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检索条件"任意字段=International Conference on Computer Technology and Development"

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Survey For Evaluating the Improvement of User's Mandarin Proficiency as a Second Language by Using Duolingo's Mobile App

Survey For Evaluating the Improvement of User's Mandarin Pro...

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2023 international conference on Networking, Electrical Engineering, computer Science, and technology, IConNECT 2023

作者： Tiocuardy, Willard Wijaya, Ricky Kurniawan, Bryan Viriya Agung Santoso Gunawan, Alexander Saputra, Karen Etania Bina Nusantara University School of Computer Science Computer Science Department Jakarta11480 Indonesia Bina Nusantara University School of Computer Science Computer Science Department Bandung Campus 40181 Indonesia

ISBN: (纸本)9798350331172

The Chinese language, also commonly known as Mandarin is the second most widely spoken language with 1.10 billion speakers, trailing only behind English. In addition, Mandarin is recognized as one of the most difficult languages in the world. Nowadays, the use of language-learning applications has become prevalent, with Duolingo standing out as one of the most popular choices. This study aims to assess the effectiveness of the Duolingo application in teaching Mandarin to second language users (L2). This research will encompass evaluations of the app's user interface (UI), user experience (UX), user development, and overall improvement. The primary objective of this study is to ascertain the suitability of Duolingo as a platform for learning Mandarin and how the application's interface impacts the learning experience of its users. The results indicate that the survey participants demonstrated significant improvement in their Mandarin skills and the UI/UX of the application received a positive rating thus contributing to effective learning. In conclusion, Duolingo is well-suited for L2 beginners aiming to grasp the basics of a new language, and consistency in learning is crucial for achieving positive advancements. © 2023 IEEE.

关键词： User interfaces

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Green Leaf Disease Detection Using Raspberry Pi and Random Forest Machine Learning Algorithm: An Automated Approach for Precision Agriculture 10

Green Leaf Disease Detection Using Raspberry Pi and Random F...

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10th international conference on Smart Computing and Communication, ICSCC 2024

作者： Ranganathan, Chitra Sabapathy Basha, Amjath Ali Jakul Vijayalakshmi, A. Mohankumar, N. Sujatha, S. Sasikala, C. Mphasis Corporation Tamil Nadu Chennai India University of Technology and Applied Sciences Computer Engineering Electrical and Electronic Section Department of Engineering Ibra Oman Chennai Institute of Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Maharashtra Nagpur India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Biomedical Engineering Tamil Nadu Chennai India Sri Sai Ranganathan Engineering College Department of Electronics and Communication Engineering Tamil Nadu Coimbatore India

ISBN: (纸本)9798350363104

Farm production and food security are affected by green leaf diseases. Modern disease detection technologies may discover plant health issues early. This work uses Raspberry Pi, a cheap and flexible single-board computer, along the advanced Random Forest machine learning technique to identify a green leaf disease. To identify crop diseases, the study uses automated, non-invasive technologies. From labeled images of good and sickly leaves, the algorithm extracts color histograms and texture, and form. It feeds a Random Forest classifier these properties for classifying data with high dimensions. Several green leaf diseased plant species were examined. This method detected plant health concerns with high precision. Movement and low cost make the Raspberry Pi ideal for remote and resource-limited agriculture, increasing farming accuracy. Helping farmers and users identify and develop green leaf diseases improves efficient farming. This methodology has the potential to improve crop management, optimize pesticide usage, and increase agricultural productivity. These results might contribute to the development of advanced automatic disease detection techniques to address green leaf diseases in modern agriculture. © 2024 IEEE.

关键词： computer Vision Green Leaf Plant Disease Detection Precision Agriculture raspberry pi

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Interactive Design of Smart Screen Mirroring for Multimedia Projector Using Combined JDK and Rasberry Pi 5

Interactive Design of Smart Screen Mirroring for Multimedia ...

