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检索条件"机构=Dept. of Computer Eng. and Computer Science"

共 9538 条记录，以下是41-50 订阅

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Investigating the Performance of Different Machine Learning Models for Forecasting Li-ion Battery Core Temperature Under Dynamic Loading Conditions

Investigating the Performance of Different Machine Learning ...

引用

2024 IEEE International Conference on Electro Information Technology, eIT 2024

作者： Arafat, Kazi Abdullah Al Yousuf Bhuiyan, Sharif Md Mahamud, Rajib Parvez, Imtiaz Atish Dipankar Uni. of Sci. and Tech. Dept. of Computer Science and Engineering Dhaka Bangladesh Idaho State University Dept. of Mechanical Eng. PocatelloID83201 United States Utah Valley University Dept. of Computer Science and Eng. Dept. of Computer Sci. OremUT84058 United States

This study delves into the difficulties involved in managing heat, within lithium ion (Li ion) batteries for vehicles emphasizing the impact of temperature on battery performance and safety. It stresses the importance of maintaining temperatures to extend battery lifespan and boost efficiency. By employing machine learning (ML) methods to predict battery temperatures and optimize thermal management systems this research aims to enhance battery safety, performance and longevity. The study compares models like Recurrent Neural Networks (RNNs) Multi Layer Perceptrons (MLPs) and Linear Regression to evaluate their effectiveness in forecasting battery temperature under conditions. The results suggest that incorporating ML into battery management systems can result in efficient and safer operations for automotive applications. A detailed analysis of the three ML models-MLP, RNN and Linear Regression-is carried out for predicting battery temperatures. The main objective is to assess and contrast their accuracy in forecasting based on data. Simulation outcomes such as Mean Absolute Error (MAE) Coefficient of Determination (R2) and Root Mean Squared Error (RMSE) offer insights into each models performance across criteria. This study contributes towards advancing methodologies for predicting battery temperatures thereby improving decision-making processes, for energy management systems and related uses. © 2024 IEEE.

关键词： Decision making

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Gemini: Graph-based Versatile News Search eng.ne

Gemini: Graph-based Versatile News Search Engine

引用

2023 International Conference on Intelligent Systems for Communication, IoT and Security, ICISCoIS 2023

作者： Babu, Sharan Samudrala, Srikar Pulabaigari, Viswanath Padmanabha Reddy, Y.C.A. Babu, Ch.Madhu B v Raju Institute of Technology Dept. of Computer Science and Eng. NarsapurMedak Telangana India IIIT Sricity Dept. of Computer Science and Eng. Chittoor Andhra Pradesh India

ISBN: (纸本)9798350335835

Productivity and time value are always important in today's fast-paced world. It is especially true for tasks like 'News Search' where a consumer wants Fast access to events around the world, but in this event, Don't rush, be a fair and eclectic cause. This paper aims to show how the state-of-The-Art natural language processing techniques with a graph database can be used to de-duplicate redundant content from data sources and help readers to save time. A graph database uses a data model consisting of nodes, edges, and properties to store and retrieve data. In this paper, A specific schema used in this paper called the hypernode graph where the hyper-nodes connect to nodes that are relevant. These hypernodes are themselves linked by semantics. Similarity assessment offers innovative methods for modeling the News de-duplication problem in a scalable way. This work provides an extensible system that can easily extend for non-news use cases as well. © 2023 IEEE.

关键词： Search eng.nes

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Enhancing DDoS Attack Detection: A Hybrid SVM-Decision Tree Ensemble Approach 15

Enhancing DDoS Attack Detection: A Hybrid SVM-Decision Tree ...

引用

15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Muduli, Debendra Bhatta, Sandesh Shookdeb, Shantanu Adhikari, Aayush Sapkota, Aaron Chaturvedi, Prabhat C. V. Raman Global University Dept. of Computer Science and Eng. Bhubaneswar India

ISBN: (纸本)9798350370249

A DDoS attack overwhelms a network or server with an excessive amount of traffic, disrupting its normal operation and rendering it inaccessible to legitimate users. DDoS attacks involve coordinated efforts from multiple compromised devices, known as botnets, flooding the target with traffic simultaneously. This flood of data exhausts the target's resources, causing service disruptions. Machine learning aids in DDoS attack detection by analyzing network traffic patterns. It learns to differentiate between normal and malicious activity by studying features such as packet rates, traffic volume, and protocol anomalies. By training on labeled datasets containing both benign and attack traffic, machine learning algorithms can identify deviations indicative of DDoS attacks, thereby enhancing network security. The hybrid machine learning model combines the precision of Support Vector Machines with the decision-making clarity of Decision Trees to effectively combat DDoS attacks. This innovative model achieves an impressive accuracy rate of 99.98% against the CICDDoS2019 dataset, demonstrating its exceptional performance in detecting various DDoS attack vectors. © 2024 IEEE.

