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检索条件"任意字段=2023 International Conference on Computer Engineering and Distance Learning, CEDL 2023"

共 40755 条记录，以下是4331-4340 订阅

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Plant Leaf Disease Detection: A Deep Hybrid learning Approach

Plant Leaf Disease Detection: A Deep Hybrid Learning Approac...

引用

2023 IEEE international conference on Modelling, Simulation and Intelligent Computing, MoSICom 2023

作者： Kumar, Santosh Singh, Shivam National Institute of Technology Department of Computer Engineering Kurukshetra136119 India

ISBN: (纸本)9798350393415

Recent breakthroughs in Deep learning have led to a shift in the classification methods for plant infections towards automated feature detection. In this work, we evaluate the performance of existing algorithms for identifying plant leaf diseases on a particular dataset. In addition, we aim to identify the most efficient methods for detecting and classifying plant leaf diseases. First, we employed state-of-the-art techniques such as VGG16, VGG19, and RESNET50. Due to the small number of members per class in the data set, these techniques could not produce satisfactory results. On the other hand, traditional machine learning procedures require an expert to identify and validate feature extraction approaches. Consequently, our goal has been to design a hybrid model combining both advantages. We envisioned developing a method for detecting plant leaf diseases. © 2023 IEEE.

关键词： Convolutional neural networks

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A learning Resource Management Model for high-enrollment Programming courses in engineering 4

A Learning Resource Management Model for high-enrollment Pro...

引用

4th international conference on Electrical, Communication and computer engineering, ICECCE 2023

作者： Lopez-Chila, Roberto Mora-Saltos, Nelson Cedeño-Tello, Alicia Llerena-Izquierdo, Joe Gie-Ticea Educational Innovation Group Universidad Politécnica Salesiana Guayaquil Ecuador TICAD Researh Group Universidad Politécnica Salesiana Guayaquil Ecuador GIEDIC Researh Group Universidad Politécnica Salesiana Guayaquil Ecuador Gieaci Researh Group Universidad Politécnica Salesiana Guayaquil Ecuador

ISBN: (纸本)9798350369694

In courses where enrollment is high, especially in courses in the first years of university study, the set of elements necessary for the curriculum implies that the same professor develops the study materials, adapts them, and integrates them into the virtual learning environment provided by the institution. Higher education institutions offer courses for a common subject each semester, which generates an administrative juncture. This paper aims to present a learning resource management model for high enrollment programming courses in engineering. It highlights the collaborative work initiated in the pandemic by a team of teachers who share academic and research interests to enhance student learning. An empirical-analytical work methodology with a quasi-experimental and longitudinal approach was developed. Four phases of work are developed in semestral periods at the Universidad Politécnica Salesiana, Guayaquil, Ecuador. From October 2019 to September 2023, in semesters numbered 55 to 63. About 240 students participate in this offer each semester. The survey technique is used in period 62, to a group of 151 students in a randomized manner to identify the set of significant activities for their learning. The results, with 95% confidence and a margin of error of 4.9%, determine that the learning activities housed in the high enrollment Programming courses have allowed students to achieve a 78% satisfaction rate for the use of activities that allow them to improve their knowledge in the topics addressed. It is concluded that having a base course for its duplication and integration of new learning elements, allow teachers to decrease the burden of configuration, integration and selection of activities, directing their efforts to create new, improve and optimize the existing ones, awakening the teacher's creativity and directing their efforts to the retention of students, as well as a belonging to worry about students at risk or with academic difficulties. © 2023 IEEE.

关键词： Students

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GNSS Spoofing Detection Based Correlation and distance for Vehicle Application 7

GNSS Spoofing Detection Based Correlation and Distance for V...

引用

7th IEEE international conference on Smart Internet of Things, SmartIoT 2023

作者： Yang, Yanzhao Guo, Jun Wang, Shouwei Yang, Jiesen Liu, Hong China Automotive Innovation Corporation Department of Automotive Cybersecurity Nanjing China Software Engineering Institute East China Normal University Shanghai China

ISBN: (纸本)9798350316575

Autonomous vehicle (AV) has been a hot topic in recent years, but some safety accidents of some self-driving cars have also aroused people's concern about the safety of AV. Among all AV security threats, GNSS spoofing attack is becoming a technology that can be used artificially and has an excellent potential threat to AV security. This paper proposes a GNSS spoofing detection method based on correlation and distance. The spoofing attack simulation experiment results suggest that a delayed attack can be detected with nearly 99.2% probability in one second alarm time. © 2023 IEEE.

