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检索条件"任意字段=4th International Conference on Optimization Learning Algorithms and Applications"

共 13501 条记录，以下是4911-4920 订阅

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An Efficient Privacy-Preserving Machine learning for Blockchain Network

An Efficient Privacy-Preserving Machine Learning for Blockch...

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Intelligent Technologies (CONIT), international conference on

作者： Aakash Sonkar Suyash Kumar Sahu Ashu Nayak Devbrat Sahu Pushpalata Verma Rovin Tiwari Department of CSE GEC Raipur India Department of CSE Shri Rawatpura Sarkar University Raipur India Department of CSE SSIPMT Raipur India Department of CSE BIT Raipur India Department of ECE RGPV Bhopal India

ISBN: (数字)9798350349900

ISBN: (纸本)9798350349917

Blockchain innovation has as of late drawn in a great deal of interest as a potential major advantage for various applications. By the by, there are as yet significant snags with blockchain organizations’ presentation and versatility. AI approaches give possible solutions to improving and advancing blockchain network activities around here. there is an additional opportunity for supporting the proficiency of blockchain networks: choice tree calculations. these calculations are prestigious for being basic, interpretable, and adaptable. this study dives into choice tree-based AI procedures that are planned in light of blockchain networks. We go over the hypothesis behind choice trees and how they work comparable to blockchain innovation. We likewise give a careful survey of the writing on blockchain network issues settled by choice tree calculations, including pertinent exploration, strategies, and contextual investigations. We trust that by doing this examination, we will show how choice tree AI approaches might work on the proficiency, security, and adaptability of blockchain networks.

关键词： Surveys Technological innovation Machine learning algorithms Prevention and mitigation Machine learning Writing Search problems

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Machine learning-Based DC to AC Dual Quasi Z-source Converter for Distribution Generation applications 4

Machine Learning-Based DC to AC Dual Quasi Z-source Converte...

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4th international conference on Power, Control and Computing Technologies, ICPC2T 2025

作者： Uppara, Rajesh Priya, S. Department of EEE Siddaganga Institute of Technology Karnataka Tumakuru India

ISBN: (纸本)9798331530402

Using artificial neural networks to simulate a dual QZS converter high step up dc to ac converter is the main goal of this work. the standard converter of QZS with PI controller could be modified by incorporating an ANN controller, diode, inductor, and two capacitors to resemble the dual QZSconverter. the recommended dual QZS converter achieves all the benefits of a standard QZS converter, including continuous input current, voltage boost, and enhanced reliability. Furthermore, the recommended dual QZS converter reduces shoot through duty cycle by 30% without adding additional switches to the typical QZS converter, all at the same voltage boost factor. Acquired in both shoot through and non-shot through modes, simulation results are shown along with a description of the dual QZS converter. © 2025 IEEE.

关键词： Computer aided instruction

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Power Plant Coal Inventory Forecasting Based on LightGBM with Bayesian optimization 5

Power Plant Coal Inventory Forecasting Based on LightGBM wit...

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5th international conference on Power and Energy applications, ICPEA 2022

作者： Zhang, Min Song, Yuan Zhang, Zhijun Lu, Liang CHN Energy Beijing China

ISBN: (纸本)9781665487610

Accurate prediction of coal inventory in power plants is an important prerequisite for the smooth implementation of power plant supply guarantee policies. It is of great significance to accurately predict the supply risk of coal in advance. the coal inventory of hydropower plants has the characteristics of periodicity, randomness and seasonality, and is affected by coal input, coal consumption, meteorology and special policy events. Aiming at the problems that the prediction results of the existing power plant coal inventory prediction algorithm are not accurate enough, and the prediction is only based on historical data such as power generation and coal consumption, this paper proposes a new coal inventory prediction method for hydropower plants, which uses the learning advantage of the LightGBM model based on Bayesian optimization to the regression problem, determines the local optimal hyperparameter configuration, and uses the histogram algorithm and the gradient unilateral sampling algorithm, Reduce the number of training samples and features in the iteration to avoid over fitting. In this paper, the experimental analysis is carried out on the historical data of a hydropower plant. Finally, the accuracy of the model is evaluated by statistical indicators such as RMSE, Mae and correlation coefficient. Experiments show that compared with other algorithms, the method proposed in this paper has higher accuracy and is of great significance for the prediction of coal inventory in hydropower plants. © 2022 IEEE.

