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International Conference on Water Resources, Ocean, and Environmental engineering, ICWROEE 2024

作者： Blessy, Sardar Rechel Supraja, Balerao Rathi, Kushal Devi, Kamalini Vasanth, K. Srilatha, Pulipati Department of Artificial Intelligence and Data Science Chaitanya Bharathi Institute of Technology Telangana Hyderabad500075 India Department of Civil Engineering Chaitanya Bharathi Institute of Technology Telangana Hyderabad500075 India Department of Electronics and Communications Engineering Chaitanya Bharathi Institute of Technology Telangana Hyderabad500075 India

ISBN: (纸本)9789819776986

This survey paper conducts a thorough examination of the global challenge posed by urban flash floods, analyzing the intricate factors influencing the occurrence and severity of these events. Drawing on a diverse array of literature and case studies, the study delves into the complex dynamics of urban flash floods and their far-reaching impacts on human populations, infrastructure, and the natural environment. Highlighting the imperative for proactive flood risk management and resilient urban planning, the research puts forth actionable recommendations to bolster urban resilience in the face of mounting flood risks. In addition to evaluating machine learning algorithms like Iso clustering and ensemble classifiers for rapid flood mapping and precise flood susceptibility assessment, the study also explores the utilization of satellite imagery for enhanced flood monitoring and analysis. By integrating insights from both machine learning techniques and satellite imaging, the research aims to provide a comprehensive understanding of urban flash floods and offer practical strategies to mitigate their adverse effects. The study underscores the critical importance of implementing robust flood management measures, advanced early warning systems, and sustainable urban development practices to address the escalating threat of flash floods in urban areas and safeguard vulnerable communities, critical infrastructure, and ecological systems from the devastating consequences of these events. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Risk management

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Evaluation of Logistic Regression and Decision Tree Models for Lung Cancer Prediction Using Survey data

Evaluation of Logistic Regression and Decision Tree Models f...

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2024 IEEE International Conference on Electronic Systems and Intelligent Computing, ICESIC 2024

作者： Narayana, K.E. Jayashree, K. ThavaVinu, A. Sindhuja, M. Jaeyalakshmi, M. Rajalakshmi Engineering College Department of Information Technology Tamilnadu Chennai India Panimalar Engineering College Department of Artificial Intelligence and Data Science Tamilnadu Chennai India Prathyusha Engineering College Department of Information Technology Chennai India Acharya University Department of Computer Science and Engineering Karakul Uzbekistan Rajalakshmi Engineering College Department Of Computer Science And Engineering Chennai India

ISBN: (纸本)9798331522988

Lung cancer is one of the most deadly and ubiquitous forms of cancer globally. Early detection can make a significant difference in survival rates, prognosis, etc. Background The present study compares the performance of logistic regression and decision trees using a survey-based dataset for predicting lung cancer. This dataset has a total of 284 cases with 16 features;behavioral patterns, symptomatic data, and demographic details. Logistic regression is a binary classification algorithm that uses input features to model the probability of lung cancer presence given these input features using for example a sigmoid function. Using Decision Trees can identify significant fields in a dataset while reducing Gini Impurity for models that categorize patients based on feature values. The performance of both models is evaluated according to accuracy, F1-score, precision, and recall measures. From the preliminary results, it looks like the Decision Tree works a little better than the logistic regression model which gets an accuracy of 94%, but this one does with more accuracy (97%). This result emphasizes the inclusion of logistic regression within this context, as it may be able to predict lung cancer with greater accuracy. Additionally, to ensure the generalization and robustness of the model, further investigation and trials will focus on optimizing performance through ensemble approaches, hyperparameter tuning, and cross-validation. This comparative analysis demonstrates the effectiveness of machine learning techniques in developing prediction models for early lung cancer detection, an important step toward a quick and appropriate intervention. © 2024 IEEE.

关键词： Lung cancer

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Predicting the friction angle of clays using a multi-layer perceptron neural network enhanced by yeo-johnson transformation and coral reefs optimization

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Journal of Rock Mechanics and Geotechnical engineering 2024年第10期16卷 3982-4003页

作者： Libing Yang Trung Nguyen-Thoi Trung-Tin Tran College of Civil Engineering and Architecture Hunan Institute of Science and TechnologyYueyang414006China Laboratory for Applied and Industrial Mathematics Institute for Computational Science and Artificial IntelligenceVan Lang UniversityHo Chi Minh CityViet Nam Faculty of Mechanical-Electrical and Computer Engineering School of TechnologyVan Lang UniversityHo Chi Minh CityViet Nam Department of Information Technology Greenwich VietnamFPT UniversityDa Nang550000Viet Nam

