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检索条件"任意字段=5th International Conference on Fun with Algorithms"

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Convolutions of String Data Structures: A Case Study of Finding Number of Palindrome Substrings in a String 5

Convolutions of String Data Structures: A Case Study of Find...

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5th international conference on Advancement in Data Science, E-Learning and Information Systems, ICADEIS 2023

作者： Kumar, Kamal Jain, Anuj Kumar Tiwari, Raj Gaurang Jain, Nitin Witarsyah, Deden Misra, Alok National Institute of Technology Uttarakhand Dept. of Computer Science and Engineering Uttarakhand Srinagar India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Manav Rachna International Institute of Research and Studies Career Development Centre Haryana Faridabad India Telkom University Information System Departement Bandung Indonesia

ISBN: (纸本)9798350303414

In computer science, substring search or string Matching is a vulnerable problem when text resources are very large. Productivity of diverse scraping applications depend on the effectiveness of searching algorithms. Palindrome string finding is one of these problems which has its use in many applications. the problem is about finding the number of palindrome substrings within a string. this paper discusses a naive and dynamic programming-based algorithm for finding several palindrome strings. the time complexity of algorithm has also been analyzed and an effort has been made to improve it. © 2023 IEEE.

关键词： Dynamic programming

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Semiconductor Material Porosity Segmentation in Flame Retardant Materials SEM Images Using Data Augmentation and Transfer Learning 5

Semiconductor Material Porosity Segmentation in Flame Retard...

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5th international conference on Computer Systems and Communication Technology, ICCSCT 2023

作者： Lu, Siwei Zhao, Xiaofang Liu, Huazhu Liang, Hongjie College of Electronic and Information Engineering Shenzhen University Shenzhen City China International School of Microelectronics Dongguan University of Technology Dongguan City China

ISBN: (纸本)9781643684963

Non-halogenated flame retardants are becoming the trend in the development of polymer flame retardant materials due to their high flame retardant efficiency and low generation of toxic smoke gases. Non-halogenated flame retardants achieve flame retardancy by forming a dense char layer and generating non-combustible gases, with the micro-porous structure of the char residue being crucial for studying the flame retardant mechanism. this study focuses on the segmentation of pores in scanning electron microscopy (SEM) images of the combustion char layer of non-halogenated flame retardant materials, which are cropped and labeled to form a unified dataset. We investigate the SEM image pore segmentation using data augmentation and transfer learning, addressing the challenge of limited sample size. We explore the impact of different data augmentation techniques and transfer learning on model performance. Additionally, we compare convolutional neural network (CNN) segmentation algorithms with traditional segmentation methods. Experimental results demonstrate that CNN segmentation algorithms outperform traditional methods in terms of segmentation accuracy. Offline data augmentation enhances model stability compared to online data augmentation, and adopting transfer learning significantly improves model performance metrics. Specifically, when training with VGG backbone weights through transfer learning, the average pixel accuracy and average intersection over union reach 94.49% and 89.88%, respectively. © 2024 the Authors.

关键词： Image segmentation

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2022 5th international Youth Scientific and Technical conference Relay Protection and Automation, RPA 2022

2022 5th International Youth Scientific and Technical Confer...

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5th international Youth Scientific and Technical conference Relay Protection and Automation, RPA 2022

ISBN: (纸本)9798350399912

the proceedings contain 27 papers. the topics discussed include: synchronous parameter estimation methods for salient pole generators based on steady state synchrophasor measurements;parameters identification of park three-circuit generator model;analysis of operation of measuring devices in the conditions of cyberattacks. review;relevance of the use of combined methods for calculating the reliability of the RPA of the digital substation in the mass use of ICT, taking into account the impact of cyber-attacks;analysis of existing approaches to the implementation of automatic synthesis of algorithms;development of a methodology for the management of electric distribution grids;research of the efficiency of under-frequency load shedding and special automatics switching-off of load in case of large shortages of active power in micro-power systems with MINI-HPPS;development of a distributed intelligent control system for electricity distribution grids. review of distribution grid control systems;and modification of the method for finding the frequency parameters of transients in unstable systems.

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Proceedings - 2022 5th international conference on Computational Intelligence and Communication Technologies, CCICT 2022

Proceedings - 2022 5th International Conference on Computati...

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5th international conference on Computational Intelligence and Communication Technologies, CCICT 2022

ISBN: (纸本)9781665472241

the proceedings contain 95 papers. the topics discussed include: identification of diseases in cassava leaves using convolutional neural network;nutrition monitoring and health awareness system based on crowd sourced food images using transfer learning;risk detection of stroke using classifier algorithms;model for predicting cardiac health using deep learning classifier;automated detection of diabetic retinopathy: a comprehensive study;a review on security schemes for electronic health records;comparison of pre-processing and feature extraction techniques for Alzheimer detection using MRI images;a big data analytics framework for analyzing global tourism trends of Indians using social media analytics;credit card fraud detection using advanced machine learning techniques;deep learning techniques for object detection in images: exploratory study;analysis and development of e-commerce web application;plant disease identification using plant images;an implemented review for intent creation using different clustering techniques;and a systematic analysis and capturing of complete and latent fingerprints.

