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arXiv 2023年

作者： Tian, Yan Xu, Zhaocheng Ma, Yujun Ding, Weiping Wang, Ruili Gao, Zhihong Cheng, Guohua He, Linyang Zhao, Xuran School of Computer Science and Technology Zhejiang Gongshang University Hangzhou310018 China School of Mathematical and Computational Sciences Massey University Auckland0632 New Zealand School of Information Science and Technology Nantong University Nantong226019 China School of Data Science and Artificial Intelligence Wenzhou University of Technology Wenzhou325000 China Department of Big Data in Health Science The First Affiliated Hospital of Wenzhou Medical University Wenzhou325000 China Zhejiang Engineering Research Center of Intelligent Medicine Wenzhou325000 China Jianpei Technology Co. Ltd Hangzhou311200 China

The task of multimodal cancer detection is to determine the locations and categories of lesions by using different imaging techniques, which is one of the key research methods for cancer diagnosis. Recently, deep learning-based object detection has made significant developments due to its strength in semantic feature extraction and nonlinear function fitting. However, multimodal cancer detection remains challenging due to morphological differences in lesions, interpatient variability, difficulty in annotation, and imaging artifacts. In this survey, we mainly investigate over 150 papers in recent years with respect to multimodal cancer detection using deep learning, with a focus on datasets and solutions to various challenges such as data annotation, variance between classes, small-scale lesions, and occlusion. We also provide an overview of the advantages and drawbacks of each approach. Finally, we discuss the current scope of work and provide directions for the future development of multimodal cancer detection. Copyright © 2023, The Authors. All rights reserved.

关键词： Computer vision

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Context-Based Meta Reinforcement Learning for Robust and Adaptable Peg-in-Hole Assembly Tasks

arXiv

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arXiv 2024年

作者： Shokry, Ahmed Gomaa, Walid Zaenker, Tobias Dawood, Murad Menon, Rohit Maged, Shady A. Awad, Mohammed I. Bennewitz, Maren Humanoid Robots Lab Center for Robotics University of Bonn Germany Lamarr Institute for Machine Learning and Artificial Intelligence Bonn Germany Cyber Physical Systems Lab Egypt Japan University of Science and Technology Alexandria Egypt Faculty of Engineering Alexandria University Alexandria Egypt Mechatronics Department Ain Shams University Cairo Egypt

Autonomous assembly is an essential capability for industrial and service robots, with Peg-in-Hole (PiH) insertion being one of the core tasks. However, PiH assembly in unknown environments is still challenging due to uncertainty in task parameters, such as the hole position and orientation, resulting from sensor noise. Although context-based meta reinforcement learning (RL) methods have been previously presented to adapt to unknown task parameters in PiH assembly tasks, the performance depends on a sample-inefficient procedure or human demonstrations. Thus, to enhance the applicability of meta RL in real-world PiH assembly tasks, we propose to train the agent to use information from the robot’s forward kinematics and an uncalibrated camera. Furthermore, we improve the performance by efficiently adapting the meta-trained agent to use data from force/torque sensor. Finally, we propose an adaptation procedure for out-of-distribution tasks whose parameters are different from the training tasks. Experiments on simulated and real robots prove that our modifications enhance the sample efficiency during meta training, real-world adaptation performance, and generalization of the context-based meta RL agent in PiH assembly tasks compared to previous approaches. Copyright © 2024, The Authors. All rights reserved.

关键词： Reinforcement learning

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WaterTS: Integrating Enhanced Transformer, Sliding Block, and Channel Independence for Long-term Water Quality Prediction

WaterTS: Integrating Enhanced Transformer, Sliding Block, an...

