检索结果-内蒙古大学图书馆

3rd International Conference for Innovation in Technology, INOCON 2024

作者： Ravi Kiran, T.S. Srisaila, A. Shankar, G. Siva Sowjanya, Bodasingi Lakshmanarao, A. P.B. Siddhartha College of Arts & Science Technology Department of Computer Science A.P Vijayawada India V.R Siddhartha Engineering College Department of Information Technology A.P Vijayawada India Aditya Engineering College Department of Ai&ml Surampalem India Centurion University of Technology and Management Department of CSE Andhra Pradesh Vizianagaram India Aditya Engineering College Department of It Surampalem India

ISBN: (纸本)9798350381931

Diabetes, a prevalent and complex medical condition, demands accurate predictive models for early detection and effective management. This paper introduces a novel approach for diabetes prediction by combining genetic algorithm-based feature selection with ml classification. By combining the genetic algorithm's capability to optimize feature selection and the predictive power of ml classifier, this work offers a promising avenue for improving diabetes prediction accuracy. Two datasets from Kaggle were collected. Initially, RF applied on both datasets. Later, datasets ae balanced using oversampling technique "ADASYN". Later, genetic algorithm is employed to optimize feature selection, with the fitness function minimizing the negative accuracy of the model. The selected features are then used to train a final model, and the accuracy is evaluated on the test set. The results showcase the effectiveness of the proposed approach in enhancing diabetes prediction accuracy when compared to base model. Results from both datasets shown accuracy enhancement with GA feature selection. The selected features provide valuable insights into the influential factors contributing to diabetes outcomes. © 2024 IEEE.

关键词： Genetic algorithms

来源：评论

学校读者我要写书评

暂无评论

Improving Medical Imaging Diagnostics with Deep Convolutional Networks for Early Detection and Treatment

Improving Medical Imaging Diagnostics with Deep Convolutiona...

引用

2025 International Conference on Computing for Sustainability and Intelligent Future, COMP-SIF 2025

作者： Krishnakumar, K. Gurrapu, Omprakash Reshma, P. Krishnammal, P. Muthu Khan, Huma Qamar Amane, Ninad Saveetha School of Engineering Chennai India Volvo Trucks North America NC United States Dhanalakshmi Srinivasan College of Engineering Department of Artificial Intelligence and Data Science Coimbatore India VIT-AP University Amaravati India Joginpally B R Engineering College Department of Computer Science and Engineering Hyderabad India Bms Institute of Technology and Management Department of Ai&ml Bangalore India

ISBN: (纸本)9798331538538

Diagnostic imaging is an important technique which helps in diagnosing several diseases at the initial stage in order to take necessary treatments. Nevertheless, conventional techniques tend to have issues with regard to precision, speed, and quantitatively assessing multidimensional medical information. In this paper, we reveal the potential of deep convolutional networks (DCNs) in the field of medical image diagnostic. It means that, with the help of deep learning algorithms DCNs can learn meaningful features of medical images and improve diagnostic accuracy and speed. The near proposed technique involves the use of deep convolutional network model to analyse medical images for diagnosis of diseases such as cancer, heart diseases and neurological disorders. The obtained experimental results demonstrate that the presented model has the highest accuracy of 96.5% with very high sensitivity and specificity coefficients compared to other methods. Further, the interpretability of the proposed DCN model and different aspects of it are discussed to support clinical validation. Through this research, the effectiveness of deep learning in revolutionizing the diagnosis for medical imaging diagnostics while early disease detection and treatment is established. © 2025 IEEE.

关键词： Neuroimaging

来源：评论

学校读者我要写书评

暂无评论

Advances in Big Data and Data Mining: Techniques and Applications in Data Fusion for Enhanced Insights and Decision-Making

Advances in Big Data and Data Mining: Techniques and Applica...

引用

2025 IEEE International Conference on Computational, Communication and Information Technology, ICCCIT 2025

作者： Goyal, Himanshu Rai Shrivastava, Anurag Nagpal, Amandeep Reddy, R. Akhilesh Yadav, Kanchan Revathi, R. Graphic Era Deemed to Be University Department of Computer Science & Engineering Dehradun India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Tamilnadu Chennai India Lovely Professional University Phagwara 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)9798331512965

The advancement of big data has accelerated over the years to change how companies utilize data to drive decisions. And so, this paper aims to look at big data and data mining in different perspectives while highlighting the importance of data fusion in decision making in the current society. Many times, data fusion combines different data sources to provide better and broader results. Clustering of raw data, anomaly detection and machine learning are discussed as well as their use in healthcare, financial management and smart cities. This paper also presents such issues as data heterogeneity, data quality assurance, as well as scalability issues. Lastly, tomorrow's possibilities, such as artificial intelligence and quantum computing, are presented as possible enhancements of the data fusion effectiveness are mentioned due to the expected marked advancement in real-time and adaptive decisionmaking systems. © 2025 IEEE.