关键词： Support vector machines

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Hand Gesture Recognition and an App for Deaf and Dumb People

Hand Gesture Recognition and an App for Deaf and Dumb People

引用

2023 International Conference on Circuit Power and Computing Technologies, ICCPCT 2023

作者： Harikrishnan, H. Johny, Joel Mathews, Marshel C Suresh, Meghana Joseph, Honey Amal Jyothi College of Engineering Dept. of Computer Science & Eng. Kottayam India

ISBN: (纸本)9798350333244

In this paper, a system for enhancing communication between sign language users and non-users is presented. The system recognizes and converts sign language into text and voice using picture data collected by a computer webcam. A masking approach is used to preprocess the picture data, then a Convolutional Neural Network (CNN) algorithm is used for feature extraction and classification. The system was trained using a dataset of masked images representing the 26 letters of the eng.ish alphabet, totaling 45500 images for training and 6500 images for testing. The system's goal is to make it simpler for those who use sign language and those who do not to communicate with one another. Keywords: Hand Gesture Recognition, Feature Extraction, Pre-Processing, Classification, CNN. © 2023 IEEE.

关键词： Feature extraction

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Automated Power Point Generation using Natural Language Processing Techniques 9

Automated Power Point Generation using Natural Language Proc...

引用

9th International Conference on Smart Computing and Communications, ICSCC 2023

作者： Thomas, Sebin John, V.G. Chacko, Josin Shajahan, Mariyam Sunny, Sharon Amal Jyothi College of Engineering Dept. of Computer Science & Eng. Kottayam India

ISBN: (纸本)9798350314090

Presentations have become an important part of the work life whether it is a student or an employee or a businessman. But creating a presentation is a time consuming process. The proposed method aims to reduce the amount of work put into creating a presentation by automatically generating an outline or a framework of the presentation based on text input. The system is a website that will be able to convert a text file or a pdf document into a presentation. If it is a text document, segments of text are created by analyzing the text and grouping sentences that belong to the same topic. Then, each of these segments will be summarized and key points will be generated. These points will be distributed over several slides. Appropriate titles will be generated for each slide based on the contents. If it is a pdf document, the titles are extracted from the document itself. The segments are already present as pdf documents and have a structure. The presentation can be exported as *** file which lets the user to edit the file easily and make any changes if necessary. The system uses Google App Script to generate a file ***, and uses API's to summarize text, generate key points and to generate titles. © 2023 IEEE.

关键词： Natural language processing systems

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Estimation of Multi-Attribute Differential Graphs with Non-Convex Penalties

Estimation of Multi-Attribute Differential Graphs with Non-C...

引用

2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Tugnait, Jitendra K. Dept. of Electrical & Computer Eng. Auburn University AuburnAL36849 United States

ISBN: (纸本)9798350368741

We consider the problem of estimating differences in two multi-attribute Gaussian graphical models (GGMs) which are known to have similar structure, using a penalized D-trace loss function with nonconvex penalties. The GGM structure is encoded in its precision (inverse covariance) matrix. Existing methods for multi-attribute differential graph estimation are based on a group lasso penalized loss function. In this paper, we consider a penalized D-trace loss function with nonconvex (log-sum and smoothly clipped absolute deviation (SCAD)) penalties. Two proximal gradient descent methods are presented to optimize the objective function. Theoretical analysis establishing local consistency in support recovery, local convexity and estimation in high-dimensional settings is provided. We illustrate our approach with a numerical example. © 2025 IEEE.

关键词： ifferential graph learning log-sum multiattribute graphs SCAD penalties undirected graph

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Contrasting Power Consumption and Trading in Residential Microgrids Considering Dynamic Pricing

Contrasting Power Consumption and Trading in Residential Mic...

引用

2024 IEEE Energy Conversion Congress and Exposition, ECCE 2024

作者： Rahman, Md. Mizanur Sushil, Ankon Babla Touma, Daniela Wolter Ferreira University of South Alabama Electrical and Computer Eng. Dept. Mobile United States

ISBN: (纸本)9798350376067

Dynamic Pricing (DP) schemes are becoming a popular solution offered by Utility Grids Companies (UG) to shift the loads to non-peak hours. This strategy supports the reduction of energy consumption in peak hours since the price of power is significantly increased at those hours. Moreover, residential microgrids (MG) can support customers who adopt DP. However, the adaptation of MG with photovoltaic panels (PV) and energy storage systems (ESS) requires advanced techniques to control the power flow among different suppliers (PV, UG, and ESS) and load (including charging ESS). The previous knowledge of how the load behaves and how PV and ESS can interact with this load supports control of power flow. This paper presents a sensitivity analysis of the power consumption and trading of a residential MG in Alabama, considering the DP offered by Alabama Power. Real-time solar insolation and load power consumption data from a local apartment were collected for one-year period. The data is used to simulate different case scenarios and provide a detailed analysis of the savings that the load would have if the simulated MG was installed. © 2024 IEEE.

