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Film and Television Special Effects Production Based on Modern Technology: from the Perspective of Statistical machine learning 4

Film and Television Special Effects Production Based on Mode...

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4th international conference on Smart Systems and Inventive Technology, ICSSIT 2022

作者： Wang, Yi Department of Digital Creativity and Animation Shenzhen Polytechnic University Shenzhen518055 China

ISBN: (纸本)9781665401180

Based on statistical machine learning, this paper studies film and television special effects production under the background of modern technology. First, it introduces the new challenges of film and television post-production technology, which requires a new development direction. then based on modern technology analysis, the role of special effects production technology in film and television post-production is analyzed, and it is necessary to strengthen related research. therefore, starting with the concept of film and television production, analyzing the technology of film and television post-production special effects according to the specific process and relationship of film and television production, and explaining its application in two-dimensional and three-dimensional animation, it has become the key research content of the current film and television post-production work. © 2022 IEEE

关键词： Special effects

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A local enhancement method to distinguish target direction 4

A local enhancement method to distinguish target direction

引用

4th international conference on Signal Processing and computer Science, SPCS 2023

作者： Zhang, Zheng Liu, Zhipeng Hangzhou Application Acoustics Institute Zhejiang Hangzhou310023 China

ISBN: (纸本)9781510672505

In order to solve the problem that the peak of the weak target in the azimuthal energy spectrum is lower than the background when using the passive sonar systems to detect targets, and it is unable to identify the target azimuth visually and quickly, a post-processing method that can identify the target azimuth and perform local enhancement is proposed in this paper. this method uses background equalization technology to compress the dynamic range of the energy spectrum. the background threshold of energy spectrum after normalization processing is estimated, and target azimuth is extracted. Combined with the background threshold, the correction coefficient is constructed in segments to achieve the local enhancement at the target azimuth. this method is able to distinguish targets and background visually and quickly, has the function of highlighting and identifying the azimuth of the target based on simulation and measured data. © 2023 SPIE.

关键词： Data visualization

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A Review of Research on the application of Deep Reinforcement learning in Unmanned Aerial Vehicle Resource Allocation and Trajectory Planning 4

A Review of Research on the Application of Deep Reinforcemen...

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4th international conference on machine learning, Big Data and Business Intelligence, MLBDBI 2022

作者： Cai, Yanan Zhang, Erqing Qi, Yali Lu, Likun Beijing Institute of Graphic Communication School of Information Engineering Beijing China

ISBN: (纸本)9798350333947

In recent years, Unmanned Aerial Vehicle (UAV) has played an important role in the field of wireless communication with its high mobility and high controllability. In this paper, we focus on the application of deep reinforcement learning (DRL) in UAV resource allocation and trajectory planning. DRL is used to design the decision deployment of UAVs, optimize data transfer rate, throughput, energy efficiency and other metrics, and learn better path planning strategies to make UAVs' decisions more intelligent. We summarize the reinforcement learning (RL) methods, describe the relevant research in the field of UAV wireless communication in recent years, and investigate the algorithmic mechanisms of DRL such as deep Q network (DQN) and deep deterministic policy gradient (DDPG) algorithm. © 2022 IEEE.

关键词： Reinforcement learning

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Image Domain Translation for Few-Shot learning 27th

Image Domain Translation for Few-Shot Learning

引用

27th international conference on Pattern Recognition, ICPR 2024

作者： Chen, Liangyuan He, Zhenan Zhang, Hai College of Computer Science Sichuan University Chengdu China

ISBN: (纸本)9783031781827

Few-shot learning is crucial in machine learning and computer vision. It enables models to recognize new objects with limited labeled data, addressing the challenge of data scarcity and expanding the application of machine learning to domains with scarce data. Previous methods built metric space using labeled data from the base set and then classified queried images from the novel set by finding the nearest class prototype. However, due to the presence of poor-quality data in the novel set, the class prototype often exhibits instability. In response to this challenge, this paper proposes a Semantic Conditional Translation Network for reconstructing stable class prototypes. Specifically, images are first divided into edge domain (i.e., images at the cluster edge) and prototype domain (i.e., images at the cluster center). then, an Enhanced Generative Adversarial Network is introduced to learn the translation from edge toward prototype, where a Non-parametric Classification Regularizer is designed to enlarge the discriminability of the translated samples. Meanwhile, class definitions are exploited as semantics providing precise descriptions and enhancing translation performance. Experimental results demonstrate that the proposed method obtains competitive results on four benchmark datasets. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Generative adversarial networks

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Innovative Severity Assessment of Almond Leaf Diseases using Federated learning CNNs 4

Innovative Severity Assessment of Almond Leaf Diseases using...

