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

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Sea Surface Temperature Prediction using Sample Convolution and Interaction Network 15

Sea Surface Temperature Prediction using Sample Convolution ...

引用

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

作者： Gayathri, P. Yalavarthi, Rahul Varma, Jayanth Santosh Gopakumar, G. Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Amritapuri India

ISBN: (纸本)9798350370249

Predicting oceanic phenomena plays a very important role in ensuring maritime safety, managing coastal areas and preparing them for any disasters like cyclone or tsunami. There are several factors that contribute to these severe weather events among which Sea Surface Temperature(SST) is a crucial one. Warmer oceans can often lead to stronger storms or even frequent heavy downpours, highlighting the importance of accurately forecasting SST. This study explores the performance of a deep learning model called Sample Convolution and Interaction Network (SCINet) in forecasting SST. This model follows a decompose-downsample-convolve-interact architecture that helps in learning complex trends in the data. On comparing SCINet’s performance with well established time-series models like Long-Short Term Memory (LSTM) network, it was able to achieve almost 4% - 20% lower Mean Absolute Error (MAE) than LSTM on various testing points. This highlights SCINet’s potential effectiveness in time-series forecasting, contributing to the ongoing development and revolutionary advancements in deep learning models for time series prediction. ©2024 IEEE.

关键词： Long short-term memory

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Sound Sight-A TTS and STT Integrated Model Using OCR and ASR

Sound Sight-A TTS and STT Integrated Model Using OCR and ASR

引用

2024 international conference on Innovative computing, Intelligent Communication and Smart Electrical Systems, ICSES 2024

作者： Deepak Kumar, A. Anush Varath, S. Aravindan, R. Balamurali, A. St. Joseph's Institute of Technology Department of Computer Science and Engineering Chennai India

ISBN: (数字)9798331543624

ISBN: (纸本)9798331543617

This paper discusses the feasibility of using an automated pipeline that is text-to-speech and speech-to-text, which will be powered with the usage of Optical Character Recognition (OCR) that may be used for extracting text from images or documents and Automatic Speech Recognition (ASR) which can be used for the benefit of using algorithms and machine learning techniques to give more accurate results. The paper also explores the system architecture, with also a look at the methodology as it implements the technologies. © 2024 IEEE.

关键词： Speech recognition

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Problems of Building Machine learning Models in teaching Students 5

Problems of Building Machine Learning Models in Teaching Stu...

引用

5th international conference on Control in Technical Systems, CTS 2023

作者： Zhukova, Nataly A. Subbotin, Alexey N. Saint-Petersburg Federal Research Centre of the Russian Academy of Sciences St. Petersburg Russia Saint Petersburg Electrotechnical University 'LETI' St. Petersburg Russia

ISBN: (纸本)9798350304664

The article deals with the problems that arise in the process of teaching students in courses involving the development of machine learning models. The subject area is analyzed. The main problems are the low speed of building models and their verification. A solution method based on the use of fog and cloud computing environments is proposed. The conducted experimental studies showed that the time for building models decreased by 22.17 times, the speed of the teacher's work increased by 47 %. The presented method of increasing the speed of building models can be applied in laboratory work, in the management of final qualifying works. Also, the method is applicable for building machine learning models in solving practical problems in the field of production, urban economy, in the provision of transport services, etc. © 2023 IEEE.

关键词： Machine learning

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teaching Exploration on Calculation Method Under the Background of Emerging engineering Education 5th

Teaching Exploration on Calculation Method Under the Backg...

引用

5th EAI international conference on Multimedia Technology and Enhanced learning, ICMTEL 2023

作者： Bi, Shuhui Ma, Liyao Xu, Yuan Yan, Xuehua School of Electrical Engineering University of Jinan Shandong China

ISBN: (纸本)9783031505799

The Calculation Method is a complicated theoretical and strong practical subject, which is used to study and solve the problem of numerical approximate-calculation and is used to solve mathematical problems on the computer. Under the background of emerging engineering education and under the requirement of current concept and requirements of engineering education professional certification, this paper explores the teaching ideas of Calculation Method course based on the teaching status and the existing problems. The innovative educational reform of the course is discussed from the aspects of the integration and expansion of teaching content, the combination of online and offline teaching mode driven by teaching purpose, the construction of teaching system and so on. So as to improve the knowledge of the course, increase the interaction between teachers and students, release the initiative of students, and achieve the purpose of improving the teaching effect of the course. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications engineering 2024.

