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

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Research on intelligent Seedling Environment Monitoring And Early-warning System Based on Machine learning Algorithm 4

Research on Intelligent Seedling Environment Monitoring And ...

引用

4th IEEE International conference on Information Technology, Big Data and Artificial Intelligence, ICIBA 2024

作者： Yu, Caiping Ren, Dongyan Xie, Zongzhi Guangxi Vocational Normal University School of Computer and Information Engineering China

ISBN: (纸本)9798350363586

With the rise of intelligent agriculture, the intelligent transformation of agriculture has been realised, bringing new development to agricultural production. However, in traditional agriculture, the regulation of seedling growth conditions mainly relies on manual management, and it is difficult to accurately predict and monitor complex ecological changes manually, which increases operational uncertainty and leads to economic losses brought about by low production efficiency. therefore, this study is based on the machine learning-based environmental monitoring and warning system for seedlings, introducing the hardware design, technical framework, machine learning algorithms in the random forest and convolutional neural network applications. through the experimental data, the speed and accuracy of the system in environmental abnormality detection are verified. the experimental results show that the system can effectively warn of environmental risks, provide scientific basis and reduce unnecessary losses. In the future, with the promotion and use of the system, it can greatly promote the effective combination of artificial intelligence and intelligent agriculture, and promote the sustainable development of agriculture. © 2024 IEEE.

关键词： Environmental monitoring

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OpenRAG: Open-source Retrieval-Augmented Generation Architecture for Personalized learning 4

OpenRAG: Open-source Retrieval-Augmented Generation Architec...

引用

4th International conference on Artificial Intelligence, Robotics, and Communication, ICAIRC 2024

作者： Shan, Richard North Carolina School of Science and Mathematics Department of Data Science DurhamNC United States

ISBN: (纸本)9798331531225

this paper introduces OpenRAG, an open-source Retrieval-Augmented Generation (RAG) system architecture designed to enhance GenAI applications in personalized learning. the architecture is modular with loosely coupled components: Generator, User Interface, Indexing subsystem, Retriever, and Orchestration module. the research applies cutting-edge design patterns for retrieval, generation, indexing, storage, user interactions, and performance optimization, whereas the data flow and processing pipeline are seamlessly integrated for customization and adaptability. A case study demonstrates a proof-of-concept implementation of OpenRAG used in an online learning platform, resulting in significant increases in learner engagement and education efficiency. Key design concerns such as computational efficiency, content accuracy, learning styles, and user experience are discussed along with their solutions. © 2024 IEEE.

关键词： Indexing (of information)

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Yummy YOLO: A Deep learning Approach Towards Indian Culinary Classification 4

Yummy YOLO: A Deep Learning Approach Towards Indian Culinary...

引用

4th International conference on intelligent Technologies, CONIT 2024

作者： Mandavkar, Maithili Abhijit Hattarki, Harsha D Nadagoudar, Vishnu V Mallibhat, Kaushik Iyer, Nalini C KLE Technological University School of Electronics and Communication Engineering Hubli India

ISBN: (纸本)9798350349900

Food image classification is a growing research area as it has many advantages, whether in the health sector to maintain a proper diet or in the tourism sector to help tourists know about dishes. In this paper, the authors have compared two approaches to categorize the Indian food images into ten categories. VGG-16, a Convolution Neural Network model, and object detection algorithm YOLOv8 (You Only Look Once, version 8) were used towards the task of Indian food classification and VGG-16 performed with an accuracy of 80.51 %, and the YOLOv8 model out performed with an accuracy of 98.2 %. © 2024 IEEE.

关键词： Food ingredients

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Current Situation and Prospect of intelligent Monitoring and Maintenance of Power System 4

Current Situation and Prospect of Intelligent Monitoring and...

