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3rd International Conference on Electronics and Renewable Systems, ICEARS 2025

作者： Maniraj, S.P. Monisha, J. Amutha, R. Kavitha, R. Shriidhar, P.J. Srinivasan, C. Srm Institute of Science and Technology School of Computing Department of Data Science and Business Systems Kattankulathur Tamilnadu Chennai India Tamil Nadu Chennai India Ultra College of Engineering and Technology Department of Electrical and Electronics Engineering Tamil Nadu Madurai India Chennai Institute of Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Vinayaka Missions Kirupananda Variyar Engineering College Department of Computer Science and Engineering Tamil Nadu Salem India Saveetha Universityi Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798331509675

With the increasing water shortage and climatic uncertainty, creative strategies for effective water management in agriculture are necessary. This research investigates the integration of Artificial Intelligence (AI), particularly Artificial Neural Networks (ANN), with solar-powered Internet of Things (IoT) networks to enhance water utilization in agricultural activities. The proposed system facilitates accurate irrigation scheduling using real-time data from soil moisture sensors, meteorological stations, and crop health monitoring devices. The ANN model analyses this data to predict ideal irrigation schedules and volumes, minimizing water waste while improving crop yields. The solar-powered IoT framework provides sustainability and reduces dependence on traditional energy sources. Field trials indicate that the AI-enhanced system may promote water saving by as much as 30% relative to traditional irrigation techniques. Moreover, using AI enables adaptive learning, enabling the system to enhance its irrigation tactics using historical and real-time data. This novel method conserves water resources and fosters sustainable agriculture practices, coinciding with global initiatives to address water shortages and assure food security. The findings underscore the potential of technology-based solutions to revolutionize agricultural water management for a sustainable future. © 2025 IEEE.

关键词： Smart agriculture

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On Statistical Rates and Provably Efficient Criteria of Latent Diffusion Transformers (DiTs) 38

On Statistical Rates and Provably Efficient Criteria of Late...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Hu, Jerry Yao-Chieh Wu, Weimin Li, Zhuoru Pi, Sophia Song, Zhao Liu, Han Center for Foundation Models and Generative AI Northwestern University EvanstonIL60208 United States Department of Computer Science Northwestern University EvanstonIL60208 United States Department of Statistics and Data Science Northwestern University EvanstonIL60208 United States Simons Institute for the Theory of Computing UC Berkeley BerkeleyCA94720 United States

We investigate the statistical and computational limits of latent Diffusion Transformers (DiTs) under the low-dimensional linear latent space assumption. Statistically, we study the universal approximation and sample complexity of the DiTs score function, as well as the distribution recovery property of the initial data. Specifically, under mild data assumptions, we derive an approximation error bound for the score network of latent DiTs, which is sub-linear in the latent space dimension. Additionally, we derive the corresponding sample complexity bound and show that the data distribution generated from the estimated score function converges toward a proximate area of the original one. Computationally, we characterize the hardness of both forward inference and backward computation of latent DiTs, assuming the Strong Exponential Time Hypothesis (SETH). For forward inference, we identify efficient criteria for all possible latent DiTs inference algorithms and showcase our theory by pushing the efficiency toward almost-linear time inference. For backward computation, we leverage the low-rank structure within the gradient computation of DiTs training for possible algorithmic speedup. Specifically, we show that such speedup achieves almost-linear time latent DiTs training by casting the DiTs gradient as a series of chained low-rank approximations with bounded error. Under the low-dimensional assumption, we show that the statistical rates and the computational efficiency are all dominated by the dimension of the subspace, suggesting that latent DiTs have the potential to bypass the challenges associated with the high dimensionality of initial data. © 2024 Neural information processing systems foundation. All rights reserved.

