检索结果-内蒙古大学图书馆

3rd IEEE Delhi Section Flagship Conference, DELCON 2024

作者： Safia Naveed, S. Ameen, Nabeena Nulin Jeriba, J. Kcg College of Technology Department of Computer Science and Engineering Chennai India B. S. Abdur Rahman Crescent Institute of Science and Tech Department of Information Technology India

ISBN: (纸本)9798331518592

Heart attacks remain a primary cause of global mortality, highlighting the urgent need for systems that enable early detection and intervention. This study proposes a Smart IoT-based system designed to detect and monitor heart attack symptoms in real-time, thus facilitating timely medical response. The system integrates wearable sensors to collect continuous data on heart rate, stress levels, and positional changes, which is then processed through Arduino IDE and transmitted to an IoT server. Machine learning algorithms, including Support Vector Regression (SVR) and Artificial Neural Networks (ANN), are employed to analyze these signals for abnormal patterns indicative of a potential heart attack. Major findings indicate that this system accurately detects heart rate irregularities, and stress fluctuations and can alert healthcare providers, significantly improving remote monitoring capabilities. The implications of this study suggest that IoT-enabled monitoring systems can substantially enhance patient outcomes by allowing for early diagnosis and proactive care in remote settings, thereby reducing the risk of fatal heart events. © 2024 IEEE.

关键词： Wearable sensors

来源：评论

学校读者我要写书评

暂无评论

Text recognition using improved dual attention based on textual double embedding network with aquila optimization algorithm

引用

International Journal of information technology (Singapore) 2024年 1-15页

作者： Singhdev, Harsiddhi Gupta, Shruti Srivastava, Vivek Saxena, Apoorva Department of Computer Science and Engineering ABES Engineering College Uttar Pradesh Ghaziabad India Department of Computer Science and Engineering Axis Institute of Technology & amp Management Uttar Pradesh Kanpur India

Text recognition is the process that changes an image of text into a system readable text format. Different approaches were suggested related to text detection but the existing methods accuracy is low and error rate is high. Therefore, a text recognition using Improved Dual-attention based on Textual Double Embedding (IDTDE) with aquila optimization algorithm is proposed in this manuscript for effective text recognition. In this method the input image is taken from several datasets. Usually, the images have some kind of noise in it, and to eliminate that, the Structural interval gradient filtering preprocessing technique is used. Then, the ternary pattern and discrete wavelet technique is use to extract best features. Next, residual-based temporal attention convolutional neural network is utilized for the text classification and character identification are effectively attained by and IDTDE network. The Aquila optimization algorithm is used to optimize the network for best extracted text as output. Experimental outcomes demonstrate that the introduced approach accomplishes the high accuracy rates of 98.14%, 98.89%, and 90.47% on the IIIT5K, ICDAR 2013, and ICDAR 2015 datasets, respectively, surpassing the performance of existing frameworks. © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： Aquila optimizer Character recognition Gradient filtering Improved self-attention Text recognition

来源：评论

学校读者我要写书评

暂无评论

A Deep CNN-Based Approach for Revolutionizing Bengali Handwritten Numeral Recognition 2nd

A Deep CNN-Based Approach for Revolutionizing Bengali Handwr...

引用

2nd International Conference on Big Data, IoT and Machine Learning, BIM 2023

作者： Islam, Sudipta Progga Parvin, Farjana Department of Computer Science and Engineering Rajshahi University of Engineering and Technology Rajshahi Bangladesh

ISBN: (纸本)9789819989362

Recognition of Bengali handwritten digits is a fascinating and demanding research problem that has garnered significant interest from researchers in the fields of pattern recognition. In this paper, a task-oriented deep convolutional architecture for recognizing handwritten Bengali digits is proposed. The main goal is to get a high level of accuracy while using a small number of parameters. The proposed architecture is designed to address the challenges posed by the complex and diverse nature of handwritten numerals in Bengali script, while also being computationally efficient with only 1.08 million trainable parameters. The performance of the model was evaluated by conducting experiments on two commonly used benchmark datasets of handwritten numerals in Bengali script, CMATERdb-3.1.1 and BanglaLekha-isolated-numerals. Different augmentation techniques were utilized to enhance the diversity and size of the training set, which led to improved robustness and generalization of the model. On the CMATERdb-3.1.1 dataset, the proposed model achieved an accuracy of 99.28%, and on the BanglaLekha-isolated-numerals dataset, it achieved an accuracy of 99.12%, outperforming several state-of-the-art models with comparable or larger numbers of parameters. The results suggest that this task-oriented model can be an efficient and effective solution for the recognition of Bengali handwritten numerals, with potential applications in document analysis, digitization, and text recognition. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： computer vision

