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检索条件"任意字段=2024 International Conference on Computing, Machine Learning and Data Science, CMLDS 2024"

共 13410 条记录，以下是4961-4970 订阅

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A SnF Scheduling Method for HFL Over Edge computing Power Optical Network 22

A SnF Scheduling Method for HFL Over Edge Computing Power Op...

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22nd international conference on Optical Communications and Networks, ICOCN 2024

作者： Zhang, Jia Lin, Xiao Hong, Zhixiang Wu, Guiping Li, Jun Chen, Zhen Sun, Weiqiang Lou, Zhilan College of Physics and Information Engineering Fuzhou University Fuzhou China School of Electronic and Information Engineering Soochow University Suzhou China Shanghai Jiao Tong University State Key Laboratory of Advanced Optical Communication Systems and Networks Shanghai China School of Data Sciences Zhejiang University of Finance and Economics Hangzhou China

A SnF scheduling method is presented to schedule data transfers in the HFL aggregation process across the ECPON. Studies demonstrate that the proposed method outperforms conventional methods in terms of network perfor... 详细信息

ISBN: (纸本)9798350367652

关键词： Edge computing power network hierarchical federated learning model aggregation store-and-forward

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Vehicle Detection in Unmanned Aerial Imagery Through Advance You Only Look Once Architectures 15

Vehicle Detection in Unmanned Aerial Imagery Through Advance...

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

作者： Nishitha, U. Lokesh, V. Kaushik, Tata Singh, Rimjhim Padam Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science & Engineering Bengaluru India

ISBN: (纸本)9798350370249

The detection of vehicles by unmanned aerial vehicles (UAVs) is gaining substantial attention in the field of traffic control. In aerial images, vehicles have a unique perspective and take up less pixels than other objects in the broader data set.. This study uses the YOLOv8 (You Only Look Once version 8) deep learning architecture to propose an enhanced method of vehicle recognition in aerial photos. This work goes beyond the conventional YOLOv8 implementation by introducing new methods for data augmentation to enhance the efficiency and the model's resiliency. To build a diverse and enriched dataset, the suggested methodology makes use of cutting-edge techniques such image cropping, contrast correction, and brightness improvement. Training the YOLOv8 model on this enhanced dataset produces encouraging results;on a different test set with ground truth annotations, a noteworthy achievement of 7 0 % accuracy in vehicle detection was made. This accomplishment highlights the effectiveness of our strategy and shows a notable increase in accuracy over earlier approaches. The study demonstrates the potential of YOLOv8 in tackling the difficulties of aerial vehicle detection and highlights the crucial role that data augmentation plays in improving model performance and resilience. © 2024 IEEE.

关键词： Aerial photography

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data Augmentation Techniques for Medical Image Segmentation - A Review

Data Augmentation Techniques for Medical Image Segmentation ...

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2024 international conference on Computer and Applications, ICCA 2024

作者： Kozah, Naney Dornaika, Fadi Charafeddine, Jinan El Jaam, Jihad University of the Basque Country UPV/EHU Department of Computer Science and Artificial Intelligence San Sebastian Spain University of the Basque Country UPV/EHU Ikerbasque Basque Foundation for Science San Sebastian Spain Léonard de Vinci Pôle Universitaire Research Center Paris La Défense92916 France School of Computing and Data Liverpool J. Mores University Liverpool United Kingdom

ISBN: (纸本)9798350367560

Segmentation of medical images is crucial for accu-rate diagnosis, treatment planning and intervention. However, the scarcity of data and the variability of different imaging modalities, including MRI, CT, PET and ultrasound, pose a major challenge for deep learning models. This review discusses traditional and advanced data augmentation techniques used to improve segmentation performance. In addition to geometric transformations such as rotation and scaling, advanced approaches such as Generative Adversarial Networks (GANs) and superpixel-based augmentations are also evaluated. The review also highlights novel methods such as Local-and-Contour Aware Grid Mixing (LCAMix) and Hard and Soft Mixing (HSMix) that preserve anatomical boundaries and effectively augment datasets. The analysis concludes with practical insights for clinical applications and suggests integrating these techniques into data pipelines to increase the generalizability of models. Future research directions focus on DICOM-specific augmentation strategies and optimization of computational requirements to improve clinical applicability. © 2024 IEEE.

关键词： Image segmentation

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Investigation on Preserving Privacy of Electronic Medical Record using Split learning 5

Investigation on Preserving Privacy of Electronic Medical Re...

