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16th International Conference on Intelligent Robotics and Applications, ICIRA 2023

作者： Meng, Hui Chen, Lingkai Zhu, Shiqiang Fei, Chentao Zhang, Yong Zheng, Miaojuan Han, Jianda Song, Wei School of Intelligent Science and Technology Hangzhou Institute for Advanced Study University of Chinese Academy of Sciences 1 Sub-lane Xiangshan Hangzhou310024 China Research Center for Intelligent Robotics Research Institute of Interdisciplinary Innovation Zhejiang Laboratory Hangzhou311100 China Hangzhou ElephantChain Network Technology Co. Ltd. Hangzhou China Faculty of Mechanical Engineering and Automation Zhejiang Sci-Tech University Hangzhou310018 China College of Artificial Intelligence and Tianjin Key Laboratory of Intelligent Robotics Institute of Robotics and Automatic Information Systems Nankai University Tianjin300350 China

ISBN: (纸本)9789819964888

Biomedical robotics has had a significant impact on flexible ureteroscopy (FURS) for the management of nephrolithiasis. The biomedical robots provide a suitable and safe platform for FURS. However, operating the robots expertly still represents a challenge for surgeons. Vision-based navigation is an effective means to simplify the operations of robots by surgeons, which has great potential to improve surgical efficiency. To achieve vision-based navigation, the localization of kidney stones in endoscopic images is the first step. In this paper, we propose a stone segmentation method (SSM) based on zero-shot learning, which consists of segmentation anything model (SAM), an image preprocessing module, and a mask postprocessing module. The SAM segments all objects in endoscopic images, which lack semantic information. The preprocessing module is designed to suppress interference of reflective area in endoscopic images, and the mask postprocessing module is used to filter noise masks. The experimental results on our collected endoscopic images indicate that our method achieves close accuracy on stone segmentation compared with supervised U-Net and VGG_U-Net, which significantly outperforms the Ostu and the SAM. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023.

关键词： Endoscopy

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Enhancing Robustness of Time Series Predictions with Deep Learning Techniques

Enhancing Robustness of Time Series Predictions with Deep Le...

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International Conference on Futuristic Technologies (INCOFT)

作者： Nithya M Neeraj Sharma K.S. Bhuvaneshwari AR Aravind Snehal R. Rathi Umesh.M. Daivagna Department of Computer Science and Information Technology Jain (Deemed to be University) Bangalore Karnataka Department of Electrical Engineering Vivekananda Global University Jaipur India Department of Artificial Intelligence and Data scienceKarpagam College of Engineering Coimbatore Department of Electronics and Communication Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai Department of Computer Engineering Vishwakarma Institute of Information Technology Pune India Department of uGDX ATLAS SkillTech University Mumbai Maharastra India

The use of deep studying techniques for making time collection predictions has multiplied whole, because of the truth that these techniques have proven to have excellent effects on obligations, which include type, object detection, and herbal language processing. This paper looks at how deep mastering methods can enhance the robustness and accuracy of time collection predictions. The authors explore various processes using extended short-term reminiscence networks, characteristic engineering, and switch learning to make more correct and robust predictions. They also examine the consequences of including noise in the dataset and combining deep getting to know architectures with different devicegaining knowledge of algorithms. Effects show that the deep getting-to-know techniques can lessen prediction mistakes, even if the facts are noisy or incomplete while outperforming conventional strategies. In conclusion, the authors indicate that those deeply gaining systems knowledge can make more fantastic and accurate time collection predictions.

