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2024 4th International Conference on Computer, Remote Sensing and Aerospace (CRSA 2024) will be held at Osaka, Japan on July 5-7, 2024.
Conference Webiste: https://ais.cn/u/MJVjiu
---Call For Papers---
The topics of interest for submission include, but are not limited to:
1. Algorithms
Image Processing
Data processing
Data Mining
Computer Vision
Computer Aided Design
......
2. Remote Sensing
Optical Remote Sensing
Microwave Remote Sensing
Remote Sensing Information Engineering
Geographic Information System
Global Navigation Satellite System
......
3. Aeroacoustics
Aeroelasticity and structural dynamics
Aerothermodynamics
Airworthiness
Autonomy
Mechanisms
......
All accepted papers will be published in the Conference Proceedings, and submitted to EI Compendex, Scopus for indexing.
Important Dates:
Full Paper Submission Date: May 31, 2024
Registration Deadline: May 31, 2024
Conference Date: July 5-7, 2024
For More Details please visit:
Invitation code: AISCONF
*Using the invitation code on submission system/registration can get priority review and feedback
I m currently doing a research on Data mining in Digital marketing and will like to get your opinion
1. The effects of mining and its impact in digital marketing
2. Does mining artificially alter organizations marketing campaign and if yes what are the pros and cons. if no, please state your reason or observations
3. is data mining the future of digital marketing, will mining determine the profitability of organizations in the nearest future.
4. Any other advise on this topic to aid my research.
the most prominent commercial data mining software applications currently available to fraud examiners to assist with investigations?
2024 4th International Conference on Machine Learning and Intelligent Systems Engineering (MLISE 2024) will be held on June 28- June 30, 2024 in Zhuhai China.
MLISE is conducting exciting series of symposium programs that connect researchers, scholars and students to industry leaders and highly relevant information. The conference will feature world-class presentations by internationally renowned speakers, cutting-edge session topics and provide a fantastic opportunity to network with like-minded professionals from around the world. MLISE propose new ideas, strategies and structures, innovating the public sector, promoting technical innovation and fostering creativity in development of services.
---Call For Papers---
The topics of interest for submission include, but are not limited to:
1. Machine Learning
- Deep and Reinforcement learning
- Pattern recognition and classification for networks
- Machine learning for network slicing optimization
- Machine learning for 5G system
- Machine learning for user behavior prediction
......
2. Intelligent Systems Engineering
- Intelligent control theory
- Intelligent control system
- Intelligent information systems
- Intelligent data mining
- AI and evolutionary algorithms
......
All papers, both invited and contributed, will be reviewed by two or three experts from the committees. After a careful reviewing process, all accepted papers of MLISE 2024 will be published in the MLISE 2024 Conference Proceedings by IEEE (ISBN: 979-8-3503-7507-7), which will be submitted to IEEE Xplore, EI Compendex, Scopus for indexing.
Important Dates:
Submission Deadline: April 26, 2024
Registration Deadline: May 26, 2024
Conference Dates: June 28-30, 2024
For More Details please visit:
Invitation code: AISCONF
*Using the invitation code on submission system/registration can get priority review and feedback
I m currently doing a research on Data mining in Digital marketing and will like to get your opinion
1. The effects of mining and its impact in digital marketing
2. Does mining artificially alter organizations marketing campaign and if yes what are the pros and cons. if no, please state your reason or observations
3. is data mining the future of digital marketing, will mining determine the profitability of organizations in the nearest future.
4. Any other advise on this topic to aid my research.
2024 IEEE 7th International Conference on Computer Information Science and Application Technology (CISAT 2024) will be held on July 12-14, 2024 in Hangzhou, China.
---Call For Papers---
The topics of interest for submission include, but are not limited to:
◕ Computational Science and Algorithms
· Algorithms
· Automated Software Engineering
· Bioinformatics and Scientific Computing
......
◕ Intelligent Computing and Artificial Intelligence
· Basic Theory and Application of Artificial Intelligence
· Big Data Analysis and Processing
· Biometric Identification
......
◕ Software Process and Data Mining
· Software Engineering Practice
· Web Engineering
· Multimedia and Visual Software Engineering
......
◕ Intelligent Transportation
· Intelligent Transportation Systems
· Vehicular Networks
· Edge Computing
· Spatiotemporal Data
All papers, both invited and contributed, the accepted papers, will be published and submitted for inclusion into IEEE Xplore subject to meeting IEEE Xplore's scope and quality requirements, and also submitted to EI Compendex and Scopus for indexing. All conference proceedings paper can not be less than 4 pages.
