检索结果-内蒙古大学图书馆

arXiv 2025年

作者： El Housni, Omar Elmachtoub, Adam N. Sheth, Harsh Shi, Jiaqi School of Operations Research and Information Engineering Cornell Tech Cornell University New YorkNY10044 United States Department of Industrial Engineering and Operations Research Data Science Institute Columbia University New YorkNY10027 United States

An opaque product is a product for which only partial information is disclosed to the buyer at the time of purchase. Opaque products are common in sectors such as travel and online retail, where the car type or product color is hidden in the opaque product. Opaque products enable sellers to target customers who prefer a price discount in exchange for being flexible about the product they receive. In this paper, we integrate opaque products and traditional products together into the multinomial logit (MNL) choice model and study the associated price and assortment optimization problems. For the price optimization problem, we surprisingly show that uniform pricing is optimal which implies it has the same optimal pricing solution and value as the traditional MNL model. While adding an opaque product priced at the revenue-maximizing price may enhance revenue given arbitrary traditional product prices, this advantage disappears when all prices are optimized jointly. For the assortment optimization problem, we show that the revenue-maximizing assortment is nested-by-valuation for uniformly priced products. For non-uniformly priced cases, we propose a natural nested-by-revenue-and-valuation heuristic that performs extremely well in an extensive numerical study. Copyright © 2025, The Authors. All rights reserved.

关键词： Sales

来源：评论

学校读者我要写书评

暂无评论

Multi-Agent System Based data Mining Technique for Supplier Selection

Multi-Agent System Based Data Mining Technique for Supplier ...

引用

International Conference on Power Energy, Environment and Intelligent Control (PEEIC)

作者： Jagjit Singh Dhatterwal Kuldeep Singh Kaswan Jay Singh Mahaveer Singh Naruka D Govardhan Artificial Intelligence and Data Science Koneru Lakshmaiah Education Foundation Guntur AP India School of Computing Science and Engineering Galgotias University Greater Noida UP India Department of Electrical and Electronics Engineering G L Bajaj Institute of Technology and Management Greater Noida India Department of Aeronautical Engineering Institute of Aeronautical Engineering Hyderabad

Different target knowledge representations, which correspond to different hypothesis spaces, are suitable for learning various types of target functions. A distinct learning algorithm, capitalizing on an underlying structure to guide the exploration of the hypothesis space, leverages each of these hypothesis representations. Consequently, making decisions on these matters entails navigating an expansive landscape of alternative approaches to determine the one that best aligns with the defined learning problem. To select an appropriate learning algorithm that performs optimally for the given problem and target function, it is essential to analyze the interplay between the size and completeness of the hypothesis space, the availability of training examples, the learner's prior knowledge, and the confidence in a hypothesis's ability to generalize correctly to unseen data points. This challenge often arises in the context of poorly integrated systems. The objective of this paper is to introduce a specific approach to addressing these challenges, involving the use of enhanced elicitation techniques and tools, improved development paradigms, knowledge modeling languages, ontologies, and advanced methods for system maintenance. However, in the past decade, an alternative reasoning paradigm and computational problem-solving approach have garnered significant attention, involving the use of Multi-Agent System (MAS) based data Mining (DM) techniques. This paper proposes the integration of MAS and DM for supplier selection, with a focus on partitioning-based algorithms such as K-means, Farthest First, Expectation Maximization, and Filtered Clusterer. These algorithms are compared based on factors such as dataset size, the number of clusters, and the time required for cluster formation. Finally, within this integrated model, MAS-DM is exemplified through an illustrative result-matching process.

关键词：

来源：评论

学校读者我要写书评

暂无评论

AI-Driven Mobile Healthcare: Societal Impact, Workforce Adaptation, and Enhanced Predictive Modeling for Diabetes Risk Assessment

AI-Driven Mobile Healthcare: Societal Impact, Workforce Adap...

