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40th international conference on machine learning

作者： Sellke, Mark Amazon Core AI Cambridge MA 02142 USA

We advance the study of incentivized bandit exploration, in which arm choices are viewed as recommendations and are required to be Bayesian incentive compatible. Recent work of (Sellke & Slivkins, 2022) has shown that for the special case of independent arms, after collecting enough initial samples, the popular Thompson sampling algorithm becomes incentive compatible. This was generalized to the combinatorial semibandit in (Hu et al., 2022). We give an analog of this result for linear bandits, where the independence of the prior is replaced by a natural convexity condition. This opens up the possibility of efficient and regret-optimal incentivized exploration in high-dimensional action spaces. In the semibandit model, we also improve the sample complexity for the pre-Thompson sampling phase of initial data collection.

关键词： machine learning

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DISTRIBUTION OF CARBON STORAGE AND POTENTIAL STRATEGIES TO ENHANCE CARBON SEQUESTRATION CAPACITY IN SINGAPORE 29th

DISTRIBUTION OF CARBON STORAGE AND POTENTIAL STRATEGIES TO E...

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29th international conference of the Association-for-Computer-Aided-Architectural-Design-Research-in-Asia (CAADRIA)

作者： Li, Jiongye Stouffs, Rudi Natl Univ Singapore Dept Architecture Singapore Singapore

ISBN: (纸本)9789887891826

The expansion of urbanization leads to significant changes in land use, consequently affecting carbon storage. This research aims to investigate the carbon loss due to land use alterations and proposes strategies for mitigation. Utilizing existing land use data from 2017 and 2022, along with simulated data for 2025 generated by an ANN model and Cellular Automata, we identified changes in land use. These changes were then correlated with variations in carbon storage, both gains and losses. Our findings reveal a significant loss of 36,859 metric tons of carbon storage from 2017 to 2022. The projection for 2025 estimates a further reduction, reaching a total loss of 83,409 metric tons. By employing the LISA method, we identified that low-carbon storage zones are concentrated in the southeast region of the research site. By overlaying these zones with areas of carbon storage loss, we pinpointed regions severely affected by carbon depletion. Consequently, we propose that mitigation strategies should be imperatively implemented in these identified areas to counteract the trend of carbon storage loss. This approach offers urban planners a solution to identify areas experiencing carbon storage decline. Moreover, our research methodology provides a novel framework for scholars studying similar carbon issues.

关键词： Land use and land cover (LULC) Changes Simulated LULC machine learning Model Carbon Storage Changes GIS

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Efficient and Robust Multirobot Navigation and Task Allocation Using Soft Actor Critic 2

Efficient and Robust Multirobot Navigation and Task Allocati...

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2nd international conference on machine learning and data engineering, icmlde 2023

作者： Hersi, Abdikafi Hashi Divya Udayan, J. Amrita School of Computing Amrita Vishwa Vidyapeetham Amritapuri India Amrita School of Computing Amrita Vishwa Vidyapeetham Amritapuri India

In this research, we present a novel solution to the problem of multi-robot task allocation (MRTA) by utilizing a variant of the state-of-art Deep Reinforcement learning algorithm, Soft Actor-Critic (SAC) within Multirobot Navigation Systems(MRS). Our approach addresses the existing research gaps in MRTA for MRS, which have received limited attention and have proven challenging to solve using conventional methods. Through extensive simulation evaluations, we demonstrate that our system outperforms current state-of-the-art methods in terms of performance metrics. These findings highlight the effectiveness and superiority of our proposed approach in overcoming the inherent challenges of large-scale MRS. Additionally, we contribute to the field by proposing improvements to the Task Allocation Index (TAI) metric, enabling a more comprehensive evaluation of MRTA performance. © 2024 Elsevier B.V.. All rights reserved.

关键词： Reinforcement learning

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LesNet: An Automated Skin Lesion Deep Convolutional Neural Network Classifier through Augmentation and Transfer learning 2

LesNet: An Automated Skin Lesion Deep Convolutional Neural N...