关键词： Forecasting

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Efficient Equivariant Transfer learning from Pretrained Models 37

Efficient Equivariant Transfer Learning from Pretrained Mode...

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37th conference on Neural Information Processing Systems (NeurIPS)

作者： Basu, Sourya Katdare, Pulkit Sattigeri, Prasanna Chenthamarakshan, Vijil Driggs-Campbell, Katherine Das, Payel Varshney, Lav R. Univ Illinois Champaign IL 61820 USA IBM Res Yorktown Hts NY USA

ISBN: (纸本)9781713899921

Efficient transfer learning algorithms are key to the success of foundation models on diverse downstream tasks even with limited data. Recent works of Basu et al. (2023) and Kaba et al. (2022) propose group averaging (equitune) and optimization-based methods, respectively, over features from group-transformed inputs to obtain equivariant outputs from non-equivariant neural networks. While Kaba et al. (2022) are only concerned with training from scratch, we find that equitune performs poorly on equivariant zero-shot tasks despite good finetuning results. We hypothesize that this is because pretrained models provide better quality features for certain transformations than others and simply averaging them is deleterious. Hence, we propose lambda-equitune that averages the features using importance weights, lambda s. these weights are learned directly from the data using a small neural network, leading to excellent zero-shot and finetuned results that outperform equitune. Further, we prove that lambda-equitune is equivariant and a universal approximator of equivariant functions. Additionally, we show that the method of Kaba et al. (2022) used with appropriate loss functions, which we call equizero, also gives excellent zero-shot and finetuned performance. Both equitune and equizero are special cases of lambda-equitune. To show the simplicity and generality of our method, we validate on a wide range of diverse applications and models such as 1) image classification using CLIP, 2) deep Q-learning, 3) fairness in natural language generation (NLG), 4) compositional generalization in languages, and 5) image classification using pretrained CNNs such as Resnet and Alexnet.

关键词： Image classification

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Cardiac Vascular Disease Prediction Using Stacked Ensemble Algorithm for Type-2 Diabetes Mellitus

Cardiac Vascular Disease Prediction Using Stacked Ensemble A...

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Sentiment Analysis and Deep learning (ICSADL), international conference on

作者： C. Selvarathi Manikandan. T G. Madhu Mitha Sharmila S Sengolrajan thanasingh P. Pandiaraja Department of Computer Science and Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai Tamilnadu India Department of ECE Vivekanandha College of Engineering for Women (Autonomous) Thiruchengode Tamilnadu India Department of Electrical and Electronics Engineering Vivekanandha College of Engineering for Women Thiruchengode Tamilnadu India Department of ECE Vivekanandha College of Engineering for Women Thiruchengode Tamilnadu India Department of Electrical and Electronics and Engineering Kongunadu college of Engineering and Technology (Autonomous) Trichy Tamilnadu India

ISBN: (数字)9798331523923

ISBN: (纸本)9798331523930

Cardiac vascular disease (CVD) is a most serious disease associated with highest humanity and complaint rates in the global population. the risk of developing CVD consciously increases in patients having long-term diabetes difficulties. Consequently, it is necessary to predict the association between CVD and type-2 Diabetes Mellitus (T2DM) for effective decision making to initiate appropriate treatment of T2DM patients. First, the proposed research work to examine the purpose of machine learning (ML) techniques to predict the risk of death rate in CVD patients with T2DM. then the proposed method is mainly focusing to predict the survival rate of regularity of CVD patient's incidence with T2DM. However, the dataset is unbalanced and handling such unbalanced dataset is one of the major challenges addressed using cost sensitive learning algorithms for reducing the misclassification rate. this proposed method stacked ensemble with cost-sensitive learning that combines the prediction results of the three base classifiers to predict the survival rate of patients with cardiovascular disease and T2DM. the proposed stacked ensemble algorithm uses a 10 fold cross validation method with network search optimization to tune the hyper-parameter of the meta classifier model. the proposed algorithm is used to measure the performance of dataset being evaluated by splitting the data into 80% training and 20% testing. the analysis of outcomes displays that the survival rate of heart failure patients is better predicted using the cost-sensitive stacked ensemble learning technique to achieves precision of 93% and higher accuracy. the unbalanced class and cost sensitive learning may help to address the class imbalance issue. the incorrect weight assignment may lead to overfitting to the minority class while underperforming on the majority class.

