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共 26731 条记录，以下是31-40 订阅

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Optimizing Crop Yield - An IoT and ML-based Soil Testing and Crop Recommendation System 3

Optimizing Crop Yield - An IoT and ML-based Soil Testing and...

引用

3rd International Conference on Intelligent data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Prathosh, S. Veerasamy, K. Dept. of Computer Science Karpagam Academy of Higher Education Coimbatore India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527549

-Agriculture is the primary source of income for a large segment of the Indian population, making this sector essential to achieving high crop yields for food security and economic stability. There is a growing concern that the misuse of fertilizers is disrupting nutrient balance, which negatively affects soil health and reduces productivity. Moreover, inderterminate climate patterns have affected crop yield leading to extensive farming losses. To address these challenges, a system leveraging the Internet of Things (IoT) and Machine Learning (ML) was developed for improved soil testing, real-time monitoring, and crop guidance. This innovative system employed a network of sensors that evaluated critical soil characteristics such as temperature, moisture, pH, and NPK levels. The data collected by these sensors was processed through a microcontroller and analyzed using the Random Forest algorithm, resulting in precise nutrient recommendations. Additionally, the robustness of the model was further enhanced through Stacking and Adaptive Feature Fusion techniques. Additionally, the system included a built-in plant health monitoring module that utilized Integrated Convolutional Neural Networks (CNNs) to analyze images captured by integrated cameras, allowing for the early detection of plant diseases and timely interventions to prevent outbreaks. This solution refines agricultural practices through precise nutrient management and early disease identification, ultimately improving crop yields and promoting soil sustainability. In addition, this initiative has the potential to significantly transform the livelihoods of millions of farmers across India. The proposed approach outperformed existing algorithms achieving a 99.5% accuracy. © 2025 IEEE.

关键词： Convolutional neural networks

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Activity Prediction for Localizing the Events in Imagined Speech EEG Signals 13

Activity Prediction for Localizing the Events in Imagined Sp...

引用

13th International Winter Conference on Brain-computer Interface, BCI 2025

作者： Balasubramanian, Arun Pandey, Kartik Veer, Gautam Samanta, Debasis Dept. of Computer Science and Enginnering Indian Institute of Technology Kharagpur India

ISBN: (纸本)9798331521929

Imagined speech electroencephalogram (EEG) signals are often collected for longer durations than necessary, leading to a difficulty in understanding the generation of EEG during the task as it is likely that most of the data collected is that of resting state. Developing a filter to identify the segments of the trials with actual information and remove those that contain the EEG from resting states could significantly advance our understanding of EEG signals in neuroscience and biomedical engineering. This work uses derivatives-based features to form the feature vectors used to train the classifiers. Based on feature importance analysis, it has been found that the derivative-based features contributed more to the classification than the traditionally preferred feature, band power in the alpha frequency band. Using the proposed features, the classification performance experienced a significant enhancement, surpassing the results reported in previous studies. The greater accuracy of the classifiers with the proposed features implies that they are effective at filtering out the resting state segments from imagined speech EEG signals. © 2025 IEEE.

关键词： Electroencephalography

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Advances in Remote Sensing Technologies for Ecological Monitoring and Applications in Carbon Stock Estimation 3

Advances in Remote Sensing Technologies for Ecological Monit...

引用

3rd International Conference on Intelligent data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Sadhasivam, Shankaripriya Gayathri, G. Poongothai, P. Pavithra, S. Dept. of Computer Science and Engineering Chennai Institute of technology Chennai India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527549

This review explores recent advancements that are made in remote sensing, enabling detailed analysis of our ecosystem through high resolution and multispectral imaging. The recent advancements in remote sensing includes developments useful for generation of synthetic images using GAN such as StyleGAN3 and for estimation of carbon stocks in forest using OBIA. Ecotones like mangroves and saltmarsh are crucial for carbon sequestration but mapping them accurately was challenging due to spectral similarity and seasonal variations. Focusing on coastal protection by this review examines the mapping methodologies such as random forest classification, OBIA and FID for habitat distinction, canopy area assessment and analysis. Through these technologies data accuracy can be enhanced for better monitoring. This review concludes with a discussion of AI-powered image synthesis and classification. © 2025 IEEE.

关键词： Carbon sequestration

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Advancing Pediatric Appendicitis Diagnosis: Harnessing CNN and Hybrid Models with Ultrasound Imaging

Advancing Pediatric Appendicitis Diagnosis: Harnessing CNN a...

