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A New Dawn for Tomato-spotted wilt virus Detection and Intensity Classification: A CNN and LSTM Ensemble Model 4

A New Dawn for Tomato-spotted wilt virus Detection and Inten...

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4th international conference for Emerging Technology, INCET 2023

作者： Sharma, Rishabh Kukreja, Vinay Vats, Satvik Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Graphic Era Hill University Computer Science and Engineering Dehradun India

ISBN: (纸本)9798350335750

Tomato-spotted wilt virus (TSWV) is a severe plant disease that causes significant economic losses in tomato production worldwide. Early detection and intensity classification of TSWV-infected tomato plants is critical for effective disease management. this study proposes a novel TSWV detection and intensity classification approach based on a convolutional neural network (CNN) and a long short-term memory (LSTM) network ensemble model. A dataset comprising 30,000 images of tomato plants infected with TSWV was gathered and annotated with six intensity levels, ranging from 0 (indicating no symptoms) to 5 (indicating severe symptoms). A framework approach was developed, with aiming to enhancing the model's performance r proposed approach achieved an overall accuracy of 97.37% on the test set, outperforming several state-of-the-art approaches. We also performed a statistical analysis of the inter-intensity level variability of the classification accuracy and found that the accuracy increased with the intensity level. Our results suggest that the proposed approach has the potential to be used in the early detection and intensity classification of TSWV-infected tomato plants, which could aid in the timely application of preventive measures and reduce the economic losses caused by TSWV. © 2023 IEEE.

关键词： Viruses

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Research on steel profiles surface defect detection algorithm based on machine vision 4

Research on steel profiles surface defect detection algorith...

引用

4th international conference on Image Processing and Intelligent Control, IPIC 2024

作者： Li, Shiyi Fu, Zemin Zhao, Zhifan School of Mechanical Engineering Shanghai Institute of Technology Shanghai China

ISBN: (纸本)9781510682313

Steel profiles is widely used in many different fields and is a fundamental raw material in the industrial production process. However, surface defects often occur during the steel production process due to differences in testing environments and production raw materials. therefore, it is crucial to detect surface defects in steel profiles. However, with the progress of society, science and technology, traditional machine vision inspection methods are increasingly difficult to meet the requirements of modern industrial production in terms of accuracy and real-time performance in the actual inspection process. In this context, this thesis provides an in-depth analysis of deep learning-based defect detection algorithms and improves the state-of-the-art YOLOv5 object detection algorithm according to the characteristics of steel profiles surface defects in order to improve the detection speed and accuracy. First, the steel dataset is regrouped using the K-means algorithm based on IOU measured distances to generate different sets of anchor frames. Second, MixUp was integrated into the Mosaic data extension to reduce overfitting and improve model generalization. then, the network structure was improved and an attention module was added to further improve the ability to extract network features. © 2024 SPIE.

关键词： Machine vision

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Research on cross-media dissemination mechanism of generative AI artworks based on ISM model 4

Research on cross-media dissemination mechanism of generativ...

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4th international conference on artificial Intelligence and Computer engineering, ICAICE 2023

作者： Liu, Yanghe Institute of Fine Art Renmin University of China Beijing100080 China

ISBN: (纸本)9798400708831

the rapid development of artificial Intelligence (AI) has facilitated the digital generation and cross-media dissemination of artworks, which has led to an explosion of artistic information and increased aesthetic opportunities. However, this also presents a dilemma for the sustainable development of generative AI artworks in media communication. In this paper, we aim to explore the cross-media communication mechanism of generative AI artworks using the ISM model. Firstly, we conduct a literature review to identify the influencing factors of cross-media communication of generative AI artworks. We then select 10 direct factors by merging similar ones. Secondly, we analyze the interaction between each factor, determine the adjacency matrix and reachability matrix, and construct the explanatory structural model. Finally, we establish the hierarchical relationship between the various factors of cross-media communication of generative AI artworks and their logical architecture. Our findings indicate that the algorithmic recommendation mechanism of the media and the audience's interest in appreciating AI art creations are the most direct reasons, while the diversity of art communication forms, the formal aesthetics of AI artworks, and the social commentary on AI artworks serve as the underlying reasons. the research presented in this paper is of practical significance in addressing the cross-media communication dilemma of generative AI artworks and ultimately contributes to the improvement of the overall aesthetic quality of society. © 2023 ACM.

