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arXiv 2023年

作者： Manole, Tudor Balakrishnan, Sivaraman Niles-Weed, Jonathan Wasserman, Larry Statistics and Data Science Center Massachusetts Institute of Technology United States Department of Statistics and Data Science Carnegie Mellon University United States Machine Learning Department Carnegie Mellon University United States Center for Data Science New York University United States Courant Institute of Mathematical Sciences New York University United States

One of the central objects in the theory of optimal transport is the Brenier map: the unique monotone transformation which pushes forward an absolutely continuous probability law onto any other given law. A line of recent work has analyzed L2 convergence rates of plugin estimators of Brenier maps, which are defined as the Brenier map between density estimators of the underlying distributions. In this work, we show that such estimators satisfy a pointwise central limit theorem when the underlying laws are supported on the flat torus of dimension d ≥ 3. We also derive a negative result, showing that these estimators do not converge weakly in L2 when the dimension is sufficiently large. Our proofs hinge upon a quantitative linearization of the Monge-Ampère equation, which may be of independent interest. This result allows us to reduce our problem to that of deriving limit laws for the solution of a uniformly elliptic partial differential equation with a stochastic right-hand side, subject to periodic boundary conditions. Copyright © 2023, The Authors. All rights reserved.

关键词： Stochastic systems

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data Governance in the Big data Era: A Scalable Framework for Effective Management and Regulatory Compliance

Data Governance in the Big Data Era: A Scalable Framework fo...

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Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES), International Conference on

作者： Vasanth S G Saravanan Keerthana S Madhu Karthick A Data Science and Applications Indian Institute of Technology Chennai Madras India Department of ECE Sri Sai Ram Institute of Technology Chennai West Tambaram India Department of Artificial Intelligence and Machine Learning St. Joseph's College of Engineering OMR Chennai India Department of CSE (Cyber Security) R.M.K. College of Engineering and Technology Thiruvallur India

ISBN: (数字)9798331543624

ISBN: (纸本)9798331543631

While big data enhances the capacity of organizations to make choices, the traditional data governance approaches are not productive enough to manage the explosion of volume, variety, and velocity of data. This inadequateness leads to loss of data integrity, inabilities to adhere to legal frameworks and poor use of available data. This study provides a solution by providing an easier way to apply data governance concepts using a framework that is elastic and scalable enough for today's data environment. In addition to this, study helps a firm maintain quality of data, compliance, and interactivity through the use of metadata strategies. This relatively new tool helps to manage data governance more effective and innovatively so that the organization can harness the business aspects within big data through the proper deployment of the data governance structure.

关键词： Scalability data integrity Medical services Big data Metadata Regulation data governance Safety Security Usability

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A Review on the Role of Artificial Intelligence in Personalized learning

A Review on the Role of Artificial Intelligence in Personali...

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Innovation in technology (APCIT), Asia Pacific Conference on

作者： Rukaiyya Qureshi Shital Hajare Prateek Verma Department of Artificial Intelligence and Data Science Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research (DU) Sawangi Meghe Wardha Maharashtra India Department of Artificial Intelligence and Machine Learning Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research (DU) Sawangi Meghe Wardha Maharashtra

ISBN: (数字)9798350361537

ISBN: (纸本)9798350361544

This article explores the revolutionary role of Artificial Intelligence (AI) in personalized learning, focusing on its potential to revolutionize educational processes and improve learning outcomes. This paper provides insights into how AI technologies can be used to tailor instruction, identify learning needs, and provide specific strategies that cater to different students' psychological, cognitive, and motivational preferences. The article examines case studies and examples to demonstrate different uses of AI in personalized learning settings, such as autonomous tutoring systems, personalized education channels, and analysis based on data. However, the use of AI in education raises ethical, technological, and policy concerns that require careful study and proactive actions to assure fair access, preserve learner privacy, and eliminate algorithmic biases. Ultimately, finally, the article emphasizes AI's transformative potential in personalized learning and advocates for coordinated efforts among educators, academics, and policymakers to leverage its benefits while tackling accompanying obstacles

关键词： Technological innovation Privacy Ethics Reviews Education Psychology Focusing

