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Toward Fine-grained Image Retrieval with Adaptive Deep Learning for Cultural Heritage Image

computer Systems science & Engineering 2023年第2期44卷 1295-1307页

作者： Sathit Prasomphan Department of Computer and Information Science Faculty of Applied ScienceKing Mongkut’s University of Technology North Bangkok1518 Pracharat 1 Rd.WongsawangBangsueBangkok10800Thailand

Fine-grained image classiﬁcation is a challenging research topic because of the high degree of similarity among categories and the high degree of dissimilarity for a speciﬁc category caused by different poses and scales.A cul-tural heritage image is one of theﬁne-grained images because each image has the same similarity in most *** the classiﬁcation technique,distinguishing cultural heritage architecture may be difﬁ*** study proposes a cultural heri-tage content retrieval method using adaptive deep learning forﬁne-grained image *** key contribution of this research was the creation of a retrieval mod-el that could handle incremental streams of new categories while maintaining its past performance in old categories and not losing the old categorization of a cul-tural heritage *** goal of the proposed method is to perform a retrieval task for *** learning for new classes was conducted to reduce the re-training *** this step,the original class is not necessary for re-train-ing which we call an adaptive deep learning *** heritage in the case of Thai archaeological site architecture was retrieved through machine learn-ing and image *** analyze the experimental results of incremental learning forﬁne-grained images with images of Thai archaeological site architec-ture from world heritage provinces in Thailand,which have a similar *** aﬁne-grained image retrieval technique for this group of cultural heritage images in a database can solve the problem of a high degree of similarity among categories and a high degree of dissimilarity for a speciﬁc *** proposed method for retrieving the correct image from a database can deliver an average accuracy of 85 *** deep learning forﬁne-grained image retrieval was used to retrieve cultural heritage content,and it outperformed state-of-the-art methods inﬁne-grained image retrieval.

关键词： Fine-grained image adaptive deep learning cultural heritage image retrieval

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Rice Bacterial Infection Detection Using Ensemble Technique on Unmanned Aerial Vehicles Images

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computer Systems science & Engineering 2023年第2期44卷 991-1007页

Establishing a system for measuring plant health and bacterial infection is critical in ***,the farmers themselves,who observed them with their eyes and relied on their experience in analysis,which could have been *** inspection can determine which plants reﬂect the quantity of green light and near-infrared using infrared light,both visible and eye using a *** goal of this study was to create algorithms for assessing bacterial infections in rice using images from unmanned aerial vehicles(UAVs)with an ensemble classiﬁcation *** neural networks in unmanned aerial vehi-cles image were *** convey this interest,the rice’s health and bacterial infec-tion inside the photo were *** project entailed using pictures to identify bacterial illnesses in *** shape and distinct characteristics of each infection were *** symptoms were deﬁned using machine learning and image processing *** steps of a convolution neural network based on an image from a UAV were used in this study to determine whether this area will be affected by *** proposed algorithms can be utilized to classify the types of rice deceases with an accuracy rate of 89.84 percent.

关键词： Bacterial infection detection adaptive deep learning unmanned aerial vehicles image retrieval

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IoT-enabled monitoring and comparative analysis of machine learning techniques for detection of plant diseases in marigolds

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International Journal of information technology (Singapore) 2024年 1-8页

作者： Jomsri, Pijitra Prangchumpol, Dulyawit Poonsilp, Kittiya Department of Information Technology Faculty of Science and Technology Suan Sunandha Rajabhat University 1 U-Thong Nok Road Dusit Bangkok Thailand Department of Computer Science Faculty of Science and Technology Suan Sunandha Rajabhat University 1 U-Thong Nok Road Dusit Bangkok Thailand

