检索结果-内蒙古大学图书馆

3rd IEEE Asian Conference on Innovation in technology, ASIANCON 2023

作者： Sira, Rishi Kumar Tanjavur, Irthiqua Moin Guru Prasad, M.S. Rajan, Abraham Laxman Mohan Kumar, T.G. Dept. of Computer Science and Engg. Dehradun India Nagarjuna College of Engineering and Technology Dept. of Computer Science and Engg. Bengaluru India School of Computing and Information Technology Reva University Bengaluru India Nitte Meenakshi Institute of Technology Dept. of Information Science and Engg. Bengaluru India

ISBN: (纸本)9798350302288

Illegal, unreported, and unregulated (IUU) fishing practices pose significant threats to marine ecosystems and global fisheries sustainability. The detection and classification of loitering events, where fishing vessels spend an extended period in a specific area, are critical for identifying potential IUU activities. This research proposes a comprehensive approach that combines supervised and semi-supervised learning techniques to effectively detect and classify loitering events. By leveraging various machine learning algorithms, including logistic regression, Gaussian mixture models, support vector machines, and random forests, accurate predictions can be made to enhance surveillance and combat IUU fishing. © 2023 IEEE.

关键词： Logistic regression

来源：评论

学校读者我要写书评

暂无评论

Multispectral Satellite Data Classification Using Rank Correlation Similarity Index and Automatic Labeling Technique

引用

SN computer Science 2024年第8期5卷 1-20页

作者： Ain, Arindom Gogoi, Minakshi Chutia, Dibyajyoti Dept. of Computer Science and Engineering GIMT Under Assam Science and Technology University Assam Guwahati India Dept. of Computer Science and Engineering Girijananda Chowdhury University Assam Guwahati India NESAC Umium Shillong India

Multispectral satellite image classification is crucial in remote sensing and image analysis, serving various applications like water area extraction, urban region classification, and vegetation monitoring. Model-based classification, particularly supervised learning approaches, heavily relies on manually annotated training samples, which are labor-intensive and subject to human bias. However, solely depending on features derived from individual pixels may fail to capture spatial contextual information effectively and manage mixed pixels and various types of noise, resulting in classification inaccuracies. To address these challenges, this research proposes a novel unsupervised learning approach and automatic labeling technique (ALT). This approach utilizes collective information from groups of pixels instead of relying solely on individual pixels for identification and extraction of meaningful unique patterns which serves as the most significant factor for satellite image classification. A new similarity index, the patch similarity index (PSI), is generated by incorporating the rank correlation similarity index (RCSI). The PSI is used to determine the similarity factor both between the patches and within the patch. Subsequently, a clustering algorithm is employed to group pixels into four distinct classes: vegetation, water, built-ups, and open land. Additionally, ALT streamlines the post-classification process by reducing the need for manual intervention in mapping clusters to the actual class label. The proposed method offers a more accurate and reliable unsupervised classification for multispectral satellite imagery, promising enhanced efficiency and reduced manual labor. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Automatic labeling technique Frequent masking Multispectral Patch similarity index Rank Correlation

来源：评论

学校读者我要写书评

暂无评论

Enhancing Reliability in Hybrid Cross-Chain Models: Adaptive Thresholds for Performance and Adaptability 22

Enhancing Reliability in Hybrid Cross-Chain Models: Adaptive...

引用

22nd IEEE/ACIS International Conference on Software engineering Research, Management and Applications, SERA 2024

作者： Seol, Jongho Kancharla, Abhilash Kim, Jongyeop Middle Georgia State University dept. of information technology MaconGA United States The University of Tampa dept. of computer science TampaFL United States Georgia Southern University dept. of information technology StatesboroGA United States

