检索结果-内蒙古大学图书馆

2024 International Conference on Communication, computer sciences and engineering, IC3SE 2024

作者： Chaturvedi, Prateek Thethi, H. Pal Reddy, N. V. Uma Kalaiarasi, M. Al-Rubaye, Taqi Mohammed Khattab Anusha, K. Amity University Greater Noida Department Of Mechanical Engineering India Lovely Professional University Phagwara India New Horizon College Of Engineering Department Of Artificial Intelligence And Machine Learning Bangalore India Institute Of Aeronautical Engineering Department Of Computer Science And Engineering Telangana Hyderabad India College Of Medical Technologies The Islamic University Department Of Medical Laboratory Technology Najaf Iraq Mlr Institute Of Technology Department Of CSE-AI&ML Telangana Hyderabad India

ISBN: (纸本)9798350366846

Effective modeling approaches are required for the management and operation of the Energy Internet due to the system's complexity and mobility. New hybrid models that link the Energy Internet's digital, real-world, and social components are the primary focus of this article. We propose a novel approach that integrates adaptive reinforcement learning, social behavior analysis, and multi-agent coordination optimization to enhance the system's performance, robustness, and long-term sustainability. We intend to address issues that arise when several factors, such as evolving technology, user behavior, and social interactions, converge by employing these mixed modeling approaches. Because of this, the Energy Internet setting will run more efficiently and effectively. Through extensive simulations and case studies, we demonstrate that our proposed method outperforms conventional ones. Our main focus is on how technology can enhance energy management, better allocate resources, and back user-centered and socially inclusive policies. To ensure the seamless operation of the Energy Internet environment's cyber, physical, and social components, the results highlight the significance of mixed modeling methodologies. © 2024 IEEE.

关键词： Multi agent systems

来源：评论

学校读者我要写书评

暂无评论

An Intelligent Thyroid Diagnosis System Utilizing Multiple Ensemble and Explainable Algorithms with Medical Supported Attributes

IEEE Transactions on Artificial Intelligence

引用

IEEE Transactions on Artificial Intelligence 2024年第6期5卷 2840-2855页

作者： Sutradhar, Ananda Al Rafi, Mustahsin Ghosh, Pronab Shamrat, F M Javed Mehedi Moniruzzaman, Md. Ahmed, Kawsar Azad, A.K.M. Bui, Francis M. Chen, Li Moni, Mohammad Ali Daffodil International University Department of Computer Science and Engineering Dhaka1216 Bangladesh Lakehead University Department of Computer Science Thunder BayONP7B 5E1 Canada University of Malaya Department of Computer System and Technology Kuala Lumpur50603 Malaysia Lakehead University Department of Software Engineering Thunder BayONP7B 5E1 Canada University of Saskatchewan Department of Electrical and Computer Engineering SaskatoonSK S7N 5A9 Canada Imam Mohammad Ibn Saud Islamic University Faculty of Science Department of Mathematics and Statistics Riyadh13318 Saudi Arabia Charles Stuart University AI and Cyber Futures Institute BathurstNSW2795 Australia

The widespread impact of thyroid disease and its diagnosis is a challenging task for healthcare experts. The conventional technique for predicting such a vital disease is complex and time-consuming. A data-driven approach may offer predictive solutions, but it relies on all relevant attributes, which are computationally expensive. Hence, we propose a novel machine learning (ml) based disease prediction system that could potentially predict it by considering three crucial steps. First, to reduce the dimension of the dataset, three feature selection techniques were employed, including feature importance (FIS), information gain selections (IGS), and least absolute shrinkage and selection operator (LAS). Moreover, recommended medical references were considered while developing a feature set having the identical attributes as high-risk factors (HRF). Second, the models, including the three stage hybrid classifier (3SHC) and the three stage hybrid artificial neural network (3SHANN), are used as classifiers on the training data set. Third, a local interpretable model-agnostic explanations (LIME) to the 3SHC with the HRF samples was applied to individually explain the predictions. Then, the overall behaviors of both gender and age categories were explored with the help of a partial dependence plot (PDP). Finally, the proposed system is validated with extensive experiments, where the 3SHC achieves an accuracy (ACC) of 99.29%, which can play a crucial role in preventing thyroid disease and alleviating stress in the healthcare sector. © 2020 IEEE.

关键词： Neural networks

来源：评论

学校读者我要写书评

暂无评论

Revolutionizing Cancer Diagnosis with Deep Learning: A Case Study Using U-Net 7

Revolutionizing Cancer Diagnosis with Deep Learning: A Case ...

