Wanner, Thomas
Dissertation Note: | Thesis (Ph.D.) -- University of Adelaide, School of Social Sciences, 2019 |
Keywords: | Mining sustainable development political ecology corporate social responsibility Ghana |
Provenance: | This electronic version is made publicly available by the University of Adelaide in accordance with its open access policy for student theses. Copyright in this thesis remains with the author. This thesis may incorporate third party material which has been used by the author pursuant to Fair Dealing exceptions. If you are the owner of any included third party copyright material you wish to be removed from this electronic version, please complete the take down form located at: http://www.adelaide.edu.au/legals |
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3d face reconstruction using deep learning.
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A deep learning approach for clustering a multi-class dataset, aerial imagery pixel-level segmentation, a framework for understanding business process remaining time predictions, a hybrid model for pedestrian motion prediction, algorithms for center-based trajectory clustering, allocation decision-making in service supply chain with deep reinforcement learning, analyzing policy gradient approaches towards rapid policy transfer, an empirical study on dynamic curriculum learning in information retrieval, an explainable approach to multi-contextual fake news detection, an exploration and evaluation of concept based interpretability methods as a measure of representation quality in neural networks, anomaly detection in image data sets using disentangled representations, anomaly detection in polysomnography signals using ai, anomaly detection in text data using deep generative models, anomaly detection on dynamic graph, anomaly detection on finite multivariate time series from semi-automated screwing applications, anomaly detection on multivariate time series using gans, anomaly detection on vibration data, application of p&id symbol detection and classification for generation of material take-off documents (mtos), applications of deep generative models to tokamak nuclear fusion, a similarity based meta-learning approach to building pipeline portfolios for automated machine learning, aspect-based few-shot learning, aspect-based few-shot learning, assessing bias and fairness in machine learning through a causal lens, assessing fairness in anomaly detection: a framework for developing a context-aware fairness tool to assess rule-based models, a study of an open-ended strategy for learning complex locomotion skills, a systematic determination of metrics for classification tasks in openml, a universally applicable emm framework, automated machine learning with gradient boosting and meta-learning, automated object recognition of solar panels in aerial photographs: a case study in the liander service area, automatic data cleaning, automatic scoring of short open-ended questions, automatic synthesis of machine learning pipelines consisting of pre-trained models for multimodal data, automating string encoding in automl, autoregressive neural networks to model electroencephalograpy signals, balancing efficiency and fairness on ride-hailing platforms via reinforcement learning, benchmarking audio deepfake detection, better clustering evaluation for the openml evaluation engine, bi-level pipeline optimization for scalable automl, block-sparse evolutionary training using weight momentum evolution: training methods for hardware efficient sparse neural networks, boolean matrix factorization and completion, bootstrap hypothesis tests for evaluating subgroup descriptions in exceptional model mining, bottom-up search: a distance-based search strategy for supervised local pattern mining on multi-dimensional target spaces, bridging the domain-gap in computer vision tasks, can time series forecasting be automated: a benchmark and analysis.
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82 Data Mining Essay Topic Ideas & Examples
🏆 best data mining topic ideas & essay examples, 💡 good essay topics on data mining, ✅ most interesting data mining topics to write about.
- Data Mining Classifiers: The Advantages and Disadvantages One of the major disadvantages of this algorithm is the fact that it has to generate distance measures for all the recorded attributes.
- Data Mining Role in Companies The increasing adoption of data mining in various sectors illustrates the potential of the technology regarding the analysis of data by entities that seek information crucial to their operations.
- Data Mining in Social Networks: Linkedin.com One of the ways to achieve the aim is to understand how users view data mining of their data on LinkedIn.
- Disadvantages of Using Web 2.0 for Data Mining Applications This data can be confusing to the readers and may not be reliable. Lastly, with the use of Web 2.
- The Data Mining Method in Healthcare and Education Thus, I would use data mining in both cases; however, before that, I would discover a way to improve the algorithms used for it.
- Data Mining Tools and Data Mining Myths The first problem is correlated with keeping the identity of the person evolved in data mining secret. One of the major myths regarding data mining is that it can replace domain knowledge.
- Hybrid Data Mining Approach in Healthcare One of the healthcare projects that will call for the use of data mining is treatment evaluation. In this case, it is essential to realize that the main aim of health data mining is to […]
- Terrorism and Data Mining Algorithms However, this is a necessary evil as the nation’s security has to be prioritized since these attacks lead to harm to a larger population compared to the infringements.