来源：评论

学校读者我要写书评

暂无评论

STARS Assisted Semi-Grant-Free NOMA Communications

引用

IEEE Transactions on Wireless Communications 2025年第6期24卷 4904-4919页

作者： Xie, Jin Yue, Xinwei Zhao, Jingjing Zou, Yixuan Liu, Yuanwei Liu, Rongke Ding, Zhiguo Key Laboratory of Information and Communication Systems China Beijing Information Science and Technology University Key Laboratory of Modern Measurement and Control Technology Ministry of Education Beijing100101 China Beihang University School of Electronic and Information Engineering Beijing100191 China Queen Mary University of London School of Electronic Engineering and Computer Science LondonE1 4NS United Kingdom The University of Hong Kong Department of Electrical and Electronic Engineering Hong Kong Hong Kong Kyung Hee University Department of Electronic Engineering Gyeonggi-do Yongin-si17104 Korea Republic of Khalifa University Department of Computer and Information Engineering Abu Dhabi United Arab Emirates

This paper investigates the performance of simultaneously transmitting and reflecting surface (STARS) assisted semi-grant-free non-orthogonal multiple access network with randomly distributed users. By deploying STARS, the transmit signals of grant-based user (GBU) and grant-free users (GFUs) can be exquisitely adjusted to reduce interference. We propose a maximum channel scheduling (MCS) protocol that allows a GFU to access GBU’s channel with the assistance of STARS. In particular, the impacts of perfect/imperfect successive interference cancellation (pSIC/ipSIC) on MCS protocol are taken into account. To characterize the performance of STARS aided MCS (STARS-MCS) network, we derive the expressions of outage probability for GBU and GFU with pSIC/ipSIC. By applying convolution theorem and Laplace transform, the asymptotic expressions of outage probability and diversity orders for GBU and GFU are attained. We further design a STARS-based power control (SPC) strategy to eliminate the outage probability error floor and improve the outage performance. Numerical results show that: 1) The performance of STARS-MCS outperforms the existing benchmarks in terms of outage probability and system throughput;2) The SPC strategy can effectively improve the performance of the STARS-MCS network and eliminate the outage probability error floor at high signal-to-noise ratios;and 3) By adjusting reflection and transmission coefficients of STARS, the outage performance of GBU and GFU can be greatly enhanced. © 2025 IEEE. All rights reserved.

关键词： Laplace transforms

来源：评论

学校读者我要写书评

暂无评论

Efficiently Enumerating All Spanning Trees of a Plane 3-Tree: (Extended Abstract) 13th

Efficiently Enumerating All Spanning Trees of a Plane 3-Tr...

引用

13th International Symposium on Algorithms and Complexity, CIAC 2023

作者： Yanhaona, Muhammad Nur Nomaan, Asswad Sarker Rahman, Md. Saidur Department of Computer Science and Engineering BRAC University Dhaka Bangladesh Graph Drawing and Information Visualization Laboratory Department of Computer Science and Engineering Bangladesh University of Engineering and Technology Dhaka Bangladesh

ISBN: (纸本)9783031304477

A spanning tree T of a connected, undirected graph G is an acyclic subgraph having all vertices and a minimal number of edges of G connecting those vertices. Enumeration of all possible spanning trees of undirected graphs is a well-studied problem. Solutions exist for enumeration for both weighted and unweighted graphs. However, these solutions are either space or time efficient. In this paper, we give an algorithm for enumerating all spanning trees in a plane 3-tree that is optimal in both time and space. Our algorithm exploits the structure of a plane 3-tree for a conceptually simpler alternative to existing general-purpose algorithms and takes time and space, where the given graph has n vertices, m edges, and spanning trees. This is a substantial improvement in both time and space complexity compared to the best-known algorithms for general graphs. We also propose a parallel algorithm for enumerating spanning trees of a plane 3-tree that has (formula presented) space complexities for p parallel processors. This second algorithm is useful when storing the spanning trees is important. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Undirected graphs