引用

Pervasive Computational Technologies (ICPCT), International Conference on

作者： Shreyas Rajendra Hole Vinothkumar Kolluru Ranita Gangul Sagar Kolekar Advaitha Naidu Chintakunta Sudeep Mungara Computer Science & Engineering (AI& ML) Dayananda Sagar University Bangalore India Department of data Science Stevens Institute of Technology Hoboken NJ U.S.A. Computer Science & Engineering Delaware State University Bangalore India Department of Civil Engineering Symbiosis Institute of Technology (SIT) Symbiosis International (Deemed University) Pune India Department of Computer Science University of North Carolina at Charlotte Charlotte NC U.S.A. Masters in Information Systems Stevens Institute of Technology Hoboken NJ U.S.A.

ISBN: (数字)9798331508685

ISBN: (纸本)9798331519476

With advancements in artificial intelligence, it is natural for the health industry to focus on IOT devices. AI could promote a paradigm shift in how doctors and patients interact through immediate updates, predictive features, and remote access. The focus of this study is the outreach and employment aspects revolving around AI-enabled health care, with special attention to mHealth solutions. We searched through published data and came across 112 relevant studies identifying keywords such as accessibility, empowerment, and workforce. One of the most interesting findings was the fact that AI targeting mobile applications has the possibility of increasing access to health services by approximately 60 percent for rural areas, as well as increasing the patient cooperative rate by approximately 45%, where a predictive health tracking device has been utilized. We proposed two new measures: the Physical Activity and Nutrition Index (PANI) and Health Status Index (HSI), allowing for better diabetes risk predictions with a 0.82 AUC-ROC and better generalizability than traditional baselines, with an improvement of 12%. When identifying the best available ML models, including Logistic Regression, Random Forest, XGBoost, LightGBM, and Bayesian Neural Networks, we found that the hybrid Random Forest + Linear SVC model had the best performance with recall and accuracy of 0.77 and 0.72, respectively. The ability of the model to demonstrate both interpretability and specificity was evident. It was also observed that synthetic data augmentation (SMOTE) increased the recall for minority class predictions by 18% but did not reduce specificity.

关键词： Adaptation models Uncertainty Neural networks Medical services Predictive models Diabetes Bayes methods Risk management Indexes Random forests

来源：评论

学校读者我要写书评

暂无评论

Theoretical and Experimental Studies on Characterisation and Collection of Particulate Matter in Single-Wire, Single-Stage Electrostatic Precipitator with Square Cross-Section

SSRN

引用

SSRN 2024年

作者： Kumar, Aiswarya A. Nawale, Prashant Rohidas Mayya, Y.S. Sahu, Manoranjan Aerosol and Nanoparticle Technology Laboratory Environmental Science and Engineering Department Indian Institute of Technology Bombay Mumbai400076 India Interdisciplinary Program in Climate Studies Indian Institute of Technology Bombay Mumbai400076 India Centre for Machine Intelligence and Data Science Indian Institute of Technology Bombay Mumbai400076 India Chemical Engineering Department Indian Institute of Technology Bombay Mumbai400076 India

Electrostatic precipitation (ESP) is a technology widely used to remove particulate matter (PM) from industrial gas streams. To adopt the same for varying scales as well as for different clean air delivery applications, there exists a requirement for the development of comprehensive, readily adaptable, reasonably good, comparable, rigorous, step-by-step analytical theory and experimental validation of same for design of modular units of ESP. In this regard, current study conducted theoretical and experimental studies to investigate corona characterisation and PM collection efficiency in a modular unit of a single-wire, single-stage, wire-plate ESP with square cross-sectional geometry. The theory integrated the corona discharge with particle transport while the experimental study was conducted using a laboratory scale ESP. Best agreement between I-V characteristics of theory and experiment was obtained while adjusting inception electric field to 12.35´105 Vm-1 as well as the ion diffusion coefficient value to 0.0647´10-4 m2s-1. Relative error for local minimum efficiency between theory as well as experiment for three tested flow rates 30 LPM, 50 LPM and 100 LPM were 5.37%, 1.36%, 14.58% and was 1.36%, 7.52% and 6.23% for three potentials 9 kV, 11 kV as we all as 13 kV respectively. As accuracy and reliability of present model are verified in terms of collecting efficiency at different operating conditions, present model can facilitate the design and scale-up of ESPs for indoor PM control with high collection efficiency. The study also illustrated a sample calculation on the applicability of this filter-less technology for air cleaning in an indoor environment. © 2024, The Authors. All rights reserved.

