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检索条件"任意字段=4th Conference on Learning and Intelligent Optimization"

共 11565 条记录，以下是551-560 订阅

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Integrating Convolutional Neural Networks (CNN) and Machine learning for Accurate Identification of Autism Spectrum Disorder Using Facial Biomarkers 4

Integrating Convolutional Neural Networks (CNN) and Machine ...

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4th International conference on Emerging Systems and intelligent Computing, ESIC 2024

作者： Guttikonda, Keerthi Ashvitha, Yerninti Reddy, Velagala Sai Ranga Krishna, Ramba Murali Sandeep, Penumala Sr Gudlavalleru Engineering College Dept. of Cse Andhra Pradesh Gudlavalleru521356 India

ISBN: (纸本)9798350349856

the work presents a novel approach to address the challenging task of accurately identifying Autism Spectrum Disorder (ASD) by Incorporating Convolutional Neural Networks (CNN) into conventional machine learning methodologies, with a specific focus on leveraging facial biomarkers. Facial biomarkers offer a non-invasive avenue for ASD detection, relying on subtle expressions and features as early indicators. the intricate nature of these biomarkers necessitates advanced analytical tools, prompting the fusion of CNN, known for its prowess in image analysis, with traditional machine learning for enhanced *** research emphasizes the potential of this integrated approach to advance ASD identification methodologies. Swift recognition is pivotal for prompt intervention, significantly shaping the trajectory of individuals navigating the autism spectrum. By concentrating on facial biomarkers and combining the strengths of deep learning with established machine learning principles, our methodology aims to provide a nuanced and precise understanding of ASD-related *** employ a meticulously curated dataset from Kaggle, comprising images representing both ASD and non-ASD cases in children. the study involves training, validation, and testing phases, incorporating a CNN model followed by a comparative analysis with a traditional machine learning model. the obtained results not only illuminate the effectiveness of our proposed integrated approach but also showcase potential applications in the realm of neurological disorder diagnostics. © 2024 IEEE.

关键词： Diseases

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2023 4th International conference on Big Data Analytics and Practices, IBDAP 2023

2023 4th International Conference on Big Data Analytics and ...

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4th International conference on Big Data Analytics and Practices, IBDAP 2023

ISBN: (纸本)9798350300192

the proceedings contain 41 papers. the topics discussed include: generating combination of biblical baby names using recurrent neural network (RNN) and optimization comparison;DeepTestDroid a platform for automated application testing using deep learning;data-to-question generation using deep learning;the online communication station in city of learning: management of city learning for senior citizen;hybrid firefly algorithm with Rao algorithm for optimization problems;a hybrid deep learning neural network for recognizing exercise activity using inertial sensor and motion capture system;and a machine learning and deep learning integrated model to detect criminal activities.

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Proactive Network optimization Using Deep learning in Predicting IoT Traffic Dynamics 4

Proactive Network Optimization Using Deep Learning in Predic...

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4th International conference on Innovative Practices in Technology and Management, ICIPTM 2024

作者： Priya, S. Anu Bhat, Niyati Kanna, B. Rajesh Rajalakshmi, S. Jeyavathana, R. Beaulah Srimathi, S. Department of Computer Applications Chennai600117 India University of Mumbai B.E Computer Engineering Mumbai India Alagappa Chettiyar College of Engineering and Technology Department of Electronics and Communication Engineering Karaikudi India Rmk College of Engineering and Technology Department of Electronics and Communication Engineering India Srm Institute of Science and Technology Computational Intelligence Kattankulathur Chennai India Simats Saveetha School of Engineering Department of Bio Technology Tamilnadu Chennai India

ISBN: (纸本)9798350307757

Problems have arisen in allocating and managing network resources effectively because of the proliferation of IoT gadgets. this study aims to provide a deep learning-based strategy for forecasting and analyzing IoT traffic patterns to solve this problem. the model uses recurrent neural networks (RNNs) with Long Short-Term Memory (LSTM) cells to capture the complex temporal dependencies in IoT data streams. Following careful data gathering from various IoT sources, a thorough preprocessing phase is performed to account for missing values and outliers. Effective modeling is enabled by applying feature engineering approaches to distill meaningful information and by temporally aggregating the dataset. the heart of the study is the creation of a sequence-to-sequence architecture that allows for reliable forecasting of future IoT traffic trends. Mean Squared Error (MSE) and other performance metrics measure the quality of the proposed model's predictions. In addition to its predictive abilities, the model's analytical section reveals interesting facts about the fundamental habits underpinning IoT traffic. this dual purpose helps network managers better comprehend the ever-changing IoT ecosystem and allocate scarce resources. the results of this study have important implications for network optimization, providing a preventative strategy for dealing with the difficulties posed by the exponential growth of IoT data traffic. Regarding accuracy and interpretability, the suggested deep learning model significantly contributes to the development of intelligent network resource management in the IoT era. © 2024 IEEE.

