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检索条件"任意字段=2024 IEEE International Conference on Cognitive Computing and Complex Data"

共 1578 条记录，以下是431-440 订阅

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Transforming Multidimensional Time Series into Interpretable Event Sequences for Advanced data Mining 5

Transforming Multidimensional Time Series into Interpretable...

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5th international conference on Intelligent computing and Human-Computer Interaction, ICHCI 2024

作者： Yan, Xu Jiang, Yaoting Liu, Wenyi Yi, Didi Wei, Jianjun Trine University Phoenix United States Carnegie Mellon University Pittsburgh United States Independent Research Nanjing China Independent Research Seattle United States Washington University in St. Louis St Louis United States

ISBN: (纸本)9798350368284

This paper introduces a novel spatiotemporal feature representation model designed to address the limitations of traditional methods in multidimensional time series (MTS) analysis. The proposed approach converts MTS into one-dimensional sequences of spatially evolving events, preserving the complex coupling relationships between dimensions. By employing a variable-length tuple mining method, key spatiotemporal features are extracted, enhancing the interpretability and accuracy of time series analysis. Unlike conventional models, this unsupervised method does not rely on large training datasets, making it adaptable across different domains. Experimental results from motion sequence classification validate the model's superior performance in capturing intricate patterns within the data. The proposed framework has significant potential for applications across various fields, including backend services for monitoring and optimizing IT infrastructure, medical diagnosis through continuous patient monitoring and health trend analysis, and internet businesses for tracking user behavior and forecasting sales. This work offers a new theoretical foundation and technical support for advancing time series data mining and its practical applications in human behavior recognition and other domains. © 2024 ieee.

关键词： Time series

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A LLMs-Assisted Framework for Parkinson's Disease Assessment Based on PPMI dataset 7

A LLMs-Assisted Framework for Parkinson's Disease Assessment...

引用

7th international conference on Algorithms, computing and Artificial Intelligence, ACAI 2024

作者： Gao, Zhenyu Ni, Qin Liu, Wen Zhang, Lei College of Information Science and Technology Donghua University Shanghai China Shanghai International Studies University The Key Laboratory of Multilingual Education with AI Shanghai China

ISBN: (纸本)9798331529314

The assessment of diseases such as Parkinson's disease, which are chronic neurodegenerative conditions, is an extremely complex and time-consuming process. Self-assessment scales or interviews in the outpatient often lead to the loss of key information. Large Language Models (LLMs) show abilities in capturing subtle linguistic differences and handling long texts, enabling the extraction of a significant amount of key information while ensuring data integrity and user privacy. We focus attention on people with Parkinson's disease (PwP). In response to these issues, we propose a framework in this paper. Firstly, we use large language model (LLM) with chain-of-thought (CoT) prompt to rephrase patient self-report texts from real-outpatient structured data as a supervised fine-tuning (SFT) corpus. Secondly, we fine-tune bidirectional encoder representation from transformers (BERT) with Low-Rank Adaptation (LoRA) to enable it to under-stand and extract the semanteme of self-report, thus predicting each MDS-UPDRS item. Finally, we designed experiments on a large amount of test data to evaluate the effectiveness of the framework. The results indicate that the accuracy on this task has been improved to 95.36%, which is a 6.7% increase compared to the best-performing model. © 2024 ieee.

关键词： Neurodegenerative diseases

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Examining Transactional Decentralization on the Ethereum Blockchain 6

Examining Transactional Decentralization on the Ethereum Blo...

引用

6th international conference on Blockchain computing and Applications, BCCA 2024

作者： Juodis, Mindaugas Filatovas, Ernestas Goštautas, Donatas Paulavičius, Remigijus Institute of Data Science and Digital Technologies Vilnius University Vilnius Lithuania Gjensidige Lithuania Vilnius Lithuania

ISBN: (纸本)9798350351538

The decentralized nature of blockchain ecosystems involves a complex interplay of various sources, entities, and decision-makers that act collectively, yet independently, avoiding unnecessary concentration or dominance. Decentralization can be assessed through various dimensions, such as consensus, network, governance, transaction, and wealth. This work presents an overview of the current landscape of transactional decentralization. Assessing decentralization requires robust metrics capable of distinguishing and incorporating factors such as the distribution of participants between addresses, blocks, and transactions. Therefore, in this work, we actively utilize and examine well-known statistical metrics, including the decentralization index, Gini coefficient, Nakamoto coefficient, Herfindahl-Hirschman index, and Shannon entropy, using Ethereum blockchain data over a period of seven years. Our analysis delves into the intricate dynamics of decentralized transactions, uncovering the evolving trends and challenges that define decentralization. This research contributes to the ongoing discourse surrounding blockchain decentralization, providing information on the measurement and evaluation of the status of decentralization using raw and grouped data from the Ethereum blockchain. © 2024 ieee.

