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5th International Conference on Advances in Distributed Computing and Machine Learning, ICADCML 2024

作者： Bedane, Tarikwa Tesfa Kebede, Kidistie Bizuneh Mohapatra, Sudhir Kumar Das, Tapan Kumar Tripathy, Asis Kumar Department of Software Engineering Addis Ababa Science and Technology University Addis Ababa Ethiopia Agricultural Transformation Institute Addis Ababa Ethiopia Faculty of Emerging Technologies Sri Sri University Cuttack India School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore India

ISBN: (纸本)9789819735228

Given maize’s significant role as a staple crop, it becomes imperative to carry out precise crop yield prediction to ensure food security. This research employs machine learning algorithms to analyze historical data related to maize crop yields, with the aim of developing a predictive model. The outcome of this study can serve as a valuable tool for pre-harvest planning and measuring crop production per unit of land area. The dataset utilized in this research spans from 2010 to 2021 and was sourced from Ethiopia’s Central Statistical Agency. Three machine learning regression algorithms—Logistic Regression, XGBoost, and Random Forest—were employed to design the predictive model. After data preparation and preprocessing, a total of 436,358 records were used for model development. The models were further trained with reduced features using principal component analysis (PCA). The experimental results demonstrate that the Random Forest algorithm, trained with a reduced number of features through PCA, yielded superior performance, with a lower error rate and a high R2 score of 0.98. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Machine learning

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Blockchain Development 7th

Blockchain Development

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7th International Conference on Smart Computing and Communication, SmartCom 2022

作者： Xie, Siqi Cai, Jiahong Zhu, Hangyu Yang, Ce Chen, Lin Xiao, Weidong School of Computer Science and Engineering Hunan University of Science and Technology Xiangtan411201 China Hunan Key Laboratory for Service Computing and Novel Software Technology Xiangtan411201 China Guangdong Financial High-Tech Zone "Blockchain +" Fintech Research Institute Foshan528253 China School of Software Engineering Xiamen University of Technology Xiamen361024 China

ISBN: (纸本)9783031281235

In recent years, blockchain research has set off an upsurge in academia, and it is called the next generation of value Internet. Because of its decentralization, anonymity, security, immutability, traceability and other characteristics, blockchain is gradually accepted and developed by people. With the deepening of research and the integration of technologies such as deep learning, blockchain has gradually been applied to various fields such as credit reporting, government, medical care, and industrial Internet of Things, not just the initial virtual currency field. This article mainly discusses the three important stages of blockchain public chain development, namely Bitcoin, Ethereum, and meta-verse, and introduces some basic supporting technologies of blockchain, as well as the research status and future trends of blockchain. Simple Analysis. By vertically introducing the development history of the blockchain, researchers can have a more concrete understanding of the status quo of the blockchain, and provide ideas for blockchain-related research. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Smart contract

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Error Analysis of Spherically Constrained Least Squares Reformulation in Solving the Stackelberg Prediction Game 38

Error Analysis of Spherically Constrained Least Squares Refo...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Li, Xiyuan Liu, Weiwei School of Computer Science Wuhan University China National Engineering Research Center for Multimedia Software Wuhan University China Institute of Artificial Intelligence Wuhan University China Hubei Key Laboratory of Multimedia and Network Communication Engineering Wuhan University China

The Stackelberg prediction game (SPG) is a popular model for characterizing strategic interactions between a learner and an adversarial data provider. Although optimization problems in SPGs are often NP-hard, a notable special case involving the least squares loss (SPG-LS) has gained significant research attention recently [1, 2, 3]. The latest state-of-the-art method for solving the SPG-LS problem is the spherically constrained least squares reformulation (SCLS) method proposed in the work of [3]. However, the paper [3] lacks theoretical analysis on the error of the SCLS method, which limits its large-scale applications. In this paper, we investigate the estimation error between the learner obtained by the SCLS method and the actual learner. Specifically, we reframe the estimation error of the SCLS method as a Primary Optimization (PO) problem and utilize the Convex Gaussian min-max theorem (CGMT) to transform the PO problem into an Auxiliary Optimization (AO) problem. Subsequently, we provide a theoretical error analysis for the SCLS method based on this simplified AO problem. This analysis not only strengthens the theoretical framework of the SCLS method but also confirms the reliability of the learner produced by it. We further conduct experiments to validate our theorems, and the results are in excellent agreement with our theoretical predictions. © 2024 Neural information processing systems foundation. All rights reserved.

