DSpace community: 人工智慧學系

Double Exponential Smoothing Slime Mould Algorithm For Disease Detection In Iot Healthcare System

Thu, 02 Jul 2026 04:05:25 GMT

title: Double Exponential Smoothing Slime Mould Algorithm For Disease Detection In Iot Healthcare System abstract: This paper presents an algorithm, called the double exponential smoothing slime mould algorithm (DeSSMA), which is formulated to train deep learning models for the precise detection of diseases in patients. The DeSSMA is designed by integrating the principles of double exponential smoothing with the slime mould algorithm. The parameters, including energy depletion, link lifetime (LLT), and distance, are considered by the proposed DeSSMA as objectives aimed at optimizing data routing efficiency. In the base station, a deep residual network (DRN) is trained using the proposed DeSSMA algorithm, which is utilized for disease detection following the processes of data preprocessing, augmentation, and feature selection. Finally, performance evaluation of the DeSSMA-DRN framework is conducted using metrics such as energy consumption, LLT, accuracy, sensitivity, specificity, and receiver operating characteristic. The findings reveal that the proposed framework achieved a minimal energy depletion rate of 0.412 (J), an LLT rate of 0.318, an increased accuracy rate of 0.959, a high sensitivity rate of 0.967, and a specificity rate of 0.931.

Role of Corporate Social Responsibility in the Financial Sustainability of Sports Organizations

Thu, 02 Jul 2026 04:05:23 GMT

title: Role of Corporate Social Responsibility in the Financial Sustainability of Sports Organizations abstract: Corporate social responsibility (CSR) has gradually more important for sports organizations as they seek to improve their financial sustainability. A growing number of sports organizations are recognizing the strategic value and potential benefits of CSR and are implementing CSR initiatives into their operations. The study explores the impact of CSR on the financial sustainability of sports organizations. Through a complete assessment of the literature and analysis of relevant data, the study examines the impact of various CSR initiatives, such as sponsorship and funding, charities through partnerships, organizational commitment, and stakeholder satisfaction on the financial sustainability of sports organizations. Furthermore, the study analyzes the impact of external factors, such as global economic recession and proper fund utilization on the financial sustainability of sports organizations. The findings reveal that proper implementation of CSR initiatives can positively impact the financial sustainability of sports organizations. CSR partnerships with charitable organizations can provide an effective way for sports organizations to improve their social profile, while also helping to generate revenue and financial stability. The study also highlights the significance of stakeholder satisfaction and organizational commitment towards CSR activities in improving financial sustainability. The outcome of this study underscores the importance of CSR in the financial sustainability of sports organizations and provides insights into how sports organizations can leverage CSR to improve their long-term financial sustainability while also benefiting society at large. The findings show that factors, such as sponsorship and funding, proper fund utilization, global economic recession, charities through CSR partnerships, organizational commitment to CSR activities, and stakeholder satisfaction have a significant impact on the financial improvement of sports organizations. The factor “improving social profile” doesn’t have an impact on the financial improvement of sports organizations.

Web-Based Personalized Machine Learning Recommendations to Enhance Shared Decision-Making in Prostate-Specific Antigen Screening: Randomized Controlled Trial

Tue, 23 Jun 2026 04:06:11 GMT

title: Web-Based Personalized Machine Learning Recommendations to Enhance Shared Decision-Making in Prostate-Specific Antigen Screening: Randomized Controlled Trial abstract: Background: Prostate‑specific antigen (PSA) screening involves complex trade‑offs between early detection and the risks of overdiagnosis. For older adults (aged ≥50 years), shared decision‑making (SDM) is often hindered by limited health literacy, sensory or cognitive impairments, and multimorbidity, which complicate risk comprehension. Traditional decision aids provide foundational knowledge but are often nonpersonalized. Machine learning (ML) may offer individualized recommendations, yet the psychological and behavioral effects of ML‑assisted SDM in geriatric populations remain poorly characterized. Objective: This study aimed to develop and evaluate a web‑based, ML‑driven decision aid integrated into an SDM workflow to provide personalized PSA screening recommendations and to assess its effects on decisional conflict (primary outcome), state anxiety, and decision satisfaction among middle‑aged and older men. Methods: The study followed a 2‑stage design. First, a model establishment group (n=507) was used to train and evaluate 6 ML algorithms based on clinical and values‑clarification data. A random forest model was selected for its superior performance (mean area under the curve 0.933, SD 0.350; 95% CI 0.902-0.963). Second, a randomized controlled trial was conducted with 367 participants (mean age 64.34, SD 10.30 years) randomly assigned 1:1 to the ML suggestion group (MLSG; n=185) or the control group (CG; n=182). Both groups received video‑based education, counseling, and values clarification; only the MLSG received an ML‑generated "second opinion" recommendation. Primary and secondary outcomes were assessed using the Decisional Conflict Scale (DCS), Spielberger State‑Trait Anxiety Inventory (STAI), and Satisfaction with Decision scale. Results: In the randomized controlled trial (n=367), the MLSG reported significantly lower decisional conflict than the CG (total DCS score: mean difference [MD] -3.77, 95% CI -5.55 to -1.99; Cohen d=-0.44; P<.001). The MLSG reported greater perceived support (DCS7: adjusted P=.03), more adequate advice (DCS9: adjusted P<.001), and higher decision confidence (DCS10: adjusted P=.03; DCS11: adjusted P<.001). Regarding psychological well‑being, although total anxiety scores did not differ, the MLSG reported reduced worry (STAI item 6: MD -0.98, 95% CI -1.20 to -0.76; d=-0.89; adjusted P<.001) and increased calmness (STAI item 1: MD 0.30, 95% CI 0.06-0.54; d=0.25; adjusted P=.01). Decision satisfaction was higher in the MLSG across all items (total Satisfaction with Decision score: MD -7.38, 95% CI -8.54 to -6.18; P<.001). Behavioral choices were strongly influenced by the ML recommendation: participants in the MLSG who received an "accept" recommendation were more likely to select "accept" (34/67, 50.7%) than those in the CG (44/182, 24.2%; P<.001). When the system suggested "not now," only 17.8% (21/118) chose "accept," which was lower than in the CG. Conclusions: Integrating personalized ML recommendations into SDM workflows provides emotional scaffolding for older men, reducing decisional distress and enhancing confidence without undermining autonomy. By addressing geriatric‑specific vulnerabilities through a facilitated digital interface, this ML‑driven approach complements traditional clinical consultations. These findings support the scalable integration of artificial intelligence-assisted decision support to foster patient‑centered care in aging populations.