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5th international conference on Big Data and Artificial Intelligence, BDAI 2022

5th International Conference on Big Data and Artificial Inte...

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5th international conference on Big Data and Artificial Intelligence, BDAI 2022

ISBN: (纸本)9781665470810

the proceedings contain 37 papers. the topics discussed include: classification of human emotion from speech data using deep learning;a Chinese-Malay neural machine translation model based on ca-transformer and transfer learning;fixed-wing stalled maneuver control technology based on deep reinforcement learning;PhysioVec: a multi-stage deep-learning framework for searching online health information with breath sound;automatic machine learning classification algorithms for stability detection of smart grid;short-term power load forecasting technology based on bi-directional LSTM quantile regression;detecting anomalies in satellite telemetry data based on causal multivariate temporal convolutional network;regularized minimum maximum probability SVM based on radius-margin ratio;event classification with imbalanced and missing data for an air-handling unit;and a dimensionality reduction and reconstruction method for data with multiple connected components.

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2022 5th international conference on Information Communication and Signal Processing, ICICSP 2022

2022 5th International Conference on Information Communicati...

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5th international conference on Information Communication and Signal Processing, ICICSP 2022

ISBN: (纸本)9781665485890

the proceedings contain 147 papers. the topics discussed include: a study and comparison of different sparse Bayesian learning algorithms in DOA estimation;system error real-time estimation of radar network based on iterative generalized least squares;parameters estimation in polarimetric ula with cross-distribution dipole pairs;a DOA estimation algorithm of multi-target recognition based on polarization-sensitive arrays;alternating projection based unitary matrix completion method for DOA estimation in nonuniform noise;constrained adaptive monopulse estimation for planar arrays;a novel neural network approach for coherent source DOA estimation;a weak signal detection method based on spatial spectrum-LSTM neural network;beat signal analysis for terahertz frequency modulation continuous wave;research on motion recognition technology based on nanogenerator;and rolling bearing vibration signal feature extraction based on SVD and mathematical morphological fractal dimension spectrum.

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An Optimized DDoS Attack Detection Using Deep Convolutional Generative Adversarial Networks 5

An Optimized DDoS Attack Detection Using Deep Convolutional ...

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5th international conference on Inventive Research in Computing Applications, ICIRCA 2023

作者： Akana, Chandra Mouli Venkata Srinivas Kumar, Arvind Tiwari, Mohit Yunus, Aslanov Zabit Vijayakumar, E. Singh, Mukesh Bonam Venkata Chalamayya Engineering College Odalarevu India School of Computer Applications Manav Rachna International Institute of Research and Studies India Bharati Vidyapeeth's College of Engineering Department of Computer Science and Engineering Delhi India Azerbaijan State University of Economics - Unec Baku Azerbaijan KIT-Kalaignar Karunanidhi Institute of Technology Department of Computer Applications Tamilnadu Coimbatore India Graphic Era University Department of Computer Science and Engineering Dehradun India

ISBN: (纸本)9798350321425

Attacks known as Distributed Denial-of-Service (DDoS) are rising as a result of recent, dramatic increase in demand for Internet access. When the amount and characteristics of network traffic, which may include harmful DDoS contents, expand dramatically, traditional basic algorithms using machine learning for classifying DDoS attacks frequently fail because it cannot automatically extract high-value characteristics. In order to effectively identify and classify DDoS attacks, a hybrid technique called DFNN-SAE-DCGAN that combines three deep learning-based models is suggested. the Deep Feed-Forward Neural Network (DFNN) and Stacked Auto encoder offers an efficient method for extracting features that identifies the most pertinent feature sets without the assistance of a person. To avoid the operational overhead and presumption associated with processing massive set of features with distortion and redundant characteristic values, the Deep Convolutional Generative Adversarial Networks (DCGAN) component of the proposed model classifies the attacks into various DDoS attack types using the restricted and minimized characteristic sets generated by the DFNN-SAE as inputs. the experimental results show a very high and resilient accuracy rate and an F1-score of 98.5%, which is higher than the performance of many similar approaches. these results were acquired by thorough and extensive trials on various performance aspects on the CICDDoS2019 dataset. this demonstrates that the suggested methodology may be utilized to defend against the increasing amount of DDoS attacks. © 2023 IEEE.

关键词： Denial-of-service attack

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A Novel Gooseneck Barnacle Optimizer Based Machine Learning Approach for Rainfall Prediction 5

A Novel Gooseneck Barnacle Optimizer Based Machine Learning ...

