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Quadratic Funding in Blockchain-Based DAO: Empirical Analysis and Future Prospects 4

Quadratic Funding in Blockchain-Based DAO: Empirical Analysi...

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4th IEEE International Conference on Digital Twins and Parallel intelligence, DTPI 2024

作者： Lin, Fei Zhang, Tengchao Ma, Siji Huang, Jun Song, Ping Li, Juanjuan Macau University of Science and Technology Faculty of Innovation Engineering 999078 China Chinese Academy of Sciences State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Beijing100190 China

ISBN: (纸本)9798350349252

This paper provides an in-depth analysis of the application of the Quadratic Funding (QF) mechanism in blockchain-based Decentralized Autonomous Organizations (DAOs) of the QF mechanism. Then, the empirical analysis on the influence of QF on promoting project funding is examined, taking the first round of BNB funding on the DoraHacks platform as the exemplar. The focus has been put on its performance on community voting and fund allocation as well as its real-world impact. Finally, based on the analysis results, the paper proposes improvements to the QF mechanism and presents forward-looking strategies for applying Large Language Models (LLMs) in decentralized decision-making. These insights provide valuable perspectives for understanding and optimizing Decentralized Finance (DeFi). © 2024 IEEE.

关键词： Decentralized finance

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Quantitative Assessment in Dynamic PET/CT for Hepatocellular Carcinoma through Bayesian Prior information-based Actor-Critic algorithm 24

Quantitative Assessment in Dynamic PET/CT for Hepatocellular...

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13th International Conference on Computing and Pattern Recognition, ICCPR 2024

作者： Li, Siming Huang, Jingchun He, Jianfeng Wang, Shaobo Faculty of Information Engineering and Automation Kunming University of Science and Technology Yunnan Key Laboratory of Artificial Intelligence Kunming Yunnan China PET/CT Center Hospital of Kunming University of Science and Technology First People's Hospital of Yunnan Kunming Yunnan China

ISBN: (纸本)9798400717482

Dynamic positron emission tomography/computed tomography (PET/CT) is a promising medical imaging technique that enables the visualization of biological processes via tracer activity. Analyzing the time-activity curves (TACs) of PET using pharmacokinetic models allows for the extraction of quantifiable physiological information relevant to hepatocellular carcinoma (HCC). Estimation of the pharmacokinetic model parameters requires advanced algorithms. The challenge lies in achieving accurate parameter estimation and obtaining parameter distributions that align with physiological characteristics. This paper introduces a reinforcement learning-based Actor-Critic (AC) algorithm and proposes a Bayesian prior information-based Actor-Critic (BPAC) algorithm. A dynamic parameter estimation environment for agent interaction was established, incorporating Bayesian optimization. The preceding information obtained through Bayesian optimization was used to design the reward and penalty signals in the interaction environment, and sampled as the initial value for the agent to estimate the parameters of 24 real HCC cases. Receiver operating characteristic (ROC) analyses were performed, using P-values and areas under the curve (AUC) to evaluate diagnostic performance between normal and tumor groups. Compared to the AC, the BPAC demonstrates statistically significant differences in K1, k2, k3, fa, and vb (all P © 2024 Copyright held by the owner/author(s).

关键词： Pharmacokinetics

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Hybrid Particle Swarm and Gray Wolf optimization for Prediction of Appliances in Low-Energy Houses

Hybrid Particle Swarm and Gray Wolf optimization for Predict...

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2022 International Telecommunications Conference, ITC-Egypt 2022

作者： El-Kenawy, El-Sayed M. Eid, Marwa M. Abdelhamid, Abdelaziz A. Ibrahim, Abdelhameed Takieldeen, Ali E. Elkhalik, Said H. Abd Delta University Faculty of Artificial Intelligence Department of Artificial Intelligence Mansoura Egypt Ain Shams University Faculty of Computer and Information Sciences Department of Computer Science Cairo Egypt Mansoura University Faculty of Engineering Computer Engineering Department Mansoura Egypt Delta University for Science and Technology Faculty of Artificial Intelligence Egypt

