检索结果-内蒙古大学图书馆

4th International Conference on Pattern Recognition and Artificial Intelligence, ICPRAI 2024

作者： Sim, Victor Ho, Shirley Winchester College Winchester United Kingdom Department of Physics And Center for Data Science New York University New York United States Centre for Computational Astrophysics Flatiron Institute New York United States Department of Physics and Center for Data Science New York University New YorkNY United States Department of Astrophysical Sciences Princeton University Princeton United States

ISBN: (纸本)9789819787043

To improve the application of existing natural language advancements to 3D representations, we present a novel approach for the efficient construction of 3D point clouds from image and text inputs. Unlike existing methods that focus on generating plausible views, our method extends functionality to yield accurate 3D representations much more quickly. Leveraging a CLIP-based segmentation model, our method can reconstruct the 3D representation of given data accurately, even with a limited number of input images. Furthermore, in many disciplines, reconstructing point clouds from image and text inputs will require robustness against perturbations of image and text inputs. We achieve this by encoding geometric and semantic confidence intervals for our outputs. Notably, our inclusion of semantic confidence values proves effective in mitigating false positives (i.e., identifying objects irrelevant to the text prompt), a prevalent problem among adjacent works. We believe that our work will improve the robustness of predictions and reconstructions in fields such as robotics, architectural imaging, and medical imaging. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Medical robotics

来源：评论

学校读者我要写书评

暂无评论

Predict+Optimize for Packing and Covering LPs with Unknown Parameters in Constraints 37

Predict+Optimize for Packing and Covering LPs with Unknown P...

引用

37th AAAI Conference on Artificial Intelligence, AAAI 2023

作者： Hu, Xinyi Lee, Jasper C.H. Lee, Jimmy H.M. Department of Computer Science and Engineering The Chinese University of Hong Kong N.T. Shatin Hong Kong Department of Computer Sciences Institute for Foundations of Data Science University of Wisconsin–Madison WI United States

ISBN: (纸本)9781577358800

Predict+Optimize is a recently proposed framework which combines machine learning and constrained optimization, tackling optimization problems that contain parameters that are unknown at solving time. The goal is to predict the unknown parameters and use the estimates to solve for an estimated optimal solution to the optimization problem. However, all prior works have focused on the case where unknown parameters appear only in the optimization objective and not the constraints, for the simple reason that if the constraints were not known exactly, the estimated optimal solution might not even be feasible under the true parameters. The contributions of this paper are two-fold. First, we propose a novel and practically relevant framework for the Predict+Optimize setting, but with unknown parameters in both the objective and the constraints. We introduce the notion of a correction function, and an additional penalty term in the loss function, modelling practical scenarios where an estimated optimal solution can be modified into a feasible solution after the true parameters are revealed, but at an additional cost. Second, we propose a corresponding algorithmic approach for our framework, which handles all packing and covering linear programs. Our approach is inspired by the prior work of Mandi and Guns, though with crucial modifications and re-derivations for our very different setting. Experimentation demonstrates the superior empirical performance of our method over classical approaches. Copyright © 2023, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Constrained optimization

来源：评论

学校读者我要写书评

暂无评论

An Improving Genetic Algorithm with Local Search for Solving Capacitated Vehicle Routing Problem 9

An Improving Genetic Algorithm with Local Search for Solving...

引用

9th IEEE International Conference on computational Intelligence and Applications, ICCIA 2024

作者： Poonpanit, Maythar Punkong, Narong Ratanavilisagul, Chiabwoot Kosolsombat, Somkiat Department of Computer and Information Sciences Bangkok Thailand Digital Technology for Business Faculty of Management Science Kanchanaburi Thailand Data Science and Innovation College of Interdisciplinary Studies Thammasat University Pathum Thani Thailand

ISBN: (纸本)9798350352214

Capacitated Vehicle Routing Problem (CVRP) is the vehicle routing problem in which vehicles with limited space have to drop off items at different places. This item consists of certain amounts, weight or volume, and the vehicles can carry a limited load. Many researchers try to solve the CVRP by the meta-heuristic algorithms. The Genetic Algorithm (GA) is a classical method to solve the CVRP. To enhance performance of GA for solving CVRP, the recently proposed GA combines simulated annealing (SA) or a hybrid genetic and simulated annealing algorithm (HGSA). However, both GA and SA enhance population diversity and lead to over distribution of population. If the distribution is too much population, it prevents convergence of population to search for answers. It affects the search that obtains a bad answer. This paper proposed an Improving Genetic Algorithm with Local Search or GALS. The proposed method uses GA for decreasing or solving trapping into local optimum from searching and improving solution by local search techniques. The proposed method does not interrupt the population convergence, so it obtains a good answer. Experiments conducted on twenty-seven maps dataset have shown that the proposed method achieves encouraging performance compared to a hybrid genetic and simulated annealing algorithm. © 2024 IEEE.

