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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Larsen, Kasper Green Montasser, Omar Zhivotovskiy, Nikita Department of Computer Science Aarhus University Denmark Department of Statistics and Data Science Yale University United States Department of Statistics University of California Berkeley United States

Multi-distribution or collaborative learning involves learning a single predictor that works well across multiple data distributions, using samples from each during training. Recent research on multi-distribution learning, focusing on binary loss and finite VC dimension classes, has shown near-optimal sample complexity that is achieved with oracle efficient algorithms. That is, these algorithms are computationally efficient given an efficient ERM for the class. Unlike in classical PAC learning, where the optimal sample complexity is achieved with deterministic predictors, current multi-distribution learning algorithms output randomized predictors. This raises the question: can these algorithms be derandomized to produce a deterministic predictor for multiple distributions? Through a reduction to discrepancy minimization, we show that derandomizing multi-distribution learning is computationally hard, even when ERM is computationally efficient. On the positive side, we identify a structural condition enabling an efficient black-box reduction, converting existing randomized multi-distribution predictors into deterministic ones. © 2024 Neural information processing systems foundation. All rights reserved.

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Optimizing cervical cancer classification using transfer learning with deep gaussian processes and support vector machines

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Discover Artificial Intelligence 2024年第1期4卷 73页

作者： Ahishakiye, Emmanuel Kanobe, Fredrick Department of Networks Data Science and Artificial Intelligence Kyambogo University Kampala Uganda Department of Computer Science Kyambogo University Kampala Uganda

Background: Cervical cancer is the fourth most frequent cancer in women worldwide. Even though cervical cancer deaths have decreased significantly in Western countries, low and middle-income countries account for nearly 90% of cervical cancer deaths. While Western countries are leveraging the powers of artificial intelligence (AI) in the health sector, most countries in sub-Saharan Africa are still lagging. In Uganda, cytologists manually analyze Pap smear images for the detection of cervical cancer, a process that is highly subjective, slow, and tedious. Machine learning (ML) algorithms have been used in the automated classification of cervical cancer. However, most of the MLs have overfitting limitations which limits their deployment, especially in the health sector where accurate predictions are needed. Methods: In this study, we propose two kernel-based algorithms for automated detection of cervical cancer. These algorithms are (1) an optimized support vector machine (SVM), and (2) a deep Gaussian Process (DGP) model. The SVM model proposed uses an optimized radial basis kernel while the DGP model uses a hybrid kernel of periodic and local periodic kernel. Results: Experimental results revealed accuracy of 100% and 99.48% for an optimized SVM model and DGP model respectively. Results on precision, recall, and F1 score were also reported. Conclusions: The proposed models performed well on cervical cancer detection and classification, and therefore suitable for deployment. We plan to deploy our proposed models in a mobile application-based tool. The limitation of the study was the lack of access to high-performance computational resources. © The Author(s) 2024.

关键词： Lung cancer

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Penalized M-Estimation Based on Standard Error Adjusted Adaptive Elastic-Net

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Journal of Systems science & Complexity 2023年第3期36卷 1265-1284页

作者： WU Xianjun WANG Mingqiu HU Wenting TIAN Guo-Liang LI Tao School of Statistics and Mathematics Zhongnan University of Economics and LawWuhan 430073China School of Statistics and Data Scicence Qufu Normal UniversityQufu 273165China Department of Statistics and Data Science Southern University of Science and TechnologyShenzhen 518055China

When there are outliers or heavy-tailed distributions in the data, the traditional least squares with penalty function is no longer applicable. In addition, with the rapid development of science and technology, a lot of data, enjoying high dimension, strong correlation and redundancy, has been generated in real life. So it is necessary to find an effective variable selection method for dealing with collinearity based on the robust method. This paper proposes a penalized M-estimation method based on standard error adjusted adaptive elastic-net, which uses M-estimators and the corresponding standard errors as weights. The consistency and asymptotic normality of this method are proved theoretically. For the regularization in high-dimensional space, the authors use the multi-step adaptive elastic-net to reduce the dimension to a relatively large scale which is less than the sample size, and then use the proposed method to select variables and estimate parameters. Finally, the authors carry out simulation studies and two real data analysis to examine the finite sample performance of the proposed method. The results show that the proposed method has some advantages over other commonly used methods.