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Virtual Palette: An Efficient Object Tracking Tool

Virtual Palette: An Efficient Object Tracking Tool

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Sustainable Computing and Smart Systems (ICSCSS), International Conference on

作者： R. Madhura S. Nikkath Bushra Devi P. P Sasigresa P Department of Computing Technologies SRM Institute of Science and Technology Kattangulathur Chennai Tamil Nadu India Department of Artificial Intelligence and Machine Learning Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Computer Science and Engineering (Data Science) Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology

ISBN: (数字)9798350379990

ISBN: (纸本)9798350391558

Virtual Palette is a cutting-edge tool designed to enhance audience participation by providing an alternative to conventional jamboards for educators. Leveraging object tracking, a fundamental component of computer vision, it facilitates seamless and intuitive interactions with the audience. The video analysis process comprises three primary steps: object detection, object tracking across frames, and object behavior analysis. This sophisticated approach necessitates meticulous consideration of elements such as accurate object representation, feature selection for tracking, object recognition, and object tracking. One of the primary advantages of Virtual Palette over traditional mouse or touchpad devices is its ability to support clear and legible drawing and effective interaction in virtual environments. This feature is particularly beneficial for individuals with hearing impairments, aiding in the identification of patterns and drawings. The development of Virtual Palette involves overcoming challenges such as precise three-dimensional finger movement tracking and optimizing the technology for virtual backgrounds. Overall, Virtual Palette offers a dynamic and interactive teaching experience, akin to the advantages provided by screen recording for creating tutorials and virtual documentation.

关键词： Fingers Virtual environments Tutorials Transforms Object detection Green computing Feature extraction

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Autonomous Vehicles: Steering the Future of Transportation with Self-Driving technology and Advanced AI Systems

Autonomous Vehicles: Steering the Future of Transportation w...

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Global Conference for Advancement in technology (GCAT)

作者： Daniel Nareshkumar. M Desidi Narsimha Reddy A. Sowmiya V. Samuthira Pandi Rayala Sateesh N. Hindumathy Department of Electronics and Communication Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences SIMATS University Chennai Tamil Nadu India Data Consultant (Data Governance Data Analytics: Enterprise Performance Management AI&ML) Soniks Consulting LLC Texas USA Department of EEE Rajalakshmi Engineering College Chennai Centre for Advanced Wireless Integrated Technology Chennai Institute of Technology Chennai Tamil Nadu India Department of ECE MLR Institute of Technology Hyderabad Department of Artificial Intelligence and Machine Learning Vel Tech Rangarajan Dr.Sagunthala R & D Institute of Science and Technology Chennai

ISBN: (数字)9798350376685

ISBN: (纸本)9798350376692

Autonomous vehicles (AVs) are a game-changer in the transportation industry; they will alter cityscapes, cut down on accidents, and reimagine how people experience mobility. This study investigates the complex relationship between autonomous vehicles and high-tech AI systems, looking at how these two technologies can influence transportation in the future. The paper highlights crucial developments in sensor fusion, machine learning algorithms, and real-time data processing that are essential for the development of fully autonomous systems by analyzing the present status of AV technology. In the first part of the study, the basic technologies that support AVs are described. These technologies include LiDAR, radar, cameras, and GPS. When taken together, they allow AVs to perceive their surroundings and locate themselves. After that, it delves into how AI is helping AVs understand sensor data, make judgments in real-time, and carry out complicated maneuvers, showcasing how advancements in deep learning, neural networks, and predictive analytics are powering these capabilities. Integrating AVs into current transportation infrastructures presents both potential and obstacles, which the paper delves into. Critical situation safety, legal frameworks, cybersecurity risks, and AI decision-making ethics are all covered. In addition to the environmental advantages of improved traffic flow and decreased emissions, the study delves into the possible socioeconomic implications, including shifts in urban design, public transit, and the labor market. The research delves into the existing deployment tactics and the path for scaling AV operations through a thorough analysis of case studies, pilot projects, and industry reports. It determines how prepared different groups are to accept this revolutionary technology, including the general public, lawmakers, and manufacturers. To sum up, the study highlights how important it is for academic institutions, business executives, and governm

关键词： Ethics Machine learning algorithms Urban planning Decision making Sensor fusion data processing Real-time systems Safety Computer security Autonomous vehicles

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Cost-Effective Communication in UDN in Indoor and Outdoor Environment via Machine learning

Cost-Effective Communication in UDN in Indoor and Outdoor En...

