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Automatic Scene Generation: State-of-the-Art Techniques, Models, Datasets, Challenges, and Future Prospects

IEEE Access 2025年 13卷 95753-95796页

作者： Ahmed Fime, Awal Mahmud, Saifuddin Das, Arpita Islam, Md. Sunzidul Kim, Jong-Hoon Southern Illinois University School of Computing CarbondaleIL62901 United States Bradley University Department of Computer Science and Information Systems PeoriaIL61625 United States The University of Texas at Arlington Department of Computer Science and Engineering ArlingtonTX76019 United States Khulna University of Engineering and Technology Department of Computer Science and Engineering Khulna9203 Bangladesh Kent State University Advanced Telerobotics Research Laboratory Department of Computer Science KentOH44240 United States

Automatic scene generation is an essential area of research with applications in robotics, recreation, visual representation, training and simulation, education, and more. This survey provides a comprehensive review of the current state-of-the-arts in automatic scene generation, focusing on techniques that leverage machine learning, deep learning, embedded systems, and natural language processing (NLP). We categorize the models into four main types: Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs), Transformers, and Diffusion Models. Each category is explored in detail, discussing various sub-models and their contributions to the field. We also review the most commonly used datasets, such as COCO-Stuff, Visual Genome, and MS-COCO, which are critical for training and evaluating these models. Methodologies for scene generation are examined, including image-to-3D conversion, text-to-3D generation, graph-based methods, and interactive scene generation. Evaluation metrics such as Fréchet Inception Distance (FID), Kullback-Leibler (KL) Divergence, Inception Score (IS), Intersection over Union (IoU), and Mean Average Precision (mAP) are discussed in the context of their use in assessing model performance. The survey identifies key challenges and limitations in the field, such as maintaining realism, handling complex scenes with multiple objects, and ensuring consistency in object relationships and spatial arrangements. By summarizing recent advances and pinpointing areas for improvement, this survey aims to provide a valuable resource for researchers and practitioners working on automatic scene generation. © 2013 IEEE.

关键词： Deep learning

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Deep learning:Applications,architectures,models,tools,and frameworks:A comprehensive survey

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CAAI Transactions on Intelligence technology 2023年第3期8卷 581-606页

作者： Mehdi Gheisari Fereshteh Ebrahimzadeh Mohamadtaghi Rahimi Mahdieh Moazzamigodarzi Yang Liu Pijush Kanti Dutta Pramanik Mohammad Ali Heravi Abolfazl Mehbodniya Mustafa Ghaderzadeh Mohammad Reza Feylizadeh Saeed Kosari School of Computer Science and Technology Harbin Institute of Technology(Shenzhen)ShenzhenChina Department of Cognitive Computing Institute of Computer Science and EngineeringSaveetha School of EngineeringSaveetha Institute of Medical and Technical SciencesChennaiIndia Department of Computer Science Islamic Azad UniversityTehranIran Computer Engineering Department Bu‐Ali Sina UniversityHamedanIran Department of Mathematics and Statistics Iran University of Science and TechnologyTehranIran Department of Mathematics and Statistics University of Northern British ColumbiaBCCanada Peng Cheng Laboratory ShenzhenChina Deprtment of Technology Jodhpur Institute of Engineering&TechnologyJodhpurIndia Department of Computer Engineering Sadjad University of TechnologyMashhadIran Department of Electronics and Communications Engineering Kuwait College of Science and TechnologyDoha DistrictKuwait Department of Artificial Intelligence Smart University of Medical SciencesTehranIran Department of Industrial Engineering Shiraz BranchIslamic Azad UniversityShirazIran Institute of Computing Science and Technology Guangzhou UniversityGuangzhouChina

Deep Learning(DL)is a subfield of machine learning that significantly impacts extracting new *** using DL,the extraction of advanced data representations and knowledge can be made *** effective DL techniques help to find more hidden *** learning has a promising future due to its great performance and *** need to understand the fundamentals and the state‐of‐the‐art of DL to leverage it effectively.A survey on DL ways,advantages,drawbacks,architectures,and methods to have a straightforward and clear understanding of it from different views is explained in the ***,the existing related methods are compared with each other,and the application of DL is described in some applications,such as medical image analysis,handwriting recognition,and so on.

关键词： data mining data privacy deep learning

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The Future of Online Shopping: Blockchain- Powered E-Commerce Platforms

The Future of Online Shopping: Blockchain- Powered E-Commerc...

