Carlos Francisco Moreno-García

Bio: Carlos Francisco Moreno-García is an academic researcher from Robert Gordon University. The author has contributed to research in topics: Computer science & Artificial intelligence. The author has an hindex of 9, co-authored 43 publications receiving 248 citations. Previous affiliations of Carlos Francisco Moreno-García include Rovira i Virgili University.

New trends on digitisation of complex engineering drawings.

Abstract: Engineering drawings are commonly used across different industries such as oil and gas, mechanical engineering and others. Digitising these drawings is becoming increasingly important. This is mainly due to the legacy of drawings and documents that may provide rich source of information for industries. Analysing these drawings often requires applying a set of digital image processing methods to detect and classify symbols and other components. Despite the recent significant advances in image processing, and in particular in deep neural networks, automatic analysis and processing of these engineering drawings is still far from being complete. This paper presents a general framework for complex engineering drawing digitisation. A thorough and critical review of relevant literature, methods and algorithms in machine learning and machine vision is presented. Real-life industrial scenario on how to contextualise the digitised information from specific type of these drawings, namely piping and instrumentation diagrams, is discussed in details. A discussion of how new trends on machine vision such as deep learning could be applied to this domain is presented with conclusions and suggestions for future research directions.

Effectiveness of social marketing strategies to reduce youth obesity in European school-based interventions: a systematic review and meta-analysis

Abstract: Context: The use of social marketing to modify lifestyle choices could be helpful in reducing youth obesity. Some or all of the 8 domains of the National Social Marketing Centre’s social marketing benchmark criteria (SMBC) are often used but not always defined in intervention studies. Objective: The aim of this review is to assess the effectiveness of European school-based interventions to prevent obesity relative to the inclusion of SMBC domains in the intervention. Data Sources: The PubMed, Cochrane, and ERIC databases were used. Study Selection: Nonrandomized and randomized controlled trials conducted from 1990 to April 2014 in participants aged 5 to 17 years were included. Data Extraction: After the study selection, the 8 domains of the SMBC were assessed in each included study. Results: Thirty-eight publications were included in the systematic review. For the meta-analysis, randomized controlled trials (RCTs) reporting body mass index or prevalence of overweight and obesity were considered. Eighteen RCTs with a total of 8681 participants included at least 5 SMBC. The meta-analysis showed a small standardized mean difference in body mass index of −0.25 (95%CI, −0.45 to −0.04) and a prevalence of overweight and obesity odds ratio of 0.72 (95%CI, 0.5–0.97). Conclusion: Current evidence indicates that the inclusion of at least 5 SMBC domains in school-based interventions could benefit efforts to prevent obesity in young people. PROSPERO registration number: CRD42014007297.

CDSMOTE: class decomposition and synthetic minority class oversampling technique for imbalanced-data classification

Abstract: Class-imbalanced datasets are common across several domains such as health, banking, security, and others. The dominance of majority class instances (negative class) often results in biased learning models, and therefore, classifying such datasets requires employing some methods to compact the problem. In this paper, we propose a new hybrid approach aiming at reducing the dominance of the majority class instances using class decomposition and increasing the minority class instances using an oversampling method. Unlike other undersampling methods, which suffer data loss, our method preserves the majority class instances, yet significantly reduces its dominance, resulting in a more balanced dataset and hence improving the results. A large-scale experiment using 60 public datasets was carried out to validate the proposed methods. The results across three standard evaluation metrics show the comparable and superior results with other common and state-of-the-art techniques.

A graph repository for learning error-tolerant graph matching.

Abstract: In the last years, efforts in the pattern recognition field have been especially focused on developing systems that use graph based representations. To that aim, some graph repositories have been presented to test graph-matching algorithms or to learn some parameters needed on such algorithms. The aim of these tests has always been to increase the recognition ratio in a classification framework. Nevertheless, some graph-matching applications are not solely intended for classification purposes, but to detect similarities between the local parts of the objects that they represent. Thus, current state of the art repositories provide insufficient information. We present a graph repository structure such that each register is not only composed of a graph and its class, but also of a pair of graphs and a ground-truth correspondence between them, as well as their class. This repository structure is useful to analyse and develop graph-matching algorithms and to learn their parameters in a broadly manner. We present seven different databases, which are publicly available, with these structure and present some quality measures experimented on them.