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5th international conference on Sustainable Technologies for Industry 5.0, STI 2023

作者： Ahmed, Marzia Sulaiman, Mohd Herwan Rahman, Mostafijur Mohamad, Ahmad Johari Faculty of Electrical & Electronics Engineering Technology Pahang Malaysia Daffodil International University Department of Software Engineering Dhaka Bangladesh Green University of Bangladesh Department of Computer Science and Engineering Bangladesh

ISBN: (纸本)9798350394290

In dynamic meteorological prediction, accurate rainfall forecasting is a mystery. In a complex and dynamic natural environment with unpredictable sky movements, we propose an innovative methodology that forecasts weekly average rainfall patterns using the Gooseneck Barnacle Optimizer and the Least Squares Support Vector Machine (LSSVM). the significance of rainfall prediction is shown by its widespread implications, including public health. However, conventional single-model techniques and machine-learning methods must represent rainfall pattern changes, limiting our preparation. With a 2014-2018 dataset from reliable meteorological libraries, our research explores the possibility of this novel combination. the Gooseneck Barnacle Optimizer (GBO) serves as the bedrock of our methodology, introducing a novel evolutionary algorithm inspired by the intricate mating behaviors of gooseneck barnacles. GBO aptly captures the dynamic interplay of factors such as navigational sperm casting properties, food availability, food attractiveness, wind direction, and intertidal zone wave movement during mating, culminating in two crucial optimization stages: exploration and exploitation. In contrast to previous algorithms like the Barnacle Mating Optimizer (BMO), GBO stands out by more accurately emulating the unique mating behaviors of gooseneck barnacles. the prediction job is performed by integrating the Least Squares Support Vector Machines (LSSVM) algorithm with GBO as an objective function, using the optimized hyper-parameter values. the results suggest that the GBO algorithm exhibits superior performance compared to existing methodologies. this is accomplished by efficiently augmenting the original random population for a particular problem, resulting in a convergence towards the global optimum and producing significantly enhanced optimization outcomes. © 2023 IEEE.

关键词： Rain

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UAV countermeasures: status quo, breakthroughs, challenges and future prospects 5

UAV countermeasures: status quo, breakthroughs, challenges a...

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5th international conference on Physics and Engineering Mathematics, ICPEM 2024

作者： Wei, Zairan Liu, Weiwei General Education College Shandong Huayu University of Technology 968 University East Road Shandong Dezhou City China

ISBN: (数字)9781510689169

ISBN: (纸本)9781510689152

In recent years, Unmanned Aerial Vehicles (hereinafter referred to as UAVs) have been extensively utilized, however, the security risks associated with them are prominent and pose a serious threat to critical areas. Consequently, there is an urgent need for research and development of UAV countermeasures. this paper provides a comprehensive analysis of countermeasures against UAVs, encompassing the classification and principles of signal interference, physical countermeasures, and laser-based countermeasures. Regarding key technological advancements, phased array radar and opto-electronic monitoring technology have undergone significant innovations in monitoring and identification capabilities. the former employs multi-unit coordination to accurately detect various types of UAVs while the latter combines multi-spectral imaging with advanced algorithms for precise identification purposes. the intelligent countermeasure decision system utilizes machine learning to construct an evaluation system that makes intelligent choices and dynamically optimizes responses based on threat levels and environmental factors. the development of countermeasure equipment focuses on miniaturization, integration, optimization of interference equipment through new components as well as enhancing reliability through multifunctional system integration components. Despite achievements obtained through laboratory simulation and field testing evaluations systems still face challenges such as complex electromagnetic environments, legal ethics considerations, and cost-effectiveness concerns. therefore it is imperative to integrate a comprehensive multi-technology research and development framework while strengthening international cooperation efforts along with standard setting initiatives to ensure that safety standards are met while promoting further advancements in the field of UAV countermeasures. © 2025 SPIE.

关键词： international cooperation

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Stroke Probability Prediction and Data Visualization Based on Multiple Machine Learning Models 5

Stroke Probability Prediction and Data Visualization Based o...

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5th international conference on Computer Information Science and Application Technology, CISAT 2022

作者： Huang, Xinyue Qi, Meng Wang, Yihan Yang, Juntian Computer Science and Software Engineering Maynooth International Engineering College Fuzhou University Fuzhou350108 China Department of Food Science & Technology School of Agriculture and Biology Shanghai Jiao Tong University Shanghai200240 China College of Chemistry and Molecular Engineering Peking University Beijing100081 China Depu Foreign Language School Chongqing500000 China

ISBN: (纸本)9781510660076

the issue of stroke happening in world range acting a significant problem for human health. Until 2019, there were about 411 death cases per day in America which is 3 years earlier than the appearance of e-cigarettes in 2022. However, the judgement between different doctors could be variable and a slightly human error in complex situations could lead to a bad estimation result. To deal with this, the prediction of stroke probability of patients could provide an objective reference for doctors. this study made a data treatment of factors choosing and translation then calculating 4 accuracy of models and finally an application. To be more specific, an online stroke dataset was employed for factor analysis firstly and three machine learning algorithms were utilized to select most important stroke-related factors. 6 most influential factors were further passed into four machine learning models for training. the work gave the result of the highest accuracy performance of Random Forest between the four models which could be assistance of the prediction in stroke. © 2022 SPIE.

关键词： Forecasting

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