What is the "nature" of the cognitive processes and contents of an artificial neural network? In other words, how does an artificial intelligence fundamentally "think," and in what form does its knowledge reside? The psychology of artificial intelligence, as predicted by Asimov (1950), aims to study this AI probing and explainability-sensitive matter. This study requires a neuronal level of cognitive granularity, so as not to be limited solely to the secondary macro-cognitive results (such as cognitive and cultural biases) of synthetic neural cognition. A prerequisite for examining the latter is to clarify some epistemological milestones regarding the cognitive status we can attribute to its phenomenology.

Emotion analysis in texts suffers from two major limitations: annotated gold-standard corpora are mostly small and homogeneous, and emotion identification is often simplified as a sentence-level classification problem. To address these issues, we introduce a new annotation scheme for exploring emotions and their causes, along with a new French dataset composed of autobiographical accounts of an emotional scene. The texts were collected by applying the Cognitive Analysis of Emotions developed by A. Finkel to help people improve on their emotion management. The method requires the manual analysis of an emotional event by a coach trained in Cognitive Analysis. We present a rule-based approach to automatically annotate emotions and their semantic roles (e.g. emotion causes) to facilitate the identification of relevant aspects by the coach. We investigate future directions for emotion analysis using graph structures.

Ontology reuse aims to foster interoperability and facilitate knowledge reuse. Several approaches are typically evaluated by ontology engineers when bootstrapping a new project. However, current practices are often motivated by subjective, case-by-case decisions, which hamper the definition of a recommended behaviour. In this chapter we argue that to date there are no effective solutions for supporting developers' decision-making process when deciding on an ontology reuse strategy. The objective is twofold: (i) to survey current approaches to ontology reuse, presenting motivations, strategies, benefits and limits, and (ii) to analyse two representative approaches and discuss their merits.

Mphasis, an information technology solutions provider specializing in cloud and cognitive services, announced that it has been granted a US patent for its artificial intelligence (AI) system for tracking, managing, and analyzing data from unstructured data sources. The newly issued patent – U.S. Patent No. 10,353929, relates to leveraging machine learning algorithms to analyze free text from a variety of communication channels including news and editorial articles, blogs, emails, consumer complaints, and social media. The patented system uses Natural Language Processing (NLP) algorithms to process the data in real-time. The patented algorithms have been integrated as part of Mphasis' NextLabs solutions such as HyperGrafTM, a comprehensive, feature-rich, business intelligence, and analytics solution, as well as DeepInsightsTM, a cognitive intelligence platform, which enables enterprises to gain faster and more effective access to insights from data. These solutions are some of Mphasis' latest offerings focusing on emerging paradigms of innovation such as artificial intelligence, machine learning, and deep learning.

Example- List of URLS listed for a search query in search engine Experiments are conducted using real web services datasets and the outcome of the experiments using machine learning confirms an improvement over existing methods in Page Ranking. Supervised Learning algorithms are, K-Nearest Neighbour Ranking Static Ranking 8. KNN RANKING Many supervised learning problems are "classification" problems. KNN is one type of many different classification algorithms. The sheer number of both good and bad pages on the Web has led to an increasing reliance on search engines for the discovery of useful information. Users rely on search engines not only to return pages related to their search query, but also to separate the good from the bad, and order results so that the best pages are suggested first.

Example- List of URLS listed for a search query in search engine Experiments are conducted using real web services datasets and the outcome of the experiments using machine learning confirms an improvement over existing methods in Page Ranking. Supervised Learning algorithms are, K-Nearest Neighbour Ranking Static Ranking 8. KNN RANKING Many supervised learning problems are "classification" problems. KNN is one type of many different classification algorithms. The sheer number of both good and bad pages on the Web has led to an increasing reliance on search engines for the discovery of useful information. Users rely on search engines not only to return pages related to their search query, but also to separate the good from the bad, and order results so that the best pages are suggested first.