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Journal articles on the topic 'Data-efficient Deep Learning'

Author: Grafiati

Published: 6 September 2023

Last updated: 31 July 2025

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1

Dr., Sumit Chaudhary, Neha Singh Ms., and Pankaj Salaiya. "Time-Efficient Algorithm for Data Annotation using Deep Learning." Indian Journal of Artificial Intelligence and Neural Networking (IJAINN) 2, no. 5 (2022): 8–11. https://doi.org/10.54105/ijainn.E1058.082522.

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<strong>Abstract:</strong> Current generation emphasis on the Digital world which creates a lot of unbeneficial data. The paper is about data annotation using deep learning as there is a lot of data available online but which data is useful can be labeled using these techniques. The unstructured data is labeled by many techniques but implementation of deep learning for labeling the unstructured data results in saving the time with high efficiency. In this paper introduce a method for data annotation, for that we can use unlabeled data as input and it is classified using the K-Nearest Neighbor

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Chaudhary, Dr Sumit, Ms Neha Singh, and Salaiya Pankaj. "Time-Efficient Algorithm for Data Annotation using Deep Learning." Indian Journal of Artificial Intelligence and Neural Networking 2, no. 5 (2022): 8–11. http://dx.doi.org/10.54105/ijainn.e1058.082522.

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Current generation emphasis on the Digital world which creates a lot of unbeneficial data. The paper is about data annotation using deep learning as there is a lot of data available online but which data is useful can be labeled using these techniques. The unstructured data is labeled by many techniques but implementation of deep learning for labeling the unstructured data results in saving the time with high efficiency. In this paper introduce a method for data annotation, for that we can use unlabeled data as input and it is classified using the K-Nearest Neighbor algorithm. K-Nearest Neighb

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Biswas, Surojit, Grigory Khimulya, Ethan C. Alley, Kevin M. Esvelt, and George M. Church. "Low-N protein engineering with data-efficient deep learning." Nature Methods 18, no. 4 (2021): 389–96. http://dx.doi.org/10.1038/s41592-021-01100-y.

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Edstrom, Jonathon, Yifu Gong, Dongliang Chen, Jinhui Wang, and Na Gong. "Data-Driven Intelligent Efficient Synaptic Storage for Deep Learning." IEEE Transactions on Circuits and Systems II: Express Briefs 64, no. 12 (2017): 1412–16. http://dx.doi.org/10.1109/tcsii.2017.2767900.

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Feng, Wenhui, Chongzhao Han, Feng Lian, and Xia Liu. "A Data-Efficient Training Method for Deep Reinforcement Learning." Electronics 11, no. 24 (2022): 4205. http://dx.doi.org/10.3390/electronics11244205.

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Data inefficiency is one of the major challenges for deploying deep reinforcement learning algorithms widely in industry control fields, especially in regard to long-horizon sparse reward tasks. Even in a simulation-based environment, it is often prohibitive to take weeks to train an algorithm. In this study, a data-efficient training method is proposed in which a DQN is used as a base algorithm, and an elaborate curriculum is designed for the agent in the simulation scenario to accelerate the training process. In the early stage of the training process, the distribution of the initial state i

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Hu, Wenjin, Feng Liu, and Jiebo Peng. "An Efficient Data Classification Decision Based on Multimodel Deep Learning." Computational Intelligence and Neuroscience 2022 (May 4, 2022): 1–10. http://dx.doi.org/10.1155/2022/7636705.

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A single model is often used to classify text data, but the generalization effect of a single model on text data sets is poor. To improve the model classification accuracy, a method is proposed that is based on a deep neural network (DNN), recurrent neural network (RNN), and convolutional neural network (CNN) and integrates multiple models trained by a deep learning network architecture to obtain a strong text classifier. Additionally, to increase the flexibility and accuracy of the model, various optimizer algorithms are used to train data sets. Moreover, to reduce the interference in the cla

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Mairittha, Nattaya, Tittaya Mairittha, and Sozo Inoue. "On-Device Deep Learning Inference for Efficient Activity Data Collection." Sensors 19, no. 15 (2019): 3434. http://dx.doi.org/10.3390/s19153434.

