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International Conference on Business Intelligence and Data Analytics, BIDA 2024

作者： Ramalakshmi, K.S. Karmakar, Kingshuk Bajaj, Krishna Abhimanyu, Kumar Srinivasa, Gowri PES Center for Pattern Recognition Department of Computer Science and Engineering PES University Bengaluru India

ISBN: (纸本)9789819777167

This paper presents a deep learning-based solution to the challenge of weed infestation in the context of modern agriculture. We leverage visual similarities of weed species through a hierarchical classification approach and explore real-time weed detection using state-of-the-art deep learning models. Our experiments using MobileNetV2 and NASNetMobile, optimized for edge applications, yield remarkable accuracies of 98.90 and 97.58%, respectively. Additionally, the paper explores real-time weed detection using the YOLOv7 and YOLOv8 framework, achieving a precision of 0.9860 and 0.9902, respectively. The deployment of these advanced technologies on the edge offers a transformative solution for weed management, ensuring precise and targeted weedicide application, thereby minimizing environmental harm and streamlining the weed management process for farmers. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Hierarchical Clustering Image Classification MobileNetV2 NASNetMobile Object Detection Weed Management YOLO

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SNOBERT: A Benchmark for Clinical Notes Entity Linking in the SNOMED CT Clinical Terminology 27th

SNOBERT: A Benchmark for Clinical Notes Entity Linking in ...

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27th International Conference on pattern recognition, ICPR 2024

作者： Kulyabin, Mikhail Sokolov, Gleb Galaida, Aleksandr Maier, Andreas Arias-Vergara, Tomas Pattern Recognition Lab Department of Computer Science Friedrich-Alexander-Universität Erlangen-Nürnberg Erlangen Germany Moscow Russia

ISBN: (纸本)9783031781186

The extraction and analysis of insights from medical data, primarily stored in free-text formats by healthcare workers, presents significant challenges due to its unstructured nature. Medical coding, a crucial process in healthcare, remains minimally automated due to the complexity of medical ontologies and restricted access to medical texts for training Natural Language Processing models. In this paper, we proposed a method, "SNOBERT," of linking text spans in clinical notes to specific concepts in the SNOMED CT using BERT-based models. The method consists of two stages: candidate selection and candidate matching. The models were trained on one of the largest publicly available datasets of labelled clinical notes. SNOBERT outperforms other classical methods based on deep learning, as confirmed by the results of a challenge in which it was applied. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Natural language processing systems

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Rotational Operating Deflection Shapes Analysis with High-Speed Camera 41st

Rotational Operating Deflection Shapes Analysis with High-Sp...

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41st IMAC, A Conference and Exposition on Structural Dynamics, 2023

作者： Bogatikov, Peter Herfert, Daniel Gollnick, Maik Department of Structural Dynamics/Pattern Recognition Society for the Advancement of Applied Computer Science Berlin Germany

ISBN: (纸本)9783031349096

By using high-speed cameras, non-contact and high-resolution measurements of vibrations are possible for beginners and experts with little configuration effort. This makes surface measurements feasible, where each image pixel can be used as a vibration sensor. By using optical flow algorithms, measurements are possible without pretreatment of the target. This also applies to color homogeneous structures. This technique is very well suited for vibration analysis and online condition monitoring of buildings, such as bridges and wind turbines. Within the scope of this chapter, this technique is used for the first time to investigate the model of a wind turbine in terms of an order-based operating deflection shapes analysis. In addition to the deformation data, the speed signal will also be generated from the video to perform an order analysis, so that the operating deflection shape analysis can be visualized depending on the order or speed. © 2024, The Society for Experimental Mechanics, Inc.

关键词： High speed cameras

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Classification of Recurrent Concepts with Metafeature-based Model Selection 24

Classification of Recurrent Concepts with Metafeature-based ...

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24th IEEE International Conference on Data Mining Workshops, ICDMW 2024

作者： Komorniczak, Joanna Ksieniewicz, Pawel Wroclaw University of Science and Technology Pattern Recognition Team Department of Systems and Computer Networks Wroclaw Poland

ISBN: (纸本)9798331530631

Processing non-stationary data streams has gained a significant research interest in the recent years. Most of the current state-of-the-art approaches in the literature exhibit hybrid solutions based on an ensemble learning paradigm, extending its parallel architecture with a drift detector. Such integrated mechanism of concept drift detection often relies on the metadescription of incoming data. The article presents a proposition of a novel hybrid classifier - the Metafeature Concept Selector. The method employs the analysis of statistical metafeatures calculated on subsequent data batches to identify the currently processed concept. The work is concluded with an extensive experimental analysis of synthetic data streams with recurrent concepts, various dimensionality and various concept change frequencies, showing that the proposed approach brings improvement when integrated with state-of-the-art base incremental learners. © 2024 IEEE.