关键词： Electrostatic precipitators

来源：评论

学校读者我要写书评

暂无评论

Enhancing Earthquake Prediction through Hybrid Deep Learning Approach

Enhancing Earthquake Prediction through Hybrid Deep Learning...

引用

Integrated Circuits and Communication Systems (ICICACS), IEEE International Conference on

作者： M. Kandan Swagata Sarkar Meenakshi Sharma V. Rajeswari P. Poonkuzhali Srimathi S Department of Computing Technologies School of Computing Faculty of Engineering and Technology SRM Institute of Science and Technology Kattankulathur India Artificial Intelligence and Data Science Sri Sairam Engineering College Chennai India Department of CSE Global Group of Institute Amritsar Information Technology Panimalar Engineering College R.M.D Engineering College Kavaraipettai Department of Bio Technology Saveetha School of Engineering SIMATS Chennai Tamilnadu India

Earthquakes, one of the greatest natural catastrophes, kill people and harm the economy. Heavy earthquakes and tsunamis may kill thousands and cost millions. Globally, predicting major earthquakes is important. Because earthquake prediction is difficult, researchers employ machine learning to improve forecasts. Many countries have early earthquake warning systems to save lives. Temporal rather than geographical characteristics dominated previous research. A similarity between earthquake and non-seismic signals makes real-time earthquake detection with cellphones or IoT devices problematic. This system may change entity interactions using ML and an IoT network. EEWS metrics are tracked using IoT. However, ML can assess these actions and determine smart city risk reduction and catastrophe management strategies. A deep learning approach for continuous earthquake forecasting utilising past seismic events is proposed. This research predicts earthquakes by position and depth using machine learning and deep learning. ConvLSTM, a Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN), extracts important waveform elements to make the classifier reliable in earthquake parameters.

关键词：

来源：评论

学校读者我要写书评

暂无评论

A Resilience Perspective on C-V2X Communication Networks under Imperfect CSI

arXiv

引用

arXiv 2024年

作者： Shui, Tingyu Saad, Walid Chen, Mingzhe Bradley Department of Electrical and Computer Engineering Virginia Tech ArlingtonVA22203 United States Department of Electrical and Computer Engineering Frost Institute for Data Science and Computing University of Miami Coral GablesFL33146 United States

Cellular vehicle-to-everything (C-V2X) networks provide a promising solution to improve road safety and traffic efficiency. One key challenge in such systems lies in meeting different quality-of-service (QoS) requirements of coexisting vehicular communication links, particularly under imperfect channel state information (CSI) conditions caused by the highly dynamic environment. In this paper, a novel analytical framework for examining the resilience of C-V2X networks in face of imperfect CSI is proposed. In this framework, the adaptation phase of the C-V2X network is studied, in which an adaptation power scheme is employed and the probability distribution function (PDF) of the imperfect CSI is estimated. Then, the resilience of C-V2X networks is studied through two principal dimensions: remediation capability and adaptation performance, both of which are defined, quantified, and analyzed for the first time. Particularly, an upper bound on the estimation’s mean square error (MSE) is explicitly derived to capture the C-V2X’s remediation capability, and a novel metric named hazard rate (HR) is exploited to evaluate the C-V2X’s adaptation performance. Afterwards, the impact of the adaptation power scheme on the C-V2X’s resilience is examined, revealing a tradeoff between the C-V2X’s remediation capability and adaptation performance. Simulation results validate the framework’s superiority in capturing the interplay between adaptation and remediation, as well as the effectiveness of the two proposed metrics in guiding the design of the adaptation power scheme to enhance the system’s resilience. Copyright © 2024, The Authors. All rights reserved.