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2024 International Conference on Signal Processing, Computation, Electronics, Power and Telecommunication, IConSCEPT 2024

作者： Kiran, Ajmeera Sri, Nemani Divya Ayyasamy, Ramesh Kumar Chinnasamy, P. Vellanki, Sreelakshmi Vujwala, Maddula MLR Institute of Technology Department of Computer Science and Engineering Hyderabad India Faculty of Information and Communication Technology Kampar Malaysia School of Computing Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Tamilnadu India

ISBN: (纸本)9798331540685

A cutting-edge online marketplace that uses blockchain technology to transform how we purchase and sell goods and services is known as a blockchain- powered e-commerce platform. This platform uses a decentralized network of computers, also known as nodes, to maintain a transparent and impenetrable record of all transactions instead of depending on a central authority like a bank or a company. A blockchain-based e-commerce platform is a secure, transparent, and trusted online marketplace. It does away with the need for intermediaries, improves security, and provides a world market where users can confidently conduct peer-to-peer transactions. This novel strategy could change how online commerce and shopping are performed. © 2024 IEEE.

关键词： Marketplaces

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MFN: Explainable DNA triple helixes Stabilized Design based on mCGR and flow network

MFN: Explainable DNA triple helixes Stabilized Design based ...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Wen, Xiaoru Sun, Lijun Xie, Lei Zheng, Yanfen Cao, Ben Wang, Bin Dalian University Key Laboratory of Advanced Design and Intelligent Computing Ministry of Education School of Software Engineering Dalian China Dalian University of Technology School of Computer Science and Technology Dalian China

ISBN: (纸本)9798350386226

DNA triple helix structure, as a highly specific gene targeting tool, enable gene regulation by precisely identifying and binding to target DNA sequences. However, the limits of design quality and efficiency affect their wide application in gene therapy. Therefore, in this paper, we propose an antiparallel DNA triple helixes design method-MFN based on matrix chaotic game representation (mCGR) and flow network. Leveraging the structural characteristics of DNA sequences, this method employs the mCGR algorithm to construct an initial matrix, generating a set of DNA sequences that conform to foundational constraints. Then, these sequences are mapped as stream network nodes to screen crosstalk structures by path search, and the whole process is observable and interpretable. Experimental results show that MFN significantly improves the design efficiency of triple helix structure and reduces crosstalk phenomenon. Wet experiments further verify the effectiveness of the method. In summary, MFN achieves an efficient and high-quality design of DNA triple helixes and provides a new idea for targeted gene therapy. © 2024 IEEE.

关键词： Crosstalk

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A Model for the Analytical Performance of Data Lake in Stock Market Analysis with Databricks Delta Lake

A Model for the Analytical Performance of Data Lake in Stock...

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2023 International Conference on Self Sustainable Artificial Intelligence Systems, ICSSAS 2023

作者： Kamalakkannan, S. Yasmin, A. Arunkumar, R. Kavitha, P. School of Computing Sciences Vels Institute of Science Technology & Advanced Studies Department of Information Technology Chennai Pallavaram India Jbas College for Women Department of Computer Science Chennai India University of South Wales Faculty of Computing Engineering and Science Treforest/Newport Campus United Kingdom School of Computing Sciences Vels Institute of Science Technology & Advanced Studies Department of Computer Applications Chennai Pallavaram India

ISBN: (纸本)9798350300857

Stock market investments are highly rewarding but also high in risk. Modern investors use variety of tools to take informed investment decisions. In the current era of digital world, financial service industry has generated huge volume and immense verities of data with extreme speed. Due to the rapid growth in data collection and the heterogeneous nature and complexity of the data, there is a need for Big Data analytical solution that would be able to deal with the stock market data. Large volumes of unstructured, heterogeneous raw data can be stored in a massively scalable manner using data lakes, which are the ideal solution to the big data storage conundrum. The ability of a data lake to preserve data in its original format while processing it at runtime using a schema on-read technique is its key feature. The challenge faced in the data lake is performing analytics which is a significant tool to calculate and analyze the stock market. The proposed architecture of Azure Databricks DeltaLake (ADDL) with Azure DataLake Storage Generation 2 (ADLSG2) is used for analytical processes like Fibonacci retracement for better stock analysis, which aid in forecasting the market price for better investment. As a result, the research focus is to produce a storage having read as well as write capabilities by taking into consideration the Extract-Load-Transform (ELT) operation on the datasource. In this experimental databricks implementation, runtime is performed using open source of Apache Spark API and a highly improved execution engine, which results in a significant performance improvement when comparing to the standard source of Apache Spark available on the ADLS platform. Additionally, the Fibonacci retracement level calculation is achieved with the analytics and forecasting of test close price with various ML and DL techniques such as KNN, LSTM are compared with original price of the test data for better prediction of forecast close price. © 2023 IEEE.

