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The Film Industry Leaps into Artificial intelligence: Scope and Challenges by the Filmmakers 1st

The Film Industry Leaps into Artificial Intelligence: Scope ...

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1st FICR international conference on Rising Threats in Expert Applications and Solutions, FICR-TEAS 2020

作者： Datta, Angana Goswami, Ruchi Jaipur India

ISBN: (纸本)9789811560132

Artificial intelligence (AI) is usually associated with assisting humans with tasks that are either ordinary or can be done better with automation. Since then technology grew, representation of AI and robotics also evolved in films, especially the robots, which has given surplus roles from the antagonist, protagonist, the romantic, to a deadly weapon capable of machine learning. With the advent of CGI in the 2000s, an AI flareup took place in the world of cinema and came up with worthy concepts of AI-based movies. Today, the AI system can robotically deliver advanced visual effects which makes it easier for performers to act their alternative characters and creatures. Data Analytics and AI technology have already made their debut in films and are going to play a bigger role in the filmmaking process. Till now, films were made on AI. In the future, AI will be writing films, robots will perform, and animation will be done by a deep learning algorithm process. The study aimed to observe the AI technologies used in the film industry. The methods adopted to achieve the goals of the study is the content analysis method. The study will conclude that technology is constantly improving, and AI can become an inescapable force that could renovate the films and take it to the next level. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Data Analytics

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Improved Moth Flame Optimization with Deep Convolutional Neural Network for Colorectal Cancer Classification using Biomedical Images

Improved Moth Flame Optimization with Deep Convolutional Neu...

引用

international conference on Advances in Information Technology (ICAIT)

作者： Parimala S Kumutha R. Faiza Iram Sunena Rose M V Nanmaran R A Angelpreethi Department of Computer Science and Applications SRM Institute of Science and Technology Humanities and science (Physics) S. A. Engineering College Chennai India School of computer science and Artificial intelligence SR university warangal Telangana Department of Computer Science and Engineering JAIN (Deemed-to-be University) Bengaluru Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Tamilnadu India Information Technology St.Joseph's College tiruchirappalli

ISBN: (数字)9798350383867

ISBN: (纸本)9798350383874

Colorectal cancer (CRC) affects the large intestinal colon. CRC treated radiation, chemotherapy, and immunotherapy. If aberrant cells, which are predicted to become cancer cells, are detected early, CRC may be effectively managed. Histopathological analysis of tissue specimens is the most common way to diagnose CRC. Histopathological examination and appropriate therapy are the only ways to improve cancer survival. AI in colon and lung cancer histopathology analysis may help doctors identify instances faster, cheaper, and easier. Medical image processing may use ML devices for pattern recognition and resolution. This paper presents an Improved Moth Flame Optimisation with Deep Convolutional Neural Network (IMFO-DCNN) method for biomedical image-based colorectal cancer classification. The IMFO-DCNN approach uses the DCNN model to automatically extract biomedical picture properties. The IMFO method fine-tunes DCNN model hyperparameters to improve detection performance. The last step uses a Random Forest (RF) classification algorithm to effectively recognise and categorise biological pictures. Methodically estimating the IMFO-DCNN methodology on biological pictures improves CRC classification accuracy and resilience over traditional techniques. CRC detection and classification are efficient and reliable using IMFO-DCNN.

关键词： Radio frequency Accuracy Biological system modeling Fires Colorectal cancer Feature extraction Classification algorithms Convolutional neural networks Optimization Biomedical imaging

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Combining Deep Features with the Invariant Local Binary pattern features for Skin Cancer Classification

Combining Deep Features with the Invariant Local Binary Patt...

