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12th International Conference on learning Representations, ICLR 2024

作者： Yan, Tom Zhang, Chicheng Machine Learning Department Carnegie Mellon University United States Department of Computer Science University of Arizona United States

The standard active learning setting assumes a willing labeler, who provides labels on informative examples to speed up learning. However, if the labeler wishes to be compensated for as many labels as possible before learning finishes, the labeler may benefit from actually slowing down learning. This incentive arises for instance if the labeler is to be replaced by the ML model once it is trained. In this paper, we initiate the study of learning from a strategic labeler, who may abstain from labeling to slow down learning. We first prove that strategic abstention can prolong learning, and propose a novel complexity measure and representation to analyze the query complexity of the learning game. Next, we develop a near-optimal deterministic algorithm, prove its robustness to strategic labeling, and contrast it with other active learning algorithms. We also analyze extensions that encompass more general learning goals and labeler assumptions. Finally, we characterize the query cost of multi-task active learning, with and without abstention. Our first exploration of strategic labeling aims to consolidate our theoretical understanding of the imitative nature of ML in human-AI interaction. © 2024 12th International Conference on learning Representations, ICLR 2024. All rights reserved.

关键词： learning algorithms

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COMPONENT-BASED SYNTHESIS OF STRUCTURAL VARIANTS OF SIMULATION MODELS FOR CHANGEABLE MATERIAL FLOW SYSTEMS

COMPONENT-BASED SYNTHESIS OF STRUCTURAL VARIANTS OF SIMULATI...

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2024 Winter Simulation Conference, WSC 2024

作者： Winkels, Jan Özkul, Felix Sutherland, Robin Löhn, Jannik Wenzel, Sigrid Rehof, Jakob Department of Computer Science TU Dortmund University Germany University of Kassel Germany Lamarr Institute for Machine Learning and Artificial Intelligence Dortmund Germany

ISBN: (纸本)9798331534202

Despite relevant research endeavors, modeling efforts related to the building of discrete-event simulation models for planning changeable material flow systems still limit their practical application. This is because simulation experts have to model many possible structural variants and compare them based on key performance indicators such as throughput, workload or investment costs, while also ensuring sufficient system changeability. This article presents a methodology for reducing efforts for structural variation during the experimental phase of a simulation study. Starting from a valid initial simulation model, structural variants of this simulation model are automatically generated by applying component-based software synthesis which uses combinatorial logic;thereby, a range of simulation models is provided for the user. This paper presents the outlined methodology using a case study and places it in the research context of reducing efforts associated with the design and execution of simulation experiments. © 2024 IEEE.

关键词： Discrete event simulation

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Energy Aware Routing Mechanism Using AODV Protocol For Low Energy Consumption in WSN 5

Energy Aware Routing Mechanism Using AODV Protocol For Low E...

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5th IEEE International Conference on Cybernetics, Cognition and machine learning Applications, ICCCMLA 2023

作者： Shanmugavalli, R. Krishnaveni, M. Dhivyaprabha, T.T. Subashini, P. Center for Machine Learning Intelligence Avinashilingam Institute for Home Science and Higher Education for Women Department of Computer Science Coimbatore India

ISBN: (纸本)9798350338287

Wireless Sensor Network (WSN) is a self-configured and infrastructure-less network that is used to monitor the environmental conditions and transfer sensor data to the desired destination in a particular region. Energy consumption is the most important concern in WSN, which is considered as an active research area. Routing selection is one method that is used to optimize energy in WSN. There are many protocols for discovering a route between two nodes. However, the performance of the Ad hoc On-Demand Distance Vector (AODV) routing protocol is a more suitable one. It is a generic reactive protocol for routing mostly used in MANET (Mobile Ad Hoc Networks) and WSNs (Wireless Sensor Networks). This protocol supports unicasting and multicasting and will also identify the shortest path. In this paper, energy-aware routing is implemented in the AODV protocol which is derived from nodes remaining energy. The remaining energy of node is computed by Max-Min energy algorithm in order to extend the network's life span and facilitates to keep the network lively. The performance of AODV is compared with Modified AODV protocols. The comparison is done by various performance metrics such as PDR (Packet Delivery Ratio), throughput, delay time, loss rate, and energy consumption. Analysis on the experimental results showed that MAODV protocol gives better results than traditional AODV protocol and it is also inferred that MAODV avoids too much energy consumption of nodes in the network. © 2023 IEEE.

