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2nd international workshop on Recent Advances in Digital Security: Biometrics and Forensics, BioFor 2019, 1st international workshop on pattern recognition for Cultural Heritage, PatReCH 2019, 1st international workshop eHealth in the Big data and Deep learning Era, e-BADLE 2019, international workshop on Deep Understanding Shopper Behaviors and Interactions in Intelligent Retail Environments, DEEPRETAIL 2019 and Industrial session held at the 20th international Conference on Image Analysis and Processing, ICIAP 2019

2nd International Workshop on Recent Advances in Digital Sec...

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2nd international workshop on Recent Advances in Digital Security: Biometrics and Forensics, BioFor 2019, 1st international workshop on pattern recognition for Cultural Heritage, PatReCH 2019, 1st international workshop eHealth in the Big data and Deep learning Era, e-BADLE 2019, international workshop on Deep Understanding Shopper Behaviors and Interactions in Intelligent Retail Environments, DEEPRETAIL 2019 and Industrial session held at the 20th international Conference on Image Analysis and Processing, ICIAP 2019

ISBN: (纸本)9783030307530

The proceedings contain 39 papers. The special focus in this conference is on Recent Advances in Digital Security. The topics include: Modern vs Diplomatic Transcripts for Historical Handwritten Text recognition;improving Ancient Cham Glyph recognition from Cham Inscription Images Using data Augmentation and Transfer learning;Oracle Bone Inscription Detector Based on SSD;shot Boundary Detection for Automatic Video Analysis of Historical Films;the Epistle to Cangrande Through the Lens of Computational Authorship Verification;a Cockpit of Measures for Image Quality Assessment in Digital Film Restoration;augmented Reality for the Valorization and Communication of Ruined Architecture;classification of Arabic Poems: from the 5th to the 15th Century;a Page-Based Reject Option for Writer Identification in Medieval Books;on the Cross-Finger Similarity of Vein patterns;minimizing Training data for Reliable Writer Identification in Medieval Manuscripts;fusion of Visual and Anamnestic data for the Classification of Skin Lesions with Deep learning;slide Screening of Metastases in Lymph Nodes via Conditional, Fully Convolutional Segmentation;A learning Approach for Informative-Frame Selection in US Rheumatology Images;a Serious Game to Support Decision Making in Medical Education;nerve Contour Tracking for Ultrasound-Guided Regional Anesthesia;Skin Lesions Classification: A Radiomics Approach with Deep CNN;semantic 3D Object Maps for Everyday Robotic Retail Inspection;collecting Retail data Using a Deep learning Identification Experience;a Large Scale Trajectory dataset for Shopper Behaviour Understanding;Improving Multi-scale Face recognition Using VGGFace2;An IOT Edge-Fog-Cloud Architecture for Vision Based Pallet Integrity;the Vending Shopper Science Lab: Deep learning for Consumer Research.

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PhD Symposium, Posters, Demos, and A Web for more inclusive, sustainable and prosperous societies, WEB-for-GOOD 2024 and 1st international workshop on AI and Web data Analytics, AIWDA 2024 form the 25th international Conference on Web Information Systems Engineering, WISE 2024

PhD Symposium, Posters, Demos, and A Web for more inclusive,...

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PhD Symposium, Posters, Demos, and A Web for more inclusive, sustainable and prosperous societies, WEB-for-GOOD 2024 and 1st international workshop on AI and Web data Analytics, AIWDA 2024 form the 25th international Conference on Web Information Systems Engineering, WISE 2024