ISBN: (纸本)9798350391343

In the realm of Decentralized Finance (DeFi), this manuscript introduces a Hybrid Cross-Chain Model. As DeFi architectures grapple with the complexities of monolithic single-chain platforms, our proposed model orchestrates a symphony of multiple chains to facilitate seamless cross-chain communication, offering a poised solution to scalability and transaction speed challenges. Incorporating modeling effects and simulations, our rigorous performance evaluation underscores the model's excellence and includes an in-dept. analysis of its performance, particularly focusing on robust security measures. The model is positioned as a cornerstone in an interconnected DeFi landscape by emphasizing stringent measures to ensure data integrity and uphold consensus mechanisms. User-centric enhancements promise swift transaction confirmations and reduced fees, improving the overall experience. The abstract culminates with a comparative analysis, positioning the Hybrid Cross-Chain Model as an innovative solution with profound implications for the future of DeFi. This manuscript advocates for ongoing research and development, heralding a new era of sophistication and resilience in decentralized finance. © 2024 IEEE.

关键词： Decentralized finance

来源：评论

学校读者我要写书评

暂无评论

Discriminative Vector Learning with Application to Single Channel Speech Separation 48

Discriminative Vector Learning with Application to Single Ch...

引用

48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Tan, Ha Minh Liang, Kai-Wen Wang, Jia-Ching National Central University Dept. of Computer Science and Information Engineering Taiwan Nguyen Tat Thanh University Faculty of Information Technology Viet Nam

ISBN: (纸本)9781728163277

In this paper, we introduce a discriminative vector learning method and apply it to single-channel speech separation. First, speech samples are transformed into discriminative vectors using two backbone networks. These vectors are easily separated by simple clustering algorithms. Among them, vectors with lower similarity are separated into different clusters, while vectors in the same cluster have higher similarity. This property is very important in image segmentation, audio separation, and data clustering problems. In our work, we design the network architecture to improve the discriminativeness of vectors through learning, taking this task as spectrogram segmentation. Experiments show that our method significantly improves performance compared to other deep clustering methods for speech separation. © 2023 IEEE.

关键词： Vectors

来源：评论

学校读者我要写书评

暂无评论

Land Use Land Cover Classification Using Machine Learning and Remote Sensing Data: A Case Study of Karnaprayag, Uttarakhand, India 1

Land Use Land Cover Classification Using Machine Learning an...

引用

1st International Conference on Electronics, Communication and Signal Processing, ICECSP 2024

作者： Saini, Rashmi Singh, Suraj Govind Ballabh Pant Institute of Engineering and Technology Dept. of Computer Science and Engineering Pauri Garhwal India

ISBN: (纸本)9798350364590

Land use and land cover (LULC) classification is crucial for planning and sustainable development, especially in mountainous regions. This study focuses on the Tehsil Karnaprayag, situated in the vicinity of the Greater Himalaya. With a total area of 1858.903 km2 and elevations ranging from 647m to 7641m. Using multispectral Sentinel-2 imagery and Machine Learning techniques i.e. Extreme gradient boosting (XGBoost) and K-nearest neighbor (KNN). This study aims to classify seven distinct land cover classes: snow, forest, water bodies, grassland, agricultural land, fallow land, and built-up areas. The classification results demonstrate the effectiveness of XGBoost classifier achieving an accuracy of 90.22% and a kappa value of 0.882, while KNN achieved an accuracy of 88.59% with a kappa value of 0.862. The analysis reveals that forest and fallow land are the predominant land cover types in the study area, constituting nearly 60% and 15% of the total area respectively. Notably, XGBoost exhibits the highest class-specific accuracy of 97.60% for the snow class, demonstrating its utility in discriminating between challenging land cover types. This research highlights the importance of LULC classification in informing land management strategies and facilitating sustainable development initiatives in mountainous regions. The findings provide valuable insights into the spatial distribution and composition of land cover in Tehsil Karnaprayag, essential for effective planning and conservation efforts in the Himalayan ecosystem. © 2024 IEEE.

关键词： Sustainable development

来源：评论

学校读者我要写书评

暂无评论

Transfer Learning Approaches for Channel State Prediction in Heterogeneous Fading Networks

Transfer Learning Approaches for Channel State Prediction in...