关键词： Adaptive elastic net -estimation oracle property standard error

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Identification of Human Activity from Video Streaming Smartphone data Using Intensified VGG16

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International Journal of Engineering, Transactions B: Applications 2025年第6期38卷 1340-1352页

作者： Yadav, R.K. Daniel, A. Semwal, V.B. Department of Computer Science and Engineering Amity University Gwalior India Data Science and Engineering Manipal University Jaipur India Department of Computer Science and Engineering Maulana Azad National Institute Technology Bhopal India

Human activity recognition (HAR) techniques pick out and interpret human behaviors and actions by analyzing data gathered from various sensor devices. HAR aims to recognize and automatically categorize human activities using patterns and attributes taken from sensor data. HAR is complex in implementing the algorithm for a self-recorded dataset, including challenges such as age variation, wearing different clothes, environment and surface, the direction of the smartphone camera, and many more. The paper aims to propose a VGG16 deep learning framework including an activation function and different optimizers for classifying human activity from the real-time captured dataset;further, we compare the evaluated results with existing results. The proposed methods achieved 99.88% accuracy with excellent precision, recall, and F_measure values. Comparing the evaluated result with existing outcomes over the WISDM and UCI-HAR datasets. The new things in the article are a self-captured dataset of various aged male, female, and healthy volunteers to perform seven activities. Furthermore, this research uses Tensor Processing Units (TPU) available on Kaggle to improve classification accuracy while reducing error rates and speeding up execution. ©2025 The author(s).

关键词： Smartphones

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Different Methods to Estimate Stress-Strength Reliability Function for Modified Exponentiated Lomax Distribution

Iraqi Journal for Computer Science and Mathematics

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Iraqi Journal for computer science and Mathematics 2025年第2期6卷 99-106页

作者： Al-Rassam, Raya Salim Mohammed, Khalida Ahmed Rashed, Safwan Nathem Department of Statistics & Informatics College of Computer Science & Mathematics University of Mosul Iraq

When ensuring the reliability of device or the suitability of a material, it is necessary to take into consideration the stress cases in the operating environment. This means that the uncertainty about the reality environmental stress must be taken into as random. The stress-strength (S-S) model treated the stress and strength variables as random. In the simplest form of stress-strength model, y represents the stress put on the unit by the operating environment, and the strength of the unit represented by x. A unit is able to perform its required function if its stress imposed on it is less than the strength of the unit. In this paper, the stress-strength reliability estimation for the modified exponentiated Lomax distribution, which is generalization of the Lomax distribution, with an unknown shape parameter and a known scale parameters is studied using different methods. These methods include the maximum likelihood method, Bayesian estimation method under a quadratic loss function, and the least squares method for complete data. The estimators are compared based on Markov Chain Monte Carlo (MCMC) simulations using R-Studio, evaluated by the mean square error (MSE) criteria. The simulation results show that the maximum likelihood estimators are the best in two cases: the first is when the sample sizes are equal and the second is when the shape parameter of the strength variable is greater than the shape parameter of the stress variable. While least squares estimators are the beast if the strength sample size is smaller than the stress sample size. Finally when the strength sample size is greater than the stress sample size, then the best estimators differ between the maximum likelihood estimators and Bayesian estimators. Bayesian estimators become the best when the shape parameter of stress variable is larger than the shape parameter of the strength variable. © 2025 The Author(s).

