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检索条件"任意字段=International Conference on Teaching and Learning in Computing and Engineering"

共 107108 条记录，以下是121-130 订阅

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AI-Powered Advertisement Design: A PIL-Based Approach for Quality and Performance Analysis 3

AI-Powered Advertisement Design: A PIL-Based Approach for Qu...

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3rd international conference on Intelligent and Innovative Technologies in computing, Electrical and Electronics, IITCEE 2025

作者： Chilupuri, Harshitha Ramesh, G. Praveen, J. Gude, Venkataramaiah Gokaraju Rangaraju Institute of Engineering and Technology Dept. of Artificial Intelligence and Machine Learning Hyderabad India Gokaraju Rangaraju Institute of Engineering and Technology Dept. of Computer Science and Engineering Hyderabad500090 India Gokaraju Rangaraju Institute of Engineering and Technology Department of Electrical and Electronics Engineering Telangana Hyderabad500 090 India Gp Technologies Llc United States

ISBN: (纸本)9798331515911

The paper presents the development of a Data Science solution that applies generative AI models in order to create high-quality images to be used in visual marketing and advertising. Using the Python library PIL (Pillow) along with machine learning frameworks, the solution combines predefined product images with the selected backgrounds, enhancing image synthesis techniques for more appealing and contextually relevant visuals. This leads to improved efficiency and effectiveness in the creation of content by automatically placing product images onto the background images used in marketing campaigns. The approach demonstrated a 98.06% SSIM (Structural similarity Index) score, indicating significant visual quality improvement. Initial results suggest a 15% enhancement in visual appeal compared to traditional image creation methods. © 2025 IEEE.

关键词： Data Science

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"empowering Smart teaching with AIGC in the Ïntegration of Specialization and Innovation\": An Exploration and Practice" 3

"empowering Smart Teaching with AIGC in the Ïntegration of ...

引用

3rd international conference on Artificial Intelligence and Education, ICAIE 2024

作者： Wang, Yu Wang, Bi Zhengzhou University of Economics and Business Henan Zhengzhou China Guizhou Medical University Guiyang Guizhou China

ISBN: (纸本)9798400712692

With the rapid development of Artificial Intelligence Generated Content (AIGC) technology and computer science innovations, its application in education is being explored. This paper examines and experiments with the innovative use of Artificial Intelligence Generated Content (AIGC) in a 'specialized and integrated' educational and teaching model, assessing its impact on pedagogical innovation, improving student creativity, and optimizing educational resources. Using computer technologies such as machine learning, data analytics, and natural language processing, AIGC can dynamically generate tailored instructional content and interactive learning experiences. While reviewing the implementation approaches and insights of AIGC-driven teaching and learning, we propose a viable educational framework that combines AIGC and computing technologies to enhance the intelligence, personalization, and flexibility of the teaching and learning process. This approach aims to provide scientific and practical guidance for engineering education reform in the context of emerging digital technologies. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： AIGC Integration of Specialization and Innovation Intelligent teaching Personalized learning

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Exploring Few-Shot learning to Enhance NLP's Cross-Domain Capabilities

Exploring Few-Shot Learning to Enhance NLP's Cross-Domain Ca...

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2025 international conference on computing for Sustainability and Intelligent Future, COMP-SIF 2025

作者： Balakrishnan, T. Suresh Gururama Senthilvel, P. Ebenezar, U. Samson Karthikeyan, L. Kishan, B.S. Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Chennai India Saveetha University Department of Computer Science and Engineering Chennai India Bms Institute of Technology and Management Department of Mechanical Engineering Bangalore India

ISBN: (纸本)9798331538538

Here, few shot learning (FSL) has been identified as a valuable method for improving the flexibility of NLP when dealing with new and different datasets with small amounts of training data. Most of the traditional NLP models are affected by shift since they are based on a big data set of a different domain. To overcome this challenge, FSL facilitates training of models to extrapolate from a few examples and not necessarily from numerous samples that may be present in LMMs. In this paper, I delve into meta-learning, prompt-based learning, and transfer learning as some of the methods that can be applied to boost NLP's cross-domain performance. We explain how these methods contribute to enhance performance in the use of fewer resources and evaluate the efficiency of those techniques on textual classification, sentiment analysis, and machine translation. Also, the pretrained GPT and BERT are beneficial to few-shot learning as it takes place through contextual learning. Based on our results, the conclusion is made that the application of FSL techniques in NLP pipelines enhances generalization and reduces the time and expenses on data annotation. Yet, some of the challenges that are associated with text analytics are data bias, sensibility of the prompts and computational problems. Finally, we propose some future works for FSL such as: the proposal of a new FSL model that combines traditional supervised and unsupervised learning;the presentation of a new FSL model based on a reinforcement learning theory. Hence, with the use of FSL, NLP models can perform well in different domains of application with little to no labeled data hence transferring knowledge to different domains to increase efficiency and scale in knowledge. © 2025 IEEE.

关键词： Sentiment analysis

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Enhancing Tactile Sensor Technology with Neuromorphic Models and Machine learning 6

Enhancing Tactile Sensor Technology with Neuromorphic Models...

