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The EmpkinS-EKSpression Reappraisal Training Augmented With Kinesthesia in Depression: One-Armed Feasibility Study

JMIR Formative Research 2025年 9卷 e65357页

作者： Keinert, Marie Schindler-Gmelch, Lena Rupp, Lydia Helene Sadeghi, Misha Richer, Robert Capito, Klara Eskofier, Bjoern M. Berking, Matthias Department of Clinical Psychology and Psychotherapy Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) Erlangen Germany Machine Learning and Data Analytics Lab Department of Artificial Intelligence in Biomedical Engineering (AIBE) Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) Erlangen Germany Translational Digital Health Group Institute of AI for Health Helmholtz Zentrum Muenchen Neuherberg Germany

Background: Harboring dysfunctional depressogenic cognitions contributes to the development and maintenance of depression. A central goal of cognitive behavioral therapy (CBT) for depression is to invalidate such cognitions via cognitive reappraisal (CR). However, relatively low remission rates and high dropout rates in CBT demonstrate the need for further improvement. Potentially, the effects of CBT could be enhanced by addressing not only dysfunctional depressogenic cognitions but also body states associated with depression. This may be done, for example, by systematically pairing the invalidation of depressogenic cognitions with the performance of antidepressive kinesthesia. Objective: This study aimed to examine the feasibility and clinical potential of a smartphone-based cognitive restructuring task that required users to deliberately perform antidepressive kinesthesia in conjunction with the rejection of depressogenic statements and the affirmation of antidepressive statements. This feasibility study was conducted as a precursor to a large-scale randomized controlled trial. Methods: In total, 10 healthy participants engaged in a single 90-120-minute session of smartphone-based CR training. During the training, they completed 2 phases in which they were required to reject 20 depressogenic and affirm 20 antidepressive statements, respectively. Diagnostic assessments were conducted 1 week (T1) before and directly prior (T2) to the training, and again directly posttraining (T3) and at a 2-week follow-up posttraining (T4). Feasibility outcomes assessed at T3 included intervention safety recorded by study therapists, compliance, technical feasibility, usability assessed using the Short Version of the User Experience Questionnaire (UEQ-S), and acceptability assessed using the UEQ-S and self-developed items. Preliminary clinical potential was evaluated via single-item ratings of current depressed and positive mood assessed continuously during the training. Feasibility

关键词： cognitive reappraisal depression facial expression kinesthesia mobile phone smartphone-based intervention

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Automated Pipeline for Regional Epicardial Adipose Tissue Distribution Analysis in the Left Atrium

Automated Pipeline for Regional Epicardial Adipose Tissue Di...

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15th International Workshop on Statistical Atlases and Computational Models of the Heart, STACOM 2024, Held in Conjunction with MICCAI 2024

作者： Lluch, Èric Castañeda, Eduardo Weiss, Maximilian E. R. Vizitiu, Anamaria Jacob, Athira Jami, Jitin Audigier, Chloé Meister, Felix Mihalef, Viorel Passerini, Tiziano Siemens Healthineers Digital Technologies and Innovation Erlangen Germany Pattern Recognition Lab Department of Computer Science Friedrich-Alexander University Erlangen-Nürnberg Erlangen Germany Siemens SRL Brasov Romania Siemens Healthineers Digital Technology and Innovation Princeton United States Machine-Learning and Data Analytics Friedrich-Alexander-Universität Erlangen-Nürnberg Erlangen Germany Advanced Clinical Imaging Technology Siemens Healthineers AG Lausanne Switzerland

ISBN: (纸本)9783031877551

Atrial fibrillation (AF) is the most common cardiac arrhythmia, affecting approximately 3% of the global population and rising to 11% in individuals over 80 years old. The distribution of epicardial adipose tissue (EAT) around the atria plays a significant role in the pathophysiology of AF, yet its regional analysis remains underexplored. This paper introduces an automated pipeline capable of segmenting 14 atrial regions from patient CT angiograms, a notable advancement over previous attempts to automate the creation of synthetic anatomical or physics-based twins. Our fully automatic pipeline requires no human supervision and provides the regional EAT quantification successfully on 91.9% of the analyzed patients for which the LA and its structures were automatically segmented (1797 out of 1954 patients), including the automatic segmentation of the aorta and pericardium to assess EAT volume around the left atrium. By applying principal component analysis of the regional EAT to analyze a separate dataset of 84 individuals who were clinically labeled, we identified a distinct group that contained all the patients with AF. This finding suggests that there is a significant link between the pattern of fat distribution and AF. This novel approach has the potential to enhance our understanding of AF and improve patient outcomes by enabling large-scale, regional fat analysis in an automated manner on all patients with accurate segmentation of left atrial structures. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Principal component analysis

