This chapter presents theoretical and practical aspects associated to the implementation of a combined model-based/data-driven approach for failure prognostics based on particle filtering algorithms, in which the current esti- mate of the state PDF is used to determine the operating condition of the system and predict the progression of a fault indicator, given a dynamic state model and a set of process measurements. In this approach, the task of es- timating the current value of the fault indicator, as well as other important changing parameters in the environment, involves two basic steps: the predic- tion step, based on the process model, and an update step, which incorporates the new measurement into the a priori state estimate. This framework allows to estimate of the probability of failure at future time instants (RUL PDF) in real-time, providing information about time-to- failure (TTF) expectations, statistical confidence intervals, long-term predic- tions; using for this purpose empirical knowledge about critical conditions for the system (also referred to as the hazard zones). This information is of paramount significance for the improvement of the system reliability and cost-effective operation of critical assets, as it has been shown in a case study where feedback correction strategies (based on uncertainty measures) have been implemented to lengthen the RUL of a rotorcraft transmission system with propagating fatigue cracks on a critical component. Although the feed- back loop is implemented using simple linear relationships, it is helpful to provide a quick insight into the manner that the system reacts to changes on its input signals, in terms of its predicted RUL. The method is able to manage non-Gaussian pdf’s since it includes concepts such as nonlinear state estimation and confidence intervals in its formulation. Real data from a fault seeded test showed that the proposed framework was able to anticipate modifications on the system input to lengthen its RUL. Results of this test indicate that the method was able to successfully suggest the correction that the system required. In this sense, future work will be focused on the development and testing of similar strategies using different input-output uncertainty metrics.

This data set belongs to the paper "Video-to-Model: Unsupervised Trace Extraction from Videos for Process Discovery and Conformance Checking in Manual Assembly", submitted on March 24, 2020, to the 18th International Conference on Business Process Management (BPM).Abstract: Manual activities are often hidden deep down in discrete manufacturing processes. For the elicitation and optimization of process behavior, complete information about the execution of Manual activities are required. Thus, an approach is presented on how execution level information can be extracted from videos in manual assembly. The goal is the generation of a log that can be used in state-of-the-art process mining tools. The test bed for the system was lightweight and scalable consisting of an assembly workstation equipped with a single RGB camera recording only the hand movements of the worker from top. A neural network based real-time object classifier was trained to detect the worker’s hands. The hand detector delivers the input for an algorithm, which generates trajectories reflecting the movement paths of the hands. Those trajectories are automatically assigned to work steps using the position of material boxes on the assembly shelf as reference points and hierarchical clustering of similar behaviors with dynamic time warping. The system has been evaluated in a task-based study with ten participants in a laboratory, but under realistic conditions. The generated logs have been loaded into the process mining toolkit ProM to discover the underlying process model and to detect deviations from both, instructions and ground truth, using conformance checking. The results show that process mining delivers insights about the assembly process and the system’s precision.The data set contains the generated and the annotated logs based on the video material gathered during the user study. In addition, the petri nets from the process discovery and conformance checking conducted with ProM (http://www.promtools.org) and the reference nets modeled with Yasper (http://www.yasper.org/) are provided.

Peer-to-peer (P2P) networks are gaining popularity in many applications such as file sharing, e-commerce, and social networking, many of which deal with rich, distributed data sources that can benefit from data mining. P2P networks are, in fact,well-suited to distributed data mining (DDM), which deals with the problem of data analysis in environments with distributed data,computing nodes,and users. This article offers an overview of DDM applications and algorithms for P2P environments,focusing particularly on local algorithms that perform data analysis by using computing primitives with limited communication overhead. The authors describe both exact and approximate local P2P data mining algorithms that work in a decentralized and communication-efficient manner.

Abstract The objective of this work is to improve the quality of the information that belongs to the database CubaCiencia, of the Institute of Scientific and Technological Information. This database has bibliographic information referring to four segments of science and is the main database of the Library Management System. The applied methodology was based on the Decision Trees, the Correlation Matrix, the 3D Scatter Plot, etc., which are techniques used by data mining, for the study of large volumes of information. The results achieved not only made it possible to improve the information in the database, but also provided truly useful patterns in the solution of the proposed objectives.