Important Dates:
Full Paper Submission Date: April 14, 2024
Submission Date: May 12, 2024
Registration Deadline: June 14, 2024
Conference Dates: July 12-14, 2024
For More Details please visit:
Invitation code: AISCONF
*Using the invitation code on submission system/registration can get priority review and feedback
2024 5th International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2024) will be held in Shenzhen, China, from June 14 to 16, 2024.
---Call For Papers---
The topics of interest for submission include, but are not limited to:
(1) Artificial Intelligence
- Intelligent Control
- Machine learning
- Modeling and identification
......
(2) Sensor
- Sensor/Actuator Systems
- Wireless Sensors and Sensor Networks
- Intelligent Sensor and Soft Sensor
......
(3) Control Theory And Application
- Control System Modeling
- Intelligent Optimization Algorithm and Application
- Man-Machine Interactions
......
(4) Material science and Technology in Manufacturing
- Artificial Material
- Forming and Joining
- Novel Material Fabrication
......
(5) Mechanic Manufacturing System and Automation
- Manufacturing Process Simulation
- CIMS and Manufacturing System
- Mechanical and Liquid Flow Dynamic
......
All accepted papers will be published in the Conference Proceedings, which will be submitted for indexing by EI Compendex, Scopus.
Important Dates:
Full Paper Submission Date: April 1, 2024
Registration Deadline: May 31, 2024
Final Paper Submission Date: May 14, 2024
Conference Dates: June 14-16, 2024
For More Details please visit:
Invitation code: AISCONF
*Using the invitation code on submission system/registration can get priority review and feedback
2024 3rd International Conference on Biomedical and Intelligent Systems (IC-BIS 2024) will be held from April 26 to 28, 2024, in Nanchang, China.
It is a comprehensive conference which focuses on Biomedical Engineering and Artificial Intelligent Systems. The main objective of IC-BIS 2024 is to address and deliberate on the latest technical status and recent trends in the research and applications of Biomedical Engineering and Bioinformatics. IC-BIS 2024 provides an opportunity for the scientists, engineers, industrialists, scholars and other professionals from all over the world to interact and exchange their new ideas and research outcomes in related fields and develop possible chances for future collaboration. The conference also aims at motivating the next generation of researchers to promote their interests in Biomedical Engineering and Artificial Intelligent Systems.
Important Dates:
Registration Deadline: March 26, 2024
Final Paper Submission Date: April 22, 2024
Conference Dates: April 26-28, 2024
---Call For Papers---
The topics of interest for submission include, but are not limited to:
- Biomedical Signal Processing and Medical Information
· Biomedical signal processing
· Medical big data and machine learning
· Application of artificial intelligent for biomedical signal processing
......
- Bioinformatics & Intelligent Computing
· Algorithms and Software Tools
· Algorithms, models, software, and tools in Bioinformatics
· Biostatistics and Stochastic Models
......
- Gene regulation, expression, identification and network
·High-performance computational systems biology and parallel implementations
· Image Analysis
· Inference from high-throughput experimental data
......
For More Details please visit:
I am trying to train a CNN model in Matlab to predict the mean value of a random vector (the Matlab code named Test_2 is attached). To further clarify, I am generating a random vector with 10 components (using rand function) for 500 times. Correspondingly, the figure of each vector versus 1:10 is plotted and saved separately. Moreover, the mean value of each of the 500 randomly generated vectors are calculated and saved. Thereafter, the saved images are used as the input file (X) for training (70%), validating (15%) and testing (15%) a CNN model which is supposed to predict the mean value of the mentioned random vectors (Y). However, the RMSE of the model becomes too high. In other words, the model is not trained despite changing its options and parameters. I would be grateful if anyone could kindly advise.
Can any one suggest this topic is better for PhD work or not. Topic is "Study on the Data Mining Techniques in Healthcare Sector with emphasis on Breast Cancer".
What are the possibilities of applying generative AI in terms of conducting sentiment analysis of changes in Internet users' opinions on specific topics?
What are the possibilities of applying generative artificial intelligence in carrying out sentiment analysis on changes in the opinions of Internet users on specific topics using Big Data Analytics and other technologies typical of Industry 4.0/5.0?
