检索结果-内蒙古大学图书馆

2nd International Conference on Machine learning and Autonomous Systems, ICMLAS 2025

作者： Jikar, Nayan Tale, Yash Tale, Abhay Barhate, Aditya Verma, Prateek Jikar, Aman Datta Meghe Institute of Higher Education and Research Faculty of Engineering and Technology Department of Artificial Intelligence and Machine Learning Maharashtra Wardha442001 India Datta Meghe Institute of Higher Education and Research Faculty of Engineering and Technology Department of Artificial Intelligence and Data Science Maharashtra Wardha442001 India Datta Meghe Institute of Higher Education and Research Faculty of Engineering and Technology Department of Computer Science and Engineering Maharashtra Wardha442001 India

ISBN: (纸本)9798331505745

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. © 2025 IEEE.

关键词： Global warming

来源：评论

学校读者我要写书评

暂无评论

Translation System for the Visually Impaired from English to Braille

Translation System for the Visually Impaired from English to...

引用

Communication & Computing (WCONF), World Conference on

作者： J. Aswini Lokesh Krishnaa M C. Lakshmipriya G. Lavanya Siva Subramanian R Dept of Artificial Intelligence & Machine Learning Saveetha Engineering College(Autonomous) Department of Artificial Intelligence and a Science Saveetha Engineering College (Autonomous) Department CSE S.A. Engineering College Department of Artificial Intelligence and Data Science Saveetha Engineering College(Autonomous) Department CSE R.M.K College of Engineering and Technology

ISBN: (数字)9798350395327

ISBN: (纸本)9798350395334

The amount of information in the modern world is huge and is growing at a rapid speed because of new technological advancements and new researches being conducted every day. Internet has paved a pivotal role in the distribution of information easily which has enriched human knowledge. This interconnected digital landscape has significantly enhanced accessibility and the flow of information, marking a transformative shift in how knowledge is shared and updated. Various software and websites enable the translation of information into any language, providing flexibility and accessibility across diverse linguistic preferences which helps people to grow their knowledge. But the Visually Impaired have no standard means to translate information to Braille which has caused a significant information gap for them. They are unable to get updated to new information and the information is not available for them in their language. This paper proposes to use deep learning which facilitates the translation of English text to Braille that aids the visually impaired in staying updated and also get access to information in Braille language.

关键词： Training Knowledge engineering Braille Accuracy Visual impairment Linguistics Software Internet Reliability Standards

来源：评论

学校读者我要写书评

暂无评论

The Role of IoT and Machine learning in Automating Space Docking: Challenges, Advancements, and Future Prospects 2

The Role of IoT and Machine Learning in Automating Space Doc...

引用

2nd International Conference on Machine learning and Autonomous Systems, ICMLAS 2025

ISBN: (纸本)9798331505745

Space docking is an important aspect of modern-day space exploration, enabling crewed missions, shipment transport, and spacecraft refuelling. Traditionally, manual docking has trusted astronaut precision and floor manipulation coordination, which has grown to be inefficient and blunders-susceptible in deep-space missions because of conversation delays. Integrating the Internet of Things (IoT) and Machine learning (ML) is reworking area docking by introducing self-reliant, actual-time selection-making structures. IoT-enabled sensors continuously monitor spacecraft role, pace, and orientation, while ML algorithms optimize docking manoeuvres, beautify predictive upkeep, and make certain collision avoidance. Reinforcement learning similarly allows adaptive management strategies, making self-sufficient docking more efficient and dependable. This paper explores the improvements in IoT and ML for automating space docking, reading real-global programs along with SpaceX Dragon, NASA's AR&D systems, and China's Tianzhou cargo deliver. By addressing key demanding situations-precision, pace, and safety-those technologies pave the way for sustainable space exploration, including future Mars missions and autonomous area stations. The observation highlights how AI-driven docking may be integral for deep-space operations, making sure safer and more green missions are made. © 2025 IEEE.