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IEEE International Conference on Automation science and engineering (CASE)

作者： Jing Bi Lifeng Xu Ziqi Wang Haitao Yuan Shichao Chen Mu Gu MengChu Zhou Faculty of Information Technology Beijing University of Technology Beijing China School of Automation Science and Electrical Engineering Beihang University Beijing China State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences and Beijing Huairou Academy of Parallel Sensing Beijing China Beijing Aerospace Smart Manufacturing Technology Development Co. Ltd Beijing China Department of Electrical and Computer Engineering New Jersey Institute of Technology Newark NJ USA

ISBN: (数字)9798350358513

ISBN: (纸本)9798350358520

Nowadays, the deterioration of water resources leads to negative ecological impacts. To effectively inhibit the deterioration of water resources, a water quality prediction model based on enhanced transformer, sliding block, and channel independence (WaterTS) is proposed by comprehensively analyzing the indicators of water resources and making long-term predictions of the dissolved oxygen index. WaterTS adopts a sliding block method to extract the short-term temporal features of the water quality series and combine them with channel independence to make independent predictions of multi-featured data. Moreover, it upgrades the internal encoder structure of the transformer and improves the attention mechanism to Probsparse-attention and Auto-Correlation to speed up the prediction speed. Furthermore, Post LayerNormal is adjusted to Pre LayerNormal, which makes the training gradient more stable and enhances the accuracy of predictions. Experiments are conducted using real-world water environment data, and comparison results with state-of-the-art prediction models show that the WaterTS achieves accurate predictions on both short-term and long-term water quality data.

关键词： Training Accuracy Biological system modeling Time series analysis Water quality Predictive models Transformers Feature extraction Real-time systems Water resources

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To What Extent Do Disadvantaged Neighborhoods Mediate Social Assistance Dependency? Evidence from Sweden

arXiv

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arXiv 2022年

作者： Lin, Cheng Daoud, Adel Brandén, Maria Institute for Analytical Sociology Department of Management and Engineering Linköping University Norrköping Sweden The Division of Data Science and Artificial Intelligence for the Social Sciences Department of Computer Science and Engineering Chalmers University of Technology Gothenburg Sweden Department of Sociology Stockholm University Stockholm Sweden

Occasional social assistance prevents individuals from a range of social ills, particularly unemployment and poverty. It remains unclear, however, how and to what extent continued reliance on social assistance leads to individuals becoming trapped in social assistance dependency. In this paper, we build on the theory of cumulative disadvantage and examine whether the accumulated use of social assistance over the life course is associated with an increased risk of future social assistance recipiency. We also analyze the extent to which living in disadvantaged neighborhoods constitutes an important mechanism in the explanation of this association. 17-year Swedish register data allow us to apply causal mediation analysis, and thereby quantify the extent to which the likelihood of ending up in social assistance dependency is affected by residing in disadvantaged neighborhoods. Our findings show the accumulation of social assistance over the studied period is associated with a more than the four-fold increase on a risk ratio scale for future social assistance recipiency, compared to never having received social assistance during the period examined. Our results suggest that the indirect effect of disadvantaged neighborhoods is weak to moderate. Therefore, social assistance dependency may be a multilevel process. © 2022, CC BY.

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Proceedings of the Fortieth Conference on Uncertainty in artificial intelligence

作者： Zhiqiang Zhang Yongqiang Jiang Qian Gao Zhipeng Wang Key Laboratory of Computing Power Network and information Security Ministry of Eaucation Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan China and Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) Jinan China and Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China China Telecom Digital Intelligence Techonology Co Jinan China

Existing multimodal recommendation research still faces some challenges, such as not being able to fully mine the implicit relevance information of neighbor nodes, and the unreasonable weight allocation to imbalanced nodes. To address the aforementioned challenges, this paper introduces a new multimodal recommendation model called NS-MAR+. Specifically, the model firstly constructs a neighbor similarity graph convolutional network to capture the implicit relevance information and reasonably assigns the attention weights through the graph attention mechanism. Secondly, the model introduces a modal alignment and fusion mechanism by using a multilayer perceptron (MLP) to map image and text features into a shared space for comparison and fusion. In addition, the model constructs a user co-interaction graph and an item semantic graph based on the original information and performs graph convolution operations to enhance the preference information of users and items, and to better capture the interactions and internal features between users and items. Finally, MLP is employed to aggregate user and item representations and predict personalized recommendation rankings. To validate the experiment's efficacy, this paper compares with several leading multimodal recommendation models on public datasets with a performance improvement of 1% to 3%. The experimental outcomes indicate that the model in this paper has good superiority and accuracy in multimodal recommendation tasks.