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Problems and Prospectives of Big data Storage and Processing Standartization

Problems and Prospectives of Big Data Storage and Processing...

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2019 International Multi-Conference on Engineering, Computer and Information Sciences, SIBIRCON 2019

作者： Pavlovskiy, Evgeniy N. Stream Data Analytics and Machine Learning Lab Novosibirsk State University Novosibirsk Russia

ISBN: (纸本)9781728144016

In the paper, we analyze the problem of standardization in the domain of storage and processing of big data in the application to the Internet of things. We highlight the underlying problems of big data;analyze the scientific, technological and economic barriers to the development of big data. We offer perspective research directions in the domain of development of standards of data representation. © 2019 IEEE.

关键词： data structures

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Binary Brain Tumor Classification With Semantic Features Using Convolutional Neural Network

Binary Brain Tumor Classification With Semantic Features Usi...

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2022 Ural Symposium on Biomedical Engineering, Radioelectronics and Information Technology, USBEREIT 2022

作者： Khue, Luu Minh Sao Pavlovskiy, Evgeniy Novosibirsk State University Stream Data Analytics and Machine Learning Laboratory Novosibirsk Russia

ISBN: (数字)9781665460927

ISBN: (纸本)9781665460927

In this study, we provide segmentations of brain tumors as semantic features to a simple convolutional neural network (CNN) to improve the classification results. The Siberian Brain Tumor dataset (SBT) of 1452 magnetic resonance (MR) images of Russian people's brains is used for training, validation, and testing. We preprocess MR images by removing the swelling region surrounding a brain tumor (edema) from the segmentation and eliminating slices that contain less than 163 pixels of tumor. The binary classifier is a simple network of four convolutional layers for feature extractions and two linear layers for classification. The network is trained and validated with 5-fold cross-validation. We train another CNN with similar configuration on images without provided segmentation and compare the testing results. The two networks are evaluated with four metrics: accuracy, sensitivity, specificity, and F1 scores. The classifier trained with segmentation achieve the highest accuracy score of 0.92, sensitivity of 0.934, specificity of 0.91, and F1 score of 0.90 using ensemble approach. © 2022 IEEE.

关键词： Magnetic resonance

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Improving Brain Tumor Multiclass Classification With Semantic Features

Improving Brain Tumor Multiclass Classification With Semanti...

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2022 Ural-Siberian Conference on Computational Technologies in Cognitive Science, Genomics and Biomedicine, CSGB 2022

作者： Sao Khue, Luu Minh Pavlovskiy, Evgeniy Novosibirsk State University Stream Data Analytics and Machine Learning Laboratory Novosibirsk Russia

ISBN: (数字)9781665452885

ISBN: (纸本)9781665452885

Histopathological examination of biopsy tissues is still utilized to diagnose and classify brain cancers today. The current approach is inconvenient, time-consuming, and prone to human mistake. These disadvantages emphasize the significance of establishing a fully automated deep learning-based system for classifying brain tumors. In this paper, we suggest an approach to improve the classification for four types of brain tumors by providing the classifier with segmentation as semantic features. 1,452 multi model magnetic resonance images from the Siberian Brain Tumor dataset (SBT) are used for training, validation, and testing. The training and validation are implemented with our experimental simple convolutional neural network and a pre-trained VGG16. Best performed models are selected and tested on both SBT and the Brain Tumor Segmentation Challenge 2020 dataset (BraTS). The models with segmentation outperform all models without segmentation on the same dataset. We also found that, compare to a general purposed network such as VGG16, a simple convolutional neural network trained on a specific task have better generalization when tested with a public dataset. © 2022 IEEE.