Decoupled Detection and Category-Level 6D Pose Estimation for Robot Grasping

Thu, 30 Apr 2026 04:05:39 GMT

title: Decoupled Detection and Category-Level 6D Pose Estimation for Robot Grasping abstract: 6D object pose estimation is an essential component for robotic grasping. Most existing deep learning-based approaches focus on instance-level pose estimation, which requires prior object models and consequently limits their applicability on unseen objects in real-world scenarios. In contrast, category-level 6D pose estimation adopts Normalized Object Coordinate Space (NOCS) maps to represent intra-class object geometry, enabling pose prediction without relying on predefined object models and thus improving generalization to unseen instances. However, the original NOCS-based category-level framework typically trains NOCS prediction and object classification in a joint manner, which introduces NOCS regression error among inter-class instances with similar appearances, thereby degrading pose estimation accuracy. To address this issue, we integrate the YOLOv8 object detection with SegFormer and propose a novel Category-Level SegFormer for 6D Object Pose Estimation (CLSF-6DPE). By decoupling object classification from NOCS regression through independent learning branches, the proposed framework significantly improves pose estimation performance. Furthermore, we validate the practical feasibility of CLSF-6DPE by integrating it with a robotic gripper via the Robot Operating System (ROS) in a Real-World grasping setup. Experimental results on the CAMERA and Real-World datasets demonstrate that the proposed method achieves mAP scores of 93.8% and 81.1%, respectively. Overall, the proposed method provides a modular and effective solution for category-level pose estimation in real-world robotic grasping applications.

Integrating deep learning and groundwater dynamics for drought vulnerability assessment under climate scenarios

Tue, 28 Apr 2026 04:06:07 GMT

title: Integrating deep learning and groundwater dynamics for drought vulnerability assessment under climate scenarios abstract: Drought increasingly threatens agricultural sustainability, particularly in groundwater-dependent regions where irrigation and aquifer recharge are closely linked. Taiwan's Zhuoshui River alluvial fan exemplifies this risk: long-term intensive pumping and rising climate extremes have amplified drought vulnerability. Yet most existing drought indices treat groundwater implicitly, and many AI studies focus on groundwater prediction without translating results into integrated vulnerability metrics. This study develops an AI-driven framework to assess future drought risk from climate, groundwater, and socio-environmental drivers. Groundwater level was predicted using a hybrid Convolutional Neural Network–Backpropagation model (CNN-BP) calibrated with 22 years of basin-wide gridded precipitation, temperature, and SPI data, together with groundwater levels from 18 monitoring wells. CNN-BP outperforms a BPNN benchmark, improving the correlation coefficient by 35.85% and reducing MAE by 19.51%, enabling robust projections for 2021–2100. These groundwater forecasts are then integrated with climatic (SPI), physiographic (soil, land use, elevation, slope, distance to river) and socio-economic (population) drivers to construct the Deep Learning-based Comprehensive Drought Vulnerability Indicator (DCDVI) under SSP1-2.6 and SSP5-8.5. Scenario results indicate consistent intensification of drought vulnerability relative to the historical baseline. SSP1-2.6 yields milder drought conditions and slower groundwater decline, while SSP5-8.5 leads to stronger drying and higher vulnerability. Under SSP5-8.5, highly vulnerable areas increase from 27.31% to 41.26% by 2081–2100. Overall, DCDVI provides a scalable, climate-responsive indicator that converts AI-based groundwater forecasts into actionable vulnerability maps. The framework provides a transferable decision-support tool for drought-prone, groundwater-reliant farming systems under climate change.

A CNN-transformer framework for air quality forecasting to support aeolian dust management in river basins

Tue, 28 Apr 2026 04:06:05 GMT

title: A CNN-transformer framework for air quality forecasting to support aeolian dust management in river basins abstract: Accurately forecasting riverbed aeolian dust emissions (PM10) in complex watershed environments is a critical engineering challenge, shaped by the intricate interdependencies among hydrometeorological factors, land surface dynamics, and anthropogenic pollution sources. Traditional models often struggle to capture these nonlinear interactions, limiting their utility for real-time environmental decision-making. This study presents a novel hybrid deep learning framework—combining a 3D Convolutional Neural Network (CNN), dual 1D CNNs, and a Transformer architecture—to enhance the predictive accuracy and interpretability of PM1110 forecasts in Taiwan’s Jhuoshuei River Basin. The model harnesses the spatial feature extraction of the 3D CNN, temporal pattern recognition of the 1D CNNs, and long-range dependency modeling of the Transformer to learn complex, multiscale relationships across diverse environmental variables. Extensive quantitative and qualitative evaluations demonstrate the model’s superior performance over conventional approaches, particularly in capturing seasonal variability and the mitigating effects of water infrastructure (e.g., Jiji Weir discharge) on dust emissions. The model effectively anticipates pollution peaks, offering critical lead time for the implementation of targeted interventions such as reservoir releases or dust suppression. Beyond technical innovation, this research provides actionable insights into the dynamic coupling of atmospheric, hydrological, and operational factors. The model’s scalability and generalizability position it as a robust decision-support tool for engineers, environmental managers, and policymakers. By bridging AI-driven modeling with practical engineering applications, this study advances the field of environmental informatics and supports the development of adaptive, knowledge-based systems for sustainable air quality and watershed management.

A case study on the application of a data-driven (XGBoost) approach on the environmental and socio-economic perspectives of agricultural groundwater management

Tue, 28 Apr 2026 04:06:00 GMT

title: A case study on the application of a data-driven (XGBoost) approach on the environmental and socio-economic perspectives of agricultural groundwater management abstract: Climate-induced extreme hydrological events threaten irrigation water resources and crop production. Groundwater serves as a vital source of irrigation during periods of surface water scarcity; however, excessive and unsustainable abstraction has resulted in land subsidence. While reducing groundwater over-extraction can alleviate this issue, it may also compromise agricultural productivity, particularly during drought conditions. To address this, a reliable assessment tool is needed to balance sustainable groundwater extraction and agricultural productivity. This study develops a groundwater level prediction model using the extreme gradient boosting (XGB) algorithm, employing power consumption, precipitation, and groundwater level data as input features. Bayesian optimization was used to determine the best-fit hyperparameters, resulting in RMSE, MAE, and R² values ranging from 0.923 to 2.497 m, 0.709–2.132 m, and 0.057–0.914, respectively, during model validation. Model testing from January 2022 to June 2023 showed a strong correlation between monitored and predicted levels, indicating effective trend capture, despite slight overestimations during the dry seasons. Scenario predictions showed that a 50 % reduction in power consumption for double-crop rice led to groundwater level increases of 0.41–2.31 m in the wet season and 0.54–2.52 m in the dry season, maintaining safe thresholds. However, current fallowing subsidies recover only a fraction of the economic profit from rice production, limiting policy adoption. To improve long-term effectiveness, this study recommends institutionalizing adaptive fallowing policies, such as seasonally adjusted quotas based on real-time groundwater and rainfall indicators, and tiered subsidy schemes according to groundwater risk levels. Embedding these tools within broader agricultural governance frameworks can enhance policy responsiveness and sustainability. The proposed model supports both short-term decision-making and long-term climate-informed groundwater management by balancing environmental protection with food security and economic viability.