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Sentinel Rover: Cutting-Edge Wireless Mine Detection and Alert System for High-Risk Terrains

Sentinel Rover: Cutting-Edge Wireless Mine Detection and Ale...

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Self Sustainable artificial intelligence Systems (ICSSAS), International Conference on

作者： R. Hemalatha K. Sangeethalakshmi M. Venkatesan D. Anitha C. Srinivasan Department of Electronics and Communication Engineering Shree Sathyam College of Engineering and Technology Salem Tamil Nadu India Department of Electronics and Communication Engineering R.M.K. College of Engineering and Technology Chennai Tamil Nadu India Department of Artificial Intelligence and Data Science Panimalar Engineering College Chennai Tamil Nadu India Department of Information Technology Muthayammal Engineering College (Autonomous) Namakkal Tamil Nadu India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India

The “Sentinel Rover (SR)” is a state-of-the-art, wirelessly operated land rover intended to revolutionize mine clearance operations in high-risk terrains. The rover, equipped with cutting-edge sensor technology, seeks to detect and warn of planted land explosives, mitigating dangers to human personnel. It can transverse rocky terrains and withstand potential explosives due to its robust chassis and agile mobility. The rover's sensor suite includes metal detectors, ground-penetrating radar, thermal imaging, acoustic and optical sensors, ensuring comprehensive mine detection. data processing and fusion methods increase precision and decrease false positives. When the system detects a potential land mine, it transmits alerts to remote operators or command centers via various communication technologies. With safety mechanisms to prevent accidental detonations and non-ignitable components, the SR operates with the highest dependability and durability. While the automaton is vital, human expertise is essential for mission planning and decision-making. The SR provides a safer and more effective method for addressing land mine hazards.

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Efficient Hybrid Multi-Population Genetic Algorithm for Multi-UAV Task Assignment in Consumer Electronics Applications

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IEEE Transactions on Consumer Electronics 2025年

作者： Bai, Xiaoshan Jiang, Haoyu Li, Chao Ullah, Inam Dabel, Maryam M. Al Bashir, Ali Kashif Wu, Zongze Sam, Shuzhi Shenzhen University College of Mechatronics and Control Engineering National Engineering Laboratory for Big Data System Computing Technology Shenzhen518060 China Shenzhen University College of Mechatronics and Control Engineering Shenzhen518060 China Shenzhen University Shenzhen City Joint Laboratory of Autonomous Unmanned Systems and Intelligent Manipulation Shenzhen518060 China Tashkent State University of Economics Department of Artificial Intelligence Tashkent100066 Uzbekistan University of Hafr Al Batin Department of Computer Science and Engineering College of Computer Science and Engineering Saudi Arabia Manchester Metropolitan University Manchester United Kingdom Shenzhen518060 China National University of Singapore Department of Electrical and Computer Engineering Singapore117576 Singapore

In recent years, as people’s living standards have improved and consumption concepts have been transformed, the demand for purchasing consumer electronics online has continued to grow, further stimulating the development of the logistics industry. Consequently, how to deliver consumer electronics to households at minimal cost has become a crucial factor that limits the development of the consumer technology industry. To tackle this problem, this paper studies the task assignment problem for multiple initially dispersed UAVs to deliver products to target locations while minimizing their total operation time. Each UAV can continuously provide delivery services to multiple target locations within its limited loading capacity and operation time. To solve this problem, we propose several hybrid multipopulation genetic algorithms. First, a novel crossover operator for the genetic algorithms is designed, through which a single parent chromosome can generate offspring individually. Second, two mutation mechanisms are performed to increase gene diversity. Third, multiple local search strategies are employed to enhance the populations’ fitness during each iteration of evolution. An improved 2-opt local search strategy is applied to optimize individual chromosomes when their similarity with the current best chromosome falls below a prescribed threshold. Alternatively, local search strategies are utilized for 1-opt, 2h-opt and interchange processes. Combining local search strategies, genetic operators, and the multi-population mechanism leads to several hybrid multi-population genetic algorithms. Numerical simulations and experimental tests demonstrate that the hybrid multi-population genetic algorithm, integrated with the improved 2-opt and 1-opt local search strategies, exhibits superior performance among the designed hybrid genetic algorithms, the minimum marginal cost algorithm (MMA), and the existing popular Co-evolutionary Multi-population Genetic Algorithm (CMGA). In exp