引用

7th International Conference on Contemporary Computing and Informatics, IC3I 2024

作者： Goyal, Himanshu Rai Al-Farouni, Mohammed Deepak, A. Reddy, R. Akhilesh Yadav, Kanchan Revathi, V. Graphic Era Deemed to be University Department of Computer Science & Engineering Dehradun India The Islamic university College of technical engineering Department of computers Techniques engineering Najaf Iraq Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha School of Engineering Tamilnadu Chennai India Mlr Institute of Technology Department of CSE-AI&ML Telangana Hyderabad India Gla University Department of Electrical Engineering Mathura India New Horizon College of Engineering Department of Applied Sciences Bangalore India

ISBN: (纸本)9798350350067

The U-Net architecture is a convolutional neural network model that is well-known for its skills in biomedical image segmentation. This study investigates the revolutionary potential that the U-Net architecture presents in the context of cancer diagnosis. We guarantee that the input is of high quality by using sophisticated preprocessing methods, which we do by constructing a robust U-Net-based system that is specifically designed for the segmentation and classification of tumour regions in medical imaging data. An exhaustive examination that makes use of criteria such as the Dice similarity coefficient, precision, recall, and F1 score indicates that our model performs substantially better than conventional techniques when it comes to identifying and distinguishing malignant tissues. Case studies that cover a wide range of cancer types provide further validation of the flexibility and practical application of the system. These studies also highlight the significant influence that deep learning has on increasing diagnosis accuracy, lowering the risk of human error, and overall patient outcomes in the field of cancer treatment. © 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

Mask R-CNN-based Occlusion Anomaly Detection Considering Orientation in Manufacturing Process Data

引用

IEIE Transactions on Smart Processing and Computing 2022年第6期11卷 393-399页

作者： Lee, Seo-El Choi, So-Eun Park, Geon Kang, Ye-Yeon Baek, Ji-Won Chung, Kyungyong Department of Public Safety BigData Kyonggi University Suwon Korea Republic of Department of Industrial Management Engineering Kyonggi University Suwon Korea Republic of Division of Computer Science and Engineering Kyonggi University Suwon Korea Republic of Department of Computer Science Kyonggi University Suwon Korea Republic of Division of AI Computer Science and Engineering Kyonggi University Suwon Korea Republic of

In a manufacturing process, data analysis is conducted to identify defective products in real time to lower their massive production and improve the rate of efficient production. In the production process, it is difficult to find defective products mixed with normal products. Therefore, it is necessary to detect defective products generated in the production process and reduce the risk of their production. Consequently, this study proposes the Mask R-CNN-based occlusion anomaly detection method in consideration of the orientation of manufacturing process data. The proposed method uses Mask R-CNN to find abnormal objects, such as occluded objects, in a manufacturing process line. In the manufacturing process, some products are hidden. Accordingly, preprocessing in consideration of multiple orientations is applied to generate data. The generated data is performed to detect occlusions and anomalies using Mask R-CNN. The mean of IoU was compared to evaluate the detection accuracy of YOLO and Mask R-CNN. YOLO showed excellent performance when there was a constant distance and orientation and no occluded object. However, Mask R-CNN performed excellently when there was any occluded object and the orientation was considered. Therefore, for occlusion anomaly detection in a manufacturing process, Mask R-CNN can reduce the production rate of defective products. Copyrights © 2022 The Institute of Electronics and Information Engineers.

关键词： Object detection

来源：评论

学校读者我要写书评

暂无评论

Automated Student Assignment Evaluation Based on Information Retrieval and Statistical Techniques 2nd

Automated Student Assignment Evaluation Based on Information...

引用

2nd Congress on Control, Robotics, and Mechatronics, CRM 2024

作者： Samya, Banoth Archana, M. Ramana, T. Venkata Raju, Kachapuram Basava Ramineni, Kamalakar Department of CSE CVR College of Engineering Hyderabad India Department of AI Anurag University Hyderabad India School of Computer Science & Artificial Intelligence SR University Telangana Warangal India

ISBN: (纸本)9789819770939

Efficient automated evaluation is urgently needed in programming courses within the field of online education. This study introduces a model that focuses on the TF-IDF algorithm, a reliable technique for information retrieval, to simplify the assessment procedure in programming courses with ten distinct categories. TF-IDF is a numerical statistical technique that measures the relevance of tokens. When combined with cosine similarity, it yields impressive outcomes in document scoring and ranking. The suggested paradigm greatly decreases the amount of time and effort teachers need to spend on manual assessment. The experimental results confirm the effectiveness of the model, demonstrating its strong ability to automatically assess programming tasks. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Teaching

来源：评论

学校读者我要写书评

暂无评论

Spatiotemporal Traffic Analysis: Deep Hybrid Approach for Resource and Cyberattacks Prediction 19

Spatiotemporal Traffic Analysis: Deep Hybrid Approach for Re...

