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Plant Leaf Disease Detection and Classification Using MobileNetV3 and Wavelet Kernel Extreme Learning Machine 3

Plant Leaf Disease Detection and Classification Using Mobile...

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3rd ieee international conference on distributed computing and Electrical Circuits and Electronics, ICDCECE 2024

作者： Shruthi, B.S. Narasimha Murthy, M.S. Prakash, Chidurala Sai Riad Al-Fatlawy, Ramy Sundaram, S. Meenakshi Malnad College Of Engineering Department Of Information Science Hassan India Bms Institute Of Technology And Management Department Of Computer Science And Engineering Bangalore India Sr University Department Of Electrical And Electronics Engineering Warangal India College Of Technical Engineering The Islamic University Department Of Computers Techniques Engineering Najaf Iraq Nitte Meenakshi Institute Of Technology Department Of Ai & Ds Bengaluru India

ISBN: (纸本)9798350318609

Detection of plant disease is the most important aspect in agriculture, aimed at identifying and diagnosing disease that affect plants. If the plant disease detected earlier, that helps to avoid risk from damaging plants and the soil. However, there is a difficulty to differentiate the diseased and healthy leaves due to similarity of disease symptoms. To solve this problem, an effective Wavelet Kernel Extreme Learning Machine (WKELM) classifier is proposed for distinguishing diseased and healthy plants. Images from the PlantVillage dataset are preprocessed using Bilateral filter and then segmented by Adaptive Gaussian Threshold (AGT) to separate the diseased portion from leaf. MobileNetV3 a Convolutional Neural Network (CNN) used for effective feature extraction with the hyper parameters which are tuned using effective Emperor Penguin Optimizer (EPO) and classification is done by the WKELM for accurate results of plant disease. The experimental results of the proposed model performed by the evaluation metrics achieved accuracy of 99.7%, recall of 99.6%, precision of 99.6%, F1-score of 99.7% which is greater than other existing deep learning models like CNN, Transfer learning with Deep CNN models, CNN - Visual Geometry Group (VGG). © 2024 ieee.

关键词： Convolutional neural networks

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Optimized Operation Model of Virtual Power Plant Considering Carbon Trading 3

Optimized Operation Model of Virtual Power Plant Considering...

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3rd international conference on Smart Grids and Energy systems, SGES 2024

作者： Zhang, Jian Zhang, Kai Zhang, Ye Li, Qingchun Feng, Zhanwen Dong, Bo Liaoning Electric Power Trading Center Co. Ltd. Liaoning China

ISBN: (纸本)9798331539634

In the face of global fossil energy shortages and environmental challenges, renewable energy generation, represented by wind and solar, is growing rapidly worldwide. However, these distributed energy sources are difficult to participate in the electricity market independently due to the volatility of their output. By integrating wind and solar power generation and energy storage systems and jointly participating in electricity market transactions, virtual power plants effectively solve the instability and randomness of distributed power generation in market operation. In addition, as an emerging participant in the power market, the virtual power plant's participation in the carbon trading market not only helps to promote the utilization of new energy sources but also enhances its overall economic returns through environmental benefits. This study explores the optimization operation strategy of virtual power plants in the carbon trading and electricity market. © 2024 ieee.

关键词： distributed energy

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2024 ieee international conference on Blockchain and distributed systems Security, ICBDS 2024

2024 IEEE International Conference on Blockchain and Distrib...

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3rd ieee international conference on Blockchain and distributed systems Security, ICBDS 2024

ISBN: (纸本)9798350354348

The proceedings contain 186 papers. The topics discussed include: blockchain-based decentralized digital forensics case management system using IPFS;design and implementation of public distribution system using blockchain;blockchain solutions for nursing: a new paradigm in healthcare security and data management;unlocking the potential of blockchain integration in secured framework of mental health;detection of fake physical certificates using a blockchain-based certificate verification and issuer validation system;a collaborative health care system: a pilot based on blockchain;blockchain based goods authentication system;blockchain based digital multimedia content authentication system: using IPFS and Ethereum;and decentralized applications for agricultural supply chain: exploring the potential on the Ethereum blockchain using smart contracts.

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Analysis of Highly Sensitive Gas sensor Based on Tunnel FETs: A Review 3

Analysis of Highly Sensitive Gas Sensor Based on Tunnel FETs...