Peer-to-peer (P2P) networks are gaining popularity in many applications such as file sharing, e-commerce, and social networking, many of which deal with rich, distributed data sources that can benefit from data mining. P2P networks are, in fact,well-suited to distributed data mining (DDM), which deals with the problem of data analysis in environments with distributed data,computing nodes,and users. This article offers an overview of DDM applications and algorithms for P2P environments,focusing particularly on local algorithms that perform data analysis by using computing primitives with limited communication overhead. The authors describe both exact and approximate local P2P data mining algorithms that work in a decentralized and communication-efficient manner.

The purpose of data mining analysis is always to find patterns of the data using certain kind of techiques such as classification or regression. It is not always feasible to apply classification algorithms directly to dataset. Before doing any work on the data, the data has to be pre-processed and this process normally involves feature selection and dimensionality reduction. We tried to use clustering as a way to reduce the dimension of the data and create new features. Based on our project, after using clustering prior to classification, the performance has not improved much. The reason why it has not improved could be the features we selected to perform clustering are not well suited for it. Because of the nature of the data, classification tasks are going to provide more information to work with in terms of improving knowledge and overall performance metrics. From the dimensionality reduction perspective: It is different from Principle Component Analysis which guarantees finding the best linear transformation that reduces the number of dimensions with a minimum loss of information. Using clusters as a technique of reducing the data dimension will lose a lot of information since clustering techniques are based a metric of 'distance'. At high dimensions euclidean distance loses pretty much all meaning. Therefore using clustering as a "Reducing" dimensionality by mapping data points to cluster numbers is not always good since you may lose almost all the information. From the creating new features perspective: Clustering analysis creates labels based on the patterns of the data, it brings uncertainties into the data. By using clustering prior to classification, the decision on the number of clusters will highly affect the performance of the clustering, then affect the performance of classification. If the part of features we use clustering techniques on is very suited for it, it might increase the overall performance on classification. For example, if the features we use k-means on are numerical and the dimension is small, the overall classification performance may be better. We did not lock in the clustering outputs using a random_state in the effort to see if they were stable. Our assumption was that if the results vary highly from run to run which they definitely did, maybe the data just does not cluster well with the methods selected at all. Basically, the ramification we saw was that our results are not much better than random when applying clustering to the data preprocessing. Finally, it is important to ensure a feedback loop is in place to continuously collect the same data in the same format from which the models were created. This feedback loop can be used to measure the model real world effectiveness and also to continue to revise the models from time to time as things change.

This chapter presents theoretical and practical aspects associated to the implementation of a combined model-based/data-driven approach for failure prognostics based on particle filtering algorithms, in which the current esti- mate of the state PDF is used to determine the operating condition of the system and predict the progression of a fault indicator, given a dynamic state model and a set of process measurements. In this approach, the task of es- timating the current value of the fault indicator, as well as other important changing parameters in the environment, involves two basic steps: the predic- tion step, based on the process model, and an update step, which incorporates the new measurement into the a priori state estimate. This framework allows to estimate of the probability of failure at future time instants (RUL PDF) in real-time, providing information about time-to- failure (TTF) expectations, statistical confidence intervals, long-term predic- tions; using for this purpose empirical knowledge about critical conditions for the system (also referred to as the hazard zones). This information is of paramount significance for the improvement of the system reliability and cost-effective operation of critical assets, as it has been shown in a case study where feedback correction strategies (based on uncertainty measures) have been implemented to lengthen the RUL of a rotorcraft transmission system with propagating fatigue cracks on a critical component. Although the feed- back loop is implemented using simple linear relationships, it is helpful to provide a quick insight into the manner that the system reacts to changes on its input signals, in terms of its predicted RUL. The method is able to manage non-Gaussian pdf’s since it includes concepts such as nonlinear state estimation and confidence intervals in its formulation. Real data from a fault seeded test showed that the proposed framework was able to anticipate modifications on the system input to lengthen its RUL. Results of this test indicate that the method was able to successfully suggest the correction that the system required. In this sense, future work will be focused on the development and testing of similar strategies using different input-output uncertainty metrics.