引用

IEEE Punecon

作者： Ritesh Maurya Drishti Arora T Gopalakrishnan Ankita R Deshpande Malay Kishore Dutta Mohan Karnati Amity Centre for Artificial Intelligence Amity University Noida India Dept. of Computer Science and Engineering Amity University Noida India Department of Information Technology Manipal Academy of Higher Education Manipal Institute of Technology Bengaluru Manipal India

Melanoma, a potentially fatal form of skin cancer, represents a global health concern, necessitating efficient and precise detection for improved patient outcomes. However, the subjective and daunting nature of manual examination and diagnosing skin lesions by dermatologists often leads to delayed identification or misdiagnosis. This research paper introduces a novel technique for automated melanoma classification to address this pressing problem. The proposed research methodology combines deep features with rotation-invariant handcrafted local-binary pattern (RI-LBP) features for skin lesion classification. RI-LBP extracts microstructures present at the local level whereas deep features capture the high-level intuitive features present in a skin lesion image. After combining RI-LBP with the deep features, the combined feature vector is used to train and test the XGBoost machine learning classifier for classification purposes. Moreover, the investigation undertakes a comparative analysis of numerous machine learning algorithms to evaluate their efficacy in the classification of melanoma using the suggested combined feature set. The XGBoost classifier has obtained an Area Under the Curve value of 0.91 in differentiating pernicious melanomas and benign skin lesion dermoscopic images. Thus, the proposed method is suitable for aiding dermatologists, enhancing the efficacy and precision of melanoma identification and treatment, and ultimately yielding superior patient care and outcomes.

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An In-Depth I/O pattern analysis in HPC Systems 28

An In-Depth I/O Pattern Analysis in HPC Systems

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28th Annual IEEE international conference on High Performance Computing, Data, and Analytics (HiPC)

作者： Bang, Jiwoo Kim, Chungyong Wu, Kesheng Sim, Alex Byna, Suren Sung, Hanul Eom, Hyeonsang Seoul Natl Univ Dept Comp Sci & Engn Seoul South Korea Lawrence Berkeley Natl Lab Computat Res Div Berkeley CA USA Sangmyung Univ Dept Game Design & Dev Seoul South Korea

ISBN: (纸本)9781665410168

High-performance computing (HPC) systems consist of thousands of compute nodes, storage systems and high-speed networks, providing multiple layers of I/O stack with high complexity. By adjusting the diverse configuration settings that HPC systems provide, the I/O performance of applications can be improved. However, it is challenging to identify the optimal configuration settings without a thorough knowledge of the system, as each of the different I/O characteristics of applications can be an important factor for parameter decision. In this paper, we use multiple machine learning approaches to perform an in-depth analysis on I/O behaviors of HPC applications and to search for the optimal configuration settings for jobs sharing similar I/O characteristics. Improved by maximum 0.07 H-squared score, our results in overall show that jobs run on the HPC systems can obtain the predicted I/O performance for different configuration parameters with a high accuracy, using the proposed machine learning-based prediction models.

关键词： I/O characteristic Unsupervised learning Feature selection Clustering Prediction model High performance computing

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A Multi-Resolution Mutual Learning Network for Multi-Label ECG Classification

A Multi-Resolution Mutual Learning Network for Multi-Label E...

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IEEE international conference on Bioinformatics and Biomedicine (BIBM)

作者： Wei Huang Ning Wang Panpan Feng Haiyan Wang Zongmin Wang Bing Zhou School of Computer and Artificial Intelligence Zhengzhou University Zhengzhou China Cooperative Innovation Center of Internet Healthcare Zhengzhou University Zhengzhou China School of Communication and Information Engineering Nanjing University of Posts and Telecommunications Nanjing China School of Computer Science Zhengzhou University of Aeronautics Zhengzhou China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Electrocardiograms (ECG), essential for diagnosing cardiovascular diseases. In recent years, the application of deep learning techniques has significantly improved the performance of ECG signal classification. Multi-resolution feature analysis, which captures and processes information at different time scales, can extract subtle changes and overall trends in ECG signals, showing unique advantages. However, common multi-resolution analysis methods based on simple feature addition or concatenation may lead to the neglect of low-resolution features, affecting model performance. To address this issue, this paper proposes the Multi-Resolution Mutual Learning Network (MRMNet). MRM-Net includes a dual-resolution attention architecture and a feature complementary mechanism. The dual-resolution attention architecture processes high-resolution and low-resolution features in parallel. Through the attention mechanism, the high-resolution and low-resolution branches can focus on subtle waveform changes and overall rhythm patterns, enhancing the ability to capture critical features in ECG signals. Meanwhile, the feature complementary mechanism introduces mutual feature learning after each layer of the feature extractor. This allows features at different resolutions to reinforce each other, thereby reducing information loss and improving model performance and robustness. Experiments on the PTB-XL and CPSC2018 datasets demonstrate that MRM-Net significantly outperforms existing methods in multi-label ECG classification performance. The code for our framework will be publicly available at https://***/wxhdf/MRM.