引用

5th IEEE international conference on technology, Informatics, Management, Engineering and Environment, TIME-E 2024

作者： Yapinus, Pin Panji Lukman, Selvi Tanubrata, Markus Wong, Hendry Universitas Kristen Maranatha Computer Engineering Department Bandung Indonesia School of Computer Science Universitas Bina Nusantara Bandung Indonesia

ISBN: (纸本)9798350364743

Multimedia projector has been beneficial in enhancing the learning system. New innovations are conducted to improve the reliability and easiness of utilizing the multimedia projector. However, the utilization of VGA (Video Graphics Array) cable in multimedia projector comes with some significant limitations that contribute to their eventual obsolescence. As a multimedia projector can support the learning skills for both teacher and students, the limitation of space especially in the device localization must be under investigation. This research aims to introduce a smart mirroring device in which Java JDK and Raspberry Pi 3 is implemented for software and hardware utilization. The research has resulted an average delay of 1.75 seconds of device mirroring in which this delay time has contributed to a more practical easiness for respondents whenever presentation must be conducted. The combination of Java JDK and Raspberry Pi 3 can replace VGA cable and other devices wherever needed. For future works, a more thorough consideration must be focused on the steadiness of objects, download speed and the utilized internet provider as signals are transmitted using Wi-Fi (Wireless Fidelity). © 2024 IEEE.

关键词： Obsolescence

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Improving Offline Handwritten Text Recognition Accuracy with ADAM and SGD Optimizers and Convolutional Neural Networks Models 4

Improving Offline Handwritten Text Recognition Accuracy with...

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4th international conference on Intelligent Cybernetics technology and Applications, ICICyTA 2024

作者： Sarwo Binus Online Learning Bina Nusantara University Computer Science Department Jakarta Indonesia

ISBN: (数字)9798331506490

ISBN: (纸本)9798331506490

Offline Handwritten Text Recognition (OHTR) is a challenging topic in pattern recognition and computer vision due to the variability and complexity of handwriting styles. This study explores the effectiveness of Convolutional Neural Networks (CNN) in improving the accuracy of OHTR systems. Specifically, this study evaluates the performance of ADAM and Stochastic Gradient Descent (SGD) optimizers in training a CNN model. The experiments were conducted on a public handwritten dataset to assess the impact of optimizers on the convergence speed, stability, and overall accuracy of the model. The results show that although both optimizers have advantages, the SGD optimizer performs superiorly in terms of faster convergence and higher accuracy in most scenarios. However, ADAM shows better generalization under certain dataset conditions, indicating that the choice of optimizer may depend on the dataset's characteristics and the specific model's requirements. In this study, it was concluded that using the SGD CNN model gave better results. From 5 experiments with SGD, 5 correct results and 1 incorrect result were obtained, while using ADAM, 2 correct results, and 3 incorrect results were obtained. The researcher realized that things could change based on different CNN conditions and parameters. This study contributes to the development of a more accurate OHTR system by providing a deeper understanding of the effectiveness of optimizers in CNN training. The results of this study are expected to be a guide in choosing the right optimizer for future OHTR systems, with the hope of improving the accuracy and reliability of handwritten text recognition. © 2024 IEEE.

关键词： Convolutional neural networks

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SPEAR: Design and Implementation of an Advanced Virtual Assistant 4

SPEAR: Design and Implementation of an Advanced Virtual Assi...

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4th international conference on Sustainable Expert Systems, ICSES 2024

作者： Jain, Garima Shukla, Amita Bairwa, Nitesh Kumar Chaudhary, Anamika Patel, Ashish Jain, Ankush Noida Institute of Engineering and Technology Department of Computer Science and Buisness Systems Greater Noida India Noida Institute of Engineering and Technology Department of Artificial Intelligence Greater Noida India Noida Institute of Engineering and Technology Department of Data Science Greater Noida India Dronacharya Group of Institutions Department of Artificial Intelligence and Machine Learning Greater Noida India Netaji Subhash University of Technology Department of Computer Science and Enginnering Delhi India

