检索结果-内蒙古大学图书馆

arXiv 2022年

作者： Yang, Linyi Li, Jiazheng Dong, Ruihai Zhang, Yue Smyth, Barry Westlake Institute for Advanced Study Westlake University China School of Engineering Westlake University China The Insight Centre for Data Analytics University College Dublin Ireland Department of Computer Science University of Warwick United Kingdom

Financial forecasting has been an important and active area of machine learning research because of the challenges it presents and the potential rewards that even minor improvements in prediction accuracy or forecasting may entail. Traditionally, financial forecasting has heavily relied on quantitative indicators and metrics derived from structured financial statements. Earnings conference call data, including text and audio, is an important source of unstructured data that has been used for various prediction tasks using deep earning and related approaches. However, current deep learning-based methods are limited in the way that they deal with numeric data;numbers are typically treated as plain-text tokens without taking advantage of their underlying numeric structure. This paper describes a numeric-oriented hierarchical transformer model (NumHTML) to predict stock returns, and financial risk using multimodal aligned earnings calls data by taking advantage of the different categories of numbers (monetary, temporal, percentages etc.) and their magnitude. We present the results of a comprehensive evaluation of NumHTML against several state-of-the-art baselines using a real-world publicly available dataset. The results indicate that NumHTML significantly outperforms the current state-of-the-art across a variety of evaluation metrics and that it has the potential to offer significant financial gains in a practical trading context. © 2022, CC BY-NC-ND.

关键词： Investments

来源：评论

学校读者我要写书评

暂无评论

Electric Vehicle Charging Demand Prediction using Multiresolution Sinusoidal Neural Network Optimized with Addax Optimization

Electric Vehicle Charging Demand Prediction using Multiresol...

引用

Trends in Material science and Inventive Materials (ICTMIM), International Conference on

作者： Rajesh Munirathnam Rohit P. Jadhav Nilesh M. Mahajan Amit Barve M. E. Shashi Kumar Natrayan L Data & Analytics Division TechnoGen Inc Hillsborough New Jersey USA Department of Robotics and Automation D.Y. Patil College of Engineering Akurdi Pune Maharashtra Department of Computer Science and Engineering Parul Institute of Technology Faculty of Engineering and Technology Parul University Limda Gujarat Department of Mechanical Engineering Amrita School of Engineering Amrita Vishwa Vidyapeetham Bengaluru Karnataka Department of Mechanical Engineering Saveetha School of Engineering SIMATS Chennai Tamil Nadu

ISBN: (数字)9798331501488

ISBN: (纸本)9798331501495

Electric Vehicle (EV) charging demand prediction, while essential for optimizing charging infrastructure and energy management, faces challenges such as data inaccuracies and uncertainties in user behavior patterns. These issues define inaccurate demand forecasts which cause the wrong placement of charging stations and distribution of energy. Also, as the pattern of the EV charging is not static may change due to many factors such as climate, time of day, price preference among others, the prediction models may not handle the dynamism and hence lower the reliability of the forecast results. To overcome these drawbacks, this manuscript proposes an efficient approach for EV charging demand prediction. The data is gathered from a dataset on EV charging. The data is then sent to pre-processing. Using the Maximum Correntropy Quaternion Kalman Filter (MCQKF), the pre-processing section eliminates missing values and normalizes the input. To forecast EV charging demand, the Multiresolution Sinusoidal Neural Network (MSNN) receives the results of the pre-processing data. MSNN’s weight parameter is optimized using Addax Optimization (AO). The proposed MSNN-AO is utilized within the MATLAB platform. The proposed MSNN-AO technique is compared with the existing techniques such as Long Short-Term Memory Neural Network (LSTMNN), Heterogeneous Spatial-Temporal Graph Convolutional Network (HSTGNN) and Artificial Neural Networks (ANN), respectively. The MSNN-AO method achieves an accuracy of 97%, precision of 95%, and a Root Mean Square Error (RMSE) of 2.2%, demonstrating its superior performance in predicting EV charging demand. This highlights the proposed method’s effectiveness in minimizing prediction errors, reducing RMSE by 22.36%, and improving precision by 14.89% compared to existing methods. The MSNN-AO method’s higher accuracy and precision, coupled with its robust performance, make it a reliable and efficient solution for EV charging demand forecasting.

