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Machine Learning Based Early Prediction of Parkinson's Disease for Sustainable Healthcare 1st

Machine Learning Based Early Prediction of Parkinson's Disea...

1st International Conference on Sustainable Development through Machine Learning, Artificial Intelligence, and IoT, ICSD 2023

作者： Chauhan, Ritu Mehta, Khushi Alankar, Bhavya Kaur, Harleen Artificial Intelligence and IoT Lab Center for Computational Biology and Bioinformatics Amity University UP Noida India Amity Institute of Information Technology Amity University UP Mumbai India Department of Computer Science and Engineering School of Engineering Sciences and Technology Jamia Hamdard New Delhi India

ISBN: (纸本)9783031470547

The healthcare sector and technology are working hand-in-hand, which has led researchers to focus on Parkinson's disease. In a world where medical science and technology intertwined, there was a group of researchers determined to unravel the mysteries of Parkinson's disease. Medical science with machine learning algorithms, aimed to develop a robust prediction model for diagnosing this complex neurological disorder. The researchers explored various algorithms including Xgboost, Naive Bayes, Tree Classifier, KNN model, SVC model, Logistic Model, Random Forest, AdaBoost, and Gradient Boosting, using a diverse dataset encompassing clinical measurements, genetic information, and demographic factors. The researchers utilized various machine learning algorithms to uncover patterns and correlations, shed light on relationships, make accurate predictions, foster insights, differentiate Parkinson's disease, understand odds and probabilities, tackle uncertainty, and improve accuracy. By combining these algorithms, they constructed a prediction model for the accurate diagnosis of Parkinson's disease, bringing us closer to unraveling its mysteries. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Adaptive boosting

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Advanced Zero-Shot Text-to-Speech for Background Removal and Preservation with Controllable Masked Speech Prediction

Advanced Zero-Shot Text-to-Speech for Background Removal and...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Leying Zhang Wangyou Zhang Zhengyang Chen Yanmin Qian AI Institute Department of Computer Science and Engineering Auditory Cognition and Computational Acoustics Lab MoE Key Lab of Artificial Intelligence Shanghai Jiao Tong University Shanghai China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

The acoustic background plays a crucial role in natural conversation. It provides context and helps listeners understand the environment, but a strong background makes it difficult for listeners to understand spoken words. The appropriate handling of these backgrounds is situation-dependent: Although it may be necessary to remove background to ensure speech clarity, preserving the background is sometimes crucial to maintaining the contextual integrity of the speech. Despite recent advancements in zero-shot Text-to-Speech technologies, current systems often struggle with speech prompts containing backgrounds. To address these challenges, we propose a Controllable Masked Speech Prediction strategy coupled with a dual-speaker encoder, utilizing a task-related control signal to guide the prediction of dual background removal and preservation targets. Experimental results demonstrate that our approach enables precise control over the removal or preservation of background across various acoustic conditions and exhibits strong generalization capabilities in unseen scenarios.

关键词： Oral communication Predictive models Signal processing Acoustics Text to speech Speech processing

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Advancing Non-intrusive Suppression on Enhancement Distortion for Noise Robust ASR

Advancing Non-intrusive Suppression on Enhancement Distortio...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Wei Wang Siyi Zhao Yanmin Qian AI Institute Department of Computer Science and Engineering Auditory Cognition and Computational Acoustics Lab MoE Key Lab of Artificial Intelligence Shanghai Jiao Tong University Shanghai China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Recent advancements in speech enhancement (SE) techniques have greatly improved speech clarity and intelligibility in challenging acoustic environments. However, integrating SE into automatic speech recognition (ASR) systems often results in performance degradation due to artifacts introduced during the enhancement process. While various methods have enhanced recognition accuracy in SE-ASR systems, they often require fine-tuning or re-training of SE or ASR models, which is impractical in many real-world applications. In this paper, we propose a lightweight distortion suppression (DS) network that addresses these artifacts without modifying the SE or ASR models, treating them as fixed black boxes. The DS module operates on the time-frequency (T-F) bands of the original and enhanced complex spectrograms, efficiently compensating for SE distortions using the original T-F information. We validate our approach through experiments on both Mandarin and English ASR tasks using monaural and multi-channel SE frontends, across various ASR backends. Results show that the DS module significantly improves the performance of SE-ASR systems, even when used with robust commercial ASR backends.