关键词： Energy utilization

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UNCCER: Unified Network for Cancer Classification and Efficient Representation using Microarray Data 18

UNCCER: Unified Network for Cancer Classification and Effici...

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18th International Conference on Open Source Systems and Technologies, ICOSST 2024

作者： Younis, Haseeb Brahmi, Horiya Imane Byrne, Jonathan Minghim, Rosane University College Cork Department of Computer Science Ireland Intel Ireland Limited Machine Learning Group Ireland

ISBN: (纸本)9798331508692

In recent years, there has been a significant advance in the use of machine learning (ML) techniques to extract gene expression data from microarray databases, particularly in cancer-related research. There no unified method for classifying cancer microarray data, even after ML adoption. Due to the high dimensionality of microarray data, it is difficult to extract the relevant features and provide insights that can be helpful in identifying cancer types and stages. In this paper, we propose a Unified Network for Cancer Classification and Efficient Representation (UNCCER) using Deep learning (DL) on cancer microarray data. To implement this methodology, we employed a microarray database (CuMiDa) that has 78 carefully curated datasets for different types of cancers. Our single model has the capability to learn the patterns, cluster instances into their corresponding classes, and classify the cancer. We also used the Uniform Manifold Approximation and Projection (UMAP) to visualise, in low dimension, the instance separation both on original data and transformed data by our methodology. Using the proposed methodology, we achieved average 94% average accuracy, precision, recall, F1 Score, and 91% G-Mean. © 2024 IEEE.

关键词： Lung cancer

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Design of Weed Detection Technique Using Yolo Models

Design of Weed Detection Technique Using Yolo Models

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2024 IEEE Students Conference on Engineering and Systems, SCES 2024

作者： Sreehari, M. Jain, Ankur Vellore Institiute of Technology Artificial intelligence and Machine Learning Department of computer science engineering specialization Bhopal India

ISBN: (纸本)9798350374711

The use of the ADAM (Adaptive Moment Estimation) and SGD (Stochastic Gradient Descent) algorithms to optimize the YOLOv7(You Only Look Once), YOLOv8, and YOLO-NAS models for weed detection in agricultural landscapes is investigated in this study. Offering a thorough comparative study based on mean Average Precision (mAP), recall, and precision measures is the goal. When compared to YOLOv7, YOLOv8 regularly performs better, exhibiting balanced recall and higher precision. With remarkable mAP scores at IOU (Intersection Over Union) 0.50, YOLOv8 ADAM in particular sticks out, demonstrating strong weed identification abilities. The YOLOv7 ADAM exhibits a precision-recall trade-off, with precision being slightly sacrificed for recall. Model superiority is not solely determined by the optimization algorithm used (ADAM vs. SGD);YOLOv8 variations perform admirably. For real-world implementation, practical factors like processing efficiency and deployment viability are even more important than quantitative criteria. This work establishes the foundation for comprehending the complex trade-offs and advantages that are present in every model and algorithm, in addition to helping with the educated selection of the best YOLO models for weed detection. Future research directions in object detection in agriculture, including ensemble techniques and hyperparameter tuning, promise more advancements in this subject as technology develops. © 2024 IEEE.

