检索结果-内蒙古大学图书馆

Informatica (Slovenia) 2025年第5期49卷 37-48页

作者： Remmach, Hassnae Razak, Siti Fatimah Abdul Ullah, Arif Yogarayan, Sumendra Sayeed, Md Shohel Mrhari, Amine Computer Systems Engineering Laboratory Cadi Ayyad University Marrakesh40000 Morocco Faculty of Information Science and Technology Multimedia University Ayer Keroh75450 Malaysia Research in Computer Science Laboratory Faculty of Sciences Ibn Tofail University Kenitra Morocco

In a number of industries, including computer graphics, robotics, and medical imaging, three-dimensional reconstruction is essential. In this research, a CNN-based Multi-output and Multi-Task Regressor with deep learning capabilities is proposed for three-dimensional object reconstruction from 3D point cloud. Our approach is grounded in the original Point Net architecture, which addresses the difficulties associated with convolution when applied to point clouds. Firstly, this paper is modified using a Multi-Output Regressor to accurately recreate Super forms from 3D point clouds. Using this method, we first extract features from the 3D point cloud using Point Net. After that, a Multi-Output Regressor receives these data and uses them to anticipate the Super shape parameters needed to reconstruct the shape. Taking in the data, the Multi-Output Regressor retrieved characteristics from Point Net and simultaneously predicts several outcomes. Second, a Multi-Task Regressor is used to modify the Point Net. The network gains from the capacity to transfer knowledge from one task to another, improving the model's overall performance. The model would forecast the ten parameters needed to create the shape in the case of rebuilding Super shapes. The test findings were better than expected;they are intriguing in terms of prediction accuracy and cost, and they update the result by 80%, which is a good accomplishment for the study. © 2025 Slovene Society Informatika. All rights reserved.

关键词： 3D reconstruction

来源：评论

学校读者我要写书评

暂无评论

Hybrid DCNN-SVM Architecture for Optimizing Sugarcane Leaf Disease Classification

IAENG International Journal of Computer Science

引用

IAENG International Journal of computer science 2025年第4期52卷 974-985页

作者： Kurniawan, Rudi Siaga, Erna Samsuryadi, Samsuryadi Susilo, Andrianto Tri Sunardi, Lukman Faculty of Engineering Science Universitas Bina Insan Lubuklingau31626 Indonesia Faculty of Agriculture Universitas Sriwijjaya Palembang30862 Indonesia Faculty of Computer Science Universitas Sriwijaya Palembang30862 Indonesia

Sugarcane leaf diseases are major agricultural issues that lead to reduced production and economic losses. Early detection is crucial for controlling their spread. This study introduces a solution for identifying sugarcane leaf diseases using image processing and deep learning techniques. Preprocessing steps include normalization for pixel scale stabilization and Gaussian blur for noise reduction. Features are extracted using a pre-trained DenseNet201 model, and classification is performed using a Support Vector Machine (SVM) with an RBF kernel. The proposed hybrid DCNN-SVM architecture achieves a test accuracy of 96.74%, demonstrating effectiveness in disease classification. Comprehensive evaluations highlight the model’s strengths and areas for improvement, providing a foundation for future research in plant disease detection. © (2025.0), (International Association of Engineers). All Rights Reserved.

关键词： Noise abatement

来源：评论

学校读者我要写书评

暂无评论

Fine-Tuned Attribute Weighted Naïve Bayes with Modified Partial Instances Reduction for Gaming Disorder Classification

HighTech and Innovation Journal

引用

HighTech and Innovation Journal 2025年第1期6卷 67-80页

作者： Latubessy, Anastasya Wardoyo, Retantyo Musdholifah, Aina Kusrohmaniah, Sri Doctoral Program Department of Computer Science and Electronics Faculty of Mathematics and Natural Science Universitas Gadjah Mada Yogyakarta55281 Indonesia Department of Informatics Engineering Faculty of Engineering Universitas Muria Kudus Indonesia Department of Computer Science and Electronics Faculty of Mathematics and Natural Science Universitas Gadjah Mada Yogyakarta55281 Indonesia Department of Psychology Faculty of Psychology Universitas Gadjah Mada Yogyakarta55281 Indonesia

