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26th Irish Machine Vision and Image Processing Conference, IMVIP 2024

作者： Nosheen, Iqra Iqbal, Talha Ullah, Ihsan Ennis, Cathy Madden, Michael G. School of Computer Science University of Galway Ireland Insight SFI Research Centre for Data Analytics University of Galway Ireland School of Computer Science Technological University Dublin Ireland

ISBN: (纸本)9781837242672

Traditional methods for depth estimation rely on supervised learning with resource-intensive LiDAR data. Virtual synthetic datasets provide a cost-effective alternative, but bridging the domain gap between synthetic and real-world data remains a significant challenge. In existing work, this gap is addressed through domain adaptation techniques, aligning the feature distributions of synthetic (source) and real-world (target) domains. Our study explores the efficacy of different encoder architectures (ResNet variants with 35, 50, 101, 101-with-attention, and 152 convolution layers) and two batch sizes (2 and 4) for the depth estimation task. Our experiments show that ResNet101 without and with attention mechanisms provide the best performance across 2 and 4 batch sizes, respectively, compared to the other models. Conversely, the deeper architecture considered, ResNet152, shows the lowest performance, indicating that increasing the network depth does not necessarily lead to improved results for depth estimation tasks. This study's findings provide valuable insights for developing more effective depth estimation algorithms, and it suggests future directions in hyperparameter optimization and semantic consistency modeling. © This is an open access article published by the IET under the Creative Commons Attribution License (http://***/licenses/by/3.0/)

关键词： Semantics

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Towards Exploring Large Molecular Space:An Efficient Chemical Genetic Algorithm

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Journal of computer science & Technology 2022年第6期37卷 1464-1477页

作者： Jian-Fu Zhu Zhong-Kai Hao Qi Liu Yu Yin Cheng-Qiang Lu Zhen-Ya Huang En-Hong Chen Anhui Province Key Laboratory of Big Data Analysis and Application School of Computer Science and TechnologyUniversity of Science and Technology of ChinaHefei 230026China

Generating molecules with desired properties is an important task in chemistry and *** efficient method may have a positive impact on finding drugs to treat diseases like *** mining and artificial intelligence may be good ways to find an efficient ***,both the generative models based on deep learning and the work based on genetic algorithms have made some progress in generating molecules and optimizing the molecule’s ***,existing methods have defects in the experimental evaluation *** methods also need to be improved in efficiency and *** solve these problems,we propose a method named the Chemical Genetic Algorithm for Large Molecular Space(CALM).Specifically,CALM employs a scalable and efficient molecular representation called molecular *** we design corresponding crossover,mutation,and mask operators inspired by domain knowledge and previous *** apply our genetic algorithm to several tasks related to molecular property optimization and constraint molecular *** results of these tasks show that our approach outperforms the other state-of-the-art deep learning and genetic algorithm methods,where the z tests performed on the results of several experiments show that our method is more than 99%likely to be *** the same time,based on the experimental results,we point out the defects in the experimental evaluation standard which affects the fair evaluation of all previous *** these defects helps to objectively evaluate the performance of all work.

关键词： data mining molecular generation genetic algorithm drug discovery artificial intelligence

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Angle-uniform parallel coordinates

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Computational Visual Media 2023年第3期9卷 495-512页

作者： Kaiyi Zhang Liang Zhou Lu Chen Shitong He Daniel Weiskopf Yunhai Wang School of Computer Science and Technology Shandong UniversityQingdao 266237China National Institute of Health Data Science Peking UniversityBeijing 100191China Visualization Research Center(VISUS) University of Stuttgart70569 StuttgartGermany

We present angle-uniform parallel coordinates,a data-independent technique that deforms the image plane of parallel coordinates so that the angles of linear relationships between two variables are linearly mapped along the horizontal axis of the parallel coordinates *** being a common method for visualizing multidimensional data,parallel coordinates are ineffective for revealing positive correlations since the associated parallel coordinates points of such structures may be located at infinity in the image plane and the asymmetric encoding of negative and positive correlations may lead to unreliable *** address this issue,we introduce a transformation that bounds all points horizontally using an angleuniform mapping and shrinks them vertically in a structure-preserving fashion;polygonal lines become smooth curves and a symmetric representation of data correlations is *** further propose a combined subsampling and density visualization approach to reduce visual clutter caused by *** method enables accurate visual pattern interpretation of data correlations,and its data-independent nature makes it applicable to all multidimensional *** usefulness of our method is demonstrated using examples of synthetic and real-world datasets.

