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Using voice analysis as an early indicator of risk for depression in young adults

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arXiv 2024年

作者： Scherer, Klaus R. Burkhardt, Felix Reichel, Uwe D. Eyben, Florian Schuller, Björn W. Department of Psychology University of Geneva Geneva Switzerland audEERING GmbH Gilching Germany Technical University of Munich MRI Munich Germany University of Augsburg Augsburg Germany Imperial College London United Kingdom Munich Center for Machine Learning Munich Germany Munich Data Science Institute Munich Germany

Increasingly frequent publications in the literature report voice quality differences between depressed patients and controls. Here, we examine the possibility of using voice analysis as an early warning signal for the development of emotion disturbances in young adults. As part of a major interdisciplinary European research project in four countries (ECoWeB), examining the effects of web-based prevention programs to reduce the risk for depression in young adults, we analyzed a large number of acoustic voice characteristics in vocal reports of emotions experienced by the participants on a specific day. We were able to identify a number of significant differences in acoustic cues, particularly with respect to the energy distribution in the voice spectrum, encouraging further research efforts to develop promising non-obtrusive risk indicators in the normal speaking voice. This is particularly important in the case of young adults who are less likely to exhibit standard risk factors for depression such as negative life experiences. © 2024, CC BY.

关键词： Risk assessment

The Role of Blockchain Technology in Supply chain Management for data Security

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The Role of Blockchain Technology in Supply chain Management...

Blockchain and Distributed Systems Security (ICBDS), IEEE International Conference on

作者： Dipika R. Birari Vijay U. Rathod Aarti Dandavate Nilesh Shelke Ranjit Kumar Masira M. S Kulkarni Department of Information Technology Army Institute of Technology Pune Department of Artificial Intelligent and Machine Learning G H Raisoni College of Engineering and Management Wagholi Pune Department of Computer Engineering Dhole Patil College of Engineering Pune Department of Computer Science and Engineering Symbiosis Institute of Technology Nagpur Campus Symbiosis International Deemed University Pune India Department of Computer Engineering Ajeenkya D Y Patil University Pune Department of Cyber Security and Data Science G H Raisoni College of Engineering and Management Wagholi Pune

ISBN: (数字)9798350354348

ISBN: (纸本)9798350354355

In a traditional supply chain system, the process of producing raw materials so that a product may be delivered to a customer is a manual operation with insufficient data and transaction security. The entire process is time consuming and requires a lot of effort. The undivided process is generally ineffective and unreliable for consumers. The data security, authenticity, the inclusion of will significantly improve the time management and transaction procedures of conventional supply chain management systems. data is shielded from illegal access via the ground-breaking, decentralized technology known as blockchain. After smart contracts are put into place, the complete supply chain management (SCM) will be satisfied with the customer. When the mediator is contracted, which is possible in various ways, the plan becomes more reliable. This article places particular emphasis on lowering third party involvement in the supply chain system and enhancing data security. Conventional supply chain management techniques have several serious shortcomings. Some of them include a lack of traceability, difficulties maintaining product safety and quality, a failure to track and manage inventories in stores and warehouses, escalating supply chain costs, and so on. The reduction of third-party involvement in the supply chain system and the improvement of data security are the main goals of this article. This enhances process accessibility, effectiveness, and timeliness overall. They will feel safer throughout the payment procedure, which is the main benefit. Yet in this investigation, a smart contract and a peer-to-peer encrypted system were both used. There are a few additional features as well.

关键词： Supply chain management Costs Supply chains Smart contracts Reliability engineering Raw materials Blockchains Peer-to-peer computing Product safety

Enhanced Vision Transformer with Lion Optimizer for Accurate Classification of Monkeypox Skin Images

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Enhanced Vision Transformer with Lion Optimizer for Accurate...