关键词： Training Costs Accuracy Predictive models Prediction algorithms Classification algorithms Diabetes Ensemble learning Cardiovascular diseases Cancer

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NLP Based Sentiment Analysis Using Particle Swarm optimization with Logistic Regression for Digital Education

NLP Based Sentiment Analysis Using Particle Swarm Optimizati...

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Cybernetics, Cognition and Machine learning applications (ICCCMLA), IEEE international conference on

作者： G. Clara Barathi Priyadharshini Prasanalakshmi Balaji Mousmi Ajay Chaurasia Subhranginee Das Sushama Rani Dutta Department of Computer Applications Karpagam Academy of Higher Education Coimbatore Department of Computer Science King Khalid University Abha Saudi Arabia Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Hyderabad India

ISBN: (数字)9798331505790

ISBN: (纸本)9798331505806

Identification and categorization of user sentiments from a text passage is known as sentiment analysis into several sentiments. For example, using feelings like happiness, sadness, anger, or positive, negative, or neutral to gauge how the user feels about a certain topic or item. these days, a variety of websites and applications collectively referred to as online educational platforms allow users to obtain a higher degree of education across a range of areas. For this reason, digital education provides a platform for the dissemination of knowledge. Among the most active areas within NLP, web mining, and text mining research is sentiment analysis. It is significant in a variety of subjects, including as the social sciences and management sciences, where evaluating the efficacy of learning technology depends heavily on student input. Due to the proliferation of educational institutions, many students have become enthralled with online learning portals that provide cost-free courses. Every year, a huge number of students enrol in these expansive online courses and then revisit their opinions regarding the course material and level of instruction. Additionally, share ideas on blogs on how to raise the standard of instruction by expressing both good and negative opinions. However, an assortment of ML techniques are available to assess these ratings and ascertain which virtual education platform is the most user-friendly and optimal. this study examined natural language processing (NLP)-based sentiment analysis and social media evaluations that were manually gathered from educational applications. To categorise the reviewer’s comment for digital education, Particle Swarm Optimisation with Logistic Regression (PSOLR) outperformed all other existing Naïve Bayes, Decision Tree, and Logistic Regression algorithms in terms of accuracy (93.75%).

关键词： Text mining Sentiment analysis Logistic regression Machine learning algorithms Accuracy Education Web mining Bayes methods Decision trees Particle swarm optimization

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Flamingo Search Algorithm and Its Application to Path Planning Problem 4

Flamingo Search Algorithm and Its Application to Path Planni...

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4th international conference on Artificial Intelligence and Pattern Recognition, AIPR 2021

作者： Wang, Zhiheng Liu, Jianhua School of Computing Xi'an University of Posts and Telecommunications China

ISBN: (纸本)9781450384087

After studying the migratory and foraging behavior of flamingos, this paper proposes a new swarm intelligence optimization algorithm: flamingos search algorithm. At the same time, in order to verify the optimization effect and stability of FSA, this paper conducts tests and comparisons on 20 different benchmark functions, and standard deviation of the proposed FSA outperformed the other algorithms with regard to 20 test functions and path planning applications. Finally, this paper applies FSA to the path optimization problem, and the test results show that FSA has good engineering application potential, especially in the solution of path planning problems with good optimization performance. Experimental code for this article can be obtained from this website: https://***/18280426650/FSA. © 2021 ACM.

关键词： Constrained optimization

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Resource optimization of Power Line Communication Network Based on Monte Carlo Method 4th

Resource Optimization of Power Line Communication Network Ba...