- Transforming Coded and Text Data Before Data Mining However, to complete data mining, it is necessary to transform the data according to the techniques that are to be used in the process.
- Data Mining and Machine Learning Algorithms The shortest distance of string between two instances defines the distance of measure. However, this is also not very clear as to which transformations are summed, and thus it aims to a probability with the […]
- Summary of C4.5 Algorithm: Data Mining 5 algorism: Each record from set of data should be associated with one of the offered classes, it means that one of the attributes of the class should be considered as a class mark.
- Ethnography and Data Mining in Anthropology The study of cultures is of great importance under normal circumstances to enhance the understanding of the same. Data mining is the success secret of ethnography.
- Issues With Data Mining It is necessary to note that the usage of data mining helps FBI to have access to the necessary information for terrorism and crime tracking.
- Large Volume Data Handling: An Efficient Data Mining Solution Data mining is the process of sorting huge amount of data and finding out the relevant data. Data mining is widely used for the maintenance of data which helps a lot to an organization in […]
- Levi’s Company’s Data Mining & Customer Analytics Levi, the renowned name in jeans is feeling the heat of competition from a number of other brands, which have come upon the scene well after Levi’s but today appear to be approaching Levi’s market […]
- Cryptocurrency Exchange Market Prediction and Analysis Using Data Mining and Artificial Intelligence This paper aims to review the application of A.I.in the context of blockchain finance by examining scholarly articles to determine whether the A.I.algorithm can be used to analyze this financial market.
- “Data Mining and Customer Relationship Marketing in the Banking Industry“ by Chye & Gerry First of all, the article generally elaborates on the notion of customer relationship management, which is defined as “the process of predicting customer behavior and selecting actions to influence that behavior to benefit the company”.
- Data Mining Techniques and Applications The use of data mining to detect disturbances in the ecosystem can help to avert problems that are destructive to the environment and to society.
- Ethical Data Mining in the UAE Traffic Department The research question identified in the assignment two is considered to be the following, namely whether the implementation of the business intelligence into the working process will beneficially influence the work of the Traffic Department […]
- Canadian University Dubai and Data Mining The aim of mining data in the education environment is to enhance the quality of education for the mass through proactive and knowledge-based decision-making approaches.
- Data Mining and Customer Relationship Management As such, CRM not only entails the integration of marketing, sales, customer service, and supply chain capabilities of the firm to attain elevated efficiencies and effectiveness in conveying customer value, but it obliges the organization […]
- E-Commerce: Mining Data for Better Business Intelligence The method allowed the use of Intel and an example to build the study and the literature on data mining for business intelligence to analyze the findings.
- Ethical Implications of Data Mining by Government Institutions Critics of personal data mining insist that it infringes on the rights of an individual and result to the loss of sensitive information.
- Data Warehouse and Data Mining in Business The circumstances leading to the establishment and development of the concept of data warehousing was attributed to the fact that failure to have a data warehouse led to the need of putting in place large […]
- Data Mining: Concepts and Methods Speed of data mining process is important as it has a role to play in the relevance of the data mined. The accuracy of data is also another factor that can be used to measure […]
- Data Mining Technologies According to Han & Kamber, data mining is the process of discovering correlations, patterns, trends or relationships by searching through a large amount of data that in most circumstances is stored in repositories, business databases […]
- Data Mining: A Critical Discussion In recent times, the relatively new discipline of data mining has been a subject of widely published debate in mainstream forums and academic discourses, not only due to the fact that it forms a critical […]
- Commercial Uses of Data Mining Data mining process entails the use of large relational database to identify the correlation that exists in a given data. The principal role of the applications is to sift the data to identify correlations.
