LEADER 09891nam 22007335 450 001 9910765491003321 005 20250602172744.0 010 $a9783031473661 010 $a3031473663 024 7 $a10.1007/978-3-031-47366-1 035 $a(MiAaPQ)EBC30960572 035 $a(Au-PeEL)EBL30960572 035 $a(DE-He213)978-3-031-47366-1 035 $a(CKB)29019296100041 035 $a(OCoLC)1411309492 035 $a(EXLCZ)9929019296100041 100 $a20231120d2024 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aNew Technologies, Artificial Intelligence and Smart Data $e10th International Conference, INTIS 2022, Casablanca, Morocco, May 20?21, 2022, and 11th International Conference, INTIS 2023, Tangier, Morocco, May 26?27, 2023, Revised Selected Papers /$fedited by Mohamed Tabaa, Hassan Badir, Ladjel Bellatreche, Azedine Boulmakoul, Ahmed Lbath, Fabrice Monteiro 205 $a1st ed. 2024. 210 1$aCham :$cSpringer Nature Switzerland :$cImprint: Springer,$d2024. 215 $a1 online resource (216 pages) 225 1 $aCommunications in Computer and Information Science,$x1865-0937 ;$v1728 311 08$aPrint version: Tabaa, Mohamed New Technologies, Artificial Intelligence and Smart Data Cham : Springer,c2023 327 $aIntro -- Preface -- Organization -- Contents -- Artificial Intelligence -- Machine Learning for the Analysis of Human Microbiome in Inflammatory Bowel Diseases: Literature Review -- 1 Introduction -- 2 The Relation Between Inflammatory Bowel Diseases and the Microbiome -- 3 Existing Approaches for Microbiome Analysis -- 4 Machine Learning Techniques for Microbiome Analysis -- 5 Applications of Machine Learning for the Analysis of the Microbiome in Inflammatory Bowel Diseases -- 6 Limitations and Challenges of Using Machine Learning for Microbiome Analysis in IBD -- 7 Conclusion -- References -- Systematic Mapping Study on Applications of Deep Learning in Stock Market Prediction -- 1 Introduction -- 2 Related Works -- 3 Research Method -- 4 Systematic Mapping Results -- 5 Synthesis and Future Works -- 6 Conclusion -- References -- Exploring the Knowledge Distillation -- 1 Introduction -- 2 Related Work -- 3 Methods and Background -- 3.1 Knowledge Distillation -- 3.2 Teacher Assistant Knowledge Distillation -- 3.3 Self-distillation -- 4 Experimental Results -- 4.1 Datasets -- 4.2 Experiment Settings -- 4.3 Evaluation Metric -- 4.4 Performance Evaluation -- 5 Conclusion -- References -- Intelligent Traffic Congestion and Collision Avoidance Using Multi-agent System Based on Reinforcement Learning -- 1 Introduction -- 2 Literature Review -- 2.1 Multi-agent System -- 2.2 Intelligent Traffic Congestion -- 2.3 Collision Avoidance -- 2.4 Reinforcement Learning -- 3 Proposed System -- 3.1 System Architecture -- 3.2 Methodology -- 3.3 Design Objectives -- 3.4 Mathematical Model -- 4 Findings -- 5 Conclusion -- 6 Future Work -- References -- BERT for Arabic NLP Applications: Pretraining and Finetuning MSA and Arabic Dialects -- 1 Introduction -- 2 Word Embeddings and BERT -- 3 The BERT Model: Background -- 3.1 Bert Architecture. 327 $a3.2 BERT Pre-training and Fine-Tuning -- 4 BERT for Arabic NLP Applications: Pre-training and Fine-Tuning -- 4.1 Training Datasets -- 4.2 Pre-training BERT Models -- 4.3 Fine-Tuning for Arabic NLP Applications -- 4.4 The Computational Cost -- 5 Results and Discussion -- 6 Conclusion -- References -- VacDist MAS for Covid-19 Vaccination Distribution: Palestine as a Case Study -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 3.1 The Proposed MAS -- 3.2 System Environments and Their Agents -- 3.3 Data Source -- 3.4 VacDist Framework -- 4 Results -- 4.1 Decision-Making Strategy Results -- 4.2 Machine Learning Distribution Strategy Based Cities Results -- 4.3 Distribution Strategy Based Citizen Results -- 5 Discussion -- 6 Conclusion -- References -- Smart E-Waste Management System Utilizing IoT and DL Approaches -- 1 Introduction -- 2 Related Work -- 3 Object Detection Model -- 3.1 Cloud, Fog, or Local Computing -- 3.2 Choosing a Deep-Learning Framework -- 3.3 Choosing an Architecture for Object Detection -- 3.4 Dataset Collection -- 3.5 Training Phase -- 4 The Proposed System -- 4.1 System Architecture -- 4.2 System Components -- 4.3 System Workflow -- 4.4 Prototype Implementation (Hardware) -- 4.5 System Cost -- 5 Conclusion and Future Work -- References -- Data Technologies -- Towards a Platform for Higher Education in Virtual Reality of Engineering Sciences -- 1 Introduction -- 2 Digital Transformation in Higher Education: State of Art -- 2.1 Virtual Reality -- 2.2 Augmented Reality -- 2.3 Metaverse -- 2.4 Virtual 3D Technologies in STEM Higher Education -- 2.5 Students' Perspectives on Using AR and VR -- 2.6 Teachers' Perspectives on Using AR and VR -- 2.7 Pedagogy of Learning in STEM Higher Education -- 2.8 Supporting Different Resource Types for Learning -- 2.9 WebXR -- 3 Discussion -- 3.1 The Implementation -- 3.2 How It Works. 