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arXiv 2023年

作者： Patil, Pratik Du, Jin-Hong Department of Statistics University of California BerkeleyCA94720 United States Department of Statistics and Data Science Carnegie Mellon University PittsburghPA15213 United States Machine Learning Department Carnegie Mellon University PittsburghPA15213 United States

We establish precise structural and risk equivalences between subsampling and ridge regularization for ensemble ridge estimators. Specifically, we prove that linear and quadratic functionals of subsample ridge estimators, when fitted with different ridge regularization levels λ and subsample aspect ratios ψ, are asymptotically equivalent along specific paths in the (λ, ψ)-plane (where ψ is the ratio of the feature dimension to the subsample size). Our results only require bounded moment assumptions on feature and response distributions and allow for arbitrary joint distributions. Furthermore, we provide a data-dependent method to determine the equivalent paths of (λ, ψ). An indirect implication of our equivalences is that optimally tuned ridge regression exhibits a monotonic prediction risk in the data aspect ratio. This resolves a recent open problem raised by Nakkiran et al. [1] for general data distributions under proportional asymptotics, assuming a mild regularity condition that maintains regression hardness through linearized signal-to-noise ratios. Copyright © 2023, The Authors. All rights reserved.

关键词： Aspect ratio

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Smart Telemedicine Using IoT by Integrating 5G and Block-Chain Techniques

Smart Telemedicine Using IoT by Integrating 5G and Block-Cha...

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International Conference on Contemporary Computing and Informatics (IC3I)

作者： Shailee Lohmor Choudhary Rinku Sharma Dixit Deepak Das K. Ranjith Singh V. Dinesh Babu Department of Artificial Intelligence & Machine Learning - Data Science New Delhi Institute of Management New Delhi India Agricultural Engineering Karunya Institute of Science and Technology Coimbatore India Department of Computer Science Karpagam Academy of Higher Education Coimbatore India Department of Information Technology Karpagam Institute of Technology Coimbatore India

There are many medical applications for the Internet of Health Things (IoHT). In order to offer patients with more intelligent and effective health diagnoses, contemporary IoHT incorporates wellness items such sensors with continuously monitored medical equipment for the evaluation and handling of a medical chart. Using the combination of 5G networks and ethereum technology, we presented the IoT using a fog medical decision assistance system in this study for the forecast and monitoring of illness along with its severities. Due to its visibility, a frame provides a method for keeping and exchanging knowledge which is safe. Ethereum has various uses in the medical industry and therefore can enhance monitoring equipment, clinical study storage systems, mobile medical software, exchanging and keeping of digital media files, and insurer details stockpiling. The suggested structure will assemble a cloud provider will hold client information from the health equipment that's going to be connected to the individual, along with any pertinent health records. The use of 5G with cryptocurrency technologies allows the reliable transfer of patient information at high transmission rates with short reaction times. In addition, a Deep Neural (NN) classification is employed to forecast illnesses as well as the degree of underlying severity. The suggested model is tested using a variety of classifiers. In order to determine which classification is appropriate for the information, the effectiveness of various classifier is compared by contrasting the results. The NN improved a 98.98 average accuracy. This NN is additionally trained on the information such so this can forecast the outcome of the information category that carries no labels. A taught Naive Bayes is more accurate than conventional classifications at making predictions and effectively displaying the findings.

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Monitoring the Student's Entry and Exit Time in the Classroom

Monitoring the Student's Entry and Exit Time in the Classroo...

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Research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication Engineering (RMKMATE), International Conference on

作者： S. Usharani K. Vijayaragavan A. Balachandar P. Manju Bala Department of Artificial Intelligence and Machine Learning IFET College of Engineering (An Autonomous Institution) Villupuram India Department of Computer Science and Engineering IFET College of Engineering (An Autonomous Institution) Villupuram India Department of Artificial Intelligence amd Data Science IFET College of Engineering (An Autonomous Institution) Villupuram India

