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

作者： Gurbade, Viraj Vijay Pacharaaney, Utkarsha Puri, Chetan Department of Artificial Intelligence and Data Science Maharashtra Wardha442001 India Department of Computer Science and Design Maharashtra Wardha442001 India

ISBN: (纸本)9798331523923

This is an essential tool used in the orthodontic practice for the assessment of craniofacial structures and as a basis in initiating treatment planning. Within the pre-AI/deep learning era, it was achieved through the procedure of manual tracing on 2D radiographs, individual variability, and quite time-consuming to obtain the established norm but which was susceptible. These technologies apply CNNs to properly detect landmarks and reduce the occurrence of human mistake, ensuring reproducibility of measurements across analyses. Automated systems demonstrated promising performance for landmark detection both on 2D and 3D images, particularly within the history of 3D imaging techniques such as cone-beam computed tomography (CBCT), offering a sweeping view of craniofacial anatomy. Studies show that training these AI models based on large datasets enables generalization to many images, thereby allowing landmark placement in reliable ways, with improved diagnostic accuracy;however, there's a challenge in achieving model robustness across different populations and complex 3D data. Future directions include the integration of AI with augmented reality, enabling real-time diagnostics to further depict and adjust clinicians' abilities to visualize treatment plans. With advancements in AI in cephalometry, the model may be hybrid to combine AI automation with manual verification, thus optimising reliability for clinical use, paving the way for more personalized and accurate orthodontic care. © 2025 IEEE.

关键词： computerized tomography

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A Multimodal Multiobjective Evolutionary Algorithm for Filter Feature Selection in Multilabel Classification

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第9期5卷 4428-4442页

作者： Hancer, Emrah Xue, Bing Zhang, Mengjie Victoria University of Wellington Center for Data Science and Artificial Intelligence School of Engineering and Computer Science Wellington6140 New Zealand Mehmet Akif Ersoy University Department of Software Engineering Burdur15039 Turkey

Multilabel learning is an emergent topic that addresses the challenge of associating multiple labels with a single instance simultaneously. Multilabel datasets often exhibit high dimensionality with noisy, irrelevant, and redundant features. In recent years, multilabel feature selection (MLFS) has gained prominence as a crucial and emerging machine learning task due to its ability to handle such data effectively. However, existing approaches for MLFS often prioritize top-ranked features based on intrinsic data criteria, disregarding relationships within the feature subset. Additionally, compared with conventional feature selection, multiobjective evolutionary algorithms (MOEAs) have not been widely explored in the context of MLFS. This study aims to address these gaps by proposing a multimodal multiobjective evolutionary algorithm (MMOEA) called MMDE_SICD which incorporates a preelimination scheme, an improved initialization scheme, an exploration scheme inspired by genetic operations and a statistically inspired crowding distance scheme. The results show that the proposed MMDE_SICD algorithm can outperform a variety of MOEAs and MMOEAs as well as conventional MLFS algorithms. Notably, this study is the first of its kind to consider MLFS as a multimodal multiobjective problem. © 2020 IEEE.

关键词： Multiobjective optimization

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Analyzing Gray Matter Structural Networks Using Diffusion Tensor Imaging

Analyzing Gray Matter Structural Networks Using Diffusion Te...

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2024 International Conference on Communication, computer sciences and Engineering, IC3SE 2024

作者： Singh, Raghvendra Bharti, Dilip Kumar Chaudhary, Preeti Tripathi, Akhilesh Kanaujia, Vishal Kumar Verma, Aditya Grater Noida India Abes Engineering College Computer Science And Engineering-Data Science Ghaziabad India Graphic Era Hill University Computer Science And Engineering Dehradun India Galgotia University Computer Science And Engineering Deapartment Greater Noida India