327 $a4 Conclusion -- References -- Robust Intelligent Control for Two Links Robot Based ACO Technique -- 1 Introduction -- 2 Optimal Neural Network Sliding Mode Control Design -- 2.1 Controller Design -- 2.2 Neural Network Representation: -- 2.3 ACO Training Algorithm -- 3 Simulation Results -- 4 Conclusion -- References -- Multimodal Learning for Road Safety Using Vision Transformer ViT -- 1 Introduction -- 2 Litterature Review -- 3 Problem Definition -- 3.1 Vision Transformer (ViT) -- 3.2 Model Architecture -- 4 Model Application: The Identification of High-Risk Areas for Smart Camera Locations -- 4.1 Dataset -- 4.2 Experimental Setup -- 4.3 Results -- 5 Conclusion -- References -- Intelligent Multi-agent Distributed System for Improving and Secure Travel Procedures: Al-Karama-King Hussein Bridge Study Case -- 1 Introduction -- 2 System Architecture -- 3 Logical Data Structure -- 4 IRMADS as Distributed System -- 4.1 Scalability -- 4.2 Openness -- 4.3 Resource Sharing -- 5 IRMADS Database Based on Blockchain Data Architecture -- 6 State Diagram Distribution for IRMADS -- 7 Agent-Based and Discrete Event Simulation for IRMADS -- 7.1 Agent and Traveler Data Structure -- 7.2 Simulation Work Flow -- 7.3 Simulation Results and Analysis -- 8 Conclusion -- References -- A New Approach for the Analysis of Resistance to Change in the Digital Transformation Context -- 1 Introduction -- 2 Related Work -- 3 Background -- 4 Proposed Approach -- 5 Illustrative Example -- 6 Results and Discussion -- 7 Conclusion -- References -- Augmented Data Warehouses for Value Capture -- 1 Introduction -- 2 Related Work -- 3 Motivating Example -- 4 DW Augmentation for Value Capture -- 4.1 GO Augmented-DW -- 4.2 On-Demand DW Augmentation -- 4.3 Value Metrics for DW Augmentation -- 5 Experiments -- 5.1 Experimental Setup -- 5.2 Obtained Results -- 6 Conclusion -- References. 327 $aA Parallel Processing Architecture for Querying Distributed and Heterogeneous Data Sources -- 1 Introduction -- 2 Related Work -- 3 A Parallel Processing Architecture for Aggregated Search in the Web of Data -- 4 Querying Heterogeneous Sources with Our Search Engine -- 5 Demonstration and Results -- 5.1 Data Sources and the Query -- 5.2 Execution Results -- 6 Conclusion -- References -- Comprehensive Data Life Cycle Security in Cloud Computing: Current Mastery and Major Challenges -- 1 Introduction -- 2 Data Life Cycle -- 3 Literature Review -- 3.1 Secure Data Creation -- 3.2 Ensuring Secure Data Exchange -- 3.3 Secure Data Deletion -- 4 Research Gap in Data Life Cycle -- 5 Conclusion -- References -- Author Index. 330 $aThis volume constitutes selected papers presented at the 10th International Conference on Innovation and New Trends in Information Technology, INTIS 2022, held in Casablanca, Morocco, in May 2022, and 11th International Conference on Innovation and New Trends in Information Technology, INTIS 2023, held in Tangier, Morocco, in May 2023. After the thorough peer review process, 4 papers were selected from the 27 submissions received for INTIS 2022, and 11 papers were selected from the 33 submissions received for INTIS 2023. The presented papers cover the mail topics of data-enabled systems/applications: data source layer, network layer, data layer, learning layer, and reporting layers while considering non-functional properties such as data privacy, security, and ethics. 410 0$aCommunications in Computer and Information Science,$x1865-0937 ;$v1728 606 $aArtificial intelligence 606 $aComputer engineering 606 $aComputer networks 606 $aComputers, Special purpose 606 $aArtificial Intelligence 606 $aComputer Engineering and Networks 606 $aComputer Communication Networks 606 $aSpecial Purpose and Application-Based Systems 615 0$aArtificial intelligence. 615 0$aComputer engineering. 615 0$aComputer networks. 615 0$aComputers, Special purpose. 615 14$aArtificial Intelligence. 615 24$aComputer Engineering and Networks. 615 24$aComputer Communication Networks. 615 24$aSpecial Purpose and Application-Based Systems. 676 $a006.3 700 $aTabaa$b Mohamed$01449002 701 $aBudayr$b H?assa?n$01859503 701 $aBellatreche$b Ladjel$01449004 701 $aBoulmakoul$b Azedine$01449005 701 $aLbath$b Ahmed$01449006 701 $aMonteiro$b Fabrice$01449007 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910765491003321 996 $aNew Technologies, Artificial Intelligence and Smart Data$94464500 997 $aUNINA