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Harris Hawks Optimizer with Graph Convolutional Network Based Weed Detection in Precision Agriculture

computer Systems science & engineering 2023年第8期46卷 1533-1547页

作者： Saud Yonbawi Sultan Alahmari T.Satyanarayana Murthy Padmakar Maddala E.Laxmi Lydia Seifedine Kadry Jungeun Kim Department of Software Engineering College of Computer Science and EngineeringUniversity of JeddahJeddahSaudi Arabia King Abdul Aziz City for Science and Technology RiyadhKingdom of Saudi Arabia Chaitanya Bharathi Institute of Technology HyderabadTelanganaIndia Department of Civil Engineering Vignan’s Institute of Information and Technology(A)DuvvadaVisakhapatnamAP530049India Department of Computer Science and Engineering Vignan’s Institute of Information TechnologyVisakhapatnam530049India Department of Applied Data Science Noroff University CollegeKristiansandNorway Artificial Intelligence Research Center(AIRC) College of Engineering and Information TechnologyAjman UniversityAjmanUnited Arab Emirates Department of Electrical and Computer Engineering Lebanese American UniversityByblosLebanon Department of Software Kongju National UniversityCheonan31080Korea

Precision agriculture includes the optimum and adequate use of resources depending on several variables that govern crop *** agriculture offers a novel solution utilizing a systematic technique for current agricultural problems like balancing production and environmental *** control has become one of the significant problems in the agricultural *** traditional weed control,the entire field is treated uniformly by spraying the soil,a single herbicide dose,weed,and crops in the same *** more precise farming,robots could accomplish targeted weed treatment if they could specifically find the location of the dispensable plant and identify the weed *** may lessen by large margin utilization of agrochemicals on agricultural fields and favour sustainable *** study presents a Harris Hawks Optimizer with Graph Convolutional Network based Weed Detection(HHOGCN-WD)technique for Precision *** HHOGCN-WD technique mainly focuses on identifying and classifying weeds for precision *** image pre-processing,the HHOGCN-WD model utilizes a bilateral normal filter(BNF)for noise *** addition,coupled convolutional neural network(CCNet)model is utilized to derive a set of feature *** detect and classify weed,the GCN model is utilized with the HHO algorithm as a hyperparameter optimizer to improve the detection *** experimental results of the HHOGCN-WD technique are investigated under the benchmark *** results indicate the promising performance of the presented HHOGCN-WD model over other recent approaches,with increased accuracy of 99.13%.

关键词： Weed detection precision agriculture graph convolutional network harris hawks optimizer hyperparameter tuning

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Intelligent System to Maximize Customer Experience in Restaurants 5

Intelligent System to Maximize Customer Experience in Restau...

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5th International Conference on Advancements in Computing, ICAC 2023

作者： Jayalal, R.U.A. Nimeshika, Y.E. Samarasekara, G.N.N. Wijerathna, G.R.C.I. Gamage, Narmada Gunathilake, Poojani Sri Lanka Institute of Information Technology Department of Computer Systems Engineering Malabe Sri Lanka Sri Lanka Institute of Information Technology Department of Computer Science and Software Engineering Malabe Sri Lanka

ISBN: (纸本)9798350358131

With technological improvements in the restaurant industry, competition is fierce, and a restaurant's success depends on providing excellent and luxury customer satisfaction. The food industry has had to continuously advance with technology in order to adequately satisfy the wants of people leading increasingly luxurious lives. While some restaurants still rely on manpower, the majority are eager to advance with technology in order to improve the standard of their offerings and enhance their customers' experiences. In order to optimize customer satisfaction and experience, this research project intends to investigate the potential of intelligence in the restaurant business. This research has explored intelligent systems such as digital self-service menus for ordering food, augmented reality (AR) visualization systems for customers, the observation of chefs in the kitchen, and the usage of robots to serve meals. This research paper analyzes the advantages of automating restaurant operations and how to do so to enhance customer satisfaction. Overall, this study has the potential to transform the restaurant industry and increase customer satisfaction. © 2023 IEEE.

关键词： Augmented reality

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A Time Series Analysis-Based Forest Disturbance Time Backtracking Method in Northeastern China, the Changbai Mountain 7

A Time Series Analysis-Based Forest Disturbance Time Backtra...

