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Efficient protection of golden nutri cereal implementing enhanced disease identification injecting hybrid split-attention mechanism through Novel SANDNES Mechanism

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Multimedia Tools and Applications 2024年第38期83卷 85499-85521页

作者： Divya, A. Sungeetha, D. Ramesh, Sundar Department of Electronics and Communication Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai602105 India Department of Networking and Communications School of Computing Faculty of Engineering and Technology SRM Institute of Science and Technology Tamilnadu Kattankulathur Chennai India

Millets, often referred to as "Nutri Cereals," hold significant potential for enhancing future food availability. Over the past 50 years, advancements such as the use of fertilizers, insecticides, and controlled irrigation have markedly increased the productivity of pearl millet crops worldwide. According to the 2020 review report by the Directorate of Millets Development, 693 million hectares of agricultural land are dedicated to pearl millet cultivation. However, managing crop diseases remains a substantial challenge for farmers. Effective crop disease management is crucial for sustainable agriculture, yet traditional algorithms often struggle with accurate detection and computational inefficiency especially in the case of golden cereal termed as "Pearl Millet". Our study introduces a novel approach for disease detection in pearl millet crops using aerial imagery, named Split-Attention Networks with Disentangled Nonlocal and Edge Supervision (SANDNES). This method leverages advanced neural network architectures to enhance disease detection accuracy and efficiency. Our comprehensive numerical evaluations demonstrate that SANDNES outperforms traditional U-Net and U-Net + Nonlocal baseline models Comprehensive evaluations demonstrate SANDNES' exceptional performance, achieving an impressive average F1 score of 89.04%, significantly surpassing other methods in accuracy and efficiency. Specifically, SANDNES achieved notable performance improvements, with F1 scores of 94.88% for healthy crops, 85.96% for honeydew, 82.97% for smut, and 92.69% for ergot. This method leverages split-attention mechanisms for precise feature localization, with Disentangled Nonlocal and Edge Supervision enhancing contextual reasoning and boundary delineation. SANDNES superior computational efficiency and high accuracy make it suitable for real-time precision agriculture applications. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024

关键词： Crops

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Advanced 3D-printed antibacterial nano 58S bioglass/AgNPs/CeO2-based scaffolds for bone tissue engineering

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Ceramics International 2025年第15期51卷 20939-20953页

作者： Singh, Indrajeet Kong, Ing Viswakarma, Ashok Balani, Kantesh Advanced Polymer and Composite Materials Laboratory Department of Engineering School of Computing Engineering and Mathematical Sciences La Trobe University BendigoVIC3550 Australia Department of Materials Science and Engineering Indian Institute of Technology Kanpur208016 India Department of Mechanical Engineering Indian Institute of Technology Kanpur208016 India

58S bioglass (BG) has been considered an effective bioceramic in bone tissue engineering due to its good osteoinductive and osteoconductive properties;however, its poor antibacterial and printing properties remain a challenge in the development of ideal patient-specific, fully interconnected, bone-like scaffolds. To address this issue, this study presents novel 3D-printed BG/AgNPs/CeO2 (BGAC3D) composite scaffolds. These scaffolds exhibit a fully interconnected pore network (≈99 %) with a porosity of ≈64 % and optimally designed pore and strut sizes of 404 ± 15 μm and 394 ± 30 μm, respectively, making them suitable for bone healing applications. Adding AgNPs and ceria nanoparticles significantly increased the shear-thinning properties of Pluronic F-127/BG/AgNPs/CeO2-based ink, as compared to Pluronic F-127/BG-based ink. As a result, BGAC3D scaffold showed improved dimensional accuracy during printing relative to pristine BG. Nanoindentation studies revealed substantial increase in the mechanical properties of BGAC3D scaffolds, with elastic modulus, hardness, and compressive strength showing enhancement of 76 % (57.9 ± 8 GPa), 24 % (3.1 ± 1 GPa), and 275 % (1.5 ± 0.4 MPa), respectively. The antibacterial properties of BGAC3D scaffolds were investigated using disc diffusion and bacterial adhesion assays, which demonstrated an exceptional bactericidal efficacy of 99.3 % against Escherichia coli (E. coli) and 99.4 % against Staphylococcus aureus (S. aureus) compared to pristine BG. The cytocompatibility of the scaffolds was assessed using resazurin assays with mouse BMSCs and MTT assays with mouse fibroblasts, revealing enhanced cell viability for the BGAC3D composition compared to BG. Additionally, both BG and BGAC3D composites showed similar apatite mineralization on their surfaces, as observed under a scanning electron microscope. These findings indicate that BGAC3D scaffolds possess the necessary mechanical, antibacterial, and cytocompatibility properties, making th