引用

4th IEEE international conference on Smart Technologies in Computing, Electrical and Electronics, ICSTCEE 2023

作者： Sharma, Vikrant Kukreja, Vinay Mehta, Shiva thapliyal, Siddhant Graphic Era Hill University Computer Science And Engineering Uttarakhand Dehradun India Chitkara University Institute Of Engineering And Technology Chitkara University Punjab India Graphic Era Deemed To Be University Computer Science And Engineering Uttarakhand Dehradun India

ISBN: (纸本)9798350384628

the study details implementing a federated learning framework using a Convolutional Neural Network (CNN) to assess the severity of almond leaf diseases across a group of six different customers, placing it at the forefront of agritech innovation. this research methodically stratifies the condition into four severity categories - Mild (1-25%), Moderate (26-50%), Severe (51-75%), and Very Severe (76-100%) - giving a granular lens through which the affliction's evolution is examined. the method is new because it makes use of federated learning. this model safeguards the privacy and decentralization of data while drawing on the wisdom of many via the use of disparate datasets. Central to the approach is federated averaging to combine the results of locally trained CNN models on individual client datasets. Using the specifics of each client's data, this method generates a unified model that incorporates a thorough knowledge of illness trends. the outcome analysis proves the approach's efficacy and provides new and exciting perspectives. In terms of precision (93.93%), recall (93.98%), and F1-score (93.94%), Client qg-1 stood out as the gold standard. there was a wide range of results from one client to the next, with qg-2 maintaining a solid posture (Precision: 88.51%, Recall: 88.54%, F1-score: 88.50%, Accuracy: 94%) and qg-3 struggling (Precision: 65.44%, Recall: 65.49%, F1-score: 65.43%, Accuracy: 83%). Metrics such as accuracy, precision, recall, and F1-scores for clients qg-4, qg-5, and qg-6 all ranged in the upper echelons, which is to say, very well. this journey of discovery not only sheds light on the efficacy of federated learning in agricultural disease detection but paves the way for more studies in distant and decentralized data settings. © 2023 IEEE.

关键词： Convolutional neural networks

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A Self-play and Sentiment-Emphasized Comment Integration Framework Based on Deep Q-learning in a Crowdsourcing Scenario : Extended Abstract 39

A Self-play and Sentiment-Emphasized Comment Integration Fra...

引用

39th IEEE international conference on Data Engineering, ICDE 2023

作者： Rong, Huan Sheng, Victor S. Ma, Tinghuai Zhou, Yang Al-Rodhaan, Mznah Nanjing University of Information Science & Technology School of Artificial Intelligence Nanjing China Texas Tech University Department of Computer Science Lubbock United States Nanjing University of Information Science & Technology School of Computer & Software Nanjing China Auburn University Department of Computer Science & Software Engineering Auburn United States King Saud University College of Computer & Information Sciences Riyadh Saudi Arabia

ISBN: (纸本)9798350322279

Crowdsourcing is a sourcing model where individuals or organizations obtain goods and services from a large, relatively open and often rapidly evolving group of internet users. the most common way that crowdsourcing can facilitate machine learning is to annotate instances with labels [1]. However, the same instance may have inconsistent class labels, in the eyes of various annotators. therefore, current efforts in crowdsourcing mainly focus on the truth inference or label integration, to remove inconsistent labels or to alleviate biased labeling. In turn, instances with the integrated labels could facilitate the training on machine learning models. the future direction of crowdsourcing is to apply more fine-grained truth inference methods to different application domains [2]. Consequently, we evolve toward another challenging problem of comment integration. that is, how can we integrate or summarize the core opinions of multiple product comments obtained from users, rather than the discrete labels. © 2023 IEEE.

关键词： Crowdsourcing

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Forecasting Demand for Donuts Using a machine learning Approach 8

Forecasting Demand for Donuts Using a Machine Learning Appro...

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8th international conference on Electrical, Telecommunication and computer Engineering, ELTICOM 2024

作者： Sembiring, Nurhayati Sari Harahap, Wanda Burma Khalif, Farhan Abiyyahda Chandra, Karina Herlim, Christian Universitas Sumatera Utara Department of Industrial Engineering Medan Indonesia

ISBN: (纸本)9798331531249

UKM Donat XYZ is a company focused on producing donuts with a daily capacity of 4,000 to 5,000 units, offering more than 20 flavor variants. However, the company experiences monthly sales fluctuations and often encounters issues with unsold donut stock due to difficulties in accurately predicting production volumes manually. the research was conducted to forecast donut demand in 2024 using a machine-learning approach to address this challenge. the study leveraged historical daily sales data from 2022 and 2023, totaling 730 data points. Two methods were employed: the Naïve-Bayes algorithm and Prophet. Evaluation of the results indicated that the Naïve- Bayes algorithm achieved better accuracy than Prophet, with a Mean Square Error (MSE) of 428,328.92 for Naïve-Bayes. At the same time, Prophet had an MSE of 27,178,280. this research provides valuable insights for the company to efficiently manage its production and optimize donut stock. By utilizing predictive models like Naïve-Bayes, the company can better forecast market demand and avoid situations of potential stock shortages or excesses that could lead to losses. It also aids in more accurate production planning and efficient resource management. this research is anticipated to serve as a foundation for the company to enhance its strategies in meeting consumer demand more timely and effectively. Consequently, UKM Donat XYZ can improve its operational performance and reduce potential losses from wasted products due to errors in demand estimation. © 2024 IEEE.

