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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Ma, Xinghua Fang, Xinyan Zou, Mingye Luo, Gongning Wang, Wei Wang, Kuanquan Qiu, Zhaowen Gao, Xin Li, Shuo Faculty of Computing Harbin Institute of Technology Harbin China Computational Bioscience Research Center King Abdullah University of Science and Technology Thuwal Saudi Arabia Faculty of Computing Harbin Institute of Technology Shenzhen China College of Computer and Control Engineering Northeast Forestry University Harbin China Department of Computer and Data Science Case Western Reserve University Cleveland United States Department of Biomedical Engineering Case Western Reserve University Cleveland United States

ISBN: (纸本)157735897X

Coronary Artery Disease (CAD) poses a significant threat to cardiovascular patients worldwide, underscoring the critical importance of automated CAD diagnostic technologies in clinical practice. Previous technologies for lesion assessment in Coronary CT Angiography (CCTA) images have been insufficient in terms of interpretability, resulting in solutions that lack clinical reliability in both network architecture and prediction outcomes, even when diagnoses are accurate. To address the limitation of interpretability, we introduce the Trusted Lesion-Assessment Network (TLA-Net), which provides a clinically reliable solution for multi-view CAD diagnosis: (1) The causality-informed evidence collection constructs a causal graph for the diagnostic process and implements causal interventions, preventing confounders’ interference and enhancing the transparency of the network architecture. (2) The clinically-aligned uncertainty integration hierarchically combines Dirichlet distributions from various views based on clinical priors, offering confidence coefficients for prediction outcomes that align with physicians’ image analysis procedures. Experimental results on a dataset of 2,618 lesions demonstrate that TLA-Net, supported by its interpretable methodological design, exhibits superior performance with outstanding generalization, domain adaptability, and robustness. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

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Context-aware Multimodal AI Reveals Hidden Pathways in Five Centuries of Art Evolution

arXiv

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arXiv 2025年

作者： Kim, Jin Lee, Byunghwee You, Taekho Yun, Jinhyuk Department of Intelligent Semiconductors Soongsil University Seoul06978 Korea Republic of School of Digital Humanities & Computational Social Sciences Korea Advanced Institute of Science and Technology Daejeon34141 Korea Republic of Luddy School of Informatics Computing and Engineering Indiana University BloomingtonIN47408 United States Institute for Social Data Science Pohang University of Science and Technology Pohang37673 Korea Republic of Center for Digital Humanities & Computational Social Sciences Korea Advanced Institute of Science and Technology Daejeon34141 Korea Republic of School of AI Convergence Soongsil University Seoul06978 Korea Republic of

The rise of multimodal generative AI is transforming the intersection of technology and art, offering deeper insights into large-scale artwork. Although its creative capabilities have been widely explored, its potential to represent artwork in latent spaces remains underexamined. We use cutting-edge generative AI, specifically Stable Diffusion, to analyze 500 years of Western paintings by extracting two types of latent information with the model: formal aspects (e.g., colors) and contextual aspects (e.g., subject). Our findings reveal that contextual information differentiates between artistic periods, styles, and individual artists more successfully than formal elements. Additionally, using contextual keywords extracted from paintings, we show how artistic expression evolves alongside societal changes. Our generative experiment, infusing prospective contexts into historical artworks, successfully reproduces the evolutionary trajectory of artworks, highlighting the significance of mutual interaction between society and art. This study demonstrates how multimodal AI expands traditional formal analysis by integrating temporal, cultural, and historical contexts. Copyright © 2025, The Authors. All rights reserved.