关键词： Sensitivity analysis

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Mentorship and Legitimate Peripheral Participation in the Research Laboratory 54

Mentorship and Legitimate Peripheral Participation in the Re...

引用

54th IEEE Frontiers in Education Conference, FIE 2024

作者： Hordemann, Glen Quek, Francis Texas A&m University Dept. of Electrical Eng. and Computer Science College StationTX United States

ISBN: (纸本)9798350351507

This research-to-practice paper investigates how undergraduates organically transition into researchers through the lens of Legitimate Peripheral Participation, Legitimate Peripheral Participation is a contextual learning phenomenon through which new members become core members of a community. These peripheral members begin with the execution of peripheral low-risk and simple activities, gradually accruing knowledge and experience as they take on more complex and essential activities. They gain legitimacy and access to more complex involvement through interaction with, and mentorship by, senior members of the community. Our research examines how seven undergraduate students, who are involved with a research lab, transition from peripheral roles to core membership through observation and peripheral eng.gement, skill development, community integration, increasing responsibility, and collaborative projects. These undergraduates interact with and are mentored by both graduate students and faculty members in an organic fashion, with no assigned mentorship roles existing between the undergraduate students and graduate students or faculty. The focus of our research is not on how one recruits undergraduates to work in a research lab, but on how the identity of an undergraduate as a researcher, both in their own mind and in those of their coworkers, is nurtured and developed. Through the process of surveys and interviews we extract the core elements of the undergraduates' experience and explore factors that might contribute to or hinder their transition into being more active researchers. The degree of involvement and identification as a researcher varied significantly between the undergraduate students. The key elements we discover at social eng.gement, physical presence, and near-peer interaction. © 2024 IEEE.

关键词： Students

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BERT Goes to SQL School: Improving Automatic Grading of SQL Statements

BERT Goes to SQL School: Improving Automatic Grading of SQL ...

引用

2023 Congress in computer science, computer eng.neering, and Applied Computing, CSCE 2023

作者： Rivas, Pablo Schwartz, Donald R. Quevedo, Ernesto School of Eng. & Computer Science Baylor University Dept. of Computer Science United States School of Computer Science & Math. Marist College Dept. of Computing Technology United States

ISBN: (纸本)9798350327595

Automated grading of SQL queries is a challeng.ng task due to the complexity of the language and the variety of acceptable solutions for a given problem. In this paper, we propose a novel approach that leverages deep learning with a BERT model to understand the syntax and semantics of SQL statements. By training BERT on a dataset of SQL queries and their corresponding grades, we create a model that can automatically grade new questions accurately. Our experiments demonstrate that the proposed methodology achieves high accuracy and consistency in grading SQL queries, outperforming existing state-of-the-art models. Furthermore, we provide an analysis of the model's explainability, revealing a new capability that can be extremely beneficial for understanding the decision-making process. Overall, our work demonstrates the potential of deep learning with BERT for improving the efficiency and accuracy of SQL query grading. © 2023 IEEE.

关键词： bert deep learning machine learning natural language processing sql

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Adaptive Traffic Signal Timing: Leveraging YOLOv10 and computer Vision for Real-Time Optimization 8

Adaptive Traffic Signal Timing: Leveraging YOLOv10 and Compu...

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8th IEEE International Conference on Computational System and Information Technology for Sustainable Solutions, CSITSS 2024

作者： Papanashi, Suhas Chadaga, Manya Kshithi, R. Huddar, Santosh S. Sreelakshmi, K. Ramakanth Kumar, P. Rv College of Engineering Dept. of Electronics and Telecomm Bengaluru India Rv College of Engineering Dept. of Computer Science & Eng. Bengaluru India

ISBN: (纸本)9798331505462

Traffic congestion poses a significant challeng. in modern urban environments, particularly in densely populated cities. Traditional traffic signals, which operate on fixed timings, often require manual adjustments to respond to varying traffic densities, leading to inefficient traffic flow. This issue arises because fixed intervals do not account for the dynamic nature of traffic patterns throughout the day. While developing intelligent systems that adjust signal durations dynamically offers a potential solution, such systems involve substantial financial investment and extended implementation timelines. This paper proposes a method to optimize green light durations by analyzing footage collected from a traffic junction in Beng.luru. This analysis can be performed using existing CCTV footage or manually gathered data. Even limited sample sizes can provide valuable insights for preliminary adjustments. The approach utilizes YOLOv10, a state-of-the-art computer vision algorithm, to extract relevant traffic data. The data is processed by an algorithm designed to reallocate green light durations, which are then compared with the original timings. An experimental setup involved recording one-minute videos over three days at the selected junction and applying the proposed algorithm to assess and refine signal durations. The results demonstrate the feasibility of using such algorithms to improve traffic signal management effectively. © 2024 IEEE.

关键词： Investments

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