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An AI-Enabled Framework Within Reach for Enhancing Healthcare Sustainability and Fairness

arXiv

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arXiv 2024年

作者： Huang, Bin Zhao, Changchen Liu, Zimeng Hong, Shenda Zhang, Baochang Lu, Hao Liu, Zhijun Wang, Wenjin Liu, Hui AI Research Center Hangzhou Innovation Institute Beihang University Zhejiang Hangzhou China School of Computer Science Hangzhou Dianzi University China School of Automation Science and Electrical Engineering Beihang University China Institute of Medical Technology Health Science Center of Peking University National Institute of Health Data Science Peking University China Institute of Artificial Intelligence Beihang University China Information Hub Hong Kong University of Science and Technology China Department of Biomedical Engineering City University of Hong Kong China Department of Biomedical Engineering Southern University of Science and Technology China Institute of Medical Information Chinese Academy of Medical Sciences Peking Union Medical College China

Good health and well-being is among key issues in the United Nations 2030 Sustainable Development Goals. The rising prevalence of large-scale infectious diseases and the accelerated aging of the global population are driving the transformation of healthcare technologies. In this context, establishing large-scale public health datasets, developing medical models, and creating decision-making systems with a human-centric approach are of strategic significance. Recently, by leveraging the extraordinary number of accessible cameras, groundbreaking advancements have emerged in AI methods for physiological signal monitoring and disease diagnosis using camera sensors. These approaches, requiring no specialized medical equipment, offer convenient manners of collecting large-scale medical data in response to public health events. Therefore, we outline a prospective framework and heuristic vision for a camera-based public health (CBPH) framework utilizing visual physiological monitoring technology. The CBPH can be considered as a convenient and universal framework for public health, advancing the United Nations Sustainable Development Goals, particularly in promoting the universality, sustainability, and equity of healthcare in low- and middle-income countries or regions. Furthermore, CBPH provides a comprehensive solution for building a large-scale and human-centric medical database, and a multi-task large medical model for public health and medical scientific discoveries. It has a significant potential to revolutionize personal monitoring technologies, digital medicine, telemedicine, and primary health care in public health. Therefore, it can be deemed that the outcomes of this paper will contribute to the establishment of a sustainable and fair framework for public health, which serves as a crucial bridge for advancing scientific discoveries in the realm of AI for medicine (AI4Medicine). Copyright © 2024, The Authors. All rights reserved.

关键词： Electronic health record

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Impact of E-Waste on Pregnant Lady, Newborn Baby, and Fetus

Impact of E-Waste on Pregnant Lady, Newborn Baby, and Fetus

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Technological Advancements in Computational sciences (ICTACS), International Conference on

作者： Ankita Tiwari N. Ramesh Babu Nitin Rakesh Mandeep Kumar Jagjit Singh Dhattarwal Abhishek Bhola Department of Engineering Mathematics College of Engineering Koneru Lakshmaih Education Foundation Guntur India Department of Mathematucs School of Advnced Sciences Vellore Institute of Technology Chennai India Department of Computer Science and Engineering Symbiosis Institute of Technology Nagpur Campus Symbiosis International (Deemed University) Pune India School of Law Sharda University Greater Noida India Department of Artificial Intelligence & Data Science Koneru Lakshmaiah Education Foundation Vaddeswaram Andhra Pradesh India Chaudhary Charan Singh Haryana Agricultural University College of Agriculture Bawal

ISBN: (数字)9798350387490

ISBN: (纸本)9798350387506

Hazardous materials included in electronic trash (e-waste) can affect fetuses, neonates, and expectant mothers. Preterm birth, birth irregularities, and miscarriage are among the risks associated with pregnant women who are exposed to e-waste chemicals. Exposure to e-waste puts newborns at risk for respiratory and neurodevelopmental disorders. Developmental irregularities and growth restrictions are possible in fetuses. To reduce these risks and protect mother and child health, effective methods including appropriate e-waste discarding and public health programmes are important.

