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

共 13504 条记录，以下是611-620 订阅

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Enhancing YouTube Spam Filtration Efficiency through Deep learning Based Techniques 4

Enhancing YouTube Spam Filtration Efficiency Through Deep Le...

引用

4th international conference on Technological Advancements in Computational Sciences, ICTACS 2024

作者： Sinhal, Arpana Kumar, Pradeep Aggarwal, Gaurav Manipal University Jaipur Jaipur India Amity University Tashkentl00128 Uzbekistan

ISBN: (纸本)9798350387490

In this paper Deep learning (DL) based techniques for filtering YouTube comments are explored. this work primarily focuses on the applications of Feedforward Neural Networks (FNNs) and Recurrent Neural Networks (RNNs) to increase the accuracy of spam detection. the conventional ML based techniques are comparatively less efficient than DL based approach while handling the complexity of natural language in order to learn the hierarchical representations. this study covers the model architecture and training of FNNs and RNNs. the experimental results represent the ability in accurate classification of YouTube comments. the results achieved in this work shows that Deep learning models are more efficient with increased accuracy in spam detection over huge datasets. © 2024 IEEE.

关键词： FNNs RNNs UCI Machine learning Repository

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Deep learning-based Classification of Cataract Infected Eyes and Normal Eyes using Convolutional Neural Network 4

Deep Learning-based Classification of Cataract Infected Eyes...

引用

4th international conference on Expert Clouds and applications, ICOECA 2024

作者： Ashitha Sandrin, A. Denisha, M. Karunya Institute of Technology and Sciences Department of Computer Science and Engineering Coimbatore India

ISBN: (数字)9798350385793

ISBN: (纸本)9798350385793

Cataract, a common eye disease characterized by clouding of the natural lens of the eye, is a serious threat to visual health. If left untreated, they can lead to blurred vision and even blindness, underscoring the importance of timely intervention. To address the urgent need for accurate cataract diagnosis, this study provides an improved approach using convolutional neural networks (CNN) to distinguish cataract eyes from eyes with normal vision. Because cataracts are the most common cause of visual impairment worldwide, recognition and accurate classification are critical for prompt intervention and mitigation of potential harms. Taking advantage of recent advances in deep learning, especially CNNs, this method introduces a new feature extraction technique that uses hyperparameter optimization. the systematic exploration of the hyperparameter space determines the optimal configuration that maximizes the efficiency of the classification algorithm to distinguish cataract from normal eyes. Experimental validation on a comprehensive fundus image dataset demonstrates the effectiveness of this approach, achieving robust performance and high classification accuracy. A deep learning approach with optimization techniques will improve the efficiency and accuracy of cataract diagnosis, offering promising solutions to alleviate the global burden of vision loss and blindness. © 2024 IEEE.

关键词： Convolutional neural networks

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Multimodality in Media Retrieval 24

Multimodality in Media Retrieval

引用

4th Annual international conference on Multimedia Retrieval (ICMR)

作者： Pegia, Maria Eirini CERTH ITI Thessaloniki Greece Reykjavik Univ Reykjavik Iceland

ISBN: (纸本)9798400706028

the quest for retrieving relevant media for a given query is well-studied and has various applications. Modern publicly available media collections provide diverse modalities of the same objects, which can enhance search. Our research delves into enhancing media retrieval by effectively representing and querying multimodal data. In the retrieval methods' ranking procedure, we examine efficiency through techniques like approximate nearest neighbor (ANN) indexing and high-performance computing (HPC). Our method, MuseHash, is proposed for single media object retrieval and is applied to images and 3D objects, outperforming existing methods on diverse datasets. Moreover, it significantly reduces execution times with ANN and HPC. Future plans include considering multimodality in the video retrieval domain.

关键词： Media Retrieval Multimodality optimization

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Machine learning Driven Energy Efficiency Framework Using Digital Twin: A Review 4

Machine Learning Driven Energy Efficiency Framework Using Di...

引用

4th IEEE international conference on ICT in Business Industry and Government, ICTBIG 2024

作者： Ramarumo, Andrew Selvaraj, Rajalakshmi Segwele, Tshiamo Kuthadi, Venumadhav thapelo, Tsaone Swaabow Botswana Internatinal University of Science and Technology Department of Computing and Informatics Palapye Botswana

ISBN: (纸本)9798331518981

the research aimed to investigate the intersection of machine learning, edge computing, digital twin technology, and energy efficiency optimization in the context of packet filtering frameworks. this study provides a structured and systematic approach to identify, collect, and analyze relevant literature on the subject matter. this study utilized different search strings to retrieve relevant papers, focusing on specific keywords such as "machine learning,""edge computing,""digital twin,""energy optimization,"and "packet filtering"to gather research articles from reputable sources within the timeframe of 2013 to 2023. the final selection of papers was refined by exporting citations to BibTeX format and utilizing the Kullback-Leibler Divergence (KLD) to categorize and prioritize the results based on the relevance and importance of keywords. Following a reproducible methodology and adhering to established research practices, we compiled a comprehensive dataset of 392 papers for in-depth review and analysis. this study can enable the researchers to have an overview of the existing literature, identify research opportunities, analyze different methodologies, and propose novel ideas and directions, to inform a cohesive framework for guidance on addressing energy efficiency challenges in edge computing through the integration of machine learning and digital twin. © 2024 IEEE.

关键词： Adversarial machine learning

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Exploring Multi-source Mobile applications Association Discovery Based on Representation learning 4th

Exploring Multi-source Mobile Applications Association Disco...