关键词： Representation learning Attention mechanisms pattern classification Electrocardiography Feature extraction Rhythm Market research Robustness Data mining Signal resolution

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Follow Me: Hierarchical Parallel Execution Synchronization in Behavior Trees

Follow Me: Hierarchical Parallel Execution Synchronization i...

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IEEE international conference on robotics and Biomimetics

作者： Yongjie Ma Jiexin Zhang Yunlong Wu Yanzhen Wang Artificial Intelligence Research Center (AIRC) Defense Innovation Institute Beijing China Tianjin Artificial Intelligence Innovation Center Tianjin China

ISBN: (纸本)9781665405362

The Behavior Tree (BT) has gained considerable popularity in many areas such as robotics and artificial intelligence, due to its advantages of modularity, reactivity, reusability, readability, etc. In the classic BT, tasks under the Parallel node are assumed to be independent of each other and cannot be executed synchronously. To address this problem, we introduce hierarchy into parallel tasks by dividing parallel tasks into leader and follower tasks. In addition, we define the state value for each task node to achieve efficient synchronization between tasks. We also propose two new parallel operators to solve the synchronous execution of parallel tasks in BTs. We integrate our approach into a popular BT framework and evaluate it by multiple experiments. Results show that our approach improves the parallel execution in traditional BT models and has great practical significance.

关键词： conferences Biomimetics Biological system modeling Parallel processing Synchronization Task analysis Artificial intelligence

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Leveraging OpenFlamingo for Multimodal Embedding analysis of C2C Car Parts Data 26th

Leveraging OpenFlamingo for Multimodal Embedding Analysis o...

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26th international conference on Artificial intelligence and Applications, ICAI 2024, held as part of the World Congress in Computer Science, Computer Engineering and Applied Computing, CSCE 2024

作者： Rashid, Maisha Binte Rivas, Pablo Department of Computer Science Baylor University Texas United States

ISBN: (纸本)9783031866227

In this paper, we aim to investigate the capabilities of multimodal machine learning models, particularly the OpenFlamingo model, in processing a large-scale dataset of consumer-to-consumer (C2C) online posts related to car parts. We have collected data from two platforms, OfferUp and Craigslist, resulting in a dataset of over 1.2 million posts with their corresponding images. The OpenFlamingo model was used to extract embeddings for the text and image of each post. We used k-means clustering on the joint embeddings to identify underlying patterns and commonalities among the posts. We have found that most clusters contain a pattern, but some clusters showed no internal patterns. The results provide insight into the fact that OpenFlamingo can be used for finding patterns in large datasets but needs some modification in the architecture according to the dataset. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Large datasets

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Extracting User Daily Routine Activity patterns from UWB Sensor Data in Indoor Environment 29th

Extracting User Daily Routine Activity Patterns from UWB Sen...

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29th international conference on Collaboration Technologies and Social Computing (CollabTech)

作者： Fawad, Muhammed Rahim Hayama, Tessai Nagaoka Univ Technol Niigata 16031 Japan

ISBN: (纸本)9783031421402;9783031421419

In recent years, location-based technologies for ubiquitous environments have transformed individuals' current location data into valuable assets. To establish advanced indoor location-based services, highly accurate positioning technology is required to precisely recognize and predict the movements of humans and objects. Hence, we proposed a method for extracting a user's activity patterns from time-series ultra-wideband (UWB) tag data in indoor environments. The proposed method consists of three steps: 1) estimate the user's stay regions from the user's location history using UWB sensors attached to the user, 2) assign each stay region to significant indoor activities, and 3) mine the activity patterns and their characteristics of the user from the sequence of indoor activities. In our experiments, we confirmed that the proposed method performed better in recognizing activity regions indoors and that the activity patterns of each member in the laboratory were discovered in a practical environment using the proposed method.