Time in Market (TIM) is a metric to describe the time period of a product from its market entry to its decline and disappearance from the market. The concept is often used implicit to describe the acceleration of product life cycles, innovation cycles and is an essential part of the product life cycle concept. It can be assumed that time in markets is an important indicator for manufacturers and marketers to plan and evaluate their market success. Moreover, time in markets are necessary to measure the speed of product life cycles and their implication for the general development of product lifetime. This article’s major contributions are to presenting (1) time in markets as a highly relevant concept for the assessment of product life cycles, although the indicator has received little attention so far, (2) explaining an automated internet-based data mining approach to gather semi-structured product data from 5 German internet shops for electronic consumer goods and (3) presenting initial insights for a period of a half to one year on market data for smartphones. It will turn out that longer periods of time are needed to obtain significant data on time in markets, nevertheless initial results show a high product rollover rate of 40-45% within one year and present a time in market below 100 days for at least 16% of the captured products. Due to the current state of work, this article is addressed to researchers already engaged in data mining or interested in the application of it.

This thesis lays the ground work for enabling scalable data mining in massively parallel dataflow systems, using large datasets. Such datasets have become ubiquitous. We illustrate common fallacies with respect to scalable data mining: It is in no way sufficient to naively implement textbook algorithms on parallel systems; bottlenecks on all layers of the stack prevent the scalability of such naive implementations. We argue that scalability in data mining is a multi-leveled problem and must therefore be approached on the interplay of algorithms, systems, and applications. We therefore discuss a selection of scalability problems on these different levels. We investigate algorithm-specific scalability aspects of collaborative filtering algorithms for computing recommendations, a popular data mining use case with many industry deployments. We show how to efficiently execute the two most common approaches, namely neighborhood methods and latent factor models on MapReduce, and describe a specialized architecture for scaling collaborative filtering to extremely large datasets which we implemented at Twitter. We turn to system-specific scalability aspects, where we improve system performance during the distributed execution of a special class of iterative algorithms by drastically reducing the overhead required for guaranteeing fault tolerance. Therefore we propose a novel optimistic approach to fault-tolerance which exploits the robust convergence properties of a large class of fixpoint algorithms and does not incur measurable overhead in failure-free cases. Finally, we present work on an application-specific scalability aspect of scalable data mining. A common problem when deploying machine learning applications in real-world scenarios is that the prediction quality of ML models heavily depends on hyperparameters that have to be chosen in advance. We propose an algorithmic framework for an important subproblem occuring during hyperparameter search at scale: efficiently generating samples from block-partitioned matrices in a shared-nothing environment. For every selected problem, we show how to execute the resulting computation automatically in a parallel and scalable manner, and evaluate our proposed solution on large datasets with billions of datapoints.

