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3D Reconstruction From Traditional Methods to Deep learning 10

3D Reconstruction From Traditional Methods to Deep Learning

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10th IEEE international conference on Cyber Security and Cloud computing and 9th IEEE international conference on Edge computing and Scalable Cloud, CSCloud-EdgeCom 2023

作者： Yang, Lan Yang, Chaoyi Xie, Rui Liu, Jingnian Zhang, Huan Tan, Wenjin QuanZhou University of Information Engineering Software College QuanZhou362000 China School of Computer Science and Engineering Hunan University of Science and Technology Xiangtan411201 China

ISBN: (纸本)9798350312461

Vision is one of the important pathways for human perception of external information, with over 80% of perception being acquired through vision. How to enable computers to possess efficient and flexible visual systems similar to humans has always been a hot topic in the field of computer science. One of the main goals of computer vision research is to reconstruct the geometric structure of 3D objects visible on the visible surfaces from 2D photos. Recently, this technology has become mature enough and its applications range from autonomous driving, virtual reality, cultural heritage preservation and restoration, among others, with significant research value. In this paper, we summarize the key technical issues in 3D reconstruction from existing technologies, first by summarizing traditional methods of 3D reconstruction, then analyzing commonly used deep learning methods for 3D reconstruction and their application scenarios in different fields. Finally, we conclude and provide an outlook on future development directions. © 2023 IEEE.

关键词： Deep learning

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Machine learning-Based Generative Design Optimization of the Energy Efficiency of Florida Single-Family Houses

Machine Learning-Based Generative Design Optimization of the...

引用

ASCE international conference on computing in Civil engineering (I3CE)

作者： Elias, Rita Issa, Raja R. A. Univ Florida Rinker Sch Construct Management Gainesville FL 32611 USA Univ Florida Rinker Sch Construct Management Ctr Adv Construct Informat Modeling Gainesville FL 32611 USA

ISBN: (数字)9780784485224

ISBN: (纸本)9780784485224

Designers usually assess only a few house design alternatives simulating their energy use, due to time and cost constraints. This study aims at developing a machine learning (ML)-based generative design (GD) framework to automate the design process of Florida's detached residences while optimizing their energy performance. An artificial neural network (ANN) model was developed using the machine learning platform TensorFlow along with some Python-based Keras libraries based on a big dataset of around 17,000 newly constructed detached residences in Florida between the years 2009 and 2021. The GD framework was established using Autodesk Dynamo and Autodesk Revit internal generative design tool that uses the Non-Dominated Sorting Genetic Algorithm (NSGA-II). The ANN model was created to predict the required capacities of the cooling and heating systems in detached houses considering 10 independent variables relating to the house geometry and its energy performance. The ANN was then integrated within the GD framework for performance evaluation purposes. The findings of this study included a fully automated design of energy-efficient detached houses greatly reducing the financial strain and time consumed by designers/developers using traditional techniques.

关键词： Machine learning

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Signal Quality Prediction for Cognitive Radio Networks using Deep learning Techniques 15

Signal Quality Prediction for Cognitive Radio Networks using...

引用

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

作者： Dev, Sudhanshu Santhoshkumar, M.P. Goyal, Udita Raja Praveen, K.N. Pathak, Kishor R. Ravivarman, G. Chitkara University Institute of Engineering and Technology Chitkara University Centre for Interdisciplinary Research for Business and Technology Punjab India Prince Shri Venkateshwara Padmavathy Engineering College Department of Mechanical Engineering Chennai127 India Quantum University Research Center Quantum University India School of Engineering and Technology Computer Science and Engineering Karnataka Bangalore India Vishwakarma Institute of Technology Department of Information Technology Pune India Karpagam Academy of Higher Education Department of Electrical and Electronics Engineering Coimbatore641021 India

