Question Paper Solutions of chapter Overview of data mining and predictive analytics of Data Mining, 6th Semester , B.Tech in Computer Science & Engineering (Artificial Intelligence and Machine Learning)

Dataset with 72000 pins from 117 users in Pinterest. Each pin contains a short raw text and an image. The images are processed using a pretrained Convolutional Neural Network and transformed into a vector of 4096 features.

This dataset was used in the paper "User Identification in Pinterest Through the Refinement of a Cascade Fusion of Text and Images" to idenfity specific users given their comments. The paper is publishe in the Research in Computing Science Journal, as part of the LKE 2017 conference. The dataset includes the splits used in the paper.

There are nine files. text_test, text_train and text_val, contain the raw text of each pin in the corresponding split of the data. imag_test, imag_train and imag_val contain the image features of each pin in the corresponding split of the data. train_user and val_test_users contain the index of the user of each pin (between 0 and 116). There is a correspondance one-to-one among the test, train and validation files for images, text and users. There are 400 pins per user in the train set, and 100 pins per user in the validation and test sets each one.

If you have questions regarding the data, write to: jc dot gomez at ugto dot mx

Question Paper Solutions of chapter Validation strategies of Data Mining, 6th Semester , B.Tech in Computer Science & Engineering (Artificial Intelligence and Machine Learning)

Question Paper Solutions of chapter Supervised Learning for classification of Data Mining, 6th Semester , B.Tech in Computer Science & Engineering (Artificial Intelligence and Machine Learning)

Background and methodsSystematic reviews, i.e., research summaries that address focused questions in a structured and reproducible manner, are a cornerstone of evidence-based medicine and research. However, certain steps in systematic reviews, such as data extraction, are labour-intensive, which hampers their feasibility, especially with the rapidly expanding body of biomedical literature. To bridge this gap, we aimed to develop a data mining tool in the R programming environment to automate data extraction from neuroscience in vivo publications. The function was trained on a literature corpus (n = 45 publications) of animal motor neuron disease studies and tested in two validation corpora (motor neuron diseases, n = 31 publications; multiple sclerosis, n = 244 publications).ResultsOur data mining tool, STEED (STructured Extraction of Experimental Data), successfully extracted key experimental parameters such as animal models and species, as well as risk of bias items like randomization or blinding, from in vivo studies. Sensitivity and specificity were over 85% and 80%, respectively, for most items in both validation corpora. Accuracy and F1-score were above 90% and 0.9 for most items in the validation corpora, respectively. Time savings were above 99%.ConclusionsOur text mining tool, STEED, can extract key experimental parameters and risk of bias items from the neuroscience in vivo literature. This enables the tool’s deployment for probing a field in a research improvement context or replacing one human reader during data extraction, resulting in substantial time savings and contributing towards the automation of systematic reviews.

Question answering (QA) is the field of information retrieval (IR) aimed at answering questions from paragraphs in natural language processing (NLP). Essentially, IR is a technique to retrieve and rank documents based on keywords, while in the QA system, answers to questions are retrieved based on the paragraph's content. The Kurdish language belongs to the Indo-European family spoken by 30-40 million people worldwide. Almost all Kurdish people speak Sorani and Kurmanji dialects. In this dataset, the Sorani dialect is used as the first attempt to collect and create a Kurdish News Question-Answering Dataset (KNQAD). The texts are collected from numerous Kurdish news websites covering various fields such as religion, social issues, art, health, economy, politics, sports, and more. In this project, 15,002 question-answer pairs are created manually from 15,002 paragraphs. Three preprocessing steps are implemented on the raw text paragraphs: stemming, removing stop words, and removing special characters.

Question Paper Solutions of chapter Data pre-processing and clean-up of Data Mining, 6th Semester , B.Tech in Computer Science & Engineering (Artificial Intelligence and Machine Learning)

Due to increasing use of technology-enhanced educational assessment, data mining methods have been explored to analyse process data in log files from such assessment. However, most studies were limited to one data mining technique under one specific scenario. The current study demonstrates the usage of four frequently used supervised techniques, including Classification and Regression Trees (CART), gradient boosting, random forest, support vector machine (SVM), and two unsupervised methods, Self-organizing Map (SOM) and k-means, fitted to one assessment data. The USA sample (N = 426) from the 2012 Program for International Student Assessment (PISA) responding to problem-solving items is extracted to demonstrate the methods. After concrete feature generation and feature selection, classifier development procedures are implemented using the illustrated techniques. Results show satisfactory classification accuracy for all the techniques. Suggestions for the selection of classifiers are presented based on the research questions, the interpretability and the simplicity of the classifiers. Interpretations for the results from both supervised and unsupervised learning methods are provided.

Question Paper Solutions of chapter Model assessment measures of Data Mining, 6th Semester , B.Tech in Computer Science & Engineering (Artificial Intelligence and Machine Learning)

Dataset-1: Question-answer pairs on “Lucene” collected from StackOverflow. As mentioned in paper [36], we first get 5,587 questions and 7,872 answers from the StackOverflow with tag “lucene”, where 1,826 questions with positive votes are kept and labeled. We use these question and their 2,460 answers for original classifier training and testing.