关键词： Pregnancy Pediatrics Osteoporosis Toxic chemicals Anemia Electronic waste Nutrients Safety Public healthcare Phthalates

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RankCSE: Unsupervised Sentence Representation Learning via Learning to Rank

arXiv

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arXiv 2023年

作者： Liu, Jiduan Liu, Jiahao Wang, Qifan Wang, Jingang Wu, Wei Xian, Yunsen Zhao, Dongyan Chen, Kai Yan, Rui Wangxuan Institute of Computer Technology Peking University China Center for Data Science AAIS Peking University China Meituan China Meta AI United States National Key Laboratory of General Artificial Intelligence China BIGAI Beijing China School of Economics Peking University China Gaoling School of Artificial Intelligence Renmin University of China China Engineering Research Center of Next-Generation Intelligent Search and Recommendation Ministry of Education China

Unsupervised sentence representation learning is one of the fundamental problems in natural language processing with various downstream applications. Recently, contrastive learning has been widely adopted which derives high-quality sentence representations by pulling similar semantics closer and pushing dissimilar ones away. However, these methods fail to capture the fine-grained ranking information among the sentences, where each sentence is only treated as either positive or negative. In many real-world scenarios, one needs to distinguish and rank the sentences based on their similarities to a query sentence, e.g., very relevant, moderate relevant, less relevant, irrelevant, etc. In this paper, we propose a novel approach, RankCSE, for unsupervised sentence representation learning, which incorporates ranking consistency and ranking distillation with contrastive learning into a unified framework. In particular, we learn semantically discriminative sentence representations by simultaneously ensuring ranking consistency between two representations with different dropout masks, and distilling listwise ranking knowledge from the teacher. An extensive set of experiments are conducted on both semantic textual similarity (STS) and transfer (TR) tasks. Experimental results demonstrate the superior performance of our approach over several state-of-the-art baselines. Copyright © 2023, The Authors. All rights reserved.

关键词： Semantics

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Toward multi-target self-organizing pursuit in a partially observable Markov game

arXiv

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arXiv 2022年

作者： Sun, Lijun Chang, Yu-Cheng Lyu, Chao Shi, Ye Shi, Yuhui Lin, Chin-Teng Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation Department of Computer Science and Engineering Southern University of Science and Technology China Centre for Artificial Intelligence CIBCI Lab Faculty of Engineering and Information Technology University of Technology Sydney Australia College of Computer and Information Science Southwest University China School of Information Science and Technology ShanghaiTech University China

The multiple-target self-organizing pursuit (SOP) problem has wide applications and has been considered a challenging self-organization game for distributed systems, in which intelligent agents cooperatively pursue multiple dynamic targets with partial observations. This work proposes a framework for decentralized multi-agent systems to improve the implicit coordination capabilities in search and pursuit. We model a self-organizing system as a partially observable Markov game (POMG) featured by large-scale, decentralization, partial observation, and noncommunication. The proposed distributed algorithm-fuzzy self-organizing cooperative coevolution (FSC2) is then leveraged to resolve the three challenges in multi-target SOP: distributed self-organizing search (SOS), distributed task allocation, and distributed single-target pursuit. FSC2 includes a coordinated multi-agent deep reinforcement learning (MARL) method that enables homogeneous agents to learn natural SOS patterns. Additionally, we propose a fuzzy-based distributed task allocation method, which locally decomposes multi-target SOP into several single-target pursuit problems. The cooperative coevolution principle is employed to coordinate distributed pursuers for each single-target pursuit problem. Therefore, the uncertainties of inherent partial observation and distributed decision-making in the POMG can be alleviated. The experimental results demonstrate that by decomposing the SOP task, FSC2 achieves superior performance compared with other implicit coordination policies fully trained by general MARL algorithms. The scalability of FSC2 is proved that up to 2048 FSC2 agents perform efficient multi-target SOP with almost 100% capture rates. Empirical analyses and ablation studies verify the interpretability, rationality, and effectiveness of component algorithms in FSC2. Copyright © 2022, The Authors. All rights reserved.

关键词： Intelligent agents

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Augmented Reality-Assisted Pediatric Surgery: A Machine Learning-Based Approach for Enhancing Accuracy

Augmented Reality-Assisted Pediatric Surgery: A Machine Lear...

