检索结果-内蒙古大学图书馆

2024 International Conference on Advances in Modern Age Technologies for Health and Engineering Science, AMATHE 2024

作者： Roy, Prosenjit Riad, Md Jahid Alam Akter, Lija Hasan, Nobanul Shuvo, Mahfuzur Rahman Quader, Montaser Abdul Quader, Mozdaher Abdul Anwar, Ahmed Selim Prairie View A & M University Department Of Computer Science TX United States Washington University Of Science And Technology Vienna United States Uttara University Department Of Computer Science Bangladesh International American University Department Of Management Information System Los AngelesCA United States Green University Of Bangladesh Department Of Computer Science And Engineering Bangladesh Department Of Eee Bangladesh

ISBN: (纸本)9798350371567

This research searches into the use of Deep learning in sentiment analysis, focusing on the analysis of German patient reviews with two neural network architectures: a Bidirectional Long Short-Term Memory (BiLSTM) model with pretrained embeddings and a BERT (Bidirectional Encoder Representations from Transformers) model. The primary goal is to determine how effective these models are at categorising patient sentiments into 'poor' and 'excellent' categories. We used a dataset of approximately 500,000 reviews, each with a numerical rating and a textual comment. To facilitate model input, the text data was preprocessed. Our findings show a significant difference in performance between the two models, with the BERT model performing better in terms of accuracy and balanced classification across sentiment categories, emphasising the importance of model selection and application. The BERT model demonstrated superior performance, achieving a remarkable accuracy of 98.4%. It exhibited high precision, recall, and F1-scores, particularly in classifying 'poor' sentiments (0.98, 0.99, and 0.99, respectively). BiLSTM model with pretrained embeddings demonstrated a pronounced dip in performance for the 'excellent' category, with zero scores across precision, recall, and F1-score metrics, while retaining high performance for the 'poor' category. LSTM Without Pretrained Embeddings model's accuracy of the model is 91%,LSTM With Pretrained Embeddings result 97%.The study underscores the critical influence of model selection and the incorporation of pretrained embeddings in achieving high accuracy and balanced classification across sentiment categories. This research contributes to the ongoing conversation about the application of advanced machine learning techniques in the analysis of patient feedback and sentiment classification. © 2024 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

FIT: Fairness-Aware Intelligent Traffic Signal Control with Deep Reinforcement Learning 23

FIT: Fairness-Aware Intelligent Traffic Signal Control with ...

引用

23rd IEEE International Conference on High Performance Computing and Communications, 7th IEEE International Conference on Data Science and systems, 19th IEEE International Conference on Smart City and 7th IEEE International Conference on Dependability in Sensor, Cloud and Big Data systems and Applications, HPCC-DSS-SmartCity-DependSys 2021

作者： Chen, Jian Zhang, Zhengwei Feng, Jianhang Zhu, Kun The Key Lab. of Information System Requirement of China Electronics Technology Group Corporation Nanjing China College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing China

ISBN: (纸本)9781665494571

The fast development of learning techniques make it possible to intelligently adjust the traffic signal, also the promotion of the future network(e.g. 6G) provides the possibility to adjust the signal in real time. Several studies have proposed learning-based methods for signal control achieving superior performance compared with traditional methods. However, these methods mainly focus on improving the total efficiency for passing the intersection. To this end, a higher priority is usually given to the lane with larger traffic, which could cause the lane with few vehicles to be starved for a long time. Although traditional methods have better performance in preventing starvation, the efficiency still needs to be greatly improved. In this paper, we consider both the fairness and efficiency in intelligent traffic signal control and try to relieve such starvation while improving the total efficiency. Specifically, a deep reinforcement learning based approach is proposed to dynamically control the traffic signal according to real-time traffic information. Inspired by the proportional fair scheduling (PFS) in wireless networks, a new fairness-aware traffic signal control model is designed to maintain a good trade-off between efficiency and fairness. Extensive experiments are conducted to demonstrate that our method achieves better fairness while also provides a good efficiency guarantee. © 2021 IEEE.

关键词： 6G mobile communication Learning systems Smart cities Wireless networks Reinforcement learning Telecommunication traffic Real-time systems

来源：评论

学校读者我要写书评

暂无评论

Studying of an Adaptive Unscented Kalman Filter as a Target Tracking Algorithm with Matlab Simulation 2

Studying of an Adaptive Unscented Kalman Filter as a Target ...