关键词： Autonomous vehicles

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Automatic Detection and Classification of Wild Animal Species Using YOLO Models 3

Automatic Detection and Classification of Wild Animal Specie...

引用

3rd international conference on Pervasive Computing and Social Networking, ICPCSN 2023

作者： Thangaraj, Rajasekaran Charly Jerome, J. Sanjith, M. Rithick Saran, K. Sasikumar, Sudev Vijayakumar, S. Kpr Institute of Engineering and Technology Department of Computer Science and Engineering Coimbatore India Kpr Institute of Engineering and Technology Department of English Coimbatore India

ISBN: (纸本)9798350322842

Using a camera trapping framework, which employs cameras activated by sensors to record a burst of pictures of animals in their habitat, it is feasible to monitor animals in the open. This paper explores the effectiveness of YOLOv3 and YOLOv5 object detection algorithms in automated animal tracking through the use of computer vision. A dataset of six animal classes was utilized to train and test both models. The results demonstrate that YOLOv5-m achieves an 81% detection accuracy rate, surpassing that of YOLOv3. In addition, the two models were evaluated based on detection speed and model size, with YOLOv5-m providing higher accuracy and comparable speed and model size to YOLOv3. Overall, these findings highlight the potential of computer vision and deep learning in automated animal tracking, and emphasize the importance of selecting appropriate algorithms for various use cases. © 2023 IEEE.

关键词： Animals

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Conceptual Architecture for the Provision and Aggregation of Universal Digital Twins within Containerization Environments

Conceptual Architecture for the Provision and Aggregation of...

引用

2023 international conference on Electrical, computer, Communications and Mechatronics engineering, ICECCME 2023

作者： Häußermann, Tim Lehmann, Joel Rache, Alessa Reichwald, Julian Mannheim University of Applied Sciences Virtual Engineering Competence Center Mannheim68163 Germany

ISBN: (纸本)9798350322972

Digital Twins (DTs) are technological cutting-edge enablers for future industrial revolutions. Intelligent behavior is nevertheless not often granted to them so that DTs are tied as passive data representations organized in repository-like structures. In order to create intrinsic value, intelligent structures, and approaches must be established leveraging DTs through intelligence and autonomy. The concept can be easily transferred to other application domains, which offers particular advantages for use-case-independent architecture developments. This paper elaborates on a methodology for creating DTs device independently deployed on arbitrary computing nodes with variable configurations tailored to its specific requirements. A learning capability is induced by the migration and adoption of new intelligent code components. After requirements on DTs are derived, a conceptual architecture is outlined. A partial implementation substantiates the feasibility of the presented structure. © 2023 IEEE.

关键词： Cyber Physical System

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Plant Pathology Detection for Massive Heterogeneous Data Using Deep learning 14

Plant Pathology Detection for Massive Heterogeneous Data Usi...

引用

14th international conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Chaudhari, Avinash Jani, Kuntesh Patel, Sunil Modasa Gujarat Technological University Government Engineering College Information Technology Department Modasa India Gujarat Technological University L.D. College of Engineering Information Technology Department Ahmedabad India Gujarat Technological University L.D. College of Engineering Computer Engineering Department Ahmedabad India

ISBN: (纸本)9798350335095

The manual inspection of plant disease is a difficult and time-consuming task that requires expertise in the area of plant pathology which is not always available. Deep learning models have shown significant efficiency in solving image classification problems. They can be used in the field of precision agriculture for disease management tasks. State-of-the-art deep learning models are used in several studies to automatically detect diseases based on the plant leaves symptoms. Most of the studies have been carried out on single or limited plants. In this study large dataset having a total number of 1,02,113 images is created by combining 16 openly available datasets. A comparative performance analysis of three CNN models, ResNet50, MobileNetV2, and InceptionV3 has been carried out after training with the dataset. InceptionV3 has achieved the highest classification accuracy of 0.90. MobileNetV2 has less training time compared to others. © 2023 IEEE.

关键词： Large dataset

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Smart Healthcare System for Disease Prediction using Hybrid Machine learning 2

Smart Healthcare System for Disease Prediction using Hybrid ...

引用

2nd international conference on Augmented Intelligence and Sustainable Systems, ICAISS 2023

作者： Singla, Sanjay Singh, Simranjit Nazz, Ammena Kang, Sandeep Wassay, Md. Abdul Akshita, Akshita Chandigarh University Computer Science Engineering Punjab India

ISBN: (纸本)9798350325799

Accurate disease prediction is essential in healthcare systems to enable early identification and intervention for better patient outcomes. This paper provides a hybrid strategy for predicting diseases and assesses its effectiveness with and without hyperparameter adjustment. The outcomes show that the hybrid technique can predict the presence or absence of disease with an accuracy of 94.3% without hyperparameter adjustment. However, the performance of the hybrid technique significantly improves with hyperparameter optimization, achieving a remarkable accuracy of 99.1%. These results highlight the significance of hyperparameter tuning in machine learning model optimization and its potential to improve illness prediction systems' performance and accuracy. With its high accuracy and capacity for complicated pattern adaptation, the proposed hybrid technique shows potential for improving disease prediction in existing healthcare systems. © 2023 IEEE.

