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检索条件"任意字段=3rd International Conference on Machine Learning and Data Engineering, ICMLDE 2024"

共 6844 条记录，以下是391-400 订阅

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Optimizing MEMS Optical Arrays with Silicon Micro Mirrors using machine learning Techniques 3

Optimizing MEMS Optical Arrays with Silicon Micro Mirrors us...

引用

3rd international conference on Computational Modelling, Simulation and Optimization, ICCMSO 2024

作者： Joshi, Vilas Ramrao Sharma, Surender Kumar Galande, Vijay Agatrao Sachin Patil, Pallavi Patil, Ushadevi Suttarwala, Hussain Sharma, Deepak K. Savitribai Phule Pune University Department of Computer Engineering Pune India Poornima University Department of Electrical Engineering Jaipur India KJEI's Trinity Academy of Engineering Department of Engineering Science Pune India Genba Sopanrao Moze College of Engineering Department of Civil Engineering Pune India American Express Senior Software Engineer PhoenixAZ85054 United States University of Technology Jaipur India

ISBN: (纸本)9798350361391

In the rapidly evolving landscape of optical microsystems, the infusion of machine learning (ML) techniques stands out as a catalyst for unprecedented progress. This research paper delves into the intricate interplay between Micro-Electro-Mechanical Systems (MEMS), optical arrays, and silicon micro mirrors, shedding light on the transformative prowess unleashed by machine learning in optimizing these intricate components. Focusing on the limitations posed by conventional designs, the paper not only illuminates the challenges encountered but also presents a compelling narrative of how ML algorithms emerge as formidable allies, poised to surmount these hurdles with ingenuity and precision. The exploration extends beyond a mere technical discourse;it unfolds as a narrative of innovation, where the convergence of MEMS, optical arrays, and silicon micro mirrors, guided by the intelligence of machine learning, transcends traditional boundaries, propelling optical microsystems into an era of unparalleled efficiency and adaptability. This research beckons a new epoch, where the intricate dance of silicon micro mirrors and the intricate mechanics of MEMS find harmony through the symphony of machine learning, reshaping the landscape of optical microsystems and inspiring future possibilities that were once deemed beyond reach. © 2024 IEEE.

关键词： Microchannels

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Environmental Monitoring using IoT using Ardunio UNO and data Analysis 3

Environmental Monitoring using IoT using Ardunio UNO and Dat...

引用

3rd international conference on Automation, Computing and Renewable Systems, ICACRS 2024

作者： Chaitanya, R. Vijaya Patil, Amulya C. Reddy, G. Harishwar Bharat Mounica, Patil Perumal, K. Muruga Vardhaman college of engineering Hyderabad India

ISBN: (纸本)9798331532420

The increasing global challenges of resource depletion, and climate change underscore the critical need for robust environmental monitoring. The integration of Internet of Things (IoT) technology has ushered in a new era of Environmental Monitoring (EM), enabling real-time, data-driven insights into air quality, water pollution, and waste management. EM systems leverage sophisticated sensor networks to collect comprehensive environmental data. These sensors, coupled with IoT connectivity, facilitate the transmission of information to centralized platforms for analysis and interpretation. By examining a multitude of research studies, this paper categorizes and evaluates the methodologies and findings related to air monitoring. Key areas of focus include the types of sensors employed, IoT architectures, data processing techniques, and the practical applications of derived insights. machine learning algorithms are then incorporated to extract valuable patterns from the collected data, enabling predictive modelling, and early warning systems. © 2024 IEEE.

关键词： Environmental monitoring

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Enhancing Bharatanatyam Face Emotion Recognition Using machine learning Techniques 3

Enhancing Bharatanatyam Face Emotion Recognition Using Machi...

引用

3rd IEEE international conference on data, Decision and Systems, ICDDS 2024

ISBN: (纸本)9798350363890

Facial Expression Analysis of Bharatanatyam a Classical South Indian Dance Using machine learning Techniques The research explores how emotion alters facial expressions, particularly the areas of expression in the eyebrows and surrounding structures, lips, and mouth region. Local binary patterns (LBP), grey-level co-occurrence matrix (GLCM), Histogram of Oriented Gradients (HOG) and invariants descriptors such as Discrete cosine transforms (DCT) or oriented fast and rotated brief (ORB). Finally, using SVM (Support Vector machine), KNN (K-Nearest Neighbours), DT (Decision Tree), and RF (Random Forest) classifiers the deafest features are tested separately. Experiments are carried out via a Bharatanatyam face image database, and it is inferred that the combination of DCT with RF gives the best performance in classification. From the evaluations for colour-scale and grey-scale images, we can see that DCT with RF has an accuracy of 82.14%, and 80.40% is colour scale and greyscale respectively According to the above results, it is concluded that DCT along with RF has successfully categorized emotional facial expression of Bharatanatyam. © 2024 IEEE.

关键词： Support vector machines

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Evaluation and Optimization of machine learning Algorithms in Personalized Marketing 3

Evaluation and Optimization of Machine Learning Algorithms i...