- A Discussion on the Acceptability of Data Mining Today, more than ever before, individuals, organizations and governments have access to seemingly endless amounts of data that has been stored electronically on the World Wide Web and the Internet, and thus it makes much […]
- Applying Data Mining Technology for Insurance Rate Making: Automobile Insurance Example
- Applebee’s, Travelocity and Others: Data Mining for Business Decisions
- Applying Data Mining Procedures to a Customer Relationship
- Business Intelligence as Competitive Tool of Data Mining
- Overview of Accounting Information System Data Mining
- Applying Data Mining Technique to Disassembly Sequence Planning
- Approach for Image Data Mining Cultural Studies
- Apriori Algorithm for the Data Mining of Global Cyberspace Security Issues
- Database Data Mining: The Silent Invasion of Privacy
- Data Management: Data Warehousing and Data Mining
- Constructive Data Mining: Modeling Consumers’ Expenditure in Venezuela
- Data Mining and Its Impact on Healthcare
- Innovations and Perspectives in Data Mining and Knowledge Discovery
- Data Mining and Machine Learning Methods for Cyber Security Intrusion Detection
- Linking Data Mining and Anomaly Detection Techniques
- Data Mining and Pattern Recognition Models for Identifying Inherited Diseases
- Credit Card Fraud Detection Through Data Mining
- Data Mining Approach for Direct Marketing of Banking Products
- Constructive Data Mining: Modeling Argentine Broad Money Demand
- Data Mining-Based Dispatching System for Solving the Pickup and Delivery Problem
- Commercially Available Data Mining Tools Used in the Economic Environment
- Data Mining Climate Variability as an Indicator of U.S. Natural Gas
- Analysis of Data Mining in the Pharmaceutical Industry
- Data Mining-Driven Analysis and Decomposition in Agent Supply Chain Management Networks
- Credit Evaluation Model for Banks Using Data Mining
- Data Mining for Business Intelligence: Multiple Linear Regression
- Cluster Analysis for Diabetic Retinopathy Prediction Using Data Mining Techniques
- Data Mining for Fraud Detection Using Invoicing Data
- Jaeger Uses Data Mining to Reduce Losses From Crime and Waste
- Data Mining for Industrial Engineering and Management
- Business Intelligence and Data Mining – Decision Trees
- Data Mining for Traffic Prediction and Intelligent Traffic Management System
- Building Data Mining Applications for CRM
- Data Mining Optimization Algorithms Based on the Swarm Intelligence
- Big Data Mining: Challenges, Technologies, Tools, and Applications
- Data Mining Solutions for the Business Environment
- Overview of Big Data Mining and Business Intelligence Trends
- Data Mining Techniques for Customer Relationship Management
- Classification-Based Data Mining Approach for Quality Control in Wine Production
- Data Mining With Local Model Specification Uncertainty
- Employing Data Mining Techniques in Testing the Effectiveness of Modernization Theory
- Enhancing Information Management Through Data Mining Analytics
- Evaluating Feature Selection Methods for Learning in Data Mining Applications
- Extracting Formations From Long Financial Time Series Using Data Mining
- Financial and Banking Markets and Data Mining Techniques
- Fraudulent Financial Statements and Detection Through Techniques of Data Mining
- Harmful Impact Internet and Data Mining Have on Society
- Informatics, Data Mining, Econometrics, and Financial Economics: A Connection
- Integrating Data Mining Techniques Into Telemedicine Systems
- Investigating Tobacco Usage Habits Using Data Mining Approach
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- Advisor: Suresh Kalathur
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Trending Data Mining Thesis Topics
Data mining seems to be the act of analyzing large amounts of data in order to uncover business insights that can assist firms in fixing issues, reducing risks, and embracing new possibilities . This article provides a complete picture on data mining thesis topics where you can get all information regarding data mining research
How does data mining work?
- A standard data mining design begins with the appropriate business statement in the questionnaire, the appropriate data is collected to tackle it, and the data is prepared for the examination.
- What happens in the earlier stages determines how successful the later versions are.
- Data miners should assure the data quality they utilize as input for research because bad data quality results in poor outcomes.
- Establishing a detailed understanding of the design factors, such as the present business scenario, the project’s main business goal, and the performance objectives.
- Identifying the data required to address the problem as well as collecting this from all sorts of sources.
- Addressing any errors and bugs, like incomplete or duplicate data, and processing the data in a suitable format to solve the research questions.
- Algorithms are used to find patterns from data.
- Identifying if or how another model’s output will contribute to the achievement of a business objective.
- In order to acquire the optimum outcome, an iterative process is frequently used to identify the best method.
- Getting the project’s findings suitable for making decisions in real-time
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Data Mining Tasks
- Data mining finds application in many ways including description, Analysis, summarization of data, and clarifying the conceptual understanding by data description
- And also prediction, classification, dependency analysis, segmentation, and case-based reasoning are some of the important data mining tasks
- Regression – numerical data prediction (stock prices, temperatures, and total sales)
- Data warehousing – business decision making and large-scale data mining
- Classification – accurate prediction of target classes and their categorization
- Association rule learning – market-based analytical tools that were involved in establishing variable data set relationship
- Machine learning – statistical probability-based decision making method without complicated programming
- Data analytics – digital data evaluation for business purposes
- Clustering – dataset partitioning into clusters and subclasses for analyzing natural data structure and format
- Artificial intelligence – human-based Data analytics for reasoning, solving problems, learning, and planning
- Data preparation and cleansing – conversion of raw data into a processed form for identification and removal of errors
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How to work on a data mining thesis topic?