Ornamental flower cultivation is highly popular and a critical career that creates income for the farmers. Among these marigold flower is a top-selling flowering plant across most of the countries. Further, this plant has many insect-repellent properties. Despite farming these plants economic benefits, multiple risks are associated with cultivating them. Plant disease plays a critical role which reduces the quality and quantity of flower production flowers and contributes primarily to the loss of production or exports. Manual disease detection strategies are expensive and highly prone to error. Thus, multiple early detection of plant diseases by the Internet of Things (IoT) and Artificial Intelligence (AI) technologies are introduced to reduce the burden of the disease. Most of them are complex and cost high. In conjunction with these, a sensor model suitable for collecting data on marigold leaf diseases is presented in this research. A Machine Learning (ML) technique based on the latest Neural Network (NN) called EFFICIENTNET is employed to isolate the diseased plants from the healthier plants. Experiments conducted and comparisons with existing K-Nearest Neighbor (KNN) techniques showed that the EFFICIENTNET-B0 technique had shown remarkable performance with 97.70% accuracy. © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： EfficientNet-B0 and K-Nearest Neighbor algorithm Images Analysis Machine Learning Marigold Leaf Disease

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A Design Study on Integrated Complex Coefficient Filters with Peripheral Circuits for Accurate Frequency Response Measurement

IEEJ Transactions on Electronics, Information and Systems

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IEEJ Transactions on Electronics, information and Systems 2025年第1期145卷 16-24页

作者： Shouno, Kazuhiro Sato, Lina Fukagawa, Kanta Tanimoto, Hiroshi Muto, Cosy Division of Information Engineering University of Tsukuba 1-1-1 Tennoudai Ibaraki Tsukuba305-8573 Japan Graduate School of Science and Technology Degree Programs in Systems and Information Engineering Master’s Program in Computer Science University of Tsukuba Japan Kitami Institute of Technology 165 Koen-cho Hokkaido Kitami090-8507 Japan Faculty of Education Nagasaki University 1-14 Bunkyo-machi Nagasaki852-8521 Japan

This paper proposes a measurement technique for an integrated complex filter. The proposed method is based on two measurement methods with integrated circuitry for calibration. It is accomplished by applying square waves to an FUT (Filter Under Test) instead of sinusoidal waves or by superposing transfer characteristics obtained by applying a single sinusoidal wave to an FUT. In both of the above methods, the output signal connected to the external measurement equipment via a 50 Ω coaxial cable and so on. In this case, the output signal of the FUT should be amplified by internal or external amplifiers, which degrades the response. However, it is cancelled out by the proposed calibration circuitry with some analog switches. In addition to the above, for the RC polyphase filter, the source resistance and the load capacitance degrade the measurement results and their effect is discussed. The validity of the proposal is confirmed through computer simulation for a third-order RCPF whose passband is 8–40 MHz fabricated by Phenitec 0.6 µm CMOS process. It is shown that the measurement error is −1.10 dB for the square-wave method and −1.00 dB and −0.43 degrees for the superposition method at 40 MHz. © 2025 The Institute of Electrical Engineers of Japan.

关键词： Capacitance

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Advancing Mixed Reality Game Development: An Evaluation of a Visual Game Analytics Tool in Action-Adventure and FPS Genres

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Proceedings of the ACM on Human-computer Interaction 2024年第CHI PLAY期8卷 1-32页

作者： Sargolzaei, Parisa Rastogi, Mudit Zaman, Loutfouz Faculty of Business and Information Technology Ontario Tech University 2000 Simcoe St N OshawaONL1G 0C5 Canada Computer Science and Technology SRM Institute of Science and Technology SRM Nagar Chengalpattu District Tamil Nadu Kattankulathur603203 India

In response to the unique challenges of Mixed Reality (MR) game development, we developed GAMR, an analytics tool specifically designed for MR games. GAMR aims to assist developers in identifying and resolving gameplay issues effectively. It features reconstructed gameplay sessions, heatmaps for data visualization, a comprehensive annotation system, and advanced tracking for hands, camera, input, and audio, providing in-depth insights for nuanced game analysis. To evaluate GAMR's effectiveness, we conducted an experimental study with game development students across two game genres: action-adventure and first-person shooter (FPS). The participants used GAMR and provided feedback on its utility. The results showed a significant positive impact of GAMR in both genres, particularly in action-adventure games. This study demonstrates GAMR's effectiveness in MR game development and suggests its potential to influence future MR game analytics, addressing the specific needs of developers in this evolving area. © 2024 ACM.