关键词： Demand Forecasting machine learning Naïve-Bayes Algorithm Prophet Algorithm

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HyFed: A Hybrid Blockchain Empowered Federated learning Privacy Fair Framework 4th

HyFed: A Hybrid Blockchain Empowered Federated Learning Priv...

引用

4th international conference on Data and Information in Online Environments

作者： Chao, Kailin Jiang, Fan Xiao, Jianmao Zhong, Yaozhang Wu, Junyi Gu, Keyang Feng, Zhiyong Jiangxi Normal Univ Sch Software Nanchang 330022 Jiangxi Peoples R China Jiangxi Prov Engn Res Ctr Blockchain Data Secur & Nanchang 330022 Jiangxi Peoples R China Tianjin Univ Coll Intelligence & Comp Tianjin 300350 Peoples R China

ISBN: (纸本)9783031807121;9783031807138

this study presents the meticulous construction of a robust experimental system framework based on a hybrid blockchain network, designed to meet the experimental needs of federated learning research. the framework leverages TensorFlow Federated (TFF) to facilitate the optimization and substitution of federated learning aggregation algorithms and the development of reputation and contribution systems. the hybrid blockchain network architecture within this framework combines the advantages of public and private blockchains, capable of processing public transactions and managing private data. An innovative data encryption and access control mechanism has been implemented, ensuring data privacy and security. Performance optimizations, including the acceleration of block production speed and database query optimization, have been carried out to enhance system efficiency. this article provides a comprehensive deployment of the framework and an analysis of its components, offering a foundation for further research. With the evolution of federated learning and blockchain technology, the proposed experimental system framework is expected to have broader application prospects and research value.

关键词： Federated learning Blockchain Smart Contract Data Security Privacy Protection

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Predicting High-Risk Areas for Crime Hotspot Using Hybrid KNN machine learning Framework 5

Predicting High-Risk Areas for Crime Hotspot Using Hybrid KN...

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5th international conference on Inventive Research in Computing applications, ICIRCA 2023

作者： Vinothkumar, K. Ranjith Kumar, S. Vikram Raj, R. Mekala, N. Sasirekha, S.P. Reshma, R. Karpagam Academy of Higher Education Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350321425

this research presents the application of the K-Nearest Neighbour (KNN) machine learning algorithm for predicting crime hotspots. the KNN algorithm is a supervised learning method that uses distance between data points to forecast outcomes. the system was used to find trends and forecast potential hotspots after being trained on previous crime data. the study also takes into account social and environmental elements that influence the frequency of crimes in hotspots. the study's findings give insight into the fundamental causes of criminal activity and show that the KNN algorithm may be successfully used to identify crime hotspots. In order to lower crime rates in high-risk locations, law enforcement agencies and urban planners can benefit from this research in establishing focused crime prevention efforts. However, the quality of the data and the algorithm's capacity to recognize pertinent patterns both affect how accurate the forecasts turn out to be. Exploring the potential and restrictions of this method to crime prediction will require more research. In general, using the KNN algorithm to anticipate crime hotspots has shown promise and has the potential to help create efficient crime prevention tactics. © 2023 IEEE.

关键词： Forecasting

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Predictive Maintenance for Array of Cherenkov Telescopes 1st

Predictive Maintenance for Array of Cherenkov Telescopes

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1st international conference on machine learning for Astrophysics, ML4ASTRO 2022

作者： Gambadoro, Salvatore Cavalieri, Salvatore Costa, Alessandro D’Urso, Diego Incardona, Federico Munari, Kevin Bruno, Pietro Department of Electrical Electronic and Computer Engineering University of Catania Catania Italy INAF Osservatorio Astrofisico di Catania Catania Italy

ISBN: (纸本)9783031341663

A predictive maintenance (PdM) approach is considered among the best solution for the maintenance of equipment. the paper aims at presenting an approach to the realization of a predictive maintenance for the array of Cherenkov telescopes. the application of PdM is able to reduce array downtimes, to increase the longevity of telescope components, and to reduce costs due to unscheduled maintenance activities. © 2023, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： machine learning

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