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Labeling activity data is a central part of the design and evaluation of human activity recognition systems. The performance of the systems greatly depends on the quantity and “quality” of annotations; therefore, it is inevitable to rely on users and to keep them motivated to provide activity labels. While mobile and embedded devices are increasingly using deep learning models to infer user context, we propose to exploit on-device deep learning inference using a long short-term memory (LSTM)-based method to alleviate the labeling effort and ground truth data collection in activity recognition

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Dheepan, G. M. Karpura, Shaik Mohammed Rafee, Prasanthi Badugu, and Sunil Kumar. "A DEEP LEARNING TECHNIQUE FOR EFFICIENT MULTIMEDIA FOR DATA COMPRESSION." ICTACT Journal on Image and Video Processing 14, no. 3 (2024): 3169–74. http://dx.doi.org/10.21917/ijivp.2024.0451.

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Medical image compression plays a pivotal role in efficient data storage and transmission, crucial for modern healthcare systems. This research proposes a convolutional transfer learning technique scheme tailored for multimedia data compression, specifically targeting medical images. In the background, the growing volume of medical imaging data and the demand for efficient storage and transmission underscore the need for innovative compression methods. Leveraging transfer learning from pre-trained convolutional neural networks (CNNs) designed for image recognition tasks, our methodology optimi

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Duan, Yanjie, Yisheng Lv, Yu-Liang Liu, and Fei-Yue Wang. "An efficient realization of deep learning for traffic data imputation." Transportation Research Part C: Emerging Technologies 72 (November 2016): 168–81. http://dx.doi.org/10.1016/j.trc.2016.09.015.

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Sashank, Madipally Sai Krishna, Vijay Souri Maddila, Vikas Boddu, and Y. Radhika. "Efficient deep learning based data augmentation techniques for enhanced learning on inadequate medical imaging data." ACTA IMEKO 11, no. 1 (2022): 6. http://dx.doi.org/10.21014/acta_imeko.v11i1.1226.

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<p class="Abstract">The world has come to a standstill with the Coronavirus taking over. In these dire times, there are fewer doctors and more patients and hence, treatment is becoming more and more difficult and expensive. In recent times, Computer Science, Machine Intelligence, measurement technology has made a lot of progress in the field of Medical Science hence aiding the automation of a lot of medical activities. One area of progress in this regard is the automation of the process of detection of respiratory diseases (such as COVID-19). There have been many Convolutional Neural Net

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Shin, Hyunkyung, Hyeonung Shin, Wonje Choi, et al. "Sample-Efficient Deep Learning Techniques for Burn Severity Assessment with Limited Data Conditions." Applied Sciences 12, no. 14 (2022): 7317. http://dx.doi.org/10.3390/app12147317.

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The automatic analysis of medical data and images to help diagnosis has recently become a major area in the application of deep learning. In general, deep learning techniques can be effective when a large high-quality dataset is available for model training. Thus, there is a need for sample-efficient learning techniques, particularly in the field of medical image analysis, as significant cost and effort are required to obtain a sufficient number of well-annotated high-quality training samples. In this paper, we address the problem of deep neural network training under sample deficiency by inve

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Petrovic, Nenad, and Djordje Kocic. "Data-driven framework for energy-efficient smart cities." Serbian Journal of Electrical Engineering 17, no. 1 (2020): 41–63. http://dx.doi.org/10.2298/sjee2001041p.

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Energy management is one of the greatest challenges in smart cities. Moreover, the presence of autonomous vehicles makes this task even more complex. In this paper, we propose a data-driven smart grid framework which aims to make smart cities energy-efficient focusing on two aspects: energy trading and autonomous vehicle charging. The framework leverages deep learning, linear optimization, semantic technology, domain-specific modelling notation, simulation and elements of relay protection. The evaluation of deep learning module together with code generation time and energy distribution cost re

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Deng, Rui, Ziqi Li, and Mingshu Wang. "GeoAggregator: An Efficient Transformer Model for Geo-Spatial Tabular Data." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 11 (2025): 11572–80. https://doi.org/10.1609/aaai.v39i11.33259.