关键词： Escherichia coli

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AI-Driven Symptom Checker: Leveraging Big Data Technologies for Real-Time Healthcare Triage and Diagnostics 5

AI-Driven Symptom Checker: Leveraging Big Data Technologies ...

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5th International Conference on Sustainable Communication Networks and Application, ICSCNA 2024

作者： Banerjee, Augnik Baibhav, Piyush Kumar, Nitin Singh, Aniket Gouthaman, P. School of Computing College of Engineering and Technology SRM Institute of Science and Technology Department of Networking and Communications Kattankulathur603203 India School of Computing SRM Institute of Science and Technology Faculty of Engineering and Technology Department of Networking and Communications Kattankulathur603203 India

ISBN: (纸本)9798331530013

The healthcare industry continues to face significant challenges in delivering timely and accurate diagnoses, particularly in remote and underserved areas where access to medical professionals is limited. The work aims to develop an AI-driven symptom checker that leverages big data technologies to offer preliminary diagnoses and support patient triage, ensuring quicker medical response times. Utilizing Hadoop for scalable data storage, Hive for advanced data analysis, and Spark for real-time distributed data processing, the system is designed to efficiently handle large volumes of patient data. Tableau is integrated for intuitive data visualization, allowing healthcare professionals to gain actionable insights and make informed decisions promptly. The proposed architecture enables the swift analysis of patient symptoms in real-time, significantly reducing diagnostic delays and enhancing the quality of care. By focusing on scalable and efficient data processing, the work addresses the need for a robust healthcare solution capable of operating in environments with limited resources. This system has the potential to transform patient care in remote areas by providing real-time analysis, identifying critical cases, and aiding in effective resource allocation. The solution's emphasis on rapid diagnosis and visualization offers a comprehensive approach to improving healthcare outcomes in underserved regions. © 2024 IEEE.

关键词： AI-Driven Symptom Checker Apache Spark Hadoop Healthcare Triage Hive Tableau

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Plant Disease Detection and Classification using Convolutional Neural Networks (CNN) 15

Plant Disease Detection and Classification using Convolution...

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15th International Conference on Advances in computing, Control, and Telecommunication Technologies, ACT 2024

作者： Sreenivasan, Mahalakshmi Selvaraj, P. Department of Computing Technologies Faculty of Engineering and Technology School of Computing SRM Institute of Science and Technology Kattankulathur Tamil Nadu Chennai603203 India

ISBN: (纸本)9798331300579

Farmers and producers may suffer significant financial losses due to plant diseases. Effective control of plant diseases requires early identification and categorization of the illnesses. However, traditional plant disease detection techniques are frequently labor- and time-intensive. Plant disease detection has found a possible collaborator in deep learning and machine learning. Consequently, a novel deep-learning technique is proposed for classifying and diagnosing plant illnesses. It uses a transfer learning methodology built on a convolutional neural network architecture consisting of 5 convolutional, 2 fully- connected, and 3 max pooling layers. The algorithm has a 98% accuracy rate when tested on a publicly accessible dataset of images of plant diseases. The CNN model was trained with the PlantVillage dataset, which is available on Kaggle. From the results and a comparison with previous works, it is evident that the chosen approach performed well in classifying plant diseases with expected accuracy. © Grenze Scientific Society, 2024.

关键词： Plant diseases

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Predictive Analytics in Hotel Recommendations Using Machine Learning 5

Predictive Analytics in Hotel Recommendations Using Machine ...

