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共 68658 条记录，以下是481-490 订阅

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Comparison of Machine Learning Algorithms for Detection of Stuttering in Speech

Comparison of Machine Learning Algorithms for Detection of S...

引用

international conference on Machine Learning and Big Data Analytics, ICMLBDA 2023

作者： Gudlavalleti, Sarvagna Devi, P. Sunitha Lakka, Ramyasri Kuchanpally, Rithika Dudekula, Sai Sonali Department of Computer Science and Engineering G Narayanamma Institute of Technology and Science Shaikpet Telangana Hyderabad India Department of Electronics and Telecommunication Engineering G Narayanamma Institute of Technology and Science Shaikpet Telangana Hyderabad India

ISBN: (纸本)9783031513374

Stuttering is a speech disorder characterized by the repetition of sounds, syllables, or words and the prolongation of sounds. An individual who stutters exactly knows what he or she would like to say but has trouble producing a normal flow of speech. This project aims to use machine learning algorithms to detect stuttering behavior in Telugu language speech samples. Stuttering is a speech disorder that affects the flow of speech, making it difficult to communicate with others. The study will collect voice samples from individuals reading the same Telugu script and generate 8- and 16-kHz samples in .wav files. The data will be annotated and coded on a 0–8 scale and will be generated as a dataset. Machine learning algorithms will be used to build a model for detecting stuttering patterns in the audio files. Finally, the model will be integrated with a user interface. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： User interfaces

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A Novel Rate of Penetration Fusion Modeling Method based on Improved Dung Beetle Optimization Algorithm and Support Vector Regression 26

A Novel Rate of Penetration Fusion Modeling Method based on ...

引用

26th international conference on Industrial technology, ICIT 2025

作者： Zhou, Yang Zhang, Zihan Chen, Shipeng Wu, Min Chen, Xin School of Automation China University of Geosciences Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan430074 China School of Future Technology China University of Geosciences Wuhan430074 China

ISBN: (纸本)9798331521950

The rate of penetration (ROP) is a critical indi-cator for evaluating drilling efficiency. Developing an accurate ROP model is essential for optimizing drilling performance and addressing process control challenges. However, ROP modeling in deep geological drilling is complicated by nonlinearity, diverse working conditions, and high-dimensional variations. To overcome these challenges, a fusion modeling approach for ROP is proposed. First, the fuzzy C-means clustering method is applied to classify drilling data into different working con-ditions. Based on this classification, support vector regression is employed to develop ROP sub-models, effectively addressing nonlinearity. To further enhance model accuracy, an improved dung beetle optimization algorithm (IDBO) is designed to deter-mine optimal model parameters and integrate the sub-models, thereby resolving issues related to multiple working conditions and high-dimensional variations. The IDBO incorporates four key enhancements, average weight, chaos disturbance, modified local search, and re-updating of the best solution, to strength-en its global search capability. Comparative results using the IEEE CEC2017 benchmark test functions demonstrate that the proposed algorithm outperforms others in 12 test functions, highlighting its strong global optimization ability. Additionally, results from real-world drilling data validate the effectiveness of the proposed modeling approach in practical applications. © 2025 IEEE.

关键词： Clustering algorithms

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Architectural Framework for Multi-Modal Video Generation via Fine-Tuned Stable Diffusion Models 24

Architectural Framework for Multi-Modal Video Generation via...

引用

3rd international conference on Artificial Intelligence and Intelligent Information Processing, AIIIP 2024

作者： Wei, Shuai Nan Liu, Yuan Yuan Yang, Yu Tianjin College University of Science and Technology Beijing Tianjin China

ISBN: (纸本)9798400707308

In recent years, large language models (LLMs) like ChatGPT have driven advancements in artificial intelligence (AI) across various domains, pushing the boundaries of multi-modal applications. The emergence of Stable Diffusion XL in April 2023 brought image generation into the mainstream. The generation of videos from textual input poses a significant computational challenge within computer science. Nonetheless, recent advancements in text-to-video artificial intelligence (AI) technologies have showcased notable progress within this domain. [1] Following the rapid development of LLMs, OpenAI’s release of the Sora promotional video in February 2024 marked a significant milestone in video generation. In this paper, we explore a novel approach to generating coherent videos by fine-tuning Stable Diffusion, enabling the production of temporally consistent video frames. Our architecture integrates LLM-based text expansion, multi-frame generation using Stable Diffusion, and final video synthesis. Experimental results demonstrate that our method significantly improves the temporal coherence of generated videos. © 2024 Copyright held by the owner/author(s).

关键词： Coherent Frame Generation

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DocHFormer: Document Image Dewarping via Harmonized Modeling of Hierarchical Priors 27th

DocHFormer: Document Image Dewarping via Harmonized Modelin...

