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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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Charting a New Course: Encouraging Programming Class Participation and Interest Through Multimedia and Flipped Classroom Activities 14th

Charting a New Course: Encouraging Programming Class Partic...

引用

14th international conference on Methodologies and Intelligent Systems for Technology Enhanced learning, mis4TEL 2024

作者： Nikolov, Ivan Department of Architecture Design and Media Technology Aalborg University Aalborg9000 Denmark

ISBN: (纸本)9783031855603

The integration of new technologies and evolving learning preferences necessitates a reevaluation of teaching methods in higher education. Beyond traditional lectures and textbooks, students now seek increased engagement and interactive learning experiences. This shift is particularly crucial in engineering and computer programming courses. In this paper, we showcase the development of three project-based learning courses across different semesters in Aalborg University’s Medialogy education. These courses were strategically redesigned to leverage multimedia materials and embrace flipped classroom learning, aiming to enhance class participation and overall student engagement. We provide an overview of the advantages and challenges associated with these teaching designs, present initial results indicating improved student engagement, and discuss potential pitfalls and future directions to fortify this approach. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Contrastive learning

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Analytical Approach Towards Radiological Image-based Fracture Assessment Using Artificial Intelligence 1

Analytical Approach Towards Radiological Image-based Fractur...

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1st international conference on computing, Sciences and Communications, ICCSC 2024

作者： Sylvia, Takhellambam Dubey, Sanjay Kumar Thongam, Khelchandra Amity University Computer Science & Engineering Dept. Uttar Pradesh Noida India National Institute of Technology Manipur Computer Science & Engineering Dept. Imphal India

ISBN: (纸本)9798350353648

The utilization of Artificial Intelligence (AI) techniques has been prevalent in various applications and the gradual increase in implementation towards healthcare sectors has scaled up. Computer aided diagnosis has emerged as a major research area directed toward efficient use of resources. Fracture of bone has been a popular health problem associated with risk for mortality. Fractures are frequent in the long bones or the weight-bearing bones. AI techniques to accurately detect and classify fractures are still a challenging task beyond the conventional methods. Our objective is to assess the effectiveness of multiple algorithms in identifying and categorizing ankle fractures using radiological images. This review paper focuses on several available approaches of Deep-learning (DL) and Machine-learning (ML) regarding Ankle Fracture Classification and Identification based on Radiological Images for Medical Applications. The goal is to assist physicians and orthopaedics with simple and realistic decision support systems. In addition to a comprehensive analysis of the research, this study looked at several real-world examples of AI applications in the healthcare industry. Consequently, it is critical to have a fundamental grasp of these ML and DL algorithms to be ready for upcoming advancements in AI. We have reviewed and classified scientific conference and journal articles used for various AI techniques and offer future utility as a comparative analysis for further research in healthcare sectors. © 2024 IEEE.

关键词： Deep learning

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Proposed Edge computing Neural Accelerator 4

Proposed Edge Computing Neural Accelerator

引用

4th IEEE international conference on ICT in Business Industry and Government, ICTBIG 2024

作者： Singh, Vikram Aditya Verma, Rajesh Saini, Dori Singh Berwal, Krishan Vishwakarna, Sanotsh Kr Military College of Telecommunication Engineering Faculty of Telecommunication Engineering Indore India Computer Discount Warehouse Indore India Indian Institute of Technology Indore India

ISBN: (纸本)9798331518981

Edge computing is revolutionizing data processing, especially for applications like surveillance and traffic control systems. The data being processed closer to the source (e.g., cameras or sensors), with this edge computing reduces communication latency, bandwidth consumption, and power usage, addressing limitations of centralized cloud systems. The integration of machine learning (ML) in edge environments enhances surveillance systems, enabling real-time analytics, such as object detection and activity recognition. Convolutional Neural Networks (CNNs), widely used in video analytics, facilitate instant object recognition at the edge. However, the computational demands of deep neural networks (DNNs) require hardware accelerators like DNN accelerators for efficient deployment. These accelerators, implemented using technologies like Field-Programmable Gate Arrays (FPGAs) and Application-Specific Integrated Circuits (ASICs), improve latency and energy efficiency. Key benefits include parallelism, optimized memory management, and energy-efficient processing, essential for resource-constrained environments like surveillance drones. This paper explores the architecture of deep learning accelerators, such as the Edge computing Neural Accelerator (ECNA), focusing on their role in real-time surveillance systems. By leveraging binary-weight convolutional layers and systolic array designs, ECNA significantly enhances processing speed and energy efficiency, enabling real-time video analytics while consuming minimal power. © 2024 IEEE.