引用

3rd IEEE international conference on Distributed Computing and Electrical Circuits and Electronics, ICDCECE 2024

作者： Guo, Chunxiang Jiao, Pengfei School Of Economics And Management Shaanxi Fashion Engineering University Shaanxi Xi'an China

ISBN: (纸本)9798350318609

With the development of society and the continuous updating of economic models, the importance of marketing for business operations is becoming increasingly high. However, due to the traditional marketing model adopting a popular strategy, personalized recommendations are insufficient. In recent years, machine learning algorithms have been widely applied and developed rapidly in various industries. Therefore, this article studied the effectiveness evaluation and optimization of machine learning algorithms in personalized marketing, and discussed the business recommendation marketing of banks as the research point. The study was conducted using the dataset provided by the Kaggle data competition website. Firstly, customer information was analyzed and divided into 12 explanatory variables and 1 response variable. Next, the Apache Nifi tool was used to collect information, ensuring the security of customer information. Then, the Informatica data Quality tool was used to clean the information data and organize it into features that can be utilized by the algorithm. The chi square test was used to test the correlation of features, determine the value of a as 0.05, and select the appropriate feature component to input into the model. Finally, the GBDT (Gradient Boosting Decision Trees) algorithm was used to analyze features, determine the actual needs of customers, and provide personalized recommendations to customers. The research results showed that the model had a 94% precision in judging the personal wishes of customers, and had strong anti-interference ability and good robustness. This study can accurately infer customers' intentions based on customer information, providing a new approach for exploring personalized marketing. © 2024 IEEE.

关键词： Sales

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Intelligent machine learning Framework for Classification of Vehicular Traffic in Internet of Vehicles 3

Intelligent Machine Learning Framework for Classification of...

引用

3rd international conference on Power Electronics and IoT Applications in Renewable Energy and its Control, PARC 2024

作者： Tripathy, Biswajit Tripathy, Subhranshu Sekhar Bebortta, Sujit Mukherjee, Tanmay Ravenshaw University Departmenet of Computer Science Cuttack75303 India KIIT Deemed to Be University School of Computer Engineering Bhubaneswar751024 India University Department of Computer Science and Engineering Bhubaneswar751030 India

ISBN: (纸本)9798350309409

In a world that is always changing due to population growth, it is difficult to forecast how routes will behave in the future, especially with regard to traffic. In this work, Traci was used to generate traffic-related data in a real-world setting, including waiting periods, coordinates, speed, and displacement. After that, cars were categorized according to how long they took to wait, and performance was examined using a variety of machine learning methods, such as Random Forest (RF), K-Nearest Neighbor (KNN), Support Vector machine (SVM), and K-means. The RF algorithm fared better than other algorithms, according to empirical studies, in estimating waiting times and evaluating traffic conditions. After that, the study used the Rapidly Exploring Random Tree (RRT) algorithm, a path-planning and simplification technique, to find vehicle travel paths that would not cause collisions. By decreasing wait times and improving customer satisfaction, this strategy aims to optimize journey routes and cut related expenses. Drones are used in the proposed system, and base stations and ad hoc networks work together to improve overall performance. © 2024 IEEE.

关键词： Vehicles

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Financial Market Risk Prediction Systems Based on data Analysis 3

Financial Market Risk Prediction Systems Based on Data Analy...

引用

3rd international conference on Artificial Intelligence and Autonomous Robot Systems, AIARS 2024

作者： Lan, Ming Guilin University Guilin Guangxi541006 China

ISBN: (纸本)9798350376173

The complexity and dispersion of risks in financial markets pose significant challenges to the decision-making of financial institutions and investors. In order to deal with these risks, the financial market risk prediction system based on data analysis was studied. With the rapid development of big data and machine learning technologies, it is possible to identify and predict risk points in financial markets, more accurately by exploring and analyzing data, thus providing an important basis for good decision making. The results showed that deep learning data analysis had high accuracy and stability in predicting financial market risks, with an accuracy rate of 94.79%, far exceeding the traditional artificial intelligence algorithm. This research has far-reaching implications not only for the stability and development of the financial sector, but also for protecting the interests of investors and maintaining the stability of the overall economy. © 2024 IEEE.

关键词： data accuracy

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A Review on Applications of IoT in Healthcare 3

A Review on Applications of IoT in Healthcare

引用

3rd international conference on Electronics and Renewable Systems, ICEARS 2025

作者： Thakare, Vedant Verma, Prateek Datta Meghe Institute of Higher Education and Research Faculty of Engineering and Technology Department of Computer Science and Engineering Maharashtra Wardha442001 India Datta Meghe Institute of Higher Education and Research Faculty of Engineering and Technology Department of Artificial Intelligence and Machine Learning Maharashtra Wardha442001 India

ISBN: (纸本)9798331509675

This article examines how the Internet of Things (IoT) is rapidly transforming the medical field. IoT technologies are revolutionizing patient care, increasing productivity, and boosting results in a variety of ways, from wearable technology and telemedicine to smart hospitals and personalized medicine. Predictive analytics, remote patient monitoring, and the use of AI and machine learning for more precise diagnosis and treatment regimens are important applications. The future of IoT in healthcare is extremely bright, with developments in 5G, AI, and other cutting-edge technologies set to further transform the sector and enhance human health, even while issues like data security and privacy still need to be resolved. © 2025 IEEE.