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Traffic Anomaly Detection Using Optimized Faster R-CNN

Traffic Anomaly Detection Using Optimized Faster R-CNN

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2024 International Conference on Electronics, Electrical and Information engineering, ICEEIE 2024

作者： Karim, Shahid Qadir, Akeel Han, Gao Li, Jing Ibrar, Muhammad Bibi, Irfana School of Information Engineering Xi'an Eurasia University 710065 China Software College Shenyang Normal University Shenyang China Department of Computer Science Faculty of Computing and Information Technology University of Punjab Punjab Lahore Pakistan

ISBN: (数字)9781510686809

ISBN: (纸本)9781510686793

This paper introduces a new method for traffic anomaly identification within the Faster R-CNN framework by combining the DenseNet module with the region proposals (RPs) processing block. The suggested approach seeks to improve feature extraction and RP creation in aerial imagery or CCTV frames to get beyond the drawbacks of the current methods. Discriminative features are extracted through DenseNet, and the RPs block makes it easier to provide a wide range of accurate and varied area proposals. Contrast correction techniques are applied to overcome the difficulties presented by low-resolution and low-contrast images frequently seen in surveillance scenarios. Comparing experimental results to state-of-the-art techniques, one can observe notable resilience and detection accuracy increases. The suggested method works exceptionally well in various traffic scenarios and environmental circumstances, indicating its potential for practical implementation in traffic surveillance systems. Overall, the study contributes to advancing the efficacy and reliability of anomaly detection in urban traffic environments. © 2024 SPIE.

关键词： Aerial photography

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Emergency Transportation and Structural Prediction and Optimization of Goods Based on ARIMA and Multi-objective E-commerce Logistics Network Model 1

Emergency Transportation and Structural Prediction and Optim...

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1st IEEE International Conference on Applied Intelligence and Sustainable Computing, ICAISC 2023

作者： Nie, Yiyang Chen, Xiang Wu, Xuanli Deng, Zihao Hunan University of Science and Technology School of Computer Science and Engineering Xiangtan China Hunan Key Laboratory for Service Computing and Novel Software Technology Xiangtan China Hunan University of Science and Technology Shcool of Mathematics and Computational Science Xiangtan China

ISBN: (纸本)9798350323795

With the development of the internet, online shopping has gradually become a popular way of shopping among the general public, which in turn poses a greater challenge to the e-commerce logistics network. Predicting the volume of goods for each logistics site and route, and establishing a model for quickly adjusting the network in response to sudden situations can save logistics network costs and improve its efficiency. Therefore, this article has done the following work: first, it establishes an effective logistics network volume prediction model;second, for the two scenarios of network structure that cannot be modified and can be modified, it provides a model that can effectively guide the adjustment of the volume of goods on each route when a logistics site is closed;finally, it establishes a logistics network evaluation model to evaluate the importance of logistics sites and routes. © 2023 IEEE.