关键词： Degradation Time-frequency analysis Acoustic distortion Closed box Speech enhancement Acoustics Noise robustness Noise measurement Spectrogram Automatic speech recognition

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Real-Time Smart Phone-Enabled Eyes Disease Classification

Real-Time Smart Phone-Enabled Eyes Disease Classification

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Advancement in Technology (ICONAT), International Conference for

作者： Bollem Poojitha G.V Nikitha K.S Sampath N Neelima T.V Smitha Department of Electronics and Communication Engineering Amrita School of Engineering Bengaluru Amrita Vishwa Vidyapeetham India Computational Science Lab Amrita School of Engineering Bengaluru Amrita Vishwa Vidyapeetham India

ISBN: (数字)9798350354171

ISBN: (纸本)9798350354188

Eye diseases occur when the tissues around the cornea and other parts of the eye are damaged. Some may be minor which doesn’t have much impact on the vision and if the damage is severe it may lead to vision loss. Early detection of such damage of tissue needs to be addressed. This paper proposes a revolutionize eye care with a real-time eye disease classification system accessible via smartphones. Using a powerful convolutional neural network (CNN) the proposed model identifies quickly and accurately cataracts, diabetic retinopathy, glaucoma, and normal eyes. Developed and tested in PyCharm, this model ensures top-notch performance. The cloud-based interface, built with Streamlit, offers a user-friendly and interactive experience for both healthcare professionals and patients. This innovative solution makes it easy to classify eye diseases on the spot, especially benefiting those in remote or underserved areas. This innovative technology is designed to make high-quality eye care accessible to everyone, regardless of their location. By incorporating advanced deep learning techniques, our application will analyze eye fundus images in real-time, ensuring accurate and prompt diagnosis. This approach not only helps in detecting eye diseases at an early stage but also empowers healthcare providers to offer better and more efficient care to the patients.

关键词： Accuracy Medical services User interfaces Eye diseases Rendering (computer graphics) Real-time systems Mobile applications Convolutional neural networks Usability Smart phones

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Fast multiplication of random dense matrices with sparse matrices

Fast multiplication of random dense matrices with sparse mat...

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International Symposium on Parallel and Distributed Processing (IPDPS)

作者： Tianyu Liang Riley Murray Aydın Buluç James Demmel Electrical Engineering and Computer Science Department UC Berkeley Computational Research Division Lawrence Berkeley National Lab International Computer Science Institute Sandia National Laboratories

ISBN: (数字)9798350387117

ISBN: (纸本)9798350387124

This work focuses on accelerating the multiplication of a dense random matrix with a (fixed) sparse matrix, which is frequently used in sketching algorithms. We develop a novel scheme that takes advantage of blocking and recomputation (on-the-fly random number generation) to accelerate this operation. The techniques we propose decrease memory movement, thereby increasing the algorithm’s parallel scalability in shared memory architectures. On the Intel Frontera architecture, our algorithm can achieve 2x speedups over libraries such as Eigen and Intel MKL on some examples. In addition, with 32 threads, we can obtain a parallel efficiency of up to 45%.We also present a theoretical analysis for the memory movement lower bound of our algorithm, showing that under mild assumptions, it’s possible to beat the data movement lower bound of general matrix-matrix multiply (GEMM) by a factor of M, where M is the cache size. Finally, we incorporate our sketching method into a randomized algorithm for overdetermined least squares with sparse data matrices. Our results are competitive with SuiteSparse for highly overdetermined problems; in some cases, we obtain a speedup of 10x over SuiteSparse.