引用

2025 International Conference on Pervasive Computational Technologies, ICPCT 2025

作者： Kulkarni, Supritha Sharma, Sanchit Manavi, J. Vachana, U. Bhargavi, M.S. Bangalore Institute of Technology Dept. of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798331508685

Pediatric appendicitis, an acute inflammatory disease, arises from the obstruction of the appendix, often due to inflammation or a fecalith. This common abdominal emergency in children presents diagnostic challenges due to its varied clinical manifestations, which can lead to misdiagnosis or higher perforation rates stemming from atypical symptoms. This research investigates the use of deep learning, more especially Convolutional Neural Networks (CNNs), by utilizing ultrasonic imaging data, which is less expensive, non-invasive, and quicker than the other approaches now in use. Several variants are evaluated including a basic CNN and more advanced hybrid models such as CNN-Support Vector Regression, CNN-Transformer, and Hierarchical Deep CNN, to identify the most optimal model to significantly enhance diagnostic accuracy, for young patients facing this condition. The study's main goal is to develop a highly accurate and dependable diagnostic tool for evaluating the severity of pediatric appendicitis by differentiating between simple and complex cases and identifying whether surgery is required to inform treatment plans. Various challenges that need to be tackled are managing significant dataset imbalances, enhancing interpretability and transparency for real-world clinical settings, and ensuring the robustness and adaptability of all these models across various healthcare environments. © 2025 IEEE.

关键词： Pediatrics

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EXAMINING CRITICAL FACTORS IN FCA-SAPO FRAMEWORK: A QUALITATIVE AND QUANTITATIVE STUDY ON THE ADOPTION OF FOG COMPUTING IN SAUDI ARABIAN PUBLIC ORGANISATIONS BY IT EMPLOYEES 18

EXAMINING CRITICAL FACTORS IN FCA-SAPO FRAMEWORK: A QUALITAT...

引用

18th International Conferences IADIS Information Systems 2025 and 23rd International Conference on e-Society 2025

作者： Alyami, Mohammed Beloff, Natalia White, Martin Dept. of Informatics University of Sussex Brighton United Kingdom Dept. of Computer Science Jazan University Jazan Saudi Arabia

ISBN: (纸本)9789898704658

Fog computing is a rapidly evolving domain within information technology and enterprise computing, providing organisations with the opportunity to greatly improve efficiency and productivity. This is particularly pertinent in developing countries, where effective resource utilisation is critical. Despite its potential, there has been limited research on the factors influencing the adoption of fog computing in Saudi Arabia, as well as the associated benefits and challenges. This study seeks to fill this gap by examining the various factors, advantages, and obstacles related to fog computing adoption within Saudi Arabian public organisations. To address the absence of a context-specific model, we employed a mixed-methods approach, gathering qualitative data through semi-structured interviews with 15 IT managers, revealing that complexity was the sole factor hindering adoption. Quantitative data was then collected from 665 IT managers and employees to validate the framework’s constructs, showing that privacy, complexity, awareness, and senior management support were insignificant among 12 factors in influencing adoption intention. The quantitative analysis complements these findings, indicating that while the majority of factors significantly influence the adoption process, a portion of the factors were found to be insignificant. Findings from both qualitative and quantitative analyses highlight the importance of technical, organisational, environmental, and financial contexts in the adoption process. These insights provide valuable guidance for IT managers in the Saudi public sector, emphasising the need to consider these factors comprehensively to achieve successful fog computing implementation. © 2025 Proceedings of the International Conferences IADIS Information Systems 2025 and e-Society 2025. All rights reserved.

关键词： Fog computing

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Multispectral Images and IoT Based Tea Plantation Monitoring: A Proposed System Architecture 5

Multispectral Images and IoT Based Tea Plantation Monitoring...

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5th International Conference on Advanced Research in Computing, ICARC 2025

作者： Kariyawasam, K.P.W.D.V. Kahandagamage, P.N. Fernando, M.R.R. Fernandopulle, J.M. Jayasinghearachchi, Vishan Dissanayaka, Kapila Dept. of Software Engineering Sri Lanka Dept. of Information Technology Sri Lanka Dept. of Computer Science Sri Lanka

ISBN: (纸本)9798331530983

In recent years, the Sri Lankan tea industry has fallen behind its competitors in the global tea market. This decline is caused by the challenges in productivity and resource management due to the limitations of traditional crop monitoring methods. This study presents a prototype system architecture that integrates multispectral imagery and IoT technologies to enhance plantation monitoring. The proposed system uses drones to capture high-resolution multispectral images and IoT devices to collect real-time environmental data, providing a comprehensive approach to assessing plant yield and detecting stress. Built on a hexagonal architecture, the system emphasizes modularity, reliability, and scalability by ensuring a clear separation of core functionalities from external components. This design facilitates easy adaptation to various crop types and agricultural contexts, enabling flexibility in deployment and use. This flexible framework can serve as a blueprint for improving decision-making and management practices across diverse plantation environments. © 2025 IEEE.

关键词： Risk perception

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IOT Based Multicrop Soil Moisture Control and Monitoring System 1

IOT Based Multicrop Soil Moisture Control and Monitoring Sys...