Advanced groundwater level forecasting with hybrid deep learning model: Tackling water challenges in Taiwan's largest alluvial fan

Tue, 28 Apr 2026 04:05:58 GMT

title: Advanced groundwater level forecasting with hybrid deep learning model: Tackling water challenges in Taiwan's largest alluvial fan abstract: Groundwater is crucial for food security and economic development, yet it faces growing threats from over-extraction and extreme weather events. The Zhuoshui River alluvial fan, Taiwan’s largest, has long served as a key water source. However, recent climate change and industrial expansion have significantly affected groundwater recharge and quality, contributing to land subsidence. Accurate forecasting of groundwater levels is essential to ensuring environmental sustainability in the region. This study presents a novel hybrid deep learning model, CNN-BP, which integrates Convolutional Neural Networks (CNN) with Backpropagation Neural Networks (BPNN) to forecast groundwater levels three days in advance at 25 monitoring stations across the Zhuoshui River alluvial fan. The CNN-BP model was benchmarked against a standalone BPNN model. Both models were trained on a dataset of 7,291 daily hydro-geo-meteorological records from 2000 to 2019, including groundwater levels, rainfall, streamflow, temperature, evaporation, and lithology. The study emphasizes comprehensive input selection, feature extraction, and hyperparameter tuning, with Random Forest utilized to filter input factors from 20 rainfall stations, thereby improving forecast accuracy and reliability. The CNN-BP model significantly outperformed the BPNN model, achieving R2 values between 0.94 and 0.98 across various stations and effectively mitigating time-delay issues. The study also explored the relationship between forecast errors and the fan’s lithological characteristics, providing valuable insights for land-use planning and groundwater management. Validation during Typhoons Haitang and Maria further demonstrated the model’s capability to predict groundwater recharge under intense rainfall conditions. By integrating environmental and social factors such as drought frequency, population density, and recharge potential, this study underscores the need for targeted water management strategies. The findings offer critical insights for future regional approaches to groundwater management, promoting sustainable practices across watersheds. Ultimately, this study serves as a valuable resource for informed decision-making in land-use planning and water resource management, advancing the sustainable utilization of groundwater in the Zhuoshui River alluvial fan.

Intelligent Urban Flood Management Using Real-Time Forecasting, Multi-Objective Optimization, and Adaptive Pump Operation

Tue, 28 Apr 2026 04:05:56 GMT

title: Intelligent Urban Flood Management Using Real-Time Forecasting, Multi-Objective Optimization, and Adaptive Pump Operation abstract: Climate-induced extreme rainfall events are increasing the intensity and frequency of flash floods, highlighting the urgent need for advanced flood management systems in climate-resilient cities. This study introduces an Intelligent Flood Control Decision Support System (IFCDSS), a novel AI-driven solution for real-time flood forecasting and automated pump operations. The IFCDSS integrates multiple advanced tools: machine learning for rapid short-term water level forecasting, NSGA-III for multi-objective optimization, the TOPSIS for robust multi-criteria decision-making, and the ANFIS for real-time pump control. Implemented in the flood-prone Zhongshan Pumping Station catchment in Taipei, the IFCDSS leveraged real-time sensor data to deliver accurate water level forecasts within five seconds for the next 10–30 min, enabling proactive and informed operational responses. Performance evaluations confirm the system’s scientific soundness and practical utility. Specifically, the ANFIS achieved strong accuracy (R2 = 0.81), with most of the prediction errors being limited to a single pump unit. While the conventional manual operations slightly outperformed the IFCDSS in minimizing flood peaks—due to their singular focus—the IFCDSS excelled in balancing multiple objectives: flood mitigation, energy efficiency, and operational reliability. By simultaneously addressing these dimensions, the IFCDSS provides a robust and adaptable framework for urban environments. This study highlights the transformative potential of intelligent flood control to enhance urban resilience and promote sustainable, climate-adaptive development.

AI-driven weather downscaling for smart agriculture using autoencoders and transformers

Tue, 28 Apr 2026 04:05:53 GMT

title: AI-driven weather downscaling for smart agriculture using autoencoders and transformers abstract: Artificial Intelligence (AI) is reshaping agriculture by driving smarter, data-driven practices that enhance regional weather forecasting and support proactive, informed decision-making. Advances in Big Data, IoT, Remote Sensing, and Machine Learning are accelerating this transformation, with Transformer architectures increasingly pivotal in refining agricultural management strategies, especially in Taiwan. In this study, we develop a hybrid Convolutional Autoencoder and LSTM-based Transformer Network (CAE-LSTMT) to downscale six-hour simulation data into precise hourly forecasts, validated using 55,538 temperature and relative humidity records (2020–2023) from Taiwan’s Jhuoshuei River basin, provided by the Central Weather Administration (CWA). The model was trained (70 %), validated (10 %), and tested (20 %) to optimize its configuration and performance. This CAE-LSTMT model substantially enhances spatiotemporal weather forecast resolution, transforming six-hour regional data into hourly forecasts with improved accuracy. It yields temperature forecast gains of 5.66 % to 20.39 % and relative humidity improvements of 8.05 % to 12.76 %, with reduced forecast biases compared to traditional LSTM models. The model demonstrates exceptional accuracy in vapor pressure deficit (VPD) predictions, achieving mean absolute errors (MAE) between 0.15 to 0.21 kPa across regions and 0.16 to 0.20 kPa seasonally, significantly outperforming the CWA model. Accurate VPD forecasts allow farmers to manage irrigation and minimize crop stress, directly supporting plant health and yield optimization. For heat index classification, the model achieves up to 96 % ACCURACY, with mean absolute percentage errors (MAPE) of 4 % to 23 %, significantly exceeding the CWA model’s ACCURACY range of 35 % to 79 % and MAPE of 29 % to 70 %. This high precision in heat index forecasting empowers farmers to protect crops and livestock against heat stress. By extracting critical features from high-dimensional data, the CAE-LSTMT model advances environmental downscaling for multi-site, multi-horizon weather data, showing significant promise for Smart Agriculture and Health Advisory Systems. This approach offers precise, actionable forecasts, optimizing agricultural practices and reducing climate-related risks, underscoring its impact on sustainable agricultural and environmental management.