引用

6th international conference on Mobile computing and Sustainable Informatics, ICMCSI 2025

作者： Lenka, Komali Jyothi, Pulaparthi Lakshmi Asha Brahma Ramesh, Kallakuri N.V.P.S. Lanka, Dasaradha Ramayya Srikanth, M. Rajesh, Jonnapalli Tulasi Dept of IT S.R.K.R.Engineering College India India AP Bhimavaram India Dept of Artificial intelligence and Machine learning Aditya University India Dept of IT SRKR Engineering College Bhimavaram India

ISBN: (纸本)9798331522667

This study presents a novel approach to enhancing touch sensor technology by utilizing machine learning techniques and neuromorphic models. Tactile sensing has struggled to accurately understand complex physical interactions, despite its significance for artificial touch and robotic dexterity. Our approach improves the classification of tactile data by using neuromorphic computing—models inspired by brain processing—to identify changes in the environment, variations in force, and textures. We evaluate our model on benchmark tactile datasets and demonstrate its superior sensory resolution and increased adaptability. For robotics, prosthetics, and human-machine interfaces, the results are very important because they show that neuromorphic tactile sensors improve the accuracy and speed of data classification. This study showcases the potential of neuromorphic models to improve tactile perception, paving the way for more advanced and adaptable sensory systems. © 2025 IEEE.

关键词： Prosthetics

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Automated diabetes diagnosis and risk assessment using machine learning 2nd

Automated diabetes diagnosis and risk assessment using machi...

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2nd international conference on Next-Generation computing and Information Systems, ICNGCIS 2023

作者： Mathukiya, Deep Kumar, Yogesh Koul, Apeksha Department of Computer Science and Engineering School of Technology Pandit Deendayal Energy University Gandhinagar India Punjabi University Punjab Patiala India

ISBN: (纸本)9781032738659

Currently, Diabetes is a prevalent issue worldwide, affecting millions of people. The detection machinery for this disease is only available at healthcare centers, so individuals with diabetes are only aware of their condition when they visit and undergo a checkup. Prolonged diabetes can lead to various complications like heart disorders, kidney disease, nerve damage, and diabetic retinopathy. However, the risk can be reduced if the disease at its early stage is predicted. By utilizing a deep learning approach, this model can easily determine if an individual is diabetic or not. The aim of the research is to develop machine learning model that can identify diabetic people on the basis of their clinical data. The machine learning algorithms utilized in this study include Naive Bayes (NB), k-nearest neighbor (KNN), Stochastic Gradient Descent (SGD), Decision tree (DT), Support Vector Machine (SVM), Random Forest (RF), and Logistic Regression (LR). We have implemented efficient pre-processing techniques to detect as well as handle missing/ NAN and duplicate values and later trained our models with the cleaned dataset. During execution, we found that Random Forest as well as Decision Tree performed well for both diabetic and non-diabetic class by computing 100% and 99% accuracy respectively. © 2025 The Author(s).

关键词： Decision trees

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Exploring Computer Science Student Perceptions on Service learning: Online and On-Campus Modality Comparative Study 16

Exploring Computer Science Student Perceptions on Service Le...

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16th international conference on Society and Information Technologies, ICSIT 2025

作者： Wright, Sarah Lara, Miguel School of Computing and Design California State University Monterey Bay SeasideCA93955 United States

ISBN: (纸本)9781950492831

This study examines computer science students’ perceptions of their service learning experiences, comparing fully online and on-campus modalities. Service learning is a teaching approach that integrates community engagement with academic learning, offering students opportunities to apply technical knowledge in real-world settings while addressing community needs. An electronic mixed methods survey was developed, tested for validity and reliability, and distributed to 110 college students enrolled in both modalities of our 400-level Race, Gender, and Class in the Digital World course. About 57% of students responded to the survey: 50 on campus and 13 fully online. Using survey data, this research evaluates students’ views on skill development, career preparation, and their ability to apply computer science concepts to practical challenges. Additionally, qualitative analysis of open-ended responses reveals insights into students’ reflections on social responsibility, professional growth, and challenges faced during their service learning. The findings indicate minimal differences between the two modalities, suggesting that service learning offers consistent benefits regardless of how it is delivered. However, unique challenges were highlighted, including managing expectations in virtual collaborations and navigating non-technical client relationships. Implications for curriculum design, strategies to foster deeper community impact, and directions for future research are discussed, emphasizing the value of service learning in computer science education. © 2025 international conference on Society and Information Technologies. All Rights Reserved.

关键词： Students

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An Innovative Method for Detecting Brain Tumors that Combines Artificial Intelligence (AI), Machine learning (ML), and Deep learning with Artificial Data Augmentation 6

An Innovative Method for Detecting Brain Tumors that Combine...

