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检索条件"任意字段=International Conference on Computer Application and System Modeling"

共 180298 条记录，以下是301-310 订阅

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FlyWheel: A Robotic Platform for modeling Fly Visual Behavior 13th

FlyWheel: A Robotic Platform for Modeling Fly Visual Behavi...

引用

13th international conference on Biomimetic and Biohybrid systems, Living Machines 2024

作者： Nourse, William R. P. Quinn, Roger D. Department of Electrical Computer and Systems Engineering Case Western Reserve University ClevelandOH44106 United States Department of Mechanical and Aerospace Engineering Case Western Reserve University ClevelandOH44106 United States

ISBN: (纸本)9783031725968

An ongoing problem in robotics is the calculation of body motion given motion in the visual field, also known as ego-motion estimation. This is a problem which has been solved in the visual system of most animals, including the fruit fly Drosophila melanogaster. Here we present FlyWheel, an open-source robotic platform for studying models of the visual motion-processing system in insects. We showcase a dataset of rotational motion data in real-world conditions using the robot, and use a simplified model of the motion pathway in Drosophila as a baseline for further comparison and development. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Flywheels

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MATHevation: Enhancing Student Academic Performances in Mathematics Through a Gamified Learning Web application with Reward system 24

MATHevation: Enhancing Student Academic Performances in Math...

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8th international conference on E-Society, E-Education and E-Technology, ESET 2024

作者： Samonte, Mary Jane Lim, Madeleine Maiquez, Ernesto Gabriel Tuala, Francoise Therese School of Information Technology Mapua University Makati City Philippines

ISBN: (纸本)9798400707094

Mathematical skills are essential for living and survival. TIMSS 2019 and PISA 2022 results show that the Philippines ranked last in mathematics in international assessments. computer educational games influence academic achievement motivation. In connection with the challenges faced by public grade 8 students to the difficulty of mathematics, the development of the web-based application aims to teach students more about math and further develop their skills through motivation by using gamification and reward systems. This study focuses on the design and development of a web application incorporating gamification functionality to enhance the usability, motivation, and engagement of the grade 8 students’ learning probability to promote student learning. The study was conducted at Benigno"Ninoy" S. Aquino High School. A quantitative research design was used in this study to examine the academic performance of grade 8 students. This quantitative approach was used to conduct a study to identify the differences between the experimental and control groups. The data was gathered through a quiz-type activity for the controlled and experimental groups. The study findings revealed that the experimental group, which was exposed to the gamified web application, had higher mean scores than the control group. The statistical analysis revealed a significant difference between the two groups. With a T-statistics = 10.748957 and P-value = 1.908744e-11, this demonstrates a considerable difference in knowledge development between traditional learning methods and the use of a web application. The findings validated the research question, which asked whether there was a substantial difference between the two methods of teaching. These findings have important implications for high school education, demonstrating the potential of the Web application’s gamification design as a great tool for engaging and educating Grade 8 students about probability. © 2024 Copyright held by the owner/autho

关键词： Contrastive Learning

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application of Artificial Intelligence Discriminant Models in Educational Reform 14

Application of Artificial Intelligence Discriminant Models i...

引用

14th international conference on Educational and Information Technology, ICEIT 2025

作者： Cao, Chunhong Dong, Shichao Geng, Rong Ji, Ce School of Computer Science and Technology Northeastern University Shenyang China

ISBN: (纸本)9798331540883

Classroom teaching evaluation is a key link in improving the quality of education and teaching and optimizing teaching management. Based on the artificial intelligence discriminant model, this paper explores the specific application of mathematical models in classroom teaching evaluation, aiming to reduce the influence of subjective factors through a data-driven approach, and at the same time, construct a scientific evaluation index system and weight allocation mechanism. This study adopts a multivariate statistical method combining cluster analysis and discriminant analysis to systematize the teaching evaluation data, so as to realize a more scientific and objective teaching evaluation system. This study provides new theoretical and practical support for educational management departments and schools, and promotes the reform and innovation of classroom teaching evaluation. © 2025 IEEE.

关键词： Multivariant analysis

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Research on the application of High-Dimensional Feature Fusion and Machine Learning Algorithms in Malicious Document Detection 24

Research on the Application of High-Dimensional Feature Fusi...

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8th international conference on Electronic Information Technology and computer Engineering, EITCE 2024

作者： Zhou, Jiaoli Wu, Ying Jing, Ru Hainan Vocational University of science and technology Hainan Haikou China

ISBN: (纸本)9798400710094

With the rapid development of Internet technology, the dissemination of malicious documents has become increasingly rampant, and traditional detection methods for malicious documents have failed to meet the current complex and diverse security requirements. The high-dimensional features of malicious documents, including instruction-level, function-level, PE header information, and byte-level code, pose challenges for detection. This paper deeply investigates the application of high-dimensional feature fusion and machine learning algorithms in malicious document detection. Through feature extraction and fusion techniques, key features of malicious documents are extracted from multiple dimensions, and advanced machine learning algorithms such as Convolutional Neural Networks (CNN), Support Vector Machines (SVM), Random Forests (RF), and LightGBM are used to model and analyze the fused high-dimensional features. Experimental results show that the combination of high-dimensional feature fusion and machine learning algorithms significantly improves the accuracy and efficiency of malicious document detection, providing effective means to address increasingly complex network security threats. © 2024 Copyright held by the owner/author(s).

