检索结果-内蒙古大学图书馆

machine learning and Autonomous Systems (ICMLAS), International Conference on

作者： Nayan Jikar Yash Tale Abhay Tale Aditya Barhate Prateek Verma Aman Jikar Department of Artificial Intelligence and Machine Learning Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research Wardha Maharashtra India Department of Artificial Intelligence and Data Science Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research Wardha Maharashtra India Department of Computer Science and Engineering Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research Wardha Maharashtra India

ISBN: (数字)9798331505745

ISBN: (纸本)9798331505752

Climate change is one of the biggest challenges of the 21st century, with profound environmental, economic, and social impacts. machine learning (ML) has emerged as a transformative tool to address climate challenges through data-driven solutions, offering predictive capabilities for extreme weather events and enhancing the efficiency of clean-energy systems. This technology benefits the optimization of renewable energy generation, intelligent grid management, and resource utilization towards reducing greenhouse gas emissions. Moreover, ML promotes climate justice through targeted region-specific intervention and inclusive solution-making for the most vulnerable. Several technical and ethical challenges associated with data quality, scalability, and model generalization must still be overcome for ML to realize its potential. This paper reviews the role of ML in climate change prediction and mitigation discusses ongoing real-world applications and outlines the future research agenda necessary to address existing challenges. Integrating ML with emerging technologies like the Internet of Things (IoT) and explainable AI, alongside global collaboration and a focus on climate justice, will be essential in advancing effective climate action.

关键词： Climate change Renewable energy sources Ethics Prevention and mitigation Scalability data integrity Collaboration data models Internet of Things Meteorology

来源：评论

学校读者我要写书评

暂无评论

Structured Multi-Track Accompaniment Arrangement via Style Prior Modelling

arXiv

引用

arXiv 2023年

作者： Zhao, Jingwei Xia, Gus Wang, Ziyu Wang, Ye Institute of Data Science NUS Singapore School of Computing NUS Singapore Integrative Sciences and Engineering Programme NUS Graduate School Singapore Machine Learning Department MBZUAI United Arab Emirates Computer Science Department NYU Shanghai China

In the realm of music AI, arranging rich and structured multi-track accompaniments from a simple lead sheet presents significant challenges. Such challenges include maintaining track cohesion, ensuring long-term coherence, and optimizing computational efficiency. In this paper, we introduce a novel system that leverages prior modelling over disentangled style factors to address these challenges. Our method presents a two-stage process: initially, a piano arrangement is derived from the lead sheet by retrieving piano texture styles;subsequently, a multi-track orchestration is generated by infusing orchestral function styles into the piano arrangement. Our key design is the use of vector quantization and a unique multi-stream Transformer to model the long-term flow of the orchestration style, which enables flexible, controllable, and structured music generation. Experiments show that by factorizing the arrangement task into interpretable sub-stages, our approach enhances generative capacity while improving efficiency. Additionally, our system supports a variety of music genres and provides style control at different composition hierarchies. We further show that our system achieves superior coherence, structure, and overall arrangement quality compared to existing baselines. © 2023, CC BY.

关键词： Vector quantization

来源：评论

学校读者我要写书评

暂无评论

Urban Land Cover Classification with Efficient Hybrid Quantum machine learning Model

Urban Land Cover Classification with Efficient Hybrid Quantu...

引用

Congress on Evolutionary Computation

作者： Fan Fan Yilei Shi Xiao Xiang Zhu Data Science in Earth Observation Technical University of Munich (TUM) Munich Germany German Aerospace Center (DLR) Remote Sensing Technology Institute (IMF) Wessling Germany School of Engineering and Design Technical University of Munich (TUM) Munich Germany Munich Center for Machine Learning Munich Germany

ISBN: (数字)9798350308365

ISBN: (纸本)9798350308372

Urban land cover classification aims to derive crucial information from earth observation data and categorize it into specific land uses. To achieve accurate classification, sophisticated machine learning models trained with large earth observation data are employed, but the required computation power has become a bottleneck. Quantum computing might tackle this challenge in the future. However, representing images into quantum states for analysis with quantum computing is challenging due to the high demand for quantum resources. To tackle this challenge, we propose a hybrid quantum neural network that can effectively represent and classify remote sensing imagery with reduced quantum resources. Our model was evaluated on the Local Climate Zone (LCZ)-based land cover classification task using the TensorFlow Quantum platform, and the experimental results indicate its validity for accurate urban land cover classification.

关键词： Earth Analytical models Quantum computing Computational modeling Land surface machine learning Feature extraction

来源：评论

学校读者我要写书评

暂无评论

A Blockchain-Based Intelligent IoT Communication System for Enhanced Security, Reliability, and Efficiency

A Blockchain-Based Intelligent IoT Communication System for ...