来源：评论

学校读者我要写书评

暂无评论

A DEEP LEARNING-BASED FAKE NEWS DETECTING SYSTEM

A DEEP LEARNING-BASED FAKE NEWS DETECTING SYSTEM

引用

2023 IET International Conference on engineering Technologies and Applications, ICETA 2023

作者： Chen, Po-Kai Nguyen, Khai-Thinh Feng, Zhiquan Tai, Tzu-Chiang Wang, Jia-Ching Department of Computer Science and Information Engineering National Central University Zhongli Taiwan Department of Computer Science and Information Engineering Providence University Taichung Taiwan

ISBN: (纸本)9781839539848

Since the birth of the Internet, social media has gradually taken up an increasingly important role in our lives. Whether it's food, clothing, housing, transportation, or keeping up with the latest events, we all rely on the vast amount of news and information provided by social media. Therefore, many unscrupulous business entities that publish all kinds of false information for profit, and the general public is easily misled because of their limited knowledge reserve. In this paper, we embrace the FNC-1 challenge as the foundation for crafting our innovative fake news detection system. In the course of our exploration, we discerned that the FNC-1 dataset was marred by issues pertaining to both class imbalance and data scarcity. To surmount these intricacies, we introduce an original data augmentation approach hinging on the principles of deep learning. Experimental results show that our proposed method outperforms state-of-the-art(SOTA) fake news detection approaches by 6.9% F1 score on the FNC-1 dataset. © The Institution of engineering & technology 2023.

关键词： Deep learning

来源：评论

学校读者我要写书评

暂无评论

AssignExpert - Host and Grade Programming Assignments in Educational Institutions 14

AssignExpert - Host and Grade Programming Assignments in Edu...

引用

14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Chankhore, Neha Jatti, Ashwini Nathani, Vivek Desai, Varda Malkan, Ronak Tattikota, Nikhil Symbiosis Institute of Technology Computer Science and Engineering Department Pune India Symbiosis Institute of Technology Information Technology Department Pune India

ISBN: (纸本)9798350335095

AssignExpert is created using *** and runs Docker under the hood to sandbox the execution of user-submitted programs. The platform allows faculties and students to sign up with their employee or student roll numbers. Faculties can create a class and give assignments with a deadline. Students can join a class and see the assignments. The assignment page is bundled with a code editor, using which the assignment can be submitted. Faculties have the option to reveal the points scored or defer this action to a later period of time. This paper further describes the theoretical and practical motivations for all the design and implementation decisions. The code for the platform is open-source and is available on GitHub. © 2023 IEEE.

关键词： Students

来源：评论

学校读者我要写书评

暂无评论

An Effective Lung Cancer Diagnosis Model Using Pre-Trained CNNs

引用

computer Modeling in engineering & sciences 2025年第4期143卷 1129-1155页

作者： Majdi Rawashdeh Muath A.Obaidat Meryem Abouali Dhia Eddine Salhi Kutub Thakur Department of Business Information Technology Princess Sumaya University for TechnologyAmman11941Jordan Department of Computer Engineering Al Yamamah UniversityRiyadh11512Saudi Arabia Department of Computer Science John Jay College and the Graduate CenterThe City University of New YorkNew YorkNY 10019USA Department of Computer Science John Jay College at the City University of NewYorkNewYorkNY 10019USA LIMOSE Laboratory Department of Computer ScienceM’hamed Bougara UniversityBoumerdes35000Algeria Department of Professional Security Studies New Jersey City UniversityJersey CityNJ 07305USA

Cancer is a formidable andmultifaceted disease driven by genetic aberrations and metabolic *** 19% of cancer-related deaths worldwide are attributable to lung and colon cancer,which is also the top cause of death *** malignancy has a terrible 5-year survival rate of 19%.Early diagnosis is critical for improving treatment outcomes and survival *** study aims to create a computer-aided diagnosis(CAD)that accurately diagnoses lung disease by classifying histopathological *** uses a publicly accessible dataset that includes 15,000 images of benign,malignant,and squamous cell carcinomas in the *** addition,this research employs multiscale processing to extract relevant image features and conducts a comprehensive comparative analysis using four Convolutional Neural Network(CNN)based on pre-trained models such as AlexNet,VGG(Visual Geometry Group)16,ResNet-50,and VGG19,after hyper-tuning these models by optimizing factors such as batch size,learning rate,and *** proposed(CNN+VGG19)model achieves the highest accuracy of 99.04%.This outstanding performance demonstrates the potential of the CAD system in accurately classifying lung cancer histopathological *** study contributes significantly to the creation of a more precise CNN-based model for lung cancer identification,giving researchers and medical professionals in this vital sector a useful tool using advanced deep learning techniques and publicly available datasets.