关键词： Genetic algorithms

来源：评论

学校读者我要写书评

暂无评论

Integrating Artificial Intelligence and Multiple Intelligences for Advanced Educational Models

Integrating Artificial Intelligence and Multiple Intelligenc...

引用

2024 International Conference on Electrical, Electronics and Computing Technologies, ICEECT 2024

作者： Palarimath, Suresh Maran, Pyingkodi Venkateswaran, R. Wilfred Blessing, N.R. Shiny, K.V. Renuga, S. University of Technology and Applied Sciences College of Computing and Information Sciences Salalah Oman Kongu Engineering College Department of Computer Applications Erode India University of Technology and Applied Sciences College of Computing and Information Sciences Ibri Oman Bharath Institute of Higher Education and Research Department of Computer Science and Engineering Chennai India Panimalar Engineering College Department of Artificial Intelligence and Data Science Chennai India

ISBN: (纸本)9798350378092

The aim of educational innovation is to foster students' creative and problem-solving skills via the integration of several disciplines, including science, technology, engineering, art, and mathematics. Efficiently identifying and fostering the various abilities of pupils continues to be a crucial obstacle. This research presents a sophisticated educational approach that combines the notion of multiple intelligences with artificial intelligence (AI) technology to tackle this problem. This concept improves the teaching environment by including intelligent auxiliary services for instructors and students via the use of smart speech and picture interaction. Artificial intelligence (AI) integrated into the multiple intelligence's framework enables the ability to observe, analyze, and implement personalized teaching tactics in real-time using machine learning. The suggested AI-assisted education paradigm aims to enhance teacher-student interactions and facilitate personalized instruction. The study provides evidence that this technique successfully facilitates personalized and captivating learning experiences, promoting students' diverse talents and augmenting their creativity and problem-solving capabilities. This approach seeks to fundamentally transform conventional educational techniques by addressing the disparity between existing educational practices and the future requirements of the workforce. © 2024 IEEE.

关键词： Adversarial machine learning

来源：评论

学校读者我要写书评

暂无评论

Hybrid architecture CNN-BLSTM for automatic speech recognition 3

Hybrid architecture CNN-BLSTM for automatic speech recogniti...

引用

3rd International Conference on Artificial Intelligence For Internet of Things, AIIoT 2024

作者： Dar, Muzaffar Ahmad Pushparaj, Jagalingam Vellore Institute of Technology School of Advanced Sciences Department of Mathematics Tamil Nadu Vellore India Vellore Institute of Technology School of Computer Science and Engineering Department of Computational Intelligence Tamil Nadu Vellore India

ISBN: (纸本)9798350372120

With several deep learning approaches, the domain of automatic speech recognition (ASR) has seen notable advancements in recent times. The domains of intelligent human- computer interaction and machine translation greatly benefited from accurate speech recognition. The present study introduces a hybrid architecture amalgamating a convolutional neural network (CNN) and bidirectional long short-term memory (BLSTM) for speech recognition. For aligning speech input sequences with corresponding text output sequences, it uses the connectionist temporal classification (CTC) technique. The experiments were done on the LJ speech dataset, and the results showed with an increased number of training samples, the performance of the speech recognition algorithm tended to be increased but the time taken for training gradually increased over time. Moreover, a trained speech recognition algorithm exhibits a longer training time when the recognition accuracy is lower. In this study, we implemented a hybrid deep learning model CNN-BLSTM, in conjunction with the CTC loss function, which attains a word error rate (WER) of 36.97% on the testing dataset. © 2024 IEEE.

关键词： Speech recognition

来源：评论

学校读者我要写书评

暂无评论

Age Classification through Handwriting Analysis using Convolutional Neural Networks and Pre-Segmented Offline Handwritten Gurumukhi Characters 7

Age Classification through Handwriting Analysis using Convol...