引用

2nd IEEE International Maghreb Meeting of the Conference on Sciences and Techniques of Automatic Control and computer Engineering, MI-STA 2022

作者： Ammar, Abdussalam Masaud Ellafi, Amira Youssef Bozed, Kenz Amhmed Zerek, Amer R. Faculty of Electronic Technology Tripoli Libya Benghazi University Faculty of Information Technology Department of Computer System Design Libya Zawia University Faculty of Engineering EE Department Zawia Libya

ISBN: (数字)9781665479189

ISBN: (纸本)9781665479189

The radar is considered as one of tracking application which we need to tracking unknown targets. This application is using a kalman filter. Also the extended and unscented kalman filter has been created to deal with nonlinear systems such as multidimensional radar tracking. If wideband radar is used, multiple return signals may be obtained by scattering on a single target in a single pulse. It is important to monitor multiple dispersions on an undefined number of artifacts in the specific problem considered, with a standard wideband radar model, whose motion involves a nonlinear rotational velocity component. The measurements used are scattered range, range-rate (in analogy to Doppler), nonlinear functions of the scattered position and velocity, with parameters in the state-space model. In order to evaluate the scene and observe its progression over time, the tracker design uses a Kalman Filter type to model the nonlinear dynamics and measurement items and a multi-pronged association scheme. © 2022 IEEE.

关键词： Mean square error

来源：评论

学校读者我要写书评

暂无评论

Technical Job Distribution at BSD SHARP Service Center Using Combination of Naïve Bayes and K-Nearest Neighbour

Technical Job Distribution at BSD SHARP Service Center Using...

引用

2022 IEEE Symposium on Wireless technology and Applications, ISWTA 2022

作者： Pebrianti, Dwi Ariawan, Angga Bayuaji, Luhur Mahdiana, Deni Rusdah, Rusdah Universitas Budi Luhur Magister of Computer Science Faculty of Information Technology Jakarta Indonesia International Islamic University Malaysia Faculty of Engineering Dept. of Mechanical Engineering Gombak Malaysia Universitas MNC Faculty of Computer Science Department of Computer and Media Jakarta Indonesia Universitas Budi Luhur Faculty of Information Technology Department of Information System Jakarta Indonesia

ISBN: (数字)9781665484824

ISBN: (纸本)9781665484824

Works distribution is a routine carried out every day by the head of the branch in the SHARP Service Center. The accuracy of the labor division is very important to get customer satisfaction. Inappropriate work distribution can increase complaints from customers. Currently, works distribution in SHARP Service Center is carried out manually, where the works received on the selected system is then shared through the document provided. Time taken for this process is about 1.42 minutes on average for each damage reports. Speed of Service also depends on the Head of Department's expertise and experience. In this study, an automatic system based on Machine Learning will be designed for the technicians work distribution by using a combination of k Nearest Neighbor (k-NN) and Naïve Bayes. Naïve Bayes algorithm is used to improve the feature extraction accuracy by considering the feature below the average (α). Meanwhile, k-NN algorithm is used to classify the experimental data. From the study, it is found that the best of k value for k-NN algorithm is 15. It is known that a high number of accuracy values, the labor distribution can be more accurate. The validation of the proposed method is conducted by using a confusion matrix with a composition of 80% training data and 20% test data. The single Classifier test with the Naïve Bayes algorithm produces the highest accuracy value of 72.7%, while using k-NN algorithm is 81.5%. With a combination of Naive Bayes and k-NN algorithms, the accuracy value is increasing to 86%. This result shows that the proposed method improves the accuracy by 13.3% on single Naive Bayes algorithms and 4% on a single k-NN algorithm. The results obtained show that in the manual process, the average time per job is 1.42 minutes, while by using the proposed method, the average processing time is around 0.03 seconds per job. An increase of 2480 times faster is found and confirmed during the implementation of the proposed method. © 2022 IEEE.

关键词： Customer satisfaction

来源：评论

学校读者我要写书评

暂无评论

An Effective Noise Removal Technique for Enhancing Ischemic Brain Stroke Computed Tomography Images 1

An Effective Noise Removal Technique for Enhancing Ischemic ...

