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

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Fast Underwater Scene Restoration for Non-Uniform Degradation 5

Fast Underwater Scene Restoration for Non-Uniform Degradatio...

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5th international conference on Emerging Systems and Intelligent computing, ESIC 2025

作者： Pranev, S.S. Prakash, Matheshwaran Prakash, Varun Kalai Selvi, T. Meena, R. Easwari Engineering College Chennai India

ISBN: (纸本)9798331522100

Deep learning-based underwater image restoration models achieve great performance when dealing with uniform artifacts in the image. However, most existing approaches primarily focus on distortion due to non-uniform illumination, neglecting the impact of non-homogeneous suspended particles in the water. This research introduces a system for fast underwater scene visibility enhancement using a deep multi-patch hierarchical neural network. The system aims to remove non-homogeneous artifacts in underwater environments in high speed. The proposed system is robust to different underwater environments with non-uniform artifacts. By improving the clarity of camera feeds in challenging underwater conditions, the system supports a wide range of underwater exploration applications. © 2025 IEEE.

关键词： Photointerpretation

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Comparative Analysis of Virtual teaching-learning and In-Person teaching-learning 2019-2022: Ricardo Palma University, Lima, Peru 13

Comparative Analysis of Virtual Teaching-Learning and In-Per...

引用

Decima Tercera Conferencia Iberoamericana de Complejidad, Informatica y Cibernetica, CICIC 2023 en el contexto de the 14th international Multi-conference on Complexity, Informatics, and Cybernetics, IMCIC 2023 - 13th Ibero-American conference on Complexity, Informatics and Cybernetics, CICIC 2023 in the context of the 14th international Multi-conference on Complexity, Informatics, and Cybernetics, IMCIC 2023

作者： Méndez, María T. Solís, Irma R. Gonzalez, Juan C. Centro de Estudios para Comunidades Saludables-CECOS-BRIGURP Universidad Ricardo Palma Lima15039 Peru

ISBN: (纸本)9781950492695

In 2019, Peru initiated population lockdowns for biosecurity reasons, which significantly impacted the education sector. To prevent delays in student learning, the government opted for remote teaching in various modalities, despite the technological, socioeconomic, and geographical limitations that Peru faces. Upon returning to in-person classes, deficiencies in students' educational development were observed, which were assumed to be a product of changes in the education system. It is noted that these modes of teaching and learning have social, psychological, and even physical side effects, as students and teachers are subjected to isolation, lack of physical activity, and social interaction. The objective of the study was to analyze the post-pandemic educational scenario for a group of engineering students at Ricardo Palma University in 2022. These students experienced both virtual teaching on the Blackboard Collaborate platform and later returned to in-person classes. The study aimed to analyze the advantages and disadvantages of using information technologies (IT) in virtual and in-person teaching and learning, recognize the problems, their consequences, and propose necessary adjustments. The study concludes that the most commonly used IT tools in the virtual teaching and learning process from 2019 to 2022 were the Blackboard Collaborate platform and WhatsApp. It recommends implementing a hybrid teaching approach to improve the teaching and learning process. © 2023 *** Rights Reserved.

关键词： Students

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Machine learning-Enhanced Blockchain Solutions for Sustainable Energy Consumption and Carbon Footprint Tracking

Machine Learning-Enhanced Blockchain Solutions for Sustainab...

引用

2024 OPJU international Technology conference on Smart computing for Innovation and Advancement in Industry 4.0, OTCON 2024

作者： Lakhanpal, Sorabh Kumar, S Vinod Raj, Vijilius Helena Yadav, Garima Yadav, Dinesh Kumar Saadoon, Mohammed Q. Lovely Professional University Phagwara India Institute of Aeronautical Engineering Hyderabad Dundigal India New Horizon College of Engineering Department of Applied Sciences Bangalore India GyanGanga Institute of Technology and Sciences DoCSE M.P Jabalpur India Lloyd Institute of Engineering & Technology Knowledge Park II Uttar Pradesh Greater Noida201306 India National University of Science and Technology College of Pharmacy Dhi Qar64001 Iraq

