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Intelligent Systems for Cybersecurity (ISCS), International Conference on

作者： Bhushan Fulkar Rushikesh Burle Pawan Patil Sanskruti Gaurkhade Swapnil Gundewar Utkarsha Pacharaney Department of Artificial Intelligence and Data science Datta Meghe Institute of Higher Education and Research Wardha India Department of Artificial Intelligence and Machine Learning Datta Meghe Institute of Higher Education and Research(DU) Wardha Maharashtra Department of Computer Science and Design Datta Meghe Institute of Higher Education and Research Wardha India Department of Artificial Intelligence and Machine Learning Datta Meghe Institute of Higher Education and Research Wardha India

ISBN: (数字)9798350375237

ISBN: (纸本)9798350375244

Since diabetic retinopathy (DR) is the primary cause of blindness in working-age adults worldwide, vision loss must be avoided by receiving early detection and treatment. In recent years, deep learning techniques have become more and more useful tools for automated drug discovery, possibly offering solutions to overcome the limitations of traditional screening methods. In-depth descriptions of the most recent deep learning methods for DR detection are provided in this review article. We begin by discussing the importance of DR screening, its prevalence, and the challenges faced by healthcare systems in managing this condition. Next, we discuss different deep learning architectures used for DR detection, such as CNNs and RNNs, and their combinations. We investigate how to improve the precision, resilience, and interpretability of DR detection models through the use of explainable AI, ensemble methods, transfer learning, and attention mechanisms. We also examine publicly available datasets, evaluation metrics, and preprocessing techniques that are commonly used to benchmark DR detection algorithms. We discuss the limitations and possible uses of deep learning-based deep readout detection, emphasizing the significance of large-scale, diverse datasets, the interpretability of the model, and real-world deployment concerns. The overall objectives of this review are to advance the field of automated DR detection by highlighting recent developments, compiling existing knowledge, and identifying areas that require further research.

关键词： Deep learning Diabetic retinopathy Reviews Transfer learning Imaging Transforms Medical services

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Identifying General Mechanism Shifts in Linear Causal Representations

arXiv

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arXiv 2024年

作者： Chen, Tianyu Bello, Kevin Locatello, Francesco Aragam, Bryon Ravikumar, Pradeep Department of Statistics and Data Sciences University of Texas Austin United States Booth School of Business University of Chicago United States Machine Learning Department Carnegie Mellon University United States Institute of Science and Technology Austria

We consider the linear causal representation learning setting where we observe a linear mixing of d unknown latent factors, which follow a linear structural causal model. Recent work has shown that it is possible to recover the latent factors as well as the underlying structural causal model over them, up to permutation and scaling, provided that we have at least d environments, each of which corresponds to perfect interventions on a single latent node (factor). After this powerful result, a key open problem faced by the community has been to relax these conditions: allow for coarser than perfect single-node interventions, and allow for fewer than d of them, since the number of latent factors d could be very large. In this work, we consider precisely such a setting, where we allow a smaller than d number of environments, and also allow for very coarse interventions that can very coarsely change the entire causal graph over the latent factors. On the flip side, we relax what we wish to extract to simply the list of nodes that have shifted between one or more environments. We provide a surprising identifiability result that it is indeed possible, under some very mild standard assumptions, to identify the set of shifted nodes. Our identifiability proof moreover is a constructive one: we explicitly provide necessary and sufficient conditions for a node to be a shifted node, and show that we can check these conditions given observed data. Our algorithm lends itself very naturally to the sample setting where instead of just interventional distributions, we are provided datasets of samples from each of these distributions. We corroborate our results on both synthetic experiments as well as an interesting psychometric dataset. The code can be found at https://***/TianyuCodings/iLCS. © 2024, CC BY.

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Innovative Healthcare Advancements: Harnessing Artificial and Human Intelligence for Bionic Solutions

Innovative Healthcare Advancements: Harnessing Artificial an...

