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Chaotic Fuzzy Logic for Enhanced Educational Q&A System 6

Chaotic Fuzzy Logic for Enhanced Educational Q&A System

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6th ieee international conference on Power, intelligent computing and systems, ICPICS 2024

作者： Chen, Haoyuan Shi, Nuobei Chen, Ling Lee, Raymond BNU-HKBU United International College Department of Computer Science Zhuhai China School of Applied Science and Civil Engineering Beijing Institute of Technology Zhuhai China

ISBN: (纸本)9798350374315

Online question-and-answer (Q&A) platforms are replete with extensive human resources support. This paper proposes a novel methodology of a customized Large Language Model (LLM) called Chaotic LLMs-based Educational Q&A System (CHAQS) to navigate the complexities associated with intelligent Q&A systems for educational sector. It uses an expansive dataset comprising over 383,000 educational data pairs, an intricated fine-tuning process encompassing p-tuning v2, Low Rank Adaptation (LRA), and strategies for parameter freezing at an open-source Large Language Model ChatGLM as baseline model. Also, Fuzzy Logic is implemented to regulate parameters, system's adaptability with Lee-Oscillator to refine model's response variability and precision. Experiment results showed that 5.12% improvement in Precision score, 11% increase in Recall metric, and 8% improvement in F1 score as compared to other models. © 2024 ieee.

关键词： AI-based QA System Education Fuzzy Logic Large Language Model Lee-Oscillator

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Design and Application of intelligent Teaching System Based on Artificial Intelligence Technology 2

Design and Application of Intelligent Teaching System Based ...

引用

2nd international conference on Distributed computing and Optimization Techniques, ICDCOT 2024

作者： He, Wansheng Jilin Agricultural Science and Technology University Jilin China

ISBN: (纸本)9798350382952

In order to improve teaching efficiency and achieve networked and intelligent education. The author first designed an intelligent teaching system based on artificial intelligence technology. On the basis of optimizing the system software structure, guided by the user group's demand for the teaching system, the author used artificial intelligence technologies such as machine learning algorithms, big data science, and image language intelligent recognition to design corresponding business function modules such as login module, data collection and processing module, and teaching service module. Secondly, the intelligent teaching system was tested. During the test, the teaching data acquisition speed of different stages of data nodes was around 2 seconds, and the system obtained teaching information faster. The system data processing module can accurately extract redundant teaching data. The highest accuracy rate of teaching data collection is 99.18%, which can accurately collect teaching data and provide the required content for different user groups. The maximum monitoring rate of the system for user teaching behavior is 99.68%, and the supervision of user behavior is strong. The experimental test results indicate that the running speed of each module of the system is fast, and the system has strong applicability and stability, which is suitable for teachers to carry out daily teaching activities. This system has the advantages of being contemporary and scientific, which helps to promote the development of teaching towards intelligence and efficiency. © 2024 ieee.

关键词： Behavioral research

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Adaptive Neuro-Fuzzy Inference systems for Cognitive Sensor Communication in Smart Cities

Adaptive Neuro-Fuzzy Inference Systems for Cognitive Sensor ...

引用

2024 ieee international conference on intelligent systems and advanced Applications, ICISAA 2024

作者： Pradhan, Rahul Prathiba, Ch. Kholmukhamedov, Timur Patil, B.N. Mary, S. Suma Christal Raj, I Infant GLA University Department of Computer Engineering and Applications Mathura India Vignana Bharathi Institute of Technology Department of CSM Ghatkesar Hyderabad India Turin Polytechnic University Automatic Control and Computer Engineering Department Tashkent Uzbekistan Yeshwantrao Chavan College of Engineering Department of Applied Physics Nagpur India Panimalar Engineering College Department of Information Technology Poonamalle Chennai India K. Ramakrishnan College of Engineering Department of CSE Tamil Nadu Trichy India

ISBN: (纸本)9798331539948

Modern urban development depends on optimizing cognitive sensor communications in smart cities, which is the focus of this investigation. In the era of expanding sensor networks, efficient data transmission is paramount for the success of smart city initiatives. The current state of sensor communication faces challenges like suboptimal adaptability, inefficient energy usage, and limited fault detection. This study proposes a solution through the application of an Adaptive Neuro-Fuzzy Inference System (ANFIS), identifying these challenges as opportunities for improvement. The novelty lies in ANFIS's introduction as an intelligent and adaptive solution. Its ability to learn from sensor data, adapt to changing conditions, and optimize communication parameters sets it apart in enhancing cognitive sensor communication. The research methodology involves a systematic approach, encompassing data collection, pre-processing, and ANFIS model development. The model is rigorously trained, validated, and tested for real-world effectiveness. Integration into the smart city communication system is explored, emphasizing applications in context-aware communication, energy-efficient protocols, and fault detection. Performance is evaluated using metrics like accuracy, reliability, and efficiency, demonstrating the model's efficacy in addressing drawbacks and enhancing overall sensor communication in smart cities. The findings contribute to advancing intelligent systems for urban infrastructure and services. © 2024 ieee.