关键词： teaching

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Research on Laboratory Unsafe Behavior Recognition Based on Robust Time Feature learning Advancement 2

Research on Laboratory Unsafe Behavior Recognition Based on ...

引用

2nd international conference on Signal Processing and Intelligent computing, SPIC 2024

作者： Li, Xinran Chen, Liang Liu, Wenhong Shenyang Ligong University College of Automation and Electrical Engineering Shenyang China Northeastern University College of Information Science and Engineering Shenyang China

ISBN: (纸本)9798350368888

In order to solve the problem of dangerous accidents caused by unsafe behaviors in university laboratories, We propose an improved method based on the RTFM model. This method combines Dilated convolution and multi-head attention mechanism to form a new feature aggregation module and improve the sensitivity to abnormal features. In addition, the Gated Recurrent Unit (GRU) is introduced to capture the timing information in the video, effectively solving the problem of false detection when RTFM detects anomalies. Experimental results show that compared with the original model, the method in this paper improves AUC by 4%. Effectively improves the accuracy of identifying abnormal behaviors of laboratory personnel. © 2024 IEEE.

关键词： Problem solving

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Google Applications in the Position of learning Management System 2.0 in Comparison with Moodle 15th

Google Applications in the Position of Learning Management S...

引用

15th international Scientific conference on Distance learning in Applied Informatics, DiVAI 2024

作者： Burgerová, Jana Piskura, Vladimír Faculty of Education University of Presov Presov Slovakia

ISBN: (纸本)9783031812606

The most widespread form of electronic education, which is also used by the current generation of university students, is Blended learning. LMS systems are generally used for this electronic support of face-to-face teaching. The aim of the presented study was to verify the impact of a specific e-learning environment on the knowledge and evaluation of the e-course by students of the University of Presov through a natural pedagogical experiment. By the term specific e-learning environment, we understood in the work a complex of Google applications, which were interpreted as a learning Management System. The results show a zero effect between the choice of e-learning environment, i.e. between the LMS Moodle and Google Apps platforms, with regard to the proven knowledge and subjective evaluation of the e-course across qualitatively diverse courses. We note that both educational systems are equally technologically demanding for students. Within the generation of students in whom we automatically assume computer knowledge, we cannot expect them to acquire it themselves. In the context of the results of the experiment, it is therefore necessary to devote more intensively to the promotion of the use of controlled teaching systems for tertiary education students, to ensure support for students and teachers, e.g. in the form of multimedia guides—tutorials and by combining LMS Moodle and Web 2.0, 3.0, Google Apps, or systems available at universities such as Office 365. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Contrastive learning

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Deep Reinforcement learning in Stock Trading: Evaluating DDPG and DQN Strategies 6

Deep Reinforcement Learning in Stock Trading: Evaluating DDP...

引用

6th IEEE international conference on Emerging Smart computing and Informatics, ESCI 2024

作者： Kodurupaka, Nithin Basavadeepthi, H.M. Pecheti, Shiva Teja Amudha, J. Amrita School of Computing Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350306613

This study presents a comparative analysis of the Deep Q-Network (DQN) and Deep Deterministic Policy Gradient (DDPG) reinforcement learning algorithms in the context of stock trading, focusing on historical stock price data of S&P 500 stock index. The primary objective is to evaluate the effectiveness of these RL algorithms in making trading decisions and achieving financial performance relative to a passive buy-And-hold strategy. The assessment revolves around key metrics, with a special emphasis on the buy, hold, and sell indications as essential signals for trading actions. Results indicate that DQN outperforms DDPG, showcasing a smaller total loss during the evaluation period. This suggests that DQN may offer superior capabilities in limiting financial losses and making effective trading decisions compared to DDPG, providing valuable insights into algorithm selection for dynamic trading environments. It is crucial to recognize the significance of buy, hold, and sell indications as key elements in assessing the performance of RL algorithms in stock trading scenarios. © 2024 IEEE.