Fine Tuning Attribute Weighted Naïve Bayes (FTAWNB) is a reliable modified Naïve Bayes model. Even though it is able to provide high accuracy on ordinal data, this model is sensitive to outliers. To improve the performance of FTAWNB, this research modified the Partial Instances Reduction (PIR) technique to make the FTAWNB more adaptive to outliers. Nevertheless, in contrast to the original PIR technique, which substitutes missing values for data values deemed outliers, the PIR technique suggested in this study replaces data values deemed outliers using a Naïve Bayes weighting approach. The attribute values from the outlier data are replaced with the highest probability values for the attributes in the actual class. This PIR technique is referred to as modified PIR. The FTAWNB model with modified PIR has been evaluated using the Gaming Disorder dataset. Replacing the four attributes with the least amount of information resulted in accuracy gains of 99.74%, an increase of 1.53% over the FTAWNB model. The experimental result shows that adding the modified PIR technique to the FTAWNB model can handle the outlier in the data, proving it by increasing the performance in terms of accuracy, precision, and recall without pruning the dataset used. © 2025, Ital Publication. All rights reserved.

关键词： Attribute Weighted Classification Fine-Tune Gaming Disorder Instances Reduction Naïve Bayes

来源：评论

学校读者我要写书评

暂无评论

A Novel 3D Paradigm for Target Expansion of Augmented Reality SSVEP

引用

IEEE Transactions on Neural Systems and Rehabilitation engineering 2025年 33卷 1562-1573页

作者： Cao, Beining Li-Ting Tsai, Charlie Zhou, Nan Do, Thomas Lin, Chin-Teng University of Technology Sydney Computational Intelligence and Brain-Computer Interfaces Laboratory Australian AI Institute Faculty of Engineering and Information Technology School of Computer Science UltimoNSW2007 Australia

Steady-State Visual Evoked Potentials (SSVEP) have proven to be practical in Brain-computer Interfaces (BCI), particularly when integrated with augmented reality (AR) for real-world application. However, unlike conventional computer screen-based SSVEP (CS-SSVEP), which benefits from stable experimental environments, AR-based SSVEP (AR-SSVEP) systems are susceptible to the interference of real-world environment and device instability. Particularly, the performance of AR-SSVEP significantly declines as the target frequency increases. Therefore, our study introduced a 3D paradigm that combines flicker frequency with rotation patterns as stimuli, enabling expansion of target sets without additional frequencies. In the proposed design, in addition to the conventional frequency-based SSVEP feature, bio-marker elicited by visual perception of rotation was investigated. An experimental comparison between this novel 3D paradigm and a traditional 2D approach, which increases targets by adding frequencies, reveals significant advantages. The 12-class 3D paradigm achieved an accuracy of 76.5% and an information transfer rate (ITR) of 70.42 bits/min using 1-second EEG segments. In contrast, the 2D paradigm exhibited a lower performance with 72.07% accuracy and 62.28 bits/min ITR. The result underscores the 3D paradigm’s superiority in enhancing the practical applications of SSVEP-based BCIs in AR settings, especially with shorter time windows, by effectively expanding target recognition without compromising system efficiency. © 2001-2011 IEEE.

关键词： Augmented reality

来源：评论

学校读者我要写书评

暂无评论

The Security of Using Large Language Models:A Survey With Emphasis on ChatGPT

引用

IEEE/CAA Journal of Automatica Sinica 2025年第1期12卷 1-26页

作者： Wei Zhou Xiaogang Zhu Qing-Long Han Lin Li Xiao Chen Sheng Wen Yang Xiang the School of Science Computing and Engineering Technologies Swinburne University of Technology IEEE the School of Computer and Mathematical Sciences the University of Adelaide Adelaide the Faculty of Science and Engineering Southern Cross University the School of Information and Physical Sciences the University of Newcastle

ChatGPT is a powerful artificial intelligence(AI)language model that has demonstrated significant improvements in various natural language processing(NLP) tasks. However, like any technology, it presents potential security risks that need to be carefully evaluated and addressed. In this survey, we provide an overview of the current state of research on security of using ChatGPT, with aspects of bias, disinformation, ethics, misuse,attacks and privacy. We review and discuss the literature on these topics and highlight open research questions and future *** this survey, we aim to contribute to the academic discourse on AI security, enriching the understanding of potential risks and mitigations. We anticipate that this survey will be valuable for various stakeholders involved in AI development and usage, including AI researchers, developers, policy makers, and end-users.

