检索结果-内蒙古大学图书馆

4th IEEE International Conference on Data engineering and Communication Systems, ICDECS 2024

作者： Suresh Kumar, N. Dolly Brinthakumari, S. Partheeban, N. School of Computer Science and Engineering Ramanagara India Jagan Institute of Management Studies Department of Information Technology Delhi India New Horizon Institute of Technology and Management Department of Computer Engineering Thane India Galgotias University School of Computing Science and Engineering Greater Noida India

ISBN: (纸本)9798350393354

In the dynamic landscape of blockchain technology, this research paper meticulously explores and contrasts two pivotal paradigmsSmart Contracts and Traditional Contractswithin the overarching framework of decentralized agreements. In the pursuit of this, the research diligently examines the subtle differentiations inherent in the various aspects of execution, intermediaries, trust, transparency, speed, costs, flexibility, security, and enforceability. While Smart Contracts, executed automatically through code on blockchain platforms such as Ethereum, offer unprecedented efficiency and transparency, Traditional Contracts, reliant on manual execution and potential intermediaries like lawyers, maintain a familiar yet potentially more cumbersome approach. The research extends its focus to Ethereum, a groundbreaking blockchain platform, spotlighting its instrumental role in upholding data security and integrity. The subsequent challenge under consideration is Ethereum's key aspects, such as decentralization, immutability, cryptography, smart contracts, consensus mechanisms, transparent ledger, tamper resistance, encryption, and permissioned access. A pivotal facet of this study centers on the comprehensive examination and comparison of consensus mechanisms employed in blockchain systems. This research assesses and compares key parameters of prominent blockchain consensus mechanisms, emphasizing factors like accuracy, time efficiency, scalability, and energy efficiency. The research culminates in a robust conclusion that synthesizes key findings, emphasizing Ethereum's role in ensuring data security and integrity, and the intricate landscape of consensus mechanisms. © 2024 IEEE.

关键词： Smart contract

来源：评论

学校读者我要写书评

暂无评论

Human Activity Recognition Using Efficientnet-B0 Deep Learning Model

Human Activity Recognition Using Efficientnet-B0 Deep Learni...

引用

2023 Intelligent Computing and Control for engineering and Business Systems, ICCEBS 2023

作者： Silpa, A.S. Benifa, J.V. Bibal Anu, K.P. Vijayakumar, Aiswarya Department of Computer Science Kerala Irinjalakuda India Indian Institute of Information Technology Department of Computer Science and Engineering Kerala Kottayam India

ISBN: (纸本)9798350394580

Human activity has many varieties of applications. By using HAR one can predict the behaviour of a person, one can predict the wellness of a person, whether he or she is healthy or not. It can be used in surveillance area, by which one can detect the abnormal activities of a person. Also HAR helps to detect the human activities in our daily life. In this study, implementation of efficientNet-Bo provided a model accuracy of 72 percentage for 31 epochs. This model gave a highest accuracy of 74 percentage for the epoch 11. By implementing this model on HAR dataset we can detect the human activities calling, clapping, cycling, dancing, drinking, eating, fighting, hugging, laughing etc. © 2023 IEEE.

关键词： EfficientNet-B0 HAR Human activity

来源：评论

学校读者我要写书评

暂无评论

Fake review detection using transformer-based enhanced LSTM and RoBERTa

引用

International Journal of Cognitive Computing in engineering 2024年第1期5卷 250-258页

作者： Mohawesh, Rami Bany Salameh, Haythem Jararweh, Yaser Alkhalaileh, Mohannad Maqsood, Sumbal College of Engineering Al Ain University Al Ain United Arab Emirates Hijjawi Faculty of Engineering Technology Yarmouk University Irbid Jordan Department of Computer Science Jordan University of Science and Technology Jordan College of Education Al Ain University Al Ain United Arab Emirates School of Information Technology University of Tasmania TAS Australia