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Modeling geospatial tabular data with deep learning has become a promising alternative to traditional statistical and machine learning approaches. However, existing deep learning models often face challenges related to scalability and flexibility as datasets grow. To this end, this paper introduces GeoAggregator, an efficient and lightweight algorithm based on transformer architecture designed specifically for geospatial tabular data modeling. GeoAggregators explicitly account for spatial autocorrelation and spatial heterogeneity through Gaussian-biased local attention and global positional aw

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Yue, Yang, Bingyi Kang, Zhongwen Xu, Gao Huang, and Shuicheng Yan. "Value-Consistent Representation Learning for Data-Efficient Reinforcement Learning." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 9 (2023): 11069–77. http://dx.doi.org/10.1609/aaai.v37i9.26311.

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Deep reinforcement learning (RL) algorithms suffer severe performance degradation when the interaction data is scarce, which limits their real-world application. Recently, visual representation learning has been shown to be effective and promising for boosting sample efficiency in RL. These methods usually rely on contrastive learning and data augmentation to train a transition model, which is different from how the model is used in RL---performing value-based planning. Accordingly, the learned representation by these visual methods may be good for recognition but not optimal for estimating st

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Li, Mengkun, and Yongjian Wang. "An Energy-Efficient Silicon Photonic-Assisted Deep Learning Accelerator for Big Data." Wireless Communications and Mobile Computing 2020 (December 16, 2020): 1–11. http://dx.doi.org/10.1155/2020/6661022.

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Deep learning has become the most mainstream technology in artificial intelligence (AI) because it can be comparable to human performance in complex tasks. However, in the era of big data, the ever-increasing data volume and model scale makes deep learning require mighty computing power and acceptable energy costs. For electrical chips, including most deep learning accelerators, transistor performance limitations make it challenging to meet computing’s energy efficiency requirements. Silicon photonic devices are expected to replace transistors and become the mainstream components in computing

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Lyu, Daoming, Fangkai Yang, Bo Liu, and Steven Gustafson. "SDRL: Interpretable and Data-Efficient Deep Reinforcement Learning Leveraging Symbolic Planning." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 2970–77. http://dx.doi.org/10.1609/aaai.v33i01.33012970.

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Deep reinforcement learning (DRL) has gained great success by learning directly from high-dimensional sensory inputs, yet is notorious for the lack of interpretability. Interpretability of the subtasks is critical in hierarchical decision-making as it increases the transparency of black-box-style DRL approach and helps the RL practitioners to understand the high-level behavior of the system better. In this paper, we introduce symbolic planning into DRL and propose a framework of Symbolic Deep Reinforcement Learning (SDRL) that can handle both high-dimensional sensory inputs and symbolic planni

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Yuan, Mu, Lan Zhang, Xiang-Yang Li, Lin-Zhuo Yang, and Hui Xiong. "Adaptive Model Scheduling for Resource-efficient Data Labeling." ACM Transactions on Knowledge Discovery from Data 16, no. 4 (2022): 1–22. http://dx.doi.org/10.1145/3494559.

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Labeling data (e.g., labeling the people, objects, actions, and scene in images) comprehensively and efficiently is a widely needed but challenging task. Numerous models were proposed to label various data and many approaches were designed to enhance the ability of deep learning models or accelerate them. Unfortunately, a single machine-learning model is not powerful enough to extract various semantic information from data. Given certain applications, such as image retrieval platforms and photo album management apps, it is often required to execute a collection of models to obtain sufficient l

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da Silva Lourenço, Catarina, Marleen C. Tjepkema-Cloostermans, and Michel J. A. M. van Putten. "Efficient use of clinical EEG data for deep learning in epilepsy." Clinical Neurophysiology 132, no. 6 (2021): 1234–40. http://dx.doi.org/10.1016/j.clinph.2021.01.035.

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Cuayáhuitl, Heriberto. "A data-efficient deep learning approach for deployable multimodal social robots." Neurocomputing 396 (July 2020): 587–98. http://dx.doi.org/10.1016/j.neucom.2018.09.104.

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Zhao, Junhui, Yiwen Nie, Shanjin Ni, and Xiaoke Sun. "Traffic Data Imputation and Prediction: An Efficient Realization of Deep Learning." IEEE Access 8 (2020): 46713–22. http://dx.doi.org/10.1109/access.2020.2978530.

