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3rd International Conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Mahesh, T.R. Singh, Shweta Srivastav, Vaibhav Kalra, Hitesh Surulivel Rajan, T. Yadav, Gitanjali B. Computer Science and Engineering Faculty of Engineering and Technology Karnataka Bangalore India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Vivekananda Global University Department of Computer Science & Engineering Jaipur India Chitkara University Institute of Engineering and Technology Chitkara University Centre of Interdisciplinary Research in Business and Technology Punjab India Prince Shri Venkateshwara Padmavathy Engineering College Department of Mechanical Engineering Chennai127 India Vishwakarma Institute of Information Technology Department of Artificial Intelligence & Data Science Pune India

ISBN: (纸本)9798350319125

Adaptive Modeling for actual-Time information evaluation with system studying is a critical studies area in the modern-day technology of information-in depth evaluation. Conventional gadget studying strategies are static, meaning that the models do no longer evolve as information scientist interactions occur or because the data changes over time. As a result, models can grow to be obsolete and further downstream machine overall performance will become constrained. Consequently, the want for adaptive modeling algorithms has arisen with the intention to permit dynamic, system-stage getting to know from statistics. Such strategies look to provide affordable predictions for real-time inputs, while also looking forward to enough destiny reaction behavior to make more accurate predictions for destiny, unseen facts. The purpose of this approach is to create a solution which optimally balances accuracy and flexibility for an ever converting environment. One of these system could be carried out for predictive upkeep, diagnostic analysis, cyber security detection or self-sustaining vehicle decision making. © 2023 IEEE.

关键词： Forecasting

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Discovering Innovative Applications for Real-Time data Analysis with Machine Learning 3

Discovering Innovative Applications for Real-Time Data Analy...

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3rd International Conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Jadhav, Prranjali Raja Praveen, K.N. Aggarwal, Prateek Gautam, Sandeep Naval, Preeti Nirmala, D. Vishwakarma Institute of Information Technology Department of Artificial Intelligence & Data Science Pune India Computer Science and Engineering Faculty of Engineering and Technology Karnataka Bangalore India Centre of Interdisciplinary Research in Business and Technology Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Vivekananda Global University Department of Computer Science & Engineering Jaipur India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Prince Shri Venkateshwara Padmavathy Engineering College Department of Electronics and Communication Engineering Chennai127 India

ISBN: (纸本)9798350319125

This paper investigates the capability for discovering modern programs for actual-time information analysis the use of system learning. We will take a look at the technology and strategies used in system getting to know and talk their adaptive potential to interpret and analyze dynamically changing facts inputs. Especially, we can examine existing technological methods to understand the capacity of AI to discover creative solutions to complicated challenges. We will also explore capability techniques to contain gadget gaining knowledge of into present real-time records analysis platforms and architectures. Finally, we are able to speak the programs of machine studying in real-time records evaluation and its potential to allow fast and correct decision making. This paper gives a theoretical framework to apprehend and harness the power of device gaining knowledge of to investigate and interpret real-time facts in modern approaches. © 2023 IEEE.

关键词： Information analysis

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Research on Rice Disease Identification Based on Improved ShuffleNet V2 Network

Research on Rice Disease Identification Based on Improved Sh...

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computer Technology and Information science (ISCTIS), International Symposium on

作者： Zhihuan Chen Shenming Gu Minghui Yu Zhaoquan Huang Key Laboratory of Oceanographic Big Data Mining & Application of Zhejiang Province Zhejiang Ocean University Zhoushan China School of Faculty of Electrical Engineering and Computer Science Ningbo University Ningbo China College of Mathematics and Computer Science Zhejiang Normal University Jinhua China

ISBN: (数字)9798350354560

ISBN: (纸本)9798350354577

How to efficiently and accurately identify rice diseases plays a crucial role in rice planting and production. Deploying large convolutional neural networks in practical applications is challenging due to their complex model parameters, while lightweight networks often compromise accuracy to reduce model size. In this study, we introduce a lightweight and precise network called GM-ShuffleNet for rice disease recognition. Building upon the ShuffleNet V2 network, we integrated the Ghost module and MCA attention mechanism, adjusted the network structure and number of channels, and replaced the ReLU activation function with Mish. Through comparative experiments, the accuracy of GM-ShuffleNet algorithm proposed in this paper reaches 96.20%, which is superior to ShuffleNet V2 and other excellent lightweight networks. In addition, in terms of model complexity comparison, the number of model parameters and FLOPs decreased by 82.54% and 77.44% respectively compared with the original network.