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5th international conference on Innovative data Communication Technologies and Application, ICIDCA 2024

作者： Kiruthika, M. Kumar, Abhishek Krishnasamy, Lalitha Sarveshwaran, Velliangiri Department of Computational Intelligence School of Computing SRM Institute of Science and Technology Tamilnadu Kattankulathur603203 India Punjab National Bank New Delhi110075 India Department of Computer Science and Engineering School of Engineering and Technology Christ University Karnataka Bengaluru560074 India

Artificial Intelligence is deployed in multiple areas, including healthcare. Utmost research is done in AI enabled healthcare industry because of the demands like accurate result, data security, exact prediction, huge volume of data, etc. In conventional deep learning models, the training happens with the dataset that are stored in a single device. This requires a huge storage space and highly efficient machines to train the data. Usage of big data, demands for innovative models that can be deployed and used in confined storage. Split learning is one such collaborative distributed deep learning model that allows the data to be stored in a split fashion. Split learning supports desirable features like less storage, more privacy to raw data, ability to work with resource constraints, etc., making it suitable for storing electronic medical record of patients. This paper discusses the advantages of using split learning for healthcare, the possible configurations of split learning that supports data privacy in healthcare and finally discusses the open research challenges in implementing split learning for healthcare. © 2024 The Authors. Published by Elsevier B.V.

关键词： Digital storage

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Intelligent IoT-Enabled Real-Time Monitoring System for Logistics Management 1

Intelligent IoT-Enabled Real-Time Monitoring System for Logi...

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

作者： Joshi, Pooja Singh, Vineeta Thapliyal, Nitin Joshi, Kapil Bhatt, Ashutosh Mahur, Mansi Swami Rama Himalayan University Department of CSE HSST Dehradun India Chhatrapati Shahu Ji Maharaj University Department of Computer Science and Engineering Kalyanpur Uttar Pradesh Kanpur208024 India Graphic Era Hill University Department of Computer Science and Engineering Uttarakhand India Uttaranchal University Department of CSE Uttaranchal Institute of Technology Dehradun India Swami Rama Himalayan University Department of Computer Science and Engineering HSST Uttarakhand India Infosys Hyderabad India

ISBN: (纸本)9798350353648

Result of growing digitalization and mastering the Internet Of Things (loT) is seen in logistics, one industry sector that has been revolutionized by these technologies. With the increasing need for tracking and tracing activities, efficiency of logistics has been demanding real-time monitoring to have transparency on logistic network ensuring that it is secure. This article discusses some of those troubles related to logistics management and the requirement for real-time asset, vehicle tracking that can be resolved by this study. Many current systems lack the integration and up-to-the-minute data needed for efficient management of logistics. This study suggests and implements the Internet of Things (loT)-based logistics management system where a unique hybrid model involving both hardware as well software solutions perceiving for improving standard functional demand in move competently implemented with cloud-based server. The whole system is designed to enable real-time monitoring via sensors, GPS tracking and data analytics that allows a look into all logistics operations. The results show that the proposed system improves tracking accuracy, process efficiency and also decisions-making power. This system provides real-time data that responds quickly to any disruptions or inefficiencies by generating alerts, improving overall logistics management. The study is limited by the hardware used and specific logistics test cases. We would suggest for further research to investigate the implementation of more sophisticated AI algorithms within CT and expand its application possibilities into other logistics environments. In the future, we will continue to strengthen our system by introducing machine learning algorithms for prediction analysis and including different types of loT devices as well apply this on a larger scale in multiple logistics environments to re-verify its sustainability. © 2024 IEEE.

关键词： Efficiency

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Using Accessibility Awareness Interventions to Improve computing Education 46

Using Accessibility Awareness Interventions to Improve Compu...

