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共 31059 条记录，以下是91-100 订阅

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Early Discovery of Emerging Entities in Persian Twitter with Semantic Similarity 9

Early Discovery of Emerging Entities in Persian Twitter with...

引用

9th International Conference on Web Research, ICWR 2023

作者： Yousefi, Shahin Hooshmand, Mohsen Afsharchi, Mohsen Dept. of Computer Science and Information Technology Zanjan Iran University of Zanjan Dept. of Electrical & Computer Engineering Zanjan Iran

ISBN: (纸本)9798350399691

Discovering emerging entities (EEs) is the problem of finding entities before their establishment. These entities can be critical for individuals, companies, and governments. Many of these entities can be discovered on social media platforms, e.g. Twitter. These identities have been the spot of research in academy and industry in recent years. Similar to any machine learning problem, data availability is one of the major challenges in this problem. This paper proposes EEPT that is an online clustering method able to discover EEs without any need for training on a dataset. Additionally, due to the lack of a proper evaluation metric, this paper uses a new metric to evaluate the results. The results show that EEPT is promising and finds significant entities before their establishment. © 2023 IEEE.

关键词： Semantics

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Effective Machine Learning Model for Disease Recognition and Categorization of Mango Leaves

Effective Machine Learning Model for Disease Recognition and...

引用

2024 International Conference on Computational Intelligence and Computing Applications, ICCICA 2024

作者： Kumar, Sandeep Gupta, Bhupesh Grover, Richa Chhabra, Mohit Computer Science & Engineering Dept. Ambala Mullana India Panipat Institute Of Engineering & Technology Computer Science & Engineering Dept. India

ISBN: (纸本)9798350306446

Maintaining the health and production of mango farms depends on the early detection of illnesses in mango trees. Despite the vital role agriculture plays, it remains an industry receiving limited attention from the machine-learning community. A noteworthy challenge is the lack of standardized and openly accessible agricultural datasets, impeding researchers from fully leveraging the potential of advanced computational prediction tools. The study focuses on employing deep learning and image processing techniques for diagnosing prevalent mango leaf diseases in India. The goal is to enhance both the quality and quantity of agricultural production by implementing effective disease control measures upon identifying plant diseases. The automation of plant disease detection proves particularly beneficial, reducing the monitoring efforts required by large farms. Rapid identification of leaf diseases is crucial, given that leaves serve as a plant's primary source of nutrition. Notably, none of the seven different mango leaf illnesses that have been observed in India have ever been the subject of earlier research. In this study, we provide a lightweight Resnet50 model that can reliably classify healthy mango leaves and the seven discovered illnesses on mango leaves. The performance of our proposed lightweight Resnet-50 model is evaluated against many pre-trained models, such as Exception, Resnet101, and VGG16. The findings reveal that the lightweight Resnet-50 model achieves the highest testing accuracy, reaching an impressive 95%. This underscores the model's effectiveness in automating the identification of mango leaf diseases, offering a promising solution for improving agricultural practices in India. © 2024 IEEE.

关键词： Diseases

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Domain Adaptive Target Detection with Optimal Transportation for Different Satellite SAR Images 15

Domain Adaptive Target Detection with Optimal Transportation...

引用

15th European Conference on Synthetic Aperture Radar, EUSAR 2024

作者： Qin, Jiang Zou, Bin Zhang, Lamei Qiu, Yu Dept. of Information Engineering Harbin Institute of Technology China

ISBN: (纸本)9783800762873

Satellite-borne SAR image target detection is widely used across various fields. However, there are different data distributions among SAR images from different satellite sensors and different imaging conditions. This makes it challenging for deep learning based detectors to achieve performance generalization for different data domains with distributional discrepancies. In this paper, an Optimal Transport based domain Adaptive target Detection (OTAD) method is proposed to improve target detection performance under the data distributional discrepancies in the realistic scenarios. OTAD utilizes geometry-aware clustering assignment to estimate prototypes corresponding to different geometries. Subsequently, unbalanced optimal transport is employed to map target features to source prototypes corresponding to their respective geometries. In this way, OTAD accomplishes feature adaptation between source and target domains, reducing feature distributional discrepancies while preserving intra-domain structures. Two cross-domain target detection tasks are constructed using Gaofen-3 and Sentinel-1 SAR data in the experiments. The cross-domain detection results demonstrate that OTAD effectively reduces feature distributional discrepancies and improves target detection performance for different satellite SAR images. © VDE VERLAG GMBH ∙ Berlin ∙ Offenbach.

关键词： Synthetic aperture radar

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A Lightweight and Fast Method for Water and Airport Segmentation in SAR Images 15

A Lightweight and Fast Method for Water and Airport Segmenta...