Technological Advancements in Computational sciences (ICTACS), International Conference on

作者： Roheet Bhatnagar Esraa Hassan Abeer Saber Mahmoud Y. Shams Samar Elbedwehy Department of Computer Science and Engineering Manipal University Jaipur Rajasthan India Dept. of Machine Learning Faculty of Artificial Intelligence Kafrelsheikh University Kafrelsheikh Egypt Information Technology Dept. Faculty of Computers and Artificial Intelligence Damietta University Damietta Egypt Dept. of Data Science Faculty of Artificial Intelligence Kafrelsheikh University Kafrelsheikh Egypt

ISBN: (数字)9798350387490

ISBN: (纸本)9798350387506

Monkeypox is a contagious viral infection caused by the monkeypox virus (Mpoxvirus). Characterized by skin lesions, fever, respiratory difficulties, swollen lymph nodes, and neurological complications, MPX can be fatal. Recent outbreaks have spread the disease across Europe, Australia, the United States, and Africa. Traditionally diagnosed through PCR testing of skin lesions, this process poses significant risks to medical personnel due to potential exposure to the highly contagious virus. However, the usage artificial intelligence (AI) is revolutionizing diagnostics, providing a safer and more efficient approach to MPX detection. In this paper, a new AI technique based on vision transformer and lion optimizer for classifying Monkeypox Skin Images. The presented model used for classifying the dataset and results reached accuracy of 99.6%.

关键词： Computer vision Accuracy Measles Feature extraction Transformers Skin Lesions Artificial intelligence Viruses (medical) Testing

Sharp statistical guarantees for adversarially robust gaussian classification

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arXiv 2020年

作者： Dan, Chen Wei, Yuting Ravikumar, Pradeep Computer Science Department Department of Statistics and Data Science Machine Learning Department Carnegie Mellon University

Adversarial robustness has become a fundamental requirement in modern machine learning applications. Yet, there has been surprisingly little statistical understanding so far. In this paper, we provide the first result of the optimal minimax guarantees for the excess risk for adversarially robust classification, under Gaussian mixture model proposed by Schmidt et al. (2018). The results are stated in terms of the Adversarial Signal-to-Noise Ratio (AdvSNR), which generalizes a similar notion for standard linear classification to the adversarial setting. For the Gaussian mixtures with AdvSNR value of r, we establish an excess risk lower bound of order Θ(e−(1 8 +o(1))r2nd ) and design a computationally efficient estimator that achieves this optimal rate. Our results built upon minimal set of assumptions while cover a wide spectrum of adversarial perturbations including `p balls for any p ≥ 1. Copyright © 2020, The Authors. All rights reserved.

关键词： Signal to noise ratio

Improving the Quality of Diabetic data with Large Language Model-driven Cleaning Techniques

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Improving the Quality of Diabetic Data with Large Language M...

Intelligent Systems and Advanced Applications (ICISAA), International Conference on

作者： Divya Biradar Rahul Dattangire Ruchika Vaidya NagaSuryaShivani Inti Computer Science University of Texas at Arlington Arlington Texas USA Data Engineering Independent Researcher Houston Texas USA Department of Artificial Intelligence and Machine Learning Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research (DU) Wardha Maharashtra India Computer and Information Science University of Texas at Arlington Arlington Texas USA

ISBN: (数字)9798331539948

ISBN: (纸本)9798331539955

The data-cleaning approach applies the capabilities of large language models to reduce the noise in the extracted and received data from healthcare sources. The aim will be to clean the collected and extracted data by looking up and dealing with the inaccuracies encountered during the project like missing or duplicated records, outliers, and data that will not add any value to the project. The “Diabetes” data set from Kaggle is divided into implementing the combined approach data-cleaning process such as removing duplicate entries, and emptying fields by a Large Language Model, to improve the accuracy is assessed. Three components will increase the reliability of the collected data: 1. Gateway, 2. Storage 3. Cleaning. The outcomes demonstrated the LLMbased approach’s effective impact in correcting and consistent data within the healthcare dataset.

关键词： Adaptation models Large language models Noise Medical services Cleaning data models Diabetes Safety data mining Reliability

A Review of machine learning Algorithms for Detection of Breast Cancer

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A Review of Machine Learning Algorithms for Detection of Bre...