关键词： Fuzzy neural networks

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Research on UAV Aerial Image Dehazing Algorithm Based on CycleGAN

Research on UAV Aerial Image Dehazing Algorithm Based on Cyc...

引用

2024 international conference on intelligent computing and Robotics, ICICR 2024

作者： Wang, Henan He, Yin Department of Information Systems Naval Dalian Ship Academy Liaoning Dalian China

ISBN: (纸本)9798350372274

Effective perception of the environment is a prerequisite for autonomous detection by unmanned aerial vehicles (UAVs), and accurate acquisition of visual images is the most crucial aspect. Considering that the visibility of UAV visual images is greatly affected by natural factors such as fog, and current dehazing algorithms are not very effective, this paper proposes a dehazing method for UAV images based on a cyclic generative adversarial network with an atrous spatial pyramid pooling and attention module. The unpaired image unsupervised dehazing task is performed using the constrained transfer learning ability of the CycleGAN. Considering the severe loss of edge details in actual foggy images and the complex distribution of fog, it is necessary to combine real visual features for dehazing. This paper replaces the ordinary convolution with the ASPP module and incorporates the Convolutional Block Attention Module into the network to non-uniformly process different features and areas in foggy images. Experimental results show that this method achieves better results on the foggy image dataset. ©2024 ieee.

关键词： Generative adversarial networks

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Monkeypox Disease Classification from Skin Lesion Images using Deep Convolution Neural Network 3

Monkeypox Disease Classification from Skin Lesion Images usi...

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3rd international conference on intelligent systems, advanced computing, and Communication, ISACC 2025

作者： Thangaraj, Rajasekaran Vadivel, Balasubramaniam Kumar, S. Manoj Sadesh, S. Karunakaran, P. Prakash, P. Nandha Engineering College Department of Computer Science and Engineering Erode India Sri Ramakrishna Institute of Technology Department of Computer Science and Engineering Coimbatore India KPR Institute of Engineering and Technology Department of Computer Science and Engineering Coimbatore India Velalar College of Engineering and Technology Department of Artificial Intelligence and Data Science Erode India Nandha Engineering College Department of Artificial Intelligence and Data Science Erode India Velalar College of Engineering and Technology Department of Information Technology Erode India

ISBN: (纸本)9798331523893

Recently monkeypox outbreak has raised concerns due to its increasing number of cases and diverse dermatological symptoms in 2024, which can complicate early diagnosis due to similarities with other viral infections such as measles and chickenpox. To enhance diagnostic accuracy, transfer learning models and artificial intelligence (AI) have been explored as effective tools. Pre-trained models such as VGG16, VGG19, ResNet50V2, and MobileNetV2 have been employed for monkeypox detection, each with distinct trade-offs in terms of computational efficiency and diagnostic precision. MobileNetV2 offers superior efficiency, but this comes at the cost of reduced accuracy. VGG models provide higher accuracy but require much more computational *** these models, DenseNet121 stands out by achieving 99% accuracy while requiring 37% less computational power, making it the model optimized choice for efficient and accurate monkeypox classification. © 2025 ieee.

关键词： Transfer learning

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Generative AI-Driven Automation of Business Process ReImagination 6

Generative AI-Driven Automation of Business Process ReImagin...

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6th ieee international conference on Recent Advances in intelligent Computational systems, RAICS 2024

作者： Paulose, Renjith Neelanath, Vinod Ust SmartOps Ust Kochi India Ust SmartOps Ust Trivandrum India

ISBN: (纸本)9798350381689

Traditional business process automation primarily concentrates on digitizing processes to achieve specific outcomes. However, it grapples with challenges stemming from manual configurations and inflexible workflows, restricting adaptability and creativity. This paper delves into the transformative potential of Generative Artificial Intelligence (AI) in revolutionizing process automation, addressing the limitations inherent in traditional approaches. In contrast to traditional automation, Generative AI adopts a groundbreaking approach by harnessing data from past automation solutions to identify more effective methods. This goes beyond conventional orchestration, dynamically integrating software components, automatically generating rules from historical automation solutions data, and seamlessly integrating external systems. Generative AI unlocks its potential through the analysis of past workflows, gaining insights into components, decisions, and sequences. This knowledge serves as the foundation for creating new, efficient processes based on user-described natural language, resulting in robust workflows and rules that assimilate lessons from past best practices and adapt as needed. This innovative approach signifies a notable departure from traditional constraints, paving the way for a future where business processes evolve dynamically, aligning with the efficiency demands of the ever-changing digital landscape. © 2024 ieee.

关键词： business process automation Generative AI intelligent automation Large Language Model

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Research on the Application of Contrast Attention Hypergraph Neural Networks (CAHG) in Image Classification 6

Research on the Application of Contrast Attention Hypergraph...