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Tovar, Nathaniel, Sean (Seok-Chul) Kwon, and Jinseong Jeong. "Image Upscaling with Deep Machine Learning for Energy-Efficient Data Communications." Electronics 12, no. 3 (2023): 689. http://dx.doi.org/10.3390/electronics12030689.

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Advanced algorithms of image quality enhancement have been attracting substantial attention recently due to the successful business model of video streaming services. The extremely high image quality in video streaming demands a significant increase in the transmit data rate. In turn, the required ultrahigh data rate causes the saturation of the video streaming service network if there is no remedy for this situation. Compression algorithms have contributed to the energy-efficient transmission of data; however, they have almost reached the upper bound. The demand for ultrahigh image quality by

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Jangid, Jagdish. "Efficient Training Data Caching for Deep Learning in Edge Computing Networks." International Journal of Scientific Research in Computer Science, Engineering and Information Technology 7, no. 5 (2020): 337–62. https://doi.org/10.32628/CSEIT20631113.

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Efficient caching of training data plays a pivotal role in boosting deep learning performance in edge computing networks, which frequently experience limitations in computational resources and data bandwidth. This paper explores innovative methods for optimizing data caching strategies, specifically designed to mitigate challenges such as latency, data redundancy, and inefficient resource utilization in distributed edge computing environments. The rapid increase in data generation, combined with the growing demand for real-time processing and deployment, calls for advanced techniques to effect

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Chen, Haodi. "The future of protein assembly: a deep learning paradigm for efficient and accurate data processing." Theoretical and Natural Science 73, no. 1 (2025): 267–74. https://doi.org/10.54254/2753-8818/2024.19820.

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Protein assembly is critical to understanding biomolecular functions and biological processes. In recent years, with the large increase in protein sequence data, the demand for data integration has also increased, and deep learning has made significant progress in protein structure prediction and functional analysis. Deep learning is an efficient data processing method that helps traditional experiments improve the efficiency, speed, and accuracy of data processing. Combined with deep learning, the development of new proteins is no longer limited by experimental conditions, which is of great s

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Devi, P. Aruna, D. Megala, N. Paviyasre, and S. Nithyanandh. "Robust AI Based Bio Inspired Protocol using GANs for Secure and Efficient Data Transmission in IoT to Minimize Data Loss." Indian Journal Of Science And Technology 17, no. 35 (2024): 3609–22. http://dx.doi.org/10.17485/ijst/v17i35.2342.

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Objectives: To propose an AI-based protocol to enhance the reliability and security of IoT data transmission in a robust manner. Deep learning with bio-inspired methods is employed to address real-time challenges such as route optimization, data integrity, error recovery, and end-to-end delay in order to minimize data loss and maximize the transmission rate. Methods: Generative Adversarial Networks (GANs) are used to enhance the robustness of the protocol in combination with a bio-inspired Artificial Immune System (AIS) to detect IoT network anomalies and responds to malicious activities by us

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Choi, Young-Jae, Woojin Cho, and Seungeui Lee. "Efficient Training Data Acquisition Technique for Deep Learning Networks in Radar Applications." Journal of Electromagnetic Engineering and Science 24, no. 5 (2024): 451–57. http://dx.doi.org/10.26866/jees.2024.5.r.246.

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In the field of radar, deep learning techniques have shown considerably superior performance over traditional classifiers in detecting and classifying targets. However, acquiring sufficient training data for deep learning applications is often challenging and time consuming. In this study, we propose a technique for acquiring training data efficiently using a combination of synthesized data and measured background data. We utilized graphics processing unit (GPU)-based physical optics methods to obtain the backscattered field of moving targets. We then generated a virtual dataset by mixing the

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Onofrey, John A., Lawrence H. Staib, Xiaojie Huang, et al. "Sparse Data–Driven Learning for Effective and Efficient Biomedical Image Segmentation." Annual Review of Biomedical Engineering 22, no. 1 (2020): 127–53. http://dx.doi.org/10.1146/annurev-bioeng-060418-052147.

