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Apple Leaf Disease Identification Using Resnet50

Apple Leaf Disease Identification Using Resnet50

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2024 international conference on Innovative computing, intelligent Communication and Smart Electrical systems, ICSES 2024

作者： Karthigaiveni, S. Archana, R. Ravi, Vasanth Khatoon, P Salma Chennai Institute of Technology Department of Electronics and Communication Engineering Chennai India St. Joseph's Institute of technology Department of Artificial intelligence and Data science OMR Chennai India Jain Deemed to be University Faculty of Engineering and Technology Department of Computer Science and Engineering Jain Global Campus India Mlr Institute of Technology Department of CSE-DS Hyderabad India

ISBN: (纸本)9798331543617

Apple leaf disease is a primary issue limiting apple yield and quality. The standard diagnostic approach requires considerable time for illness detection, causing farmers to frequently miss the optimal period for prevention and treatment. The detect of leaf diseases through leaf imagery is a vital study area in computer vision, with the primary objective being to devise an effective method for representing images of sick leaves. The efficient diagnosis and categorization of plant diseases constitutes a significant research aim in agriculture. This paper proposes a novel deep learning, ResNet. To evaluate the efficacy of the ResNet model, a number of comparative experiments were performed with leading transfer learning methodologies. This demonstrates that the proposed method is both possible and successful. © 2024 ieee.

关键词： CNN Diseases Leaf Resnet Transfer Learning

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Distributed data Storage Architecture Model and data Platform Design for Power Grid Dispatching 3

Distributed Data Storage Architecture Model and Data Platfor...

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3rd international conference on Energy and Electrical Power systems (ICEEPS)

作者： Xu, Yuan Su, Yinsheng Zhao, Ligang Zhou, Baorong Tang, Yingqi Huang, Guanbiao Hu, Jiaming Li, Kaihang CSG Elect Power Res Inst State Key Lab HVDC Technol Guangzhou Peoples R China CSG Power Dispatching & Control Ctr Guangzhou Peoples R China South China Univ Technol Sch Elect Power Engn Guangzhou Peoples R China

ISBN: (纸本)9798350375145;9798350375138

The construction of a distributed heterogeneous data platform for power grid dispatching faces challenges of diversity, large scale, and high performance. However, existing data platform design methods in both the power and computer science fields struggle to meet practical production requirements effectively. This paper constructs a distributed data storage architecture model for power grid dispatching, defining the elements and their relationships within the architecture. Additionally, it proposes methods for managing massive source data and distributed heterogeneous database clusters. Based on these findings, a power grid dispatching business data platform is designed. Test results indicate that the proposed architecture effectively supports the efficient execution of power grid dispatching business, providing a specialized data platform design paradigm for the power industry.

关键词： data architecture model distributed heterogeneous database cluster power grid dispatching data platform design metadata

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A dataset on Human-Cobot Collaboration for Action Recognition in Manufacturing Assembly 10

A Dataset on Human-Cobot Collaboration for Action Recognitio...

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10th international conference on Control, Decision and Information Technologies (CoDIT)

作者： Romeo, L. Maselli, M., V Dominguez, M. Garcia Marani, R. Nicora, M. Lavit Cicirelli, G. Malosio, M. D'Orazio, T. Natl Res Council CNR Italy Inst Intelligent Ind Syst & Technol Adv Mfg STIIM Rome Italy Univ La Rioja Dept Math & Comp Sci Logrono Spain

ISBN: (纸本)9798350373981;9798350373974

This paper introduces a dataset on Human-cobot collaboration for Action Recognition in Manufacturing Assembly (HARMA(3)). It is a collection of RGB frames, Depth maps, RGB-to-depth-Aligned (RGB-A) frames and Skeleton data relative to actions performed by different subjects in collaboration with a cobot for building an Epicyclic Gear Train (EGT). In particular, 27 subjects executed several trials of the assembly task, which consisted of 7 actions. data were collected in a laboratory scenario using two Microsoft (R) Azure Kinect cameras positioned in frontal and lateral positions. The dataset represents a good foundation for developing and testing advanced action recognition as well as action segmentation systems with far-reaching implications beyond human-cobot collaboration. Further potential applications include Computer Vision, Machine Learning, and Smart Manufacturing. Preliminary experiments for action segmentation by applying a state-of-the-art method on features extracted from RGB and skeletal data are presented in this paper, showing high-performance rates.

关键词： Smart manufacturing

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data Collection Strategies for Preparing ML Model for Prediction of Injuries in Gymnastics 3

Data Collection Strategies for Preparing ML Model for Predic...