关键词： Data streams

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Mental Health in Tech: Analysis of Workplace Risk Factors and Impact of COVID-19

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SN computer science 2023年第2期4卷 197页

作者： Mitravinda, K.M. Nair, Devika S. Srinivasa, Gowri PES Center for Pattern Recognition Department of Computer Science and Engineering PES University Karnataka Bengaluru India

The psychological, emotional and social well-being of an individual determines their ability to contribute and function as a social member. Several studies over the years have proven that an alarming number of people live with mental illnesses, of which only a fraction is documented. Studies conducted by Open Sourcing Mental Illness (OSMI) organization have indicated that these figures are much higher in the tech industry. We present an analysis of patterns and infer contributory factors for mental illness in the tech industry, to aid in the early detection and assess employees’ risk of diagnosis. Towards this end, the study comprises a detailed analysis, models for prediction of diagnosis, risk-based clustering and investigation into existing literature on factors contributing to mental illness. In addition to this, we have attempted to understand the impact of Covid-19 through analyzing trends of the factors influencing mental health, pre- and post-pandemic. We conclude with an insight to the impact of the COVID-19 pandemic on global mental health and the actions taken in the workplace to mitigate this. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Clustering COVID-19 Mental health Prediction Risk

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ScripTONES: Sentiment-Conditioned Music Generation for Movie Scripts 3

ScripTONES: Sentiment-Conditioned Music Generation for Movie...

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3rd International Conference on AI-ML Systems, AIMLSystems 2023

作者： Veerendranath, Vishruth Masti, Vibha Gupta, Utkarsh Chaudhuri, Hrishit Srinivasa, Gowri Pes Center For Pattern Recognition Department Of Computer Science And Engineering Pes University India

ISBN: (纸本)9798400716492

Film scores are considered an essential part of the film cinematic experience, but the process of film score generation is often expensive and infeasible for small-scale creators. Automating the process of film score composition would provide useful starting points for music in small projects. In this paper, we propose a two-stage pipeline for generating music from a movie script. The first phase is the Sentiment Analysis phase where the sentiment of a scene from the film script is encoded into the valence-arousal continuous space. The second phase is the Conditional Music Generation phase which takes as input the valence-arousal vector and conditionally generates piano MIDI music to match the sentiment. We study the efficacy of various music generation architectures by performing a qualitative user survey and propose methods to improve sentiment-conditioning in VAE architectures. © 2023 ACM.

关键词： Sentiment analysis

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Examining the dynamic characteristics of a tennis racket through high-speed videos 31

Examining the dynamic characteristics of a tennis racket thr...

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31st International Conference on Noise and Vibration Engineering, ISMA 2024 and 10th International Conference on Uncertainty in Structural Dynamics, USD 2024

作者： Herfert, D. Krause, J. Henning, K. Gollnick, M. Society for the Advancement of Applied Computer Science Department of Structural Dynamics / Pattern Recognition Volmerstrasse 3 Berlin12489 Germany

ISBN: (纸本)9789082893175

This paper presents the application of image-based vibration analysis to a tennis racket. A high-speed camera is used to record a video of the vibrating tennis racket and the recorded data is then analyzed. The simultaneous recording of thousands of measuring points enables completely new analysis options for tennis rackets, so that evaluations under playing conditions as well as investigations of arm-hand vibration effects will also be possible in the future. Non-contact measurements eliminate the need to install and wire sensors and allow analysis of delicate structures like the racket strings, where sensors are difficult to install. In image-based analysis, each pixel becomes an individual measuring point, comparable to a classical vibration sensor. In this way, sub-pixel movements that are no longer recognizable to the human eye, can be measured. The results of the analysis are compared with those of Finite Element Analysis (FEA). © 2024 Proceedings of ISMA 2024 - International Conference on Noise and Vibration Engineering and USD 2024 - International Conference on Uncertainty in Structural Dynamics. All rights reserved.

关键词： High speed cameras

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Deep Learning-Based Pipeline for the Detection of Multiple Ocular Diseases 4th

Deep Learning-Based Pipeline for the Detection of Multipl...