Nowadays, Internet marketing is developing rapidly, including viral Internet marketing used on social media sites, among others, in the form of, for example, Real-Time marketing in the formula of viral marketing. It is also marketing aimed at precisely defined groups, audience segments, potential customers of a specific advertised product and/or service offering. In terms of improving Internet marketing, new ICT information technologies and Industry 4.0/5.0 are being implemented. Marketing conducted in this form is usually preceded by market research conducted using, among other things, sentiment analysis of the preferences of potential consumers based on verification of their activity on the Internet, taking into account comments written on various websites, Internet forums, blogs, posts written on social media. In recent years, the importance of the aforementioned sentiment analysis carried out on large data sets using Big Data Analytics has been growing, thanks to which it is possible to study the psychological aspects of the phenomena of changes in the trends of certain processes in the markets for products, services, factor markets and financial markets. The development of the aforementioned analytics makes it possible to study the determinants of specific phenomena occurring in the markets caused by changes in consumer or investor preferences, caused by specific changes in the behavior of consumers in product and service markets, entrepreneurs in factor markets or investors in money and capital markets, including securities markets. The results from these analyses are used to forecast changes in the behavior of consumers, entrepreneurs and investors that will occur in the following months and quarters. In addition to this, sentiment analyses are also conducted to determine the preferences, awareness of potential customers, consumers in terms of recognition of the company's brand, its offerings, description of certain products and services, etc., using textual data derived from comments, entries, posts, etc. posted by Internet users, including social media users on a wide variety of websites. The knowledge gained in this way can be useful for companies to plan marketing strategies, to change the product and service offerings produced, to select or change specific distribution channels, after-sales services, etc. This is now a rapidly developing field of research and the possibilities for many companies and enterprises to use the results of this research in marketing activities, but not only in marketing. Recently, opportunities are emerging to apply generative artificial intelligence and other Industry 4.0/5.0 technologies to analyze large data sets collected on Big Data Analytics platforms. In connection with the development of intelligent chatbots available on the Internet, recently there have been discussions about the possibilities of potential applications of generative artificial intelligence, 5G and other technologies included in the Industry 4.0/5.0 group in the context of using the information resources of the Internet to collect data on citizens, companies, institutions, etc. for their analysis carried out using, among other things, sentiment analysis to determine the opinion of Internet users on certain topics or to define the brand recognition of a company, the evaluation of product or service offerings by Internet users. In recent years, the scope of applications of Big Data technology and Data Science analytics, Data Analytics in economics, finance and management of organizations, including enterprises, financial and public institutions is increasing. Accordingly, the implementation of analytical instruments of advanced processing of large data sets in enterprises, financial and public institutions, i.e. the construction of Big Data Analytics platforms to support organizational management processes in various aspects of operations, including the improvement of customer relations, is also growing in importance. In recent years, ICT information technologies, Industry 4.0/5.0 including generative artificial intelligence technologies are particularly rapidly developing and finding application in knowledge-based economies. These technologies are used in scientific research and business applications in commercially operating enterprises and in financial and public institutions. In recent years, the application of generative artificial intelligence technologies for collecting and multi-criteria analysis of Internet data can significantly contribute to the improvement of sentiment analysis of Internet users' opinions and the possibility of expanding the applications of research techniques carried out on analytical platforms of Business Intelligence, Big Data Analytics, Data Science and other research techniques using ICT information technology, Internet and advanced data processing typical Industry 4. 0/5.0. Most consumers of online information services available on new online media, including social media portals, are not fully aware of the level of risk of sharing information about themselves on these portals and the use of this data by technological online companies using this data for their analytics. I am conducting research on this issue. I have included the conclusions of my research in scientific publications, which are available on Research Gate. I invite you to cooperate with me.
In view of the above, I address the following question to the esteemed community of scientists and researchers:
What are the possibilities for the application of generative AI in terms of conducting sentiment analysis of changes in the opinions of Internet users on specific topics using Big Data Analytics and other technologies typical of Industry 4.0/5.0?
What are the possibilities of using generative AI in conducting sentiment analysis of Internet users' opinions on specific topics?
And what is your opinion on this topic?
What is your opinion on this issue?
Please answer,
I invite everyone to join the discussion,
Thank you very much,
Best wishes,
Dariusz Prokopowicz
The above text is entirely my own work written by me on the basis of my research.
In writing this text I did not use other sources or automatic text generation systems.
Dariusz Prokopowicz
Hello there, I am in the search for datasets of software's requirements and their use cases, in hope to be able to gather datasets of use case for the requirements to train a ML model for a research we're working on. Would anyone know any source to find such datasets ?
I want to analyze the research problem in education data mining, with machine learning algorithms, I want to build a model that suggest school students which domain to select for higher education, with evaluating the dataset of student as well as the dataset of higher education of the same student.
What is spatial and temporal mining in data mining and what are spatial data structures in data mining?
I am interested to get depper to the connection between data analysis methods and information visualization that can be generated by this data analysis. For example, data clustering (in data mining) produces a certain kind of information. Which visualization method could be used to best visualize the produced information and why?