关键词： Generative adversarial networks

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On Computing Probabilistic Explanations for Decision Trees

arXiv

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arXiv 2022年

作者： Arenas, Marcelo Barceló, Pablo Romero, Miguel Roa, Bernardo Anibal Subercaseaux Department of Computer Science PUC Chile Institute for Mathematical and Computational Engineering PUC Chile Faculty of Engineering and Science UAI Chile Millenium Institute for Foundational Research on Data Chile CENIA Chile Carnegie Mellon University Pittsburgh United States

Formal XAI (explainable AI) is a growing area that focuses on computing explanations with mathematical guarantees for the decisions made by ML models. Inside formal XAI, one of the most studied cases is that of explaining the choices taken by decision trees, as they are traditionally deemed as one of the most interpretable classes of models. Recent work has focused on studying the computation of sufficient reasons, a kind of explanation in which given a decision tree T and an instance x, one explains the decision T(x) by providing a subset y of the features of x such that for any other instance z compatible with y, it holds that T(z) = T(x), intuitively meaning that the features in y are already enough to fully justify the classification of x by T. It has been argued, however, that sufficient reasons constitute a restrictive notion of explanation. For such a reason, the community has started to study their probabilistic counterpart, in which one requires that the probability of T(z) = T(x) must be at least some value δ ∈ (0, 1], where z is a random instance that is compatible with y. Our paper settles the computational complexity of δ-sufficient-reasons over decision trees, showing that both (1) finding δ-sufficient-reasons that are minimal in size, and (2) finding δ-sufficient-reasons that are minimal inclusion-wise, do not admit polynomial-time algorithms (unless PTIME = NP). This is in stark contrast with the deterministic case (δ = 1) where inclusion-wise minimal sufficient-reasons are easy to compute. By doing this, we answer two open problems originally raised by Izza et al., and extend the hardness of explanations for Boolean circuits presented by Wäldchen et al. to the more restricted case of decision trees. On the positive side, we identify structural restrictions of decision trees that make the problem tractable, and show how SAT solvers might be able to tackle these problems in practical settings. © 2022, CC BY.

关键词： Decision trees

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Challenges and Opportunities in Integration of Human and Autonomous systems

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INCOSE International Symposium 2022年第S1期32卷 48-49页

作者： Sahar, Yotam Wagner, Michael Herrera, Francisco Hatwell, Julian Department of Industrial Engineering & Management Ariel University Ariel Israel Andalusian Research Institute in Data Science and Computational Intelligence DaSCI Institute Spain Birmingham City University United Kingdom

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Improving the second-tier classification of methylmalonic acidemia patients using a machine learning ensemble method

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World Journal of Pediatrics 2024年第10期20卷 1090-1101页

作者： Zhi-Xing Zhu Georgi ZGenchev Yan-Min Wang Wei Ji Yong-Yong Ren Guo-Li Tian Sira Sriswasdi Hui Lu Shanghai Engineering Research Center for Big Datain Pediatric Precision Medicine Center for BiomedicalInformaticsShanghai Children’s HospitalSchoolof MedicineShanghai Jiao Tong UniversityShanghaiChina Center of Excellence in Computational Molecular Biology Faculty of MedicineChulalongkorn UniversityBangkokThailand Newborn Screening Center Shanghai Children’s HospitalSchool of MedicineShanghai Jiao Tong UniversityShanghaiChina SJTU-Yale Joint Center for Biostatistics and Data Science National Center for Translational MedicineShanghai JiaoTong UniversityShanghaiChina Center for Artifcial Intelligence in Medicine ResearchAfairsFaculty of MedicineChulalongkorn UniversityBangkokThailand State Key Laboratory of Microbial Metabolism JointInternational Research Laboratory of Metabolic&Developmental SciencesDepartment of Bioinformaticsand BiostatisticsSchool of Life Sciences and BiotechnologyShanghai Jiao Tong UniversityShanghaiChina

