检索结果-内蒙古大学图书馆

International Conference on Artificial Intelligence, Blockchain, computing and Security, ICABCS 2023

作者： Narmatha, R. Prakash, M. Vennila, V. Tamil Nadu Dharmapuri India Department of Data Science and Business Systems School of Computing SRM Institute of Science and Technology Tamil Nadu Kattankulathur India Department of Computer Science and Engineering K. S. R. College of Engineering Tamil Nadu Tiruchengode India

ISBN: (纸本)9781032669663

In this modern world due to Road traffic, many people are unable to reach their destination at the correct time. For example, if a person needed to reach the hospital in critical condition due to road traffic, they are unable to reach the hospital and got stuck in traffic which will lead to death. We lost a lot of people because of this road traffic. So, Road Safety is the most important part of our daily life. Conjointly, this prediction accomplished enhanced the safety and security of women empowerment which acted as a security guard for people especially women, protecting themselves in every circumstance. Here, statistical analysis of road traffic networks of selective locations is implemented to prognosticate road traffic. Alert and divert the people who were identified in the traffic zone and found the location of the people who were identified in the unsafe zone. © 2024, CRC Press/Balkema. All rights reserved.

关键词： Hospitals

来源：评论

学校读者我要写书评

暂无评论

Machine learning approach to identity resolution for criminal profiling

引用

Journal of Cyber Security Technology 2025年

作者： Kazemian, Hassan Shrestha, Subeksha AI and Data Science Research Group School of Computing and Digital Media London Metropolitan University London United Kingdom

A common dilemma when working on criminal data is that often people manipulate their details to disguise themselves and hide their identities which leads to creating ambiguous and false identities. Deep Neural Network (DNN) is applied to work well on fraudulent and imbalanced data. Two subcategories of DNN, the Multilayer Perceptron (MLP) and Long Short-Term Memory (LSTM) with Python libraries such as TensorFlow and Keras are applied to the dataset for the detection of suspects with false identities to assist the process of analytical investigation by law enforcement agencies. Upon application of this approach to the anonymized policing dataset from SPIRIT Project funded by European Union’s Horizon, 5 main suspects with false identities were identified out of 23 targets on 39 million records. As working on criminal data is quite sensitive so to avoid data leakage while training and testing the data, K-fold validation has been applied. Furthermore, cascading both MLP and LSTM models on the policing dataset resulted in improved model prediction and accuracy compared to using each model individually. The cascaded model notably reduced false predictions for the recurring criminal patterns such as age-specific trends and most prevalent crime activities. © 2025 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Information leakage

来源：评论

学校读者我要写书评

暂无评论

Slew-Driven Layer Assignment for Advanced Non-default-rule Wires 1

引用

Proceedings of the 20th Web Information Systems and Applications Conference, WISA 2023

作者： Lu, Ren Zhang, Wei Jiang, Lieqiu Liu, Genggeng College of Computer and Data Science Fuzhou University Fuzhou350116 China Key Laboratory of Network Computing and Intelligent Information Processing Fujian Province Fuzhou350116 China

ISBN: (数字)9789819962228

ISBN: (纸本)9789819962211

With the rapid increase in circuit density in Very Large Scale Integration, the proportion of interconnect delay in circuit timing is also increasing. This makes the importance of layer assignment algorithms increasingly prominent in circuit design. However, most previous layer assignment algorithms prioritize optimizing timing exclusively from the perspective of interconnect delay, thereby disregarding the impact of slew violations on circuits. Therefore, this paper proposes a slew-driven layer assignment algorithm, which considers the timing of different routing layers and introduces non-default-rule wires to design a layer assignment algorithm that can significantly optimize delay, congestion, and slew violations. This algorithm mainly includes three key technologies: 1) Introducing non-default-rule wires technology to optimize timing and adopting a negotiation based approach to ensure that the final routing scheme does not have overflow;2) Proposing a slew prioritization strategy that comprehensively considers slew and interconnect delay during the routing process;3) Proposing a timing critical awareness strategy to further optimize the slew and interconnect delay without worsening the overflow. The experimental results show that the proposed algorithm has significant effects on optimizing delay and reducing slew violations. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Wire

来源：评论

学校读者我要写书评

暂无评论

Machine Learning and OpenCV: A Powerful Combination for Driver Drowsiness Detection

Machine Learning and OpenCV: A Powerful Combination for Driv...