关键词： Machine learning

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Deep learning Character Recognition of Handwritten Devanagari Script: A Complete Survey 1

Deep Learning Character Recognition of Handwritten Devanagar...

引用

1st IEEE international conference on Contemporary Computing and Communications, InC4 2023

作者： Sharma, Arpit Mithun, B.N. Department of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798350335774

Recognition of handwritten characters is a concept in which the single characters are classified, it is a facility of an electronic device to scan and decipher the handwritten input from a variety of sources, including written texts, images, and other digital touch-screen devices. This concept is being used in distinctive sectors such as the processing of bank checks, form data entry, and parcel posting and nowadays it is becoming a very important issue in the pattern recognition domain and a very challenging task to resolve it. Since deep learning is a crucial strategy in solving detection and pattern recognition problems, several algorithms are available to classify the characters with better prediction rates on different datasets, and ultimately, whichever algorithm gives the optimized results will be considered the best solution for the character recognition problem. As a result, various solutions proposed by the existing researchers are discussed using deep learning algorithms in this survey article. © 2023 IEEE.

关键词： Character recognition

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Predictive Maintenance by Machine learning Methods 11

Predictive Maintenance by Machine Learning Methods

引用

11th IEEE international conference on Intelligent Computing and Information Systems, ICICIS 2023

作者： Amer, Sara Mohamed, Hoda K. Badr Monir Mansour, Marvy Ain Shams University Department of Computer and Systems Engineering Cairo Egypt British University in Egypt Department of Electrical Engineering Cairo Egypt

ISBN: (纸本)9798350322101

Predictive maintenance techniques are designed to determine the state of equipment in action to help us know when we can intervene to perform maintenance on it. Predictive maintenance design applies artificial intelligence techniques such as machine learning to analyze data and monitor efficiency. In this paper, we predict the maintenance of any equipment before it stops to reduce unplanned equipment maintenance by means of machine learning algorithms. We adopted in our work the following supervised machine learning algorithms: Random Forest, Support Vector Machine, KNN, Decision Tree, Logistic Regression, Naïve Bayes, and XGBOOST. Simulations and results show that the Random Forest and XGBOOST have almost the same performance. The XGBOOST machine learning algorithm is preferred for bigger datasets compared to Random Forest and it works more effectively in the case of small datasets. Finally, the proposed predictive maintenance system is successful in identifying possible failure indicators and reducing some production stoppages as shown by our simulation results. © 2023 IEEE.

关键词： Decision trees

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WDRKNN: Weighted distance Representation based Nearest Neighbor for Hyperspectral Remote Sensing Imagery Classification 14

WDRKNN: Weighted Distance Representation based Nearest Neigh...

引用

14th international conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Sharma, Monika Biswas, Mantosh National Institute of Technology Department of Computer Engineering Haryana Kurukshetra India University of Delhi Department of Computer Science Delhi India

ISBN: (纸本)9798350335095

Hyperspectral image (HSI) classification has enormous importance in the era of remote sensing and extensively sourced in a variety of promising applications. Most HSI classifications use the K-nearest neighbor (KNN) method due to its efficiency and simplicity. Although the KNN-based classification has lot of benefits, while this classification method has two big issues: firstly, the sensitivity of the k-neighborhoods affects KNN performance and secondly, moderate majority voting utilized there especially if the size of samples is quite small. To tackle these outliers, this paper suggests the weighted distance representation-based K nearest neighbor (WDRKNN) approach for improving the image classification results. In proposed WDRKNN, initially for each pattern class k nearest neighbors belongs to testing data samples are chosen and it has been employed to imply the testing data sample as their linear combinations. The localities related to the k-nearest neighbors for each pattern class represent as the weighted distance restrict their subsequent representations related constants. Decisively, the testing sample will be classified to a specific pattern class by the WDRKNN using the least representation-based distance between the classes. Experimental assessment on two HSIs: Pavia University and Indian Pines confirms that the suggested structure can significantly enhance HSI classification as compared to related state-of-the-art methodologies. © 2023 IEEE.

关键词： Remote sensing

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