scholarly journals Development and Evaluation of an Intelligence and Learning System in Jurisprudence Text Mining in the Field of Competition Defense

2021 ◽  
Vol 11 (23) ◽  
pp. 11365
Author(s):  
Edna Dias Canedo ◽  
Valério Aymoré Martins ◽  
Vanessa Coelho Ribeiro ◽  
Vinicius Eloy dos Reis ◽  
Lucas Alexandre Carvalho Chaves ◽  
...  
Keyword(s):  
Public Administration ◽  
Learning System ◽  
Unstructured Data ◽  
Search System ◽  
Legal Decisions ◽  
Machine Learning Classification ◽  
Java Programming ◽  
Construction Techniques ◽  
Different Sources ◽  
Search Systems

A jurisprudence search system is a solution that makes available to its users a set of decisions made by public bodies on the recurring understanding as a way of understanding the law. In the similarity of legal decisions, jurisprudence seeks subsidies that provide stability, uniformity, and some predictability in the analysis of a case decided. This paper presents a proposed solution architecture for the jurisprudence search system of the Brazilian Administrative Council for Economic Defense (CADE), with a view to building and expanding the knowledge generated regarding the economic defense of competition to support the agency’s final procedural business activities. We conducted a literature review and a survey to investigate the characteristics and functionalities of the jurisprudence search systems used by Brazilian public administration agencies. Our findings revealed that the prevailing technologies of Brazilian agencies in developing jurisdictional search systems are Java programming language and Apache Solr as the main indexing engine. Around 87% of the jurisprudence search systems use machine learning classification. On the other hand, the systems do not use too many artificial intelligence and morphological construction techniques. No agency participating in the survey claimed to use ontology to treat structured and unstructured data from different sources and formats.

scholarly journals PRE-PROCESSED LATENT SEMANTIC ANALYSIS FOR AUTOMATIC ESSAY GRADING

Kursor ◽  
2017 ◽  
pp. 175 ◽  
Author(s):  
Ruth Ema Febrita ◽  
Wayan Firdaus Mahmudy
Keyword(s):  
Latent Semantic Analysis ◽  
Semantic Analysis ◽  
Learning System ◽  
Semantic Meaning ◽  
Lexical Database ◽  
Java Programming ◽  
E Learning ◽  
Manual Processing ◽  
Essay Grading ◽  
High Order Thinking

In education, essay is considered as the best tool to evaluate student’s high order thinking and understanding. In the other hand, manual processing and grading essay answers by a teacher need much time and tending to subjectivity grading. Meanwhile automatic essay grading in e-learning system find the difficulties in comparing model or key answer to student’s answer because student’s can answer the question with so various way. That means a right answer also can be so various, for they have same semantic meaning. This paper proposed automatic essay grading using Latent Semantic Analysis. But before the texts being scored, they will be pre-processed using stop words removal and synonyms checking. Calibration process implemented for dealing with the various possible right answer and help to simplify the term matrix. Implementation of this approach using Java Programming Language and WordNet as lexical database for searching the synonyms of every given words. The accuracy obtained by this method is 54.9289%.

A Machine Learning-Based System for Predicting Service-Level Failures in Supply Chains

2021 ◽  
Author(s):  
Gabrielle Gauthier Melançon ◽  
Philippe Grangier ◽  
Eric Prescott-Gagnon ◽  
Emmanuel Sabourin ◽  
Louis-Martin Rousseau
Keyword(s):  
Machine Learning ◽  
Supply Chain ◽  
Service Level ◽  
Learning System ◽  
Safety Stock ◽  
Service Levels ◽  
Sources Of Uncertainty ◽  
Supply Chain Efficiency ◽  
Percentage Points ◽  
Different Sources

