检索结果-内蒙古大学图书馆

International Conference on Computing and Networking Technology (ICCNT)

作者： Amrita Ticku Niva Tripathy Pankaj Kumar Mishra Anurag Sinha Shivangi Jadon Aditya Raj Harsh Kulshrestha Vipin Rai Rejuwan Shamim Bharti Vidyapeeth’s College of Engineering India Driems Autonomous Engineering College Cuttack India Department of Electronics and Telecommunication Engg. Rungta College of Engg. & TechnologyR1 Bhilai CG India School of Computing and Information Science IGNOU New Delhi India Dept. of Computer Science and Engineering Galgotias University Noida India Department of Computer Science and Engineering Galgotias University Noida India Dept. of Computer Science and Engineering With Data Science Maharishi University of Information Technology Noida India

Stock prediction has always been a difficult task, and numerous strategies for predicting stock market behavior have been offered. Researchers have recently begun to investigate the use of sentimental analysis and deep learning models such as Long Short-Term Memory (LSTM) to predict stock prices. We offer a stock prediction model that integrates sentimental analysis and LSTM in this research. The model considers the mood of stock market news items and use LSTM to forecast future stock prices. The results of our suggested model's evaluation on the S& amp; P 500 dataset reveal that it outperforms traditional stock prediction approaches.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Comparative Analysis of Time Series Forecasting Models for Weather Prediction: ARIMA vs. STL

Comparative Analysis of Time Series Forecasting Models for W...

引用

International Conference on Computing Communication Control and Automation (ICCUBEA)

作者： Aahash Kamble Sanika Belsare Purva Chaudhari Palash Gourshettiwar Swapnil Gundewar Department of Artificial Intellegence and Data Science Datta Meghe Institute of Higher Education and Research Wardha India Department of Artificial Intellegence and Data Science Datta Meghe Institute of Higher Education and Research(DU) Wardha Maharashtra Department of Computer Science and Medical Engineering Datta Meghe Institute of Higher Education and Research Wardha India Department of Artificial Intellegence and Machine Learning Datta Meghe Institute of Higher Education and Research Wardha India

ISBN: (数字)9798350391770

ISBN: (纸本)9798350391787

This research compares two well-known models for predicting weather: ARIMA (Auto Regressive Integrated Moving Average) and STL (Seasonal-Trend Decomposition using Loess). Accurate weather forecasts are crucial for businesses and daily life, making strong forecasting methods necessary. ARIMA finds linear patterns and time dependencies in data, while STL splits time series into seasonal, trend, and random parts. Using real-world weather data, we train and test these models. We compare their performances using MSE (Mean Squared Error) and R2 (R-squared) metrics. The research demonstrates that the STL model performs better than the ARIMA model at accurately predicting weather patterns and fitting to the data. This superior performance makes the STL model a more dependable choice for weather forecasting. This study enhances techniques for weather prediction and offers valuable information to those making decisions in weather-sensitive businesses.

关键词： Visualization Accuracy Time series analysis Weather forecasting Predictive models Prediction algorithms Market research data models Forecasting Business

来源：评论

学校读者我要写书评

暂无评论

Hierarchical Recording Architecture for Three-Dimensional Magnetic Recording

arXiv

引用

arXiv 2025年

作者： Jian, Yugen Luo, Ke Chen, Jincai Fong, Xuanyao Wuhan National Laboratory for Optoelectronics Huazhong University of Science and Technology Wuhan430074 China Department of Electrical and Computer Engineering National University of Singapore Singapore117583 Singapore School of Computer of Science and Technology Huazhong University of Science and Technology Wuhan430074 China Key Laboratory of Information Storage System Engineering Research Center of Data Storage Systems and Technology Ministry of Education of China Wuhan430074 China

Three-dimensional magnetic recording (3DMR) is a highly promising approach to achieving ultra-large data storage capacity in hard disk drives. One of the greatest challenges for 3DMR lies in performing sequential and correct writing of bits into the multi-layer recording medium. In this work, we have proposed a hierarchical recording architecture based on layered heat-assisted writing with a multi-head array. The feasibility of the architecture is validated in a dual-layer 3DMR system with FePt-based thin films via micromagnetic simulation. Our results reveal the magnetization reversal mechanism of the grains, ultimately attaining appreciable switching probability and medium signal-to-noise ratio (SNR) for each layer. In particular, an optimal head-to-head distance is identified as the one that maximizes the medium SNR. Optimizing the system’s noise resistance will improve the overall SNR and allow for a smaller optimal head-to-head distance, which can pave the way for scaling 3DMR to more recording layers. Copyright © 2025, The Authors. All rights reserved.