关键词： Genetic algorithms

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artificial intelligence-enabled discovery of a RIPK3 inhibitor with neuroprotective effects in an acute glaucoma mouse model

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Chinese Medical Journal 2025年第2期138卷 172-184页

作者： Xing Tu Zixing Zou Jiahui Li Simiao Zeng Zhengchao Luo Gen Li Yuanxu Gao Kang Zhang Guangzhou Institutes of Biomedicine and Health Chinese Academy of SciencesGuangzhouGuangdong 510530China Guangzhou National Laboratory Guangzhou International Bio IslandGuangzhouGuangdong 510530China State Key Laboratory of Ophthalmology Zhongshan Ophthalmic CenterSun Yat-sen UniversityGuangdong Provincial Key Laboratory of Ophthalmology and Visual ScienceGuangzhouGuangdong 510623China University of Chinese Academy of Sciences Beijing 100049China Guangzhou Women and Children’s Medical Center Guangzhou Medical UniversityGuangzhouGuangdong 510623China Department of Big Data and Biomedical AI College of Future TechnologyPeking UniversityBeijing 100871China Institute for Artificial Intelligence in Medicine and Faculty of Medicine Macao University of Science and TechnologyMacao Special Administrative Region 999078China

Background:Retinal ganglion cell(RGC)death caused by acute ocular hypertension is an important characteristic of acute ***-interacting protein kinase 3(RIPK3)that mediates necroptosis is a potential therapeutic target for RGC ***,the current understanding of the targeting agents and mechanisms of RIPK3 in the treatment of glaucoma remains ***,artificial intelligence(AI)technologies have significantly advanced drug *** study aimed to discover RIPK3 inhibitor with AI ***:An acute ocular hypertension model was used to simulate pathological ocular hypertension in *** employed a series of AI methods,including large language and graph neural network models,to identify the target compounds of ***,these target candidates were validated using molecular simulations(molecular docking,absorption,distribution,metabolism,excretion,and toxicity[ADMET]prediction,and molecular dynamics simulations)and biological experiments(Western blotting and fluorescence staining)in vitro and in ***:AI-driven drug screening techniques have the potential to greatly accelerate drug development.A compound called HG9-91-01,identified using AI methods,exerted neuroprotective effects in acute *** research indicates that all five candidates recommended by AI were able to protect the morphological integrity of RGC cells when exposed to hypoxia and glucose deficiency,and HG9-91-01 showed a higher cell survival rate compared to the other ***,HG9-91-01 was found to protect the retinal structure and reduce the loss of retinal layers in an acute glaucoma *** was also observed that the neuroprotective effects of HG9-91-01 were highly correlated with the inhibition of PANoptosis(apoptosis,pyroptosis,and necroptosis).Finally,we found that HG9-91-01 can regulate key proteins related to PANoptosis,indicating that this compound exerts neuroprotective effects in the retina by inhibiting the expr

关键词： Virtual screening AI model PANoptosis acute ocular hypertension HG9-91-01 Glucoma

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Usage of Computer Aided Methods for Detection and Evaluation of Breast Cancer in Mammograms

Usage of Computer Aided Methods for Detection and Evaluation...

