检索结果-内蒙古大学图书馆

International Conference on Energy, Power, Environment, Control and Computing (ICEPECC 2025)

作者： Fatima N. Al-Aswadi Wafa’ Za’al Alma’aitah Huah Yong Chan Keng Hoon Gan Zahid Iqbal Ahlam Al-Dhamari Institute of Computer Science and Digital Innovation UCSI University Kuala Lumpur 56000 Malaysia Department of Intelligence Systems Faculty of Artificial Intelligence Al-Balqa Applied University Salt 19117 Jordan School of Computer Sciences Universiti Sains Malaysia 11800 Gelugor Pulau Pinang Malaysia Department of Computer Science Air University Kharian Pakistan Department of Electrical and Computer Engineering Curtin University Malaysia Miri 98009 Sarawak Malaysia

ISBN: (数字)9781837243150

Relation extraction and classification, collectively referred to as relation discovery, are fundamental tasks in the ontology construction process. In recent years, numerous studies have dedicated their efforts to devising hybrid approaches, supporting inclusive innovation in computational linguistics and ontology engineering. These approaches merge traditional methods with Machine Learning (ML) or Deep Learning (DL) techniques to tackle relation discovery tasks. This research examines the applicability and efficacy of four distinct models—Support Vector Machine (SVM), Naive Bayes (NB), k-Nearest Neighbors (KNN), and Convolutional Neural Network (CNN)—in relation extraction and classification. Two different datasets, ACL RD-TEC 2.0 and ACLRelAcS, are utilized, alongside two distinct text representation methodologies, BOW and TF-IDF-BOW. An in-depth analysis of the results, including the advantages and limitations of each model, is presented in the results and discussion sections.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Artificial Intelligence For Realtime Face Recognition Attendance Using College Classrooms and Buses

Artificial Intelligence For Realtime Face Recognition Attend...

引用

Electrical Electronics and Computing Technologies (ICEECT), International Conference on

作者： Ahila A Hosanna Princye P Poonguzhali A Kavivendhan M. Deepa A. Arthy R. Saravanakumar Department of Electronics and Communication Engineering Sri Sairam College of Engineering Anekal Bangalore India Department of Computer Science and Business Systems Francis Xavier Engineering College Tirunelveli Tamil Nadu India Software Analyst Iconix Software Solution Tirunelveli Tamil Nadu India

ISBN: (数字)9798350378092

ISBN: (纸本)9798350378108

Teachers take attendance by having pupils sign in or check-in classes and transportation. Student absences often result from individual mistakes. This article examines a technology that records data from classroom photographs of every student's face. This research uses an Adaptive Boost Classifier, Random Forest (RF), and Deep Convolutional Neural Networks (DCNNs). The model performs well on the DCNN model with 88 and 92% accuracy and on the ResNet50 pre-trained model with 97.21% accuracy. After detecting each student's face, they recorded their present status in an Excel document. It kept the best system implementation approach based on performance.

关键词： Adaptation models Accuracy Face recognition Transportation Forestry Convolutional neural networks Pupils System implementation Random forests Residual neural networks

来源：评论

学校读者我要写书评

暂无评论

GAPTCHA: A Game-Based Image-CAPTCHA 1st

GAPTCHA: A Game-Based Image-CAPTCHA

引用

1st International Conference on Computing and Emerging Technologies, ICCET 2023

作者： Farooq, Umar Tariq, Noshina Mehmood, Danish Almufareh, Maram Fahaad Humayun, Mamoona Khan, Farrukh Aslam Department of Computer Science Shaheed Zulfikar Ali Bhutto Institute of Science and Technology Islamabad44000 Pakistan Department of Avionics Engineering Air University Islamabad44000 Pakistan Department of Information Systems College of Computer and Information Sciences Jouf University Al Jouf Sakakah72388 Saudi Arabia King Saud University Riyadh11653 Saudi Arabia

ISBN: (纸本)9783031776168

The threat of bot attacks has increased with the abundance of a diverse range of smart devices used globally. Users access the Internet for various needs, such as entertainment, education, e-commerce, and data exchange. It has tremendously increased the number of users and connected devices. The completely automated public Turing test to tell the computer and human apart (CAPTCHA) is a fully automated Turing test to differentiate a machine from a person and safeguard Internet services from spam and bot assaults. However, the distortion levels make human identification unpleasant and impossible. This paper proposes a novel CAPTCHA, named "GAPTCHA," based on a famous game called Snakes and Ladders game. It uses a simple keyboard, mouse, and touch inputs. The results showed that 3% of the people got it wrong, and about reusing the proposed GAPTCHA, 100% of the users said yes, which is a positive result for its successful user experience. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Bot (Internet)

来源：评论

学校读者我要写书评

暂无评论

Hash Tag-Based Sentiment Analysis Using A Deep Learning Approach

Hash Tag-Based Sentiment Analysis Using A Deep Learning Appr...