关键词： Decentralized finance

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Algorithm Implementation of Digital Protection System for Traditional Cultural Industry Based on Grey Clustering

Algorithm Implementation of Digital Protection System for Tr...

引用

2024 international conference on Mechatronics and Intelligent Control, ICMIC 2024

作者： Chen, Hongxia Hou, Lanbao School of Literature and Media Jingchu University of Technology Jingmen448000 China School of Mathematics and Physics Jingchu University of Technology Jingmen448000 China

ISBN: (数字)9781510686847

ISBN: (纸本)9781510686830

The protection mode of digitalization effectively transforms various national traditional cultures into digital products, providing a way of thinking for the protection of national traditional cultures. This mode still lacks the mode of how to mine the data of national traditional culture and analyze the internal relationship structure after facing the massive data, which can guide the digital protection of national traditional culture under the complex social conditions in the future. This paper discusses the significance of digitalization for the protection of traditional national culture under the environment and ideology of big data development. The possibility of protecting traditional national culture based on big data was analyzed and discussed. This paper designs and establishes the digital perception analysis system of traditional culture. The system uses grey clustering algorithm to identify the characteristics of cultural products. The experimental results show that this method can improve the efficiency of traditional cultural product classification. This method has certain reference value for the research of digital protection system of traditional cultural industry. © 2025 SPIE.

关键词： Augmented reality Visualization Telecommunications computing systems Cultural heritage data communications Image processing Video data storage Cameras

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Tropical Cyclone Detection and Tracking Using YOLOv8 Algorithm 1

Tropical Cyclone Detection and Tracking Using YOLOv8 Algorit...

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1st international conference on cognitive, Green and Ubiquitous computing, IC-CGU 2024

作者： Rout, Saroja Kumar Kumar, Kottu Santosh Sahu, Ruchismita Barik, Shekharesh Pradhan, Samarendra Department of Information Technology Hyderabad India Department of Artificial Intelligence and Machine Learning Hyderabad India Templecity Institute of Technology and Engineering Department of Computer Science & Engineering Bhubaneswar India DRIEMS University Department of Computer Science & Engineering Cuttack India Data scientist Jayapur Cuttack India

ISBN: (纸本)9798350361780

Coastal areas around the world face a great threat from tropical cyclones, which makes timely and accurate identification essential for efficient disaster response. An enhanced method for detecting tropical cyclones is proposed in this work using the YOLOv8 (You Only Look Once version 8) object identification algorithm. Using the real-time object recognition capabilities of YOLOv8, the methodology is designed to tackle the challenges associated with finding cyclonic structures in satellite data. To detect crucial features typical of tropical cyclones - like cloud formations, circular symmetry, and dynamic atmospheric fluctuations - the YOLOv8 architecture is improved. To ensure its adaptability to different climatic circumstances, the model is trained using a varied dataset of labeled satellite photos. The YOLOv8-based methodology is superior to existing methods in detecting tropical cyclones with accuracy and efficiency almost in real-time, as demonstrated by the experimental results. Because of the algorithm's excellent precision, recall, and computing economy, early warning systems and catastrophe preparedness can benefit greatly from it. This research contributes to the ongoing effort to develop detection approaches for increased resilience in vulnerable coastal areas, while also utilizing YOLOv8's capabilities to address the global threat posed by tropical cyclones. This study can help clarify the necessity of improving models and conducting better observations of Tropical Cyclones. © 2024 ieee.

关键词： Object detection

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Research on Image Encryption Method Based on complex Analytical Chaotic Map 6

Research on Image Encryption Method Based on Complex Analyti...