引用

7th International Conference on Image Information Processing, ICIIP 2023

作者： Singla, Chinu Maini, Raman Kumar, Munish Punjabi University Department of Computer Science And Engineering Punjab Patiala India Maharaja Ranjit Singh Punjab Technical University Department of Computational Sciences Punjab Bathinda India

ISBN: (纸本)9798350371406

Handwriting analysis is increasingly gaining prominence across various domains, including forensics, education, and biometrics, owing to its invaluable insights. This article introduces an innovative approach to age classification based on offline handwriting in Gurumukhi script, employing a Convolutional Neural Network (CNN) architecture, specifically the Inception-V3 model. The method utilizes pre-segmented offline Gurumukhi characters, commonly employed in the Punjabi language, as input features. This pre-segmentation process ensures precise and efficient extraction of individual characters from handwriting samples. The CNN model undergoes training on a substantial dataset of annotated Gurumukhi characters, encompassing a diverse range of age groups. The dataset comprises 74,760 Gurumukhi characters contributed by 267 writers, with 137 males and 130 females, each providing 280 characters. By inputting the extracted features into the CNN model, our approach accurately predicts individuals' age groups based on their handwriting samples. Experimental results underscore the efficacy of the proposed method, achieving an impressive accuracy rate of 92.7% in age classification. This high classification accuracy unequivocally validates the potential of our method for age classification in handwriting analysis. It underscores its applicability in forensic investigations and document verification scenarios, highlighting the promising role of handwriting analysis in various fields that necessitate age-related information. © 2023 IEEE.

关键词： Deep neural networks

来源：评论

学校读者我要写书评

暂无评论

A Novel Hybrid Architecture for Superior IoT Threat Detection through Real IoT Environments

引用

computers, Materials & Continua 2024年第11期81卷 2299-2316页

作者： Bassam Mohammad Elzaghmouri Yosef Hasan Fayez Jbara Said Elaiwat Nisreen Innab Ahmed Abdelgader Fadol Osman Mohammed Awad Mohammed Ataelfadiel Farah H.Zawaideh Mouiad Fadeil Alawneh Asef Al-Khateeb Marwan Abu-Zanona Department of Computer Science Faculty of Computer Science and Information TechnologyJerash UniversityJerash26150Jordan Computer Engineering Department College of Engineering&Information Technology Buraydah CollegesBuraydah51418Saudi Arabia Faculty of Architecture and Design Al-Zaytoonah University of JordanAmman11733Jordan Department of Computer Science and Information Systems College of Applied SciencesAlMaarefa UniversityDiriyahRiyadh13713Saudi Arabia Applied College King Faisal UniversityAl-Ahsa31982Saudi Arabia Department of Business Intelligence and Data Analysis Faculty of Financial Sciences and BusinessIrbid National UniversityIrbid21110Jordan Faculty of Information Technology Ajloun National UniversityAjlun26810Jordan Department of Management Information Systems College of Business AdministrationKing Faisal UniversityAl-Ahsa31982Saudi Arabia

As the Internet of Things(IoT)continues to expand,incorporating a vast array of devices into a digital ecosystem also increases the risk of cyber threats,necessitating robust defense *** paper presents an innovative hybrid deep learning architecture that excels at detecting IoT threats in real-world *** proposed model combines Convolutional Neural Networks(CNN),Bidirectional Long Short-Term Memory(BLSTM),Gated Recurrent Units(GRU),and Attention mechanisms into a cohesive *** integrated structure aims to enhance the detection and classification of complex cyber threats while accommodating the operational constraints of diverse IoT *** evaluated our model using the RT-IoT2022 dataset,which includes various devices,standard operations,and simulated *** research’s significance lies in the comprehensive evaluation metrics,including Cohen Kappa and Matthews Correlation Coefficient(MCC),which underscore the model’s reliability and predictive *** model surpassed traditional machine learning algorithms and the state-of-the-art,achieving over 99.6%precision,recall,F1-score,False Positive Rate(FPR),Detection Time,and accuracy,effectively identifying specific threats such as Message Queuing Telemetry Transport(MQTT)Publish,Denial of Service Synchronize network packet crafting tool(DOS SYN Hping),and Network Mapper Operating System Detection(NMAP OS DETECTION).The experimental analysis reveals a significant improvement over existing detection systems,significantly enhancing IoT security *** our experimental analysis,we have demonstrated a remarkable enhancement in comparison to existing detection systems,which significantly strength-ens the security standards of *** model effectively addresses the need for advanced,dependable,and adaptable security solutions,serving as a symbol of the power of deep learning in strengthening IoT ecosystems amidst the constantly evolving cyber threat *** achievemen

关键词： A hybrid deep learning model IoT threat detection real IoT environments cybersecurity attention mechanism

来源：评论

学校读者我要写书评

暂无评论

On the Role of Text Preprocessing in BERT Embedding-based DNNs for Classifying Informal Texts

引用

International Journal of Advanced computer science and Applications 2022年第6期13卷 927-934页

作者： Kurniasih, Aliyah Manik, Lindung Parningotan Department of Computer Science University of Nusa Mandiri Jakarta Indonesia Research Center for Data and Information Sciences National Research and Innovation Agency Cibinong Indonesia