关键词： Distributed processing Scalability Instruction sets Memory management Memory architecture Libraries Sparse matrices

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Variable stiffness design optimization of fiber-reinforced composite laminates with regular and irregular holes considering fiber continuity for additive manufacturing

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Chinese Journal of Aeronautics 2025年第3期38卷 334-354页

作者： Yi LIU Zunyi DUAN Chunping ZHOU Yuan SI Chenxi GUAN Yi XIONG Bin XU Jun YAN Jihong ZHU School of Mechanics Civil Engineering&ArchitectureNorthwestern Polytechnical UniversityXi’an 710072China Research&Development Institute of Northwestern Polytechnical University in Shenzhen Shenzhen 518063China Key Lab for Airborne Hi-performance Electro-magnetic Window RISACJinan 130012China School of System Design and Intelligent Manufacturing Southern University of Science and TechnologyShenzhen 518055China State Key Laboratory of Structural Analysis for Industrial Equipment Department of Engineering MechanicsInternational Research Center for Computational MechanicsDalian University of TechnologyDalian 116024China State IJR Center of Aerospace Design and Additive Manufacturing MIIT Lab of Metal Additive Manufacturing and Innovative DesignNorthwestern Polytechnical UniversityXi’an 710072China

Fiber-reinforced composites are an ideal material for the lightweight design of aerospace structures. Especially in recent years, with the rapid development of composite additive manufacturing technology, the design optimization of variable stiffness of fiber-reinforced composite laminates has attracted widespread attention from scholars and industry. In these aerospace composite structures, numerous cutout panels and shells serve as access points for maintaining electrical, fuel, and hydraulic systems. The traditional fiber-reinforced composite laminate subtractive drilling manufacturing inevitably faces the problems of interlayer delamination, fiber fracture, and burr of the laminate. Continuous fiber additive manufacturing technology offers the potential for integrated design optimization and manufacturing with high structural performance. Considering the integration of design and manufacturability in continuous fiber additive manufacturing, the paper proposes linear and nonlinear filtering strategies based on the Normal Distribution Fiber Optimization (NDFO) material interpolation scheme to overcome the challenge of discrete fiber optimization results, which are difficult to apply directly to continuous fiber additive manufacturing. With minimizing structural compliance as the objective function, the proposed approach provides a strategy to achieve continuity of discrete fiber paths in the variable stiffness design optimization of composite laminates with regular and irregular holes. In the variable stiffness design optimization model, the number of candidate fiber laying angles in the NDFO material interpolation scheme is considered as design variable. The sensitivity information of structural compliance with respect to the number of candidate fiber laying angles is obtained using the analytical sensitivity analysis method. Based on the proposed variable stiffness design optimization method for complex perforated composite laminates, the numerical examples cons

关键词： Variable stiffness composite laminates Discrete material interpolation scheme Normal distribution fiber optimization Discrete fiber continuous filtering strategy Additive manufacturing of composite laminates

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Choice of Optimal Voltage for DC Power Distribution Systems on More Electric Aircraft

Choice of Optimal Voltage for DC Power Distribution Systems ...

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European Conference on Power Electronics and Applications

作者： Xin Wang Jason Atkin Seang Shen Yeoh Serhiy Bozhko The Department of Electrical and Electronic Engineering Faculty of Engineering University of Nottingham Nottingham UK Computational Optimisation and Learning Lab School of Computer Science University of Nottingham Nottingham UK

ISBN: (数字)9798350364446

ISBN: (纸本)9798350364453

The new generation of aircraft will experience increasing electrical power demands because of the more electric trends and advances in recent years. Adopting higher voltage levels is widely discussed for More Electric Aircraft (MEA) Power Distribution Systems (PDSs) to efficiently deliver the increased power ratings and reduce the system weight. However, higher voltage levels also indicate the risk of electrical insulation failures. Hence, it is essential to select optimised voltage levels for onboard PDSs considering the trade-offs. In addition, the relationship between the PDS weight/efficiency and voltage levels is complex rather than linear, resulting in infeasibility issues when optimising voltage levels. This study proposes a voltage optimisation method based on selecting among a set of components, i.e., converters and cables, for onboard DC PDSs. Moreover, to improve feasibility, the Integrated Converter Component (ICC) concept is proposed to combine each converter with its lightest input/output cables as an integrated block. Hence, the optimal bus voltages can be decided by selecting the lightest and most efficient ICCs while meeting the power delivery and voltage conversion requirements.