ISBN: (数字)9781665488082

ISBN: (纸本)9781665488082

In this paper, we propose a new feature optimization prediction framework. This framework consists of two phases. The first phase is the optimization of the feature selection process, in which the dataset is preprocessed by removing non-predictive factors, then the best set of features is selected based on the hybrid modified gray wolf optimizer (GWO) and particle swarm optimization (PSO) algorithms. Whereas the second phase consists of an ensemble model based on the k-nearest neighbors (KNN) regression model, which is trained on the selected features to generate predictions of the appliances in low energy house. The proposed ensemble model consists of three regression models namely, KNN regression, multi-layer perceptron (MLP), and decision trees (DT). Experimental results show that the proposed model achieved the best accuracy and lowest error. In specific, the achieved Root Mean Square Error (RMSE) is (0.00225) and the achieved Mean Absolute Error (MAE) is (0.0002). These results emphasize the effectiveness of the proposed approach when compared with the separate application of regression models. © 2022 IEEE.

关键词： Forecasting

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Innovations and Applications of Generative Adversarial Networks (GANs)

Innovations and Applications of Generative Adversarial Netwo...

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Emerging Trends in engineering and Medical sciences (ICETEMS), International Conference on

作者： Meher Langote Nikita Zade Department of Artificial Intelligence and Data Science Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research Sawangi India

ISBN: (数字)9798331529352

ISBN: (纸本)9798331529390

This paper presents a topical understanding of Generative Adversarial Networks (GANs) and their relevance to artificial intelligence and data science. Following a brief description of what GANs are, their background, and their importance in today's AI, the paper provides the following goals, which are designed to clarify the difficulties, progress, and potential prospects in GAN technology. The paper presents an overview of GAN's basics, elements, and training procedures and discusses the issues, including mode collapse, training fluctuation, data demands, and the ethics of elaborating further on this model. In addition, it discusses promising research directions that concern GANs: new architectures, better algorithms, and combining GANs with other AI approaches. The paper also outlines the development of other GAN applications in areas like climate and material sciences, as well as the transformative role that GANs can play in solving the issues affecting the world today. Therefore, it is expected that the outcomes of the current research proposal and future possibilities can offer insights for researchers, practitioners, and enthusiasts in achieving artificial intelligence and innovative applications in GANs.

关键词： Training Ethics Technological innovation Materials science and technology data science Generative adversarial networks Market research Proposals artificial intelligence Meteorology

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ACP-based Dynamic Incentive Mechanism for Information Sharing in DAO 4

ACP-based Dynamic Incentive Mechanism for Information Sharin...

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4th IEEE International Conference on Digital Twins and Parallel intelligence, DTPI 2024

作者： Zhang, Tengchao Qin, Rui Lin, Fei Ma, Siji Macau University of Science and Technology Faculty of Innovation Engineering 999078 China Chinese Academy of Sciences State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Beijing100190 China

ISBN: (纸本)9798350349252

As a new paradigm in the Web3 era, Decentralized Autonomous Organizations (DAOs) not only embodie the spirit of decentralization and collective governance, but also are the forefront of promoting community-led innovation. However, DAOs face challenges in sustainable growth and scalability in community governance, which are closely related to the income distribution model and member participation. Therefore, the artificial systems, Computational experiments, Parallel execution (ACP) approach is applied to optimize the contribution evaluation and incentive feedback of member behavior through parallel governance and decision-making methods, so as to improve the intelligence of the DAO incentive mechanism. On this basis, the long short-term memory network (LSTM) and combinatorial game theory are used to conduct experimental verification on information sharing within the community. The experimental results show that our proposed method can not only achieve a high degree of information sharing in the community faster than other methods, but also has the ability of autonomous dynamic adjustment. It is of great significance and value to the community governance research and scenario implementation of DAOs. © 2024 IEEE.

关键词： Decentralized systems

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Enhancing the Prediction Accuracy of Brain Tumor Detection by using Fuzzy C-Means Segmentation and Support Vector Machine 5

Enhancing the Prediction Accuracy of Brain Tumor Detection b...

