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arXiv 2025年

作者： Ustinov, Andrew Yordanov, Matey Kuchma, Andrei Bychkov, Mikhail Department of Machine Learning and Data Analytics ITMO University Russia AI Innovation Hub ANTEI Limited Hong Kong Department of Integrative Systems and Design Hong Kong University of Science and Technology AI Innovation Hub ANTEI Limited Hong Kong

This paper evaluates the Audio Spectrogram Transformer (AST) architecture for synthesized speech detection, with focus on generalization across modern voice generation technologies. Using differentiated augmentation strategies, the model achieves 0.91% EER overall when tested against ElevenLabs, NotebookLM, and Minimax AI voice generators. Notably, after training with only 102 samples from a single technology, the model demonstrates strong cross-technology generalization, achieving 3.3% EER on completely unseen voice generators. This work establishes benchmarks for rapid adaptation to emerging synthesis technologies and provides evidence that transformer-based architectures can identify common artifacts across different neural voice synthesis methods, contributing to more robust speech verification systems. © 2025, CC BY-NC-ND.

关键词： Spectrographs

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A Smart Electronic Wheelchair Design Using Artificial Intelligence and Intelligent Sensors Association 2

A Smart Electronic Wheelchair Design Using Artificial Intell...

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2nd IEEE International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, ICIITCEE 2024

作者： Balamurugan, R. Kanchana, D. Anni Princy, B. Jaisudha, J. Malathi, P. Glory, E. Ajk College of Arts and Science Department of Artificial Intelligence and Machine Learning Tamil Nadu Coimbatore India Srm Institute of Science and Technology Department of Computer Applications Tamil Nadu Chennai India Panimalar Engineering College Department of Computer and Communication Engineering Tamil Nadu Chennai India Prince Shri Venkateshwara Padmavathy Engineering College Department of Ece Tamil Nadu Chennai India Simats Saveetha College of Liberal Arts and Sciences Department of Data Analytics Tamil Nadu Chennai India Simats Department of Biosciences Saveetha School of Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350306415

Since the beginning of the seventeenth century, wheelchairs have been used to transport hospital patients and those with mobility impairments. The power of people is what spreads wheelchairs. Wheelchair users propel themselves forward using their upper bodies or, more commonly, require the assistance of another person to propel them. In this work, we examine how to build an autonomous electric wheelchair for the disabled at the lowest possible cost without sacrificing quality of design or control. This research presents the planning and development of an AI-enhanced electric wheelchair that can be controlled automatically to climb stairs, making it accessible to those with mobility impairments. The chair's controls allow the user to move it in any direction and raise the landing and staircases to varying heights. The chair's DC power source (battery used) may be recharged, allowing it to function for an extended period of time. In addition, a safety system was created that monitors and provides feedback on the user's pulse rate, as well as the wheelchair's functioning state (including acceleration, climate, and inclination position). This proposed AI based Wheelchair (AIWC) design is suitable for any aged people and the proposed model is cross-validated with the conventional wheelchair design using mobile based operations handling system called Mobile-Wheelchair (MWC) to test the efficiency of the proposed design in clear manner. © 2024 IEEE.

关键词： Accelerator AI Artificial Intelligence Batteries DC Power Intelligent Sensors Mobile-Wheelchair MWC Wheelchair

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Blessemflood21: Advancing Flood Analysis with a High-Resolution Georeferenced dataset for Humanitarian Aid Support

Blessemflood21: Advancing Flood Analysis with a High-Resolut...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Vladyslav Polushko Alexander Jenal Jens Bongartz Immanuel Weber Damjan Hatic Ronald Rösch Thomas März Markus Rauhut Andreas Weinmann Image Processing Department Fraunhofer ITWM Kaiserslautern Germany Working Group Algorithms for Computer Vision Imaging and Data Analysis Darmstadt Germany Center for Machine Learning and Sensor Technology Hochschule Koblenz Remagen Germany