Introduction Methylmalonic acidemia(MMA)is a disorder of autosomal recessive inheritance,with an estimated prevalence of 1:50,***-tier clinical diagnostic tests often return many false positives[fve false positive(FP):one true positive(TP)].In this work,our goal was to refne a classifcation model that can minimize the number of false positives,currently an unmet need in the upstream diagnostics of *** We developed machine learning multivariable screening models for MMA with utility as a secondary-tier tool for false positives *** utilized mass spectrometry-based features consisting of 11 amino acids and 31 carnitines derived from dried blood samples of neonatal patients,followed by additional ratio feature *** selection strategies(selection by flter,recursive feature elimination,and learned vector quantization)were used to determine the input set for evaluating the performance of 14 classifcation models to identify a candidate model set for an ensemble model *** Our work identifed computational models that explore metabolic analytes to reduce the number of false positives without compromising *** best results[area under the receiver operating characteristic curve(AUROC)of 97%,sensitivity of 92%,and specifcity of 95%]were obtained utilizing an ensemble of the algorithms random forest,C5.0,sparse linear discriminant analysis,and autoencoder deep neural network stacked with the algorithm stochastic gradient boosting as the *** model achieved a good performance trade-of for a screening application with 6%false-positive rate(FPR)at 95%sensitivity,35%FPR at 99%sensitivity,and 39%FPR at 100%*** The classifcation results and approach of this research can be utilized by clinicians globally,to improve the overall discovery of MMA in pediatric *** improved method,when adjusted to 100%precision,can be used to further inform the diagnostic process journey of MMA and help r

关键词： Machine learning Methylmalonic acidemia Second-tier test Stacking

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Automatic text-to-SQL machine translation for scholarly publication database search

Automatic text-to-SQL machine translation for scholarly publ...

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2020 IEEE SoutheastCon, SoutheastCon 2020

作者： Deshmukh, Sulochana Bikdash, Marwan Department of Computational Data Science and Engineering North Carolina A&T State University GreensboroNC27411 United States

ISBN: (纸本)9781728168616

A database Management System (DBMS) is typically accessed using a Structured Query Language (SQL). Here, we investigate a structured method to parse questions in natural language. Our work is a text-to-SQL translator for scholarly publication database search. The work includes implementation of a phrase-based information retrieval and a Text-to-SQL translator. The information retrieval is emphasizes on the recognition of Named Entity (NE), Named Entity Attribute (NEA), Question Intention (QI) and Focus Recognition. NE is a database entry and NEA is the attribute or column name of the corresponding NE entry. QI is the type of a question. The proposed translator is a domain-specific Text-to-SQL translator. Therefore the scope for named entities is limited to the database values. Thus, we use the database itself as a knowledge-base. We derive rules comprised of phrases, predicates and substitution strings. The phrases are regular expressions and the predicates are stop-words. The usual practice of natural language processing removes stop words. Instead, we make use of many stop words as predicates, thus helping the recognition of NE, NEA and QI. The text cosine similarity measure is used to validate NE, NEA and QI. The rule-based approach is computationally effective and does not require any exhaustive training on the dataset, while providing precise translation. © 2020 IEEE

关键词： Information retrieval

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Revolutionizing Image Analysis: The Integration of MLP and GRU Architectures in Advanced Computer Vision 1

Revolutionizing Image Analysis: The Integration of MLP and G...

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1st IEEE International Conference on Green Industrial Electronics and Sustainable Technologies, GIEST 2024

作者： Nimma, Divya Sengar, Alok Singh Kaur, Chamandeep Khan, Shamim Ahmad Anushna, S.D. Raj, I Infant University of Southern Mississippi Data Analyst in UMMC PhD in Computational Science United States Noida International University School of Sciences Dept. of Computer Science Uttar Pradesh Greater Noida India Jazan University Department of Computer Science Jazan Saudi Arabia Glocal University Glocal School of Science & Technology U.P India Hyderabad India K.Ramakrishnan College of Engineering Department of CSE Tamil Nadu Trichy India

