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Intelligent and Innovative Technologies in Computing, Electrical and Electronics (IITCEE), International Conference

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

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.

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Conditional independence testing with a single realization of a multivariate nonstationary nonlinear time series

arXiv

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arXiv 2025年

作者： Wieck-Sosa, Michael Haddad, Michel F.C. Ramdas, Aaditya Department of Statistics and Data Science Carnegie Mellon University United States Department of Business Analytics and Applied Economics Queen Mary University of London United Kingdom Departments of Statistics and Data Science and Machine Learning Carnegie Mellon University United States

Identifying relationships among stochastic processes is a key goal in disciplines that deal with complex temporal systems, such as economics. While the standard toolkit for multivariate time series analysis has many advantages, it can be difficult to capture nonlinear dynamics using linear vector autoregressive models. This difficulty has motivated the development of methods for variable selection, causal discovery, and graphical modeling for nonlinear time series, which routinely employ nonparametric tests for conditional independence. In this paper, we introduce the first framework for conditional independence testing that works with a single realization of a nonstationary nonlinear process. The key technical ingredients are time-varying nonlinear regression, time-varying covariance estimation, and a distribution-uniform strong Gaussian approximation. Copyright © 2025, The Authors. All rights reserved.

关键词： Time series

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The Conditional Cauchy-Schwarz Divergence with Applications to Time-Series data and Sequential Decision Making

arXiv

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arXiv 2023年

作者： Yu, Shujian Li, Hongming Løkse, Sigurd Jenssen, Robert Príncipe, José C. Machine Learning Group UiT - The Arctic University of Norway Tromsø Norway The Quantitative Data Analytics Group Vrije Universiteit Amsterdam Netherlands Department of Electrical and Computer Engineering University of Florida GainesvilleFL32611 United States Drones and Autonomous Systems group NORCE Norwegian Research Centre Tromsø Norway Pioneer AI Centre Copenhagen University Denmark The Norwegian Computing Center Oslo Norway

The Cauchy-Schwarz (CS) divergence was developed by Príncipe et al. in 2000. In this paper, we extend the classic CS divergence to quantify the closeness between two conditional distributions and show that the developed conditional CS divergence can be elegantly estimated by a kernel density estimator from given samples. We illustrate the advantages (e.g., rigorous faithfulness guarantee, lower computational complexity, higher statistical power, and much more flexibility in a wide range of applications) of our conditional CS divergence over previous proposals, such as the conditional KL divergence and the conditional maximum mean discrepancy. We also demonstrate the compelling performance of conditional CS divergence in two machine learning tasks related to time series data and sequential inference, namely time series clustering and uncertainty-guided exploration for sequential decision making. The code of conditional CS divergence is available at https://***/SJYuCNEL/conditional_CS_divergence. Copyright © 2023, The Authors. All rights reserved.

关键词： Time series

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Monitoring medication optimization in patients with Parkinson’s disease

Monitoring medication optimization in patients with Parkinso...

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Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Hamid Moradi Julius Hannink Sabine Stallforth Till Gladow Stefan Ringbauer Martin Mayr Jürgen Winkler Jochen Klucken Bjoern M. Eskofier Department of Artificial Intelligence in Biomedical Engineering Machine Learning and Data Analytics Laboratory (MaD Lab) Friedrich Alexander University Erlangen Nuremberg Erlangen-Nuremberg Germany Portabiles HealthCare Technologies GmbH Erlangen Germany Department of Molecular-Neurology University Hospital Erlangen Erlangen Germany Medical Valley Digital Health Application Center Bamberg Germany NeuroSys GmbH Ulm Germany Luxembourg Institute of Health Centre Hospitalier du Luxembourg University of Luxembourg

Medication optimization is a common component of the treatment strategy in patients with Parkinson’s disease. As the disease progresses, it is essential to compensate for the movement deterioration in patients. Conventionally, examining motor deterioration and prescribing medication requires the patient’s onsite presence in hospitals or practices. Home-monitoring technologies can remotely deliver essential information to physicians and help them devise a treatment decision according to the patient’s need. Additionally, they help to observe the patient’s response to these changes. In this regard, we conducted a longitudinal study to collect gait data of patients with Parkinson’s disease while they received medication changes. Using logistic regression classifier, we could detect the annotated motor deterioration during medication optimization with an accuracy of 92%. Moreover, an in-depth examination of the best features illustrated a decline in gait speed and swing phase duration in the deterioration phases due to suboptimal *** relevance— Our proposed gait analysis method in this study provides objective, detailed, and punctual information to physicians. Revealing clinically relevant time points related to the patient’s need for medical adaption alleviates therapy optimization for physicians and reduces the duration of suboptimal treatment for patients. As the home-monitoring system acts remotely, embedding it in the medical care pathways could improve patients’ quality of life.