关键词： Information science Accuracy Attention mechanisms Computational modeling Buildings Production Complexity theory Convolutional neural networks Diseases

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Enhancing data Visualization in Real-Time with Machine Learning 3

Enhancing Data Visualization in Real-Time with Machine Learn...

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3rd International Conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Sripriya, T. Kachhoria, Renu Dev, Sudhanshu Mahesh, T.R. Nivesh Acharjya, Kalyan Prince Shri Venkateshwara Padmavathy Engineering College Department of Electronics and Communication Engineering Chennai127 India Vishwakarma Institute of Information Technology Department of Artificial Intelligence & Data Science Pune India Chitkara University Institute of Engineering and Technology Chitkara University Centre of Interdisciplinary Research in Business and Technology Punjab India Computer Science and Engineering Faculty of Engineering and Technology Karnataka Bangalore India Vivekananda Global University Department of Computer Science & Engineering Jaipur India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India

ISBN: (纸本)9798350319125

Statistics visualization is an effective tool for information and communicating insights from massive facts sets. However, giving complicated records in a correct and complete style becomes challenging, mainly when the statistics are dynamic and change rapidly in real time. Device learning may be leveraged to enhance records visualization, making it more particular and comprehensive. This paper discusses the integration of machine learning techniques to enhance data visualization in real-time. The exponential growth of data in recent years has made it challenging for traditional techniques to effectively visualize and interpret data in real-time. Machine learning algorithms offer the ability to analyze and classify data in real-time, providing a more accurate and efficient way to visualize complex datasets. By leveraging machine learning, data visualization can be enhanced by automatically generating visual representations that are tailored to the data and user preferences. This paper presents an overview of the different machine learning techniques that can be applied to data visualization, along with their advantages and limitations. Additionally, a case study is presented to demonstrate the effectiveness of using machine learning for real-time data visualization. Through this study, it is shown that the integration of machine learning can significantly improve the speed, accuracy, and usability of data visualization in real-time scenarios. By using including the strength of machine learning to statistics visualization, such methods can permit quicker and deeper insights into records, taking into consideration higher selection making within the modern-day international wherein facts modifications swiftly. © 2023 IEEE.

关键词： interactive interested machine potential visualization

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Sustainable North-South Africa Collaboration for Disaster and Crisis Management: A Strategic Capacity Development Framework using Open science, Artificial Intelligence and Geoinformatics

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data science Journal 2025年 24卷

作者： Ibrahim, Rania Elsayed Motshegwa, Tshiamo Benouar, Djillali Khodja, Mohamed Ead, Hamed Zhang, Lili Crawley, Francais McHunu, Nokuthula P. Hachigonta, Sepo Hodson, Simon McBride, Vanessa Yohannes, Teketel National Authority for Remote Sensing and Space Sciences Egypt National Research Foundation South Africa Department of Computer Science University of Botswana Botswana Algerian Academy for Science and Technology Algeria Faculty of Science Cairo University Egypt Egyptian Embassy in Morocco Morocco Computer Network Information Center Chinese Academy of Sciences China CODATA International Data Policy Committee Belgium University of KwaZulu Natal South Africa CODATA France International Science Council South Africa

Background: This paper explores strategies to enhance North-South collaboration to foster multi-and trans-disciplinary research in disaster risk reduction (DRR) and crisis management, emphasizing the role of open science, Artificial Intelligence (AI) and geoinformatics in developing a resilient and proactive approach to these challenges, as well as drawing on lessons learned from the COVID-19 pandemic, the paper highlights how collaboration and data sharing can enhance preparedness, response, and recovery in future crises. Methods: The study uses a mixed approach to gather and analyze information. Qualitative methods focus on gathering descriptive data through engaging with experts and a structured review. By investigating literature, a comprehensive strategic framework for minimizing disaster risk, and for crisis management, is developed to provide adequate guidance to policymakers on implementation strategies and the application of how to build the human, institutional, and technological capacity required for effective disaster and crisis management (DCM). The events were used to engage with experts to provide recommendations and opportunities to enhance crisis response, capacity development and policy. Findings: By presenting a comprehensive framework that bridges theoretical knowledge with practical application, our findings indicate that governments must address the challenges of DCM to minimize losses. The practices surrounding disaster recovery can provide opportunities for policymakers to reassess priorities and redirect resources toward building sustainable communities. Originality: There is no strategic framework that provides adequate guidance to policymakers on implementation strategies or the application of how to build the human, institutional and technological capacity required for effective DCM. This article develops a Decision Support Framework for DCM. We outline how open science promotes cross-disciplinary collaboration and the sharing of knowledg