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2024 OPJU International Technology Conference on Smart Computing for Innovation and Advancement in Industry 4.0, OTCON 2024

作者： Sapkal, Samruddhi Jadhav, Sulaxan Mallikarjun, P. Shamim, Rejuwan Ul Islam, Atowar Bamane, Kalyan D y Patil International University Artificial Intelligence and Machine Learning Pune Akurdi411044 India D y Patil International University Pune Akurdi411044 India Malla Reddy Engineering College Jntuh Department of Electrical and Electronic Engineering Hyderabad India Maharishi University of Information Technology School of Data Science Noida India University of Science and Technology Meghalaya Department of Computer Science and Electronics Ri-Bhoi Techno city Kiling Road Meghalaya Baridua793101 India D y Patil College of Engineering Department of Information Technology Pune Akurdi India

ISBN: (纸本)9798350373783

According to initial data, individuals who have been diagnosed with type 2 diabetes (T2DM) appear to be at a more chances of evolving breast cancer compared to those who have not received a T2DM diagnosis. The primary goal of the research was to estimate the efficacy of three dissimilar methods in forecasting the probability of breast cancer in T2DM patients with diverse attributes. To achieve this objective, a danger expectation model was created for breast cancer in individuals with T2DM using the primary *** ensure a comprehensive analysis, we also gathered information on potential factors that may predict the growth of breast cancer. As the population sample size was restricted, we utilized Synthetic Minority Oversampling Technology to amplify the quantity of accessible data. Random assignment of data points to training or test sets was conducted at a ratio of roughly 39 to 1. Three distinct models, specifically Artificial Neural Network (ANN), Logistic Regression (LR), and Random Forest (RF), were assessed for effectiveness using a range of performance criteria, including as F1 score, area under the receiver operation characteristic curves, recalled, and correctness. (AUC). AUC values for these models were as follows: LR had an AUC of 0.834, ANN had an AUC of 0.865, and RF had an AUC of 0.959, with RF having the greatest AUC. According to our study, the Random Forest model outperformed the LR and ANN models in correctly estimating the incidence of breast cancer in people with T2DM. © 2024 IEEE.

关键词： Diseases

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On convergence of federated averaging langevin dynamics 24

On convergence of federated averaging langevin dynamics

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Proceedings of the Fortieth Conference on Uncertainty in Artificial Intelligence

作者： Wei Deng Qian Zhang Yi-An Ma Zhao Song Guang Lin Machine Learning Research Morgan Stanley Department of Statistics Purdue University West Lafayette Halicioglu Data Science Institute University of California San Diego Adobe Research Department of Mathematics and School of Mechanical Engineering Purdue University

We propose a federated averaging Langevin algorithm (FA-LD) for uncertainty quantification and mean predictions with distributed clients. In particular, we generalize beyond normal posterior distributions and consider a general class of models. We develop theoretical guarantees for FA-LD for strongly log-concave distributions with non-i.i.d data and study how the injected noise and the stochastic-gradient noise, the heterogeneity of data, and the varying learning rates affect the convergence. Such an analysis sheds light on the optimal choice of local updates to minimize the communication cost. Important to our approach is that the communication efficiency does not deteriorate with the injected noise in the Langevin algorithms. In addition, we examine in our FA-LD algorithm both independent and correlated noise used over different clients. We observe that there is a tradeoff between the pairs among communication, accuracy, and data privacy. As local devices may become inactive in federated networks, we also show convergence results based on different averaging schemes where only partial device updates are available. In such a case, we discover an additional bias that does not decay to zero.

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A causal intervention framework for synthesizing mobility data and evaluating predictive neural networks

arXiv

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arXiv 2023年

作者： Hong, Ye Xin, Yanan Dirmeier, Simon Perez-Cruz, Fernando Raubal, Martin Institute of Cartography and Geoinformation ETH Zurich Switzerland Swiss Data Science Center ETH Zurich Switzerland EPFL Switzerland Institute for Machine Learning Department of Computer Science ETH Zurich Switzerland