关键词： Big data

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Collaborative Edge Server Placement for Maximizing QoS With Distributed Data Cleaning

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IEEE Transactions on Services computing 2025年第3期18卷 1321-1335页

作者： Liang, Yuzhu Yin, Mujun Wang, Wenhua Liu, Qin Wang, Liang Zheng, Xi Wang, Tian Beijing Normal University Institute of Artificial Intelligence and Future Networks Zhuhai519000 China Huaqiao University College of Computer Science and Technology Xiamen361021 China Hunan University College of Computer Science and Electronic Engineering Changsha410082 China Northwestern Polytechnical University School of Computer Science Xi’an710072 China Macquarie University School of Computing SydneyNSW2109 Australia

The proliferation of contaminated data on Internet of Things (IoT) devices has the potential to undermine the accuracy of data-driven decision-making by altering the distribution of original data. Existing data cleaning methods primarily depend on cloud center or cloud-edge cooperation, leading to prolonged data transmission delays and reduced cleaning accuracy. In this study, we identify edge server placement as a crucial step aligned with data cleaning and view the collaborative edge server placement with distributed data cleaning (SPDC) as a holistic problem. We comprehensively quantify the complexity of our issue through the analysis of numerous scenarios. To address this problem, we introduce a novel distributed collaborative edge framework comprising two key stages: server placement and data cleaning. We propose an optimized clustering algorithm for the former, considering the data distribution on the IoT layer and the constraints of the edge layer. For the latter, we introduce a gossip-based data cleaning algorithm that fully utilizes edge collaboration to enhance data cleaning accuracy. The algorithm exhibits an approximate performance complexity of O(ln m), where m represents the number of users’ tasks. Both theoretical analysis and experimental results reveal that our algorithm an average improvement in data cleaning accuracy of 9.02% and a reduction in delay of 36.61%, surpassing the performance of state-of-the-art works in various scenarios. © 2008-2012 IEEE.

关键词： Data accuracy

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Enhanced Prediction Of Rheumatoid Arthritis Flares Through Ensemble Learning Methods 2

Enhanced Prediction Of Rheumatoid Arthritis Flares Through E...

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2nd IEEE International Conference on Advances in Information technology, ICAIT 2024

作者： Priyadharshini, K. Vinitha, S. Sivaprakash, S. Kayalvili, S. Thilakavathy, P. Vasavi, M. Department of Computing Technologies School of Computing SRM Institute of Science and Technology Kattankulathur Campus Chennai India Department of Artificial Intelligence and Machine Learning Jerusalem College of Engineering Chennai India School of Computer Science and Engineering Vellore Institute of Technology Vellore India Department of Artificial Intelligence Kongu Engineering College Erode India Vels Institute of Science Technology and Advanced Studies Chennai India ET Department CVR College of Engineering Hyderabad India

ISBN: (纸本)9798350383867

RA is an inflammatory, autoimmune disease with an unpredictable course, during which patients experience exacerbations (increases in symptoms) and its associated complications, such as deterioration of the joint and decreased quality of life. Tipping the exact point when remission is reached or RA flares is most essential for devising optimal treatment and enhancing patient's well-being. In this study, we put forward the usage of various types of ensemble learning algorithms aimed at improved the predictive power of RA flares. We use diverse data sources, including clinical data, medical imagery, genetic profiles and patient-reported data, and ensemble methods like Random Forest, Gradient Boosting, AdaBoost and Stacking processors to help us identify the patterns automatically. Thus ensembles of models are shown to be leading in performance, reaching superior accuracy, accuracy precision, recall rate, F1-score and area under the receiver operating characteristic curve (AUC-ROC). Feature importance analysis highlights the significant impact of clinical features which include the levels of CRP, ESR, age of the patient, duration of the disease on the likelihood of RA flares among the individual variants. The sensitivity analysis shows how well the ensemble model behaves under the generation of different model parameters and input data. The presented study emphasizes the immense facilitative ability of the ensemble methods of learning as predicting RA flares and this can also provide great information about the disease process and personalized medical decision-making. The areas for future research could be improved ensembles models, identification of novel feature selection approaches and validation of model performance in a variety of clinical settings. This will give the researchers an opportunity to implement the models in practice and make clinics more effective. © 2024 IEEE.