关键词： Computer programming

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A Heterogeneous Network Model Based Analysis and Detection of RNA Enabling Convolutional Neural Network 4

A Heterogeneous Network Model Based Analysis and Detection o...

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4th international conference on Smart Electronics and Communication, ICOSEC 2023

作者： Priyatharsini, G. Soniya Aruna, M. Myneni, Madhu Bala Kumar, P. Kiran Anil Kumar, D.B. Singh, Vijay Dr. M.G.R. Educational and Research Institute Department of Computer Science and Engineering Tamil Nadu Chennai600095 India Nitte Meenakshi Institute of Technology Department of Electrical and Electronics Engineering Bangalore India Institute of Aeronautical Engineering Department of Computer Science and Engineering Telangana Hyderabad India Cvr College of Engineering Department of Computer Science and Engineering Telangana Ibrahimpatnam India School of Electrical and Electronic Engineering Reva University Karnataka Bengaluru India Graphic Era Deemed to Be University Department of Computer Science and Engineering Uttarakhand Dehradun248002 India

ISBN: (纸本)9798350300888

RNA is an important target in the research of genetic diseases and disorders since it is essential for the process of gene expression and control. Convolutional neural networks (CNNs) and other machine learning-based methods for analyzing RNA sequences have recently attracted more attention. But reliable analysis and detection are significantly hampered by the enormous variability of RNA sequences. In this research, a brand-new heterogeneous network model for CNN-based RNA analysis and detection is provided. this model accounts for the wide range of RNA sequence properties, such as length fluctuation, secondary structure, and chemical changes. A unified model that incorporates these features is created by combining 1D and 2D convolutional layers. Multiple datasets, including synthetic and real-world datasets, were used to assess proposed model's performance. the outcomes show that the model performs better in terms of accuracy, sensitivity, and specificity than current state-of-the-art approaches. Furthermore, the model's performance is greatly enhanced with the addition of 2D convolutional layers and attention processes, according to rigorous studies to assess the contributions of each component of the model. Overall, the heterogeneous network model offers a reliable and precise method for analyzing and identifying RNA sequences, which has significant ramifications for comprehending hereditary illnesses and creating targeted therapies. © 2023 IEEE.

关键词： Machine learning

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Network on Chip(NoC) Mesh Topology FPGA Verification: Real Time Operating System Emulation Framework 4

Network on Chip(NoC) Mesh Topology FPGA Verification: Real T...

引用

4th international conference on Advances in Electrical, Computing, Communication and Sustainable Technologies, ICAECT 2024

作者： Rane, Udaysing V. Panem, Charanarur Abhyankar, Girish Gad, R.S. Dnyanprassarak Mandal's College and Research Centre Department of Computer Science Goa Bardez Assagao India Indian Institute of Technology Department of Electronics and Communication Engineering Uttarakhand Roorkee India Dnyanprassarak Mandal's College and Research Centre Department of Electronics Goa Bardez Assagao India Goa University School of Physical and Applied Sciences Department of Electronics India

ISBN: (纸本)9798350343670

Network on Chip (NoC) is an upcoming paradigm that copes with the increasing complexity and communication requirement of future System on Chip (SoC). In this paper, we have modeled the NoC traffic characteristics under queuing theory, demonstrating clustered phase over synthetic traffic specially under varying packets and queue/buffer *** have also demonstrated IP core design cycle verification scalable framework in a SoC design paradigm, over a 3x2 mesh NoC framework on Intel Field Programmable Gate Array Logic (FPGA) Cyclone-II FPGA (EP2C70F896), 95nm Very large Scale Integration (VLSI) technology chipset for functional and cycle behaviour characteristics through State-of-the-art software Quartus Prime Computer Aided Design(CAD) system. the task management and synchronization of the traffic generation and reception is performed with the help of real-time operating system (RTOS) so as to demonstrate the Device Under Test (DUT). Such frameworks can helps in generating scalable NoCs and their cycle behaviours in re-configurable environments and applications.. © 2024 IEEE.