关键词： Multiobjective optimization

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Tumor type classification and candidate cancer-specific biomarkers discovery via semi-supervised learning

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Biophysics Reports 2023年第2期9卷 57-66页

作者： Peng Chen Zhenlei Li Zhaolin Hong Haoran Zheng Rong Zeng School of Computer Science and Technology University of Science and Technology of China Anhui Key Laboratory of Software Engineering in Computing and Communication University of Science and Technology of China Department of Systems Biology University of Science and Technology of China CAS Key Laboratory of Systems Biology Shanghai Institute of Biochemistry and Cell Biology Center for Excellence in Molecular Cell Science Chinese Academy of Sciences School of Life Science and Technology ShanghaiTech University

Identifying cancer-related differentially expressed genes provides significant information for diagnosing tumors, predicting prognoses, and effective treatments. Recently, deep learning methods have been used to perform gene differential expression analysis using microarray-based high-throughput gene profiling and have achieved good results. In this study, we proposed a new robust multiple-datasetsbased semi-supervised learning model, MSSL, to perform tumor type classification and candidate cancer-specific biomarkers discovery across multiple tumor types and multiple datasets, which addressed the following long-lasting obstacles:(1) the data volume of the existing single dataset is not enough to fully exert the advantages of deep learning;(2) a large number of datasets from different research institutions cannot be effectively used due to inconsistent internal variances and low quality;(3) relatively uncommon cancers have limited effects on deep learning methods. In our article, we applied MSSL to The Cancer Genome Atlas(TCGA) and the Gene Expression Comprehensive Database(GEO) pan-cancer normalized-level3 RNA-seq data and got 97.6% final classification accuracy, which had a significant performance leap compared with previous approaches. Finally, we got the ranking of the importance of the corresponding genes for each cancer type based on classification results and validated that the top genes selected in this way were biologically meaningful for corresponding tumors and some of them had been used as biomarkers, which showed the efficacy of our method.

关键词： Deep learning Tumor type classification Cancer-specific biomarkers MSSL

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Towards a global machine learning model to fill gaps in flood frequency: Random Forest to estimate MNDWI in Bangladesh

Towards a global machine learning model to fill gaps in floo...

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2024 International Conference on Machine Intelligence for GeoAnalytics and Remote Sensing, MIGARS 2024

作者： Houghton, James Hultquist, Carolynne Atlas, James Zimmer, Andrew University of Canterbury Computer Science And Software Engineering Christchurch New Zealand University of Canterbury School of Earth And Environment Christchurch New Zealand Montana State University Department of Earth Sciences BozemanMT United States

ISBN: (纸本)9798350389678

An understanding of historical trends of flooding is essential to capture current risk as more people are exposed to flood hazards than ever before. Flood modeling relies on accurate and frequent remote sensing inputs to produce reliable flood risk results. However, remote sensing datasets are limited by temporal resolution which, in the case of Landsat, leaves 87% of days without observation. This study aims to address these gaps in observation using a Random Forest model to predict the Modified Normalized Difference Water Index (MNDWI) in Landsat images by fusing gridded precipitation and elevation data. The trained model is then used to estimate MNDWI for when there are no Landsat observations. The model was evaluated on a 50 by 50 pixel test patch achieving a residual squared error between the true average MNDWI and the estimated average MNDWI of 0.48. © 2024 IEEE.

关键词： Landsat

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Self-Bidirectional Decoupled Distillation for Time Series Classification

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第8期5卷 4101-4110页

作者： Xiao, Zhiwen Xing, Huanlai Qu, Rong Li, Hui Feng, Li Zhao, Bowen Yang, Jiayi Southwest Jiaotong University School of Computing and Artificial Intelligence Chengdu610031 China Tangshan Institute of Southwest Jiaotong University Tangshan063000 China Engineering Research Center of Sustainable Urban Intelligent Transportation Ministry of Education Chengdu611756 China University of Nottingham School of Computer Science NottinghamNG7 2RD 455356 United Kingdom Xi'an Jiaotong University School of Mathematics and Statistics Xi'an710049 China Tongji University School of Software Engineering Shanghai201804 China