关键词： Maximum likelihood estimation

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Stylometry-driven framework for Urdu intrinsic plagiarism detection: a comprehensive analysis using machine learning, deep learning, and large language models

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Neural Computing and Applications 2025年第9期37卷 6479-6513页

作者： Manzoor, Muhammad Faraz Farooq, Muhammad Shoaib Abid, Adnan Department of Computer Science University of Management and Technology Lahore Pakistan Department of Data Science Faculty of Computing and Information Technology University of the Punjab Lahore Pakistan

Detecting plagiarism in documents is a well-established task in natural language processing (NLP). Broadly, plagiarism detection is categorized into two types (1) intrinsic: to check the whole document or all the passages have been written by a single author;(2) extrinsic: where a suspicious document is compared with a given set of source documents to figure out sentences or phrases which appear in both documents. In the pursuit of advancing intrinsic plagiarism detection, this study addresses the critical challenge of intrinsic plagiarism detection in Urdu texts, a language with limited resources for comprehensive language models. Acknowledging the absence of sophisticated large language models (LLMs) tailored for Urdu language, this study explores the application of various machine learning, deep learning, and language models in a novel framework. A set of 43 stylometry features at six granularity levels was meticulously curated, capturing linguistic patterns indicative of plagiarism. The selected models include traditional machine learning approaches such as logistic regression, decision trees, SVM, KNN, Naive Bayes, gradient boosting and voting classifier, deep learning approaches: GRU, BiLSTM, CNN, LSTM, MLP, and large language models: BERT and GPT-2. This research systematically categorizes these features and evaluates their effectiveness, addressing the inherent challenges posed by the limited availability of Urdu-specific language models. Two distinct experiments were conducted to evaluate the impact of the proposed features on classification accuracy. In experiment one, the entire dataset was utilized for classification into intrinsic plagiarized and non-plagiarized documents. Experiment two categorized the dataset into three types based on topics: moral lessons, national celebrities, and national events. Both experiments are thoroughly evaluated through, a fivefold cross-validation analysis. The results show that the random forest classifier achieved an ex

关键词： Deep learning

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CoverGAN: cover photo generation from text story using layout guided GAN

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Soft Computing 2025年第1期29卷 405-423页

作者： Cheema, Adeel Naeem, M. Asif Department of Data Science and Artificial Intelligence National University of Computer and Emerging Sciences Islamabad Pakistan

Generating cover photos from story text is a non trivial challenge to solve. Existing approaches focus on generating only images from given text prompt. To the best of our knowledge, non of these approaches focus on generating cover photos from a text story. The paper addresses this issue by introducing multi-object image generation with text title from a text story. We split the problem into three steps:- understanding semantics of text story, predicting layout of objects, and generating a cover photo. At start, a semantic relation was encoded between text story objects using a Scene graph, then features from graph neural network were concatenated with single object features from scene graph to create an object layout. All of these features were then passed on to the image generating part. Image generation was further divided into two phases. In the first phase, the image is generated using a scene graph image generation model. While in the second phase, the results of first phase were further enhanced using image translation model conditioned on the object layout. In final phase we generated title of the given story based on generated image. In our experiments, we used custom dataset of text stories with three animal categories along with the COCO dataset. For the image generating part, we evaluated our approach with state of the art models known as scene_gen and sg2im. Our method generated high-resolution informative cover photo with story title by positioning the objects at right locations as specified in the text story. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： Graph neural networks

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PVTAdpNet: polyp segmentation using pyramid vision transformer with a novel adapter block

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International Journal of Information Technology (Singapore) 2025年 1-16页

作者： Nezhad, Arshia Yousefi Aghaei, Helia Sajedi, Hedieh Department of Mathematics Statistics and Computer Science College of Science University of Tehran Tehran Iran