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Smart Electronics and Communication (ICOSEC), International Conference on

作者： M. Nagabhushana Rao L. Venkateswara Reddy D. Ganesh Lakkireddy. Udanth Reddy R Madana Mohana CSE Marri Laxman reddy institute of Technology & Management Hyderabad Department of CSE Joginapally B. R. Engineering College Yenkepally RR Dist Hyderabad Telangana India CSE Mohan Babu University (Erstwhile Sree Vidyanikethan Engineering College(Autonomous) Tirupati Andhra Pradesh India Dept. of ECE VIT-AP University Amaravath Artificial Intelligence & Data Science Chaitanya Bharathi Institute of Technology Hyderabad Telangana India

ISBN: (数字)9798331504403

ISBN: (纸本)9798331504410

Breast cancer accounts for an extremely high annual death rate. Many tools have been created to facilitate earlier diagnosis of breast cancer, which is the most common cancer among females. According to the research, cancer of the breast is also regarded as the second most deadly form of the disease. It is the most prominent kind of the disease and the top reason for death among females worldwide. Any progress made in the early detection and identification of cancer is crucial to maintaining good health. Therefore, it is essential to improve both the therapeutic aspect and the survival standard of patients by utilizing high precision in cancer prognosis. A reliable, effective, and speedy reaction from medical professionals is provided by an automatic disease identification system, which also reduces the danger of death. Recently, breast cancer screening methods that utilise deep learning have shown promising results, allowing for earlier identification and thereby enhancing patients’ chances of survival thanks to the introduction of artificial intelligence (AI). Deep learning reduces the need for manual intervention while extracting features, in comparison to traditional machine learning methods. We provide a brief overview of deep learning techniques, data availability, and the many breast cancer screening options, such as mammography, thermography, ultrasound, and MRI. Using demographic, laboratory, & mammographic data, this investigation sought to forecast breast cancer using various Deep learning algorithms. Ultimately, we apply artificial intelligence to breast cancer clinical trials and compare the proposed approach to current algorithms.

关键词： Deep learning Ultrasonic imaging Manuals Feature extraction Breast cancer Mammography Reliability Prognostics and health management Standards Medical diagnostic imaging

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Constructing a Blockchain-Based Loyalty Program Model for Secure and Transparent Customer Rewards in Retail Marketing

Constructing a Blockchain-Based Loyalty Program Model for Se...

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International Conference on Communication Systems and Network Technologies (CSNT)

作者： Ch. Anudeep Kathari Santosh M Sandeep Kumar Jahnavi M Sabareesh R B Kiran Bala GITAM school of business GITAM University Visakhapatnam A.P. Department of MBA CMR Institute of Technology Bengaluru India RV Institute of Management Bengaluru India Skill Development & Employability Programs Fortuna Business Solutions(R&D) Coimbatore Tamil Nadu Department of Artificial Intelligence and Data Science K. Ramakrishnan college of Engineering Trichy TN

ISBN: (数字)9798350305463

ISBN: (纸本)9798350305470

As the retail industry continues to evolve in the digital era, businesses are exploring innovative solutions to enhance customer engagement and loyalty. This research proposes a novel approach to loyalty programs by leveraging blockchain technology to create a secure and transparent model for customer rewards. Traditional loyalty programs often face challenges related to security, transparency, and trust, leading to issues such as fraud and customer skepticism. The proposed model outlines the design and implementation of a blockchainbased loyalty program, ensuring a secure and immutable ledger for tracking customer transactions and rewards. Smart contracts, self-executing contracts with predefined rules, are utilized to automate and enforce the terms of the loyalty program, reducing the potential for disputes and enhancing transparency. The decentralized nature of the blockchain ensures that customer data is securely stored and accessible only to authorized parties, addressing privacy concerns prevalent in centralized loyalty systems. This research employs a mix of theoretical analysis and practical implementation to validate the feasibility and effectiveness of the blockchain-based loyalty program model. The proposed Ethereum Blockchain model stands out with a significantly higher throughput of 5300 transactions per day, accompanied by an exceptional transaction transparency score of 99.5 .

关键词： Industries data privacy Communication systems Smart contracts Throughput Blockchains Fraud

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