关键词： Time series analysis

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Construction of digital twin workshop integrated with edge computing and deep learning 4

Construction of digital twin workshop integrated with edge c...

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2024 4th International conference on Artificial Intelligence and Industrial Technology Applications, AIITA 2024

作者： Liu, Xinning Xie, Tianxia Wu, Bo Li, Mingjun Li, Haotian School of Computer and Software Engineering Dalian Neusoft University of Information Dalian116023 China

this study addresses the challenges of real-time data synchronization and big data processing in the construction of digital twin workshops under the background of intelligent manufacturing. A solution that integrates edge computing with deep learning technology is proposed. through a multi-layered, modular framework structure, this solution achieves real-time synchronous mapping and interaction between physical workshops and virtual workshops, effectively improving production efficiency, reducing maintenance costs, and ensuring product quality. the edge computing layer in the research is responsible for preprocessing heterogeneous data from multiple sources and making rapid response decisions. Simultaneously, deep learning algorithms are employed to conduct in-depth analysis, forecasting, and optimization of the production process. Case studies demonstrate that this solution can significantly improve equipment utilization and optimize workpiece scheduling, validating its application value in actual manufacturing environments. © Published under licence by IOP Publishing Ltd.

关键词： Smart manufacturing

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Dynamic Rating optimization of Transmission Lines Using Particle Swarm Algorithm and Meteorological Data 4

Dynamic Rating Optimization of Transmission Lines Using Part...

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4th International conference on Smart Grid and Energy Internet, SGEI 2024

作者： Shen, Xinyi Zhang, Guoqing Ren, Zhiguo Xie, Yuqi Sun, Yunqi Liu, Ziqian School of Electrical Engineering and Automation Harbin Institute of Technology Harbin China

ISBN: (纸本)9798350368314

With the construction of new power systems, the development of advanced power grids that are distributed power integration, facilitate bidirectional interaction between sources, grids, and loads, and are intelligent and efficient has become a major trend for the future development of power grids in China. this paper takes the 220kV transmission line Rehe A and B lines in Changchun city as the research object. Based on historical meteorological data and temperature data from weak sections, the thermal balance equation is employed, and a particle swarm optimization algorithm is utilized to introduce error polynomials of meteorological data for algorithm optimization, thereby establishing a physical model for the dynamic rating of specific lines. By calculating the mean absolute error and root mean square error of different error polynomials, the results indicate that the established physical model for dynamic rating calculation has higher accuracy. ©2024 IEEE.

关键词： Power grids

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Node Coverage and Connectivity optimization in Heterogeneous Wireless Sensor Networks Using Improved Flower Pollination Algorithm 4

Node Coverage and Connectivity Optimization in Heterogeneous...

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4th International conference on Ubiquitous Computing and intelligent Information Systems, ICUIS 2024

作者： Mahankali, Saritha Kesavan, R. Kalaiselvan, S.A. Saveetha School of Engineering Simats Department of Computer Science & Engineering Chennai India Vardhaman College of Engineering Department of Computer Science & Engineering Telangana India Rajalakshmi Engineering Department of Artificial Intelligence & Machine Learning Chennai India

ISBN: (纸本)9798331529635

Wireless sensor networks (WSNs) rely on energy-conserving routing protocols due to the limited power and communication capabilities. the LEACH protocol has seen extensive usage despite its homogeneous network foundation, leads inadequate signal coverage. To solve the issue, this research proposes an improved flower pollination algorithm (IFPA) improves flower pollination algorithm (FPA) addresses issues with slow convergence and local optima. this research presents an enhanced clustering technique that may be utilized in heterogeneous sensor networks. Its main feature is the ability to adjust the status of data transmission. Once the observed information intensity above the predefined threshold, the data transmission connection is initiated and data is sent to the sink by the cluster heads, which filter the perceived information. Otherwise, cluster nodes keep track of the data and wait for the next batch of nodes to provide data. the good news is that the simulation results show a network lifespan that is several times longer than the LEACH protocol. the IFPA method stands out because it increases coverage rates while drastically decreasing the cost of deploying networks. As a result, the suggested method might find many uses, helping to lower costs and improve the performance of wireless sensor networks. © 2024 IEEE.

关键词： Sensor nodes

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Smart Energy Management in Solar-Powered 2BHK Residential Building Based on Forecast-Driven optimization and EV Scheduling 4

Smart Energy Management in Solar-Powered 2BHK Residential Bu...