关键词： Lung cancer machine learning computer aided diagnosis CNN medical imaging transfer learning

来源：评论

学校读者我要写书评

暂无评论

Coronavirus Disease Outbreak Prediction and Analysis Using Machine Learning and Classical Time Series Forecasting Models

Coronavirus Disease Outbreak Prediction and Analysis Using M...

引用

2024 International Conference on Artificial Intelligence and Quantum Computation-Based Sensor Applications, ICAIQSA 2024

作者： Rabby, Habibor Rahman Hasan, Mahadi Jahan, Israt Jahan, Rumana Siddiky, Rafiuddin Campbellsville University Department of Computer Science KY United States Washington University of Science and Technology Department of Information Technology VA United States Military Institute of Science and Technology Industrial and Production Engineering Dhaka Bangladesh Technology Wilmington University Department of Information Systems DE United States

ISBN: (纸本)9798331517953

This study explores the efficacy of machine learning and classical time series forecasting models in predicting COVID-19 case counts, addressing a critical global health challenge. We compared four models - Holt-Winters, ARIMA, Facebook Prophet, and XGBoost - across eight countries, forecasting both confirmed and death cases over a 14-day period. Our findings reveal that Facebook Prophet with holiday impact and XGBoost consistently outperform other models in predicting new cases, with XGBoost excelling in forecasting death cases, followed by ARIMA and Facebook Prophet. Notably, XGBoost with a mean absolute error (MAE) of 1.42, mean squared error (MSE) of 4.06, and root mean squared error (RMSE) of 2.01, demonstrated the best performance for death prediction. These results underscore the potential of advanced analytical techniques in pandemic management, offering valuable insights for public health officials and policymakers. By providing accurate short-term projections, these models can facilitate proactive decision-making, resource allocation, and intervention strategies, ultimately contributing to more effective COVID-19 mitigation efforts worldwide. This re- search not only demonstrates the practical application of machine learning in epidemiological forecasting but also highlights the importance of model selection in achieving optimal predictive performance for different aspects of the pandemic. © 2024 IEEE.

关键词： COVID-19

来源：评论

学校读者我要写书评

暂无评论

Breast Cancer Diagnosis Using Elephant Herding Optimization and Sparse Autoencoder Through Gene Expression Analysis 15th

Breast Cancer Diagnosis Using Elephant Herding Optimization ...

引用

15th International Conference on Soft Computing and Pattern Recognition, SoCPaR 2023 and 14th World Congress on Nature and Biologically Inspired Computing, NaBIC 2023

作者： Bharanidharan, N. Chakravarthy, S. R. Sannasi Kumar, V. Vinoth Kavya, B. Meghana, G. Rajaguru, Harikumar School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore India Department of Electronics and Communication Engineering Bannari Amman Institute of Technology Sathyamangalam India Department of Computer Science and Engineering Dayananda Sagar University Bangalore India Department of Computer Science and Engineering PES University RR Campus Bangalore India

ISBN: (纸本)9783031810794

Gene expression analysis is an efficient modality to detect breast cancer. But analysis of the gene expression data is a complicated task due to the involvement of large number of numerical data for each subject. This research work focuses on the development of computerized diagnosis systems for analyzing the gene expression data and classifying the subjects as either normal or breast adenocarcinoma. Sparse autoencoder is used for dimensionality reduction and Elephant Herding Optimization is used for transforming the features. Four different supervised classifiers namely support vector machine, random forest classifier, decision trees, and naive bayes classifier are examined for classification task. Among them, random forest provides the superior balanced accuracy of 86% while it was further enhanced to 92% with the help of sparse autoencoders and elephant herding optimization algorithm. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Transcription

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：