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46th Annual Conference of the German Association for Pattern Recognition, DAGM-GCPR 2024

作者： Jensen, Joakim Chanda, Sukalpa Krishnan, Narayanan C. Doermann, David Department of Computer Science and Communication Østfold University College Halden Norway Department of Data Science Indian Institute of Technology Palakkad Kanjikode India Department of Computer Science and Engineering University at Buffalo Buffalo United States

ISBN: (纸本)9783031851865

Recent advancements in text-to-image (T2I) generation have revolutionized image synthesis, but conventional text-image paired training poses challenges when confronted with limited dataset size and narrow descriptive breadth. In particular, limited descriptive breadth can significantly impair a model’s ability to generate unmentioned image features. This issue is also evident in text-to-face image generation, where a method will underperform when rendering an image based on the text description of a specific facial feature not present in the training dataset. Language-free training emerges as a promising solution to this problem, leveraging latent spaces like CLIP to facilitate generalization from image embeddings to text embeddings. However, the modality gap remains a hurdle for language-free trained models. To address this, we propose a Gaussian perturbation-based technique that enhances perturbation coverage and robustness across varying modality gap sizes without the need to train a prior model. Our method achieves new state-of-the-art results on MM-CelebA-HQ in the language-free setting, presenting a novel solution to challenges in text-to-face image generation on limited datasets. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Photointerpretation

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Empirical Analysis of Nature-Inspired Algorithms for Autism Spectrum Disorder Detection Using 3D Video dataset

arXiv

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arXiv 2025年

作者： Panchal, Aneesh Khan, Kainat Katarya, Rahul Department of Computational and Data Sciences Indian Institute of Science Karnataka Bangalore560012 India Big Data Analytics and Web Intelligence Laboratory Department of Computer Science & Engineering Delhi Technological University Delhi110042 India

Autism Spectrum Disorder (ASD) is a chronic neurodevelopmental disorder symptoms of which includes repetitive behaviour and lack of social and communication skills. Even though these symptoms can be seen very clearly in social but a large number of individuals with ASD remains undiagnosed. In this paper, we worked on a methodology for detection of ASD from 3-dimensional walking video dataset, utilizing supervised machine learning (ML) classification algorithms and using nature-inspired optimization algorithms for feature extraction from dataset. The proposed methodology involve the classification of ASD using supervised ML classification algorithm and extracting important and relevant features from the dataset using nature-inspired optimization algorithms. We also included the ranking coefficients for finding the initial leading particle. This selection of particle significantly reduces the computation time and hence, improving the total efficiency and accuracy for the ASD detection. To evaluate the efficiency of the proposed methodology, we deployed various combination of classification algorithm and nature-inspired algorithms resulting in an outstanding classification accuracy of 100% using random forest classification algorithm and gravitational search algorithm for feature selection. The application of the proposed methodology with different datasets would enhance the robustness and generalizability of the proposed methodology. Due to high accuracy and less total computation time, the proposed methodology will offer a significant contribution to the medical and academia fields, providing a foundation for future research and advancements in ASD diagnosis. Copyright © 2025, The Authors. All rights reserved.

关键词： Diseases

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Dual-Channel Convolutional Neural Network with Attention Pooling and Sand Cat Swarm Optimization for Incremental Land-Cover Classification in Satellite Image Patches 4

Dual-Channel Convolutional Neural Network with Attention Poo...

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4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Anushkannan, N.K. Karpagavalli, C. Jagadesan, A. Kavya, R.V. Geetha, K. Devi, S. Rukmani Kathir College of Engineering Department of Electronics and Communication Engineering Tamil Nadu Coimbatore641062 India Ramco Institute of Technology Department of Artificial Intelligence and Data Science Tamil Nadu Rajapalayam626117 India R.M.K Engineering College Department of Physics Tamil Nadu Thiruvallur601206 India J J College of Engineering and Technology Department of Electronics and Communication Engineering Tamil Nadu Tiruchirappalli620009 India Srm Institute of Science and Technology Department of Computing Technologies Tamil Nadu Kattankulathur603203 India Saveetha College of Liberal Arts and Sciences Simats Deemed to Be University Department of Computer Science Tamil Nadu Chennai602105 India