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7th International Conference on Pattern Recognition and Artificial Intelligence, PRAI 2024

作者： Yan, Yuxiao Zhang, Changsheng Fang, Lei Zhang, Bin Dong, Xingjun Guo, Ying College of Software Northeastern University Shenyang110819 China Environment Research Institute Shandong University Qingdao266237 China College of Computer Science and Engineering Ningxia Institute of Science and Technology Shizuishan753000 China

ISBN: (纸本)9798350350890

Forest disturbance is a crucial factor in evaluating carbon sink changes in ecological studies. Current research on forest disturbances mainly focuses on forest disturbance detection, forest disturbance classification, and other analyses and statistics of forest changes methods. These studies are mostly based on time-series images or forest remote sensing features to achieve dynamic monitoring of forest changes. However, it is often impossible to construct disturbance information with a lack of images from early remote sensing. This paper addresses this limitation by proposes a method of forest disturbance time backtracking which can get historical information on forest disturbance over a wider temporal distribution. Using this method, we ultimately achieved forest disturbance detection on early remote sensing images from the 1960s. This method is based on machine learning and deep learning algorithms to construct two forest disturbance datasets to achieve this goal. The first dataset, for categorizing forest disturbances, is the first of its kind in Northeast China. This data can be widely used in similar studies. The second dataset is the forest disturbance recovery year dataset. We estimate the timing of early forest disturbance by classifying this dataset. The experimental results showed that the first dataset has an accuracy of over 90%, and the backtracking historical disturbance results are consistent with the actual historical information. This method is significant in its ability to fill in gaps in the forest disturbance classification present in Northeast China and enhance the confidence and time span of the forest disturbance detection method. © 2024 IEEE.

关键词： Forest ecology

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Emotion and Mentality Monitoring Assistant (EMMA) 8

Emotion and Mentality Monitoring Assistant (EMMA)

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8th International Conference on Communication and Electronics Systems, ICCES 2023

作者： Kiridena, Ishini Marasinghe, Dayan Karunarathne, Ruvini Wijethunga, Kavindu Fernando, Harinda Sri Lanka Institute of Information Technology Department of Computer Science and Software Engineering Malabe Sri Lanka Sri Lanka Institute of Information Technology Department of Computer Systems Engineering Malabe Sri Lanka

ISBN: (纸本)9798350396638

Human emotions, psychology, and social well-being are all parts of mental health. It has an impact on how people feel, think, and act. It aids in figuring out how individuals act under pressure, interact with each other, and arrive at decisions. Every period of life, from childhood and adolescence to maturity, is vital for mental health. There are several elements that affect mental health issues, such as experiences with trauma or abuse in life, work-life stress, relationship difficulties, etc. Help is available for many of the mental health issues that are identified within a person. The proposed solution is implementing a friendly chatbot that is capable of capturing human emotional states (happy, angry, sad, etc.) and mental health states (depression, anxiety, stress, etc.) using human computer interaction and health record management. The proposed solution has been developed by considering both regular users and mental health practitioners. The user can communicate with the proposed assistant using text or voice to identify their emotions and mental health based on keywords, particular characteristics of voice and facial recognition used during the conversation. Once the mental health and emotion statuses are analyzed the electronic health records will be readily available on the blockchain as blockchain tokens, this could be shared with the practitioners securely. © 2023 IEEE.

关键词： Blockchain

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GoMatching: A Simple Baseline for Video Text Spotting via Long and Short Term Matching 38

GoMatching: A Simple Baseline for Video Text Spotting via Lo...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： He, Haibin Ye, Maoyuan Zhang, Jing Liu, Juhua Du, Bo Tao, Dacheng School of Computer Science National Engineering Research Center for Multimedia Software Institute of Artificial Intelligence Wuhan University China College of Computing & Data Science Nanyang Technological University Singapore