Deep neural networks are increasingly utilized in mobility prediction tasks, yet their intricate internal workings pose challenges for interpretability, especially in comprehending how various aspects of mobility behavior affect predictions. This study introduces a causal intervention framework to assess the impact of mobility-related factors on neural networks designed for next location prediction – a task focusing on predicting the immediate next location of an individual. To achieve this, we employ individual mobility models to synthesize location visit sequences and control behavior dynamics by intervening in their data generation process. We evaluate the interventional location sequences using mobility metrics and input them into well-trained networks to analyze performance variations. The results demonstrate the effectiveness in producing location sequences with distinct mobility behaviors, thereby facilitating the simulation of diverse yet realistic spatial and temporal changes. These changes result in performance fluctuations in next location prediction networks, revealing impacts of critical mobility behavior factors, including sequential patterns in location transitions, proclivity for exploring new locations, and preferences in location choices at population and individual levels. The gained insights hold value for the real-world application of mobility prediction networks, and the framework is expected to promote the use of causal inference to enhance the interpretability and robustness of neural networks in mobility applications. © 2023, CC BY-NC-SA.

关键词： Prediction models

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ON THE EXISTENCE OF POWERFUL P-VALUES AND E-VALUES FOR COMPOSITE HYPOTHESES

arXiv

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arXiv 2023年

作者： Zhang, Zhenyuan Ramdas, Aaditya Wang, Ruodu Department of Mathematics Stanford University United States Depts. of Statistics & Data Science and Machine Learning Carnegie Mellon Univ. United States Department of Statistics and Actuarial Science University of Waterloo Canada

Given a composite null P and composite alternative Q, when and how can we construct a p-value whose distribution is exactly uniform under the null, and stochastically smaller than uniform under the alternative? Similarly, when and how can we construct an e-value whose expectation exactly equals one under the null, but its expected logarithm under the alternative is positive? We answer these basic questions, and other related ones, when P and Q are convex polytopes (in the space of probability measures). We prove that such constructions are possible if and only if Q does not intersect the span of P. If the p-value is allowed to be stochastically larger than uniform under P ∈ P, and the e-value can have expectation at most one under P ∈ P, then it is achievable whenever P and Q are disjoint. More generally, even when P and Q are not polytopes, we characterize the existence of a bounded nontrivial e-variable whose expectation exactly equals one under any P ∈ P. The proofs utilize recently developed techniques in simultaneous optimal transport. A key role is played by coarsening the filtration: sometimes, no such p-value or e-value exists in the richest data filtration, but it does exist in some reduced filtration, and our work provides the first general characterization of this phenomenon. We also provide an iterative construction that explicitly constructs such processes, and under certain conditions it finds the one that grows fastest under a specific alternative Q. We discuss implications for the construction of composite nonnegative (super)martingales, and end with some conjectures and open problems. Copyright © 2023, The Authors. All rights reserved.

关键词： Stochastic systems

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Ai-Driven Automated Tool for Abdominal CT Body Composition Analysis in Gastrointestinal Cancer Management 22

Ai-Driven Automated Tool for Abdominal CT Body Composition A...

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22nd IEEE International Symposium on Biomedical Imaging, ISBI 2025

作者： Nan, Xinyu He, Meng Chen, Zifan Dong, Bin Tang, Lei Zhang, Li Peking University Center for Data Science China Peking University Cancer Hospital and Institute Key Laboratory of Carcinogenesis and Translational Research Ministry of Education Department of Radiology Beijing China Beijing China Peking University Center for Machine Learning Research Beijing China Peking University National Biomedical Imaging Center Beijing China

ISBN: (纸本)9798331520526

The incidence of gastrointestinal cancers remains significantly high, particularly in China, emphasizing the importance of accurate prognostic assessments and effective treatment strategies. Research shows a strong correlation between abdominal muscle and fat tissue composition and patient outcomes. However, existing manual methods for analyzing abdominal tissue composition are time-consuming and costly, limiting clinical research scalability. To address these challenges, we developed an AI-driven tool for automated analysis of abdominal CT scans to effectively identify and segment muscle, subcutaneous fat, and visceral fat. Our tool integrates a multi-view localization model and a high-precision 2D nnUNet-based segmentation model, demonstrating a localization accuracy of 90% and a Dice Score Coefficient of 0.967 for segmentation. Furthermore, it features an interactive interface that allows clinicians to refine the segmentation results, ensuring high-quality outcomes effectively. Our tool offers a standardized method for effectively extracting critical abdominal tissues, potentially enhancing the management and treatment for gastrointestinal cancers. The code is available at https://***/NanXinyu/***. © 2025 IEEE.

