Implementation Of Knowledge Base Research Articles

Application of FCM Clustering Algorithm in Digital Library Management System

Traditional library systems are gradually being replaced by digital libraries. Digital libraries are developing from simple database-based storage and retrieval to knowledge-based implementation. The fuzzy C-means (FCM) clustering algorithm is an example of data collection and data processing technology. It evaluates and draws conclusions based on mathematics, large data, and other technologies. In order to better improve the digital library management system, this paper applied FCM clustering algorithm to the digital library management system. Based on the in-depth study of the FCM clustering algorithm, this paper built a digital library management system. The clustering algorithm was used to cluster library borrowing records and reader information. It provided technical support and suggestions on library collection construction and book purchase and promoted book management to form a good spitting cycle. The experimental results extracted during the evaluation phase demonstrated that the overall error rate of the suggested FCM clustering algorithm for information clustering is 3.66%, which is better than the existing comparative models. This shows that applying the FCM clustering algorithm to a digital library management system has some practical significance.

Knowledge-based Implementation of Deep Reinforcement Learning Agents in Assembly

Robotic systems based on Deep Reinforcement Learning have shown great potential to enable assembly systems with higher flexibility and robustness. This paper presents a concept of a Case-Based Reasoning system to automate the implementation process, based on the assumption that similar assembly tasks have similar solutions as used as heuristics in the current manual procedure. For retrieving similar cases a digital description of the assembly task and a method to measure the similarity is introduced. The retrieved cases are then used to warmstart a Bayesian Hyperparameter Optimization. The approach is evaluated on two simulated robot task.

Multi-objective framework for cost-effective OTN switch placement using NSGA-II with embedded domain knowledge

The continuous growth of traffic coming from a plethora of bandwidth-hungry applications will drive network operators to further pursue strategies to cost-efficiently plan and dimension their transport networks. In view of this trend, this paper presents an evolutionary multi-objective design framework for routing a set of services in an optical transport network such that the key resources impacting capital expenditures (CapEx) – line interfaces and optical transport network (OTN) switches – are minimized. Particularly, the multi-objective problem is customized to select the most cost-effective network nodes to place OTN switches, while at the same time keeping the number of line interfaces required to a minimum. To solve the multi-objective design problem, different strategies were considered to produce the Pareto front of non-dominated solutions, using the Non-dominated Sorting Genetic Algorithm (NSGA-II). These strategies differ on how mutation and crossover solutions are generated: randomly or exploiting prior knowledge. The solution quality obtained with both strategies after a fixed number of generations is compared. The results indicate that embedding expert knowledge within the genetic algorithm leads to better convergence results. Moreover, the knowledge-based implementation of the genetic algorithm presents on average a 59% increase in the hyper volume rate when compared to the purely random evolutionary algorithm for the same number of generations.

An Adaptive Neuro-Fuzzy Propagation Model for LoRaWAN

This article proposes an adaptive-network-based fuzzy inference system (ANFIS) model for accurate estimation of signal propagation using LoRaWAN. By using ANFIS, the basic knowledge of propagation is embedded into the proposed model. This reduces the training complexity of artificial neural network (ANN)-based models. Therefore, the size of the training dataset is reduced by 70% compared to an ANN model. The proposed model consists of an efficient clustering method to identify the optimum number of the fuzzy nodes to avoid overfitting, and a hybrid training algorithm to train and optimize the ANFIS parameters. Finally, the proposed model is benchmarked with extensive practical data, where superior accuracy is achieved compared to deterministic models, and better generalization is attained compared to ANN models. The proposed model outperforms the nondeterministic models in terms of accuracy, has the flexibility to account for new modeling parameters, is easier to use as it does not require a model for propagation environment, is resistant to data collection inaccuracies and uncertain environmental information, has excellent generalization capability, and features a knowledge-based implementation that alleviates the training process. This work will facilitate network planning and propagation prediction in complex scenarios.

PP165 Content Instead Of Orders: Experiences Of Launching A Knowledge Base

IntroductionIn Hungary, the procedure for health technology assessment of innovative pharmaceutical products allows 13 assessors 43 calendar days to evaluate reimbursement submissions. These short timelines have created a need for smart capacity building, namely, streamlining the scientific evaluation process while making sure that the quality of the critical appraisals remain high. The objective of this study was to present and evaluate the implementation of an online knowledge base to distill community knowledge, and also for management purposes.MethodsThe scope and the content-, functional-, and technical specification was developed, and information technology security requirements were identified during the pre-implementation phase. An existing platform was chosen for adaptation, ensuring that descriptive follow-up data is available on uptake for monitoring purposes. Both the adaptation and maintenance were carried out internally by the Department of Health Technology Assessment at the National Institute of Pharmacy and Nutrition.ResultsThe key requirements identified when developing the specification were searchability, low maintenance need, low operating costs and attractivity for users. An already existing open-source, flat file content management system was chosen for adaptation. In terms of content, a health technology assessment handbook, process documentation, a news bulletin section was created, and corporate identity elements were added. Since the start of the service in September 2018, the number of total daily page downloads to the knowledge base varied between four and 1,193 (average 205 per day), with the assessment handbook topping the overall page visit statistics.ConclusionsThe implementation of this knowledge base enables the Department of Technology Assessment to rely more on the formalized community knowledge when carrying out critical appraisal, while enabling better knowledge and quality management. Uptake remains an issue on the long run, indicating a need for continuous content development.

