Welcome to the seventh edition of the biennial internationalconference on Data Science and machinelearningapplications (CDMA2022). this edition celebrates the 2030 Saudi Vision and its ambitions goals to reach the ...
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ISBN:
(纸本)9781665410144
Welcome to the seventh edition of the biennial internationalconference on Data Science and machinelearningapplications (CDMA2022). this edition celebrates the 2030 Saudi Vision and its ambitions goals to reach the top-five nations in AI advancements and deployments.
the proceedings contain 39 papers. the topics discussed include: evaluation of machinelearning to early detection of highly cited papers;towards using deep reinforcement learning for better COVID-19 vaccine distribut...
ISBN:
(纸本)9781665410144
the proceedings contain 39 papers. the topics discussed include: evaluation of machinelearning to early detection of highly cited papers;towards using deep reinforcement learning for better COVID-19 vaccine distribution strategies;an investigation of forecasting Tadawul all share index (TASI) using machinelearning;intelligent deep detection method for malicious tampering of cancer imagery;an empirical analysis of health-related campaigns on twitter Arabic hashtags;the accuracy performance of semantic segmentation network with different backbones;a comprehensive evaluation of statistical, machinelearning and deep learning models for time series prediction;depression detection in Arabic using speech language recognition;a deep learning framework for temperature forecasting;improving relevance in a recommendation system to suggest charities without explicit user profiles using dual-autoencoders;and the impact of feature selection on different machinelearning models for breast cancer classification.
the proceedings contain 35 papers. the special focus in this conference is on Mining Humanistic Data. the topics include: Digitally Assisted Planning and Monitoring of Supportive Recommendations in Canc...
ISBN:
(纸本)9783031083402
the proceedings contain 35 papers. the special focus in this conference is on Mining Humanistic Data. the topics include: Digitally Assisted Planning and Monitoring of Supportive Recommendations in Cancer Patients;CAIPI in Practice: Towards Explainable Interactive Medical Image Classification;a Deep Q Network-Based Multi-connectivity Algorithm for Heterogeneous 4G/5G Cellular Systems;simulating Blockchain Consensus Protocols in Julia: Proof of Work vs Proof of Stake;Maximum Likelihood Estimators on MCMC Sampling Algorithms for Decision Making;employing Natural Language Processing Techniques for Online Job Vacancies Classification;Probabilistic Quantile Multi-step Forecasting of Energy Market Prices: A UK Case Study;proactive Buildings: A Prescriptive Maintenance Approach;performance Meta-analysis for Big-Data Univariate Auto-Imputation in the Building Sector;non-intrusive Diagnostics for Legacy Heat-Pump Performance Degradation;a 5G-Based Architecture for Localization Accuracy;anomaly Detection in Small-Scale Industrial and Household Appliances;an Innovative Software Platform for Efficient Energy, Environmental and Cost Planning in Buildings Retrofitting;deep learning-Based Segmentation of the Atherosclerotic Carotid Plaque in Ultrasonic Images;An Intelligent Grammar-Based Platform for RNA H-type Pseudoknot Prediction;An Automated 2D U-Net Segmentation Method for the Identification of Cancer Brain Metastases Using MRI Images;the Use of Robotics in Critical Use Cases: the 5G-ERA Project Solution;fundamental Features of the Smart5Grid Platform Towards Realizing 5G Implementation;experimentation Scenarios for machinelearning-Based Resource Management;efficient Data Management and Interoperability Middleware in Business-Oriented Smart Port Use Cases;5G for the Support of Smart Power Grids: Millisecond Level Precise Distributed Generation Monitoring and Real-Time Wide Area Monitoring;monitoring Neurological Disorder Patients via Deep learning Based Facial Expressions An
the proceedings contain 23 papers. the special focus in this conference is on Skin Imaging Collaboration, Interpretability of machine Intelligence in Medical Image Computing, Embodied AI and Robotics for Healthcare Wo...