关键词： Clinical research

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The Age and Gender Detection using Deep Convolution Neural Network

The Age and Gender Detection using Deep Convolution Neural N...

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Signal Processing and Advance Research in Computing (SPARC), International Conference on

作者： Shweta Chauhan Rahul Kumar Bibek Kumar Department of artificial intelligence and Machine Learning Madhav Institute of science and technology Gwalior Madhya Pradesh India ECE Department GLA University Mathura Uttar Pradesh India School of Data Science Symbiosis University of Applied Sciences Indore Madhya Pradesh India

ISBN: (数字)9798350385205

ISBN: (纸本)9798350385212

Automatic age and gender classification has become important researcher since the rise of social platforms and social media. The main advancements in face recognition are the ability to determine a person's age and gender. This paper makes a significant contribution by providing a survey of different face recognition methods for determining age and gender. A deep neural network (DNN) known as a convolutional neural network is frequently used for natural language processing and image recognition and processing. The proposed method significantly enhances three elements: face alignment, face recognition, and face recognition accuracy. All weight vectors of people in the same age group within a given database are averaged together. To predict gender and age, picture classification and regression models have been discussed in this paper respectively. Although training the proposed network to make accurate predictions takes a lot of time, it significantly improves age and gender categorization precision. From the experimental results we compared and analyzed the work to the most recent UTK-Face benchmark for estimating age and gender.

关键词： Training Surveys Accuracy Image recognition Social networking (online) Face recognition Predictive models Vectors Natural language processing Convolutional neural networks

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Model and feature diversity for bayesian neural networks in mutual learning 23

Model and feature diversity for bayesian neural networks in ...

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Proceedings of the 37th International Conference on Neural Information Processing Systems

作者： Cuong Pham Cuong C. Nguyen Trung Le Dinh Phung Gustavo Carneiro Thanh-Toan Do Department of Data Science and AI Monash University Australia Australian Institute for Machine Learning University of Adelaide Australia Department of Data Science and AI Monash University Australia and VinAI Vietnam Centre for Vision Speech and Signal Processing University of Surrey United Kingdom

Bayesian Neural Networks (BNNs) offer probability distributions for model parameters, enabling uncertainty quantification in predictions. However, they often underperform compared to deterministic neural networks. Utilizing mutual learning can effectively enhance the performance of peer BNNs. In this paper, we propose a novel approach to improve BNNs performance through deep mutual learning. The proposed approaches aim to increase diversity in both network parameter distributions and feature distributions, promoting peer networks to acquire distinct features that capture different characteristics of the input, which enhances the effectiveness of mutual learning. Experimental results demonstrate significant improvements in the classification accuracy, negative log-likelihood, and expected calibration error when compared to traditional mutual learning for BNNs.

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No Rose for MLE: Inadmissibility of MLE for Evaluation Aggregation Under Levels of Expertise

No Rose for MLE: Inadmissibility of MLE for Evaluation Aggre...

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IEEE International Symposium on Information Theory

作者： Charvi Rastogi Ivan Stelmakh Nihar Shah Sivaraman Balakrishnan Machine Learning Department Carnegie Mellon University Computer Science Department Carnegie Mellon University Department of Statistics and Data Science Carnegie Mellon University

ISBN: (数字)9781665421591

ISBN: (纸本)9781665421607

A number of applications including crowd-sourced labeling and peer review require aggregation of labels or evaluations sourced from multiple evaluators. There is often additional information available pertaining to the evaluators’ expertise. A natural approach for aggregation is to consider the widely studied Dawid-Skene model (or its extensions incorporating evaluators’ expertise), and employ the standard maximum likelihood estimator (MLE). While MLE is in general widely used in practice and enjoys a number of appealing theoretical guarantees, in this work we provide a negative result for the MLE. Specifically, we prove that the MLE is asymptotically inadmissible for a special case of evaluation aggregation with expertise level information. We show this by constructing an alternative estimator that we show is significantly better than the MLE in certain parameter regimes and at least as good elsewhere. Finally, simulations reveal that our findings may hold in more general conditions than what we theoretically analyze.

关键词： Maximum likelihood estimation Analytical models Labeling Standards Information theory

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