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Mediterranean Conference on Control and Automation (MED)

作者： Vijayanand Digge Ramkrishna Pasumarthy Department of Electrical Engineering Indian Institute of Technology Madras Chennai India Department of Electrical Engineering Robert Bosch Center of Data Science and Artificial Intelligence Network Systems Learning Control and Evolution Group Indian Institute of Technology Madras Chennai India

ISBN: (数字)9781665406734

ISBN: (纸本)9781665406741

This paper presents a data-driven state feedback control law, based on a linear quadratic regulator (LQR) design, for systems with exogenous inputs. In general, this framework is referred to as a data-driven min-max controller, and is more robust to disturbances than the standard LQR controllers. Instead of relying on system models, in this work, the state feedback control law is computed directly from the knowledge of the inputs and the states. The LQR gain is parametrized with matrices that are directly estimated using open-loop experiment data of the system. We experimentally validate our results by implementing the data driven controller for performance management of a web-server hosted on a private cloud.

关键词： Linear systems State feedback Regulators Riccati equations Markov processes Mathematical models Parametric statistics

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Rainfall Prediction using Novel Approach for Enhancing Crop Yield

Rainfall Prediction using Novel Approach for Enhancing Crop ...

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Advanced Computing & Communication Technologies (ICACCTech), International Conference on

作者： Visweswararao Seelam Sriharsha Vikruthi Naresh Kumar Bhagavatham P V V S D Nagendrudu Goda SrinivasaRao Ayyappa Chakravarthi M. Dept of CSE PACE Institute of Technology and Sciences. Dept of CSE B V Raju Institute of Technology Telangana Narsapur Medak India Dept of CSE Vignana bharathi Institute of Technology Hyderabad India Department of Artificial Intelligence and Machine Learning Aditya University Surampalem Dept of CSE KL University Vaddeswaram Guntur Andhra Pradesh Dept of CSE-Data Science KKR & KSR Institute of Technology & Sciences Guntur Andhra Pradesh

ISBN: (数字)9798331519056

ISBN: (纸本)9798331519063

In India, agriculture is one of the fastest-growing fields to improve the country's economy. Based on several factors like climatic conditions, predicting rainfall in these stages is one of the most significant tasks. In recent years, unexpected and unpredictable weather conditions have been observed in some places. This study mainly focused on developing the Hybrid algorithm (HA) to predict rainfall in the early stages and find accurate weather conditions. The proposed approach also called as Novel Approach that improves the algorithm's strength by adopting the advanced preprocessing and feature extraction technique. The training model random forest (RF) also used to find the accurate patterns from the given dataset samples. The preprocessing technique focused on cleaning and transforming the raw data to remove the noise from the input dataset, improving the data quality. The proposed approach combines Autoencoders and Linear Regression (LR) to accurately predict rainfall. Extensive experiments are carried out to evaluate the performance of the hybrid algorithm using historical meteorological data from multiple regions with varying climatic conditions. The results show that the hybrid algorithm outperforms traditional methods and individual machine-learning models in terms of accuracy, robustness, and generalization.

关键词： Radio frequency Training Rain Accuracy Temperature Autoencoders Linear regression Time series analysis Prediction algorithms data models

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Accelerating crystal structure search through active learning with neural networks for rapid relaxations

arXiv

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arXiv 2024年

作者： Hessmann, Stefaan S.P. Schütt, Kristof T. Gebauer, Niklas W.A. Gastegger, Michael Oguchi, Tamio Yamashita, Tomoki Machine Learning Group Technische Universität Berlin Berlin10587 Germany Berlin Institute for the Foundations of Learning and Data Berlin10587 Germany BASLEARN TU Berlin BASF Joint Lab for Machine Learning Technische Universität Berlin Berlin10587 Germany Center for Spintronics Research Network Osaka University 1-3 Machikaneyama Toyonaka Osaka560-8531 Japan Department of Electrical Electronics and Information Engineering Nagaoka University of Technology 1603-1 Kamitomioka-machi Nagaoka Niigata940-2188 Japan