Despite advanced supply chain planning and execution systems, manufacturers and distributors tend to observe service levels below their targets, owing to different sources of uncertainty and risks. These risks, such as drastic changes in demand, machine failures, or systems not properly configured, can lead to planning or execution issues in the supply chain. It is too expensive to have planners continually track all situations at a granular level to ensure that no deviations or configuration problems occur. We present a machine learning system that predicts service-level failures a few weeks in advance and alerts the planners. The system includes a user interface that explains the alerts and helps to identify failure fixes. We conducted this research in cooperation with Michelin. Through experiments carried out over the course of four phases, we confirmed that machine learning can help predict service-level failures. In our last experiment, planners were able to use these predictions to make adjustments on tires for which failures were predicted, resulting in an improvement in the service level of 10 percentage points. Additionally, the system enabled planners to identify recurrent issues in their supply chain, such as safety-stock computation problems, impacting the overall supply chain efficiency. The proposed system showcases the importance of reducing the silos in supply chain management.

scholarly journals On designing an oral history search system

2017 ◽  
Vol 73 (6) ◽  
pp. 1281-1298 ◽  
Author(s):  
Iain Walker ◽  
Martin Halvey
Keyword(s):  
Oral History ◽  
Qualitative Methodology ◽  
Geographic Region ◽  
Search System ◽  
Content Type ◽  
Framework Approach ◽  
Develop Software ◽  
User Groups ◽  
And Control ◽  
Search Systems

Purpose The purpose of this paper is to conduct a UK-based assessment of oral history technology and to identify the most important features that should be available in any oral history search system. Design/methodology/approach A co-design approach involving interviews and focus groups was adopted. The framework approach with elements of grounded theory was used to analyse transcripts to identify themes. Findings The analysis found that “ethics, consent and control”, “accessibility and engagement”, “publicity and awareness”, and “innovative technologies” were the four major themes identified. It was also established that there is limited understanding of oral history in the digital age, numerous interests, ethical concerns, lack of publicity and several key attributes that those designing an oral history search system or archive should strive for. The findings also identified that further exploration into sampling selected technologies on different user groups is required in order to develop software that would benefit the field. Research limitations/implications Participants were all recruited from one geographic region. The qualitative methodology utilised could be deemed to have elements of subjectivity. Practical implications This study has identified important features of any oral history search system and offered design recommendations for any developer of an oral history search systems. Originality/value This research has validated some previous findings for oral history search systems from more limited user studies. New issues for consideration including usability, software development and marketing have also been identified.

scholarly journals How searching under time pressure impacts clinical decision making

2020 ◽  
Vol 108 (4) ◽  
Author(s):  
Anton Van der Vegt ◽  
Guido Zuccon ◽  
Bevan Koopman ◽  
Anthony Deacon
Keyword(s):  
Literature Search ◽  
Clinical Decision Making ◽  
Time Pressure ◽  
Search Time ◽  
Clinical Decision ◽  
High Time ◽  
Clinical Decisions ◽  
Search System ◽  
The Impact ◽  
Search Systems

Objective: Clinicians encounter many questions during patient encounters that they cannot answer. While search systems (e.g., PubMed) can help clinicians find answers, clinicians are typically busy and report that they often do not have sufficient time to use such systems. The objective of this study was to assess the impact of time pressure on clinical decisions made with the use of a medical literature search system.Design: In stage 1, 109 final-year medical students and practicing clinicians were presented with 16 clinical questions that they had to answer using their own knowledge. In stage 2, the participants were provided with a search system, similar to PubMed, to help them to answer the same 16 questions, and time pressure was simulated by limiting the participant’s search time to 3, 6, or 9 minutes per question.Results: Under low time pressure, the correct answer rate significantly improved by 32% when the participants used the search system, whereas under high time pressure, this improvement was only 6%. Also, under high time pressure, participants reported significantly lower confidence in the answers, higher perception of task difficulty, and higher stress levels.Conclusions: For clinicians and health care organizations operating in increasingly time-pressured environments, literature search systems become less effective at supporting accurate clinical decisions. For medical search system developers, this study indicates that system designs that provide faster information retrieval and analysis, rather than traditional document search, may provide more effective alternatives.

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