Ubiquitous Computing and Intelligent Information Systems (ICUIS), International Conference on

作者： Abhay Tale Aditya Barhate Prateek Verma Palash Gourshettiwar Department of Artificial Intelligence and Data Science Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research (DU) Wardha Maharashtra India Department of Artificial Intelligence and Machine Learning Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research (DU) Wardha Maharashtra India Department of Computer Science and Medical Engineering Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research (DU) Wardha Maharashtra India

ISBN: (数字)9798331529635

ISBN: (纸本)9798331529642

Breast cancer is still a global health concern, chiefly affecting women, as it is one of the major causes of cancer mortality. For the therapy to be effective, early diagnosis of cancer is critical to raising the survival level and increasing the effectiveness of the procedure. The advancement in the applied technique of machine learning (ML) has brought further opportunities for early and effective diagnosis of breast cancer. This review paper provides insights into how different ML algorithms including the Support Vector machine (SVM), Convolutional Neural Network (CNN), Random Forests, and many more have contributed to the detection of breast cancer. These algorithms have proved effective in providing solutions to various medical data and imaging including early diagnosis and treatment. The paper also looks at the use of ML with precision medicine to show that these technologies will help minimize false diagnoses and offer better treatment options. This review consolidates recent works and advancements to emphasize the prospect of ML in breast cancer management and calls for more studies to enhance its implementation in practice

关键词： Support vector machines machine learning algorithms Sensitivity Reviews Precision medicine Ubiquitous computing Breast cancer Vectors Convolutional neural networks Random forests

Position: Bayesian Deep learning is Needed in the Age of Large-Scale AI

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arXiv 2024年

作者： Papamarkou, Theodore Skoularidou, Maria Palla, Konstantina Aitchison, Laurence Arbel, Julyan Dunson, David Filippone, Maurizio Fortuin, Vincent Hennig, Philipp Hernández-Lobato, José Miguel Hubin, Aliaksandr Immer, Alexander Karaletsos, Theofanis Khan, Mohammad Emtiyaz Kristiadi, Agustinus Li, Yingzhen Mandt, Stephan Nemeth, Christopher Osborne, Michael A. Rudner, Tim G.J. Rügamer, David Teh, Yee Whye Welling, Max Wilson, Andrew Gordon Zhang, Ruqi Department of Mathematics The University of Manchester Manchester United Kingdom Eric and Wendy Schmidt Center Broad Institute of MIT and Harvard Cambridge United States Spotify London United Kingdom Computational Neuroscience Unit University of Bristol Bristol United Kingdom Centre Inria de l'Université Grenoble Alpes Grenoble France Department of Statistical Science Duke University United States Statistics Program KAUST Saudi Arabia Helmholtz AI Munich Germany Department of Computer Science Technical University of Munich Munich Germany Munich Center for Machine Learning Munich Germany Tübingen AI Center University of Tübingen Tübingen Germany Department of Engineering University of Cambridge Cambridge United Kingdom Department of Mathematics University of Oslo Oslo Norway Bioinformatics and Applied Statistics Norwegian University of Life Sciences Ås Norway Department of Computer Science ETH Zurich Switzerland Chan Zuckerberg Initiative CA United States Center for Advanced Intelligence Project RIKEN Tokyo Japan Vector Institute Toronto Canada Department of Computing Imperial College London London United Kingdom Department of Computer Science UC Irvine Irvine United States Department of Mathematics and Statistics Lancaster University Lancaster United Kingdom Department of Engineering Science University of Oxford Oxford United Kingdom Center for Data Science New York University New York United States Department of Statistics LMU Munich Munich Germany DeepMind London United Kingdom Department of Statistics University of Oxford Oxford United Kingdom Informatics Institute University of Amsterdam Amsterdam Netherlands Courant Institute of Mathematical Sciences Center for Data Science Computer Science Department New York University New York United States Department of Computer Science Purdue University West Lafayette United States