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1st International Conference on AIML-Applications for Engineering and Technology, ICAET 2025

作者： Barhate, Madhusudan Desai, Devam Mishra, Gaurav Phadke, Gargi Das, Siuli Bhuran, Supriya Yogesh Ramrao Adik Institute of Technology D.Y. Patil Deemed to be University Dept. of Instrumentation Engg Dept. of Computer Engg Dept. of Computer Science and Engg Navi Mumbai India

ISBN: (纸本)9798350355611

Agriculture is crucial for the economy, with China and India leading in production. Conventional irrigation methods require precise timing and quantity, varying for different crops. This paper proposes an IoT-enabled intelligent irrigation system to enhance crop productivity. The system uses a moisture sensor to activate the pump motor, and IoT technology enables remote monitoring through a web interface or mobile app. The system stores moisture data customized to specific crop requirements, reducing human intervention while ensuring optimal water supply. © 2025 IEEE.

关键词： Moisture control

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HPC master design: experience from Pisa 33

HPC master design: experience from Pisa

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33rd Euromicro International Conference on Parallel, Distributed, and Network-Based Processing, PDP 2025

作者： Danelutto, Marco Univ. of Pisa Dept. of Computer Science Italy

ISBN: (纸本)9798331524937

HPC is a widely used term, often referred to the applications, architectures and programming models and tools targeting highly parallel machines such as those of the *** lists. Recent advances in computing hardware resources require application of HPC techniques when using much smaller machines. Indeed, proper parallel programming tools and applications are needed also to exploit parallel hardware resources in personal computers (laptops, desktops, servers). This paper outlines key challenges in designing master's degree programs in HPC and shares lessons learned from various experiences in developing and implementing such programs in Italy and Europe. © 2025 IEEE.

关键词： Parallel programming

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A Hybrid Approach Using Cryptography and K-Least Significant Bit Steganography Algorithm in Cloud Computing 1

A Hybrid Approach Using Cryptography and K-Least Significant...

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1st International Conference on Advances in computer science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Goyal, Anshika Gupta, Bhumika VMSB Uttarakhand Technical University Dept. of Computer Science & Engineering Dehradun India G.B. Pant Institute of Engineering & Technology Dept. of Computer Science & Engineering Pauri India

ISBN: (纸本)9798331518578

The exponential growth of communication technologies has broadened the cloud computing ecosystem horizon to meet main communication needs. However, in-parallel upsurge in online attacks has alarmed industries to ensure optimal data security. The combination of steganography and cryptosystems has been recognized as an enhanced security model for data transfer among the main security systems now in use. In this paper, a hybrid approach using cryptography and K- Least Significant Bit Steganography algorithm in Cloud Computing (HYBC-KLSBS-CC) are proposed. Initially, input text data is retrieved from cloud storage and compressed using Huffman Compression Coding (HCC) to reduce data size. The compressed data is then encrypted with a Hybrid Cryptography Algorithm (Hyb-MRFC-ALC), combining Modified Random Fibonacci Cryptography and Asymmetric Lightweight Cryptography to ensure robust encryption. Finally, encrypted data is embedded within an image using the K-Least Significant Bit (KLSB) steganography technique, providing additional layer of security. The proposed method enhances data confidentiality, integrity, and storage efficiency in cloud environments. The proposed HYBC-KLSBS-CC approach attains 26.89%, 29.49% and 18.64% lower PSNR and 26.89%, 29.49% and 18.64% lower MSE when analyzed through existing techniques like data security dependent on cryptography steganography related with OTP procedure and Huffman coding in the cloud environment (CS-OTP-CE), a novel hybrid cryptographic framework for secure data storage in cloud computing: Integrating AES-OTP and RSA with adaptive key management and Time-Limited access control (AES-OTP-RSA-CC) and Crypto-Stegno dependent model for securing medical information on IOMT platform (CS-SMI-IOMT). © 2025 IEEE.

关键词： Cloud platforms

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Federated Learning under Attack: Game-Theoretic Mitigation of data Poisoning 18

Federated Learning under Attack: Game-Theoretic Mitigation o...

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18th IEEE International Conference on Software Testing, Verification and Validation Workshops, ICSTW 2025

作者： De Santis, Marco Esposito, Christian University of Salerno Dept. Computer Science Fisciano Italy

ISBN: (纸本)9798331534677

Federated Learning (FL) is vulnerable to various attacks, including data poisoning and model poisoning, which can degrade the quality of the global model. Current solutions are based on cryptographic primitives or byzantine aggregation but suffer from performance and/or quality worsening and are even ineffective in case of data poisoning. This work proposes a game-theoretic solution to identify malicious weights and differentiate between benign and compromised updates. We use the Prisoner's Dilemma and Signaling Games to model interactions between local learners and the aggregator, allowing a precise evaluation of the legitimacy of shared weights. Upon detecting an attack, the system activates a rollback mechanism to restore the model to a safe state. The proposed approach enhances FL robustness by mitigating attack impacts while preserving the global model's generalization capabilities. © 2025 IEEE.

关键词： Federated learning

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