关键词： Convolutional neural networks

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A Triage system Based Approach With AI-Driven Management In Bugs Resolving application

A Triage System Based Approach With AI-Driven Management In ...

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2025 international conference on Pervasive Computational Technologies, ICPCT 2025

作者： Tiwari, Praveshika Anuradha Nagra, Gagandeep Kaur Sharma, Rashmi Rakesh, Nitin Saraswat, Birendra Kumar Raj Kumar Goel Institute of Technology Computer Science & Engineering UP Ghaziabad India Institute of Management and Research Department of Management New Delhi India D.Y Patil Deemed to Be University Department of Management CBD Belapur Navi Mumbai India Symbiosis Institute of Technology Nagpur Campus Pune India

ISBN: (纸本)9798331508685

Software bugs management is one of the most crucial and time-intensive jobs for software development teams. With this ground reality, there is an urgent need to build a robust system to identify, prioritize, and fix bugs. In this paper, we present a new methodology for handling bugs, which incorporates a triage system and AI-powered methods to assist with bug management. Our triage system is a classification model to classify bugs of severity, impact, or urgency. This aids in prioritizing bugs and the workforce required to fix them. The system also uses AI to detect the issue, and special techniques like natural language processing and machine learning are used to automate the triage process. The proposed novel approach, which is AI-based, also incorporates a bug resolution management system that uses predictive analytics to deliver a forecast of how many developers need to work on a bug and how much time would be needed to resolve it based on historical data., which allows for quick and efficient detection and classification of bugs as manual effort is minimized from the dev teams. The statistical data regarding the number of bugs reported per module allows for better planning and allocation of resources, thus increasing the overall efficiency of the bug resolution process. © 2025 IEEE.

关键词： application programs

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A Design of Hybrid Model and Bayesian Neural Networks for Smart Grid Stability Prediction 1

A Design of Hybrid Model and Bayesian Neural Networks for Sm...

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1st IEEE international conference on Smart and Sustainable Developments in Electrical Engineering, SSDEE 2025

作者： Hole, Shreyas Rajendra Kolluru, Vinothkumar Salotagi, Shreekant Challagundla, Yagnesh Mungara, Sudeep Sriramkumar, R. Dayananda Sagar University AI & ML Bangalore India Stevens Institute of Technology Department of Data Science HobokenNJ07030 United States Dayananda Sagar University Computer Science & Engineering Bagalore India Herbert Wertheim College of Engineering University of Florida Department of Computer Science GainesvilleFL32611 United States Stevens Institute of Technology Masters in Information Systems HobokenNJ07030 United States

ISBN: (纸本)9798331542108

The latest evolution in power systems, is 'smart grids'that offers real time monitoring, control features as well as effective management of renewable energy sources. Nonetheless, with the increase in the system complexity, the chances for insta- bility also increase and thus it is crucial to forecast the stability accurately for the safety of the grid. Weather it is in terms of Gradient Boosting Tree Algorithm or any other traditional algorithm, these machine learning models are successful in achieving the objectives of the task but they lack a forthcoming metric to assist in high stake situations, uncertainty quantification. The paper is devoted to the analysis of the reliability of smart grid systems based on Bayesian Neural Networks (BNN) taking into account both the reliability of predictive modeling and forecasting uncertainty. The smart grid stability augmented dataset was used to benchmark the efficacy of using BNNs or BNNs combined hybrid models, one with XGBoost, for feature extraction and the other with LightGBM. The present study shows that the BNN used with the above mentioned smart grid system achieved an accuracy of 92% with precision of 0.89, recall of 0.91 and F1 of 0.90. These metrics are appending with the help of the additional features of BNN where they are capable of addressing the key questions around uncertainty and thus the metrics confirming the predictions are quite strong in terms of real-time grid stability evaluation. This article encapsulates a detailed study of BNN architecture, the role of uncertainty in machine learning models and the natural application of such models in the design of dependable and fault-tolerant smart grids. © 2025 IEEE.

关键词： Smart power grids

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Mobile Edge Computing for Efficient Vehicle Management in Smart City 14

Mobile Edge Computing for Efficient Vehicle Management in Sm...

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14th IEEE international conference on Communication systems and Network Technologies, CSNT 2025

作者： Chanu, Asem Debala Shelar, Saylee Nath, Shubha Brata Department of Computer Science and Engineering Indian Institute of Information Technology Guwahati Guwahati India Department of Computer Science and Engineering Ajeenkya DY Patil University Pune India

ISBN: (纸本)9798331531935

Mobile edge computing (MEC) promises to be a useful technology to enable applications in a smart city. However, the task offloading becomes challenging as the MEC infrastructure has limited resources. To solve the challenges of MEC, we have proposed a system architecture with an optimization algorithm to minimize the task delay in MEC. A Genetic Algorithm based task allocation algorithm finds the optimal set of edge servers for task deployment and processing. The proposed system efficiently optimizes the task delay in MEC as analyzed from the experiments performed by taking virtual machines as MEC servers. We show the usefulness of the proposed system by taking an application scenario for efficient vehicle management in a smart city. © 2025 IEEE.