ISBN: (纸本)9798331540142

Although there have been significant advances in computer vision recently, there are still difficulties in efficiently interpreting dynamic and complicated visual input. Through the integration of Gated Recurrent Units (GRUs) and Multi-Layer Perceptrons (MLPs) in advanced picture analysis, this study suggests a novel way to overcome these issues. Utilizing the advantages of both systems, the integrated model seeks to improve visual information comprehension and interpretation. Computer vision applications involving temporal context and semantic understanding are sometimes difficult for traditional approaches to handle. MLPs are good at collecting spatial features;on the other hand, GRUs are efficient at processing data sequentially. This research aims to develop and evaluate an integrated MLP-GRU model for advanced image analysis, combining the strengths of MLPs and GRUs. The model aims to achieve superior performance in tasks like object detection, image classification, and semantic segmentation. The model combines MLPs for spatial feature extraction and GRUs for capturing temporal dependencies within sequential data. The experimental results show significant improvements in accuracy of 98%, compared to traditional approaches. The model also demonstrates the ability to discern complex visual patterns and make accurate predictions. This study highlights the potential of hybrid neural network architectures in image analysis, highlighting the paradigm shift in computer vision and their ability to analyze diverse and dynamic visual data. © 2024 IEEE.

关键词： Semantic Segmentation

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Enhancing Robotic Surgery Precision and Safety Using a Hybrid Autoencoder and Deep Belief Network Approach: Real-Time Feedback and Adaptive Control from Image data

Enhancing Robotic Surgery Precision and Safety Using a Hybri...

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International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)

作者： Divya Nimma SK. Fakruddin Babavali Ravindra Changala Mahesh Manchanda Aradhana Sahu B Kiran Bala Computational Science University of Southern Mississippi Data Analyst in UMMC USA Department of Physics V.R.Siddhartha Engineering College Vijayawada Andhra Pradesh India Department of CSE Guru Nanak Institutions Technical Campus Hyderabad India Department of CSE Graphic Era Hill University Dehradun Graphic Era Deemed to be University Dehradun Uttarakhand India Department of CSE Rungta College of Engineering and Technology Bhilai Chhattisgarh India Department of AI & DS K.Ramakrishnan College of Engineering Trichy India

ISBN: (数字)9798350376425

ISBN: (纸本)9798350376432

This research work presents a new paradigm for augmenting the accuracy and safety of robotic surgery through the development of a hybrid autoencoder and DBN for online feedback and control. The proposed hybrid model takes the advantages of the autoencoder to extract the features and to reduce the dimension, and on the other hand takes the prevailing pattern recognition and decision-making ability of the DBN. The method begins with the data pre-processing of high dimension labeled surgical tool images using min-max normalization for normalization of the given data. The normalized images are then passed on to the hybrid model whereby the autoencoder weights and rebuild the images while the DBN examines such characteristics to the surgical real-time control. This approach enables the robotic system to make fine adjustments of movements during operations as it receives feedback and reacts to the dynamic environment of surgery in order to be accurate. The performance analysis revealed a substantial increase in precision, recall and overall surgical effectiveness of the model; yields a near to perfect test outcome of 99% which is higher than the traditional methods. The methods put together, bring into one revolutionary advancement in the field of robotic surgery by enhancing the safety and accuracy of the medical operations and the quality of the patients’ lives.

关键词： Accuracy Linear feedback control systems Decision making Surgery Feature extraction Real-time systems Safety Performance analysis Pattern recognition Robots

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Cell2Sentence: Teaching Large Language Models the Language of Biology 41

Cell2Sentence: Teaching Large Language Models the Language o...

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41st International Conference on Machine Learning, ICML 2024

作者： Levine, Daniel Rizvi, Syed Asad Lévy, Sacha Pallikkavaliyaveetil, Nazreen Zhang, David Chen, Xingyu Ghadermarzi, Sina Wu, Ruiming Zheng, Zihe Vrkic, Ivan Zhong, Anna Raskin, Daphne Han, Insu de Oliveira Fonseca, Antonio Henrique Caro, Josue Ortega Karbasi, Amin Dhodapkar, Rahul M. van Dijk, David Department of Computer Science Yale University New HavenCT United States School of Engineering Applied Science University of Pennsylvania PhiladelphiaPA United States School of Computer and Communication Sciences Swiss Federal Institute of Technology Lausanne Lausanne Switzerland Department of Neuroscience Yale School of Medicine New HavenCT United States Wu Tsai Institute Yale University New HavenCT United States Google United States Yale Institute for Foundations of Data Science New HavenCT United States Yale School of Engineering and Applied Science New HavenCT United States Roski Eye Institute University of Southern California Los AngelesCA United States Yale School of Medicine New HavenCT United States Cardiovascular Research Center Yale School of Medicine New HavenCT United States Interdepartmental Program in Computational Biology & Bioinformatics Yale University New HavenCT United States