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27th international conference on Pattern Recognition, ICPR 2024

作者： Zhou, Xinyue Li, Guanting Jiang, Nanfeng Wang, Da-Han Zhang, Xu-Yao Zhu, ShunZhi School of Computer and Information Engineering Xiamen University of Technology Xiamen361024 China Fujian Key Laboratory of Pattern Recognition and Image Understanding Xiamen361024 China State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9783031781186

Document Image Dewarping (DID) task aims to address the issue of geometry distortion and improve image quality. In this paper, we propose a simple but effective method, named DocHFormer, that can take hierarchical priors features of images, including document image mask and coordinate positions, as additional information to realize accurate representation. To better exploit these fused information for dewarping, we take them into a harmonized space random shuffle operation, which can stochastically rearrange the pixels across spatial space and further use inverse operation to recover the original order. This way can adapt to allocate each feature pixel with equal probability and thus make full use of multi-type features. Furthermore, we introduce this mechanism into local self-attention to use linear complexity to input resolution and also design a new feed-forward network with structural modeling to boost representation. With the help of the above components, our proposed DocHFormer can achieve competitive performance with lower complexity and also outperform the existing state-of-the-art on several popular datasets. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Image enhancement

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Design and Simulation of SCADA System to Improve Reliability of a Distribution Feeder: A Case Study on Bahir Dar, Feeder R6-G2 11th

Design and Simulation of SCADA System to Improve Reliability...

引用

11th EAI international conference on Advancement of science and technology, ICAST 2023

作者： Fenitaw, Melaku Asrat Beza, Teketay Mulu Faculty of Electrical and Computer Engineering Bahir Dar Institute of Technology – Bahir Dar University Bahir Dar Ethiopia

ISBN: (纸本)9783031641503

This paper presents a design and simulation of a supervisory control and data acquisition (SCADA) system to improve the reliability of a distribution feeder in Bahir Dar, Ethiopia. The SCADA system was modeled using MATLAB Simulink and Java programming language, and it includes sensors, circuit breakers, and a human-machine interface (HMI) for remote monitoring and control of the distribution feeder. The results of the simulation show that the proposed SCADA system has significantly improved the reliability of the distribution feeder, with a 60% reduction in the system average interruption duration index (SAIDI), a 61% reduction in the customer average interruption duration index (CAIDI), and a 1.11% improvement in the average service availability index (ASAI). © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Java programming language

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Exploration of Software Field Engineer Training Based on AI+Capability Model 24

Exploration of Software Field Engineer Training Based on AI+...

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2nd international conference on Advances in Artificial Intelligence and Applications, AAIA 2024

作者： Zhou, Junjie Wu, Qian School of Information Wuhan Software Engineering Vocational College Wuhan Open University Hubei Wuhan China School of Computer Science Wuhan University Hubei Wuhan China Hubei Engineering Research Center for Intelligent Detection and Identification of Complex Parts Wuhan China

ISBN: (纸本)9798400712883

With the rapid development of artificial intelligence technology, its application in the field of education and teaching is becoming increasingly widespread, bringing revolutionary changes to traditional education models. To cultivate high-quality software vocational skills talents, a digital management platform based on AI+capability model is constructed to explore the innovative application of artificial intelligence in education and teaching, realizing intelligent auxiliary learning functions such as personalized resource recommendation, student growth monitoring, and achievement label portrait. At the same time, a series of intelligent teaching tools such as"AI+Code Generation, AI+Inspection Assistant, AI+Process Map" will be developed to explore and promote the vertical application of big models in talent cultivation. Through innovative models such as"introducing enterprises into education, cultivating students through real-life situations, AI+empowerment, teaching according to students’ aptitude, trinity, innovative training, strengthening processes, and diverse evaluations", it has been fully applied to the talent training scene of software technology field engineers, and its teaching effectiveness is significant. © 2024 Copyright held by the owner/author(s).

关键词： Students

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LoRA-drop: Efficient LoRA Parameter Pruning based on Output Evaluation 31

LoRA-drop: Efficient LoRA Parameter Pruning based on Output ...