Using artificial intelligence methods for systematic review in health sciences: A systematic review

Abstract: The exponential increase in published articles makes a thorough and expedient review of literature increasingly challenging. This review delineated automated tools and platforms that employ artificial intelligence (AI) approaches and evaluated the reported benefits and challenges in using such methods. A search was conducted in 4 databases (Medline, Embase, CDSR, and Epistemonikos) up to April 2021 for systematic reviews and other related reviews implementing AI methods. To be included, the review must use any form of AI method, including machine learning, deep learning, neural network, or any other applications used to enable the full or semi‐autonomous performance of one or more stages in the development of evidence synthesis. Twelve reviews were included, using nine different tools to implement 15 different AI methods. Eleven methods were used in the screening stages of the review (73%). The rest were divided: two in data extraction (13%) and two in risk of bias assessment (13%). The ambiguous benefits of the data extractions, combined with the reported advantages from 10 reviews, indicating that AI platforms have taken hold with varying success in evidence synthesis. However, the results are qualified by the reliance on the self‐reporting of the review authors. Extensive human validation still appears required at this stage in implementing AI methods, though further evaluation is required to define the overall contribution of such platforms in enhancing efficiency and quality in evidence synthesis.

Machine learning

Abstract: Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

分群式取樣法於類別不平衡問題之研究;Clustering-Based Under-sampling in Class Imbalanced Data

Abstract: Abstract Class imbalance is often a problem in various real-world data sets, where one class (i.e. the minority class) contains a small number of data points and the other (i.e. the majority class) contains a large number of data points. It is notably difficult to develop an effective model using current data mining and machine learning algorithms without considering data preprocessing to balance the imbalanced data sets. Random undersampling and oversampling have been used in numerous studies to ensure that the different classes contain the same number of data points. A classifier ensemble (i.e. a structure containing several classifiers) can be trained on several different balanced data sets for later classification purposes. In this paper, we introduce two undersampling strategies in which a clustering technique is used during the data preprocessing step. Specifically, the number of clusters in the majority class is set to be equal to the number of data points in the minority class. The first strategy uses the cluster centers to represent the majority class, whereas the second strategy uses the nearest neighbors of the cluster centers. A further study was conducted to examine the effect on performance of the addition or deletion of 5 to 10 cluster centers in the majority class. The experimental results obtained using 44 small-scale and 2 large-scale data sets revealed that the clustering-based undersampling approach with the second strategy outperformed five state-of-the-art approaches. Specifically, this approach combined with a single multilayer perceptron classifier and C4.5 decision tree classifier ensembles delivered optimal performance over both small- and large-scale data sets.

A Short Survey of Recent Advances in Graph Matching

Abstract: Graph matching, which refers to a class of computational problems of finding an optimal correspondence between the vertices of graphs to minimize (maximize) their node and edge disagreements (affinities), is a fundamental problem in computer science and relates to many areas such as combinatorics, pattern recognition, multimedia and computer vision. Compared with the exact graph (sub)isomorphism often considered in a theoretical setting, inexact weighted graph matching receives more attentions due to its flexibility and practical utility. A short review of the recent research activity concerning (inexact) weighted graph matching is presented, detailing the methodologies, formulations, and algorithms. It highlights the methods under several key bullets, e.g. how many graphs are involved, how the affinity is modeled, how the problem order is explored, and how the matching procedure is conducted etc. Moreover, the research activity at the forefront of graph matching applications especially in computer vision, multimedia and machine learning is reported. The aim is to provide a systematic and compact framework regarding the recent development and the current state-of-the-arts in graph matching.

Celesstin: CAD conversion of mechanical drawings

Abstract: Celesstin, a system that converts mechanical engineering drawings into a format suitable for CAD, is described. Celesstin integrates several modules in a blackboard-based interpretation system. Once a drawing has been digitized, a first processing step separates the text and the dimensioning lines from the pure graphics part. Celesstin vectorizes the graphics part and assembles the resulting lines into blocks, the basic elements for the technical entities that it creates. The result is transferred to the CAD system. Celesstin tries to match the extracted entities with the corresponding models from the CAD library. It puts the remaining blocks and lines into different layers of the CAD description. The system is based on the assumption that even when the vectorized drawing is distorted, it can be correctly interpreted by using knowledge about the representation rules used in technical drawings and about the manufacturing technology associated with the represented objects. >