A New Collaborative Risk Assessment Model for Cloud Computing

Cloud computing has recently emerged a new paradigm by introducing potential benefits to Cloud users. Although the advantage of cloud computing are tremendous, number of security risk are emerging in association with cloud usage that need to be assessed. Assessing risk in Cloud computing environment remains an open research issue. This paper presents a collaborative risk assessment model for cloud computing, which is in compliance with all the specific characteristics of the Cloud Computing

An innovative medical waste management system in a smart city using XAI and vehicle routing optimization [version 1; peer review: 2 approved]

Background: The management of medical waste is a complex task that necessitates effective strategies to mitigate health risks, comply with regulations, and minimize environmental impact. In this study, a novel approach based on collaboration and technological advancements is proposed. Methods: By utilizing colored bags with identification tags, smart containers with sensors, object recognition sensors, air and soil control sensors, vehicles with Global Positioning System (GPS) and temperature humidity sensors, and outsourced waste treatment, the system optimizes waste sorting, storage, and treatment operations. Additionally, the incorporation of explainable artificial intelligence (XAI) technology, leveraging scikit-learn, xgboost, catboost, lightgbm, and skorch, provides real-time insights and data analytics, facilitating informed decision-making and process optimization. Results: The integration of these cutting-edge technologies forms the foundation of an efficient and intelligent medical waste management system. Furthermore, the article highlights the use of genetic algorithms (GA) to solve vehicle routing models, optimizing waste collection routes and minimizing transportation time to treatment centers. Conclusions: Overall, the combination of advanced technologies, optimization algorithms, and XAI contributes to improved waste management practices, ultimately benefiting both public health and the environment

LnaCBR:Case Based Reasoning Architecture for Intrusion Detection to Learning New Attacks

The agents used in the intrusion detection architectures have multiple characteristics namely delegation, cooperation and communication. However, an important property of agents: learning is not used. The concept of learning in existing IDSs used in general to learn the normal behavior of the system to secure. For this,normal profiles are built in a dedicated training phase, these profiles are then compared with the current activity. Thus, the IDS does not have the ability to detect new attacks. We propose in this paper, a new architecture based intrusion MAS adding a learning feature abnormal behaviors that correspond to new attack patterns detection. Thanks to this feature to update the knowledge base of attacks take place when a new plan of attack is discovered. To learn a new attack, the architecture must detect at first and then update the basic attack patterns. For the detection step, the detection approach adopted is based on the technique of Case-Based Reasoning (CBR). Thus, the proposed architecture is based on a hierarchical and distributed strategy where features are structured and separated into layers

HT-TPP: A Hybrid Twin Architecture for Thermal Power Plant Collaborative Condition Monitoring

Thermal power plants, TPP, are one of the main players in the phosphoric acid and fertilizer production value chain. The control of power plant assets involves considerable complexity and is subject to several constraints, affecting the asset’s reliability and, most importantly, plant operators’ safety. The main focus of this paper is to investigate the potential of an agent-based digital twin architecture for collaborative prognostic of power plants. Based on the ISO 13374:2015 scheme for smart condition monitoring, the proposed architecture consists of a collaborative prognostics system governed by several smart DT agents connected to both physical and virtual environments. In order to apprehend the potential of the developed agent-based architecture, experiments on the architecture are conducted in a real industrial environment. We show throughout the paper that our proposed architecture is robust and reproduces TPP static and dynamic behavior and can contribute to the smart monitoring of the plant in case of critical conditions

An architecture of an interactive multimodal urban mobility system

Throughout the world and particularly in urban areas, population growth can be listed as a direct cause of the uprising use of personal vehicles in cities around the world. Such attitude may lead to dramatic consequences, not only economically, but socially and environmentally. To meet these challenges, and to promote the use of multiple means of public transports by citizens, public authorities and transport operators seek − within the framework of the implementation of connected cities projects and intelligent − to optimize the extraction as well as the exploitation of the multimodal information by developing Interactive Systems of Assistance to the Multimodal Movement (IAMM). However, finding the optimal multimodal path for a given person is far from being a simple matter. Indeed, each potential user may have different or unique preferences regarding the: cost and/or duration of his/her journey, number of mode changes, comfort or safety levels desired. In the present study, we propose a multi-agent system which, based on the parameters entered by each user, proposes the optimal paths in the Pareto sense, including different public transport modes, private cars and parking availability

Association maladie de Verneuil et trisomie 21 – Étude internationale multicentrique de 56 cas

info:eu-repo/semantics/publishe