Knowledge Mining Research Articles

A WELL-DEVELOPED SOFTWARE TO INTEGRATE KNOWLEDGE MANAGEMENT PROCESSES INTO THE SUPPLY CHAIN PHASES

t— The paper introduces a new software called Software Service Supply Chain Knowledge Base CLOUD (SSSC KB CLOUD). The software provides a novel solution for knowledge capture and sharing during the development of the clients’ software projects. The main aim of this software is to facilitate sharing, transferring, retrieving, and retaining of knowledge among the experts’ group who are engaged in the different phases of the software development process. The software also provides a comprehensive management tool for daily work and tasks. It provides functions and abilities to ensure effective communication, search, and knowledge retrieval. In addition, the proposed software forms the group of experts who will complete and work on the new project and provides a mechanism to perform monitoring, reporting, tracking, and knowledge mining. The research also conducts quantitative testing to confirm the software’s functionality and efficiency; a questionnaire was welldeveloped and distributed among users in two different settings: three universities and a software company, then data was collected, and results were verified on whether the software improved the overall business productivity and knowledge externalization.

Mining knowledge graphs to map heterogeneous relations between the internet of things patterns

<span lang="EN-US">Patterns for the internet of things (IoT) which represent proven solutions used to solve design problems in the IoT are numerous. Similar to object-oriented design patterns, these IoT patterns contain multiple mutual heterogeneous relationships. However, these pattern relationships are hidden and virtually unidentified in most documents. In this paper, we use machine learning techniques to automatically mine knowledge graphs to map these relationships between several IoT patterns. The end result is a semantic knowledge graph database which outlines patterns as vertices and their relations as edges. We have identified four main relationships between the IoT patterns-a pattern is similar to another pattern if it addresses the same use case problem, a large-scale pattern uses a small- scale pattern in a lower level layer, a large pattern is composed of multiple smaller scale patterns underneath it, and patterns complement and combine with each other to resolve a given use case problem. Our results show some promising prospects towards the use of machine learning techniques to generate an automated repository to organise the IoT patterns, which are usually extracted at various levels of abstraction and granularity.</span>

A novel user review-based contextual recommender system

Recommendation systems are shrewd applications for knowledge mining that profoundly handle the problem of data overload. Various literature explores different philosophies to create ideas and recommends different strategies according to the needs of customers. Most of the work in the suggested structure space focuses on extending the accuracy of the recommendation by using a few possible methods where the principle purpose remains to improve the accuracy of suggestions while avoiding other plan objectives, such as the particular situation of a client. By using appropriate customer rating data, the biggest test for a suggested system is to generate substantial proposals. A setting is an enormous concept that can think of numerous points of view: for example, the community of friends of a client, time, mindset, environment, organization, type of day, classification of an item, description of the object, place, and language. The rating behavior of customers typically varies in different environments. We have proposed a new review-based contextual recommender (RBCR) system application from this line of analysis, in particular a novel recommender system, which is an adaptable, quick, and accurate piece planning framework that perceives the significance of setting and fuses the logical data using piece stunt while making expectations. We have contrasted our suggested calculation with pre- and post-sifting methods as they have been the most common methodologies in writing to illuminate the issue of setting conscious suggestion. Our studies show that considering the logical data, the display of a system will increase and provide better, appropriate and important results on various evaluation measurements.

A Novel Tradeoff Analysis between Traffic Congestion and Packing Density of Interconnection Networks for Massively Parallel Computers

From disaster prevention to mitigation, drug analysis to drug design, agriculture to food security, IoT to AI, and big data analysis to knowledge or sentiment mining, a high computation power is a prime necessity at present. As such, massively parallel computer (MPC) systems comprising a large number of nodes are gaining popularity. To interconnect these huge numbers of nodes efficiently, hierarchical interconnection networks are an attractive and feasible option. A Tori-connected flattened butterfly network (TFBN) has been proposed by the authors in a prior work for future generation MPC systems. In the previous study, the static network performance and static cost-effectiveness were evaluated. In this research, a novel trade-off factor named message traffic congestion vs. packing density trade-off factor has been proposed, which characterizes the message congestion in the network and its packing density. The factor is used to statically assess the suitability of the implementation of an interconnection network. The message traffic density, packing density, and new factor have been evaluated for the proposed network and similar competitive networks such as TTN, TESH, 2D-Mesh, 3D-Mesh, 2D-Torus, and 3D-Torus. It has been found that the performance of the TFBN is superior to the other networks.