Global optimization of crystal compositions is a significant yet computationally intensive method to identify stable structures within chemical space. The specific physical properties linked to a three-dimensional atomic arrangement make this an essential task in the development of new materials. We present a method that efficiently uses active learning of neural network force fields for structure relaxation, minimizing the required number of steps in the process. This is achieved by neural network force fields equipped with uncertainty estimation, which iteratively guide a pool of randomly generated candidates towards their respective local minima. Using this approach, we are able to effectively identify the most promising candidates for further evaluation using density functional theory (DFT). Our method not only reliably reduces computational costs by up to two orders of magnitude across the benchmark systems Si16, Na8Cl8, Ga8As8 and Al4O6, but also excels in finding the most stable minimum for the unseen, more complex systems Si46 and Al16O24. Moreover, we demonstrate at the example of Si16 that our method can find multiple relevant local minima while only adding minor computational effort. Copyright © 2024, The Authors. All rights reserved.

关键词： Density functional theory

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Flow-Based Sampling for Entanglement Entropy and the Machine learning of Defects

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Physical Review Letters 2025年第15期134卷 151601-151601页

作者： Andrea Bulgarelli Elia Cellini Karl Jansen Stefan Kühn Alessandro Nada Shinichi Nakajima Kim A. Nicoli Marco Panero Department of Physics University of Turin and INFN Turin unit Via Pietro Giuria 1 I-10125 Turin Italy Computation-Based Science and Technology Research Center The Cyprus Institute Nicosia Cyprus Deutsches Elektronen-Synchrotron DESY Zeuthen Germany Berlin Institute for the Foundations of Learning and Data (BIFOLD) Berlin Germany Machine Learning Group Technische Universität Berlin Berlin Germany RIKEN Center for AIP Tokyo Japan Transdisciplinary Research Area (TRA) Matter University of Bonn Germany Helmholtz Institute for Radiation and Nuclear Physics (HISKP) Bonn Germany Department of Physics and Helsinki Institute of Physics PL 64 FIN-00014 University of Helsinki Finland

We introduce a novel technique to numerically calculate Rényi entanglement entropies in lattice quantum field theory using generative models. We describe how flow-based approaches can be combined with the replica trick using a custom neural-network architecture around a lattice defect connecting two replicas. Numerical tests for the ϕ4 scalar field theory in two and three dimensions demonstrate that our technique outperforms state-of-the-art Monte Carlo calculations, and exhibit a promising scaling with the defect size.

关键词： Lattice theory

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A Review on Advancing Prosthetics with Artificial Intelligence: Enhancing Control, Sensory Feedback, and Adaptability

A Review on Advancing Prosthetics with Artificial Intelligen...

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International Conference on Trends in Electronics and Informatics (ICEI)

作者： Shivam Bhoyar Renuka Motghare Subodh Daronde Department of Artificial Intillengence and Data Science Datta Meghe Institute of Higher Education and Research(Demeed to be University) Wardha Maharashtra India Department of Artificial Intillengence and Machine Learning Datta Meghe Institute of Higher Education and Research (Demeed to be University) Wardha Maharashtra India Department of Biomedical Engineering Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research. (Demeed to be University) Wardha Maharashtra India

ISBN: (数字)9798331544607

ISBN: (纸本)9798331544614

Recent advancements in prosthetic technology have dramatically improved quality of life for amputees, with AI-powered devices now offering capabilities far beyond traditional prosthetics. This paper traces the evolution of prosthetic limbs from basic mechanical designs to today's intelligent systems featuring myoelectric control and neural interfaces. Modern AI integration enables three key benefits: (1) more natural movement through real-time pattern recognition, (2) enhanced sensory feedback that restores touch perception, and (3) adaptive functionality that reduces user fatigue. Breakthroughs in osseointegration and smart materials further improve comfort and usability. These innovations extend beyond limb prosthetics to include cardiac implants and spinal support systems. Looking ahead, we anticipate a new generation of affordable, autonomous prosthetics that will blur the line between human and machine capabilities.

