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Negative Affective State Vocalization Analysis of Dairy Cattle Using 3D MFCC Features with CNN-LSTM Model on an Edge Device 2nd

Negative Affective State Vocalization Analysis of Dairy Catt...

引用

2nd International Conference on Agriculture-Centric Computation

作者： Ramrakhiyani, Hitesh Arjunbhai Pandey, Sandeep Kumar Sreenivasalu, N. S. Shekhawat, Hanumant Singh Jasuja, Ravi Indian Inst Technol Guwahati Gauhati India Samsung R&D Inst Bangalore Bangalore Karnataka India Harvard Med Sch Brigham & Womens Hosp Boston MA USA XYonetx Therapeut Bangalore Karnataka India

ISBN: (纸本)9783031744396;9783031744402

Understanding livestock's emotional states (affective states) is crucial, and vocalizations offer a promising avenue for non-invasive assessment. While research has explored vocal indicators in pigs, horses, poultry, and goats, cattle remain understudied. Using sensors to collect biometric data as a means of measuring animal emotions is a topic of growing interest in agricultural technology. Cows produce distinct low and high-frequency vocalizations associated with close-range and long-distance communication, respectively, with the latter potentially linked to negative emotions. Additionally, their vocalizations are shown to be unique to each individual across various contexts. Given the challenges dairy cows face during distress, pain, or fear, studying their vocalizations during negative states is particularly important. Artificial intelligence can help achieve this objective along with anomaly detection and continuous monitoring. Further, continuous cattle monitoring requires low latency for quick actions, low cost, and scalability. Therefore, this study proposes a solution by porting a small CNN-LSTM model on a low-cost Jetson Nano edge device that can identify animal affective states for two high-frequency and low-frequency vocalization classes using 3D MFCC features in dairy cows using the cattle vocalization dataset. The training and testing accuracy of a model is 96.92% and 95.81% respectively is higher than state-of-the-art methodologies.

关键词： Animal Communication Affective states Jetson Nano

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An Optimal Scheduling Algorithm for Intelligent Embedded Heterogeneous Multicore System 4th

An Optimal Scheduling Algorithm for Intelligent Embedded Het...

引用

4th International Conference on Artificial Intelligence Logic and Applications

作者： Chen, Xinyu Huang, Guopeng Chen, Yixiang East China Normal Univ Natl Engn Res Ctr Trustworthy Embedded Software Shanghai 200062 Peoples R China

ISBN: (纸本)9789819603534;9789819603541

The intelligent embedded system is a computing system combining artificial intelligence science and embedded technology, the system combines general-purpose processor and FPGA to achieve stronger processing capability, but it also brings system hardware and software design challenges. This paper proposes an intelligent embedded system task scheduling algorithm based on heterogeneous multi-core, which reasonably allocates hardware resources on the FPGA to optimize energy consumption under the premise of meeting system reliability requirements. The algorithm adopts a critical path-based energy consumption pre-allocation strategy, and the task scheduling makes the system schedule length shortest based on system reliability. Experiments show that the method of this paper outperforms other algorithms by an average of 5.34% in terms of energy consumption, and by an average of 6.83% in terms of scheduling length, which reflects the reasonableness and advancement of the method of this paper.

关键词： Heterogeneous Multicore System Task Scheduling FPGA Energy Optimization

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Ensemble Learning with Feature Fusion for Well-Overflow Detection 5th

Ensemble Learning with Feature Fusion for Well-Overflow Dete...

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5th International Conference on Neural Computing for Advanced Applications (NCAA)

作者： Cui, Ziliang Liu, Li Xiong, Yinzhou Liu, Yinguo Su, Yu Man, Zhimin Wang, Ye Chongqing Univ Posts & Telecommun Chongqing Peoples R China Chongqing Energy Big Data Ctr Co Ltd Chongqing Peoples R China Shandong Zibo Polytech Vocat Sch Chongqing Peoples R China