关键词： Hard disk storage

来源：评论

学校读者我要写书评

暂无评论

Maximizing Crop Yields and Water Efficiency through Tailored Irrigation System Design and Crop Selection

Maximizing Crop Yields and Water Efficiency through Tailored...

引用

Information Technologies and Communications (GCITC), Global Conference on

作者： Prajakta Musale Balasaheb Jadhav Suvarna A Bahir Kaushalya Thopate Dipali Anarase Krishna Jaiswal Department of Engineering Sciences and Humanities Vishwakarma Institute of Technology Pune India Department of Artificial Intelligence and Data Science Vishwakarma Institute of Technology Pune India Department of Computer Engineering Trinity Academy of Engineering Department of Computer Engineering Vishwakarma Institute of Technology Pune India Wipro Technology

Today various water-pump operating systems are available in the market, which allows the user/farmer to operate his water-pump from a distant location using mobile app or on a phone call to the system which is integrated with the water-pump. While studying the existing system we came across a problem that, existing systems does not provide any solution for optimizing the usage of water and power. Optimizing the use of power and water in farming has become a real-time problem these days. To overcome this problem, we have proposed an IOT based system "Advanced Irrigation System". This system provides automation for operating the water pump in the farm by automatically turning ON and OFF the water-pump depending on the type of the crop and its moisture requirements. Also, proposed system provides farmer interface via a mobile application in his regional language, where he can check the moisture, humidity and temperature of his farm. Along with this proposed system has a Google Assistant integrated, using which famer can operate the water-pump remotely over a voice command.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Captions Speak Louder than Images (CASLIE): Generalizing Foundation Models for E-commerce from High-quality Multimodal Instruction data

arXiv

引用

arXiv 2024年

作者： Ling, Xinyi Peng, Bo Du, Hanwen Zhu, Zhihui Ning, Xia Department of Computer Science and Engineering The Ohio State University United States Translational Data Analytics Institute The Ohio State University United States Department of Biomedical Informatics The Ohio State University United States

Leveraging multimodal data to drive breakthroughs in e-commerce applications through Multimodal Foundation Models (MFMs) is gaining increasing attention from the research community. However, there are significant challenges that hinder the optimal use of multimodal e-commerce data by foundation models: (1) the scarcity of large-scale, high-quality multimodal benchmark datasets;and (2) the lack of effective multimodal information integration methods. To address these challenges, in this paper, we introduce MMECInstruct, the first-ever, large-scale, and high-quality multimodal instruction dataset for e-commerce. We also develop CASLIE, a simple, lightweight, yet effective framework for integrating multimodal information for e-commerce. Leveraging MMECInstruct, we fine-tune a series of e-commerce MFMs within CASLIE, denoted as CASLIE models. Our comprehensive evaluation demonstrates that CASLIE models substantially outperform 5 categories of advanced baseline models in the in-domain evaluation. Moreover, CASLIE models show strong generalizability to out-of-domain settings. MMECInstruct and CASLIE models are publicly accessible through https://***/CASLIE/. © 2024, CC BY.

关键词： Large datasets

来源：评论

学校读者我要写书评

暂无评论

Hyperparameter Tuning and Model Evaluation in Causal Effect Estimation

arXiv

引用

arXiv 2023年

作者： Machlanski, Damian Samothrakis, Spyridon Clarke, Paul Department of Computer Science and Electronic Engineering University of Essex United Kingdom Institute for Analytics and Data Science University of Essex United Kingdom Institute for Social and Economic Research University of Essex United Kingdom

The performance of most causal effect estimators relies on accurate predictions of high-dimensional non-linear functions of the observed data. The remarkable flexibility of modern Machine Learning (ML) methods is perfectly suited to this task. However, data-driven hyperparameter tuning of ML methods requires effective model evaluation to avoid large errors in causal estimates, a task made more challenging because causal inference involves unavailable counterfactuals. Multiple performance-validation metrics have recently been proposed such that practitioners now not only have to make complex decisions about which causal estimators, ML learners and hyperparameters to choose, but also about which evaluation metric to use. This paper, motivated by unclear recommendations, investigates the interplay between the four different aspects of model evaluation for causal effect estimation. We develop a comprehensive experimental setup that involves many commonly used causal estimators, ML methods and evaluation approaches and apply it to four well-known causal inference benchmark datasets. Our results suggest that optimal hyperparameter tuning of ML learners is enough to reach state-of-the-art performance in effect estimation, regardless of estimators and learners. We conclude that most causal estimators are roughly equivalent in performance if tuned thoroughly enough. We also find hyperparameter tuning and model evaluation are much more important than causal estimators and ML methods. Finally, from the significant gap we find in estimation performance of popular evaluation metrics compared with optimal model selection choices, we call for more research into causal model evaluation to unlock the optimum performance not currently being delivered even by state-of-the-art procedures. © 2023, CC BY.