I have found this http://www.visual-literacy.org/periodic_table/periodic_table.html which very good on depicting the different visualization methods but lacks explaining to what data analysis method each one of them it is connected.
Any recommended good source?
Thanks
Please suggest new research topic new computer science in data mining using machine learning
Dear Pushpraj Singh,
I give my proposal for a research topic, research thesis, thesis concept in the area of your interest:
Research Context: In recent years, the scale of various economic, financial, social, health, food, energy, nature, climate, etc. crises is increasing. As a result, the importance of improving crisis management techniques and using new ICT information technologies and Industry 4.0 for this purpose is growing. The importance of improving risk management processes using new Industry 4.0 technologies, including but not limited to i.e. Big Data Analytics and Artificial Intelligence, is also growing.
Accordingly, the research topic may address the following issue: The application of selected ICT information technologies, Industry 4.0, the technologies of the current fourth technological revolution, including Big Data Analytics, machine learning, deep learning, artificial intelligence to improve risk management systems, early warning systems within the framework of crisis management, and the improvement of forecasting models used to predict abnormal situations, events of special risk increases, emergencies, specific types of disasters, etc.
I would like to invite you to join me for scientific cooperation on this issue,
Kind regards,
Dariusz Prokopowicz
Compared to the old-fashioned and currently used emulsion type explosives, the explosive filling of the tunnel face with bulk charging provides better and higher quality vibration values. if you are drilling in the tunnel face with the Mwd (measurement while drilling) featured jumbo. Because with the mwd-capable machine, heterogeneous drilling is performed in the formation whose face surface is uneven and the drilling lengths are different. Therefore, a homogeneous charge in a heterogeneous face with an emulsion-type explosive of constant kilogram will be difficult. Therefore, I think that more stable vibration data will be obtained with bulk charging. What is your opinion?
I created my own huge dataset from different sites and labeled it on some NLP task. How can i publish it in form of Paper or article and where?
Hello everyone,
I want to find emerging pattern of blockchain applications in cybersecurity . I’ve collected and filtered my dataset which now consists of 1183 research items indexed in WoS and scopus. Which text mining algorithms can fulfill the purpose?
I found burst detection and LDA suitable but as a tourism student i want to know about other possibilities and the suggestions of professionals.
Best wishes.
Hello everyone, I’m currently working on my masters thesis in which I want to find current and future application patterns of a technology in an industry based on previous researchers done regarding the topic by analyzing the tittle, abstract, conclusion and implications of these article if it is even possible but I’m not sure which data mining method and algorithm should I use to get the best possible results. It would be great if you could give me advices and feedbacks.
Best regards.
My team and I are trying to open a dialogue about designing a Continuum of Realism for synthetic data. We want to develop a meaningful way to talk about data in terms of the degree of realism that is necessary for a particular task. We feel the way to do this is by defining a continuum that shows that as data becomes more realistic, the analytic value increases, but so does the cost and risk of disclosure. Everyone seems to be interested in generating the most realistic data, but let's be honest, sometimes that's not the level of realism that we actually need. It is expensive and carries a high reidentification risk when working with PII. Sometimes we just need data to test our code, and we can't justify using this level of realism when the risk is so high. Have you also encountered this issue? Are you interested in helping us fulfill our mission? Ultimately we are trying to save money and protect consumer privacy. We would love to hear your thoughts!
It will be for a data mining research that the objective is to classify the best time of day for the operation of the wind farm.
I would need a (tabular, i.e. not imaging or text) dataset with a hierarchically structured outcome to use as an example dataset in a new R package (but the dataset can be of any format, e.g. txt, csv or arff). It should be single-label and tree-structured, e.g. first level: classes 1, ..., 4, second level: 1.1, 1.2, 1.3, 2.1,2.2, third level: 1.1.1, 1.1.2, 1.2.1, 1.2.2, 1.2.3, 1.3.1, 1.3.2., ... .
I want to develop a research about higher school dropout and would like some help on this topic.
I am writing PhD thesis on data mining. How I can write a good "thesis Innovations"? What are the key points?
The data that is obtained from the institution database is to analyze the GPA and CGPA of 1000 students. The attributes obtained are demographic but no behavioral, income, etc. What type of data mining technique can be used to analyze this type of attributes and obtain patterns from the analysis?
Please do give reference in regards to how the techniques can be applied.
Thank you! Appreciate it.
Hello dear researchers,
I've just accepted in doctoral program with data approximately consisted of thousands observations. I am planning on data mining first to explore, classify, associate, and detecting anomaly. I used to work with Stata and wondering if stata can do such things. Do you have any suggestions about reference that connecting Stata and data mining?