ISBN: (纸本)9789819770069;9789819770076

Traditional methods for kick prediction rely on experience and basic physical models, but they struggle to maintain high accuracy under complex well conditions and varying geological environments. Moreover, conventional approaches often fail to fully leverage big data resources. Therefore, this study aims to improve the accuracy of kick prediction through machine learning models, especially by adopting an innovative feature fusion strategy to optimize the prediction model. We evaluated the comprehensive performance of six different machine learning models: GBM, LightGBM, CatBoost, XGBoost, Random Forest, and AdaBoost. Based on these evaluations, we proposed a feature score weighted fusion method. This method weights the importance scores of features based on the accuracy of each model, selecting the features that have the most significant impact on the prediction results. Experimental results show that the model utilizing the feature fusion strategy performs better on the test set than any single base model. Specifically, the GBM model, optimized through feature fusion, achieved better values in accuracy, F1 score, and F2 score, proving the effectiveness of this feature fusion strategy in enhancing the accuracy of kick predictions during well control operations. Furthermore, this study compared the performance of different models, providing valuable insights for kick prediction in well control operations. This research not only demonstrates the potential of feature fusion strategies in improving the accuracy of kick prediction but also offers new insights for the future application of machine learning technologies in the field of oilfield data analysis.

关键词： Machine learning feature fusion kill well overflow

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Text Classification Experiments on Contextual Graphs Built by N-Gram Series 4th

Text Classification Experiments on Contextual Graphs Built b...

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4th International Conference on Intelligent Systems and Pattern Recognition-ISPR

作者： Sen, Tarik Uveys Yakit, Mehmet Can Gumus, Mehmet Semih Abar, Orhan Bakal, Gokhan Abdullah Gul Univ TR-38080 Kayseri Turkiye Osmaniye Korkut Ata Univ TR-80000 Osmaniye Turkiye

ISBN: (纸本)9783031821493;9783031821509

Traditional n-gram textual features, commonly employed in conventional machine learning models, offer lower performance rates on high-volume datasets compared to modern deep learning algorithms, which have been intensively studied for the past decade. The main reason for this performance disparity is that deep learning approaches handle textual data through the word vector space representation by catching the contextually hidden information in a better way. Nonetheless, the potential of the n-gram feature set to reflect the context is open to further investigation. In this sense, creating graphs using discriminative ngram series with high classification power has never been fully exploited by researchers. Hence, the main goal of this study is to contribute to the classification power by including the long-range neighborhood relationships for each word in the word embedding representations. To achieve this goal, we transformed the textual data by employing n-gram series into a graph structure and then trained a graph convolution network model. Consequently, we obtained contextually enriched word embeddings and observed F1-score performance improvements from 0.78 to 0.80 when we integrated those convolution-based word embeddings into an LSTM model. This research contributes to improving classification capabilities by leveraging graph structures derived from discriminative n-gram series.

关键词： text-graph transformation graph convolution network deep learning text mining

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Advancing Circularity Through Digital Transformation: Harnessing Life Cycle Assessment 5th

Advancing Circularity Through Digital Transformation: Harnes...

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5th 5th Olympus - International Conference on Supply Chain

作者： Koidis, Christos Kalaitzidis, Athanasios Tsampoulatidis, Ioannis Makavou, Kassandra Bantsos, Athanasios Aptalidis, Irodotos Achillas, Charisios Aidonis, Dimitrios Engineers Business SA EFB Dekatis Evdomis Noemvriou 79 Thessaloniki 55534 Greece Int Hellen Univ Kanelopoulou 2 Katerini 60100 Greece

ISBN: (纸本)9783031693502;9783031693519

In the pursuit of sustainable production and supply chains, integrating Life Cycle Assessment (LCA) within digital platforms has been identified as a critical strategy for industries aiming to reduce their environmental footprint. As an example, the integration of LCA and other key tools within eMT Cluster's Metaplatform tailored for diverse sectors, provides a holistic strategy for assessing and ameliorating the environmental performance throughout the complete value and supply chains of products. Metaplatform enables this integrated approach through its advanced digital architecture, which is distinguished by interoperability, automation, and a suite of Industry 4.0 technologies, such as Internet-of-Things (IoT), data analytics, Artificial Intelligence (AI), and machine learning. This comprehensive framework facilitates a thorough environmental impact assessment from the extraction of raw materials to the end-of-life of products, directing stakeholders across different industries towards more sustainable and circular practices. Specifically, the platform supports the identification of efficient and environmentally friendly product designs, the optimization of production processes, and the enhancement of supply chain operations. The integration of components such as Metaplatform with LCA enhances their effectiveness, fostering sustainability and circularity throughout the production and supply chain. This approach provides significant environmental benefits, including the monitoring of greenhouse gas emissions, waste, and resource use. Such advancements significantly contribute to the development of more sustainable industrial ecosystems.