关键词： Petroleum reservoir evaluation

来源：评论

学校读者我要写书评

暂无评论

Evaluation of Deep Training and Adapting Algorithms in Medical with De-Noising EEG 3

Evaluation of Deep Training and Adapting Algorithms in Medic...

引用

3rd International Conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2023

作者： Abdulwahid, Ali Hadi Karetla, Girija Rani Ganesh, K.V.B. Kumar, A Suresh Radha, K. Shivakumar, K. Southern Technical University Engineering Technical College Control and Automation Engineering Department Basra Iraq Western Sydney University School of Computer Data and Mathematical Sciences Australia Koneru Lakshmaiah Education Foundation KL Business School Guntur Andhra Pradesh India Graphic Era Deemed to Be University Department of CSE India St. Martin's Engineering College Dept. of IT Secunderabad Telangana India SR University School of Computer Science and Artificial Intelligence Warangal Telangana India

ISBN: (纸本)9798350399264

An electroencephalogram that was captured with electrodes placed can easily get contaminated with a variety of artifacts. Here is a comparative of various electroencephalogram (EEG) de-noising techniques. Three alternative techniques are used to eliminate five different kinds of noise, and also the outcomes are evaluated. These three methods are just the Least Mean Square (LMS) technique, the Connected Neural Network, and also the Recursive Least-square (RLS) adaptable algorithm (FCNN). Period charts of the actual EEG signal, the noise EEG signal, and thus the predicted EEG signal are used to display the results. RRMSE and signal-to-noise ratio were utilized to compare the effectiveness of the 3 de-noising strategies in this study. Investigating the model parameters in this case, we discover the FCNN forecasts a better outcome compared to the other two methods. © 2023 IEEE.

关键词： Electroencephalography

来源：评论

学校读者我要写书评

暂无评论

Evaluating AI-Based Deep Learning Models for Enhancing English Reading Comprehension Skills in Students

Evaluating AI-Based Deep Learning Models for Enhancing Engli...

引用

International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)

作者： Divya Nimma V. Arunadevi Mehul Manu V. Selvi Prema S PhD in Computational Science University of Southern Mississippi Data Analyst in UMMC USA Department of Science and Humanities Dr. Mahalingam College of Engineering and Technology Pollachi India Allied Science (Physics) Graphic Era Hill University Bhimtal Graphic Era Deemed to be University Dehradun Uttarakhand India Department of Computer Science Mother Teresa Women's University Kodaikanal India Department of English Panimalar Engineering College Chennai India

ISBN: (数字)9798350376425

ISBN: (纸本)9798350376432

In the rapidly evolving field of natural language processing (NLP), enhancing model performance in understanding and generating human language has become increasingly critical. As the need for better language models increases, enhancing the current systems’ shortcomings becomes crucial. There are difficulties in existing models precisely due to the fundamental problem of striking a balance between the level of detail achievable and the amount of computational resources that a model requires for accurate estimation in practical settings. Current models, present several problems including but not limited to overfitting, average performance on out-of-sample data and less applicability for subtle linguistic features. Such obstacles limit their capability to execute in real conditions, where high degree of accuracy and precision is paramount. To overcome these challenges, this study introduces a novel framework that employs the BERT model which was developed to capture bidirectional context and achieve greater depth of the semantic relations between words. The proposed approach is designed to improve the model’s effectiveness to a considerable extent through the use of mechanisms such as attention as well as transformer architecture included in BERT. The proposed BERT model demonstrates exceptional results, achieving an accuracy of 99.10% and a precision of 98.30%. These metrics reflect the model's superior ability to understand and generate accurate language representations, surpassing existing models in both effectiveness and efficiency. This advancement underscores the potential of BERT to address critical challenges in NLP, offering a promising solution for applications requiring high precision and robust language comprehension.

关键词： Measurement Accuracy Computational modeling Semantics Estimation Bidirectional control Linguistics Transformers Encoding Natural language processing

来源：评论

学校读者我要写书评

暂无评论

AVWAC based Gradual transitions detection in presence of high Object-Camera Motions and Uneven Illuminations 6

AVWAC based Gradual transitions detection in presence of hig...