Computational resources in healthcare

AbstractHealthcare is the most important component in the life of all human beings as each individual wish to have happy, healthy, and wealthy life‐span. Most of the branches of science are dedicated to improve the healthcare. In the era of knowledge mining, informatics is playing a crucial role in different branches of research. Thus, a wide range of informatics‐based fields have emerged in the last three decades that include medical informatics, bioinformatics, cheminformatics, pharmacoinformatics, immunoinformatics, and clinical informatics. In the past, a number of reviews have been focused on the application of an informatics‐based field in the healthcare. In this review, an attempt is made to summarize the major computational resources developed in any informatics‐based field that have an application in healthcare. This review enlists computational resources in following groups ‐ drug discovery, toxicity prediction, vaccine designing, disease biomarkers, and Internet of Things. We mainly focused on freely available, functional resources like data repositories, prediction models, standalone software, mobile apps, and web services. In order to provide service to the community, we developed a health portal that maintain links related to healthcare http://webs.iiitd.edu.in/.This article is categorized under: Application Areas > Health Care

The Rare Knowledge Mining Methodological Framework for the Development of Practice Guidelines and Knowledge Translation Tools for Rare Diseases.

Rare diseases bring on a heavy health, social and economic burden that impacts patients' lives and puts pressure on the healthcare system. Furthermore, they are often associated with limited published studies to inform multidisciplinary clinical practice thus limiting evidence-based practice. Moreover, the development of knowledge translation products including clinical care guidelines are often very challenging based on the current available methodological frameworks relying mostly on critical appraisal of the published research evidence where randomized clinical trial design is considered as the gold standard. To overcome this barrier, we proposed the Rare Knowledge Mining Methodological Framework (RKMMF). The RKMMF is one possible answer to improve the development of knowledge translation products for rare diseases. This framework includes other sources of evidence including registry information and qualitative studies and the involvement of expert patients. This article documents the RKMMF structure and its application is exemplified through knowledge translation products developed for a neuromuscular population.

Strategic-level performance enhancement of a 660 MWe supercritical power plant and emissions reduction by AI approach

Power plant heat rate is a plant level performance parameter that indicates the economy of power production, equipment’s safety, and availability. In this paper, seven operating parameters, including the performance indices of integrated energy devices and the environmental conditions are incorporated for modeling the power plant heat rate by Artificial Neural Network (ANN), Support Vector Machine (SVM), and automated machine learning (AutoML) approach. The parametric significance order is determined by ANN and SVM-based Monte Carlo analytics and other machine learning-driven algorithms. Subsequently, the best-performing model is selected based on the external validation test and deployed for knowledge mining purposes. The improvement in the power plant heat rate by the parametric adjustment is achieved and subsequently, up to 3.12 percentage point (pp) increase in the thermal efficiency of the power plant is confirmed.Moreover, the fuel savings corresponding to the improved power plant heat rate are also calculated at three power generation modes. Their equivalence to an annual reduction in emissions is quantified. It is estimated that the accumulated reduction in CO2, SO2, CH4, N2O, and Hg emissions, i.e., 288.2 kilo tons / year (kt/y), can be achieved under 3.15% improvement in the power plant heat rate, corresponding to 75% power generation mode.

Classification of Problem and Solution Strings in Scientific Texts: Evaluation of the Effectiveness of Machine Learning Classifiers and Deep Neural Networks

One of the central aspects of science is systematic problem-solving. Therefore, problem and solution statements are an integral component of the scientific discourse. The scientific analysis would be more successful if the problem–solution claims in scientific texts were automatically classified. It would help in knowledge mining, idea generation, and information classification from scientific texts. It would also help to compare scientific papers and automatically generate review articles in a given field. However, computational research on problem–solution patterns has been scarce. The linguistic analysis, instructional-design research, theory, and empirical methods have not paid enough attention to the study of problem–solution patterns. This paper tries to solve this issue by applying the computational techniques of machine learning classifiers and neural networks to a set of features to intelligently classify a problem phrase from a non-problem phrase and a solution phrase from a non-solution phrase. Our analysis shows that deep learning networks outperform machine learning classifiers. Our best model was able to classify a problem phrase from a non-problem phrase with an accuracy of 90.0% and a solution phrase from a non-solution phrase with an accuracy of 86.0%.