The following are the important stages or phases in developing data mining thesis topics.
- First of all, you need to identify the present demand and address the question
- The next step is defining or specifying the problem
- Collection of data is the third step
- Alternative solutions and designs have to be analyzed in the next step
- The proposed methodology has to be designed
- The system is then to be implemented
Usually, our experts help in writing codes and implementing them successfully without hassles . By consistently following the above steps you can develop one of the best data mining thesis topics of recent days. Furthermore, technically it is important for you to have a better idea of all the tasks and techniques involved in data mining about which we have discussed below
- Data visualization
- Neural networks
- Statistical modeling
- Genetic algorithms and neural networks
- Decision trees and induction
- Discriminant analysis
- Induction techniques
- Association rules and data visualization
- Bayesian networks
- Correlation
- Regression analysis
- Regression analysis and regression trees
If you are looking forward to selecting the best tool for your data mining project then evaluating its consistency and efficiency stands first. For this, you need to gain enough technical data from real-time executed projects for which you can directly contact us. Since we have delivered an ample number of data mining thesis topics successfully we can help you in finding better solutions to all your research issues. What are the points to be remembered about the data mining strategy?
- Furthermore, data mining strategies must be picked before instruments in order to prevent using strategies that do not align with the article’s true purposes.
- The typical data mining strategy has always been to evaluate a variety of methodologies in order to select one which best fits the situation.
- As previously said, there are some principles that may be used to choose effective strategies for data mining projects.
- Since they are easy to handle and comprehend
- They could indeed collaborate with definitional and parametric data
- Tare unaffected by critical values, they could perhaps function with incomplete information
- They could also expose various interrelationships and an absence of linear combinations
- They could indeed handle noise in records
- They can process huge amounts of data.
- Decision trees, on the other hand, have significant drawbacks.
- Many rules are frequently necessary for dependent variables or numerous regressions, and tiny changes in the data can result in very different tree architectures.
All such pros and cons of various data mining aspects are discussed on our website. We will provide you with high-quality research assistance and thesis writing assistance . You may see proof of our skill and the unique approach that we generated in the field by looking at the samples of the thesis that we produced on our website. We also offer an internal review to help you feel more confident. Let us now discuss the recent data mining methodologies
Current methods in Data Mining
- Prediction of data (time series data mining)
- Discriminant and cluster analysis
- Logistic regression and segmentation
Our technical specialists and technicians usually give adequate accurate data, a thorough and detailed explanation, and technical notes for all of these processes and algorithms. As a result, you can get all of your questions answered in one spot. Our technical team is also well-versed in current trends, allowing us to provide realistic explanations for all new developments. We will now talk about the latest data mining trends
Latest Trending Data Mining Thesis Topics
- Visual data mining and data mining software engineering
- Interaction and scalability in data mining
- Exploring applications of data mining
- Biological and visual data mining
- Cloud computing and big data integration
- Data security and protecting privacy in data mining
- Novel methodologies in complex data mining
- Data mining in multiple databases and rationalities
- Query language standardization in data mining
- Integration of MapReduce, Amazon EC2, S3, Apache Spark, and Hadoop into data mining
These are the recent trends in data mining. We insist that you choose one of the topics that interest you the most. Having an appropriate content structure or template is essential while writing a thesis . We design the plan in a chronological order relevant to the study assessment with this in mind. The incorporation of citations is one of the most important aspects of the thesis. We focus not only on authoring but also on citing essential sources in the text. Students frequently struggle to deal with appropriate proposals when commencing their thesis. We have years of experience in providing the greatest study and data mining thesis writing services to the scientific community, which are promptly and widely acknowledged. We will now talk about future research directions of research in various data mining thesis topics
Future Research Directions of Data Mining
- The potential of data mining and data science seems promising, as the volume of data continues to grow.
- It is expected that the total amount of data in our digital cosmos will have grown from 4.4 zettabytes to 44 zettabytes.
- We’ll also generate 1.7 gigabytes of new data for every human being on this planet each second.
- Mining algorithms have completely transformed as technology has advanced, and thus have tools for obtaining useful insights from data.
- Only corporations like NASA could utilize their powerful computers to examine data once upon a time because the cost of producing and processing data was simply too high.