引用

2024 International Conference on Communication, computing and Internet of Things, IC3IoT 2024

作者： Siva Kumar, A. Gopirajan, P.V. Ramasamy, Jayaraj Jackson, Beulah School Of Computing Srm Institute Of Science And Technology Department Of Data Science And Business Systems Kattankulathur Chennai603203 India School Of Computing Srm Institute Of Science And Technology Department Of Computational Intelligence Kattankulathur Campus Chennai603203 India Botho University Faculty Of Engineering And Technology Department of IT Botswana Vel Tech Rangarajan Dr. Sagunthala R & D Institute Of Science And Technology Department Of Electronics And Communication Engineering Chennai India

ISBN: (纸本)9798350352689

Driver fatigue is a significant contributor to road accidents worldwide, and there is a need for efficient driver drowsiness detection systems to prevent such accidents. Computer vision techniques like OpenCV have recently been utilized to develop such strategies. This proposed work investigates the use of machine learning models and OpenCV Python package for driver drowsiness detection. The proposed system uses a camera to continuously capture the driver's face and applies image processing techniques to identify drowsiness. The system detects blinks by monitoring the driver's eyes and measuring the frequency and duration of eyelid closures. The real-time experiments conducted with the proposed method showed high accuracy in detecting drowsiness, and the system can be implemented in various settings, including cars and heavy vehicles. Ultimately, the system has some potential to reduce accidents caused by driver fatigue, thereby enhancing road safety. © 2024 IEEE.

关键词： Support vector machines

来源：评论

学校读者我要写书评

暂无评论

Enhancing Machine Learning for Situation Aware Dispatching through Generative Adversarial Network Based Synthetic data Generation 24

Enhancing Machine Learning for Situation Aware Dispatching t...

引用

Proceedings of the Winter Simulation Conference

作者： Chew Wye Chan Boon Ping Gan Wentong Cai D-SIMLAB Technologies Pte Ltd SINGAPORE and College of Computing and Data Science Nanyang Technological University SINGAPORE D-SIMLAB Technologies Pte Ltd SINGAPORE College of Computing and Data Science Nanyang Technological University SINGAPORE

ISBN: (纸本)9798331534202

Adapting dispatch rules via machine learning in a complex manufacturing environment has shown overall factory performance in various studies. However, the performance of the machine learning model depends on the training data. Limited data could reduce the prediction accuracy of the machine learning model, thereby negatively influencing the overall factory performance. Addressing this, we generate synthetic data for the lot attributes, simulate it through a discrete event simulator, and use the resulting data to improve the prediction accuracy for the machine learning model. We evaluate three synthetic data generation approaches: Latin Hypercube, Synthetic Minority Oversampling Technique, and Generative Adversarial Networks (GAN), demonstrating GAN suitability for synthetic data generation. To validate our approach, we apply two evaluation processes: Train on Real, Test on Real, and Train on Synthetic, Test on Real, showing the improved predictive accuracy of the machine learning model when trained with synthetic data.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Comfort Shoulder (Rhomboid Major) Muscles Chair Identifier System 6

Comfort Shoulder (Rhomboid Major) Muscles Chair Identifier S...

