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SGDR-YOLOv8: Training Method for Rice Diseases Detection Using YOLOv8 8th

SGDR-YOLOv8: Training Method for Rice Diseases Detection Usi...

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8th international conference on Advances in computing and Data sciences, ICACDS 2024

作者： thanh, Bui Dang Anh, Mac Tuan Khanh, Giap Dang Dong, Trinh Cong Huong, Nguyen thanh School of Electrical and Electronic Engineering Hanoi University of Science and Technology Hanoi Viet Nam Institute for Control Engineering and Automation Hanoi University of Science and Technology Hanoi Viet Nam

ISBN: (纸本)9783031709050

In Vietnam, rice stands as a vital food source for the whole population, and because of that timely identification and management of rice plant diseases is extremely crucial. Among the diseases that appear in Vietnam, blast leaf, leaf folder, and brown spot are among the most prevalent ailments that harm rice plants, directly impacting cultivation. To address this problem, deep learning, a cutting-edge solution can be used to detect plant diseases. In this paper, we present an approach utilizing Deep learning to identify illnesses affecting rice leaves. YOLOv8 is employed and trained with various strategies of Stochastic Gradient Descent with Warm Restart (SGDR) to identify diseases on rice leaves. the result of the proposed method is assessed using 309 images. the experimental findings reveal an accuracy of 88.6%, indicating superior performance compared to alternative methods. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Plant diseases

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Quantum Generative Adversarial Network for Autonomous Drone Navigation in Urban Wind Zones

Quantum Generative Adversarial Network for Autonomous Drone ...

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Mobile computing and Sustainable Informatics (ICMCSI), international conference on

作者： Anumula Swarnalatha Nalluri Archana Naveen Mukkapati Subramanian Selvakumar Eneyachew Tamir Manikandaprabu P Department of Aeronautical and Aerospace Engineering KCG College of Technology Chennai Tamil Nadu India Department of Computer Science and Engineering Vignan's Foundation for Science Technology and Research Andhra Pradesh India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh India Department of Computer Engineering Bahir Dar Institute of Technology Bahir Dar University Bahir Dar Ethiopia Department of Electrical and Computer Engineering Bahir Dar Institute of Technology Bahir Dar University Bahir Dar Ethiopia Department of Mechanical Engineering Sona College of Technology Salem Tamil Nadu India

ISBN: (数字)9798331522667

ISBN: (纸本)9798331522674

Major challenges are observed when autonomous drones fly in the complex, changeable environment such as wind zones regarding accurate path planning, avoiding the object, and completing tasks. Lack of flexibility in different situations reduces the reliability of conventional models in real time. To overcome these difficulties the Savitzky-Golay Denoising CSWin Quantum Generative Adversarial Network with Emperor Penguin Optimizer known as SGDCQGANNet-EPO was developed. the RGB data and GPS-estimated wind forces in this model undergo Savitzky-Golay Denoising; this model does not rely on global data, only RGB camera data and GPS data. Recently a CSWin transformer (CSWin-T) makes feature extraction and obstacles detection as well as wind angle, wind speed and wind position identification easier. Quantum Generative Adversarial Networks (QGANet) are then used for optimized path planning, and the model is enhanced by the Emperor Penguin Optimizer (EPO) to fine tune the generator and discriminator loss functions. More accurate compared to current approaches in accuracy and efficiency, the model achieves an accuracy of 99.28%, a low RMSE of 0.38, a recall of 99.25%, and EVA of 0.95. In Confidence using this new technique, the free roaming and self-organizing drone maneuvers in complex environments achieved remarkable results.

关键词： Accuracy Navigation Wind speed Noise reduction Generative adversarial networks Transformers Path planning Data models Real-time systems Drones

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TrackNCount - Intelligent Vehicle Tracking, Counting, and Speed Estimation Using YOLOv8 and DeepSORT Algorithms

TrackNCount - Intelligent Vehicle Tracking, Counting, and Sp...