Internet reviews significantly influence consumer purchase decisions across all types of goods and services. However, fake reviews can mislead both customers and businesses. Many machine learning (ML) techniques have been proposed to detect fake reviews, but they often suffer from poor accuracy due to their focus on linguistic features rather than semantic content. This paper presents a novel semantic- and linguistic-aware model for fake review detection that improves accuracy by leveraging advanced transformer architecture. Our model integrates RoBERTa with an LSTM layer, enabling it to capture intricate patterns within fake reviews. Unlike previous methods, our approach enhances the robustness of fake review detection and authentic behavior profiling. Experimental results on semi-real benchmark datasets show that our model significantly outperforms state-of-the-art methods, achieving 96.03 % accuracy on the OpSpam dataset and 93.15 % on the Deception dataset. To further enhance transparency and credibility, we utilize Shapley Additive Explanations (SHAP) and attention techniques to clarify our model's classifications. The empirical findings indicate that our proposed model can offer rational explanations for classifying specific reviews as fake. © 2024

关键词： Fake detection

来源：评论

学校读者我要写书评

暂无评论

Multimedia IoT Data Compression using Deep Learning Technique 2

Multimedia IoT Data Compression using Deep Learning Techniqu...

引用

2nd International Conference on Applied Artificial Intelligence and Computing, ICAAIC 2023

作者： Yathavaraj, V. Karthikeyan, N. Kousalya, A. Sindhuja, P. Dr. N.G.P. Institute of Technology Department of Computer Science and Engineering Coimbatore India Sns College of Technology Department of Computer Science and Engineering Coimbatore India Sri Krishna College of Engineering and Technology Department of Information Technology Coimbatore India United Institute of Technology Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9781665456302

Due to the considerable complexity of the multimedia data acquired, IoT (Internet of Things) and multimedia will encounter a number of energy and communication overload limits. One well-known solution to the problem of large datasets is to use the lossy compression algorithm. However, the lower transmission rate required by current lossy compression techniques to ensure endurable apparent image/picture quality only results in a minor reduction in the amount of communicated data and, as a result, a minor reduction in power and bandwidth use. An effective firmness technique is required to strike a fair equilibrium flanked by information volume and visual degradation. A deep-learning super-resolution technique is utilized in this study to restore high-resolution photographs from damaged photos with a considerable compression ratio as input. The results of the study clearly show how the suggested approach improves the graphical fidelity of compressed and down scaled images. The suggested method reduces largely the communication transparency and energy consumption of constrained multimedia IoT framework components as a result. © 2023 IEEE.

关键词： Internet of things

来源：评论

学校读者我要写书评

暂无评论

Enhanced Click Fraud Detection in Digital Advertising Through Ensemble Deep Learning 14th

Enhanced Click Fraud Detection in Digital Advertising Throug...

引用

14th International Conference on Frontier Computing, FC 2024

作者： Batool, Amreen Kim, Jisoo Byun, Yung-Cheol Department of Electronic Engineering Institute of Information Science and Technology Jeju National University Jeju63243 Korea Republic of Collage of Engineering Jeju National University Jeju63243 Korea Republic of Department of Computer Engineering Major of Electronic Engineering Institute of Information Science and Technology Jeju National University Jeju63243 Korea Republic of

ISBN: (纸本)9789819623945

As digital marketing becomes more and more crucial in today’s business strategies, the issue of click fraud. Which aims to deplete advertising budgets through activities. Has grown, leading to the need, for defenses. This research introduces a blend of Convolutional Neural Networks (CNN) Bidirectional Long Short Term Memory networks (BiLSTM) and Random Forest (RF) classifiers to effectively combat click fraud. This innovative model combines the power of learning for extracting features with RFs classification capabilities. By incorporating CNN and BiLSTM our method improves the identification of spatial features from data, which are then classified using an RF model. This ensemble approach is designed to address shortcomings in existing models that depend mainly on machine learning methods and often demand extensive feature manipulation. We showcase the efficiency of our model through testing on a dataset, achieving an impressive accuracy rate of 99.19% surpassing previous techniques. Additionally, we evaluate metrics such as precision, recall, and F1 score all highlighting the superiority of our approach. This research does not only push forward the tools for detecting click fraud but also offers a scalable framework for potential adaptation and implementation, in real world scenarios. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Blockchain Based Secure Federated Learning With Local Differential Privacy and Incentivization

IEEE Transactions on Privacy

引用

IEEE Transactions on Privacy 2024年第1期1卷 31-44页

作者： Chaudhury, Saptarshi De Reddy, Likhith Varun, Matta Sengupta, Tirthankar Chakraborty, Sandip Sural, Shamik Vaidya, Jaideep Atluri, Vijay Indian Institute of Technology Department of Computer Science and Engineering Kharagpur721302 India Rutgers University Management Science and Information Systems Department NewarkNJ07102-3122 United States