Dataset-2: Question-answer pairs on “Java” collected from StackOverflow. We need more data to train the classifier models and evaluate our approach. Then we extend our data collection scope and randomly pick 50,000 questions with tag “Java” on StackOverflow. It may cost too much time if we judge the types of these question accurately and manually. We filter all the questions using regular expressions (e.g. the question includes phrases “how to” , “how can” or “what is the best way to”, etc., are labeled with “how to” tag). Finally, 11,003 questions and the corresponding 16,255 answers are selected. Table IV briefly describes these two datasets.

Dataset-3: FAQs of seven well-known open source projects. In software development, FAQs are used by many projects as part of their documentation. Compared with the data from StackOverflow, the FAQs are more formal and accurate. We want to investigate whether our approach is valid in search- ing these questions’ answers and whether the classifiers are affected by our learning examples. Table V illustrates the 7 open source projects and the numbers of their FAQs. All of them are the top level projects (TLPs) in Apache.

Question Paper Solutions of chapter Overfitting Dilemma of Data Mining, 6th Semester , B.Tech in Computer Science & Engineering (Artificial Intelligence and Machine Learning)

This resource collects teaching materials that are originally created for the in-person course 'GEOSC/GEOG 497 – Data Mining in Environmental Sciences' at Penn State University (co-taught by Tao Wen, Susan Brantley, and Alan Taylor) and then refined/revised by Tao Wen to be used in the online teaching module 'Data Science in Earth and Environmental Sciences' hosted on the NSF-sponsored HydroLearn platform.

This resource includes both R Notebooks and Python Jupyter Notebooks to teach the basics of R and Python coding, data analysis and data visualization, as well as building machine learning models in both programming languages by using authentic research data and questions. All of these R/Python scripts can be executed either on the CUAHSI JupyterHub or on your local machine.

This resource is shared under the CC-BY license. Please contact the creator Tao Wen at Syracuse University (twen08@syr.edu) for any questions you have about this resource. If you identify any errors in the files, please contact the creator.

Air pollution directly affects human health endpoints including growth, respiratory processes, cardiovascular health, fertility, pregnancy outcomes, and cancer. Therefore, the distribution of air pollution is a topic that is relevant to all, and of direct interest to many students. Air quality varies across space and time, often disproportionally affecting minority communities and impoverished neighborhoods. Air pollution is usually higher in locations where pollution sources are concentrated, such as industrial production facilities, highways, and coal-fired power plants. The United States Environmental Protection Agency manages a national air quality-monitoring program to measure and report air-pollutant levels across the United States. These data cover multiple decades and are publicly available via a website interface. For this lesson, students learn how to mine data from this website. They work in pairs to develop their own questions about air quality or air pollution that span spatial and/or temporal scales, and then gather the data needed to answer their question. The students analyze their data and write a scientific paper describing their work. This laboratory experience requires the students to generate their own questions, gather and interpret data, and draw conclusions, allowing for creativity and instilling ownership and motivation for deeper learning gains.

Characteristics of included literature corpora and reporting prevalence for parameters to extract.

Summary of performance measures of STEED compared with manual human ascertainment.

Through the cooperation of the LouisianaView consortium members and co-sponsored with the local USGS liaison, this annual workshop is offered free to everyone interested in up-to-date information on data availability for the geospatial emergency responder. This is a 4-day virtual workshop hosts speakers from multiple Federal, State and Private Response Teams, each presenting their data, websites, links, and contacts while also fielding questions live from those in attendance, proving again and again what a cohesive and informed network of geospatial responders can mean to the inhabitants and economic base within Louisiana, the Gulf of Mexico region and the Caribbean.

Dataset available only to University of Arizona affiliates. To obtain access, you must log in to ReDATA with your NetID. Data is for research use by each individual downloader only. Sharing and/or redistribution of any portion of this dataset is prohibited.The UA Libraries have acquired XML and PDF files of two newspapers from the ProQuest Historical Newspapers collection: The New York Times (1851-1936) and The Washington Post (1877-1934). These files may be downloaded and used for text and data mining. For questions about news resources or text mining, contact Mary Feeney (mfeeney@arizona.edu), Librarian in the Research & Learning Department. NOTE: The uncompressed datasets are very large.Detailed file descriptions and MD5 hash values for each file can be found in the README.txt file.For inquiries regarding the contents of this dataset, please contact the Corresponding Author listed in the README.txt file. Administrative inquiries (e.g., removal requests, trouble downloading, etc.) can be directed to data-management@arizona.edu

Question Paper Solutions of chapter Unsupervised learning of Data Mining, 6th Semester , B.Tech in Computer Science & Engineering (Artificial Intelligence and Machine Learning)

The dataset comes from a questionnaire structured into 24 questions, which can be accessed at https://forms.gle/bUgYMfoNHh7r6ebs6. This questionnaire was completed by 956 respondents and aims to analyze the online activities carried out during March - April 2020, being distributed to teachers.
Each question is designed to reveal different aspects of the experiences, skills, and perspectives of teaching staff regarding online teaching and learning.

To protect the identity of the respondents and to obtain accurate responses, all data collected from teachers was anonymous. We did not collect any personal information whatsoever. This aspect was made clear to the respondents in the description of the questionnaire.

The market is projected to be valued at $X million in 2024, driven by factors such as increasing consumer awareness and the rising prevalence of industry-specific trends. The market is expected to grow at a CAGR of Y%, reaching approximately $Z million by 2034.