引用

6th ieee international conference on Power, intelligent computing and systems, ICPICS 2024

作者： Chen, Yuxuan Wang, Teng School of Software Engineering South China University of Technology Guangzhou China College of Technology and Data Yantai Nanshan University Yantai China

ISBN: (纸本)9798350374315

Hypergraphs, contrastive learning, and attention mechanisms are widely applied in many fields. In the field of computer vision, they are commonly used to capture complex information from images. In order to achieve better performance in image classification tasks and expand to more other tasks, this work proposes a new framework-Contrast Attention Hypergraph neural networks (CAHG), which can better capture complex relationships in image data and improve image classification performance by comparing views constructed in different ways. In the future, this framework can be extended to other fields such as multimodality. This study used a remote sensing cloud image dataset, which includes 19000 remote sensing cloud images and 7 classification methods. Compared with several baseline methods, our CAHG model has superior performance. Our downstream tasks cover various fields, including but not limited to autonomous driving, medical imaging, and many other areas. This work indicates that extracting information in multiple ways and transforming it into a hypergraph representation, followed by comparative learning, can improve performance and can be used in various tasks in the future. © 2024 ieee.

关键词： Contrastive Learning

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Asleep Adults' Breathing Patterns via data Mining of Electromyograms

Asleep Adults' Breathing Patterns via Data Mining of Electro...

引用

ieee international Workshop on intelligent data acquisition and advanced computing systems: Technology and Applications

作者： Gennady Chuiko Olga Dvornik Yevhen Darnapuk Denis Honcharov Olga Yaremchuk Petro Mohyla Black Sea National University Mykolayiv Ukraine

A study used Machine Learning to identify potential sleep disturbances by analyzing abdominal electromyograms and breathing patterns in sleeping adults. The median and peak values of envelopes of electromyograms have been used as diagnostic signs of breathing patterns. The report suggests using visual classifiers, like decision trees, to make the diagnostic process more accessible for clinicians unfamiliar with Machine Learning. The duly preprocessed abdominal electromyograms allow the forecast of the breathing pattern by decision trees with a precision of up to 0.848.

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Blockchain-Based Framework for Secure IoT data Transmission

Blockchain-Based Framework for Secure IoT Data Transmission

引用

2024 international conference on Innovative computing, intelligent Communication and Smart Electrical systems, ICSES 2024

作者： Gujar, Shantanu Sudhir Golisano College of Computing and Information Sciences Rochester Institute of Technology Department of Cyber Security RochesterNY14623 United States

ISBN: (纸本)9798331543617

In response to the growing demand for security in IoT networks. Technology that is decentralized is used in this architecture. The suggested architecture includes blockchain oracles to securely connect blockchain with off-chain Internet of Things data. By using decentralized and secure Internet of Things oracles, this guarantees verifiable and trustworthy real-time data flow. Furthermore, artificial intelligence-powered Internet of Things security procedures are used to monitor and defend Internet of Things devices against emerging cyber threats. These techniques include threat intelligence solutions supported by blockchain technology and anomaly detection powered by AI. The IOTA Tangle architecture is utilized by the framework. This architecture provides scalable and fee-free data transfers, making it ideal for Internet of Things ecosystems. Strong, secure, and effective communication between Internet of Things devices is ensured by this method. Increased data integrity, privacy, and real-time threat detection from multiple devices are also made possible by it. This study presents a potential solution to the security issues that contemporary IoT systems are now facing, as well as an examination of the practical implementation of these technologies in protecting data transmission over the Internet of Things (IoT). © 2024 ieee.

关键词： Anomaly Detection Blockchain Decentralized Oracles IoT Security IOTA Tangle Real-Time data Threat Intelligence

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Construction of a One Map Model for intelligent Evaluation of Distribution Networks Integrating Multiple Entities and Attributes 5

Construction of a One Map Model for Intelligent Evaluation o...

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5th ieee international conference on advanced Electrical and Energy systems, AEES 2024

作者： Zhang, Guangru Ren, Haodong Zhen, Wenxi Ma, Xiping Wang, Lipeng Dong, Haiying State Grid Gansu Electric Power Company Lanzhou China State Grid Gansu Electric Power Research Institute Lanzhou China Lanzhou Jiaotong University School of New Energy and Power Engineering Lanzhou China

ISBN: (纸本)9798331517724

This paper addresses the current challenges in state assessment and data storage and retrieval within distribution networks that have a high penetration of distributed power sources. It proposes a method for constructing a unified graph model of the distribution network that integrates multiple entities and attributes. The model considers the consistency of graph topology and includes various elements, such as the topological data of distribution transformers, distributed power sources, and lines. It also incorporates multiscale monitoring information and a multidimensional evaluation index system, with these elements represented as nodes and their interconnections as edges. By establishing this unified graph model, we enable intelligent evaluation of distribution networks with diverse entities and attributes. The model has been verified using Python and Neo4j software to ensure alignment with the intended objectives. This approach offers a more efficient way to analyze data and topology, paving the way for future work related to distribution networks. © 2024 ieee.

关键词： Distribution transformers

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