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Mobile computing and Sustainable Informatics (ICMCSI), international conference on

作者： B. Sathiyaprasad Paluru Ambareesh Ram Reddy Digumalla Manisri Venkatesh Department of Computer Science and Engineering Sathyabama institute of science and technology Chennai India

ISBN: (数字)9798331522667

ISBN: (纸本)9798331522674

For effective city planning and traffic control, it is now crucial to develop some effective monitoring systems for vehicle traffic in order to address this quickly growing tendency within a city. As a result, the TrackNCount project combines DeepSORT with YOLOv8 for object identification to create a smart system for tracking, counting, and speed estimation of vehicles. the idea behind this design is to use sophisticated computer vision techniques in conjunction with efficient algorithms to handle real-world scenarios. Setting up a reliable procedure that recognizes cars, gives them distinct IDs, and records their movements through video frames is our first priority. the system computes vehicle speeds using the Euclidean distance formula, manages item trajectories using double-ended queues, and detects entering and outgoing vehicles based on directional thresholds. Motion trails and bounding boxes are two further visual aids that improve the results' readability. With the added benefit of simple speed estimation feature integration, the model provides exceptional accuracy in real-time vehicle monitoring and counting. Experiments conducted on a variety of traffic scenarios consistently showed that this system can deliver precise vehicle counts in designated directions and speed calculations with a small margin of error. the findings show that TrackNCount is a viable and scalable solution for data-driven city planning and intelligent traffic management.

关键词： Computer vision Accuracy Urban planning Estimation Traffic control Weather modification Real-time systems Automobiles Reliability Monitoring

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Deep Neural Network and Convolutional Neural Network Implementation in 5G Enabled IoT for Enhanced Latency 6th

Deep Neural Network and Convolutional Neural Network Impleme...

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6th international conference on Advanced Informatics for computing Research, ICAICR 2023

作者： Upadhyay, Deepak Malhotra, Sonal Gupta, Mridul Yadav, Devansh Department of Computer Science and Engineering Graphic Era Hill University Dehradun India Department of Electronics and Communication Engineering Graphic Era Deemed to be University Dehradun India Department of Computer Science and Engineering Graphic Era Deemed to be University Dehradun India

ISBN: (纸本)9783031840586

the rapid proliferation of 5G networks and Internet of things (IoT) technology has created new opportunities and challenges in the quest for enhanced latency performance. this research paper explores the implementation of deep neural networks (DNNs) and convolutional neural networks (CNNs) in 5G-enabled IoT systems to address the latency challenge. By leveraging the power of DNNs, CNNs, and 5G networks, our study aims to significantly reduce latency and improve response times within IoT environments. through a systematic methodology, including data collection, model architecture design, training, integration with 5G infrastructure, and performance evaluation, we analyze the effectiveness of these advanced machine learning techniques. Our implementation work includes real-world use cases from diverse IoT domains, such as industrial automation, healthcare monitoring, and smart city infrastructure. this paper assess the impact of our implementation on latency reduction, response times, and overall system performance. the findings of this research provide valuable insights into the feasibility and benefits of integrating DNNs and CNNs in 5G-enabled IoT for enhanced latency, paving the way for improved IoT applications and services in latency-sensitive domains. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Convolutional neural networks

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A Modified Low Energy Consuming MAC Protocol for Wireless Sensor Networks 6th

A Modified Low Energy Consuming MAC Protocol for Wireless S...

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6th international conference on Computational Intelligence in Communications and Business Analytics, CICBA 2024

作者： Pedditi, Ramesh Babu Debasis, Kumar School of Computer Science and Engineering VIT-AP University Andhra Pradesh Amaravati522237 India

ISBN: (纸本)9783031813351

Wireless Sensor Networks(WSNs) are essential in diverse applications, spanning from environmental monitoring to industrial automation. Effectively managing energy consumption poses a significant challenge in WSNs, as sensor nodes are typically powered by limited energy sources such as batteries. Initially, we utilized the Honey Badger Algorithm to designate cluster heads. this method enables us to choose the optimal cluster head among all sensors, considering factors such as residual energy and node proximity. In this model, we use Maximum Cluster Size to avoid big clusters. the suggested frame work, Modified Low Energy Consuming Medium Access control (M-LECMAC), reduces energy utilization by decreasing network communications, leading to an extended WSN lifetime. In four different layouts, the proposed M-LECMAC model is contrasted with two pre-existing models, LEACH and LECMAC. Experimental results reveal that M-LECMAC surpasses both LEACH and LECMAC in prolonging the network’s lifetime. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Sensor nodes

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Revolutionizing Residential EV Charging through IoT-Enabled Smart Energy Metering for Sustainability

Revolutionizing Residential EV Charging Through IoT-Enabled ...