引用

4th International conference on Electronic Information Engineering and Computer Science, EIECS 2024

作者： Zhao, Bingzhen Luo, Peng Niu, Pingjuan Sun, Ximin Li, Yong Yan, Jingwen Li, Zihao Jia, Jiangkai Han, Shuzhen State Grid Ecommerce Technology CO. Ltd Tianjin China Tiangong University Tianjin China

ISBN: (纸本)9798331531409

With the growth of energy demand and the optimization of energy structure, the operation and maintenance of power systems are facing increasingly severe challenges. the introduction of intelligent monitoring and maintenance technology has become a key measure to improve the management level of power system and improve energy efficiency. this paper reviews the current situation and prospect of intelligent monitoring and maintenance of power system, starts from the concept and significance of intelligent monitoring and maintenance of power system, and expounds its important role in improving the safety, reliability and efficiency of power system. the wide application of intelligent monitoring technology in power system is discussed, including fault diagnosis and prediction, real- time monitoring and control, equipment health management, etc. At the same time, the development trend and challenges of intelligent monitoring and maintenance technology are analyzed, including the development trend of Internet of things technology, the application of big data and artificial intelligence, and the integration of visualization and virtual reality technology. through a comprehensive analysis of the current situation and prospects, this paper aims to provide useful enlightenment and guidance for the research and practice in the field of intelligent monitoring and maintenance of power systems. © 2024 IEEE.

关键词： intelligent detection power system smart gridartificial intelligence

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intelligent Transportation Activity Recognition Using Deep Belief Network 4

Intelligent Transportation Activity Recognition Using Deep B...

引用

4th IEEE Interdisciplinary conference on Electrics and Computer (INTCEC)

作者： Alazeb, Abdulwahab Khan, Danyal Jalal, Ahmad Najran Univ Dept Comp Sci Najran Saudi Arabia Air Univ Dept Comp Sci Islamabad Pakistan

ISBN: (纸本)9798350349467;9798350349450

Comprehending and analyzing a diverse range of transportation modalities within urban environments is paramount for efficient traffic management and the development of smart cities. this paper explores a novel methodology for Transportation Activity Recognition (TAR) using data derived from GPS sensors, highlighting the potential to discern and categorize distinct modalities such as walking, cycling, and vehicular transport. Utilizing machine learning and advanced feature extraction for GPS sensors, the research processes and analyzes the GPS Microsoft Geo-life dataset, aiming to accurately identify and differentiate between diverse transportation activities and patterns. these activities' effects on urban traffic flow, congestion, and transportation planning are investigated, yielding insightful information that can improve and inform traffic management plans and regulations. the findings emphasize the potential of employing GPS sensors for a detailed and activity-specific analysis of transportation modes, contributing significantly to the evolution and implementation of intelligent and sustainable urban transportation systems.

关键词： traffic activity recognition (TRA) transportation systems GPS sensor deep beliefs network

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Production Scheduling optimization Method Based on Improved Particle Swarm optimization Algorithm 4

Production Scheduling Optimization Method Based on Improved ...

引用

4th International conference on Mechatronics Technology and intelligent Manufacturing, ICMTIM 2023

作者： Wang, Chuchu Zang, Haijuan Jiangsu Institute of Technology School of Mechanical Engineering Changzhou China Jiangsu Institute of Technology School of Computer Science Changzhou China

ISBN: (纸本)9798350314434

In the environment of smart manufacturing, smart and agile production scheduling is an important measure for manufacturing companies to meet the new trend. To solve complex production scheduling problems consisting of different optimization objectives and constraints, an optimization model is constructed, an improved particle swarm optimization algorithm is proposed, and a particle selection based on the tournament selection method and a new speed update mechanism is designed. the practicality of the algorithm was verified with production data, confirming that the improved algorithm improved order completion time by 24.6% over traditional scheduling methods. © 2023 IEEE.

关键词： Production control

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Automated Loan Approval System using Machine learning 4

Automated Loan Approval System using Machine Learning

引用

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

作者： Srivastava, Swati Gla University Department of Computer Engineering and Applications UP Mathura India

ISBN: (数字)9798331529635

ISBN: (纸本)9798331529635

In today's world, it's common for people to apply loans from banks and financial institutions for various reasons. But not everyone who applies can be approved. We often hear about cases where individuals fail to repay their loans, causing significant losses for banks. Making decisions about loan approvals is risky and challenging. Previous works considered single algorithm to make predictions where accuracy is low. To enhance the accuracy is a challenge. So, the goal of this study is to use multiple machine learning algorithms to extract key information about the loan data which could help organizations make better decisions about approving or rejecting loan requests from customers. In this paper, we analyze real bank credit data and apply various machine learning algorithms to determine the creditworthiness of customers. the accuracies of 56.28%, 59.46%, 57.34%, 63.23%, and 73.79% are obtained for logistic regression, support vector machine, Decision tree classifier, Random Forest Classifier, and ensemble method respectively. the aim of this work is to train a machine learning model to predict the eligibility for the applied loan and to create an interface for different types of loans. © 2024 IEEE.