ISBN: (纸本)9798331523923

Incremental Land-Cover Classification in satellite image patches concerns the categorization of different types of land cover in satellite images. Land cover mapping from satellite imagery has applications in environmental observation, planning and resource management, and disaster response. Current land cover change classification paradigms involve heavily sampled and trained state vector data models, which do not have provisions for dynamic updates consequent upon variation in the cover density sizes, seasonal changes, incremental growth of urban sprawl, deforestation, and other natural calamities. Since new satellite images become available often, retraining your models from the very beginning is both time-consuming and computationally expensive. To tackle these issues, we propose a Dual-Channel Convolutional Neural Network with Attention Pooling and Sand Cat Swarm Optimization (DualCCNNet-AP+SCSO) for incremental classification of land cover in satellite imagery patches. This methodology utilizes satellite image patches from the So2Sat image patch dataset, where images are initially preprocessed with Iterative Self-Guided Image Filtering (ISGIF). After preprocessing, the Spike-Driven Transformer (SDT) method extracts key features, which are then classified through the Dual-Channel Convolutional Neural Network with Attention Pooling (DualCCNNet-AP), fine-tuned by Sand Cat Swarm Optimization (SCSO) for accurate incremental land-cover classification. The DualCCNNet-AP+SCSO method is implemented in Python. The effectiveness of the proposed method was assessed on the So2Sat image patch dataset, where it achieved an impressive 99.9% accuracy and 99.5% recall, outperforming current techniques. Based on these conclusions about classification accuracy, it demonstrated that the proposed approach enables the effective classification of the change, especially in the smallest class of the land cover in the satellite image patches. The results of the proposed method were bett

关键词： Convolutional neural networks

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Bridging Evolutionary Multiobjective Optimization and GPU Acceleration via Tensorization

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IEEE Transactions on Evolutionary Computation 2025年

作者： Liang, Zhenyu Li, Hao Yu, Naiwei Sun, Kebin Cheng, Ran Southern University of Science and Technology Department of Computer Science and Engineering Shenzhen518055 China The Hong Kong Polytechnic University Department of Data Science and Artificial Intelligence The Department of Computing Hong Kong Hong Kong

Evolutionary multiobjective optimization (EMO) has made significant strides over the past two decades. However, as problem scales and complexities increase, traditional EMO algorithms face substantial performance limitations due to insufficient parallelism and scalability. While most work has focused on algorithm design to address these challenges, little attention has been given to hardware acceleration, thereby leaving a clear gap between EMO algorithms and advanced computing devices, such as GPUs. To bridge the gap, we propose to parallelize EMO algorithms on GPUs via the tensorization methodology. By employing tensorization, the data structures and operations of EMO algorithms are transformed into concise tensor representations, which seamlessly enables automatic utilization of GPU computing. We demonstrate the effectiveness of our approach by applying it to three representative EMO algorithms: NSGA-III, MOEA/D, and HypE. To comprehensively assess our methodology, we introduce a multiobjective robot control benchmark using a GPU-accelerated physics engine. Our experiments show that the tensorized EMO algorithms achieve speedups of up to 1113× compared to their CPU-based counterparts, while maintaining solution quality and effectively scaling population sizes to hundreds of thousands. Furthermore, the tensorized EMO algorithms efficiently tackle complex multiobjective robot control tasks, producing high-quality solutions with diverse behaviors. Source codes are available at https://***/EMI-Group/evomo. © 2025 IEEE.

关键词： Tensors

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Sentiment Analysis of Movie Reviews: Optimizing Predictions Through TF-IDF Vectorization

Sentiment Analysis of Movie Reviews: Optimizing Predictions ...