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4th International conference on Ubiquitous Computing and intelligent Information Systems, ICUIS 2024

作者： Kumar, M. Praveen Raja, S. Charles Dharssini, A.C.Vishnu Keerthi, P. Thiagarajar College of Engineering Department of Eee Madurai India School of Electrical Engineering and Computer Science The University of Queensland Brisbane Australia

ISBN: (纸本)9798331529635

the study aims to address a comprehensive energy optimization approach for a 2BHK residential building located in Bodinayakanur, theni, Tamil Nadu, equipped with a 2 kW solar panel. the study focuses on minimizing energy costs by integrating day-ahead energy forecasting using a Long Short-Term Memory (LSTM) model with rule-based meta-heuristic optimization for load scheduling. Two scenarios are analyzed: the first optimizes the scheduling of household appliances, and the second incorporates an electric vehicle (EV) into the load profile. the optimization strategy shifts secondary loads, such as EV charging and non-essential appliances, to off-peak hours while maximizing solar energy utilization. Efficient management of energy demand and load scheduling results in significant cost savings and enhanced grid stability. the findings demonstrate the practical viability of the energy optimization framework for reducing electricity costs in real-world residential settings, especially in areas with renewable energy integration. © 2024 IEEE.

关键词： Costs

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Comparing the Performance of Supervised, Unsupervised and Hybrid learning on Medical Insurance Fraud Detection 4th

Comparing the Performance of Supervised, Unsupervised and Hy...

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4th International conference on Advanced Network Technologies and intelligent Computing-ANTIC-Annual

作者： Kumari, Saloni Jaiswal, Neelanshi Kanika Kumar, Atul Kumar, Divya Motilal Nehru Natl Inst Technol Allahabad Comp Sci & Engn Dept Prayagraj 211004 UP India

ISBN: (纸本)9783031837890;9783031837906

Medical insurance fraud is a serious challenge in the healthcare industry, and its rapid escalation necessitates improved detection mechanisms. this study examines three machine learning techniques-supervised (Random Forest), unsupervised (K-means clustering), and hybrid learning-for detecting medical insurance fraud across two datasets from some National Health Insurance Scheme (NHIS)-approved hospitals in Ghana concerning insurance claims. the performance indicators for these strategies are as follows: Random Forest attained detection accuracies of 91% and 93%, K-means clustering generated 70% and 46%, and the hybrid model produced 34% and 42%, respectively. the growing prevalence of medical insurance fraud highlights the critical need for effective detection methods to protect the integrity of healthcare systems.

关键词： medical insurance fraud random forest k-means clustering hybrid learning supervised learning unsupervised learning comparative analysis

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Enhancing Node Classification: Integrating Particle Swarm optimization with Graph Convolutional Networks 4

Enhancing Node Classification: Integrating Particle Swarm Op...

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4th International conference on intelligent Cybernetics Technology and Applications, ICICyTA 2024

作者： Nasari, Maulin Pradana, Rilo Chandra Tarigan, Gabriel Asael Masaling, Nikita Ananda Putri Tedjasulaksana, Jeffrey School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798331506490

the effectiveness of Graph Neural Networks (GNNs) in processing graph-structured data gained widespread recognition, and these models have applications in a wide range of fields. Even though GNNs have a lot of potential, optimising GNN models for particular tasks is still challenging. Within the scope of this research, a method is presented that combines Particle Swarm optimization (PSO) and Graph Convolutional Networks (GCN) to enhance the performance of node classification. Using the Amazon Computers and Amazon Photo datasets, which are both made up of product nodes that are connected to one another. the methodology includes data preprocessing with PyTorch Geometric, strategic data partitioning, and a robust hyperparameter optimization process using PSO. the modification was able to achieve an accuracy of 88.7% on the Amazon Computers dataset and 94.9% on the Amazon Photo dataset, which is higher than the traditional GCN baselines. Our GCN-PSO model demonstrated significant performance enhancements. these findings highlight the effectiveness of PSO in fine-tuning GCN hyperparameters, which ultimately results in outcomes that are more accurate and efficient. When it comes to improving graph-based data analysis, the research highlights the potential of combining GCNs with optimization techniques such as PSO. this method could be extended to other graph learning challenges in subsequent research, and alternative optimization strategies could be investigated to further push the limits of GNN performance. © 2024 IEEE.

关键词： Particle swarm optimization (PSO)

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Research on Equipment Fault Analysis based on Machine learning Algorithm 4

Research on Equipment Fault Analysis based on Machine Learni...

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2023 4th International conference on Computer Engineering and intelligent Control, ICCEIC 2023

作者： Qin, Hua Wang, Pengfei Xu, Linping Unit 92145 of Pla Hongkou Shanghai China Navy Petty Officer Academy Bengbu Anhui China

ISBN: (纸本)9798350308877

this paper builds an intelligent operation and maintenance platform based on machine learning algorithm through using the equipment level and system level fault monitoring data generated in the operation and maintenance process and combining with machine learning data analysis technology. It focuses on solving the difficult problems such as fault feature selection, fault diagnosis model classification, time series trend prediction, etc. the above research provides a decision-making reference for operating the communication links and making equipment maintenance scientifically, which has certain engineering application value and practical significance. © 2023 IEEE.

关键词： Support vector machines

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