检索结果-内蒙古大学图书馆

Design and Development An Low Cost Intelligent Irrigation System in Bamboo Farm 10

学校读者我要写书评

暂无评论

Design and Development An Low Cost Intelligent Irrigation Sy...

10th IEEE Global Conference on Consumer Electronics, GCCE 2021

作者： Yen, Ping-Yi Fanjiang, Yong-Yi Hung, Chi-Huang Jun-Bin, Lai Ku Pao Home Economics Commercial High School Graduate Institute of Applied Science And Engineering Fu Jen Catholic University Department of Data Processing New Taipei City Taiwan Fu Jen Catholic University Graduate Institute of Applied Science And Engineering Dept. of Computer Science And Information Engineering New Taipei City Taiwan Fu Jen Catholic University Lee-Ming Institute of Technology Graduate Institute of Applied Science And Engineering Dept. of Information Technology New Taipei City Taiwan Nation University of Business Department of Information Management Taipei City Taiwan

ISBN: (纸本)9781665436762

Smart agriculture has not been an unreachable technology in recent years. From the lighting control during cultivation, temperature and humidity control and other technologies have become quite easy. However, most of these technologies be used in greenhouses, and they are not as simple as expected in an open environment. In this research, a smart irrigation control performed in a bamboo farm. The water content of the soil understood through a sensing module and transmitted back to the central controller. Farmers do not necessarily have to open the water tank to discharge water as before, and then close the water tank based on experience. In order to achieve a simple and intelligent agricultural irrigation system. © 2021 IEEE.

关键词： Water tanks

Neural Annotation Refinement: Development of a New 3D dataset for Adrenal Gland Analysis

学校读者我要写书评

暂无评论

arXiv 2022年

作者： Yang, Jiancheng Shi, Rui Wickramasinghe, Udaranga Zhu, Qikui Ni, Bingbing Fua, Pascal Shanghai Jiao Tong University Shanghai China EPFL Lausanne Switzerland Dept. of Computer and Data Science Case Western Reserve University OH United States Dept. of Biomedical Engineering Case Western Reserve University OH United States

The human annotations are imperfect, especially when produced by junior practitioners. Multi-expert consensus is usually regarded as golden standard, while this annotation protocol is too expensive to implement in many real-world projects. In this study, we propose a method to refine human annotation, named Neural Annotation Refinement (NeAR). It is based on a learnable implicit function, which decodes a latent vector into represented shape. By integrating the appearance as an input of implicit functions, the appearance-aware NeAR fixes the annotation artefacts. Our method is demonstrated on the application of adrenal gland analysis. We first show that the NeAR can repair distorted golden standards on a public adrenal gland segmentation dataset. Besides, we develop a new Adrenal gLand ANalysis (ALAN) dataset with the proposed NeAR, where each case consists of a 3D shape of adrenal gland and its diagnosis label (normal vs. abnormal) assigned by experts. We show that models trained on the shapes repaired by the NeAR can diagnose adrenal glands better than the original ones. The ALAN dataset will be open-source, with 1,584 shapes for adrenal gland diagnosis, which serves as a new benchmark for medical shape analysis. Code and dataset are available at https://***/M3DV/NeAR. © 2022, CC BY.

关键词： Diagnosis

Deepfake Detection in Media Files - Audios, Images and Videos

学校读者我要写书评

暂无评论

Deepfake Detection in Media Files - Audios, Images and Video...

2020 IEEE Recent Advances in Intelligent Computational Systems, RAICS 2020

作者： Nasar, Bismi Fathima Sajini, T. Lason, Elizabeth Rose ER DCI Institute of Technology Dept. of Computer Science and Engineering ThiruvanthapuramKerala India Knowledge Resource Center CDAC ThiruvanthapuramKerala India

ISBN: (纸本)9781728190525

Recent advancement in deep learning has applied to solve various complex problems ranging from big data analytic to computer vision and human-level control. One among them is the deepfake technology which becomes a real threat to privacy, democracy, and national security. Deepfake is hyper-realistic digitally manipulated videos to depict people saying and doing things that never actually happened. This technology has been used in many fields in film industries for recreating videos without re-shooting, awareness video generation such as creating voices of those who have lost theirs or updating episodes of movies without re-shooting them at very low cost. This technology has many harmful usages in social media, pornographic sites, etc. to deface peoples which largely dominate the positive side of this application of deep learning. Also, the creation and spreading of these videos are increasing rapidly along all fields of media files. Therefore, it is very much important to develop efficient tools that can automatically detect the deepfake in these videos and thus reduce the public harm caused by such videos. In the early stages of deepfake detection, traditional technologies like signal processing, image processing, lip-syncing, etc were used but this provides very little accuracy when combined with the recent technologies of deep learning. So, here a system is proposed that can automatically detect the deepfake in media files such as images, videos, and audios. This uses an image processing approaches combined with deep learning which detects the inconsistency that exists in fake media. © 2020 IEEE.