ISBN: (纸本)9798350373783

This study looks at what blockchain technology and machine learning can do to help achieve optimal energy use and monitor carbon footprints. A method that combines federated learning with LSTM has been presented to give a real-time perspective of energy use. Furthermore, an immutable blockchain record is now accessible to publicly document carbon emissions. To achieve the above indicated goals, both possible solutions are under consideration. Studies show that the above process is more accurate, efficient, and long-lasting than several other widely used methods. This result was achieved by extensive model runs and real-world data analysis. According to the research, it can promote environmentally friendly energy habits while lessening its detrimental effects on the environment. Blockchain technology and machine intelligence together might help build a strong system to protect energy-related data, make sure it's secure, and build trust. Blockchain technology might be used to create this system. Thanks to blockchain technology, these two sites may now work together. © 2024 IEEE.

关键词： Federated learning

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Sentiment Analysis of YouTube Comments Using Machine learning Algorithm

Sentiment Analysis of YouTube Comments Using Machine Learnin...

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2024 IEEE international conference on computing, Applications and Systems, COMPAS 2024

作者： Singha, Rahul Das, Swarna Das, Kapil Hasan, Md. Premier University Department of Computer Science and Engineering Chittagong Bangladesh

ISBN: (纸本)9798331529765

The extensive user engagement on YouTube leads to a flood of comments, creating challenges for content creators who aim to understand audience sentiment. Previous studies have mainly concentrated on distinguishing between positive, negative, and neutral sentiments in comments. This study involves creating a dataset of YouTube comments and classifying them into positive, negative, interrogative, corrective, imperative, and neutral categories, which will help content creators visualize other sentiments alongside positive, negative, and neutral sentiments. Based on the sentiment, they can improve their content and also enhance user interaction. Six machine learning algorithms - Naive Bayes (NB), Logistic Regression (LR), Decision Tree (DT), Random Forest (RF), K-Nearest Neighbors (KNN), and Support Vector Machine (SVM) - are applied, with the SVM model demonstrating outstanding accuracy, reaching 94%. © 2024 IEEE.

关键词： Adversarial machine learning

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Advancing Deep learning on Edge Devices: Fine-Tuning and Deployment of YOLOv7 Model for Efficient Object Detection in AI based Computer Vision Applications 3

Advancing Deep Learning on Edge Devices: Fine-Tuning and Dep...

引用

3rd international conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Shekhar, Sudhanshu Sathwik, T.S. Pritwani, Mayank Mohana Ramakanth Kumar, P. Sreelakshmi, K. RV College of Engineering® Bengaluru India

ISBN: (纸本)9798331527549

This paper investigates the optimization and deployment of YOLOv7 deep learning model on NVIDIA Jetson Nano, an AI-focused edge computing platform for object detection in various computer vision applications. The work leverages TensorRT and quantization techniques for model acceleration for good detection accuracy. Further it examines performance metrics such as speed, accuracy, and resource utilization for image dataset. The model is trained using 80 different classes of objects and demonstrates the use of 6 classes. The average detection accuracy obtained 92.35% and the average processing time is 117.8ms. This work supports AI by demonstrating the feasibility of running deep learning models on edge devices and provides insight into the challenges and opportunities of optimizing AI models for energy-efficient, real-time operations on edge devices for various computer vision applications. © 2025 IEEE.

关键词： Adversarial machine learning

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Design and Application of an English-assisted teaching System based on Recurrent Neural Network-Long Short-Term Memory 2

Design and Application of an English-assisted Teaching Syste...