- Organizations are now using cloud-based data warehouses to accomplish any kinds of great activities with machine learning, artificial intelligence, and deep learning.
The Internet of Things as well as wearable electronics, for instance, has transformed devices to be connected into data-generating engines which provide limitless perspectives into people and organizations if firms can gather, store, and analyze the data quickly enough. What are the aspects to be remembered for choosing the best data mining thesis topics?
- An excellent thesis topic is a broad concept that has to be developed, verified, or refuted.
- Your thesis topic must capture your curiosity, as well as the involvement of both the supervisor and the academicians.
- Your thesis topic must be relevant to your studies and should be able to withstand examination.
Our engineers and experts can provide you with any type of research assistance on any of these data mining development tools . We satisfy the criteria of your universities by ensuring several revisions, appropriate formatting and editing of your thesis, comprehensive grammar check, and so on . As a result, you can contact us with confidence for complete assistance with your data mining thesis. What are the important data mining thesis topics?
Research Topics in Data Mining
- Handling cost-effective, unbalanced non-static data
- Issues related to data mining and their solutions
- Network settings in data mining and ensuring privacy, security, and integrity of data
- Environmental and biological issues in data mining
- Complex data mining and sequential data mining (time series data)
- Data mining at higher dimensions
- Multi-agent data mining and distributed data mining
- High-speed data mining
- Development of unified data mining theory
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Master thesis topics [closed]
I am looking for a thesis to complete my master, I am interested in Predictive Analytics in marketing, HR, management or financial subject, using Data Mining Application.
I have found a very interesting subject: "Predicting customer churn using decision tree" or either "Predicting employee turnover using decision tree", I looked around very hard but unfortunately couldn't find any relevant dataset to download ( Telecommunication Customer churn Dataset ).
I would like to work on a similar subject using "Decision Tree Technique".
Please suggest some topics or project that would make for a good masters thesis subject.
- data-mining
- predictive-modeling
- decision-trees
2 Answers 2
This is the approach I took:
- Find journals related to your field of studies
- Skim through the proceedings, see if there are titles that catch your interest
- Read the papers (carefully or globally) that seemed interesting
- Carefully consider the approaches and whatever future suggestions they present in their papers
- Think critically: What would you change? What do you want to find out? Don't limit yourself to data but rather orient from the perspective of research. Solutions for data might only become apparent when you know exactly what you want to examine.
I think this has advantages because these papers outline details regarding data as well -- perhaps you can use the same.
Present some papers and your idea to your prospective supervisor and he/she will make some suggestions. Researchers generally have a lot of knowledge about the possibilities and might even be curious about some things themselves.
Good luck! And enjoy.
First, talk to your thesis advisor before committing to a project. They know better than I do.
Secondly, just analyzing a new dataset using standard techniques doesn't make for a good masters thesis. Your project is expected to use some sort of novel approach.
With that said, I'd suggest that you start by reading up on existing decision tree techniques, learning why they work and what their flaws are, and try to find ways to overcome the flaws. Then, once you have your improvement, it should be relatively easy to find a dataset to apply it to.
Not the answer you're looking for? Browse other questions tagged data-mining predictive-modeling bigdata decision-trees research or ask your own question .
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The Impacts of Longwall Mining on Groundwater Systems -- A Case of Cumberland Mine Panels B5 and B6, Xinzhi Du. PDF. Evaluation of ultrafine spiral concentrators for coal cleaning, Meng Yang. Theses/Dissertations from 2009 PDF. Development of a coal reserve GIS model and estimation of the recoverability and extraction costs, Chandrakanth Reddy ...
the methodology for integrating robotic systems in undeground mining machines, peter kolapo. pdf. discrete element modeling to predict muckpile profiles from cast blasting, russell lamont. pdf. autonomous shuttle car docking to a continuous miner using rgb-depth imagery, sky rose. pdf
Replacing combustion engines with hydrogen fuel cells to power mining haul trucks: challenges and opportunities, Ayorinde Akinrinlola. Theses from 2021 PDF. The effects of rigid polyurethane foam as a confinement material on breaching charge detonations, Nathan Franz Paerschke-O'Brien. PDF
The application of probabilistic logic to identify, quantify and mitigate the uncertainty inherent to a large surface mining budget. Hager, Johann (University of Pretoria, 2014-01) Mining is a hugely expensive process and unlike manufacturing is based on an ever diminishing resource. It requires a continuous infusion of capital to sustain ...