引用

4th international conference on Artificial Intelligence Logic and applications

作者： Luo, Weiqi Zhou, Yixin Zhao, Wenman Li, Weizhuo Nanjing Univ Posts & Telecommun Sch Modern Posts Nanjing 210000 Jiangsu Peoples R China

ISBN: (纸本)9789819603534;9789819603541

With the rapid development of mobile applications, effectively discovering associations between mobile applications has become an important issue. Existing methods for discovering associations among mobile applications are limited to associations within the same application store. Besides, the methods for association discovery are restricted to a single type of representation learning approach, resulting in suboptimal results. To address this problem, this paper explores two mobile application association discovery frameworks based on representation learning for multi-source mobile applications, including iterative framework combining knowledge graph representation methods and network representation models, and entity alignment-based models to mine associations between mobile apps for this task. Experiments indicate that the proposed methods can obtain better performances than existing methods in terms of metrics.

关键词： Mobile applications Association Discovery Representation learning Entity Alignment Iterative learning

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Improving Bias Detection in NLP Models through Multitask learning with Cloze and Random Mask Tasks 4

Improving Bias Detection in NLP Models through Multitask Lea...

引用

4th international Signal Processing, Communications and Engineering Management conference, ISPCEM 2024

作者： Weng, Kexin Department of Computer Science University of Warwick Coventry United Kingdom

ISBN: (纸本)9798331528676

this paper applies a multitask learning approach designed to enhance the understanding of textual bias in natural language processing (NLP) models. By combining Cloze and Random Mask (RM) tasks, the study aims to train models that can recognize and bias better within context. the WinoBias dataset is used to verify proposed method, which includes both stereotypical and counter-stereotypical sentences. Applying model under the multitask learning framework allows it to understand diverse contextual scenarios, and then improving its ability to detect biases that might be overlooked. Experimental results evaluated on classification task indicate that this approach significantly improve the model’s performance in bias detection. the study offers a foundation for future work on reducing bias in various text analysis applications. the findings highlight the importance of developing methodologies for detecting bias in automated text processing systems, which ensures more accurate and fair outcomes in real-world applications. ©2024 IEEE.

关键词： Contrastive learning

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Research on Intelligent Control System of UAV Path Planning and Autonomous Control Based on Deep Reinforcement learning 4

Research on Intelligent Control System of UAV Path Planning ...

引用

4th IEEE international conference on Power, Electronics and Computer applications, ICPECA 2024

作者： Wu, Juan College of Mechanical and Electrical Engineering Shandong Vocational College of Industry Zibo China

ISBN: (纸本)9798350359589

this paper studies the autonomy and intelligence of UAV. the system can effectively control the lifting of the UAV through the computer, and carry out linear motion between multiple operation points, so that its position deviation in the horizontal position is less than 0.14 meters. In this paper, a deep reinforcement learning PID method based on the COACH method of correction and correction is proposed, which provides the optimal path for UAV landing on the mobile platform. First, the reinforcement learning model is trained using the correction and correction framework in the simulation environment, and then the trained model is used to output the control parameters in the simulation environment and the real environment. Experiments show that the proposed method can effectively improve the time and smoothness of UAV trajectory planning, and improve the real-time performance of the algorithm, so as to meet the needs of obstacle avoidance and planning in complex scenes. the results show that this new type of aircraft can land smoothly on a maneuvering platform. © 2024 IEEE.

关键词： Deep learning drones intelligent control path planning

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IoT Based Monitoring System for Health During Menstruation 4

IoT Based Monitoring System for Health During Menstruation

引用

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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Traffic target detection algorithm based on 4D radar and vision fusion 4

Traffic target detection algorithm based on 4D radar and vis...

引用

4th international conference on Machine learning and Intelligent Systems Engineering (MLISE)

作者： Lin, Li Feng, Lin Chongqing Univ Posts & Telecommun Sch Automat Chongqing Peoples R China

ISBN: (纸本)9798350375084;9798350375077

In the intelligent road traffic system, the sensor module is required to quickly distinguish each traffic target, but the traditional camera-based or millimeter-wave radar-based detection system is difficult to meet this requirement. Aiming at the problem that the limitation of a single sensor makes the longitudinal traffic target indistinct and the detection speed slow, a method for target detection is developed by combining 4D millimeter wave radar with a camera. the ROI of the target detection region of interest is constructed by using the characteristics of 4D millimeter wave radar with stronger perception ability and faster data processing, which reduces the computing power for the subsequent visual traffic target detection. the test results of the proposed detection algorithm in the experimental field show that the detection method is suitable for practical road traffic target detection applications.

关键词： Radar vision fusion Traffic target detection Adaptive clustering

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Research on fault identification method of ru-ral distribution network for optimal control 4

Research on fault identification method of ru-ral distributi...

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4th international conference on Intelligent Power and Systems, ICIPS 2024

作者： Ye, Jianfeng Gong, Yanling Zhang, Yanpeng Su, Gaoyang Chen, Junxue Li, Yang Gong, Ting Yicheng City Power Supply Company State Grid Hubei Electric Power Co. Ltd Hubei Xiangyang China

ISBN: (纸本)9798331532765

With the rapid economic development in rural areas of our country, the scale of the rural distribution network is ex-panding constantly. However, the existing rural distribution network still has problems, such as unreasonable network structure, weak reliability, and aging equipment. therefore, a fault identification method for rural distribution networks oriented to optimal control is proposed to improve the relia-bility of rural distribution networks. However, due to the limitation of the traditional neural network itself, it can not identify the fault samples well. therefore, an intelligent fault identification model of a rural distribution network based on deep learning is proposed. First, determine the neural network architecture;then the model is trained with the corresponding parameter optimization algorithm. Finally, the deep learning model for fault identification of rural dis-tribution networks can be obtained. the simulation results show that the method is effective. © 2024 IEEE.

关键词： deep learning model neural network parameter optimization algorithm rural distribution network

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