ISBN: (纸本)9789819614820

The proceedings contain 36 papers. The special focus in this conference is on Web Information Systems Engineering. The topics include: Effective Transparent Monitoring of Personal data;iCNN-LstM: An Incremental CNN-LstM Based Ransomware Detection System;a Mini Review on Purchaser Security in the Metaverse: Challenges and Solutions;low-Resource dataset Synthetic Generation for Hate Speech Detection;Web Open data to SDG Indicators: Towards an LLM-Augmented Knowledge Graph Solution;leveraging Sentence-Transformers to Overcome Query-Document Vocabulary Mismatch in Information Retrieval;time Distance Aware for Multi-component Graph Collaborative Filtering;scientific Documents Recommendation Based on Graph Convolutional Network;semantic Communication of Images Using Image Generation and Image Captioning Models;leveraging Optimization Techniques for Effective Arabic Query Expansion;DURLLCON: Deep Reinforcement learning for URLLC Optimization in Multi-edge Networks;FMM-RNS: A Fast HMM Map Matching Method Based on Road Network Simplification;Context-Aware Selection of machine learning as a Service (MLaaS) in IoT Environments;GraphTFD: A Fraud Detection System Based on Graph Transformer;MLGE-AC-UFD: Multi-level Graph Embedding and Approximate Computation for Unsupervised Fraud Detection;SeCORE: Quantitative Security Assurance and Evaluation Platform;developing Geospatial Web Applications Using Question Answering Engines, Knowledge Graphs and Linked data Tools;A Visual Query Builder for DBpedia;FL-PPELA: Partial Parameter Enhancement and Local Adaptive Aggregation for Personalized Federated learning;ORCPM: An Online Regional Core pattern mining System;MTRM: A Web-Miner Multi-Threshold mining Co-location patterns to Mitigate Redundancy;constrained Path Optimization on Time-Dependent Road Networks;prompt strategies for Sarcastic Meme Detection: A Comparative Analysis;Deepfake Detection in Cancer Medical Imaging Using CNN Architectures;strengthening Cybersecurity: The Influence o

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Empirical techniques for effort estimation in designing effective ML models

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international Journal of Computers and Applications 2025年第2期47卷 226-234页

作者： Padmanabha Reddy, Y.C.A. Kosuru, Sai Sathwik Sirisalla, Nageswara Rao Vivekananda, G.N. Perala, Venkata Akanksha Department of Artificial Intelligence and Machine Learning Chaitanya Bharati Institute of Technology Telangana Hyderabad India Department of CSE B V Raju Institute of Technology Telangana Narsapur India Department of Computer Science and Engineering K.S.R.M. College of Engineering AP Kadapa India School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore India

machine learning Research often involves the use of diverse libraries, modules, and pseudocodes for data processing, cleaning, filtering, pattern recognition, and computer intelligence. Quantization of Effort Required for the above cumulative processes is rarely discussed in the existing works and The time to reach the desired level of the model's functionality is essential to gauge the environment training for the model training and pre-deployment testing. In this study, we empirically defined the manual-cum-computational effort required for the model development in terms of 2-time factors: time-to-live (time until 1st code modification) and time-for-modification (time between 2 codebase changes) taken together in a mathematical model to compute Effort Factor(quantitative measure of determining effort needed to design ML algorithms). The study is novel in terms of how the effort required to create ML models is calculated with a particular focus on the manual effort direction. The results and the findings obtained can be used for determining the total time needed to synthesize ML models and frameworks in terms of code change cycles and implementation strategies marking a 25% performance increase in the current method above the standard Pipeline. © 2025 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Pipeline codes

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Analysis of learning Behaviour of Open Education Learners based on Principal Component Analysis and K-means Clustering Algorithm 1

Analysis of Learning Behaviour of Open Education Learners ba...

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1st international Conference on Intelligent Systems and Computational Networks, ICISCN 2025

作者： Wang, Sufang Dongying Vocational Institute Dongying China

ISBN: (纸本)9798331529246

Open Education Platforms have revolutionized learning by providing accessible and flexible education to a vast number of learners. However, understanding learner behaviour in such environments remains a challenge due to the diverse nature of data generated. This study explores the learning behaviour of open education learners using data mining techniques which involves the hybrid technique. A dataset containing learner interactions, engagement levels, and performance metrics was analysed using unsupervised learning algorithms. Dimensionality reduction techniques such as Principal Component Analysis (PCA) is employed to enhance computational efficiency and improve pattern recognition. Clustering techniques like K-Means is used to categorize learners into distinct groups based on learning behaviour patterns. For predictive analysis, classification models were trained, achieving an accuracy of 97.5%. Other evaluation metrics, including precision (95.2%), recall (96.7%), and F1-score (95.9%), were used to assess model performance. Insights from this analysis can help educators personalize learning experiences, enhance student engagement, and improve retention rates in open education platforms. © 2025 IEEE.