The LSC (Leicester Scientific Corpus)August 2019 by Neslihan Suzen, PhD student at the University of Leicester (ns433@leicester.ac.uk) Supervised by Prof Alexander Gorban and Dr Evgeny MirkesThe data is extracted from the Web of Science® [1] You may not copy or distribute this data in whole or in part without the written consent of Clarivate Analytics.Getting StartedThis text provides background information on the LSC (Leicester Scientific Corpus) and pre-processing steps on abstracts, and describes the structure of files to organise the corpus. This corpus is created to be used in future work on the quantification of the sense of research texts. One of the goal of publishing the data is to make it available for further analysis and use in Natural Language Processing projects.LSC is a collection of abstracts of articles and proceeding papers published in 2014, and indexed by the Web of Science (WoS) database [1]. Each document contains title, list of authors, list of categories, list of research areas, and times cited. The corpus contains only documents in English.The corpus was collected in July 2018 online and contains the number of citations from publication date to July 2018.Each document in the corpus contains the following parts:1. Authors: The list of authors of the paper2. Title: The title of the paper3. Abstract: The abstract of the paper4. Categories: One or more category from the list of categories [2]. Full list of categories is presented in file ‘List_of _Categories.txt’.5. Research Areas: One or more research area from the list of research areas [3]. Full list of research areas is presented in file ‘List_of_Research_Areas.txt’.6. Total Times cited: The number of times the paper was cited by other items from all databases within Web of Science platform [4]7. Times cited in Core Collection: The total number of times the paper was cited by other papers within the WoS Core Collection [4]We describe a document as the collection of information (about a paper) listed above. The total number of documents in LSC is 1,673,824.All documents in LSC have nonempty abstract, title, categories, research areas and times cited in WoS databases. There are 119 documents with empty authors list, we did not exclude these documents.Data ProcessingThis section describes all steps in order for the LSC to be collected, clean and available to researchers. Processing the data consists of six main steps:Step 1: Downloading of the Data OnlineThis is the step of collecting the dataset online. This is done manually by exporting documents as Tab-delimitated files. All downloaded documents are available online.Step 2: Importing the Dataset to RThis is the process of converting the collection to RData format for processing the data. The LSC was collected as TXT files. All documents are extracted to R.Step 3: Cleaning the Data from Documents with Empty Abstract or without CategoryNot all papers have abstract and categories in the collection. As our research is based on the analysis of abstracts and categories, preliminary detecting and removing inaccurate documents were performed. All documents with empty abstracts and documents without categories are removed.Step 4: Identification and Correction of Concatenate Words in AbstractsTraditionally, abstracts are written in a format of executive summary with one paragraph of continuous writing, which is known as ‘unstructured abstract’. However, especially medicine-related publications use ‘structured abstracts’. Such type of abstracts are divided into sections with distinct headings such as introduction, aim, objective, method, result, conclusion etc.Used tool for extracting abstracts leads concatenate words of section headings with the first word of the section. As a result, some of structured abstracts in the LSC require additional process of correction to split such concatenate words. For instance, we observe words such as ConclusionHigher and ConclusionsRT etc. in the corpus. The detection and identification of concatenate words cannot be totally automated. Human intervention is needed in the identification of possible headings of sections. We note that we only consider concatenate words in headings of sections as it is not possible to detect all concatenate words without deep knowledge of research areas. Identification of such words is done by sampling of medicine-related publications. The section headings in such abstracts are listed in the List 1.List 1 Headings of sections identified in structured abstractsBackground Method(s) DesignTheoretical Measurement(s) LocationAim(s) Methodology ProcessAbstract Population ApproachObjective(s) Purpose(s) Subject(s)Introduction Implication(s) Patient(s)Procedure(s) Hypothesis Measure(s)Setting(s) Limitation(s) DiscussionConclusion(s) Result(s) Finding(s)Material (s) Rationale(s)Implications for health and nursing policyAll words including headings in the List 1 are detected in entire corpus, and then words are split into two words. For instance, the word ‘ConclusionHigher’ is split into ‘Conclusion’ and ‘Higher’.Step 5: Extracting (Sub-setting) the Data Based on Lengths of AbstractsAfter correction of concatenate words is completed, the lengths of abstracts are calculated. ‘Length’ indicates the totalnumber of words in the text, calculated by the same rule as for Microsoft Word ‘word count’ [5].According to APA style manual [6], an abstract should contain between 150 to 250 words. However, word limits vary from journal to journal. For instance, Journal of Vascular Surgery recommends that ‘Clinical and basic research studies must include a structured abstract of 400 words or less’[7].In LSC, the length of abstracts varies from 1 to 3805. We decided to limit length of abstracts from 30 to 500 words in order to study documents with abstracts of typical length ranges and to avoid the effect of the length to the analysis. Documents containing less than 30 and more than 500 words in abstracts are removed.Step 6: Saving the Dataset into CSV FormatCorrected and extracted documents are saved into 36 CSV files. The structure of files are described in the following section.The Structure of Fields in CSV FilesIn CSV files, the information is organised with one record on each line and parts of abstract, title, list of authors, list of categories, list of research areas, and times cited is recorded in separated fields.To access the LSC for research purposes, please email to ns433@le.ac.uk.References[1]Web of Science. (15 July). Available: https://apps.webofknowledge.com/[2]WoS Subject Categories. Available: https://images.webofknowledge.com/WOKRS56B5/help/WOS/hp_subject_category_terms_tasca.html[3]Research Areas in WoS. Available: https://images.webofknowledge.com/images/help/WOS/hp_research_areas_easca.html[4]Times Cited in WoS Core Collection. (15 July). Available: https://support.clarivate.com/ScientificandAcademicResearch/s/article/Web-of-Science-Times-Cited-accessibility-and-variation?language=en_US[5]Word Count. Available: https://support.office.com/en-us/article/show-word-count-3c9e6a11-a04d-43b4-977c-563a0e0d5da3[6]A. P. Association, Publication manual. American Psychological Association Washington, DC, 1983.[7]P. Gloviczki and P. F. Lawrence, "Information for authors," Journal of Vascular Surgery, vol. 65, no. 1, pp. A16-A22, 2017.