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Intelligent data Communication Technologies and Internet of Things (IDCIoT), International Conference on

作者： Mohd Irfan Anne Sreyas Venkatesam Hariharan Shanmugasundaram Mahendra Kumar Gourisaria Subrata Chowdhury Sudhansu Shekhar Patra Department of Artificial Intelligence and Data Science Vardhaman College of Engineering Hyderabad Telangana India Department of Artificial Intelligence and Data Science Vardhaman College of Engineering Hyderabad India School of Computer Engineering Kalinga Institute of Industrial Technology (KIIT) Deemed to be University Bhubaneswar India Dept. of Computer Science and Engineering Sreenivasa Institute of Technology and Management Studies(A) Chittoor Andhra Pradesh India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527556

Sentiment analysis model is aimed to accurately interpret movie reviews, addressing challenges like sarcasm and noisy data. By integrating multiple machine learning classifiers Naive Bayes, Random Forest, K-Nearest Neighbors, Logistic Regression, Decision Tree, and XGBoost—the model enhances sentiment prediction accuracy. The Term Frequency-Inverse Document Frequency (TF-IDF) method is used to extract features from a dataset of 50,000 movie reviews. Performance is evaluated using accuracy, precision, recall, and F1-score, with Logistic Regression achieving the best results, including an highest accuracy of 84.33 % and an F1-score of 0.847. In comparison to traditional methods, this multi-classifier approach offers a deeper understanding of movie reviews and its influence on audience decisions. The study demonstrates the potential of using these machine learning techniques to provide real-time, scalable sentiment analysis, which is crucial for filmmakers and marketers seeking insights into audience satisfaction and box office performance.

关键词： Sentiment analysis Logistic regression Accuracy Reviews Predictive models Motion pictures Feature extraction Real-time systems Noise measurement Random forests

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Blockchain-Based Secure Cloud Storage for IoT Healthcare Using BiGRU and BiLSTM Models

Blockchain-Based Secure Cloud Storage for IoT Healthcare Usi...

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Integrated Circuits and Communication Systems (ICICACS), IEEE International Conference on

作者： Prachi Arihant Chougule Sarumathi S Ravi Kant Raj Kumar Gupta U. Pushpa Latha Anvesh Perada Department of Electrical Engineering DKTE Society's Tetile and Engineering Institute Ichalkaranji India Department of Computer Science and Engineering HKBK College of Engineering Bangalore India Department of Computer and Data Sciences YSAICDS Shoolini University Solan India Department of Physics Sardar Vallabhbhai Patel College Bhabua VKSU Ara India Department of English Malla Reddy Engineering College (Autonomous) Hyderabad India Department of Electrical and Computer Engineering Drexel University Philadelphia United States of America

ISBN: (数字)9798331508456

ISBN: (纸本)9798331508463

Machine Learning (ML) algorithms have experienced a significant increase in popularity owing to the digitisation of analogue processes and other technological advancements, like the Internet of Things (IoT). Dependable datasets are crucial for the precise forecasting and resolution abilities of these algorithms, which are indispensable in healthcare, cloud computing, engineering, and finance. Conversely, training datasets are vulnerable to manipulation, thereby skewing the results. To address this issue, blockchain-based approaches have been proposed to enhance the reliability and security of cloud-stored E-Health data generated by the Internet of Things. To improve the accuracy and efficacy of healthcare prediction models, our study focusses on an advanced strategy that integrates feature selection with model training. Optimal input data is refined by feature selection employing entropy and correlation coefficient methodologies. The proposed model, employing BiGRU and BiLSTM, surpasses conventional CNNs and BiRNNs, with an average accuracy of 94.57%. When integrated with the Internet of Things (IoT), blockchain technology ensures the secure storage and transmission of electronic health records (EHRs). This strategy enhances predictive accuracy and safeguards sensitive health data in cloud infrastructures by amalgamating feature selection techniques with robust deep learning models.