Flood resilience through hybrid deep learning: Advanced forecasting for Taipei's urban drainage system

Tue, 28 Apr 2026 04:05:50 GMT

title: Flood resilience through hybrid deep learning: Advanced forecasting for Taipei's urban drainage system abstract: The escalating impacts of climate change have intensified extreme rainfall events, placing urban drainage systems under unprecedented pressure and increasing flood risks. Addressing these challenges requires advanced flood mitigation strategies, optimized sewer operations, and responsive disaster management. This study leverages knowledge graphs to integrate diverse data sources, providing a comprehensive perspective on flood dynamics, and applies deep learning models within a Real-Time Urban Drainage Early Warning System to enhance flood management at Taipei City's Zhongshan Pumping Station in Taiwan. We proposed deep learning models, specifically Convolutional Neural Networks combined with Back Propagation Neural Networks (CNN-BP), to make multi-input multi-output multi-step (MIMOMS) forecasts on sewer water levels at intervals from 10 to 40 min (T+1 to T+4) and MIMO forecasts on the pumping station's internal (forebay) and external (river) water levels at intervals from 10 to 60 min (T+1 to T+6). The CNN-BP model exhibited superior forecast accuracy, reaching an R2 (RMSE) of 0.97 (0.08m) at T+1 for sewer water levels and an R2 (RMSE) of 0.99 (0.06m) at T+1 for both internal and external water levels. These results highlight CNN-BP's capability to accurately capture water level trends, ensuring reliable real-time responsiveness, especially during intense and sudden rainfall events. The CNN-BP's high predictive accuracy enables enhanced pump operations, strengthens early warning systems, and fosters intelligent flood control practices crucial for effective environmental management.

Digital Image Recovery and Multiple-watermarking Techniques

Wed, 15 Apr 2026 06:12:01 GMT

title: Digital Image Recovery and Multiple-watermarking Techniques abstract: The research topic of this paper is to integrate the digital image processing schemes and the watermarking techniques, and those methods will apply on the digital images and digital videos. The research topic includes three parts: (1) image recovery, colorization and enhancement, (2) multiple-watermarking techniques, and (3) the integration of image recovery and multiple-watermarking techniques. The abstracts of all chapters are described below: Chapter 1 – Image Recovery The lacuna texture synthesis is proposed for the virtual restoration of ancient Chinese paintings and digital images. Lacuna texture synthesis is a patching method, which uses the Markov Random Field (MRF) model. We eliminate the undesirable patterns, such as stains, crevices, and artifacts, and the algorithm fills the lacuna regions with the appropriate textures. The proposed scheme not only maintains a complete shape, but also prevents the edge disconnection in the final results. Chapter 2 – Visible Watermark Removal In this chapter, an image recovery algorithm for removing visible watermarks is presented. Independent component analysis (ICA) is utilized to separate source images from watermarked and reference images. Three independent component analysis approaches and five different visible watermarking methods are examined in our study. The experimental results will show that visible watermarks are successfully removed, and that the proposed algorithm is independent of both the adopted ICA approach and the visible watermarking method. Moreover, several watermarked images sourced from various websites are removed the watermarks successively. Chapter 3 – Image Colorization In the past, the artists adopted the black ink to represent various sights and objects in Chinese ink-and-wash, such as, mountain scenery, waterscape, animals, plants, etc. This chapter will introduce an effective method to colorize the Chinese ink-and-wash paintings. The proposed method not only takes fewer computing time than the conventional method, but it also can preserve the soft-gradual tone in the ink-and-wash paintings, such as, water-flowing, smog, cloud, waterfall, and shadow etc. Chapter 4 – Image Enhancement We will introduce the weighted histogram separation (WHS) in this chapter, which is presented to enhance the high dynamic range images. The property of weighted histogram separation situates between the successive mean quantization transform and the histogram equalization. Additionally, the proposed method is further applied to the local enhancement, which is termed as the adaptive weighted histogram separation (AWHS). Chapter 5 – Spatial Domain Multiple-watermarking Algorithm The objective of our study in information security is to develop a multiple watermarks embedding and extraction algorithm, which is called as spatial domain multiple-watermarking algorithm. This algorithm is one kind of quantization index modulation, it can impose bi-watermark or tri-watermark on the host image. Furthermore, the extracted watermarks not only are exploited to detect the tampered areas, but it is also used for attack classification and attack identification. Chapter 6 – Dual Domain Bi-watermarking Algorithm A dual domain bi-watermarking algorithm embeds bi-watermark into the host image in discrete-cosine-transform domain (DCT), and it is the extension of the spatial domain bi-watermarking algorithm. However, the bi-watermark can be extracted from both spatial domain and DCT domain. By the same token, two separated watermarks from the extracted bi-watermark have different capability for various compression rates, and they also reveal the different robustness against the global and the regional attacks. Chapter 7 – 2.5 Domain Tri-watermarking Algorithm In this chapter, we will introduce an integration of dual domain bi-watermarking algorithm and visual cryptography, which is named as 2.5 domain tri-watermarking algorithm (2.5D-TW). This algorithm implements tri-watermark embedding in discrete-cosine-transform domain (DCT) for video protection, but the tri-watermark can be extracted from both spatial domain and DCT domain. Three separated watermarks from the extracted tri-watermark reveal the different robustness against various attacks. According to the bit error rates of those three watermarks, the algorithm even identifies whether the attack is occurred in spatial domain or in temporal domain for video. Chapter 8 – Integration of Image Recovery and Watermarking Algorithm The key of this chapter is to integrate the image recovery scheme and the watermarking technique. The spatial domain bi-watermarking algorithm is used to add the halftone of downscaled host image into the host image. After extracting the bi-watermark from the covered image, the bi-watermark is restored to the gray-scale image using the proposed inverse halftoning, which utilizes the linear programming and quadratic programming. Furthermore, the bi-watermark is not only exploited to detect the tampered areas without prior data, but it also can be applied to recover the tampered areas in the tampered image. Chapter 9 – Linear Programming and Its Applications In 1736, the great mathematician Leonhard Euler published a paper to solve the problem of seven bridges of Königsberg, and he translated it into the graph theory problem. This study is the well-known Euler circuit problem. Here, we solve non-Euler circuit problem using mix-integer linear programming, which transforms the non-Euler circuit to the Euler one. In addition, the binary integer programming is exploited to determine the edge direction. The experimental results will show that the proposed scheme can be applied to the route planning, the continuous line drawing and the real-object production. Chapter 10 – Conclusions and Future Works Consequently, we will summarize the previous researches and describe the possible improvement and applications in the future works.