关键词： Data centers

来源：评论

学校读者我要写书评

暂无评论

Drone-Based Intelligent System for Social Distancing Compliance Using YOLOv5 and YOLOv6 with Euclidean Distance Metric

引用

SN computer science 2024年第8期5卷 972页

作者： Parkavi, A. Alex, Sini Anna Pushpalatha, M.N. Shukla, Prashant Kumar Pandey, Ankur Sharma, Sachin Department of Computer Science and Engineering M S Ramaiah Institute of Technology Bangalore India Department of Computer Science and Engineering (AI and ML) M S Ramaiah Institute of Technology Bangalore India Department of Information Science and Engineering M S Ramaiah Institute of Technology Bangalore India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram Guntur 522302 India Department of Computer Science and Engineering Manipal University Jaipur Rajasthan Jaipur India Manav Rachna International Institute of Research and Studies Faridabad India

The COVID-19 pandemic forced rigorous social distancing measures to be put in place to halt the virus’s spread;compliance was extremely difficult. In response, we have designed a highly advanced intelligent drone system that maintains and controls distance measures following social distance principles through advanced machine learning skills. This paper describes an intelligent monitoring and compliance system in public places using drone technology with a focus on social distance requirements. Boosting the efficiency of human detection in real-time video streams acquired by drones, our solution employs the You Only Look Once version 5 (YOLOv5) and YOLO version 6 (YOLOv6) object detection algorithms which are the most potent computer vision tools today. The distances between the recognized individuals are computed based on the Euclidean distance measurement to distinguish cases of nonadherence to social distance measures. Our technique integrates the recently developed deep learning models with aerial imagery and monitoring to make it easily scalable for population health surveillance. Thus, we prove impressively that the underlying inventive method works well in different environmental conditions and can potentially contribute to people’s safety in the event of a pandemic. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： COVID-19 Drones Euclidean distance metric Social distancing YOLOv6

来源：评论

学校读者我要写书评

暂无评论

ml based Text Summarization for Sentiment Analysis in Information Retrieval using Feature engineering 4

ML based Text Summarization for Sentiment Analysis in Inform...

引用

4th International Conference on Sustainable Expert Systems, ICSES 2024

作者： Shirley, C.P. Berin Jeba Jingle, I. Venkatesan, R. Tamilarasan, R. Yashvanth Ram, R.V. Joyce Beryl Princess, P. Karunya Institute of Technology and Sciences Department of Computer Science and Engineering Coimbatore India Karunya Institute of Technology and Sciences Dept. of Computer Science and Engineering Coimbatore India Karunya Institute of Technology and Sciences Dept. of Ai & Ml Coimbatore India

ISBN: (数字)9798331540364

ISBN: (纸本)9798331540364

Sentiment analysis plays a crucial role in understanding user preferences and opinions in the field of information retrieval. This research focuses on developing a system that combines text summarization and machine learning techniques to improve sentiment analysis accuracy. The proposed approach employs extractive and abstractive summarization techniques to reduce lengthy texts into concise summaries while preserving key information. Feature engineering is then applied to extract sentiment-related elements from the summary text, providing valuable insights for the SVM model. The SVM model, enhanced with feature engineering, is expected to effectively summarize text and accurately detect sentiments expressed in documents. This research contributes to the advancement of sentiment analysis techniques and provides valuable insights for decision-making and content development. © 2024 IEEE.

关键词： Support vector machines Measurement Sentiment analysis Accuracy Text summarization Machine learning Information retrieval Feature extraction Vectors Resilience

来源：评论

学校读者我要写书评

暂无评论

Comparision of Principal Component Analysis, K-means, and Generative Adversarial Network for Compression and reconstruction of medical image

Comparision of Principal Component Analysis, K-means, and Ge...