关键词： Human Activity Recognition Indoor Location-Based System Human Activity pattern analysis Sensor Network

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Experimental Study on Autonomous Food Packaging with Delta Parallel Robot and Two Fingered Gripper

Experimental Study on Autonomous Food Packaging with Delta P...

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Iranian conference on Electrical Engineering (ICEE)

作者： Mona Mohades Mojtahedi Arvin Mohammadi Mehdi Tale Masouleh Human and Robot Interaction Laboratory School of Electrical and Computer Engineering University of Tehran Tehran Iran Human and Robot Interaction Laboratory School of Mechanical Engineering University of Tehran Tehran Iran

ISBN: (数字)9798350376340

ISBN: (纸本)9798350376357

In response to the increasing supply and demand in the food industry, automated food packaging has emerged as a substitute for the manual labor that was previously required. The surge in automation is evident, with robotics playing a pivotal role in tasks ranging from sorting and packaging to labeling, and notably, pick-and-place operations which is the specific focus of this study. Leveraging robots for these tasks not only improves precision and speed but also takes packaging hygiene into account, resulting in a substantial cost reduction and heightened efficiency. Delta Parallel Robots are among the robotic systems well-suited for high-speed pick-and-place tasks. In this experimental study, the focus is on packaging several scattered chocolates, where both the container box and the chocolates will be randomly positioned and oriented. This is achieved by employing a Delta Parallel Robot paired with a two-fingered gripper, designed to incur minimal damage to the product when compared to other types of end-effectors. The positioning of the box and products will be determined through a classical image processing approach, specifically Edge Detection and Hough Transform, implemented using the OpenCV Python library. Following identification, the objects are localized through camera-robot calibration, and the resulting coordinates are transmitted to the robot as target points. Extensive testing has demonstrated that this study, employing visual perception for pick-and-place tasks, achieved a success rate of 82%. This study showcases the potential of automating packaging tasks in the food industry through the integration of robotic systems and artificial intelligence. The approach proves effective across diverse tasks, encompassing the packaging of variously shaped food items and drugs, applicable across different industries.

关键词： Parallel robots Accuracy Service robots Shape Image color analysis Robot kinematics Food industry

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BLPNet: An End-to-End Model Towards Voxelization Free 3D Object Detection 9

BLPNet: An End-to-End Model Towards Voxelization Free 3D Obj...

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Joint 9th international conference on Informatics, Electronics and Vision (ICIEV) / 4th international conference on Imaging, Vision and pattern Recognition (ICIVPR)

作者： Cui, Zhihao Zhang, Zhenhua Univ Technol Sydney Fac Engn & IT Sydney NSW Australia

ISBN: (纸本)9781728193311

Recent achievements in robotics application and autonomous navigation are accomplished through the fast progress in point cloud 3D object detection models. Many of the current single staged 3D object detection models rely heavily on the voxelization and PointNet based method for feature extraction. Although it provides an efficient way to process point cloud data, its lack of spatial and geometric relationships on both voxel and point level limited the detection accuracy of models. In this paper, we focus on these limitations of the voxelization based object detection pipeline and proposed a single-stage non-voxelization 3D object detection framework. This framework utilizes the bilateral convolution layer, region-based feature clustering and lattice to feature map layer to address the lack of spatial, and geometric relationships in both voxel and point level, which further improves 3D object detection accuracy on the KITTI dataset. Our method achieved 75.63 mAP in moderate difficulty and outperformed many influential object detection models on the KITTI benchmark leaderboard. Contribution-We proposed a voxelization free single-stage model to achieve a desirable performance on the 3D object detection task.

关键词： 3D object detection robotics vision lidar point cloud

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