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Mobile computing and Sustainable Informatics (ICMCSI), international conference on

作者： N. Sivakumar S. Charles Raja M.Meenakshi Devi Saraswathi Meena R Dept. of EEE Thiagarajar College of Engineering Madurai Tamilnadu India Dept. of Applied Mathematics and Computational Science Thiagarajar College of Engineering Madurai Tamilnadu India

ISBN: (数字)9798331522667

ISBN: (纸本)9798331522674

the rapid growth in Electric Vehicle (EV) adoption poses substantial challenges to residential energy infrastructures, especially in terms of the strain it places on local grids during charging. this paper proposes an IoT-based Smart Energy Meter System (SEM) designed to efficiently manage EV charging in residential environments. the system integrates IoT technology with advanced energy metering features, enabling real-time monitoring and control of charging operations. the system is composed of intelligent energy meters for real-time monitoring, an IoT-enabled hub for data collection and processing, and an intuitive consumers interface for customizing and managing EV charging schedules. By continuously monitoring energy consumption and charging demands, the system optimizes charging schedules to reduce peak loads, lower energy costs, and enhance grid stability. It provides residents with insights into their energy usage and charging patterns, empowering them to make informed decisions to reduce their carbon footprint. Furthermore, the system seamlessly integrates with renewable energy sources and energy storage solutions, promoting clean energy usage for EV charging. through cloud-based analytics and predictive maintenance, it ensures reliable and uninterrupted charging services. the proposed IoT-based SEM system offers a scalable and sustainable solution for managing EV charging in residential settings, supporting electric mobility while improving energy efficiency and promoting environmental sustainability.

关键词： Meters Schedules Power system stability Message services Electric vehicle charging Real-time systems Stability analysis Reliability Monitoring Predictive maintenance

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RAC-Based One-Stop Professional Course Teaching Model Exploration and Practice - Taking the Course of "Microcomputer Principles and Interface Technology" as an Example 6th

RAC-Based One-Stop Professional Course Teaching Model Explor...

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6th international conference on Computer science and Educational Informatization, CSEI 2024

作者： Wan, Xiaorong Li, Yingna Luo, Chuan Li, Chuan You, Jinguo Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming650500 China Yunnan Key Laboratory of Computer Technology Applications Kunming650500 China

ISBN: (纸本)9789819637379

In the context of the country’s requirement for academic disciplines to be aligned and mutually reinforcing with industry chains, innovation chains, and talent chains, the integration of education and industry has become an important direction for the reform of higher education. In response to the iterative demand for edge intelligence in the Intelligent Internet of things (AIOT), the course "Microcomputer Principles and Interface Technology" is deeply integrated with industry and education, establishing a hierarchical model of resource, capability, and commercialization (RAC) layers. It adopts a "one-stop" teaching mainline that starts with microcomputer theory knowledge, relies on hardware development boards, project orientation, competition innovation, and product commercialization training "five points and one line", connects knowledge product channels, integrates ideological and political education in courses, and constructs a multi-dimensional and comprehensive evaluation system to achieve the integration of industry and education from universities to enterprises, classrooms to markets, and from knowledge to products. the innovative teaching mode of this course has achieved significant results in innovative practice. this innovative teaching mode can effectively achieve the knowledge, ability, and quality goals of cultivating new engineering talents, and effectively improve the quality of talent cultivation and teaching. this teaching reform and innovation provide a reference and idea for further exploring the talent cultivation mode of schools and enterprises. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Teaching

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An Advanced Deep Learning Detection of Rice Plant Diseases Based on Residual Neural Networks 8th

An Advanced Deep Learning Detection of Rice Plant Diseases B...