关键词： Artificial intelligence (AI) ChatGPT large language models (LLMs) security

来源：评论

学校读者我要写书评

暂无评论

Deep Learning for Sentiment Analysis 4

Deep Learning for Sentiment Analysis

引用

4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Rokade, Gayatri Ughade, Radhe Gaurshettiwar, Palash Faculty of Engineering and Technology Computer Science and Medical Engineering Maharashtra Wardha India

ISBN: (纸本)9798331523923

This paper represents that one of the critical subfields of NLP, SA applies DL techniques to analyze the feelings expressed in text, image, and voice context. As new technology of Web 2.0 emerges, there are many opportunities and challenges because of the large amount of content generated from blogs, forums, social networking sites, etc. This work explores the development of DL-based SA and notes that enhancements have been made primarily on the document, sentence, phrase, as well as aspect levels. Ambiguity, sarcasm, and negation are also discussed, as well as specific domain and multimodal aspects. The work shows that it might be possible to transition to semi-supervised, and unsupervised learning to overcome real-world data limitations and offers emergent prospects in the real-time analysis, domain fine-tuning, and data enhancement. The inspiration for future research is offered by the multimodal sentiment analysis and the enhancement of the DL architectures. © 2025 IEEE.

关键词： Contrastive Learning

来源：评论

学校读者我要写书评

暂无评论

AN ITERATIVE TWO-GRID METHOD FOR STRONGLY NONLINEAR ELLIPTIC BOUNDARY VALUE PROBLEMS

引用

Journal of Computational Mathematics 2025年第3期43卷 673-689页

作者： Jiajun Zhan Lei Yang Xiaoqing Xing Liuqiang Zhong School of Computer Science and Engineering Faculty of Innovation EngineeringMacao University of Science and TechnologyMacao SAR 999078China School of Mathematical Sciences South China Normal UniversityGuangzhou 510631China

We design and analyze an iterative two-grid algorithm for the finite element discretizations of strongly nonlinear elliptic boundary value problems in this *** propose an iterative two-grid algorithm,in which a nonlinear problem is first solved on the coarse space,and then a symmetric positive definite problem is solved on the fine *** main contribution in this paper is to establish a first convergence analysis,which requires dealing with four coupled error estimates,for the iterative two-grid *** also present some numerical experiments to confirm the efficiency of the proposed algorithm.

关键词： Iterative two-grid method Convergence Strongly nonlinear elliptic problems

来源：评论

学校读者我要写书评

暂无评论

Optimizing crowd evacuation: evaluation of strategies for safety and efficiency

引用

Journal of Reliable Intelligent Environments 2025年第1期11卷 1-19页

作者： Oliveira, Hugo S. Faculty of Sciences University of Porto Porto Portugal Institute for Systems and Computer Engineering Technology and Science INESC TEC Porto Portugal

Predicting and controlling crowd dynamics in emergencies is one of the main objectives of simulated emergency exercises. However, during emergency exercises, there is often a lack of sense of danger by the actors involved and concerns about exposing real people to potentially dangerous environments. These problems impose limitations in running an emergency drill, harming the collection of valuable information for posterior analysis and decision-making. This work aims to mitigate these problems by using Agent Based Modelling (ABM) simulator to deepen the comprehension of human actions when exposed to a sudden variation in extensive crowded environmental conditions and how evacuation strategies affect evacuation performance. To assess the impact of the evacuation strategy employed, we propose a modified informed leader-flowing approach and compare it with common evacuation strategies in a simulated environment, replicating stadium benches with narrow corridors leading to different exit points. The objective is to determine the impact of each set of configurations and evacuation strategies and compare them against other established ones. Our experiments determined that agents following the crowd generally lead to a higher number of victims due to the rise of herding phenomena near the exits, which was significantly reduced when agents were guided towards the exit via knowing the exit beforehand or following leader agent with real-time information regarding exit location and exit current state, proving that relevant and controlled information in combination with Follow Leader strategies can be crucial in an emergency evacuation scenario with limited evacuation exit capabi and distribution. © The Author(s) 2024.