Attention Distribution-Aware Softmax for NPU-Accelerated On-Device Inference of LLMs: An Edge-Oriented Approximation Design

Wed, 25 Mar 2026 04:05:10 GMT

title: Attention Distribution-Aware Softmax for NPU-Accelerated On-Device Inference of LLMs: An Edge-Oriented Approximation Design abstract: Low-power NPUs enable on-device LLM inference through efficient integer and fixed-point algebra, yet their lack of native exponential support makes Transformer softmax a critical performance bottleneck. Existing NPU kernels approximate e^x using uniform piecewise polynomials to enable O(1) SIMD indexing, but this wastes computation by applying high-degree arithmetic indiscriminately in every segment. Conversely, fully adaptive approaches maximize statistical fidelity but introduce pipeline stalls due to comparator-based boundary search. To bridge this gap, we propose an attention distribution-aware softmax that uses Particle Swarm Optimization (PSO) to define non-uniform segments and variable polynomial degrees, prioritizing finer granularity and lower arithmetic complexity in attention-dense regions. To ensure efficiency, we snap boundaries into a 128-bin LUT, enabling O(1) retrieval of segment parameters without branching. Inference measurements show that this favors low-degree execution, minimizing exp-kernel overhead. Using TinyLlama-1.1B-Chat as a testbed, the proposed weighted design reduces cycles per call exp kernel (CPC) by 18.5% versus an equidistant uniform Degree-4 baseline and 13.1% versus uniform Degree-3, while preserving ranking fidelity. These results show that grid-snapped, variable-degree approximation can improve softmax efficiency while largely preserving attention ranking fidelity, enabling accurate edge LLM inference.

Heuristic Design for Humanoid Robots

Mon, 23 Mar 2026 04:05:36 GMT

title: Heuristic Design for Humanoid Robots

基於分軸變換提昇二維碼儲存技術

Fri, 20 Mar 2026 04:07:19 GMT

title: 基於分軸變換提昇二維碼儲存技術 abstract: 隨著物聯網和數位化應用的發展，二維碼作為一種重要的數據承載技術，已在行動支付、智慧物流等領域得到廣泛應用。然而，現有二維碼技術在數據容量與解碼穩定性方面仍面臨挑戰，特別是在高密度數據存儲和高損毀場景中。為了提升二維碼的儲存密度與解碼效率，本研究提出了一種基於分軸變換的創新技術，通過三維座標變換將數據嵌射至二維座標中，避免了傳統投影重疊問題，並顯著增強了數據存儲的可可靠性。研究結果表明，所提出的技術能顯著提高二維碼的儲存容量。此外，本研究的技術架構對未來高效、穩定的二維碼應用場景具有重要的理論價值和實際意義

AIの超進化に伴う日本語人材業界のトレンドを洞察するキャリアデザイン

Thu, 19 Mar 2026 04:08:22 GMT

title: AIの超進化に伴う日本語人材業界のトレンドを洞察するキャリアデザイン

「死の文学」と言われた村上春樹の1980年代までの創作群に築いた死生観―『ダンス・ダンス・ダンス』とその前の短篇集を中心に―

Thu, 19 Mar 2026 04:08:19 GMT

title: 「死の文学」と言われた村上春樹の1980年代までの創作群に築いた死生観―『ダンス・ダンス・ダンス』とその前の短篇集を中心に―

AI時代下のクリエイティブな日本文学授業への挑戦─AIと協働して村上春樹風動画制作の完成を目指して─

Thu, 19 Mar 2026 04:08:15 GMT

title: AI時代下のクリエイティブな日本文学授業への挑戦─AIと協働して村上春樹風動画制作の完成を目指して─

AI時代下の「不易流行」に相応しいクリエイティブな日本語授業を目指して

Thu, 19 Mar 2026 04:08:12 GMT

title: AI時代下の「不易流行」に相応しいクリエイティブな日本語授業を目指して

ChatGPT反證之人本精神需求下的在地永續與國際共融(共榮)：結合日語敘事力、AI、DX三把劍

Thu, 19 Mar 2026 04:08:08 GMT

title: ChatGPT反證之人本精神需求下的在地永續與國際共融(共榮)：結合日語敘事力、AI、DX三把劍

An Application of Deep Learning in the Zhuoshui River Basin for Multi-Station PM10 Forecast

Wed, 18 Mar 2026 04:05:25 GMT

title: An Application of Deep Learning in the Zhuoshui River Basin for Multi-Station PM10 Forecast

Image Regression Classification of Air Quality by Convolutional Neural Network

Wed, 18 Mar 2026 04:05:18 GMT

title: Image Regression Classification of Air Quality by Convolutional Neural Network

A study on spatiotemporal groundwater level forecasting by a hybridization of machine learning and physically-based models

Tue, 17 Mar 2026 04:08:27 GMT

title: A study on spatiotemporal groundwater level forecasting by a hybridization of machine learning and physically-based models

AI-Driven Hydro-Insights: Proactive Water Resource Management for Sustainable Agriculture in the Face of Climate Change

Tue, 17 Mar 2026 04:08:22 GMT

title: AI-Driven Hydro-Insights: Proactive Water Resource Management for Sustainable Agriculture in the Face of Climate Change

A Vision of Agriculture 4.0: Constructing Smart Agriculture through Artificial Intelligent

Tue, 17 Mar 2026 04:08:19 GMT

title: A Vision of Agriculture 4.0: Constructing Smart Agriculture through Artificial Intelligent

Feature Analysis and Anomaly Detection of Personal Protective Equipment in PCB Production Lines

Tue, 17 Mar 2026 04:08:16 GMT

title: Feature Analysis and Anomaly Detection of Personal Protective Equipment in PCB Production Lines

Siamese CNN-based Few-shot Learning for PCB Defect Detection

Tue, 17 Mar 2026 04:08:10 GMT

title: Siamese CNN-based Few-shot Learning for PCB Defect Detection abstract: Defect detection in mass production lines is often challenged by small and imbalanced datasets, making few-shot learning approaches particularly suitable. Traditional deep learning methods typically rely on large-scale datasets for training, which limit their applicability in real-world manufacturing environments. To address this limitation, this study proposes a few-shot learning model capable of identifying product defects using a limited amount of data, thereby enhancing generalization across multiple defect classes. Unlike conventional deep learning models that require extensive data, the proposed approach effectively performs defect detection with minimal samples. Specifically, we introduce a Siamese Convolutional Neural Network-based Few-Shot Learning (SCNN-FSL) framework. The Siamese network is constructed using CNN architecture and trained with a triplet loss function to optimize feature embedding. Furthermore, SCNN-FSL is integrated into an automated optical inspection (AOI) defect detection system, incorporating image preprocessing, data sampling, and object classification techniques tailored for detecting defects in electronic components on PCB production lines. Experimental results demonstrate that the proposed few-shot learning model outperforms traditional deep learning approaches, achieving higher accuracy and lower miss rates, thereby validating its effectiveness in practical industrial applications.

