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A Genetic Algorithm Based on Deep Q-learning in Optimization of Remote Sensing data Discretization

IEEE Transactions on Evolutionary Computation 2024年

作者： Chen, Qiong Ding, Weiping Hainan Medical University Hainan Academy of Medical Sciences School of Biomedical Information and Engineering Haikou571199 China Nantong University School of Artificial Intelligence and Computer Science Nantong226019 China City University of Macau Faculty of Data Science Macau999078 China

Feature discretization can improve the processing efficiency of remote sensing big data. However, the distribution of target attribute values is often difficult to ascertain, and there are complex correlations between features, making it extremely difficult to obtain an optimal discretization scheme for remote sensing data. Although feature discretization methods based on evolutionary models can achieve some considerable results, it is difficult to formulate appropriate strategies without prior knowledge as guidance, which makes searching in multidimensional space inefficient and prone to falling into local optima. Therefore, we propose a genetic algorithm based on deep Q-learning (DQLGA) to optimize the discretization scheme of remote sensing data. First, we design a state set in the crossover and mutation stages by balancing the global and local search capabilities of genetic operators to obtain an accurate mapping of states in high-dimensional feature space. Then, we simulate the variation pattern of the optimal discretization scheme by analyzing the distribution of all individuals in the population during the evolution process to construct a reasonable adaptive reward function in reinforcement learning model. Finally, we introduce a pair of deep Q-networks with the same structure by treating the calculation of Q-values as a function fitting problem to fulfil an efficient updating of massive Q-values in state transition. We compare DQLGA with state-of-the-art feature discretization methods on remote sensing data. The experimental results indicate that DQLGA significantly improves the search efficiency of feature discretization methods based on evolutionary models, achieving higher classification accuracy while further reducing the number of breakpoints. © 2024 IEEE.

关键词： Contrastive Learning

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A Sketch-Based Generation Model for Diverse Ceramic Tile Images Using Generative Adversarial Network

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Intelligent Automation & Soft Computing 2023年第9期37卷 2865-2882页

作者： Jianfeng Lu Xinyi Liu Mengtao Shi Chen Cui Mahmoud Emam School of Computer Science and Technology Hangzhou Dianzi UniversityHangzhou310018China Key Laboratory of Public Security Information Application Based on Big-Data Architecture Ministry of Public SecurityZhejiang Police CollegeHangzhou310000China Faculty of Artificial Intelligence Menoufia UniversityShebin El-Koom32511Egypt

Ceramic tiles are one of the most indispensable materials for interior *** ceramic patterns can’t match the design requirements in terms of diversity and interactivity due to their natural *** this paper,we propose a sketch-based generation method for generating diverse ceramic tile images based on a hand-drawn sketches using Generative Adversarial Network(GAN).The generated tile images can be tailored to meet the specific needs of the user for the tile *** proposed method consists of four ***,a dataset of ceramic tile images with diverse distributions is created and then pre-trained based on ***,for each ceramic tile image in the dataset,the corresponding sketch image is generated and then the mapping relationship between the images is trained based on a sketch extraction network using ResNet Block and jump connection to improve the quality of the generated ***,the sketch style is redefined according to the characteristics of the ceramic tile images and then double cross-domain adversarial loss functions are employed to guide the ceramic tile generation network for fitting in the direction of the sketch style and to improve the training ***,we apply hidden space perturbation and interpolation for further enriching the output textures style and satisfying the concept of“one style with multiple faces”.We conduct the training process of the proposed generation network on 2583 ceramic tile images *** measure the generative diversity and quality,we use Frechet Inception Distance(FID)and Blind/Referenceless Image Spatial Quality Evaluator(BRISQUE)*** experimental results prove that the proposed model greatly enhances the generation results of the ceramic tile images,with FID of 32.47 and BRISQUE of 28.44.

关键词： Ceramic tile pattern design cross-domain learning deep learning GAN generative adversarial networks ResNet Block

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Safe and Reliable Diffusion Models via Subspace Projection

arXiv

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arXiv 2025年

作者： Chen, Huiqiang Zhu, Tianqing Wang, Linlin Yu, Xin Gao, Longxiang Zhou, Wanlei Faculty of Data Science City University of Macau China School of Computer Science University of Queensland QLD Australia Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology Shandong Academy of Sciences Jinan China Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing Shandong Fundamental Research Center for Computer Science Jinan China

