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检索条件"任意字段=2024 International Conference on Machine Learning and Intelligent Computing, MLIC 2024"

共 25993 条记录，以下是31-40 订阅

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5th international conference on Advances in Information Communication Technology and computing, AICTC 2024

5th International Conference on Advances in Information Comm...

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5th international conference on Advances in Information Communication Technology and computing, AICTC 2024

ISBN: (纸本)9789819761050

The proceedings contain 86 papers. The special focus in this conference is on Advances in Information Communication Technology and computing. The topics include: Universal Representation Theory—Rigour II;revolutionizing Smart Cities: A Data-Driven Traffic Monitoring System for Real-Time Traffic Density Estimation and Visualization;Assessing the Influence of Self-service Technology on WOM: The Role of Customer Satisfaction;Universal Representation Theory—Applications II;enhancing Performance of machine learning Models in Healthcare: An Analytical Framework for Assessing and Improving Data Quality;evaluating the Enhancement of Cognitive Skills and Acquisition of New Skills in an Environment of Different Video Game Genres;hySeg-Net: A Robust Interactive Hybrid Technique for Image Segmentation and Classification in Hand Gesture Recognition;web Vulnerability Scanner;digitalization of Educational Resources in Pre-tertiary Education in Ghana: Assessing Impact, Challenges, and Opportunities in Enhancing learning Outcomes;interconnected Through Robotic System in English Foreign Language Education;transferability Cybersocialization as a Digital Civility Through Artificial Intelligence-Based Design Thinking Mindset to Modern Pedagogy;fog computing in Healthcare: Enhancing Recommendations and Real-Time Monitoring;enhancing Bank Consumer Retention: A Comprehensive Analysis of Churn Prediction Using machine-learning and Deep learning Techniques;a Contemporary Model of Kazakh National Pedagogy in the Era of Technology: Theory-Based Design Framework;the Future of Post-harvest Logistics: The Cold Chain Practices with Blockchain, IoT, and FinTech;diagnosing Sexually Transmitted Disease in Females: Leveraging Dimensionality Reduction and machine learning;a Systematic Review of Optimal Task Scheduling Methods Using machine learning in Cloud computing Environments;intelligent Urban Futures: Exploring AI and machine learning Applications in Smart Cities.

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Multi Dimensional Deep Encoding for Categorical Feature Space 24

Multi Dimensional Deep Encoding for Categorical Feature Spac...

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13th international conference on computing and Pattern Recognition, ICCPR 2024

作者： Alamuri, Madhavi Surampudi, Bapiraju Negi, Atul School of Computer and Information Sciences University of Hyderabad Telangana State Hyderabad India Cognitive Science Lab International Institute of Information Technology Telangana State Hyderabad India

ISBN: (纸本)9798400717482

Categorical data classification and clustering are essential to many fields, including pattern recognition, data mining, knowledge discovery, and machine learning. It is crucial to understand how to provide categorical input to machine learning algorithms because the majority of them are developed for numerical inputs. Any machine learning algorithm's performance depends not only on the model and hyper parameters but also on the way the data is prepared for and fed into the model. With high cardinality features and categorical predictor variables, machine learning approaches that handle categorical data frequently encounter challenges. In this scenario, we investigate categorical encoding techniques, develop a taxonomy of encoding techniques, and use the machine learning algorithms KNN, MLP, SVM, and ELM and make a comparative study. We suggest a method of hybrid encoding using CNN that gives better performance results. In this paper, we propose the machine learning strategy employing multi-dimensional encodings with CNN, which outperforms other machine learning approaches on majority datasets. It is clear that CNN's performance in learning categorical features has been much enhanced by the use of multi-dimensional encoded input vectors. In our suggested method for supervised and unsupervised encoding, we have used four distinct encodings. These results encourage us to further study the outcomes of different hybrid combinations of encodings for categorical feature classification. © 2024 Copyright held by the owner/author(s).

关键词： Categorical data

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Green and intelligent Transformation of Artificial Intelligence in Supply Chain Management: History, Current Situation and Future Prospects 24

Green and Intelligent Transformation of Artificial Intellige...

引用

5th international conference on Computer Science and Management Technology, ICCSMT 2024

作者： Huang, Ling School of Economics and Management Sichuan Minzu College Sichuan Kangding China

ISBN: (纸本)9798400709999

The Dartmouth conference in 1956 established the field of artificial intelligence (AI), which aims to simulate human intelligence. The article explores the application of AI in supply chain management and its impact on the greening and intelligent transformation of the supply chain. By analyzing the progress of big data, machine learning, and deep learning technologies, it reveals how AI promotes innovation and efficiency improvement in the supply chain. At the same time, the article also discusses the specific application of AI in all links of the supply chain such as product design, production, logistics, and after-sales service, which promotes the green and intelligent transformation of the supply chain and shows huge market potential and economic value. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Green development

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Partial Distributed Deep learning Inference Model for Image based Edge Device Cluster 24

Partial Distributed Deep Learning Inference Model for Image ...