引用

2nd international conference on Data Science and Information System, ICDSIS 2024

作者： Gui, Ling Shanghai Industrial And Commercial Polytechnic Shanghai China

ISBN: (纸本)9798350381665

English education is emerging in a path of adapted learning, and self-study, that is not limited through space and time, is supported by the modern information technology, especially the network technology. The traditional teaching techniques of teachers, classrooms, books are unable to meet requirement of information society. In this research, Deep learning (DL) technique named as Recurrent Neural Network-Long Short-Term Memory (RNN-LSTM) is proposed for English assisted teaching system. At the language practice stage, the help system act as learning, talking in English with learners. The secondary system is organized to reproduce learner English stylishness which plays a main role in task situation. The next step of the linguistic practice part involves the implementation of multiple roles for the system that match the user's definition of identity. It does import discussion topics covering different domains and is designed for the modification and restructuring of such ideas. The performance of RNN-LSTM is calculated by metrics like f1-score, precision, accuracy, and recall. The RNNLSTM achieves 98.64% f1-score, 98.83% precision, 99.27% accuracy, 98.39% recall when compared to Machine learning-based Gaussian Process Model (MLGPM). © 2024 IEEE.

关键词： Long short-term memory

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Signal Detection of MIMO System with One Bit ADCS Based on Deep learning 7

Signal Detection of MIMO System with One Bit ADCS Based on D...

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7th international conference on Contemporary computing and Informatics, IC3I 2024

作者： Shrivastava, Anurag Maindola, Meenakshi Alabdeli, Haider Gopal, K. Thandra, Nagarjuna Bansal, Saloni Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Tamilnadu Chennai India Graphic Era Deemed to be University Department of Computer Science & Engineering Uttarakhand Dehradun India The Islamic university College of technical engineering Department of computers Techniques engineering Najaf Iraq New Horizon College of Engineering Department of Mechanical Engineering Bangalore India Mlr Institute of Technology Department of Computer Science and Engineering Telangana Hyderabad India Gla University Department of Computer Engineering and Applications Uttar Pradesh Mathura India

ISBN: (纸本)9798350350067

Due to the quantization noise created by analog-to-digital converters (ADCs) with just one bit of resolution, signal identification in Multiple-Input Multiple-Output (MIMO) systems is a very difficult task to perform. This is due to the fact that MIMO systems have many inputs in addition to a large number of outputs. The conventional methods of detection have a difficult time properly handling this noise. In this study, we describe a novel strategy based on deep learning for improving signal identification performance in MIMO systems like these that employ ADCs with just one bit of resolution. The method of deep learning that we recommend takes advantage of the power of neural networks to generate intricate connections from information that has been quantized to one bit. We provide the model with the ability to learn to differentiate between the several separate delivered signals while simultaneously decreasing the impacts of quantization noise by training it on a massive dataset consisting of MIMO channel realisations. This is done by providing it with the opportunity to learn from a variety of MIMO channel realisations. In-depth simulations are carried out in order to evaluate how well our deep learning-based approach to identifying signals works, and the results obtained from these simulations are compared to the findings obtained from more conventional detection methods. The results of our research indicate that deep learning algorithms perform better than traditional ones in terms of bit error rate (BER) and symbol recognition accuracy, particularly in challenging conditions with low signal-to-noise ratios (SNR). © 2024 IEEE.

关键词： Quantization (signal)

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Detecting the Eye Movement to Control the Cursor Movement of the system 4

Detecting the Eye Movement to Control the Cursor Movement of...

引用

4th IEEE international conference on Data engineering and Communication Systems, ICDECS 2024

作者： Padma, R. Yadav, Suchitra T Keerthana, R.J. Tejeshwari, U.S. Kenche, Amulya Ramaiah University of Applied Sciences Computer Science and Engineering Bengaluru India Ramaiah University of Applied Sciences Information Science and Engineering Bengaluru India Ramaiah University of Applied Sciences Artifical Intelligence and Machine Learning Bengaluru India

ISBN: (纸本)9798350393354

A novel approach to cursor control via real-time eye movement that employs a synergistic combination of OpenCV and machine learning approaches. The ability to operate a cursor in a natural and intuitive manner using eye tracking has the potential to revolutionize human-computer interaction, particularly for people with motor limitations or those looking for more natural and efficient interfaces. The suggested method makes use of OpenCV for robust eye detection and tracking, laying the groundwork for precise gaze estimation. Convolutional Neural Networks (CNNs) are trained to recognize different gaze patterns, enabling precise and dependable cursor control. Over time, the machine learning component adapts the system to individual users, optimizing efficiency and improving user experience. This advances human-computer interaction by presenting a unique and practical cursor solution based on real-time eye movement. The combination of OpenCV, deep learning, and machine learning creates a strong and adaptable framework that paves the way for more intuitive and inclusive computing interfaces in a variety of applications. © 2024 IEEE.

