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检索条件"主题词=algorithm design"

共 564 条记录，以下是81-90 订阅

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A Path Planning Approach for Optic Inspection Based on Composite Frame 6

A Path Planning Approach for Optic Inspection Based on Compo...

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6th International Conference on Data-Driven Optimization of Complex Systems (DOCS)

作者： Zhang, Yukang Zheng, Jianchao Luo, Junwei Yan, Xiaohui Dongguan Univ Technol Sch Mech Engn Dongguan Peoples R China

ISBN: (纸本)9798350377859;9798350377842

Path planning for Automatic Optical Inspection (AOI) is a key strategy for enhancing the efficiency of AOI devices. This paper modeled the inspection issue upon the completion of imaging window planning and introduced a composite frame to optimize the problem. Swarm intelligence algorithms were employed to determine the position of imaging points, while the 2-opt algorithm was utilized to optimize the access order. The edge location method was proposed to reduce the solution space. To address the slow convergence and low accuracy issues for BSO algorithm, an improved version was proposed. The strategy of individual discussion was improved, the individual step length was adjusted dynamically according to the population position, and the competitive crossover strategy was introduced. Experimental results confirm the effectiveness of the improved brain storm algorithm and the composite frame.

关键词： Computation intelligence Brain storm optimization algorithm design Path planning Automatic optical inspection

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Demystifying Complex algorithms: A Visual Guide Through the DSA Visualizer 1

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8th Smart Trends in Computing and Communications (SmartCom)

作者： Matalia, Jeet Kathiriand, Shreya Patel, Priyanka Charotar Univ Sci & Technol CHARUSAT Chandubhai S Patel Inst Technol Fac Engn & Technol Smt Kundaben Dinsha Patel Dept Informat Technol Changa 388421 Gujarat India

ISBN: (数字)9789819713134

ISBN: (纸本)9789819713127;9789819713134

The foundation of computer science is made up of data structures and algorithms (DSA), which are necessary for creating effective software solutions and resolving challenging issues. But learning the nuances of DSAs can be difficult and frequently poses a big barrier to entry for both programmers and students. DSA Visualizers have become indispensable resources for improving the acquisition and comprehension of these important ideas in response to this challenge. This paper focuses on the visualization of data structures and algorithms. A web application has been developed to help students understand step-by-step working of different data structures and algorithms. Aim behind implementation of this paper is to make a clear understandability of various algorithms of data structures. This web app will simulate the data structure operations such as searching and sorting. In array, stack, queue, and linked list as well. Data structure and algorithm learning has been made easier thanks in large part to DSA Visualizers. Their visual and interactive method breaks down difficult ideas into easily understood models, allowing students to become more proficient in problem-solving and programming. DSA Visualizers are invaluable resources that help people of all skill levels, from beginners to experts in programming, understand DSAs, and make it easier to use them in real-world computer science applications.

关键词： Data structure and algorithm Analysis of algorithm Complexity and applications Visual representation Searching and sorting algorithm algorithm design

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Logarithmic Weighted Random Selector algorithm: A Novel Approach for Biasing Selection Based on Positional Order Without Hyperparameters 17th

Logarithmic Weighted Random Selector Algorithm: A Novel Appr...

引用

17th Workshop of Hybrid Intelligent Systems, HIS 2024, 17th Workshop on Intelligent Learning Environments, WILE 2024 and 6th Workshop on New Trends in Computational Intelligence and Applications, CIAPP 2024, held in conjunction with Mexican International Conference on Artificial Intelligence, MICAI 2024

作者： Ramos Herrera, Iván Alejandro Autonomous University of Aguascalientes Aguascalientes Mexico