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2nd international conference on machine learning and data engineering, icmlde 2023

作者： Mir, Aqib Nazir Nissar, Iqra Rizvi, Danish Raza Kumar, Ankush Research Lab Department of Computer Engineering Jamia Millia Islamia New Delhi110025 India Department of Computer Science and Engineering Punjabi University Punjab 147002 India

Skin cancer is one of the most prevalent forms of cancer around the world. Initial diagnosis relies on visual assessment of the affected area, followed by detailed dermoscopic analysis. The development of an automated system for classifying skin lesions poses a considerable challenge due to inherent noise and subtle variability in lesion images. Deep convolutional neural networks have demonstrated exceptional proficiency in image classification tasks spanning diverse domains. This paper demonstrates an end-to-end classification architecture built on top of transfer learning with data augmentation. The proposed model capitalizes on pre-trained architectures such as DenseNet, VGG-16, and Inception for feature extraction, and employs fully connected dense layers for categorizing seven distinct types of lesions. Several data augmentation techniques are also used to handle the class imbalance problem. Extensive experimentation encompassing various hyperparameters, imbalanced data scenarios, and balanced datasets was conducted to refine the automated skin lesion system. The proposed approach achieved a notable accuracy of 98% on the HAM10000 dataset and 94% on the ISIC-2019 dataset. Importantly, the experimental findings surpassed the performance of current state-of-the-art models for lesion classification. Furthermore, this paper examines the impact of class imbalance and data augmentation on the model's accuracy. © 2024 Elsevier B.V.. All rights reserved.

关键词： Convolutional neural networks

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Translating Natural Language Queries to SQL Using the T5 Model 18

Translating Natural Language Queries to SQL Using the T5 Mod...

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18th Annual IEEE international Systems conference (SysCon)

作者： Wong, Albert Pham, Lien Lee, Young Chan, Shek Sadaya, Razel Khmelevsky, Youry Clement, Mathias Cheng, Florence Wing Yau Mahony, Joe Ferri, Michael Langara Coll Math & Stat Vancouver BC Canada Okanagan Coll Math & Stat Kelowna BC Canada Okanagan Coll Comp Sci Kelowna BC Canada Harris SmartWorks Res & Dev Ottawa ON Canada

ISBN: (纸本)9798350358810;9798350358803

This paper presents the development process of a natural language to SQL model using the T5 model as the basis. The models, developed in August 2022 for an online transaction processing system and a data warehouse, have a 73% and 84% exact match accuracy respectively. These models, in conjunction with other work completed in the research project, were implemented for several companies and used successfully on a daily basis. The approach used in the model development could be implemented in a similar fashion for other database environments and with a more powerful pre-trained language model.

关键词： Natural Language Processing data Query System Text-to-SQL Speech-to-SQL Deep learning machine learning T5 Model Human-machine-Systems Energy Systems

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Prediction of pressure drop in solid-liquid two-phase pipe flow for deep-sea mining based on machine learning

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OCEAN engineering 2024年 304卷

作者： Wan, Chuyi Zhu, Hongbo Xiao, Shengpeng Zhou, Dai Bao, Yan Liu, Xu Han, Zhaolong Shanghai Jiao Tong Univ Sch Ocean & Civil Engn Minhang Campus Shanghai 200240 Peoples R China Shanghai Jiao Tong Univ Sichuan Res Inst Chengdu 610213 Peoples R China

In deep -sea mining, the accurate and rapid prediction of the pressure drop in a solid-liquid two-phase pipe flow (SLPF) with different parameters including particles, pipes, and flow fields, remains an issue yet to be fully resolved. In this study, an extensive investigation of the pressure drop in a slpf is conducted using machine-learning techniques. By collecting 1290 sets of data from 13 experimental papers and performing analysis and processing, we obtain a machine-learning ensemble algorithm capable of accurately predicting the pipe -pressure drop based on random forest (RF), back propagation (BP), and polynomial regression (PR) algorithms. The performance of the ensemble algorithm surpasses that of the other three algorithms, whether applied to pure substance (PS) particles or mixed particles (MP) containing PS and equivalent particles. For PS particles, the particle concentration and particle diameter -to -pipe diameter (PTP) account for the second and third weights influencing the pressure drop. Using the computational fluid dynamics (CFD)-discrete element method (DEM), this can be attributed to the significant kinetic energy loss caused by the collisions and friction between the particles and pipe wall and the excessive gravity of the particles, which influences the pressure drop.

关键词： Deep-sea mining machine learning Ensemble algorithm Pipe flow Hydraulic conveying

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Prediction of Ground Water Level in Rajasthan State Using machine learning

Prediction of Ground Water Level in Rajasthan State Using Ma...

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2022 international conference on machine learning and data engineering, icmlde 2022

作者： Srivastava, Devesh Kumar Shukla, Aditi Jemni, Divyans SIT Manipal University Jaipur Rajasthan Jaipur India Accenture Pvt Ltd Karnataka Banglore India

Groundwater is best portrayed as the world's truly covered up treasure. It has had an effect in giving safe drinking water during dry season. It is the world's biggest open store of fresh water (barring ice sheets and icy masses) and comprises around 94% of all fresh water. Rajasthan witnesses water scarcity problem therefor, it aims at forecasting the rainfall in the state, based on different features provided by the state government. The motivation of the paper is to predict the groundwater level so that proper conservative methods can be applied to raise the level of ground water. In this paper, a comparative analysis of different individual models is used on the derived dataset and two new hybrid models are added to increase the efficiency and accuracy of the prediction. © 2023 The Authors. Published by Elsevier B.V.