This dataset includes google scholar articles listing on data mining, this is very helpful in many educational research works. This dataset contains 936 unique entries. including title, description, author names, article link, cited by and related articles.

This dataset is about books. It has 2 rows and is filtered where the book series is Advances in data mining and database management (ADMDM) book series. It features 9 columns including author, publication date, language, and book publisher.

Full title: Mining Distance-Based Outliers in Near Linear Time with Randomization and a Simple Pruning Rule Abstract: Defining outliers by their distance to neighboring examples is a popular approach to finding unusual examples in a data set. Recently, much work has been conducted with the goal of finding fast algorithms for this task. We show that a simple nested loop algorithm that in the worst case is quadratic can give near linear time performance when the data is in random order and a simple pruning rule is used. We test our algorithm on real high-dimensional data sets with millions of examples and show that the near linear scaling holds over several orders of magnitude. Our average case analysis suggests that much of the efficiency is because the time to process non-outliers, which are the majority of examples, does not depend on the size of the data set.

These are artificially made beginner data mining datasets for learning purposes.

Case study:

• FEELS LIKE HOME is an interior design company, which has about 100 000 registered customers and provide services for more than 200 000 clients annually.
• The range of the products can be divided in 5 major classes: Decor accessories, Furniture, Textiles, Lighting and Art with an option to purchase Limited Edition versions for an extra charge. These goods can be distributed by 3 channels: Physical stores, yearly catalogs and the companies’ website.
• FEELS LIKE HOME has been doing a great job during recent years, achieving decent profits and revenues, but the future remains volatile. In order to solve the problem of instability the company is planning to launch new marketing program, especially to improve the accuracy of marketing campaigns.

The aim of FeelsLikeHome_Campaign dataset is to create project is in which you build a predictive model (using a sample of 2500 clients’ data) forecasting the highest profit from the next marketing campaign, which will indicate the customers who will be the most likely to accept the offer.

The aim of FeelsLikeHome_Cluster dataset is to create project in which you split company’s customer base on homogenous clusters (using 5000 clients’ data) and propose draft marketing strategies for these groups based on customer behavior and information about their profile.

FeelsLikeHome_Score dataset can be used to calculate total profit from marketing campaign and for producing a list of sorted customers by the probability of the dependent variable in predictive model problem.

This paper proposes a scalable, local privacy preserving algorithm for distributed Peer-to-Peer (P2P) data aggregation useful for many advanced data mining/analysis tasks such as average/sum computation, decision tree induction, feature selection, and more. Unlike most multi-party privacy-preserving data mining algorithms, this approach works in an asynchronous manner through local interactions and it is highly scalable. It particularly deals with the distributed computation of the sum of a set of numbers stored at different peers in a P2P network in the context of a P2P web mining application. The proposed optimization based privacy-preserving technique for computing the sum allows different peers to specify different privacy requirements without having to adhere to a global set of parameters for the chosen privacy model. Since distributed sum computation is a frequently used primitive, the proposed approach is likely to have significant impact on many data mining tasks such as multi-party privacy-preserving clustering, frequent itemset mining, and statistical aggregate computation.

This dataset is about books. It has 1 row and is filtered where the book is An introduction to data analysis in R : hands-on coding, data mining, visualization and statistics from scratch. It features 7 columns including author, publication date, language, and book publisher.