Monitoring the student's entry and exit time into a classroom is a crucial task. This system will be a solution for performing that crucial task. In this system, for face detection and face recognition, two pre-trained models were imported and utilized to create face embeddings for a collection of photographs kept in a file directory. The face embeddings and corresponding labels are then used to train an SVM classifier using the scikit-learn library's SVC class. To detect and recognize the students, the pre-trained face detection and recognition models were loaded using OpenCV's dnn module. Then the saved face embeddings and labels were also loaded using pickle, which was previously generated by a face recognition model. Face detection is performed using a Caffe model trained on the SSD framework, while face recognition is done using OpenFace. The system identifies the student's faces using embeddings that were generated by pre-processing the images and were saved in the face_*** file. It then matches these embeddings to the corresponding labels that were saved in the *** file. Then the input is captured from the webcam and the optical flow analysis is applied to detect motion in the video stream. After that, it initializes the input image with a blurred, grayscale image and sets the starting points for the motion tracking. It then loops over the captured frames, blurs and converts them to grayscale, and uses calcOpticalFlowPyrLK to calculate the new points based on the old ones. If the new points are within a certain range, it draws a line between them on the input image and displays it. If the points move outside a certain threshold, it triggers an event to log the entry or exit of a student from a classroom and appends these details into a text file accordingly. Finally, this text file is sent through email to a specified list of recipients (faculties) on a daily basis.

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Deep-learning-enabled multimodal data fusion for lung disease classification

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Informatics in Medicine Unlocked 2023年 42卷

作者： Kumar, Sachin Ivanova, Olga Melyokhin, Artyom Tiwari, Prayag Big Data and Machine Learning Laboratory South Ural State University Chelyabinsk Russian Federation Computer Science Department South Ural State University Chelyabinsk Russian Federation Department of Computer Science Aalto University Espoo Finland

The recent pandemic has revealed the urgent need for lung disease diagnosis at early stages in humans. Deep learning-based automatic diagnosis methods typically rely on single-modality data such as medical imaging. However, analysis of single modality data is not so reliable to diagnose the disease at its early stages. Clinical data, blood tests together with imaging methods are very powerful and reliable sources to detect the presence of disease in the human body. This study attempts to use medical imaging data with clinical information to develop a multimodal fusion approach to detect lung disease. Two architectures of multimodal network based on late and intermediate fusion is proposed. Besides, an approach of adapting batch size is also introduced. Experiments show that the performance of intermediate fusion is better than the late fusion model with both direct and adaptive batch size approach. © 2023 The Authors

关键词： Clinical data Deep learning Lung disease Medical imaging Multimodal fusion

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Classification under nuisance parameters and generalized label shift in likelihood-free inference 24

Classification under nuisance parameters and generalized lab...

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Proceedings of the 41st International Conference on machine learning

作者： Luca Masserano Alex Shen Michele Doro Tommaso Dorigo Rafael Izbicki Ann B. Lee Department of Statistics and Data Science and Machine Learning Department Carnegie Mellon University Pittsburgh Department of Statistics and Data Science Carnegie Mellon University Pittsburgh Department of Physics and Astronomy Università di Padova Padova Italy Istituto Nazionale di Fisica Nucleare Sezione di Padova Italy and Lulea Techniska Universitet Lulea Sweden and Universal Scientific Education and Research Network Italy Department of Statistics Universidade Federal de São Carlos São Paulo Brazil

An open scientific challenge is how to classify events with reliable measures of uncertainty, when we have a mechanistic model of the data-generating process but the distribution over both labels and latent nuisance parameters is different between train and target data. We refer to this type of distributional shift as generalized label shift (GLS). Direct classification using observed data X as covariates leads to biased predictions and invalid uncertainty estimates of labels Y. We overcome these biases by proposing a new method for robust uncertainty quantification that casts classification as a hypothesis testing problem under nuisance parameters. The key idea is to estimate the classifier's receiver operating characteristic (ROC) across the entire nuisance parameter space, which allows us to devise cutoffs that are invariant under GLS. Our method effectively endows a pretrained classifier with domain adaptation capabilities and returns valid prediction sets while maintaining high power. We demonstrate its performance on two challenging scientific problems in biology and astroparticle physics with data from realistic mechanistic models.