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Sparsity is a powerful concept to exploit for high-dimensional machine learning and associated representational and computational efficiency. Sparsity is well suited for medical image segmentation. We present a selection of techniques that incorporate sparsity, including strategies based on dictionary learning and deep learning, that are aimed at medical image segmentation and related quantification.

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Deng, Yuhao, Chengliang Chai, Kaisen Jin, et al. "Two Birds with One Stone: Efficient Deep Learning over Mislabeled Data through Subset Selection." Proceedings of the ACM on Management of Data 3, no. 3 (2025): 1–28. https://doi.org/10.1145/3728289.

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Using a large training dataset to train a big and powerful model -- a typical practice in modern deep learning, often suffers from two major problems: the expensive and slow training process and the error-prone labels. The existing approaches, targeting either speeding up the training by selecting a subset of representative training instances (subset selection) or eliminating the negative effect of mislabels during training (mislabel detection), do not perform well in this scenario due to overlooking one of these two problems. To fill this gap, we propose Deem, a novel data-efficient framework

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Yuan, Hang. "Current perspective on artificial intelligence, machine learning and deep learning." Applied and Computational Engineering 19, no. 1 (2023): 116–22. http://dx.doi.org/10.54254/2755-2721/19/20231019.

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Artificial intelligence has exploded in the past few years, especially after 2015. Much of it is due to the widespread use of GPUs, which has made parallel computing faster, cheaper, and more efficient. Of course, the combination of infinite expansion of storage capacity and sudden explosion of data torrent (big data) also makes image data, text data, transaction data, mapping data comprehensive and massive explosion. The wave of artificial intelligence has swept the world, and many words still plague us: artificial intelligence, machine learning, and deep learning. Many people do not have a d

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Oh, Seongjin, Jongpil Jeong, Chae-Gyu Lee, Juyoung Yoo, and Gyuri Nam. "Synergistic Training: Harnessing Active Learning and Pseudo-Labeling for Enhanced Model Performance in Deep Learning." WSEAS TRANSACTIONS ON COMPUTERS 22 (September 18, 2023): 114–19. http://dx.doi.org/10.37394/23205.2023.22.14.

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This research addresses the growing need for efficient data labeling methods by leveraging deep learning models. The proposed approach combines pre-training and active learning to automate the labeling process and reduce reliance on human annotators. In the pre-training phase, two deep learning models are trained using labeled data, adjusting the data ratio to ensure approximately 50% accuracy on the test set. In the active learning phase, the models generate pseudo labels for unlabeled data based on a confidence threshold, and the selected data is used to improve the models' performance throu

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He, Yan, Bin Fu, Jian Yu, Renfa Li, and Rucheng Jiang. "Efficient Learning of Healthcare Data from IoT Devices by Edge Convolution Neural Networks." Applied Sciences 10, no. 24 (2020): 8934. http://dx.doi.org/10.3390/app10248934.

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Wireless and mobile health applications promote the development of smart healthcare. Effective diagnosis and feedbacks of remote health data pose significant challenges due to streaming data, high noise, network latency and user privacy. Therefore, we explore efficient edge and cloud design to maintain electrocardiogram classification performance while reducing the communication cost. These contributions include: (1) We introduce a hybrid smart medical architecture named edge convolutional neural networks (EdgeCNN) that balances the capability of edge and cloud computing to address the issue f

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Bhat, Sanjit, David Lu, Albert Kwon, and Srinivas Devadas. "Var-CNN: A Data-Efficient Website Fingerprinting Attack Based on Deep Learning." Proceedings on Privacy Enhancing Technologies 2019, no. 4 (2019): 292–310. http://dx.doi.org/10.2478/popets-2019-0070.

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Abstract In recent years, there have been several works that use website fingerprinting techniques to enable a local adversary to determine which website a Tor user visits. While the current state-of-the-art attack, which uses deep learning, outperforms prior art with medium to large amounts of data, it attains marginal to no accuracy improvements when both use small amounts of training data. In this work, we propose Var-CNN, a website fingerprinting attack that leverages deep learning techniques along with novel insights specific to packet sequence classification. In open-world settings with

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Wu, Chunyi, and Ya Li. "FLOM: Toward Efficient Task Processing in Big Data with Federated Learning." Security and Communication Networks 2022 (January 27, 2022): 1–16. http://dx.doi.org/10.1155/2022/5277362.