Beyond the text detection and recognition tasks in image text spotting, video text spotting presents an augmented challenge with the inclusion of tracking. While advanced end-to-end trainable methods have shown commendable performance, the pursuit of multi-task optimization may pose the risk of producing sub-optimal outcomes for individual tasks. In this paper, we identify a main bottleneck in the state-of-the-art video text spotter: the limited recognition capability. In response to this issue, we propose to efficiently turn an off-the-shelf query-based image text spotter into a specialist on video and present a simple baseline termed GoMatching, which focuses the training efforts on tracking while maintaining strong recognition performance. To adapt the image text spotter to video datasets, we add a rescoring head to rescore each detected instance's confidence via efficient tuning, leading to a better tracking candidate pool. Additionally, we design a long-short term matching module, termed LST-Matcher, to enhance the spotter's tracking capability by integrating both long- and short-term matching results via Transformer. Based on the above simple designs, GoMatching delivers new records on ICDAR15-video, DSText, BOVText, and our proposed novel test set with arbitrary-shaped text termed ArTVideo, which demonstrates GoMatching's capability to accommodate general, dense, small, arbitrary-shaped, Chinese and English text scenarios while saving considerable training budgets. © 2024 Neural information processing systems foundation. All rights reserved.

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Sequential Kernel Goodness-of-fit Testing 41

Sequential Kernel Goodness-of-fit Testing

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41st International Conference on Machine Learning, ICML 2024

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

Goodness-of-fit testing, a classical statistical tool, has been extensively explored in the batch setting, where the sample size is ***, practitioners often prefer methods that adapt to the complexity of a problem rather than fixing the sample size *** batch tests are generally unsuitable for streaming data, as valid inference after data peeking requires multiple testing corrections, resulting in reduced statistical *** address this issue, we delve into the design of consistent sequential goodness-of-fit *** the principle of testing by betting, we reframe this task as selecting a sequence of payoff functions that maximize the wealth of a fictitious bettor, betting against the null in a repeated *** conduct experiments to demonstrate the adaptability of our sequential test across varying difficulty levels of problems while maintaining control over type-I errors. Copyright 2024 by the author(s)

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Devil is in Details: Locality-Aware 3D Abdominal CT Volume Generation for Self-Supervised Organ Segmentation 24

Devil is in Details: Locality-Aware 3D Abdominal CT Volume G...

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32nd ACM International Conference on Multimedia, MM 2024

作者： Wang, Yuran Wan, Zhijing Qiu, Yansheng Wang, Zheng National Engineering Research Center for Multimedia Software Hubei Key Laboratory of Multimedia and Network Communication Engineering Institute of Artificial Intelligence School of Computer Science Wuhan University Wuhan China

ISBN: (纸本)9798400706868

In the realm of medical image analysis, self-supervised learning (SSL) techniques have emerged to alleviate labeling demands, while still facing the challenge of training data scarcity owing to escalating resource requirements and privacy constraints. Numerous efforts employ generative models to generate high-fidelity, unlabeled 3D volumes across diverse modalities and anatomical regions. However, the intricate and indistinguishable anatomical structures within the abdomen pose a unique challenge to abdominal CT volume generation compared to other anatomical regions. To address the overlooked challenge, we introduce the Locality-Aware Diffusion (Lad), a novel method tailored for exquisite 3D abdominal CT volume generation. We design a locality loss to refine crucial anatomical regions and devise a condition extractor to integrate abdominal priori into generation, thereby enabling the generation of large quantities of high-quality abdominal CT volumes essential for SSL tasks without the need for additional data such as labels or radiology reports. Volumes generated through our method demonstrate remarkable fidelity in reproducing abdominal structures, achieving a decrease in FID score from 0.0034 to 0.0002 on AbdomenCT-1K dataset, closely mirroring authentic data and surpassing current methods. Extensive experiments demonstrate the effectiveness of our method in self-supervised organ segmentation tasks, resulting in an improvement in mean Dice scores on two abdominal datasets effectively. These results underscore the potential of synthetic data to advance self-supervised learning in medical image analysis. © 2024 ACM.

关键词： Self-supervised learning

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Semantic-Aware Pseudo-Labeling for Unsupervised Meta-Learning

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IEEE Transactions on Pattern Analysis and Machine Intelligence 2025年第7期47卷 5475-5488页

作者： Ouyang, Tianran Dong, Xingping Ye, Mang Du, Bo Shao, Ling Shen, Jianbing Wuhan University School of Computer Science National Engineering Research Center for Multimedia Software Institute of Artificial Intelligence Hubei Key Laboratory of Multimedia and Network Communication Engineering Wuhan430072 China University of Chinese Academy of Sciences UCAS-Terminus AI Lab Beijing101408 China University of Macau State Key Laboratory of Internet of Things for Smart City Department of Computer and Information Science 314100 China