In the current landscape of deep learning research, there is a predominant emphasis on achieving high predictive accuracy in supervised tasks involving large image and language datasets. However, a broader perspective reveals a multitude of overlooked metrics, tasks, and data types, such as uncertainty, active and continual learning, and scientific data, that demand attention. Bayesian deep learning (BDL) constitutes a promising avenue, offering advantages across these diverse settings. This paper posits that BDL can elevate the capabilities of deep learning. It revisits the strengths of BDL, acknowledges existing challenges, and highlights some exciting research avenues aimed at addressing these obstacles. Looking ahead, the discussion focuses on possible ways to combine large-scale foundation models with BDL to unlock their full potential. © 2024, CC BY-NC-ND.

关键词： Large datasets

Dynamics Harmonic Analysis of Robotic Systems: Application in data-Driven Koopman Modelling

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arXiv 2023年

作者： Ordoñez-Apraez, Daniel Felipe Kostic, Vladimir Turrisi, Giulio Novelli, Pietro Mastalli, Carlos Semini, Claudio Pontil, Massimiliano Computational Statistics and Machine Learning Istituto Italiano di Tecnologia Italy Dynamic Legged Systems Istituto Italiano di Tecnologia Italy Robot Motor Intelligence Heriot-Watt University United Kingdom Florida Institute for Human and Machine Cognition United States Department of Computer Science University College London United Kingdom

We introduce the use of harmonic analysis to decompose the state space of symmetric robotic systems into orthogonal isotypic subspaces. These are lower-dimensional spaces that capture distinct, symmetric, and synergistic motions. For linear dynamics, we characterize how this decomposition leads to a subdivision of the dynamics into independent linear systems on each subspace, a property we term dynamics harmonic analysis (DHA). To exploit this property, we use Koopman operator theory to propose an equivariant deep-learning architecture that leverages the properties of DHA to learn a global linear model of the system dynamics. Our architecture, validated on synthetic systems and the dynamics of locomotion of a quadrupedal robot, exhibits enhanced generalization, sample efficiency, and interpretability, with fewer trainable parameters and computational *** Codes 43-08 © 2023, CC BY.

关键词： Dynamical systems

Towards Symbolic XAI – Explanation Through Human Understandable Logical Relationships Between Features

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arXiv 2024年

作者： Schnake, Thomas Jafari, Farnoush Rezaei Lederer, Jonas Xiong, Ping Nakajima, Shinichi Gugler, Stefan Montavon, Grégoire Müller, Klaus-Robert Berlin Institute for the Foundations of Learning and Data – BIFOLD Berlin10623 Germany Machine Learning Group Technical University of Berlin Berlin10623 Germany RIKEN AIP Tokyo103-0027 Japan Department of Artificial Intelligence Korea University Seoul136-713 Korea Republic of Max Planck Institut für Informatik Saarbrücken66123 Germany Department of Mathematics and Computer Science Free University of Berlin Berlin14195 Germany

Explainable Artificial Intelligence (XAI) plays a crucial role in fostering transparency and trust in AI systems, where traditional XAI approaches typically offer one level of abstraction for explanations, often in the form of heatmaps highlighting single or multiple input features. However, we ask whether abstract reasoning or problem-solving strategies of a model may also be relevant, as these align more closely with how humans approach solutions to problems. We propose a framework, called Symbolic XAI, that attributes relevance to symbolic queries expressing logical relationships between input features, thereby capturing the abstract reasoning behind a model’s predictions. The methodology is built upon a simple yet general multi-order decomposition of model predictions. This decomposition can be specified using higher-order propagation-based relevance methods, such as GNN-LRP, or perturbation-based explanation methods commonly used in XAI. The effectiveness of our framework is demonstrated in the domains of natural language processing (NLP), vision, and quantum chemistry (QC), where abstract symbolic domain knowledge is abundant and of significant interest to users. The Symbolic XAI framework provides an understanding of the model’s decision-making process that is both flexible for customization by the user and human-readable through logical formulas. Copyright © 2024, The Authors. All rights reserved.

关键词： Decision making