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3rd International Conference of Reliable information and Communication technology, IRICT 2018

作者： Alruqimi, Mohammed Aknin, Noura Al-Hadhrami, Tawfik James-Taylor, Anne Information Technology and Modeling Systems Research Unit Abdelmalek Essaadi University Tétouan Morocco School of Science and Technology Nottingham Trent University Nottingham United Kingdom

ISBN: (纸本)9783319990064

Handling large-scale heterogeneous data in the IoT and processing it in real time will be a key factor towards realizing IoT services in users’ daily lives. In this regard, semantic domain ontologies are increasingly seen as a solution for enabling interoperability across heterogeneous data and sensor-based applications. Several ontologies have been proposed with the aim of addressing interoperability issues and various aspects of IoT device observation. However, for most of these ontologies, they are either in the domain of sensor networks or the much broader domain of the IoT. Furthermore, these ontologies have shown slow improvement, as they have been developed by limited groups of domain experts. This paper proposes a model that exploits the collective intelligence which emerges from social tagging systems to generate up-to-date domain-specific ontologies. The evaluation of the proposed model, using a dataset extracted from BibSonomy, demonstrated that the model could effectively learn a domain terminology, and identify more meaningful semantic information for the domain terminology. Furthermore, the proposed model introduces a simple and effective method for the common problems related to tag ambiguity. © Springer Nature Switzerland AG 2019.

关键词： Ontology

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Broadband ring dielectric resonator antenna for satellite and 5G applications

Broadband ring dielectric resonator antenna for satellite an...

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2020 International Symposium on Advanced Electrical and Communication Technologies, ISAECT 2020

作者： Yamoun, Jihad Ben Aknin, Noura Abdelmalek Essaadi University Information Technology and Modeling Systems Research Group Faculty of Sciences Tetouan Morocco

ISBN: (纸本)9781665422222

In this work, a ring dielectric resonator antenna is designed, with a parasite strip placed in the inside surface of the ring, the dielectric resonator is an Acrylonitrile butadiene styrene mounted on Aluminum ground plane and excited by an adjacent coaxial probe for its ease of fabrication. The antenna is designed by using CST Microwave Studio and HFSS software. The result of simulation shows a wideband about 62.42%, the bandwidth of the structure is found ranging from 17.3GHz to 34GHz, which make it suitable for satellite and 5G applications. © 2020 IEEE.

关键词： Microwave antennas

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CSO to Solve the Shop Scheduling Problem: Survey 1

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2nd International Conference on Advanced Intelligent systems for Sustainable Development, AI2SD 2019

作者： Bouzidi, Abdelhamid Riffi, Mohammed Essaid Information Technology and Modeling Systems Research Unit Sciences Faculty Abdelmalek Essaadi University Tetouan Morocco Laroseri Laboratory FS-UCD El Jadida Morocco

ISBN: (数字)9783030366773

ISBN: (纸本)9783030366766

The Shop scheduling problem is a class of scheduling problem used in different area application as industry, characterized by a set of jobs that should be executed in determined number of machines, the aim of this is to find the optimal schedule to enrich the minimal makespan. This paper presents the Cat Swarm optimization to solve the three class of the problem in study, which are the Job Shop Scheduling problem (JSSP), the Flow Shop Scheduling Problem (FSSP) and the Open Shop Scheduling Problem (OSSP), the result obtained by the application of this method to solve the Three problem, are collected and compared with the existing to conclude the efficiency of the CSO method to solve the real problems in study. © 2020, Springer Nature Switzerland AG.

关键词： Job shop scheduling

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Global system for telematics: E-safety services validation 13

Global system for telematics: E-safety services validation

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13th World Congress on Intelligent Transport systems and Services, ITS 2006

作者： Ségarra, Gérard Telematics Research Unit RENAULT Information Technology Systems Division United Kingdom

The GST (Global System for Telematics) European Integrated project is now producing its results. Renault has been strongly involved with its French partners in prototyping and assessing the open system architecture and services proposed by this project. This paper is reporting about the obtained results, focusing on e-Safety services which have been integrated in the French storyboard. As described, these services are fully using the GST Telematics infrastructure insuring the services discovery, their subscription and their secure provisioning and operation in a flexible, open environment. The here below considered safety services are including medical emergency assistance, car breakdown assistance and road hazard warning. This last set of services is based on Car to Car communication and cooperation and is paving the way for a new E.C project started in 2006 called SAFESPOT.