关键词： Genetic algorithms

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Analysis and design of pervasive computing intelligent surveillance system integrating optical sensors and behavioral analysis for industrial internet 2

Analysis and design of pervasive computing intelligent surve...

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2nd international conference on Big Data, Computational Intelligence, and applications, BDCIA 2024

作者： Yao, Di Luo, Chenyao Cheng, Weiwei Song, Huaju Beijing100083 China Portland Institute Nanjing University of Posts and Telecommunications Nanjing210003 China School of Electronic and Optical Engineering Nanjing University of Science and Technology Nanjing210094 China School of Environmental Science NanJing XiaoZhuang University Nanjing210017 China

ISBN: (纸本)9781510689053

Nowadays, the surveillance system performing as an indispensable part for modern industrial Internet has attracted much attention. With the continuous progress of microprocessor technology, sensor technology, particularly optical sensors, and network technology, it has entered the era of pervasive computing, which leads to the breakthrough of the surveillance system and the emergence of intelligent environment safety monitoring systems. However, the intelligence of surveillance systems is still a significant challenge. In this paper, we propose a surveillance system for detecting and recognizing people based on their behaviors from images or video sequences using computer vision techniques and optical sensor data, which is implemented by designing the system architecture through a hierarchical structure, applying key techniques such as background subtraction, modeling, and optical sensing analysis, and experimentally processing and testing a variety of behavioral samples. The alarm information generated by the proposed surveillance system aided by pervasive computing system can be delivered to remote terminals. Experimental results reveal that the environmental monitoring and alarm information can be processed in real time with devices, such as cell phones, telephones, and PCs. Therefore, the proposed approach can improve the performance of the surveillance systems. © 2025 SPIE.

关键词： Alarm systems

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CPT Manager: An Open Environment for Constrained Path-Based Testing 18

CPT Manager: An Open Environment for Constrained Path-Based ...

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18th IEEE international conference on Software Testing, Verification and Validation Workshops, ICSTW 2025

作者： Klima, Matej Bures, Miroslav Holotik, Daniel Herczeg, Maximilian Miltner, Marek Zanocco, Chad FEE Czech Technical University in Prague Dept. of Computer Science Prague Czech Republic Stanford University Civil and Environmental Engineering StanfordCA United States The Czech Technical University in Prague FEE Dept. of Economics Management and Humanities Prague Czech Republic

ISBN: (纸本)9798331534677

Path-based Testing is a common technique to test system Under Test (SUT) processes. Generally, a directed graph that models a system's workflow is input to the test path generation process, as well as the selected test coverage criterion. Several algorithms are proposed in the literature that traverse the graph and facilitate the generation of test cases for the selected coverage criterion. However, a plain directed graph used for modeling SUT processes does not allow for the capture of real-life dependencies and constraints between actions in the tested processes, which might pose a limit in application of this technique. Therefore, we defined an extended model that allows the specification of constraints upon the graph's elements and a set of algorithms that allow the generation of the set of test cases that satisfy the given constraints with the edge coverage. Considering the fact that in path-based testing, there is no platform in which engineers and researchers can share SUT models to be further assembled into open datasets to test performance of evolved path-based testing MBT algorithms, especially for the given problem of test paths generation with the constraints, this paper presents a summary of the problem and a novel management for creation and management of SUT models with constraints that allows the generation of test paths as well as to serve as a platform for creation of such benchmark datasets. © 2025 IEEE.

关键词： Data flow graphs

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Evaluating the Benefits of Long Duration Storage: modeling and Analysis of a Seasonal Energy Storage system against a Decarbonizing Power system 58

Evaluating the Benefits of Long Duration Storage: Modeling a...

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58th Hawaii international conference on system Sciences, HICSS 2025

作者： Kumthekar, Ninad Rudkevich, Aleksandr Tabors, Richard Tabors Caramanis Rudkevich United States Newton Energy Group United States

ISBN: (纸本)9780998133188

Long Duration Energy Storage (LDES) will play a critical role in successful decarbonization of the electric sector. LDES is needed to manage weather-driven energy needs and resources across time and will significantly reduce the required investment in energy production and transmission. LDES benefits accrue at multiple time scales ranging from days to seasons to years. An adequate evaluation of these benefits is a challenging analytical task as it requires the use of multiple modeling techniques: (1) Capacity Expansion modeling to size and site LDES based on its impact on future generation and transmission;(2) Operational Scheduling to ration use of storage inventory across time;and (3) Energy and Ancillary Services modeling to emulate storage operations at daily and intra-day timescales. In this paper, we discuss the coordination of these three models to assess LDES within a large regional electricity market. Our methodology uses sequentially optimized and coordinated decision cycles. From this, we provide a range of simulated metrics assessing benefits of a large-scale LDES at various time scales. © 2025 IEEE computer Society. All rights reserved.

关键词： Grid Decarbonization Long Duration Storage modeling Market Operation

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