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Revolutionizing UAV: Experimental Evaluation of IoT-Enabled Unmanned Aerial Vehicle-Based Agricultural Field Monitoring Using Remote Sensing Strategy

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Remote Sensing in Earth Systems sciences 2024年第4期7卷 411-425页

作者： Narayanappa, Gireesh Babu Chandanadur Abbas, Syed Hauider Annamalai, Lavanya Meenakshi, Ramakrishnan Singh, Mangal Yadav, Tumikipalli Nagaraju Kumar, Aarthi Ramesh Department of CSE/ISE BMS Institute of Technology and Management Bengaluru India Department of Computer Science & amp Engineering Integral University Lucknow India Department of ECE Dr. MGR Educational and Research Institute Chennai India Department of CSE Chennai Institute of Technology Chennai India Department of Electronics and Telecommunication Engg Symbiosis International (Deemed University) Symbiosis Institute of Technology Pune India Department of Computer Science and Engineering Koneru Lakshmaih Education Foundation AP Vaddeswaram India Department of ECE R.M.D Engineering College R. S. M Nagar Gummidipoondi India

Agricultural monitoring has evolved significantly with the advent of unmanned aerial vehicles (UAVs), which offer innovative solutions for precision farming. Traditional agricultural practices often rely on manual inspection and limited ground-based sensors, which can be time-consuming and prone to inaccuracies. This paper explores the integration of UAV and ground-based IoT sensor data through advanced data fusion techniques to enhance data reliability and precision. Our hybrid machine learning model (HMLM), combining random forest and support vector machine algorithms, is employed for accurate crop health assessment, disease detection, and yield prediction. The implementation of UAV-based agricultural monitoring systems leverages the unique capabilities of quadcopters and fixed-wing UAVs. Quadcopters are utilized for detailed field surveys and spot checks, offering high-resolution imaging for precise inspections, while fixed-wing UAVs are deployed for large-scale monitoring, providing extensive coverage and long-flight endurance. Equipped with advanced IoT devices and sensors, including multispectral and hyperspectral cameras, GPS modules, and real-time communication systems, these UAVs enable comprehensive data collection and analysis. Our hybrid machine learning model (HMLM) improves classification accuracy and predictive power, achieving an impressive accuracy of 98.74%, leading to precise crop management and higher yields. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Agricultural monitoring Artificial intelligence (AI) Deep learning GPS modules Hybrid machine learning model (HMLM) Internet of things (IoT) Precision agriculture Sustainable farming Unmanned aerial vehicles (UAVs)

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Cancer Prediction using Clinical and Genomic Data Fusion: A Systematic Review 6

Cancer Prediction using Clinical and Genomic Data Fusion: A ...

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6th International Conference on Cybernetics and Intelligent System, ICORIS 2024

作者： Julian, Kelvin Nugroho, Kuncahyo Setyo Nirwantono, Rudi Pardamean, Bens Computer Science Department BINUS Graduate Program Bina Nusantara University Jakarta Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia Biotechnology Department Faculty of Engineering Bina Nusantara University Jakarta Indonesia

ISBN: (纸本)9798331539603

This systematic review provides a comprehensive overview of the methods used to integrate genomic and clinical data in cancer prediction. The review includes 19 studies across various cancers, including breast, colorectal, melanoma, lung, pancreatic, and thyroid. The studies employed different methods to combine genomic and clinical data, including weighted polygenic risk scores, genetic and non-genetic risk scores, and different machine learning algorithms. The results show significant improvements in model prediction performance accuracy across multiple studies. The review highlights the potential benefits of integrating genetic and phenotypic information to improve disease risk prediction models and inform personalized healthcare strategies. © 2024 IEEE.

关键词： Lung cancer

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Early Detection of ADHD in Juveniles using recurrent neural networks 3

Early Detection of ADHD in Juveniles using recurrent neural ...

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3rd International Conference for Emerging Technology, INCET 2022

作者： Chintamaneni, Anish Agrawal, Sarvesh Yelamancheli, Chandana Sowmya. Archana, G.R Reddy, Sainandan Joy, Ajin Karunya Institute of Technology and Sciences Department of Computer Science and Engineering Tamil Nadu Coimbatore India Icfai University Department of Computer Science and Engineering Hyderabad India Karpagam College of Engineering Department of Computer Science and Engineering Tamil Nadu Coimbatore India Indian Institute of Technology Department of Civil Engineering Hyderabad India JSPMs' Rajarshi Shahu College of Engineering Department of Electronics and Telecommunication Engineering Maharashtra Pune India

ISBN: (纸本)9781665494991

ADHD, or attention deficit and hyperactivity disorder, is a common problem among youngsters that falls under mental diseases. It is one of the most studied disorders, as many industrialized countries with advanced technology cannot diagnose it. In the land of Ethiopia, a study was carried out. The prevalence rate, which is the proportion of the population affected by diseases at a given period, is expected to be around 7 percent among youngsters. If preschool teachers or child care teachers understand what ADHD is and what research is being done on it, they can help the child and inform their parents about the child's concerns. This would also benefit the research community because the data would be provided after the teachers read about the research. Furthermore, teachers can spot ADHD in its early stages, which can aid in early treatment. However, because not every teacher is aware of the study, various machine learning and deep learning techniques have been utilized to solve the current problem statement. The paper's primary goal is to use a Recurrent Neural Network to detect ADHD at an early stage and increase the correctness of the problem statement. © 2022 IEEE.

