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Crop Yield Prediction Using Deep Reinforcement Learning 2

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Crop Yield Prediction Using Deep Reinforcement Learning

2nd International Conference on Trends in Electrical, Electronics and computer Engineering, TEECCON 2023

作者： Deivasigamani, Sudhakar Jaya Mabel Rani, A. Natchadalingam, R. Vijayakarthik, P. Bala Sendhil Kumar, G. Reddy, Pundru Chandra Shaker Digipulse Technologies Inc Scrum Master NJ United States Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Tamil Nadu Chennai India R.L.Jalappa Institute of Technology CSE-Data Science Karnataka Bengaluru India R.L.Jalappa Institute of Technology Computer Science and Engineering Karnataka Bengaluru India Sri Manakula Vinayagar Engineering College Master of Business Administration Puducherry India Lovely Professional University School of Computer Science and Engineering Punjab Phagwara India

ISBN: (纸本)9798350339949

Predicting crop production using information about the environment, soil, water, and crops themselves is an area ripe for investigation. Deep learning-based models often extract crop attributes for prediction. These techniques have the following drawbacks: The quality of the extracted characteristics substantially affects the efficacy of those models, and there is no simple non-linear or linear mapping between raw data and crop production estimates. Deep RL guides and inspires to compensate. Deep reinforcement learning uses RL and DL to accurately forecast agricultural yields from raw data. A Deep Recurrent Q-Network model predicts agricultural yield. This model uses Q-Learning reinforcement learning and a Recurrent Neural Network deep learning algorithm. data parameters feed the Recurrent Neural network's layers. Q-learning networks forecast agricultural yields from data. Q-values are linear mappings from Recurrent Neural Network output values. A threshold and parametric features help the reinforcement learning agent predict crop productivity. Finally, the agent is awarded a final score that takes into account the steps taken to both reduce error and improve forecast accuracy. By maintaining the original data distribution, the suggested model efficiently predicts the crop production with an accuracy of 93.7%, exceeding competing models. © 2023 IEEE.

关键词： Crops

Time-Slotted On-Demand Predictive Routing for UAV Networks

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Time-Slotted On-Demand Predictive Routing for UAV Networks

2025 IEEE Wireless Communications and Networking Conference, WCNC 2025

作者： Feng, Houze Wang, Jingjing Chen, Jianrui Hou, Xiangwang Wang, Jiacheng Sun, Geng Niyato, Dusit School of Cyber Science and Technology Beihang University Beijing China Tsinghua University Department of Electronic Engineering Beijing China College of Computing and Data Science Nanyang Technological University Singapore College of Computer Science and Technology Jilin University Changchun China

ISBN: (纸本)9798350368369

Flying ad hoc networks (FANETs) composed of small unmanned aerial vehicles (UAVs) are flexible, inexpensive, and fast to deploy, which have been used in an increasing number of mission scenarios. However, unstable link quality and frequently changing network topology pose significant challenges for adopting existing routing protocols in mobile ad hoc networks (MANETs). In this paper, we propose a time-slotted on-demand predictive (TSDP) routing protocol designed specifically for UAV networks. The TSDP protocol introduces a novel approach to route selection by incorporating multiple criteria, including delivery ratio, adjacent degree, and mobility prediction factor, to ensure reliable and efficient data transmission. By addressing high latency in route discovery and excessive broadcast overhead, TSDP employs a time-slotted communication mechanism that reduces packet drop rates and enhances route stability. Simulation results demonstrate that TSDP consistently outperforms ad hoc on-demand distance vector (AODV) and dynamic source routing (DSR) protocols in terms of throughput, packet delivery ratio, end-to-end delay, and overhead, particularly in highly dynamic network environments. © 2025 IEEE.

关键词： Drones

A Survey of Neural Code Intelligence: Paradigms, Advances and Beyond

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arXiv 2024年

作者： Sun, Qiushi Chen, Zhirui Xu, Fangzhi Cheng, Kanzhi Ma, Chang Yin, Zhangyue Wang, Jianing Han, Chengcheng Zhu, Renyu Yuan, Shuai Guo, Qipeng Qiu, Xipeng Yin, Pengcheng Li, Xiaoli Yuan, Fei Kong, Lingpeng Li, Xiang Wu, Zhiyong Shanghai AI Laboratory Shanghai China School of Data Science and Engineering East China Normal University Shanghai China School of Computer Science Fudan University Shanghai China NetEase Fuxi AI Lab Zhejiang China Google DeepMind Mountain ViewCA United States Singapore School of Computer Science and Engineering Nanyang Technological University Singapore Department of Computer Science The University of Hong Kong Hong Kong