引用

15th European Conference on Synthetic Aperture Radar, EUSAR 2024

作者： Qiu, Yu Zou, Bin Zhang, Lamei Dept. of Information Engineering Harbin Institute of Technology China

ISBN: (纸本)9783800762873

In large scene SAR images, the extraction of regions of interest is crucial for improving the accuracy of target detection. Airports and water bodies within these scenes often contain aircraft and ships, which are primary targets for detection. SAR images exhibit similar scattering characteristics for airport runways and water bodies. In light of this, our paper presents a lightweight and fast segmentation method applicable to both airport and water regions(LFSAW). Firstly, we construct a lightweight and efficient concatenate module (LECM) for rapid feature extraction. Subsequently, we create a specialized module for SAR images that extracts edge information (DIEM) from ground truth and utilizes it for supervision while simultaneously enhancing the segmentation performance by extracting detailed information. Finally, we employ a multi-task loss function to jointly supervise the edge detail and segmentation information. Furthermore, to facilitate simultaneous segmentation of airport and water regions, we build a dataset comprising 7931 image slices designed specifically for airport-water body segmentation tasks. © VDE VERLAG GMBH ∙ Berlin ∙ Offenbach.

关键词： Synthetic aperture radar

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5G the Missing Piece in Digital Transformation of the Construction Sector 28

5G the Missing Piece in Digital Transformation of the Constr...

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28th International computer Science and engineering Conference, ICSEC 2024

作者： Chnaidini, Shaik Khan, M. Jebran Ullah, Fasee Khan, Arafat Ahmad Prashar, Deepak Gulf American University Dept. of Civil Engineering Fujairah United Arab Emirates Sarhad University of Science and Information Technology Dept of Computing and Information Technology Peshawar Pakistan Uinversiti Teknology Petronas Dept. of Computer and Information Sciences Seri Iskandar Malaysia Khonkaen University Dept. of Computer Science Khon Kaen Thailand Lovely Professional University Dept. of Computer Science and Engineering Punjab India

ISBN: (纸本)9798350366860

The 5th generation of wireless technology with a continuum leap rushing towards the future in the world of communication has a puzzle that enables the autonomous machine to work in site making a louder voice that shows the era where even if we cannot be fully robotic, we can make the machines do the work by sitting on a faraway control room. Making ways clear to see the work done miles away on construction sites the 5th generation has not only brought the people close through communication but also made them work collegiately with a clearer view front. The paper discusses 5G and the other related technologies that are emerging all over the world and how it is needed specifically in the construction industry. In the present paper, we tried to shed light on the acceptance and implementation of 5G technology in the construction sector. It is seen that the construction sector in India is lagging behind when it comes to implementation and availability of required support and resources for the 5G implementation. © 2024 IEEE.

关键词： Construction industry

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IoT Based Night Patrol Robot Leveraging Voice technology for Superior Surveillance

IoT Based Night Patrol Robot Leveraging Voice Technology for...

引用

2025 International Conference on Multi-Agent Systems for Collaborative Intelligence, ICMSCI 2025

作者： Libina, M. Dhevendhiran, R. Praveenkumar, B. Ifet College of Engineering Dept. of Information Technology Villupuram India

ISBN: (数字)9798331509828

ISBN: (纸本)9798331509828

It designs and implements an IoT-based night patrol robot that has improved surveillance capabilities by combining voice technology. The patrol robot system is remote-controlled, patrols designated areas at night, and provides real-time monitoring through onboard sensors and live video feed. This robot provides an easy interface to human operators that can give voice commands. There is no need for physical interactions in terms of remote control or status updates that can run on the desired location from instructions from the control room. The night patrol human risks lead to reduced awareness, vulnerabilities, physical health, and work-life balance. Motion detection, along with the sensing of environmental conditions, and secure wireless communication ensure real-time alerts for suspicious activities in areas like cities, critical areas, and construction sites. The patrol robot is efficient, scalable, and cost-effective in enhancing traditional security systems. The robot can run in a crucial area that human patrol cannot do, ensuring reliability, flexibility, and adaptability to the environment it roams. It includes technologies like motion detection, location tracking, and voice controls that can reduce human intervention while providing improvements in safety in high-risk environments. The robot can increase the emergency patrol assistant by monitoring live video streaming to control rooms. The results from prototype testing indicate effectiveness in performing patrol duties and in responding to voice-based instructions in varying environmental conditions. © 2025 IEEE.

关键词： Remote control

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Note on 'RAKS: robust authentication and key agreement scheme for satellite infrastructure'

Note on 'RAKS: robust authentication and key agreement schem...

引用

2023 International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2023

作者： Tsai, Chun-Chieh Sun, Chin-Yu Yang, Shih-Hsuan National Taipei University of Technology Dept. of Computer Science and Information Engineering Taiwan

ISBN: (纸本)9798350324174

Low Earth orbit (LEO) satellites play an important role in modern communication systems. Recently, Khan et al. [1] designed a robust authentication and key agreement scheme (RAKS) for LEO satellite communication. They also performed security analysis of RAKS to verify the declared safety. Upon careful examination, however, we discovered several security flaws in RAKS, including insufficient information for user identification, vulnerability to elaborate DoS attacks, and inaccurate estimation of computational costs. In this paper, we highlight these flaws and suggest potential modifications to improve the scheme. © 2023 IEEE.

关键词： Satellites

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Automated Bengali Sign Language Character Classification with Deep Learning Techniques 15

Automated Bengali Sign Language Character Classification wit...