The accurate prediction of the friction angle of clays is crucial for assessing slope stability in engineering *** study addresses the importance of estimating the friction angle and presents the development of four soft computing models:YJ-FPA-MLPnet,YJ-CRO-MLPnet,YJ-ACOC-MLPnet,and *** of all,the Yeo-Johnson(YJ)transformation technique was used to stabilize the variance of data and make it more suitable for parametric statistical models that assume normality and equal *** technique is expected to improve the accuracy of friction angle prediction *** friction angle prediction models then utilized multi-layer perceptron neural networks(MLPnet)and metaheuristic optimization algorithms to further enhance performance,including flower pollination algorithm(FPA),coral reefs optimization(CRO),ant colony optimization continuous(ACOC),and cuckoo search algorithm(CSA).The prediction models without the YJ technique,***-MLPnet,CRO-MLPnet,ACOC-MLPnet,and CSA-MLPnet,were then compared to those with the YJ technique,***-FPA-MLPnet,YJ-CRO-MLPnet,YJ-ACOC-MLPnet,and *** these,the YJ-CRO-MLPnet model demonstrated superior reliability,achieving an accuracy of up to 83%in predicting the friction angle of clay in practical engineering *** improvement is significant,as it represents an increase from 1.3%to approximately 20%compared to the models that did not utilize the YJ transformation technique.

关键词： Natural hazards Slope stability Friction angle Clay Soft computing models Geotechnical engineering

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AI-Powered Bird Species Identifier and Behavior Analyzer

AI-Powered Bird Species Identifier and Behavior Analyzer

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International Conference on ent Computing and Control Systems (ICICCS)

作者： G. Janani Alias Pandeeswari Ahalya. G Atchaya. P Delffie. E Department of Artificial Intelligence and Data Science PSNA College of Engineering and Technology Dindigul TamilNadu India

ISBN: (数字)9798331512088

ISBN: (纸本)9798331512095

Although Tamil Nadu is home to a wide variety of bird species, the absence of easily accessible tools and comprehensive data frequently makes it difficult for enthusiasts, researchers, and conservationists to identify and comprehend these species. A web program that can identify different bird species from user-uploaded images or audio recordings is being developed in order to solve this problem. Each discovered species’ physical characteristics, habitats, food, migratory patterns, and conservation statuses will all be covered in detail by the platform. The portal supports educational and conservation efforts while raising awareness of Tamil Nadu's distinctive bird species by utilizing deep learning for precise identification. AI is used here to enhance accuracy in image and audio detection, enabling efficient and reliable species recognition.

关键词： Deep learning Image recognition Habitats Ecosystems Birds Control systems Audio recording Reliability Biodiversity Portals

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Large circuit models: opportunities and challenges

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science China(Information sciences) 2024年第10期67卷 25-66页

作者： Lei CHEN Yiqi CHEN Zhufei CHU Wenji FANG Tsung-Yi HO Ru HUANG Yu HUANG Sadaf KHAN Min LI Xingquan LI Yu LI Yun LIANG Jinwei LIU Yi LIU Yibo LIN Guojie LUO Hongyang PAN Zhengyuan SHI Guangyu SUN Dimitrios TSARAS Runsheng WANG Ziyi WANG Xinming WEI Zhiyao XIE Qiang XU Chenhao XUE Junchi YAN Jun YANG Bei YU Mingxuan YUAN Evangeline F.Y.YOUNG Xuan ZENG Haoyi ZHANG Zuodong ZHANG Yuxiang ZHAO Hui-Ling ZHEN Ziyang ZHENG Binwu ZHU Keren ZHU Sunan ZOU Huawei Noah’s Ark Lab School of Integrated Circuits Peking University Faculty of Electrical Engineering and Computer Science Ningbo University Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Department of Computer Science and Engineering The Chinese University of Hong Kong School of Integrated Circuits Southeast University Huawei HiSilicon Peng Cheng Laboratory School of Computer Science Peking University School of Microelectronics State Key Laboratory of Integrated Chips and System Fudan University School of Artificial Intelligence Shanghai Jiao Tong University

