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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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Investigating the Sixth Generation Mobile Networks: Present Situations, Challenges and Possible Solutions 1

Investigating the Sixth Generation Mobile Networks: Present ...

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

作者： Pushparajesh, V. Gautam, Anupam Kumar Faculty of Engineering and Technology Department of Electrical and Electronics Engineering Karnataka Ramnagar District India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India

ISBN: (纸本)9798331518578

A number of industries are benefiting from the introduction of 5G technology, including eHealth, automation, robots, and smart infrastructure. The fast expansion of 5G has presented substantial problems in terms of network complexity and maintenance, despite the fact that it delivers high-speed connection and supports a wide variety of developing technologies. Consequently, studies are already under way to establish the specifications for 6G, the next generation of mobile networks. Examining the technological hurdles that prevent 5G from reaching its full potential, this essay investigates the ever-changing 5G environment. Furthermore, it delves into the 6G architecture framework and explores how spectrum management can back the capabilities needed by future networks. Improving network speed, achieving ultra-low latency, and connecting millions of devices are all important goals that must be met in order to serve applications of the future. Although there will be new communication possibilities brought about by the upgrade from 5G to 6G, there will also be significant challenges in areas such as network architecture, infrastructure scalability, and optimal spectrum usage. This article provides a high-level summary of these important problems, explains how people are working to solve them, and lays the groundwork for a strong and long-lasting 6G network. © 2025 IEEE.

关键词： 6G Mobile Network Challenges Network Speed Smart Infrastructure Spectrum Usage

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A Smart Irrigation System for Coconut Farming using IoT 3

A Smart Irrigation System for Coconut Farming using IoT

引用

3rd international conference on Electronics and Renewable Systems, ICEARS 2025

作者： Russia, S. Mohanapriya, B. Gowrishankar, V. Parimala Devi, M. Pradeep, K.K. Brindha, P. Velalar College of Engineering and Technology Department of Computer Science and Engineering India Velalar College of Engineering and Technology Department of Electronics and Communication Engineering Erode India Erode Sengunthar Engineering College Department of Electronics and Communication Engineering Erode India

ISBN: (数字)9798331509675

ISBN: (纸本)9798331509675

Coconut farming is an essential agricultural practice that contributes significantly to the global economy by providing valuable products such as coconut water, oil, and meat. However, the management of coconut plantations faces numerous challenges, including labor shortages, pest control, and the demand for precise irrigation and fertilization. This paper proposes an integrated smart farming system that leverages advanced sensor technologies and automation to address these challenges. The system employs precision irrigation and fertigation methods, facilitated by a centralized motor controller and solenoid valves, to deliver water and nutrients efficiently to the base of each coconut tree. A pH sensor monitors soil acidity or alkalinity, ensuring optimal nutrient availability and promoting sustainable farming practices by reducing chemical inputs. Additional features include a tree motion sensor and a tilt sensor for monitoring tree stability under adverse weather conditions. Soil moisture sensors, temperature, humidity, and light intensity sensors provide real-time data to enhance decision-making. Farmers can remotely monitor and control the system via a Node-RED dashboard, receiving alerts through platforms like Telegram. By integrating these technologies, the proposed system aims to optimize resource utilization, enhance crop yield, and reduce the environmental impact of coconut farming. © 2025 IEEE.

关键词： Soil moisture

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FractureAI Revolutionizing Bone Fracture Detection with Deep Learning CNNs for Precision Medical Imaging 3

FractureAI Revolutionizing Bone Fracture Detection with Deep...

引用

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

作者： Sudha, I. Ramesh, P.S. Durgadevi, P. Sundari, G. Senthilvadivu, S. Narang, Sudha Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Simats University Computer Science and Engineering Tamil Nadu Chennai India Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Cse TamilNadu Chennai India Srm Institute of Science and Technology Department of Computer Science and Engineering Vadapalani campus Chennai-26 India C. Abdul Hakeem College of Engineering and Technology Department of Cse India Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Computer Science and Engineering TamilNadu Chennai India Maharaja Agrasen Institute of Technology Sector 22 Delhi Rohini India

ISBN: (纸本)9798331542375

Detection of bone fractures is part and parcel of clinical diagnosis, previously relied on radiological interpretation of X-ray or CT scans. However, manually reviewed system involves human intervention and it is vulnerable to reveal more false negatives or delayed at revealing certain fractures. To counter these challenges, this research proposes Fracture AI, a deep learning system that employs CNNs, specifically ResNet-50 for fracture identification and categorization. With the help of transfer learning and techniques such as Grad-CAM for fracture localization, the system can reach remarkable results with the accuracy of 96.2%, precision of a 97.9%, recall of a 96.9%, and the F1-score of a 97.4%. The results presented here show how Fracture Ai can enhance the diagnostic process enabling the identification of fractures in a shorter time, especially in emergency departments, has been clearly shown here and the AI solution is accurate. © 2025 IEEE.

关键词： Mammography

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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...

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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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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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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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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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Qualitative and Quantitative Perspectives on Video Watermarking Approaches 3

Qualitative and Quantitative Perspectives on Video Watermark...

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

作者： Kalra, Vaishali Sharma, Chirag Lovely Professional University Department of Computer Science and Engineering Jalandhar India

ISBN: (纸本)9798331542375

The technique of video watermarking performs a critical part in ensuring protection of digital video content by embedding an imperceptible yet detectable mark into the video stream. This paper explores both the qualitative and quantitative aspects of various video watermarking techniques. The study categorizes watermarking methods into spatial domain and frequency domain techniques, providing a comprehensive comparison of their effectiveness regarding visual being imperceptible and resilient to attacks, computational complexity, along with embedding capacity. Important quantitative evaluation measures are provided, such as PSNR(Peak Signal to Noise Ratio), Bit Error Rate(BER), and Structural Similarity Index(SSIM). © 2025 IEEE.

关键词： Frequency response

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Enhancing traffic light recognition in autonomous vehicles: A novel convolutional neural network approach for fog removal using efficient channel attention 4

Enhancing traffic light recognition in autonomous vehicles: ...

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4th international conference on computer Vision, Application, and Algorithm, CVAA 2024

作者： Ji, Shuai Zhang, Huangnian Dong, Xingwen The College of Nuclear Technology and Automation Engineering Chengdu University of Technology Sichuan Chengdu610059 China

ISBN: (数字)9781510687622

ISBN: (纸本)9781510687615

Adverse environmental conditions such as haze, rain, and dust can significantly impair visibility for vehicles in motion, leading to blurred vision. Traditional de-fogging techniques often encounter limitations, including reliance on pre-existing assumptions, color distortion, and inadequate real-time performance. Consequently, these methods may fail to effectively distinguish traffic light images post-de-fogging, thereby hindering target recognition in autonomous driving systems. This study introduces a convolutional neural network that integrates deep and shallow feature fusion utilizing the Efficient Channel Attention mechanism. By employing an atmospheric scattering model, shallow features from fogged images are extracted through convolutional layer operations, while deep features are obtained using parallel multi-scale convolution kernels. The integration of shallow and deep features is achieved through a series of fusion steps, enhanced by the application of an effective channel attention mechanism. Ultimately, a transmittance map corresponding to the fog map is derived through nonlinear regression, facilitating the recovery of fog-free images and improving the recognition capabilities of the YOLOv8 model. Experimental results demonstrate that this approach effectively identifies traffic light targets during simulations of vehicles navigating traffic intersections. Furthermore, the quality of fog removal achieved by this method surpasses that of other algorithms, as evidenced by both subjective evaluations and objective analyses. © 2025 SPIE.

关键词： Convolutional neural networks

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