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5th International Conference on IoT Based Control Networks and Intelligent Systems, ICICNIS 2024

作者： Revathi, M. Kalaiarasi, P. Jeevitha, S. Akilandasowmya, G. St. Joseph's Institute of Technology Artificial Intelligence & Data Science Chennai India Kalasalingam University Department of Machine Learning Krishnankoil India Jerusalem College of Engineering Information Technology Chennai India Sri Sairam Institute of Technology Computer and Communication Engineering Chennai India

ISBN: (纸本)9798331518097

Brain is an important organ of the human body that helps in the proper functioning of the all the organs. Without brain, the functions of other organs will not happen since they cannot run without the commands of brain. Brain tumor refers to a situation where an abnormal group of cells are found in some part of the brain. There are many types of brain tumor that are identified. The tumors in brain is independent of age and it can grow at anytime. The symptoms that is exhibited by the brain tumor depends on the size of the tumor cell as well as its location. Machine learning algorithms helps in the earlier identification of the disease which can be used to give proper treatment. In this paper, a fuzzy c means based segmentation of image is done. Then classification of the segmented image is done by using Support Vector Machine. The results shows 98% percentage of accuracy. © 2024 IEEE.

关键词： Support vector machines

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IoT Based Monitoring System for Health During Menstruation 4

IoT Based Monitoring System for Health During Menstruation

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4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Khadse, Chinmaya Hajare, Shital Sheikh, Nishad Dahule, Roshni Kate, Priya Datta Meghe Institute of Higher Education and Research Faculty of Engineering and Technology Department of Computer Science and Engineering Sawangi Maharashtra Wardha442001 India Datta Meghe Institute of Higher Education and Research Faculty of Engineering and Technology Department of Artificial Intelligence and Machine Learning Sawangi Maharashtra Wardha442001 India Datta Meghe Institute of Higher Education and Research Faculty of Engineering and Technology Department of Computer Science and Engineering Sawani Maharashtra Wardha442001 India

ISBN: (纸本)9798331523923

The integration of IoT and ML brought forth new prospective solutions in menstrual health management, pertaining to continuous monitoring of physiological signals and personalization of insights for women. This article discusses the usage of IoT, smartphones, and cloud-based systems for tracking vital signs, temperature, pulse rate, and flow of menstruation that will enable pro-active management of the cycle. The use of ML algorithms, including supervised models-like Random Forest and Logistic Regression-and complex techniques like RNNs and LSTM networks-offers added advantages for predicting menstrual cycles and ovulation. These technologies offer personalized health recommendations, early diagnosis of menstrual disorders, and help in communicating with healthcare providers in a very short span of time. While offering huge potential to improve women's health, concerns around data privacy, ethical considerations, and equitable access to these digital health solutions are highlighted. The existing systems showed improvements yet they encountered problems because of insufficient data diversity and health system integration as well as insufficient personalization and imprecise cycle predictions. data privacy issues and security threats that stem from ML models along with constant bias concerns prevent broad acceptance of ML systems in general use. This research study analyzes IoT and ML applications in menstrual health today and introduces their advantages as well as the barriers that require resolution before mass acceptance can occur. The proposal introduces an approach to create menstrual tracking software through a combination of rule-based systems with ML algorithms that generates precise forecasts alongside tailored information for women who have PCOS or PCOD. © 2025 IEEE.

关键词： Electronic health record

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A Study Of Dynamic Thresholds Power Detection Spectrum Sensing Techniques In CRN 9

A Study Of Dynamic Thresholds Power Detection Spectrum Sensi...