Interest in supporting Federated Learning (FL) using blockchains has grown significantly in recent years. However, restricting access to the trained models only to actively participating nodes remains a challenge even today. To address this concern, we propose a methodology that incentivizes model parameter sharing in an FL setup under Local Differential Privacy (LDP). The nodes that share less obfuscated data under LDP are awarded higher quantum of tokens, which they can later use to obtain session keys for accessing encrypted model parameters updated by the server. If one or more of the nodes do not contribute to the learning process by sharing their data, or share only highly perturbed data, they earn less number of tokens. As a result, such nodes may not be able to read the new global model parameters if required. Local parameter sharing and updating of global parameters are done using the distributed ledger of a permissioned blockchain, namely HyperLedger Fabric (HLF). Being a blockchain-based approach, the risk of a single point of failure is also mitigated. Appropriate chaincodes, which are smart contracts in the HLF framework, have been developed for implementing the proposed methodology. Results of an extensive set of experiments firmly establish the feasibility of our approach. © 2024 IEEE.

关键词： Federated learning

来源：评论

学校读者我要写书评

暂无评论

Predicting the Specific Gravity of Must During Fermentation Using Machine Learning Models 32nd

Predicting the Specific Gravity of Must During Fermentation ...

引用

32nd International Conference on Organization and technology of Maintenance, OTO 2023

作者： Kovačević, Ivana Orić, Mihaela Tolić, Ivana Hartmann Nyarko, Emmanuel Karlo Faculty of Electrical Engineering Computer Science and Information Technology Osijek Croatia Kneza Trpimira 2B Osijek31000 Croatia

ISBN: (纸本)9783031514937

In this article, the process of fermentation in winemaking is investigated. The authors analyze fermentation data and propose several machine learning models to assess the effectiveness of ongoing fermentation processes based on measurements of specific gravity and temperature. To evaluate the performance of each model, various metrics such as R-squared, Mean Squared Error, Root Mean Squared Error, and Maximum Absolute Error are employed. The results suggest that the models have the potential to predict fermentation outcomes in winemaking and provide winemakers with a valuable tool to optimize the process and ensure high-quality wine production. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Machine learning

来源：评论

学校读者我要写书评

暂无评论

Localization Improvements in Faster Residual Convolutional Neural Network Model for Temporomandibular Joint – Osteoarthritis Detection 1st

Localization Improvements in Faster Residual Convolutional N...

引用

1st International Conference on Artificial Intelligence of Things, ICAIoT 2023

作者： Vijaya Kumar, K. Baskaran, Santhi Department of Computer Science and Engineering Puducherry Technological University Puducherry India Department of Information Technology Puducherry Technological University Puducherry India

ISBN: (纸本)9783031487736

To recognize the Osteoarthritis of the Temporomandibular Joint (TMJ-OA) from panoramic dental X-ray images, deep learning algorithms are widely used these days. Among others, an Optimized Generative Adversarial Network with Faster Residual Convolutional Neural Network (OGAN-FRCNN) was recently achieved better FRCNN training by providing more synthetic images for TMJ-OA recognition. However, the localization of a small condyle OA region was ineffective because of the complex background, occlusion and low-resolution images. Hence, this article proposes the OGAN with Progressive Localization-improved FRCNN (PLFRCNN) model for TMJ-OA recognition. First, the OGAN can augment the number of panoramic X-ray scans. Then, ResNet101 can extract the Feature maps (F-maps) at multiple levels, followed by the Feature Pyramid Network (FPN) with a Region-of-Interest (RoI)-grid attention for multiscale F-map extraction. Those F-maps are given to the Modified Region Proposal Network (MRPN), which applies a multiscale convolution feature fusion and an Improved Non-Maximum Suppression (INMS) scheme for creating the Region Proposals (RPs) with more information. To resolve the localizing variance and obtain the proposal F-maps, the improved RoI pooling based on bilinear interpolation merges both F-maps and RPs. Moreover, the fully connected layer is used to classify those F-maps into corresponding classes and localize the target Bounding Box (BB). Additionally, the BB regression in the TMJ-OA localization stage is enhanced by the new Intersection-Over-Union (IOU) loss function. Finally, the test outcomes reveal that the OGAN-PLFRCNN model attains an accuracy of 98.18% on the panoramic dental X-ray corpus, in contrast to the classical CNN models. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Generative adversarial networks

来源：评论

学校读者我要写书评

暂无评论

Evaluation of Machine Learning Approaches for Accurate Heart Disease Prediction 14

Evaluation of Machine Learning Approaches for Accurate Heart...