关键词： parallel coordinates multidimensional data deformation correlations

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A Fine-Tuned DenseNet-Based Diagnosis Approach for Multi-Class Breast Cancer Classification 2

A Fine-Tuned DenseNet-Based Diagnosis Approach for Multi-Cla...

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2nd International Conference on Computational Intelligence, Communication Technology and Networking, CICTN 2025

作者： Kansal, Kanika Kansal, Kajal Department of Computer Science and Engineering Data Science ABES Engineering College Uttar Pradesh Ghaziabad India School of Computer Science Engineering and Technology Bennett University Uttar Pradesh Greater Noida India

ISBN: (数字)9798331530389

ISBN: (纸本)9798331530389

Breast cancer is a prevalent tumor across women and is associated with a high mortality rate. Prompt diagnosis is one of the biggest challenges that needs to be addressed globally, as it can considerably improve survival rates. An automatic diagnosis system based on deep learning models is critical for improving detection, prediction accuracy, and survivability. This study used various DenseNet-based architectures for multi-class classification using the BUSI dataset. The experiments indicate that the fine-tuned DenseNet201 model outperforms with an accuracy of 98.28%. The results demonstrate the promising capability of the fine-tuned DenseNet201 model to have a high potential for further implementation in practice for achieving early diagnosis and highly individualized approaches for better treatment. The further development of extensive and diverse data sets will be essential for developing new diagnostic methods and improving the existing ones. © 2025 IEEE.

关键词： Lung cancer

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FCIGAN: A Three-dimensional Ink Rendering Brushwork Based on Generative Adversarial Network

Journal of Network Intelligence

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Journal of Network Intelligence 2024年第3期9卷 1348-1358页

作者： Wang, Qinghua Lin, Wenru Lan, Wei Huang, Jingwei Chen, Yang Long, Xinling Zheng, Lujia Huang, Huilong Zhang, Fuquan School of Computer and Data Science Minjiang University Fuzhou University Town No.200 Xiyuangong Road Fuzhou350108 China Fujian Province University Fuzhou350300 China College of Computer and Big Data Fuzhou University Fuzhou China

Three-dimensional ink rendering is a NPR (Non-Photorealistic rendering) art style, widely used in a range of fields, including gaming and ***, CycleGAN is a standard image transformation model, however, it prefers Western painting methods, resulting in less-than-ideal ink rendering effects. As a result, this investigation focuses on the characteristics of ink painting by merging attention processes with three-dimensional depth information. It emphasizes differences in ink color depth to depict the relationship between distance and three-dimensional perception of things in the picture. It also makes use of methods like line thickness and curves to show the contours and textures of things. Additionally, to make the picture as close to the real traditional Chinese ink painting as possible, we use the thickness and bending of lines to show the outline and texture of objects, and highlight the key points or depict some details in the picture by leaving blank. This method is named FCIGAN (Freehand Chinese Ink Generative Adversarial Network). We have also gathered and created a matching dataset called FCI Images to aid in the experiment’s smooth progression. In conclusion, the experimental results show that, the FCIGAN approach suggested in this research has much better performance when compared to conventional techniques and may be used more effectively in the 3D rendering field. © 2024, Taiwan Ubiquitous Information CO LTD. All rights reserved.

关键词： Generative adversarial networks

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CoMO-NAS: Core-Structures-Guided Multi-Objective Neural Architecture Search for Multi-Modal Classification 24

CoMO-NAS: Core-Structures-Guided Multi-Objective Neural Arch...