关键词： National security

A Blockchain-Based data-Sharing Framework for Cloud Based Internet of Things Systems with Efficient Smart Contracts

学校读者我要写书评

暂无评论

A Blockchain-Based Data-Sharing Framework for Cloud Based In...

IEEE International Conference on Communications Workshops, ICC

作者： Kadiyala Ramana R.Madana Mohana C. Kishor Kumar Reddy Gautam Srivastava Thippa Reddy Gadekallu Department of Artificial Intelligence & Data Science Chaitanya Bharathi Institute of Technology Hyderabad India Department of CSE Stanley College of Engineering and Technology for Women Hyderabad India Dept. of Math and Computer Science Brandon University Brandon MB Canada Vellore Institute of Technology Vellore India Department of Electrical and Computer Engineering Lebanese American University Byblos Lebanon

As the Internet of Things (IoT) has advanced, data sharing has become a crucial function of cloud computing. However, data security remains a significant challenge in this field. This research proposes a blockchain-based data-sharing system that prioritizes data security and efficiency. The system includes efficient smart contracts and security gateways that record data in the cloud using blockchain. If suspicious behaviour is detected, the blockchain is checked by the centralized cloud, and the responsible party for any malicious gateway behaviour is held accountable. Authentication and data exchange algorithms are used to ensure data security. Additionally, to reduce the burden on end-users, smart contracts in blockchain use highly complex partial decryption algorithms. To satisfy data restriction safety criteria, blockchain achieves traceability of historical actions through open and transparent supervision. Experimental findings demonstrate that the proposed technique is effective in ensuring the safety and efficiency of information exchange between various clients.

关键词：

ChatGPT-guided Semantics for Zero-shot Learning

学校读者我要写书评

暂无评论

arXiv 2023年

作者： Shubho, Fahimul Hoque Chowdhury, Townim Faisal Cheraghian, Ali Saberi, Morteza Mohammed, Nabeel Rahman, Shafin Dept. of Electrical and Computer Engineering North South University Bangladesh Australian Institute for Machine Learning University of Adelaide Australia Data61 Commonwealth Scientific and Industrial Research Organisation Australia School of Computer Science and DSI University of Technology Sydney Australia

Zero-shot learning (ZSL) aims to classify objects that are not observed or seen during training. It relies on class semantic description to transfer knowledge from the seen classes to the unseen classes. Existing methods of obtaining class semantics include manual attributes or automatic word vectors from language models (like word2vec). We know attribute annotation is costly, whereas automatic word vectors are relatively noisy. To address this problem, we explore how ChatGPT, a large language model, can enhance class semantics for ZSL tasks. ChatGPT can be a helpful source to obtain text descriptions for each class containing related attributes and semantics. We use the word2vec model to get a word vector using the texts from ChatGPT. Then, we enrich word vectors by combining the word embeddings from class names and descriptions generated by ChatGPT. More specifically, we leverage ChatGPT to provide extra supervision for the class description, eventually benefiting ZSL models. We evaluate our approach on various 2D image (CUB and AwA) and 3D point cloud (ModelNet10, ModelNet40, and ScanObjectNN) datasets and show that it improves ZSL performance. Our work contributes to the ZSL literature by applying ChatGPT for class semantics enhancement and proposing a novel word vector fusion method. © 2023, CC BY.

关键词： Zero-shot learning

ChatGPT-guided Semantics for Zero-shot Learning

学校读者我要写书评

暂无评论

ChatGPT-guided Semantics for Zero-shot Learning

Proceedings of the Digital Image Computing: Technqiues and Applications (DICTA)

作者： Fahimul Hoque Shubho Townim Faisal Chowdhury Ali Cheraghian Morteza Saberi Nabeel Mohammed Shafin Rahman Dept. of Electrical and Computer Engineering North South University Bangladesh Australian Institute for Machine Learning University of Adelaide Australia Data61 Commonwealth Scientific and Industrial Research Organisation Australia School of Computer Science and DSI University of Technology Sydney Australia