关键词： Visual analytics

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Neuro-Mimetic Developmental Architecture for Continual Learning through Self-Organizing Multimodal Perception Coordination

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IEEE Access 2025年 13卷 91625-91641页

作者： Dawood, Farhan Masuyama, Naoki Loo, Chu Kiong Iqra University Department of Computing & Technology Islamabad Pakistan Osaka Metropolitan University Graduate School of Informatics 1-1 Gakuen-cho Naka-ku Sakai-Shi Osaka599-8531 Japan University of Malaya Faculty of Computer Science and Information Technology Department of Artificial Intelligence Kuala Lumpur50603 Malaysia

Developing cognitive capabilities in autonomous agents stands as a challenging yet pivotal task. In order to construct cohesive representations and extract meaningful insights from the environment, the brain utilizes patterns from a variety of sensory inputs, including vision and sound. Furthermore, related sensory inputs can activate each other’s representations, highlighting the brain’s ability to associate and integrate information across different sensory channels – key to cognitive development and adaptive learning. Current learning methods often mirror the developmental process of infants, who enhance their cognition through guidance and exploration. These methods often struggle with issues such as catastrophic forgetting and stability-plasticity trade-offs. This study presents a novel brain-inspired hierarchical autonomous framework, Cognitive Deep Self-Organizing Neural Network (CDSN), designed to enable autonomous agents to acquire object concepts dynamically. The architecture includes dual parallel audio-visual information pathways, incorporating three layers based on a Topological Kernel CIM-based Adaptive Resonance Neural Network (TC-ART). The first layer, referred to as the receptive layer, learns and organizes visual attributes and object names autonomously in an unsupervised manner. Subsequently, the second layer, the concept layer, distills clustered results from the corresponding receptive layer to create succinct symbol representation. In a synchronized manner, visual and auditory concepts are combined concurrently in the third layer, the associative layer, to establish real-time associative connections between the modalities. Furthermore, this layer introduces a top-down response approach, allowing agents to independently retrieve associated modalities and adapt acquired knowledge in a hierarchical manner. Experimental evaluations conducted on object datasets demonstrate the proposed architecture’s efficacy in online learning and the association o

关键词： Multilayer neural networks

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The Development of a Question-Answering System for Learning Article using Natural Language Processing 5

The Development of a Question-Answering System for Learning ...

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5th International Conference on Control, Robotics, and Intelligent System, CCRIS 2024

作者： Sriharee, Gridaphat Boonyamarn, Kewalee Department of Computer and Information Science Faculty of Applied Science King Mongkut’s University of Technology North Bangkok 1518 Pracharat 1 Wonsawang Bangsur Bangkok10800 Thailand

ISBN: (数字)9781510685871

ISBN: (纸本)9781510685864

This paper proposes developing a question-answering (QA) system for Thai learning articles. This system is a closed-domain QA system. Thai text processing was proposed to retrieve answers relevant to the natural language-based questions specified by users. The methodology to generate the keywords/topics used for retrieving answers was introduced. Matching and selecting methods for obtaining the best answer using cosine similarity is the support technique. Creating the corpus database, which contains Thai synset - a set of words with similar meanings or that may relate to each other, was explained. With the proposed corpus database, the question-answering can extract and analyze the meaning of the unbound sentence in Thai. The proposed QA system performed well, with a precision score of 87.67%, a recall score of 73.35%, and an F1 score of 81.91%. © 2024 SPIE.

关键词： Contrastive Learning

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FEATURE SELECTIONS AND EXPLORATORY ANALYSIS FOR ML STOCK PREDICTIONS USING SET50 9

FEATURE SELECTIONS AND EXPLORATORY ANALYSIS FOR ML STOCK PRE...