关键词： Decision making

来源：评论

学校读者我要写书评

暂无评论

Simple, robust and systematic QCA clocking scheme for area-efficient nanocircuits

引用

International Journal of Electronics Letters 2025年第2期13.0卷 210-218页

作者： Goswami, Mrinal Sharma, Tonmoy Jyoti Nath Boruah, Arpita Programme of Computer Science & Engineering Faculty of Engineering Assam Down Town University Assam Guwahati India

Quantum-dot cellular automata (QCA) represent an innovative forefront in nanotechnology, exploring the utilisation of quantum dots as carriers of data in computational systems. Temporal controls govern the coordination of information transmission and synchronisation within QCA circuits. Nevertheless, most research in QCA overlooks the crucial aspect of appropriately arranging the fundamental clocking mechanism. Consequently, QCA circuits often exhibit irregularities in their clocking regions, rendering them unsuitable for practical implementation. Adopting a consistent and scalable clocking framework is necessary to address this challenge and facilitate efficient design with optimal routing possibilities within clocking zones. Without a clearly defined and standardised clocking structure, QCA circuit designs remain incomplete and lack viability for fabrication. Numerous QCA clocking methodologies have been proposed, showcasing commendable ingenuity. Nonetheless, these approaches exhibit room for enhancement, particularly in tackling issues concerning design intricacies, such as establishing robust feedback loops and efficient area utilisation. A novel approach termed Simple, Robust and Systematic (SRS) scheme has been introduced in response to these challenges. Emphasising heightened feedback pathways, optimal spatial allocation and streamlined design, this scheme aims to overcome existing limitations. © 2025 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Feedback path QCA symmetric clocking Qcadesigner quantum-dot cellular automata (QCA) wire-crossing

来源：评论

学校读者我要写书评

暂无评论

A QoS-based technique for load balancing in green cloud computing using an artificial bee colony algorithm

引用

Journal of Experimental and Theoretical Artificial Intelligence 2025年第2期37卷 307-342页

作者： Tabagchi Milan, Sara Jafari Navimipour, Nima Lohi Bavil, Hamed Yalcin, Senay Department of Computer Engineering Tabriz Branch Islamic Azad University Tabriz Iran Department of Computer Engineering Faculty of Engineering and Natural Sciences Kadir Has University Istanbul Turkey Future Technology Research Center National Yunlin University of Science and Technology Douliou Taiwan Department of Computer Engineering University of Applied Sciences & Technology Tabriz Iran Department of Computer Engineering Nisantasi University Istanbul Turkey

Nowadays, high energy amount is being wasted by computing servers and personal electronic devices, which produce a high amount of carbon dioxide. Thus, it is required to decrease energy usage and pollution. Many applications are utilised by green computing to save energy. Scheduling of tasks acts as an important process to reach the mentioned goals. It is worth stating that the vital characteristic of task scheduling in green clouds is the load balancing of tasks on virtual machines. Efficient load balancing moves tasks from overloaded to underloaded virtual machines to maintain the Quality of Service (QoS). This issue is an NP-complete problem, so this research suggests a new technique based on the behavioural structure of artificial bee behaviour. This method aims to improve QoS while lowering energy usage in green computing. In addition, the honey bees are considered the removed tasks from overloaded virtual machines and a candidate for migrating selected tasks with the lowest priority. The CloudSim testing findings demonstrate that the technique is successful in QoS, makespan, and energy usage compared to other ways. © 2023 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Cloud computing

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：