关键词： Contrastive learning

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Cross-Modality Image Quality Prediction for Time-Resolved CT from Breathing Signals

Cross-Modality Image Quality Prediction for Time-Resolved C...

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workshop on Longitudinal Disease Tracking and Modeling with Medical Images and data, LDTM 2024, 5th international workshop on Multiscale Multimodal Medical Imaging, MMMI 2024, 1st workshop on machine learning for Multimodal/-sensor Healthcare data, ML4MHD2024 and workshop on Multimodal learning and Fusion Across Scales for Clinical Decision Support, ML-CDS 2024 held in conjunction with the 27th international Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2024

作者： Schwarz, Annette Dickmann, Jannis Hofmann, Christian Szkitsak, Juliane Bert, Christoph Maier, Andreas Arias-Vergara, Tomás Pattern Recognition Lab Friedrich-Alexander-Universität Erlangen-Nürnberg Erlangen Germany Siemens Healthineers AG Forchheim Germany Department of Radiation Oncology Universitätsklinikum Erlangen Friedrich-Alexander-Universität Erlangen-Nürnberg Erlangen Germany Erlangen Germany

ISBN: (纸本)9783031845246

Four-dimensional computed tomography (4DCT) is a time-resolved, multi-modal imaging method that captures respiratory signals synchronised with the CT scan in order to track the movement of the lung. It is routinely used in radiation therapy treatment planning for lung or liver cancer patients. However, image artifacts during 4DCT scans caused by irregular patient breathing negatively impact treatment outcomes. This work proposes a method to automatically detect patients at high risk for severe image artifacts even before the scan is conducted based on an pre-scan analysis of their breathing. This can help to take proactive measures to improve image quality, such as changing the scan mode or providing in-depth patient coaching. A deep neural network is trained to predict the image quality score of 28 lung CT phantom scans, each rated by ten clinical experts. Different pretrained networks are investigated for feature generation and combined with two linear output heads to predict the average expert image quality score of unseen scans. We were able to predict the quality of a 4DCT with a mean absolute error of 8% using only the one-dimensional breathing signal as input. This accuracy is comparable to the rating consistency of our clinical experts, which were rating the images directly. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Radiotherapy

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Discovering Causal Relationships in Noisy Web data for Sentiment Classification Using Attention Mechanisms

Discovering Causal Relationships in Noisy Web Data for Sen...

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作者： Mihoubi, Miloud Zerkouk, Meriem Chikhaoui, Belkacem Artificial Intelligence Institute University of Téluq 5800 rue Saint-Denis bureau 1105 MontrealQCH2S 3L5 Canada

ISBN: (纸本)9789819614820

In the era of big data, sentiment analysis on social media platforms presents unique challenges due to the noisy and unstructured nature of the data. This study introduces a novel deep learning approach for discovering causal relationships within such noisy web data using an attention-enhanced architecture. The proposed model leverages an embedding layer, bidirectional LstM, attention mechanisms, global average pooling, and dropout techniques, followed by a dense layer with sigmoid activation for binary classification. We validated our model on the Sentiment140 dataset, achieving a test accuracy of 82.36% and an F1-score of 0.8246, outperforming traditional machine learning models such as Logistic Regression, Support Vector machines (SVM), and Random Forests. An extensive ablation study was conducted to assess the contributions of key model components, confirming the critical role of attention mechanisms and bidirectional LstM in improving performance and interpretability. Beyond its superior performance in sentiment classification, our model provides insights into the causal dynamics driving sentiment shifts on social media. This work significantly contributes to the fields of web data mining and causal inference by offering a robust framework capable of handling the complexities of noisy, high-dimensional data commonly encountered in online environments.. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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Curating Reagents in Chemical Reaction data with an Interactive Reagent Space Map 1st

Curating Reagents in Chemical Reaction Data with an Interact...