感謝表現として多文化多言語社会の台湾にトランスフォーメーションした村上春樹の「小確幸」ランブクピティヤ　ディヌーシャ編

Tue, 10 Mar 2026 04:10:51 GMT

title: 感謝表現として多文化多言語社会の台湾にトランスフォーメーションした村上春樹の「小確幸」ランブクピティヤ　ディヌーシャ編

洋行者漱石の文明開化との苦闘と回帰昇華

Tue, 10 Mar 2026 04:10:46 GMT

title: 洋行者漱石の文明開化との苦闘と回帰昇華

パートナーシップの観点から見た『ノルウェイの森』の男同士の関係変容―「死は生の対極としてではなく、その一部として存在している」の言葉に注目して―

Tue, 10 Mar 2026 04:10:40 GMT

title: パートナーシップの観点から見た『ノルウェイの森』の男同士の関係変容―「死は生の対極としてではなく、その一部として存在している」の言葉に注目して―

「死の文学」と言われた村上春樹の1980年代までの創作群に築いた死生観―『ダンス・ダンス・ダンス』とその前の短篇集を中心に―

Tue, 10 Mar 2026 04:10:34 GMT

title: 「死の文学」と言われた村上春樹の1980年代までの創作群に築いた死生観―『ダンス・ダンス・ダンス』とその前の短篇集を中心に―

日語翻譯教育之於AI，AI之於日語翻譯教育的課題：AI賦能學習者創新自主學習的活教材

Tue, 10 Mar 2026 04:10:28 GMT

title: 日語翻譯教育之於AI，AI之於日語翻譯教育的課題：AI賦能學習者創新自主學習的活教材 abstract: 「後AI時代」一詞出現，代表AI被廣泛通用現象業已形成。又大腦學習為主的教師與LLM學習為主的數位原生世代學習者間出現的學習模式的世代差異，教師如何克服兩者間差異性，而進行教與學，乃是本稿撰寫的動機。當著眼於日語學習5項技能中的「譯」，理念貫穿以下4步驟，先以教師傳承豐富的教學經驗的點出長年以來學生容易犯錯之處。再由教師介紹學習者有利改善的AI工具，且與學生並肩作戰找出對策，解決學生學習的問題。繼之借力AI讓學生於課外時間進行自主學習。最後由教師認證自主學習成效，給予評價。依此理念，借力AI為應用策略，師生聯袂將自製日語翻譯自主學習教材，廣為推行。如此地使得AI賦能學習者自製自主學習的日語翻譯活教材，足以讓日語翻譯教育之於AI，AI之於日語翻譯教育的課題，得以永續發展。

A CNN-transformer framework for air quality forecasting to support aeolian dust management in river basins

Mon, 09 Mar 2026 04:06:41 GMT

High-spatiotemporal-resolution PM2.5 forecasting by hybrid deep learning models with ensembled massive heterogeneous monitoring data

Fri, 06 Mar 2026 04:08:30 GMT

title: High-spatiotemporal-resolution PM2.5 forecasting by hybrid deep learning models with ensembled massive heterogeneous monitoring data abstract: High-resolution real-time air quality forecasting can alert decision-makers and residents about forthcoming air pollution events and refine air quality management. The Environmental Protection Administration in Taiwan has deployed numerous low-cost air quality microsensors near industrial zones lately to facilitate local air quality monitoring. Nevertheless, the frequent occurrence of missing sensor data due to problems of mobile transmission, frontend/backend device malfunction, or other unforeseen issues would raise difficulty in making quick responses to air pollution incidents. This study proposed a hybrid deep learning model (AE-CNN-BP) collaborating an Autoencoder (AE), a Convolutional Neural Network (CNN), and a Back Propagation Neural Network (BPNN) to effectively extract crucial features from big data for making successive high-spatiotemporal-resolution forecasts of PM2.5 concentrations 4 h ahead. The proposed model was trained and tested in three industrial zones densely installed with microsensors in Kaohsiung City of Taiwan. A high pollution incident was selected to evaluate model performance. The results show that the proposed model could reliably produce nice high-spatiotemporal-resolution forecasts for 12 air quality monitoring stations and 485 microsensors, with Coefficient of Determination (R2) values and Root Mean Squared Error (RMSE) of 0.82 (0.76) and 11.05 (12.75) μg/m3 in the training (testing) stage, respectively. For the selected incident, the Mean Absolute Percentage Error (MAPE) values of the proposed model were 22.3% and 27.1% at T+1 and T+4, respectively. This study demonstrates that the proposed deep learning model based on ensemble datasets of sparsely distributed monitoring stations and densely deployed microsensors can offer reliable high-spatiotemporal-resolution air quality forecasts, benefiting environmental studies and informed policymaking by accounting for local-scale variations in PM2.5 concentrations.

Develop a hybrid machine learning model for promoting microbe biomass production

Fri, 06 Mar 2026 04:08:27 GMT

title: Develop a hybrid machine learning model for promoting microbe biomass production abstract: Since the cultivation condition of microbe biomass production (mycelia yield) involves a variety of factors, it’s a laborious process to obtain the optimal cultivation condition of Antrodia cinnamomea (A. cinnamomea). This study proposed a hybrid machine learning approach (i.e., ANFIS-NM) to identify the potent factors and optimize the cultivation conditions of A. cinnamomea based on a 32 fractional factorial design with seven factors. The results indicate that the ANFIS-NM approach successfully identified three key factors (i.e., glucose, potato dextrose broth, and agar) and significantly boosted mycelia yield. The interpretability of ANFIS rules made the cultivation conditions visually interpretable. Subsequently, a three-factor five-level central composite design was used to probe the optimal yield. This study demonstrates the proposed hybrid machine learning approach could significantly reduce the time consumption in laboratory cultivation and increase mycelia yield that meets SDGs 7 and 12, hitting a new milestone for biomass production.