Sequence-based analysis and prediction of lantibiotics: A machine learning approach

Lantibiotics, an important group of ribosomally synthesized peptides, represent an important arsenal of novel promising antimicrobials showing high potency in fighting against the prevalence of antibiotic resistance among microbial pathogens. However, due to the lack of high throughput strategies for the isolation and identification of these compounds, our information regarding their structure and especially sequence-based properties is far from complete. Therefore, in the present study, a comprehensive sequence-based analysis of these peptides was performed with the help of machine learning approach together with a feature selection technique. Meanwhile, an attempt to develop an accurate computational model for prediction of lantibiotics was made via constructing two datasets of 280 and 190 lantibiotic and non-lantibiotic antimicrobial peptide sequences, respectively. Based on the conducted approach and as a result of our search for a subset of relevant features of lantibiotics, particular types of sequenced-based features were observed to be preferred in lantibiotics, the knowledge-based implementation of which can be used as strategies for lantibiotic bioengineering purposes. Moreover, a SMO-based classifier was developed for the prediction of lantibiotics with the accuracy and specificity values of 88.5% and 94%, respectively which shows the great potential of the developed algorithm for the prediction of lantibiotcs. Conclusively, the accurate predictor algorithm as well as the identified sequence-based distinctiveness properties of lantibiotics can give valuable information in both the fields of lantibiotic discovery and bioengineering.

Oracle Database 10g: a platform for BLAST search and Regular Expression pattern matching in life sciences

As database management systems expand their array of analytical functionality, they become powerful research engines for biomedical data analysis and drug discovery. Databases can hold most of the data types commonly required in life sciences and consequently can be used as flexible platforms for the implementation of knowledgebases. Performing data analysis in the database simplifies data management by minimizing the movement of data from disks to memory, allowing pre-filtering and post-processing of datasets, and enabling data to remain in a secure, highly available environment. This article describes the Oracle Database 10g implementation of BLAST and Regular Expression Searches and provides case studies of their usage in bioinformatics. http://www.oracle.com/technology/software/index.html

An automated modelling and reformulation system for design optimization

Many optimization problems in engineering rely on the use of computer algorithms for solving nonlinear programming (NLP), mixed-integer linear programming (MILP) and mixed-integer nonlinear programming (MINLP) models. Although a number of powerful optimization packages are currently available, it is still far from trivial for design engineers to properly formulate optimization models. Since a qualitative knowledge base does exist for expert modelling of optimization problems, there is an incentive to develop computer systems that can encode this knowledge. In this work we develop some of this qualitative knowledge and describe a knowledge-based implementation for automatic model reformulation. The resulting program, REFORM, symbolically reformulates nonlinear and mixed-integer models to increase the robustness and efficiency of the respective algorithms. Models specified in GAMS input files are automatically preprocessed by REFORM to generate a new, well-formulated model. Moreover, its capabilities include constraint reformulation to induce linearity and convexity, tightening of bounds, problem scaling and fixing binary (0–1) variables a priori. While some of these represent common knowledge in the area of optimization, the tight bounding procedure is a novel method that exploits monotonicity in the linear and nonlinear equations, and has applications for fixing binary variables. Computational results are presented to illustrate how the implementation of qualitative knowledge-based problem reformulation can improve the solution of poorly developed models.

Context-based automated detection of epileptogenic sharp transients in the EEG: elimination of false positives

This paper describes a knowledge-based system for the elimination of false positives in the automated detection of epileptogenic sharp transients in the EEG. The system makes comprehensive use of spatial and temporal context information available on 16 channels of EEG, EKG, EMG, and EOG. A knowledge-based implementation is used because of the ease with which it allows the contextual rules to be expressed and refined. The resulting system is shown to be capable of rejecting a wide variety of artifacts commonly found in EEG recordings, artifacts that cause numerous false positive detections in systems making less comprehensive use of context.

Adaptive interface design: a symmetric model and a knowledge-based implementation

Adaptation in the user-system interface can be designed to benefit either the system or the user. This paper presents a general model to portray that symmetry and to describe the essential components of the user-oriented version of an adaptive interface system. A function-based interpretation of the process is also characterized. Finally, the types of knowledge bases upon which the adaptation is dependent are described with reference to a prototype system being implemented. Especially those users who have limited understanding of computer technology, such as the numerous workers in offices, are expected to be future beneficiaries of adaptive interface research.