关键词： Groundwater

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Federated learning on low-power Arduino Nano33 BLE Sense to predict the length of stay using a linear regression model 2

Federated learning on low-power Arduino Nano33 BLE Sense to ...

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2nd international conference on machine learning and data engineering, icmlde 2023

作者： Sriram, S. Rk, Hariharathmajan Barathi Babu, M. Pradeep, Amal Karthi, R. Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Coimbatore India

Federated learning (FL) is a collaborative learning paradigm where multiple clients are used to build the model without sharing data and preserving privacy. An FL-based linear regression model is designed to predict the length of stay for patients at hospitals using the low-power Arduino Nano 33 BLE Sense microcontroller unit (MCU). FL uses a distributed learning technique that allows model building from decentralized data sources. The Arduino Nano 33 BLE Sense is a compact and energy-efficient MCU providing an ideal platform for implementing FL in resource-constrained environments. FL algorithms aggregate model parameters from multiple Arduino clients and collectively train and build a predictive model to estimate the length of stay at the hospital by patients. Experiments were conducted to understand the performance of FL on clients with data of equal and varying sizes and heterogeneous data from multiple sources. The performance of the algorithm is evaluated based on Mean Absolute Error (MAE), Percentage Decrease in Training error (PDTE), and Percentage Difference with Optimal Testing (PDOT) value. Experimental results show that the number of local epochs and FL rounds affects the convergence of clients to the optimal value. The experimental results demonstrate the applicability of FL on low-power MCUs, preserving privacy which is a core requirement for healthcare solutions. © 2024 Elsevier B.V.. All rights reserved.

关键词： Forecasting

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Advancing AI-Driven Network Anomaly Detection: A Comparative Study Employing Big data Analytics 10

Advancing AI-Driven Network Anomaly Detection: A Comparative...

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10th international conference on Big data and Information Analytics

作者： Zhao, Baokang Zeng, Zengri Luo, Zijin Zhang, Zhaoyuan Liu, Hacheng Natl Univ Def Technol Sch Comp Changsha Peoples R China Hunan Univ Humanities Sci & Technol Sch Informat Loudi Peoples R China Changsha Univ Sci & Technol Sch Comp & Commun Engn Changsha Peoples R China

ISBN: (纸本)9798350354638;9798350354621

In the expanding realms of the Internet and IoT, the surge in network data both drives the digital economy and intensifies cybersecurity vulnerabilities. Network anomaly detection is essential for protecting against security threats. This paper conducted a comprehensive comparative study by applying big data analytics and sophisticated machine learning to enhance intelligent network anomaly detection. It confronts challenges such as data heterogeneity and model standardization, conducting extensive experiments across six datasets with a range of algorithms, from classical decision trees to cutting-edge CNN, LSTM, and Transformer models, the GWO algorithm was also employed for feature selection, and it was combined with the KNN algorithm to optimize classification performance. The evaluation focuses on metrics such as accuracy, recall rate, F1 score, and training time, revealing the performance of these algorithms with high-dimensional and imbalanced data. Notably, random forest shows exceptional detection performance on the CIC-MalMem-2022 dataset, while random forest and GBDT excel in accuracy and training speed on the RT-IoT2022 dataset. These insights are critical for creating more effective detection systems.

关键词： Network Anomaly Detection Big data Analysis machine learning

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Research on Fire Risk Prediction Method Based on Multi-Source data Perception 24

Research on Fire Risk Prediction Method Based on Multi-Sourc...

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4th international conference on Internet of Things and machine learning

作者： Kong, Fanteng Zhu, Ning Zhu, Wenjun Xu, Hengpeng Wang, Jun Ludong Univ Yantai Peoples R China Tianjin Normal Univ Tianjin Peoples R China

ISBN: (纸本)9798400710353

In order to effectively reduce the risk of fire occurrence, research on fire risk prediction has been rapidly developing. However, current fire risk prediction mainly relies on the experience of regulatory personnel, lacking scientific and systematic methods. To address this issue, this study utilizes the random forest model to accomplish fire prediction by analyzing multi-source fire data with smoke and flame information. Experimental results indicate that the prediction performance of the random forest model is more superior on smoke data to that on flame data, suggesting that smoke information might be a more promising indicator for fire alarming.

关键词： Fire prediction Random forest Ensemble learning

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