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6th ieee international conference on Power, Intelligent computing and Systems, ICPICS 2024

作者： Sun, Yanling Li, Yingqi Shenyang Jianzhu University Department of Science Shenyang China Shenyang Jianzhu University Computer Science and Engineering Shenyang China

ISBN: (纸本)9798350374315

Targeting the security issues associated with digital image transmission across networks, a digital image encryption technique based on Piecewise linear chaotic map (PWLCM) and complex analytical chaotic map (ICON) was presented. Firstly, ICON was introduced to overcome the problem that the traditional chaotic map is too simple and the generated random sequence is too single. The HMAC-SHA256 hash algorithm is used to calculate the plaintext image and generate the key stream required for encryption, which improves the plaintext sensitivity of the algorithm. The algorithm employs a scrambling-diffusion framework. During the scrambling phase, it integrates overall scrambling and separate scrambling across three channels to significantly diminish pixel correlations. In the diffusion phase, it utilizes XOR operations alongside S-box replacement diffusion to extensively spread pixel values. Experimental findings indicate that the encryption method exhibits robust key sensitivity, minimal correlation among neighboring pixels in encrypted images, and adeptness in countering differential attacks. Even if the encrypted data is missing, most of the information can still be obtained after decryption, which shows that the scheme has high security and reliability. © 2024 ieee.

关键词： Piecewise linear techniques

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Simulation of Teaching Management data System Based on PSO Algorithm 3

Simulation of Teaching Management Data System Based on PSO A...

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3rd international conference on data Analytics, computing and Artificial Intelligence, ICDACAI 2024

作者： Zhang, Min Jiangxi Institute of Applied Science and Technology Jiangxi Nanchang330100 China

ISBN: (纸本)9798331519032

Swarm intelligence algorithms have developed rapidly in recent years, and are widely used in solving complex practical problems, such as decentralized control centers, high conditional coupling or inability to establish system models. Particle swarm optimization (PSO) algorithm is an optimization method based on swarm intelligence, which originates from the study of the motion behavior of birds. Because of its simple principle, convenient program design, small number of parameters and no strong coupling relationship, PSO has become a hot topic in the field of control optimization. In the management of each university, the teaching management data system is the core part of the whole campus information construction, which is mainly responsible for managing the learning situation of all students, the teaching situation of teachers and the unified management and configuration of the use of teaching resources. Therefore, how to make better use of computers as an auxiliary management means to develop a stable and efficient management system is the key problem faced by the current campus information construction. According to the actual needs of college teaching management, a college teaching management data system is developed and designed to help universities better manage college students. This paper uses PSO algorithm to study the teaching management data system. © 2024 ieee.

关键词： Swarm intelligence

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Integrating Multi-Omics data for Advanced Diabetes Prediction and Understanding 7

Integrating Multi-Omics Data for Advanced Diabetes Predictio...

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7th international conference on Contemporary computing and Informatics, IC3I 2024

作者： Mishra, Soumya Ranjan Dash, Sachikanta Padhy, Sasmita Kumar, Naween Dash, Yajnaseni Giet University Gunupur India Giet University Department of Cse Gunupur India Vit Bhopal University School of Computing Science and Engineering Madhya Pradesh Bhopal India Bennett University School of Computer Science Engineering and Technology Uttar Pradesh Greater Noida India Bennett University School of Artificial Intelligence Uttar Pradesh Greater Noida India

ISBN: (纸本)9798350350067

Diabetes is a major global health issue, requiring new approaches to understanding its complex etiology and improving predictions of effective management and treatment. Current methods typically use centralized models, which have privacy concerns and cannot fully capture correlations in multimodal datasets. To address these problems, we introduce a new model using integrated learning and causal theory. Our system uses three datasets: the Pima Indian Diabetes dataset (PIDD), 130 U.S. Appl. Clinical Diabetes dataset, and physiological summaries. For analysis in our proposed method, we use various machine learning methods with ensemble method. Here we achieve an accuracy of 93% for PIDD 92% for US 130 clinical datasets and 90% for physically collected datasets. By integrating genetic, proteomic, and metabolomic data, we better understand diabetic mechanisms and potential therapeutic targets. Our model addresses the limitations of current methods, improves prediction accuracy, and data privacy, and provides insight into the aetiology of diabetes. This approach provides a strong foundation for future study. © 2024 ieee.