Density-Based Dynamically Self-Parameterized Clustering for Material Inspection

Abstract The ultrasonic based nondestructive testing (NDT) is the common technique used to perform material testing using ultrasonic signals. These signals are difficult to interpret and the examiner has to focus on every sampling signal to observe the changes in characteristics of signals. The core target points of the proposed work are used to identify the size and position of the defects as fast as possible. For that, an unsupervised machine learning approach is proposed to analyze defects such as shrinkage, porosity, crack, discontinuity, lack of fusion, lack of penetration and overlap. This would be helpful in the domain of material science and knowledge mining to study the structural integrity of metals during the manufacturing process and can be applied in automobile industries to increase the quality of manufactured parts. The proposed work incorporates a novel Density-Based Dynamically Self-Parameterized Clustering for Material Inspection (DBDSPCMI) method to effectively predict and identify the defect size and its position. The proposed method proves to be effective with an accuracy of 97.04% in measuring defect size and 95% in identifying defect position.

On assigning probabilities to new hypotheses

• Knowledge mining on varying sets of compound hypotheses is inspected. • Bayesian learning is equipped with technique allowing to extend the set of hypotheses. • The technique copes with learning with less data than the amount of parameters. • The use of data forecasters as a knowledge source in parameter estimation is refined. • A rich set of interesting related research and application problems is presented. The paper proposes the way how to assign a proper prior probability to a new, generally compound, hypothesis. To this purpose, it uses the minimum relative-entropy principle and a forecaster-based knowledge transfer. Methodologically, it opens a way towards enriching the standard Bayesian framework by the possibility to extend the set of models during learning without the need to restart. The presented use scenarios concern: (a) creating new hypotheses, (b) learning problems with an insufficient amount of data, and (c) sequential Monte Carlo estimation. They indicate a strong application potential of the proposed technique. Related interesting open research problems are listed.

IMPLEMENTATION OF DYNAMIC AND FAST MINING ALGORITHMS ON INCREMENTAL DATASETS TO DISCOVER QUALITATIVE RULES

Association Rule Mining is an important field in knowledge mining that allows the rules of association needed for decision making. Frequent mining of objects presents a difficulty to huge datasets. As the dataset gets bigger and more time and burden to uncover the rules. In this paper, overhead and time-consuming overhead reduction techniques with an IPOC (Incremental Pre-ordered code) tree structure were examined. For the frequent usage of database mining items, those techniques require highly qualified data structures. FIN (Frequent itemset-Nodeset) employs a node-set, a unique and new data structure to extract frequently used Items and an IPOC tree to store frequent data progressively. Different methods have been modified to analyze and assess time and memory use in different data sets. The strategies suggested and executed shows increased performance when producing rules, using time and efficiency.

A Study on Knowledge Mining Methods based on Data Warehouse

A Study on Knowledge Mining Methods based on Data Warehouse

SARS-CoV-2 genome datasets analytics for informed infectious disease surveillance

