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

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An Enhanced Opposition-Based Golden-Sine Whale Optimization Algorithm 7th

An Enhanced Opposition-Based Golden-Sine Whale Optimization ...

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7th international conference on Cognitive computing (ICCC) Held as Part of the Services conference Federation Meeting (SCF)

作者： Lu, Yong Yi, Chao Li, Jiayun Li, Wentao Minzu Univ China Sch Informat Engn Beijing Peoples R China

ISBN: (纸本)9783031516702;9783031516719

The Whale Optimization Algorithm is an ingenious method conceived by researchers, drawing inspiration from the feeding behavior of humpback whales. Characterized by its simple structure, limited parameters, high efficiency, and robust optimization capacity, WOA has been extensively applied across multiple domains to address various challenges. Nonetheless, it has been found that the algorithm demonstrates low global exploration capability, inadequate search precision, and susceptibility to local optima entrapment. Many enhancements have been suggested in the literature, with Opposition-Based learning emerging as a particularly effective technique for improving the quality of the initial population. In the present study, we put forth the Enhanced Opposition-Based strategy, which integrates supplementary constraints into the existing Opposition-Based learning framework, generating a more refined initial population. Furthermore, we introduce the Golden Sine Algorithm to modify the optimization approach of WOA, fostering an equilibrium between global exploration and exploitation abilities. In our evaluation, the proposed algorithm is assessed on nine classic benchmark functions with a dimensionality of 500, and compared with the original WOA, An enhanced whale optimization algorithm (eWOA), and the Elite Opposition-Based Golden-Sine Whale Optimization Algorithm (EGolden-SWOA). The results exemplify the superior performance of our proposed algorithm, underscoring its potential application in the optimization of truss structure design problems, the results indicate that ESWOA outperforms other enhanced algorithms, such as eWOA and EGolden-SWOA, in terms of its performance in engineering optimization. This signifies that ESWOA can be effectively applied to engineering optimization problems.

关键词： Whale optimization algorithm Opposition-Based learning Golden Sine algorithm engineering optimization

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Smart Traffic Management System For Emergency Vehicles Using Deep learning 15

Smart Traffic Management System For Emergency Vehicles Using...

引用

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

作者： Nirmaladevi, P. Pavithra, M. Jp Sundar Tharma, S. Manoj, V. Hariprasath, A. Kongu Engineering College Electronics and Communication Engineering Erode Perundurai638060 India

ISBN: (纸本)9798350370249

Road congestion is the main cause for slower speed, long waiting time for the emergency vehicles. The project proposes a Smart Traffic Management System for ambulances to reduce road congestion, improve response time, and reduce waiting times. It uses modern communication, computation, and sensing technologies to minimize response time, travel time, and waiting time. The system uses Dijkstra's shortest path algorithm to calculate optimal routes based on real-time traffic conditions, road closures, and congestion, ensuring the ambulance is always on the most efficient route. The Smart Traffic Management System uses the YOLO deep learning algorithm to detect and track ambulances in real-time, enabling rapid and accurate vehicle recognition in complex traffic scenarios. Once detected, the system controls traffic signals along the ambulance's route, prioritizing green lights when approaching intersections. This intelligent traffic signal control balances the needs of the emergency vehicle with traffic flow, minimizing delays and improving response times. The system's combination of YOLO-based vehicle detection, Dijkstra path optimization, and intelligent traffic signal control makes it a valuable tool for modern urban emergency services, reducing ambulance response times. © 2024 IEEE.

关键词： Deep learning Dijkstra algorithm Shortest Path Traffic Management Yolo

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MDLGO: Integrated Multimodal Deep learning Framework for Enhanced Precision in Glaucoma Diagnosis Using OCT A-scans 6

MDLGO: Integrated Multimodal Deep Learning Framework for Enh...