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With the diversification and individuation of user requirements as well as the rapid development of computing technology, the large-scale tasks processing for big data in edge computing environment has become a research focus nowadays. Many recent efforts for task processing are designed and implemented based on some traditional protocols and optimization methods. Therefore, it is more difficult to explore the task allocation strategy that maximizes the overall system revenue from the perspective of global load balancing. In order to overcome this problem, a large-scale tasks processing approa

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Li, Jiangneng, Haitao Yuan, Gao Cong, Han Mao Kiah, and Shuhao Zhang. "MAST: Towards Efficient Analytical Query Processing on Point Cloud Data." Proceedings of the ACM on Management of Data 3, no. 1 (2025): 1–27. https://doi.org/10.1145/3709702.

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The proliferation of 3D scanning technology, particularly within autonomous driving, has led to an exponential increase in the volume of Point Cloud (PC) data. Given the rich semantic information contained in PC data, deep learning models are commonly employed for tasks such as object queries. However, current query systems that support PC data types do not process queries on semantic information. Consequently, there is a notable gap in research regarding the efficiency of invoking deep models for each PC data query, especially when dealing with large-scale models and datasets. To address this

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Wang, Yang, Yutong Li, Ting Wang, and Gang Liu. "Towards an energy-efficient Data Center Network based on deep reinforcement learning." Computer Networks 210 (June 2022): 108939. http://dx.doi.org/10.1016/j.comnet.2022.108939.

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Shiloh, Lihi, Avishay Eyal, and Raja Giryes. "Efficient Processing of Distributed Acoustic Sensing Data Using a Deep Learning Approach." Journal of Lightwave Technology 37, no. 18 (2019): 4755–62. http://dx.doi.org/10.1109/jlt.2019.2919713.

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Yi, Deliang, Xin Zhou, Yonggang Wen, and Rui Tan. "Efficient Compute-Intensive Job Allocation in Data Centers via Deep Reinforcement Learning." IEEE Transactions on Parallel and Distributed Systems 31, no. 6 (2020): 1474–85. http://dx.doi.org/10.1109/tpds.2020.2968427.

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Jeong, Seunghwan, Gwangpyo Yoo, Minjong Yoo, Ikjun Yeom, and Honguk Woo. "Resource-Efficient Sensor Data Management for Autonomous Systems Using Deep Reinforcement Learning." Sensors 19, no. 20 (2019): 4410. http://dx.doi.org/10.3390/s19204410.

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Hyperconnectivity via modern Internet of Things (IoT) technologies has recently driven us to envision “digital twin”, in which physical attributes are all embedded, and their latest updates are synchronized on digital spaces in a timely fashion. From the point of view of cyberphysical system (CPS) architectures, the goals of digital twin include providing common programming abstraction on the same level of databases, thereby facilitating seamless integration of real-world physical objects and digital assets at several different system layers. However, the inherent limitations of sampling and o

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Zhang, Lan, Yu Feng Nie, and Zhen Hai Wang. "Image De-Noising Using Deep Learning." Applied Mechanics and Materials 641-642 (September 2014): 1287–90. http://dx.doi.org/10.4028/www.scientific.net/amm.641-642.1287.

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Deep neural network as a part of deep learning algorithm is a state-of-the-art approach to find higher level representations of input data which has been introduced to many practical and challenging learning problems successfully. The primary goal of deep learning is to use large data to help solving a given task on machine learning. We propose an methodology for image de-noising project defined by this model and conduct training a large image database to get the experimental output. The result shows the robustness and efficient our our algorithm.

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Benladgham, Rafika, Fethallah Hadjila, and Adam Belloum. "Efficient Privacy-Utility Optimization for Differentially Private Deep Learning." International journal of electrical and computer engineering systems 16, no. 5 (2025): 377–95. https://doi.org/10.32985/ijeces.16.5.3.