关键词： Telematics

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Optimizing Deep LSTM Model through Hyperparameter Tuning for Sensor-Based Human Activity Recognition in Smart Home

Informatica (Slovenia)

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Informatica (Slovenia) 2023年第10期47卷 109-122页

作者： El Ghazi, Mariam Aknin, Noura Information Technology and Modeling Systems Research Unit Abdelmalek Essaadi University Tetouan Morocco

Human Activity Recognition (HAR) holds significant potential in healthcare, smart homes, sports, and security, mainly benefiting the well-being of elderly individuals and dependents. This research introduces an innovative deep learning-based approach to HAR, using wearable sensors in smart home environments. In this paper, we conduct a comprehensive review of the state of the art, offering insights into existing methods, classification techniques, their performances, hyperparameter tuning strategies, findings, limitations, and future directions. We propose an LSTM-based deep model enriched with batch normalization and perform a hyperparameter tuning using Bayesian Optimization;then, we evaluate the model on the PAMAP2 public dataset. The model outperforms previous studies, achieving remarkable performance metrics, including accuracy at 97.71%, F1 score, precision, and recall, approaching 96.66%, 96.85%, and 96.55%, respectively. We plan to assess the model's generalization capabilities for future work by training it on diverse datasets such as Opportunity and WISDM. Furthermore, we aim to enhance the model by exploring hybrid deep model architectures and alternative hyperparameter tuning approaches. These efforts maximize the model's efficiency and adaptability in real-world scenarios. © 2023 Slovene Society Informatika. All rights reserved.

关键词： Long short-term memory

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eComP: an architecture that supports P2P networking among ubiquitous computing devices 2

eComP: an architecture that supports P2P networking among ub...

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2nd International Conference on Peer-to-Peer Computing

作者： Kameas, A Mavrommati, I Ringas, D Wason, P Computer Technology Institute Research Unit 3 Ambient Information Systems Group Greece Indian Institute of Technology Guwahati India

ISBN: (纸本)0769518109

In the new paradigm of computer use, the computer ceases to exist as an integrated multi-task device, but disintegrates into a task-oriented collection of networked devices. These devices do not resemble to computers yet they have computational abilities. None of these concepts will be realised without appropriate support from communication technologies - P2P networking being the primary candidate. This paper describes part of the research being conducted in the Extrovert Gadgets project geared towards applying P2P computing solutions to the context of networked everyday objects.

关键词： ubiquitous computing middleware P2P computing

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A range-gated CMOS SPAD array for real-time 3D range imaging

A range-gated CMOS SPAD array for real-time 3D range imaging

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2019 Image Sensors and Imaging systems Conference, IMSE 2019

作者： Ruokamo, Henna Hallman, Lauri Kostamovaara, Juha Faculty of Information Technology and Electrical Engineering Circuits and Systems Research Unit University of Oulu Finland

A range image of a scene is produced with a solid-state time-of-flight system that uses active illumination and a time-gated single photon avalanche diode (SPAD) array. The distance to a target from the imager is measured by delaying the time gate in small steps and counting the photons of the pixels in each delay step in successive measurements. To achieve a high frame rate, the number of delay steps needed is minimized by limiting the scanning of the depth only to the range of interest. To be able to measure scenes with objects in different ranges, the array has been divided into groups of pixels with independently controlled time gating. This paper demonstrates an algorithm that can be used to control the time gating of the pixel groups in the sensor array to achieve depth maps of the scene with the time-gated SPAD array in real time and at a 70 Hz frame rate. © 2019, Society for Imaging Science and technology

关键词： Pixels

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Adjustment of sampling locations in rail-geometry datasets: Using dynamic programming and nonlinear filtering

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systems and Computers in Japan 2006年第1期37卷 61-70页

作者： Kamiyama, Masako Higuchi, Tomoyuki Track Technology Division Railway Technical Research Institute Kokubunji 185-8540 Japan Tokyo 105-8569 Japan Department of Statistical Modeling Institute of Statistical Mathematics Research Organization of Information and Systems Tokyo 106-8569 Japan Track Technology Division Railway Technical Research Institute Japan Society for Industrial and Applied Mathematics Japan Statistical Society Department of Statistical Modeling Institute of Statistical Mathematics Research Organization of Information and Systems Society of Geomagnetism and Earth Planetary and Space Sciences American Geophysical Union American Statistical Association