引用

1st International Conference on Innovations in Communications, Electrical and computer Engineering, ICICEC 2024

作者： Puneeth, S.P. Poornima, B. PremaSudha, B.G. Patil, N.S. Puneetha, B.H. Jadekar, Ranjana B. Visvesvaraya Technological University Bapuji Institute of Engineering and Technology Davangere Department of Information Science and Engineering Karnataka Belagavi India Visvesvaraya Technological University Siddaganga Institute of Technology Tumkur Department of Mca Karnataka Belagavi India Visvesvaraya Technological University Bapuji Institute of Engineering and Technology Davangere Department of Computer Science and Business System Karnataka Belagavi India Visvesvaraya Technological University Bapuji Institute of Engineering and Technology Davangere Department of Computer Science and Engineering Karnataka Belagavi India

ISBN: (纸本)9798350376517

Ischemic stroke significantly affects millions of people motor and cognitive skill. Early identification of ischemic stroke helps in preventing loss of life. Usually, noise is present in Computed-Tomography (CT) during acquisition and the noise degrades the visibility of anatomical structures and subtle abnormalities, making it difficult for radiologists to accurately diagnose and interpret medical conditions. Thus, denoising CT medical images is crucial in preserving information and restoring images contaminated with noise. The Convolution-Neural-Network (CNN) has been used to remove the noise with good effect;however, the performance resulted in a loss of clarity and fine details preservation that rendered the CT images unsuitable. In addressing this, presented an enhanced noise removal techniques namely Morphology-Aware CNN (MA-CNN) to enhance brain CT image of ischemic stroke patients for better segmentation, classification to enhance diagnosis. The MA-CNN performance is studied in term of Peak-signal-to-noise-ratio (PSNR);the overall result attained shows the proposed model exhibit higher PSNR in comparison with existing denoiser methods and assures good performance in maintaining the quality of the image and fine details' preservation. © 2024 IEEE.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

A Comparative Study of Transformers Embeddings for Question Answering in Arabic Private Documents

A Comparative Study of Transformers Embeddings for Question ...

引用

Big Data, Cloud Computing, Data Science & Engineering (BCD)

作者： Essam Al Daoud Ghassan Samara Laith Al Daoud Mohammad Al Jaidi Computer Science Department Zarqa University Jordan Computer Information System Department Jordan University of Science and Technology Jordan

Question answering (QA) tasks in natural language processing (NLP) are tricky, particularly when used with Arabic private documents. This is due to the complexity of Arabic language and the lack of sufficient annotated datasets for Arabic QA. Recent research findings have demonstrated the effectiveness of transformer embeddings for English QA. Their efficiency for QA in Arabic private documents, however, has not been fully investigated. In this study, various transformer embeddings for QA in Arabic private documents are compared. The effectiveness of these embeddings is evaluated by assessing a golden dataset extracted from private documents. The results show that the accuracy of QA in Arabic private documents can be significantly improved by combining transformer embeddings with count vectorization. Furthermore, different transformer embeddings have distinct strengths and weaknesses.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Collaborative Mobile Charging Vehicles Placement: A Reinforcement Learning Approach 23

Collaborative Mobile Charging Vehicles Placement: A Reinforc...

引用

作者： Ni, Jianing Liang, Rupeng Wu, Hao The Key Lab. of Information System Requirement of China Electronics Technology Group Corporation Nanjing China College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing China

ISBN: (纸本)9781665494571

Nowadays, there are increasing demand from electric vehicles that need to be charged during peak hours in a city. The number of traditional fixed charging stations is limited, which may not be sufficient to satisfy the charging demand of electric vehicle users. Mobile charging vehicles can be scheduled at fixed positions to serve as a fixed charging station, providing charging services for electric vehicle users, for relieving the pressure of mass charging demand. In this paper, the charging scheduling problem of mobile charging vehicle is modeled as a Markov Decision Process(MDP) problem, and a reinforcement learning method based on policy evaluation is adopted to solve this problem. The decision of the specific position to which the mobile charging vehicle is scheduled is determined by this method. In addition, the decision of how to dispatch electric vehicle charging orders is determined by a fixed algorithm. We propose two different order dispatching algorithms. According to the simulation, our proposed reinforcement learning method has superior results in terms of the efficiency of order processing and user satisfaction compared with the traditional greedy method. The two different electric vehicle charging order dispatching algorithms have their own advantages and disadvantages in terms of processing speed and effectiveness. © 2021 IEEE.

关键词： Greedy algorithms Smart cities Simulation Decision making Reinforcement learning Markov processes Charging stations

来源：评论

学校读者我要写书评

暂无评论

Metric Learning with Distillation for Overcoming Catastrophic Forgetting 16th

Metric Learning with Distillation for Overcoming Catastrop...