Hi,
Most of the researchers knew R Views website which is:
Please, I am wondering if this website contains all R packages available for researchers.
Thanks & Best wishes
Osman
I am completely new to WEKA and I am trying to load this file that I got from kaggle to WEKA but is meet with error. How do I find the solution to change the format of .crv to ARFF file.
this is where I got the file, and I have cleaned the extra columns
Thank you very much.
my topic is the " fraud detection in banking sector by using data mining techniques " so i am looking for the data set in banking and how t use that data set.
The current technological revolution, known as Industry 4.0, is determined by the development of the following technologies of advanced information processing:
Big Data database technologies, cloud computing, machine learning, Internet of Things, artificial intelligence, Business Intelligence and other advanced data mining technologies.
Which of these technologies are applicable or will be used in the future in the education process?
Please reply
Best wishes
Hi everyone,
I am facing this problem in my MA thesis:
I have two time series datasets. The first dataset has numerical features and the second one has binary variables. I found in the literature these three methods that are able to determine the correlation between the two datasets:
- logistic regression
-biserial point correlation
- Kruskal Wallis H test
Unfortunately, I could not find out if these methods are still applicable when the data are time series? I would appreciate some advice/explanations to figure this out :)
another question would be if I can use one of these methods, are there any limitations if my continuous variable is nonlinear?
Thanks in advance for your help :)
PS
both my datasets are stationary
# Data mining #correlation #time series analysis
What do you consider are the implications of Big Data on urban planning practice?
Good evening dear researchers,
I have a data set from KEGG database. it is in CSV format. I was trying to convert it into arff format using WEKA for further analysis.
It keeps giving me an error saying that it is not recognized by WEKA as a csv file. I searched for it, then I found that the file needs to be cleansed and put into a suitable data structure for it to be valid and ready to be analyzed.
unfortunately, I do not have the ability or the knowledge now to do that and I need it as soon as possible.
Can anyone help with the problem? thank you so much for your time.
kind regards.
attachments:
-data set csv file.
-error png clip.
We are looking at the application of data mining in water quality space. There are several articles to begin with and refer, and it is a bit confusing. Trying to narrow down the scope.
Hi everyone, well the thing is im trying to apply spatial data mining to a set of vector and raster files so i need a way to convert my raster archives into a csv in order to run the mining
A little bit of background, my thesis is about applying data mining in archeology with the intention of modeling archaeological sites, currently im struggling to convert the rasters in csv to run the data mining
Could you please recommend to me a package or tool for the drop3 instance selection method?
Well,
I am a very curious person. During Covid-19 in 2020, I through coded data and taking only the last name, noticed in my country that people with certain surnames were more likely to die than others (and this pattern has remained unchanged over time). Through mathematical ratio and proportion, inconsistencies were found by performing a "conversion" so that all surnames had the same weighting. The rest, simple exercise of probability and statistics revealed this controversial fact.
Of course, what I did was a shallow study, just a data mining exercise, but it has been something that caught my attention, even more so when talking to an Indian researcher who found similar patterns within his country about another disease.
In the context of pandemics (for the end of these and others that may come)
I think it would be interesting to have a line of research involving different professionals such as data scientists; statisticians/mathematicians; sociology and demographics; human sciences; biological sciences to compose a more refined study on this premise.
Some questions still remain:
What if we could have such answers? How should Research Ethics be handled? Could we warn people about care? How would people with certain last names considered at risk react? And the other way around? From a sociological point of view, could such a recommendation divide society into "superior" or "inferior" genes?
What do you think about it?
=================================
Note: Due to important personal matters I have taken a break and returned with my activities today, February 13, 2023. I am too happy to come across many interesting feedbacks.
Dear all,
Why forward selection search is very popular and widely used in FS based on mutual information such as MRMR, JMI, CMIM, and JMIM (See )? Why other search approaches such as the beam search approach is not used? If there is a reason for that, kindly reply to me.
Data mining has a broad discussion of how to manipulate data mining on other algorithms.
How can I distinctively differentiate between 'data mining', 'data analysis', and 'data analytics'?
Is there any example to add, towards proper understanding of the differences?
Thank you!
One of my master students is currently conducting a preliminary study to find out the maturity of the Cross Industry Standard Process for Big Data (CRISP4BigData) for use in Big Data projects. I would like to invite all scientists, Big Data experts, project managers, data engineers, data scientists from my network to participate in the following survey. Feel free to share!
I'm an undergraduate doing a Software Engineering degree. I'm looking for a research topic for my final year project. If anyone has any ideas or research topics or any advice on how or where to find one please post them.