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4th International Conference on Machine Intelligence and Signal Processing, MISP 2022

作者： Angadi, Ananya Bhat, Aneesh N. Ankitha, P. Kumar, Parul S. Srinivasa, Gowri PES Center for Pattern Recognition Department of Computer Science and Engineering PES University Bengaluru India

ISBN: (纸本)9789819900466

computer-aided diagnostic systems provide decision support for the early and accessible detection of ocular diseases. While predictive models exist for certain common diseases in isolation, there is a need to detect multiple diseases at the same time to ensure that rarer conditions do not go undiagnosed. In the present work, we propose a workflow to detect the presence of seven ocular diseases. We first determine the disease risk using EfficientNetB3 pre-trained on ImageNet. Next, for common diseases with a relatively larger representation of images in the dataset, we utilize a Vanilla CNN to classify each disease. For relatively less common diseases, characterized by fewer representative samples in the dataset, we employ transfer learning using EfficientNetB3 pre-trained on ImageNet. To deal with the class imbalance in the original data, we create representative datasets with respect to each disease. The resulting models were found to have high sensitivity (0.8–1.0) and promising accuracy (0.7–0.96). © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Deep learning

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Ressource-Efficient Moth Detection for Pest Monitoring with YOLOv5

Ressource-Efficient Moth Detection for Pest Monitoring with ...

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2023 IEEE Symposium Series on Computational Intelligence, SSCI 2023

作者： Farooq, M. Tallal Leipert, Martin Maier, Andreas Christlein, Vincent Friedrich-Alexander-Universität Erlangen-Nürnberg Pattern Recognition Lab Department of Computer Science Erlangen91058 Germany

ISBN: (纸本)9781665430654

Moths pose a significant threat to agricultural crops, and identifying them accurately is crucial for effective pest monitoring and crop conservation efforts. However, manually evaluating glue traps is a time-consuming and labor-intensive process, which has led to the development of automated solutions. In this study, we present a deep learning-based automated detection pipeline that can detect moths in images captured by field traps with pheromone-emitting glue pads. To train our model, we collected a comprehensive dataset that includes moths from various environments, such as agricultural plants, homes, and food production facilities. We augmented this dataset and included additional glue pad datasets, enabling the model to detect moths regardless of the species. We base our model on the YOLOv5 algorithm and fine-tune it using transfer learning, which enables us to identify moths in real-time and on embedded hardware. Our evaluation of the algorithm reveals that it achieves an average precision of 98.2 % on a test dataset, which outperforms reference models from previous research. We also assess the model's ability to handle disturbances such as other insects, varying lighting conditions, and foreign objects. Importantly, our solution maintains a tiny memory footprint and low inference time of 2.3 ms, making it a highly efficient and effective tool for moth detection in the field. © 2023 IEEE.

关键词： Insect Detection Moths Pest Management Pheromone Trap

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Mutual Information-Guided GA for Bayesian Network Structure Learning (Extended Abstract) 40

Mutual Information-Guided GA for Bayesian Network Structure ...

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40th IEEE International Conference on Data Engineering, ICDE 2024

作者： Yan, Kefei Fang, Wei Lu, Hengyang Zhang, Xin Sun, Jun Wu, Xiaojun Jiangnan University Jiangsu Provincial Engineering Laboratory of Pattern Recognition and Computational Intelligence Department of Computer Science and Technology Wuxi214122 China

ISBN: (纸本)9798350317152

Bayesian network structure learning (BNSL) from data is an NP-hard problem. Genetic algorithms are powerful for solving combinatorial optimization problems, but the lack of effective guidance results in slow convergence and low accuracy regarding BNSL. To address this problem, we propose a mutual information (MI) guided genetic algorithm (MIGA) for BNSL in this paper, which uses MI to effectively search BN structures. In the initialization phase of MIGA, the population is generated by adding additional constraints based on MI to reach a higher score without losing diversity. By employing normalized MI and defining the population support, the potential dominance in the population can be identified and then used to design a novel crossover operator in order to preserve the dominant genes with a higher probability. Moreover, with the guidance of MI for removing loops from the structures, infeasible solutions can be handled in a straightforward and practical way. The proposed MIGA is evaluated on eleven well-known benchmark datasets and compared with four GA-based methods and four other state-of-the-art BNSL algorithms. Experimental results show that MIGA outperforms the compared algorithms in convergence and learning accuracy. © 2024 IEEE.

关键词： Genetic algorithms

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