引用

Artificial Intelligence and Data engineering (aiDE), International Conference on

作者： Anusha Prashanth Shetty Deepthi L Raksha Puthran Surendra Shetty Mohammad Anzil Parveez Shravan Kumar Department of Master of Computer Applications NMAM Insitute of Technology Nitte (Deemed to be University) Nitte Karkala India Department of Computer Science and Engineering (AI&ML) Sahyadri College of Engineering and Management Mangalore India

ISBN: (数字)9798331527518

ISBN: (纸本)9798331527525

Medical images are essential in healthcare, but their large file sizes present challenges for storage and transmission. This study proposes a framework for compressing color medical images while preserving key visual information. We apply three compression models: Principal Component Analysis (PCA), K-Means clustering, and Generative Adversarial Networks (GANs), and evaluate their performance based on Compression Ratio (CR), Peak Signal-to-Noise Ratio (PSNR), Mean Squared Error (MSE), and Structural Similarity Index (SSIM). K-Means with 100 clusters outperforms the other models, achieving the highest compression (CR of 1.20), with excellent image quality (PSNR of 66.77 dB, SSIM of 1.0000). PCA shows moderate performance with a CR of 2.45, while GAN provides the least effective compression and image quality. These results demonstrate that K-Means offers the best trade-off between compression and image fidelity.

关键词： Measurement Image quality Analytical models Image coding PSNR Medical services Generative adversarial networks Image reconstruction Principal component analysis Biomedical imaging

来源：评论

学校读者我要写书评

暂无评论

Diabetic Retinopathy Severity Detection an Automated Tool 1

引用

World Conference on Information Systems for Business Management, ISBM 2023

作者： Rao, Supriya B. Shetty, S. Shailesh Padmanayana Thanmayee, S. Divya Department of AI&ML New Horizon College of Engineering Bangalore India Department of CS and Business System Srinivas Institute of Technology Mangalore India Department of CSE Srinivas Institute of Technology Mangalore India Department of Data Science Srinivas University Institute of Engineering and Technology Mangalore India Department of CSE AJ Institute of Engineering and Technology Mangalore India

ISBN: (数字)9789819986125

ISBN: (纸本)9789819986118

Diabetics start to occur when there is not enough insulin secretion in the body. Diabetes is the primary cause of diabetic retinopathy in patients, which leads to vision impairment. The main reason for vision impairment is that lesions get accumulated in the retina. Early detection of diabetic retinopathy using the fundus images surely helps in faster recovery of the patient. Manual detection of the severity of diabetic retinopathy requires time, which may cause a delay in cure. To automate the process of severity detection, a convolutional neural network (CNN) is developed and presented, which is capable to predict the severity of the diabetic retinopathy in the fundus images. A model developed can learn the features of the retinal images and predict either of the five stages of diabetic retinopathy that is non-proliferative diabetic retinopathy, mild non-proliferative diabetic retinopathy, moderate non-proliferative diabetic retinopathy, severe non-proliferative diabetic retinopathy, and proliferative diabetic retinopathy. Images were enhanced using various methods. After conducting tests on the model using random test data, the model proved to be reliable resulting in 95.5% specificity and 93.5% sensitivity. With this specificity and sensitivity, our model is reliable when deployed to the real world where the doctors in the medical field can rely on in detecting the presence of diabetic retinopathy. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Image enhancement

来源：评论

学校读者我要写书评

暂无评论

Enhancing Data Privacy and Accountability in Smart Grids with Blockchain Technology

Enhancing Data Privacy and Accountability in Smart Grids wit...

引用

Electrical Electronics and Computing Technologies (ICEECT), International Conference on

作者： G. Indiravathi M. P. Kumar Anumulagundam Usha Roshan Kumar T. Lohith Shaik Shahid Department of Computer Science and Engineering Siddharth Institute of Engineering & Technology Chittoor Andhra Pradesh India Department of Computer Science and Engineering (AI&ML) Siddharth Institute of Engineering & Technology Chittoor Andhra Pradesh India

ISBN: (数字)9798350378092

ISBN: (纸本)9798350378108

Smart grids offer promising opportunities for energy efficiency but raise concerns about data privacy and transparency. This paper presents a new framework for Secure and Auditable Private Data Sharing designed specifically for smart grids, with a focus on its functionality as a password manager. By utilizing smart contracts and blockchain technology, the framework creates explicit guidelines for data usage that guarantee clear oversight of data access and intended uses. In addition to managing smart grid data, the framework securely stores passwords and credentials, providing users with a comprehensive solution for data security and access management. The use of off-chain smart contract execution and trusted execution environments maximizes performance. To further assure transactional reliability during the result dissemination and payment procedures, the framework employs a two-phase atomic delivery mechanism. Energy service providers gain from optimum contracts because they encourage high-quality data exchange and customer participation through the use of contract theory. Extensive simulations validate the efficacy of the proposed approach, enabling users to securely access their data remotely through a cloud platform integrated with open-source services.

