31 research outputs found
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
Game theory framework for MAC parameter optimization in energy-delay constrained sensor networks
Optimizing energy consumption and end-to-end (e2e) packet delay in energy-constrained, delay-sensitive wireless sensor networks is a conflicting multiobjective optimization problem. We investigate the problem from a game theory perspective, where the two optimization objectives are considered as game players. The cost model of each player is mapped through a generalized optimization framework onto protocol-specific MAC parameters. From the optimization framework, a game is first defined by the Nash bargaining solution (NBS) to assure energy consumption and e2e delay balancing. Secondy, the Kalai-Smorodinsky bargaining solution (KSBS) is used to find an equal proportion of gain between players. Both methods offer a bargaining solution to the duty-cycle MAC protocol under different axioms. As a result, given the two performance requirements (i.e., the maximum latency tolerated by the application and the initial energy budget of nodes), the proposed framework allows to set tunable system parameters to reach a fair equilibrium point that dually minimizes the system latency and energy consumption. For illustration, this formulation is applied to six state-of-the-art wireless sensor network (WSN) MAC protocols: B-MAC, X-MAC, RI-MAC, SMAC, DMAC, and LMAC. The article shows the effectiveness and scalability of such a framework in optimizing protocol parameters that achieve a fair energy-delay performance trade-off under the application requirements
Energy-efficient cluster-based security mechanism for intra-WBAN and inter-WBAN communications for healthcare applications
Multiple Benefits through Smart Home Energy Management Solutions—A Simulation-Based Case Study of a Single-Family-House in Algeria and Germany
Commercial Technologies for Advanced Light Control in Smart Building Energy Management Systems: A Comparative Study
This work investigates the economic, social, and environmental impact of
adopting different smart lighting architectures for home automation in two
geographical and regulatory regions: Algiers, Algeria, and Stuttgart, Germany.
Lighting consumes a considerable amount of energy, and devices for smart
light-ing solutions are among the most purchased smart home devices. As
commercial-ized solutions come with variant features, we empirically evaluate
through this study the impact of each one of the energy-related features and
provide insights on those that have higher energy saving contribution. The
study started by investigating the state-of-the-art of commercialized ICT-based
light control solutions, which allowed the extraction of the energy-related
features. Based on the outcomes of this study, we generated simulation
scenarios and selected evaluations metrics to evaluate the impact of dimming,
daylight harvesting, scheduling, and motion detection. The simulation study has
been conducted using \textit{EnergyPlus} simulation tool, which enables
fine-grained realistic evaluation. The results show that adopting smart
lighting technologies have a payback period of few years, and that the use of
these technologies has positive economic and societal impacts, as well as on
the environment by considerably reducing gas emissions. However, this positive
contribution is highly sensitive to the geographical location, energy prices,
and the occupancy profile
Multiple Benefits through Smart Home Energy Management Solutions—A Simulation-Based Case Study of a Single-Family-House in Algeria and Germany
From both global and local perspectives, there are strong reasons to promote
energy efficiency. These reasons have prompted leaders in the European Union
(EU) and countries of the Middle East and North Africa (MENA) to adopt policies
to move their citizenry toward more efficient energy consumption. Energy
efficiency policy is typically framed at the national, or transnational level.
Policy makers then aim to incentivize microeconomic actors to align their
decisions with macroeconomic policy. We suggest another path towards greater
energy efficiency: Highlighting individual benefits at microeconomic level. By
simulating lighting, heating and cooling operations in a model single-family
home equipped with modest automation, we show that individual actors can be led
to pursue energy efficiency out of enlightened self-interest. We apply
simple-to-use, easily, scalable impact indicators that can be made available to
homeowners and serve as intrinsic economic, environmental and social motivators
for pursuing energy efficiency. The indicators reveal tangible homeowner
benefits realizable under both the market-based pricing structure for energy in
Germany and the state-subsidized pricing structure in Algeria. Benefits accrue
under both the continental climate regime of Germany and the Mediterranean
regime of Algeria, notably in the case that cooling energy needs are
considered. Our findings show that smart home technology provides an attractive
path for advancing energy efficiency goals. The indicators we assemble can help
policy makers both to promote tangible benefits of energy efficiency to
individual homeowners, and to identify those investments of public funds that
best support individual pursuit of national and transnational energy goals