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Automated Detection of Tomato Leaf Diseases Using Convolutional Neural Networks 3

Automated Detection of Tomato Leaf Diseases Using Convolutio...

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3rd IEEE international conference on automation and Computation, AUTOCOM 2025

作者： Yadav, Hemant Kumar Srivastava, Varsha Banoth, Sreenu Rohini Rawat, Daksh Rawat, Priyansha Iimt College of Engineering Uttar Pradesh Greater Noida201310 India School of Computer Science and Engineering Iilm University Uttar Pradesh Greater Noida201306 India Chandigarh Engineering College Chandigarh Group of Colleges Department of Computer Science and Engineering Jhanjeri Punjab Mohali140307 India Graphic Era Hill University Dehradun India

ISBN: (纸本)9798331542375

In this study, an automated system to detect tomato leaf diseases with Convolutional Neural Networks (CNNs) is introduced to aid farmers in early disease identification to avert crop losses and promote yield. The dataset is multiple classes of tomato leaf diseases, and the model was trained and tuned extensively for its performance. In particular, a high training accuracy of 95.73%, and validation accuracy of 89.85% was achieved by CNN. Furthermore, the system's precision, recall, and F1 metric were around 90%, indicating the balanced system performance. The proposed CNN model is shown to be more reliable and faster than the traditional image processing techniques and hence is an appropriate choice for real time agricultural applications. The integration of modern varieties technologies in AI based approaches in agriculture which will feed productivity while maintaining Sustainability. The results indicate that the proposed system could provide an effective means for disease detection for farmers and may alleviate some of the need for chemical treatments. © 2025 IEEE.

关键词： Convolutional neural networks

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Semantic segmentation for virtual-real fusion data processing in nonferrous metal process industry 24

Semantic segmentation for virtual-real fusion data processin...

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8th international conference on computer Science and Application engineering, CSAE 2024

作者： Yin, Zhiqiang Zhao, Defeng Feng, Yong Li, Yingna School of Information Engineering and Automation Kunming University of Science and Technology Yunnan Kunming China Yunnan Radio Co. Ltd. Yunnan Kunming China

ISBN: (纸本)9798400718090

By combining high-precision virtual models and actual data, virtual-real fusion technology can significantly improve the production efficiency, quality control and safety of non-ferrous metal process industry, and optimize the production process. In the data processing of virtual-real fusion, environmental understanding is the basis for the correct interaction between virtual objects and the real world in the virtual-real fusion. Virtual-real convergence, especially in Augmented Reality (AR), often requires advanced image understanding techniques to improve user experience and provide more interactivity. Semantic segmentation plays a key role in this process, so this paper proposes a semantic segmentation method that uses RGB and point cloud images as well as deep image fusion to solve the heterogeneous problem of multi-source data in virtual-real fusion data processing, and improve the ability of machine vision system to understand the real world environment in virtual-real fusion. Firstly, Agent Attention is introduced for the decoder stage processing features, which combines the advantages of traditional attention and linear attention. Secondly, a feature fusion method is introduced for adaptive fusion of features between various stages. Experiments were carried out on the NYU Depth Dataset V2 and KITTI-360 data sets, and the segmentation results of the proposed method were 53.7% and 56.7% respectively on mIoU. In addition, the method is applied to the field of ore segmentation, and the mIoU reaches 81.7%, which also achieves good segmentation effect. © 2024 Copyright held by the owner/author(s).

关键词： Semantic Segmentation

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Swin-BFB-UNet: A swin transformer-based UNet combined with bottleneck fusion block for vertebra segmentation

Swin-BFB-UNet: A swin transformer-based UNet combined with b...