Within the electronic design automation(EDA) domain, artificial intelligence(AI)-driven solutions have emerged as formidable tools, yet they typically augment rather than redefine existing methodologies. These solutions often repurpose deep learning models from other domains, such as vision, text, and graph analytics, applying them to circuit design without tailoring to the unique complexities of electronic circuits. Such an “AI4EDA” approach falls short of achieving a holistic design synthesis and understanding,overlooking the intricate interplay of electrical, logical, and physical facets of circuit data. This study argues for a paradigm shift from AI4EDA towards AI-rooted EDA from the ground up, integrating AI at the core of the design process. Pivotal to this vision is the development of a multimodal circuit representation learning technique, poised to provide a comprehensive understanding by harmonizing and extracting insights from varied data sources, such as functional specifications, register-transfer level(RTL) designs, circuit netlists,and physical layouts. We champion the creation of large circuit models(LCMs) that are inherently multimodal, crafted to decode and express the rich semantics and structures of circuit data, thus fostering more resilient, efficient, and inventive design methodologies. Embracing this AI-rooted philosophy, we foresee a trajectory that transcends the current innovation plateau in EDA, igniting a profound “shift-left” in electronic design methodology. The envisioned advancements herald not just an evolution of existing EDA tools but a revolution, giving rise to novel instruments of design-tools that promise to radically enhance design productivity and inaugurate a new epoch where the optimization of circuit performance, power, and area(PPA) is achieved not incrementally, but through leaps that redefine the benchmarks of electronic systems' capabilities.

关键词： AI-rooted EDA large circuit models (LCMs) multimodal circuit representation learning circuit optimization

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Reputation-Based Streamlet: An Enhanced BFT Consensus Algorithm for Improved Performance and Scalability 29

Reputation-Based Streamlet: An Enhanced BFT Consensus Algori...

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29th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2023

作者： Fang, Bo Zhao, Dancheng Wang, Taotao Wang, Hao Zhang, Shengli Yang, Qing Monash University Department of Data Science and Artificial Intelligence Melbourne Australia Shenzhen University Blockchain Technology Research Center College of Electronics and Information Engineering Shenzhen China

ISBN: (纸本)9798350330717

This work presents Reputation-Based Streamlet (RBStreamlet), an improved version of the Streamlet consensus algorithm, designed to address limitations such as high bandwidth load and high communication complexity during the Propose and Vote phases. RBStreamlet introduces a reputation mechanism for leader election and fork selection, along with erasure coding and Merkle trees to reduce data transmission volume in the Propose phase. Furthermore, RBStreamlet leverages threshold signature technology to decrease the message complexity in the Vote phase. We implement the proposed RBStreamlet algorithm in a prototype blockchain system and build a network emulator to evaluate its performance under conditions resembling a practical wide-area network. Through extensive experiments, RBStreamlet demonstrates superior performance, scalability, and resilience to Byzantine attacks compared to the original Streamlet, especially in bandwidth-limited scenarios. These findings suggest its potential for practical applications in blockchain systems. © 2023 IEEE.

关键词： blockchain Byzantine fault tolerance consensus algorithm reputation streamlet

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Principled Design of Translation, Scale, and Rotation Invariant Variation Operators for Metaheuristics

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Chinese Journal of Electronics 2023年第1期32卷 111-129页

作者： TIAN Ye ZHANG Xingyi HE Cheng TAN Kay Chen JIN Yaochu Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education Institutes of Physical Science and Information Technology Anhui University School of Artificial Intelligence Anhui University School of Electrical and Electronic Engineering Huazhong University of Science and Technology Department of Computing The Hong Kong Polytechnic University Faculty of Technology Bielefeld University

A large number of metaheuristics have been proposed and shown high performance in solving complex optimization problems. While most variation operators in existing metaheuristics are empirically designed,new operators are automatically designed in this work,which are expected to be search space independent and thus exhibit robust performance on different *** work first investigates the influence of translation invariance, scale invariance, and rotation invariance on the search behavior and performance of some representative operators. This work then deduces the generic form of translation, scale, and rotation invariant operators, and proposes a principled approach for the automated design of operators, which searches for high-performance operators based on the deduced generic form. The experimental results demonstrate that the operators generated by the proposed approach outperform state-of-the-art ones on a variety of problems with complex landscapes and up to 1000decision variables.

关键词： Deep learning Metaheuristics Reinforcement learning Benchmark testing Search problems Robustness Behavioral sciences

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Optimized Detection of Continuous Gravitational-Wave Signals using Convolutional Neural Network 3

Optimized Detection of Continuous Gravitational-Wave Signals...