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

作者： Mundada, Rupesh Goverdhan Nawgaje, Devesh D. Shri Sant Gajanan Maharaj College of Engineering Department of Electronics and Telecommunication Engineering Maharashtra Shegaon India

ISBN: (纸本)9798350306613

Glaucoma, a leading cause of irreversible blindness, necessitates accurate and early diagnosis to prevent visual impairment. Current methods relying solely on OCT A-scans often fall short in specificity and sensitivity, and may overlook subtle anomalies indicative of early glaucoma. Furthermore, delays in diagnosis can contribute to disease progression. This paper introduces an innovative deep learning model that amalgamates OCT A-scans, fundus images, and visual field data, leveraging the strengths of multi-modal fusion to provide a comprehensive analysis for glaucoma diagnosis. Transfer learning, utilizing pre-trained models like InceptionV3 and ResNet50, was employed to enhance feature extraction, alongside anomaly detection methods including One-Class SVM and Isolation Forest to identify atypical glaucoma presentations. Moreover, the integration of an attention mechanism in segmentation models, specifically V-Net and SegNet, facilitated precise delineation of regions of interest in OCT A-scans. The proposed model significantly outperformed existing methods, enhancing precision by 8.5%, accuracy by 3.9%, recall by 8.3%, AUC by 4.9%, and specificity by 5.5%, while concurrently reducing diagnosis delay by 10.4%. These substantial improvements underscore the potential of our method in revolutionizing glaucoma diagnosis, ultimately contributing to timely intervention and better patient outcomes. © 2024 IEEE.

关键词： Anomaly Detection Deep learning Glaucoma Multi-Modal Fusion Optical Coherence Tomography (OCT)

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Towards a Framework for learning Content Analysis in K-12 AI/ML Education 53

Towards a Framework for Learning Content Analysis in K-12 AI...

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53rd IEEE ASEE Frontiers in Education international conference, FIE 2023

作者： Waite, Jane Tshukudu, Ethel Cucuiat, Veronica Whyte, Robert Sentance, Sue Raspberry Pi Foundation Cambridge United Kingdom University of Cambridge Raspberry Pi Computing Education Research Centre Cambridge United Kingdom

ISBN: (纸本)9798350336429

In recent years, significant interest in AI has resulted in many K-12 learning resources being developed for teachers and students. However, a consensus on which concepts and skills should be developed has yet to be reached. In addition, there is limited research on how these resources can be used by teachers. As AI/machine learning is not currently mandated in many CS curricula, supporting teachers is an ongoing challenge. Analysing resources currently available to teachers provides us with a starting point to consider how resources could be designed to better support teaching and learning in this area. In this paper, we surveyed the landscape of resources available and categorised 307 AI/ML teaching resources based on their core learning focus. We devised and employed a learning content analysis framework, SEAME, to determine how resources covered AI at multiple levels: (i) Social & Ethical;(ii) Application;(iii) Model, and;(iv) Engine. We found that most resources focused on the Application level (80%) with many covering the Model level (60%) and fewer at the Social & Ethical (40%) and Engine (27%) levels. We found little consensus about what to teach and how with many resources failing to specify target ages. Similarly, we found few examples of resources with professional development opportunities or appropriate lesson documentation, indicative of the challenges teachers face in teaching about AI. We propose that the SEAME framework provides an innovative starting point for teachers and researchers to review resources and consider what a progression of AI concepts and skills might look like that is comprehensive and simple to use. Likewise, it provides a common language for articulating the learning focus of resources. In order to support teachers encountering a new strand of CS, we suggest that further work in developing age-appropriate curricula and better documentation for AI resources is needed. © 2023 IEEE.

关键词： AI computing education K-12 ML

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Design and Practice of teaching Demonstration System for Water Quality Prediction Experiment Based on EasyDL 7

Design and Practice of Teaching Demonstration System for Wat...