引用

6th International Conference on Communications, Information System and Computer Engineering, CISCE 2024

作者： Moshayedi, Ata Jahangir Yu, Yuexin Savoji, Kimia Andani, Mehran Emadi School of Information Engineering Jiangxi University of Science and Technology Jiangxi Ganzhou China School of Computing Clemson University Biomedical Data Science and Informatics ClemsonSC United States University of Verona Department of Neurosciences Biomedicine and Movement Sciences Verona Italy

ISBN: (纸本)9798350353174

Nowadays, Sitting is one of the frequent activities of humans. particularly with the rise of office work, which is leading to an increase in the sitting periods for many individuals. Research highlights the importance of studying sitting posture to enhance seat quality, correct sitting methods, and prevent various health issues like neck and shoulder strains, etc. This article focuses on designing an IoT-based system that enables web-based data visualization without specialized software that can enable distance sampling. Through temperature sensors (DS18b20 and SHT11) and an FSR sensor, the system can identify distinct patterns in the main rhomboid muscles (the most important muscles of humans). The results show a significant relationship between individuals' body mass (BMI) and the observed muscle patterns, highlighting the importance of this correlation for researchers and industry designers alike. © 2024 IEEE.

关键词： Seats

来源：评论

学校读者我要写书评

暂无评论

Adaptive Evolutionary Reinforcement Learning Algorithm with Early Termination Strategy 23

Adaptive Evolutionary Reinforcement Learning Algorithm with ...

引用

23rd International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2024

作者： Wu, Xiaoqiang Zhu, Qingling Lin, Qiuzhen Chen, Weineng Li, Jianqiang College of Computer Science and Software Engineering Shenzhen University Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China School of Computer Science and Engineering South China University of Technology Guangzhou China

Evolutionary reinforcement learning algorithms (ERLs), which combine evolutionary algorithms (EAs) with reinforcement learning (RL), have demonstrated significant success in enhancing RL performance. However, most ERLs rely heavily on Gaussian mutation operators to generate new individuals. When the standard deviation is too large or small, this approach will result in the production of poor or highly similar offspring. Such outcomes can be detrimental to the learning process of the RL agent, as too many poor or similar experiences are generated by these individuals. In order to alleviate these issues, this paper proposes an Adaptive Evolutionary Reinforcement Learning (AERL) method that adaptively adjusts both the standard deviation and the evaluation process. By tracking the performance of new individuals, AERL maintains the mutation strength within a suitable range without the need for additional gradient computations. Moreover, the proposed AERL approach early terminates unnecessary evaluations and discards experiences arising from poor individuals, thereby resulting in enhanced learning efficiency. Empirical assessments conducted on a variety of continuous control problems demonstrate the effectiveness of the AERL method. © 2024 International Foundation for Autonomous Agents and Multiagent Systems.

关键词： Reinforcement learning

来源：评论

学校读者我要写书评

暂无评论

Multi-Task Pointwise Mutual Information Learning for Bearing Remaining Useful Life Cross-Domain Imbalanced Regression

引用

IEEE Internet of Things Journal 2025年

作者： Hu, Tao Mo, Zhenling Zhang, Zijun City University of Hong Kong College of Computing Department of Data Science Kowloon Hong Kong

In modern industry, the Industrial Internet of Things (IIoT) has enabled system health analytics and monitoring through continuous data collection and networked connectivity. Bearing remaining useful life (RUL) prediction is one pivotal analytical task in preventing industrial system failures and optimizing maintenance schedules. Existing prediction methods using data face two critical engineering challenges: (1) performance degrades when deployed to unseen operational domains, and (2) imbalanced sensor data distributions cause biased predictions. Two challenges can even co-exist, which further limits the effectiveness of prediction methods using data. This study proposes a multi-task pointwise mutual information learning (MPML) based prediction model development method to tackle bearing RUL prediction under this compound challenge. MPML offers several key innovations through the following developments. First, an auxiliary task-assisted multi-task model learning scheme is devised to obtain task-wise generalizability for learning invariant latent features, and theoretical analysis is provided to explain the invariant feature learning mechanism. Furthermore, pointwise mutual information (PMI) modeling with a statistical explanation is proposed to impose adaptive biases, rectifying penalties for RUL misprediction in minority groups. Consequently, MPML addresses the unseen domain prediction through the multi-task learning scheme and effectively handles the data imbalance with the novel PMI-assisted loss. Extensive computational experiments are conducted to demonstrate the superiority of MPML, achieving a 33.12 square error compared to state-of-the-art methods. © 2014 IEEE.