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ACM/IEEE 44th international conference on Software Engineering: Software Engineering Education and Training (ICSE-SEET)

作者： Liu, Yang Mangano, Domenic Neupane, Krishna Prasad Malachowsky, Samuel Krutz, Daniel E. Rochester Inst Technol B Thomas Golisano Coll Comp & Informat Sci Dept Software Engn Rochester NY 14623 USA

ISBN: (纸本)9798400704987

Research demonstrates that we continue to be deficient in creating inclusive and equitable software, with a lack of empathy among software developers attributed to this issue, even if unintentional. Although increasing empathy has been shown to be part of the solution, unfortunately there is a lack of data that can be analyzed that shows how empathy-building and accessibility awareness interventions can be utilized in computing education, along with a shortage of free, public, and easily adoptable educational material regarding empathy and the creation of accessible software. Through large in-person studies that involve real-world participants, we are able to contribute to the knowledge regarding the potential benefits that can be used to improve computing education by implementing empathy-building strategies in the curriculum. Two forms of accessibility awareness exercises are utilized to display the effectiveness of empathy-based learning and teach the importance of accessibility in computing, including experiential and expression-based interventions. In these interventions, we expect to find an increase in awareness and empathy in participants, as well as an increase in participant interest in the topic of software inclusivity and accessibility by placing the participant in a situation they may encounter in the real world. We additionally present a web-hosted, easily adoptable experiential and expression-based educational lab to support the inclusion of inclusive development and/or empathy-focused topics into a variety of curricula formats. Complete materials are available on the project website: https://***

关键词： Experiential Education computing Education Empathy-Building Interventions

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A Deep learning Approach to Classify Skin Cancer

A Deep Learning Approach to Classify Skin Cancer

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2024 IEEE international conference on Intelligent computing and Emerging Communication Technologies, ICEC 2024

作者： Sharma, Mansi Kumar, Praveen Department of Computer Science and Design Wardha India Department of Computer Science and Medical Engineering Wardha India

ISBN: (纸本)9798331508432

A effective treatment for skin cancer depends on early discovery, which may be a major around the world health issue. This study offers a interesting strategy for dependably classifying skin injuries as benign or malignant by utilizing convolutional neural networks (CNNs). Convolutional Neural Networks (CNNs) are a promising strategy with incredible potential for skin cancer screening. CNNs are well-known for their capacity to observe complex designs from picture data and are able of accurately recognizing benign or malignant skin injuries. Analysts can make exceptionally valuable demonstrative devices by utilizing enormous datasets of skin injury photos to prepare CNN models. Methods like information augmentation, ensemble approaches, transfer learning, attention mechanisms, and generative ill-disposed systems can be utilized to make strides execution indeed encourage. Besides, making mobile and cloud-based apps and examining logical AI can progress the impact and openness of skin cancer diagnosis. CNNs are idealize for this reason because of their reputation for being able to extract complex patterns from visual information. Our approach involves employing a sizable dataset of photographs of skin injuries that have been meticulously labeled to prepare a CNN show. The demonstrate is planning to secure discriminative properties, such as asymmetry, uneven borders, color change, distance across, and creating characteristics, that distinguish benign from malignant tumors. The results of our tests appear how well our proposed CNN-based strategy works to distinguish skin cancer at high accuracy rates. The demonstrate ceaselessly beats out conventional machine learning approaches, illustrating the benefits of profound learning strategies in this field. This ponder progresses the field of dermatology's computer-aided conclusion, giving a possibly valuable instrument to assist therapeutic specialists distinguish and treat skin cancer early on. © 2024 IEEE.

关键词： Convolutional neural networks

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t-SNE Based on Halton Sequence Initialized Mountaineering Team-Based Optimization Algorithm 14

t-SNE Based on Halton Sequence Initialized Mountaineering Te...

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14th IEEE international conference on Electronics Information and Emergency Communication, ICEIEC 2024

作者： Tang, Yue Nai, Wei Lei, Nengwei Li, Dan Yang, Zan School of Electronic Information Huzhou College Zhejiang Province Huzhou313000 China Huzhou Key Laboratory for Urban Multidimensional Perception and Intelligent Computing Zhejiang Province Huzhou313000 China Public Teaching and Research Department Huzhou College Zhejiang Province Huzhou313000 China

ISBN: (纸本)9798350361889

t-distributed stochastic neighbor embedding (t-SNE) is a classic manifold learning algorithm in the field of machine learning (ML), and it is a non-linear data dimensionality reduction method. Manifold learning generally constructs homeomorphic mappings from high-dimensional spaces to low dimensional spaces based on different mathematical ideas. The idea of t-SNE is to pursue the probability isomorphism of data distribution before and after projection from high-dimensional space to low dimensional space. Thus, its essence can be seen as solving the minimization problem of Kullback-Leibler (KL) divergence that measures the similarity of probability distribution before and after projection. t-SNE algorithm usually uses gradient dependent line search optimization methods to solve, but gradient dependent optimization methods are often prone to fall into the trap of local optimal solutions and unable to find the global one. To address this issue, in this paper, a non-derivative optimization method, which is called Halton sequence initiated mountaineering team-based optimization (HSMTBO) has been proposed to realize the parameter optimization of t-SNE, and its feasibility and advantages can be verified through numerical experiments. © 2024 IEEE.