ISBN: (纸本)9798350370249

Cognitive radio networks (CRNs) are getting increasingly famous due to their potential to deliver excessive-bandwidth offerings to numerous users. Despite this, the signal satisfactory of those networks can be fantastically variable and hard to predict, leading to degraded communication. Currently, deep gaining knowledge of strategies had been proposed as a method to improve the capability of CRNs to correctly expect signal first-rate, allowing multiplied device reliability. On this paper, we gift an assessment of present deep gaining knowledge of tactics to be expecting signal excellent in CRNs, focusing at the structure and application of convolutional neural networks (CNNs), recurrent neural networks (RNNs) and deep reinforcement studying (DRL). The overview additionally examines the overall performance of those processes in terms of accuracy, scalability and generalization to new environments. Subsequently, we provide a survey of capability future guidelines for signal first-rate prediction in CRNs using deep getting to know. © 2024 IEEE.

关键词： Deep reinforcement learning

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Detection and Classification of Brain Tumor Using Machine learning 3

Detection and Classification of Brain Tumor Using Machine Le...

引用

3rd international conference on Smart Technologies and Systems for Next Generation computing, ICSTSN 2024

作者： Kale, Prachi V. Gadicha, Ajay B. Dalvi, G.D. P R Pote College of Engineering & Management Department of Computer Science and Engineering Maharashtra Amravati India P R Pote College of Engineering & Management Department of AI Maharashtra Amravati India Allard College Department of EXTC Maharashtra Pune India

ISBN: (纸本)9798350391565

Diagnosing a brain tumor typically necessitates the expertise of a radiologist, whose skills and knowledge are essential to the process. Since the overall number of individuals has grown, so too has the quantity of medical data required to be managed, rendering outdated methods extremely costly and inefficient. Many investigators looked into different techniques that had been quick and effective in identifying and categorizing brain tumours. Currently, Machine learning (ML) techniques have gained popularity in the development of computerized systems that can quickly and effectively diagnose or segment brain tumours. The deep learning (DL) enables the application of ML frameworks for brain tumor identification. This paper proposed various ML models to analyze the MRI images. First, preprocessing and augmentation algorithms were applied to the MRI brain images. We evaluated the performance of several models, including LR, SVC, kNN, NB, NN, RF, and K-means clustering. The analysis focused on accuracy, precision, recall, F1-score, and AUC. LR (96%), NN (95%), and RF (96%) showed the highest performance across all metrics, effectively distinguishing between tumor and no-tumor images and providing reliable results. © 2024 IEEE.

关键词： K-means clustering

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20th EAI international conference on Mobile and Ubiquitous Systems: computing, Networking and Services, MobiQuitous 2023

20th EAI International Conference on Mobile and Ubiquitous S...

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20th EAI international conference on Mobile and Ubiquitous Systems: computing, Networking and Services, MobiQuitous 2023

ISBN: (纸本)9783031639913

The proceedings contain 65 papers. The special focus in this conference is on Mobile and Ubiquitous Systems: computing, Networking and Services. The topics include: High-Performance Features in Generalizable Fingerprint-Based Indoor Positioning;SCORE: Scalable Contact Tracing over Uncertain Trajectories;faultBit: Generic and Efficient Wireless Fault Detection Using the Internet of Things;deepHeteroIoT: Deep Local and Global learning over Heterogeneous IoT Sensor Data;Federated Reinforcement learning for Automated LoRaWAN Management in Industrial IoT;a Hybrid Approach to Monitor Context Parameters for Optimising Caching for Context-Aware IoT Applications;LOADHoC: Towards the Automatic Local Distribution of Computation Using Existing IoT Devices;e-Go Bicycle Intelligent Speed Adaptation System for Catching the Green Light;FedGCS: Addressing Class Imbalance in Long-Tail Federated learning;FedRC: Representational Consistency Guided Model Uploading Mechanism for Asynchronous Federated learning;RADEAN: A Resource Allocation Model Based on Deep Reinforcement learning and Generative Adversarial Networks in Edge computing;a Stream Data Service Framework for Real-Time Vehicle Companion Discovery;KS-Autoformer: An Autoformer-Based SOC Prediction Framework for Electric Vehicles;deep Reinforcement learning-Based Multi-node Collaborative Task Offloading Optimization in 6G Space-Air-Ground Integrated Networks;securing Wireless Communication in Critical Infrastructure: Challenges and Opportunities;cross-User Activity Recognition via Temporal Relation Optimal Transport;selfAct: Personalized Activity Recognition Based on Self-Supervised and Active learning;a Novel Method for Wearable Activity Recognition with Feature Evolvable Streams;let’s Vibrate with Vibration: Augmenting Structural engineering with Low-Cost Vibration Sensing;research on Data Drift and Class Imbalance in Android Malware Detection;Reputation-Based Dissemination of Trustworthy Information in VANETs;reputation System

