Telemedicine framework using case-based reasoning with evidences

Telemedicine is the medical practice of information exchanged from one location to another through electronic communications to improve the delivery of health care services. This research article describes a telemedicine framework with knowledge engineering using taxonomic reasoning of ontology modeling and semantic similarity. In addition to being a precious support in the procedure of medical decision-making, this framework can be used to strengthen significant collaborations and traceability that are important for the development of official deployment of telemedicine applications. Adequate mechanisms for information management with traceability of the reasoning process are also essential in the fields of epidemiology and public health. In this paper we enrich the case-based reasoning process by taking into account former evidence-based knowledge. We use the regular four steps approach and implement an additional (iii) step: (i) establish diagnosis, (ii) retrieve treatment, (iii) apply evidence, (iv) adaptation, (v) retain. Each step is performed using tools from knowledge engineering and information processing (natural language processing, ontology, indexation, algorithm, etc.). The case representation is done by the taxonomy component of a medical ontology model. The proposed approach is illustrated with an example from the oncology domain. Medical ontology allows a good and efficient modeling of the patient and his treatment. We are pointing up the role of evidences and specialist's opinions in effectiveness and safety of care

A deep tech architecture for intelligent IoT systems

[EN]The increase in the number of connected devices on the Internet of Things (IoT), interactions and the amount of data raises a number of issues. Two major problems are limitations in terms of network latency and bandwidth. While cloud-based infrastructures give us access to scalable, on-demand storage and processing services that can scale to the requirements of the Internet of Things (IoT), these centralized resources can create unacceptable delays and performance problems for devices that have latency-sensitive applications, such as health monitoring and emergency response applications. This article has been created for the PhD thesis that aims to create a deep tech architecture for intelligent IoT systems

Data Security Enhancement in 4G Vehicular Networks Based on Reinforcement Learning for Satellite Edge Computing

The vehicular network provides the dedicated short-range communication (DSRC) with IEEE 802.11p standard. The VANET model comprises of cellular vehicle-to-everything communication with wireless communication technology. Vehicular Edge Computing exhibits the promising technology to provide promising Intelligent Transport System Services. Smart application and urban computing. Satellite edge computing model is adopted in vehicular networks to provide services to the VANET communication for the management of computational resources for the end-users to provide access to low latency services for maximal execution of service. The satellite edge computing model implemented with the 4G vehicular communication network model subjected to data security issues. This paper presented a Route Computation Deep Learning Model (RCDL) to improve security in VANET communication with 4G technology. The RCDL model uses the route establishment model with the optimal route selection. The compute route is transmitted with the cryptographic scheme model for the selection of optimal route identified from the satellite edge computing model. The proposed RCDL scheme uses the deep learning-based reinforcement learning scheme for the attack prevention in the VANET environment employed with the 4G technology communication model. The simulation results expressed that proposed RCDL model achieves the higher PDR value of 98% which is ~6% higher than the existing model. The estimation of end-to-end delay is minimal for the RCDL scheme and improves the VANET communication

Smart territories

The concept of smart cities is relatively new in research. Thanks to the colossal advances in Artificial Intelligence that took place over the last decade we are able to do all that that we once thought impossible; we build cities driven by information and technologies. In this keynote, we are going to look at the success stories of smart city-related projects and analyse the factors that led them to success. The development of interactive, reliable and secure systems, both connectionist and symbolic, is often a time-consuming process in which numerous experts are involved. However, intuitive and automated tools like “Deep Intelligence” developed by DCSc and BISITE, facilitate this process. Furthermore, in this talk we will analyse the importance of complementary technologies such as IoT and Blockchain in the development of intelligent systems, as well as the use of edge platforms or fog computing