ISBN:
(纸本)9783031776090
the proceedings contain 23 papers. the special focus in this conference is on Skin Imaging Collaboration, Interpretability of machine Intelligence in Medical Image Computing, Embodied AI and Robotics for Healthcare Workshop and MICCAI Workshop on Distributed, Collaborative and Federated learning. the topics include: DeCaF 2024 Preface;i2M2Net: Inter/Intra-modal Feature Masking Self-distillation for Incomplete Multimodal Skin Lesion Diagnosis;from Majority to Minority: A Diffusion-Based Augmentation for Underrepresented Groups in Skin Lesion Analysis;segmentation Style Discovery: Application to Skin Lesion Images;a Vision Transformer with Adaptive Cross-Image and Cross-Resolution Attention;lesion Elevation Prediction from Skin Images Improves Diagnosis;DWARF: Disease-Weighted Network for Attention Map Refinement;PIPNet3D: Interpretable Detection of Alzheimer in MRI Scans;Detecting Unforeseen Data Properties with Diffusion Autoencoder Embeddings Using Spine MRI Data;interpretability of Uncertainty: Exploring Cortical Lesion Segmentation in Multiple Sclerosis;TextCAVs: Debugging Vision Models Using Text;evaluating Visual Explanations of Attention Maps for Transformer-Based Medical Imaging;Exploiting XAI Maps to Improve MS Lesion Segmentation and Detection in MRI;EndoGS: Deformable Endoscopic Tissues Reconstruction with Gaussian Splatting;VISAGE: Video Synthesis Using Action Graphs for Surgery;a Review of 3D Reconstruction Techniques for Deformable Tissues in Robotic Surgery;SurgTrack: CAD-Free 3D Tracking of Real-World Surgical Instruments;MUTUAL: Towards Holistic Sensing and Inference in the Operating Room;Complex-Valued Federated learning with Differential Privacy and MRI applications;enhancing Privacy in Federated learning: Secure Aggregation for Real-World Healthcare applications;federated Impression for learning with Distributed Heterogeneous Data;A Federated learning-Friendly Approach for Parameter-Efficient Fine-Tuning of SAM in 3D Segmentation;probing the Effic
the proceedings contain 16 papers. the special focus in this conference is on machinelearning in Clinical Neuroimaging. the topics include: Brain-Cognition Fingerprinting via Graph-GCCA with Contrastive Lea...
ISBN:
(纸本)9783031787607
the proceedings contain 16 papers. the special focus in this conference is on machinelearning in Clinical Neuroimaging. the topics include: Brain-Cognition Fingerprinting via Graph-GCCA with Contrastive learning;hyperBrain: Anomaly Detection for Temporal Hypergraph Brain Networks;SpaRG: Sparsely Reconstructed Graphs for Generalizable fMRI Analysis;a Lightweight 3D Conditional Diffusion Model for Self-explainable Brain Age Prediction in Adults and Children;SOE: SO(3)-Equivariant 3D MRI Encoding;towards a Foundation Model for Cortical Folding;a Lesion-Aware Edge-Based Graph Neural Network for Predicting Language Ability in Patients with Post-stroke Aphasia;DISARM: Disentangled Scanner-Free Image Generation via Unsupervised Image2Image Translation;segmenting Small Stroke Lesions with Novel Labeling Strategies;a Progressive Single-Modality to Multi-modality Classification Framework for Alzheimer’s Disease Sub-type Diagnosis;Surface-Based Parcellation and Vertex-wise Analysis of Ultra High-resolution ex vivo 7 tesla MRI in Alzheimer’s disease and related dementias;Self-supervised Pre-training Tasks for an fMRI Time-Series Transformer in Autism Detection;is Your Style Transfer Doing Anything Useful? An Investigation into Hippocampus Segmentation and the Role of Preprocessing;GAMing the Brain: Investigating the Cross-Modal Relationships Between Functional Connectivity and Structural Features Using Generalized Additive Models.
the rise of Generative Artificial Intelligence (GAI) has brought new transformations to education. AI-driven educational applicationsthat employ human-machine collaborative teaching are expected to become one of the ...
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In various scientific and engineering disciplines, decision-making processes frequently rely on optimization strategies to identify the best solution from a multitude of potential options. Traditional optimization met...
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Cancer is one of the most dreadful illnesses that plague mankind. the illness has a high mortality rate. there are numerous kinds of this illness. It is challenging to identify these diseases in their early stages. Re...
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Cancer is one of the most dreadful illnesses that plague mankind. the illness has a high mortality rate. there are numerous kinds of this illness. It is challenging to identify these diseases in their early stages. Recent studies have shown the significance of machinelearning and Deep learning techniques in disease diagnosis. the most promising methods are presented in this study employing several machinelearning and deep learning algorithms and their comparative study to determine the specific type of cancer sickness that a patient has. Additionally, it offers the most effective models for each disease type currently in use analyzed using Accuracy and AUC ROC metrics.
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