The LScDC (Leicester Scientific Dictionary-Core Dictionary)April 2020 by Neslihan Suzen, PhD student at the University of Leicester (ns433@leicester.ac.uk/suzenneslihan@hotmail.com)Supervised by Prof Alexander Gorban and Dr Evgeny Mirkes[Version 3] The third version of LScDC (Leicester Scientific Dictionary-Core) is formed using the updated LScD (Leicester Scientific Dictionary) - Version 3*. All steps applied to build the new version of core dictionary are the same as in Version 2** and can be found in description of Version 2 below. We did not repeat the explanation. The files provided with this description are also same as described as for LScDC Version 2. The numbers of words in the 3rd versions of LScD and LScDC are summarized below. # of wordsLScD (v3) 972,060LScDC (v3) 103,998 * Suzen, Neslihan (2019): LScD (Leicester Scientific Dictionary). figshare. Dataset. https://doi.org/10.25392/leicester.data.9746900.v3 ** Suzen, Neslihan (2019): LScDC (Leicester Scientific Dictionary-Core). figshare. Dataset. https://doi.org/10.25392/leicester.data.9896579.v2[Version 2] Getting StartedThis file describes a sorted and cleaned list of words from LScD (Leicester Scientific Dictionary), explains steps for sub-setting the LScD and basic statistics of words in the LSC (Leicester Scientific Corpus), to be found in [1, 2]. The LScDC (Leicester Scientific Dictionary-Core) is a list of words ordered by the number of documents containing the words, and is available in the CSV file published. There are 104,223 unique words (lemmas) in the LScDC. This dictionary is created to be used in future work on the quantification of the sense of research texts. The objective of sub-setting the LScD is to discard words which appear too rarely in the corpus. In text mining algorithms, usage of enormous number of text data brings the challenge to the performance and the accuracy of data mining applications. The performance and the accuracy of models are heavily depend on the type of words (such as stop words and content words) and the number of words in the corpus. Rare occurrence of words in a collection is not useful in discriminating texts in large corpora as rare words are likely to be non-informative signals (or noise) and redundant in the collection of texts. The selection of relevant words also holds out the possibility of more effective and faster operation of text mining algorithms.To build the LScDC, we decided the following process on LScD: removing words that appear in no more than 10 documents (