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Exploring the Capabilities of a Language Model-Only Approach for Depression Detection in Text data

Exploring the Capabilities of a Language Model-Only Approach...

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IEEE EMBS International Conference on Information Technology Applications in Biomedicine (ITAB)

作者： Misha Sadeghi Bernhard Egger Reza Agahi Robert Richer Klara Capito Lydia Helene Rupp Lena Schindler-Gmelch Matthias Berking Bjoern M. Eskofier Department Artificial Intelligence in Biomedical Engineering Machine Learning and Data Analytics Lab Friedrich-Alexander-Universität Erlangen- Nürnberg (FAU) Erlangen Germany Department of Computer Science Chair of Visual Computing Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) Erlangen Germany Syenah GMBH Eschborn Germany Chair of Clinical Psychology and Psychotherapy Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) Erlangen Germany

Depression is a prevalent and debilitating mental health condition that requires accurate and efficient detection for timely and effective treatment. In this study, we utilized the E-DAIC (Extended Distress Analysis Interview Corpus-Wizard-of-Oz) dataset, an extended version of the DAIC-WOZ dataset, which consists of semi-clinical interviews conducted by an animated virtual interviewer called Ellie, controlled by a human interviewer in another room. With 275 participants, the E-DAIC dataset represents a valuable resource for investigating depression detection methods. Our aim is to predict PHQ-8 scores through text analysis. Leveraging state-of-the-art speech processing, LLM-based text summarization, and a specialized depression detection module, we demonstrate the transformative potential of language data analysis in enhancing depression screening. By overcoming the limitations of manual feature extraction methods, our automated techniques provide a more efficient and effective means of evaluating depression. In our evaluation, we achieve robust accuracy on the development set of the E-DAIC dataset, with a Mean Absolute Error (MAE) of 3.65 in estimating PHQ-8 scores from recorded interviews. This remarkable performance highlights the efficacy of our approach in automatically predicting depression severity. Our research contributes to the growing evidence supporting the use of LLMs in mental health assessment, showcasing the role of innovative technologies in advancing patient care for depression.

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Retraction Note: Efficient user authentication protocol for distributed multimedia mobile cloud environment

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Journal of Ambient Intelligence and Humanized Computing 2024年第1期15卷 275-275页

作者： Vivekanandan, Manojkumar Sastry, V. N. Srinivasulu Reddy, U. Center for Mobile Banking (CMB) Institute for Development and Research in Banking Technology (IDRBT) Hyderabad India Machine Learning and Data Analytics Lab Department of Computer Applications National Institute of Technology Tiruchirappalli India

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DSGD-CECA: Decentralized SGD with Communication-Optimal Exact Consensus Algorithm

arXiv

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arXiv 2023年

作者： Ding, Lisang Jin, Kexin Ying, Bicheng Yuan, Kun Yin, Wotao Department of Mathematics University of California Los AngelesCA United States Department of Mathematics Princeton University PrincetonNJ United States Google Inc. Los AngelesCA United States Center for Machine Learning Research Peking University Beijing China AI for Science Institute Beijing China National Engineering Labratory for Big Data Analytics and Applications Beijing China Decision Intelligence Lab. Alibaba US BellevueWA United States

Decentralized Stochastic Gradient Descent (SGD) is an emerging neural network training approach that enables multiple agents to train a model collaboratively and simultaneously. Rather than using a central parameter server to collect gradients from all the agents, each agent keeps a copy of the model parameters and communicates with a small number of other agents to exchange model updates. Their communication, governed by the communication topology and gossip weight matrices, facilitates the exchange of model updates. The state-of-the-art approach uses the dynamic one-peer exponential-2 topology, achieving faster training times and improved scalability than the ring, grid, torus, and hypercube topologies. However, this approach requires a power-of-2 number of agents, which is impractical at scale. In this paper, we remove this restriction and propose Decentralized SGD with Communication-optimal Exact Consensus Algorithm (DSGD-CECA), which works for any number of agents while still achieving state-of-the-art properties. In particular, DSGD-CECA incurs a unit per-iteration communication overhead and an Õ(n3) transient iteration complexity. Our proof is based on newly discovered properties of gossip weight matrices and a novel approach to combine them with DSGD’s convergence analysis. Numerical experiments show the efficiency of DSGD-CECA. Copyright © 2023, The Authors. All rights reserved.

关键词： Stochastic systems

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A Lightweight Robust Approach for Automatic Heart Murmurs and Clinical Outcomes Classification from Phonocardiogram Recordings

A Lightweight Robust Approach for Automatic Heart Murmurs an...