引用

16th International Conference on Bio-Inspired Computing: Theories and Applications, BIC-TA 2021

作者： Yu, Piaoyao He, Juanjuan Min, Qilang Zhu, Qi College of Computer Science and Technology Wuhan University of Science and Technology Wuhan China Hubei Province Key Laboratory of Intelligent Information Processing and Real-Time Industrial System Wuhan China

ISBN: (纸本)9789811912528

In incremental learning, reducing catastrophic forgetting always adds additional weights to the classification layer when a new task comes. Moreover, it focuses on prediction accuracy, ignores mislabeling differences, and causes the learned features to be scattered. This paper removes the softmax and integrates depth metric learning to compute image embeddings, which doesn’t need to add extra weights or additional space for new classes. The distances of the data mapped in the old and new task spaces are calculated separately. Then, the distillation of depth metric learning is used to make the two distances as similar as possible to improve the performance of the old knowledge. The experimental results on Cifar10 and Tiny-ImageNet show that the proposed method can effectively alleviate catastrophic forgetting and enhance the effectiveness of incremental learning. © 2022, Springer Nature Singapore Pte Ltd.

关键词： Distillation

来源：评论

学校读者我要写书评

暂无评论

Review of Data Visualization Techniques in IoT Data 8

Review of Data Visualization Techniques in IoT Data

引用

8th International Conference on information technology Trends, ITT 2022

作者： Il-Agure, Zakea Dempere, Juan Computer Information System Higher Colleges of Technology Ras Al Khaimah United Arab Emirates Faculty of Business Higher Colleges of Technology Ras Al Khaimah United Arab Emirates

ISBN: (数字)9781665487429

ISBN: (纸本)9781665487412

Data visualization is considered the most efficient solution in IoT. It visualizes the endless stream of information collected by the respective smart systems to exponentially increase its value due to the meaningful insights it provides. Notably, numerous tools and techniques visualize the meaningful information obtained. It is, however, prudent that the analysis of this information considers the possibilities of anomalies within the data. The proper mechanisms must be implemented to detect these anomalies to ensure that the visualized information is accurate and reliable. This research discussion delves into reviewing current literature on the different tools and techniques that could detect anomalies in IoT. The review would reveal that the machine learning and deep learning approaches are the most-used approaches for anomaly detection. Several images would emerge regarding the visualization tools, including Grafana, Tableau, Orange, Power Bi tool, Kibabana, and Plotly. Tableau was identified as the popular and leading software used by numerous entities for visualizing the data in meaningful insights leading to powerful decisions. Some of its salient features include offering Api's and sophisticated developer tools such that different companies could use to meet specific needs, it has a less learning curve and has the ability to handle with extensive amounts of data generated in the network. Notably, the fusion of Big Data and IoT will continue to develop and advance both the existing and new visualization tools. These developments will arise because the IoT networks will continue to obtain data from various sources as the number of smart devices increases tremendously. © 2022 IEEE.

关键词： Data visualization

来源：评论

学校读者我要写书评

暂无评论

Discounted Iterative Adaptive Critic Designs With Novel Stability Analysis for Tracking Control

引用

IEEE/CAA Journal of Automatica Sinica 2022年第7期9卷 1262-1272页

作者： Mingming Ha Ding Wang Derong Liu School of Automation and Electrical Engineering University of Science and Technology BeijingBeijing 100083China Faculty of Information Technology the Beijing Key Laboratory of Computational Intelligence and Intelligent Systemthe Beijing Laboratory of Smart Environmental Protectionand the Beijing Institute of Artificial IntelligenceBeijing University of TechnologyBeijing 100124China Department of Electrical and Computer Engineering University of Illinois at ChicagoChicago IL 60607 USA IEEE

The core task of tracking control is to make the controlled plant track a desired *** traditional performance index used in previous studies cannot eliminate completely the tracking error as the number of time steps *** this paper,a new cost function is introduced to develop the value-iteration-based adaptive critic framework to solve the tracking control *** the regulator problem,the iterative value function of tracking control problem cannot be regarded as a Lyapunov function.A novel stability analysis method is developed to guarantee that the tracking error converges to *** discounted iterative scheme under the new cost function for the special case of linear systems is ***,the tracking performance of the present scheme is demonstrated by numerical results and compared with those of the traditional approaches.

关键词： Adaptive critic design adaptive dynamic programming(ADP) approximate dynamic programming discrete-time nonlinear systems reinforcement learning stability analysis tracking control value iteration(VI)

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：