The term Artificial Intelligence (AI) has recently become a hot topic, however, there are currently some misunderstandings about this term, for instance, it has been used as a synonym for Machine Learning (ML), however, ML is only a part of the whole AI. There are two main reasons to explain this, the first one is the fact that ML is the best known of all techniques, and the second one is because of the similarities between learning and "intelligent behaviour". Machine Learning is the ability to train a computer system to perform a task by giving as input either data or, alternatively, an equivalent source of information that allows it to automatically associate, segment and/or classify said data. In other words, the computer can learn something in a certain way and therefore, act with "intelligence". Moreover, ML is not a synonym for AI, because AI does not only focus on learning, AI has more factors to be able to operate autonomously in new and uncertain environments and adapt to them accordingly.

The unsupervised K- Nearest Neighbour (KNN) algorithm is perhaps the most straightforward machine learning algorithm. However, a simple algorithm does not mean that analyzing the results is equally simple. As per my research, there are not many documented approaches to analyzing the results of the KNN algorithm. In this article, I will show you how to analyze and understand the results of the unsupervised KNN algorithm. I will be using a dataset on cars.

It's simple math, really: In a family with eight children, it stands to reason, surely one of them must be queer. Bridgerton has defied other expectations of a Regency-era love story: It is set in an alternate universe where the upper class is fully integrated and race is not an issue. The show's first two seasons focus on interracial romances, and the second season at least obliquely references the history of British colonialism in India. There's one obvious candidate for such a storyline: On the show, Eloise is the most outspoken, most feminist Bridgerton sibling. She is not interested in becoming a debutante, delaying her appearance to pursue another year of studies. She often dismisses marriage, questioning why a husband and children are all that are waiting in store for women.

K-nearest neighbors (KNN) is a type of supervised learning machine learning algorithm and is used for both regression and classification tasks. KNN is used to make predictions on the test data set based on the characteristics of the current training data points. This is done by calculating the distance between the test data and training data, assuming that similar things exist within close proximity. The algorithm will have stored learned data, making it more effective at predicting and categorising new data points. When a new data point is inputted, the KNN algorithm will learn its characteristics/features.

K-Nearest Neighbor(KNN) algorithm is a poplar model and falls under the Supervised Learning and it can be used to solve both classification and regression problems. In this article, I would be giving you a detailed explanation and how this model works. K-Nearest Neighbor is one of the simplest Machine Learning algorithms based on Supervised Learning technique. KNN algorithm assumes the similarity between the new data and available data and put the new case into the category that is most similar to the available categories. The value of the K is very important.

Satellite imagery is now widely used in the defense sector for monitoring locations of interest. Although the increasing amount of data enables pattern identification and therefore prediction, carrying this task manually is hardly feasible. We hereby propose a cased-based reasoning approach for automatic prediction of rare events on strategic sites. This method allows direct incorporation of expert knowledge, and is adapted to irregular time series and small-size datasets. Experiments are carried out on two use-cases using real satellite images: the prediction of submarines arrivals and departures from a naval base, and the forecasting of imminent rocket launches on two space bases. The proposed method significantly outperforms a random selection of reference cases on these challenging applications, showing its strong potential. Keywords: Predictive analysis · Case-based reasoning · Earth observation · Submarine activity · Space launch.

We investigate case-based reasoning (CBR) problems where cases are represented by abstract factors and (positive or negative) outcomes, and an outcome for a new case, represented by abstract factors, needs to be established. To this end, we employ abstract argumentation (AA) and propose a novel methodology for CBR, called AA-CBR. The argumentative formulation naturally allows to characterise the computation of an outcome as a dialogical process between a proponent and an opponent, and can also be used to extract explanations for why an outcome for a new case is (not) computed.

The addition of a robot to a team can be difficult if the human teammates do not trust the robot. This can result in underutilization or disuse of the robot, even if the robot has skills or abilities that are necessary to achieve team goals or reduce risk. To help a robot integrate itself with a human team, we present an agent algorithm that allows a robot to estimate its trustworthiness and adapt its behavior accordingly. As behavior adaptation is performed, using case-based reasoning (CBR), information about the adaptation process is stored and used to improve the efficiency of future adaptations.

We present a case-based approach to character identification in natural language text in the context of our Voz system. Voz first extracts entities from the text, and for each one of them, computes a feature-vector using both linguistic information and external knowledge. We propose a new similarity measure called Continuous Jaccard that exploits those feature-vectors to compute the similarity between a given entity and those in the case-base, and thus determine which entities are characters or not. We evaluate our approach by comparing it with different similarity measures and feature sets. Results show an identification accuracy of up to 93.49%, significantly higher than recent related work.

This paper presents a case-based reasoning (CBR) application for discovering similar patients with non-specific musculoskeletal disorders (MSDs) and recommending treatment plans using previous experiences. From a medical perspective, MSD is a complex disorder as its cause is often bounded to a combination of physiological and psychological factors. Likewise, the features describing the condition and outcome measures vary throughout studies. However, healthcare professionals in the field work in an experience-based way, therefore we chose CBR as the core methodology for developing a decision support system for physiotherapists which would assist them in the process of their co-decision making and treatment planning. In this paper, we focus on case representation and similarity modeling for the non-specific MSD patient data as well as we conducted initial experiments on comparing patient profiles.