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9th International Conference on Advanced Computing and Communication Systems, ICACCS 2023

作者： Keerthika, J. AnandaKumar, H. Priya, V. Yuvalatha, S. Gayathri, N. Sri Eshwar College of Engineering Department of Computer Science and Engineering Coimbatore India Dr NGP Institute of Technology Department of Artificial Intelligence and Data Science Coimbatore India Bannari Amman Institute of Technology Department of Computer Science and Business System Sathyamangalam India

ISBN: (纸本)9798350397376

A Cognitive radio is the mobile communication technologies which help to utilize the efficient need of radio spectrum origin. The performance of CRN is significantly impacted by the spectrum sensing function. The problem of bandwidth has risen as a more specific one with breakthroughs in remote communications. By forgiving unlicensed users to usage the available spectrum in primary networks is the solution to the CR technology. An innovative method for enhancing the electromagnetic spectrum's use efficiency, cognitive radio is a encouraging technology. Spectrum sensing has high spectral resolution capabilities and aids in the detection of the spectrum holes. The various methodologies are used to enhance the spectrum sensing essence. These techniques are distinguished by a sensing threshold, which is crucial to the performance of the sensing. The most important element in selecting the best threshold to optimize spectrum sensing performance is the dynamic threshold in the proposed system's spectrum detection algorithm. The energy earned from the licensed user over a precise time of observation, then the dynamic threshold is commutated between two levels. The estimation of the noise ambiguity factor forms the basis for threshold evaluations. These criteria are used to hike the likelihood of detection and contraction the possibility of false alarms. © 2023 IEEE.

关键词： Cognitive radio

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Supply Chain Management Using Blockchain Security Enhancement 1

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4th International Conference on Advances in Information Communication technology and Computing, AICTC 2022

作者： Sharma, Aditi Goar, Vishal Kuri, Manoj Chowdhary, Chiranji Lal School of Engineering and Digital Sciences Nazarbayev University Astana Kazakhstan Department of Artificial Intelligence and Data Science Engineering College Bikaner Bikaner India School of Information Technology and Engineering VIT University Vellore India

ISBN: (数字)9789811998881

ISBN: (纸本)9789811998874

The cybersecurity of contemporary systems has drawn much more focus from both academic and industrial viewpoints. The blockchain-based approach has recently become more popular due to how straightforward it is, especially in the management of supply chains. This highlights how crucial the quality factors are from a supply chain management standpoint. Numerous sectors have come to realize how crucial it is to have reliable supply chain and logistics solutions. The introduction of blockchain technology has given rise to several possible breakthroughs in the management and monitoring of corporate operations, specifically supply chain procedures. The blockchain and, more precisely, a smart contract technology that was utilized to manage the process of creating, verifying, and inspecting data over the supply chain management process were both discussed in this study. Next, discuss blockchain cybersecurity in the context of supply chains. Security protection needs to be powerful sufficient to shield the assets and data from dangers, as the smart contract increasingly controls the movement of data over many places. The paper then examines the primary security breaches that have an impact on the data on the blockchain and offers a fix. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Smart contract

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Enhanced Brain Stroke Prediction: An Ensemble of Random Forest, Logistic Regression and XGBoost 2

Enhanced Brain Stroke Prediction: An Ensemble of Random Fore...

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2nd IEEE International Conference on Emerging Research in Computational science, ICERCS 2024

作者： Sundaram, Karthic Lanitha, B. Kamaraj, K. Ramamoorthy, Arun Kumar KPR Institute of Engineering and Technology Department of Information Technology Coimbatore India Karpagam Academy of Higher Education Department of Artificial Intelligence and Data Science Coimbatore India University of South Wales Digital Forensics and Cyber Security Treforest United Kingdom

ISBN: (纸本)9798331534950

Stroke, a leading cause of disability and mortality globally, necessitates early prediction and intervention to mitigate its devastating effects. Recognizing the challenges faced by stroke survivors, the focus shifts to the crucial role of rehabilitation and lifestyle changes in optimizing recovery and quality of life. Machine learning (ML) algorithms emerges as a powerful tool for predicting stroke risk and enhancing patient outcomes. ML algorithms, by analysing extensive datasets, can identify patterns and early warning signs, enabling more personalized treatment plans. To enhance prediction accuracy, an ensemble model is proposed, leveraging Random Forest (RF), Logistic Regression (LR) and XGBoost techniques. The ensemble model exhibits an impressive accuracy rate of 97.4%, exceeding other models. © 2024 IEEE.

关键词： Prediction models

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