ISBN: (数字)9798350360325

ISBN: (纸本)9798350360332

Floods are an increasingly common global threat, causing emergencies and severe damage to infrastructure. During crises, organisations such as the World Food Programme use remotely sensed imagery, typically obtained through drones, for rapid situational analysis to plan life-saving actions. Computer Vision tools are needed to support task force experts on-site in the evaluation of the imagery to improve their efficiency and to allocate resources strategically. We introduce the BlessemFlood21 dataset to stimulate research on efficient flood detection tools. The imagery was acquired during the 2021 Erftstadt-Blessem flooding event and consists of high-resolution and georeferenced RGB-NIR images. In the resulting RGB dataset, the images are supplemented with detailed water masks, obtained via a semi-supervised human-in-the-loop technique, where in particular the NIR information is leveraged to classify pixels as either water or non-water. We evaluate our dataset by training and testing established Deep learning models for semantic segmentation. With BlessemFlood21 we provide labeled high-resolution RGB data and a baseline for further development of algorithmic solutions tailored to flood detection in RGB imagery.

关键词： Deep learning Training Semantic segmentation Human in the loop Sensors Floods Task analysis

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A Comprehensive Review of Supervised learning Algorithms in Healthcare Applications

A Comprehensive Review of Supervised Learning Algorithms in ...

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

作者： Aditya Barhate Abhay Tale Nayan Jikar Prateek Verma Praveen Kumar Prajyot Yesankar 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

Supervised learning has revolutionized the concept of personalization in treatment with the development of Precision Medicine. This review aims to provide a systematic analysis of the utilization of supervised learning in healthcare and its functions in diagnostics and prognoses, recommendations for treatment, and patient surveillance. Classification algorithms such as Logistic Regression, Support Vector Machines, and Decision Trees. Regression techniques such as Linear Regression, Polynomial Regression, and Neural Networks. Ensemble methods such as Bagging, Boosting, and Stacking. Deep learning techniques such as Convolutional Neural Networks, Recurrent Neural Networks, and Long Short-Term Memory are discussed for their efficiency and applicability in various healthcare applications. There are strengths highlighted by the review including enhanced diagnostic capabilities and patient-targeted therapy alongside weaknesses such as quality of data, interpretability as well as compatibility with existing healthcare systems. This review gives a more generalized approach to how supervised learning algorithms help to enhance healthcare results and identifies the likely topic that requires more research in the future.

关键词： Industries Deep learning Ethics Systematics Reviews Precision medicine Supervised learning Medical services Classification algorithms Tuning

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Application of Machine learning Algorithms for Analyzing Sentiments Using Twitter dataset

Application of Machine Learning Algorithms for Analyzing Sen...

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

作者： Bhairavi Rewatkar Aditya Barhate Prateek Verma 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 Artificial Intelligence and Machine Learning Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research Wardha Maharashtra India

ISBN: (数字)9798350379990

ISBN: (纸本)9798350391558

Sentiment analysis is a Natural Language Processing (NLP) approach used to determine whether a piece of text is negative, positive, or neutral based on the emotions it expresses. Despite advancements, several challenges persist in this field, including handling sarcasm, context ambiguity, and the presence of noisy data. Recent techniques, such as deep learning models and transformer-based approaches, have improved accuracy but still face limitations in computational efficiency and real-time analysis. This article employs Machine learning (ML) techniques to analyze text and determine the sentiments of reviews shared on Twitter, aiming to address these challenges. The proposed system extracts attributes from the text, such as the sentiment polarity (whether the sentiment is negative or positive) and the topic or theme of the discussion. A comparison with prior work on sentiment analysis in tweets is presented, along with a comprehensive study of feature extraction and representation methods. The study evaluates the performance of six classifiers: Naïve Bayes, Decision Tree, Logistic Regression, Support Vector Machine (SVM), Gradient Boosting, and Random Forest, based on different feature extraction techniques. Using the Bag of Words technique, SVM achieved the highest accuracy of 94.58%, while using term frequency, Random Forest showed the highest accuracy of 94.99%. The developed algorithms for sentiment analysis in tweets are useful for analyzing social feedback, product surveys, and social media comments. Combining neural networks and deep learning improves the accuracy of sentiment analysis and provides opportunities to analyze various types of emotions, enabling proactive responses to public sentiment.

