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Proceedings of the 41st International Conference on Machine Learning

作者： Ali AhmadiTeshnizi Wenzhi Gao Madeleine Udell Department of Management Science and Engineering Stanford University CA Institute for Computational and Mathematical Engineering Stanford University CA Department of Management Science and Engineering Stanford University CA and Institute for Computational and Mathematical Engineering Stanford University CA

Optimization problems are pervasive in sectors from manufacturing and distribution to healthcare. However, most such problems are still solved heuristically by hand rather than optimally by state-of-the-art solvers because the expertise required to formulate and solve these problems limits the widespread adoption of optimization tools and techniques. This paper introduces OptiMUS, a Large Language Model (LLM)-based agent designed to formulate and solve (mixed integer) linear programming problems from their natural language descriptions. OptiMUS can develop mathematical models, write and debug solver code, evaluate the generated solutions, and improve its model and code based on these evaluations. OptiMUS utilizes a modular structure to process problems, allowing it to handle problems with long descriptions and complex data without long prompts. Experiments demonstrate that OptiMUS outperforms existing state-of-the-art methods on easy datasets by more than 20% and on hard datasets (including a new dataset, NLP4LP, released with this paper that features long and complex problems) by more than 30%. The implementation and the datasets are available at https://***/teshnizi/OptiMUS.

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Machine Learning Approach for Handling Imbalanced Students' Performance Data 15

Machine Learning Approach for Handling Imbalanced Students' ...

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15th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： Sujatha, E. Divya, S. Sakthivelan, R.G. Gopirajan, P.V. Thandalam Chennai India Chennai India Department of Computer Science and Engineering Gopalan College of Engineering and Management Bengaluru India Department of Computational Intelligence School of Computing SRM Institute of Science and Technology Kattankulathur campus Chennai India

ISBN: (纸本)9798331300579

Imbalanced student performance data in educational institutions is crucial for any machine learning prediction model. It affects the efficiency of classifiers and challenges the sampling methods for having a more significant number of features. The proposed model was designed to predict the student's performance by comparing the results with popular similar models. The Kaggle dataset having 386 rows and 33 columns of student data, was used in this study. In addition, the Portugal database was considered for experimental analysis, containing 394 students and 19 attributes. It proves that the Random Forest classifier yields the highest accuracy at 84.15% in handling imbalanced datasets. The results show that the SVM-SMOTE is higher accuracy as, 94.76%, than the other sampling methods in predicting the student's performance with various features. © Grenze Scientific Society, 2024.

关键词： Adversarial machine learning

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Morpho-contour exponential estimation algorithm for predicting breast tumor growth from MRI imagery

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International Journal of Information Technology (Singapore) 2024年 1-16页

作者： Kaladevi, P. Kanimozhi, N. Nirmala, B. Sivasankari, R. Department of Computer Science and Engineering K. S. Rangasamy College of Technology (Autonomous) Tamil Nadu Tiruchengode 637215 India Department of Computational Intelligence School of Computing SRM Institute of Science and Technology Kattankulathur Tamil Nadu Chennai 603203 India Computer Science and Engineering Sri Ramachandra Faculty of Engineering and Technology Sri Ramachandra Institute of Higher Education and Research Porur Chennai 600116 India Department of Computer Science Engineering B. S. Abdur Rahman Crescent Institute of Science and Technology Vandalur Tamil Nadu Chennai 600 048 India

Breast tumor grows rapidly have an elevated risk of progressing to a metastatic state, leading to the spread of tumor tissues to other regions of the body. Early detection of such metastatic sites can be facilitated by predicting these tumors' growth rate. In this context, we introduce a novel Morpho-Contour Exponential Estimation (MoCEE) method to estimate the potential growth rate of identified breast tumors using magnetic resonance imaging (MRI) datasets. This methodology not only effectively captures morphological traces but also pinpoints regions indicative of infectious spread. This precision is achieved by enhancing the Mask R-CNN with an active contour function, ensuring the accurate segmentation of the breast tumor. Once segmented, the tumor's growth rate is computed using the exponential growth model, which accurately determines the rate by analyzing the distance between two distinct random points on the tumor's segmented region. Subsequently, this derived information, processed through a gradient boosting approach, predicts the likelihood of tumor growth across two different breast tumor categories. In our study, we assessed the proposed MoCEE method from two crucial angles. Firstly, we rigorously evaluated its ability to accurately segment both benign and malignant breast tumors. Our analysis involved comparing MoCEE's performance to that of various deep learning networks, yielding impressive segmentation accuracy percentages, such as 93.12% and 92.22% for benign and malignant tumors in BUSnet, and even higher values for other networks. Secondly, we explored MoCEE's capability to estimate tumor size, considering different probability growth rates (k^=0.2, 0.3, 0.4, and 0.5). Notably, proposed MoCEE proved highly accurate in predicting tumor growth rates across this range of probabilities. These findings highlight MoCEE's versatility and robustness, making it a valuable tool for precise breast tumor analysis and prediction. © The Author(s), under exclusiv