关键词： Multi-task learning

来源：评论

学校读者我要写书评

暂无评论

Hierarchical Clustering Combination Method Using Intuitionistic Fuzzy Similarity in Software Module Clustering

Hierarchical Clustering Combination Method Using Intuitionis...

引用

2023 International Conference on Networking and Network Applications, NaNA 2023

作者： Xia, Hong Wang, Bo Zhang, Yongkang Chen, Yanping School of Computer Science and Technology Xi'an University of Posts and Telecommunications China Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing China Xi 'An Key Laboratory of Big Data and Intelligent Computing Shaanxi Xi'an710121 China

ISBN: (纸本)9798350327380

In order to solve the problem that the similarity method used in software module clustering can produce arbitrary decision, and the description matrix of dendrogram generated by base clustering in hierarchical clustering combination does not consider more characteristics of entities, a hierarchical clustering combination method based on intuitionistic fuzzy similarity (HCC-IFS) was proposed. In the process of entity similarity calculation, the intuitionistic fuzzy set theory and similarity calculation method are used to provide membership and non-membership attributes for each entity, so as to improve the merging efficiency and solve arbitrary decision problems. In the description matrix representation of base clustering, the absolute and relative features obtained from the dendrogram are used to construct the description matrix, and the generated description matrix is combined. Finally, the proposed algorithm is verified by experiments. Experimental results show that HCC-IFS can produce better clustering results and improve the recovery quality of software architecture. © 2023 IEEE.

关键词： Fuzzy set theory

来源：评论

学校读者我要写书评

暂无评论

Classification of COVID-19 Symptoms Using Multilayer Perceptron

Iraqi Journal for Computer Science and Mathematics

引用

Iraqi Journal for Computer science and Mathematics 2023年第4期4卷 99-110页

作者： Azam, Nurulain Nusrah Mohd Ismail, Mohd Arfian Mohamad, Mohd Saberi Ibrahim, Ashraf Osman Jeba, Shermina Faculty of Computing Universiti Malaysia Pahang Al-Sultan Abdullah Pekan 26600 Malaysia Center of Excellence for Artificial Intelligence & Data Science Universiti Malaysia Pahang Al-Sultan Abdullah Lebuhraya Tun Razak Gambang26300 Malaysia Health Data Science Lab Department of Genetics and Genomics College of Medicine and Health Sciences United Arab Emirates University P.O. Box 17666 Al Ain Abu Dhabi United Arab Emirates Creative Advanced Machine Intelligence Research Centre Faculty of Computing and Informatics Universiti Malaysia Sabah Jalan UMS Kota Kinabalu Sabah 88400 Malaysia Department of Computing Muscat College Muscat Oman

The COVID-19 virus had easily affected people worldwide through direct contact. Individuals diagnosed with positive COVID-19 virus may be affected with many symptoms, such as fever, tiredness, dry cough, difficulty in breathing, sore throat, chest pain, nasal congestion, runny nose, and diarrhea. An individual can also be diagnosed with COVID-19 even when he does not have any symptoms or be in contact with an infected person. data classification was required due to the size of COVID-19 data that will be analyzed for future countermeasures determination. Some problems in data classification occurred due to unorganized data, such as time consumption, human error in complexity of symptom features and the diagnosis process data needed expert knowledge. This study aimed to use the artificial neural network (ANN) approach, which was multilayer perceptron (MLP) to classify the COVID-19 data by using patient symptom data. The MLP process involved data collection, data normalization, MLP design, MLP training, testing, and MLP verification. From the experiments, the MLP method was able to obtain an accuracy rate of 77.10%. In conclusion, the MLP method could classify the COVID-19 data and achieve a high accuracy rate. © 2023 College of Education, Al-Iraqia University. All rights reserved.

关键词： Multilayer neural networks

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：