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

共 108002 条记录，以下是4431-4440 订阅

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Bridging the Modality Gap: Generative Adversarial Networks for T1-T2 MRI Image Translation 15

Bridging the Modality Gap: Generative Adversarial Networks f...

引用

15th international conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Pandey, Mayank Kunda, Naga Sai Shreya Pranave, K.C. Singh, Tripty Nair, Rekha R. Amrita Vishwa Vidyapeetham Amrita School of Computing Dept. of Computer Science and Engineering Bengaluru India Alliance University Alliance College of Engineering and Design Dept. of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350370249

CycleGAN, a significant deep learning model, has transformed medical imaging by providing a remedy for translating MRI images between different modalities. Its ability to transform T1-weighted to T2-weighted images is crucial in healthcare environments where obtaining multiple scans can be time-consuming and burdensome for patients. This can be achieved by utilizing CycleGAN, which generates images that contains vital information that will help the healthcare professionals to diagnose the patient without exposing them to further radiations. This unleashes an effective and safer way of diagnosing patients. The adaptability of CycleGAN also allows scalability and the ability to customize it for different types of medical images, where scanning can be achieved with less exposure to radiation, without loosing or compromising any important information. © 2024 IEEE.

关键词： Discriminator GAN Generator Magnetic Resonance Image T1 T2

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A Heterogeneous Semi-Supervised learning Network for Road Damage Detection 9

A Heterogeneous Semi-Supervised Learning Network for Road Da...

引用

9th international conference on Intelligent computing and Signal Processing, ICSP 2024

作者： Guo, Chen Su, Yiheng Zuo, Chen Chang'an University School of Information Engineering Xi'an China

ISBN: (纸本)9798350376548

Road damage detection is important for intelligent traffic systems and the sustainability of transportation infrastructure. Utilizing deep learning methodologies, we can achieve automation and improve efficiency in the context of road damage detection. However, conventional deep learning-base object detection methods require extensive labeled data and struggle with generalization. In order to alleviate the demand of data annotations, we introduce a semi-supervised road damage detection method using an ensemble of heterogeneous detectors. Cascade RCNN, YOLO, and DETR models are employed for diverse learning and reduced cognitive bias. Iterative training and pseudo-label generation have a positive effect on model robustness. An adaptive weight fusion framework improves detection accuracy. Validated on a drone-captured highway database, the method indicates superior performance. Our method trained on a dataset of 200 labeled images and 5000 unlabeled images, reaches 89% mAP50, marking a 21% enhancement compared to Soft Teacher. It provides an economical, precise, and dependable approach for road maintenance and repair operations. © 2024 IEEE.

关键词： Semi-supervised learning

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Enhancing Healthcare through the Symbiosis of Blockchain and Machine learning in Preprocessing Skin Disease Images: A Review 15

Enhancing Healthcare through the Symbiosis of Blockchain and...

引用

15th international conference on Advances in computing, Control, and Telecommunication Technologies, ACT 2024

作者： Rokade, Sonali Mishra, Nilamadhab School of Computer Science and Engineering Vellore Institute of Technology Bhopal India

ISBN: (纸本)9798331300579

Recent advancements in skin disease identification leverage machine learning for automated diagnosis. However, safeguarding the integrity of sensitive medical data remains a top priority. This paper explores the integration of machine learning and blockchain in skin disease identification preprocessing, promising trust, transparency, and accuracy in dermatological diagnosis. The preprocessing workflow starts with gathering a diverse, labeled skin image dataset, followed by data cleaning and augmentation to enhance quality and quantity. Standardization methods like resizing and normalization ensure data consistency, while addressing class imbalances prevents model bias. Blockchain technology is introduced to secure data integrity. Each skin image and its metadata are encrypted and timestamped on a decentralized blockchain, guaranteeing tamper-proof authenticity. Machine learning models, typically based on CNNs, extract features and classify diseases, benefiting from high-quality preprocessed data. During training, blockchain securely records model parameters and progress, ensuring transparency and auditability, crucial for medical accountability and *** fusion of machine learning and blockchain enhances diagnostic accuracy while addressing data privacy and security concerns, potentially revolutionizing dermatological healthcare with a trustworthy, automated diagnosis system. © Grenze Scientific Society, 2024.

关键词： Data integrity

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Machine learning Assisted Sanitizer Quality Identification Using Permittivity Measurement 11

Machine Learning Assisted Sanitizer Quality Identification U...