关键词： Accuracy Uncertainty Quaternions Energy resolution Electric vehicle charging Reliability Kalman filters Spatial resolution Root mean square Optimization

来源：评论

学校读者我要写书评

暂无评论

Paddy Diseases Multi-Class Classification using CNN Variants

Paddy Diseases Multi-Class Classification using CNN Variants

引用

Information Technology (ACIT)

作者： Danial Izzat Anwar Bin Abdul Aziz Marina Yusoff Nurain Ibrahim Raed Alazaidah College of Computing Informatics and Mathematics Universiti Teknologi MARA Shah Alam Malaysia Institute for Big Data Analytics and Artificial Intelligence (IBDAAI) College of Computing Informatics and Mathematics Universiti Teknologi MARA Shah Alam Malaysia Department of Computer Science Faculty of Information Technology Zarqa University Zarqa Jordan

Agriculture is critical to ensuring and securing food supply for human consumption. Paddy crops are an agricultural practice that produces one of the highest food yields in agriculture. Paddy diseases must now be detected and diagnosed as soon as possible rather than later. Farmers currently use the naked eye to observe suspected infected paddies. The suspected infected paddy will then be sampled and sent to the laboratory for further examination. These methods are highly inefficient, time-consuming, and error prone. Most paddy farmers lack the knowledge to make an informed and accurate diagnosis of which paddies are infected by which diseases. This paper explores using deep learning algorithms to detect and classify various paddy diseases automatically. This method will take an image of paddy leaves and classify it based on the disease category indicated by the idea. The PaddyDoctor dataset from the Kaggle website was used to train and test the deep-learning models. Convolutional Neural Network (CNN) and Transfer Learning models, including MobileNet, MobileNetv2, and InceptionV3, have been constructed. The results of an experiment involving different dataset split ratios, learning rates, and batch sizes. CNN with MobileNetV2 outperformed CNN with MobileNetV1 and CNN with InceptionV3 by 98.05 percent, 80.08 percent, and 83.98 percent, respectively.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Uncertainty Estimation for Twitter Inference

Uncertainty Estimation for Twitter Inference

引用

International Conference on Computational science and Computational Intelligence (CSCI)

作者： Meng Hsiu Tsai Nicole Marie Ely Yingfeng Wang Department of Computer Science and Engineering University of Tennessee at Chattanooga Chattanooga United States Department of Data Science and Business Analytics Florida Polytechnic University Lakeland United States

ISBN: (纸本)9781665458429

Twitter provides a platform for users to express their opinions in the form of Twitter messages called tweets. Analyzing tweets from a specific city or demographical group requires geographical or demographical information. Unfortunately, most Twitter users do not provide these details in their profiles. To overcome this challenge, tools have been developed for inferring users' geographical and demographical information from Twitter data. Using inference results is risky due to the lack of uncertainty estimation of these results. Here, we present a framework to estimate uncertainties of Twitter inference results. The effectiveness of this framework is verified in experiments.