引用

7th international conference on Electronic Information Technology and Computer engineering (EITCE)

作者： Wang, Ting Wu, Yanjun GuangDong Vocat Coll Post & Telecom Guangzhou Guangdong Peoples R China

ISBN: (纸本)9798400708305

The intelligent Internet of Things, as a new trend in the development of the Internet of Things, has received widespread attention. However, students majoring in the Internet of Things, especially vocational college students, generally have weak theoretical foundations in artificial intelligence and poor ability to build AI models. This requires that the practical teaching design of AI+IOT should be as visually as possible. Based on the practical needs of teaching and water quality prediction, a water quality prediction experimental teaching demonstration system has been designed, which includes the entire process of water quality data collection, data transmission, data cloud storage, and intelligent prediction. He prediction indicators R2_Score for Temp, PH, Salinity (PSU), and DO based on the EasyDL platform reached 0.840, 0.434, 0.593, and 0.784, MAPE reached 0.31%, 0.27%, 2.27%, and 4.94%, Compared with models such as RNN /LSTM /SRU/ BI-S-SRU, etc., the water quality prediction based on EasyDL has a smaller MAPE value, demonstrating better prediction performance. Moreover, the creation and training of the model are simple and convenient, making it very suitable for teaching demonstrations and interdisciplinary learning or research.

关键词： EasyDL Machine learning Practice teaching Water Quality Prediction

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Enhancing Image Classification with Federated learning through a Flower Simulation Study on the CIFAR100 Dataset 8

Enhancing Image Classification with Federated Learning throu...

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8th international conference on Electronics, Communication and Aerospace Technology, ICECA 2024

作者： Akilandeswari, A. Ramamoorthy, M. Akila, D. Hajira, A.B. Radhika, S. Chandramohan, S. Saveetha University Saveetha School of Engineering Simats Chennai India Saveetha University Saveetha College of Liberal Arts and Sciences Simats Chennai India Karpaga Vinayaga College of Engineering and Technology Chennai India Sri Chandrasekharendra Saraswathi Viswa Mahavidyalaya Chennai India

ISBN: (纸本)9798350367904

The aim of this study is to enhance image classification using Federated learning (FL) applied to the CIFAR100 dataset. We created a decentralized FL environment with multiple clients, leveraging the Flower framework and Ray for distributed computing. The methodology involved federated averaging for parameter updates, client-specific data handling, and performance evaluation using various metrics. Our results demonstrate a significant improvement in model performance, with loss decreasing from 0.060846 to 0.038253 and accuracy increasing from 41.64% to 57.96% over five training rounds. This study concludes that FL is effective in improving image classification while preserving data privacy and decentralization. © 2024 IEEE.

关键词： Federated learning

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Content and Skills for teaching Software Process Improvement in the Computer Science Course: A Mapping of ACM / IEEE, SBC, SWEBOK, CMMI and MR-MPS-SW Assets 53

Content and Skills for Teaching Software Process Improvement...

引用

53rd IEEE ASEE Frontiers in Education international conference, FIE 2023

作者： De Oliveira Colares, Adolfo Francesco Furtado, Julio Cezar Costa Oliveira, Sandro Ronaldo Bezerra Pará Belém Brazil Federal University of Amapá Department of Exact and Technological Sciences Amapá Macápa Brazil

ISBN: (纸本)9798350336429

This Research to Practice Full Paper seeks to map the assets of ACM / IEEE, SBC and SWEBOK guide specifically in the knowledge area of Software Process Improvement (SPI) to create a set of contents and competences to be addressed in the classroom, aligned with international and Brazilian references using the good practices of CMMI DEV v2.0 and MR-MPS-SW 2023. The teacher is the main gear for conducting teaching, research, and extension, being responsible for selecting the methods to be used to improve the teaching-learning process. SPI is a set of partially ordered steps with the intention of achieving a goal within the context of software development and involves understanding existing processes and changing them to increase product quality and / or reduce costs and development time. Thus, teaching SPI needs to be addressed in computing courses to meet the wishes of the software industry. © 2023 IEEE.

关键词： ACM/IEEE asset mapping CMMI DEV MR-MPS-SW SBC software process improvement SWEBOK

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Dry Power Transformer Fault Diagnosis Using Deep learning: A Review

Dry Power Transformer Fault Diagnosis Using Deep Learning: A...