关键词： Circular Economy Digital Transformation Life Cycle Assessment (LCA) Sustainability

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GRAM: Graph Regularizable Assessment Metric 1st

GRAM: Graph Regularizable Assessment Metric

引用

1st MICCAI Meets Africa Workshop

作者： Touihri, Mariem Nebli, Ahmed Univ Manouba Higher Sch Digital Econ ESEN Ariana Tunisia

ISBN: (纸本)9783031791024;9783031791031

Here, we propose the Graph Regularizable Assessment Metric (GRAM), a customizable tool for evaluating the quality of generated brain graphs. Current geometric deep learning methods often lack robust quantification techniques for assessing the synthetic brain graphs integrity. GRAM addresses this gap by proportionally combining a set of existing graph metrics to establish a linear correlation between distortions' levels and metric values of ground-truth graphs. To evaluate the performance of our model, we generated a synthetic dataset of structural brain connectomes which was derived from an existing dataset and used to simulate a set of predicted connectomes from a generative model with controlled levels of distortions. Our results show that GRAM outperforms single metrics in quantifying the distortion between generated and original graphs. This approach is a significant step towards establishing a universal graph quality index for graph-based predictive studies.

关键词： Predicted brain graphs Quality metrics Customized metrics

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Navigating the Edge: A Comprehensive Comparison of Machine Learning Algorithms on Prominent Edge Platforms 4th

Navigating the Edge: A Comprehensive Comparison of Machine L...

引用

4th International Conference on Intelligent Systems and Pattern Recognition-ISPR

作者： Sayed, Aya Nabil Al-Hasan, Tamim Mahmud Bensaali, Faycal Nhlabatsi, Armstrong Qatar Univ Dept Elect Engn Coll Engn Doha Qatar Qatar Univ Coll Engn KINDI Ctr Comp Res Doha Qatar

ISBN: (纸本)9783031821493;9783031821509

As the demand for deploying Machine Learning (ML) and Deep Learning (DL) models at the network's edge continues to grow, it becomes imperative to evaluate and compare the performance of various edge platforms. This study introduces an innovative edge-based pipeline for analyzing home energy and environmental data and addressing challenges in managing imbalanced label distributions. The comparison between random under-sampling and over-sampling strategies reveals the superiority of over-sampling, significantly improving accuracy and f1-score. More specifically, over-sampling using the Decision Tree (DT) technique enhanced accuracy and f1-score by 4.97% and 9.24%, respectively, when compared to the original results. The paper expands its contribution by conducting a thorough performance evaluation of various edge platforms, emphasizing critical metrics. The evaluated edge platforms include a Raspberry Pi 4b+, Jetson Nano Developer Kit, Odroid XU4, and Coral Dev Board. The study conducted an exhaustive comparison of the runtime of these prominent edge platforms when running ML/DL algorithms for tabular and image datasets during the testing phase. The Jetson Nano, particularly with its built-in GPU, demonstrates superior performance in ML/DL tasks, showcasing accelerated processing times for Convolutional Neural Network (CNN)-based algorithms. These findings emphasize the efficacy of the Jetson Nano in handling computationally intensive tasks for edge-based applications.

关键词： Edge Computing Machine Learning Deep Learning Energy Efficiency

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Adaptive Edge-Based AIoT Architecture for Efficient Retraining and Sustainable Monitoring of Ephemeral Streams 11th

Adaptive Edge-Based AIoT Architecture for Efficient Retraini...

引用

11th Latin American Conference on High Performance Computing

作者： Arratia, Benjamin Cecilia, Jose M. Manzoni, Pietro Hernandez, Daniel Zennaro, Marco Univ Politecn Valencia Dept Comp Engn DISCA Valencia Spain Sciling S L Valencia Spain Abdus Salam Int Ctr Theoret Phys ICTP Trieste Italy

ISBN: (纸本)9783031800832;9783031800849

Monitoring water flows in highly anthropized ecosystems is crucial for assessing eutrophication in water bodies. Ephemeral streams in semi-arid areas present significant challenges for environmental monitoring due to their sporadic flow (2-3 days/year), high intensity, and remote locations (often lacking connectivity and energy sources). Autonomous artificial vision systems deployed at the edge offer a viable solution for this context due to their low cost and high deployment security. However, deploying these systems in different locations requires frequent model retraining to adapt to new environmental conditions, which increases energy consumption and computational resources. This paper proposes an Artificial intelligence of things (AIoT) architecture to identify the presence of water in ephemeral streams, enabling in-place model retraining without compromising the general characteristics of the model. Our results demonstrate that this approach provides a more sustainable and scalable solution for monitoring ephemeral flows, achieving a 64% faster retraining time and a 73% lower energy consumption compared to complete retraining, while maintaining an F1 score around 0.90 for both old and new datasets.