关键词： Uncertainty Social networking (online) Scientific computing Blogs Urban areas Estimation Computational intelligence

来源：评论

学校读者我要写书评

暂无评论

Relative magnetic field and slipping effect on Casson dusty fluid of two phase fluctuating flow over inclined parallel plate

引用

South African Journal of Chemical Engineering 2023年 44卷 135-146页

作者： khan, Dolat Kumam, Poom Kumam, Wiyada Suttiarporn, Panawan Rehman, Ali Fixed Point Research Laboratory Fixed Point Theory and Applications Research Group Center of Excellence in Theoretical and Computational Science (TaCS-CoE) Faculty of Science King Mongkut's University of Technology Thonburi (KMUTT) 126 Pracha Uthit Rd. Bang Mod Bangkok Thung Khru 10140 Thailand Center of Excellence in Theoretical and Computational Science (TaCS-CoE) Faculty of Science King Mongkut's University of Technology Thonburi (KMUTT) 126 Pracha Uthit Rd. Bang Mod Bangkok Thung Khru 10140 Thailand Applied Mathematics for Science and Engineering Research Unit (AMSERU) Program in Applied Statistics Department of Mathematics and Computer Science Faculty of Science and Technology Rajamangala University of Technology Thanyaburi (RMUTT) Pathum Thani 12110 Thailand Faculty of Science Energy and Environment King Mongkut's University of Technology North Bangkok Rayong Campus Rayong 21120 Thailand Special Interest Group for Modelling and Data Analytics Faculty of Ocean Engineering Technology and Informatics Universiti Malaysia Terengganu Terengganu Kuala Nerus 21030 Malaysia

Due to its significant applications in magnetic devices for cell separation, magnetic drugs for cancer tumor treatment, blood flow adjustment during surgery, magnetic endoscopy, and fluid pumping in industrial and engineering processes, magnetohydrodynamics (MHD) has a wide range of applications. Therefore, the aim of this study to investigate the Relative Magnetic Field and slipping effect on Casson dusty fluid of two-phase fluctuating flow over inclined parallel plate. More exactly, the relative magnetic phenomena are fixed relatively to the plate (MFFRP) or fixed relatively to the fluid (MFFRF). The mathematical modeling was established as the set of partial differential equations for dusty Casson fluid. Buckingham's pi theorem is used to find out the dimensionless variables, to make our system dimensionless. The solution in perturb form is to find out by incorporating Poincare–Lighthill perturbation technique (PLPT). To know in-depth different parameters the graphical results for both velocities are plotted with the help of software Mathcad- 15. It is clearly detected that the relative magnetic field performs a vital part in the fluid as well as particle motion. Moreover, the relative magnetic field play a vital role in the decreasing of skin friction of both plates. © 2023

关键词： Brinkman fluid Dust particles Fluctuating vertical plate Inclined relative magnetic field PLPT

来源：评论

学校读者我要写书评

暂无评论

Intrusion Detection and Prevention at the End-User Level in Smart Grid Cyber-Physical Systems

Intrusion Detection and Prevention at the End-User Level in ...

引用

Innovations in High Speed Communication and Signal Processing (IHCSP), International Conference on

作者： N. V. Balaji Sachin Mittal K.K. Senthilkumar Gourav Sood Manjunatha R D.S. Wankhede Department of Computer Science Karpagam Academy of Higher Education Coimbatore India Centre of Research Impact and Outcome Chitkara University Rajpura Punjab India Department of ECE Prince Shri Venkateshwara Padmavathy Engineering College Chennai India Chitkara Centre for Research and Development Chitkara University Himachal Pradesh India School of Sciences Data Analytics and Mathematical Sciences JAIN Deemed to be University Bangalore Karnataka India Computer Engineering Vishwakarma Institute of Technology Pune India

ISBN: (数字)9798350368949

ISBN: (纸本)9798350368956

In order to keep the electric power system running smoothly and reliably, it is constantly monitored and controlled by an intelligent cyber layer that consists of data processing algorithms and a wide network of sensors. Cyberattacks on this data processing and collecting system might halt operations and have catastrophic physical consequences like system breakdowns. A common kind of attack is the FDIA. To avoid detection by the state estimator's inherent anomaly detection capabilities, an attacker tries to trick the grid's underlying control system into producing disturbances. On top of that, there are major issues with customer-end security. For instance, criminals might intercept, alter, or replay wireless transmissions of power consumption signals sent by consumers to their utility provider. Consequently, smart grid security and privacy are major apprehensions. In order to prevent cyberattacks on the vital infrastructures of the electrical system, this thesis helps. New detection systems for the generating and transmission side, as well as the end-user customer side infrastructure, are the primary focus of the study. To protect the generating and transmission sides from cyber-attack, we provide a system based on intelligent sensor weights and an optimization strategy based on sophisticated grey wolves. In addition, to safeguard end-user customer data from cyberattacks, we provide a gaussian mixture model–based privacy protection strategy and an artificial neural structure–based intelligent loop detection method.

