Machine Learning

Machine Learning can be defined in various ways related to a scientific domain concerned with the design and development of theoretical and implementation tools that allow building systems with some Human Like intelligent behavior. Machine learning addresses more specifically the ability to improve automatically through experience

Chatbot de Suporte para Plataforma de Marketing Multicanal

E-goi is an organization which provides automated multichannel marketing possibilities. Given its system’s complexity, it requires a not so smooth learning curve, which means that sometimes costumers incur upon some difficulties which directs them towards appropriate Costumer Support resources. With an increase in the number of users, these Costumer Support requests are somewhat frequent and demand an increase in availability in Costumer Support channels which become inundated with simple, easily-resolvable requests. The organization idealized the possibility of automating significant portion of costumer generated tickets with the possibility of scaling to deal with other types of operations. This thesis aims to present a long-term solution to that request with the development of a chatbot system, fully integrated with the existing enterprise modules and data sources. In order to accomplish this, prototypes using several Chatbot management and Natural Language Processing frameworks were developed. Afterwards, their advantages and disadvantages were pondered, followed by the implementation of its accompanying system and testing of developed software and Natural Language Processing results. Although the developed overarching system achieved its designed functionalities, the master’s thesis could not offer a viable solution for the problem at hand given that the available data could not provide an intent mining model usable in a real-world context.A E-goi é uma organização que disponibiliza soluções de marketing digital automatizadas e multicanal. Dada a complexidade do seu Sistema, que requer uma curva de aprendizagem não muito suave, o que significa que os seus utilizadores por vezes têm dificuldades que os levam a recorrer aos canais de Apoio ao Cliente. Com um aumento de utilizadores, estes pedidos de Apoio ao Cliente tornam-se frequentes e requerem um aumento da disponibilidade nos canais apropriados que ficam inundados de pedidos simples e de fácil resolução. A organização idealizou a possibilidade de automatizar uma porção significativa de tais pedidos, podendo escalar para outro tipo de operações. Este trabalho de mestrado visa apresentar uma proposta de solução a longo prazo para este problema. Pretende-se o desenvolvimento de um sistema de chatbots, completamente integrado com o sistema existente da empresa e variadas fontes de dados. Para este efeito, foram desenvolvidos protótipos de várias frameworks para gestão de chatbots e de Natural Language Processing, ponderadas as suas vantagens e desvantagens, implementado o sistema englobante e realizados planos de testes ao software desenvolvido e aos resultados de Natural Language Processing. Apesar do sistema desenvolvido ter cumprido as funcionalidades pelas quais foi concebido, a tese de mestrado não foi capaz de obter uma solução viável para o problema dado que com os dados disponibilizados não foi possível produzir um modelo de deteção de intenções usável num contexto real

New Weighting Schemes for Document Ranking and Ranked Query Suggestion

Term weighting is a process of scoring and ranking a term’s relevance to a user’s information need or the importance of a term to a document. This thesis aims to investigate novel term weighting methods with applications in document representation for text classification, web document ranking, and ranked query suggestion. Firstly, this research proposes a new feature for document representation under the vector space model (VSM) framework, i.e., class specific document frequency (CSDF), which leads to a new term weighting scheme based on term frequency (TF) and the newly proposed feature. The experimental results show that the proposed methods, CSDF and TF-CSDF, improve the performance of document classification in comparison with other widely used VSM document representations. Secondly, a new ranking method called GCrank is proposed for re-ranking web documents returned from search engines using document classification scores. The experimental results show that the GCrank method can improve the performance of web returned document ranking in terms of several commonly used evaluation criteria. Finally, this research investigates several state-of-the-art ranked retrieval methods, adapts and combines them as well, leading to a new method called Tfjac for ranked query suggestion, which is based on the combination between TF-IDF and Jaccard coefficient methods. The experimental results show that Tfjac is the best method for query suggestion among the methods evaluated. It outperforms the most popularly used TF-IDF method in terms of increasing the number of highly relevant query suggestions

Advances in Condition Monitoring, Optimization and Control for Complex Industrial Processes

The book documents 25 papers collected from the Special Issue “Advances in Condition Monitoring, Optimization and Control for Complex Industrial Processes”, highlighting recent research trends in complex industrial processes. The book aims to stimulate the research field and be of benefit to readers from both academic institutes and industrial sectors

Advances in Public Transport Platform for the Development of Sustainability Cities

Modern societies demand high and varied mobility, which in turn requires a complex transport system adapted to social needs that guarantees the movement of people and goods in an economically efficient and safe way, but all are subject to a new environmental rationality and the new logic of the paradigm of sustainability. From this perspective, an efficient and flexible transport system that provides intelligent and sustainable mobility patterns is essential to our economy and our quality of life. The current transport system poses growing and significant challenges for the environment, human health, and sustainability, while current mobility schemes have focused much more on the private vehicle that has conditioned both the lifestyles of citizens and cities, as well as urban and territorial sustainability. Transport has a very considerable weight in the framework of sustainable development due to environmental pressures, associated social and economic effects, and interrelations with other sectors. The continuous growth that this sector has experienced over the last few years and its foreseeable increase, even considering the change in trends due to the current situation of generalized crisis, make the challenge of sustainable transport a strategic priority at local, national, European, and global levels. This Special Issue will pay attention to all those research approaches focused on the relationship between evolution in the area of transport with a high incidence in the environment from the perspective of efficiency