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Advances in Computer science, Electrical, Electronics, and Communication Technologies (CE2CT), International Conference on

作者： Sumedha Bhardwaj Swapna Narla Md Nurul Absur Shuvo Dutta J B Hyzil S M Krishna Ganesh Department of School of Computing DIT University Dehradun Uttarakhand India Tek Yantra Inc California USA Computer Science CUNY Graduate Center New York New York USA Department of Physics Western Michigan University USA Department of Zoology Govt College for Women Affiliated to University of Kerala Thiruvananthapuram Department of Artificial Intelligence and Data Science New Prince Shri Bhavani College of Engineering and Technology Chennai India

ISBN: (数字)9798331518578

ISBN: (纸本)9798331518585

Numerous advancements have been identified as potentially disruptive innovations in the surgical workplace as surgical workflow continues to improve in the digital age. The application of virtual reality (VR) and augmented reality (AR) within health care to enhance the use of data for medical reasons is unavoidable given their rapid expansion in availability, affordability, and accessibility. Using augmented reality may help paediatric orthopaedic and trauma surgeons overcome the difficult problem of comprehending the spatial linkages among the implants, anatomy, and their instruments. As needs be, the current examination proposes another AR of the surgical scene that deftly incorporates the various information sources given by a mobile C-arm and a Kinect RGB-depth sensor. This paper present a learning-based worldview to perceive the significant items or life structures in both Kinect and data of X-ray, and to produce an object unambiguous pixel-wise alpha guide that permits importance based combination of the X-ray pictures and the video inside a solitary view. We demonstrate very encouraging results in 10 simulated procedures with the goal of giving physicians a better grasp of the surgical environment and an enhanced perception.

关键词： Pediatrics Accuracy Navigation Instruments Surgery Medical services Information age Object recognition Augmented reality X-ray imaging

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Hybrid Optimization Algorithm to Mitigate Phishing URL Attacks In Smart Cities

Hybrid Optimization Algorithm to Mitigate Phishing URL Attac...

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Innovative Practices in technology and Management (ICIPTM), International Conference on

作者： G. Sujatha Manimuthu Ayyannan S. Gayathri Priya V. Arun A. N. Arularasan M Jogendra Kumar Department of Electronics and Communication Sri Venkateswara College of Engineering Tirupati Andhra Pradesh Senior Software Engineering Manager AI/ML and Data Expert San Francisco Bay Area California USA Department of Electronics and Communication R. M. D. Engineering College Kavaraipettai Tamil Nadu Department of Computer Science &Engineering Madanapalle Institute of Technology Madanapalle Andhra Pradesh Department of Artificial Intelligence &Data Science Panimalar Engineering College Chennai Tamil Nadu Department of Computer Science Koneru Lakshmaiah Education Foundation Vaddeswaram Andhra Pradesh

To improve the detection of phishing sites, this proposal introduces feature weights for intelligent phishing site detection based on hybrid bio-inspired algorithms. The proposed approach uses Gray Wolf Optimization (GWO) and Firefly Algorithm (FF), which examines a wide range of website attributes, to more precisely identify phishing sites. Then employs artificial Neural Network (ANN) to classify different website elements according to the importance of each component in differentiating between legitimate and phishing websites using bioinspired-based recommended site feature weights. According to experimental findings, the suggested hybrid bioinspired-based feature weighting greatly improved classification accuracy, true positive (TPR), and negative rates (TNR), as well as precision and F1 score. Phishing is an online crime that entails the gathering of private information like passwords, account numbers, and credit card numbers. Attackers use alluring URLs to entice phony website visitors. Recently, artificial intelligence-based phishing detection has seen some success, and in this study, ANN was used to detect phishing. This ANN classifier may make phishing websites simpler to spot. However, it has been shown that the effectiveness of detection can be increased by applying a genetic algorithm to improve feature selection.

关键词： Uniform resource locators Smart cities Phishing Biological system modeling artificial neural networks Feature extraction Classification algorithms

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MetaMask: revisiting dimensional confounder for self-supervised learning 22

MetaMask: revisiting dimensional confounder for self-supervi...