关键词： Wireless communication Privacy Wireless sensor networks Technological innovation data processing Smart meters Sensor systems Smart grids Security Intelligent sensors

来源：评论

学校读者我要写书评

暂无评论

$\mathop {\mathtt {HAM}}$HAM: Hybrid Associations Models for Sequential Recommendation

引用

IEEE Transactions on Knowledge and data Engineering 2021年第10期34卷 4838-4853页

作者： Bo Peng Zhiyun Ren Srinivasan Parthasarathy Xia Ning Department of Computer Science and Engineering The Ohio State University Columbus OH USA Department of Biomedical Informatics The Ohio State University Columbus OH USA Department of Biomedical Informatics Department of Computer Science and Engineering Translational Data Analytics Institute The Ohio State University Columbus OH USA

Sequential recommendation aims to identify and recommend the next few items for a user that the user is most likely to purchase/review, given the user's purchase/rating trajectories. It becomes an effective tool to help users select favorite items from a variety of options. In this manuscript, we developed hybrid associations models ( $\mathop {\mathtt {HAM}}\limits$ ) to generate sequential recommendations using three factors: 1) users’ long-term preferences, 2) sequential, high-order and low-order association patterns in the users’ most recent purchases/ratings, and 3) synergies among those items. $\mathop {\mathtt {HAM}}\limits$ uses simplistic pooling to represent a set of items in the associations, and element-wise product to represent item synergies of arbitrary orders. We compared $\mathop {\mathtt {HAM}}\limits$ models with the most recent, state-of-the-art methods on six public benchmark datasets in three different experimental settings. Our experimental results demonstrate that $\mathop {\mathtt {HAM}}\limits$ models significantly outperform the state of the art in all the experimental settings. with an improvement as much as 46.6 percent. In addition, our run-time performance comparison in testing demonstrates that $\mathop {\mathtt {HAM}}\limits$ models are much more efficient than the state-of-the-art methods. and are able to achieve significant speedup as much as 139.7 folds.

关键词： Adaptation models Benchmark testing Trajectory Tools Recurrent neural networks Markov processes Image color analysis

来源：评论

学校读者我要写书评

暂无评论

Examining the Challenges in Implementing AIDriven Systems for Automated data science

Examining the Challenges in Implementing AIDriven Systems fo...

引用

International Conference on Computing and Networking Technology (ICCNT)

作者： T Vanaja Priya M. Shelke Sandeep Gautam Rakesh Kumar Yadav Santosh Chowhan G. Ravivarman Department of Information Technology Prince Shri Venkateshwara Padmavathy Engineering College Chennai Department of Information Technology Vishwakarma Institute of Technology Pune INDIA Department of Computer Science & Engineering Vivekananda Global University Jaipur India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Department of Data Analytics and Mathematical Sciences Jain (Deemed to be University) Bangalore Karnataka India Department of Electrical and Electronics Engineering Karpagam Academy of Higher Education Coimbatore

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

- this paper provides an overview brand new the contemporary in employing synthetic Intelligence (AI) strategies to automate statistics technological know-how procedures. It highlights state-of-the-art the demanding situations related to the mixing of those structures with current infrastructure and implementations, as well as ability solutions. It also outlines the significance trendy thinking about a broad range latest AI-driven system, which include supervised and unsupervised studying, Deep Neural network (DNN) architectures, version-based reinforcement present day, and lively state-of-the-art. The paper in addition discusses how every of these systems can be used to automate sure components modern the records technology system, and the way their use can bring about stronger accuracy and effectiveness brand new records science operations. In the end, it evaluates latest the important thing concerns which want to be taken into account while defining and implementing an AIpushed device for computerized information technological know-how.