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1st international workshop on AI in Drug Discovery (AIDD)

作者： Andronov, Mikhail Andronova, Natalia Wand, Michael Schmidhuber, Jurgen Clevert, Djork-Arne SUPSI USI IDSIA CH-6900 Lugano Switzerland Pfizer Res & Dev Machine Learning Res Friedrichstr 110 D-10117 Berlin Germany SUPSI Inst Digital Technol Personalized Healthcare CH-6900 Lugano Switzerland KAUST AI Initiat Thuwal 23955 Saudi Arabia

ISBN: (纸本)9783031723803;9783031723810

The increasing use of machine learning and artificial intelligence in chemical reaction studies demands high-quality reaction data, necessitating specialized tools enabling data understanding and curation. Our work introduces a novel methodology for reaction data examination centered on reagents - essential molecules in reactions that do not contribute atoms to products. We propose an intuitive tool for creating interactive reagent space maps using distributed vector representations, akin to word2vec in Natural Language Processing, capturing the statistics of reagent usage within datasets. Our approach enables swift assessment of reagent action patterns and identification of erroneous reagent entries, which we demonstrate using the USPTO dataset. Our contributions include an open-source web application for visual reagent pattern analysis and a table cataloging around six hundred of the most frequent reagents in USPTO annotated with detailed roles. Our method aims to support organic chemists and cheminformatics experts in reaction data curation routine.

关键词： Reagents word2vec USPTO Chemical data curation

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Enhanced Classification Performance through Joint Mutual Information for Feature Selection in Life Science data 1

Enhanced Classification Performance through Joint Mutual Inf...

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1st international Conference on Intelligent Systems and Computational Networks, ICISCN 2025

作者： Saha, Soumitra Kumar, Rakesh Gupta, Meenu Zanke, Pankaj Chandigarh University Department of Cse Mohali India Spaiens International Corporation Atlanta United States

ISBN: (纸本)9798331529246

Multifarious approaches exist for data preprocessing in machine learning (ML), pattern recognition, and data mining, and feature selection (FS) is regarded as the most efficacious technique for dimensionality reduction in a structured way. The existence of irrelevant, redundant, and disproportionate features directly diminishes the model's performance and dramatically expands its complexity. Distinguishable FS algorithms can outperform others, and the most influential aspect of a well-performing FS algorithm is discovering momentous features that help optimize model performance by processing data more efficiently. Therefore, this study introduces a novel FS technique based on the joint mutual information (FSJMI) technique for life science data. This technique declines the number of features and enhances the model's performance, or at least maintains it in most cases, making it a unique and promising approach. It also enriches predictive accuracy while minimizing computational overhead by substituting the correlation between the features and the correlation substitution of the class values with the features. Comprehensive experiments utilizing ten diverse life science datasets have been conducted to validate the suggested model's performance. The experimental results demonstrate a substantial improvement in the proposed techniques compared to other well-known FS techniques and no selection (NoSel) in the primary dataset. The results conveyed using widely used ML algorithms such as Decision Tree (DT), K Nearest Neighbour (KNN), Naive Bayes (NB) Classifier, and Logistic Regression (LR) reveal that the presented algorithm demonstrated superior performance and outperformed NoSel and other FS algorithms in mean accuracy by at least 2.7%, 3.466%, 2.038%, and 1.792%, respectively. Additionally, regarding the LR model, the proposed algorithm outperforms one of the FS models by at most 11.631%. These findings of the proposed algorithm regarding outcomes highlight its capabili

关键词： Dimensionality reduction

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Back to the Future: Challenges of Sparse and Irregular Medical Image Time Series

Back to the Future: Challenges of Sparse and Irregular M...