关键词： Recurrent neural networks

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Brain Tumor Detection and Segmentation Using Deep Learning Models 6

Brain Tumor Detection and Segmentation Using Deep Learning M...

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6th World Symposium on Artificial Intelligence, WSAI 2024

作者： Abiodun, Moses Kazeem Adeniyi, Abidemi Emmanuel Awotunde, Joseph Bamidele Aworinde, Halleluyah Tinuoye, George Imoize, Agbotiname Lucky Li, Chun-Ta Lee, Cheng-Chi Landmark University Department of Computer Science Kwara State Omu-Aran Nigeria Bowen University Department of Computer Science Osun State Iwo Nigeria University of Ilorin Department of Computer Science Kwara State Ilorin Nigeria University of Lagos Faculty of Engineering Department of Electrical and Electronics Engineering Akoka Nigeria Fu Jen Catholic University Bachelor's Program of Artificial Intelligence and Information Security New Taipei City Taiwan Fu Jen Catholic University Department of Library and Information Science New Taipei City Taiwan

ISBN: (纸本)9798350374162

This study focuses on brain tumor detection and segmentation using Convolutional Neural Networks (CNN) with architectures of Fully Convolutional Net-work (FCN) and VGG16. The dataset imported for this study consists of brain MRI images, which were preprocessed to improve the overall image resolution. Data augmentation techniques were applied to heighten the variability and volume of the training data. For feature extraction, CNN models were utilized, specifically the VGG16 architectures. This model was trained on a preprocessed dataset to learn the important features associated with the presence of brain tumor. Image segmentation was performed to precisely outline and identify the tumor region within the brain images. FCN architecture was employed for this task, which allowed pixel-level classification of tumor regions. Finally, image classification was carried out to group brain images based on the absence or presence of a tumor. The trained models were evaluated using accuracy, precision, recall and F1score as the performance metrics. The results of the study demonstrated promising outcomes, achieving an accuracy score of 93% and a precision, recall and f1score ranging from 75% to 100% based on the specific type of tumor enlisted. This indicates that the developed CNN models, with the FCN and VGG16 architectures, effectively detect and segment brain tumors from MRI images. The findings of this study contribute to the field of medical image analysis and gives a foundation for further research and development in brain tumor diagnosis and treatment. The study recommends the use of more deep learning algorithms for the process of detecting brain tumors. © 2024 IEEE.

关键词： Convolutional neural networks

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Design of Green Power Clouds for Intelligent Virtual Power Plants

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IEEE Transactions on Automation science and engineering 2024年 1-12页

作者： Ou, Ting-Chia Tieng, Hao Tsai, Tsung-Han Li, Yu-Yong Hung, Min-Hsiung Cheng, Fan-Tien Program on Smart and Sustainable Manufacturing National Cheng Kung University Tainan Taiwan Institute of Manufacturing Information and Systems National Cheng Kung University Tainan Taiwan Department of Computer Science and Information Engineering Chinese Culture University Taipei Taiwan

Traditional virtual power plants (VPPs) combine power from distributed energy resources (DER) to supply energy to users. However, they fall short of net-zero goals because of neglecting carbon footprints during power aggregation. This paper proposes a novel intelligent virtual power plant framework ( $i$ VPPF) to address this gap. $i$ VPPF comprises a central $i$ VPP ( $i$ VPP $_{\mathrm{C}})$ and several regional $i$ VPPs ( $i$ VPP $_{\mathrm{n}})$ , n $=$ E, S, M, and N. These $i$ VPP $_{\mathrm{n}}$ are geographically distributed systems for intelligently managing $i$ VPPs in four regions: east, south, middle, and north, respectively, while the $i$ VPP $_{\mathrm{C}}$ is responsible for dispatching power across $i$ VPP $_{\mathrm{n}}$ . We built $i$ VPP $_{\mathrm{C}}$ and $i$ VPP $_{\mathrm{n}}$ on individual green power clouds, which can provide abundant computing resources and realize intelligence through AI technologies for $i$ VPPF. We also design universal computing devices called cyber-physical agents (CPAs) to collect essential data on manufacturing, carbon footprint, and energy usage for $i$ VPP $_{\mathrm{n}}$ . $i$ VPP $_{\mathrm{n}}$ can intelligently control DERs based on the collected data. Also, $i$ VPPF can empower enterprises to participate in power balancing services offered by Taipower, thereby enhancing the flexibility of the overall power grid. Furthermore, we integrate $i$ VPPF with the I4.2-GiM framework, offering intelligent carbon and energy management capabilities to achieve the net-zero goal. The testing results show that $i$ VPPF can significantly reduce energy usage (up to 25.6%) and carbon emissions (up to 509 kg) through power dispatch. Thus, the proposed $i$ VPPF promises to contribute economic benefits for businesses and the pursuit of net-zero emissions. Note to Practitioners—This paper proposes an intelligent virtual power plant framework $({\it i} \text{VPPF})$ consisting of a central coordinator $({\it i}\text{VPP}_{\text{C}})