引用

19th International Conference on Ubiquitous Information Management and Communication, IMCOM 2025

作者： Lee, Hyeonmin Kim, Dongchan Beom, Donggyu Byun, Gyurin Vo, Van-Vi Choo, Hyunseung Department of AI Systems Engineering Sungkyunkwan University Suwon16419 Korea Republic of Department of Electrical and Computer Engineering Sungkyunkwan University Suwon16419 Korea Republic of Convergence Research Institute Sungkyunkwan University Suwon16419 Korea Republic of Department of Computer Science and Engineering Sungkyunkwan University Suwon16419 Korea Republic of

ISBN: (纸本)9798331507817

This study proposes a hybrid deep learning approach to address the complexity and dynamic characteristics of modern network environments. The research integrates Graph Neural Networks (GNNs) and Convolutional Long and Short-Term Memory (ConvLSTM) networks to predict future network traffic and detect cyberattacks. The model was trained and evaluated on the CICIDS 2018 dataset which includes various types of normal and malicious traffic. Grid-based representations were created from the dataset. The attack type of each timestamp were visualized using distinct colors to the grid images. These images are sued as input of ConvLSTM model. Protocol and destination port are modeled as nodes in a graph, with volume of data flow represented as weighted edges. This graph-based representation is designed to capture unique traffic patterns of specific services and enhance the analysis of spatial and temporal features. Experimental results show a 13% increase in macro F1 score and a 4% increase in weighted F1 score, highlighting the effectiveness of the proposed method. This research highlights its potential to enable proactive responses through the prediction of potential cyberattack occurrences. © 2025 IEEE.

关键词： Convolutional Long Short-Term Memory Cyberattack Detection Deep Learning Graph Neural Networks Network Traffic Prediction

来源：评论

学校读者我要写书评

暂无评论

Predictive Analytics For Groundwater Resources Using SVM And Deep Radial Basis Approaches In Smart City Planning For Feature Extraction And Prediction 2

Predictive Analytics For Groundwater Resources Using SVM And...

引用

2nd International Conference on Disruptive Technologies, ICDT 2024

作者： Padmavathi, N. Rafee, Shaik Mohammad Ahmed, Salman Kanagavalli, N. Gangwar, Prashant Kumar Meenakshi, S. Rmd Engineering College Department of Electronics and Instrumentation Engineering Tamilnadu Chennai India Sasi Institute of Technology & Engineering Department of Ai&ml Andhra Pradesh Tadepalligudem India Aligarh Muslim University Interdisciplinary Department of Remote Sensing & Gis Applications Uttar Pradesh Aligarh India Rajalakshmi Institute of Technology Department of Computer Science and Engineering Tamilnadu Chennai India Woldia University Department of Construction Technology & Management Woldia Town Ethiopia Tamilnadu Kavarapettai India

ISBN: (纸本)9798350371055

The growth of smart cities is happening at the same time that there is an increasing demand for water resources. Controlling groundwater in a responsible manner is absolutely necessary in order to ensure sustainability. When it comes to groundwater forecast, traditional methods are frequently wrong, which in turn leads to inefficient use of resources and bad planning. Specifically, the research employs a two technique, which combines Support Vector Machines (SVMs) for efficient feature extraction with DRB Approaches (DRBAs) for accurate prediction. SVM is excellent at extracting significant properties from complicated datasets, whereas DRB takes use of neural network capabilities to produce variable predictions. DRB employs neural network capabilities. When compared to methods that are considered to be more conventional, the findings indicate a significant improvement in the accuracy of this prediction. It is exciting to see that the combined support vector machine and DRB model are producing positive results when it comes to estimating groundwater resources. © 2024 IEEE.

关键词： Deep Radial Basis Groundwater Resources Predictive Analytics Smart City SVM

来源：评论

学校读者我要写书评

暂无评论

Consistent diffusion meets tweedie: training exact ambient diffusion models with noisy data 24

Consistent diffusion meets tweedie: training exact ambient d...