Background: The COVID-19 pandemic has ravaged economies, health systems, and lives globally. Concerns surrounding near total economic collapse, loss of livelihood and emotional complications ensuing from lockdowns and commercial inactivity, resulted in governments loosening economic restrictions. These concerns were further exacerbated by the absence of vaccines and drugs to combat the disease, with the fear that the next wave of the pandemic would be more fatal. Consequently, integrating disease surveillance mechanism into public healthcare systems is gaining traction, to reduce the spread of community and cross-border infections and offer informed medical decisions. Methods: Publicly available datasets of coronavirus cases around the globe deposited between December, 2019 and March 15, 2021 were retrieved from GISAID EpiFluTM and processed. Also retrieved from GISAID were data on the different SARS-CoV-2 variant types since inception of the pandemic. Results: Epidemiological analysis offered interesting statistics for understanding the demography of SARS-CoV-2 and helped the elucidation of local and foreign transmission through a history of contact travels. Results of genome pattern visualization and cognitive knowledge mining revealed the emergence of high intra-country viral sub-strains with localized transmission routes traceable to immediate countries, for enhanced contact tracing protocol. Variant surveillance analysis indicates increased need for continuous monitoring of SARS-CoV-2 variants. A collaborative Internet of Health Things (IoHT) framework was finally proposed to impact the public health system, for robust and intelligent support for modelling, characterizing, diagnosing and real-time contact tracing of infectious diseases. Conclusions: Localizing healthcare disease surveillance is crucial in emerging disease situations and will support real-time/updated disease case definitions for suspected and probable cases. The IoHT framework proposed in this paper will assist early syndromic assessments of emerging infectious diseases and support healthcare/medical countermeasures as well as useful strategies for making informed policy decisions to drive a cost effective, smart healthcare system.

Organizing knowledge to enable faster data interpretation in COVID-19 research

Enormous volumes of COVID-19 research data have been published and this continues to increase daily. This creates challenges for researchers to interpret, prioritize and summarize their own findings in the context of published literature, clinical trials, and a multitude of databases. Overcoming the data interpretation bottleneck is vital to help researchers to be more efficient in their quest to identify COVID-19 risk factors, potential treatments, drug side-effects, and much more. As a proof of concept, we have organized and integrated a range of COVID-19 and human biomedical data and literature into a knowledge graph (KG). Here we present the datasets we have integrated so far and the content of the KG which consists of 674,969 biological concepts and over 1.6 million relationships between them. The COVID-19 KG is available via KnetMiner, an interactive online platform for gene discovery and knowledge mining, or via RDF and Neo4j graph formats which can be searched programmatically through SPARQL and Cypher endpoints. KnetMiner is a road mapped ELIXIR UK service. We hope this integrated resource will enable faster data interpretation and discovery of linkages between genes, drugs, diseases and many more types of information relating to COVID-19.

An intelligent approach for mining knowledge graphs of online news

Automatic extraction of background knowledge of news articles, published over the web is a complex task and less explored by knowledge-mining researchers. Suppose there is no source of background or related information available on editorial, in this case, the reader needs to search it, which is a time-consuming process of manually going over possibly an in-definite number of knowledge sources to check its authenticity. This paper aims to resolve this issue of fetching associated information regarding any text by identifying the entities in text and fetching the related entities. This work has proposed a model that can automate this process by generating a semantic network to build a Knowledge Graph on any chunk of textual content. It identifies the entities and their corresponding relations and further expands them to extract related entities from non-trivial knowledge sources. Extracted entities and relations are further converted into a node(s) and edge(s) in an environment where knowledge is represented in a graph format to generate a Knowledge Graph. The mining of such a Knowledge Graph is a complex task by acquiring and integrating different information from heterogeneous sources. The resulting Knowledge Graph would be significant to be used as a knowledge base for future news.

Agile Innovation Process Model Based on Computer-Aided Patent Knowledge Mining and Functional Analogy

Computer-Aided Design and Applications is an international journal on the applications of CAD and CAM. It publishes papers in the general domain of CAD plus in emerging fields like bio-CAD, nano-CAD, soft-CAD, garment-CAD, PLM, PDM, CAD data mining, CAD and the internet, CAD education, genetic algorithms and CAD engines. The journal is aimed at all developers and users of CAD technology to ptovide CAD solutions for various stages of design and manufacturing. The journal publishes all about Computer-Aided Design and Computer-Aided technologies.