引用

Advanced Computing Technologies (ICoACT), International Conference on

作者： Dinesh Kumar K J. Gobinath Jayavadivel Ravi S. Saravanakumar Senthil P B. Prabhu Shankar Department of Computer Science and Engineering Vel Tech University Chennai India Department of Computer Science and Engineering Rajiv Gandhi College of Engineering Chennai India School of Computer Science and Engineering Presidency University Bengaluru India Department of Information Technology Paavai Engineering College Paachal India Department of Artificial Intelligence and Data Science T.J.S.Engineering college Chennai India Department of Artificial Intelligence and Machine Learning Vel Tech University Chennai India

ISBN: (数字)9798331509859

ISBN: (纸本)9798331509866

This research proposes a new approach for developing and implementing a smart communication system of IoT through developmental blockchain technology. Regarding the proposed solution, it is suggested that a distributed ledger can improve security, reliability, and efficiency of communication of IoT devices. They tackle issues such as data integrity, privacy and security in IoT networks as well as the problem of trust in such networks. The solution provides a decentralised and secure IoT ecosystem communication hence allowing monitoring, controlling and administration of the IoT build ecosystems in real time. Blockchain technology, however, when combined with nextgeneration communication interfaces and modern data analysis techniques, can offer various superior characteristics such as device identification, origin of information, etc. to IoT applications. It also enhances the dependability and capacity of IoT systems regarding capability and efficiency. Analysis and performance comparisons substantiate the performance benefits of the proposed intelligent communication system based on blockchain technology In addition these theoretical and performance analyses and experimental evaluation confirm the applicability and potential of the blockchain-based intelligent communication system to transform IoT communication models and facilitate the new generation of eIOT applications in a number of fields. The key to the solution to incorporate blockchain fundamental infrastructure to augment the communication channel of IoT devices. Using cryptography and consensus methodologies with the decentralized framework, integrity and confidentiality of the data is properly maintained because of the barriers of malicious interception or manipulation of the data. The communication system also boosts trust within the network participants because it offers a decentralized and immutable ledger of data transactions on the blockchain. Instead, one of the compelling benefits of this solution

关键词： Performance evaluation Computers Communication systems Ecosystems Transforms Real-time systems Blockchains Internet of Things Reliability Monitoring

来源：评论

学校读者我要写书评

暂无评论

Giant Trevally Optimizer (GTO): Enhancing HVDC Transmission Capacity with Optimized Fault Current Limiters and HVDC Circuit Breaker Parameters

Giant Trevally Optimizer (GTO): Enhancing HVDC Transmission ...

引用

Electronics and Renewable Systems (ICEARS), International Conference on

作者： M. Siva Ramkumar Saranya. N Gokul Gopan Rahmath Ulla Baig Josha Daniel S Babu M Department of ECE SNS College of Technology India Department of Artificial Intelligence and Data Science Karpagam College of Engineering India Department of Mechanical Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences (SIMATS) Saveetha University Chennai India Department of Artificial Intelligence and Machine Learning Cambridge Institute of Technology K R Puram Bangalore Department of EEE Hindustan College of Engineering and Technology India Master of Engineering in Embedded Systems Hindusthan College of Engineering and Technology India

ISBN: (数字)9798331509675

ISBN: (纸本)9798331509682

The multi-terminal HVDC system relies heavily on circuit breakers (CBs) and fault current limiters (FCLs) for protection and performance reliability. This research describes a new Giant Trevally optimizer-based strategy for improving the performance of 4-terminal HVDC systems by optimizing crucial circuit breakers and fault current limiters. The proposed method uses time domain estimation to correctly capture fault dynamics, such as traveling waves and reflection coefficients and finds the worst-case fault locations. The multi-objective optimization seeks to optimize the overcurrent, overvoltage handling, fault clearance time, and energy absorption in arresters. This technology provides an effective, dependable protection solution that reduces fault recovery time and increases system stability. The output pulse of the HVDC system shows very low THD results 1.98% for the minimum load condition, 0.87% for median load, and 0.04% for peak load conditions. The results established the simulation of the model in a MATLAB environment.

关键词： HVDC transmission Circuit breakers Stability analysis Circuit faults Fault current limiters Time-domain analysis Protection Voltage control Integrated circuit reliability Optimization

来源：评论

学校读者我要写书评

暂无评论

An Optimized Hybrid Deep learning and IoT-Enabled Health System in Retina Image Analysis for Blindness Detection

An Optimized Hybrid Deep Learning and IoT-Enabled Health Sys...