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HUMAN-IN-THE-LOOP: TOWARDS LABEL EMBEDDINGS FOR MEASURING CLASSIFICATION DIFFICULTY

arXiv

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arXiv 2023年

作者： Hechinger, Katharina Zhu, Xiao Xiang Koller, Christoph Kauermann, Göran Department of Statistics Ludwig-Maximilians-University Munich Germany Chair of Data Science in Earth Observation Technical University of Munich Munich Germany Munich Center for Machine Learning Munich Germany German Aerospace Center Wessling Germany

Uncertainty in machine learning models is a timely and vast field of research. In supervised learning, uncertainty can already occur in the first stage of the training process, the annotation phase. This scenario is particularly evident when some instances cannot be definitively classified. In other words, there is inevitable ambiguity in the annotation step and hence, not necessarily a single "ground truth" associated with each instance. The main idea of this work is to drop the assumption of a ground truth label and instead embed the annotations into a multidimensional space. This embedding is derived from the empirical distribution of annotations in a Bayesian setup, modeled via a Dirichlet-Multinomial framework. We estimate the model parameters and posteriors using a stochastic Expectation Maximisation algorithm with Markov Chain Monte Carlo steps. The methods developed in this paper readily extend to various situations where multiple annotators independently label instances. To showcase the generality of the proposed approach, we apply our approach to three benchmark datasets for image classification and Natural Language Inference, where multiple annotations per instance are available. Besides the embeddings, we can investigate the resulting correlation matrices, which reflect the semantic similarities of the original classes very well for all three exemplary datasets. Copyright © 2023, The Authors. All rights reserved.

关键词： Classification (of information)

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Review of machine learning and Deep learning algorithms for Personality traits classification

Review of Machine Learning and Deep Learning algorithms for ...

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

作者： Satish N Bhadane Prateek Verma Department of Artificial Intelligence and Data Science Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research (DU) Sawangi Wardha Maharashtra India Department of Artificial Intelligence and Machine Learning Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research Sawangi Wardha Maharashtra India

ISBN: (数字)9798331528713

ISBN: (纸本)9798331528720

Nowadays, people deal with major psychological issues on a daily basis, often in the absence of a desired support system. In the field of psychology, focus is on the thoughts, feelings, behaviors, goals, and interests of individuals. The psychologist conventionally considers various types of personality traits, like the Big 5 personality traits, the Myers-Briggs Type Indicator (MBTI), the OCEAN Model (Openness, Conscientiousness, Agreeableness, Extraversion, Neuroticism), and other methods also considered for measuring personality. To forecast personality traits like extraversion, agreeableness, openness, conscientiousness, and neuroticism, consider the name "Big Five personality traits" It is a universal model to forecast these personality traits. Studies on personality prediction that identify personality disorders from user content on social media sites like Facebook, Twitter, and WhatsApp primarily rely on user behavior on these platforms. A number of popular Deep learning (DL) techniques have advanced recently, including Deep Belief Networks (DBNs), Convolution Neural Networks (CNNs), and Recurrent Neural Networks (RNNs). Examine these methods in terms of single and multiple modalities, including psychological, audio, video, text, and image information. Researchers currently focus on predicting personality traits using single-modality text features, static facial images, audio, and video clips, including interview recordings and YouTube videos. Therefore, the current study highlights that personality prediction mostly relies on written text, audio and video datasets used in machine learning (ML) and DL models. Additional investigation is needed to resolve ethical concerns and justify the use of psychological datasets and DL models to evaluate personality traits. By combining personality traits with a psychological dataset, this study imagines a time when people everyday struggles can be effectively addressed. The integration of ML and DL in the development of p

关键词： Industries Deep learning Video on demand Recurrent neural networks Social networking (online) Reviews Psychology Sea measurements Predictive models Web sites

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Making all pairwise comparisons in multi-arm clinical trials without control treatment

arXiv

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arXiv 2024年

作者： Burnett, T. Jaki, T. Department of Mathematical Sciences University of Bath BathBA2 7AY United Kingdom MRC Biostatistics Unit University of Cambridge CambridgeCB2 0SR United Kingdom Department of Machine Learning and Data Science University of Regensburg Regensburg Germany

The standard paradigm for confirmatory clinical trials is to compare experimental treatments with a control, for example the standard of care or a placebo. However, it is not always the case that a suitable control exists. Efficient statistical methodology is well studied in the setting of randomised controlled trials. This is not the case if one wishes to compare several experimental with no control arm. We propose hypothesis testing methods suitable for use in such a setting. These methods are efficient, ensuring the error rate is controlled at exactly the desired rate with no conservatism. This in turn yields an improvement in power when compared with standard methods one might otherwise consider using, such as a Bonferroni adjustment. The proposed testing procedure is also highly flexible. We show how it may be extended for use in multi-stage adaptive trials, covering the majority of scenarios in which one might consider the use of such procedures in the clinical trials setting. With such a highly flexible nature, these methods may also be applied more broadly outside of a clinical trials setting. Copyright © 2024, The Authors. All rights reserved.