Neural Code Intelligence – leveraging deep learning to understand, generate, and optimize code – holds immense potential for transformative impacts on the whole society. Bridging the gap between Natural Language and programming Language, this domain has drawn significant attention from researchers in both research communities over the past few years. This survey presents a systematic and chronological review of the advancements in code intelligence, encompassing over 50 representative models and their variants, more than 20 categories of tasks, and over 700 related works. We follow the historical progression to trace the paradigm shifts across different research phases (e.g., from modeling code with recurrent neural networks to the era of Large Language Models). Concurrently, we highlight the major technical transitions in models, tasks, and evaluations spanning through different stages. For applications, we also observe a co-evolving shift. It spans from initial endeavors to tackling specific scenarios, through exploring a diverse array of tasks during its rapid expansion, to currently focusing on tackling increasingly complex and varied real-world challenges. Building on our examination of the developmental trajectories, we further investigate the emerging synergies between code intelligence and broader machine intelligence, uncovering new cross-domain opportunities and illustrating the substantial influence of code intelligence across various domains. Finally, we delve into both the opportunities and challenges associated with this field, alongside elucidating our insights on the most promising research directions. An ongoing, dynamically updated project and resources associated with this survey have been released at https://***/QiushiSun/Awesome-Code-Intelligence. © 2024, CC BY.

关键词： Recurrent neural networks

Image Matching Algorithm Under Adverse Conditions Based on SIFT

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Image Matching Algorithm Under Adverse Conditions Based on S...

IEEE International Symposium on Information (IT) in Medicine and Education, ITME

作者： Hao Chen Xinrong Cao Zuoyong Li Lihui Lin College of Computer and Big Data Fuzhou University Fuzhou China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control School of Computer and Big Data Minjiang University Fuzhou China Fujian Key Laboratory of Big Data Application and Intellectualization for Tea Industry School of Mathematics and Computer Science Wuyi University Fujian China

ISBN: (数字)9798350356670

ISBN: (纸本)9798350356687

Image matching technology is crucial in computer vision applications. However, the traditional SIFT (Scale-Invariant Feature Transform) algorithm often faces challenges under adverse conditions, such as a high number of mismatched points and few accurately matched points. Additionally, in practical scenarios, environmental factors like lighting changes and image blurring further reduce image similarity. Therefore, this paper proposes an improved SIFT-based image matching algorithm to address these challenges. First, SIFT is used to extract image features, and a bidirectional FLANN (Fast Library for Approximate Nearest Neighbors) matching strategy is employed to initially filter out incorrect features. Then, the RANSAC (Random Sample Consensus) algorithm is used to screen out unreliable matching point pairs. Finally, the matching accuracy is evaluated based on the remaining pure matching point pairs. Experimental results demonstrate that, compared to the traditional SIFT algorithm, this improved algorithm achieves higher matching accuracy and robustness under adverse conditions, exhibiting superior performance.

关键词： Location awareness Matched filters Accuracy Image matching Lighting Transforms Feature extraction Robustness Libraries Information technology

Constraint Boundary Wandering Framework: Enhancing Constrained Optimization with Deep Neural Networks

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IEEE Transactions on Pattern Analysis and Machine Intelligence 2025年 PP卷 PP页

作者： Wu, Shuang Chen, Shixiang Shen, Li Zhang, Lefei Tao, Dacheng Wuhan University National Engineering Research Center for Multimedia Software School of Computer Science Wuhan430072 China University of Science and Technology of China Key Laboratory of the Ministry of Education for Mathematical Foundations and Applications of Digital Technology School of Mathematical Sciences China Shenzhen Campus of Sun Yat-sen University School of Cyber Science and Technology Shenzhen518107 China Nanyang Technological University College of Computing and Data Science 639798 Singapore

Constrained optimization problems are pervasive in various fields, and while conventional techniques offer solutions, they often struggle with scalability. Leveraging the power of deep neural networks (DNNs) in optimization, we present a novel learning-based approach, the Constraint Boundary Wandering Framework (CBWF), to address these challenges. Our contributions include introducing a boundary wandering strategy inspired by the active-set method, enhancing equality constraint feasibility, and treating the Lipschitz constant as a learnable parameter. Additionally, we evaluate the regularization term, illustrating that the nonsmooth L2 norm yields superior results. Extensive testing on synthetic datasets and the ACOPT dataset demonstrates CBWF's superiority, outperforming existing deep learning-based solvers in terms of both objective and constraint loss. © 1979-2012 IEEE.

关键词： Constrained optimization

A Simplifying and Learnable Graph Convolutional Attention Network for Unsupervised Knowledge Graphs Alignment

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arXiv 2024年

作者： Cai, Weishan Ma, Wenjun Jiang, Yuncheng School of Computer Science Guangdong University of Education Guangzhou510303 China Aberdeen Institute of Data Science and Artificial Intelligence South China Normal University Foshan528225 China School of Computer Science South China Normal University Guangzhou510631 China School of Artificial Intelligence School of Software South China Normal University Foshan528225 China