Advancing climate-resilient flood mitigation: Utilizing transformer-LSTM for water level forecasting at pumping stations

Fri, 06 Mar 2026 04:08:23 GMT

title: Advancing climate-resilient flood mitigation: Utilizing transformer-LSTM for water level forecasting at pumping stations abstract: Proactive management of pumping stations using artificial intelligence (AI) technology is vital for effectively mitigating the impacts of flood events caused by climate change. Accurate water level forecasts are pivotal in advancing the intelligent operation of pumping stations. This study proposed a novel Transformer-LSTM model to offer accurate multi-step-ahead forecasts of the flood storage pond (FSP) and river water levels for the Zhongshan pumping station in Taipei, Taiwan. A total of 19,647 ten-minute-based datasets of pumping operation and storm sewer, FSP, and river water levels were collected between 2014 and 2020 and further divided into training (70 %), validation (10 %), and test (20 %) datasets for model construction. The results demonstrate that the proposed model dramatically outperforms benchmark models by producing more accurate and reliable water level forecasts at 10-minute (T + 1) to 60-minute (T + 6) horizons. The proposed model effectively enhances the connections between input factors through the Transformer module and increases the connectivity across consecutive time series using the LSTM module. This study reveals interconnected dynamics among pumping operation and storm sewer, FSP, and river water levels, enhancing flood management. Understanding these dynamics is crucial for effective execution of management strategies and infrastructure revitalization against climate impacts. The Transformer-LSTM model's forecasts encourage water practices, resilience, and disaster risk reduction for extreme weather events.

An Artificial Intelligence-Powered Environmental Control System for Resilient and Efficient Greenhouse Farming

Fri, 06 Mar 2026 04:08:20 GMT

title: An Artificial Intelligence-Powered Environmental Control System for Resilient and Efficient Greenhouse Farming abstract: The rise in extreme weather events due to climate change challenges the balance of supply and demand for high-quality agricultural products. In Taiwan, greenhouse cultivation, a key agricultural method, faces increasing summer temperatures and higher operational costs. This study presents the innovative AI-powered greenhouse environmental control system (AI-GECS), which integrates customized gridded weather forecasts, microclimate forecasts, crop physiological indicators, and automated greenhouse operations. This system utilizes a Multi-Model Super Ensemble (MMSE) forecasting framework to generate accurate hourly gridded weather forecasts. Building upon these forecasts, combined with real-time in-greenhouse meteorological data, the AI-GECS employs a hybrid deep learning model, CLSTM-CNN-BP, to project the greenhouse’s microclimate on an hourly basis. This predictive capability allows for the assessment of crop physiological indicators within the anticipated microclimate, thereby enabling preemptive adjustments to cooling systems to mitigate adverse conditions. All processes run on a cloud-based platform, automating operations for enhanced environmental control. The AI-GECS was tested in an experimental greenhouse at the Taiwan Agricultural Research Institute, showing strong alignment with greenhouse management needs. This system offers a resource-efficient, labor-saving solution, fusing microclimate forecasts with crop models to support sustainable agriculture. This study represents critical advancements in greenhouse automation, addressing the agricultural challenges of climate variability.

深度學習：環境資料數位化的應用

Fri, 06 Mar 2026 04:08:18 GMT

title: 深度學習：環境資料數位化的應用 abstract: 深度學習能精準捕捉資料中的非線性特徵和劇烈變化，實現高精度預測。本研究將其應用於多個環境議題，包括全臺灣的空氣污染、臺北市的下水道水位預測及溫室微氣候預測。空污預測使用了全臺灣環保署測站的歷史資料，涵蓋六個污染因子和兩個氣象因子，模型引入了注意力機制，成功解決傳統深度學習的梯度消失問題，顯著提升未來72小時的預測精度，誤差（root-mean-square error, RMSE）在8.5至13.2 μg/m^3之間。在臺北市的下水道水位預測中，本研究採用了DNN-AE模型，能穩定預測未來10至60分鐘水位。在所有DNN-AE模型中，C-AE模型在結構上更具優勢，卷積層能有效提取時間特徵，特別是在處理時間變化資料時，能捕捉更細緻的趨勢，顯著提高預測精度，誤差（RMSE）在T+1至T+6為0.21至0.51 m，預測結果最佳且穩定。對於溫室微氣候預測，本研究使用XGBoost對彰化伸港各微氣候因子進行特徵篩選，分析了溫度、相對濕度和光照強度等關鍵影響因素。結果顯示影響溫度、相對濕度和光照強度的主要特徵分別為溫度、短波輻射（日射量）和RH，反映了捲簾和遮蔽系統對溫室內部微氣候的影響。結果顯示，ANFIS模型在溫度預測方面表現最佳，R^2值超過0.8，CNN則在相對濕度與光照強度的預測上表現良好。綜合這些應用，臺灣的環境預測技術將能進一步推動智慧城市的發展，邁向更高階的數位化未來。

Watershed groundwater level multistep ahead forecasts by fusing convolutional-based autoencoder and LSTM models

Fri, 06 Mar 2026 04:08:14 GMT

title: Watershed groundwater level multistep ahead forecasts by fusing convolutional-based autoencoder and LSTM models abstract: The development of deep learning-based groundwater level forecast models can tackle the challenge of high dimensional groundwater dynamics, predict groundwater variation trends accurately, and manage groundwater resources effectively, thereby contributing to sustainable water resources management. This study proposed a novel ConvAE-LSTM model, which fused a Convolutional-based Autoencoder model (ConvAE) and a Long Short-Term Memory Neural Network model (LSTM), to provide accurate spatiotemporal groundwater level forecasts over the next three months. The HBV-light and LSTM models are chosen as benchmarks. An ensemble of point data and the corresponding derived images concerning the past (observations) and the future (forecasts from a conceptual model) of groundwater levels at 33 groundwater wells in Jhuoshuei River basin of Taiwan between 2000 and 2019 constituted the case study. The findings showcase the effectiveness of the ConvAE-LSTM model in extracting crucial features from both point and imagery datasets. This model successfully establishes spatiotemporal dependencies between regional images and groundwater level data over diverse time frames, leading to accurate multi-step-ahead forecasts of groundwater levels. Notably, the ConvAE-LSTM model exhibits a substantial improvement, with the R-squared values showing an increase of more than 18%, 22%, and 49% for the R1, R2, and R3 regions, respectively, compared to the HBV-light model. Additionally, it outperforms the LSTM model in this regard. This study represents a noteworthy milestone in environmental modeling, offering key insights for designing sustainable groundwater management strategies to ensure the long-term availability of this vital resource.