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Generative Deduplication For Socia Media data Selection

arXiv

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arXiv 2024年

作者： Li, Xianming Li, Jing Department of Computing Hong Kong Research Centre on Data Science & Artificial Intelligence The Hong Kong Polytechnic University Hong Kong

Social media data exhibits severe redundancy caused by its noisy nature. It leads to increased training time and model bias in its processing. To address this issue, we propose a novel Generative Deduplication framework for social media data selection by removing semantically duplicate data. While related work involves data selection in task-specific training, our model acts as an efficient pre-processing method to universally enhance social media NLP pipelines. Specifically, we train a generative model via self-supervised learning to predict a keyword to capture the semantics of noisy social media text for deduplication. Meanwhile, time-dimensional Gaussian noise is added to improve training complexity and avoid learning trivial features. Extensive experiments suggest that our model can better reduce training samples while improving performance than baselines. The results show our model’s potential to broadly advance social media language understanding in effectiveness and efficiency. © 2024, CC BY.

关键词： Self-supervised learning

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AGENT-ORIENTED JOINT DECISION SUPPORT FOR data OWNERS IN AUCTION-BASED FEDERATED LEARNING

arXiv

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arXiv 2024年

作者： Tang, Xiaoli Yu, Han Li, Xiaoxiao College of Computing and Data Science Nanyang Technological University Singapore Department of Electrical and Computer Engineering The University of British Columbia Canada

Auction-based Federated Learning (AFL) has attracted extensive research interest due to its ability to motivate data owners (DOs) to join FL through economic means. While many existing AFL methods focus on providing decision support to model tusers (MUs) and the AFL auctioneer, decision support for data owners remains open. To bridge this gap, we propose a first-of-its-kind agent-oriented joint Pricing, Acceptance and Sub-delegation decision support approach for data owners in AFL (PAS-AFL). By considering a DO’s current reputation, pending FL tasks, willingness to train FL models, and its trust relationships with other DOs, it provides a systematic approach for a DO to make joint decisions on AFL bid acceptance, task sub-delegation and pricing based on Lyapunov optimization to maximize its utility. It is the first to enable each DO to take on multiple FL tasks simultaneously to earn higher income for DOs and enhance the throughput of FL tasks in the AFL ecosystem. Extensive experiments based on six benchmarking datasets demonstrate significant advantages of PAS-AFL compared to six alternative strategies, beating the best baseline by 28.77% and 2.64% on average in terms of utility and test accuracy of the resulting FL models, respectively. Copyright © 2024, The Authors. All rights reserved.

关键词： Costs

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Leveraging anatomical constraints with uncertainty for pneumothorax segmentation

Health Care Science

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Health Care science 2024年第6期3卷 456-474页

作者： Han Yuan Chuan Hong Nguyen Tuan Anh Tran Xinxing Xu Nan Liu Centre for Quantitative Medicine Duke-NUS Medical SchoolSingapore Department of Biostatistics and Bioinformatics Duke UniversityDurhamNorth CarolinaUSA Department of Diagnostic Radiology Singapore General HospitalSingapore Institute of High Performance Computing Agency for ScienceTechnology and ResearchSingapore Programme in Health Services and Systems Research Duke-NUS Medical SchoolSingapore Institute of Data Science National University of SingaporeSingapore

Background:Pneumothorax is a medical emergency caused by the abnormal accumulation of air in the pleural space—the potential space between the lungs and chest *** 2D chest radiographs,pneumothorax occurs within the thoracic cavity and outside of the mediastinum,and we refer to this area as“lung+space.”While deep learning(DL)has increasingly been utilized to segment pneumothorax lesions in chest radiographs,many existing DL models employ an end-to-end *** models directly map chest radiographs to clinician-annotated lesion areas,often neglecting the vital domain knowl-edge that pneumothorax is inherently ***:We propose a novel approach that incorporates the lung+space as a constraint during DL model training for pneumothorax segmentation on 2D chest *** circumvent the need for additional annotations and to prevent potential label leakage on the target task,our method utilizes external datasets and an auxiliary task of lung *** approach generates a specific constraint of lung+space for each chest ***,we have incorporated a discriminator to eliminate unreliable constraints caused by the domain shift between the auxiliary and target ***:Our results demonstrated considerable improvements,with average performance gains of 4.6%,3.6%,and 3.3%regarding intersection over union,dice similarity coefficient,and Hausdorff *** results were con-sistent across six baseline models built on three architectures(U-Net,LinkNet,or PSPNet)and two backbones(VGG-11 or MobileOne-S0).We further con-ducted an ablation study to evaluate the contribution of each component in the proposed method and undertook several robustness studies on hyper-parameter selection to validate the stability of our ***:The integration of domain knowledge in DL models for medical applications has often been *** research underscores the significance of incorporating medical domain kno