关键词： Carbon dioxide

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Multi-Normalization Residual Graph Convolutional Network for 3D Human Pose Estimation

Multi-Normalization Residual Graph Convolutional Network for...

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2025 International Workshop on Advanced Imaging Technology, IWAIT 2025

作者： Pramono, Andy Chang, I-Cheng Puspasari, Betty Dewi Animation Study Program Universitas Negeri Malang Jawa Timur Malang Indonesia Department of Computer Science and Information Engineering National Dong Hwa University Hualien Taiwan Department of Informatics Engineering Institut Teknologi Nasional Malang Jawa Timur Malang Indonesia

ISBN: (数字)9781510688131

ISBN: (纸本)9781510688124

3D human pose estimation (HPE) has improved significantly through Graph Convolutional Networks (GCNs), which effectively model body part ***, GCNs have limitations, including uniform feature transformations across nodes and reliance on skeleton-based graphs that may miss complex motion *** address these issues, we introduce a Multi-Normalization Residual Graph Convolutional Network that fine-tunes the graph structure through affinity multi-normalization and activation, allowing the representation of additional connections beyond the *** extensive ablation study shows that this approach enhances performance with minimal overhead while maintaining the same model size, consistently outperforming state-of-the-art techniques on two benchmark datasets. © 2024 SPIE.

关键词： Musculoskeletal system

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A motion-level-aware denoising framework for x-ray fluoroscopic images

A motion-level-aware denoising framework for x-ray fluorosco...

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Medical Imaging 2023: Physics of Medical Imaging

作者： Jo, Geonhui Jang, Minhee Jeon, Sun-Young Kim, Wonjin Choi, Jang-Hwan Department of Computer Science and Engineering Ewha Womans University Seoul03760 Korea Republic of Division of Mechanical and Biomedical Engineering Graduate Program in System Health Science and Engineering Ewha Womans University Seoul03760 Korea Republic of

ISBN: (纸本)9781510660311

Fluoroscopy in a low-dose tube output is used to reduce the damage associated with radiation exposure. However, lowering the radiation dose inevitably increases random noise in x-ray images, resulting in poor diagnostic image quality, which requires noise reduction for accurate diagnosis. Also, in the case of non-static objects, the image is blurred due to motion. The most-used denoiser with a recursive filter (RF) preserves details well when applied to temporal data, but it is vulnerable to motion blur. Existing convolutional neural network (CNN)-based algorithms with single-frame input cannot use the temporary context, and others with multi-frame input are good for motion detection but poor for detail preservation. Therefore, we propose a motion-level-aware denoising framework to combine the results of RF- and CNN-based algorithms depending on the pixel-wise magnitude of motion to complement each other. The data we use are fluoroscopy images taken in continuous time, and we aim at many-to-one so that one frame is denoised by considering sequential frames. Also, since both RF- and CNN-based algorithms used in our architecture are many-to-one methods, they can consider spatiotemporal information. In the multi-frame input, the difference in intensity of each pixel between frames is calculated to obtain a moving map. Depending on the factor value from the moving map, the final image is obtained by reflecting the outputs of the RF- and CNN-based algorithms. If the factor value is high, the pixel intensity of the final image is like the CNN-based output, which is good for motion detection, and vice versa, it more reflects the intensity of RF output, which is excellent in perceptual quality. Therefore, it prevents motion blur and does not over-smooth microdetails, such as bones and muscles. The results show that combining the two outputs together records higher peak signal-to-noise ratio (PSNR) and has better perceptual quality for diagnosis than using only one method. F

关键词： Deep learning

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Autonomic Computing Based Respiratory Disorders Assessment Using Speech Parameters: A Systematic Review 7

Autonomic Computing Based Respiratory Disorders Assessment U...

