Showing papers by "Henrique Vicente published in 2018"

A Deep-Big Data Approach to Health Care in the AI Age

Abstract: The intersection of these two trends is what we call The Issue and it is helping businesses in every industry to become more efficient and productive. One's aim is to have an insight into the development and maintenance of comprehensive and integrated health information systems that enable sound policy and effective health system management in order to improve health and health care. Undeniably, different sorts of technologies have been developed, each with their own advantages and disadvantages, which will be sorted out by attending at the impact that Artificial Intelligence and Decision Support Systems have to everyone in the healthcare sector engaged to quality-of-care, i.e., making sure that doctors, nurses, and staff have the training and tools they need to do their jobs.

A Deep Learning Approach to Case Based Reasoning to the Evaluation and Diagnosis of Cervical Carcinoma

Abstract: Deep Learning (DL) is a new area of Machine Learning research introduced with the objective of moving Machine Learning closer to one of its original goals, i.e., Artificial Intelligence (AI). DL breaks down tasks in ways that makes all kinds of machine assists seem possible, even likely. Better preventive healthcare, even better recommendations, are all here today or on the horizon. However, keeping up the pace of progress will require confronting currently AI’s serious limitations. The last but not the least, Cervical Carcinoma is actuality a critical public health problem. Although patients have a longer survival rate due to early diagnosis and more effective treatment, this disease is still the leading cause of cancer death among women. Therefore, the main objective of this article is to present a DL approach to Case Based Reasoning in order to evaluate and diagnose Cervical Carcinoma using Magnetic Resonance Imaging. It will be grounded on a dynamic virtual world of complex and interactive entities that compete against one another in which its aptitude is judged by a single criterion, the Quality of Information they carry and the system’s Degree of Confidence on such a measure, under a fixed symbolic structure.

A Data Mining Approach to Improve Inorganic Characterization of Amanita ponderosa Mushrooms.

Abstract: Amanita ponderosa are wild edible mushrooms that grow in some microclimates of Iberian Peninsula. Gastronomically this species is very relevant, due to not only the traditional consumption by the rural populations but also its commercial value in gourmet markets. Mineral characterisation of edible mushrooms is extremely important for certification and commercialization processes. In this study, we evaluate the inorganic composition of Amanita ponderosa fruiting bodies (Ca, K, Mg, Na, P, Ag, Al, Ba, Cd, Cr, Cu, Fe, Mn, Pb, and Zn) and their respective soil substrates from 24 different sampling sites of the southwest Iberian Peninsula (e.g., Alentejo, Andalusia, and Extremadura). Mineral composition revealed high content in macroelements, namely, potassium, phosphorus, and magnesium. Mushrooms showed presence of important trace elements and low contents of heavy metals within the limits of RDI. Bioconcentration was observed for some macro- and microelements, such as K, Cu, Zn, Mg, P, Ag, and Cd. A. ponderosa fruiting bodies showed different inorganic profiles according to their location and results pointed out that it is possible to generate an explanatory model of segmentation, performed with data based on the inorganic composition of mushrooms and soil mineral content, showing the possibility of relating these two types of data.

Evolutionary Computation on Road Safety

Abstract: This study examines the psychological research that focuses on road safety in Smart Cities as proposed by the Vulnerable Road Users (VRUs) sphere. It takes into account qualities such as VRUs’ personal information, their habits, environmental measurements and things data. With the goal of seeing VRUs as active and proactive actors with differentiated feelings and behaviours, we are committed to integrating the social factors that characterize each VRU into our social machinery. As a result, we will focus on the development of a VRU Social Machine to assess VRUs’ behaviour in order to improve road safety. The formal background will be to use Logic Programming to define its architecture based on a Deep Learning approach to Knowledge Representation and Reasoning, complemented with an Evolutionary approach to Computing.

Planning, Managing and Monitoring Technological Security Infrastructures

Abstract: Over the past few decades many different Information Technologies (IT) policies have been introduced, including COSO, ITIL, PMBook, CMM, ISO 2700x, Six Sigma, being COBIT IT (Control Objectives for IT) the framework that encompasses all IT and Information Systems (IS) governance activities at the organization’s level. As part of the applicability of quality services certification (ISO 9001) in all IT services of a public institution, it is presented a case study aimed at planning, managing and monitoring technological security infrastructures. It followed the guidelines for the ISO 2700x family, COBIT, ITIL and other standards and conducted a survey to complement the IT process’s objectives. With regard to an action-research methodology for problem-solving (i.e., a kind of attempt to improve or investigate practice) and according to the issue under analyze, the question is put into the terms, viz. “How can the ISO 2700x, COBIT, ITIL and other guidelines help with the planning, management and monitoring of technological security infrastructures and minimize the risk management of IT and IS?”. Indeed, it may be resolved that it is possible to achieve the goals of planning, managing and monitoring a technological security infrastructure. In the future, we will use Artificial Intelligence based approaches to problem solving such as Artificial Neural Networks and Cased Based Reasoning, to evaluate this issue.

A Many-Valued Empirical Machine for Thyroid Dysfunction Assessment

Abstract: Thyroid Dysfunction is a clinical condition that affects thyroid behaviour and is reported to be the most common in all endocrine disorders. It is a multiple factorial pathology condition due to the high incidence of hypothyroidism and hyperthyroidism, which is becoming a serious health problem requiring a detailed study for early diagnosis and monitoring. Understanding the prevalence and risk factors of thyroid disease can be very useful to identify patients for screening and/or follow-up and to minimize their collateral effects. Thus, this paper describes the development of a decision support system that aims to help physicians in the decision-making process regarding thyroid dysfunction assessment. The proposed problem-solving method is based on a symbolic/sub-symbolic line of logical formalisms that have been articulated as an Artificial Neural Network approach to data processing, complemented by an unusual approach to Knowledge Representation and Argumentation that takes into account the data elements entropic states. The model performs well in the thyroid dysfunction assessment with an accuracy ranging between 93.2% and 96.9%.