引用

11th IEEE international conference on Advances in computing and Communications, ICACC 2024

作者： Thushara, H.P. Mridula, S. Neema, K. CUSAT Division of Electronics School of Engineering Kerala India CUSAT Department of Electronics Kerala India

ISBN: (数字)9798350379136

ISBN: (纸本)9798350379136

Sanitizers play a crucial role in hygiene and the prevention of infectious diseases by reducing harmful microorganisms on surfaces. This paper focuses on alcohol-based sanitizers, specifically those containing ethanol and isopropyl alcohol, known for their rapid action and broad spectrum efficacy. The effectiveness of these sanitizers depends on the alcohol content, with water enhancing their performance. Traditional quality detection methods are accurate but time-consuming. This work proposes a support vector machine based classification model combined with a complex permittivity measurement system to assess sanitizer quality. The model achieves 97.2% accuracy for a frequency range of 0.2-20 GHz at 25°C, with improved accuracy of 99% in the 2-5 GHz range, identifying this as the optimal frequency range for sanitizer classification. This method is compatible with any permittivity measurement system with measurement error ±15%. Significant time savings in quality assessment is also achieved. © 2024 IEEE.

关键词： Permittivity measurement

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An analysis comparing CT and X-ray images for COVID-19 identification employing deep learning and image processing methods 3

An analysis comparing CT and X-ray images for COVID-19 ident...

引用

3rd international conference on Smart Technologies and Systems for Next Generation computing, ICSTSN 2024

作者： Prabu, R. Dhinakar, P. Bharath Institute of Higher Education and Research Department of Electronics and Communication Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350391565

This study presents a comparative analysis of deep learning approaches for the identification of COVID-19 pneumonia in medical imaging, specifically focusing on CT and X-ray modalities. To aid healthcare professionals in quickly identifying infected individuals during the COVID-19 pandemic, accurate and efficient diagnostic tools are urgently required. In order to identify COVID-19 more quickly and accurately, deep learning algorithms combined with medical imaging data produced results. Computed Tomography (CT) and X-ray imaging are commonly used modalities for assessing lung involvement in COVID-19 patients, each offering unique advantages and challenges. Leveraging deep learning techniques, such as convolutional neural networks (CNNs), this analyzes the performance of models trained on large datasets of labeled CT and X-ray images. Our analysis includes preprocessing methods, model architectures, training procedures, and evaluation metrics to assess the efficacy of these approaches. Furthermore, this discusses the clinical implications, challenges, and ethical considerations associated with integrating deep learning-based diagnostic tools into routine clinical practice. © 2024 IEEE.

关键词： COVID-19

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Use of Generative AI for Audio Speech Recognition: Methods and Selection Criteria 6

Use of Generative AI for Audio Speech Recognition: Methods a...

引用

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

作者： Parmanand, Satish L. Chandankhede, Pankaj H. Deshmukh, Atul R. Electronics and Telecommunication Engineering Department G H Raisoni College of Engineering Nagpur India

ISBN: (纸本)9798331522667

The generative AI has transformed human speech recognition systems audio-wise it was impossible prior. In this paper, various generative AI methods for speech recognition like End-to-end deep learning models, such as Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs) and Transformer Models are described. A comprehensive evaluation framework is introduced in it to aid in the selection of appropriate generative AI models with respect to their performance and computational efficiency as well as language/dialect neutrality. Results indicate that, though generative AI substantially improves the performance of speech recognition in relation to accuracy and robustness, some guidelines need to be defined for an effective selection procedure aimed at maximizing system efficiency. Furthermore, the study highlights a call for further work to improve integration of these powerful AI models in practice-oriented solutions and establish their effectiveness and scalability for practical deployment. Improving these guidelines will allow for more accurate and trustworthy speech recognition systems going forward. © 2025 IEEE.

关键词： Generative adversarial networks

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Exploration of Blended teaching in Data Mining Course Based on STEM Education Concept 2023

Exploration of Blended Teaching in Data Mining Course Based ...

引用

8th international conference on Information and Education Innovations, ICIEI 2023

作者： Zhu, Yingxue Liu, Mingyou Wang, Yue Wu, Tao School of Biology and Engineering Guizhou Medical University China

ISBN: (纸本)9798400700613

With the deep development of big data, artificial intelligence and other technologies, data thinking ability, engineering practice ability and theoretical innovation ability have become the core educational objectives for colleges and universities to cultivate high quality composite "new engineering"talents. In view of the current situation that the teaching content of traditional computer courses is boring and difficult to understand, the teaching is teacher-centered, which is difficult to stimulate students' interest in learning, and the course content is limited by credit hours to improve the teaching effect, this paper takes the teaching mode design of data mining course as an example to explore the teaching design of data mining course in colleges and universities based on the concept of STEM engineering education, and proposes an inquiry-based teaching design concept which is student-centered, problem-oriented and case-driven, and uses online and offline blended teaching method to realize students' personalized learning needs and encourage students to realize active learning, aims to cultivate students' data thinking ability, engineering practice ability and theoretical innovation ability, and to cultivate teamwork and the ability to use data mining knowledge to analyze and solve problems in the process of project practice, so as to explore a new path for cultivating high-quality composite "new engineering"talents. © 2023 ACM.