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6th international conference on Mobile computing and Sustainable Informatics, ICMCSI 2025

作者： Appavu, Narenthirakumar Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Tamil Nadu Chennai602105 India

ISBN: (纸本)9798331522667

Effective treatment and better patient outcomes are made possible by early brain tumor identification. Traditional diagnostic techniques are trustworthy, but they frequently depend on unreliable and time-consuming manual examination. In order to improve brain tumor diagnosis from MRI images, this work combines HOG The Histogram of Oriented Gradients (HOG)HOG for extracted features, HOG Support Vector Machine (SVM)HOG clustering over segmentation, HOG the ResNet50HOG for deep learning-mediated classification, including HOG based data augmentation HOG. SVM clustering overcomes the drawbacks of linear clustering techniques by efficiently segmenting tumor zones. Texture-based characteristics that offer useful information for classification, such contrast and homogeneity, are extracted using HOG. The ResNet50 model fine-tunes its layers according to the retrieved characteristics, delivering great accuracy thanks to its strong deep learning architecture. By mitigating data scarcity, synthetic data augmentation ensures the generalizability and robustness of the model. Results from experiments on publically accessible datasets show notable gains in specificity, accuracy, and efficiency, underscoring the usefulness of this strategy in offering dependable and expandable automated detection of brain tumors systems. © 2025 IEEE.

关键词： Deep learning

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Obstacle Avoidance: Transfer learning and FPGA for Acceleration 6

Obstacle Avoidance: Transfer Learning and FPGA for Accelerat...

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6th international conference on Mobile computing and Sustainable Informatics, ICMCSI 2025

作者： Jeyalakshmi, V. Chakkrapani, Arjun Jansirani, Preethaa Kripa, S. Vaithiyanathan, D. Department of Electronics and Communication Engineering College of Engineering Guindy Tamil Nadu Chennai600025 India Department of Electronics and Communication Engineering NIT Delhi India

ISBN: (纸本)9798331522667

Obstacle detection and avoidance have historically been manual processes. With the advent of neural networks, many processing tasks have been made autonomous with more efficient by obtaining a wide variety of data from their surroundings to perform the next move. Training these networks is computationally intensive, which became the driving force behind transfer learning. Field Programmable Gate Arrays (FPGAs) which revolutionized reconfigurable logic is the next choice considered the opportunity to interleave the processing tasks between a general-purpose processor and dedicated logic blocks. Extending this concept to collision detection and using hardware acceleration to speed up the decision process can be highly beneficial, especially in such time critical applications. In this article, the idea above has been reviewed, and a method to implement the system has been proposed. © 2025 IEEE.

关键词： Convolutional neural networks

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Postprandial Blood Glucose Level Prediction through combined Machine learning, Meta-learning and XAI 25

Postprandial Blood Glucose Level Prediction through combined...

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26th international conference on Distributed computing and Networking, ICDCN 2025

作者： Aruna Devi, B. Karthik, N. Computer Science and Engineering National Institute of Technology Puducherry Puducherry Karaikal India

ISBN: (纸本)9798400710629

Postprandial blood glucose (PPBG) is the glucose level measured after a meal and it is one of the main factors for Type 2 Diabetes Mellitus (T2DM) management. This work aims to predict PPBG level of T2DM patients by addressing the problems of clinical interpretability, limited number of patient dataset and generalizability. The proposed work aims to build a machine learning (ML) model that integrates appropriate feature selection, meta-learning to improve the generalizability and Explainable Artificial Intelligence (XAI) for interpretability. The preliminary work employed Random Forest (RF) feature selection, Independent Component Analysis (ICA) feature extraction technique and followed by training of five ML models such as Support Vector Regression (SVR), Xtreme Gradient Boosting (XGB), Random Forest (RF), Convolutional Neural Network (CNN) and Multilayer Perceptron (MLP). RF produced low Root Mean Square Error (RMSE) of 30.91 followed by CNN and MLP. © 2025 Copyright held by the owner/author(s).

关键词： Convolutional neural networks

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Position Paper: Enhancing the learning and Mastery of Academic Writing in the Serbian Language Through an AI Tool with Adaptive Scaffolding 17

Position Paper: Enhancing the Learning and Mastery of Academ...

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17th international conference on Computer Supported Education, CSEDU 2025

作者： Francišković, Teodor Sakal Gajić, Dušan Luburić, Nikola Slivka, Jelena Department of Computing and Control Engineering Faculty of Technical Sciences University of Novi Sad Trg Dositeja Obradovića 6 Novi Sad Serbia

ISBN: (纸本)9789897587467

Academic writing is a significant challenge for many learners striving for proficiency. Adaptive scaffolding techniques and AI tools in education have proven effective in addressing this challenge and supporting learners in improving their academic writing skills when used correctly. This position paper proposes combining adaptive scaffolding techniques with AI tools in a public university's final year academic writing course to enhance the learning experience and mastery of academic writing skills in Serbian. The proposed plan outlines the course structure and details how the AI-driven adaptive scaffolding techniques will be integrated to support the learning experience, focusing on summative and formative feedback from the AI tool. The proposed plan is a work in progress. It will be implemented in the next iteration of the course for evaluation, taking into account potential counter-arguments and their impact on the tool's development and the student's learning experience and outcomes. This study will analyse our plan's effectiveness in enhancing the learning experience and outcomes. The expected outcome is to assist students in their learning while contributing to the development of AI in education and the Serbian language. Copyright © 2025 by SCITEPRESS - Science and Technology Publications, Lda.

关键词： Students

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