引用

Self Sustainable Artificial Intelligence Systems (ICSSAS), International Conference on

作者： J Yamini T Kanimozhi A Jenefa K Bagyalakshmi K Aarthi P Dhivya Department of Artificial Intelligence and Machine learning Kalaignar Karunanidhi Institute of Technology Coimbatore India Department of Artificial Intelligence and Data Science Sri Eshwar College of Engineering Coimbatore India Department of Computer Science and Engineering PPG Institute of Technology Coimbatore India Department of Mathematics SNS College of Technology Coimbatore India Department of Artificial Intelligence and Data Science Kalaignar Karunanidhi Institute of Technology Coimbatore India Department of Computer Science and Engineering Bannari Amman Institute of Technology Erode India

ISBN: (数字)9798350368413

ISBN: (纸本)9798350368420

Early signs of blind identification and retina disorder in retina image has become prominent. Deep learning techniques are used to improve the accuracy in detecting pathological conditions from the retina images. However, the models have some challenges such as suboptimal feature extraction and inefficient filtering methods. Most of the traditional methods often result in overfitting and under fitting, thus decreasing the generalizability and robustness of the model. The proposed hybrid approach combines bilateral filtering with Convolutional Neural Networks (CNN). In this approach, bilateral filtering acts as a pre-processing technique to enhance image quality by reducing noise and preserving edges for the correct extraction of features. The CNN is applied for feature extraction and it is used to self-learn complex patterns from filtered images. The proposed approach gives an accuracy of $95.65 \%$ with a loss of 0.18 without overfitting and underfitting.

关键词： Optical filters Accuracy Filtering Noise Predictive models Retina Feature extraction Convolutional neural networks Diseases Overfitting

来源：评论

学校读者我要写书评

暂无评论

Deep Neural Quality of Service Prediction for Unmanned Aircraft System Communications

Deep Neural Quality of Service Prediction for Unmanned Aircr...

引用

International Wireless Communications and Mobile Computing Conference, IWCMC

作者： Patrick Purucker Christian Reil Alfred Höß Björn W. Schuller Department of Electrical Engineering Media Technology and Computer Sciences University of Applied Sciences Amberg-Weiden Amberg Germany CHI MRI Technical University of Munich Germany Munich Data-Science Institute (MDSI) Germany Munich Center for Machine Learning (MCML) Germany GLAM Imperial College London UK

ISBN: (数字)9798350361261

ISBN: (纸本)9798350361278

Commercial Unmanned Aircraft Systems (UAS) have a wide range of applications, including package delivery, inspection and search and rescue missions. For the operation of Unmanned Aircraft Vehicles (UAV) Beyond Visual Line of Sight (BVLOS), reliable long-range communication is essential. The cellular network is one possible solution, but there are issues such as signal loss and frequent handovers at higher altitudes. To mitigate these issues, our work proposes the use of two cellular links from different providers prioritised according to Quality of Service (QoS) prediction. We evaluate multiple AI-based model architectures for the prediction, and find that the model consisting of Gated Recurrent Units (GRU) and convolutional layers outperforms the others. The models are trained and tested on real-world data and show a reduction in latency peaks, thereby increasing connection resilience. Moreover, the prediction pipeline is designed to be executable on the UAV side and is not limited to a specific geographical area, making it applicable to real-world scenarios. Finally, we present a pre-flight path planning algorithm that takes QoS into account when calculating the flight path in order to further improve communication. To support the research community, we publicly share the dataset used to obtain our results.

关键词： Adaptation models Heuristic algorithms Quality of service Computer architecture Predictive models Autonomous aerial vehicles Modems

来源：评论

学校读者我要写书评

暂无评论

Computational Approach for Respiratory Pressure Parameters in Neonatal Ventilation 24

Computational Approach for Respiratory Pressure Parameters i...

引用

24th IEEE International Conference of Young Professionals in Electron Devices and Materials, EDM 2023

作者： Lipchak, Daria Chupov, Aleksey Dolganov, Anton Zhdanov, Aleksei Jt. Stock Co. Prod. Association Urals Optical and Mechanical Plant Named after Mr. E. S. Yalamov Medical Devices R and D Bureau Yekaterinburg Russia Engineering School of Information Technologies Telecommunications and Control Systems Ural Federal University Named after the First President of Russia B.N. Yeltsin Yekaterinburg Russia University of Erlangen-Nuremberg Machine Learning and Data Analytics Lab Erlangen Germany

ISBN: (纸本)9798350336870

In the context of mechanical ventilation, the accurate calculation of secondary pressure parameters is essential for clinicians to effectively assess a patient's clinical condition. In the case of neonatal resuscitation with the T-piece, both the inspiratory and expiratory triggers are manually operated. The device provides a constant flow at the initiation of inspiration and reduces the flow to limit output pressure according to the set peak inspiratory pressure. However, without an algorithmic classification of the inspiration and expiration pressure, data analysis may only be performed postfactum. To address the need for the separate trend analysis of PIP and PEEP in non-invasive mechanical ventilation with a T-piece, there is a requirement to develop and implement an accurate algorithm for respiratory pressure parameters calculation. In this study, we review the current state-of-the-art methods for the calculation of respiratory pressure parameters for non-invasive mechanical ventilation with a T-piece. We then present the development and implementation of an algorithm for respiratory pressure parameters calculation and discuss the results of the analysis. This algorithm is designed to provide accurate and reliable measurements of PIP and PEEP in neonatal resuscitation with the T-piece, allowing clinicians to more effectively monitor and assess a patient's clinical condition during mechanical ventilation. The results demonstrate the effectiveness of the algorithm in accurately calculating PIP and PEEP values, providing valuable insights into the patient's respiratory mechanics and helping to inform clinical decision-making. © 2023 IEEE.