Large-scale text-to-image (T2I) diffusion models have revolutionized image generation, enabling the synthesis of highly detailed visuals from textual descriptions. However, these models may inadvertently generate inappropriate content, such as copyrighted works or offensive images. While existing methods attempt to eliminate specific unwanted concepts, they often fail to ensure complete removal—allowing the concept to reappear in subtle forms. For instance, a model may successfully avoid generating images in Van Gogh’s style when explicitly prompted with "Van Gogh", yet still reproduce his signature artwork when given the prompt "Starry Night". In this paper, we propose SAFER, a novel and efficient approach for thoroughly removing target concepts from diffusion models. At a high level, SAFER is inspired by the observed low-dimensional structure of the text embedding space. The method first identifies a concept-specific subspace Sc associated with the target concept c. It then projects the prompt embeddings onto the complementary subspace of Sc, effectively erasing the concept from the generated images. Since concepts can be abstract and difficult to fully capture using natural language alone, we employ textual inversion to learn an optimized embedding of the target concept from a reference image. This enables more precise subspace estimation and enhances removal performance. Furthermore, we introduce a subspace expansion strategy to ensure comprehensive and robust concept erasure. Extensive experiments demonstrate that SAFER consistently and effectively erases unwanted concepts from diffusion models while preserving generation quality. © 2025, CC BY.

关键词： Embeddings

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A Novel CAD Framework with Visual and Textual Interpretability: Multimodal Insights for Predicting Respiratory Diseases

A Novel CAD Framework with Visual and Textual Interpretabili...

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International Artificial Intelligence and data Processing Symposium (IDAP)

作者： Raza Mukhlis Saied Salem Hyunwook Kwon Jamil Hussain Ahmet Arif Aydin Mugahed A. Al-Antari Department of Artificial Intelligence and Data Science College of AI Convergence Sejong University Seoul Korea Department of Computer Engineering Faculty of Engineering Inonu University Malatya Turkey

ISBN: (数字)9798331531492

ISBN: (纸本)9798331531508

Generating textual interpretability using recent advancements in large language models (LLMs) is crucial for enhancing the efficiency of comprehensive computer-aided diagnosis (CAD) systems. This improves transparency between medical staff, intelligent CAD systems, and end-users by creating a trustworthy and effective intermediate medical diagnosis environment. In this paper, an innovative explainable throughout CAD system is introduced, designed to predict diseases from Chest X-rays (CXR) in a comprehensive scenario. The primary goal is to undertake multiple tasks that reduce the burden on medical staff and enrich CAD outcomes, including classification, visual explanations (heatmaps), and textual report generation. The proposed CAD system is developed through eight key steps: data Collection and Annotation, data Preparation, Text Vectorizations (Indexing), Visual Encoder, RAG-Fusion, Structural Prompt, XAI LLmTextual Reasoning (LLM Model), and Final Output (LLM textual report, image classification, and heatmap localization). The AI-based CAD system is trained and evaluated using the public benchmark MIMIC-CXR dataset with 14 different classes. The classification performance achieved an overall accuracy of $70 \%$, precision of $70 \%$, and F1-score of $0.60 \%$, while for text report generation, the system obtained an average BERTScore precision of 0.83, RougeL 0.16, and a Meteor score of 0.28. These promising results suggest the potential for further improvement of the CAD system and its applicability to real-world medical tasks.

关键词： Heating systems Location awareness Visualization Solid modeling Accuracy Pulmonary diseases data models Meteors Medical diagnostic imaging X-ray imaging

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Evaluation of the Montenegrin Academic Digital Innovation Hub 27

Evaluation of the Montenegrin Academic Digital Innovation Hu...

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27th International Conference on Information Technology, IT 2023

作者： Lakovic, Luka Kara, Peter A. Ognjanovic, Ivana Bokor, Laszlo Sendelj, Ramo Reich, Christoph Roganovic, Milovan Zoulias, Emmanouil Erakovic, Jevto Radusinovic, Tanja Rakocevic, Nada Simon, Aniko University of Donja Gorica Faculty for Information Systems and Technologies Podgorica81000 Montenegro Budapest University of Technology and Economics Faculty of Electrical Engineering and Informatics Department of Networked Systems and Services Budapest1111 Hungary Faculty of Science Engineering and Computing Kingston University Wireless Multimedia and Networking Research Group Department of Computer Science School of Computer Science and Mathematics LondonKT1 2EE United Kingdom University of Donja Gorica Humanistic Studies Podgorica81000 Montenegro Furtwangen University Institute of Data Science Cloud Computing and It Security Furtwangen78120 Germany Clinical Centre of Montenegro Clinic for Neurology Podgorica81000 Montenegro National and Kapodistrian University of Athens Faculty of Nursing School of Health Sciences Goudi 11527 Greece Chamber of Economy of Montenegro Podgorica81000 Montenegro Sigma Technology Budapest1093 Hungary

ISBN: (纸本)9798350397512

In the era of global digitalization, digital innovation hubs have become essential, as they function as transforming forces. In this paper, we provide an analysis of the Montenegrin Academic Digital Innovation Hub by applying multi-criteria qualitative evaluation over several criteria: process flow of fully digitized operations of the Hub (with aspects of the innovation life-cycle, ethics, and security), resource management, strategic goals, and feasibility. The paper also proposes a model of comprehensive resource management via the perspective of smart specialization priorities of Montenegro. © 2023 IEEE.

