Intelligent blockchain management for distributed knowledge graphs in IoT 5G environments

This article introduces a new problem of distributed knowledge graph, in IoT 5G setting. We developed an end-to-end solution for solving such problem by exploring the blockchain management and intelligent method for producing the better matching of the concepts and relations of the set of knowledge graphs. The concepts and the relations of the knowledge graphs are divided into several components, each of which contains similar concepts and relations. Instead of exploring the whole concepts and the relations of the knowledge graphs, only the representative of these components is compared during the matching process. The framework has outperformed state-of-the-art knowledge graph matching algorithms using different scenarios as input in the experiments. In addition, to confirm the usability of our suggested framework, an in-depth experimental analysis has been done; the results are very promising in both runtime and accuracy.publishedVersio

Sensor data fusion for the industrial artificial intelligence of things

The emergence of smart sensors, artificial intelligence, and deep learning technologies yield artificial intelligence of things, also known as the AIoT. Sophisticated cooperation of these technologies is vital for the effective processing of industrial sensor data. This paper introduces a new framework for addressing the different challenges of the AIoT applications. The proposed framework is an intelligent combination of multi-agent systems, knowledge graphs and deep learning. Deep learning architectures are used to create models from different sensor-based data. Multi-agent systems can be used for simulating the collective behaviours of the smart sensors using IoT settings. The communication among different agents is realized by integrating knowledge graphs. Different optimizations based on constraint satisfaction as well as evolutionary computation are also investigated. Experimental analysis is undertaken to compare the methodology presented to state-of-the-art AIoT technologies. We show through experimentation that our designed framework achieves good performance compared to baseline solutions.publishedVersio

Hybrid intelligent framework for automated medical learning

This paper investigates the automated medical learning and proposes hybrid intelligent framework, called Hybrid Automated Medical Learning (HAML). The goal is the efficient combination of several intelligent components in order to automatically learn the medical data. Multi agents system is proposed by using distributed deep learning, and knowledge graph for learning medical data. The distributed deep learning is used for efficient learning of the different agents in the system, where the knowledge graph is used for dealing with heterogeneous medical data. To demonstrate the usefulness and accuracy of the HAML framework, intensive simulations on medical data were conducted. A wide range of experiments were conducted to verify the efficiency of the proposed system. Three case studies are discussed in this research, the first case study is related to process mining, and more precisely on the ability of HAML to detect relevant patterns from event medical data. The second case study is related to smart building, and the ability of HAML to recognize the different activities of the patients. The third one is related to medical image retrieval, and the ability of HAML to find the most relevant medical images according to the image query. The results show that the developed HAML achieves good performance compared to the most up-to-date medical learning models regarding both the computational and cost the quality of returned solutions.publishedVersio

Hybrid intelligent framework for automated medical learning

This paper investigates the automated medical learning and proposes hybrid intelligent framework, called Hybrid Automated Medical Learning (HAML). The goal is the efficient combination of several intelligent components in order to automatically learn the medical data. Multi agents system is proposed by using distributed deep learning, and knowledge graph for learning medical data. The distributed deep learning is used for efficient learning of the different agents in the system, where the knowledge graph is used for dealing with heterogeneous medical data. To demonstrate the usefulness and accuracy of the HAML framework, intensive simulations on medical data were conducted. A wide range of experiments were conducted to verify the efficiency of the proposed system. Three case studies are discussed in this research, the first case study is related to process mining, and more precisely on the ability of HAML to detect relevant patterns from event medical data. The second case study is related to smart building, and the ability of HAML to recognize the different activities of the patients. The third one is related to medical image retrieval, and the ability of HAML to find the most relevant medical images according to the image query. The results show that the developed HAML achieves good performance compared to the most up-to-date medical learning models regarding both the computational and cost the quality of returned solutionspublishedVersio

Deep Learning and Dempster-Shafer Theory Based Insider Threat Detection

Organizations' own personnel now have a greater ability than ever before to misuse their access to critical organizational assets. Insider threat detection is a key component in identifying rare anomalies in context, which is a growing concern for many organizations. Existing perimeter security mechanisms are proving to be ineffective against insider threats. As a prospective filter for the human analysts, a new deep learning based insider threat detection method that uses the Dempster-Shafer theory is proposed to handle both accidental as well as intentional insider threats via organization's channels of communication in real time. The long short-term memory (LSTM) architecture is applied to a recurrent neural network (RNN) in this work to detect anomalous network behavior patterns. Furthermore, belief is updated with Dempster's conditional rule and utilized to fuse evidence to achieve enhanced prediction. The CERT Insider Threat Dataset v6.2 is used to train the behavior model. Through performance evaluation, our proposed method is proven to be effective as an insider threat detection technique

Law in the present future : approaching the legal imaginary of smart cities with science (and) fiction

This doctoral research concerns smart cities, describing digital solutions and social issues related to their innovative technologies, adopted models, and major projects around the world. The many perspectives mentioned in it were identified by online tools used for the textual analysis of two databases that were built from relevant publications on the main subject by authors coming from media and academia. Expected legal elements emerged from the applied process, such as privacy, security, transparency, participation, accountability, and governance. A general review was produced on the information available about the public policies of Big Data in the two municipal cases of Rio de Janeiro and Montréal, and their regulation in the Brazilian and Canadian contexts. The combined approaches from science and literature were explored to reflect on the normative concerns represented by the global challenges and local risks brought by urban surveillance, climate change, and other neoliberal conditions. Cyberpunk Science Fiction reveals itself useful for engaging with the shared problems that need to be faced in the present time, all involving democracy. The results achieved reveal that this work was, in fact, about the complex network of practices and senses between (post)modern law and the imaginary of the future.Cette recherche doctorale centrée sur les villes intelligentes met en évidence les solutions numériques et les questionnements sociétaux qui ont trait aux technologies innovantes, ainsi qu’aux principaux modèles et projets développés autour d’elles à travers le monde. Des perspectives multiples en lien avec ces développements ont été identifiées à l’aide d’outils en ligne qui ont permis l’analyse textuelle de deux bases de données comprenant des publications scientifiques et des écrits médiatiques. De ce processus analytique ont émergé des éléments juridiques relatifs aux questions de vie privée, de sécurité, de transparence, de participation, d’imputabilité et de gouvernance. De plus, à partir de ces informations a été réalisée une revue des politiques publiques relatives aux mégadonnées dans les villes de Rio de Janeiro et de Montréal, ainsi que des réglementations nationales du Canada et du Brésil en lien avec ce sujet. Finalement, à travers l’exploration d’écrits scientifiques et fictionnels de la littérature, les principaux enjeux normatifs soulevés localement et mondialement par la surveillance urbaine, les changements climatiques et les politiques néolibérales ont pu être mis à jour. Le courant cyberpunk de la science-fiction s’est avéré particulièrement utile pour révéler les principaux problèmes politiques, en lien avec la préservation de la démocratie, auxquelles sont confrontées nos sociétés présentement. Les résultats de la recherche démontrent finalement la présence d’un réseau de pratiques et de significations entre le droit (post)moderne et les représentations imaginaires du futur