关键词： Privacy Leadership Accuracy Artificial neural networks computer architecture data science Safety Information technology Artificial intelligence Investment

来源：评论

学校读者我要写书评

暂无评论

Joint Convexity of Error Probability in Blocklength and Transmit Power in the Finite Blocklength Regime

arXiv

引用

arXiv 2022年

作者： Zhu, Yao Hu, Yulin Yuan, Xiaopeng Gursoy, M. Cenk Poor, H. Vincent Schmeink, Anke Information Theory and Data Analytics RWTH Aachen University Aachen52074 Germany Department of Electrical Engineering and Computer Science Syracuse University NY13210 United States Department of Electrical Engineering Princeton University PrincetonNJ08544 United States

To support ultra-reliable and low-latency services for mission-critical applications, transmissions are usually carried via short blocklength codes, i.e., in the so-called finite blocklength (FBL) regime. Different from the infinite blocklength regime where transmissions are assumed to be arbitrarily reliable at the Shannon’s capacity, the reliability and capacity performances of an FBL transmission are impacted by the coding blocklength. The relationship among reliability, coding rate, blocklength and channel quality has recently been characterized in the literature, considering the FBL performance model. In this paper, we follow this model, and prove the joint convexity of the FBL error probability with respect to blocklength and transmit power within a region of interest, as a key enabler for designing systems to achieve globally optimal performance levels. Moreover, we apply the joint convexity to general use cases and efficiently solve the joint optimization problem in the setting with multiple users. We also extend the applicability of the proposed approach by proving that the joint convexity still holds in fading channels, as well as in relaying networks. Via simulations, we validate our analytical results and demonstrate the advantage of leveraging the joint convexity compared to other commonly-applied approaches. Copyright © 2022, The Authors. All rights reserved.

关键词： Fading channels

来源：评论

学校读者我要写书评

暂无评论

SciQAG: A Framework for Auto-Generated science Question Answering dataset with Fine-grained Evaluation

arXiv

引用

arXiv 2024年

作者： Wan, Yuwei Liu, Yixuan Ajith, Aswathy Grazian, Clara Hoex, Bram Zhang, Wenjie Kit, Chunyu Xie, Tong Foster, Ian GreenDynamics Pty. Ltd KensingtonNSW Australia Department of Linguistics and Translation City University of Hong Kong Hong Kong Department of Computer Science University of Chicago ChicagoIL United States School of Mathematics and Statistics University of Sydney CamperdownNSW Australia DARE ARC Training Centre in Data Analytics for Resources and Environments Australia School of Photovoltaic and Renewable Energy Engineering University of New South Wales KensingtonNSW Australia School of Computer Science and Engineering University of New South Wales KensingtonNSW Australia

We introduce SciQAG, a novel framework for automatically generating high-quality science question-answer pairs from a large corpus of scientific literature based on large language models (LLMs). SciQAG consists of a QA generator and a QA evaluator, which work together to extract diverse and research-level questions and answers from scientific papers. Utilizing this framework, we construct a large-scale, high-quality, open-ended science QA dataset containing 188,042 QA pairs extracted from 22,743 scientific papers across 24 scientific domains. We also introduce SciQAG-24D, a new benchmark task designed to evaluate the science question-answering ability of LLMs. Extensive experiments demonstrate that finetuning LLMs on the SciQAG dataset significantly improves their performance on both open-ended question answering and scientific tasks. To foster research and collaboration, we make the datasets, models, and evaluation codes publicly available, contributing to the advancement of science question answering and developing more interpretable and reasoningcapable AI systems. Copyright © 2024, The Authors. All rights reserved.

关键词： Question answering

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：