关键词： Stochastic systems

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GITA: Graph to Visual and Textual Integration for Vision-Language Graph Reasoning 38

GITA: Graph to Visual and Textual Integration for Vision-Lan...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Wei, Yanbin Fu, Shuai Jiang, Weisen Zhang, Zejian Zeng, Zhixiong Wu, Qi Kwok, James T. Zhang, Yu Department of Computer Science and Engineering Southern University of Science and Technology China Department of Computer Science and Engineering Hong Kong University of Science and Technology Hong Kong Australia Institute for Machine Learning University of Adelaide Australia Tencent China

Large Language Models (LLMs) are increasingly used for various tasks with graph structures. Though LLMs can process graph information in the textual format, they overlook the rich vision modality, which is an intuitive way for humans to comprehend structural information and conduct general graph reasoning. The potential benefits and capabilities of representing graph structures as visual images (i.e., visual graph) are still unexplored. To fill the gap, we innovatively propose an end-to-end framework, called Graph to vIsual and Textual IntegrAtion (GITA), which incorporates visual graphs into general graph reasoning. Besides, we construct the Graph-based Vision-Language Question Answering (GVLQA) dataset from existing graph data, which is the first vision-language dataset for general graph reasoning. Extensive experiments on the GVLQA dataset and five real-world datasets show that GITA outperforms mainstream LLMs on general graph reasoning. Moreover, experimental results demonstrate the effectiveness of the layout augmentation on visual graphs and pretraining on the GVLQA dataset. © 2024 Neural information processing systems foundation. All rights reserved.

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A Framework for Fluid Motion Estimation using a Constraint-Based Refinement Approach

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machine Graphics and Vision 2023年第3-4期32卷 17-43页

作者： Doshi, Hirak Nori, Uday Kiran Department of Mathematics and Computer Science Sri Sathya Sai Institute of Higher Learning Andhra Pradesh India

Physics-based optical flow models have been successful in capturing the deformities in fluid motion arising from digital imagery. However, a common theoretical framework analyzing several physics-based models is missing. In this regard, we formulate a general framework for fluid motion estimation using a constraint-based refinement approach. We demonstrate that for a particular choice of constraint, our results closely approximate the classical continuity equation-based method for fluid flow. This closeness is theoretically justified by augmented Lagrangian method in a novel way. The convergence of Uzawa iterates is shown using a modified bounded constraint algorithm. The mathematical well-posedness is studied in a Hilbert space setting. Further, we observe a surprising connection to the Cauchy-Riemann operator that diagonalizes the system leading to a diffusive phenomenon involving the divergence and the curl of the flow. Several numerical experiments are performed and the results are shown on different datasets. Additionally, we demonstrate that a flow-driven refinement process involving the curl of the flow outperforms the classical physics-based optical flow method without any additional assumptions on the image data. © 2023 institute of Information Technology, Warsaw University of Life sciences - SGGW. All rights reserved.

关键词： Optical flows

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Comparative Assessment of machine learning Models for Predicting Glucose Intolerance Risk

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SN computer science 2024年第7期5卷 894页

作者： Kumar, B. P. Pradeep Manoj, H.M. Department of Computer Science and Design Atria Institute of Technology Affiliated to VTU Karnataka Bangalore India Department of Artificial Intelligence and Machine Learning BMS Institute of Technology and Management Affiliated to VTU Karnataka Bangalore India

This study focuses on predictive analytics and healthcare, specifically the prediction of Glucose Intolerance, a chronic metabolic disease with significant global health implications. The research aims to develop efficient tools for risk assessment and early identification, offering medical practitioners reliable instruments for identifying high-risk patients and implementing preventive measures in a timely manner. A significant challenge in managing Glucose Intolerance is the absence of effective and precise prediction models. Traditional risk assessment techniques often fail to capture the multifaceted nature of Glucose Intolerance development, leading to delayed treatments and suboptimal patient outcomes. To address this issue, we conducted a comprehensive study utilizing various machine learning algorithms, including Decision Trees, Random Forest, Gradient Boosting, CatBoost, K-Nearest Neighbors (KNN), Support Vector Classifier (SVC), Logistic Regression, and a Voting Classifier, to predict Glucose Intolerance. The primary objective was to identify the most effective combination of features and models for accurate predictions. Key factors considered included patient demographics, lifestyle characteristics, medical history, and genetic susceptibility, which were used to build robust and personalized prediction models. We conducted a comparative analysis of the machine learning models' performance based on cross-validated accuracy with test-train splits and folds: 0.2, 6;0.05, 4;0.1, 2;0.2, 6;and 0.05, 2. The results showed that Random Forest achieved test accuracies of 74%, 76%, 82%, 75%, and 79%;KNN achieved 70%, 72%, 74%, 75%, and 72%;SVC achieved 74%, 72%, 77%, 77%, and 72%;Logistic Regression achieved 73%, 76%, 79%, 73%, and 76%;Gradient Boosting achieved 72%, 79%, 75%, 73%, and 79%;XGBoost achieved 72%, 79%, 77%, 74%, and 79%;CatBoost achieved 74%, 76%, 79%, 75%, and 76%;Decision Tree achieved 60%, 83%, 74%, 70%, and 79%;and Voting Classifier achieved 74%, 7

