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检索条件"任意字段=4th International Conference on Smart Learning Ecosystems and Regional Development"

共 899 条记录，以下是61-70 订阅

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IoT based Vehicle Monitoring System for Safety Standards in smart Cities 4

IoT based Vehicle Monitoring System for Safety Standards in ...

引用

4th international conference on Pervasive Computing and Social Networking (ICPCSN)

作者： Piruthiviraj, P. Tarun, S. R. Tarun, M. K. Tarun, M. New Horizon Coll Engn Bengaluru Dept Elect & Commun Bengaluru India

ISBN: (数字)9798350386349

ISBN: (纸本)9798350386356;9798350386349

the rapid urbanization and increasing number of vehicles in smart cities have increased the importance of ensuring road safety. this model proposes the development and implementation of an Internet of things (IoT)-based vehicle monitoring system to increase the safety standards in smart city environments. Leveraging cutting-edge technology, the system continuously monitors and evaluates on-road vehicle behavior in real-time, providing essential data for traffic management and accident prevention. By integrating a variety of in-car sensors and communication devices, the proposed system collects and transmits data to a centralized server, including information on the vehicle's position, speed, acceleration, and braking patterns. this data enables the detection of potentially hazardous situations, such as aberrant driving patterns and traffic congestion. the IoT-based Vehicle Monitoring System not only monitors individual vehicle behavior but also facilitates communication between vehicles and infrastructure, fostering a cooperative traffic ecosystem. With the incorporation of advanced analytics algorithms, the proposed system can identify factors affecting road safety and enhance its prediction capabilities over time. By harnessing technologies like Artificial Intelligence (AI) and Machine learning (ML), the proposed system continuously evolves to address evolving safety challenges. the ultimate goal of this study is to contribute to the development of smarter and safer cities by proactively addressing safety and traffic management concerns, thereby reducing traffic congestion, accidents, and creating a more secure urban transportation network.

关键词： Internet of things Traffic Monitor Safety Node Microcontroller unit

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Predicting Quality of Ground Water using Different Deep learning Models 4

Predicting Quality of Ground Water using Different Deep Lear...

引用

4th IEEE international conference on smart Technologies in Computing, Electrical and Electronics, ICSTCEE 2023

作者： Dsouza, Jeshma Nishitha Santhosh, Saba Shivani, M. Jins, Dain St. Joseph's University Department Of Advanced Computing Bangalore India

ISBN: (纸本)9798350384628

the evaluation of water quality is critical to ensuring the health of ecosystems and human populations. We delve into the domain of water quality assessment in this study by developing an innovative approach that combines traditional WAWQI calculations with cutting-edge deep learning algorithms like-Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Backpropagation Neural Network (BPNN), Long Short-Term Memory (LSTM), Bidirectional LSTM (Bi-LSTM), Gated Recurrent Unit (GRU) and Artificial Neural Networks (ANN). Our key research goals include computing WQI using comprehensive water quality metrics and applying deep learning techniques to improve the index's accuracy and prediction potential. We aim to discover hidden patterns and linkages within water quality data that are not observable using standard methods by combining WQI with deep learning models. the performance metrics we considered for the evaluating our models were Accuracy, Precision, Recall and F1 Score. © 2023 IEEE.

关键词： Long short-term memory

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development of a Digital Stethoscopy System for Detection of Heart Diseases 4

Development of a Digital Stethoscopy System for Detection of...