ISBN: (纸本)9798331540364

This research presents the development and evaluation of SPEAR, an advanced voice-activated personal desktop assistant designed to address challenges in existing virtual assistant technology, such as limited language support, low recognition accuracy, and inadequate task automation. Despite recent advancements in Natural Language Processing (NLP) and machine learning, implementation in current systems remains subpar. SPEAR achieves an 88% overall command execution success rate, with a speech recognition accuracy of 92% and an average response time of 1.8 seconds, demonstrating efficiency in real-time interactions. Developed using Python and machine learning libraries, SPEAR supports 15 voice commands across multiple platforms, excelling in note creation (95% success), web searching (94%), and information retrieval (92%). Its natural language understanding score of 0.86 and context retention rate of 75% highlight its conversational capabilities. While the assistant performs well in messaging functionality (85% success) and currently supports three languages, expanding its multilingual capabilities is essential for broader user engagement. Metrics indicate a satisfaction score of 8.2/10 and an average of 12 daily interactions per user, showcasing its practical utility. Additionally, the system's text-to-speech quality (8.5/10) and error recovery rate (82 %) affirm its robustness. These findings contribute valuable insights for future research, focusing on enhancing multilingual support, broadening task categories, and improving context retention, ultimately positioning SPEAR to rival established industry leaders by offering a comprehensive suite of services. © 2024 IEEE.

关键词： Speech recognition

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ShadowNets: Efficient and Accurate Face Recognition for Resource-Constrained Devices 25

ShadowNets: Efficient and Accurate Face Recognition for Reso...

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25th international conference on Digital Image Computing: Techniques and Applications, DICTA 2024

作者： Mirza, Inzela Iqbal, Shahzaib Alhaqbani, Bandar Alshammari, Bandar Khan, Tariq Razzak, Imran Department of Computing Islamabad Pakistan Tcc Research and Development Labs Technology Control Company Riyadh Saudi Arabia Computer and Information Sciences College Jouf University Saudi Arabia School of Computer Science and Engineering University of New South Wales Sydney Australia

ISBN: (纸本)9798350379037

Over recent years, deep convolutional neural networks have advanced the field of face recognition in both verification and identification applications. In this paper, we present ShadowNets, a highly efficient and compact face recognition network. We employed multiple shadow blocks in a cascading manner where the output of each shadow block is given as the input to the next shadow block. By integrating the advantages of Focal Modulation Blocks, Shadow Block and Ghost Modules, our proposed ShadowNets achieves superior face recognition performance, optimized specifically for edge devices. This network architecture not only minimizes computational costs and storage needs but also delivers high accuracy, making it suitable for deployment in edge environments. Our extensive experiments on challenging benchmark face datasets showcase the outstanding performance by achieving accuracy 98.85% on LFW, 84.37% on CFP-FP, and 91.23% on AgeDB-30 which validates the effectiveness of ShadowNets over state-of-the-art lightweight and deep face recognition models. © 2024 IEEE.

关键词： Convolutional neural networks

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Decentralized Application Identification via Burst Feature Aggregation 26

Decentralized Application Identification via Burst Feature A...

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26th international conference on computer Supported Cooperative Work in Design, CSCWD 2023

作者： Yang, Chen Wang, Can Zhang, Weidong Zhang, Huiyi Wu, Xuangou Anhui University of Technology School of Computer Science and Technology Ma'Anshan China Anhui Engineering Research Center for Intelligent Applications and Security of Industrial Internet Ma'Anshan China

ISBN: (纸本)9798350331684

With the development of blockchain technology, de-centralized applications (DApps) are increasingly being developed and deployed on blockchain platforms. However, the complex data validation mechanism and strict encryption protocol settings of blockchain often lead to sparse traffic behavior of DApps. This sparsity poses a challenge for existing encrypted traffic identification methods to extract distinguishable DApps traffic features. In this study, we propose a novel approach for identifying DApps traffic features by observing the differences in burst timing features of DApps. We introduce a continuous burst feature matrix (CBFM) method based on burst feature aggregation that can aggregate sparse features and express the burst timing differences of DApps encrypted traffic. Additionally, we design a deep learning classifier to automatically extract the features contained in the CBFM. Our experimental results on real datasets demonstrate that the proposed CBFM method achieves a classification accuracy of 94%, outperforming state-of-the-art methods. © 2023 IEEE.