Role of economic, and social parameters affecting life satisfaction and happiness during pre and post Covid era: a study with Marx’s perspective

A cognitive, objective, and subjective evaluation of the judgment of one’s perspective of looking at life defines as life satisfaction and happiness. There is a strong association between life satisfaction, joy, and external parameters, including environmental and socioeconomic factors and green innovation technologies. Marx’s theory on life satisfaction provides an exciting insight and defines that economic resources are necessary to live comfortably. The core objective of this paper is to examine the effects of contributing parameters concerning life satisfaction and happiness (LSH) in China from 2005 to 2020. For this purpose, data collected for the dependent variable collect World Happiness Index and World Bank official website. Nine independent variables related to LSH discuss freedom to make life choices (FMLC); GDP growth; Social contribution (SC); Employment rate (ER); Social support (SS); Innovation and development (ID); Life expectancy (LE); Coverage of social safety (CSS); High qualification (HQ). The maximum LSH value is 5.77, with a mean value of 5.13. The highest coefficient correlation value with LSH is CO2, with a positive correlation coefficient value of 0.80, followed by GDPG, with a negative coefficient value of 0.80. PC1 explains 76.74% of results, whereas MLR produces 0.91 R2 (p-value: 0.093, Residual standard error: 0.181). There is a need to understand correlates and determinants in further detail to set up a framework that enables policy-makers to incorporate well-being and life satisfaction measures in carving new public policies

Short Papers of the 11th Conference on Cloud Computing Conference, Big Data & Emerging Topics (JCC-BD&ET 2023)

Compilación de los short papers presentados en las 11vas Jornadas de Cloud Computing, Big Data & Emerging Topics (JCC-BD&ET2023), llevadas a cabo en modalidad híbrida durante junio de 2023 y organizadas por el Instituto de Investigación en Informática LIDI (III-LIDI) y la Secretaría de Posgrado de la Facultad de Informática de la UNLP en colaboración con universidades de Argentina y del exterior.Facultad de Informátic

Time And Space Efficient Techniques For Facial Recognition

In recent years, there has been an increasing interest in face recognition. As a result, many new facial recognition techniques have been introduced. Recent developments in the field of face recognition have led to an increase in the number of available face recognition commercial products. However, Face recognition techniques are currently constrained by three main factors: recognition accuracy, computational complexity, and storage requirements. The problem is that most of the current face recognition techniques succeed in improving one or two of these factors at the expense of the others. In this dissertation, four novel face recognition techniques that improve the storage and computational requirements of face recognition systems are presented and analyzed. Three of the four novel face recognition techniques to be introduced, namely, Quantized/truncated Transform Domain (QTD), Frequency Domain Thresholding and Quantization (FD-TQ), and Normalized Transform Domain (NTD). All the three techniques utilize the Two-dimensional Discrete Cosine Transform (DCT-II), which reduces the dimensionality of facial feature images, thereby reducing the computational complexity. The fourth novel face recognition technique is introduced, namely, the Normalized Histogram Intensity (NHI). It is based on utilizing the pixel intensity histogram of poses\u27 subimages, which reduces the computational complexity and the needed storage requirements. Various simulation experiments using MATLAB were conducted to test the proposed methods. For the purpose of benchmarking the performance of the proposed methods, the simulation experiments were performed using current state-of-the-art face recognition techniques, namely, Two Dimensional Principal Component Analysis (2DPCA), Two-Directional Two-Dimensional Principal Component Analysis ((2D)^2PCA), and Transform Domain Two Dimensional Principal Component Analysis (TD2DPCA). The experiments were applied to the ORL, Yale, and FERET databases. The experimental results for the proposed techniques confirm that the use of any of the four novel techniques examined in this study results in a significant reduction in computational complexity and storage requirements compared to the state-of-the-art techniques without sacrificing the recognition accuracy

EMPIRICAL RESEARCH ON HUMAN-AI COLLABORATIVE ARCHITECTURAL DESIGN PROCESS THROUGH A DEEP LEARNING APPROACH

北九州市立大学博士(工学)The purpose of this thesis is to explore how AI technologies intervene in the architectural design process and to discuss the importance and approaches that drive the paradigm shift towards human-AI collaboration in architectural design. The research is conducted from two perspectives: theoretical and practical. At the theoretical level, how AI technologies affect architectural design through technological evolution is analyzed, as well as the advantages, disadvantages and trends of different AI networks in sustainably analyzing and optimizing different kinds of architectural designs. Further, based on this, the methodology of how to develop a reflection on the nature of technology and data is discussed. At the practical level, AI methods that are inventive and capable of performance-based design are constructed and trained. And the basic process of human-AI collaborative architectural design is presented with an empirical study. The results of this thesis not only provide a theoretical reference and methodological basis for future research on human-AI collaborative architectural design at a broader and higher level but also attempt to explore new ideas and methods for the field of architectural design during the evolution of the old and new paradigms, ultimately realizing the purpose of sustainable development of the B&C industry.doctoral thesi