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3rd International Conference on artificial intelligence and Signal Processing, AISP 2023

作者： Duraisamy, Premkumar Natarajan, Yuvaraj Niranjani, V. Parvathy, K. KPR Institute of Engineering and Technology Department of Artificial Intelligence and Data Science Coimbatore India KPR Institute of Engineering and Technology Department of Computer Science and Engineering Coimbatore India Sri Eshwar College of Engineering Department of Computer Science and Engineering Coimbatore India Dr NGP Institute of Technology Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350320749

Identifying gravitational waves produced by binary black hole mergers has sparked an unheard-of revolution in physics and astronomy. However, due to the low magnitudes of gravitational wave signals and the inevitability of detector noise, proper processing of gravitational wave data is in high demand. In this research, we propose a deep learning technique termed convolution neural network based on a set of time-series data from '***/datasets/andradaolteanu/g2net-gravitational-wave-dataset' that contains simulated gravitational waves with noise. This work uses a convolution neural network and Fourier transformation-based gravitational wave signal recognition model (CNN). According to experimental findings, the frequency-domain signal graph describes the gravitational wave signal's features more accurately than the time-domain signal. The signal recognition accuracy has been improved using CNN's convolutional processing. © 2023 IEEE.

关键词： Gravity waves

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A survey on the visual perception of humanoid robot

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Biomimetic intelligence & Robotics 2025年第1期5卷 3-14页

作者： Teng Bin Hanming Yan Ning Wang Milutin N.Nikolić Jianming Yao Tianwei Zhang The Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen 518172China College of Intelligent Systems Science and Engineering Harbin Engineering UniversityHarbin 150001China College of Physics and Optoelectronic Engineering Shenzhen UniversityShenzhen 518060China College of Mechatronics Engineering Nanjing University of Aeronautics and AstronauticsNanjing 210016China Faculty of Technical Sciences University of Novi SadNovi Sad 21102Serbia Electronic science and technology Guangdong University of TechnologyGuangzhou 510006China

In recent years,humanoid robots have gained significant attention due to their potential to revolutionize various industries,from healthcare to manufacturing.A key factor driving this transformation is the advancement of visual perception systems,which are crucial for making humanoid robots more intelligent and *** the progress,the full potential of vision-based technologies in humanoid robots has yet to be fully *** review aims to provide a comprehensive overview of recent advancements in visual perception applied to humanoid robots,specifically focusing on applications in state estimation and environmental *** summarizing key developments and analyzing the challenges and opportunities in these areas,this paper seeks to inspire future research that can unlock new capabilities for humanoid robots,enabling them to better navigate complex environments,perform intricate tasks,and interact seamlessly with humans.

关键词： advanced technologies visual perception autonomous navigation human robot interaction environmental interaction state estimation humanoid robots advancement visual perception systemswhich

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A Multi-Dimensional Deep Learning Approach for Enhanced Cardiovascular Disease Diagnosis using ECG Signals 8

A Multi-Dimensional Deep Learning Approach for Enhanced Card...

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8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2024

作者： Evangelin Sonia, S.V. Nedunchezhian, R. Rajalakshmi, M. Kavya, S.P. Parveen, H. Summia Jerusha Miraclin Dulcie, B. Karunya Institute of Technology and Sciences Division of Cse Coimbatore India Coimbatore Institute of Technology Computer Science and Engineering Coimbatore India Coimbatore Institute of Technology Information Technology Coimbatore India Kpr Institute of Engineering and Technology Department of Artificial Intelligence and Data Science Coimbatore India Sri Eshwar college of Engineering Computer Science and Engineering Coimbatore India Karunya Institute of Technology and Sciences Computer Science and Engineering Tamil Nadu Coimbatore India

ISBN: (纸本)9798350376425

The diagnosis of cardiovascular disease is essential for timely clinical intervention and patient prognose. Although electrocardiogram is performed frequently for cardiovascular disease diagnosis, its interpretation requires experts. This study first introduces a Multi-Dimensional Deep Neural Network aimed to improve the accuracy and efficiency for cardiovascular disease diagnosis from electrocardiogram data. The Multi-Dimensional Deep Neural Network architecture of our electrocardiogram signal model, which coalesces convolutional and recurrent neural networks (layers) to learn spatial/temporal features. We developed the model on a large annotated dataset of electrocardiogram recordings using training and validation datasets, yielding personal performance compared to baseline methods as well as known deep learning models. We summarize the various statistics-sensitivity, specificity;overall accuracies-that are calculated for our Multi-Dimensional Deep Neural Network indicating that our model is both robust and reliable in terms of diagnostics. This research could lead to more advanced neural networks in medical diagnostics, which may provide clinicians with an effective tool for the clinical diagnosis of cardiovascular diseases. © 2024 IEEE.

关键词： Convolutional neural networks

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