关键词： Limbs Technological innovation Prosthetic limbs Reviews Tactile sensors Surgery Real-time systems Usability Smart materials Prosthetics

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Facial Expression Recognition Using Multi-Block Deep CNN

Facial Expression Recognition Using Multi-Block Deep CNN

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International Conference on Circuits, Communication, Control and Computing

作者： Priyadarshini V Srinivasulu Reddy U Venkata Rami Reddy Chirra Mrudula M Suneetha M Dept. of Computer Science & Engineering National Institute of Technology Andhra Pradesh India Dept. of Computer Applications Machine Learning & Data Analytics lab National Institute of Technology Tiruchirappalli India School of Computer Science & Engineering VIT-AP University Amaravathi India Department of CSE Malla Reddy College of Engineering Telangana India Independent Researcher Amaravati India

ISBN: (数字)9798331528539

ISBN: (纸本)9798331528546

Emotions play a crucial role in decision-making, moral judgments, and other cognitive processes. The goal of this work is to identify people's facial emotions from face images. Facial expression recognition (FER), a highly diverse field with many commonalities between classes, is a challenging problem. The capabilities of current FER systems are limited in this regard. In this work, a multi-block Deep Convolutional Neural Networks (DCNN) model was proposed to identify facial expressions. Five blocks with different computational components were defined in a multi-block DCNN to extract the unique characteristics from input images. The second model (DCNN-SVM) is developed with an ensemble of SVM classifier to improve predictability and stability. The third model (DCNN-MV) is based on the majority voting with 4 classifiers SVM, KNN, Random Forest, and Decision Tree. The CK+ dataset was utilized and expanded via image data augmentation to enhance model performance and generalization. By adjusting hyperparameters, the suggested DCNN model's accuracy was investigated. The DCNN model reports an accuracy score of 97.71%. Both DCNN-SVM and DCNN-MV models achieved 97.79% accuracy. On the CK+ dataset, the proposed models demonstrated good accuracy in identifying facial expressions. When compared to existing models, the proposed model outperformed existing models in terms of accuracy.

关键词： Support vector machines Accuracy Computational modeling Face recognition Stability analysis Circuit stability Convolutional neural networks Integrated circuit modeling Thermal stability Random forests

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Enhancing Alzheimer's Disease Diagnosis Using Multi-Relation Graph Convolutional Networks and Structural MRI data

Enhancing Alzheimer's Disease Diagnosis Using Multi-Relation...

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IoT Based Control Networks and Intelligent Systems (ICICNIS), International Conference on

作者： P. Rajesh Kanna C. Gunasundari M. Senthamarai P. Pandiaraja P. Nithin K. Chitra Department of Computer Science and Engineering Bannari Amman Institute of Technology Erode Tamil Nadu India Department of Computer Science and Engineering SRM Institute of Science and Technology Trichy Tamil Nadu India Department of Artificial Intelligence and Data Science Nandha Engineering College Erode Tamil Nadu India Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu India Department of Artificial Intelligence and Machine Learning Bannari Amman Institute of Technology Erode Tamil Nadu India Department of Computer Applications Kongu Engineering College Erode Tamil Nadu India

ISBN: (数字)9798331518097

ISBN: (纸本)9798331518103

Alzheimer's disease (AD) has substantial obstacles to early detection, which frequently leads to therapy delays. In this article a unique method that uses structural MRI data and Multi-Relation Graph Convolutional Networks (MR-GCN) to improve AD diagnostic accuracy. Which can capture the nuanced interactions that characterize neurodegenerative disorders by representing the intricate relationships between various brain regions as a multi-relational graph. This strategy provides a comprehensive picture of brain function and pathology through the combination of relational data and structural MRI features. Using a large dataset, the resusts are evaluated and contrasted in MR-GCN method with other machine-learning techniques and single-relation graph models. The findings demonstrate that this method outperforms current models in terms of classification accuracy and resilience, achieving higher sensitivity and specificity in distinguishing between individuals in good health and various stages of AD. Then for further exploration the interpretability of the model is identified in critical brain regions that support the diagnosing process. This work provides a promising framework for the integration of sophisticated graph-based medical imaging tools, which could lead to more sophisticated clinical applications in the diagnosis and monitoring of neurodegenerative diseases in addition to improving our understanding of how AD progresses.