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Hybrid CNN-BiLSTM Model for Power Quality Disturbance Classification 1

Hybrid CNN-BiLSTM Model for Power Quality Disturbance Classi...

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1st IEEE international conference on Advanced Power engineering and Energy, APEE 2024

作者： Lin, Jiajian Tavalaei, Jalal Yeo, Lit Yen Zhou, Yutong Afrouzi, Hadi Nabipour Ektesabi, Mehran Motamed Swinburne University of Technology Faculty of Engineering Computing and Science Kuching Malaysia College of Mechancial Engineering Taishan University Taian China School of Science Computing and Engineering Technologies Swinburne University of Technology HawthornVIC Australia

ISBN: (纸本)9798350360936

With the evolution of intelligent grid tech, the need for monitoring power quality has markedly risen, essential for the continuous functioning of the power grid. Addressing this matter, this research proposes a combined approach combining Convolutional Neural Networks (CNN) with Bidirectional Long Short-Term Memory (BiLSTM) networks. The model effectively catches spatial or temporary aspects of power quality disruptions using CNN and employs BiLSTM to document long-term or temporal aspects, thus creating a profound correlation between input and output sequences. By substantially decreasing reliance on conventional physical attributes, this technique markedly enhances the precision in detecting power quality anomalies. Crucially, this technique, independent of particular original samples and existing information, holds widespread applicability and is applicable in identifying patterns in various other intricate systems. Research on experiments indicates that, as opposed to conventional machine learning frameworks, deep learning approaches are more effective in detecting disruptions in power quality. Our hybrid model excels in identifying power quality disturbance (PQD) and produces highly accurate recognition outcomes by autonomously analyzing central features of the disturbance indications. © 2024 IEEE.

关键词： Convolutional neural networks

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Making Health Care Smarter: The Role of Machine learning Algorithms 6

Making Health Care Smarter: The Role of Machine Learning Alg...

引用

6th international conference on Contemporary computing and Informatics, IC3I 2023

作者： Bisht, Amita Dhiman, Diksha Yamsani, Nagendar Uttaranchal Institute of Technology Uttaranchal University Department of Computer Science and Engineering Dehradun India Shivalik College of Engineering Department of Computer Science and Engineering Dehradun India School of Computer Science and Aartificial Intelligence SR University Warangal India

ISBN: (纸本)9798350304480

Machine learning algorithms make a significant contribution to disease prediction. The main objective of this is to support researchers and practitioners in choosing the most suitable machine learning algorithm for the healthcare sector. In today's world machine learning (ML) algorithm has a essential role in many sectors like medical diagnosis, predictive analytics, security and in finance. Many people today are dealing with serious illnesses that must be detected in the starting stages of development in order to begin therapy on schedule. The diseases may become incurable once reaching this stage. By using machine learning algorithms, we can discover the patterns and predict the diseases. This study attempts to briefly describe several machine learning approaches and to discuss the function of these algorithms in the field of healthcare, including diabetes, cancer diagnosis, brain tumor detection, bioinformatics, and many more applications. © 2023 IEEE.

关键词： Cancer Diagnosis Disease Prediction Diseases Healthcare Machine learning

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A Way of Making Smart Health Through Collaborating Machine learning with the Bibliometrics 4

A Way of Making Smart Health Through Collaborating Machine L...