关键词： Life cycle

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FACOS: Enabling Privacy Protection Through Fine-Grained Access Control with On-chain and Off-chain System

arXiv

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arXiv 2024年

作者： Liu, Chao Hou, Cankun Jiang, Tianyu Ning, Jianting Qiao, Hui Wu, Yusen Fujian Provincial Key Laboratory of Data Intensive Computing the Key Laboratory of Intelligent Computing and Information Processing Fujian China The School of Computer Science and Technology Xidian University Shaanxi Xi’an710071 China The Department of Computer Science University of Miami MiamiFL33146 United States College of Computer and Cyber Security Fujian Normal University Fuzhou350007 China The Faculty of Data Science City University of Macau China

data-driven landscape across finance, government, and healthcare, the continuous generation of information demands robust solutions for secure storage, efficient dissemination, and fine-grained access control. Blockchain technology emerges as a significant tool, offering decentralized storage while upholding the tenets of data security and accessibility. However, on-chain and off-chain strategies are still confronted with issues such as untrusted off-chain data storage, absence of data ownership, limited access control policy for clients, and a deficiency in data privacy and auditability. To solve these challenges, we propose a permissioned blockchain-based privacy-preserving fine-grained access control on-chain and off-chain system, namely FACOS. We applied three fine-grained access control solutions and comprehensively analyzed them in different aspects, which provides an intuitive perspective for system designers and clients to choose the appropriate access control method for their systems. Compared to similar work that only stores encrypted data in centralized or non-fault-tolerant IPFS systems, we enhanced off-chain data storage security and robustness by utilizing a highly efficient and secure asynchronous Byzantine fault tolerance (BFT) protocol in the off-chain environment. As each of the clients needs to be verified and authorized before accessing the data, we involved the Trusted Execution Environment (TEE)-based solution to verify the credentials of clients. Additionally, our evaluation results demonstrated that our system1 offers better scalability and practicality than other state-of-the-art designs. We deployed our system on Alibaba Cloud and Tencent Cloud and conducted multiple evaluations. The results indicate that it takes about 2.79 seconds for a client to execute the protocol for uploading and about 0.96 seconds for downloading. Compared to other decentralized systems, our system exhibits efficient latency for both download and upload operations.

关键词： Access control

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Performance and Security Evaluation of DBMS Models for E-Commerce and Website Session Tracking

Performance and Security Evaluation of DBMS Models for E-Com...

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Research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication Engineering (RMKMATE), International Conference on

作者： Sathishkumar Veerappamplayam Easwaramoorthy Javid Iqbal R. Geetha Rajakumari Shaik Shabana Anjum Usha Moorthy Department of Data Science and Artificial Intelligence Faculty of Engineering and Technology Sunway University Selangor Darul Ehsan Malaysia Department of Artificial Intelligence and Data Science Sri Eshwar College of Engineering Coimbatore Tamilnadu India School of Computer Science Faculty of Innovation and Technology Taylor's University Malaysia Department of Information Technology Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Manipal Karnataka India

ISBN: (数字)9798331598488

ISBN: (纸本)9798331598495

The exponential increase of data within businesses has a substantial impact on the performance of database management systems. As businesses deal with growing volumes of data, selecting the correct DBMS becomes critical to ensuring efficiency and data integrity. In this paper, we present our experimental insights into how different data models utilized by database management systems (DBMS) perform under varying conditions, using two scenarios: Sales Performance Analysis and Real-Time Analytics for Web Traffic. This paper examines the various data models used by DBMS, notably MySQL (Relational Model), MongoDB (Document-Based Model), Redis (Key-Value Model), Cassandra (Wide Column Model), and Neo4j (Graph Model). We evaluate the strengths and limitations of these data models in terms of data creation, manipulation, retrieval, and database security measures such as access control and data integrity for each scenario. The findings provide useful information for optimizing DBMS selection to improve data management and operational effectiveness in the face of increasing data demands.