Over the years, many deep learning algorithms have been developed for time series classification (TSC). A learning model's performance usually depends on the quality of the semantic information extracted from lower and higher levels within the representation hierarchy. Efficiently promoting mutual learning between higher and lower levels is vital to enhance the model's performance during model learning. To this end, we propose a self-bidirectional decoupled distillation (self-BiDecKD) method for TSC. Unlike most self-distillation algorithms that usually transfer the target-class knowledge from higher to lower levels, self-BiDecKD encourages the output of the output layer and the output of each lower level block to form a bidirectional decoupled knowledge distillation (KD) pair. The bidirectional decoupled KD promotes mutual learning between lower and higher level semantic information and extracts the knowledge hidden in the target and nontarget classes, helping self-BiDecKD capture rich representations from the data. Experimental results show that compared with a number of self-distillation algorithms, self-BiDecKD wins 35 out of 85 University of California, Riverside (UCR) 2018 datasets and achieves the smallest AVeraGe (AVG)_rank score, namely 3.2882. In particular, compared with a nonself-distillation baseline, self-BiDecKD results in 58/8/19 regarding 'win'/'tie'/'lose.' © 2020 IEEE.

关键词： Semantics

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Solving Sparse Reward Tasks Using Self-Balancing Exploration and Exploitation

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Journal of Internet Technology 2025年第3期26卷 293-301页

作者： Kong, Yan Wei, Junfeng Hsia, Chih-Hsien School of Computer and Software Nanjing University of Information Science and Technology China Department of Computer Science and Information Engineering National Ilan University Taiwan Department of Business Administration Chaoyang University of Technology Taiwan

A core challenge in applying deep reinforcement learning (DRL) to real-world tasks is the sparse reward problem, and shaping reward has been one effective method to solve it. However, due to the enormous state space and sparse rewards in the real world, a large number of useless samples may be generated, leading to reduced sample efficiency and potential local optima. To address this issue, this study proposes a self-balancing method of exploration and development to solve the issue of sparse rewards. Firstly, we shape the reward function according to the evaluated progress, to guide the agent’s learning of high-reward samples. Secondly, we construct a dual-trajectory exploration network, which provides intrinsic rewards based on the novelty of states and the trajectory difference of sibling agents to encourage the agent to explore and adjust the balance between exploration and exploitation. This method effectively prevents the generation of a large amount of useless training data during the interaction between the agent and the environment, resolves local optimal dilemmas through state novelty, and adjusts the strategy in a timely manner to solve sparse reward tasks. Our method outperforms basic reinforcement learning (RL) and curiosity-driven incentives in these experimental tasks. The self-balancing exploration and exploitation approach in our research provides a new perspective and effective solution for addressing the problem of sparse rewards, thereby advancing the application of DRL in real-world problems and achieving greater success. © 2025 Taiwan Academic Network Management Committee. All rights reserved.

关键词： Deep learning

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Beam Splitting Sensing for Terahertz Near-Field Channel Estimation 3

Beam Splitting Sensing for Terahertz Near-Field Channel Esti...

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3rd International Conference on Intelligent Communications and Computing, ICC 2023

作者： Tang, Shaochen Dong, Zhicheng Zhou, Weixi Cai, Donghong Zhou, Enzhi Lan, Ping College of Information Science Technology Tibet University Lhasa850000 China School of Computer Science Sichuan Normal University Chengdu610066 China College of Cyber Security Jinan University Guangzhou510632 China School of Computer and Software Engineering Xihua University Chengdu610039 China

ISBN: (纸本)9798350308327

Terahertz technology is considered as one of the key techniques for future 6G due to its ultra-wide bandwidth and ultra-high communication rate. However, terahertz communication channel estimation faces several significant challenges, one is that extremely-large-scale MIMO communication typically occurs in the near field, which will result in traditional far-field channel estimation schemes no longer being suitable. The other is the beam splitting effect caused by terahertz wideband communication, which leads to a large impact on the steering vector caused by different subcarrier frequencies. In order to solve these problems, this paper first studied the model of THz near-field beam splitting;secondly discussed the construction method of the THz near-field polar dictionary matrix;and then proposed a two-stage beam splitting sensing (Two-stage-BSS) algorithm to improve the accuracy of near-field channel estimation and reduce the impact of beam splitting. The experimental results validate the effectiveness of the proposed algorithm. © 2023 IEEE.

关键词： Channel estimation

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