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Computers in Cardiology (CinC)

作者： Hui Lu Julia Beatriz Yip Tobias Steigleder Stefan Grießhammer Maria Heckel Naga Venkata Sai Jitin Jami Bjoern Eskofier Christoph Ostgathe Alexander Koelpin Chair of Electronics and Sensor Systems Brandenburg University of Technology Cottbus Germany Institute of High-Frequency Technology Hamburg University of Technology Hamburg Germany Department of Palliative Medicine Universitätsklinikum Erlangen Erlangen Germany Machine Learning and Data Analytics (MaD) Lab Friedrich-Alexander-Universität Erlangen-Nürnberg Erlangen Germany

Cardiac auscultation provides an efficient and cost-effective way for cardiac disease pre-screening. The George B. Moody PhysioNet Challenge 2022 aimed to detect heart murmurs and clinical outcomes with heart sound recordings from multiple auscultation locations. Our team HearHeart proposed a lightweight convolutional neural network (CNN) to detect heart murmurs and a random forest model to classify clinical outcomes. 128 Melspectrogram features and wide features like the socio-demographic data and statistical features are extracted. Different techniques are employed to migrate the data imbalance and model the overfitting problem. We used two data augmentation methods, noise injection and spectrogram augmentation in time and frequency domain to increase the training samples and avoid overfitting during training. Besides, weighted loss functions are applied to both tasks to deal with data imbalance. In the end, we ensembled the models from cross-validation and used voting for the final classification. We achieved a murmur score of 0.791, and a clinical outcome score of 11731.64 on 5-fold cross-validation in the hidden validation set. While on the hidden test set, we achieved a murmur score of 0.780, and a clinical outcome score of 12110, placing our team 1st and 10th in the challenge tasks, respectively.

关键词： Heart Training Frequency-domain analysis Feature extraction data models Recording Convolutional neural networks

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Big data = Big Insights? Operationalising Brooks’ Law in a Massive GitHub data Set

arXiv

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arXiv 2022年

作者： Gote, Christoph Schweitzer, Frank Mavrodiev, Pavlin Scholtes, Ingo Department of Systems Design ETH Zurich Weinbergstrasse 56/58 Zurich8092 Switzerland Department of Computer Science XV - Machine Learning for Complex Networks Julius-Maximilians-Universität Würzburg Friedrich-Bergius-Ring 30 Würzburg97076 Germany Data Analytics Group University of Zurich Binzmühlestrasse 14 Zurich8050 Switzerland

Massive data from software repositories and collaboration tools are widely used to study social aspects in software development. One question that several recent works have addressed is how a software project’s size and structure influence team productivity, a question famously considered in Brooks’ law. Recent studies using massive repository data suggest that developers in larger teams tend to be less productive than smaller teams. Despite using similar methods and data, other studies argue for a positive linear or even super-linear relationship between team size and productivity, thus contesting the view of software economics that software projects are diseconomies of scale. In our work, we study challenges that can explain the disagreement between recent studies of developer productivity in massive repository data. We further provide, to the best of our knowledge, the largest, curated corpus of GitHub projects tailored to investigate the influence of team size and collaboration patterns on individual and collective productivity. Our work contributes to the ongoing discussion on the choice of productivity metrics in the operationalisation of hypotheses about determinants of successful software projects. It further highlights general pitfalls in big data analysis and shows that the use of bigger data sets does not automatically lead to more reliable insights. Copyright © 2022, The Authors. All rights reserved.

关键词： Productivity

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Reproducing kernel Hilbert space, Mercer's theorem, eigenfunctions, Nyström method, and use of kernels in machine learning: Tutorial and survey

arXiv

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arXiv 2021年

作者： Ghojogh, Benyamin Ghodsi, Ali Karray, Fakhri Crowley, Mark Department of Electrical and Computer Engineering Machine Learning Laboratory University of Waterloo WaterlooON Canada Department of Statistics and Actuarial Science David R. Cheriton School of Computer Science Data Analytics Laboratory University of Waterloo WaterlooON Canada Department of Electrical and Computer Engineering Centre for Pattern Analysis and Machine Intelligence University of Waterloo WaterlooON Canada

This is a tutorial and survey paper on kernels, kernel methods, and related fields. We start with reviewing the history of kernels in functional analysis and machine learning. Then, Mercer kernel, Hilbert and Banach spaces, Reproducing Kernel Hilbert Space (RKHS), Mercer's theorem and its proof, frequently used kernels, kernel construction from distance metric, important classes of kernels (including bounded, integrally positive definite, universal, stationary, and characteristic kernels), kernel centering and normalization, and eigenfunctions are explained in detail. Then, we introduce types of use of kernels in machine learning including kernel methods (such as kernel support vector machines), kernel learning by semi-definite programming, Hilbert-Schmidt independence criterion, maximum mean discrepancy, kernel mean embedding, and kernel dimensionality reduction. We also cover rank and factorization of kernel matrix as well as the approximation of eigenfunctions and kernels using the Nyström method. This paper can be useful for various fields of science including machine learning, dimensionality reduction, functional analysis in mathematics, and mathematical physics in quantum mechanics. Copyright © 2021, The Authors. All rights reserved.

关键词： Functional analysis

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