引用

International Conference on Advanced Computing and Communication systems (ICACCS)

作者： R. Venkatesan M. Sangeetha S. Nivedha M. Selvakumar K.S. Chandru P. Parthasarathi Department of Information Technology Bannari Amman Institute of Technology Erode India Department of Information Technology K S Rangasamy College of Technology Namakkal India Dept. of Computer Science & Engineering Hindusthan College of Engg and Technology Coimbatore India Dept. Computer Science & Business Systems Bannari Amman Institute of Technology Erode India Dept. Computer Science and Engineering Bannari Amman Institute of Technology Erode India

ISBN: (数字)9798350384369

ISBN: (纸本)9798350384376

Social commerce uses a variety of social networking sites like Facebook, Instagram, Twitter, Pinterest, and business Whatsapp as a platform to promote products and services. Since advertising, a product, or service in s-comm will reach as high as a possible number of users on networking sites. Ads will spread virally by sharing among friends and friends of friends. So that wider reachability is possible and shortly will get the customer for our business. The success of an s-comm campaign is measured by the degree to which consumers interact with the sellers through review comments, likes, and shares between friends. Customer behavior analysis is needed for s-comm marketing because social commerce can significantly boost economic growth. The sentiment analysis of customer recommendations was investigated using a dataset of women's s-comm apparel hashtags and reviews. So for such reviews are experimented with different machine learning algorithms (Decision tree, SVM, Random Forest, KNN, Naïve Bayes and LSTM). These models handled the common English reviews and compared the metrics (Accuracy, Sensitivity and Specificity) with one another. Instead of working with individual model, this paper introduces a novel hybrid approach that integrates CNN and Ada Boost LSTM algorithms to provide better results in sentiment analysis when compared to other machine learning algorithms mentioned above. While considering the hash tag with common review comments the hybrid system is out performed. The comparison analysis is done for performance matrices namely accuracy of 98%, specificity of 98.1%, and sensitivity of 98.1%. In the s-commerce transaction industry, this hybrid approach will help to create a deeper understanding of consumer sentiment analysis and psychologically attract clients.

关键词： Deep learning Sentiment analysis Analytical models Accuracy Sensitivity Machine learning algorithms Reviews Social networking (online) Biological system modeling Long short term memory

来源：评论

学校读者我要写书评

暂无评论

Robust Event-Triggered Control of Nonlinear systems Under a Global Sector-Bound Condition

引用

Guidance, Navigation and Control 2022年第2期2卷 59-79页

作者： Pengpeng Zhang Tengfei Liu Zhong-Ping Jiang Department of Control Science&Engineering Frontiers Science Center for Intelligent Autonomous SystemsTongji UniversityShanghai 201804P.R.China State Key Laboratory of Synthetical Automation for Process Industries Northeastern UniversityShenyang 110819P.R.China Department of Electrical and Computer Engineering New York University370 Jay StreetBrooklynNY 11201USA

This paper studies an event-tniggered control problem for nonlinear systems subject to both external disturbancoes and dy namic *** is assumed that the system satisfies a global sector bound *** avold infnitely fast samplng,a novel eventriggred sampling mechanism is propoeed,which use8 not only the measuned system state but also an estimation of the inluence of the *** the propoeed design,the intersampling intervals an be lower bounded by a poeitive constant,and it is independent of botb external disturbances and dynamie ***,the doeedl loop event-tniggered system i proved to be input-torstate stable with repect to the extemal *** smalgain techmigues are;used for the stability analysis of the dloeeil-bop system.

关键词： Event-triggered control external disturbance dynamic uncertainty input-to-state stability

来源：评论

学校读者我要写书评

暂无评论

An Efficient Detection and Classification of Plant Diseases using Deep Learning Approach

An Efficient Detection and Classification of Plant Diseases ...