关键词： Support vector machines Deep learning Sentiment analysis Accuracy Machine learning algorithms Social networking (online) Reviews

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New theoretical ISM-K2 Bayesian network model for evaluating vaccination effectiveness

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Journal of Ambient Intelligence and Humanized Computing 2023年第9期14卷 12789-12805页

作者： Xie, Xiaoliang Xie, Bingqi Xiong, Dan Hou, Muzhou Zuo, Jinxia Wei, Guo Chevallier, Julien School of Mathematics and Statistics Hunan University of Technology and Busin Ess Changsha410205 China School of Mathematics and Statistics Central South University Changsha410083 China Key Laboratory of Hunan Province for Statistical Learning and Intelligent Computation Hunan University of Technology and Business Hunan Changsha410205 China Institute of Big Data and Internet Innovation Hunan University of Technology and Business Changsha410205 China Department of Mathematics and Computer Science University of North Carolina at Pembroke PembrokeNC28372 United States 184 boulevard Saint-Germain Paris75006 France 2 rue de la Liberté Saint-Denis93526 France

Aiming at the difficulty in obtaining a complete Bayesian network (BN) structure directly through search-scoring algorithms, authors attempted to incorporate expert judgment and historical data to construct an interpretive structural model with an ISM-K2 algorithm for evaluating vaccination effectiveness (VE). By analyzing the influenza vaccine data provided by Hunan Provincial Center for Disease Control and Prevention, risk factors influencing VE in each link in the process of "Transportation—Storage—Distribution—Inoculation" were systematically investigated. Subsequently, an evaluation index system of VE and an ISM-K2 BN model were developed. Findings include: (1) The comprehensive quality of the staff handling vaccines has a significant impact on VE;(2) Predictive inference and diagnostic reasoning through the ISM-K2 BN model are stable, effective, and highly interpretable, and consequently, the post-production supervision of vaccines is enhanced. The study provides a theoretical basis for evaluating VE and a scientific tool for tracking the responsibility of adverse events of ineffective vaccines, which has the value of promotion in improving VE and reducing the transmission rate of infectious diseases. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Bayesian networks

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DEFT - A Scaled Electric Vehicle Platform for Testing Autonomous Driving Algorithms

TechRxiv

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TechRxiv 2024年

作者： Kumar, Subhadeep Pasumarthy, Ramkrishna Bhatt, Nirav P. Department of Electrical Engineering Indian Institute of Technology Madras Chennai600036 India Department of Electrical Engineering Robert Bosch Center of Data Sciences and Artificial Intelligence Network Systems Learning Control and Evolution Group Indian Institute of Technology Madras Chennai600036 India Department of Biotechnology Robert Bosch Center of Data Sciences and Artificial Intelligence Initiative for Biological Systems Engineering Indian Institute of Technology Madras Chennai600036 India

Scaled-down vehicles provide a controlled and repeatable environment for testing autonomous driving algorithms, expediting development while mitigating risks associated with full-scale vehicle testing. This paper introduces a meticulously designed scaled-down electric vehicle, emphasizing vehicle state measurement, precise control, perception sensing, and electric safety. Our approach utilizes actuators with feedback, provides enhanced reliability and precision, and torque across all speed ranges, in contrast to conventional scaled platforms built with actuators of radio-controlled cars. Apart from the conventional sensors used in the existing scaled platforms, our vehicle incorporates novel sensor modules to measure wheel angular velocities, steering angles, and battery cell voltages, which can also be seamlessly integrated into these platforms. We demonstrate the vehicle’s motion with manual and autonomous operation and showcase its features for validation. © 2024, CC BY.

关键词： Automobile testing

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Dynamic Channel Allocation Using Reinforcement learning Algorithm for Multiple Input Multiple Output Systems 3

Dynamic Channel Allocation Using Reinforcement Learning Algo...