关键词： Reinforcement learning

来源：评论

学校读者我要写书评

暂无评论

Disease and Medications Text Visualization Using Scattertext

Disease and Medications Text Visualization Using Scattertext

引用

Intelligent Control, Computing and Communications (IC3), International Conference on

作者： Rama Krishna K Kaipa Sandhya Praveen Gujjar J Raghavendra M Devadas Vani Hiremani Sapna R Department of Artificial Intelligence and Machine Learning Impact college of Engineering and Applied Sciences Bengaluru India Department of Data Science Impact college of Engineering and applied sciences Bengaluru India Faculty of Management Studies JAIN (Deemed-to-be University) Bengaluru India Department of Information Technology Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education (MAHE) Manipal India Symbiosis Institute of Technology Symbiosis International (Deemed) University Pune India

ISBN: (数字)9798331527495

ISBN: (纸本)9798331527501

Before text data can be analysed and visualised, it must be thoroughly cleaned due to its messy nature. data visualizations use the data to tell an engaging and simple-to-read story. That is what the Scattertext tool does. Scatter text allows which terms stand out in your corpora and how their frequency varies across categories. The dataset used in this paper includes reviews of drugs. This dataset offers a useful window into how people discuss their thoughts and experiences using various drugs. The drug names, user ratings, and the actual written reviews are among the details included in the dataset. This dataset consists of drug reviews collected from various sources, hence expressing a wide range of opinions and feelings. Each row in this dataset represents the experience a user had with a given medication and hence allows us to explore those emotions that are associated with any particular set of drugs. Understanding opinion expressed in the review about drugs might go a long way in helping decision-making processes in both healthcare and pharmaceuticals. The scatter plot distinguishes between labelled points and unlabelled ones by using a custom algorithm for Scattertext. By hovering their cursor over the labelled areas done client-side, users can interactively see which terms correspond to which points.

关键词： Drugs Reviews Decision making data visualization Medical services Market research Feature extraction Diseases Intelligent control

来源：评论

学校读者我要写书评

暂无评论

The Role of technology in Modern Education

The Role of Technology in Modern Education

引用

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

作者： Priya Kate Utkarsha Pacharaney Abhay Tale Aditya Barhate Nishad Sheikh Chinmaya Khadse Department of Computer Science and Engineering Faculty of Engineering & Technology Datta Meghe Institute of Higher Education & Research Wardha Maharashtra India Department of Artificial Intelligence and Machine Learning Faculty of Engineering & Technology Datta Meghe Institute of Higher Education & Research Wardha Maharashtra India Department of Artificial Intelligence and Data Science Faculty of Engineering & Technology Datta Meghe Institute of Higher Education & Research Wardha Maharashtra India

ISBN: (数字)9798331528713

ISBN: (纸本)9798331528720

It can be seen that technology has played a big role in today’s education through the use of smart boards and AI. Some of the advantages are that it affords students better access and opportunity for participation and brings more flexibility and efficiency to the teaching process and allows students to experience learning in new ways. However, there are also disadvantages such as digital divides, screen time, privacy concerns, and technical limitations. After highlighting the implications, the article analyses potential developments such as AI tutors and 5G connectivity as potential frontier technologies for the next era of learning technologies. Concisely, technology is an enhancement resource that should be embraced and complement quality teaching. The appropriate policy and continuous enhancement are the two crucial strategies for the education systems to implement technology to foster students with competencies required in the twenty-first century. Further research is needed regarding such matters as time spent in front of screens, media education, and implementation of new technologies in schools.

关键词： Training Industries Privacy Education Medical services learning (artificial intelligence) Media Stakeholders Security Digital divide

来源：评论

学校读者我要写书评

暂无评论

Deep Reinforcement learning In Autonomous Cars To Preventing Road Accident

Deep Reinforcement Learning In Autonomous Cars To Preventing...

引用

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

作者： Rushikesh Thote Aditya Barhate Abhay Tale Prateek Verma Department of Computer Science and Design Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research Wardha Maharashtra India 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

ISBN: (数字)9798331528713

ISBN: (纸本)9798331528720

The integration of autonomous vehicles (AVs) into modern transportation systems presents both unprecedented opportunities and challenges, particularly in ensuring road safety and reducing accidents. This paper explores the application of Deep Reinforcement learning (DRL) techniques to enhance decision-making processes in AVs, aiming to proactively prevent road accidents. By simulating complex driving environments, DRL algorithms enable AVs to learn optimal driving strategies through continuous interaction and feedback. We introduce a DRL-based framework that incorporates sensor data fusion, real-time environment analysis, and adaptive control mechanisms. Experimental results from simulated urban and highway scenarios demonstrate a significant reduction in collision rates and improved response to unforeseen obstacles compared to traditional rule-based systems. The findings underscore the potential of DRL in fortifying the reliability and safety of autonomous driving technologies.