关键词： Convolutional neural networks

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Massive data clustering by multi-scale psychological observations

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National Science Review 2022年第2期9卷 43-51页

作者： Shusen Yang Liwen Zhang Chen Xu Hanqiao Yu Jianqing Fan Zongben Xu National Engineering Laboratory of Big Data Analytics Xi'an Jiaotong University Industrial Artificial Intelligent Center Pazhou Laboratory Department of Mathematics and Statistics University of Ottawa Center for Statistics and Machine Learning Princeton University

Clustering is the discovery of latent group structure in data and is a fundamental problem in artificial intelligence,and a vital procedure in data-driven scientific research over all ***,existing methods have various limitations,especially we ak cognitive interpretability and poor computational scalability,when it comes to clustering massive datasets that are increasingly available in all ***,by simulating the multi-scale cognitive observation process of humans,we design a scalable algorithm to detect clusters hierarchically hidden in massive *** observation scale changes,following the Weber-Fechner law to capture the gradually emerging meaningful grouping *** validated our approach in real datasets with up to a billion records and 2000 dimensions,including taxi trajectories,single-cell gene expressions,face images,computer logs and *** approach outperformed popular methods in usability,efficiency,effectiveness and robustness across different domains.

关键词： massive data clustering psychological observation Weber–Fechner law cognitive interpretability computational scalability

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Simple unsupervised keyphrase extraction using sentence embeddings 22

Simple unsupervised keyphrase extraction using sentence embe...

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22nd Conference on Computational Natural Language learning, CoNLL 2018

作者： Bennani-Smires, Kamil Musat, Claudiu Hossmann, Andreaa Baeriswyl, Michael Jaggi, Martin Data Analytics and AI Swisscom AG Switzerland Machine Learning and Optimization Laboratory EPFL Switzerland

ISBN: (纸本)9781948087728

Keyphrase extraction is the task of automatically selecting a small set of phrases that best describe a given free text document. Supervised keyphrase extraction requires large amounts of labeled training data and generalizes very poorly outside the domain of the training data. At the same time, unsupervised systems have poor accuracy, and often do not generalize well, as they require the input document to belong to a larger corpus also given as input. Addressing these drawbacks, in this paper, we tackle keyphrase extraction from single documents with EmbedRank: a novel unsupervised method, that leverages sentence embeddings. EmbedRank achieves higher F-scores than graph-based state of the art systems on standard datasets and is suitable for real-time processing of large amounts of Web data. With EmbedRank, we also explicitly increase coverage and diversity among the selected keyphrases by introducing an embedding-based maximal marginal relevance (MMR) for new phrases. A user study including over 200 votes showed that, although reducing the phrases’ semantic overlap leads to no gains in F-score, our high diversity selection is preferred by humans. © 2018 Association for Computational Linguistics.

关键词： Embeddings

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Insect classification and detection in field crops using modern machine learning techniques

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Information Processing in Agriculture 2021年第3期8卷 446-457页

作者： Thenmozhi Kasinathan Dakshayani Singaraju Srinivasulu Reddy Uyyala Machine Learning and Data Analytics Lab Centre of Excellence in Artificial IntelligenceDepartment of Computer ApplicationsNational Institute of TechnologyTiruchirappalliTiruchirappalli 620015Tamil NaduIndia

The agriculture sector has an immense potential to improve the requirement of food and supplies healthy and nutritious *** insect detection is a challenging task for farmers as a significant portion of the crops are damaged,and the quality is degraded due to the pest *** insect identification has the drawback of requiring well-trained tax-onomists to identify insects based on morphological features *** were conducted for classification on nine and 24 insect classes of Wang and Xie dataset using the shape features and applying machine learning techniques such as artificial neural net-works(ANN),support vector machine(SVM),k-nearest neighbors(KNN),naive bayes(NB)and convolutional neural network(CNN)*** paper presents the insect pest detec-tion algorithm that consists of foreground extraction and contour identification to detect the insects for Wang,Xie,Deng,and IP102 datasets in a highly complex *** 9-fold cross-validation was applied to improve the performance of the classification *** highest classification rate of 91.5%and 90%was achieved for nine and 24 class insects using the CNN *** detection performance was accomplished with less com-putation time for Wang,Xie,Deng,and IP102 datasets using insect pest detection *** comparison results with the state-of-the-art classification algorithms exhibited considerable improvement in classification accuracy,computation time perfor-mance while apply more efficiently in field crops to recognize the *** results of classification accuracy are used to recognize the crop insects in the early stages and reduce the time to enhance the crop yield and crop quality in agriculture.