关键词： Probability distributions

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Electric Vehicle Charging Demand Prediction using machine learning Algorithms 8

Electric Vehicle Charging Demand Prediction using Machine Le...

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8th international conference on Inventive Systems and Control, ICISC 202

作者： Hitesh, K. N. B. Sai Teja, P. Saran Kumar, J. Ram Srm Institute of Science and Technology Computing Technologies Chennai India

ISBN: (纸本)9798350386578

The increasing concerns regarding climate change, notably the surge in greenhouse gas emissions, have attained paramount significance in the agendas of both U.S. and global policy makers. Transportation, responsible for 29% of U.S. greenhouse gas emissions according to the U.S. Environmental Protection Agency (EPA), stands as the nation's leading contributor to this challenge. In parallel, substantial policy initiatives at the national and state levels are underway, and key automotive manufacturers like Ford, GM, and Volvo have committed to a complete transition to electric vehicles (EVs) within the next decade, heralding a profound shift in the automotive industry. This impending transformation, combined with public and private sector efforts, is poised to generate a substantial increase in EV demand. Governments and public utilities can use this research to build and manage public charging networks strategically by applying machine learning models to forecast energy consumption patterns inside EV charging infrastructure. By proactively forecasting future EV charger energy consumption, this study aims to pinpoint regions of high demand, facilitating optimized infrastructure expansion and resource utilization, aligning with sustainability goals and the evolving EV landscape. We harnessed a real-time dataset comprising 433,172 electric vehicle transactions, including 91,255 new vehicle registrations. We integrated data from the National Renewable Energy Laboratory's API, encompassing 1,686 charging stations in Washington. Our modeling process, applied uniformly across counties, included seasonal decomposition, training and testing data partitioning, gridsearch with pmdarima for parameter optimization, and SARIMAX modeling for forecasting. Visualization aided in assessing forecast accuracy, contributing to our overall study on EV charging demand prediction. © 2024 IEEE.

关键词： Automotive industry

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12th international conference on Culture and computing, C and C 2024, Held as Part of the 26th HCI international conference, HCII 2024

12th International Conference on Culture and Computing, C an...

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12th international conference on Culture and computing, C and C 2024, Held as Part of the 26th HCI international conference, HCII 2024

ISBN: (纸本)9783031611469

The proceedings contain 27 papers. The special focus in this conference is on Culture and computing. The topics include: Preliminary Exploration of User Experience in the learning-Through-Play Language learning App Duolingo;taking Culture as the Axis to Discuss User Experience in Tour Guide Mobile Application of Taichung City;Market-Driven HCI Design Education Analysis and Cultivation Strategy;contentRank: Towards a Scoring and Ranking System for Screen Media Products Using Critical Reception data;discussion of User Experience for Streaming Platform by Using Mouse Trajectory;feasibility Study on Touch Screen Interaction Technology Based on "The Sword of King Goujian" Exhibit;exploring User Behavior Based on Metaverse: A Modeling Study of User Experience Factors;Research on AR Cultural Heritage Museum Application Design Driven by User Demands;studying the Usability of the Yunlin Puppet Theater Website;construction of Immersive Art Space Using Mirror Display and Its Evaluation Through Heart Rate Measurements;color Constancy Assuming Viewing Works Using a Display;application of Interactive Installation Art Design Based on Generalization Theory;effect of Art’s Increasing Human Creativity and Motivation When Viewed in an Immersive Environment;data Shed: Interactive Art in the Service of data Fluency;the (Un)Answered Question: A data science Powered Music Experiment;Spatial Interaction Elements in AR-Glasses-Based Touristic Service Scenario Design;the Inheritance of Traditional Weaving Skills of Indigenous Settlement and the Practice of Micro-industry and the Dream of Weaving Craft Village;design of Interactive Digital Virtual Display Application for Chinese Heritage Traditional Herbal Medicine Culture;digital Storytelling of Intangible Cultural Heritage: A Multimodal Interactive Serious Game for Teaching Folk Dance.

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