关键词： constrained optimization deep transfer learning diagnostic radiology pneumothorax detection semantic segmentation

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An Automatic Sleep Arousal Detection Method by Enhancing U-Net with Spatial-channel Attention

An Automatic Sleep Arousal Detection Method by Enhancing U-N...

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2022 IEEE International Conference on Big data, Big data 2022

作者： Su, Yen-Ning Kuo, Chih-En National Chung Hsing University Institute of Data Science and Information Computing Taichung Taiwan National Chung Hsing University Department of Applied Mathematics Taichung Taiwan

ISBN: (纸本)9781665480451

Sleep apnea is the most prevalent sleep disorder. In severe cases, it can even lead to sudden death. To diagnose sleep apnea, it is critical to measure the number of sleep arousal times per hour. Current clinical standard procedures require patients to visit a sleep center or hospital for polysomnography (PSG), in which the patient's physiological signals are recorded during the night, and a sleep technician then interprets these signals manually to determine the sleep-wake pattern. With the rising number of sleep disorders, sleep technologists cannot afford such a high workload, which makes the development of an automated sleep arousal detection system critical to reducing medical costs. When PSG is recorded, a large number of wires will also interfere with the patient and cause measurement distortion. In this study, a spatial-channel attention (SCA) U-Net for automatic sleep arousal detection was proposed, which was trained and tested on PhysioNet 2018 sleep PSG dataset. Considering clinical applicability and reducing patient disturbance when recording physiological signals, only five physiological signal channels were required for our proposed model. It is much lower than other methods in the literature. The A AUPRC of our proposed model was and 39%. The results showed the proposed method is highly accurate and requires fewer channel signals, that also demonstrated the clinical applicability and robustness of the proposed method. © 2022 IEEE.

关键词： Convolutional neural networks

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Enhancing Fruit and Vegetable Detection in Unconstrained Environment with a Novel dataset

arXiv

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arXiv 2024年

作者： Khanna, Sandeep Chattopadhyay, Chiranjoy Kundu, Suman Department of Computer Science and Engineering Indian Institute of Technology Jodhpur India School of Computing and Data Sciences FLAME University India

Automating the detection of fruits and vegetables using computer vision is essential for modernizing agriculture, improving efficiency, ensuring food quality, and contributing to technologically advanced and sustainable farming practices. This paper presents an end-to-end pipeline for detecting and localizing fruits and vegetables in real-world scenarios. To achieve this, we have curated a dataset named FRUVEG67 that includes images of 67 classes of fruits and vegetables captured in unconstrained scenarios, with only a few manually annotated samples per class. We have developed a semi-supervised data annotation algorithm (SSDA) that generates bounding boxes for objects to label the remaining non-annotated images. For detection, we introduce the Fruit and Vegetable Detection Network (FVDNet), an ensemble version of YOLOv7 featuring three distinct grid configurations. We employ an averaging approach for bounding-box prediction and a voting mechanism for class prediction. We have integrated Jensen-Shannon divergence (JSD) in conjunction with focal loss to better detect smaller objects. Our experimental results highlight the superiority of FVDNet compared to previous versions of YOLO, showcasing remarkable improvements in detection and localization performance. We achieved an impressive mean average precision (mAP) score of 0.78 across all classes. Furthermore, we evaluated the efficacy of FVDNet using open-category refrigerator images, where it demonstrates promising results. Copyright © 2024, The Authors. All rights reserved.