关键词： Convolutional neural networks

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Exploring Advanced Techniques in Computer and Network Forensics for Enhanced Cybersecurity

Exploring Advanced Techniques in Computer and Network Forens...

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2024 OPJU international Technology conference on Smart computing for Innovation and Advancement in Industry 4.0, OTCON 2024

作者： Nagpal, Amandeep Kumar, S Vinod Reddy, Uma Radha, K. Kumar, Ashwani Abdulla, Namaat R. Lovely Professional University Phagwara India Institute of Aeronautical Engineering Hyderabad Dundigal India New Horizon College of Engineering Department of Artificial Intelligence and Machine Learning Bangalore India St. Martin's Engineering College Dept. of It Telangana Secunderabad India Lloyd Institute of Engineering & Technology Knowledge Park II Uttar Pradesh Greater Noida201306 India National University of Science and Technology College of Health and Medical Technology Dhi Qar64001 Iraq

ISBN: (纸本)9798350373783

Because cybersecurity is continually changing, it's crucial to have effective, adaptable defenses. This paper demonstrates a novel computer security method. This is done via blockchain technology, enhanced cryptography, threat intelligence integration, machine learning-based strangeness detection, and behavioral analysis. After carefully comparing our solution to others, we feel it is the best way to secure computer and network systems. The Machine learning-Based Anomaly Identification approach accurately identifies anomalies using complex mathematical calculations and repetitive procedures to adjust parameters. Blockchain technology unites odd data into a decentralized ledger using encryption for forensics efficiency. Homomorphic encryption and quantum-resistant signatures protect the program against new attacks. The proposed method exhibits superior performance across various metrics, achieving a detection accuracy of 98.5% with a low false positive rate of 1.0%. Visualizations elucidate trade-offs, trends, and classification metrics. This cybersecurity framework offers a promising solution for addressing the dynamic challenges posed by a wide range of cybersecurity threats. Its adaptability, versatility, and collective efficacy position it as a noteworthy advancement in the pursuit of securing digital environments from evolving and sophisticated cyber threats. © 2024 IEEE.

关键词： Adversarial machine learning

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Secure Data Transmission in IoT Networks: A Machine learning-Based Approach 9

Secure Data Transmission in IoT Networks: A Machine Learning...

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9th international conference on Science, Technology, engineering and Mathematics, ICONSTEM 2024

作者： Subathra, K. Vignesh, G.R. Babu, S. B. G. Tilak Mendhe, Dinesh Yada, Ramesh Kumar Maranan, Ramya Sri Sairam Engineering College Department of Mathematics Tamil Nadu Chennai44 India Ssn College of Engineering Department of Computer Science Tamil Nadu India Aditya Engineering College Department of Ece Surampalem India Office of Research Computing Rutgers The State University of New Jersey New Brunswick United States Icfai University Fst Department Chhattisgarh Raipur India Saveetha School of Engineering Simats Department of Research And Innovation Chennai India

ISBN: (纸本)9798350365092

This research studies the application of machine learning to enhance the security of data transport for the. of Things (IoT). Conventional encryption methods might not be adequate in Internet of Things scenarios because of the fluidity and limited resources presented by these contexts. In the course of our research, we make use of fictional Internet of Things data in order to evaluate the effectiveness of machine learning models for anomaly detection, intrusion detection, and data encryption. When it comes to resolving difficulties related to the security of the internet of things (IoT), the findings suggest that machine learning is better to traditional cryptography methodologies. The Internet of Things (IoT) security is going to be strengthened by the introduction of new strategies, dynamic security frameworks, privacy-preserving methodologies, and practical implementations in the near future. It is possible that the implementation of machine learning will make the ecosystem of the Internet of Things (IoT) more safe and efficient, which would, in turn, foster innovation across a wide range of industries. © 2024 IEEE.

关键词： Internet of things

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