In unsupervised meta-learning, the clustering-based pseudo-labeling approach is an attractive framework, since it is model-agnostic, allowing it to synergize with supervised algorithms to learn from unlabeled data. However, the pseudo-labels suffer from clustering noise and semantic chaos problems, further impacting the effectiveness of meta-learning. In this paper, we analyze and optimize the pseudo-labeling process, including encoding and clustering, aiming to generate semantic-like pseudo-labels to narrow the gap between unsupervised and supervised meta-learning. First, during the encoding, we observe that the embedding space of existing methods lacks clustering-friendly properties, which is the primary reason for clustering noise. To address this issue, we minimize the inter-to-intra-class similarity ratio to generate clustering-friendly embedding features and validate our approach through comprehensive experiments. Then, during the clustering, we find that the semantic quality of pseudo-labels is not adequately controlled, resulting in semantic chaos of pseudo-labels. We propose a semantic-stability index to measure the semantic quality of pseudo-labels quantitatively. Based on this index, we propose the Semantic-aware Pseudo-label Reassignment mechanism to generate semantic-like pseudo-labels for all samples. Our approach is model-agnostic and can easily be integrated into existing supervised methods. To demonstrate its generalization ability, we integrate it into two representative algorithms: MAML and EP. The results on three main few-shot benchmarks clearly show that the proposed method achieves significant improvement compared to state-of-the-art models. Notably, our approach also outperforms the corresponding supervised method in three tasks. © 1979-2012 IEEE.

关键词： Latent semantic analysis

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CHAR: Composite Head-body Activities Recognition with A Single Earable Device

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IEEE Transactions on Mobile Computing 2025年第7期24卷 6532-6549页

作者： Zhu, Peizhao Zhu, Yuzheng Li, Wenyuan He, Yanbo Zou, Yongpan Wu, Kaishun Leung, Victor C. M. Shenzhen University College of Computer Science and Software Engineering 3688 Nanhai Ave Shenzhen518060 China Hong Kong University of Science and Technology Information Hub 1 Duxue Road Guangzhou511453 China Shenzhen MSU-BIT University Artificial Intelligence Research Institute Shenzhen518172 China

The increasing popularity of earable devices stimulates great academic interest to design novel head gesture-based interaction technologies. But existing works simply consider it as a singular activity recognition problem. This is not in line with practice since users may have different body movements such as walking and jogging along with head gestures. It is also beneficial to recognize body movements during human-device interaction since it provides useful context information. As a result, it is significant to recognize such composite activities in which actions of different body parts happen simultaneously. In this paper, we propose a system called CHAR to recognize composite head-body activities with a single IMU sensor. The key idea of our solution is to make use of the inter-correlation of different activities and design a multi-task learning network to extract shared and specific representations. We implement a real-time prototype and conduct extensive experiments to evaluate it. The results show that CHAR can recognize 60 kinds of composite activities (12 head gestures and 5 body movements) with high accuracies of 89.7% and 85.1% in sufficient data and insufficient data cases, respectively. © 2002-2012 IEEE.

关键词： Gesture recognition

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Advanced Bearing Fault Diagnosis Using Cuckoo Optimization and KAN Algorithms 4

Advanced Bearing Fault Diagnosis Using Cuckoo Optimization a...

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4th International Conference on Electronic Information engineering and computer science, EIECS 2024

作者： Wang, Lingchao Ai, Qiang Yan, Hailong Hao, Ming Li, Xiaoxin School of Information Engineering Liaoning Institute of Science and Engineering Jinzhou China College of Computer Qinghai Normal University Xining China Software College Liaoning Technical University Huludao China

ISBN: (纸本)9798331531409

This study proposes a bearing fault diagnosis method that combines the Cuckoo Optimization Algorithm (COA) with the KAN algorithm. COA, as an intelligent optimization algorithm, is primarily used to find the optimal hyperparameters of the model, thereby enhancing the efficiency and robustness of the diagnosis model. The KAN algorithm is employed to build an intelligent classification model, achieving accurate recognition of multiple fault modes. Experimental results show that the proposed method maintains high diagnostic accuracy even with limited data, providing an effective solution for bearing fault diagnosis. © 2024 IEEE.

关键词： Fault detection

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