引用

2024 international conference on computer Vision and Image Processing, CVIP 2024

作者： Gao, Yifan Li, Min Miao, Hongxia Jiang, Junfeng College of Information Science and Engineering Hohai University Jiangsu Changzhou China College of Artificial Intelligence and Automation Hohai University Jiangsu Changzhou China Key Laboratory of Maritime Intelligent Cyberspace Technology Hohai University Ministry of Education Jiangsu Changzhou China

ISBN: (纸本)9781510688384

Segmenting the vertebrae is an important part of diagnosing spine diseases and planning surgery. The vertebra's complex anatomy, featuring similar shapes of neighboring bones and vague boundaries, presents substantial challenges for precise semantic segmentation. Traditional methods often depend heavily on local features, making it difficult to capture global context and resulting in less accurate recognition. Consequently, automated and efficient segmentation approaches are crucial to overcoming these issues. This study introduces an innovative Swin-BFB-UNet architecture derived from UNet to enhance vertebra segmentation. We include the Swin Transformer into the encoder to capture both global and local information, hence enhancing vertebra segmentation accuracy. The bottleneck fusion block (BFB) fuses multi-scale features and semantic information, enhancing the model's feature representation. Furthermore, we employ both Dice Loss and Cross-Entropy Loss to alleviate the adverse effects of foreground-background area imbalance. We conducted annotations and experiments on the lateral X-ray images in the BUU-LSPINE dataset. The experimental findings indicate that our model exceeds other leading methodologies, attaining a mDice of 90.3%, hence evidencing its proficiency in precise vertebra segmentation. © 2025 SPIE.

关键词： Semantic Segmentation

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An Improved U-Net Architecture for Continuous Non-Invasive Arterial Blood Pressure Estimation 24

An Improved U-Net Architecture for Continuous Non-Invasive A...

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8th international conference on Electronic Information Technology and computer engineering, EITCE 2024

作者： Lin, Dongmei Li, Yahui Chen, Fuming Chou, Yongxin College of Electrical and Information Engineering Lanzhou University of Technology Gansu Lanzhou China Medical Support Center The 940th Hospital of Joint Logistics Support Force of Chinese People’s Liberation Army Gansu Lanzhou China School of Electrical and Automation Engineering Changshu Institute of Technology Jiangsu Changshu China

ISBN: (纸本)9798400710094

Cardiovascular diseases account for a significant portion of all fatalities, causing significant loss of life globally each year. Currently, continuous blood pressure monitoring is the most feasible option, given the limitations of invasive monitoring methods and the challenges in accuracy, complexity, and reliability of non-invasive techniques. We have delved into the advantages of deep learning and have eliminated the dependency on manual feature extraction. Consequently, we have introduced a network that incorporates information from various scales and an attention mechanism to tackle the deficiencies in existing methods, which might fail to consider the diverse scales inherent in PPG signals. This network accurately estimates continuous ABP waveforms from PPG signals, end-to-end, compensating for the phase shift and preserving waveform characteristics. Experiments demonstrate that our approach yields an mean absolute error of 3.20 ± 4.34 mmHg for DBP, 3.43 ± 3.91 mmHg for MAP, and 4.53 ± 6.22 mmHg for SBP estimation, respectively, outperforming existing works and meeting the stringent standards of both BHS and AAMI. These results demonstrate the effectiveness and potential application of this method in non-invasive blood pressure monitoring. © 2024 Copyright held by the owner/author(s).

关键词： Blood pressure

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Standardized Post-Quantum Cryptography and Recent Developments in Quantum computers 1

Standardized Post-Quantum Cryptography and Recent Developmen...

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1st international conference on Advances in computer Science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Chib, Shiney Devarajan, Harshitha Raghavan Chundru, Swathi Pulivarthy, Padmaja Isaac, R. Augustian Oku, KrishnaMurthy Meghe Institute of Management Studies Department of Management Datta Maharashtra Nagpur India New York University Department of Computer Science United States Motivity labs INC United States Samsung Korea Republic of Saveetha Engineering College Department of Artificial Intelligence and Machine Learning Tamil Nadu Chennai602105 India AT&T Services Inc Tech Lead Infrastructure/Cloud Architecture Department of Automation platform Development India

ISBN: (纸本)9798331518578

Traditional cryptographic frameworks are seriously threatened by the quick development of quantum computing, especially those that depend on public-key cryptosystems like RSA and ECC. Post-quantum cryptography (PQC) seeks to create cryptographic methods that are impervious to quantum computer assaults in order to overcome this difficulty. The National Institute of Standards and Technology (NIST), which has selected and standardised quantum-resistant techniques like CRYSTALS-Kyber for key encapsulation and CRYSTALS-Dilithium for digital signatures, has sparked recent advancements in PQC. At the same time, the need for reliable PQC solutions has increased due to developments in quantum computing, such as IBM's improvements in qubit stability and Google's 'quantum supremacy' assertion. In order to protect data security in the post-quantum age, PQC regulations must be adopted globally, as highlighted by the intersection of these fields. Current developments in PQC standardisation and the changing state of quantum computing technology are highlighted in this paper. © 2025 IEEE.