关键词： And gradient boosting approach Breast tumors Exponential growth model Mask R-CNN Morpho-contour exponential estimation (MoCEE)

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Enhancing Lung Disease Identification with Multimodal Data Fusion and Deep Learning CNN Approach 5

Enhancing Lung Disease Identification with Multimodal Data F...

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5th International Conference on Smart Electronics and Communication, ICOSEC 2024

作者： Grace Shalini, T. Shiny, G. Susan Saranya, R. Suresh Babu, P. Kavitha, R. Atheeswaran, Athiraja Srm Institute of Science and Technology School of Computing Department of Computational Intelligence Chengalpattu Kattankulathur India Sethu Institute of Technology Department of information technology Kariapatti India Velammal college of engineering and technology Department of Eee Madurai India Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Krishnankoil India Bannari Amman Institute Of Technology Department Of Computer Science And Engineering Erode Sathyamangalam India Hyderabad India

ISBN: (纸本)9798331504403

Lung diseases, encompassing a wide spectrum from chronic conditions to acute infections, present a significant burden on global healthcare systems, necessitating precise and timely diagnostic methodologies. In this study, we propose a pioneering approach leveraging multimodal data fusion and deep learning Convolutional Neural Network (CNN) algorithms for enhanced identification of lung diseases. Our methodology integrates diverse data sources including medical images, clinical reports, and patient demographics to capture a comprehensive representation of lung pathology. We employ a state-of-the-art CNN architecture for medical image analysis, trained on a large-scale dataset encompassing various lung diseases and healthy lung tissue. Through extensive experimentation and validation, the approach achieved remarkable performance, with an accuracy of 94%, sensitivity of 92%, specificity of 93%, and an F1-score of 93% in accurately identifying and classifying lung abnormalities. Furthermore, we demonstrate the clinical utility of our method in providing valuable insights to healthcare professionals, aiding in timely diagnosis, prognosis, and treatment planning. Our findings highlight the potential of multimodal data fusion coupled with deep learning CNN algorithms in advancing the field of lung disease identification, offering a promising avenue for improving healthcare practices and patient outcomes in respiratory medicine. © 2024 IEEE.

关键词： Lung cancer

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Enhancing Medical Image Diagnosis with Convolutional Neural Networks 2

Enhancing Medical Image Diagnosis with Convolutional Neural ...

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2nd IEEE International Conference on Advances in Information Technology, ICAIT 2024

作者： Mahadevan, K. Pushparani, S. SashiRekha, K. Sivagami, V.M. Usharani, R. Department of Computer Science and Engineering CARE College of Engineering Tamil Nadu Trichy India Department of Computer Science and Engineering Meenakshi College of Engineering Tamil Nadu Chennai India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Tamil Nadu Chennai India Department of Information Technology Sri Venkateswara College of Engineering Tamil Nadu Chennai India Department of Computational Intelligence School of Computing Faculty of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Chennai India