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3rd international conference on Ubiquitous computing and intelligent Information systems, ICUIS 2023

作者： Bhoir, Pawan Narawade, Vaibhav Ramrao Adik Institute of Technology D y Patil Deemed to Be University Research Scholar Dept of Information Technology Nerul India Ramrao Adik Institute of Technology D y Patil Deemed to Be University Dept of Information Technology Nerul India

ISBN: (纸本)9798350306989

Gymnastics is a sport that is associated with a high risk of injury. Injury prevention is crucial in gymnastics to ensure the safety and well-being of athletes. Machine learning (ML) models can be used to predict the likelihood of injuries in gymnastics, but the accuracy of these models depends on the quality and quantity of the data used to train them. In this paper, we describe the data collection strategies used to gather injury data for the development of an ML model for predicting injuries in gymnastics. We discuss the strengths and limitations of these strategies and suggest areas for future research. Our findings highlight the importance of collecting comprehensive and standardized injury data to improve injury prediction models and ultimately, injury prevention in gymnastics. © 2023 ieee.

关键词： Sports

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IRS-Aided Caching with Channel Power-Delay-Profile via DDPG and Self-Supervision Learning 9

IRS-Aided Caching with Channel Power-Delay-Profile via DDPG ...

引用

9th international conference on Computer and Communication systems (ICCCS)

作者： Huang, Huang Zhou, Fasheng Guangzhou Univ Sch Elect & Commun Engn Key Lab On Chip Commun & Sensor Chip Guangdong Hi Guangzhou Peoples R China

ISBN: (纸本)9798350350227;9798350350210

The 5G information age requires a large number of marginal nodes distributed around users to provide digital services. By caching and processing data at edge nodes close to users, edge caching technology can effectively collaborate with cloud computing and edge computing to improve the transmission efficiency as well as network response time. Meanwhile, intelligent reflective surface (IRS) is a promising technology for achieving high reductions in hardware cost and power consumption as compared to traditional relaying systems. It provides reliable and scalable backhaul transmission for base stations and access points in ultra-dense networks, which is in line with the requirements of 5G green technology. Therefore, in this paper, we propose a collaborative edge caching method that effectively combines IRS-aided wireless communication and channel power-delay-profile to design joint caching decisions in a centralized manner through limited cache capacity, improving the quality of content delivery in information transmission. Then we proposed a deep deterministic policy gradient learning method based on the framework of deep reinforcement learning along with self-supervision learning to predict IRS phases and collaborative caching strategies. The numerical simulation results show that the proposed algorithm has better convergence speed and learning accuracy than that of the existing algorithms, and is expected to be applied for future massive and dynamical content delivery applications.

关键词： Deep deterministic policy gradient (DDPG) edge caching intelligent reflecting surface average power-delay-profile(APDP) spectrum efficiency (SE) self-supervision learning

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Empowering smart traffic avoidance using Natural language processing 3

Empowering smart traffic avoidance using Natural language pr...

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3rd international conference on intelligent and Innovative Technologies in computing, Electrical and Electronics, IITCEE 2025

作者： Kalaiselvi, V.K.G. Vishnu Chandar, K.R. Vishwa, G. Tirunagari, Sharanya Hariharan, Shanmugasundaram Krishnamoorthy, Murugaperumal Sri Sairam Engineering College Department of Information Technology Tamilnadu India Vardhaman College of Engineering Department of Artificial Intelligence and Data Science Hyderabad India Vardhaman College of Engineering Department of Electrical and Electronics Engineering Hyderabad India

ISBN: (纸本)9798331515911

The integration of Natural Language Processing (NLP) into smart traffic systems offers a novel approach to enhancing real-time traffic management and optimization. This paper explores how NLP can be leveraged to interpret and analyze vast amounts of unstructured traffic-related data, such as social media updates, traffic reports, and incident logs. A key component of this approach is the use of a BERT (Bidirectional Encoder Representations from Transformers) classifier, which enables more accurate extraction and classification of relevant traffic information from textual data. By processing this data in real time, the BERT-based NLP system can provide more precise traffic predictions, identify congestion patterns, and optimize traffic flow dynamically. The study highlights the potential of NLP, particularly through the use of BERT, in improving the efficiency and responsiveness of traffic management systems, ultimately leading to reduced travel times, lower emissions, and enhanced urban mobility. The app also includes user-friendly interfaces that allow drivers and city planners to access real-time updates, personalized route recommendations, and predictive analytics. The system's adaptability ensures that it can respond quickly to unexpected incidents, such as accidents or road closures, further enhancing urban mobility. The study highlights the transformation potential of this app in improving the efficiency and responsiveness of traffic management systems, ultimately leading to reduced travel times, lower emissions, and a significant improvement in overall urban transportation experiences. © 2025 ieee.

关键词： Efficiency

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Beamforming Technique for Terahertz Massive MIMO System Aided by Holographic Reconfigurable intelligent Surfaces using Approximate Message Passing

Beamforming Technique for Terahertz Massive MIMO System Aide...