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32nd ACM International Conference on Multimedia, MM 2024

作者： Fu, Pinhan Liang, Xinyan Qian, Yuhua Guo, Qian Wei, Zhifang Li, Wen Institute of Big Data Science and Industry Shanxi University Taiyuan China School of Computer Science and Technology Taiyuan University of Science and Technolog Taiyuan China

ISBN: (纸本)9798400706868

Most existing NAS-based multi-modal classification (MMC-NAS) methods are optimized using the classification *** can not simultaneously provide multiple models with diverse perferences such as model complex and classification performance for meeting different users' demands. Combining NAS-MMC with multi-objective optimization is a nature way for this issue. However, the challenge problem of this solution is the high computation cost. For multi-objective optimization, the computing bottleneck is pareto front search. Some higher-quality MMC models (namely core structures, CSs) consisting of high-quality features and fusion operators are easier to identify. We find that CSs have a close relation with the pareto front (PF), i.e., the individuals lying in PF contain the CSs. Based on the finding, we propose an efficient multi-objective neural architecture search for multi-modal classification by applying CSs to guide the PF search (CoMO-NAS). In conclusion, experimental results thoroughly demonstrate the effectiveness of our CoMO-NAS. Compared to state-of-the-art competitors on benchmark multi-modal tasks, we achieve comparable performance with lower model complexity in shorter search time. © 2024 ACM.

关键词： Multiobjective optimization

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Leveraging user-item interactions and hotel characteristics for hotel recommendations in Indonesia with graph neural networks 9th

Leveraging user-item interactions and hotel characteristics ...

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9th International Conference on computer science and Computational Intelligence, ICCSCI 2024

作者： Panarto, Francis Alexander Phanghegar, Tyrone Matthew Gunardi, Vincent Cenggoro, Tjeng Wawan Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia

Travelers usually check information about the destination before they decide to go. However, sometimes the information is too much to handle and causes confusion. A filtering mechanism is needed to help them make their choices. The solution is to implement a recommendation system;some common techniques that are used for this are Collaborative Filtering and content-based filtering, but both techniques have their flaws. Therefore, we propose a solution to combine those two methods through Graph Neural Network architectures to help fix their flaws. The proposed method will be used for recommending hotels in Indonesia using the HotelRec dataset as the base dataset and hotel features acquired from the TripAdvisor API. We use several GNN architectures, such as GCN, LightGCN, GraphSAGE, and GAT, to implement our approach. Then, we compare it to several baseline models, such as SVD and NeuMF, which are common baselines in recommendation systems. The performance of these models is calculated based on three metrics: HitRate@K, NDCG@K, and Recall@K. Based on the result of this experiment, we found that incorporating hotel features into the learning process of the model, accompanied by user-item interactions, boosted the model's performance by at least 20% in all metrics, especially in datasets with a smaller number of interactions. © 2024 The Authors.

关键词： Collaborative filtering

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Word-of-Mouth Promotion: How to Attract Consistent Consumers as a Promoter for the B2C Model 2

Word-of-Mouth Promotion: How to Attract Consistent Consumers...

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2nd IEEE International Conference on Trends in Quantum Computing and Emerging Business Technologies, TQCEBT 2024

作者： Gunavathi, R. Vijayalakshmi, S. Savithri, M. Thamarai Selvi, S.B. Christ University School of Data Science India K.S.Rangasamy College of Technology Computer Science and Engineering India

ISBN: (纸本)9798350384277

The primary goal of this research article is to discover the consumers' behaviour while spending time on the e-commerce platform and to use the consumers who have positive Word-of-Mouth on the products to motivate them as promoters through positive Word of Mouth behaviour. The behavioural factors considered in this study are Relationship value, Trust value, Satisfaction level and Word of Mouth. The trial model includes the consumers who use an e-commerce platform for their online shopping in India. A proper questionnaire with four components was created and used to collect the sample data. Totally 300 responses have been received and analysed with the help of structured equation model and SPSS and AMOS software. The findings suggest that the 'Word of Mouth' technique can be used as a tool to increase the number of consumers in an online platform, particularly e-commerce. We investigated how Relationship Value and Trust Value can be used as key factors to motivate consumers' positive WoM behaviour. This research would be more beneficial to the B2C model. The research has done only for Indian e-commerce portals for survey. There is a scope to do research for the global level e-commerce market. Future study focuses on dynamic attributes for relationship values. This research work will help the researchers who is working on B2C model and consumer behavioural models. This model would be used for any online transactions-based services. As best of the knowledge of the authors' this study is the novel idea to understand the consumers' behaviour for purchasing items through the positive WoM. This work can be adopted for any e-commerce platform. © 2024 IEEE.