引用

4th IEEE international conference on smart Information Systems and Technologies, SIST 2024

作者： Baikuvekov, Meirzhan Baikuvekova, Aida Sultan, Daniyar Department of Information Systems Al-Farabi Kazakh National University Almaty Kazakhstan

ISBN: (纸本)9798350374865

this research paper presents the development of a Digital Stethoscopy System for the Detection of Heart Diseases using machine learning techniques on phonocardiograms. With cardiovascular diseases remaining a leading cause of global morbidity and mortality, the need for accurate and timely cardiac diagnostics is paramount. the proposed system integrates cutting-edge technology, including digital stethoscopy and machine learning, to address the limitations of traditional cardiac auscultation methods. the core components of the system, including phonocardiographic signal acquisition, an advanced Analog Front End, a robust microcontroller, and sophisticated machine learning algorithms, are meticulously designed and evaluated. Extensive data preparation, model training, and rigorous testing are undertaken to assess the system's diagnostic capabilities. Results indicate that this innovative system has the potential to provide reliable and real-time cardiac diagnostics, offering a promising solution for early detection and intervention in heart diseases. Furthermore, the research underscores the importance of large and diverse datasets for training machine learning models and emphasizes the significance of continuous validation and evaluation. the outcomes of this study hold promise for improving cardiac healthcare, enhancing patient outcomes, and advancing the field of cardiology, with recommendations for further research and clinical validation to refine and optimize the system for widespread clinical implementation. © 2024 IEEE.

关键词： Phonocardiography

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smart Stick Detecting Obstacles Early Using Random Forest Algorithm 4

Smart Stick Detecting Obstacles Early Using Random Forest Al...

引用

4th international conference of Science and Information Technology in smart Administration, ICSINTESA 2024

作者： Viet, thanh Pham Minh, Bac Nguyen Van, Tinh Tran Manh, Hoang Tran Tan, Nghia Duong Huu, Phat Nguyen VNU-HCM High School for the Gifted HoChiMinh Viet Nam School of Electronic and Electrical Engineering Hanoi Viet Nam

ISBN: (纸本)9798350376111

In everyday life, blind people always have to face more difficulty. In modern technology, we can design convenient devices to help them live more easily. this paper focuses on designing a product for them. Specifically, a smart cane will be designed to help blind people avoid objects. To identify and classify objects, we use the random forest algorithm. Our main contribution is to build a detection system and classify obstacles early using cheap sensors and cameras. the results show that the model achieves an accuracy of up to 9 7. 8 7% at a distance of up to 94 cm. the results show that the device is promised feasibility when applied in practice. © 2024 IEEE.

关键词： AI image recognition machine learning Random forest algorithm smart stick

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Multi-Layered Edge Computing Architecture of smart Information System for Cross-View People Tracking 4

Multi-Layered Edge Computing Architecture of Smart Informati...

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4th international conference on Machine learning and Intelligent Systems Engineering (MLISE)

作者： Wang, Pei Liu, Jiaming Zhang, Hui Tsinghua Univ Inst Publ Safety Res Beijing 100084 Peoples R China

ISBN: (纸本)9798350375084;9798350375077

Pedestrian detection and tracking have long been popular topics in both research and application. In intelligent building video surveillance systems, these techniques can be used to detect and track pedestrians within monitored areas, identifying suspicious behaviors. Currently, most widely-used pedestrian detection and tracking systems are based on a centralized architecture with a central server. Such systems suffer from high latency, poor scalability, and limited robustness. To address these issues, this paper proposes an end-to-end pedestrian detection and tracking system architecture utilizing edge computing devices. this architecture divides complex building structures into logical multi-layer structures using edge computing devices, solving the aforementioned problems, facilitating building security management, and reducing the complexity of pedestrian tracking algorithms in large buildings. Finally, a demo system is built to verify the feasibility of the proposed architecture.

关键词： Intelligent Security Pedestrian Detection Pedestrian Tracking Edge Computing

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ICDAR 24: Intelligent Cross-Data Analysis and Retrieval 24

ICDAR 24: Intelligent Cross-Data Analysis and Retrieval

引用

4th Annual international conference on Multimedia Retrieval (ICMR)

作者： Dao, Minh-Son Riegler, Michael Alexander Dang-Nguyen, Duc-Tien Tran, Hanh-Nhi Kiran, Rage Uday Komamizu, Takahiro Natl Inst Informat & Commun Technol Big Data Integrat Res Ctr Tokyo Japan Simula Metropolitan Ctr Digital Engn Oslo Norway Bergen Univ Bergen Norway French Mil Acad St Cyr Coetquidan Paris France Univ Aizu Aizu Wakamatsu Japan Nagoya Univ Nagoya Aichi Japan