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The optimization of differentially private deep learning models in medical data analysis using efficient hyper-parameter tuning is still a challenging task. In this context, we address the fundamental issue of balancing privacy guarantees with model utility by simultaneously optimizing model parameters and privacy parameters across two primary medical datasets, with additional validation on PathMNIST. Our framework encompasses both tabular data (Wisconsin Breast Cancer dataset) and medical imaging (BreastMNIST and PathMNIST), implementing four distinct optimization approaches: Grid Search, Ran

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Neelima, Mrs P. "Human Activity Recognition Using Deep Learning." International Journal for Research in Applied Science and Engineering Technology 13, no. 4 (2025): 3605–10. https://doi.org/10.22214/ijraset.2025.69027.

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Abstract: Human Activity Recognition (HAR) focuses on the detection and classification of human movements using data collected from various sources, including video and wearable sensors. This paper presents a deep learning-based approach for identifying daily activities such as walking, running, sitting, and standing by utilizing accelerometer and gyroscope sensor data. HAR plays a vital role in domains like healthcare, smart environments, and fitness tracking. The study employs machine learning (ML) and deep learning (DL) models to address the challenges of data variability and real-time proc

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Lubab, H. Albak, Rafi Omar Al-Nima Raid, and Hamid Salih Arwa. "Palm print verification based deep learning." TELKOMNIKA (Telecommunication, Computing, Electronics and Control) 19, no. 3 (2021): 851–57. https://doi.org/10.12928/telkomnika.v19i3.16573.

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In this paper, we consider a palm print characteristic which has taken wide attentions in recent studies. We focused on palm print verification problem by designing a deep network called a palm convolutional neural network (PCNN). This network is adapted to deal with two-dimensional palm print images. It is carefully designed and implemented for palm print data. Palm prints from the Hong Kong Polytechnic University Contact-free (PolyUC) 3D/2D hand images dataset are applied and evaluated. The results have reached the accuracy of 97.67%, this performance is superior and it shows that our propos

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Deeksha, Nargotra, and Vinod Sharma Prof. "Grape Leaf Disease Detection Using Deep Learning." Journal of Scientific Research and Technology (JSRT) 1, no. 5 (2023): 128–39. https://doi.org/10.5281/zenodo.8285236.

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An AI function called as deep learning (sometimes deep structured learning or hierarchical learning) attempts to mimic the brain's pattern recognition and data analysis capabilities. Deep learning is a branch of AI's machine learning that makes use of unstructured or unlabeled data to train its networks. Using deep learning to identify diseases in grape leaves has several benefits. First, it facilitates accurate and timely illness diagnosis, which in turn paves the way for efficient and timely disease management. Second, deep learning models are capable of processing massive volumes of

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Pant, Sakshi. "Deep Learning for Personalized Healthcare Recommendations." International Journal for Research in Applied Science and Engineering Technology 12, no. 11 (2024): 470–75. http://dx.doi.org/10.22214/ijraset.2024.65093.

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Personalized healthcare refers to an evolving paradigm of providing appropriate medical treatments based on the particularities of the individual patient, where evidence-based management is enhanced with the use of technologies. Deep learning (DL) is placed within the umbrella of efficient systems known as artificial intelligence (AI), it assists in performing data processing with more accuracy, and making suggestions based on the unique health information of the health record e.g. EHRs, images and genetic data among others. This article gives an overview of deep learning techniques in develop

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Blank, Andreas, Lukas Baier, Oguz Kedilioglu, Xuebei Zhu, Maximilian Metzner, and Jörg Franke. "Effiziente KI-Adaption durch synthetische Daten/Efficient AI Adaption using Synthetic Data." wt Werkstattstechnik online 111, no. 10 (2021): 759–62. http://dx.doi.org/10.37544/1436-4980-2021-10-105.

