and partitioning-based clustering, to obtain fair-capacitated clustering. We introduce the multi-fair capacitated (MFC) students-topics grouping problem that satisfies students' preferences while ensuring balanced group cardinalities and maximizing the diversity of members regarding the protected attribute. We propose three approaches: a greedy heuristic approach, a knapsack-based approach using vanilla maximal 0-1 knapsack formulation, and an MFC knapsack approach based on group fairness knapsack formulation. In short, the findings described in this thesis demonstrate the importance of fairness-aware ML in educational settings. We show that bias-aware data analysis, fairness measures, and fairness-aware ML models are essential aspects to ensure fairness in EDM and the educational environment.Ministry of Science and Culture of Lower Saxony/LernMINT/51410078/E

    Undergraduate Catalog of Studies, 2022-2023

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    Adaptive Grey Wolf Optimization Technique for Stock Index Price Prediction on Recurring Neural Network Variants

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    In this paper, we propose a Long short-term memory (LSTM) and Adaptive Grey Wolf Optimization (GWO)--based hybrid model for predicting the stock prices of the Major Indian stock indices, i.e., Sensex. The LSTM is an advanced neural network that handles uncertain, nonlinear, and sequential data. The challenges are its weight and bias optimization. The classical backpropagation has issues of dangling on local minima or overfitting the dataset. Thus, we propose a GWO-based hybrid approach to evolve the weights and biases of the LSTM and the dense layers. We have made the GWO more robust by introducing an approach to improve the best possible solution by using the optimal ranking of the wolves. The proposed model combines the GWO with Adam Optimizer to train the LSTM. Apart from the LSTM, we have also implemented the Adaptive GWO on other variants of Recurring Neural Networks (RNN) like LSTM, Bi-Directional LSTM, Gated Recurrent Units (GRU), and Bi-Directional GRU and computed the corresponding results. The Adaptive GWO here evolves the initial weights and biases of the above-discussed neural networks. In this research, we have also compared the forecasting efficiency of our proposed work with a particle-warm optimization (PSO) based hybrid LSTM model, simple Grey-wolf Optimization (GWO), and Adaptive PSO. According to the experimental findings, the suggested model has effectively used the best initial weights, and its results are the best overall

    Forecasting the Real Estate Housing Prices Using a Novel Deep Learning Machine Model

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    There is an urgent need to forecast real estate unit prices because the average price of residential real estate is always fluctuating. This paper provides a real estate price prediction model based on supervised regression deep learning with 3 hidden layers, a Relu activation function, 100 neurons, and a Root Mean Square Propagation optimizer (RMS Prop). The model was developed using actual data collected from 28 Egyptian cities between 2014 and 2022. The model can forecast the price of a real estate unit based on 27 different variables. The model is created in two stages: adjusting the parameters to obtain the best ones using a sensitivity k-fold technique, then optimizing the result. 85 percent of the real estate unit data gathered was used in training and developing the model, while the other 15 percent was used in validating and testing. By using a dropout regularization technique of 0.60 on the model layers, the final developed model had a maximum error of 10.58%. After validation, the model had a maximum error of about 9.50%. A graphical user interface (GUI) tool is developed to make use of the final predictive model, which is very simple for real estate developers and decision-makers to use. Doi: 10.28991/CEJ-SP2023-09-04 Full Text: PD

    Designing a combined Markov-bayesian model in order to predict stock prices in the stock exchange

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    Investing in shares offered on the stock exchange is one of the most profitable options in the capital market. The stock market has a non-linear and chaotic system that is influenced by political, economic, and psychological conditions. Forecasting time series, such as stock price forecasting, is one of the most important problems in the field of economics and finance because the data is unstable and has many variables that are influenced by many factors. There are many ways to predict stock prices. Non-linear intelligent systems such as artificial neural networks, fuzzy neural networks, and genetic algorithms can be used to predict stock prices. In this research, a hybrid system based on Bayesian networks and the Markov model is proposed to predict the daily trend of the stock market. Bayesian networks are used to specify relationships between variables in forecasting. Finally, the Markov model is used to predict the market trend in the sets extracted from the Bayesian network. The evaluation criteria in the proposed system show the high efficiency of this method

    Tradition and Innovation in Construction Project Management

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    This book is a reprint of the Special Issue 'Tradition and Innovation in Construction Project Management' that was published in the journal Buildings

    Researches regarding the evolution, magnitude and complexity of the impact generated by the economic activities on the East Jiu River