关键词： Quantum cryptography

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Comparison of the applicability of spatio-temporal fusion algorithms in the sanjiang confluence area of yibin city 6

Comparison of the applicability of spatio-temporal fusion al...

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6th international conference on Geoscience and Remote Sensing Mapping, GRSM 2024

作者： Zhang, Yuanyang Zhao, Liangjun Ning, Feng Wang, Yinqing School of Computer Science and Engineering Sichuan University of Science and Engineering Yibin644000 China Sichuan Key Provincial Research Base of Intelligent Tourism Yibin644000 China School of Automation and Information Engineering Sichuan University of Science and Engineering Yibin644000 China

ISBN: (纸本)9781510688018

Obtaining high-quality remote sensing images is crucial for ecological environment monitoring in the Yangtze River basin and for the protection and development of the regions along the river. Currently, due to the challenges faced by satellite remote sensing instruments in simultaneously acquiring high spatial and temporal resolution data, spatio-temporal fusion technology is one of the main methods to address data gaps and the difficulty of obtaining high-quality data. This study uses a portion of the Sanjiang Confluence area in Yibin City as the experimental area, selecting Landsat and MODIS data as data sources. Four spatio-temporal fusion algorithms (STARFM, FSDAF, ISTRUM, FSDAF2.0) are employed to achieve the spatio-temporal fusion of Landsat and MODIS data, analyzing the applicability of these four algorithms in the Yibin area. The results indicate that the ISTRUM algorithm has the smallest overall error compared to the actual images, with a high degree of restoration for spatial and spectral information, yielding the best predictive fusion results and demonstrating good applicability in areas with high heterogeneity. © 2025 SPIE.

关键词： Landsat

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Research on supply chain intelligent bid evaluation management system based on AHP-TOPSIS 24

Research on supply chain intelligent bid evaluation manageme...

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5th international conference on computer Science and Management Technology, ICCSMT 2024

作者： Yang, Zheng Jin, Hao Chen, Shuai Sun, Jinzhou Tang, Fei Chen, Mo E-commerce Co. Ltd. Fujian Xiamen China Wuhan Hangke Logistics Co.Ltd. Hubei Wuhan China School of Information Management Wuhan University Hubei Wuhan China School of Electrical and Automation Wuhan University Hubei Wuhan China Hubei Engineering and Technology Research Center for AC/DC Intelligent Distribution Network Hubei Wuhan China College Students Engineering Training and Innovation Practice Center Hubei Wuhan China

ISBN: (纸本)9798400709999

The intelligent transformation of the bid evaluation process in supply chains has become a prominent area of *** paper analyzes the functional framework of the intelligent bid evaluation management system for supply chains and ranks the importance of functions involved in the evaluation ***, the paper examines the challenges faced by existing supply chain platforms during their intelligent *** then proposes an evaluation system for the functional importance of bid evaluation, utilizing the Analytic Hierarchy Process (AHP) and TOPSIS *** converting subjective judgments into objective data, this study comprehensively analyzes three key factors in the bid evaluation process: bid integrity, risk level, and evaluation *** weights are determined based on expert assessments, leading to the optimization and ranking of intelligent bid evaluation *** results reveal that tender integrity and supplier risk are critical in the evaluation process, with functions such as core term extraction, integrity detection, and format error identification playing essential *** research not only offers scientific decision support for the development of intelligent bid evaluation management systems in the supply chain but also provides a theoretical foundation for advancing supply chain intelligence. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： AHP

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Oral Cancer Classification Using a Hybrid Attention-Aided Deep Learning Model 5th

Oral Cancer Classification Using a Hybrid Attention-Aided D...