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8th international conference on Electronic Information Technology and Computer Engineering, EITCE 2024

作者： Xiao, Dun Wang, Xiaogang Yang, Zhihe Huang, Chao School of Electronic and Information Shanghai Dianji University Shanghai China

ISBN: (纸本)9798400710094

In recent years, the development of AI technology has driven the development of network edge applications such as smart manufacturing, smart factories, and smart cities. Deep neural networks are increasingly being deployed in smart manufacturing edge application scenarios to carry out edge detection tasks. However, there is a gap between the expensive DNN computing network and the limited computing power of the edge devices. To deploy compute-intensive neural networks on resource-constrained edge devices, traditional approaches rely either on offloading workloads to a remote central cloud or on optimizing compute on local edge devices. However, the method of uploading tasks to the central cloud is significantly affected by the unreliability and delay of the WAN, and the sensitive information of users is easy to be leaked during transmission, and the local computing method is also limited by the computing power. The latest machine learning models have great demands on memory, computing, and energy, which poses challenges to distributed computing and data aggregation for heterogeneous and resource-constrained edge devices. In order to address these challenges, this paper proposes solutions for different image types in intelligent manufacturing scenarios. When images are processed in the production process of intelligent manufacturing, this paper adopts the method of horizontal partitioning of the DNN model to perform DNN-based inference tasks on the edge device cluster. In order to reduce the consumption of network bandwidth, a dynamic priority scheduling algorithm is used to optimize the distribution of inference tasks at the edge nodes to improve the inference speed. Finally, experiments show that the edge node optimization scheduling method proposed in this paper has a good effect on the performance of running time, communication cost and energy consumption of edge devices. © 2024 Copyright held by the owner/author(s).

关键词： Smart manufacturing

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Research on enterprise internal audit risk prediction model based on machine learning 24

Research on enterprise internal audit risk prediction model ...

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2024 international conference on Economic Data Analytics and Artificial Intelligence, EDAI 2024

作者： Li, Na Wuxi Vocational Institute of Commerce Jiangsu Wuxi China

ISBN: (纸本)9798400713255

This study constructs a machine learning-based internal audit risk prediction model to address the complex and dynamic financial risks faced by modern enterprises. By utilizing algorithms such as Random Forest, Support Vector machine (SVM), and Logistic Regression, we analyze five years of internal financial data and perform feature selection, coupled with grid search and cross-validation to optimize model parameters. The experimental results indicate that the Random Forest model performs best in terms of accuracy, F1 score, and AUC value, effectively identifying high-risk transactions. The research conclusions provide an intelligent tool for enterprise audit risk management and offer insights for future model optimization and application research. © 2024 Copyright held by the owner/author(s).

关键词： Prediction models

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machine learning-Based Hyper-Heuristics: A Clear Insight 24

Machine Learning-Based Hyper-Heuristics: A Clear Insight

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7th international conference on Computational Intelligence and intelligent Systems, CIIS 2024

作者： Bouazza, Wassim LS2N Laboratory Nantes Universite Ecole Centrale Nantes Cnrs LS2N Umr 6004 NantesF-44000 France

ISBN: (纸本)9798400717437

In various scientific and engineering disciplines, decision-making processes frequently rely on optimization strategies to identify the best solution from a multitude of potential options. Traditional optimization methods have proven effective for many challenges;however, they face substantial difficulties when applied to complex, dynamic, and large-scale scenarios. Hyper-heuristics (HHs) have emerged as a promising alternative, offering a flexible and adaptive approach by optimizing the selection or creation of heuristics rather than directly tackling problem solutions. The integration of machine learning (ML) with hyper-heuristics has significantly propelled this field forward, enabling data-driven methods to enhance the selection and creation of heuristics. This article provides a comprehensive overview of the current landscape of machine learning-based hyper-heuristics. It categorizes and analyzes existing approaches, emphasizing the various ML techniques employed and their specific applications. Furthermore, it identifies emerging trends and future research directions aimed at enhancing the efficiency, adaptability, and robustness of HH methodologies through advanced ML techniques. By consolidating these insights, this review aims to equip researchers and practitioners with a deep understanding of the evolving landscape and potential opportunities for leveraging machine learning in the design of optimization heuristics. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Adversarial machine learning

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Use neural Networks to Recognize Students' Handwritten Letters and Incorrect Symbols

Use neural Networks to Recognize Students' Handwritten Lette...

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2024 international conference on Mechatronics and intelligent Control, ICMIC 2024

作者： Zhu, Jia Jun Yang, Zichuan Hong, Binjie Song, Jiacheng Wang, Jiwei Chen, Tianhao Yang, Shuilan Lan, Zixun Ma, Fei Department of Information and Computing Science Xi'an Jiaotong-Liverpool University Suzhou China School of Engineering Suzhou Centennial College Suzhou China Department of Applied Mathematics Xi'an Jiaotong-Liverpool University Suzhou China GROW Inc. Suzhou China EasyStack Inc. Nanjing China TAL Education Group Beijing China

ISBN: (数字)9781510686847

ISBN: (纸本)9781510686830

Correcting students' multiple-choice answers is a repetitive and mechanical task that can be considered an image multi-classification task. Assuming possible options are 'abcd' and the correct option is one of the four, some students may write incorrect symbols or options that do not exist. In this paper, five classifications were set up - four for possible correct options and one for other incorrect writing. This approach takes into account the possibility of non-standard writing options. © 2025 SPIE.