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2023 International Conference on Artificial Intelligence and Knowledge Discovery in Concurrent Engineering, ICECONF 2023

作者： Karman, K. Nattar Velmurugan, V. Raju, Kommisetti Murthy Sajana, T. Vijayalakshmi, V. Dhanraj, JoshuvaArockia Department of Artificial Intelligence and Machine Learning Saveetha School of Engineering Tamil Nadu Chennai602105 India Department of Electronics and Communication Engineering Vel Tech Rangarajan and Dr.Sagunthala RandD Institute of Science and Technology Tamil Nadu Chennai600062 India Department of Electronics and Communication Engineering Shri Vishnu Engineering College for Women Andhra Pradesh West Godavari India Department of Artificial Intelligence and Data Science KoneruLakshmaiah Education Foundation Andhra Pradesh Vaddeswaram522502 India Department of Networking and Communications School of Computing SRM Institute of Science and Technology Tamil Nadu Kattankulathur603203 India Department of Mechatronics Engineering Hindustan Institute of Technology and Science Tamil Nadu Chennai603103 India

ISBN: (纸本)9798350334357

In general, applications on a densely populated network are slower. When there are no opportunities to interact with the devices on the network, the user is forced to communicate at some cost. Thus, the inconsistency that exists there increases the cost over that dense network. The single way to evaluate prospect usage is to look at that have used in the earlier period. This paper proposes an upgraded approach to upgrading existing services within the Ultra Dense cloud Network (UDN). In this way the cost for the services available there is calculated in advance and a system is developed to manage it effectively. This proposed method clearly shows that the effective cost can be managed by combining high-costservices and low-costservices separately. This will help to manage the various communication processes to achieve high effectiveness in Ultra Dense cloud Network (UDN). The proposed method achieves 93.28% of Cost-effective data Management.49% of Application log failure, 42 % of system log failure, 98% of network log management, 96% of security log management, 99% cost effective management. © 2023 IEEE.

关键词： Cost effectiveness

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Segment Together: A Versatile Paradigm for Semi-Supervised Medical Image Segmentation

arXiv

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arXiv 2023年

作者： Zeng, Qingjie Xie, Yutong Lu, Zilin Lu, Mengkang Wu, Yicheng Xia, Yong School of Computer Science and Engineering Northwestern Polytechnical University China Australian Institute for Machine Learning University of Adelaide Australia Department of Data Science & AI Faculty of Information Technology Monash University Australia

Annotation scarcity has become a major obstacle for training powerful deep-learning models for medical image segmentation, restricting their deployment in clinical scenarios. To address it, semi-supervised learning by exploiting abundant unlabeled data is highly desirable to boost the model training. However, most existing works still focus on limited medical tasks and underestimate the potential of learning across diverse tasks and multiple datasets. Therefore, in this paper, we introduce a Versatile Semi-supervised framework (VerSemi) to point out a new perspective that integrates various tasks into a unified model with a broad label space, to exploit more unlabeled data for semi-supervised medical image segmentation. Specifically, we introduce a dynamic task-prompted design to segment various targets from different datasets. Next, this unified model is used to identify the foreground regions from all labeled data, to capture cross-dataset semantics. Particularly, we create a synthetic task with a cutmix strategy to augment foreground targets within the expanded label space. To effectively utilize unlabeled data, we introduce a consistency constraint. This involves aligning aggregated predictions from various tasks with those from the synthetic task, further guiding the model in accurately segmenting foreground regions during training. We evaluated our VerSemi model on four public benchmarking datasets. Extensive experiments demonstrated that VerSemi can consistently outperform the second-best method by a large margin (e.g., an average 2.69% Dice gain on four datasets), setting new SOTA performance for semi-supervised medical image segmentation. The code will be released. © 2023, CC BY.