引用

6th International Conference on Information Systems and computer Networks, ISCON 2023

作者： Chandwani, Kamal S. Namdeo, Varsha Malode, Sanjay Agarkar, Poonam T. Giradkar, Narendra P. Patil, Prashant R. Sarvepalli Radhakrishnan University Deptt. of Computer Science and Engg Madhya Pradesh Bhopal India K. D. K. College of Engineering Deptt. of Artificial Intelligence and Data Science Maharashtra Nagpur India Yeshwantrao Chavan College of Engg Deptt. of Electronics and Telecommunication Engg Maharashtra Nagpur India Smt. Radhikatai Pandav College of Engineering Deptt. of Electronics and Telecommunication Engineering Maharashtra Nagpur India Smt. Radhikatai Pandav College of Engineering Department of Management Studies Maharashtra Nagpur India

ISBN: (纸本)9798350346961

Advanced Multimedia Technology had provided a new lease of life to current videos and made life amazing in the field of entertainment. On the other hand, special lightening effects, rapid object-camera motions and scene blending have posed significant challenges in content based video retrieval. These factors have made special effects in close consistency with the gradual transitions to fool even a robust system. We propose an efficient and accurate gradual detection system to detect fade out and fade in transitions using a simple approach. The average value of wavelet approximation component (AVWAC) at level 5 for all the scene frames is used to analyze the presence of fade out and fade in. The position of global maxima and global minima points using AVWAC in successive frames are used to analyze and find the origin and end frames of respective gradual transitions. A fade out is detected when a global maxima is followed by a global minima whereas fade in are result of occurrence of global minima followed by global maxima. A common threshold efficiently determines the occurance of gradual transition. When the minimum value of AVWAC exceeds the threshold the peakvalley algorithm finds the occurance of the gradual transition. The technique is computationally simple and faster since the existence of either of the gradual transitions is verified using an experimentally found threshold before the analysis. The method is effective in high illuminations, rapid object and camera motion videos and fast color changing scenes when tested with real videos.. © 2023 IEEE.

关键词： Special effects

来源：评论

学校读者我要写书评

暂无评论

A Novel Design of Secured BlockChain Assisted Land Registration Mechanism by using Enhanced Verification Principles

A Novel Design of Secured BlockChain Assisted Land Registrat...

引用

2023 IEEE International Conference on Research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication engineering, RMKMATE 2023

作者： Prabu, R.T. Hemalatha, R. Madhini, M. Diwakaran, S. Senthilkumar, V. Thiyaneswaran, B. Dept. Electronics and Communication Saveetha School of Engineering Chennai India Dept. Computer Science Engineering St.Joseph College of Engineering Chennai India Dept. Electronics and Communication S.A.Engineering College Chennai India Dept. Master of Comp. Applns Kalasalingam Academy of Res. & Edn. Srivilliputhur India Department of Data Science VelTech Multitech Engg. College Chennai India Dept. Electronics & Communication Sona College of Technology Salem India

ISBN: (纸本)9798350305708

In the contemporary world, safeguarding data has assumed paramount importance, prompting numerous industrial sectors to fortify their defenses against potential hackers. In this landscape, blockchain has emerged as a cutting-edge technology that empowers peers to securely exchange various assets, including currency, financial documents, and land properties. Operating as an open-source public network, blockchain eliminates the need for a central authority. Instead, it facilitates peer-to-peer interactions, ensuring that transactions, value transfers, and data sharing are authenticated by all interconnected nodes within the network. The conventional process of land registration, characterized by its sluggishness and complexity, involves multiple intermediaries and is susceptible to instances of fraudulent and counterfeit land transfers. To address these challenges, the paper introduces a conceptual framework for blockchain-based land registry systems. By analyzing existing frameworks and scrutinizing the robust strategies and concepts they employ, the paper establishes connections that culminate in the creation of this conceptual framework. The proposed solution in the paper revolves around the secure transfer of land ownership through blockchain technology. This solution circumvents the need for intermediaries, allowing buyers and sellers to conduct land ownership transactions seamlessly using the Ethereum network. By leveraging the power of blockchain, this solution aims to mitigate the vulnerabilities and inefficiencies inherent in traditional land registration processes, ushering in a more secure, efficient, and transparent approach to managing land ownership transactions. © 2023 IEEE.

关键词： Blockchain Land Registration land registry systems

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：