ISBN: (纸本)9798400706028

Our workshop aims to provide a platform for both academic and industrial professionals engaged in the analysis and retrieval of cross-data from diverse perspectives, with a particular emphasis on wearable and ambient sensors, lifelog cameras, social networks, and surrounding sensors. Despite numerous studies exploring individual viewpoints, there remains a significant gap in the analysis and retrieval of cross-data to maximize benefits for humanity. Additionally, challenges such as data security and distributed learning for cross-modal model training and inference arise when dealing with large and distributed datasets. We invite researchers to contribute to this initiative, with the overarching goal of fostering the development of a smart and sustainable society through the efficient utilization of intelligent cross-data analysis and retrieval techniques.

关键词： cross-data analysis privacy-preserving prediction model time-series data forecast cross-modal models image clustering and segmentation social multimedia hidden knowledge reveal transformer federated learning

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Face Recognition for Blurry Images Using Deep learning 4

Face Recognition for Blurry Images Using Deep Learning

引用

4th international conference on Computer Communication and Artificial Intelligence (CCAI)

作者： Nukala, Saihemanth Yuan, Xiaohong Roy, Kaushik Odeyomi, Olusola T. North Carolina A&T Univ Comp Sci Dept Greensboro NC 27411 USA

ISBN: (纸本)9798350362770;9798350362763

the rapid growth of visual surveillance and personal identification systems in smart environments has empowered the development of real-time face recognition, even under imperfect conditions. Among these challenges, recognizing faces in blurry images stands as a significant barrier to achieving consistent identification results. In this paper, we combine advanced machine learning algorithms and deep learning frameworks, integrating Generative Adversarial Networks (GANs), specifically the Generative Facial Prior (GFPGAN), to restore clarity to blurred facial images. the primary objective is to enhance the accuracy of face recognition in real-world scenarios where image quality may be compromised. In the proposed approach, the GFPGAN model serves as the primary mechanism for blind face restoration, transforming blurred images into clear and recognizable visuals. Upon achieving clarity in the image, a customized Convolutional Neural Network (CNN) is deployed to perform face recognition tasks. To showcase the efficiency of the proposed system, a comprehensive comparative analysis is carried out, comparing the results of the pretrained models VGG-Face, FaceNet, CNN, and DeepFace. Our experiments showed a high validation accuracy of 83.72% for FaceNet. Our experimental results demonstrate that the combination of GFPGAN preprocessing and deep learning models significantly improves blurry images face recognition accuracy.

关键词： face recognition computer vision deep learning deblur generative adversarial networks

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Advancing Neuro-Oncological Diagnostics: A Comparative Analysis of Machine and Deep learning Models for Brain Tumor Classification 4

Advancing Neuro-Oncological Diagnostics: A Comparative Analy...

引用

4th international conference of Science and Information Technology in smart Administration, ICSINTESA 2024

作者： Sharika, T.R. Devassy, Deepa Willson Joseph, C. Reshma, M.R. Muraleedharan, Amrutha Jayaraj, Akshaya Adi Shankara Institute of Engineering and Technology Kalady Dept.of Cse Ernakulam India Adi Shankara Institute of Engineering and Technology Kalady Dept.of CSE-AI Ernakulam India

ISBN: (纸本)9798350376111

Brain tumor classification represents a pivotal intersection of medical diagnostics and advanced computational techniques, where the precise categorization of neoplastic growths is essential for guiding treatment strategies and improving patient outcomes. this research investigates the efficacy of various machine learning models in the categorization of brain tumors through medical imaging. We meticulously evaluated and contrasted four algorithms: support vector machine (SVM), logistic regression, random forest, and a convolutional neural network (CNN) enhanced by transfer learning. the study involved training and validating these models using a collection of medical images depicting brain tumors and assessing their performance through metrics like accuracy, precision, recall, and F1-score. Findings indicate that each algorithm is capable of high precision in tumor classification, with the CNN employing transfer learning showing superior performance across multiple metrics. While SVM and logistic regression offer straightforward solutions for binary classification challenges, random forest and CNN with transfer learning are adept at navigating more intricate datasets and identifying non-linear feature-class relationships. the implications of this study highlight the advanced capabilities of deep learning models in the domain of medical diagnostics and shed light on the comparative advantages and limitations of various machine learning approaches © 2024 IEEE.