引用

Evolutionary Algorithms and Soft Computing Techniques (EASCT), International Conference on

作者： Jagadeesan S Deepakraj E Venkadeshan Ramalingam Ilayaraja Venkatachalam Manojkumar Vivekanandan Manjula R School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore India Information Technology Department University of Technology and Applied Sciences - Shinas Branch Sultanate of Oman School of Computer Science and Engineeing Vellore Institute of Technology Vellore India Department of Computer Science & Engineering School of Engineering and Applied Sciences SRM University-AP Amaravati Andhra Pradesh Department of Computing Technology College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur India

Crop failure caused by disease-causing pests is an important problem. Farmers struggle with disease management and detection due to inadequate interventions. The goal is to create an automatic system to efficiently identify plant diseases from photos while reducing crop losses and increasing productivity. Machine learning algorithms offer a faster and cheaper alternative to visual inspection by experts. The main goal is to perform image analysis for early diagnosis and effective disease control. The use of CNN architecture for plant disease classification and detection offers a promising solution for plant health monitoring and risk mitigation. Given the threats to crop productivity and global food security, reliable methods for early detection and accurate classification are essential. CNNs enable efficient analysis of vast plant image databases, enabling accurate plant disease identification with speed and accuracy. The multi-layer CNN approach extracts feature and refines the representation, facilitating accurate prediction and accurate diagnosis. Transfer learning methods accelerate system development and allow adaptation to plant disease-specific databases. Combining computer vision algorithms with CNN architecture enables real-time monitoring, early disease detection and targeted intervention, reducing yield losses and improving crop management. This approach uses AI, image analysis, and plant pathology to solve the challenges of sustainable agriculture and plant diseases. System performance is measured by various performance metrics.

关键词：

来源：评论

学校读者我要写书评

暂无评论

A Proactive Management Assistant Chatbot for Software engineering Teams: Prototype and Preliminary Evaluation 3

A Proactive Management Assistant Chatbot for Software Engine...

引用

3rd Novel Intelligent and Leading Emerging sciences Conference, NILES 2021

作者： Hefny, Abdelrahman H. Dafoulas, Georgios A. Ismail, Manal A. National Egyptian E-Learning University Faculty of Computers Information Technology Giza Egypt Middlesex University Faculty of Science and Technology Computer Science Department London United Kingdom Helwan University Faculty of Engineering Computer Engineering and Systems Department Cairo Egypt

ISBN: (纸本)9781665421577

This work presents a prototype and preliminary evaluation experiments of a management conversational assistant for software engineering teams. Such teams commonly rely on text chat for communications using specialized collaboration platforms such as Slack or Microsoft Teams. A conversational assistant integrated into these platforms can help in several tasks related to project and team management, thus benefiting both managers and software developers. The proposed design of the conversational assistant is described in terms of its architectural design and interaction design, and the important design decisions are explained. Preliminary evaluation shows that the proposed chatbot can be used effectively to interact with users reactively and proactively over public and private channels. However, for better results, a specific intent classifier should be used for each combination of user roles and channel types. In addition, interacting to messages with unspecified target over public channels is affected by the relatively limited intent classifier accuracy and still requires improvement. © 2021 IEEE.

关键词： Human resource management

来源：评论

学校读者我要写书评

暂无评论

An Over the Air Software Update System for IoT Microcontrollers based on WebAssembly

An Over the Air Software Update System for IoT Microcontroll...

引用

International Conference on Distributed Computing in Sensor systems (DCOSS)

作者： Ivan Zyrianoff Luca Sciullo Lorenzo Gigli Angelo Trotta Carlos Kamienski Marco Di Felice Department of Computer Science and Engineering University of Bologna Italy Advanced Research Center on Electronic Systems "Ercole De Castro" University of Bologna Italy Federal University of ABC (UFABC) Brazil