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2024 international conference on Sustainable Power and Energy, ICSPE 2024

作者： Tiwari, Priyanka Singh, Shweta Singh, Vinod Kumar Maharishi University of Information Technology Lucknow Dept of Electrical Engineering UP India SR Group of Institutions Dept. of Electrical Engineering UP Jhansi India

ISBN: (纸本)9798350366051

Dry power transformers to be most effectively used in industrial, commercial, and even utility applications. They are ideal for both low voltage and medium ones hence assuring comfort in providing the needs of utility company. In this article, we summarize the applications of deep learning on vibration-based diagnosis of deformed core detection in dry power transformer based on systematic review from related literature. The perspective of data-driven, that is to say integrating various technologies related with deep learning into traditional on-the-spot fault diagnosis, especially vibration analysis and acoustic signal processing technology;infrared thermal imaging technique coupled with typical diagnostic sinkhole transformation matrix algorithm by using neural network computing. Next, we consider the impact of emerging research in these areas on transformer reliability and discuss several examples from recent literature that show how intelligent systems can be used to improve this. © 2024 IEEE.

关键词： Thermography (imaging)

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In-Depth Investigation of Artificial Intelligence - Blockchain Integration Strategies for Security Enhancement 7

In-Depth Investigation of Artificial Intelligence - Blockcha...

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7th international conference on Circuit Power and computing Technologies, ICCPCT 2024

作者： Jothy, C.R. Judith, J.E. Noorul Islam Centre for Higher Education Computer Science and Engineering Tamilnadu Kanyakumari India

ISBN: (数字)9798350372816

ISBN: (纸本)9798350372816

Blockchain being a distributed ledger preserves data in a highly secured environment which cannot be modified even by the user who created the data. This security aspect makes the blockchain technology popular in recent years and was adopted by many organizations to secure their data. The other technology which is very popular recent times is the Artificial Intelligence (AI). Nowadays, day to day life activities are tied up with the use of AI. Knowingly or unknowingly we are using AI in various circumstances. As the world becomes digitalized almost in all aspects, security threats are also has been growing in different dimensions. This paper makes a deep survey on various research articles which integrates AI and blockchain technology to improve the overall security. This paper also analyses the various methodologies and the machine learning algorithms used by different researchers to secure data on a blockchain so that the highly secured data can be automated with the integration of AI. If proper integration techniques were implemented with appropriate Machine learning algorithms the security will be much improved. This paper concludes by providing a path way to the upcoming researchers to move their research based on the problems identified by comparing various research articles. © 2024 IEEE.

关键词： Adversarial machine learning

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Enhancing Detection of Breast Cancer Through a Novel Framework: A Machine learning Approach with Explainable AI Interpretation 1

Enhancing Detection of Breast Cancer Through a Novel Framewo...

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1st international conference for Women in computing, InCoWoCo 2024

作者： Vidyashree, R. Kodipalli, Ashwini Rao, Trupthi Global Academy of Technology Dept. of Computer Science & Engineering Karnataka Bengaluru India Global Academy of Technology Dept. of Artificial Intelligence & Data Science Karnataka Bangalore India

ISBN: (纸本)9798331518943

Breast cancer, the most prevalent cancer among women, significantly contributes to increased mortality rates and ranks as the second leading cause of cancer-related deaths in women. Early detection is crucial for effective treatment, yet traditional diagnostic approaches often involve multiple laboratory tests supervised by medical experts. To mitigate human error and expedite breast cancer detection, there is a pressing need for automated systems capable of accurate and timely diagnosis. This study aims to investigate the application of machine learning (ML) techniques in classifying breast cancer using features from digitized images of fine-needle aspiration (FNA) breast mass samples. Machine learning methods are instrumental in addressing this imperative. The study conducts a comparative analysis of various ML classification methods, including Logistic Regression, Support Vector Machine (SVM), K Nearest Neighbor (KNN), Decision Trees, Bagging-Boosting, and ensemble models, using a breast cancer dataset to establish a novel framework. The performance of each algorithm is assessed based on metrics such as accuracy, precision, recall, and F1 score. The findings underscore the effectiveness of ensemble classifier models, achieving a notable accuracy rate of 98.87%. Additionally, the integration of explainable Artificial Intelligence enhances the interpretability of the models by providing valuable insights into their decision-making processes. © 2024 IEEE.

关键词： Contrastive learning

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