关键词： Road accidents Reviews Decision making Transportation Transforms Deep reinforcement learning Road safety Safety Reliability Stress

来源：评论

学校读者我要写书评

暂无评论

Smart Healthcare: Harnessing the Power of Machine learning for Predictive Analysis

Smart Healthcare: Harnessing the Power of Machine Learning f...

引用

Engineering and technology (PICET), Parul International Conference on

作者： Aditya Barhate Praveen Kumar Prateek Verma Nayan Jikar Abhay Tale Vaibhav Hikre 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 Computer Science and Medical Engineering 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

ISBN: (数字)9798350369748

ISBN: (纸本)9798350369755

Smart healthcare employs cloud computing, the Internet of Things (IoT), and supercomputers to manage the healthcare system. Medical data storage volume is increasing, necessitating the adoption of Machine learning (ML) models for predicting outcomes, particularly in the treatment of chronic illnesses. The trend of care personalization and technological integration is highlighted by the development of ML applications for clinical decision support systems, including real-time predictive analytics. Predictive analytics applications employ ML and Artificial Intelligence (AI) to forecast patient risks, outcomes, and treatment responses. Predictive analytics is more important in healthcare when it comes to detecting Alzheimer's disease, cardiovascular events, radiology, and other conditions. Therefore, the current study highlights the change in illness identification, medical imaging, and personalized medication brought about by ML and Predictive Analytics in Precision Medicine (PM). However, ethical concerns and the deployment of transparent AI models still need more research. This study anticipates a time when healthcare will be quicker, better informed, and more patient-centered by using the insights provided by intelligent healthcare data. By effectively saving patient lives, the use of ML in healthcare ushers in a new era for the industry.

关键词： Ethics Solid modeling Medical services Machine learning Radiology Supercomputers Real-time systems Internet of Things Predictive analytics Biomedical imaging

来源：评论

学校读者我要写书评

暂无评论

Verification and Rectification of Error in Topology of Conserved Networks

引用

IFAC-PapersOnLine 2022年第30期55卷 43-48页

作者： Satya Jayadev P Arsh Bawa Nirav Bhatt Department of Electrical Engineering Department of Metallurgical and Materials Engineering Department of Biotechnology Robert Bosch Centre for Data Science and Artificial Intelligence (RBCDSAI) prospective Center for Network Systems Learning Control and Evolution Indian Institute of Technology Madras Chennai India

The knowledge of the underlying topology is essential for understanding and manipulating power grids, water distribution networks, biological networks. At times, the topology may be reported (or recorded) erroneously, mostly owing to human mistakes in reporting. The networks can be represented as a graph in which entities are represented as nodes and interactions among nodes as edges. This work focuses on the study of a specific type of error in topology that occurs when the incidence of an edge in the network is incorrectly reported. We propose a methodology to detect, isolate, and rectify this type of error using a single noisy measurement of flows along all the edges of a conserved network. We first show that this type of error generates specific error signatures, which enables error diagnosis, when the data is noise-free. An approach based on a series of statistical tests is developed to handle noisy data for online error detection and rectification. Simulation studies are performed to test the robustness of the proposed methodology.

关键词： Network topology Error detection Networks Verification Flow data Statistical hypothesis testing

来源：评论

学校读者我要写书评

暂无评论

Machine learning With data Assimilation and Uncertainty Quantification for Dynamical Systems:A Review

