Abstract

Abstract
This paper reviewed machine learning algorit hms, particularly deep learning architectures applied to end-of-line testing systems in industrial environment. In industry, data is also produced when any product is being manufactured. All this information registered when manufacturing a specific product can be manipulated and interpreted using Machine Learning algorithms. Therefore, it is possible to draw conclusions from data and infer valuable results that can positively impact the future of the production line. The reviewed papers showed that machine learning algorithms play a crucial role in detecting, isolating, and preventing anomalies, helping operators make decisions, and allowing industries to save resources. © International Journal of Emerging Technology and Advanced Engineering.All right reserved.

Abstract
New challenges arise with the upsurge of a Big Data era. Huge volumes of data, from the most varied natures, gathered from different sources, collected in different timings, often with high associated uncertainty, make the decision-making process a harsher task. Current methods are not ready to deal with characteristics of the new problems. This paper proposes a novel data selection methodology that filters big volumes of data, so that only the most correlated information is used in the decision-making process in each given context. The proposed methodology uses a clustering algorithm, which creates sub-groups of data according to their correlation. These groups are then used to feed a forecasting process that uses the relevant data for each situation, while discarding data that is not expected to contribute to improving the forecasting results. In this way, a faster, less computationally demanding, and effective forecasting is enabled. A case study is presented, considering the application of the proposed methodology to the filtering of electricity market data used by forecasting approaches. Results show that the data selection increases the forecasting effectiveness of forecasting methods, as well as the computational efficiency of the forecasts, by using less yet more adequate data.

Abstract
Information systems can be useful tools to understand complex agroecological systems. Farmers and extensionists in the Global South may not have access to such information systems due limited resources, skills and opportunities. End-user development (EUD) has the potential to best suit local needs and conditions. This article summarises and furthers a research and development effort targeting communities as well as agroecological extension professionals and organisations in the Serra da Capivara Territory, Piaui, Brazil. In a retrospective ethnographic study we observed information abundance, topdown IS bias, informational competence and digital infrastructure limitations. A set of requirements was identified, with Portuguese syntax and semantics being crucial for an EUD solution. Based on these requirements a multiparadigm controlled natural language is specified and described as well as a prototype implementation and evaluation method. This language should provide a language that enables the end-user to develop IS suitable to their needs and conditions.

Abstract
Artificial intelligence is transforming the way chatbots are created and used. The recent boom of artificial intelligence development is creating a whole new generation of intelligent approaches that enable a more efficient and effective design of chatbots. On the other hand, the increasing need and interest from the industry in artificial intelligence based solutions, is guaranteeing the necessary investment and applicational know-how that is pushing such solutions to a new dimension. Some relevant examples are e-commerce, health or education, which is the main focus of this work. This paper studies and analyses the impact that artificial intelligence models and solutions is having on the design and development of chatbots, when compared to the previously used approaches. Some of the most relevant current and future challenges in this domain are highlighted, which include language learning, sentiment interpretation, integration with other services, or data security and privacy issues.

Abstract
Objectives: Healthcare professionals' high risk of infection and burnout in the first months of the COVID19 pandemic probably hindered their much-needed preparedness to respond. We aimed to inform how individual and institutional factors contributed for the preparedness to respond during the first months of a public health emergency. Study design: Cross-sectional study. Methods: We surveyed healthcare workers from a Local Health Unit in Portugal, which comprises primary health care centers and hospital services, including public health units and intensive care units, in the second and third months of the COVID-19 epidemic in Portugal. The 460 answers, completed by 252 participants (about 10% of the healthcare workers), were analyzed using descriptive statistics and multiple logistic regressions. We estimated adjusted odds ratios for the readiness and willingness to respond. Results: Readiness to respond was associated with the perception of adequate infrastructures (aOR = 4.04, P < 0.005), lack of access to personal protective equipment (aOR = 0.26, P < 0.05) and organization (aOR = 0.31, P < 0.05). The willingness to act was associated with the perception of not being able to make a difference (aOR = 0.05, P < 0.005), risk of work-related burnout (aOR = 21.21, P < 0.01) and experiencing colleagues or patients' deaths due to COVID-19 (aOR = 0.24, P < 0.05). Conclusions: Adequate organization, infrastructures, and access to personal protective equipment may be crucial for workers' preparedness in a new public health emergency, as well workers' understanding of their roles and expected impact. These factors, together with the risk of work-related burnout, shall be taken into account in the planning of the response of healthcare institutions in future public health emergencies.