引用

31st international conference on Computational Linguistics, COLING 2025

作者： Zhou, Hongyun Lu, Xiangyu Xu, Wang Zhu, Conghui Zhao, Tiejun Yang, Muyun Faculty of Computing Harbin Institute of Technology China Department of Computer Science & Technology Tsinghua University Beijing China

ISBN: (纸本)9798891761964

Low-Rank Adaptation (LoRA) is currently the most commonly used Parameter-efficient fine-tuning (PEFT) method. However, it still faces high computational and storage costs to models with billions of parameters. Most previous studies have tackled this issue by using pruning techniques. Nonetheless, these efforts only analyze LoRA parameter features to evaluate their importance, such as parameter count, size, and gradient. In fact, the output of LoRA directly impacts the fine-tuned model. Preliminary experiments indicate that a fraction of LoRA possesses significantly high output values, substantially influencing the layer output. Motivated by the observation, we propose LoRA-drop. Concretely, LoRA-drop evaluates the importance of LoRA based on the LoRA output. Then we retain LoRA for important layers and the other layers share the same LoRA. We conduct abundant experiments with models of different scales on NLU and NLG tasks. Results demonstrate that LoRA-drop can achieve performance comparable to full fine-tuning and LoRA while retaining 50% of the LoRA parameters on average. © 2025 Association for Computational Linguistics.

关键词： Computational linguistics

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Document Image Shadow Removal via Frequency Information-Oriented Network 27th

Document Image Shadow Removal via Frequency Information-Ori...

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27th international conference on Pattern Recognition, ICPR 2024

作者： Yang, Fan Zhou, Xinyue Jiang, Nanfeng Wang, Da-Han Zhang, Xu-Yao Li, Guantin Man, Wang Wu, Yun School of Computer and Information Engineering Xiamen University of Technology Xiamen361024 China Fujian Key Laboratory of Pattern Recognition and Image Understanding Xiamen361024 China State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9783031781186

Removing shadows from document images can significantly improve the Quality of Experience (QoE) and boost the performance of the downstream document analysis and recognition tasks. However, existing methods still have limited generalization ability on complex document images and are prone to disrupt the image details. To address this issue, we consider the different shadow types that impact the image content on different frequency sub-bands. This motivates us to exploit frequency-domain information and further design a Frequency Information-oriented Deshadow Network (FID-Net). The proposed FID-Net mainly uses two elaborated modules, named Frequency Feature Extractor (FFE) and a Frequency Feature Refinement (FFR). FFE can generate low/high-frequency features through adaptively decomposing spectra of the shadow image. After that, FFR further refines both frequency features with mutual information operations. With the proposed key designs, extensive experimental results on the commonly used benchmarks demonstrate that the proposed method can learn discriminative shadows and achieve favorable performance against state-of-the-art approaches. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Image enhancement

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Oracle Bone Inscription Image Retrieval Based on Improved ResNet Network 27th

Oracle Bone Inscription Image Retrieval Based on Improved Re...

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27th international conference on Pattern Recognition, ICPR 2024

作者： Ding, Jun Wang, Jiaoyan Aysa, Alimjan Xu, Xuebin Ubul, Kurban School of Computer Science and Technology Xinjiang University Ürümqi830046 China Xinjiang Multilingual Information Technology Key Laboratory Xinjiang University Ürümqi830046 China Joint International Research Laboratory of Silk Road Multilingual Cognitive Computing Xinjiang University Urumqi830046 China

ISBN: (纸本)9783031783043

Research on oracle bone inscription image retrieval is important for applications in academic and cultural heritage areas. The current oracle bone dataset faces problems such as the low similarity between the same category, the high similarity between the different categories, and imbalanced sample distribution. In addition, due to the complex background of oracle bone images, existing network models have certain limitations in extracting image features. To address these challenges, this study first adopts a Siamese network-based image retrieval method to learn feature representations of similar and dissimilar images. Subsequently, the existing dataset was partitioned, providing a practical and usable retrieval dataset for the oracle bone image retrieval field. Finally, an improved network model based on ResNet is proposed and integrated into the Siamese network framework. The model achieves the highest retrieval MP and MAP values of 83.26% and 90.68%, respectively, which is better than the current research. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Content based retrieval

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Advancing Cucumber Disease Detection in Agriculture Through Machine Vision and Drone technology 2nd

Advancing Cucumber Disease Detection in Agriculture Through...

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2nd international conference on Cyber Intelligence and Information Retrieval, CIIR 2023

作者： Rahman, Syada Tasfia Vasker, Nishat Ahammed, Amir Khabbab Hasan, Mahamudul Department of Computer Science and Engineering East West University Dhaka Bangladesh

ISBN: (纸本)9789819776023

This study uses machine vision and drone technologies to propose a unique method for the diagnosis of cucumber disease in agriculture. The backbone of this research is a painstakingly curated dataset of hyperspectral photographs acquired under genuine field conditions. Unlike earlier datasets, this study included a wide variety of illness types, allowing for precise early-stage detection. The model achieves an excellent 87.5% accuracy in distinguishing eight unique cucumber illnesses after considerable data augmentation. The incorporation of drone technology for high-resolution images improves disease evaluation. This development has enormous potential for improving crop management, lowering labor costs, and increasing agricultural productivity. This research, which automates disease detection, represents a significant step toward a more efficient and sustainable agricultural future. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Drones

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