关键词： Training Cloud computing Accuracy Bidirectional long short term memory Medical services Predictive models Feature extraction Internet of Things Security Secure storage

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Closer through commonality: Enhancing hypergraph contrastive learning with shared groups

arXiv

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arXiv 2025年

作者： Roh, Daeyoung Han, Donghee Kim, Daehee Han, Keejun Yi, Mun Graduate School of Data Science KAIST Korea Republic of School of Computer Engineering Hansung University Korea Republic of Department of Industrial and Systems Engineering KAIST Korea Republic of

Hypergraphs provide a superior modeling framework for representing complex multidimensional relationships in the context of real-world interactions that often occur in groups, overcoming the limitations of traditional homogeneous graphs. However, there have been few studies on hypergraph-based contrastive learning, and existing graph-based contrastive learning methods have not been able to fully exploit the high-order correlation information in hypergraphs. Here, we propose a Hypergraph Fine-grained contrastive learning (HyFi) method designed to exploit the complex high-dimensional information inherent in hypergraphs. While avoiding traditional graph augmentation methods that corrupt the hypergraph topology, the proposed method provides a simple and efficient learning augmentation function by adding noise to node features. Furthermore, we expands beyond the traditional dichotomous relationship between positive and negative samples in contrastive learning by introducing a new relationship of weak positives. It demonstrates the importance of fine-graining positive samples in contrastive learning. Therefore, HyFi is able to produce high-quality embeddings, and outperforms both supervised and unsupervised baselines in average rank on node classification across 10 datasets. Our approach effectively exploits high-dimensional hypergraph information, shows significant improvement over existing graph-based contrastive learning methods, and is efficient in terms of training speed and GPU memory cost. The source code is available at https://***/Noverse0/***. © 2025, CC BY.

关键词： Contrastive Learning

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Super-Resolution of Diffusion-Weighted Images via TDI-Conditioned Diffusion Model 15th

Super-Resolution of Diffusion-Weighted Images via TDI-Cond...

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15th International Workshop on Computational Diffusion MRI, CDMRI 2024, held in conjunction with 27th International Conference on Medical Image Computing and computer-Assisted Intervention, MICCAI 2024

作者： Ma, Jiquan Teng, Yujun Chen, Geng Jiang, Haotian Zhang, Kai Liu, Feihong Rekik, Islem Shen, Dinggang School of Computer Science and Technology Heilongjiang University Harbin China National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology School of Computer Science and Engineering Northwestern Polytechnical University Xi’an China School of Biomedical Engineering and State Key Laboratory of Advanced Medical Materials and Devices ShanghaiTech University Shanghai China School of Information Science and Technology Northwest University Xi’an China BASIRA Lab Imperial-X and Department of Computing Imperial College London London United Kingdom

ISBN: (纸本)9783031869198

Diffusion-Weighted Imaging (DWI) is a significant technique for studying white matter. However, it suffers from low-resolution obstacles in clinical settings. Post-acquisition Super-Resolution (SR) can enhance the resolution of DWIs and has gained increasing research interest in recent years. An advanced generative model, the Diffusion Model (DM), exhibits particularly promising performance in image SR. However, effective conditions are required to bootstrap the DM for DWI SR. To this end, we proposed the first DM-based DWI SR model with two effective conditions based on low-solution DWIs and Track Density Imaging (TDI) maps, which possess rich high-resolution prior knowledge Additionally, we consider another condition based on features from low-resolution DWIs. These two conditions are integrated into our model, which comprises three components: DWI Resolution Enhancer (DRE), DWI Feature Extractor (DFE), and TDI Feature Extractor (TFE). DRE combines low-resolution DWI features from DFE with TDI features from TFE to progressively generate high-resolution DWIs. We performed extensive experiments on DWIs of normal subjects from human connectome projects and patients with Parkinson’s disease. The results demonstrate that our model outperforms existing DWI SR models, both qualitatively and quantitatively. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Neurodegenerative diseases

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Automatic Repeat Request Design in AoI-Aware Broadcast With Heterogeneous Direct and Relay-Assisted Users

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IEEE Internet of Things Journal 2025年