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4th EAI international conference on 6G for Future Wireless Networks, 6GN 2021

作者： Chen, Peiru Chen, Zhixiong Zhi, Leixin Zhang, Lixia School of Electrical and Electronic Engineering North China Electric Power University Hebei Baoding071003 China Information and Communication Branch of State Grid Shanxi Electric Power Company Shanxi Taiyuan030021 China

ISBN: (纸本)9783031042447

the power system communication network uses power lines as a medium, which is an important means to ensure the safe, stable, and economic operation of the power grid. In order to improve the throughput of the power line communication (PLC) network and realize the optimization of network resources, a method based on the machine learning algorithm, namely the Monte Carlo method, is proposed to optimize the media access control (MAC) layer protocol of the PLC. Firstly, based on the lognormal fading and impulse noise in the physical layer of the PLC channel, and the IEEE 1901 CSMA backoff mechanism in the MAC layer, the main factors leading to packet loss are analyzed. Secondly, the throughput calculation model based on the above packet loss factors is established. Finally, according to the idea of Monte Carlo algorithm, the MAC layer contention window selection algorithm is established based on the principle of maximum throughput. And compared with the original algorithm standard simulation results, the effectiveness of the method is verified. the results show that the contention window value obtained based on the proposed algorithm can achieve higher system throughput, and has certain practical reference value in the corresponding PLC network scenario. © 2022, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： learning algorithms

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An Accurate learning-Based Performance/Power Model for System-Level Design of a Multicore Multithreaded Network Processor 4

An Accurate Learning-Based Performance/Power Model for Syste...

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4th international conference on Information and Computer Technologies (ICICT)

作者： Hafezan, Mohamad Azari, Hossein Dabaghan, Amir Beigi, Leila Univ Tehran Dept Elect & Comp Engn Tehran Iran Microsoft Corp Dept Artificial Intelligent AI Platform Seattle WA USA Bu Ali Sina Univ Dept Ind Engn Hamadan Hamadan Iran

ISBN: (纸本)9781665413992

In the network applications domain, different network environments and scenarios demand various line rates, and restrain the design of the network processor by several constraints such as power and area. Additionally, new network services and applications with different processing requirements are increasingly emerging day by day. In this regard, having a multi-objective and flexible performance model that can be used to minimize the cost and time of designing network processors is an inevitable need. In this paper, we propose an accurate and fast prediction model that exploits just a few numbers of system-level parameters to estimate the performance and power of a commercial network processor, Intel IXP2800. the proposed design methodology uses a non-linear learning algorithm - a combination of the polynomial transformation of design parameters and higher-order spline functions - which in the face of newly introduced applications needs only a small training set, i.e. a small number of simulations, to train the model. Our experimental results show the proposed models can achieve a median error rate as low as 8.7 percent for performance and 2.6 percent for power metric.

关键词： performance model non-linearity predictor variables polynomial transformation spline functions

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Performance Assessment of Meta-Heuristic and WOA algorithms for End-to-End Connection Weights Enhancement of (DeepLABNet)

Performance Assessment of Meta-Heuristic and WOA Algorithms ...

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international conference on Adaptive Science & Technology (ICAST)

作者： Mustapha Abdulrahman Lawal Okere Chidiebere Emmanuel Fatima Umar Zambuk Emmanuel Ramson Nannin Abdulmutalib Abdullahi Tapidi Blessing Abu Department of Computer Science Abubakar Tafawa Balewa University Bauchi Nigeria Department of Computer Science Federal Polytechnic Kaltungo Gombe Department of Computer Science University of Calabar Cross River Nigeria

ISBN: (数字)9798350385403

ISBN: (纸本)9798350385410

Radial Basis Function (RBF) neural networks are widely used in artificial intelligence applications. However, traditional training algorithms often suffer from local optima stagnation and slow convergence, reducing network efficiency. this study investigates using the Whale optimization Algorithm (WOA) to optimize RBF neural networks for improved cancer classification accuracy. WOA addresses the limitations of traditional algorithms by avoiding local optima and ensuring global nonlinear parameter optima. We evaluated WOA-RBF on cancer datasets and compared its performance with conventional algorithms, including Genetic Algorithm, Particle Swarm optimization, Ant Colony optimization, and Differential Evolution. Results indicate WOA-RBF outperforms other algorithms in precision and convergence, achieving 97% average classification accuracy for ovarian tumours and 96% for pancreatic tumours. WOA-RBF demonstrated faster convergence and better generalization capabilities. this study fills the gap in optimizing RBF neural networks using WOA, providing a reliable solution for cancer classification tasks. this research contributes to developing more accurate cancer diagnosis systems using RBF neural networks optimized with WOA.

关键词： Training Accuracy Machine learning algorithms Neural networks Whale optimization algorithms Classification algorithms Reliability Cancer Convergence Tumors

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