Colorectal cancer is one of the most prevalent cancers in the world. It illustrates the effectiveness of early detection and treatment of precursor polyps to prevent progression to malignancy. Despite the pivotal role of colonoscopy in detecting colorectal cancer and polyps, its efficacy has been marred by high miss rates attributed to heterogeneity in polyps and observer variability. Recent advancements in deep learning have significantly improved the automation of polyp detection and segmentation systems. In this study, we introduce the Pyramid Vision Transformer Adapter Residual Network (PVTAdpNet) to enhance existing models. As such, PVTAdpNet also presents an encoder-decoder architecture with additional upsampling layers by fusing some principles from the Pyramid Vision Transformer model with a novel residual block and adapter base skip connection. The lightweight design of PVTAdapNet enables real-time inference, making it suitable for clinical integration. This research contributes to developing advanced computer-aided diagnosis systems for improved polyp detection and early cancer diagnosis. PVTAdpNet obtains a high Dice coefficient of 0.8851 and a mean Intersection over Union of 0.8167 on out-of-distribution polyp datasets. Evaluation of the PolypGen dataset demonstrates PVTAdpNet's capability for real-time, accurate performance within familiar distributions. The source code of our network is available at https://***/ayousefinejad/***. © Bharati Vidyapeeth's Institute of computer Applications and Management 2025.

关键词： Adapter residual network Colonoscopy Colorectal cancer Deep learning Polyp detection Pyramid vision transformer Transformer

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data-driven slicing for dimension reduction in regressions:A likelihood-ratio approach

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science China Mathematics 2024年第3期67卷 647-664页

作者： Peirong Xu Tao Wang Lixing Zhu Department of Statistics Shanghai Jiao Tong UniversityShanghai 200240China Center for Statistics and Data Science Beijing Normal University at ZhuhaiZhuhai 519087China

To efficiently estimate the central subspace in sufficient dimension reduction,response discretization via slicing its range is one of the most used methodologies when inverse regression-based methods are ***,existing slicing schemes are almost all ad hoc and not widely ***,how to define datadriven schemes with certain optimal properties is a longstanding problem in this *** research described here is then ***,we introduce a likelihood-ratio-based framework for dimension reduction,subsuming the popularly used methods including the sliced inverse regression,the sliced average variance estimation and the likelihood acquired ***,we propose a regularized log likelihood-ratio criterion to obtain a data-driven slicing scheme and derive the asymptotic properties of the estimators.A simulation study is carried out to examine the performance of the proposed method and that of existing methods.A data set concerning concrete compressive strength is also analyzed for illustration and comparison.

关键词： full-likelihood approach adaptive slicing regularization second-order method

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An intelligent approach for autism spectrum disorder diagnosis and rehabilitation features identification

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Neural Computing and Applications 2024年第4期37卷 2557-2580页

作者： Ghnemat, Rawan Al-Madi, Nailah Awad, Mohammad Computer Science department Princess Sumaya University for Technology Amman Jordan Data Science department Princess Sumaya University for Technology Amman Jordan

Autism spectrum disorder (ASD) affects 1 in 100 children globally. Early detection and intervention can enhance life quality for individuals diagnosed with ASD. This research utilizes the support vector machine-recursive feature elimination (SVM-RFE) method in its approach for ASD classification using the phenotypic and Automated Anatomical Labeling (AAL) Brain Atlas datasets of the Autism Brain Imaging data Exchange preprocessed dataset. The functional connectivity matrix (FCM) is computed for the AAL data, generating 6670 features representing pair-wise brain region activity. The SVM-RFE feature selection method was applied five times to the FCM data, thus determining the optimal number of features to be 750 for the best performing support vector machine (SVM) model, corresponding to a dimensionality reduction of 88.76%. Pertinent phenotypic data features were manually selected and processed. Subsequently, five experiments were conducted, each representing a different combination of the features used for training and testing the linear SVM, deep neural networks, one-dimensional convolutional neural networks, and random forest machine learning models. These models are fine-tuned using grid search cross-validation (CV). The models are evaluated on various metrics using 5-fold CV. The most relevant brain regions from the optimal feature set are identified by ranking the SVM-RFE feature weights. The SVM-RFE approach achieved a state-of-the-art accuracy of 90.33% on the linear SVM model using the data Processing Assistant for Resting-State Functional Magnetic Resonance Imaging pipeline. The SVM model’s ability to rank the features used based on their importance provides clarity into the factors contributing to the diagnosis. The thalamus right, rectus right, and temporal middle left AAL brain regions, among others, were identified as having the highest number of connections to other brain regions. These results highlight the importance of using traditional ML models fo

关键词： Diseases

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