引用

2024 IEEE International Conference on Intelligent Signal Processing and Effective Communication Technologies, INSPECT 2024

作者： Upadhyay, Deepak Sharma, Kunj Bihari Gupta, Mridul Upadhyay, Abhay Venu, Nookala Graphic Era Hill University Dept. of Computer Science and Engineering Dehradun India Graphic Era deemed to be University Dept. of Computer Science and Engineering Dehradun India ABV Indian Institute of Information Technology and Management Data Science and Quantum Computing Gwalior India

ISBN: (纸本)9798350379525

In this work, we leverage state-of-the-art machine learning algorithms to predict Channel State information (CSI), for enhancing performance of wireless communication systems. The simulation and analysis stop with transfer learning to finetune, adaptive domain adaptations, multitasking (MTL) as well a few shot meta-learning models framework under different network packet loss rate scenarios. By generalizing better from one domain to another, transfer learning pushes the performance beyond what traditional methods can obtain. Further, fine-tuning also helps to align the source and target domain distributions in feature space by a partial (or full) reduction of re-stylization error. This work proposes multi-Task learning as a tool to mitigate loses in light of predicting across fading scenarios. Considering multi-task learning also provides benefits from shared learnings, so experience is pooled between the tasks which indirectly improve prediction as well and it helps in training both of these algorithms together. Moreover, the few-shot learning models prove to be highly desirable as they deliver state-of-the-art performance with minimal training data for difficult problems. These results show the potential of AI-based methods to improve CSI prediction, and have implications for improving reliability and throughput when deploying real wireless communication systems. © 2024 IEEE.

关键词： Contrastive Learning

来源：评论

学校读者我要写书评

暂无评论

A Detailed Analysis of Secrecy and Performance Metrics in Hybrid RF/ FSO Communication Systems: Implications of Composite Weibull and Log-normal Fading with M Turbulent Channels

A Detailed Analysis of Secrecy and Performance Metrics in Hy...

引用

2024 IEEE International Conference on Intelligent Signal Processing and Effective Communication Technologies, INSPECT 2024

ISBN: (纸本)9798350379525

In this paper, we have analyzed the secrecy performance and system operation performance for hybrid RF/FSO systems against composite Weibull-log-normal fading channel with M turbulent channels. The performance is evaluated by carrying out extensive simulations to calculate Signal-to-Noise Ratio (SNR), Bit Error Rate (BER) and outage probability, we also re-evaluate channel capacity and secrecy capacity with respect of environmental conditions. Compared with RF only system, FSO only standalone scheme and some meaningful orthogonal-modulation multicarrier IM/DD-based schemes proposed by the literatures. Similarly, the utilization of adaptive modulation schemes improved resistance to fading and roughness in systems. The above results confirm the hybridization of both RF and FSO a comprehensive solution for dependable communication in different and even hostile surrounding conditions which set this work as reliable foundation point towards forthcoming research activity or application relevance within all range telecommunication sector. © 2024 IEEE.

关键词： Bit error rate

来源：评论

学校读者我要写书评

暂无评论

Deepfake Detection Using Multi-Modal Fusion Combined with Attention Mechanism 4

Deepfake Detection Using Multi-Modal Fusion Combined with At...

引用

4th International Conference on Sustainable Expert Systems, ICSES 2024

作者： Shirley, C.P. Berin Jeba Jingle, I. Abisha, M.B. Venkatesan, R. Yashvanth Ram, R.V. Elango, Elakkiya Karunya Institute of Technology and Sciences Dept. of Computer Science and Engineering Coimbatore India Government Arts College for Woman Dept. of computer Science Sivaganga India

ISBN: (数字)9798331540364

ISBN: (纸本)9798331540364

The proliferation of deepfake technology poses a significant challenge to the authenticity of digital content. This research explores the application of multimodal fusion techniques to enhance deepfake detection accuracy. By combining visual and audio features, the proposed method leverages the complementary nature of different data types to detect discrepancies introduced by deepfake manipulation. An attention mechanism is incorporated to focus on salient regions within each modality, further improving detection accuracy. Convolutional Neural Networks (CNNs) and Mel-Frequency Cepstral Coefficients (MFCCs) are employed for feature extraction, followed by feature fusion for deepfake detection. This approach demonstrates the effectiveness of multimodal fusion in combating the evolving threat of deepfake technology. By advancing deepfake detection techniques, this research contributes to safeguarding the integrity of digital content and preserving trust in media. © 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

Comparative Advances in Financial Sentiment Analysis:A Review of BERT,FinBert, and Large Language Models 3

Comparative Advances in Financial Sentiment Analysis:A Revie...