关键词： Data privacy Data security Smart contracts Passwords Reliability theory Energy efficiency Data models Smart grids Blockchains Guidelines

来源：评论

学校读者我要写书评

暂无评论

Hybrid Deep Learning Model for Spectrum Sensing Classification in Cognitive Radio Networks

Hybrid Deep Learning Model for Spectrum Sensing Classificati...

引用

2024 International Conference on Distributed Systems, computer Networks and Cybersecurity, ICDSCNC 2024

作者： Padmakala, S. Alsalami, Zaid Supriya, R. Sahana, M.P. Deepthi, Palakurthy Saveetha University Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Chennai India The Islamic University College of Technical Engineering Department of Computers Techniques Engineering Najaf Iraq Nitte Meenakshi Institute of Technology Department of Ise Bengaluru India Dayananadha Sagar College of Engineering Department of Cse Bangalore India Gokaraju Rangaraju Institute of Engineering and Technology Department of Ai&ml Hyderabad India

ISBN: (纸本)9798350375442

Spectrum sensing (SS) uses cognitive radio technology at base transceiver stations in order to discover the licensed spectra used by Primary Users (PUs). This enables the SUs to make wireless network transmissions without causing interference. Cognitive Radio Networks (CRNs) help in the problem on the restricted radio frequency spectrum that wireless devices depend on. For enhanced classification of SS in CRN, the proposed research focused on using hybrid Deep Learning (DL) model by integrating Convolutional Neural Network (CNN) and Long Short Term Memory (LSTM). Initially, the data is generated through eight modulation types considering the varied oversampling rate. The spatial features of signal are extracted through CNN and temporal features are identified by LSTM. the Cooperative Spectrum Sensing (CSS) algorithm is integrated increase the reliability of spectrum detection. The hyper parameters of the trained model are fine tuned for enhanced classification of SS. Experimental results showed that the proposed LSTM-CNN-CSS achieved sensing accuracy at 98.34%, precision at 95.62%, recall at 96.77% when compared to the traditional SS classification models such as Ensemble Convolutional Recurrent Neural Network (ECRNN) and LSTM-Extreme Learning Machine (LSTM-ELM). © 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

Securing Wireless and Optical Networks: Advanced Strategies for Network and Information Security in Modern Communication Systems

Securing Wireless and Optical Networks: Advanced Strategies ...

引用

2025 IEEE International Conference on Computational, Communication and Information Technology, ICCCIT 2025

作者： Dhasmana, Gulshan Sharma, Vishal Boob, Nandini Shirish Aravind, K. Reddy, R. Akhilesh Yadav, Kanchan Graphic Era Deemed to Be University Department of Computer Science & Engineering Dehradun India Lovely Professional University Phagwara India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Tamilnadu Chennai India New Horizon College of Engineering Department of Electronics and Communication Engineering Bangalore India MLR Institute of Technology Department of CSE-AI&ML Telangana Hyderabad India GLA University Department of Electrical Engineering Mathura India

ISBN: (纸本)9798331512965

In the era of rapidly changing communication systems, securing wireless and optical networks has become of essence to protect sensitive data and ensure operation. This study addresses network and information security in current communication systems by studying cutting edge techniques to enhance network and information security in wireless and optical networks. The wireless networks are convenient and may be moved around, but they are also prone to hacking, illegal access or some other cyberattacks. As optical networks possess both a large capacity and relatively low latency, it is not without special security issues such as fibre tapping and signal jamming. This study presents detailed analyses of state of the art authentication techniques, intrusion detection systems (IDS), and encryption algorithms specifically designed for these networks. Then there are, such as Quantum Key Distribution (QKD) which almost assuarly unbreakable techniques are examined alongside conventional cryptographic methods. Additionally, anomaly detection based on machine learning is explored for real time threat identification in optical and wireless channels. It stresses the need of the cross domain solutions and multi layered security frameworks covering the network layer and the physical security measures. Through case studies and recent developments in cyber threats and network resilience, this article provides a thorough understanding of how these tactics could be used to make networks such as wireless network and optical network more resilient against increasing cyber risks. © 2025 IEEE.

关键词： Network intrusion

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：