关键词： Convolutional neural networks

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Blockchain-Supported Platform for Collaborative Livestreaming of STEM Courses 4

Blockchain-Supported Platform for Collaborative Livestreamin...

引用

4th international conference on Electronic Information Engineering and Computer Technology, EIECT 2024

作者： Sun, Geng School of Computer Engineering Chongqing College of Humanities and Technology Chongqing China Vermilion Cloud Sydney Australia

ISBN: (纸本)9798331528850

With the rapid development and increasing maturity of emerging technologies such as 5G communication, artificial intelligence, blockchain technology, and computer supported collaborative work, the internet is entering a new era - Web3. this transformation brings both opportunities and challenges to the field of education. Addressing current issues faced by universities, such as security risks in resource storage, lack of platform creation incentives, complexity in copyright confirmation, unsatisfactory user interaction experiences, and insufficient supply of high-quality educational resources, this paper explores and proposes an innovative solution - the 'CoTeach' smart education platform. this platform integrates the core concepts of Web3, and data-driven artificial intelligence technology. It aims to reshape the teaching and learning experience, as well as the collaborative development of professional knowledge. Specifically, the 'CoTeach' platform ensures secure storage and immutability of educational resources through blockchain technology, reducing risks associated with resource storage. By utilizing collaborative livestreaming mechanisms, it provides economic incentives for creators, contributors, and learners, fostering greater community participation. the transparency and smart contract functionality of blockchain simplify the copyright confirmation process, safeguarding creators' rights. the platform enhances user interaction through gamification design, making the learning process more engaging and enjoyable. Finally, artificial intelligence technology optimizes resource recommendations and personalized learning paths, addressing the shortage of high-quality educational resources. © 2024 IEEE.

关键词： Collaborative learning

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Optimization of MLP-Regressor for Predicting Student's Cumulative Grade Point Average (GPA) 4

Optimization of MLP-Regressor for Predicting Student's Cumul...

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4th international conference of Science and Information Technology in smart Administration, ICSINTESA 2024

作者： Setiawan, Hermawan Tampati, Ihsan Fadli Putra, I Gede Maha Girinoto Politeknik Siber Dan Sandi Negara Department of Cryptographic Engineering Bogor Indonesia

ISBN: (纸本)9798350376111

In the digital age, vast amounts of data offer opportunities to enhance student outcomes and achieve sustainable educational goals. Educational institutions aim to identify factors influencing academic performance and predict exam scores to provide tailored support to students in need. Educational Data Mining (EDM) techniques enable in-depth analysis of students' historical data. Prior research has identified factors such as learning methods, study environment, parental involvement, and pressure as significant influencers of academic achievement. this study takes a novel approach by training a deep learning model on historical cumulative GPA data from Politeknik Siber dan Sandi Negara using regression to accurately predict cumulative GPA. the model is implemented in a web application, alerting educators to students at risk of declining cumulative GPA. the Multi-layer Perceptron (MLP) regressor is employed for training and evaluation, yielding optimized configurations for the number of neurons, hidden layer depth, and Adam optimizer with constant learning rate. Evaluation results using MSE, RMSE, MAE, R-squared, and Breusch-Pagan Test show that the model has good performance. Future research could focus on expanding the dataset to achieve greater predictive accuracy thereby empowering institutions to offer targeted support to students in need. © 2024 IEEE.

关键词： Contrastive learning

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