关键词： Mesh Topology Network on Chip Real Time Operating System System on Chip Validation Verification

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A graph structure clustering based spatial-temporal graph convolutional network model for autism spectrum disorder classification 4

A graph structure clustering based spatial-temporal graph co...

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4th international conference on Biomedicine and Bioinformatics engineering, ICBBE 2024

作者： Chen, Xunying Shao, Lizhen Beijing Engineering Research Center of Industrial Spectrum Imaging School of Automation and Electrical Engineering University of Science and Technology Beijing Beijing100083 China

ISBN: (纸本)9781510682443

In this paper, we propose a graph structure clustering-based spatio-temporal graph convolutional network (GSC-GCN) to classify Autism Spectrum Disorder. Firstly, a sliding window with fixed size is utilized to sample the preprocessed resting-state functional magnetic resonance imaging time series. then functional connectivity matrices for all the subsequences are constructed and a series of dynamical temporal and spatial graphs are built accordingly. Next, to effectively reduce dimensionality and capture key functional connections, three different sizes of coarsening graphs are generated using clustering methods. Subsequently, a spatial-temporal graph convolution network is designed which focuses on the temporal dynamics of graph series as well as the spatial characteristics of functional brain network. In order to verify the effectiveness of our model, we test our GSC-GCN on the Autism Brain Imaging Data Exchange dataset and compare it with the state-of-the-art techniques. Results show that GSC-GCN outperforms other methods, indicating that the comprehensive use of both temporal and spatial features deserves further exploration. © 2024 SPIE.

关键词： Diseases

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Remote Patient Monitoring Innovations Using IoT for Improved Healthcare Outcomes 4

Remote Patient Monitoring Innovations Using IoT for Improved...

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4th international conference on Technological Advancements in Computational sciences, ICTACS 2024

作者： Veeraiah, Vivek Shetty, Aishwarya D. thejaswini, K.O. Srinivas, P. Tripathi, Sudhanshu Mohammad, Faiz Sri Siddhartha Academy of Higher Education Dept. of Computer Science Karnataka India Nmamit Dept. of Cse Nitte India Sri Siddhartha Academy of Higher Education Dept. of Physiology Karnataka India Sri Siddhartha Academy of Higher Education Dept. of Pediatrics Karnataka India Amity University Tashkent Uzbekistan Rungta College of Engineering and Technology Dept. of Cse Chhattisgarh Bhilai India

ISBN: (纸本)9798350387490

RPM has now become a revolutionary concept in the field of healthcare through IoT technology improvement to patients' care. New RPM systems on IoT have been covered by the study which permits constant health indicators monitoring of the patient even under non-conventional clinical settings by means of real-time capacity. Such systems have RT-PCR using IoT sensors, wearable devices, and data analytics coupled with immediate access to intervention, chronic health checking, and personalized therapies. this article includes a debate of the state-of-the-art in the field of IoT. Among them the enhanced methods of data manipulation, wireless link technologies, and intelligent measurement of critical signs by sensors. Some of the concerns addressed to with regard to RPM based on Internet of things include patient engagement, privacy of data, and integration. In this paper, RPM is analyzed in context with chronic diseases, with an evaluation of its impact on medication compliance and scenarios of its potential for lessening hospitalization in existing scholarly articles. the findings presented in the paper reveal how advancements in the context of IoT can modify the ways of the healthcare distribution through the promotion of enhanced health result from the patient-centered therapy implemented continuously. © 2024 IEEE.