引用

2024 international conference on Integrated Circuits, Communication, and computing systems, ICIC3S 2024

作者： Suban, A. Vasanth, S. Vigneshwaran, M. Velammal College of Engineering and Technology Department of ECE Madurai India

ISBN: (纸本)9798350364088

In the rising landscape of 5G technology, the surge in device proliferation and population growth heralds a massive increase in expected users. To harness the full potential of this expansion, we propose a novel approach integrating Terahertz (THz) technology with Massive MIMO (Multiple-input and Multiple-output) systems, bolstered by Holographic Reconfigurable intelligent Surfaces (RIS). This innovation aims to amplify data rates while mitigating the high-power consumption inherent in conventional methods. Our methodology prioritizes adaptability, addressing the dynamic nature of user mobility and channel variability by leveraging reconfigurable channels facilitated by RIS. By harnessing shorter wavelengths enabled by THz frequencies, we not only enhance data transmission efficiency but also circumvent the need for longer wavelengths, thereby reducing power consumption. To optimize system performance further, we employ a sum rate maximization holographic beamforming algorithm, tailored to minimize power consumption while maximizing throughput. Additionally, we tackle the challenge of high interference by meticulously designing beamforming vectors. Our approach unfolds in two stages: first, the design of a Terahertz Massive MIMO system, and second, the prediction of RIS channels utilizing the Approximate Message Passing (AMP) technique. © 2024 ieee.

关键词： Message passing

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Innovating Smart Buildings and Cities Through Agent-Based Modeling, Discrete Event Simulation and Sensor-Based data Analytics

Innovating Smart Buildings and Cities Through Agent-Based Mo...

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ieee international Workshop on intelligent data acquisition and advanced computing systems: Technology and Applications

作者： Juliane Lopinski Sabine Sachweh Claudia Müller IDiAL Institute University of Applied Sciences and Arts Dortmund Dortmund Germany University Siegen Siegen

The paper emphasizes the significance of sensor-based data analytics (SBDA) for modeling and simulation in a smart city, using the methods discrete event simulation (DES) and agent-based modeling (ABM). Moreover, the project reviewing paper presents several selected project examples, such as CitySim, AIRE, Smart Grid, and the Gotland project that demonstrate the potential of SBDA in smart buildings and urban systems and analyse possible advantages of the application of the SBDA method. Furthermore, the result table demonstrates the possibilities of using SBDA in ABM and DES in other sectors, thus obtaining accurate results in a modeling system in the future. These findings have significant implications for policymakers, urban planners, and stakeholders in smart city development who seek to enhance the quality of life for urban citizens. The paper contributes to a better understanding of advantages of using sensor-based data analysis in ABM and DES and the method's potential benefit for other sectors. It provides a basis for analysis on some methodologies in the field of smart cities and buildings. It is intended to serve as an overview for future researchers and to contribute to the understanding of general concepts of research institutions.

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Empirical Analysis and AI based intelligent Evaluation of Korean Teachers' Guiding Ability in MOOC based on data Clustering

Empirical Analysis and AI based Intelligent Evaluation of Ko...

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2022 international conference on Sustainable computing and data Communication systems, ICSCDS 2022

作者： Li, Haiyan Eastern Liaoning University Dandong118001 China

ISBN: (纸本)9781665478847

Evaluation of Korean teachers' ability in MOOC environment based on the fingerprint data collection with the AI tools and the recognition is conducted in this paper. There are many data protocols in the wireless sensor networks, but cluster-based hierarchical routing protocols have received more attention due to improved scalability. Hence, this paper is based on the traditional model to construct the efficient data collection methods. The estimation model is validated through the test on the collected data. This research study has simulated the proposed algorithm and the evaluation accuracy is high. Furthermore, to estimate the performance of the model under different conditions, the AI model is designed to meet with the high frequency model scenario, and the traditional MOOC model is improved. © 2022 ieee.

关键词： data acquisition

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Towards Real-world Deployment of NILM systems: Challenges and Practices 14

Towards Real-world Deployment of NILM Systems: Challenges an...

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14th ieee international conference on Sustainable computing and Communications, SustainCom 2024

作者： Xue, Junyu Zhang, Yu Wang, Xudong Wang, Yi Tang, Guoming The Chinese Univ of Hong Kong Hong Kong Hong Kong The Chinese Univ of Hong Kong Shenzhen China Southern University of Science and Technology Shenzhen China Pengcheng Laboratory Shenzhen China Heyuan Bay Area Digital Economy Technology Innovation Center Heyuan China Guangzhou China

ISBN: (纸本)9798331521059

Non-intrusive load monitoring (NILM), as a key load monitoring technology, can much reduce the deployment cost of traditional power sensors. Previous research has largely focused on developing cloud-exclusive NILM algorithms, which often result in high computation costs and significant service delays. To address these issues, we propose a three-tier framework to enhance the real-world applicability of NILM systems through edgecloud collaboration. Considering the computational resources available at both the edge and cloud, we implement a lightweight NILM model at the edge and a deep learning based model at the cloud, respectively. In addition to the differential model implementations, we also design a NILM-specific deployment scheme that integrates Gunicorn and NGINX to bridge the gap between theoretical algorithms and practical applications. To verify the effectiveness of the proposed framework, we apply real-world NILM scenario settings and implement the entire process of data acquisition, model training, and system deployment. The results demonstrate that our framework can achieve high decomposition accuracy while significantly reducing the cloud workload and communication overhead under practical considerations. © 2024 ieee.

关键词： Edge-cloud collaboration Energy management Machine learning Non-intrusive load monitoring

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