检索结果-内蒙古大学图书馆

Proceedings of the Winter Simulation Conference

作者： Christopher W. H. Davis Antonie J. Jetter Philippe J. Giabbanelli Microsoft USA Dept. of Engineering & Technology Management Portland State University USA Dept. of Computer Science & Software Engineering Miami University Oxford USA

ISBN: (纸本)9798350369663

A predictive simulation is built on a conceptual model (e.g., to identify relevant constructs and relationships) and serves to estimate the potential effects of 'what-if' scenarios. Developing the conceptual model and plausible scenarios has long been a time-consuming activity, often involving the manual processes of identifying and engaging with experts, then performing desk research, and finally crafting a compelling narrative about the potential futures captured as scenarios. Automation could speed-up these activities, particularly through text mining. We performed the first review on automation for simulation scenario building. Starting with 420 articles published between 1995 and 2022, we reduced them to 11 relevant works. We examined them through four research questions concerning data collection, extraction of individual elements, connecting elements of insight and (degree of automation of) scenario generation. Our review identifies opportunities to guide this growing research area by emphasizing consistency and transparency in the choice of datasets or methods.

关键词：

来源：评论

学校读者我要写书评

暂无评论

An Effective Recurrent Neural Network (RNN) based Intrusion Detection via Bi-directional Long Short-Term Memory

An Effective Recurrent Neural Network (RNN) based Intrusion ...

引用

2021 International Conference on Intelligent Technologies, CONIT 2021

作者： Sivamohan, S. Sridhar, S.S. Krishnaveni, S. Dept of Computer Science and Engineering SRMIST Kattankulathur Chennai India Dept of Software Engineering SRMIST Kattankulathur Chennai India

ISBN: (纸本)9781728185835

The evolution of communication and information systems has raised the volume of data distributed through the internet. As an effect, a majority of digital resources have been increased, so does the challenge of cybersecurity. Intrusion detection systems (IDSs) are closely connected to a holistic approach for preventing cyberattacks. Due to the high utilization of network traffic in the cyber world, conventional machine learning approaches used in intrusion detection systems are becomes ineffective. Recently evolved deep learning techniques are successfully applied in the detection and classification of threats at both the network and host levels, with a focus on deep learning. This study proposed an efficient IDS based on Recurrent Neural Network (RNN) via Bi-directional Long Short- Term Memory (RNN BiLSTM). The strategy uses a two-step mechanism to develop the expertise of the suggested solution to address network problems. This research aims to determine the algorithm's processing time and increase attack classification accuracy. The proposed model was evaluated on the CICIDS2017 intrusion detection dataset. The Random forest and Principal component analysis algorithms were used to detecting the valuable features and eliminating the unwanted features from the given dataset. The findings revealed that BLSTM outperform all other RNN architectures in terms of classification accura 98.48%. © 2021 IEEE.

关键词： Long short-term memory

来源：评论

学校读者我要写书评

暂无评论

Improved Residual U-Net for COVID-19 Lung Infection Multi-Class Segmentation in CT Image

Improved Residual U-Net for COVID-19 Lung Infection Multi-Cl...

引用

2022 International Conference of Advanced Technology in Electronic and Electrical engineering, ICATEEE 2022

作者： Khouloud, Sahib Ahlem, Melouah Fadel, Toure Amel, Slim LRI Badji-Mokhtar Annaba University Dept. Computer Science Annaba23000 Algeria Trois-Rivieres Laboratory of Software Engineering Dept. du Quebec Quebec Canada

ISBN: (纸本)9798350346749

COVID-19, the new coronavirus, is a threat to global public health. Today, there is an urgent need for automatic COVID-19 infection detection tools. This work proposes an automatic COVID-19 infection detection system based on CT image segmentation. A deep learning network developed from an improved Residual U-net architecture extracts infected areas from a CT lung image. We tested the system on COVID-19 public CT images. An evaluation using the F1 score, sensitivity, specificity and accuracy proved the effectiveness of the proposed network. Besides, experimental results showed that the proposed network performed well in extracting infection regions so, it can assist experts in COVID-19 infection detection. © 2022 IEEE.

关键词： Coronavirus

来源：评论

学校读者我要写书评

暂无评论

Deep Learning-Based Image Analysis for Detecting Nutrient Deficiencies of Tomato Plants

Deep Learning-Based Image Analysis for Detecting Nutrient De...