ISBN: (纸本)9798350383867

This paper is a comparative analysis of medical image diagnosis algorithms with Convolutional Neural Networks (CNNs) and other methods;such as Support Vector Machine (SVM), Random Forest, and k-nearest Neighbors (k-NN) The study performs evaluations on datasets with multimodalities like medical images and diagnostic and various imaging modalities. Results shows that CNNs have the upper hand over traditional, static algorithms across several metrics that assesses the performance of the system such as accuracy, precision, recall, and F1. In particular, CNN precision was 90.5% while the recall was 93.7%, and the accuracy was 92.1% as well as F1-score 92.0%. In addition, the high area under the Receiver Operating Characteristic curve (AUC-ROC) of over 0.95 for the CNN also signed for its excellent discriminatory power. These outcomes only exemplify CNN usefulness in the diagnostic area because it is an exact and accurate method for analyzing medical images. Although the nets CNNs are deployed in clinical cases may help address concerns regarding interpretability, compliance with regulations, and ethical opinions, such issues still exist and require further exploration. Overall, this research takes the first steps in directing algorithmic approach towards medical diagnosis and show the feasibility in deployment of CNNs as inseparable pieces of equipment in modern healthcare. © 2024 IEEE.

关键词： Random forests

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Random hyperbolic graphs in d+1 dimensions

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Physical Review E 2024年第5期109卷 054131-054131页

作者： Gabriel Budel Maksim Kitsak Rodrigo Aldecoa Konstantin Zuev Dmitri Krioukov Faculty of Electrical Engineering Mathematics and Computer Science Department of Physics Network Science Institute Department of Computing and Mathematical Sciences Department of Physics Department of Mathematics Department of Electrical & Computer Engineering

We consider random hyperbolic graphs in hyperbolic spaces of any dimension d+1≥2. We present a rescaling of model parameters that casts the random hyperbolic graph model of any dimension to a unified mathematical framework, leaving the degree distribution invariant with respect to the dimension. Unlike the degree distribution, clustering does depend on the dimension, decreasing to 0 at d→∞. We analyze all of the other limiting regimes of the model, and we release a software package that generates random hyperbolic graphs and their limits in hyperbolic spaces of any dimension.

关键词： Network formation & growth Network structure

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A Novel Approach of Disease Diagnostic Prediction Using SMOTE Ensemble Classification 2nd

A Novel Approach of Disease Diagnostic Prediction Using SMOT...

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2nd International Conference on Deep sciences for Computing and Communications, IconDeepCom 2023

作者： Gajendran, Sudhakaran Arunarani, A.R. Nair, Akhil R. Logeswari, G. Elakkiya, R. School of Electronics Engineering Vellore Institute of Technology Tamilnadu Chennai India Department of Computational Intelligence School of Computing SRM Institute of Science and Technology Kattankulathur Tamilnadu Chennai India School of Computer Science and Engineering Vellore Institute of Technology Tamilnadu Chennai India Department of Computer Science Birla Institute of Technology and Science Pilani Dubai Campus Dubai United Arab Emirates

ISBN: (纸本)9783031689079

Hepatitis is a liver inflammation caused primarily by viral infection. There are several methods that help with its diagnosis. However, the available datasets for Hepatitis research contain missing values that are imputed by median values. The Synthetic Minority Oversampling Technique (SMOTE) oversamples the minority class, while feature selection algorithms extract critical features from the actual data. One significant disadvantage of existing approaches is their inability to efficiently deal with missing data, resulting in lower statistical effectiveness. In this paper, a novel approach for predicting disease diagnostic is proposed. Firstly, recursive feature elimination (RFE) is used to remove features from the total number declared, followed by univariate analysis, which provides a score to each feature. The principal component analysis (PCA) compresses data. The mutual information score and ReliefF are two automatic feature selection approaches that restore key features. The synthetic minority oversampling technique (SMOTE) oversamples the minority class to obtain balanced data. Several experiments have been conducted using the hepatitis dataset and the framework has shown to predict the disease much more accurately than existing methods. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Diseases

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Pre-trained language models in Spanish for health insurance coverage 5

Pre-trained language models in Spanish for health insurance ...