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作者： Disch, Nico Albert Peretzke, Robin Roy, Saikat Ulrich, Constantin Zimmerer, David stiefelhagen, Rainer Kleesiek, Jens Maier-Hein, Klaus Division of Medical Image Computing German Cancer Research Center Heidelberg Germany HIDSS4Health - Helmholtz Information and Data Science School for Health Karlsruhe Heidelberg Germany Faculty of Mathematics and Computer Science University of Heidelberg Heidelberg Germany Pattern Analysis and Learning Group Department of Radiation Oncology Heidelberg University Hospital Heidelberg Germany Karlsruhe Institute of Technology Karlsruhe Germany University Hospital Essen Essen Germany University Hospital Essen West German Cancer Center Essen Essen Germany Medical Faculty Heidelberg University of Heidelberg Heidelberg Germany

ISBN: (纸本)9783031845246

In longitudinal medical image analysis, most work focuses on regularly sampled images, or on tasks like regression or classification. However, in the clinical context, images are frequently generated irregularly due to factors such as cost constraints. This work tackles the problem of irregularly sampled longitudinal medical imaging by evaluating methods in segmentation and reconstruction tasks. We examine the reconstruction for real-life MRI from patients with Alzheimer’s Disease (AD), where the model predicts future MRI scans from an arbitrary number of input images sampled at irregular intervals. However, experiments show that most models cannot surpass the performance of using an image from the same patient at a different time point as a baseline. Therefore, we conducted experiments on a synthetic dataset to better isolate effects in temporal learning. These experiments offer insights into model behavior and serve as a benchmark for validating models’ longitudinal learning capability, thus providing a straightforward method to assess temporal understanding in a controlled environment. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Neurodegenerative diseases

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Longitudinal Segmentation of MS Lesions via Temporal Difference Weighting

Longitudinal Segmentation of MS Lesions via Temporal Diffe...

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作者： Rokuss, Maximilian R. Kirchhoff, Yannick Roy, Saikat Kovacs, Balint Ulrich, Constantin Wald, Tassilo Zenk, Maximilian Denner, stefan Isensee, Fabian Vollmuth, Philipp Kleesiek, Jens Maier-Hein, Klaus Division of Medical Image Computing Heidelberg Germany Faculty of Mathematics and Computer Science Heidelberg University Heidelberg Germany HIDSS4Health - Helmholtz Information and Data Science School for Health Karlsruhe Heidelberg Germany Medical Faculty Heidelberg Heidelberg University Heidelberg Germany Helmholtz Imaging German Cancer Research Center Heidelberg Germany Department of Neuroradiology Heidelberg University Hospital Heidelberg Germany Division for Computational Neuroimaging Department of Neuroradiology Heidelberg University Hospital Heidelberg Germany University Hospital Essen Essen Germany University Hospital Essen West German Cancer Center Essen Essen Germany Pattern Analysis and Learning Group Department of Radiation Oncology Heidelberg University Hospital Heidelberg Germany

ISBN: (纸本)9783031845246

Accurate segmentation of Multiple Sclerosis (MS) lesions in longitudinal MRI scans is crucial for monitoring disease progression and treatment efficacy. Although changes across time are taken into account when assessing images in clinical practice, most existing deep learning methods treat scans from different timepoints separately. Among studies utilizing longitudinal images, a simple channel-wise concatenation is the primary albeit suboptimal method employed to integrate timepoints. We introduce a novel approach that explicitly incorporates temporal differences between baseline and follow-up scans through a unique architectural inductive bias called Difference Weighting Block. It merges features from two timepoints, emphasizing changes between scans. We achieve superior scores in lesion segmentation (Dice Score, Hausdorff distance) as well as lesion detection (lesion-level F1 score) as compared to state-of-the-art longitudinal and single timepoint models across two datasets. Our code is made publicly available at: ***/MIC-DKFZ/Longitudinal-Difference-Weighting. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Image segmentation

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