引用

15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Akter, Tahmina Mahmud, Tanjim Barua, Tikle Naher, Sultana Rokeya Hossain, Mohammad Shahadat Andersson, Karl Port City International University Dept. of Computer Science and Engineering Chittagong Bangladesh Rangamati Science and Technology University Dept. of Computer Science and Engineering Rangamati4500 Bangladesh University of Information Technology and Sciences Dept. of Computer Science and Engineering Dhaka Bangladesh University of Chittagong Dept. of Computer Science and Engineering Chittagong4331 Bangladesh Luleå University of Technology Cybersecurity Laboratory Luleå97187 Sweden

ISBN: (纸本)9798350370249

Recognition of Bengali sign language characters is crucial for facilitating communication for the deaf and hard-of-hearing population in Bengali-speaking regions, which encompass approximately 430 million people worldwide. Despite the significant number of individuals requiring this support, research on Bengali sign language character recognition remains underdeveloped. This article presents a novel approach to categorize Bengali sign language characters using the Ishara-Lipi dataset, based on convolutional neural networks (CNNs) and pretrained models. We evaluated our approach using metrics such as accuracy, precision, recall, F1-score, and confusion matrices. Our findings indicate that the CNN model achieved the highest performance with an accuracy of 98%, followed by VGG19 with 94% and ResNet variants achieving around 88%. The proposed model demonstrates robust and efficient classification capabilities, significantly bridging the gap in existing literature. This study holds substantial promise for enhancing assistive technology, thereby improving social inclusion and quality of life for Bengalispeaking deaf and hard-of-hearing individuals. © 2024 IEEE.

关键词： Bengali sign language CNNs Deaf and hard-of-hearing community Image classification

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High Gain, E-Shaped Microstrip Antenna with Two Identical Slits for 5G Applications in the 60 GHz Band 3

High Gain, E-Shaped Microstrip Antenna with Two Identical Sl...

引用

3rd International Conference on Emerging Smart Technologies and Applications, eSmarTA 2023

作者： Ali, Ala'a N. S. Gaid, AbdulGuddoos S. A. Saeed, Mamoon M. Saeed, Rashid A. Hawash, Burkan Taiz University Faculty of Engineering & Information Technology Dept. of Communications & Computer Taiz Yemen Faculty of Engineering Dept. Communications & Electronics Engineering Sana'a Yemen College of Computers & Information Technology Taif University Dept. of Computer Engineering Taif21944 Saudi Arabia Faculty of Information Science & Technology Selangor Malaysia

ISBN: (纸本)9798350305333

This article presents a simple, high-gain, E-shaped antenna suitable for 5G applications in the unlicensed 60 GHz band, characterized by high absorption and path losses. In order to optimize the reflection coefficient performance, enhance the gain, and adjust the resonance frequency of the proposed antenna, two identical slits are incorporated into the front edge of the radiating patch. The proposed structure is built on a Rogers RT/Duroid 5880 dielectric substrate, characterized by a thickness of 0.118 mm, a dielectric constant of 2.2, and a tangent loss of 0.0009. The optimized design with a compact size of 3.8 x 6 x 0.118 mm3 achieves an impedance bandwidth ( © 2023 IEEE.

关键词： Radiation efficiency

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Explainability to Business: Demystify Transformer Models with Attention-based Explanations 2

Explainability to Business: Demystify Transformer Models wit...

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2nd International Conference on Applied Artificial Intelligence and Computing, ICAAIC 2023

作者： Thiruthuvaraj, Rajasekhar Jo, Ashly Ann Raj, Ebin Deni Indian Institute of Information Technology Dept. of Computer Science & Engineering Kottayam India

ISBN: (纸本)9781665456302

Recently, many companies are relying on Natural Language Processing (NLP) techniques to understand the text data generated daily. It has become very critical to deal with this data because finding the sentiments of text and summarizing them will help the company understand the pain points of the customers posting reviews on social media or understand the experience of the customer. These requirements have increasingly demanded many advanced algorithms to deal the text data. The introduction of Transformers led to businesses adopting NLP methods more and more to keep up with their needs. Models like Bidirectional Encoder Representations from Transformers (BERT) and Generative Pre-trained Transformers (GPT), state-of-the-art results were achieved with billions of parameters learned. Although these advancements improved the accuracy and expanded the use of algorithms to a wide range of NLP tasks like language translation, text summarization, and language modeling. Businesses are more interested in the Explainability of the model compared to its accuracy. Explainable Artificial Intelligence (XAI) plays an important role to comprehend the complexities of the model as well as the influence of weights on predictions. In this paper, the complexities of the transformer model are unraveled by presenting a straightforward method for computing explainable predictions. The DistilBERT model is chosen as an example to implement the explainable system due to its lighter nature. Combining the strengths of a Posthoc expla-nation with those of a self-learning neural network, the method makes it simple to scale it to other algorithms to implement. With technologies like python, PyTorch, and Hugging Face, a detailed step-by-step algorithmic computation is demonstrated to explain the predictions from the attention-based explanations. © 2023 IEEE.

关键词： Forecasting

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