引用

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

作者： Atul Shankar, Pranav Rallabandi, Srikari Singabhattu, Bhavya Shinde, Ashish School of Engineering and Technology Computer Science and Engineering Haryana India Pes University Computer Science and Engineering Bangalore India Vidya Jyothi Institute of Technology Artificial Intelligence Hyderabad India Malla Reddy Engineering College for Women Computer Science and Engineering Hyderabad India Srmist Ramapuram Information Technology Chennai India

ISBN: (纸本)9798350335095

Heart disease is a global health concern, and early identification is crucial. Machine learning (ML) shows promise in predicting heart disease [5], but choosing the best algorithm is challenging. Comparing ML models is vital for selecting the optimal one. This study compared six ML algorithms: Linear Regression, Logistic Regression, K-Nearest Neighbours (KNN), Decision Tree, Random Forest, and SVM (Support Vector Machine). Accuracy, precision, recall, and F1 score assessed their *** excelled with a precision score of 0.92, followed by Random Forest at 0.91. SVM performed well with 0.88 accuracies. Logistic Regression and Decision Tree scored 0.86, while Linear Regression was the least accurate (0.55). While evaluating the precision, recall, and F1 scores confirmed KNN's superiority. Other models varied in *** findings highlight the pros and cons of ML models for heart disease prediction. While KNN was the most accurate in this study, further research is needed to identify the optimal algorithm for different datasets. ML models can revolutionize cardiology by quickly forecasting and identifying the cardiac disease, improving patient outcomes. © 2023 IEEE.

关键词： Predictive analytics

来源：评论

学校读者我要写书评

暂无评论

The Challenges and Opportunities of Cloud Security as a Service for Enhancing IoT Security

The Challenges and Opportunities of Cloud Security as a Serv...

引用

2023 IEEE International Conference on Paradigm Shift in information Technologies with Innovative Applications in Global Scenario, ICPSITIAGS 2023

作者： Mandal, Sanjeev Kumar Hemelatha, S. Sasirekha, R. Chavan, Gurunath T. Kaneria, Ojasvee Jaffar, A Mohamed Department of Computer Science and Information Technology Karnataka Bangalore India Karpagam Academy of Higher Education Department of Computer Science Engineering Coimbatore India Prince Shri Venkateshwara Padmavathy Engineering College Department of Computer Science and Engineering Chennai India Vishwakarma Institute of Information Technology Department of Information Technology Pune India Vivekananda Global University Department of Computer Science & Engineering Jaipur India Atlas SkillTech University Department of Isme Maharastra Mumbai India

ISBN: (纸本)9798350329773

The internet of things (Iota) has had a remarkable impact on modern lifestyles, revolutionizing the way we display, communicate, and have interaction with the sector round us. In conjunction with this new generation, but, come new challenges for organizations in terms of safety. Iota devices frequently lack the security features wished for defensive towards cyber attacks and statistics robbery, leaving them prone to malicious actors. Cloud safety as a carrier (Cases) might also provide the solution to those challenges, via providing a more suitable layer of protection for Iota networks. This new sort of protection answer utilizes cloud-based technologies to provide comfortable get admission to, multi-thing authentication, safety analytics, and strong management abilities for Iota networks. With the aid of imparting this comfy environment, Cases can offer organizations with the warranty that their Iota information can remain comfortable, even within the face of ever-evolving cyber threats. At the identical time, however, heavily investing in Cases increases a number of questions. Factoring in implementation fees, compliance requirements, and commercial enterprise goals may be a daunting task for corporations, and Cases might not always offer a greatest solution. It's vital for businesses to correctly determine their safety necessities when thinking about Cases, and to understand the benefits. © 2023 IEEE.

关键词： Cybersecurity

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：