ISBN: (纸本)9783031838781

The "Logarithmic Weighted Random Selector" (LWRS) introduces a novel algorithm designed for selecting randomly one item from a list, with the bias that the further to the beginning of the list each one is, the more likely it is to be selected. Unlike traditional selection and sampling methods that rely on numerical fitness scores or require hyperparameter tuning, LWRS employs a logarithmic weighting mechanism to naturally favor items based on their position. It arose from the problem of requiring a method that allows weighting a selection based on the order of preference on a list of elements, for phenomena for which numerical weighting values are unknown but only their order, which unlike algorithms such as Rank-based Selection, does not require configuration of hyperparameters. Different mechanisms were explored, such as a Linear Selection method and Exponential Selection. Discovering that utilizing a logarithmic scale, LWRS achieves a non-numerical preference bias, distinguishing itself from classic methods of weighted random sampling as Fitness-proportionate selection and Tournament Selection, which do require defined numerical weights, contributing to the field with a straightforward, parameter-free approach to weighted random selection. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： algorithm design Computer Science Evolutionary Computing Random Sampling Randomized algorithm Selection Method

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Efficient Sorting Suffixes of Big Alphabets

Efficient Sorting Suffixes of Big Alphabets

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Data Compression Conference (DCC)

作者： Nong, Ge Zhang, Sen Sun Yat Sen Univ Guangzhou 510275 Peoples R China SUNY Coll Oneonta Oneonta NY 12309 USA

ISBN: (纸本)9798350385885;9798350385878

An algorithm SACA-m is proposed to sort all suffixes of a read-only input string of n characters with alphabet size n(O(1)) in O (n) time and O(n(1/2)) workspace. It can be applied to sort suffixes of a general alphabet in O (n log n) time and O(n(1/2)) workspace. This algorithm can be revised to a succinct variant SACA-1 to reuse the space of suffix array for O (1) workspace while keeping O (n) time. The time and space performance of both algorithms are evaluated by experiments on realistic and artificial datasets. These new results give the best time and space complexities for sorting suffixes of big alphabets.

关键词： algorithm design Performance evaluation Suffix sorting

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Usability Evaluation of an Online Inquiry-based Learning Platform for Computational Thinking (CT-ONLINQ) 23

Usability Evaluation of an Online Inquiry-based Learning Pla...

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23rd IEEE International Conference on Advanced Learning Technologies (ICALT)

作者： Jha, Nitesh Kumar Bhowmik, Plaban Kumar Bhagat, Kaushal Kumar Indian Inst Technol Kharagpur Adv Technol Dev Ctr Kharagpur West Bengal India Indian Inst Technol Kharagpur GS Sanyal Sch Telecommun Kharagpur West Bengal India

ISBN: (纸本)9798350300543

With the increased focus on Computational Thinking (CT) in education, there has been an increase in the development of learning platforms to teach CT. The current study developed an Online Inquiry-based learning platform for Computational Thinking (CT-ONLINQ) to support CT activities using Inquiry-based Learning (IBL) pedagogy. IBL-based CT steps include algorithm design, analysis, and comparison of algorithms. The platform allows students to explore multiple solutions and provides hints as support during problem-solving activities. A 4-week experimental study was conducted to evaluate the usability of the online platform. A total of 79 9th-grade students volunteered to participate in this study to complete six activities. Subsequently, the students completed the SUS questionnaire and open-ended feedback questions. Results showed that around 80% of the students scored above the "Good" category (70-80), with a total average score of 78.45. Also, we analyzed the difference in rating scores among groups based on multiple background factors. Findings showed an average rating above 4 with no significant difference between the ratings among factors such as gender and math performance (below-above average math scores). In addition, analysis of the feedback comments showed that the platform is user-friendly, with students enthusiastic about learning coding on the platform.

关键词： Computational Thinking Inquiry-based Learning Online Platform algorithm design System Usability Scale

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Reconfigurable Power Amplifiers for Spectrum Sharing in Arrays

Reconfigurable Power Amplifiers for Spectrum Sharing in Arra...