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8th international conference on Advances in computing and Data sciences, ICACDS 2024

作者： Huong, Nguyen thanh Lan, Nguyen Dang Dong, Trinh Cong thanh, Bui Dang School of Electrical and Electronic Engineering Hanoi University of Science and Technology Hanoi Viet Nam Institute for Control Engineering and Automation Hanoi University of Science and Technology Hanoi Viet Nam

ISBN: (纸本)9783031709050

For countries with the large rice export in the world, taking care of rice plants for them to grow healthily is very necessary. One of the techniques is to detect diseases promptly, at the right time, on the right diseases in order that necessary treatment can be taken to minimize the harmful effects of diseases on rice plants as well as prevent them from breaking out into epidemics. Previously, disease detection was carried out manually, which took a lot of time and labor. Nowadays, automated methods have been increasingly applied for precision agriculture applications. In the article, a method to automatically detect rice diseases based on machine learning, especially deep learning concentrated method is proposed. In this article, our study concentrates on detecting four most common rice diseases: brown spot, hispa, blast and blight. the dataset for training, testing and validation collected and constructed from Kaggle and Google dataset includes approximately 400 images for each type of rice leaf diseases. From the experiment to assess the performance of the proposed learning method, the accuracy can reach approximately 97%. thereby, the method shows feasibility and reasonableness, with extensively potential usage for practical precision agriculture application. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Adversarial machine learning

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Analysis of Hand Shapes with Convolutional Neural Networks for Sign Language Recognition 5th

Analysis of Hand Shapes with Convolutional Neural Networks f...

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5th international conference on Recent Developments in control, automation and Power engineering, RDCAPE 2023

作者： Bhatia, Sandeep Naib, Bharat Bhushan Goel, Amit Kumar Verma, Anurag Gangwar, Sachin Goel, Neha School of Computing Science and Engineering Galgotias University Uttar Pradesh Greater Noida India School of Engineering and Technology Apeejay Stya University Sohna Gurugram India Department of Electronics and Communication Engineering RKGIT Ghaziabad Uttar Pradesh Ghaziabad India Department of ECE RKGIT Ghaziabad India

ISBN: (纸本)9789819600465

According to the late theoretical physicist Stephen Hawking, having a disability need not prevent you from succeeding. People with talents should always work to make the lives of disabled people better. the deaf and dumb benefit from a variety of technological developments and studies. Numerous message boards are used by people to spread their message. Text messages can now be sent using rewritable pads, however doing so requires time and isn’t always successful. Technologies like machine learning and open CV can be employed to progress the situation. these can be created into automatic editors for anyone with limitations like as speech or hearing loss. Hand gestures can be used to convey messages. With the aid of open CV and machine learning, we can capture their hand motions and translate them into a language that we can all comprehend. this study article will look at how to implement a model that will assist the disabled with this problem and challenges associated with sign language recognition. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Motion analysis

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Smooth Imaging System Using Learning Generative Adversarial Network 5th

Smooth Imaging System Using Learning Generative Adversarial ...

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5th international conference on Recent Developments in control, automation and Power engineering, RDCAPE 2023

作者： Saraswat, Shipra Rani, Kajal Kanotra, Ishita Singh, Aditya Kumar Amity School of Engineering and Technology Amity University Noida Noida India Department of Computer Science and Engineering ABES Institute of Technology Affiliated to AKTU Uttar Pradesh Lucknow Ghaziabad India

ISBN: (纸本)9789819600465

A method for smoothing the blur images by deblurring them using GAN is discussed in the paper. the method includes learning techniques to detect the type of filters applied to the image, all possible edges present in the image and counting all those details into account. this method makes assumptions about the filters on the image and uses techniques to de-filter all the affected pixels. this method can also handle the unconstrained blurs caused due to overexposure, underexposure, shaking of the camera lens, zooming of pixels and optical noise. Due to these unconstrained measures of the photograph, the image becomes blurred or semi-blur. the proposed method also focuses on constrains of prior information about blurring filters and parametric definitions of filters used. this method detects all possible blurred edges in its own way using the contrast of all blurry areas. the colour contrasts of blurred images are different from that of deblurred images. Also, it can work on both single-frame and multi-frame scenarios. the constrained parameters of the blurry areas in multi-frames are used in achieving improved quality images. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Discriminators

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