关键词： Electronic health record

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Advancing Multi-Modal Beam Prediction with Multipath-Like data Augmentation and Efficient Fusion Mechanism 24

Advancing Multi-Modal Beam Prediction with Multipath-Like Da...

引用

3rd international conference on Frontiers of Artificial Intelligence and machine learning (FAIML)

作者： Zhu, Qihao Wang, Yu Li, Wenmei Huang, Hao Yin, Jie Guo, Lantu Lin, Yun Gui, Guan Coll Telecommun & Informat Engn NJUPT Nanjing Jiangsu Peoples R China Jiangsu Police Inst Dept Comp Informat & Cyber Secur Nanjing Jiangsu Peoples R China China Res Inst Radiowave Propagat Qingdao Peoples R China Harbin Engn Univ Coll Informat & Commun Engn Harbin Peoples R China

ISBN: (纸本)9798400709777

In millimeter-wave and terahertz band communication systems, it is crucial to ensure accurate beam prediction for optimizing network performance and enhancing signal transmission efficiency. Traditionally, beam prediction has relied on single-modal data, which cannot capture the comprehensive environmental information necessary for optimal accuracy. Acknowledging this limitation, our paper shifts the focus towards multi-modal beam prediction (MMBP). Building upon existing MMBP methods, we introduce an advanced MMBP method that incorporates multipath-like data augmentation (MLDA) and an efficient fusion mechanism. This method is designed to significantly enhance the accuracy and robustness of beam prediction. Experimental results underscore the effectiveness of this method, which can achieve an impressive optimal distance-based beam score of 89.1%.

关键词： Multi-modal beam prediction (MMBP) Multipath-Like data augmentation (MLDA) Efficient fusion mechanism

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Efficient Bitrate Ladder Construction using Transfer learning and Spatio-Temporal Features 13

Efficient Bitrate Ladder Construction using Transfer Learnin...

引用

13th Iranian/3rd international machine Vision and Image Processing conference (MVIP)

作者： Falahati, Ali Safavi, Mohammad Karim Elahi, Ardavan Pakdaman, Farhad Gabbouj, Moncef Avanco Tehran Iran Tampere Univ Tampere Finland

ISBN: (纸本)9798350350494;9798350350500

Providing high-quality video with efficient bitrate is a main challenge in video industry. The traditional one-size-fits-all scheme for bitrate ladders is inefficient and reaching the best content-aware decision is computationally impractical due to extensive encodings required. To mitigate this, we propose a bitrate and complexity efficient bitrate ladder prediction method using transfer learning and spatio-temporal features. We propose: (1) using feature maps from well-known pre-trained DNNs to predict rate-quality behavior with limited training data;and (2) improving highest quality rung efficiency by predicting minimum bitrate for top quality and using it for the top rung. The method tested on 102 video scenes demonstrates 94.1% reduction in complexity versus brute-force at 1.71% BD-Rate expense. Additionally, transfer learning was thoroughly studied through four networks and ablation studies.

关键词： HTTP Adaptive Streaming (HAS) Bitrate ladder CRF Transfer learning Pareto Front

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A Comprehensive Review and Analysis of Advancements in Breast Cancer Detection and Classification 3

A Comprehensive Review and Analysis of Advancements in Breas...

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3rd IEEE Delhi Section Flagship conference, DELCON 2024

作者： Kaur, Baljit Popli, Renu Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (纸本)9798331518592

The comprehensive review analyzes machine learning (ML) in breast cancer detection, prediction, diagnosis, and treatment. The review study covers research from 2018-2024 on breast cancer prediction, detection, and diagnosis using machine learning tools/techniques. It compared different models, and performance metrics, which utilized datasets such as the Wisconsin breast cancer database and the online database for screening mammography. It encourages further research and innovation in breast cancer prediction to enhance ML-based disease forecasting. The review highlights research gaps and current techniques and suggests areas for future enhancement. This review explores machine learning algorithms to improve the effectiveness of breast cancer detection and classification. Recent research has highlighted using federated and deep learning to encourage cooperative data analysis while protecting patient privacy. Enhancing computing efficiency, researching advanced machine learning techniques like deep learning, and validating models on large amounts of data are among the main areas of key research gaps. machine learning, deep learning, and federated learning techniques improve the accuracy and efficiency of breast cancer detection. These technologies improved results for breast cancer and personalized healthcare. Future research should focus on scalability, real-world validation, and integration of real-time systems to advance early detection and therapy planning for breast cancer. © 2024 IEEE.

关键词： Federated learning

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