关键词： Distribution functions

来源：评论

学校读者我要写书评

暂无评论

CMATH: Cross-Modality Augmented Transformer with Hierarchical Variational Distillation for Multimodal Emotion Recognition in Conversation

arXiv

引用

arXiv 2024年

作者： Zhu, Xiaofei Cheng, Jiawei Yang, Zhou Chen, Zhuo Wang, Qingyang Yao, Jianfeng College of Computer Science and Engineering Chongqing University of Technology Chongqing400054 China College of Computer and Data Science Fuzhou University Fuzhou350108 China Wind Tunnel Center China Automotive Engineering Research Institute Co. Ltd. Chongqing401120 China Big Data Center Chongqing Changan Automobile Co. Ltd. Chongqing400023 China

Multimodal emotion recognition in conversation (MER) aims to accurately identify emotions in conversational utterances by integrating multimodal information. Previous methods usually treat multimodal information as equal quality and employ symmetric architectures to conduct multimodal fusion. However, in reality, the quality of different modalities usually varies considerably, and utilizing a symmetric architecture is difficult to accurately recognize conversational emotions when dealing with uneven modal information. Furthermore, fusing multi-modality information in a single granularity may fail to adequately integrate modal information, exacerbating the inaccuracy in emotion recognition. In this paper, we propose a novel Cross-Modality Augmented Transformer with Hierarchical Variational Distillation, called CMATH, which consists of two major components, i.e., Multimodal Interaction Fusion and Hierarchical Variational Distillation. The former is comprised of two submodules, including Modality Reconstruction and Cross-Modality Augmented Transformer (CMA-Transformer), where Modality Reconstruction focuses on obtaining high-quality compressed representation of each modality, and CMA-Transformer adopts an asymmetric fusion strategy which treats one modality as the central modality and takes others as auxiliary modalities. The latter first designs a variational fusion network to fuse the fine-grained representations learned by CMATransformer into a coarse-grained representations. Then, it introduces a hierarchical distillation framework to maintain the consistency between modality representations with different granularities. Experiments on the IEMOCAP and MELD datasets demonstrate that our proposed model outperforms previous state-of-the-art baselines. Implementation codes can be available at https://***/ cjw-MER/CMATH. Copyright © 2024, The Authors. All rights reserved.

关键词： Emotion Recognition

来源：评论

学校读者我要写书评

暂无评论

Predicting gastric cancer response to anti-HER2 therapy or anti-HER2 combined immunotherapy based on multimodal data

引用

Signal Transduction and Targeted Therapy 2024年第9期9卷 4137-4148页

作者： Zifan Chen Yang Chen Yu Sun Lei Tang Li Zhang Yajie Hu Meng He Zhiwei Li Siyuan Cheng Jiajia Yuan Zhenghang Wang Yakun Wang Jie Zhao Jifang Gong Liying Zhao Baoshan Cao Guoxin Li Xiaotian Zhang Bin Dong Lin Shen Center for Data Science Peking UniversityBeijingChina Department of Gastrointestinal Oncology Key Laboratory of Carcinogenesis and Translational Research(Ministry of Education)Peking University Cancer Hospital and InstituteBeijingChina Department of Pathology Key Laboratory of Carcinogenesis and Translational Research(Ministry of Education)Peking University Cancer Hospital and InstituteBeijingChina Department of Radiology Key Laboratory of Carcinogenesis and Translational Research(Ministry of Education)Peking University Cancer Hospital and InstituteBeijingChina National Biomedical Imaging Center Peking UniversityBeijingChina Department of General Surgery Nanfang HospitalSouthern Medical UniversityGuangzhouChina Guangdong Provincial Key Laboratory of Precision Medicine for Gastrointestinal Tumor GuangzhouChina Department of Medical Oncology and Radiation Sickness Peking University Third HospitalBeijingChina National Engineering Laboratory for Big Data Analysis and Applications Peking UniversityBeijingChina Beijing International Center for Mathematical Research(BICMR) Peking UniversityBeijingChina Center for Machine Learning Research Peking UniversityBeijingChina