关键词： Risk management

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Landscape of sign language research based on smartphone apps: coherent literature analysis, motivations, open challenges, recommendations and future directions for app assessment

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Universal Access in the Information Society 2024年第2期23卷 687-702页

作者： David, Dianes Alamoodi, A.H. Albahri, O.S. Zaidan, B.B. Zaidan, A.A. Garfan, Salem Ismail, Amelia Ritahani Albahri, A.S. Alsinglawi, Belal Malik, R.Q. Perak Malaysia Future Technology Research Center National Yunlin University of Science and Technology 123 University Road Section 3 Yunlin Douliou64002 Taiwan Faculty of Engineering and IT The British University in Dubai Dubai United Arab Emirates International Islamic University Malaysia Kuala Lumpur Malaysia Baghdad Iraq School of Computer Data and Mathematical Sciences Western Sydney University Sydney Australia Medical Intrumentation Techniques Engineering Department Al-Mustaqbal University College Babylon Iraq Computer Techniques Engineering Department Mazaya University College Thi-Qar Nassiriya Iraq

Numerous nations have prioritised the inclusion of citizens with disabilities, such as hearing loss, in all aspects of social life. Sign language is used by this population, yet they still have trouble communicating with others. Many sign language apps are being created to help bridge the communication gap as a result of technology advances enabled by the widespread use of smartphones. These apps are widely used because they are accessible and inexpensive. The services and capabilities they offer and the quality of their content, however, differ greatly. Evaluation of the quality of the content provided by these applications is necessary if they are to have any kind of real effect. A thorough evaluation like this will inspire developers to work hard on new apps, which will lead to improved software development and experience overall. This research used a systematic literature review (SLR) method, which is recognised in gaining a broad understanding of the study whilst offering additional information for future investigations. SLR was adopted in this research for smartphone-based sign language apps to understand the area and main discussion aspects utilised in the assessment. These studies were reviewed on the basis of related work analysis, main issues, discussions and methodological aspects. Results revealed that the evaluation of sign language mobile apps is scarce. Thus, we proposed a future direction for the quality assessment of these apps. The findings will benefit normal-hearing and hearing-impaired users and open up a new area where researchers and developers could work together on sign language mobile apps. The results will help hearing and non-hearing users and will pave the way for future collaboration between academicians and app developers in the field of sign language technology. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023.

关键词： Smartphones

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HySpecNet-11k: a Large-Scale Hyperspectral dataset for Benchmarking Learning-Based Hyperspectral Image Compression Methods

HySpecNet-11k: a Large-Scale Hyperspectral Dataset for Bench...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Martin Hermann Paul Fuchs Begüm Demir Faculty of Electrical Engineering and Computer Science Technische Universität Berlin Germany BIFOLD - Berlin Institute for the Foundations of Learning and Data Germany

The development of learning-based hyperspectral image compression methods has recently attracted great attention in remote sensing. Such methods require a high number of hyperspectral images to be used during training to optimize all parameters and reach a high compression performance. However, existing hyperspectral datasets are not sufficient to train and evaluate learning-based compression methods, which hinders the research in this field. To address this problem, in this paper we present HySpecNet-11k that is a large-scale hyperspectral benchmark dataset made up of 11,483 nonoverlapping image patches. Each patch is a portion of 128 × 128 pixels with 224 spectral bands and a ground sample distance of 30 m. We exploit HySpecNet-11k to benchmark the current state of the art in learning-based hyperspectral image compression by focussing our attention on various 1D, 2D and 3D convolutional autoencoder architectures. Nevertheless, HySpecNet-11k can be used for any unsupervised learning task in the framework of hyperspectral image analysis. The dataset, our code and the pre-trained weights are publicly available at https://***.