关键词： Neuroimaging Pathology Accuracy Graph convolutional networks Magnetic resonance imaging Machine learning Sensitivity and specificity Brain modeling Alzheimer's disease Resilience

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

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

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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

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Sentiment Analysis Using Machine learning Methods

Sentiment Analysis Using Machine Learning Methods

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Intelligent & Innovative Practices in Engineering & Management (IIPEM), International Conference on

作者： Abhishek Badholia Tarun Dhar Diwan Preeti Narooka Pravin B Khatkale Ankit Vishnoi Keshav Kaushik Department of Data Science Shri Shankaracharya Institute of Professional Management and Technology Raipur Atal Bihari Vajpayee University Bilaspur India Artificial Intelligence and Machine Learning School of Computer and Engineering Manipal University Jaipur India Sanjivani University Kopargaon Maharashtra India Department of Computer Science and Engineering Graphic Era Deemed to be University Dehradun Uttarakhand India Amity School of Engineering and Technology Amity University Punjab Mohali India

ISBN: (数字)9798350390049

ISBN: (纸本)9798350390056

Machine learning (ML) will be utilized to evaluate sentiment analysis in this project. This study aims to do this. Sentiment analysis is a popular natural language processing area. This is a natural language processing subfield. This section identifies textual senses. Due to the growing availability of digital text across platforms, sentiment analysis is now vital for many applications. This is because sentiment analysis has become essential. This technology might be used for market research, political analysis, and consumer feedback. A few instances. This study analyzes several machine learning methods, to name a few. This category includes SVM, Naive Bayes, Random Forest, and RNNs. To clarify, several datasets are used to assess these methodologies. To get more information. Preprocessing and feature engineering are crucial to sentiment analysis model construction, as this paper shows. The research emphasises these two phases. In addition, it provides insights into how these approaches are used in constantly changing digital situations.

关键词： Support vector machines Sentiment analysis Recurrent neural networks Transforms Market research Robustness Naive Bayes methods Noise measurement Random forests Resilience

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Artificial Intelligence-Driven Protein Folding System Employing Alpha Fold

Artificial Intelligence-Driven Protein Folding System Employ...

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Multi-Agent Systems for Collaborative Intelligence (ICMSCI), International Conference on

作者： Sowmiya R M. Pravin Kumar T. M. Sathish Kumar Rasika P M. Poornima Devi S. Priya Dharshini Department of Artificial Intelligence and Data Science SNS College of Engineering Coimbatore Tamil Nadu India Department of Medical Electronics Velalar College of Engineering and Technology (Autonomous) Erode Tamil Nadu India Department of Electronics and Communication Engineering K.S.R. College of Engineering Namakkal Tamil Nadu India Department of Pharmaceutics SNS College of Pharmacy and Health Sciences Coimbatore Tamil Nadu India Department of Artificial Intelligence and Machine Learning SNS College of Technology Coimbatore Tamil Nadu India Department of Pharmaceutics Periyar College of Pharmaceutical Sciences Tiruchirappalli Tamil Nadu India

ISBN: (数字)9798331509828

ISBN: (纸本)9798331509835

Artificial Intelligence (AI) is a main role to solve the lot of real-live issues. One of the significant challenges in the bioinformatics that researchers have been identified recently is understand the protein folding. First of all, people want to give a big hand off to the advancements because, day to day activities is performed in 3-dimensional basis that is formed based on a sequence of amino acids using protein folds. The folding of proteins is mostly related to three major issues. The first issue is the lack of a specific folding code. There is a folding mechanism in the second one. The ability to ascertain the 3-dimensional structure of proteins presents the last challenge. The structural components and microscopic machinery that make up human cells are called proteins. They provide vital tasks include storing oxygen, breaking down food, and creating scaffolding to support cells in maintaining their structure. Here, Amino acids is formed based on a single chain mechanism because amino acids folds safely and it performs a unique structure that is incorporated here. All living things, including viruses, are composed of proteins, including every element of human body and every other organism. The computational approach can be used to predict the structure of minuscule proteins. In this research work explained about how artificial intelligence is incorporated in predictions of protein structure and artificial intelligence give the solution for the problem of problem folding.

关键词： Proteins Hands Computer viruses Microscopy Amino acids Organisms Artificial intelligence Machinery Collaborative intelligence Periodic structures

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