关键词： data integrity data models database systems Real-time systems Knowledge management Communications technology Performance analysis Electronic commerce Faces Business

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Drone Gesture Control using OpenCV and Tello

Drone Gesture Control using OpenCV and Tello

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Conference on Information Communication Technology and Society (ICTAS)

作者： Nokuthula Khoza Pius Owolawi Vusumuzi Malele Department of Computer Systems Engineering Faculty of Information and Communication Technology Soshanguve Campus Tshwane University of Technology Pretoria South Africa Unit for Data Science and Computing School of Computer Science and Information Systems Vanderbijlpark Campus North-West University Vanderbijlpark South Africa

Tracking a person with an onboard camera is a very difficult and perhaps technically impossible if one camera is used. In this regard, real-life projects use a series of cameras to achieve the task. The advent of camera installed drone technology has made it possible for researchers and designers to resolve the tracking issues of a single or multiple stagnant devices. This paper presents a method that makes it possible for a drone to recognize hand gestures as control mechanism and follow the person who control it. Furthermore, the method assists in drone object detection using an integrated depth camera that provides video capturing. The OpenCV and Tello were used in this project. A stabilized depth video is for use with current person trackers like the OpenCV tracker. The estimated Tello pose deduced from vision to stabilize the depth image by warping it to a virtual-static camera. This method allows the Tello to obtain not only the tracked person's position and orientation but also their full body pose, which can then be used, for instance, to recognize hand gestures and alter the Tello's behavior. This work has a potential of contributing or enhancing cutting-edge applications such as hands-free photography and videography.

关键词：

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Optimized Hybrid CNN-GRU Approach for Accurate Prediction of Cardiovascular Diseases: Integrating Medical Imaging and Clinical data

Optimized Hybrid CNN-GRU Approach for Accurate Prediction of...

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Communication, Control and Intelligent Systems (CCIS)

作者： Prasanna V Suresh Kumar N Divyaprabha P R Sundaram Department of Data Science and Business Systems School of Computing Faculty of Engineering and Technology SRM Institute of Science and Technology Chennai India Department of Information Technology Sri Ramakrishna Engineering College Coimbatore India Department of Computer Science and Engineering Sri Ramakrishna Engineering College Coimbatore India Department of Computer Science and Engineering Ganesh College of Engineering Salem India

ISBN: (数字)9798331528201

ISBN: (纸本)9798331528218

As the primary cause of death globally, cardiovascular diseases (CVDs) demand precise and timely prediction to enhance patient outcomes. Other examples of conventional approaches for CVD prediction include statistical models as well as machine learning models such as a single model that often struggle to address complex and comprehensive data from clinical records and Medical Imaging. To address these issues, the concept proposed here is to integrate the medical imaging data with clinical data as new apart from utilizing the hybrid CNN–GRU model to predict cardiovascular diseases with higher accuracy and interpretability. While the GRU module deals with the time sequences of clinical data including past history and other test results, the CNN module is to extract the spatial features from the medical images. Thus, by integrating these modules, it is more accurate to predict the data of the patient as a synergy of spatial and temporal arrangements. Cross-validation techniques and performance indices are used for model training and testing on a diverse data set containing imaging and other clinical-related data. Experimental results depict that the suggested hybrid CNN-GRU model performs much better than the traditional approaches and has higher levels of predicted accuracy and robustness. It is also feasible to generalize this integrated strategy to other medical disorder where multi-modal data analysis is crucial, besides enhancing the capacity to estimate CVDs. The findings underline the importance of integrating doctors’ information with radiological images and applying new patterns of neural networks to augmenting clinical judgment. The suggested work is implemented using python.

关键词： Analytical models Accuracy data analysis Computational modeling Predictive models Feature extraction data models Convolutional neural networks Cardiovascular diseases Biomedical imaging

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Imitation Learning from Purified Demonstrations

arXiv

引用

arXiv 2023年

作者： Wang, Yunke Dong, Minjing Zhao, Yukun Du, Bo Xu, Chang School of Computer Science National Engineering Research Center for Multimedia Software Institute of Artificial Intelligence Wuhan Institute of Data Intelligence Wuhan University China Department of Computer Science City University of Hong Kong China School of Computer Science Faculty of Engineering The University of Sydney Australia

Imitation learning has emerged as a promising approach for addressing sequential decision-making problems, with the assumption that expert demonstrations are optimal. However, in real-world scenarios, most demonstrations are often imperfect, leading to challenges in the effectiveness of imitation learning. While existing research has focused on optimizing with imperfect demonstrations, the training typically requires a certain proportion of optimal demonstrations to guarantee performance. To tackle these problems, we propose to purify the potential noises in imperfect demonstrations first, and subsequently conduct imitation learning from these purified demonstrations. Motivated by the success of diffusion model, we introduce a two-step purification via diffusion process. In the first step, we apply a forward diffusion process to smooth potential noises in imperfect demonstrations by introducing additional noise. Subsequently, a reverse generative process is utilized to recover the optimal demonstration from the diffused ones. We provide theoretical evidence supporting our approach, demonstrating that the distance between the purified and optimal demonstration can be bounded. Empirical results on MuJoCo and RoboSuite demonstrate the effectiveness of our method from different aspects. Copyright © 2023, The Authors. All rights reserved.

关键词： Contrastive Learning

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