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Transformer-Based Multi-Modal Learning for Multi-Label Remote Sensing Image Classification

Transformer-Based Multi-Modal Learning for Multi-Label Remot...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： David Sebastian Hoffmann Kai Norman Clasen Begüm Demir Faculty of Electrical Engineering and Computer Science Technische Universität Berlin Germany BIFOLD – Berlin Institute for the Foundations of Learning and Data Germany

In this paper, we introduce a novel Synchronized Class Token Fusion (SCT Fusion) architecture in the framework of multi-modal multi-label classification (MLC) of remote sensing (RS) images. The proposed architecture leverages modality-specific attention-based transformer encoders to process varying input modalities, while exchanging information across modalities by synchronizing the special class tokens after each transformer encoder block. The synchronization involves fusing the class tokens with a trainable fusion transformation, resulting in a synchronized class token that contains information from all modalities. As the fusion transformation is trainable, it allows to reach an accurate representation of the shared features among different modalities. Experimental results show the effectiveness of the proposed architecture over single-modality architectures and an early fusion multi-modal architecture when evaluated on a multi-modal MLC dataset. The code of the proposed architecture is publicly available at https://***/rsim/sct-fusion.

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Transformer-based Multi-Modal Learning for Multi Label Remote Sensing Image Classification

arXiv

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arXiv 2023年

作者： Hoffmann, David Sebastian Clasen, Kai Norman Demir, Begüm Faculty of Electrical Engineering and Computer Science Technische Universität Berlin Germany BIFOLD – Berlin Institute for the Foundations of Learning and Data Germany

In this paper, we introduce a novel Synchronized Class Token Fusion (SCT Fusion) architecture in the framework of multimodal multi-label classification (MLC) of remote sensing (RS) images. The proposed architecture leverages modality-specific attention-based transformer encoders to process varying input modalities, while exchanging information across modalities by synchronizing the special class tokens after each transformer encoder block. The synchronization involves fusing the class tokens with a trainable fusion transformation, resulting in a synchronized class token that contains information from all modalities. As the fusion transformation is trainable, it allows to reach an accurate representation of the shared features among different modalities. Experimental results show the effectiveness of the proposed architecture over single-modality architectures and an early fusion multi-modal architecture when evaluated on a multi-modal MLC dataset. The code of the proposed architecture is publicly available at https://***/rsim/sct-fusion. © 2023, CC BY.

关键词： Image classification

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Investigating and Implementing the Efficiency of Image Restoration Techniques in Digital Image Processing 3

Investigating and Implementing the Efficiency of Image Resto...

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3rd International Conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Acharjya, Kalyan Yuvaraj, S. Jadhav, Varsha D. Sidhu, Amritpal Sinha, Deepak Kumar Kaneria, Ojasvee Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Prince Shri Venkateshwara Padmavathy Engineering College Department of Electronics and Communication Engineering Chennai127 India Vishwakarma Institute of Information Technology Department of Artificial Intelligence & Data Science Pune India Centre of Interdisciplinary Research in Business and Technology Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Computer Science and Engineering Faculty of Engineering and Technology Karnataka Bangalore India Vivekananda Global University Department of Computer Science & Engineering Jaipur India

ISBN: (纸本)9798350319125

This look at explores the efficiency of diverse photo recovery strategies, inclusive of noise reduction, histogram change, facet enhancement, polishing, and smoothing, in digital picture processing. An extensive evaluation of the techniques becomes conducted to assess their effectiveness in restoring degraded virtual pictures. Numerous objective metrics, together with Structural Similarity (SSIM) Index, peak sign-to-Noise Ratio (PSNR), and mean Squared blunders (MSE), have been used to quantitatively examine the first-rate of the restored photographs. Effects indicated that noise discount and area enhancement furnished the finest improvements within the snap shots. Histogram changes and sprucing also stepped forward the high-quality of the pictures, at the same time as the smoothing technique ended in a moderate lower in pleasant. This examines offers a detailed insight into the efficiency of various photo healing techniques and can serve as a reference for in addition investigations. © 2023 IEEE.

关键词： Investigations Smoothing Strategies Structural Techniques

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