关键词： Semantics

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Role of Machine learning in Climate Change Prediction and Mitigation

Role of Machine Learning in Climate Change Prediction and Mi...

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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

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Smart Telemedicine Using IoT by Integrating 5G and Block-Chain Techniques

Smart Telemedicine Using IoT by Integrating 5G and Block-Cha...

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International Conference on Contemporary Computing and Informatics (IC3I)

作者： Shailee Lohmor Choudhary Rinku Sharma Dixit Deepak Das K. Ranjith Singh V. Dinesh Babu Department of Artificial Intelligence & Machine Learning - Data Science New Delhi Institute of Management New Delhi India Agricultural Engineering Karunya Institute of Science and Technology Coimbatore India Department of Computer Science Karpagam Academy of Higher Education Coimbatore India Department of Information Technology Karpagam Institute of Technology Coimbatore India

There are many medical applications for the Internet of Health Things (IoHT). In order to offer patients with more intelligent and effective health diagnoses, contemporary IoHT incorporates wellness items such sensors with continuously monitored medical equipment for the evaluation and handling of a medical chart. Using the combination of 5G networks and ethereum technology, we presented the IoT using a fog medical decision assistance system in this study for the forecast and monitoring of illness along with its severities. Due to its visibility, a frame provides a method for keeping and exchanging knowledge which is safe. Ethereum has various uses in the medical industry and therefore can enhance monitoring equipment, clinical study storage systems, mobile medical software, exchanging and keeping of digital media files, and insurer details stockpiling. The suggested structure will assemble a cloud provider will hold client information from the health equipment that's going to be connected to the individual, along with any pertinent health records. The use of 5G with cryptocurrency technologies allows the reliable transfer of patient information at high transmission rates with short reaction times. In addition, a Deep Neural (NN) classification is employed to forecast illnesses as well as the degree of underlying severity. The suggested model is tested using a variety of classifiers. In order to determine which classification is appropriate for the information, the effectiveness of various classifier is compared by contrasting the results. The NN improved a 98.98 average accuracy. This NN is additionally trained on the information such so this can forecast the outcome of the information category that carries no labels. A taught Naive Bayes is more accurate than conventional classifications at making predictions and effectively displaying the findings.

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A PDE Framework of Consensus-Based Optimization for Objectives with Multiple Global Minimizers

arXiv

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arXiv 2024年

作者： Fornasier, Massimo Sun, Lukang Technical University of Munich School of Computation Information and Technology Department of Mathematics Munich Germany Munich Center for Machine Learning Munich Germany Munich Data Science Institute Germany King Abdullah University of Science and Technology Thuwal Saudi Arabia KAUST AI Initiative Thuwal Saudi Arabia

Consensus-based optimization (CBO) is an agent-based derivative-free method for non-smooth global optimization that has been introduced in 2017 [40], leveraging a surprising interplay between stochastic exploration and Laplace’s principle. In addition to its versatility and effectiveness in handling high-dimensional, non-convex, and non-smooth optimization problems, this approach lends itself well to theoretical analysis. Indeed, its dynamics is governed by a degenerate nonlinear Fokker–Planck equation, whose large time behavior explains the convergence of the method. Recent results [10, 23] provide guarantees of convergence under the restrictive assumption of unique global minimizer for the objective function. In this work, we propose a novel and simple variation of CBO to tackle non-convex optimization problems with multiple global minimizers. Despite the simplicity of this new model, its analysis is particularly challenging because of its nonlinearity and nonlocal nature. We prove existence of solutions of the corresponding nonlinear Fokker–Planck equation and we show exponential concentration in time to the set of minimizers made of multiple smooth, convex, and compact components. Our proofs require combining several ingredients, such as delicate geometrical arguments, new variants of a quantitative Laplace principle, ad hoc regularizations and approximations, and regularity theory for parabolic equations. Ultimately, this result suggests that the corresponding CBO algorithm, formulated as an Euler–Maruyama discretization of the underlying empirical stochastic process, tends to converge to multiple global minimizers. © 2024, CC BY.

关键词： Fokker Planck equation

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