The success of current Entity Alignment (EA) task depends largely on the supervision information provided by labeled data. Considering the cost of labeled data, most supervised methods are difficult to apply in practical scenarios. Therefore, more and more works based on contrastive learning, active learning or other deep learning techniques have been developed, to solve the performance bottleneck caused by the lack of labeled data. However, the existing unsupervised EA methods still have some limitations, either their modeling complexity is high or they cannot balance the effectiveness and practicality of alignment. To overcome these issues, we propose a Simplifying and Learnable graph convolutional attention network for Unsupervised Knowledge Graphs alignment method (SLU). Specifically, we first introduce LCAT, a new and simple framework as the backbone network to model the graph structure of two KGs. Then we design a reconstruction method of relation structure based on potential matching relations for efficiently filtering invalid neighborhood information of aligned entities, to improve the usability and scalability of SLU. Impressively, a similarity function based on consistency is proposed to better measure the similarity of candidate entity pairs. Finally, we conduct extensive experiments on three datasets of different sizes (15K and 100K) and different types (cross-lingual and monolingual) to verify the superiority of SLU. Experimental results show that SLU significantly improves alignment accuracy, outperforming 25 supervised or unsupervised methods, and improving 6.4% in Hits@1 over the best baseline in the best case. Copyright © 2024, The Authors. All rights reserved.

关键词： Contrastive Learning

LKFormer: Large Kernel Transformer for Infrared Image Super-Resolution

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arXiv 2024年

作者： Qin, Feiwei Yan, Kang Wang, Changmiao Ge, Ruiquan Peng, Yong Zhang, Kai School of Computer Science and Technology Hangzhou Dianzi University China Shenzhen Research Institute of Big Data China CVL ETH Zurich Switzerland

Given the broad application of infrared technology across diverse fields, there is an increasing emphasis on investigating super-resolution techniques for infrared images within the realm of deep learning. Despite the impressive results of current Transformer-based methods in image super-resolution tasks, their reliance on the self-attentive mechanism intrinsic to the Transformer architecture results in images being treated as one-dimensional sequences, thereby neglecting their inherent two-dimensional structure. Moreover, infrared images exhibit a uniform pixel distribution and a limited gradient range, posing challenges for the model to capture effective feature information. Consequently, we suggest a potent Transformer model, termed Large Kernel Transformer (LKFormer), to address this issue. Specifically, we have designed a Large Kernel Residual Attention (LKRA) module with linear complexity. This mainly employs depth-wise convolution with large kernels to execute non-local feature modeling, thereby substituting the standard self-attentive layer. Additionally, we have devised a novel feed-forward network structure called Gated-Pixel Feed-Forward Network (GPFN) to augment the LKFormer's capacity to manage the information flow within the network. Comprehensive experimental results reveal that our method surpasses the most advanced techniques available, using fewer parameters and yielding considerably superior performance. Copyright © 2024, The Authors. All rights reserved.

关键词： Convolution

Efficient Segment-Level Waveform Anomaly Detection for Memory Devices

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Efficient Segment-Level Waveform Anomaly Detection for Memor...

IEEE International Conference on Communications (ICC)

作者： Ching-Lun Tai Peter Harrington School of Electrical and Computer Engineering Georgia Institute of Technology Technology and Products Group - Data Science Micron Technology

With the emergence of big data and advanced technologies, memory devices play an important role. However, there is a lack of efficient approaches for waveform anomaly detection to validate the reliability of memory devices under various operations. In this paper, we propose an efficient segment-level waveform anomaly detection method for memory devices that includes the stages of waveform segmentation and anomaly analysis. Being compatible with general waveform sequences, the proposed method features lightweight computational complexity. During the waveform segmentation stage, we execute downsampled segment collection and downsampled segment pair matching to obtain downsampled segments that are associated with individual operations. During the anomaly analysis stage, we execute ensemble denoising, entrywise alignment, anomaly presence examination through excess kurtosis, and anomaly detection with density-based spatial clustering of applications with noise (DBSCAN) to predict anomaly locations. Simulation results confirm the outstanding performance of the proposed method in terms of matching accuracy, excess kurtosis measurements, and segment-level anomaly detection.

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THE DKU POST-CHALLENGE AUDIO-VISUAL WAKE WORD SPOTTING SYSTEM FOR THE 2021 MISP CHALLENGE: DEEP ANALYSIS

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arXiv 2023年

作者： Wang, Haoxu Cheng, Ming Fu, Qiang Li, Ming School of Computer Science Wuhan University Wuhan China Data Science Research Center Duke Kunshan University Kunshan China Alibaba Group China

This paper further explores our previous wake word spotting system ranked 2-nd in Track 1 of the MISP Challenge 2021. First, we investigate a robust unimodal approach based on 3D and 2D convolution and adopt the simple attention module (SimAM) for our system to improve performance. Second, we explore different combinations of data augmentation methods for better performance. Finally, we study the fusion strategies, including score-level, cascaded and neural fusion. Our proposed multimodal system leverages multimodal features and uses the complementary visual information to mitigate the performance degradation of audio-only systems in complex acoustic scenarios. Our system obtains a false reject rate of 2.15% and a false alarm rate of 3.44% in the evaluation set of the competition database, which achieves the new state-of-the-art performance by 21% relative improvement compared to previous systems. Related resource can be found at: https://***/Mashiro009/DKU_WWS_MISP. Copyright © 2023, The Authors. All rights reserved.

关键词： Wakes