ISBN: (纸本)9798350366846

Recent advancements in scientific imaging techniques have allowed for the visualization and analysis of elaborate structural networks within the human brain. one of these techniques, referred to as diffusion tensor imaging (DTI), has end up more and more famous inside the have a look at of grey be counted structural networks. DTI measures the diffusion of water molecules within the brain, providing statistics approximately the integrity of white remember pathways and structural connectivity between special mind areas. The first step in reading grey count number structural networks the usage of DTI is to acquire DTI facts from a topic's brain. This entails the use of magnetic resonance imaging (MRI) and specialized imaging sequences to seize the diffusion records. The ensuing statistics is then processed to generate three-D maps of white count number tracts and their connections, known as tractography. Once tractography is entire, a number of community evaluation techniques may be applied to the information to benefit insights into the corporation and characteristic of the mind's structural networks. Those techniques consist of measures of community density, connectivity strength, and network centrality, which could assist discover key regions and pathways inside the community. One not unusual application of DTI in gray count structural community analysis is the look at of neurological disorders which includes Alzheimer's ailment and a couple of sclerosis. Through evaluating the structural connectivity patterns in healthy people to people with the sickness. The research focus described in the abstract revolves around using DTI to study the structural networks of the human brain, with a particular emphasis on gray matter, and its applications in understanding neurological disorders. © 2024 IEEE.

关键词： Diffusion tensor imaging

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MEPNet: Medical Entity-Balanced Prompting Network for Brain CT Report Generation 39

MEPNet: Medical Entity-Balanced Prompting Network for Brain ...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Zhang, Xiaodan Shi, Yanzhao Ji, Junzhong Zheng, Chengxin Qu, Liangqiong College of Computer Science Beijing University of Technology Beijing China School of Computing and Data Science The University of Hong Kong Hong Kong

ISBN: (纸本)157735897X

The automatic generation of brain CT reports has gained widespread attention, given its potential to assist radiologists in diagnosing cranial diseases. However, brain CT scans involve extensive medical entities, such as diverse anatomy regions and lesions, exhibiting highly inconsistent spatial patterns in 3D volumetric space. This leads to biased learning of medical entities in existing methods, resulting in repetitiveness and inaccuracy in generated reports. To this end, we propose a Medical Entity-balanced Prompting Network (MEPNet), which harnesses the large language model (LLM) to fairly interpret various entities for accurate brain CT report generation. By introducing the visual embedding and the learning status of medical entities as enriched clues, our method prompts the LLM to balance the learning of diverse entities, thereby enhancing reports with comprehensive findings. First, to extract visual embedding of entities, we propose Knowledge-driven Joint Attention to explore and distill entity patterns using both explicit and implicit medical knowledge. Then, a Learning Status Scorer is designed to evaluate the learning of entity visual embeddings, resulting in unique learning status for individual entities. Finally, these entity visual embeddings and status are elaborately integrated into multi-modal prompts, to guide the text generation of LLM. This process allows LLM to self-adapt the learning process for biased-fitted entities, thereby covering detailed findings in generated reports. We conduct experiments on two brain CT report generation benchmarks, showing the effectiveness in clinical accuracy and text coherence. © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Report generators

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English-to-Low-Resource Translation: A Multimodal Approach for Hindi, Malayalam, Bengali, and Hausa 9

English-to-Low-Resource Translation: A Multimodal Approach f...

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9th Conference on Machine Translation, WMT 2024

作者： Hatami, Ali Banerjee, Shubhanker Arcan, Mihael Buitelaar, Paul McCrae, John Philip Insight SFI Research Centre for Data Analytics Data Science Institute University of Galway Ireland ADAPT Centre Data Science Institute University of Galway Ireland Lua Health Galway Ireland School of Computer Science University of Galway Ireland

ISBN: (纸本)9798891761797

Multimodal machine translation leverages multiple data modalities to enhance translation quality, particularly for low-resourced languages. This paper uses a multimodal model that integrates visual information with textual data to improve translation accuracy from English to Hindi, Malayalam, Bengali, and Hausa. This approach employs a gated fusion mechanism to effectively combine the outputs of textual and visual encoders, enabling more nuanced translations that consider both language and contextual visual cues. The model’s performance was evaluated against the text-only machine translation model based on BLEU, ChrF2 and TER. Experimental results demonstrate that the multimodal approach consistently outperforms the text-only baseline, highlighting the potential of integrating visual information in low-resourced language translation tasks. ©2024 Association for Computational Linguistics.