关键词： Crop pest classification Crop insect detection Image processing machine learning Image segmentation

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LSTM based Soft-Sensor for Estimating Nitrate Concentration in Aquaponics pond 3

LSTM based Soft-Sensor for Estimating Nitrate Concentration ...

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3rd International Conference for Innovation in Technology, INOCON 2024

作者： Dharshan, A. Kumar, Purushottam Ravimaran, S. Srinivasulu Reddy, U. Saranathan College of Engineering Department of Artificial Intelligence and Data Science Trichy India National Institute of Technology Artificial Intelligence Machine Learning & Data Analytics Lab Trichy India National Institute of Technology Department of Computer Applications Machine Learning & Data Analytics Lab Trichy India

ISBN: (纸本)9798350381931

In the field of aquaponics, where fish and plants coexist in a symbiotic environment, closely monitoring nitrate levels in the water is crucial due to their profound impact on aquatic and plant well-being. Traditional nitrate measurement methods are often time-consuming and costly. Various approaches, including first principles, IoT-based sensors, and machine learning-based soft sensors, have been attempted to address this challenge. However, these efforts face challenges such as expensive sensors, infrequent data collection, multistage data processing using limited sensor types, and the need for regular maintenance like cleaning and calibration. Additionally, varied environmental conditions affect sensor suitability for different water environments, and even some machine learning-based soft sensors have proven inaccurate. In response, soft sensors, especially deep learning-based ones, have gained prominence in industrial applications for their adaptability and accuracy. These sensors provide real-time insights into complex processes without requiring expensive hardware. In this study, an innovative solution was introduced using Long Short-Term Memory (LSTM) technology, a neural network architecture in deep learning known for capturing complex temporal patterns. LSTM is well-suited for modeling and predicting nitrate concentration changes in aquaponics, trained with extensive data collected from various aquaponic ponds. Through rigorous evaluation, a remarkable MSE value of 0.00074 and an impressive R-squared score of 0.98 were achieved, holding potential for scaling up to commercial applications, benefiting aquaponics operations, supporting researchers, and enhancing sustainability and productivity in aquaponic systems. © 2024 IEEE.

关键词： Nitrates

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ProbSAINT: Probabilistic Tabular Regression for Used Car Pricing

ProbSAINT: Probabilistic Tabular Regression for Used Car Pri...

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2024 IEEE International Conference on Big data, Bigdata 2024

作者： Madhusudhanan, Kiran Behrens, Gunnar Stubbemann, Maximilian Schmidt-Thieme, Lars University Of Hildesheim Information Systems and Machine Learning Lab Vwfs Data Analytics Research Center Germany Volkswagen Financial Services Ag Data Analytics & Ai Engineering United States

ISBN: (纸本)9798350362480

Used car pricing is a critical aspect of the automotive industry, influenced by many economic factors and market dynamics. With the recent surge in online marketplaces and increased demand for used cars, accurate pricing would benefit both buyers and sellers by ensuring fair transactions. However, the transition towards automated pricing algorithms using machine learning necessitates the comprehension of model uncertainties, specifically the ability to flag predictions that the model is unsure about. Although recent literature proposes the use of boosting algorithms or nearest neighbor-based approaches for swift and precise price predictions, encapsulating model uncertainties with such algorithms presents a complex challenge. We introduce ProbSAINT, a model that offers a principled approach for uncertainty quantification of its price predictions, along with accurate point predictions that are comparable to state-of-the-art boosting techniques. Furthermore, acknowledging that the business prefers pricing used cars based on the number of days the vehicle was listed for sale, we show how ProbSAINT can be used as a dynamic forecasting model for predicting price probabilities for different expected offer durations. Our experiments further indicate that ProbSAINT is especially accurate in instances where it is highly certain. This proves the applicability of its probabilistic predictions in real-world scenarios where trustworthiness is crucial. © 2024 IEEE.

关键词： Automotive industry

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