We introduce Cell2Sentence (C2S), a novel method to directly adapt large language models to a biological context, specifically single-cell transcriptomics. By transforming gene expression data into"cell sentences," C2S bridges the gap between natural language processing and biology. We demonstrate cell sentences enable the finetuning of language models for diverse tasks in biology, including cell generation, complex cell-type annotation, and direct data-driven text generation. Our experiments reveal that GPT-2, when fine-tuned with C2S, can generate biologically valid cells based on cell type inputs, and accurately predict cell types from cell sentences. This illustrates that language models, through C2S finetuning, can acquire a significant understanding of single-cell biology while maintaining robust text generation capabilities. C2S offers a flexible, accessible framework to integrate natural language processing with transcriptomics, utilizing existing models and libraries for a wide range of biological applications. Copyright 2024 by the author(s)

关键词： Gene expression

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Leveraging Deep Learning Techniques for Objective and Subjective Assessment of English Language Proficiency

Leveraging Deep Learning Techniques for Objective and Subjec...

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Intelligent Systems and Advanced Applications (ICISAA), International Conference on

作者： J Naga Madhuri Divya Nimma M. Mythili Punit Pathak Veera Ankalu Vuyyuru M. Karthik Department of English Velagapudi Rama krishna Siddhartha Engineering College (Deemed to be University) Kanuru Vijayawada India PhD in Computational Science University of Southern Mississippi Data Analyst in UMMC USA Department of English Nandha Engineering College(Autonomous) Erode India Department of School of Liberal Arts and Human Sciences Auro university Surat India Department of CSE Koneru Lakshmaiah Education Foundation Vaddeswaram Andhra Pradesh India Department of AI & DS K. Ramakrishnan College of Engineering Trichy India

ISBN: (数字)9798331539948

ISBN: (纸本)9798331539955

Objective and subjective assessments are two common methods used to evaluate English language proficiency. In order to measure English language proficiency both objectively and subjectively, this research suggests using a hybrid CNN-BERT model. With the use of Bidirectional Encoder Representations from Transformers (BERT) and Convolutional Neural Networks (CNN), the framework seeks to offer a thorough assessment of English language competency that addresses either objective and subjective elements. Unlike conventional approaches, our method offers a comprehensive assessment by integrating subjective components (like spoken language analysis and reading comprehension) with objective assessments (like grammar and vocabulary). This dataset includes a wide range of text examples, covering various levels of proficiency and linguistic nuances. Next, the collected data is preprocessed using tokenization techniques, breaking down the text into smaller, manageable units. This preprocessing step enhances the efficiency and effectiveness of the subsequent model. Following data preprocessing, a hybrid CNN-BERT model is applied to the processed data. The CNN component captures local features, while the BERT model captures contextual information effectively. By combining the strengths of both CNN and BERT, the proposed model ensures a robust assessment of English language proficiency. The CNN-BERT model offers a dual assessment, covering both objective and subjective aspects of English language proficiency. The objective assessment focuses on grammatical accuracy, vocabulary usage, and syntactic structures. Meanwhile, the subjective assessment delves into fluency, coherence, and overall language proficiency. Experimental results demonstrate that the proposed CNN-BERT model outperforms existing approaches in both objective and subjective assessments. The model achieves 96.5% accuracy in evaluating English language proficiency, making it a valuable tool for language educators, rese

关键词： Vocabulary Accuracy Bidirectional control Syntactics Linguistics Transformers Encoding Tokenization Convolutional neural networks Intelligent systems

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