关键词： Machine translation

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data Analysis with Model Explainability for Air Quality and Pollution Prediction of Urban Areas in Edge Networks 12th

Data Analysis with Model Explainability for Air Quality an...

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12th International Conference on Big data and Artificial Intelligence, BDA 2024

作者： Singh, Akansha Chakraborty, Soumendu Adhikari, Mainak Department of Computer Science Indian Institute of Information Technology Lucknow Lucknow India School of Data Science Indian Institute of Science Education and Research Thiruvananthapuram Thiruvananthapuram India

ISBN: (纸本)9783031818202

Air quality significantly impacts human health and economic conditions, making precise and timely assessment crucial in urban areas. Existing studies often fail to predict pollution accurately in smaller areas due to varying meteorological conditions. Most existing prototypes rely on standard ML models, trained with generic, publicly available datasets, which often lack local dynamics, adaptability, and synchronization. To address these challenges, in this paper, we propose a new Hybrid Edge-Cloud Air Quality Monitoring System. For initial model training a robust testbed is created by data Fusion of air quality data from diverse sources like Application Programming Interfaces and public datasets. The system trains and refines ML models in the cloud, identifies key features to reduce feature dimensions, and leverages local decision-making using site-specific data collected by sensors on edge devices. Sensors monitor local environmental parameters for real-time analysis and decision-making, ensuring accurate predictions for specific urban locations while optimizing performance and minimizing resource usage on edge devices. Explainable AI techniques improve transparency and confidence in decisions. The model is validated by simulating various ML models on the testbed, with and without feature extraction. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Prediction models

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An improved estimation method of secondary path uncertainties for feedback active noise control earphones

Noise Control Engineering Journal

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Noise Control Engineering Journal 2024年第3期72卷 166-174页

作者： Guo, Jianfeng Xun, Ziyou Ren, Zhenxing Yin, Lan Wang, Han The College of Computer Science and Technology College of Data Science Taiyuan University of Technology Taiyuan030024 China The Key Laboratory of Noise and Vibration Research Institute of Acoustics Chinese Academy of Sciences Beijing100190 China

The fixed feedback control has been widely used for active noise-canceling earphones due to its simple structure and higher performance for low-frequency noise reduction. Although some disk-based models have been done to design robust controllers, they usually overestimate the uncertainty and thus limit noise reduction. In this article, we analyze the distribution of measured secondary paths and propose the tri-rectangle model to provide the uncertainty bound. The cascade biquad controllers are optimized by solving the multi-constrained optimization problem, with H2 performance criteria and H1 performance criteria and constraints of secondary path uncertainty and noise amplification. Simulation and experiment results show that the proposed tri-rectangle model provides a more accurate estimate of the uncertainty than disk-based models, and the designed controller achieves good noise attenuation with acceptable noise enhancement and strong stability. © 2024 Institute of Noise Control Engineering.

关键词： Controllers

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Enhancing Weakly Supervised Medical Segmentation via Heterogeneous Co-training with Box-Wise Augmentation and Pseudo-Label Filtering 6th

Enhancing Weakly Supervised Medical Segmentation via Hetero...