引用

3rd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Mahendran, Manish Barath Gokul, Aswin Kumar Lakshmi, Poornima Pavithra, S. Dept. of Computer Science and Engineering Chennai Institute of Technology Tamil Nadu Chennai600069 India

ISBN: (纸本)9798331527549

Capturing market sentiments and supporting well-informed financial decision-making depend on the developing field of Financial Sentiment Analysis (FSA). Natural language processing (NLP) has made significant strides in comprehending and categorizing sentiment in intricate financial texts, especially with the use of Large Language Models (LLMs). The application of various LLMs, such as Bidirectional Encoder Representations form Transformers(BERT) and Financial BERT (FinBERT), as well as distilled models, such as DistilBERT and DistilRoBERTa, on a variety of financial datasets, including Financial Phrase Bank and LexisNexis news articles, was the main focus of this review article. highlighting different approaches such as model fine-tuning, zero-shot and few-shot learning, and prompt engineering. The study focuses on practical model predictions through a case study of sentimental analysis of cryptocurrencies. While FinBERT, a financial variant of BERT, exhibits high accuracy and robustness, other LLMs exhibit varying degrees of success based on the dataset and domain requirements. The analysis concentrates on the difficulties, compromises, and potential paths for improving LLMs for financial sentiment analysis. © 2025 IEEE.

关键词： Zero-shot learning

来源：评论

学校读者我要写书评

暂无评论

Blockchain-Based Music Streaming Services: A Novel Approach to Transparent Artist Control 3

Blockchain-Based Music Streaming Services: A Novel Approach ...

引用

3rd IEEE International Conference on Blockchain and Distributed Systems Security, ICBDS 2024

作者： Ashtagi, Rashmi Deshmukh, Tanmay Jadhav, Swati Bidwe, Ranjeet Akkalkot, Afsha Buchade, Amar Uke, Nilesh Banerjee, Sudipta Vishwakarma Institute of Technology Dept. of Computer Engineering Pune India Symbiosis Institute of Technology Dept. of Cse Pune India Coep Technological University Dept. Name of Computer Engineering Pune India Data Science Vishwakarma Institute Technology Dept. of Artificial Intelligence Pune India Indira College of Engineering and Management Pune India Symbiosis Institute of Technology Pune India

ISBN: (纸本)9798350354348

Artists have historically used music as a form of personal expression. However, due to the centralized nature of music streaming services, artists receive only a small percentage of the revenue generated and have no control over their content produced. To address these issues, a revolutionary technique is needed that provides artists with greater control and transparency over their output. This paper focuses on a decentralized strategy that utilizes blockchain technology to facilitate direct interaction between artists and users, enabling artists to upload their music, sell songs, and receive royalties. The implementation of blockchain technology could potentially revolutionize the music industry by ensuring that rightful owners receive payment for their music while reducing the waste of paying intermediaries. Without intermediaries involved in the distribution process, the music industry could pay out more royalties to rights holders automatically and instantaneously. The proposed solution also addresses piracy concerns through the use of encryption and watermarking techniques. Blockchains provide security, confidentiality, tamper-proofing, traceability, control, and oversight over information. By developing a secure, self-installing, cross-platform reader positioned on the header of the digital content file, The research empowers data owners to manage the flow of information even after it has been made available. The suggested approach enables content owners to control their digital content and monitor its most popular components after distribution. The blockchain-based system also benefits from token distribution among the content's owner, distributor, and consumer. The simulation results demonstrate that our approach is capable of overcoming the flaws of the existing system. © 2024 IEEE.

关键词： Blockchain

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：