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5th Workshop on Clinical Natural Language Processing, ClinicalNLP 2023. held at ACL 2023

作者： Aracena, Claudio Rodríguez, Nicolás Rocco, Victor Dunstan, Jocelyn Faculty of Physical and Mathematical Sciences University of Chile Chile Millennium Institute Foundational Research on Data Chile Chilean Safety Association Chile Department of Computer Science Catholic University of Chile Chile Institute for Mathematical and Computational Engineering Catholic University of Chile Chile Center for Mathematical Modeling University of Chile Chile

ISBN: (纸本)9781959429883

The field of clinical natural language processing (NLP) can extract useful information from clinical text. Since 2017, the NLP field has shifted towards using pre-trained language models (PLMs), improving performance in several tasks. Most of the research in this field has focused on English text, but there are some available PLMs in Spanish. In this work, we use clinical PLMs to analyze text from admission and medical reports in Spanish for an insurance and health provider to give a probability of no coverage in a labor insurance process. Our results show that fine-tuning a PLM pretrained with the provider's data leads to better results, but this process is time-consuming and computationally expensive. At least for this task, fine-tuning publicly available clinical PLM leads to comparable results to a custom PLM, but in less time and with fewer resources. Analyzing large volumes of insurance requests is burdensome for employers, and models can ease this task by pre-classifying reports that are likely not to have coverage. Our approach of entirely using clinical-related text improves the current models while reinforcing the idea of clinical support systems that simplify human labor but do not replace it. To our knowledge, the clinical corpus collected for this study is the largest one reported for the Spanish language. © 2023 Association for computational Linguistics.

关键词： Health insurance

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Design,simulation,and testing of a tunable MEMS multi-threshold inertial switch

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Microsystems & Nanoengineering 2024年第2期10卷 37-46页

作者： Qiu Xu Rodrigo T.Rocha Yousef Algoos Eric Feron Mohammad I.Younis Faculty of Electronic Information Engineering Huaiyin Institute of TechnologyHuai’an223003PR China Computer Electrical and Mathematical Science and Engineering DivisionKing Abdullah University of Science and TechnologyThuwal23955-6900Saudi Arabia Piezoelectric Microsystem Technologies Silicon Austria LabsVillach9524Austria Physical Sciences and Engineering Division King Abdullah University of Science and TechnologyThuwal23955Saudi Arabia Mechanical Engineering Department State University of New YorkBinghamtonNY13902USA

This paper presents a tunable multi-threshold micro-electromechanical inertial switch with adjustable threshold *** demonstrated device combines the advantages of accelerometers in providing quantitative acceleration measurements and g-threshold switches in saving power when in the inactive state upon experiencing acceleration below the *** designed proof-of-concept device with two thresholds consists of a cantilever microbeam and two stationary electrodes placed at different positions in the sensing *** adjustable threshold capability and the effect of the shock duration on the threshold acceleration are analytically investigated using a nonlinear beam *** are shown for the relationships among the applied bias voltage,the duration of shock impact,and the tunable *** fabricated prototypes are tested using a shock-table *** analytical results agree with the experimental *** designed device concept is very promising for the classification of the shock and impact loads in transportation and healthcare applications.

关键词： tunable acceleration testing

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Detection of Medicinal Plants Using Machine Learning 12th

Detection of Medicinal Plants Using Machine Learning

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12th International Conference on Recent Trends in Computing, ICRTC 2024

作者： Ravikumar, S. Eugene Berna, I. Babu, R. Arockia Raj, Y. Vijay, K. Department of Computer Science and Engineering Vel Tech Rangarajan Dr Sagunthala R&D Institute of Science and Technology Chennai India Department of Artificial Intelligence and Machine Learning Bannari Amman Institute of Technology Erode India Department of Computational Intelligence School of Computing SRM Institute of Science and Technology Kattankulathur Campus Chennai India Department of Computer Science and Engineering PSNA College of Engineering and Technology Dindigul India Department of Artificial Intelligence and Machine Learning Rajalakshmi Engineering College Chennai India

ISBN: (纸本)9789819789450

An exciting new direction in plant categorization and identification is the use of machine learning techniques for medicinal plant detection. Using a variety of photo datasets containing medicinal plants, this technique trains models using ANNs. The ML model learns the complex traits that distinguish various plant species by using data from pre-trained models or by developing bespoke architectures. Thanks to the trained model’s precision in visually recognizing therapeutic plants, everyone from researchers to ecologists can reap the benefits. Methods for medicinal plant identification that are both accurate and objective are crucial to biodiversity research and attempts to safeguard resources. Considerations of ethics, model calibration, and data curation should be incorporated into the construction of such systems. Gather any and all images of plants used for medical purposes. Various viewpoints, lighting scenarios, and plant types will enhance the set. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Plant diseases

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