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9th International Conference on Big Data Analytics, Data Mining and Computational Intelligence, BigDaCI 2024, the 10th International Conference on Connected Smart Cities, CSC 2024 and the 16th International Conference on e-Health, EH 2024, Part of the 18th Multi Conference on computer science and information Systems 2024, MCCSIS 2024

作者： Sriharee, Gridaphat Department of Computer and Information Science Faculty of Applied Science King Mongkut’s University of Technology North Bangkok 1518 Pracharat 1 Wonsawang Bangsur Bangkok10800 Thailand

ISBN: (纸本)9789898704597

Stock prediction using machine learning is an interesting topic for investors. However, the performance of the prediction relies on different techniques and the train data set. Feature selection is an important factor in creating the model. In this paper, feature selection based on correlation was proposed, and exploratory analysis of SET50 from Thailand Stock market was experimented. The fundamental data – OHLC, percent change, and three technical indicators – SMA, RSI, and EMA were applied in the experimentation. The fundamental ML models: linear regression, logistic regression, ANN, decision tree, random forest, and XGBoost were conducted to evaluate the model’s performance based on the proposed features. The result of the experiments showed that multiple-parameter model performed better than single-parameter model. RSI was the most powerful indicator, and different periods can be a critical factor for different ML models. © 2024 Big Data Analytics, Data Mining and Computational Intelligence. All Rights Reserved.

关键词： Adversarial machine learning

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Graphene oxide-coated microbottle resonators for relative humidity sensing

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Optoelectronics Letters 2024年第7期20卷 400-405页

作者： Huda Adnan Zain Malathy Batumalay Md Ashadi Md Johari Hazli Rafis Abdul Rahim Sulaiman Wadi Harun Department of Electrical Engineering Faculty of EngineeringUniversity of MalayaKuala Lumpur 50603Malaysia Faculty of Data Science and Information Technology INTI International UniversityNilai 71800Malaysia1 Faculty of Electrical and Electronic Engineering Technology Universiti Teknikal Malaysia MelakaMelaka 76100Malaysia Faculty of Electronic and Computer Engineering Universiti Teknikal Malaysia MelakaMelaka 76100Malaysia

Graphene oxide(GO)is a 2D coating material used to improve fiber optics sensors’response to relative *** resonators(MBRs)have garnered more attention as sensing media *** MBR with a 190μm diameter was coated with ***,tapered fiber light coupling was used to investigate the relative humidity sensing performance in the range of 35—70%RH at 25℃.The MBR showed a higher Q factor before and after GO *** sensitivity of 0.115 dB/%RH was recorded with the 190μm GO-coated MBR sample compared to a sensitivity of 0.022 dB/%RH for the uncoated MBR *** results show that the MBR can be used in fiber optic sensing applications for environmental sensing.

关键词： humidity oxide bottle

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Task Consistent Prototype Learning for Incremental Few-Shot Semantic Segmentation 27th

Task Consistent Prototype Learning for Incremental Few-Shot...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Xu, Wenbo Wu, Yanan Jiang, Haoran Wang, Yang Wu, Qiang Zhang, Jian Faculty of Engineering and Information Technology University of Technology Sydney Sydney2007 Australia School of Computer and Information Technology Beijing Jiaotong University Beijing100044 China Department of Computer Science and Software Engineering Concordia University MontrealH3G2J1 Canada

ISBN: (纸本)9783031783463

Incremental Few-Shot Semantic Segmentation (iFSS) tackles a task that requires a model to continually expand its segmentation capability on novel classes using only a few annotated examples. Typical incremental approaches encounter a challenge that the objective of the base training phase (fitting base classes with sufficient instances) does not align with the incremental learning phase (rapidly adapting to new classes with less forgetting). This disconnect can result in suboptimal performance in the incremental setting. This study introduces a meta-learning-based prototype approach that encourages the model to learn how to adapt quickly while preserving previous knowledge. Concretely, we mimic the incremental evaluation protocol during the base training session by sampling a sequence of pseudo-incremental tasks. Each task in the simulated sequence is trained using a meta-objective to enable rapid adaptation without forgetting. To enhance discrimination among class prototypes, we introduce prototype space redistribution learning, which dynamically updates class prototypes to establish optimal inter-prototype boundaries within the prototype space. Extensive experiments on iFSS datasets built upon PASCAL and COCO benchmarks show the advanced performance of the proposed approach, offering valuable insights for addressing iFSS challenges. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Semantic Segmentation

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