International Journal of Engineering and Advanced Technology FAQ - ResearchHelpDesk - International Journal of Engineering and Advanced Technology (IJEAT) is having Online-ISSN 2249-8958, bi-monthly international journal, being published in the months of February, April, June, August, October, and December by Blue Eyes Intelligence Engineering & Sciences Publication (BEIESP) Bhopal (M.P.), India since the year 2011. It is academic, online, open access, double-blind, peer-reviewed international journal. It aims to publish original, theoretical and practical advances in Computer Science & Engineering, Information Technology, Electrical and Electronics Engineering, Electronics and Telecommunication, Mechanical Engineering, Civil Engineering, Textile Engineering and all interdisciplinary streams of Engineering Sciences. All submitted papers will be reviewed by the board of committee of IJEAT. Aim of IJEAT Journal disseminate original, scientific, theoretical or applied research in the field of Engineering and allied fields. dispense a platform for publishing results and research with a strong empirical component. aqueduct the significant gap between research and practice by promoting the publication of original, novel, industry-relevant research. seek original and unpublished research papers based on theoretical or experimental works for the publication globally. publish original, theoretical and practical advances in Computer Science & Engineering, Information Technology, Electrical and Electronics Engineering, Electronics and Telecommunication, Mechanical Engineering, Civil Engineering, Textile Engineering and all interdisciplinary streams of Engineering Sciences. impart a platform for publishing results and research with a strong empirical component. create a bridge for a significant gap between research and practice by promoting the publication of original, novel, industry-relevant research. solicit original and unpublished research papers, based on theoretical or experimental works. Scope of IJEAT International Journal of Engineering and Advanced Technology (IJEAT) covers all topics of all engineering branches. Some of them are Computer Science & Engineering, Information Technology, Electronics & Communication, Electrical and Electronics, Electronics and Telecommunication, Civil Engineering, Mechanical Engineering, Textile Engineering and all interdisciplinary streams of Engineering Sciences. The main topic includes but not limited to: 1. Smart Computing and Information Processing Signal and Speech Processing Image Processing and Pattern Recognition WSN Artificial Intelligence and machine learning Data mining and warehousing Data Analytics Deep learning Bioinformatics High Performance computing Advanced Computer networking Cloud Computing IoT Parallel Computing on GPU Human Computer Interactions 2. Recent Trends in Microelectronics and VLSI Design Process & Device Technologies Low-power design Nanometer-scale integrated circuits Application specific ICs (ASICs) FPGAs Nanotechnology Nano electronics and Quantum Computing 3. Challenges of Industry and their Solutions, Communications Advanced Manufacturing Technologies Artificial Intelligence Autonomous Robots Augmented Reality Big Data Analytics and Business Intelligence Cyber Physical Systems (CPS) Digital Clone or Simulation Industrial Internet of Things (IIoT) Manufacturing IOT Plant Cyber security Smart Solutions – Wearable Sensors and Smart Glasses System Integration Small Batch Manufacturing Visual Analytics Virtual Reality 3D Printing 4. Internet of Things (IoT) Internet of Things (IoT) & IoE & Edge Computing Distributed Mobile Applications Utilizing IoT Security, Privacy and Trust in IoT & IoE Standards for IoT Applications Ubiquitous Computing Block Chain-enabled IoT Device and Data Security and Privacy Application of WSN in IoT Cloud Resources Utilization in IoT Wireless Access Technologies for IoT Mobile Applications and Services for IoT Machine/ Deep Learning with IoT & IoE Smart Sensors and Internet of Things for Smart City Logic, Functional programming and Microcontrollers for IoT Sensor Networks, Actuators for Internet of Things Data Visualization using IoT IoT Application and Communication Protocol Big Data Analytics for Social Networking using IoT IoT Applications for Smart Cities Emulation and Simulation Methodologies for IoT IoT Applied for Digital Contents 5. Microwaves and Photonics Microwave filter Micro Strip antenna Microwave Link design Microwave oscillator Frequency selective surface Microwave Antenna Microwave Photonics Radio over fiber Optical communication Optical oscillator Optical Link design Optical phase lock loop Optical devices 6. Computation Intelligence and Analytics Soft Computing Advance Ubiquitous Computing Parallel Computing Distributed Computing Machine Learning Information Retrieval Expert Systems Data Mining Text Mining Data Warehousing Predictive Analysis Data Management Big Data Analytics Big Data Security 7. Energy Harvesting and Wireless Power Transmission Energy harvesting and transfer for wireless sensor networks Economics of energy harvesting communications Waveform optimization for wireless power transfer RF Energy Harvesting Wireless Power Transmission Microstrip Antenna design and application Wearable Textile Antenna Luminescence Rectenna 8. Advance Concept of Networking and Database Computer Network Mobile Adhoc Network Image Security Application Artificial Intelligence and machine learning in the Field of Network and Database Data Analytic High performance computing Pattern Recognition 9. Machine Learning (ML) and Knowledge Mining (KM) Regression and prediction Problem solving and planning Clustering Classification Neural information processing Vision and speech perception Heterogeneous and streaming data Natural language processing Probabilistic Models and Methods Reasoning and inference Marketing and social sciences Data mining Knowledge Discovery Web mining Information retrieval Design and diagnosis Game playing Streaming data Music Modelling and Analysis Robotics and control Multi-agent systems Bioinformatics Social sciences Industrial, financial and scientific applications of all kind 10. Advanced Computer networking Computational Intelligence Data Management, Exploration, and Mining Robotics Artificial Intelligence and Machine Learning Computer Architecture and VLSI Computer Graphics, Simulation, and Modelling Digital System and Logic Design Natural Language Processing and Machine Translation Parallel and Distributed Algorithms Pattern Recognition and Analysis Systems and Software Engineering Nature Inspired Computing Signal and Image Processing Reconfigurable Computing Cloud, Cluster, Grid and P2P Computing Biomedical Computing Advanced Bioinformatics Green Computing Mobile Computing Nano Ubiquitous Computing Context Awareness and Personalization, Autonomic and Trusted Computing Cryptography and Applied Mathematics Security, Trust and Privacy Digital Rights Management Networked-Driven Multicourse Chips Internet Computing Agricultural Informatics and Communication Community Information Systems Computational Economics, Digital Photogrammetric Remote Sensing, GIS and GPS Disaster Management e-governance, e-Commerce, e-business, e-Learning Forest Genomics and Informatics Healthcare Informatics Information Ecology and Knowledge Management Irrigation Informatics Neuro-Informatics Open Source: Challenges and opportunities Web-Based Learning: Innovation and Challenges Soft computing Signal and Speech Processing Natural Language Processing 11. Communications Microstrip Antenna Microwave Radar and Satellite Smart Antenna MIMO Antenna Wireless Communication RFID Network and Applications 5G Communication 6G Communication 12. Algorithms and Complexity Sequential, Parallel And Distributed Algorithms And Data Structures Approximation And Randomized Algorithms Graph Algorithms And Graph Drawing On-Line And Streaming Algorithms Analysis Of Algorithms And Computational Complexity Algorithm Engineering Web Algorithms Exact And Parameterized Computation Algorithmic Game Theory Computational Biology Foundations Of Communication Networks Computational Geometry Discrete Optimization 13. Software Engineering and Knowledge Engineering Software Engineering Methodologies Agent-based software engineering Artificial intelligence approaches to software engineering Component-based software engineering Embedded and ubiquitous software engineering Aspect-based software engineering Empirical software engineering Search-Based Software engineering Automated software design and synthesis Computer-supported cooperative work Automated software specification Reverse engineering Software Engineering Techniques and Production Perspectives Requirements engineering Software analysis, design and modelling Software maintenance and evolution Software engineering tools and environments Software engineering decision support Software design patterns Software product lines Process and workflow management Reflection and metadata approaches Program understanding and system maintenance Software domain modelling and analysis Software economics Multimedia and hypermedia software engineering Software engineering case study and experience reports Enterprise software, middleware, and tools Artificial intelligent methods, models, techniques Artificial life and societies Swarm intelligence Smart Spaces Autonomic computing and agent-based systems Autonomic computing Adaptive Systems Agent architectures, ontologies, languages and protocols Multi-agent systems Agent-based learning and knowledge discovery Interface agents Agent-based auctions and marketplaces Secure mobile and multi-agent systems Mobile agents SOA and Service-Oriented Systems Service-centric software engineering Service oriented requirements engineering Service oriented architectures Middleware for service based systems Service discovery and composition Service level agreements (drafting,