Thanks in advance ✌
Hi, Could you please guide me how to conduct Latent Semantic Analysis through text mining for my business research, any website, book or tutorial videos? so I can apply this method for my research project. Thanks in advance. Kind regards Bushra Aziz
Hi,
Thank you for help.
How to make the scheduling process in CloudSim an environment for my reinforcement learning model ?
I am looking for a justification to associate data mining with big data analytics, however, many researches have observed that in addition to the characteristics of the data, there is a line of thought that guides a question of taxonomy, that is, data mining is a step in the big data analytics, can I think of it this way? Or is there something I'm not considering?
Modern politics is characterized by many aspects which were not associated with traditional politics. Big data is one of them. Data mining is being done by political parties as they seek help from data scientists to arrive at various patterns to identify behavior of voters. Question is, what are the various ways in which big data is being used by modern political parties and leaders?
I require some suggestions and need a health insurance dataset where text mining can be possible.Any recent papers addressing dataset can be helpful
I have a data set that contains a text field for approximately more than 3000 records, all of which contain notes from the doctor. I need to extract specific information from all of them, for example, the doctor's final decision and the classification of the patient, so what is the most appropriate way to analyze these texts? should I use information retrieval or information extraction, or the Q and A system will be fine
Which tools solves prediction problems effectively other than python based ?
Problem statement: Google Trend Analysis and Paradigm Shift of Online Education
Platforms during the COVID-19 Pandemic
I would like to know what methodoligies, Data preprocessing techniques methods ,data mining methods,metrics used for this Analysis.
How do data mining researchers test or evaluate their data mining model's EFFICIENCY?
or an ISO cert evaluation?
The model created is an output of the hypothesis and theory in my mind that I want to test so I unlikely want to use other people to evaluated the model like a system.
Since data mining evalation metrics alone can not be use to support the study.
I am searching for a study/research of way I can back up my study for the efficancy of the model created.
Feel free to educate me. I would love to hear your thoughts.
Hi everybody,
I would like to do part of speech tagging in an unsupervised manner, what are the potential solutions?
Please suggest R packages and codes for text ming (or any other programming) to search pubmed database.
Data Mining (DM) is a process of extracting and discovering patterns in large data sets including methods of Machine Learning (including Deep Learning and Statistical Learning), Statistics, and Database Systems.
Machine Learning (ML) is the study of computer algorithms that improve automatically through experience and by the use of data.
It would seem very simplistic to consider the ML only as a part of the larger field of the DM.
From a very rough and general point of view, DM and ML are part of the mathematics.
From another point of view, more precise but more obsolete, they are both seen as a part of Artificial Intelligence.
I would like to propose to consider both disciplines as overlapping for most of their methods.
Do you have at least 3 differences between DM and ML to report?
Dear Madam,
Please advise about post Doc supervisors in the university in the field of educational data mining and learning analytics for strengthening university decision making. I will be grateful
how to measure classification errors using weka. can we take the value of RSME or etc to utilize for taken the classification rate?
I'm searching about autoencoders and their application in machine learning issues. But I have a fundamental question.
As we all know, there are various types of autoencoders, such as Stack Autoencoder, Sparse Autoencoder, Denoising Autoencoder, Adversarial Autoencoder, Convolutional Autoencoder, Semi- Autoencoder, Dual Autoencoder, Contractive Autoencoder, and others that are better versions of what we had before. Autoencoder is also known to be used in Graph Networks (GN), Recommender Systems(RS), Natural Language Processing (NLP), and Machine Vision (CV). This is my main concern:
Because the input and structure of each of these machine learning problems are different, which version of Autoencoder is appropriate for which machine learning problem.
I am very new to these forecasting methods. Can someone help me with how to forecast the next period using these methods?
I have weekly demand data where I classified them into lumpy, erratic, and smooth demands. As Croston's forecasting method is the best suited for smooth and SBA method for lumpy, I require their forecasting process to plan the demand for the next weekly period.
Is there any other method to forecast lumpy and smooth demands other than this method?
Thank you in advance
I am looking a free of charge International Conference in metaheuristic algorithm or data mining issue, is there ay one can help me?
I have past 4 years of weekly demand data. There are various products with their demand values. I am trying to calculate the future weekly values for a year. The data doesn't follow any trend and it is random. There are many weeks with Zero demands too.
I am very new to time series analysis. Can someone help me in suggesting an appropriate method?