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7th International Symposium on Multidisciplinary Studies and Innovative Technologies, ISMSIT 2023

作者： Shrivastava, Poonam Tripathi, Neeta Dewangan, Bhupesh Kumar Singh, Bikesh Kumar Choudhury, Tanupriya Kotecha, Ketan Dewangan, Sanjana Sstc Department of Electronics & Telecommunication Chhattisgarh Bhilai India School of Engineering Op Jindal University Department of Computer Science and Engineering Raigarh496109 India National Institute of Technology Department of Biomedical Engineering Chhattisgarh Raipur India Pune411045 India Pune411045 India School of Science Op Jindal University Raigarh India

ISBN: (纸本)9798350342154

Respiratory disorders (RD) are common health problems, and they represent a huge health burden to people around the world. Nowadays, speech analysis is a very important research area in the biomedical field. Therefore, speech analysis can be a useful tool for pre-diagnosing of RD. This review paper aimed to select the features of speech affected by RD and to define autonomic computing in terms of self-care and self-management in disease assessment using the machine learning (ML) paradigm. Selected articles in this work have been published in the last decade and are written in the English language.722 articles in total were obtained, of which 15 were chosen based on the meta-analyses (PRISMA) approach, and for systematic literature reviews (SLR), the reporting items were preferred. Articles were compiled by searching different databases such as Google Scholar, Crossref, and PubMed. This SLR mainly contributed to identifying the role of autonomic computing in the application of self-management for the assessment of RD in ML classifiers. It was observed that most of the research work on respiratory disorders with self-management applying speech analysis was done during the year 2012. Then gradually less work was done in this area until 2018 and in 2019, it increased again and stayed finally stable until 2022. It was concluded that the important information provided by this SLR is efficient model to detect and characterize RDs by applying speech parameters using autonomic computing. © 2023 IEEE.

关键词： Machine learning

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Quantum Attacks on Public Cryptosystems 6

Quantum Attacks on Public Cryptosystems

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6th International Conference on Contemporary Computing and Informatics, IC3I 2023

作者： Singh, Puneet Hasabnis, Amey R. Rajpoot, Subhadra Singh, Pankaj College of Telecommunication Engineering Faculty of Communication Engineering Military MP Mhow India Amiuty University Department of Applied Sciences Uttar Pradesh Greater Noida India BITS Pilani Pilani India Department of Computer Science and Engineering Uttar Pradesh Greater Noida India

ISBN: (纸本)9798350304480

Quantum Key Distribution (QKD) networks have emerged as a revolutionary technology in ensuring secure communication in the digital age. By leveraging the principles of quantum mechanics, QKD allows two parties to establish a secret key securely, which can subsequently be used for encrypting and decrypting sensitive information. While QKD holds immense promise for enhancing cybersecurity, it also presents several challenges that necessitate further research to achieve its full potential. The research presented here examines the development of QKD networks, their real-world use, as well as the general QKD network architecture and the most recent advancements in QKD network deployments. © 2023 IEEE.

关键词： Quantum Key Distribution (QKD Networks

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An adaptive energy detection scheme using estimation of noise uncertainty for cognitive radio

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International Journal of Information Technology (Singapore) 2025年 1-6页

作者： Venugopal, Anita Gautam, Ashu Suma, S. Anbarasu, P. Kulkarni, Shailesh V. Bombale, Girisha Ramhari IT Department Dhofar University Salalah Oman ECE Manav Rachna International Institute of Research and Studies Haryana Faridabad India Department of Computer Science and Engineering CMR Technical Campus Telangana Hyderabad India Department of EEE Sri Eshwar College of Engineering Tamil Nadu Coimbatore India Department of Electronics and Telecommunication Vishwakarma Institute of Technology Pune India Shree Ramchandra College of Engineering Affiliated to Savitribai Phule Pune University Lonikand India

Cognitive radio (CR) technology is a new approach to the enhancement of the utilization of the electromagnetic spectrum. The problem of spectrum scarcity mainly stems from inefficient and irregular allocation of frequency bands. This problem is solved by CR systems by allowing better use of the available spectrum by other technologies. In this work, an adaptive energy detection method for the efficient operation of spectrum sensing (SS) in cognitive radio systems is proposed. The proposed approach engages a dynamic threshold energy detection scheme where enhanced energy of the primary signals is detected for the side lobe signals and a generalized energy detector. Depending on the activity of primary users (PU’s), the scheme determines decision thresholds based on the uncertainty in the noise value. The given adaptive mechanism enhances the detection probability (Pd) and reduces the false alarm probability (Pfa) to produce effective spectrum sensing. The results show that the proposed scheme attains the highest Pd (0.85) and the lowest Pfa (0.71), thus surpassing existing SS techniques. © Bharati Vidyapeeth's Institute of computer Applications and Management 2025.

关键词： Cognitive radio (CR) Dynamic spectrum access technique Energy detection Noise uncertainty Spectrum sensing (SS)

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