The development of a mining method selection model through a detailed assessment of multi-criteria decision methods. In the past decades, attempts were made to build a systematic approach to mining method selection (MMS) Ooriad et al, (2018). This is because MMS is a complex and irreversible process.
ii DECLARATION I Gladness Mmadibakisha Chadi (Student no: 38780593) hereby declare that the dissertation/thesis title: Environmental Impacts Resulting from Gold Mining Tailings Storage Facilities Within the West Rand Area in South Africa, which I hereby submit for the degree of PhD in Environmental Management in the college of
injury severity modeling in mining industry using workers' compensation data and data analytics, poorva kadrolli. pdf. simulating groundwater pollutant transport for remediation design, antrim county, michigan, abilynn raetz. pdf. thermomechanical evolution of a magmatic system during a caldera cycle: okataina volcanic center, new zealand ...
This thesis argues from the findings that, underlying the limited contribution of mining to sustainable development of mining communities in Ghana is a crisis of mining benefits sharing. The findings of this research show that, different actors including the government, mining companies and mining communities have different conceptions and ...
More than 70,000 new full-text dissertations and theses are added to the database each year through dissertation publishing partnerships with over 700 academic institutions worldwide and collaborative retrospective digitization of dissertations through UMI's Digital Archiving and Access Program. Full-text dissertations are archived as submitted ...
This collection contains theses and dissertations by Wits doctoral and masters' students.
This thesis first introduces the basic concepts of data mining, such as the definition of data mining, its basic function, common methods and basic process, and two common data mining methods, classification and clustering. Then a data mining application in network is discussed in detail, followed by a brief introduction on data mining ...
Explore past mechanical and mining engineering undergraduate theses. Theses from Semester 2, 2016 onwards are available via the UQ Library search using the below course codes: ENGG4600, ENGG4601, MECH4500, MECH4501 or MIN4123 ( Undergraduate theses) ENGG7340, ENGG7341, ENGG7342, ENGG7381, ENGG7382, ENGG7240, ENGG7241, ENGG7242, ENGG7281 or ...
Consult the top 50 dissertations / theses for your research on the topic 'Data mining.'. Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.
Achieving Long Term Fairness through Curiosity Driven Reinforcement Learning: How intrinsic motivation influences fairness in algorithmic decision making. van der Wee, W. J. (Author), Pechenizkiy, M. (Supervisor 1), Gajane, P. (Supervisor 2) & Kapodistria, S. (Supervisor 2), 28 Aug 2023. Student thesis: Master.
Consult the top 50 dissertations / theses for your research on the topic 'Platinum mines and mining - Zimbabwe.'. Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA ...
Thesis (MSc (Earth Sciences))--Stellenbosch University, 2008. This study aims to provide a practical tool for the prediction and management of dust generated by the activities of an opencast mining operation. The study was conducted on opencast gypsum mines in the semi-arid environment of the Bushmanland, 90 km north of Loeriesfontein in the ...
This thesis examines Gold Reef, a small mining locality in the Absaroka Mountains of northwestern Wyoming. Gold Reef was occupied from approximately 1895 to 1914. The mining archaeology of Wyoming has received only minimal research to date and this paper seeks to partly redress this imbalance. The primary goals of this study are to
Data Mining Role in Companies. The increasing adoption of data mining in various sectors illustrates the potential of the technology regarding the analysis of data by entities that seek information crucial to their operations. We will write a custom essay specifically for you by our professional experts. 190 writers online.
Data mining is concerned with knowledge discovery and finding patterns in. datasets through a process of applying the model to the data [13]. The model, the heart of. the data mining proce ss, is ...
Integration of MapReduce, Amazon EC2, S3, Apache Spark, and Hadoop into data mining. These are the recent trends in data mining. We insist that you choose one of the topics that interest you the most. Having an appropriate content structure or template is essential while writing a thesis.
thesis title: developing knowledge based system using data mining techniques for diagnosis and treatment of diabetes by: kedir eyasu advisors: 1. worku jimma (phd candidate) 2. takele tadese (msc) submitted to: information science department jimma, ethiopia november, 2018 ...
1. This is the approach I took: Find journals related to your field of studies. Skim through the proceedings, see if there are titles that catch your interest. Read the papers (carefully or globally) that seemed interesting. Carefully consider the approaches and whatever future suggestions they present in their papers.
Topics to study in data mining. Data mining is a relatively new thing and many are not aware of this technology. This can also be a good topic for M.Tech thesis and for presentations. Following are the topics under data mining to study: Fraud Detection. Crime Rate Prediction.