关键词： Adversarial machine learning

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An Incremental Gray-box Physical Adversarial Attack on Neural Network Training

An Incremental Gray-box Physical Adversarial Attack on Neura...

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ieee international conference on Communications (ieee ICC)

作者： Al-qudah, Rabiah Aloqaily, Moayad Ouni, Bassem Guizani, Mohsen Lestable, Thierry Mohamed Bin Zayed Univ Artificial Intelligence MB Abu Dhabi U Arab Emirates Technol Innovat Inst Abu Dhabi U Arab Emirates

ISBN: (纸本)9781538674628

Neural networks have demonstrated remarkable success in learning and solving complex tasks in a variety of fields including cognitive cities. Nevertheless, the rise of those networks in modern computing has been accompanied by concerns regarding their vulnerability to adversarial attacks. In this work, we propose a novel gradient-free, gray box, incremental attack that targets the training process of neural networks. The proposed attack, which implicitly poisons the intermediate data structures that retain the training instances between training epochs acquires its high-risk property from attacking data structures that are typically unobserved by professionals. Hence, the attack goes unnoticed despite the damage it can cause. Moreover, the attack can be executed without the attackers' knowledge of the neural network structure or training data, making it more dangerous. The proposed attack was tested under a sensitive application of secure cognitive cities, namely, biometric authentication. The conducted experiments showed that the proposed attack is effective and stealthy. Finally, the attack effectiveness property was concluded from the fact that it was able to flip the sign of the loss gradient in the conducted experiments to become positive, which is noisy and unstable training. Moreover, the attack was able to decrease the inference probability in the poisoned networks compared to their unpoisoned counterparts by 15.37%, 14.68%, and 24.88% for the Densenet, VGG, and Xception, respectively. Finally, the attack retained its stealthiness despite its high effectiveness. This was demonstrated by the fact that the attack did not cause a notable increase in the training time, in addition, the Fscore values only dropped by an average of 1.2%, 1.9%, and 1.5% for the poisoned Densenet, VGG, and Xception, respectively.

关键词： Adversarial Attacks data Poisoning Neural Networks Iris Recognition.

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Optimal Multi-Keyword Ranked Search Matching in Public Cloud with Enhanced Secure Search Scheme Over Encrypted data 9

Optimal Multi-Keyword Ranked Search Matching in Public Cloud...

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9th international conference on Science, Technology, Engineering and Mathematics, ICONSTEM 2024

作者： Radha, B. Jaya Bharathi, R. Vidhya Muthulakshmi, R. Vidhya, K. Reddy, R. Vijaya Kumar Rondon, Ruchika Sri Krishna Arts And Science College Department of Ict And Cognitive Systems Tamil Nadu Coimbatore641008 India Rmk College of Engineering And Technology Department of Computer Science Engineering Tamil Nadu Puduvoyal601206 India Panimalar Engineering College Department of Artificial Intelligence And Data Science Tamil Nadu Chennai600 123 India Kpr Institute of Engineering And Technology Department of Computer Science Engineering Tamil Nadu Arasur641407 India Lakshmaiah Education Foundation Department of Computer Science Engineering Andhra Pradesh Vaddeswaram522502 India Graphic Era Deemed To Be University Department of Petroleum Engineering Uttarakhand Dehradun248002 India

ISBN: (纸本)9798350365092

In this research work, the authors present an improved secure search algorithm that will allow for optimum multi-keyword ranked search matching in public cloud storage that uses encrypted data. The goal of the plan is to provide a method that is both safe and effective for searching for and retrieving important data stored in cloud settings, all while preserving the data's privacy and confidentiality. Experiments and simulations are carried out in order to assess the viability of the suggested plan, and mathematical expressions are used to depict the algorithms and assessment criteria. According to the findings of the research, the strategy that was presented is capable of achieving a high level of accuracy, recall, and F1-score while simultaneously reducing the number of false positives and false negatives. The research underlines how important it is to address security problems in cloud computing settings, and it gives significant insights that can be used to design cloud computing services that are both safe and efficient. In the future, possible topics of study might include further refining and optimizing of the scheme that was described, as well as the application of the method in real cloud computing systems. © 2024 ieee.

关键词： data privacy

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