关键词： Algorithmic Trading Buy-And-Hold Strategy DDPG Trading Agent Deep Neural Networks Deep Reinforcement learning DQN Portfolio Performance Total Return

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Research of OMO teaching Mode Based on Data Driven 4

Research of OMO Teaching Mode Based on Data Driven

引用

4th international conference on Educational Technology, ICET 2024

作者： Zhao, Guohui Shanghai Open University Fengxian Branch Shanghai China

ISBN: (纸本)9798350376944

The epidemic has spurred the transformation of information technology education, timely promoting the transformation of traditional classrooms to OMO, and making the OMO teaching model a new form of future education. The paper takes the teaching activity design of the course "Management Psychology"as an example, analyzes the learning situation from three dimensions: teaching preparation, teaching process, and teaching effectiveness, sorts out the design ideas, proposes teaching strategies, and optimizes teaching design. Based on the three stages of pre class, course, and post class, from obtaining demand data information, analyzing teaching behavior, to adjusting and optimizing teaching design, an OMO mode teaching design topology diagram was constructed. And rational thinking was proposed regarding the organization of teaching content and the development of teaching forms, which should follow the psychological order and characteristics of adult students. At the same time, good suggestions were put forward on how to optimize the interface of the live streaming software (Classin) and improve the custom report statistics function of the Shanghai Open University platform. This provides reference and inspiration for the OMO teaching model in the post pandemic era. © 2024 IEEE.

关键词： Classin platform Data driven OMO teaching Mode

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A Method to Reduce the Incidence of Wheat Leaf Diseases

A Method to Reduce the Incidence of Wheat Leaf Diseases

引用

2024 international conference on Advances in computing Research on Science engineering and Technology, ACROSET 2024

作者： Verma, Riya Rathore, Narendra Pal Singh Sethi, Kamal Kumar Acropolis Insitute of Technology and Research Department of Computer Science Indore India

ISBN: (纸本)9798350388800

Numerous studies have delved into the domain of wheat disease prediction, frequently leveraging meticulously curated datasets compiled by researchers. This review comprehensively examines a spectrum of research endeavours, with a primary focus on elucidating classification methodologies and the breadth of disease manifestations analysed. By systematically surveying diverse studies, we scrutinize the approaches employed in interpreting disease patterns and the methodologies employed for data collection, while also addressing their inherent limitations. Through this thorough analysis, we shed light on the intricate facets of wheat disease prediction, providing invaluable insights into the current state of the field and delineating promising avenues for future research and development. © 2024 IEEE.

关键词： CNNs (Convolutional Neural Network) DI (Deep learning) Leaf Diseases VGG Model

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Deep learning-Based Identification of Skin Cancer on Any Suspicious Lesion 4

Deep Learning-Based Identification of Skin Cancer on Any Sus...

引用

4th international conference on computing, Mathematics and engineering Technologies, iCoMET 2023

作者： Aaqib, Muhammad Ghani, Musawir Khan, Ayaz University of Engineering and Technology Dept. Mechatronics Engineering Peshawar Pakistan

ISBN: (纸本)9798350335316

Certain estimates place skin cancer as the most lethal form of the disease worldwide. Spreading to other parts of the body and requiring invasive procedures like chemotherapy and radiation therapy are the results of delayed diagnosis. As a result, automatic methods have greatly aided medical professionals and allowed even non-specialists to identify the sort of cancer a patient has. In this paper, we present a deep learning-based skin cancer identification to classify images of skin lesions as malignant or benign. This method may be used for any potentially malignant lesion. We found that using a deep learning (DL) approach that relied on a mask region-based convolutional neural network yielded the fastest and most accurate results for diagnosing skin cancer. © 2023 IEEE.

关键词： Convolutional neural networks

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