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    Ongoing development of modern society, based on consumption of goods and services, leads to the increase of compulsoriness of economic agents to face market requirements by increasing the degree of local and regional industrialization. Establishment of new economic activities generates negative pressures on the environment and surface waters, generating increased pollution, manifested by vulnerability of aquatic ecosystems to stressors. Preliminary studies carried out within the doctoral thesis entitled 'Research on the evolution, magnitude and complexity of the impact of economic activities on the East Jiu' include information on characteristic elements of the East Jiu River basin, in accordance with the Water Framework Directive 2000/60/CE. The objectives of the field research aimed to identify economic activities in the eastern Jiu Valley generating an impact on the environment (especially the mining industry, but also timber exploitation and processing, local agriculture, animal husbandry and waste storage), establishing a quarterly monitoring program of the river basin, identification of flora and fauna species and identification of areas vulnerable to potential pollution. Based on observations made in situ and on information obtained from the evolution process of the monitoring program, the appropriate methodologies for assessing physical-chemical and ecological quality of the water were selected. Study of the evolution of the impact generated by economic activities on the East Jiu was carried out by mathematical modelling, with finite volumes, of the East Jiu River basin and plotting of pollutant dispersion maps. The magnitude and complexity of impact generated by economic activities was studied by using a complex system based on fuzzy logic, designed based on interactions between natural and artificial systems, between physical-chemical indicators of water and ecosystem. The research carried out substantiates in development of necessary technical measures to reduce the impact generated by economic activities located in eastern Jiu Valley, without significantly changing the hydrodynamics of the river basin. Following research, during different research stages, methods, techniques and tools were designed and accomplished with the help of which, water and aquatic ecosystems’ quality can be assessed, as well as the impact generated by human activity on the Jiu River, at a given moment and/or continuously.:CONTENT ACKNOWLEDGEMENTS SUMMARY LIST OF FIGURES LIST OF TABLES ABBREVIATIONS INTRODUCTION PURPOSE OF THE THESIS AND RESEARCH METHODOLOGY CHAPTER 1 THE EAST JIUL RIVER HYDROGRAPHIC BASIN 1.1. Soil and subsoil of the Eastern part of Jiu Valley 1.2. Climate description of the Eastern part of Jiu Valley 1.3. Geology particularities of the Eastern part of Jiu Valley 1.4. Groundwater features of the Eastern part of Jiu Valley 1.5. Flora and fauna of the Eastern part of Jiu Valley CHAPTER 2 SOURCES OF IMPACT ON THE QUALITY OF WATER, RIPARIAN, TERRESTRIAL AND AQUATIC ECOSYSTEMS 2.1. Mining industry 2.2. Wood processing industry in the Eastern part of Jiu Valley 2.3. Urban agriculture and local animal husbandry 2.4. Inappropriate urban household waste storage CHAPTER 3 MONITORING PROGRAM AND METHODS OF EVALUATION OF THE QUALITY OF THE EAST JIUL RIVER 3.1. Establishment of monitoring (control) sections 3.2. Monitoring program of the East Jiu River basin 3.3. Sampling, transport and analysis of water samples 3.4. Methodology used to establish the water quality CHAPTER 4 QUALITY ASSESSMENT OF WATER IN THE EASTERN JIU HYDROGRAPHIC BASIN 4.1. Section 1 - Jieț River - upstream of household settlements (blank assay) 4.2. Section 2 - East Jiu River - in the area of Tirici village 4.3. Section 3 - Răscoala brook - before the confluence with East Jiu River 4.4. Section 4 - East Jiu River - after the confluence with the Răscoala brook 4.5. Section 5 - Taia River - upstream of the confluence with East Jiu River 4.6. Section 6 - East Jiu River - before the confluence with the Taia River 4.7. Section 7 - East Jiu River - after the confluence with the Taia River 4.8. Section 8 - Jiet River downstream of household settlements 4.9. Section 9 - East Jiu River - after the confluence with the Jieț River 4.10. Section 10 - East Jiu River - before the confluence with Banița River 4.11. Section 11 - Roşia River - upstream of household settlements 4.12. Section 12 - Bănița River - after the confluence with the Roșia River 4.13. Section 13 - East Jiu River - after the confluence with the Banița River 4.14. Section 14 - Maleia River - before the confluence with East Jiu River 4.15. Section 15 - Slătioara River - before the confluence with East Jiu River 4.16. Section 16 – East Jiu River - before the confluence with West Jiu River CHAPTER 5 INFLUENCES OF PHYSICAL-CHEMICAL FACTORS ON AQUATIC ICHTHYOFAUNA IN THE EAST JIU RIVER BASIN 5.1. Total suspended solids and aquatic ecosystems 5.2. Acidity or basicity reaction of surface watercourses 5.3. Aquatic ecosystem requirements for gas oversaturation 5.4. Nitrogenous compounds in watercourse 5.5. Phenols, aquatic ecosystems and water quality CHAPTER 6 ANALYSIS OF THE IMPACT GENERATED BY ECONOMIC ACTIVITIES IN THE EASTERN PART OF JIU VALLEY 6.1. Impact analysis of mining industry in the Eastern Part of Jiu Valley 6.2. The general impact of Eastern Jiu Valley dumps to water quality 6.3. Research on effective infiltration in the Eastern part of Jiu Valley 6.4. Research on groundwater quality in the Eastern part of Jiu Valley 6.5. Analysis of the impact generated by local micro-agriculture 6.6. Analysis of the impact generated by deforestation and wood processing 6.7. Analysis of the impact generated by non-compliant landfilling of waste CHAPTER 7 EVOLUTION OF THE IMPACT GENERATED BY ECONOMIC ACTIVITIES IN THE EASTERN JIU VALLEY 7.1. Analysis of the dynamic elements of the watercourse - RMA2 mode 7.2. Analysis of pollutants concentration evolution in the water course - RMA4 module 7.3. Computational field and composition of the energy model of the East Jiu River 7.4. Extension and evolution of the impact generated by economic activities on the East Jiu River 7.5. Extension and evolution of the impact caused by organic pollution of the East Jiu River CHAPTER 8 MAGNITUDE AND COMPLEXITY OF THE IMPACT GENERATED BY ECONOMIC ACTIVITIES IN THE EASTERN JIU VALLEY 8.1. Definition of input linguistic variables 8.2. Linguistic outputs of the fuzzy interference system 8.3. Defining the Black Box set of rules 8.4. Proficiency testing of complex systems based on fuzzy logic 8.5. While it is all about the wheel do not forget about the cube CONCLUSIONS AND PERSONAL CONTRIBUTIONS REFERENCE
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