关键词：

Multimodal signals subject authentication system 18

学校读者我要写书评

暂无评论

Multimodal signals subject authentication system

18th International Conference on Electrical engineering/Electronics, Computer, Telecommunications and Information Technology, ECTI-CON 2021

作者： Alotaiby, Turky N. Alshebeili, Saleh A. Alotaibi, Gaseb King Abdulaziz City for Science and Technology The National Center for Data Analytics and Artificial Intelligence Riyadh Saudi Arabia King Saud University Dept. of Electrical Engineering Ryiadh Saudi Arabia Ministry of Interior Public Security Riyadh Saudi Arabia

ISBN: (纸本)9780738111278

The ever increasing number of e-services provided to users necessitates the efficient and accurate verification of user identity. In this work, we present a user authentication model based on multimodal signals. More specifically, we investigate the extraction of statistical features using various segment lengths of photoplethysmogram (PPG), electrocardiogram (ECG) and capnogram (CO2) signals and generate a feature vector using different fusion approaches. In addition, adding to the feature vector additional features extracted from the 1st derivative of the corresponding signal is explored. Finally, a classifier is used for identity authentication. The proposed authentication system achieved an average authentication accuracy of 99.76% using a 1 second segment length and a random forest classifier. © 2021 IEEE.

关键词： Electrocardiograms

Multi-modal Representation Learning Enables Accurate Protein Function Prediction in Low-data Setting

学校读者我要写书评

暂无评论

arXiv 2024年

作者： Ünsal, Serbulent Özdemir, Sinem Kasap, Bünyamin Kalaycı, M. Erşan Turhan, Kemal Doğan, Tunca Acar, Aybar C. Department of Biostatistics and Medical Informatics Faculty of Medicine Graduate School of Health Sciences Karadeniz Technical University Trabzon Turkey Graduate School of Informatics Middle East Technical University Ankara Turkey Health Sciences University Trabzon Kanuni Training and Research Hospital Medical Microbiology Laboratory Trabzon Turkey Biological Data Science Lab Dept. of Computer Engineering Department of Computer Engineering Hacettepe University Ankara Turkey Dept. of Bioinformatics Graduate School of Health Sciences Hacettepe University Ankara Turkey

In this study, we propose HOPER (HOlistic ProtEin Representation), a novel multimodal learning framework designed to enhance protein function prediction (PFP) in low-data settings. The challenge of predicting protein functions is compounded by the limited availability of labeled data. Traditional machine learning models already struggle in such cases, and while deep learning models excel with abundant data, they also face difficulties when data is scarce. HOPER addresses this issue by integrating three distinct modalities—protein sequences, biomedical text, and protein-protein interaction (PPI) networks—to create a comprehensive protein representation. The model utilizes autoencoders to generate holistic embeddings, which are then employed for PFP tasks using transfer learning. HOPER outperforms existing methods on a benchmark dataset across all Gene Ontology categories, i.e., molecular function, biological process, and cellular component. Additionally, we demonstrate its practical utility by identifying new immune-escape proteins in lung adenocarcinoma, offering insights into potential therapeutic targets. Our results highlight the effectiveness of multimodal representation learning for overcoming data limitations in biological research, potentially enabling more accurate and scalable protein function prediction. HOPER source code and datasets are available at https://***/kansil/HOPER © 2024, CC BY-NC-ND.

关键词： Adversarial machine learning

Learning Concept-Driven Logical Rules for Interpretable and Generalizable Medical Image Classification

学校读者我要写书评

暂无评论

arXiv 2025年

作者： Gao, Yibo Zhou, Hangqi Gao, Zheyao Wang, Bomin Gao, Shangqi Wang, Sihan Zhuang, Xiahai School of Data Science Fudan University China Dept. of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong Dept. of Oncology University of Cambridge United Kingdom

The pursuit of decision safety in clinical applications highlights the potential of concept-based methods in medical imaging. While these models offer active interpretability, they often suffer from concept leakages, where unintended information within soft concept representations undermines both interpretability and generalizability. Moreover, most concept-based models focus solely on local explanations (instance-level), neglecting the global decision logic (dataset-level). To address these limitations, we propose Concept Rule Learner (CRL), a novel framework to learn Boolean logical rules from binarized visual concepts. CRL employs logical layers to capture concept correlations and extract clinically meaningful rules, thereby providing both local and global interpretability. Experiments on two medical image classification tasks show that CRL achieves competitive performance with existing methods while significantly improving generalizability to out-of-distribution data. The code of our work is available at https://***/obiyoag/crl. © 2025, CC BY.

关键词：