关键词： Algorithmic approaches Chronic disease Data-driven healthcare Early detection Glucose Intolerance Healthcare analytics machine learning Personalized medicine Prediction Predictive modelling Risk stratification

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Secure Healthcare Monitoring: Algorithms and Techniques for Heart Disease Detection and Data Privacy 1

Secure Healthcare Monitoring: Algorithms and Techniques for ...

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1st International Conference on Intelligent Systems and Computational Networks, ICISCN 2025

作者： Poonam Mallapur, Sujata V. Sharnbasva University Department of Computer Science and Engineering Kalaburagi India Sharnbasva University Department of Artificial Intelligence & Machine Learning Kalaburagi India

ISBN: (纸本)9798331529246

This survey offers the review of Healthcare Monitoring Systems (HMS) and Privacy Preservation (PP) approaches. The main objective is based on the detection of heart disease and maintain the security for patient data. The machine learning models such as SVM, decision trees, and neural networks for heart disease detection and encryption approaches such as AES, ECC, and homomorphic encryption for securing the data are the major topics that reviewed in this survey. Moreover, the survey highlights the performance evaluation for heart disease prediction and the datasets used for heart disease. It also ensures the privacy to tackle the challenges in real time monitoring. A higher accuracy of 99.8 was reached by FHE-based encryption method. Moreover, it discusses the research gaps like scalability, efficiency, and regulatory compliance which motivates the need of novel privacy-preserving techniques for healthcare systems, which is highlighted in the future scope. © 2025 IEEE.

关键词： Differential privacy

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Reducing noise using neighbourhood pixel analysis and interpretable custom kernel in CNN model for CP handwritten digit recognition

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International Journal of Information Technology (Singapore) 2024年 1-11页

作者： Muthureka, K. Srinivasulu Reddy, U. Janet, B. Machine Learning and Data Analytics Lab Department of Computer Applications National Institute of Technology Tiruchirappalli India Information Processing Lab Department of Computer Applications National Institute of Technology Tiruchirappalli India

Individuals with Cerebral Palsy (CP) are impacted lifetime barriers in their everyday activities, especially in writing phrase, which results from innate neural motor in co-ordination. Numerous studies have focused on recognizing normal people’s handwritten digits using benchmark datasets. However, there has been limited exploration into recognizing handwritten digits by individuals with CP. The handwriting of CP individuals is significantly more difficult to recognize than that of normal people because the CP digits are inaccurate, uneven, and complex to recognize. In this study, the authors improve the digit recognition accuracy of existing Convolutional Neural Network (CNN) models on collected CP handwritten digit’s dataset using two notable methods, first one is to reduce the noise using neighbourhood pixel analysis through Fast Non-Local Means (FNLM) algorithm and second one is to extract the feature using customized kernel technique. The experimental results show that the accuracy of existing state of art models are improved by 3% to 7.14% with the integration of aforementioned techniques. Moreover, a custom CNN model hybridised with the Prewit, Laplacian, Sobel filters, and custom kernel has following CP digit recognition accuracy values of 97.96%, 98.08%, 99.17%, and 99.60% respectively. © Bharati Vidyapeeth's institute of computer Applications and Management 2024.

关键词： Cerebral Palsy CNN Customized kernel initialization Edge detection Handwritten digit recognition

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