关键词： Electronic health record

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CodeMap: A Graphical Note-Taking Tool Cooperating with an Integrated Development Environment 22

CodeMap: A Graphical Note-Taking Tool Cooperating with an In...

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6th international conference on the art, science, and engineering of programming (programming)

作者： Taniguchi, Rikito Masuhara, Hidehiko Tokyo Inst Technol Tokyo Japan

ISBN: (纸本)9781450396561

Program comprehension is an important yet difficult activity in a software development process. One of the main causes of the difficulty is its cognitive overhead for maintaining the mental models, which consist of roles of program elements and relationships between them. though researchers have been working on tools to help maintaining the mental models, existing tools have high adoption barrier and support only a few programming languages, which hinders wide-range of programmers from using the program comprehension tools. We propose CodeMap, a graphical note-taking tool for offloading the mental models onto a visual representation. We designed CodeMap by considering familiarity and availability for practitioners. Our tool allows programmers to extract interested information into a graphical note with a few keyboard/mouse operations, and support many programming languages by using the language server protocol. In this paper, we present the design and implementation of CodeMap, and discuss possible features that could be useful for program comprehension.

关键词： program comprehension code navigation software engineering

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Multi-class Generative Adversarial Network for Hyperspectral Image Classification for Addressing Class Imbalance Problem 4th

Multi-class Generative Adversarial Network for Hyperspectral...

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4th international conference on Smart IoT Systems: Innovations and Computing, SSIC 2023

作者： Kumar, Deepak Kumar, Dharmender Department of Computer Science and Engineering Guru Jambheshwar University of Science and Technology Haryana Hisar India

ISBN: (纸本)9789819751990

Class-imbalanced datasets are commonly observed in hyperspectral image classification and deep learning models often tend to demonstrate a bias toward the dominant class, resulting in very low efficiency for minority classes. Data augmentation has been the most widely used strategy to mitigate class-imbalanced challenge. therefore, in this paper, the authors have introduced a data augmentation approach utilizing a multi-class generative adversarial network (MCGAN) to deal with this issue. In the proposed approach a generative adversarial network is utilized to specifically generate fake minority class samples and integrating these samples with the existing class samples. Further, a convolutional neural network (CNN) has been employed for classification of hyperspectral image samples. A series of experiments, utilizing the proposed approach, has been conducted with two most popular and publicly available datasets. the experiment results obtained are then compared with four state-of-the-art data augmentation techniques. the comparison results prove the supremacy of the proposed approach in terms of the quality of the generated samples and the classification accuracy over the state-of-the-art techniques. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Generative adversarial networks

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Automatic Speech Recognition Based on Attention-Enhanced Blockformer 4

Automatic Speech Recognition Based on Attention-Enhanced Blo...

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4th international conference on Computer Vision and Data Mining, ICCVDM 2023

作者： Liu, Wei Zhan, Tianyu Xu, Chunsheng Changchun University of Science and Technology Changchun130000 China Zhongshan Institute of Changchun University of Science and Technology Zhongshan528437 China

ISBN: (纸本)9781510674448

the Blockformer speech recognition model has recently been proposed as a state-of-the-art (SOTA) model on the Aishell-1 Chinese speech dataset. this model exhibited significant improvements in character error rate (CER) when compared to its baseline, Conformer. the key improvement of Blockformer is the addition of the Squeeze-and-Excitation (SE) block on top of Conformer, which enables better utilization of the information contained in each Conformer block. In our study of Blockformer, we identified scope for improving its block information extraction method. To this end, we used the attention mechanism to enhance the SE block's efficacy in squeezing block information. And we enhanced the model's structure in attention inference mode to align more effectively with the training structure. Under the four inference modes, namely attention, attention rescoring, ctc greedy search, and ctc prefix beam search, the CER reaches 4.67%, 4.43%, 4.75% and 4.75%. All of these rates are at the level of Blockformer or exceed it. © 2024 SPIE.

关键词： Speech recognition

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