I have some Key Informant Interview (KII) data. I want to apply Natural Language Processing (NLP) to identify the pattern in the data. Can applying NLP for analyzing KII be mentioned as data analytics tools in the report/paper?TIA
I have compiled a list of lecture note, examples, and notes from Data Mining: Concepts and Techniques (The Morgan Kaufmann Series in Data Management Systems). The attached pdf is the first iteration of the text at this point it is just a manuscript. I would appreciate feedback on how to organize and structure the text in a way that it could be presented to a publisher.
What will be the future applications of analytics of large data sets conducted in the computing cloud on computerized Business Intelligence analytical platforms in Big Data database systems in enterprise logistics management?
The analytics conducted on computerized Business Intelligence platforms is one of the key advanced information technology technologies of the fourth technological revolution, known as Industry 4.0. The current technological revolution described as Industry 4.0 is determined by the development of the following technologies of advanced information processing: Big Data database technologies, cloud computing, machine learning, Internet of Things, artificial intelligence, Business Intelligence and other advanced data mining technologies.
The analytics conducted on computerized Business Intelligence platforms currently supports business management processes, including logistics management.
In my opinion, the use of analytics of large data sets conducted in the computing cloud on computerized Business Intelligence analytical platforms in Big Data database systems in enterprise logistics management, including supply logistics, production logistics, provision of services and distribution of manufactured products and services, is currently growing.
The analytics conducted on large data sets conducted in the cloud computing on Business Intelligence computerized platforms in Big Data database systems makes it particularly easy to identify opportunities and threats to business development, allows for quick generation of analytical reports on selected issues in the economic and financial situation of the business entity. In this way, the generated reports can be helpful in the processes of enterprise logistics management, including supply logistics, production logistics, provision of services and distribution of manufactured products and services.
Do you agree with my opinion on this matter?
In view of the above, I am asking you the following question:
What will be the future applications of analytics of large data sets conducted in the computing cloud on computerized Business Intelligence analytical platforms in Big Data database systems in enterprise logistics management?
Please reply
I invite you to the discussion
The issues of the use of information contained in Big Data database systems for the purposes of conducting Business Intelligence analyzes are described in the publications:
I invite you to discussion and cooperation.
Best wishes
i am doing project on automated classification of software requirement sing NLP and machine learning approach i.e. Naive Bayes. For this i require dataset of classified software requirements. i have searched PROMISE data repository, but didnot find dataset according to my need. can someone help me it will be highly appreciated if someone tell me from where i can find and download this dataset.
dear community, I need your help regarding extracting data from the Binance platform in order to use it for a forecasting problem , for example we extract data about a certain crypto then we clean it and make it ready for use and make a forecast if we should buy it or not with adding an alarm when the time is perfect for that, using python and machine learning and statistics.
Hi everyone
I'm looking for a quick and reliable way to estimate my missing climatological data. My data is daily and more than 40 years. These data include the minimum and maximum temperature, precipitation, sunshine hours, relative humidity and wind speed. My main problem is the sunshine hours data that has a lot of defects. These defects are diffuse in time series. Sometimes it encompasses several months and even a few years. The number of stations I work on is 18. Given the fact that my data is daily, the number of missing data is high. So I need to estimate missing data before starting work. Your comments and experiences can be very helpful.
Thank you so much for advising me.
Hi Fellows,
The matrix is here at the bottom: https://statweb.stanford.edu/~jtaylo/courses/stats202/visualization.html. A similar version is seen on the book Introduction to Data Mining. It's clear that colours toward the red end indicate stronger correlation, but what attributes or variables are really correlated as shown? For example, along the main diagonal, cases of the same species show mostly perfect correlation, with a few near-perfect occurrences. Normally, a correlation is calculated with two columns of values, not two single cases.
Thanks
RP
I am working on a data mining project and would like to portray the correlation between healthcare expenditure by country and the population's life expectancy/general health and am having trouble finding sizeable data sets.
Hi
How can new data mining methods be used to assess the ecological potential of the land?
I am passionate for working on medical data. but unfortunately the disease on which I want to work, I couldn't find data in my home country. Anyone Up from medical informatics and health data mining who can collaborate with me?
Hi There!
My data has a number of features (with contain continuous data) and a response feature (class label) of categorical data (binary). My intention is to study the variation of the response feature (Class ) due to all the other features using a variety of feature selection techniques. Kindly help in pointing out right techniques for the purpose. Data is like this:
------------------------------------------------------------------
f1 f2 f3 f4 ... fn class
------------------------------------------------------------------
0.2 0.3 0.87 0.6 ... 0.7 0
0.2 0.3 0.87 0.6 ... 0.7 1
0.2 0.3 0.87 0.6 ... 0.7 0
0.2 0.3 0.87 0.6 ... 0.7 1
-------------------------------------------------------------------
I think that Generative Adversarial Networks can be used as Data Farming Means. What do you know about such an approach? Can you give another example of means for Data Farming?