ISBN: (数字)9798350369441

ISBN: (纸本)9798350369458

In recent years, the proliferation of Internet of Things (IoT) applications has been remarkable, characterized by integrating tasks across multiple devices, forming the so-called edge-cloud continuum. The ability to reconfigure the software behavior over all nodes of the continuum has become crucial to support applications with varying demands. However, located at the extreme edge of IoT deployments, IoT microcontrollers often have static behaviors. Although Over the Air (OTA) methodologies are widely supported by most devices, the reconfiguration process may involve potentially inefficient and hazardous updates of the entire firmware. In response, this paper proposes an OTA software update system for IoT microcontrollers leveraging portable WebAssembly (WASM) code. By separating the application logic from the rest of the firmware, our system ensures heterogeneity, safety, efficiency, and reliability. Furthermore, we propose an update procedure based on different network protocols (MQTT and HTTP) to facilitate dealing with groups of IoT microcontrollers in a single action. We implemented a Configuration and Administration Toolkit (CAT) to evaluate our proposal and conducted a comprehensive performance analysis using a small-scale IoT testbed. Our findings demonstrate that while the WASM logic updates notably outperform those of the entire firmware, achieving a 30x delay reduction, they incur a performance overhead that is lower than MicroPython when compared to native C++ development.

关键词： Microcontrollers Software Software reliability Safety Performance analysis Internet of Things Logic

来源：评论

学校读者我要写书评

暂无评论

Efficient Entanglement Routing for Satellite-Aerial-Terrestrial Quantum Networks

arXiv

引用

arXiv 2024年

作者： Zhang, Yu Gong, Yanmin Fan, Lei Wang, Yu Han, Zhu Guo, Yuanxiong The Department of Electrical and Computer Engineering the University of Texas San AntonioTX78249 United States The Department of Engineering Technology and Electrical and Computer Engineering the University of Houston HoustonTX77204 United States The Department of Computer and Information Sciences Temple University PhiladelphiaPA19122 United States The Department of Electrical and Computer Engineering University of Houston HoustonTX77204 United States The Department of Computer Science and Engineering Kyung Hee University Seoul446-701 Korea Republic of The Department of Information Systems and Cyber Security University of Texas San AntonioTX78249 United States

In the era of 6G and beyond, space-aerial-terrestrial quantum networks (SATQNs) are shaping the future of the global-scale quantum Internet. This paper investigates the collaboration among satellite, aerial, and terrestrial quantum networks to efficiently transmit high-fidelity quantum entanglements over long distances. We begin with a comprehensive overview of existing satellite-, aerial-, and terrestrial-based quantum networks. Subsequently, we address the entanglement routing problem with the objective of maximizing quantum network throughput by jointly optimizing path selection and entanglement generation rates (PS-EGR). Given that the original problem is formulated as a mixed-integer linear programming (MILP) problem, which is inherently intractable, we propose a Benders’ decomposition (BD)-based algorithm to solve the problem efficiently. Numerical results validate the effectiveness of the proposed PS-EGR scheme, offering valuable insights into various optimizable factors within the system. Finally, we discuss the current challenges and propose promising avenues for future research in SATQNs. © 2024, CC BY-NC-SA.

关键词： Satellite antennas

来源：评论

学校读者我要写书评

暂无评论

ALIM: Adjusting Label Importance Mechanism for Noisy Partial Label Learning 37

ALIM: Adjusting Label Importance Mechanism for Noisy Partial...

引用

37th Conference on Neural Information Processing systems, NeurIPS 2023

作者： Xu, Mingyu Lian, Zheng Feng, Lei Liu, Bin Tao, Jianhua The State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences China School of Artificial Intelligence University of Chinese Academy of Sciences China School of Computer Science and Engineering Nanyang Technological University Singapore Department of Automation Tsinghua University China Beijing National Research Center for Information Science and Technology Tsinghua University China

ISBN: (纸本)9781713899921

Noisy partial label learning (noisy PLL) is an important branch of weakly supervised learning. Unlike PLL where the ground-truth label must conceal in the candidate label set, noisy PLL relaxes this constraint and allows the ground-truth label may not be in the candidate label set. To address this challenging problem, most of the existing works attempt to detect noisy samples and estimate the ground-truth label for each noisy sample. However, detection errors are unavoidable. These errors can accumulate during training and continuously affect model optimization. To this end, we propose a novel framework for noisy PLL with theoretical interpretations, called "Adjusting Label Importance Mechanism (ALIM)". It aims to reduce the negative impact of detection errors by trading off the initial candidate set and model outputs. ALIM is a plug-in strategy that can be integrated with existing PLL approaches. Experimental results on multiple benchmark datasets demonstrate that our method can achieve state-of-the-art performance on noisy PLL. Our code is available at: https://***/zeroQiaoba/ALIM. © 2023 Neural information processing systems foundation. All rights reserved.

关键词： Benchmarking

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：