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Die Produktion ist geprägt durch einen Antagonismus aus Flexibilität und Produktivität. Objektmanipulation gestützt durch Deep-Learning-basierte, autonome Roboterfähigkeiten bietet Potenzial, bestehende Herausforderungen zu lösen. Der Aufwand zur Erzeugung zweckmäßiger Daten ist allerdings hoch. Im Beitrag vorgestellt und bewertet wird eine Methode zur zeiteffizienten Datengenerierung für die Objekterkennung mittels synthetischer Daten. &nbsp; Production is characterized by an antagonism between flexibility and productivity. Deep Learning-based autonomous robot skills for object manipulati

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Bai, Cong, Zhonghao Lin, Jinglin Zhang, and Shengyong Chen. "Dust-Mamba: An Efficient Dust Storm Detection Network with Multiple Data Sources." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 27 (2025): 27813–21. https://doi.org/10.1609/aaai.v39i27.34997.

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Accurate detection of dust storms is challenging due to complex meteorological interactions. With the development of deep learning, deep neural networks have been increasingly applied to dust storm detection, offering better learning and generalization capabilities compared to traditional physical modeling. However, existing methods face some limitations, leading to performance bottlenecks in dust storm detection. From the task perspective, existing research focuses on occurrence detection while neglecting intensity detection. From the data perspective, existing research fails to explore the u

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Shin, Tae-Ho, and Soo-Hyung Kim. "Utility Analysis about Log Data Anomaly Detection Based on Federated Learning." Applied Sciences 13, no. 7 (2023): 4495. http://dx.doi.org/10.3390/app13074495.

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Logs that record system information are managed in anomaly detection, and more efficient anomaly detection methods have been proposed due to their increase in complexity and scale. Accordingly, deep learning models that automatically detect system anomalies through log data learning have been proposed. However, in existing log anomaly detection models, user logs are collected from the central server system, exposing the data collection process to the risk of leaking sensitive information. A distributed learning method, federated learning, is a trend proposed for artificial intelligence learnin

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Taubert, Oskar, Fabrice von der Lehr, Alina Bazarova, et al. "RNA contact prediction by data efficient deep learning." Communications Biology 6, no. 1 (2023). http://dx.doi.org/10.1038/s42003-023-05244-9.

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AbstractOn the path to full understanding of the structure-function relationship or even design of RNA, structure prediction would offer an intriguing complement to experimental efforts. Any deep learning on RNA structure, however, is hampered by the sparsity of labeled training data. Utilizing the limited data available, we here focus on predicting spatial adjacencies ("contact maps”) as a proxy for 3D structure. Our model, BARNACLE, combines the utilization of unlabeled data through self-supervised pre-training and efficient use of the sparse labeled data through an XGBoost classifier. BARNA

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Herzog, Vencia D., and Stefan Suwelack. "Data-Efficient Machine Learning on 3D Engineering Data." Journal of Mechanical Design, October 14, 2021, 1–14. http://dx.doi.org/10.1115/1.4052753.

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Abstract Decisions in engineering design are closely tied to the 3D shape of the product. Limited availability of 3D shape data and expensive annotation present key challenges for using Artificial Intelligence in product design and development. In this work we explore transfer learning strategies to improve the data-efficiency of geometric reasoning models based on deep neural networks as used for tasks such as shape retrieval and design synthesis. We address the utilization of problem- related and un-annotated 3D data to compensate for small data volumes. Our experiments show promising result

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Sangha, Veer, Akshay Khunte, Gregory Holste, et al. "Biometric contrastive learning for data-efficient deep learning from electrocardiographic images." Journal of the American Medical Informatics Association, January 24, 2024. http://dx.doi.org/10.1093/jamia/ocae002.

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Abstract Objective Artificial intelligence (AI) detects heart disease from images of electrocardiograms (ECGs). However, traditional supervised learning is limited by the need for large amounts of labeled data. We report the development of Biometric Contrastive Learning (BCL), a self-supervised pretraining approach for label-efficient deep learning on ECG images. Materials and Methods Using pairs of ECGs from 78 288 individuals from Yale (2000-2015), we trained a convolutional neural network to identify temporally separated ECG pairs that varied in layouts from the same patient. We fine-tuned

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Liu, Qi, Yanjie Li, Yuecheng Liu, Ke Lin, Jianqi Gao, and Yunjiang Lou. "Data Efficient Deep Reinforcement Learning With Action-Ranked Temporal Difference Learning." IEEE Transactions on Emerging Topics in Computational Intelligence, 2024, 1–13. http://dx.doi.org/10.1109/tetci.2024.3369641.

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