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Overcoming Challenges in DevOps Education through teaching Methods 23

Overcoming Challenges in DevOps Education through Teaching M...

引用

IEEE/ACM 45th international conference on Software engineering - Software engineering Education and Training (ICSE-SEET)

作者： Ferino, Samuel Fernandes, Marcelo Cirilo, Elder Agnez, Lucas Batista, Bruno Kulesza, Uira Aranha, Eduardo Treude, Christoph Univ Fed Rio Grande do Norte Natal Brazil Fed Inst Rio Grande do Norte Mossoro Brazil Univ Fed Sao Joao del Rei Sao Joao Del Rei Brazil Univ Melbourne Parkville Australia

ISBN: (纸本)9798350322590

DevOps is a set of practices that deals with coordination between development and operation teams and ensures rapid and reliable new software releases that are essential in industry. DevOps education assumes the vital task of preparing new professionals in these practices using appropriate teaching methods. However, there are insufficient studies investigating teaching methods in DevOps. We performed an analysis based on interviews to identify teaching methods and their relationship with DevOps educational challenges. Our findings show that project-based learning and collaborative learning are emerging as the most relevant teaching methods.

关键词： DevOps teaching methods challenges mixed methods

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Enhancing Deep learning Performance Through Parallel Processing: A Comprehensive Research Study 6

Enhancing Deep Learning Performance Through Parallel Process...

引用

6th international conference on Recent Trends in Advance computing, ICRTAC 2023

作者： Thakur, Rudraditya Goel, Naman Bhavesh Vijaykumar, Vaidehi Vellore Institute of Technology Chennai School of Computer Science Engineering Chennai India

ISBN: (纸本)9798350394702

The computational demands of training complex deep learning models have hindered the widespread adoption of this transformative technology. Parallel processing techniques have emerged as a promising approach to address this challenge by distributing the computational workload across multiple processors. This research delves into the multifaceted dimensions of enhancing deep learning performance through parallel processing. It explores the theoretical underpinnings, parallelization strategies, hardware and software infrastructures, and application-specific impact of parallel processing in deep learning. © 2023 IEEE.

关键词： Application programs

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AI-Enabled Edge computing Models: Trends, Developments, and Future Implications 2

AI-Enabled Edge Computing Models: Trends, Developments, and ...

引用

2nd international conference on Edge computing and Applications, ICECAA 2023

作者： Satyam Geetha, P. Shashikala, K.S. Kumar, N. Ashok School of Engineering Mohan Babu University Department of Ece Tirupati517102 India New Horizon College of Engineering Department of Ai&ml Bangalore560103 India

ISBN: (纸本)9798350347579

This research study examines the ways in which AI-enabled edge computing (AIEC) models are being used across various industries to improve operational efficiency and enable faster decision-making. The study discusses the key trends and developments of shaping the future of these next generation computing models, including the increased adoption of EC, the growing use of IoT devices, the emergence of 5G networks, advancements in machine learning algorithms, and the focus on data security. The research study also highlights case studies from different industries to illustrate the benefits of AIEC models and the potential impacts on businesses. The findings of this research work suggests that these computing models have the potential to transform the way organizations operate, leading to increased efficiency and productivity, better customer experiences, and more informed decision-making. © 2023 IEEE.

关键词： Machine learning

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Next-Generation Intrusion Detection Systems with Machine learning and Deep learning-Based Firewalls 3

Next-Generation Intrusion Detection Systems with Machine Lea...

引用

3rd international conference on Intelligent and Innovative Technologies in computing, Electrical and Electronics, IITCEE 2025

作者： Rani, Ritu Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (纸本)9798331515911

The growing complexity of cyber-attacks has underlined the requirement of strong Intrusion Detection Systems (IDS) able to instantly recognize a variety of hazards. This work offers a strategy for improving Next-Generation Intrusion Detection Systems (NGIDs) combined with firewalls by use of machine learning (ML) and deep learning (DL) approaches. Using the NSL-KDD dataset - which includes both normal and attack network traffic - the work creates a system able to classify traffic patterns into "Normal"and "Attack"categories. The proposed method consists in four phases: data collecting, data preprocessing, feature selection and extraction, and classifier application. In the preprocessing stage, the dataset is cleaned and normalized;hence, suitable feature selection follows to increase classification accuracy. Trained and tested for classification using performance criteria including accuracy, precision, recall, and F1-score, naïve bayes and support vector machine (SVM) models are then compared in efficacy. The results reveal that the SVM classifier has low misclassification and great accuracy (97.29%), therefore proving its prospective use in advanced IDS. The article underlines how several models could be merged inside a firewall system to offer real-time network protection. Future studies will look at using deep learning techniques and ensemble methods to raise detection accuracy even more, therefore allowing NGIDS to be more adaptable and efficient in countering evolving cyber threats. © 2025 IEEE.