引用

Advancements in Computing (ICAC), International Conference on

作者： K. C. Nirasha Jayasiri Sanjeevi Chandrasiri Shalini Rupasinghe K.O. Rivindu Karunanayake Abineshh Uthayachandran M.I. Fathima Zihara Dept. of Computer Science and Software Engineering Sri Lanka Institute of Information Technology Sri Lanka

Tomato is one of the most popular crops worldwide. The success of a tomato crop is highly dependent on the health of the plants. Nutrient deficiency surveillance is typically conducted through visual inspections, which can be challenging for inexperienced farmers and home gardeners to work accurately. There is a growing need for a quick, reliable, and accurate nutrient deficiency identification system to address this issue. The proposed method is based on image processing and deep learning technologies that are highly effective for image classification tasks. Two models were trained using a dataset collected from tomato plants in Sri Lanka and evaluated using Mask Region-based Convolutional Neural Network (Mask R-CNN) and, You Only Look Once (YOLO) for deficiency classification, and results obtained 92% and 98% accuracy, respectively. Deficiency dispersion level expressed as a percentage using Mask R-CNN and followed by image processing techniques. Overall, this proposed system offers a convenient and accessible tool for farmers and home gardeners to monitor and maintain the health of their tomato plants, enabling them to achieve optimal yields and ensure profitable returns.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Effective Disease Recognition in Cucumbers: A Web-Based Application Using Transfer Learning Models 3

Effective Disease Recognition in Cucumbers: A Web-Based Appl...

引用

3rd IEEE International Conference on Robotics, Automation, Artificial-Intelligence and Internet-of-Things, RAAICON 2024

作者： Rahman, Md Shahidur Ahamed, Asif Pranto, Md Nuruzzaman Islam, Md Ariful Al Masum, Abdullah Al-Sakib, Abdullah Rahman, S.M. Arafat Haque, Rezaul Rahman, Shafiur Westcliff University Dept. of Engineering Management CA92614 United States Westcliff University College of Technology and Engineering CA92614 United States International American University Dept. of Business Administration CA90010 United States Westcliff University Dept. of Information Technology CA92614 United States East West University Dept. of Computer Science and Engineering Dhaka1212 Bangladesh Daffodil International University Dept. of Software Engineering Dhaka1216 Bangladesh

ISBN: (纸本)9798331534400

Cucumber farming plays a crucial role in Bangladesh's agricultural economy, significantly contributing to vegetable production. However, diseases like Downy Mildew, Bacterial Wilt, Anthracnose, and Belly Rot threaten crop quality and lead to financial losses. Effective management of these diseases is crucial for maintaining high crop yields and ensuring economic stability. Current disease identification methods are labor-intensive, costly, and dependent on expert knowledge. To address these challenges, this study presents an automated disease categorization system using advanced transfer learning models, including MobileNet, ShuffleNet, InceptionResNet-v2, AlexNet, and Ensemble Transfer Learning (ETL). Utilizing a dataset of 8,000 well-annotated images, enhanced through preprocessing techniques like GLRM feature extraction and InfoMAX-GAN feature selection, the ETL model achieved a remarkable accuracy of 99.79% and an F1-score of 99.78%. We also developed a user-friendly web tool for real-time diagnostics, providing invaluable support to farmers and agricultural specialists. This tool enables rapid disease detection, leading to increased productivity and a transformative approach to cucumber disease management. By reducing the reliance on chemical interventions, it promotes sustainable farming practices and safeguards human and animal health. © 2024 IEEE.

关键词： Feature Selection

来源：评论

学校读者我要写书评

暂无评论

Multi-granularity Coal Mine Risk Conceptual Prediction Based on Time-varying Cloud Model

Multi-granularity Coal Mine Risk Conceptual Prediction Based...

引用

2023 International Conference on Cyber Security, Artificial Intelligence, and Digital Economy, CSAIDE 2023

作者： Dai, Jin Hu, Sunling Zhang, Lei Dept. of Software Engineering Chongqing University of Posts and Telecommunications Chongqing China Dept. of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing China Chongqing Key Laboratory of Computational Intelligence Chongqing University of Posts and Telecommunications Chongqing China

ISBN: (纸本)9781510666597

The big data of coal mine was characterized by large scale, many influencing factors and weak correlation. The existing big data mining based on quantitative data analysis usually adopts fixed framework processing, which is easy to fall into the dilemma of overfitting and cannot acquire elastic knowledge across layers. From the perspective of coal mine monitoring and management, a multi-granularity time-varying cloud model is proposed based on a multi-granularity semantic transformation model and a grey prediction model to achieve the conceptual prediction of coal mine gas concentration risk. The new model first converts the gas concentration data into semantic concepts using a semantic transformation model, then processes the sequence of semantic concept sets for randomness and oscillation, and builds a grey prediction model for them, which is used to project future semantic concepts. The new model is compared with the existing model, and the experimental results show that the new method can not only predict coal mine risk concepts at different granularities, but also has high prediction accuracy. © 2023 SPIE.

关键词： Coal mines

来源：评论

学校读者我要写书评

暂无评论

Unveiling The Arsenal of User Data Protection Tools and Practices

Unveiling The Arsenal of User Data Protection Tools and Prac...