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5th International Conference on IoT Based Control Networks and Intelligent Systems, ICICNIS 2024

作者： Singh, Siddharth Dey, Shayan Sharma, Shreyanshi Prathaban, Banu Priya School of Computing College of Engineering and Technology SRM Institute of Science and Technology Department of Networking and Communications Kattankulathur603203 India School of Computing SRM Institute of Science and Technology Faculty of Engineering and Technology Department of Networking and Communications Kattankulathur603203 India

ISBN: (纸本)9798331518097

In a world where travellers want the perfect stay, a new system emerges as the one that goes beyond simple hotel recommendations. By analysing details like interaction words and prices, it predicts hotel ratings using a powerful Random Forest Regressor which performs better than methods like Linear Regressor. With careful data processing and encoding, it achieves high accuracy, measured through precise metrics like RMSE, MSE and Max Error. It achieves a great RMSE score of 0.424. This system offers a scalable solution, turning every hotel suggestion into a personalized experience, redefining the way people find their ideal stay. © 2024 IEEE.

关键词： Hotels

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Recent Advances in In-Memory computing:Exploring Memristor and Memtransistor Arrays with 2D Materials

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Nano-Micro Letters 2024年第7期16卷 1-30页

作者： Hangbo Zhou Sifan Li Kah-Wee Ang Yong-Wei Zhang Institute of High Performance Computing(IHPC) Agency for ScienceTechnology and Research(A*STAR)1 Fusionopolis Way#16-16 ConnexisSingapore 138632Republic of Singapore Department of Electrical and Computer Engineering National University of Singapore4 Engineering Drive 3Singapore 117583Republic of Singapore Institute of Materials Research and Engineering Agency for ScienceTechnology and Research(A*STAR)2 Fusionopolis WaySingapore 138634Republic of Singapore

The conventional computing architecture faces substantial chal-lenges,including high latency and energy consumption between memory and processing *** response,in-memory computing has emerged as a promising alternative architecture,enabling computing operations within memory arrays to overcome these *** devices have gained significant attention as key components for in-memory computing due to their high-density arrays,rapid response times,and ability to emulate biological *** these devices,two-dimensional(2D)material-based memristor and memtransistor arrays have emerged as particularly promising candidates for next-generation in-memory computing,thanks to their exceptional performance driven by the unique properties of 2D materials,such as layered structures,mechanical flexibility,and the capability to form *** review delves into the state-of-the-art research on 2D material-based memristive arrays,encompassing critical aspects such as material selection,device perfor-mance metrics,array structures,and potential ***,it provides a comprehensive overview of the current challenges and limitations associated with these arrays,along with potential *** primary objective of this review is to serve as a significant milestone in realizing next-generation in-memory computing utilizing 2D materials and bridge the gap from single-device characterization to array-level and system-level implementations of neuromorphic computing,leveraging the potential of 2D material-based memristive devices.

关键词： 2D materials Memristors Memtransistors Crossbar array In-memory computing

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Advancement in Image Restoration Through GAN-based Approach 15

Advancement in Image Restoration Through GAN-based Approach

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

作者： Rama, P. Pandiaraj, A. Angayarkanni, V. Prakash, Y. Jagadeesh, S. SRM Institute of Science and Technology Computing Technologies College of Engineering and Technology Faculty of Engineering and Technology SRM Nagar TN Kattankulathur603203 India SRM Institute of Science and Technology Computing Technologies College of Engineering and Technology SRM Nagar TN Kattankulathur603203 India

ISBN: (纸本)9798350370249

This paper introduces IR-GAN (Image Restoration Generative Adversarial Network), a groundbreaking tool accessible on GitHub, designed to elevate image quality and rejuvenate damaged or low-resolution photographs. Leveraging the power of generative facial priors, IR-GAN adeptly transforms aged images, effectively reducing blurriness and revitalizing historical snapshots with remarkable precision. At the heart of IR-GAN lies its pre-trained Generative Adversarial Network (GAN) model, meticulously crafted to intelligently fill in missing or damaged areas within images by discerning intricate patterns present in the dataset. This innovative approach enables IR-GAN to seamlessly replace distorted or blurred segments with visually coherent elements, preserving the authenticity and essence of the original photographs. Through the integration of advanced image processing techniques and state-of-the-art deep learning methodologies, IR-GAN emerges as a promising solution for digital restoration and preservation endeavours. Its ability to breathe new life into old photographs while maintaining their historical significance holds immense value for archivists, historians, and enthusiasts alike. Experimental results presented in this paper underscore IR-GAN's efficacy in the transformative process of old photographs into visually appealing images. The showcased capabilities of IR-GAN align with the burgeoning demand for sophisticated image enhancement tools within the realm of image processing, paving the way for future advancements in digital restoration and preservation technologies. © 2024 IEEE.