关键词： Environmental Intelligence Edge Computing IoT AIoT TinyML Environmental modeling

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Linking Clinical information to ICF: Inter- and Intra-Rater Agreement When Using a Coding Platform 1

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4th International Conference on Advanced Research in Technologies information Innovation and Sustainability-ARTIIS-Annual

作者： Alvarelhao, Joaquim Caravau, Hilma Rosa, Ana Filipa Rocha, Nelson Pacheco Univ Aveiro Sch Hlth Sci P-3810193 Aveiro Portugal Univ Aveiro Dept Med Sci IEETA P-3810193 Aveiro Portugal

ISBN: (数字)9783031832079

ISBN: (纸本)9783031832062;9783031832079

Mapping information to the International Classification of Functioning, Disability, and Health (ICF) continues to pose a challenge for different health professionals, especially in clinical practice contexts, but tools based on information technologies might improve the efficiency of this process. In this study, we aimed to assess the agreement and efficiency of coding Specific, Measurable, Achievable, Relevant, and Timebound (SMART) therapeutic goals to ICF using a web ICF-Coding Platform (ICF-CP). Inter- and intra-rater agreement values were analyzed, both using and not using ICF-CP (stage 1), without using ICF-CP (stage 2), and only using ICF-CP (stage 3), considering two phases (i.e., ICF-CP without additional information (phase 1) and ICF-CP with additional information (phase 2)) with the participation of two raters. The coding duration was used as the indicator for efficiency. The biggest increase in agreement occurred in the comparison between using or not using ICF-CP: stage 1, phase 1 (kappa = 0.46;95% CI, [0.26-0.66]) to stage 1, phase 2 (kappa = 0.69;95% CI, [0.53-0.85]). For the same set of records used in stage 1, the coding duration was significantly shorter using ICF-CP (means for both raters, 205 +/- 35 min vs. 30 +/- 5 min). Similarly, comparing stage 2 with stage 3, we observed that the coding duration was much shorter for both raters when using ICF-CP. The results show that ICF-CP could provide important support to the coding of clinical records, namely rehabilitation goals.

关键词： International Classification of Functioning Disability and Health Cerebral palsy Coding platform Inter-rater and intra-rater agreement

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Trustworthiness for Deep Learning Based Breast Cancer Detection Using Point-of-Care Ultrasound Imaging in Low-Resource Settings 1st

Trustworthiness for Deep Learning Based Breast Cancer Detect...

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1st MICCAI Meets Africa Workshop

作者： Wodrich, Marisa Karlsson, Jennie Lang, Kristina Arvidsson, Ida Lund Univ Ctr Math Sci Lund Sweden Lund Univ Div Diagnost Radiol Dept Translat Med Lund Sweden Skane Univ Hosp Unilabs Mammog Unit Malmo Sweden

ISBN: (纸本)9783031791024;9783031791031

Poor survival for breast cancer in low- and middle-income countries is largely attributed to late-stage diagnosis and limited access to diagnostic tools. Therefore, we propose using point-of-care ultrasound (POCUS) as a low-cost diagnostic solution paired with a deep learning (DL) model for cancer detection. While using DL has shown great potential, it is crucial to ensure the trustworthiness of a model for diagnostic support in a setting with minimally trained healthcare workers, as a wrong prediction can lead to severe consequences. In the present study, we investigated different measures of trustworthiness from the field of uncertainty quantification, including deep ensembles, Bayesian neural networks and softmax score, for the application of breast cancer detection in POCUS imaging. The results show that all methods exhibit a correlation between uncertainty scores and correctness of prediction. The correlation was strongest when using an average ensemble and an entropy-based total predictive uncertainty. When excluding 30% of the test samples based on highest uncertainty scores, the area under the receiver operating characteristic curve (AUC) for cancer detection increases significantly from 95.6% to 98.9% (with 95% confidence intervals [93.3, 97.0] to [97.5, 99.9]), comparable to an expert radiologist's performance.

关键词： Trustworthy AI Uncertainty quantification Breast cancer detection Point-of-care ultrasound low-resource settings

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