The sole use of single modality data often fails to capture the complex heterogeneity among patients,including the variability in resistance to anti-HER2 therapy and outcomes of combined treatment regimens,for the treatment of HER2-positive gastric cancer(GC).This modality deficit has not been fully considered in many ***,the application of artificial intelligence in predicting the treatment response,particularly in complex diseases such as GC,is still in its ***,this study aimed to use a comprehensive analytic approach to accurately predict treatment responses to anti-HER2 therapy or anti-HER2 combined immunotherapy in patients with HER2-positive *** collected multi-modal data,comprising radiology,pathology,and clinical information from a cohort of 429 patients:310 treated with anti-HER2 therapy and 119 treated with a combination of anti-HER2 and anti-PD-1/PD-L1 inhibitors *** introduced a deep learning model,called the Multi-Modal model(MuMo),that integrates these data to make precise treatment response *** achieved an area under the curve score of 0.821 for anti-HER2 therapy and 0.914 for combined ***,patients classified as low-risk by MuMo exhibited significantly prolonged progression-free survival and overall survival(log-rank test,P<0.05).These findings not only highlight the significance of multi-modal data analysis in enhancing treatment evaluation and personalized medicine for HER2-positive gastric cancer,but also the potential and clinical value of our model.

关键词： immunotherapy HER2 cancer

来源：评论

学校读者我要写书评

暂无评论

LLGS: Illuminating Gaussian Splatting via absorptance Modulation

LLGS: Illuminating Gaussian Splatting via absorptance Modula...

引用

International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Jianwen Gan Wenxin Li Bo Zheng Chengliang Wang Yingbo Wu National Elite Institute of Engineering Chongqing University China AI Lab Chongqing Changan Automobile Co. Ltd. China College of Computer Science Chongqing University China School of Big Data & Software Engineering Chongqing University China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Low-light images are typically characterized by low pixel intensity and color distortion, presenting a significant challenge for accurate 3D reconstruction with 3D Gaussian Splatting (3DGS). Traditional 2D enhancement methods fail to maintain consistent illumination, affecting reconstruction quality. We propose Low-Light Gaussian (LLGS), which can directly leverage low-light images for 3D reconstruction and synthesizing normal-light novel views. LLGS incorporates absorptance to simulate light behavior in low-light conditions, assuming objects maintain normal illumination while reflected light intensity attenuates due to absorptance during rendering. This approach enables the capture of authentic color information in dimly lit scenes. LLGS outperforms current enhancement algorithms and Neural Radiance Fields (NeRF) in image quality and processing efficiency, making it highly effective for low-light 3D scene reconstruction and novel view synthesis.

关键词： Image quality Three-dimensional displays Image color analysis Lighting Signal processing algorithms Modulation Speech enhancement Neural radiance field Rendering (computer graphics) Image reconstruction

来源：评论

学校读者我要写书评

暂无评论

Enhancing Speech Recognition for Hearing-Impaired Individuals Using Deep Learning Models

Enhancing Speech Recognition for Hearing-Impaired Individual...

引用

International Conference on Circuit, Power and Computing Technologies (ICCPCT)

作者： K. Kalaiarasi Winson Medidhi Niresh Kumar S S. Thenappan R. Sathiyaseelan Mohana Sundaram K Department of ECE Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Department of CSE(Data Science) Prasad V. Potluri Siddhartha Institute of Technology Kanuru Vijayawada Andhra Pradesh India Department of CSE St. Joseph's College of Engineering OMR Chennai Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai Department of Computer Science Engineering Arunai Engineering College Thiruvannamalai Tamil Nadu India Department of Computer Science and Engineering R.M.D. Engineering College Kavaraipettai Thiruvallur India

ISBN: (数字)9798350372816

ISBN: (纸本)9798350372823

This study examines the constraints of current methodologies, specifically in diverse acoustic environments, to surmount the obstacles associated with speech recognition for individuals with hearing impairments. Conventional processes frequently experience reduced accuracy and utility as a result of their incapability to dynamically adjust to evolving circumstances. This study presents an innovative approach that surpasses these constraints through the utilization of a transformer-based model. Through the implementation of sophisticated data augmentation techniques, the proposed model demonstrates exceptional performance across various acoustic environments, notwithstanding obstacles such as speaker adjustments and background noise. One notable characteristic of this methodology is its transformer architecture, which aptly captures subtle speech inflections by employing self-attention processes. Additionally, through independent learning, the model improves its ability to address the unique challenges associated with hearing-impaired speech. Integrating these components, the model achieves superior accuracy and adaptability compared to current technologies.

关键词： Adaptation models Accuracy Speech recognition Auditory system Artificial neural networks Speech enhancement Transformers

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：