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Wireless and Microwave Technology Conference (WAMICON)

作者： Baylis, Charles Peroulis, Dimitrios Egbert, Austin Roessler, Justin Fisher, Alden Marks, Robert J., II Baylor Univ Dept Elect & Comp Engn Waco TX 76798 USA Purdue Univ Elmore Family Sch Elect & Comp Engn W Lafayette IN 47907 USA

ISBN: (纸本)9798350344943

In a wireless spectrum that continues to grow more crowded, reconfigurable power amplifiers play a significant role in allowing system performance to be optimized while dynamically sharing spectrum. In phased arrays, reconfigurable amplifiers are useful in allowing both spatial and spectral resources to be shared. Recently developed high-power reconfigurable impedance matching networks, using plasma switches, allow the use of reconfigurable power amplifiers in radar transmitters, providing reconfiguration on a pulse-to-pulse basis. Fast tuning algorithms have been designed to allow closed-loop optimization of the tuner settings to maximize output power and range in real time. To allow closed-loop optimization, an in-situ measurement technique allows on-board performance assessment and solves the problem of array calibration in reconfigurable arrays. Challenges and opportunities to complete the design of phased arrays using reconfigurable power amplifiers are discussed through some application examples.

关键词： radio spectrum management power amplifiers phased arrays algorithm design measurement technology reconfigurable circuits

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design of robust algorithm for machine learning based on deep search of outliers 6

Design of robust algorithm for machine learning based on dee...

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6th International Conference on Trends in Electronics and Informatics (ICOEI)

作者： Tang, Minghu Qinghai Minzu Univ Sch Comp Sci Xining 810007 Peoples R China

ISBN: (纸本)9781665483285;9781665483292

design of robust algorithm for machine learning based on deep search of outliers is implemented in this study. The depth first search is a graph traversal method. The traversal process is essentially the process of finding the leading point of each vertex. It starts from a vertex of the graph, and the visited vertices are marked with seven visited marks. The statistical inference rules under this new theoretical system not only take into account the requirements for asymptotic performance, but also seek to obtain optimal results under the condition of existing limited information. By transforming the original problem into a dual problem, the computational complexity of the support vector machine no longer depends on the spatial dimension, but on the number of support vectors in the sample. For the robust system, the abnormal detection is considered for the application scenario. Through the Hadoop testing, the ideas are validated.

关键词： Deep Learning Robust algorithm Machine Learning Deep Search algorithm design

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Active Flux Methods With Gradient Degrees of Freedom

Active Flux Methods With Gradient Degrees of Freedom

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作者： Samani, Iman University of Michigan

学位级别：Ph.D., Doctor of Philosophy

This thesis presents a numerical approach designed to solve wave propagation problems with high accuracy on an unstructured grid. The method employs a fully explicit approach with a compact stencil, addressing both material and remote boundaries automatically. The ultimate goal is to contribute to the development of a highly accurate Navier-Stokes code for aeroacoustic predictions, specifically focusing on waves with wavelengths not significantly larger than the grid size. In essence, the aim is to accurately capture short waves using relatively coarse and cost-effective unstructured grids that can represent intricate geometric details. The underlying hypothesis is based on the belief that, for ensuring accuracy, particularly in terms of bandwidth – which is often more crucial than achieving high formal accuracy – the method must be fully-discrete and explicit. This choice is driven by the recognition that a numerical stencil, while encompassing the analytical domain of dependence to prevent instability, should not be excessively large. An overly large stencil includes irrelevant data, leading to either instability or an overly dissipative scheme. Moreover, semi-discrete methods, despite enabling high formal accuracy for long waves, prove less effective in ensuring well-behaved mid- and high-frequency behavior due to the gradual inclusion of data from outside the true domain of dependence unless the time step is significantly reduced. Any endeavor to enhance accuracy invariably involves the processing of additional information, commonly achieved by enlarging the stencil. However, in the scenario of element enlargement, where irrelevant information is incorporated into a large stencil, it not only encounters the issues previously outlined but also poses challenges on irregular grids and in proximity to boundaries. An alternative approach is to augment the set of information retained at each location, as demonstrated in methods such as Discontinuous Galerkin and Activ

关键词： Active Flux method Hermite element Wave propagation problems Scientific computing algorithm design Data structures

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AI algorithmic Bias: Understanding its Causes, Ethical and Social Implications 35

AI Algorithmic Bias: Understanding its Causes, Ethical and S...