Due to highly unstructured and noisy data, analyzing society reports in written texts is very challenging. Classifying informal text data is still considered a difficult task in natural language processing since the texts could contain abbreviated words, repeating characters, typos, slang, et cetera. Therefore, text preprocessing is commonly performed to remove the noises and make the texts more structured. However, we argued that most tasks of preprocessing are no longer required if suitable word embeddings approach and deep neural network (DNN) architecture are correctly chosen. This study investigated the effects of text preprocessing in fine-tuning a pre-trained Bidirectional Encoder Representations from Transformers (BERT) model using various DNN architectures such as multilayer perceptron (MLP), long short-term memory (LSTM), bidirectional long-short term memory (Bi-LSTM), convolutional neural network (CNN), and gated recurrent unit (GRU). Various experiments were conducted using numerous learning rates and batch sizes. As a result, text preprocessing had insignificant effects on most models such as LSTM, Bi-LSTM, and CNN. Moreover, the combination of BERT embeddings and CNN produced the best classification performance. © 2022. International Journal of Advanced computer science and *** Rights Reserved

关键词： Long short-term memory

来源：评论

学校读者我要写书评

暂无评论

Vision transformer for detecting traffic congestion using image mapping

Advances in Transportation Studies

引用

Advances in Transportation Studies 2025年 66卷 19-38页

作者： Khalfi, A. Guerroumi, M. Haid, T. Laradji, N. Faculty of Computer Science Artificial Intelligence and Data Science Department University of Science and Technology Houari Boumediene Algiers Algeria Matter Sciences and Computer Science Faculty Department of Computer Science University of Djilali Bounaama Khemis Miliana Algeria

Leveraging advanced computer vision techniques, we present a novel method for analyzing and categorizing traffic videos by utilizing an image mapping technique combined with a UniVit (Unit Vision Transformer) model. First, video data is transformed into an image dataset, where each frame undergoes a mapping process based on vehicle detection using the YOLOv8s algorithm. These mapped images are then input into a vision transformer, which utilizes attention mechanisms to identify and analyze complex patterns across frames. The approach is evaluated on the UCSD (University of California San Diego) dataset, which features diverse weather conditions such as clear, overcast, and rainy scenarios, enabling a rigorous assessment under varied environments. Our method achieves a notable accuracy score of 99.89%, surpassing previous approaches on the UCSD benchmark. This improvement underscores the effectiveness of combining image mapping with advanced vision transformers, suggesting promising applications for intelligent traffic management systems aimed at mitigating traffic issues. © 2025, Aracne Editrice. All rights reserved.

关键词： Photomapping

来源：评论

学校读者我要写书评

暂无评论

IoT-Enhanced Innovations in Drug Delivery Systems for Targeted Therapy in Autoimmune Disorders 10

IoT-Enhanced Innovations in Drug Delivery Systems for Target...

引用

10th International Conference on Advanced Computing and Communication Systems, ICACCS 2024

作者： Bhuvaneshwari, V. Sadaieswaran, R. Malathi, P. Legapriyadharshini, N. Thirumalaikumari, T. Raja, S.R. Acharya College of Engineering Department of Computer Science and Engineering Achariyapuram Puducherry India Tagore College of arts and science Department of Computer Applications Tamil Nadu Chennai India Saveetha College of Liberal Arts and Sciences Simats Deemed to be University Department of Data Analytics Tamil Nadu Chennai India Saveetha College of Liberal Arts and Sciences Saveetha Institute of Medical and Technical Sciences Department of Computer Application Tamil Nadu Chennai India Saveetha College of Liberal Arts and Sciences Simats Deemed to be University Department of Computer Science Tamil Nadu Chennai India

ISBN: (纸本)9798350384369

The growing number of autoimmune diseases necessitates the needs novel drug delivery system alternatives to improve targeted therapy. The study deals with the immediate requirement for improvements by investigating the limitations of existing systems, highlighting their limitations in precision, patient adherence, and real-time monitoring. The proposed system combines Internet of Things (IoT) technology into medicine distribution, providing an extensive structure that makes use of smart devices, sensors, and networking solutions. The integration attempts to improve patient participation and treatment efficacy by ensuring tailored regimens. The study demonstrates promising discoveries in optimizing therapeutic interventions, cost-effectiveness, and data-driven insights through an in-depth evaluation of case studies and successful implementations. The proposed IoT-enhanced drug delivery system not only addresses the limitations of existing systems but also represents an evolution for healthcare professionals and pharmaceutical businesses. Patient privacy and data security are essential for ethical and regulatory reasons. The study's results and analysis highlight the proposed system's potential for overcoming limitations and enhancing targeted therapy for autoimmune disorders, setting the groundwork for a future in which IoT seamlessly interacts with drug delivery systems to transform patient care. © 2024 IEEE.

关键词： Targeted drug delivery

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：