Peptides are biologically ubiquitous and important molecules that self-assemble into diverse structures. While extensive research has explored the effects of chemical composition and environmental conditions on self-assembly, a systematic study consolidating this data to uncover global rules is lacking. In this work, we curate a peptide assembly database through a combination of manual processing by human experts and literature mining with a large language model. As a result, we collect more than 1,000 experimental data entries with information about peptide sequence, experimental conditions and corresponding self-assembly phases. Utilizing the data, machine learning models are trained and evaluated, demonstrating excellent accuracy (> 80%) and efficiency in assembly phase classification. Moreover, we fine-tune our GPT model for peptide literature mining with the developed dataset, which exhibits markedly superior performance in extracting information from academic publications relative to the pre-trained model. This workflow can improve efficiency when exploring potential self-assembling peptide candidates, through guiding experimental work, while also deepening our understanding of the mechanisms governing peptide self-assembly.

--- phase_data_clean.csv stores 1000+ peptide self-assembly data under different experimental conditions.

---mined_paper_list.csv stores the corresponding papers we used to collect data.

--- trainset.jsonl and testset.jsonl are data we used for fine-tuning the LLM.

--- fine-tuning.ipynb: code used to fine-tune ChatGPT model.

--- pretrain.ipynb: code used to test the pretrained ChatGPT model.

--- train_and_inference.ipynb: code to use mined data to train and test a ML predictor for phase classification.

This dataset is about book subjects. It has 2 rows and is filtered where the books is Learning Data Mining with Python. It features 10 columns including number of authors, number of books, earliest publication date, and latest publication date.