A track inspection car, which measures the shape of railway tracks (hereafter, rail geometry) while it is running on rails, discretizes the measurement results at nearly fixed spatial intervals. However, the distance between the discretized locations (spatial sampling intervals) may shorten or lengthen locally due to slipping or sliding of the car wheel, and this prevents the sampling locations from aligning with those of a dataset obtained with another measuring run. The authors developed an algorithm for approximately aligning the sampling locations of the measurement datasets obtained with different runs. First, they considered this problem as the selection of the series of data corresponding to each supervised data from a training dataset, which was constructed by interpolation in order to minimize the evaluation function of a number sequence representing data points. Next, they used the maximum likelihood method to identify the unknown parameters contained in the evaluation function. This problem uses two features of the evaluation function. The first is that the evaluation function is minimized by dynamic programming, and the obtained optimum sequence is equivalent to a maximum a posteriori (MAP) estimate in the Bayesian framework. The second is that by converting the evaluation function to a general state space representation, the log likelihood of the model that includes the parameters is obtained by a nonlinear filtering method. Also, to simplify the search for the identification, they devised a parameter search procedure for the parameters in the autoregressive (AR) model. © 2005 Wiley Periodicals, Inc.

关键词： Dynamic programming

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Talking plant: Integrating plants behavior with ambient intelligence

Talking plant: Integrating plants behavior with ambient inte...

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2nd IET International Conference on Intelligent Environments, IE 06

作者： Calemis, Ioannis Goumopoulos, Christos Kameas, Achilles Research Academic Computer Technology Institute Research Unit 3 Design of Ambient Information Systems Group Patras Hellas

ISBN: (纸本)0863416632

The aim of this work is to bring the ambient intelligence (AmI) (1) concept in a new level by introducing it to living organisms - plants. In this paper we provide a concrete scenario where an augmented plant, an ePlant can be incorporated in a ubiquitous computing environment in order to work together with other augmented objects, artifacts, in order to provide to the environment status of its condition. The paper presents the enabling infrastructure and the tools that are used to make such an application, and discusses on how we can make these augmented items to collaborate in order to create mixed societies of plants and artifacts.

关键词： Ubiquitous computing

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Large-Scale Radiomics Analysis for Prostate Cancer Detection Harnessing Machine and Deep Learning Models

Large-Scale Radiomics Analysis for Prostate Cancer Detection...

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IEEE International Symposium on Biomedical Imaging

作者： Eugenia Mylona Dimitrios I. Zaridis Charalampos N. Kalantzopoulos Vasileios C. Pezoulas Nikolaos S. Tachos George K. Matsopoulos Daniele Regge Nikolaos Papanikolaou Manolis Tsiknakis Kostas Marias Dimitrios I. Fotiadis Biomedical Research Institute FORTH Ioannina Greece Unit of Medical Technology Intelligent Information Systems University of Ioannina Greece Biomedical Engineering Laboratory School of Electrical & Computer Engineering National Technical University of Athens Greece Institute of Computer Science FORTH Heraklion Greece Department of Radiology Candiolo Cancer Institute FPO-IRCCS Strada Provinciale Candiolo Italy Computational Clinical Imaging Group Champalimaud Foundation Portugal Computational Biomedicine Laboratory Institute of Computer Science FORTH Heraklion Greece Department of Electrical and Computer Engineering Hellenic Mediterranean University Heraklion Greece

ISBN: (数字)9798331520526

ISBN: (纸本)9798331520533

Prostate cancer (PCa) is the second most common cancer in men, making early detection crucial for effective treatment. This study explores the efficacy of machine learning (ML) and deep learning (DL) models in detecting PCa lesions using radiomics features derived from multiparametric MRI data. Leveraging a dataset of 7621 retrospective and 2132 prospective cases from 12 clinical centers across Europe, we compared the performance of various ML (LASSO, Random Forest, SVM, XGBoost) and DL models (CNN, MLP, Transformer, CNN&MLP). Radiomic features were extracted and refined through a robust feature selection process. Models' performance was evaluated through nested cross-validation (CV) and externally. The CNN&MLP model achieved the highest ROC AUC (0.86) and PR AUC (0.90) in CV, while LASSO showed consistent generalization in external validation. Our findings indicate that DL models excel in high-dimensional data analysis but face challenges in generalizing to external datasets, highlighting the importance of external validation for clinical applicability.

关键词： Deep learning Support vector machines Analytical models Biological system modeling Feature extraction Transformers Lesions Prostate cancer Radiomics Principal component analysis

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