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The Future of Driving: A Review on the Combination of Artificial Intelligence and Autonomous Vehicles

The Future of Driving: A Review on the Combination of Artifi...

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

作者： Nikita Zade Mayur Gawande Prateek Verma Swapnil Gundewar Department of Artificial Intelligence and Data Science Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research (DU) Sawangi Wardha Maharashtra India Department of Artificial Intelligence and Machine Learning Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research Sawangi Wardha Maharashtra India

ISBN: (数字)9798331528713

ISBN: (纸本)9798331528720

The convergence of Artificial Intelligence (AI) and Autonomous Vehicles (AV) are poised to revolutionize the automotive industry, offering safer, more efficient, and sustainable transportation solutions. This review article explores the current state of AVs, role of AI in AV, challenges and hurdles, Real world applications & development, its critical technological components, and their impact on society. This paper presents a wide array of applications of AI in AV, elaborating on the way complex sensor fusion, computer vision, and machine learning (ML) algorithms makes AVs perceive their surroundings, form critical opinions, and carry out accurate motions. AI is making real time interpretations of great quantities of data which are paving the way for safer and more effective navigation under a diversity of traffic circumstances, from sensor fusion to decision-making algorithms. The next section of this paper elaborates on the role of AI in the field of AV in perceiving, planning, and navigating through real world environment, while also talking about issues of integration and ethics. This paper further identifies its social, economic, and environmental consequences, resulting from mass use of AV. This article discusses the latest developments and opportunities in the regulatory environment, gives an overview of how AV define transportation in the near future. This paper also explores the levels of autonomy of AVs as defined by the Society of Automotive Engineers (SAE) and defines the process from driver assistance to full automation. It examines the ethical and technical considerations arising at each instant and emphasizes the key role AI continues to play in providing more autonomy while preserving security and dependability.

关键词： Ethics machine learning algorithms Reviews Navigation Decision making Sensor fusion Safety Artificial intelligence Socioeconomics Automotive engineering

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Randomized Spline Trees for Functional data Classification: Theory and Application to Environmental Time Series

arXiv

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arXiv 2024年

作者： Riccio, Donato Maturo, Fabrizio Romano, Elvira Machine Learning Engineer and Student in the Data Science Master’s Degree Program The University of Campania Luigi Vanvitelli Caserta Italy Department of Economics Statistics and Business Faculty of Technological and Innovation Sciences Universitas Mercatorum Rome Italy Department of Mathematics and Physics University of Campania Luigi Vanvitelli Caserta Italy

Functional data analysis (FDA) and ensemble learning can be powerful tools for analyzing complex environmental time series. Recent literature has highlighted the key role of diversity in enhancing accuracy and reducing variance in ensemble *** paper introduces Randomized Spline Trees (RST), a novel algorithm that bridges these two approaches by incorporating randomized functional representations into the Random Forest framework. RST generates diverse functional representations of input data using randomized B-spline parameters, creating an ensemble of decision trees trained on these varied representations. We provide a theoretical analysis of how this functional diversity contributes to reducing generalization error and present empirical evaluations on six environmental time series classification tasks from the UCR Time Series Archive. Results show that RST variants outperform standard Random Forests and Gradient Boosting on most datasets, improving classification accuracy by up to 14%. The success of RST demonstrates the potential of adaptive functional representations in capturing complex temporal patterns in environmental data. This work contributes to the growing field of machine learning techniques focused on functional data and opens new avenues for research in environmental time series *** Codes 62M10, 68T05, 65D07, 68T10 © 2024, CC BY.

关键词： Spatio-temporal data

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