引用

Proceedings of the 36th International Conference on Neural Information Processing Systems

作者： Jiangmeng Li Wenwen Qiang Yanan Zhang Wenyi Mo Changwen Zheng Bing Su Hui Xiong University of Chinese Academy of Sciences Institute of Software Chinese Academy of Sciences Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou) Gaoling School of Artificial Intelligence Renmin University of China Institute of Software Chinese Academy of Sciences Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou) Gaoling School of Artificial Intelligence Renmin University of China Beijing Key Laboratory of Big Data Management and Analysis Methods Thrust of Artificial Intelligence The Hong Kong University of Science and Technology (Guangzhou) Guangzhou HKUST Fok Ying Tung Research Institute

ISBN: (纸本)9781713871088

As a successful approach to self-supervised learning, contrastive learning aims to learn invariant information shared among distortions of the input sample. While contrastive learning has yielded continuous advancements in sampling strategy and architecture design, it still remains two persistent defects: the interference of task-irrelevant information and sample inefficiency, which are related to the recurring existence of trivial constant solutions. From the perspective of dimensional analysis, we find out that the dimensional redundancy and dimensional confounder are the intrinsic issues behind the phenomena, and provide experimental evidence to support our viewpoint. We further propose a simple yet effective approach MetaMask, short for the dimensional Mask learned by Meta-learning, to learn representations against dimensional redundancy and confounder. MetaMask adopts the redundancy-reduction technique to tackle the dimensional redundancy issue and innovatively introduces a dimensional mask to reduce the gradient effects of specific dimensions containing the confounder, which is trained by employing a meta-learning paradigm with the objective of improving the performance of masked representations on a typical self-supervised task. We provide solid theoretical analyses to prove MetaMask can obtain tighter risk bounds for downstream classification compared to typical contrastive methods. Empirically, our method achieves state-of-the-art performance on various benchmarks.1

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Adaptive sliding mode control based on maximum power point tracking for boost converter of photovoltaic system under reference voltage optimizer

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Frontiers in Energy Research 2024年 12卷

作者： Torchani, Borhen Azar, Ahmad Taher Sellami, Anis Ahmed, Saim Hameed, Ibrahim A. Kasim Ibraheem, Ibraheem Al-Obaidi, Moamin Ibrahim Jameel LISIER Laboratory ENSIT University of Tunis Tunis Tunisia College of Computer and Information Sciences Prince Sultan University Riyadh Saudi Arabia Prince Sultan University Riyadh Saudi Arabia Faculty of Computers and Artificial Intelligence Benha University Benha Egypt Department of ICT and Natural Sciences Norwegian University of Science and Technology Alesund Norway Department of Electrical Engineering College of Engineering University of Baghdad Baghdad Iraq Department of Communication Technical Engineering Uruk University Baghdad Iraq

This article presents an innovative APISMC method applied to PVS, integrating the MPPT technique for a boost converter. The primary objective of this approach is to maximize the converter’s output power while ensuring optimal operation in the face of varying environmental conditions such as solar irradiance and temperature, while dynamically adapting to variations in system parameters, as demonstrated by the obtained results. To achieve this, a RVO is employed to generate reference voltage and power. A PI controller calculates the reference current based on this power. The APISMC control modeling utilizes all its reference variables to synthesize the sliding surface and duty cycle for optimal boost converter control. Simulations conducted demonstrate superior performance in terms of stability, speed, and control of the converter compared to traditional MPPT algorithms. The main contributions of this article include an improvement in system robustness against irradiance variations, thanks to the integration of an adaptive algorithm and a PI controller within the SMC. Moreover, the proposed theoretical and practical framework enables rapid MPPT attainment by adjusting the duty cycle in real-time, optimizing maximum power extraction and ensuring stable regulation even under non-ideal conditions. Copyright © 2024 Torchani, Azar, Sellami, Ahmed, Hameed, Kasim Ibraheem and Al-Obaidi.

关键词： Solar irradiance

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