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35th IEEE International Conference on Tools with Artificial Intelligence (ICTAI)

作者： Jain, Lakshitha R. Menon, Vineetha Univ Alabama Dept Comp Sci Huntsville AL 35899 USA

ISBN: (纸本)9798350342734

The escalating usage of artificial intelligence (AI) and machine learning algorithms across diverse fields has prompted apprehension regarding the propagation of algorithmic bias, which may exacerbate instances of discrimination and inequality. algorithmic bias in AI and machine learning (ML) techniques manifests in real-world applications as a result of either insufficient data variation or augmentation availability in the AI/ML training data, or a flawed learning policy. This leads to the accidental propagation of AI bias as an unjust treatment of particular groups of individuals, owing to their race, gender [1], age, or other distinguishing attributes in practical applications. This paper offers a comprehensive analysis of algorithmic bias, encompassing its origins, ethical and social ramifications, and possible remediations. In addition, this paper introduces an innovative methodology for identifying and measuring algorithmic bias that integrates statistical analysis with input from users and domain specialists. This exposition examines distinct forms of algorithmic biases, such as selection bias, confirmation bias, and measurement bias, and examines underlying catalysts for algorithmic bias, encompassing data integrity concerns, decisions regarding algorithmic design, and institutional prejudgments. The adverse ramifications of algorithmic bias, including the perpetuation of social inequality and the impeding of societal advancement, are the focus of our examination. The present study seeks to make a contribution to the advancement of impartial [2] and equitable AI systems with the potential to foster societal progress and benefit individuals across diverse demographics by identifying the sources and repercussions of algorithmic bias and recommending efficacious interventions.

关键词： algorithmic bias Artificial intelligence Machine learning Discrimination Inequality Ethics Bias mitigation Data variation algorithm design Social impact Diversity Data integrity Selection bias Confirmation bias Measurement bias Social equality Ethical analysis Remediation User input Institutional bias Fairness Equity

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An Experimental Study of Machine Learning algorithms for Predicting Start-Up Success 6th

An Experimental Study of Machine Learning Algorithms for Pre...

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6th World Conference on Smart Trends in Systems, Security and Sustainability, WS4 2022

作者： Mishra, Ajai Jat, Dharm Singh Mishra, Durgesh Kumar Computer Science Namibia University of Science and Technology Windhoek Namibia Computer Science and Engineering Sri Aurobindo Institute of Technology Indore India

ISBN: (纸本)9789811976599

In recent years, our data storage and processing abilities have been enhanced significantly. Machine learning (ML) has become able to capture and analyze huge data without human interference. In this context, the ML techniques will also be used for measuring, predicting, and recommending the consequences of start-up success. Therefore, first, we studied some essential contributions which employ the ML models for business success. Next, a system architecture has been demonstrated that will be able to predict the success of a start-up and also recommend the key insights. Further, we proposed and implemented a start-up success prediction model. In this modeling, we initially used four supervised learning algorithms, namely support vector machine (SVM), k-nearest neighbor (KNN), Naïve Bayes, and logistic regression. In addition, two unsupervised algorithms such as k-means and fuzzy c-means (FCM) are implemented. During experiments, we have found these models are able to provide only 58% of classification accuracy. Therefore, we have proposed to use convolutional neural network (CNN) for training and prediction. In this context, we have crafted three variants of CNN models for experimental study. According to obtained results, we found the first model provides only 48% classification accuracy, the second model provides (69%) accuracy, and the third model provides higher accuracy (74%). Thus, we found promising to work with the CNN and Crunchbase datasets. Therefore, in near future, we proposed to design a CNN model with the feature engineering processes. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： algorithm design Business data analytics Handholding of start-ups Machine learning Recommendation system Support system

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