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6th IFIP TC 12 International Conference on Intelligence science, ICIS 2024

作者： Wang, You Qi, Lei Yu, Qian Shi, Yinghuan Gao, Yang State Key Laboratory of Novel Software Technology National Insititute of Health-care Data Science Nanjing University Nanjing China School of Computer Science and Engineering Southeast University Nanjing China School of Data and Computer Science Shandong Woman’s University Jinan China

ISBN: (纸本)9783031712524

In this paper, we introduce an innovative approach to weakly supervised medical image segmentation with box annotations. Different from the previous methods which simply utilize a single conventional network with the same augmentation techniques widely used in supervised segmentation, we aim to introduce diverse augmentations and heterogenous networks to leverage the box annotations for promising generalization ability. Specifically, to amplify the diversity between the contents within the box and its surroundings, we propose the interior and exterior box augmentation (IEBA) technique, in which distinct augmentation techniques are employed for regions inside and outside the bounding boxes. Also, for the purpose of selecting pseudo-labels of superior quality, we propose the pseudo-label filter module (PLFM) to eliminate unreliable pseudo-labels. Besides, as CNN demonstrates superior capabilities in acquiring local information, and ViT specializes in capturing global context, we facilitate a bidirectional learning process between CNN and ViT through quadruple cross consistency losses (QCCL). In inference, we only employ the superior model from the validation set to obtain parameter efficiency. Our approach is evaluated across four tasks on two public datasets, utilizing the 3D dice similarity coefficient as the evaluation metric. The experimental results show that the proposed method outperforms the state-of-the-art comparison methods. © IFIP International Federation for Information Processing 2025.

关键词： Image segmentation

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Machine Learning Models for Speech-Based Depression Screening

Machine Learning Models for Speech-Based Depression Screenin...

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2025 IEEE International Conference on Cybernetics and Innovations, ICCI 2025

作者： Kongchatree, Kunanon Suriyo, Ongon Pichedpan, Natvara Kongkachandra, Rachada Songmuang, Pokpong Data Science and Innovation College of Interdisciplinary Studies Thammasat University Bangkok Thailand Thammasat University Department of Computer Science Bangkok Thailand

ISBN: (纸本)9798331533267

Mental health issues significantly affect society, requiring long-term treatment that is both costly and impacts personal relationships. This research paper proposes a speech-based method to screen for depression risk as an accessible alternative. The research included five machine learning models-Logistic Regression, Support Vector Machine (SVM), K-Nearest Neighbour (KNN), Naive Bayes, and Linear Discriminant Analysis (LDA)-were used to analyze speech data from 25 participants with varying mental health conditions. Three data preparation techniques-Leave-One-Out Cross-Validation, SMOTE, and raw data-were applied to optimize the models. Results showed that SVM with SMOTE achieved the highest accuracy (98 %), emphasizing the importance of effective data preparation. This research demonstrates the potential of using speech analysis as a cost-effective tool for early mental health screening and provides a foundation for practical applications in healthcare and beyond. © 2025 IEEE.

关键词： mHealth

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Twin Transition or Competing Interests? Validation of the Artificial Intelligence and Sustainability Perceptions Inventory (AISPI)

Twin Transition or Competing Interests? Validation of the Ar...

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2025 CHI Conference on Human Factors in Computing Systems, CHI EA 2025

作者： Bush, Annika Research Center Trustworthy Data Science and Security University Alliance Ruhr Faculty of Computer Science Technical University Dortmund Dortmund Germany

ISBN: (纸本)9798400713958

As artificial intelligence (AI) and sustainability initiatives increasingly intersect, understanding public perceptions of their relationship becomes crucial for successful implementation. However, no validated instrument exists to measure these specific perceptions. This paper presents the development and validation of the Artificial Intelligence and Sustainability Perceptions Inventory (AISPI), a novel 13-item instrument measuring how individuals view the relationship between AI advancement and environmental sustainability. Through factor analysis (N = 105), we identified two distinct dimensions: ‘Twin Transition’ and ‘Competing Interests’. The instrument demonstrated strong reliability (α = .89) and construct validity through correlations with established measures of AI and sustainability attitudes. Our findings suggest that individuals can simultaneously recognize both synergies and tensions in the AI-sustainability relationship, offering important implications for researchers and practitioners working at this critical intersection. This work provides a foundational tool for future research on public perceptions of AI’s role in sustainable development. © 2025 Copyright held by the owner/author(s).

关键词： Green development

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