On-site text classification and knowledge mining for large-scale projects construction by integrated intelligent approach

A large-scale project produces a lot of text data during construction commonly achieved as various management reports. Having the right information at the right time can help the project team understand the project status and manage the construction process more efficiently. However, text information is presented in unstructured or semi-structured formats. Extracting useful information from such a large text warehouse is a challenge. A manual process is costly and often times cannot deliver the right information to the right person at the right time. This research proposes an integrated intelligent approach based on natural language processing technology (NLP), which mainly involves three stages. First, a text classification model based on Convolution Neural Network (CNN) is developed to classify the construction on-site reports by analyzing and extracting report text features. At the second stage, the classified construction report texts are analyzed with improved frequency-inverse document frequency (TF-IDF) by mutual information to identify and mine construction knowledge. At the third stage, a relation network based on the co-occurrence matrix of the knowledge is presented for visualization and better understanding of the construction on-site information. Actual construction reports are used to verify the feasibility of this approach. The study provides a new approach for handling construction on-site text data which can lead to enhancing management efficiency and practical knowledge discovery for project management.

Implementation of Data Mining in Shopping Cart Analysis using the Apriori Algorithm

Market basket analysis is one of the techniques of knowledge mining used in a broad dataset or database to find a collection of items that are interwoven. Generally used in a sale, the most relevant shopping cart data is used. This methodology has been widely applied in different multinational or foreign industries and is very useful in consumer buying preferences. Technology advances change business trends dramatically, shifting customer demands require increased surgical accuracy of business. In this research, the writer wants to analyze the shopping cart using apriori algorithm, with a dataset from the Kaggle web. Using anaconda software features with the Python programming language is expected to create knowledge overwriting consumer buying patterns. In conclusion, this pattern can be used to support industry in managing its company activities.

Graph-based managing and mining of processes and data in the domain of intellectual property

Digitalization of knowledge work in communication-intensive domains such as intellectual property protection poses great challenges but also opportunities to improve today’s working environments. The legal domain is strongly characterized by knowledge work, whereby, despite a common legal framework, creativity of individual experts is decisive. This knowledge-intensive work deals with a great amount of data objects, not only as a working basis, but also as a result. While experts heavily follow individual working styles, they still rely on a vast amount of administrative tasks, which are carried out by the supporting staff. These tasks are expected to be performed regularly, reliably and without errors, despite necessary adjustments to the current case and the changing legal framework. Today, knowledge work and administrative tasks are typically supported by different tools that are hardly integrated. Therefore, the tracing of continuous work processes based on exchanged data objects is a great challenge. This traceability is crucial, not only for legal security reasons, but also to enable mining and learning of applicable knowledge about processes. In this paper, we propose a bottom-up approach, which applies a continuously evolving graph of integrated data objects and tasks to model and store static and dynamic aspects of administrative as well as knowledge work, and test the approach in a real-world setting in the domain of intellectual property. We further present initial results of a novel dependency-based mining approach to learn data-dependent task sequences in the graph-based model and discuss several methods for enabling privacy-preserving sharing and mining.

A hybrid computational framework for intelligent inter-continent SARS-CoV-2 sub-strains characterization and prediction

Whereas accelerated attention beclouded early stages of the coronavirus spread, knowledge of actual pathogenicity and origin of possible sub-strains remained unclear. By harvesting the Global initiative on Sharing All Influenza Data (GISAID) database (https://www.gisaid.org/), between December 2019 and January 15, 2021, a total of 8864 human SARS-CoV-2 complete genome sequences processed by gender, across 6 continents (88 countries) of the world, Antarctica exempt, were analyzed. We hypothesized that data speak for itself and can discern true and explainable patterns of the disease. Identical genome diversity and pattern correlates analysis performed using a hybrid of biotechnology and machine learning methods corroborate the emergence of inter- and intra- SARS-CoV-2 sub-strains transmission and sustain an increase in sub-strains within the various continents, with nucleotide mutations dynamically varying between individuals in close association with the virus as it adapts to its host/environment. Interestingly, some viral sub-strain patterns progressively transformed into new sub-strain clusters indicating varying amino acid, and strong nucleotide association derived from same lineage. A novel cognitive approach to knowledge mining helped the discovery of transmission routes and seamless contact tracing protocol. Our classification results were better than state-of-the-art methods, indicating a more robust system for predicting emerging or new viral sub-strain(s). The results therefore offer explanations for the growing concerns about the virus and its next wave(s). A future direction of this work is a defuzzification of confusable pattern clusters for precise intra-country SARS-CoV-2 sub-strains analytics.