关键词： Network intrusion

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Soil Health Analysis Using Few-Shot learning and Deep learning 8

Soil Health Analysis Using Few-Shot Learning and Deep Learni...

引用

8th IEEE international conference on computing, Communication, Control and Automation, ICCUBEA 2024

作者： Vispute, Sushma Rajeswari, K. Kumare, Vinay Naikawadi, Muhafij Patekar, Abhishek Padavale, Pravin Pimpri Chinchwad College of Engineering Department of Computer Engineering Nigdi Pune India

ISBN: (纸本)9798350391770

This paper presents an innovative approach to soil health analysis specifically tailored for farmers in a Pune division. We addressing the unique agricultural needs of this region by leveraging advanced machine learning techniques and data-driven methodologies. Our research focused on the development of machine learning models for crop recommendation and soil classification, utilizing diverse datasets including soil nutrient information, soil card details, weather records, and soil image visuals. The first aspect of our investigation involves the implementation of a few-shot learning model for personalized crop suggestions. This model considering the unique characteristics of the soil in the Pune division, incorporating data from soil card filings, weather reports, and soil images specific to the region. By providing tailor recommendations for optimal crop selection, our model aiming to maximize agricultural productivity and sustainability for Pune division farmers. In the second phase, we employing convolutional neural networks (CNNs) for soil picture classification. This model analyzing soil visuals to accurately categorize soil types, assisting in soil classification procedures crucial for various agricultural and environmental applications within the Pune division. Our emphasis throughout this research is on developing robust and accurate machine learning models that cater to the agricultural needs of Pune division farmers. By combining cutting-edge machine learning techniques with region-specific agricultural data, including soil image data, this research aims to empower Pune division farmers with actionable insights. Our goal is to promote sustainable farming practices and contribute to the agricultural development of the region. © 2024 IEEE.

关键词： Agricultural Sustainability CNN Crop Recommendation Few-Shot learning Soil Classification Soil Health Analysis

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Strategic Patient Education Approaches for Effective Disease Management and Prevention

Strategic Patient Education Approaches for Effective Disease...

引用

2024 international conference on Sustainable computing and Integrated Communication in Changing Landscape of AI, ICSCAI 2024

作者： Aravinda, K. Rangaraju, Tejaswini Singla, Atul Shrivastava, Anurag Lavanya, C. Al-Rubaye, Taqi Mohammed Khattab New Horizon College of Engineering Bangalore India Institute of Aeronautical Engineering Dundigal Hyderabad500043 India Lovely Professional University Phagwara India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Tamilnadu Chennai India Department of Civil GRIET Bachupally Telangana Hyderabad India Department of Medical Laboratory Technology College of Medical Technologies The Islamic University Najaf Iraq

ISBN: (纸本)9798350355338

This study presents and assesses the Adaptive learning Reinforcement (ALR) approach, a new patient education strategy that improves illness prevention and treatment. Behavioral incentives and AI provide a dynamic and individualized learning environment in ALR. ALR consistently outperformed competitors in key areas. ALR's Patient Profiling Algorithm (PPA) customizes lesson plans depending on patient personality. Adaptability allows health information to be tailored to each patient's health and education. ALR emphasizes education involvement. The Reinforcement learning Algorithm (RLA) changed material based on patient responses, keeping them engaged. RLA's regular feedback helps individuals recall and utilize health knowledge. ALR's culture awareness distinguishes them. The Cultural Sensitivity Algorithm ensures that all students can understand and apply to courses by considering cultural backgrounds. Cultural relevance creates a healthcare environment that values and welcomes diversity. ALR was forward-thinking by using modern technology. The system proved straightforward to integrate with AI, virtual models, and real-time feedback. Thus, this addresses tech-savvy youth requirements and increases learning. Finally, ALR improves patient education by providing high-level customization, continuing participation, cultural understanding, technology, and peer support. © 2024 IEEE.

关键词： Contrastive learning

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Research on teaching Reform of Spark Big Data Analysis Course Based on TOPCARES-OBE 4

Research on Teaching Reform of Spark Big Data Analysis Cours...

引用

4th international conference on Big Data engineering and Education, BDEE 2024

作者： Luo, Guotao Chengdu Neusoft University Department of Intelligent Science and Engineering Chengdu China

ISBN: (纸本)9798350388268

Focusing on the issues of students' low efficiency in learning real-time big data processing and analysis techniques, as well as their weakness in solving comprehensive real-time big data problems, this article, guided by the TOPCARES teaching method and based on the OBE and student-centered educational philosophy, takes the Spark Big Data Analysis course as an example to reform theoretical teaching methods and practical teaching modes. In theoretical teaching, we adopt an outcome-oriented online and offline hybrid teaching method. In practical teaching, we use a teaching mode that combines OBE-CDIO-based classroom and extracurricular smart cloud platform project practice. This can not only consolidate students' theoretical foundation of Spark big data but also improve their ability to solve comprehensive big data problems. Finally, we study the evaluation mechanism of course goal achievement and calculate the degree of course goal achievement. By comparing the average values of course goal achievement between the 20201-20204 big data classes and the 19201-19204 big data classes, it is found that the average achievement rates of course goal 1, goal 2, and goal 3 of the 20201-20204 classes are 3%, 4%, and 2% higher than those of the 19201-19204 classes respectively. Meanwhile, a questionnaire survey was conducted among students, and the average satisfaction rate of students with the course was 92.55, indicating that the teaching reform has achieved certain results and can provide references for other similar curriculum reforms. © 2024 IEEE.