Why Particle Swarm Optimization works better for this classification problem?
Can anyone give me any strong reasons behind it?
Thanks in advance.
How many respondents are really enough?
There are two schools of thought about sample size a relatively small sample size is adequate. Perhaps 300-500 respondents can work?
Please share the paper and throw the light on text mining and meta analysis
Let consider there is a selling factor like this:
Gender | Age | Street | Item 1 | Count 1 | Item 2 | Count 2 | ... | Item N | Count N | Total Price (Label)
Male | 22 | S1 | Milk | 2 | Bread | 5 | ... | - | - | 10 $
Female | 10 | S2 | Cofee | 1 | - | - | ... | - | - | 1 $
....
We want to predict the total price for a factor based on their buyer demographic information (like gender, age, job) and also their buying items and counts. It should be mentioned that we suppose that we don't know each item's price and also, the prices will be changed during the time (so, we although will have a date in our dataset).
Now it is the main question that how we can use this dataset that contains some transactional data (items) which their combination is not important. For example, if somebody buys item1 and item2, it is equal to other guys who buy item2 and item1. So, the values of our items columns should not have any differences for their value orders.
This dataset contains both multivariate and transactional data. My question is how can we predict the label more accurately?
For example, k-nearest neighbor needs to compute the smallest one of distances between a query and a large number of data.
But, k-means clustering computes the smallest one of distances between each data and a few cluster center.
Like k-nearest neighbor, which technique requires to compute the maximum or minimum value in a large number of data?
I want to understand C5.0 algorithm for data classification , is there any one have the steps for it or the original paper that this is algorithm was presented in ?
What is the best algorithm to complement a cluster analysis (k-means) and define the ideal cluster number? I am testing the Weka data mining application, which incorporates clustering algorithms that do not require prior selection of the number of clusters. Has anyone tried it?
Hello everybody
I am solving a Social Network Analysis problem. I have 9 centrality measures in my problem and I am trying to combine them for creating a new centrality measure.
I have chosen TOPSIS as a combining method. Now I am looking for an easy method to assign appropriate weights to my criteria.
If you think you can help me and even introduce me to a better solution than TOPSIS, I will be glad if you share it with me.
Best Regards
I have seen City Pulse (see link) and they have the type of data I'm looking for, but not in large enough quantity. In the best case, the data will have recording intervals that are < 1 hour (the more frequent, the better) and have total duration of at least a month.
I would like to carry out a study (Social-Economical Categorization) on multi datasets (text data from ISPs, hospitals, Government records agencies ) using any suitable data mining technique. I read that WEKA can do the job. I am still a newbie when it comes to data mining analysis and WEKA. Kindly advise on how best I can do this.
what procedure and data should I use ?
how to structure the empirical study ?
I usually use Latent Dirichlet Allocation to cluster texts. What do you use? Can someone give a comparison between different text clustering algorithms?
Can you suggest any topic related to Big Data + Data Mining + Association Rule Mining + Predicting Consumer Behaviors
What are the various query based (Top-K Frequent Pattern Mining) techniques are being used for various purposes. So i need to know what are some new research trends in Data Mining.
I'm looking for finding frequent itemsets in sequences, which means the order of appearance of items matters in itemsets. Consider the following example :
1,2,3
1,3,2
3,1,2
Assume that the order of items matters, then if we put min_support = 3, {1,2} is frequent, because support({1,2})= 3 and every time we see {1,2} in this dataset, 2 comes after 1.
Let's consider {1,3}, we know that this itemset appears 3 times in our dataset, but is not frequent, because only in 2 transactions 3 comes after 1.
I'm looking for an algorithm that can do this for me, I found algorithms like GSP which do something similar to what i want, but they don't do exactly what i wanted to do. Can you please recommend me an algorithm which is able to find such frequent itemsets?
Thanks in advance
Can anyone help me find a tool that allows me to download the old tweets in the history of a user. I need to study the content of the tweets of 2011 from a group of users who used a # hashtag.
Is there a Python or R package for analyzing spreader nodes and community detection in the multilayer network?
Hello,
Does anyone know how to extract all twitter images under a specific hashtag using python or R? Any relevant packages?
Thank you,
Ioanna
PS I am not searching how to extract all images uploaded by a user.
I'd like to use it in a classification task.
I would like to dive into the research domain of explainable AI. What are some of the recent trending methodologies in this domain? What can be a good start to dive into this field?