Advanced groundwater level forecasting with hybrid deep learning model: Tackling water challenges in Taiwan’s largest alluvial fan

Fri, 06 Mar 2026 04:08:11 GMT

title: Advanced groundwater level forecasting with hybrid deep learning model: Tackling water challenges in Taiwan’s largest alluvial fan abstract: Groundwater is crucial for food security and economic development, yet it faces growing threats from over-extraction and extreme weather events. The Zhuoshui River alluvial fan, Taiwan’s largest, has long served as a key water source. However, recent climate change and industrial expansion have significantly affected groundwater recharge and quality, contributing to land subsidence. Accurate forecasting of groundwater levels is essential to ensuring environmental sustainability in the region. This study presents a novel hybrid deep learning model, CNN-BP, which integrates Convolutional Neural Networks (CNN) with Backpropagation Neural Networks (BPNN) to forecast groundwater levels three days in advance at 25 monitoring stations across the Zhuoshui River alluvial fan. The CNN-BP model was benchmarked against a standalone BPNN model. Both models were trained on a dataset of 7,291 daily hydro-geo-meteorological records from 2000 to 2019, including groundwater levels, rainfall, streamflow, temperature, evaporation, and lithology. The study emphasizes comprehensive input selection, feature extraction, and hyperparameter tuning, with Random Forest utilized to filter input factors from 20 rainfall stations, thereby improving forecast accuracy and reliability. The CNN-BP model significantly outperformed the BPNN model, achieving R2 values between 0.94 and 0.98 across various stations and effectively mitigating time-delay issues. The study also explored the relationship between forecast errors and the fan’s lithological characteristics, providing valuable insights for land-use planning and groundwater management. Validation during Typhoons Haitang and Maria further demonstrated the model’s capability to predict groundwater recharge under intense rainfall conditions. By integrating environmental and social factors such as drought frequency, population density, and recharge potential, this study underscores the need for targeted water management strategies. The findings offer critical insights for future regional approaches to groundwater management, promoting sustainable practices across watersheds. Ultimately, this study serves as a valuable resource for informed decision-making in land-use planning and water resource management, advancing the sustainable utilization of groundwater in the Zhuoshui River alluvial fan.

DSpace community: 人工智慧學系

Double Exponential Smoothing Slime Mould Algorithm For Disease Detection In Iot Healthcare System

Role of Corporate Social Responsibility in the Financial Sustainability of Sports Organizations

Web-Based Personalized Machine Learning Recommendations to Enhance Shared Decision-Making in Prostate-Specific Antigen Screening: Randomized Controlled Trial

Decoupled Detection and Category-Level 6D Pose Estimation for Robot Grasping

Integrating deep learning and groundwater dynamics for drought vulnerability assessment under climate scenarios

A CNN-transformer framework for air quality forecasting to support aeolian dust management in river basins

A case study on the application of a data-driven (XGBoost) approach on the environmental and socio-economic perspectives of agricultural groundwater management

Advanced groundwater level forecasting with hybrid deep learning model: Tackling water challenges in Taiwan's largest alluvial fan

Intelligent Urban Flood Management Using Real-Time Forecasting, Multi-Objective Optimization, and Adaptive Pump Operation

AI-driven weather downscaling for smart agriculture using autoencoders and transformers

Flood resilience through hybrid deep learning: Advanced forecasting for Taipei's urban drainage system

Digital Image Recovery and Multiple-watermarking Techniques

Attention Distribution-Aware Softmax for NPU-Accelerated On-Device Inference of LLMs: An Edge-Oriented Approximation Design

Heuristic Design for Humanoid Robots

基於分軸變換提昇二維碼儲存技術

AIの超進化に伴う日本語人材業界のトレンドを洞察するキャリアデザイン

「死の文学」と言われた村上春樹の1980年代までの創作群に築いた死生観―『ダンス・ダンス・ダンス』とその前の短篇集を中心に―

AI時代下のクリエイティブな日本文学授業への挑戦─AIと協働して村上春樹風動画制作の完成を目指して─

AI時代下の「不易流行」に相応しいクリエイティブな日本語授業を目指して

ChatGPT反證之人本精神需求下的在地永續與國際共融(共榮)： 結合日語敘事力、AI、DX三把劍

An Application of Deep Learning in the Zhuoshui River Basin for Multi-Station PM10 Forecast

Image Regression Classification of Air Quality by Convolutional Neural Network

A study on spatiotemporal groundwater level forecasting by a hybridization of machine learning and physically-based models

AI-Driven Hydro-Insights: Proactive Water Resource Management for Sustainable Agriculture in the Face of Climate Change

A Vision of Agriculture 4.0: Constructing Smart Agriculture through Artificial Intelligent

Feature Analysis and Anomaly Detection of Personal Protective Equipment in PCB Production Lines

Siamese CNN-based Few-shot Learning for PCB Defect Detection

感謝表現として多文化多言語社会の台湾にトランスフォーメーションした村上春樹の「小確幸」ランブクピティヤ ディヌーシャ編

洋行者漱石の文明開化との苦闘と回帰昇華

パートナーシップの観点から見た『ノルウェイの森』の男同士の関係変容―「死は生の対極としてではなく、その一部として存在している」の言葉に注目して―

「死の文学」と言われた村上春樹の1980年代までの創作群に築いた死生観―『ダンス・ダンス・ダンス』とその前の短篇集を中心に―

日語翻譯教育之於AI，AI之於日語翻譯教育的課題：AI賦能學習者創新自主學習的活教材

A CNN-transformer framework for air quality forecasting to support aeolian dust management in river basins

High-spatiotemporal-resolution PM2.5 forecasting by hybrid deep learning models with ensembled massive heterogeneous monitoring data

Develop a hybrid machine learning model for promoting microbe biomass production

Advancing climate-resilient flood mitigation: Utilizing transformer-LSTM for water level forecasting at pumping stations

An Artificial Intelligence-Powered Environmental Control System for Resilient and Efficient Greenhouse Farming

深度學習：環境資料數位化的應用

Watershed groundwater level multistep ahead forecasts by fusing convolutional-based autoencoder and LSTM models

Advanced groundwater level forecasting with hybrid deep learning model: Tackling water challenges in Taiwan’s largest alluvial fan

ChatGPT反證之人本精神需求下的在地永續與國際共融(共榮)：結合日語敘事力、AI、DX三把劍

感謝表現として多文化多言語社会の台湾にトランスフォーメーションした村上春樹の「小確幸」ランブクピティヤ　ディヌーシャ編