关键词： Systolic arrays

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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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An Intelligent System For Skin Cancer Detection Using Deep learning Techniques

An Intelligent System For Skin Cancer Detection Using Deep L...

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2024 international conference on Advances in Data engineering and Intelligent computing Systems, ADICS 2024

作者： Rajasekar, Vani Nirmala Devi, K. Sathya, K. Santhosh, A. Saran, S. Senthilvel, K. Kongu Engineering College Computer Science And Engineering Erode India

ISBN: (纸本)9798350364828

Skin cancer diagnosis poses significant challenges in the medical field due to its varying presentations and the expertise required for accurate classification. The complexity of skin lesions and the need for specialized expertise often hinder efficient diagnosis. The proposed work provides a reliable and effective method for classifying skin cancer using convolutional neural networks. The CNN model uses deep learning to automatically identify complex patterns and features. The model allows for the accurate and automated classification of skin lesions into benign and malignant categories. The proposed model is trained on a vast and diverse dataset that includes different types of skin cancer, specifically the 'Skin Cancer MNIST HAM10000' dataset, which comprises 10,000 images of various skin lesions. Data augmentation approaches are utilized to enhance performance even more, allowing the model to generalize well over a range of skin textures, lighting situations, and lesion sizes. In addition to expediting the identification of skin cancer, the suggested CNN-based method demonstrates impressive accuracy, achieving an overall accuracy rate of approximately 98.10%. The automatic classification system is a valuable addition to a doctor's toolkit, as skin cancer cases increase. Early detection aids in the fight against this crippling sickness, which ultimately leads to progress. © 2024 IEEE.

关键词： Convolutional neural networks

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Deep Semi-Supervised learning with Fine-Grained Dynamic Weights and Pseudo-Label Constraints

Deep Semi-Supervised Learning with Fine-Grained Dynamic Weig...

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2023 international conference on Communications, computing and Artificial Intelligence, CCCAI 2023

作者： Li, Kuan Lian, Qianzhi Gao, Can Dongguan University of Technology School of Cyberspace Security Dongguan China Shenzhen University College of Computer Science and Software Engineering Shenzhen China

ISBN: (纸本)9798350311297

Semi-supervised learning is comonly trained using a large amount of unlabeled data and a small amount of labeled data. Existing works have achieved excellent results through consistent regularization methods. However, when designing the loss function, they set a loss function for labeled and unlabeled data separately, ignoring the connection between labeled and unlabeled data. Furthermore, unlabeled data all share the same weight, but the weights of different unlabeled data should be different. Based on these observations, a pair loss is proposed in this paper to strengthen the connection between labeled and unlabeled data;in addition, a dynamic weight is designed for each unlabeled data based on EMA (Exponential Moving Average) when calculating the loss function. Finally, a deep semi-supervised learning method called DWPC is proposed by combining dynamic weighting and pair loss. Experimental results on several datasets show that the proposed method improves performance in many scenarios. © 2023 IEEE.

关键词： Deep 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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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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Ecological Surveillance and Crisis Management: Early Forest Fire Prediction and Endangered Species Identification 6

Ecological Surveillance and Crisis Management: Early Forest ...

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

作者： Ashok, K. Chatterjee, Pratik Jha, Aman Kumar Herle, Akshay Gupta, Shaksham Das, Saumyadeep New Horizon College of Engineering Computer Science and Engineering Bangalore India New Horizon College of Engineering Electronics and Communication Engineering Bangalore India

ISBN: (纸本)9798350306613

In the realm of ecological surveillance and crisis management, a pioneering approach combining some of the advanced machine learning techniques with bioacoustic analysis is to be delved. The proposed system integrates the predictive capabilities of Mel-frequency cepstral coefficients (MFCC) and other sophisticated models of Deep learning like CRNN which is a hybrid and composite neural network architecture that combines elements of both the convolutional layers and recurrent layers, for the dual objectives of early forest fire prediction and endangered species identification. The soundscapes of avian habitats are leveraged for early forest fire prediction, wherein a classification system discerns sounds produced by birds into two categories: Panic and normal. This real-Time analysis enables timely intervention strategies in the event of potential fire outbreaks. Simultaneously, the study addresses the critical issue of endangered species identification. By employing MFCC and other sound-based models, the system recognizes and classifies species-specific vocalizations during predefined observation periods. The quantification of hits, where a detected sound precisely matches a trained species' vocal signature, serves as a decisive metric in determining the endangered status of a species. © 2024 IEEE.

关键词： Convolutional Recurrent Neural Networks Mel frequency cepstral coefficients

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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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