关键词： Fruits

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Agent-Oriented Joint Decision Support for data Owners in Auction-Based Federated Learning

Agent-Oriented Joint Decision Support for Data Owners in Auc...

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Xiaoli Tang Han Yu Xiaoxiao Li College of Computing and Data Science Nanyang Technological University Singapore Department of Electrical and Computer Engineering The University of British Columbia Canada

ISBN: (数字)9798350390155

ISBN: (纸本)9798350390162

关键词： Training Economics Systematics Federated learning Ecosystems Buildings Pricing

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Freeze the Backbones: a Parameter-Efficient Contrastive Approach to Robust Medical Vision-Language Pre-Training

Freeze the Backbones: a Parameter-Efficient Contrastive Appr...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Jiuming Qin Che Liu Sibo Cheng Yike Guo Rossella Arcucci Department of Computing Imperial College London Data Science Institute Imperial College London Department of Earth Science and Engineering Imperial College London Department of Computer Science and Engineering The Hong Kong University of Science and Technology

Modern healthcare often utilises radiographic images alongside textual reports for diagnostics, encouraging the use of Vision-Language Self-Supervised Learning (VL-SSL) with large pre-trained models to learn versatile medical vision representations. However, most existing VL-SSL frameworks are trained end-to-end, which is computation-heavy and can lose vital prior information embedded in pre-trained encoders. To address both issues, we introduce the backbone-agnostic Adaptor framework, which preserves medical knowledge in pre-trained image and text encoders by keeping them frozen, and employs a lightweight Adaptor module for cross-modal learning. Experiments on medical image classification and segmentation tasks across three datasets reveal that our framework delivers competitive performance while cutting trainable parameters by over 90% compared to current pre-training approaches. Notably, when fine-tuned with just 1% of data, Adaptor outperforms several Transformer-based methods trained on full datasets in medical image segmentation.

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Paddy Plant Disease Detection using Hybrid Deep Learning Algorithms 8

Paddy Plant Disease Detection using Hybrid Deep Learning Alg...

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8th International Conference on Electronics, Communication and Aerospace Technology, ICECA 2024

作者： Suguna, R. Deepa, S. Kanimozhi, T. Chandru, M. Arun, G. Harshini, M. Bannari Amman Institute of Technology Department of Computer Science and Engineering Sathyamangalam Tamilnadu Erode638401 India Kongu Engineering College Perundurai Department of Computer Technology - Pg Tamilnadu Erode638060 India Sri Eshwar College of Engineering Department of Artificial Intelligence and Data Science Coimbatore641202 India College Department of Computer Science Erode India Vel Tech Rangarajan Dr Sagunthala R&d Institute of Science and Technology School of Computing Department of Computer Science and Engineering Chennai India Kongu Engineering College Tamilnadu Perundurai Erode638060 India

ISBN: (纸本)9798350367904

Agriculture is the backbone of India's economy, supporting a large portion of the population and establishing the country as a key global player in food production. India ranks as the second-largest producer of both paddy and wheat. However, agricultural productivity is highly susceptible to various plant diseases, which can significantly reduce crop yields and quality. Paddy, one of the most important staple crops, is particularly vulnerable to diseases caused mainly by viral and bacterial related diseases. The climate conditions are also play a vital role in paddy plant diseases. This research work proposes a deep learning-based approach for early detection and classification of paddy diseases. The proposed research devises a hybrid model that combines the VGG19 and ResNet50 algorithms, the system analyses images of both healthy and disease-affected paddy leaves, sourced from the Kaggle repository. This research focuses on predicting four major paddy diseases that can cause yield losses of up to 80%, and in severe cases, can destroy an entire crop: (i) Paddy Blast, (ii) Bacterial Leaf Blight, (iii) Sheath Blight, and (iv) Ufra Disease. The proposed model achieves a high accuracy rate of 98% in predicting paddy diseases and offers a rapid classification process, showcasing the potential of advanced image processing and deep learning techniques to mitigate the impact of paddy plant diseases on agricultural productivity. © 2024 IEEE.

关键词： Crops

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