引用

IEEE/CAA Journal of Automatica Sinica 2023年第6期10卷 1361-1387页

作者： Sibo Cheng César Quilodrán-Casas Said Ouala Alban Farchi Che Liu Pierre Tandeo Ronan Fablet Didier Lucor Bertrand Iooss Julien Brajard Dunhui Xiao Tijana Janjic Weiping Ding Yike Guo Alberto Carrassi Marc Bocquet Rossella Arcucci Data Science Institute Department of ComputingImperial College LondonSW72AZ London Department of Earth Science and Engineering Imperial College LondonSW72AZ London Department of Computer Science and Engineering Hong Kong University of Science and TechnologyHong Kong 999077China the IMT Atlantique Lab-STICCUMR CNRS 6285France and OdysseyInria/IMTFrance.P.Tandeo is also with RIKEN Center for Computational ScienceKobeJapan the CEREA École des Ponts and EDF R&Dîle-de-FranceFrance the Laboratoire Interdisciplinaire des Sciences du Numérique CNRSParis-Saclay UniversityF-91403OrsayFrance the Electricitéde France(EDF) 78401 ChatouFranceInstitut de Mathématiques de Toulouse31062 ToulouseFrance and SINCLAIR AI LabSaclayFrance the Sorbonne University ParisFranceand also with Nansen Environmental and Remote Sensing Center(NERSC)BergenNorway the School of Mathematical Sciences Tongji UniversityShanghai 200092China the Mathematical Institute for Machine Learning and Data Science KU Eichstaett-IngolstadtBavariaGermany the School of Information Science and Technology Nantong UniversityNantong 226019China the Department of Physics and Astronomy“Augusto Righi” University of Bologna40124 BolognaItaly

data assimilation(DA)and uncertainty quantification(UQ)are extensively used in analysing and reducing error propagation in high-dimensional spatial-temporal *** applications span from computational fluid dynamics(CFD)to geoscience and climate ***,much effort has been given in combining DA,UQ and machine learning(ML)*** research efforts seek to address some critical challenges in high-dimensional dynamical systems,including but not limited to dynamical system identification,reduced order surrogate modelling,error covariance specification and model error correction.A large number of developed techniques and methodologies exhibit a broad applicability across numerous domains,resulting in the necessity for a comprehensive *** paper provides the first overview of state-of-the-art researches in this interdisciplinary field,covering a wide range of *** review is aimed at ML scientists who attempt to apply DA and UQ techniques to improve the accuracy and the interpretability of their models,but also at DA and UQ experts who intend to integrate cutting-edge ML approaches to their ***,this article has a special focus on how ML methods can overcome the existing limits of DA and UQ,and vice *** exciting perspectives of this rapidly developing research field are also *** Terms-data assimilation(DA),deep learning,machine learning(ML),reduced-order-modelling,uncertainty quantification(UQ).

关键词： assimilation overcome apply

来源：评论

学校读者我要写书评

暂无评论

Intrusion Detection in Cloud Architecture Using Machine learning 4

Intrusion Detection in Cloud Architecture Using Machine Lear...

引用

4th International Conference on Advances in Computing, Communication Control and Networking, ICAC3N 2022

作者： Govinda Rajulu, G. Santhoshkumar, R. Venkatesan, D. Kalvikkarasi, S. Santosh Kumar Patra, P. St. Martin's Engineering College Department of Computer Science and Design India St. Martin's Engineering College Department of Artificial Intelligence and Data Science India St. Martin's Engineering College Department of Artificial Intelligence and Machine Learning Department of CSE India Podhagai College of Engineering & Technology Professor in Ece Department India St. Martin's Engineering College India

ISBN: (纸本)9781665474368

The research of Intrusion Detection Systems is known to be among the most challenging aspects of Cloud Security. In the context of the cloud, security breaches such as denial of service, scanning, injection of malicious code, viruses, worms, and the breaking of passwords are becoming increasingly commonplace. In the event that these attacks are not stopped in a timely manner, the company's reputation may be put in jeopardy, and it may sustain financial harm. As a consequence of this, it is essential to safeguard the cloud against dangers of this kind. In this research, we provide solutions to defend cloud systems by incorporating some of the most successful methods of intrusion detection. Within IDS, our primary focus has been placed on two critical aspects: the effectiveness of the detection techniques and the pace of detection. In order to overcome these obstacles, we suggest utilising machine learning in conjunction with parallelization. Approaches based on machine learning gain information from the data itself, hence diminishing the significance of the human expert. In order to identify intrusions, statistical models, safe system approaches, neural networks, and a variety of other methods are utilised. The purpose of this study is to improve the accuracy of intrusion detection in cloud computing by presenting a new approach to intrusion detection that is based on existing intrusion detection systems and the various architectures that those systems employ. © 2022 IEEE.

关键词： Cloud computing architecture

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：