关键词： Education and training machine learning Data modeling Neural networks Image classification Visual process modeling Computer vision technology Deep learning Performance modeling Transformers

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international conference on computing and machine learning, CML 2024

International Conference on Computing and Machine Learning, ...

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international conference on computing and machine learning, CML 2024

ISBN: (纸本)9789819778386

The proceedings contain 37 papers. The special focus in this conference is on computing and machine learning. The topics include: ViTaL: An Advanced Framework for Automated Plant Disease Identification in Leaf Images Using Vision Transformers and Linear Projection for Feature Reduction;Enhancing Path Planning with Obstacles Via a Combined Dijkstra-LbCS Methodology;revitalising Volunteerism: The Transformative Influence of Artificial Intelligence in Volunteer Management;optimizing the U-Net Model for Segmenting the Lung Opacity Regions in Chest Radiographs;intelligent Surveillance Camera System Based on Object Tracking;data-Efficient Training for Effective Paraphrase Retrieval Techniques Using Language Models to Identify Research Gaps;Enhancing IoT Anomaly Detection with DBSCAN—A Data-Driven Approach;Enhancing Efficiency and Functionality of Voice-Controlled Cars Through NLP Techniques and Additional Features;ensembling of Performance Metrics in Credit Risk Assessment Using machine learning Analytics;GUARDING THE GATE: Innovative Solutions for Third-Party App Vulnerabilities;prediction of Compressive Strength of Geopolymer Concrete via machine learning Techniques;Personalized Stress Mitigation Through EEG Based Stress Classification and Music Recommendation;detection and Prediction of Alzheimer’s Disease Using Rule Induction Method;Heart Disease Prediction Using Bagging QSVC Algorithm;loopholes in Security Implementation of Cloud and IoT Environment: Case Study of a Recent Security Incident;speckle Noise Removal from Three-Dimensional Ultrasound Images of Ovaries;Leveraging 3D Faster R-CNN for 3D Dental X-ray Restoration and Treatment Identification;rainfall Prediction in Diverse Indian Regions Using machine learning Approaches;climacteric Fruit Skin Defect Region Segmentation by Boundary Identification Using Morphologically Enhanced Watershed;Decoding Sentiments: Harnessing the Power of NLP for Comparative Analysis of ML Algorithms.

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Human-machine Symbiosis in action: Innovation of Foreign Language Teaching Mode in Higher Education Empowered by ChatGPT 24

Human-Machine Symbiosis in action: Innovation of Foreign Lan...

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7th international conference on Educational Technology Management, ICETM 2024

作者： Xue, Qiaoyu Li, Huiting School of Foreign Languages for Business Guangxi University of Finance and Economics Guangxi Nanning China

ISBN: (纸本)9798400717468

Human-machine symbiosis, a new type of human-machine relationship, has become the future trend in the field of education. Employing a descriptive approach and case study analysis, the article describes the goal, learning ways, role and division of labor of human-machine symbiosis in foreign language learning, and initially explores the innovation of human-machine symbiosis in foreign language teaching mode in higher education based on ChatGPT. Combined with the analysis of teaching cases, the mode realizes intelligent generation of teaching resources, human-machine speaking training, language analysis and feedback, personalized learning customization through diagnostic learning before class, accompanying learning during class, and evaluation learning after class. Admittedly, the human-machine symbiosis mode of foreign language teaching has potential risks, which should be reasonably used to avoid countermeasures to ensure the sustainable development of the teaching mode. © 2024 Copyright held by the owner/author(s).

关键词： Adversarial machine learning

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machine learning-Based Crop Recommendation for IoT-Enabled Smart Agriculture 24

Machine Learning-Based Crop Recommendation for IoT-Enabled S...

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9th international conference on Cloud computing and Internet of Things, CCIOT 2024

作者： Sonata, Ilvico Computer Science Department Binus Graduate Program Bina Nusantara University Indonesia Jakarta Indonesia

ISBN: (纸本)9798400717161

Human food needs are increasing all the time. This condition is in line with the increasing world population and exacerbated by climate change. Agricultural products are one of the sources of food to meet food needs. The use of technology is one of the keys to success in increasing agricultural production. One of the technologies used in agricultural technology is the Internet of Things (IoT). IoT in agricultural technology can be used to monitor soil and weather conditions to ensure that these conditions are in accordance with the needs of agricultural crops. The use of machine learning technology can be combined with IoT to help predict the types of crops that are suitable for planting based on soil and weather conditions. The results of the experiment show that the machine learning model can be used to predict the right types of plants to plant based on soil and weather conditions obtained through IoT. This method can be used to increase agricultural yields. Copyright © 2024 held by the owner/author(s). Publication rights licensed to ACM.

关键词： Smart agriculture

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