关键词： Data centers

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StraightLine: An End-to-End Resource-Aware Scheduler for Machine learning Application Requests 3

StraightLine: An End-to-End Resource-Aware Scheduler for Mac...

引用

3rd international conference on Artificial Intelligence, Internet of Things and Cloud computing Technology, AIoTC 2024

作者： Ching, Cheng-Wei Guan, Boyuan Xu, Hailu Hu, Liting University of California Santa Cruz Department of Computer Science and Engineering Santa CruzCA United States Center of Florida International University MiamiFL United States California State University Department of Computer Engineering and Computer Science Long BeachCA United States

ISBN: (纸本)9798331530280

The life cycle of machine learning (ML) applications consists of two stages: model development and model deployment. However, traditional ML systems (e.g., training-specific or inference-specific systems) focus on one particular stage or phase of the life cycle of ML applications. These systems often aim at optimizing model training or accelerating model inference, and they frequently assume homogeneous infrastructure, which may not always reflect real-world scenarios that include cloud data centers, local servers, containers, and serverless platforms. We present StraightLine, an end-to-end resource-aware scheduler that schedules the optimal resources (e.g., container, virtual machine, or serverless) for different ML application requests in a hybrid infrastructure. The key innovation is an empirical dynamic placing algorithm that intelligently places requests based on their unique characteristics (e.g., request frequency, input data size, and data distribution). In contrast to existing ML systems, StraightLine offers end-to-end resource-aware placement, thereby it can significantly reduce response time and failure rate for model deployment when facing different computing resources in the hybrid infrastructure. © 2024 IEEE.

关键词： Resource allocation

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Self-organizing interval type-2 fuzzy neural network based on singular value decomposition and QR decomposition

Self-organizing interval type-2 fuzzy neural network based o...

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2023 international conference on Cloud computing, Performance computing, and Deep learning, CCPCDL 2023

作者： Wang, Panchao Zhao, Taoyan Liaoning Petrochemical University School of Information and Control Engineering Fushun China

ISBN: (纸本)9781510666474

In this paper, a self-organizing interval type-2 fuzzy neural network based on singular value decomposition and QR decomposition with column pivoting (SVD-QR)method is proposed to solve the identification problem of nonlinear systems. The network model can realize both structure learning and parameter learning. Firstly, in the aspect of structure learning, error criterion and completeness criterion of fuzzy rules are used to judge whether the rules are growing or not, and at the same time, SVD-QR method is used to find the less active rules to delete. Secondly, a sliding-window second-order algorithm with forgetting factor is used to optimize the parameters. Finally, the proposed model is applied to one typical nonlinear examples for identification. The experimental result shows that the proposed model can produce a relatively compact network structure and has the high identification and the forecast precision. © 2023 SPIE.

关键词： Singular value decomposition

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Research on human pose and action detection algorithm based on YOLOv8 2

Research on human pose and action detection algorithm based ...

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2nd international conference on Big Data, Computational Intelligence, and Applications, BDCIA 2024

作者： Cheng, Weizhong Wang, Xuefei School of Information Engineering Suihua University Heilongjiang Suihua China School of Electrical Engineering Suihua University Heilongjiang Suihua China

ISBN: (纸本)9781510689053

Human pose and action detection technology has significant applications in areas such as smart security, sports monitoring, and human-computer interaction. The development of deep learning has driven the demand for real-time and efficient detection algorithms in this domain. Although existing human action detection algorithms have improved accuracy, their model complexity and computational demands hinder practical use in portable devices and edge computing platforms. To address this, the paper presents a lightweight human pose and action detection algorithm based on the YOLOv8 framework, designed to meet the high-precision, low-latency needs of edge computing and portable devices. In the feature extraction phase, GhostNet's lightweight structure is used to reduce model parameters, while the CBAM attention mechanism, applied in the feature fusion phase, enhances the model's attention to critical features. This approach significantly decreases computational load while maintaining high accuracy. Experimental results indicate that the algorithm achieves a real-time detection rate exceeding 40 frames per second on edge computing platforms, demonstrating high accuracy and robustness across various human action scenarios. This offers strong technical support for human pose detection in smart security and wearable devices. © 2025 SPIE.

关键词： Portable equipment

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