关键词： Marketplaces

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Secure RFID-Assisted Authentication Protocol for Vehicular Cloud Computing Environment

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IEEE Transactions on Intelligent Transportation Systems 2024年第9期25卷 12528-12537页

作者： Saleem, Muhammad Asad Li, Xiong Mahmood, Khalid Tariq, Tayyaba Alenazi, Mohammed J. F. Das, Ashok Kumar University of Electronic Science and Technology of China School of Computer Science and Engineering Chengdu611731 China Institute for Cyber Security School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu611731 China Sichuan University Key Laboratory of Data Protection and Intelligent Management Ministry of Education Chengdu610065 China Shenzhen Institute for Advanced Study University of Electronic Science and Technology of China Shenzhen518000 China National Yunlin University of Science and Technology Graduate School of Intelligent Data Science Yunlin Douliu64002 Taiwan National Yunlin University of Science and Technology Graduate School of Engineering Science and Technology Yunlin Douliu64002 Taiwan King Saud University College of Computer and Information Sciences Department of Computer Engineering Riyadh11451 Saudi Arabia International Institute of Information Technology Center for Security Theory and Algorithmic Research Hyderabad500032 India

Vehicular network technology has made substantial advancements in recent years in the field of Intelligent Transportation Systems. Vehicular Cloud Computing (VCC) has emerged as a novel paradigm with a substantial increase in data exchange within Vehicular ad-hoc networks (VANETs). VCC integrates cloud computing, vehicular networking, and Internet of Things (IoT) technologies. It enables Infrastructure-to-Vehicle (I2V), Vehicle-to-Vehicle (V2V), and Vehicle-to-Device (V2D) communication. VCC optimizes vehicle, cloud infrastructure, and IoT resources while addressing significant communication security and vehicle-user privacy challenges. To address these issues, we developed an RFID-based authentication protocol for VCC based on a Henon map using a hash function. In addition, we also incorporated a Physical Unclonable Function (PUF) to resist physical tampering attacks. We validate the protocol's security formally and informally. The formal security analysis is conducted through a widely used RoR model. We use the Scyther simulation tool to verify the proposed protocol's security against various attacks. Moreover, we compare the performance of our protocol with similar existing protocols across important performance parameters such as communication and computation overheads and security attributes. The proposed protocol yields substantial improvements, demonstrating a 40.74% reduction in computation overhead and a 13.03% decrease in communication overhead as compared to related protocols, delivering both enhanced performance and resource efficiency. The analysis demonstrates its capacity to support secure communication in the VCC environment and satisfy desirable security attributes. © 2024 IEEE.

关键词： Authentication

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A Novel Pipeline for Virus Integration Sites Detection in Tumor Genomes Using Deep Learning

A Novel Pipeline for Virus Integration Sites Detection in Tu...

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2024 IEEE International Conference on Big data, Bigdata 2024

作者： Williams, Lorrayya Gupta, Vibhuti Meharry Medical College School of Applied Computational Sciences Department of Computer Science & Data Science NashvilleTN United States

ISBN: (纸本)9798350362480

Cancer is one of the leading causes of death worldwide. Pathogenic viruses are estimated to be responsible for 15% of all human cancers globally and pose significant threats to public health. Viruses integrate their genetic material into the host genome, increasing the risk of cancer promoting changes in it. To understand the molecular mechanisms of virus-mediated cancers, it is crucial to identify viral insertion sites in cancer genomes. However, this effort is hindered by the rapidly increasing volume of tumor sequencing data, along with the challenges of accurate data analysis caused by high viral mutation rates and the difficulty of aligning short reads to the reference genome. Thus it is crucial to develop an efficient method for virus integration site detection in tumor genomes. This paper proposes a novel pipeline to identify viral integration sites leveraging deep Convolutional Neural Networks (CNN). Our contributions are twofold: (i) We propose and integrate three novel matrix generation methods into the pipeline, developed after aligning the host and viral genomes with their respective reference genomes.;(ii) We employ one-hot encoded images with reduced computational complexity to represent viral integration sites and harness the capabilities of Deep CNN networks for detection. The paper illustrates our proposed approach and presents experiments conducted using both synthetic and real sequencing data. Our preliminary experimental results are promising, showcasing the effectiveness of the proposed methods in detecting viral integration sites. © 2024 IEEE.

关键词： Convolutional neural networks

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