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3rd International Conference on Integrated Circuits and Communication Systems, ICICACS 2025

作者： Bittla, Srinivasa Rao Riadhusin, Raami Divyaraj, G.N. Aravindh, S. Ahila, B. College of Technical Engineering The Islamic University Department of Computers Techniques Engineering Al Diwaniyah Iraq Nitte Meenakshi Institute of Technology Department of Artificial Intelligence and Machine Learning Bengaluru India New Prince Shri Bhavani College of Engineering and Technology Department of Mechanical Engineering chennai India Dhanalakshmi Srinivasan College of Engineering Technology Department of Artificial Intelligence and Data Science Mamallapuram India

ISBN: (纸本)9798331508456

In recent years, Multiple-Input Multiple-Output systems (MIMO) play a crucial role in modern wireless networks by enhancing spectral efficiency and data rates. Traditional static, heuristic-based allocation methods struggled to adapt to real-time variations, handling high-dimensional state spaces, minimizing interference, and ensuring fairness in channel allocation. However, decision-making in dynamic environments requires intelligent and scalable approach that enables real-time decision-making by efficiently learning and adapting to changing network conditions. To overcome these issues. This research proposed Deep Q-Network with Prioritized Experience Replay (DQN-PER) for dynamic channel allocation for MIMO Systems. DQN-PER prioritizes high-value learning experiences and while also improving training efficiency and convergence speed. PER prioritizes important experiences based on Temporal-Difference (TD) error by allowing the model to focus on more informative transitions, which speeds up learning and improves adaptability. Simulation results demonstrate enhanced spectral efficiency, reduced packet loss, and improved Quality of Service (QoS) in dynamic wireless environments. These findings establish DQN-PER as a promising solution for intelligent and adaptive MIMO resource allocation. The proposed method achieved better results in terms of a packet drop rate 0.04 compared to existing method Advantage Actor-Critic (A2C). © 2025 IEEE.

关键词： Reinforcement learning

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Band selection in Hyperspectral Images using information similarity ranking

Band selection in Hyperspectral Images using information sim...

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Machine Intelligence for GeoAnalytics and Remote Sensing (MIGARS), International Conference on

作者： T. Hitendra Sarma R. Dharma Reddy K. Mrudula K. Rammohan Rao Sankalp Dhondi Murali Kanthi Department of Information Technology Vasavi College of Engineering Hyderabad India Department of Mathematics GNITS Hyderabad India Machine Learning Engineer Brane Enterprises Pvt. Ltd. Hyderabad India Department of Data Science CMR Technical Campus Hyderabad India

ISBN: (数字)9798350389678

ISBN: (纸本)9798350389685

Band selection in hyperspectral images is crucial for reducing redundant features and improving subsequent tasks like classification and segmentation. This article presents a novel band ranking method based on information similarity measures from the concepts of information theory. We introduce the Normalized Mutual Information for similarity and Variation of Information for dissimilarity to create a ranking scheme based on the affinity of a band to become a cluster center. We explore hybrid schemes combining structural and informational attributes. Supervised approaches using label-wise similarities using ground truth supervision are also investigated. Evaluation against state-of-the-art band selection algorithms on popular datasets, using both traditional Machine learning and Deep learning models, demonstrates superior results. This study underscores the significance of informational and structural attributes in unsupervised band selection and the potential for further enhancement through supervised similarity with ground truth information.

关键词： Deep learning Image segmentation Machine learning algorithms Clustering algorithms Task analysis Mutual information Machine intelligence

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Object Proxy Patterns for Accelerating Distributed Applications

arXiv

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arXiv 2024年

作者： Pauloski, J. Gregory Hayot-Sasson, Valerie Ward, Logan Brace, Alexander Bauer, André Chard, Kyle Foster, Ian Department of Computer Science University of Chicago ChicagoIL United States Data Science and Learning Division Argonne National Laboratory LemontIL United States Department of Computer Science Illinois Institute of Technology ChicagoIL United States

Workflow and serverless frameworks have empowered new approaches to distributed application design by abstracting compute resources. However, their typically limited or one-size-fits-all support for advanced data flow patterns leaves optimization to the application programmer—optimization that becomes more difficult as data become larger. The transparent object proxy, which provides wide-area references that can resolve to data regardless of location, has been demonstrated as an effective low-level building block in such situations. Here we propose three high-level proxy-based programming patterns—distributed futures, streaming, and ownership—that make the power of the proxy pattern usable for more complex and dynamic distributed program structures. We motivate these patterns via careful review of application requirements and describe implementations of each pattern. We evaluate our implementations through a suite of benchmarks and by applying them in three meaningful scientific applications, in which we demonstrate substantial improvements in runtime, throughput, and memory usage. Copyright © 2024, The Authors. All rights reserved.

关键词： Open source software

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