Kidney Care—A Personal Assistant Assessment

Abstract: A cognitive disability is a medical condition that, despite all technological progress, still does not have a cure, i.e., there are cases where the physician may use medication, but the only purpose is to decrease the progression of the disease, not its cure. This is the case in many situations, and in particular in kidney illnesses, which have a dominant impact on a person well being, i.e., the assistance to an individual to whom was diagnosed cognitive disabilities is essential, where the location of the individual is not decisive or important. Hence, the presence of a Personal Assistance Service can become a cornerstone in achieving independence and quality of life. Therefore, the objective of this work is to present an intelligent system aimed at an endless individuals monitoring and alerting system, based on a Logical Programming approach to Knowledge Representation and Reasoning, and centre on RapidMiner, a software platform that provides an integrated environment for machine learning, predictive analysis or application development and deployment. It undergoes a Case Based approach to computing that tracks patient’s performance, learn and deliver content when it is needed, and assures that patient’s key information is changed into the indispensable ongoing knowledge.

Predicative Vagueness in Lung Metastases in Soft Tissue Sarcoma Screening

Abstract: Soft Tissue Sarcomas (STSs) pose a potential risk for the development of lung metastases, which in turn results in a negative prognosis for patients. Presumptions about the occurrence of these abnormalities during STSs treatment would have countless implications for both patients and healthcare professionals as they could increase the efficacy of the treatment and improve overall survival. Prediction is based on a creative Logic Programming, Case Based Reasoning approach to problem solving, that is complemented with an unusual approach to Knowledge Representation and Reasoning, as it takes into consideration not only the data items entropic states but introduces the concept of Vague’s Predicate Extension.

A Case-Based Reasoning Approach to GBM Evolution

Abstract: GlioBastoma Multiforme (GBM) is an aggressive primary brain tumor characterized by a heterogeneous cell population that is genetically unstable and resistant to chemotherapy. Indeed, despite advances in medicine, patients diagnosed with GBM have a median survival of just one year. Magnetic Resonance Imaging (MRI) is the most widely used imaging technique for determining the location and size of brain tumors. Indisputably, this technique plays a major role in the diagnosis, treatment planning, and prognosis of GBM. Therefore, this study proposes a new Case Based Reasoning approach to problem solving that attempts to predict a patient’s GBM volume after five months of treatment based on features extracted from MR images and patient attributes such as age, gender, and type of treatment.

Towards Road Safety

Abstract: This meta-analytic study analyzes psychological research focusing on road safety as proposed by the Vulnerable Road Users (VRUs) approach, paying attention to attributes such as VRUs habits, environment and things. Aiming to consider VRUs as active and proactive actors, with differentiated feelings and behaviours, we were required to integrate in our study the social factors that characterize each VRU. In fact, many studies have shown how social factors may put at risk a human being, with special incidence in socioeconomic groups where lower social positions correlate with an increased exposure to danger. Therefore, diminishing social inequities by addressing the social factors that motivate them developed into an explicit goal to enhance road safety. Undeniably, one way to examine how such factors affect injury is to study them at different degrees. Hence, focusing on such attributes, which may be indicative of inequalities in social status, we are able to quantify their degree of disorder in terms of an entropic metric. The computational framework is to be understood under a mathematical logic approach to computing.

Quality Management in Training Companies

Abstract: This study was carried out in training companies and aims to evaluate customer satisfaction. It focusses at the Organizations’ Quality-of-Management (QoM) that is in itself a major competitive advantage to differentiate them. Indeed, the universe of discourse is set in order to consider not only the complex relationships among the entities that populate it, but also to take into account its inner structure, where incomplete, unknown or even self-contradictory information or knowledge are present. One’s goal is at the development of a comprehensive and integrated computational model to ensure the Organizations’ Performance and its QoM in order to fulfill customer’s requirements. It is based on a Logic Programming approach to Knowledge Representation and Reasoning and grounded on an Artificial Neural Networks approach to computing.

Awareness of School Learning Environments

Abstract: Now, and in the times that follow, student education should focus on developing inclusive skills such as problem-solving and decision-making, where the role of the learning environment plays a crucial part, i.e., it is a process where the screen of the universe of discourse is accomplished in order to consider not only the complex relationships that flow among the objects that populate it, but also its inner structure, co-existing incomplete/unknown or even self-contradictory information or knowledge. As a result, we will focus on the development of an Intelligent Social Machine to assess Learning Environments in high schools, based on factors like School and Disciplinary Climates as well as Parental Involvement. The formal background will be to use Logic Programming to define its architecture based on a Deep Learning-Big Data approach to Knowledge Representation and Reasoning, complemented by an Evolutionary approach to Computing grounded on Virtual Intellects.

Prediction of Neoadjuvant Chemotherapy Outcome in Breast Cancer Patients

Abstract: Breast Cancer is the most common invasive cancer in women worldwide. Indeed, it is imperative to investigate which factors influence the development of this disease in order to improve the efficiency of the treatment and to allow for a balanced follow-up. In fact, this article has in mind an original Case Based Reasoning (CBR) approach to problem solving, complemented with a novel approach to Knowledge Representation and Reasoning that takes into consideration the data items entropic states. It works towards cancer’s assessment after Neoadjuvant Chemotherapy in terms of its size, shape, and texture.