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4th IEEE international conference on Advance computing and Innovative Technologies in engineering, ICACITE 2024

作者： Vignesh, T. Sruthi, P.S. Mohanraj, T. Kumar, Gaurav Varkale, Akshay Sheela, M.Sahaya Prince Shri Venkateshwara Padmavathy Engineering College Department of Computer Science and Engineering TN India Nehru Institute of Technology Department of Computer Science and Business System Tamil Nadu T.M. Palayam India Karpagam Academy of Higher Education Department of Computer Science Engineering India Chitkara University Chitkara Centre for Research and Development Himachal Pradesh India M.P Bhopal India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Electronics and Communication Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350360165

It datasets are widely utilized, shared, and repurposed without providing enough context for understanding the decision-making processes that led to their creation. It is necessary to modify system creation and use procedures to handle the concrete effects of how knowledge for model building is envisioned and used in real-world applications as AI systems assume more and more important roles. Documents are created at every phase of the data generation lifecycle to improve communication, aid in decision-making, and highlight the need of careful data work. By putting out this paradigm, we want to shed light on the choices and duties that are often disregarded while creating datasets, which is a crucial first step in bridging the duty of care gap in AI. This framework provides an essential and important resource for guaranteeing ethical AI activities, and it is in line with current developments in auditing procedures. © 2024 IEEE.

关键词： Adversarial machine learning

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Application of Data Mining in Physical Education Experiment teaching Guidance 3rd

Application of Data Mining in Physical Education Experiment ...

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3rd EAI international conference on Application of Big Data, Blockchain, and Internet of Things for Education Informatization, BigIoT-EDU 2023

作者： Hao, Lv Lan, Wang Shao, Yi Guan, Xinxin Gai, Feng Physical Culture Institute Yili Normal University Xinjiang Yining835000 China Key Laboratory of College Student Physique Monitoring Center Yili Normal University Yining China School of Cyber Security and Information Technology Yili Normal University Xinjiang Yining835000 China Yantai Vocational College Shandong264670 China

ISBN: (纸本)9783031631290

The application of data mining in physical education experimental teaching guidance can situation and performance in experimental courses, and provide personalized teaching guidance and feedback. By analyzing students’ experimental data and behaviors, data mining can discover their learning patterns, difficulties, and potential, and provide data-driven teaching strategies and resources for teachers. Through data mining technology, teachers can analyze students’ data indicators in experiments, such as action data, time data, energy data, etc., in order to identify students’ strengths and weaknesses in different actions or techniques, and help them provide targeted feedback and guidance. Teachers can use data mining techniques to find students’ learning patterns, such as their mastery speed and process of different experimental skills, so that they can conduct differentiated teaching based on students’ learning patterns. In summary, the data mining in physical education experimental teaching guidance can provide teachers with a comprehensive understanding and students’ learning situation, and help teachers provide personalized teaching guidance and feedback. This will promote the improvement of students’ experimental abilities and the optimization of learning outcomes. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications engineering 2024.

关键词： Data mining

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Research on the Application of Virtual Simulation Technology in the IoT programming class

Research on the Application of Virtual Simulation Technology...

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2022 international conference on Internet of Things and Machine learning, IoTML 2022

作者： Xu, Fangqin Gao, Xia Fu, Wanying Shanghai Publishing and Printing College Shuifeng road 100 Shanghai200933 China

ISBN: (纸本)9781510664913

With the development of information technology, the teaching of IoT technology has gradually become popular, but as an important part of hands-on training, the practical aspects of the course are restricted by various factors such as equipment and venue, resulting in unsatisfactory results. Through the introduction of virtual simulation technology in teaching, the IoT programming course is no longer constrained by the above factors, so that the course can reflect the characteristics of IoT applications, improve the implementation effect and teaching quality of the IoT programming course, and stimulate students' interest in learning. © 2023 SPIE.

关键词： teaching

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