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

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
On rugged slopes overlooking the Douro River we find the Alto Douro Wine Region in Portugal, populated by plantations in schist lands of difficult access and mostly manual work. The combined features of this region are a source of motivation to explore remote sensing techniques associated with artificial intelligence. In this paper, a preliminary approach for terrace vineyards detection is presented. This is a key-enabling task towards the achievement of important goals such as multi-temporal crop evaluation and cultures characterization. The proposed methodology consists in the application of a deep learning model (U-net) to detect the terrace vineyards using satellite images dataset acquired with Google Earth Pro. The proposed methodology showed very promising detection capabilities.

Abstract

Abstract
Non-native plant species can have a negative impact in the ecosystems and in local economies when they spread uncontrollably. Monitoring tools can support their management and spread. In this paper, an exploratory approach is presented for pixelwise detection of Acacia dealbata from UAV-based imagery acquired from RGB and multispectral sensors. Four machine learning algorithms-k-nearest neighbors (KNN), random forest (RF), adaptive boosting (AdaBoost) and a linear kernel SVM (LSVM)-Are trained using four datasets (hue, saturation and value-HSV, multispectral-MSP, RGB and a combination of all features) and their classification performance is evaluated. RF classifier obtained the overall best performance, with an accuracy above 86% in all data combinations, with LSVM showing the poorer results. Obtained results are encouraging for monitoring invasive species and can serve as a base for future improvements to detect invasive species.