关键词： Generative adversarial networks

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Cloud Detection and Nowcasting for Ship Navigation 15

Cloud Detection and Nowcasting for Ship Navigation

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15th International Conference on Advances in computing, Control, and Telecommunication Technologies, ACT 2024

作者： Sreenivasan, Mahalakshmi Sai Kumar, Kanuri S.V.S. Selvaraj, P. Department of Computing Technologies Faculty of Engineering and Technology School of Computing SRM Institute of Science and Technology Kattankulathur Tamil Nadu Chennai603203 India

ISBN: (纸本)9798331300579

Navigating through oceans is challenging. Sailors are disadvantaged in determining the most appropriate and safe path, free from hurdles and extreme weather conditions, especially on pitch-dark nights. Ships rely on their communication with satellites for navigation. When this connection breaks, the ship loses its ability to navigate and follow the required path. In such scenarios, the Geographical Information System (GIS) and Satellite Imagery-based algorithms are best suited to assist. It is necessary to focus on cloud behavior to ensure the success of ocean endeavors. Cloud Detection is performed by the methods of K-Means clustering and MRCNN. The approach of Mean Path Adjustment (MPA) is used to trace the center of mass to predict Cloud Motion;thereby, building a convolutional LSTM model for Cloud Nowcasting. Collectively, these methods help in detecting clouds, predicting their motion, and helping sailors decide the route to follow, based on the weather conditions. This system works well when the communication between the ship and satellite breaks. A high similarity index of 0.8363 is achieved with the test images. © Grenze Scientific Society, 2024.

关键词： Satellite imagery

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Data Encryption in 6G Networks: A Zero- Knowledge Proof Model 8

Data Encryption in 6G Networks: A Zero- Knowledge Proof Mode...

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8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2024

作者： Khamesra, Aryan Selvaraj, P. Singh, Isha Srm Institute of Science and Technology Faculty of Engineering and Technology School of Computing Department of Computing Technologies Chennai Tamil Nadu Kattankulathur603203 India

ISBN: (纸本)9798350376425

This work presents an innovative algorithm demonstrating the effectiveness of zero-knowledge proofs (ZKPs) in network security. By integrating Advanced Encryption Standard (AES) and Rivest-Shamir-Adleman (RSA) for key generation, the algorithm showcases their applicability in enhancing security measures within 6G networks. It highlights the utility of ZKPs in bolstering data privacy and security by enabling entities to validate knowledge without compromising sensitive information. The algorithm shows its capability to ensure robust communication security through comprehensive simulations, thereby laying the groundwork for dependable next-generation communication infrastructures. © 2024 IEEE.

关键词： Ciphertext

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Peer To Peer Fund Raising using Blockchain 15

Peer To Peer Fund Raising using Blockchain

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15th International Conference on Advances in computing, Control, and Telecommunication Technologies, ACT 2024

作者： Reddy, Boyapati Jane Prakeerth Vasu, Aaruni Mohankumar, Pradeep K. Department of Computing Technologies Faculty of Engineering and Technology School of Computing SRM Institute of Science and Technology Kattankulathur Tamil Nadu Chennai603203 India

ISBN: (纸本)9798331300579

Crowdfunding has emerged as a transformative financial model in the contemporary business landscape, providing a decentralized avenue for entrepreneurs, creatives, and innovators to secure funding for a wide range of projects. This Project explores the multifaceted nature of crowdfunding, encompassing its origins, evolution, and the diverse platforms that facilitate this alternative form of financing. The study delves into the key mechanisms and principles that underpin successful crowdfunding campaigns, emphasizing the role of social media, community engagement, and trust-building in fostering financial support. Furthermore, the project investigates the impact of crowdfunding on traditional funding channels, examining how it has disrupted conventional investment models and empowered individuals and small businesses. The project also addresses the challenges and risks associated with crowdfunding, including issues of accountability, regulatory concerns, and the potential for project failure. © Grenze Scientific Society, 2024.

关键词： Decentralized finance

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