关键词： Blockchain

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Research on Teaching Model Innovation in the Context of Smart Learning 18th

Research on Teaching Model Innovation in the Context of Smar...

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18th international conference on computer Science and Education, ICCSE 2023

作者： Wang, Hao Li, Huiyan Hospitality Institute of Sanya Sanya China Sanya Universtiy Sanya China

ISBN: (纸本)9789819707362

Information technology is an important source and force for the current education reform and development. It has improved the innovation of teaching methods and teaching strategies as a tool;It has expanded the scope of teaching resources as the content;It has leaded the development of education as a tendency. It has spawned a new educational form——smart education. The concept of smart education has contributed to the occurrence of smart learning, and smart learning can be realized in a smart learning context. In the smart learning environment, not only the educational philosophy, models, and forms has changes greatly, but also the methods in teaching and learning will appear revolutionary changes. Therefore, research on the teaching strategy in the smart learning context is one of the issues that must be studied and solved by the current teaching model innovation and reform environment, and it is also the prerequisite for ensuring the implementation of smart learning. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： computer aided instruction

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Leveraging Pre-trained Deep Learning Models for Orange Leaf Disease Classification 9

Leveraging Pre-trained Deep Learning Models for Orange Leaf ...

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9th IEEE international conference for Convergence in technology, I2CT 2024

作者： Vats, Satvik Singh, Abhay Narayan Kukreja, Vinay Sharma, Rishabh Graphic Era Hill University Computer Science And Engineering Uttarakhand Dehradun India Uttar Pradesh Greater Noida201310 India Chitkara University Institute of Engineering And Technology Chitkara University Punjab India

ISBN: (纸本)9798350394474

The research work investigates the use of pre-trained deep learning models, VGG16 and EfficientNetB3, for multi-class classification of orange leaf diseases. The paper aims at improving the accuracy and efficiency of disease diagnosis in orange crops which is also a contribution to the development of agricultural diagnostic tools. Using intensive data preprocessing, model adaptation, and fine-tuning, the research shows the models' ability to detect five specific disease classes as well as a healthy leaf. The VGG16 model achieved an overall accuracy of 89.5%, whereas the EfficientNetB3 model exceeded this with an accuracy of 92.3%, which demonstrates that the latter is better at capturing disease patterns with an optimally balanced network. The findings demonstrate that pre-trained models can be used in plant pathology and provide avenues for new research in agricultural technology. Through the strengths of VGG16 and EfficientNetB3, this study underlines the great advantages of deep learning models in disease detection procedures to promote sustainable and productive agricultural practices. © 2024 IEEE.

关键词： Precision agriculture

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development of Cloud-Based LoRa Web Service 5

Development of Cloud-Based LoRa Web Service

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5th international conference on computer Communication and the Internet, ICCCI 2023

作者： Tanonvarakitti, Yawinta Sutthisangiam, Nikorn King Mongkut's University of Technology North Bangkok Department of Computer and Information Science Bangkok10800 Thailand

ISBN: (纸本)9798350326956

This paper presents a web server to support the LoRaWAN network, which is a low-power and long-range wireless communication. The system developed by LoRaServer, which is open-source software and adds functionality to be able to collect data and show results with a graph to the web server. In addition, the system has alerts when a data error occurs by sending e-mail to the user. This paper simulates the LoRaWAN network, using Lopy and Pysense boards from Pycom company, which is a microcontroller board that can use LoRa by mean Semtech LoRa transceiver SX1272 chip at a 923 MHz frequency. We divided Lopy into 3 nodes and 1 gateway. From the experimental results, the system can send and receive data on the LoRaWAN network and show results to the web server developed © 2023 IEEE.

关键词： Open source software

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