关键词： Sensitivity analysis

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Efficient MMSE Equalization for Direct-Sequence Spread-Spectrum Underwater Communications

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IEEE Internet of Things Journal 2025年第11期12卷 16325-16335页

作者： Liu, Mengzhuo Liu, Jun Peng, Zheng Cui, Jun-Hong Jilin University College of Computer Science and Technology Changchun130012 China Beihang University School of Electronic and Information Engineering Beijing100191 China Shenzhen Institute for Advanced Study University of Electronic Science and Technology of China Shenzhen518110 China Shenzhen Institute for Advanced Study UESTC Shenzhen518110 China Smart Ocean Technology Company Ltd. Shenzhen518057 China

To facilitate the exploration and exploitation of underwater resources, autonomous systems and underwater acoustic networks (UANs) are deployed for tasks unsuitable for direct human intervention and exchange information between devices. To keep the reliability of information exchanged, direct-sequence spread spectrum (DSSS) communication is commonly adopted for this scenario. To simplify the channel equalization process in DSSS communication, a minimum-mean-square-error (MMSE) equalizer is often utilized. However, the characteristics of underwater acoustic channels and acoustic modem, including long delay spreads and limited computational resources, lead to high computational complexity for MMSE equalization, thereby reducing decoding efficiency. To address this challenge, we propose a refined MMSE equalizer, termed the efficient MMSE equalizer (EME). Unlike conventional MMSE methods, the EME approach involves initially despreading the received chip sequence, followed by equalizing on the noisy symbols. By reducing the size of the correlation matrix in the core computational step of MMSE equalization, our method significantly improves computational efficiency. We assess the computational complexity of the proposed EME approach in comparison to conventional MMSE equalization and validate its performance through simulations and experimental studies. The results demonstrate that the EME achieves a bit error rate (BER) performance comparable to that of conventional MMSE equalization under high signal-to-noise ratio (SNR) conditions, while significantly enhancing computational efficiency. © 2014 IEEE.

关键词： Equalizers

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Enhancing Wildlife Monitoring Through Infrared Imaging: A Deep Learning Approach for Improved Animal Region Segmentation and Species Identification

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Journal of Harbin institute of technology(New series) 2024年

作者： V Janani C Shanthi Department of Computer Science， School of Computing Sciences， Vels Institute of Science Technology and Advanced Studies

Infrared imaging has proven to be a powerful tool in various fields， with particular significance in wildlife monitoring. In this paper， we present an extension of our previous research， focusing on advancing the segmentation of animal regions in enhanced infrared images and expanding the scope to include species identification. Our proposed methodology builds upon the success of the R-CNN (Region-based Convolutional Neural Network) object detection to improve the accuracy and robustness of animal region segmentation， while simultaneously extending our model’s capabilities to identify and classify the species within those regions. By fine-tuning the R-CNN model on a larger dataset that includes annotated infrared images and species labels， we enhance its capacity to not only accurately segment animal regions but also classify them into specific species categories. To assess the performance of our extended model， we employ a comprehensive set of evaluation metrics， including pixel-based metrics like Intersection over Union (IoU)， as well as species classification accuracy. Our results demonstrate significant improvements in both region segmentation accuracy and species identification compared to our previous work and existing methods. This research showcases the potential of deep learning techniques， combined with transfer learning， to advance wildlife monitoring applications using infrared imaging.

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VIEPred: Predicting Viral Immune Evasion with Multimodal Representation Learning

VIEPred: Predicting Viral Immune Evasion with Multimodal Rep...

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2024 International Conference on Computational Intelligence and Communication System, CCICS 2024

作者： Liang, Banqing Wang, Na Huang, Hao Hou, Yijie Qiang, Xiaoli Institute of Computing Science and Technology Guangzhou University Guangzhou510006 China School of Computer Science and Cyber Engineering Guangzhou University Guangzhou510006 China

Predicting coronaviral immune evasion is crucial for identifying and responding to new COVID-19 variants in advance, thereby optimizing vaccine development and public health strategies to prevent further outbreaks. Hence, we present VIEPred, a model designed to predict mutations in every amino acid of the RBD. However, protein representation learning is a challenge task due to the hierarchical structure of proteins. Our method uses a sequence encoder based on dilated convolutions and attention mechanisms to generate sequence embeddings and a structure encoder employing geometric vector perceptrons (GVP) and graph neural networks (GNN) to produce protein structure representations. These features are combined using a cross-attention fusion module and subsequently processed through a fully connected layer to output the probability of viral immune evasion. Extensive ablation studies led to an optimal model with ACC of 88.4% and AUC of 86.8%. Compared to existing prediction methods, VIEPred demonstrates superior predictive performance across most evaluation metrics for both SARS-CoV-2 and influenza viruses. © Published under licence by IOP Publishing Ltd.

关键词： Graph neural networks

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