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5th international conference on Medical Imaging and computer Aided Diagnosis, MICAD 2024

作者： Dutta, Srinjan Roy, Jibesh Mandal, Diptarka Kaplun, Dmitrii Shpakovskaya, Irina Sarkar, Ram Department of Computer Science and Engineering Jalpaiguri Government Engineering College Jalpaiguri India School of Computer Engineering and Mathematical Sciences Defence Institute of Advanced Technology Pune India Artificial Intelligence Research Institute China University of Mining and Technology Xuzhou China Department of Automation and Control Process Saint Petersburg Electrotechnical University "LETI" Saint Petersburg Russia Department of Computer Science and Engineering Jadavpur University West Bengal Kolkata India I.P. Pavlov Saint Petersburg First State Medical University St. Petersburg Russia

ISBN: (纸本)9789819638628

Oral cancer remains a critical global health challenge, characterized by high morbidity and mortality due to late-stage diagnosis. This paper addresses the need for improved diagnostic accuracy by introducing a novel hybrid-attention aided deep learning model for oral cancer classification. Our approach integrates the EfficientNetB0 model for feature extraction with a customized hybrid attention module, combining self, spatial, and soft attention mechanisms to emphasize crucial features. The model has been trained and evaluated on both microscopic and macroscopic oral cancer datasets, demonstrating its generalizability. Our proposed model achieves an accuracy of 96.97% on a macroscopic dataset and 92% on a microscopic dataset, with respective F1-scores of 0.97 and 0.89, outperforming baseline models. This hybrid attention-aided deep learning model shows significant potential in improving early detection and treatment planning of oral cancer. Source code and additional results of this work can be found at the GitHub repository. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Contrastive Learning

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Detection and Mitigation of Backdoor Attacks on x-Apps 25th

Detection and Mitigation of Backdoor Attacks on x-Apps

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25th international conference on Web Information Systems engineering, WISE 2024

作者： Naim, Rouaa Gelban, Hams Badawy, Ahmed Computer Science and Engineering Department Qatar University Doha Qatar

ISBN: (纸本)9789819605750

The integration of artificial intelligence (AI) and machine learning (ML) within the Open Radio Access Network (O-RAN) xApps introduces significant enhancements to network automation and anomaly detection. However, this open architecture increases vulnerability to sophisticated attacks, including backdoor threats. This paper proposes the use of an xLSTM autoencoder for detecting anomalies in O-RAN, specifically focusing on its ability to model long-term dependencies in network traffic. xLSTM, with its enhanced memory mechanisms, addresses the limitations of traditional models like LSTM by improving both detection accuracy and computational efficiency in real-time environments. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Radio access networks

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SVM-based malware classification through deep learning method 5

SVM-based malware classification through deep learning metho...

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5th international conference on Signal Processing and computer Science, SPCS 2024

作者： Shi, Jiawei Wang, Wancheng Zhang, Meng Yao, Qinbo Cheng, Shiyuan Zhan, Zhifei College of Artificial Intelligence and Automation Hohai University Nanjing China

ISBN: (数字)9781510686731

ISBN: (纸本)9781510686724

Various daily applications, including image categorization, natural language comprehension, and voice identification, heavily rely on fully connected and convolutional neural networks. When tackling classification problems, conventional deep learning architectures typically employ softmax activation functions to normalize outputs and minimize the model's cross-entropy loss. In this study, we present a novel malware classification model that integrates hybrid support vector machines with neural networks. Notably, this model substitutes the conventional softmax layer in neural networks with a support vector machine, thereby shifting the learning process towards minimizing margin losses instead of cross-entropy losses. This modification enhances the precision of malware classification for both standard machine learning algorithms and several prevalent deep learning models. To validate our proposed model, we employed the Malimg dataset, comprising malware images derived from binary malware samples. Subsequently, we trained an DL-SVM model to assign scores to each distinct malware family. Experimental outcomes revealed that the malware classification model achieved an accuracy of 96.31%, a precision rate of 97.00%, a recall rate of 97.35%, and an F1 score of 97.16%, respectively. © 2025 SPIE.

关键词： Convolutional neural networks

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