关键词： Adversarial machine learning

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Focal Iou loss: More attentive learning for bounding box regression 24

Focal Iou loss: More attentive learning for bounding box reg...

引用

4th International conference on Internet of things and Machine learning

作者： Liao, Yuan Cao, Peixia Nanchang Hangkong Univ Sch Informat Engn Nanchang Jiangxi Peoples R China Jiangxi Prov Ctr Dis Control & Prevent Finance Off Nanchang Peoples R China

ISBN: (纸本)9798400710353

In this paper, we investigate a more efficient IoU loss based bounding box localization mechanism on top of end-to-end target detection frameworks to further improve the regression accuracy of object detection methods. Aiming at the limited spatial perception ability of deep convolution features, this paper proposes an optimization strategy by combining shallow spatial feature feed-forward mechanism (SSFF) with focal IoU loss function for bounding box regression tasks in target detection. this strategy firstly constructs a channel to transfer important location information in shallow spatial features to deep spatial features to reduce the loss of spatial details. Secondly, it constructs a focal IoU loss function based on the IoU loss, which dynamically adjusts loss weights according to the regression difficulty of different bounding boxes, to improve the positioning ability of regression networks on difficult bounding boxes. the experimental results show that the proposed methods can effectively improve the regression accuracy of target detection models.

关键词： Target detection Bounding box regression IoU loss function Focal loss function

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Collaborative Filtering Based Module Recommendation to Boost Learners Achievement 4

Collaborative Filtering Based Module Recommendation to Boost...

引用

4th International conference on intelligent Technologies, CONIT 2024

作者： Shabnam Ara, S.J. Tanuja, R. Manjula, S.H. Uvce Bangalore University Dept of Cse Bengaluru India

ISBN: (纸本)9798350349900

In today's world learners have access to a plethora of information in online learning platforms to hone their skills in different fields. Although a large number of choices may seem good, but it also leads to choice overload. Recommender systems (RS) can play a pivotal role in personalizing e-learning experiences by providing tailored content through various techniques. One commonly employed technique in such systems is collaborative filtering (CF). In this study, we utilize autoencoders within a CF framework to suggest course modules to learners. Our approach demonstrates superior performance in enhancing personalized learning experiences, addressing challenges posed by high-dimensional and sparse data. Empirical results confirm the effectiveness of our model, surpassing traditional methods like KNN, SVD, SVD++, and NMF in terms of accuracy. this research contributes to advancing e-learning recommendation systems, highlighting the potential of autoencoder-based collaborative filtering in optimizing learner engagement and performance. © 2024 IEEE.

关键词： Collaborative learning

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Federated Multi-Task learning on Non-IID Data Silos: An Experimental Study 24

Federated Multi-Task Learning on Non-IID Data Silos: An Expe...

引用

4th Annual International conference on Multimedia Retrieval (ICMR)

作者： Yang, Yuwen Lu, Yuxiang Huang, Suizhi Sirejiding, Shalayiding Lu, Hongtao Ding, Yue Shanghai Jiao Tong Univ Shanghai Peoples R China Shanghai Jiao Tong Univ AI Inst MoE Key Lab Artificial Intelligence Shanghai Peoples R China

ISBN: (纸本)9798400706028

the innovative Federated Multi-Task learning (FMTL) approach consolidates the benefits of Federated learning (FL) and Multi-Task learning (MTL), enabling collaborative model training on multi-task learning datasets. However, a comprehensive evaluation method, integrating the unique features of both FL and MTL, is currently absent in the field. this paper fills this void by introducing a novel framework, FMTL-Bench, for systematic evaluation of the FMTL paradigm. this benchmark covers various aspects at the data, model, and optimization algorithm levels, and comprises seven sets of comparative experiments, encapsulating a wide array of non-independent and identically distributed (Non-IID) data partitioning scenarios. We propose a systematic process for comparing baselines of diverse indicators and conduct a case study on communication expenditure, time, and energy consumption. through our exhaustive experiments, we aim to provide valuable insights into the strengths and limitations of existing baseline methods, contributing to the ongoing discourse on optimal FMTL application in practical scenarios. the source code can be found at https://***/youngfish42/FMTL-Benchmark.

关键词： Federated learning Multi-Task learning Dense Prediction

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