关键词： Data mining

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Deep learning and Dynamic BPSO for Road Accident Severity Prediction using Twitter Data 15

Deep Learning and Dynamic BPSO for Road Accident Severity Pr...

引用

15th international conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Raul, Sanjib Kumar Rout, Rashmi Ranjan Somayajulu, Dvln National Institute of Technology Department of Computer Science and Engineering Telangana Warangal506004 India

ISBN: (纸本)9798350370249

The traffic and medical authorities utilize accident severity prediction through social media postings for better administration and robust emergency response. Deep learning-based models can be used for accident severity prediction using social media data. However, most of the deep learning methods can only perform well when trained with an optimal set of hyper-parameters. This necessitates the use of meta-heuristics in conjunction with deep learning. Moreover, the increase in depth of the networks warrants partial training of the network to make the training process efficient. In this work, we propose a novel partial training-based hybrid deep neural network on convolutional neural networks (CNN) and long short-term memory (LSTM), which is partially trained, and the hyperparameters are optimized using a dynamic version of binary particle swarm optimization (DBPSO) known as accident tweet hybrid deep dynamic BPSO (AT-HDBPSO). Experiments show that the proposed AT-HDBPSO turns out to be efficient in training compared to traditional deep learning-based training. Finally, the proposed method outperforms other baseline and state-of-the-art techniques in prediction accuracy. © 2024 IEEE.

关键词： Tweets

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A Machine learning-Based Approach for Anomaly Detection for Secure Cloud computing Environments

A Machine Learning-Based Approach for Anomaly Detection for ...

引用

2023 international conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2023

作者： Parameswarappa, Priya Shah, Taral Lanke, Govinda Rajulu University of the Cumberlands School of Technology United States Pace Univerty School of Information System United States Birla Institute of Technology and Science Data Science and Engineering India

ISBN: (纸本)9781665474511

The concept of "cloud computing"has been presented as a promising strategy for providing online service hosting and distribution. Despite the widespread adoption of cloud computing, security remains a top priority. Several secure methods have been devised to safeguard communication in such scenarios, with the majority of these solutions based on attack signatures. Unfortunately, these technologies cannot always detect every possible danger. A machine learning method was recently outlined. The judgment could be inaccurate if the training set is missing examples from a certain category. In this research, an innovative firewall strategy for safe cloud-based computing is presented using machine learning system. The proposed methods estimate the final assault category categorization by combining the judgments of the nodes from the past with the decision of the machine learning algorithm in the present, a technique termed most frequent decision. Both learning efficiency and system precision are improved by this method. Our results are based on UNSW-NB-15, a publicly available dataset. According to the evidence provided by our data, it improves anomaly detection by 97.68 percent. A Machine learning-Based Approach for Anomaly Detection for Secure Cloud computing Environments © 2023 IEEE.

关键词： Anomaly detection

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Advancing AI-Driven BETC Models for Transformative Educational Data Mining and learning Analytics 24

Advancing AI-Driven BETC Models for Transformative Education...

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2024 international Symposium on Artificial Intelligence for Education, ISAIE 2024

作者： Chen, Zhiqiang Chen, Yanru Jinhua University of Vocational Technology Zhejiang Jinhua China Jinhua No.1 High School Zhejiang Jinhua China

ISBN: (纸本)9798400707100

This research paper presents an innovative integration of Artificial Intelligence (AI) with the British Technology Education Council (BETC) method, specifically tailored for civil engineering education. The study introduces an AI-driven BETC model that integrates real-world engineering data, industry standards, and virtual construction technologies to significantly enhance learning efficiency and teaching feedback precision. The model was rigorously evaluated through quantitative metrics, demonstrating a substantial increase in pre-class task completion rates from 30% to 87%, accuracy of achievements from 38% to 82%, and student satisfaction from 56% to 97%. These results not only validate the effectiveness of the AI-BETC model but also highlight the transformative potential of AI in educational data mining and learning analytics within the civil engineering sector. The study's findings underscore the importance of adopting advanced technologies in educational practices to improve outcomes and prepare students for the challenges of the future. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Students

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