关键词： Resuscitation

来源：评论

学校读者我要写书评

暂无评论

Building a data Lake for Power BI in the Cloud: A Review on Utilizing Cloud Storage Services for Large datasets

Building a Data Lake for Power BI in the Cloud: A Review on ...

引用

Artificial Intelligence in Education and Industry 4.0 (IDICAIEI), DMIHER International Conference on

作者： Viraj Vijay Gurbade Prateek Verma Swapnil Gundewar Vijendra Singh Bramhe Department of Artificial Intelligence and Data Science Faculty of Engineering and Technology Datta Meghe Institute of Higher Education & Research (DU) Wardha Maharashtra India Department of Artificial Intelligence and Machine Learning Faculty of Engineering and Technology Datta Meghe Institute of Higher Education & Research (DU) Wardha Maharashtra India School of Computing Science and Engineering VIT Bhopal University Sehore Madhya Pradesh India

ISBN: (数字)9798331528713

ISBN: (纸本)9798331528720

A data lake is a centralized repository where you may store both structured and unstructured data at any scale. Unlike typical data warehouses, which need data structure for storage, data lakes allow you to store raw data in its natural state. This adaptability makes data lakes suitable for storing large volumes of various data kinds, including photographs, videos, log files, sensor data, and others. When integrating a data lake with Power Business Intelligence (BI), organizations can harness the power of both technologies to gain valuable insights from their data. In the realm of data-driven decision-making, organizations face the challenge of managing and extracting insights from vast and diverse datasets, a task conventional on-premises storage solutions often struggle with due to the exponential growth of data. Thus, the adoption of more scalable and flexible alternatives becomes imperative. Cloud-based data lakes emerge as transformative solutions, offering unparalleled scalability, accessibility, cost-effectiveness, and advanced security protocols. This paper discuss the concept of constructing a cloud-based data lake optimized for Power BI, leveraging leading cloud storage services like Azure data Lake Storage (ADLS) and Amazon S3. By seamlessly integrating Power BI with the cloud data lake, organizations can overcome traditional storage limitations and reap numerous benefits. The practical applications in advanced analytics, customer insights, IoT data management, and data discovery underscore the real-world utility of cloud data lakes. The integration of Power BI with a cloud data lake augments efficient data analysis and self-service analytics potential, offering organizations a comprehensive solution for deriving actionable insights from their data assets.

关键词： Cloud computing Scalability Decision making Buildings Organizations Big data applications Security data mining Business intelligence Self-service

来源：评论

学校读者我要写书评

暂无评论

Exploring the Tangible Impact of Artificial Intelligence and machine learning: Bridging the Gap between Hype and Reality

Exploring the Tangible Impact of Artificial Intelligence and...

引用

Advanced Computing and Emerging Technologies (ACET), International Conference on

作者： Rahul Dattangire Ruchika Vaidya Divya Biradar Ashish Joon Data Engineering Publicis Sapient Houston Texas USA Department of Artificial Intelligence and Machine Learning Datta Meghe Institute of Higher Education and Research (DU) Wardha Maharashtra India Computer Science University of Texas at Arlington Arlington Texas USA Software Developer Andhus Technologies Inc Pensacola FL

ISBN: (数字)9798350367720

ISBN: (纸本)9798350367737

Beyond being merely trendy terms in technology, machine learning (ML) and artificial intelligence (AI) have directly influenced every industry. This article thus demystifies the true meaning of these technologies by sifting through the sultry descriptions. Here, we explore how AI and ML enhance efficiency, decision-making, and problem-solving in various sectors including health, Agriculture, finance, and production as we discover real-world applications. Continuing with the discussion of implementation challenges, the aspects of bias, ethical pitfalls, and report cards are examined. In addition, the article also covered how the integration of AI as well as ML remains a way of improving processes, creating better user experiences, and spurring innovation. This paper, which is also based on case studies, discusses the relationship between social welfare and AI and the importance of AI’s development and use being done responsibly. Considering all the discussed aspects, this volume provides a rather nuanced perspective on AI and ML, unmasking the myths around these concepts to reveal the fact that they have a highly significant impact on the current reality and its projected evolutions.

关键词： Technological innovation Ethics Decision making Finance machine learning Production Pressing Medical services Writing Problem-solving

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：