引用

Humanitarian Technology Conference (KHI-HTC), IEEE Karachi Section

作者： Syed Moiz Abdul Ahad Abro Mansoor Ebrahim Abdul Basit Abro Dept. of Computer Science Iqra University Main Campus Karachi Pakistan Dept. of Software Engineering Iqra University Main Campus Karachi Pakistan Dept. of Computer Science Indus University Karachi Pakistan

In our rapidly evolving digital landscape, the imperative of safeguarding personal data has surged in significance. As lives increasingly intertwine with digital technologies, personal information has grown markedly, parallel to the escalating threats posed by cyberattacks and data breaches. This research paper provides a comprehensive exploration of data security and privacy, aiming to impart a profound understanding of the intrinsic value of data. It meticulously examines essential methodologies such as encryption, access control, multifactor authentication, and systems, all of which collectively form the bedrock of a resilient security framework. Additionally, this study delves into the intricate regulatory landscape that governs data protection, placing particular emphasis on well-established frameworks such as the General Data Protection Regulation and the Health Insurance Portability and Accountability Act. It highlights emergent trends in data security, including the integration of artificial intelligence and machine learning for threat detection, while also addressing the distinctive challenges posed by the proliferation of Internet of Things (IoT) devices. In conclusion, this paper underscores the paramount importance of personal data protection in our digital age, offering invaluable insights and guidance for organizations and individuals endeavoring to bolster their defenses and uphold data integrity within our interconnected world.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Enhancing Energy Consumption of a Node in Wireless Sensor Networks Using Quick Response Code 3

Enhancing Energy Consumption of a Node in Wireless Sensor Ne...

引用

3rd International Conference on Inventive Research in Computing Applications, ICIRCA 2021

作者： Nidhya, M.S. Veronica, Benita K. J. Jayakarthik, R. Parvathavardhini, K. Pmist Dept of Software Engineering Thanjavur India Mother Teresa University Dept of Computer Science Kodaiakanal India Vistas Dept of Computer Science Chennai India

ISBN: (纸本)9780738146270

Sending messages, retransmission of identical messages, and sensing information in the monitored environment can quickly reduce the energy consumption of a sensor node present in a wireless sensor network. Due to repeated delivery of the same message, the battery loses power and drops dead. Node closer to the sink node will quickly deplete its energy by forwarding the messages. Total network lifetime will also be decreased. To overcome this, this research work use Quick response code (QR) for message transmission and reject the transmission of the same message several times. © 2021 IEEE.

关键词： Energy utilization

来源：评论

学校读者我要写书评

暂无评论

Deep Reinforcement Learning Algorithms for Option Hedging

arXiv

引用

arXiv 2025年

作者： Neagu, Andrei Godin, Frédéric Kosseim, Leila Dept. of Computer Science and Software Engineering Concordia University Montréal Canada Dept. of Mathematics & Statistics Concordia University Montréal Canada

Dynamic hedging is a financial strategy that consists in periodically transacting one or multiple financial assets to offset the risk associated with a correlated liability. Deep Reinforcement Learning (DRL) algorithms have been used to find optimal solutions to dynamic hedging problems by framing them as sequential decision-making problems. However, most previous work assesses the performance of only one or two DRL algorithms, making an objective comparison across algorithms difficult. In this paper, we compare the performance of eight DRL algorithms in the context of dynamic hedging;Monte Carlo Policy Gradient (MCPG), Proximal Policy Optimization (PPO), along with four variants of Deep Q-Learning (DQL) and two variants of Deep Deterministic Policy Gradient (DDPG). Two of these variants represent a novel application to the task of dynamic hedging. In our experiments, we use the Black-Scholes delta hedge as a baseline and simulate the dataset using a GJR-GARCH(1,1) model. Results show that MCPG, followed by PPO, obtain the best performance in terms of the root semi-quadratic penalty. Moreover, MCPG is the only algorithm to outperform the Black-Scholes delta hedge baseline with the allotted computational budget, possibly due to the sparsity of rewards in our environment. © 2025, CC BY.

关键词： Learning algorithms

来源：评论

学校读者我要写书评

暂无评论

Spatial Mini Golf: Game-Based Spatial Intelligence Testing and Training Using VR and Non-VR Platforms

Spatial Mini Golf: Game-Based Spatial Intelligence Testing a...

引用

International Conference on Image and Vision Computing New Zealand, IVCNZ

作者： Patrick Oliver Andy Fong Zixuan Wang Burkhard C. Wünsche Dept. of Software Engineering University of Auckland Auckland New Zealand School of Computer Science University of Auckland Auckland New Zealand

ISBN: (数字)9798331518776

ISBN: (纸本)9798331518783

Spatial intelligence is a key determinant of students' motivation and success in pursuing a STEM career. To improve students' participation in STEM subjects it is important to test and train spatial skills in an enjoyable and unobtrusive manner. We present Spatial Mini Golf, a game designed to test and enhance spatial intelligence, targeting four sub-skills: visual perception, mental rotation, spatial visualization, and visuospatial working memory. Two versions-VR and desktop-were compared. While VR increases engagement, it also presents control challenges that raise cognitive load and extend task completion times. Performance correlates with spatial intelligence, particularly in desktop environments, but VR reduces performance gaps among lower-ability participants. Our research suggest that both VR and desktop games are suitable for testing and training spatial reasoning skills. VR tools might be more accessible for users with low spatial skills if intuitive and easy-to-use interfaces are provided, but results might get affected by users' hand-eye coordination.

关键词： Training Visualization engineering profession Games Cognitive load Testing Visual perception

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：