关键词： Insulation Power demand Power distribution Europe Optimization methods Voltage Market research Energy efficiency Aircraft Cables

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Accurate calculation of bubble and droplet properties in diffuse-interface two-phase simulations

arXiv

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arXiv 2025年

作者： Nathan, Pranav J. Jain, Suhas S. Flow Physics and Computational Science Lab George W. Woodruff School of Mechanical Engineering Georgia Institute of Technology AtlantaGA United States

In this paper, we address the challenge of accurately calculating droplet/bubble properties (e.g., volume, number) in diffuse-interface two-phase flow simulations. Currently, flood-fill algorithms can truncate a significant portion of the volume of droplets/bubbles contained within the diffuse interface region or artificially merge multiple droplets/bubbles. This error is also dependent on the volume fraction cutoff value, which is typically chosen to be 0.5 arbitrarily, in the flood-fill algorithms. We propose a simple volume-correction approach that incorporates an analytical approximation of the truncated volume to correct for the missing droplet/bubble volumes. This proposed method results in accurately recovering the dispersed phase volumes with minimal volume error over a wide range of volume fraction cutoff values;and hence, can also accurately recover the number of droplets/bubbles. This can be a valuable tool for accurate calculation of drop/bubble size distributions for analysis and for Eulerian-to-Lagrangian conversion of the dispersed phase in multi-scale modeling approaches. © 2025, CC BY.

关键词： Two phase flow

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Knowledge Distillation from Discriminative Model to Generative Model with Parallel Architecture for Speech Enhancement

Knowledge Distillation from Discriminative Model to Generati...

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International Symposium on Chinese Spoken Language Processing

作者： Tingxiao Zhou Leying Zhang Yanmin Qian Department of Computer Science and Engineering Auditory Cognition and Computational Acoustics Lab MoE Key Lab of Artificial Intelligence Al Institute Shanghai Jiao Tong University Shanghai

ISBN: (数字)9798331516826

ISBN: (纸本)9798331516833

Generative speech enhancement methods, especially diffusion methods, have recently gained attention. However, current generative methods face several unresolved issues, such as lagging performance compared to discriminative methods on conventional enhancement metrics and slower inference speeds. In order to address these challenges, we propose a knowledge distillation approach from discriminative to generative models, and we design a parallel architecture with a latent vector fusion module to leverage their respective strengths. We conduct experiments and ablation studies on the LibriMix dataset to validate its effectiveness. The results demonstrate that our approach substantially improves performance over baseline systems in terms of speech quality and inference speed.

关键词： Measurement Speech enhancement Vectors Parallel architectures Faces

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Insights from Hyperparameter Scaling of Online Speech Separation

Insights from Hyperparameter Scaling of Online Speech Separa...

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International Symposium on Chinese Spoken Language Processing

作者： Xin Zhou Wangyou Zhang Chenda Li Yanmin Qian Department of Computer Science and Engineering Auditory Cognition and Computational Acoustics Lab MoE Key Lab of Artificial Intelligence AI Institute Shanghai Jiao Tong University Shanghai

ISBN: (数字)9798331516826

ISBN: (纸本)9798331516833

With the rapid development of deep learning, a large number of models with excellent performance for speech separation tasks have emerged in the literature. Despite their impressive performance, these models usually cannot be used for online speech separation tasks due to high computational and memory costs. In recent years, some approaches tailored for online speech separation have been developed to achieve state-of-the-art performance while keeping the model lightweight. However, it is often unclear how these models can scale with their architecture-specific hyperparameters as they are under-explored in the literature. To fill this gap, this paper investigates the effect of hyper-parameter scaling on the model performance, number of parameters, and computational cost based on two representative online speech separation models, BSRNN and SkiM. Experimental results on the Libri2Mix dataset reveal the relationship between model performance and composite hyperparameter configurations. We also derive the patterns of the number of parameters and computational costs varying with hyperparameters. Based on the results of the analysis, we give suggestions for scaling the two models and summarize two common trends.

关键词： Degradation Deep learning Analytical models Costs computational modeling Market research computational efficiency Speech processing Guidelines

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