引用

Proceedings of the 41st International Conference on Machine Learning

作者： Giannis Daras Alexandros G. Dimakis Constantinos Daskalakis Department of Computer Science University of Texas at Austin and Archimedes AI Department of Electrical and Computer Engineering University of Texas at Austin Department of Electrical Engineering and Computer Science MIT and Archimedes AI

Ambient diffusion is a recently proposed framework for training diffusion models using corrupted data. Both Ambient Diffusion and alternative SURE-based approaches for learning diffusion models from corrupted data resort to approximations which deteriorate performance. We present the first framework for training diffusion models that provably sample from the uncorrupted distribution given only noisy training data, solving an open problem in Ambient diffusion. Our key technical contribution is a method that uses a double application of Tweedie's formula and a consistency loss function that allows us to extend sampling at noise levels below the observed data noise. We also provide further evidence that diffusion models memorize from their training sets by identifying extremely corrupted images that are almost perfectly reconstructed, raising copyright and privacy concerns. Our method for training using corrupted samples can be used to mitigate this problem. We demonstrate this by fine-tuning Stable Diffusion XL to generate samples from a distribution using only noisy samples. Our framework reduces the amount of memorization of the fine-tuning dataset, while maintaining competitive performance.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Hybrid Quantum Computing and Deep Learning Approaches for Enhancing Wireless Communication Security

Hybrid Quantum Computing and Deep Learning Approaches for En...

引用

Emerging Systems and Intelligent Computing (ESIC), International Conference on

作者： Ramu V Kalyan Barla Kavitha V Srinivas Aluvala Sankepally Chandrasekhar A.Athiraja Department of CSE (AI&ML) KITS Warangal Hanamkonda Telangana Department of IT Bannari Amman Institute of Technology Erode School of Computer Science & Artificial Intelligence SR University Warangal Department of Mechanical Engineering Malla Reddy Engineering College (Autonomous) Telangana Department of CSE (AI&ML) Malla Reddy Engineering College (Autonomous) Telangana

ISBN: (数字)9798331522100

ISBN: (纸本)9798331522117

A potential way to improve wireless communication security in the face of increasingly complex cyberthreats is to combine deep learning techniques with hybrid quantum computing. By combining the processing benefits of quantum algorithms with the pattern recognition powers of deep learning, this article tackles the crucial problems of protecting wireless networks, which are vulnerable to eavesdropping, spoofing, and data breaches. The research uses a hybrid framework in which deep learning models like convolutional neural networks (CNNs) identify and stop possible invasions in real time, while quantum key distribution (QKD) guarantees safe data transfer. Simulations and experimental validation in a 5G network environment show that the proposed system has a 40% reduction in latency and a 35% increase in intrusion detection accuracy compared to traditional security methods, like standard encryption techniques and rule-based intrusion detection systems that lack quantum cryptographic mechanisms and advanced ai-driven pattern recognition. The findings demonstrate that integrating quantum and ai-driven methods to build a more robust wireless communication infrastructure is feasible. The study concludes by highlighting the potential of hybrid quantum-ai systems as a critical first step in protecting next-generation networks.

关键词： Deep learning 5G mobile communication Computational modeling Wireless networks Intrusion detection Real-time systems Quantum key distribution Security Artificial intelligence Optimization

来源：评论

学校读者我要写书评

暂无评论

Automated Detection of Alzheimer's Disease Using EfficientNet-B3 Architecture with Transfer Learning 25

Automated Detection of Alzheimer's Disease Using EfficientNe...

引用

25th International Arab Conference on Information Technology, ACIT 2024

作者： Aburomman, Ahmad Abuowaida, Suhaila Al-Momani, Adai Al Henawi, Esraa Elsoud, Esraa Abu Salah, Zaher Alzoubi, Haneen Chan, Huah Yong Khouj, Mohammed Zarqa University Department of Data Science and Ai Zarqa Jordan Al-Bayt University Department of Computer Science Ai Mafraq Jordan Zarqa University Department of Computer Science Zarqa Jordan The Hashemite University Department of Information Technology Zarqa Jordan Applied Science Private University Faculty of Information Technology Amman Jordan Universiti Sains Malaysia School of Computer Sciences Pulau Pinang Gelugor11800 Malaysia University of Business and Technology Department of Electrical Engineering Jeddah21432 Saudi Arabia

ISBN: (纸本)9798331540012

In order to bring forward expansion in both the accuracy and the processing speed of the detection of Alzheimer's disease, an automated approach for Alzheimer's disease diagnosis with passable EfficientNet-B3 neural network and transfer learning techniques is provided in this work. After direct fitness on a large number of annotated brain images, the model of EfficientNet-B3 is further trained, in an effort to obtain worse depicted quality connected with Alzheimer's Disease. To hasten the process of training and to make the model fit for this specific task, transfer learning was employed. The results of these experiments prove the efficiency of our approach to the diagnostic Alzheimer's disease compared to the most advanced and regular deep learning methods. Thus, the model may also withstand the test amidst diverse clinical populations by performing well in dementia classification with precision and recall performance coupled with over 99% accuracy. These findings demonstrate how the EfficientNet-B3 architecture and transfer learning enhance generalization across. © 2024 IEEE.

关键词： Image annotation

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：