作者： Wang, Xueer Pan, Haoyuan Chan, Tse-Tin Li, Jianqiang Shenzhen University College of Computer Science and Software Engineering Shenzhen China The Education University of Hong Kong Department of Mathematics and Information Technology Hong Kong Shenzhen University National Engineering Laboratory for Big Data System Computing Technology The College of Computer Science and Software Engineering Shenzhen China

This paper investigates the design of automatic repeat request (ARQ) protocols in age of information (AoI)-aware broadcast systems with heterogeneous users, including both direct and relay-assisted users. In this setup, a direct user receives status updates directly via a single-hop link, while a relay-assisted user can receive status updates through either a direct single-hop link or a two-hop relay-assisted link. While ARQ is commonly used to ensure reliable transmission in error-prone wireless networks, previous studies suggest that ARQ does not improve the average AoI in single-hop networks. However, its impact on AoI in systems with relays, particularly those involving heterogeneous users, remains unclear. We address this gap by analyzing the average AoI under different ARQ strategies, introducing a transmission limit k ≥ 0 at the relay. Here, k = 0, k = 1, and k > 1 correspond to non-relay, non-ARQ-at-relay, and truncated-ARQ-at-relay strategies, respectively. Utilizing a unified Markov chain framework that models the transmission processes for each user type, we derive the theoretical average AoI. Our results show that the direct user benefits most from non-relay and non-ARQ strategies, similar to single-hop systems. In contrast, the relay-assisted user achieves optimal performance with truncated-ARQ-at-relay, leveraging both direct and relay-assisted links. For the overall system, applying a non-ARQ-at-relay approach strikes a balance in AoI between the direct and relay-assisted users, leading to a more stable and lower system-wide average AoI. © 2014 IEEE.

关键词： Mobile telecommunication systems

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Malignancy Detection in Lung and Colon Histopathology Images by Transfer Learning with Class Selective Image Processing

Recent Advances in Computer Science and Communications

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Recent Advances in computer Science and Communications 2025年第4期18卷

作者： Sukhavasi, Vidyullatha Kulkarni, Shridhar Raghavendran, V. Dastagiraiah, C. Apat, Shraban Kumar Reddy, Pundru Chandra Shaker Department of Computer Science and Engineering BVRIT HYDERABAD College of Engineering for Women Hyderabad India Department of Data Sciences Harrisburg University of Science & Technology HarrisburgPA United States Tamil Nadu Chennai India Computer Science and Engineering Anurag University Telangana Hyderabad India Geethanjali College of Engineering and Technology Telangana Hyderabad India School of Computer Science and Artificial Intelligence SR University Telangana Warangal India

Aims & Background: Due to its ferocity, enormous metastatic potential, and variability, cancer is responsible for a disproportionately high number of deaths. Cancers of the lung and colon are two of the most common forms of the disease in both sexes worldwide. The excellence of treatment and the endurance rate for cancer patients can be greatly improved with early and precise diagnosis. Objectives & Methodology: We suggest a computationally efficient and highly accurate strategy for the rapid and precise diagnosis of lung and colon cancers as a substitute for the standard approaches now in use. The training and validation procedures in this work made use of an enormous dataset consisting of lung and colon histopathology pictures. There are 25,000 Histopathological Images (HIs) in the dataset, split evenly among 5 categories (mostly lung and colon tissues). Before training it on the dataset, a pretrained neural network (AlexNet) had its four layers fine-tuned. Results: The study enhances malignancy detection in lung and colon histopathology images by applying transfer learning with class-selective image processing. Instead of enhancing the entire dataset, a targeted contrast enrichment was applied to images from the underperforming class, improving the model's accuracy from 92.3% to 99.2% while reducing computational overhead. Conclusion: This approach stands out by emphasizing class-specific enhancements, leading to significant performance gains. The results meet or exceed established CAD metrics for breast cancer histological images, demonstrating the method's efficiency and effectiveness. © 2025 Bentham Science Publishers.

关键词： Lung cancer

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