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3rd ieee international conference on Power Electronics, Intelligent Control and Energy systems, ICPEICES 2024

作者： Bhatt, Jai Kumar Gopal, Girdhar Verma, Tarun Engineering Malaviya National Institute of Technology Department of Electronics and Communication Rajasthan Jaipur302017 India School of Electronics Indian Institute of Information Technology Himachal Pradesh Una177209 India

ISBN: (纸本)9798350387193

This work reviews the hydrogen Gas sensors based on TFETs that utilizes multiple current injection methods for quantum mechanical band-to-band tunneling to achieve significantly higher performance than a conventional metal-oxide-semiconductor field-effect transistor (MOSFET) for gas species detection in ambient conditions The source region is employed to improve gate control over tunneling junction electrostatics, leading to increment in drain current sensitivity over the standard Tunnel-FET. The fundamental physics of the sensor is investigated using a thorough electrostatic analysis of the tunneling junction in the presence of gas molecule adsorption. The sensitivity is examined for different gate and drain biases, and the acceptable biasing range of operation is advised. The paper's primary contribution is a comparative study of sensors, including the gate bias dependence of sensitivity and the relationship between required work-function modulation and subthreshold swing. © 2024 ieee.

关键词： MOS devices

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Advanced Camouflage Robot for Military Spying Applications using sensor Fusion and Machine Learning 3

Advanced Camouflage Robot for Military Spying Applications u...

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3rd ieee international conference on Blockchain and distributed systems Security, ICBDS 2024

作者： Kulkarni, Neeraj P Khan, Subim N. Rathod, Rahul Ramkishan Choure, Ajay Raosaheb Chinchole, Mahesh Gokul Jspm Rajarshi Shahu College of Engineering Tathawade Pune India Soet D Y Patil University Ambi Pune India Bhivarabai Sawant Institute Of Technology And Research E&tc Department Wagholi Pune India Nutan Maharashtra Institute of Engineering and Technology E&tc Department Pune India

ISBN: (纸本)9798350354348

The current defense application scenario. the ability to blend into the environment is crucial for reducing detection and enhancing operational efficiency. This paper presents the development of an advanced camouflage robot to dynamically adapt its color to match its surroundings. The proposed system utilizes a combination of machine learning, computer vision, and sensor fusion to achieve camouflaging using the RGB LED panels mounted on the robot to adapt to the color of the surroundings. The robot is equipped with a video camera system that captures real-time video feed of the environment. The captured video is then processed by a trained machine learning model, which analyzes the color distribution in the surroundings. Simultaneously, the robot uses RGB color sensors to accurately detect the colors present in its immediate vicinity. The data from both the camera and the sensors are integrated to determine the predominant color around the robot. The hybrid camouflaging approach involves feeding the combined data into the machine learning model, which then calculates the color of maximum distribution. The robot then activates its RGB panels, adjusting their color to blend with the identified predominant color in the environment thereby making it difficult to locate. This process allows the robot to effectively camouflage itself, minimizing its visual footprint. KNN and sensor fusion techniques are used to achieve this task. © 2024 ieee.

关键词： Nearest neighbor search

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A Hybrid Machine Learning Intrusion Detection System for Wireless sensor Networks 20

A Hybrid Machine Learning Intrusion Detection System for Wir...

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20th ieee international Wireless Communications and Mobile computing (ieee IWCMC) - Green and Intelligent Communications

作者： Zhang, Hongwei Zaman, Marzia Jain, Achin Sampalli, Srinivas Dalhousie Univ Fac Comp Sci Halifax NS B3H 4R2 Canada Cistel Technol Res & Dev Ottawa ON K2E 7V7 Canada Norleaf Networks Res & Dev Gatineau PQ J8Y 2V5 Canada

ISBN: (纸本)9798350361261;9798350361278

Federated Learning (FL) has emerged as a novel distributed Machine Learning (ML) approach, to tackle the challenges associated with data privacy and overload in ML-based intrusion detection systems (IDSs). Drawing inspiration from the FL architecture, we have introduced a hybrid ML IDS tailored for Wireless sensor Networks (WSNs). This system is crafted to leverage ML for achieving a two-layer intrusion detection mechanism in WSNs free from constraints posed by specific attack types. The architecture follows a server-client model compatible with the configuration of sensor nodes, sink nodes, and gateways in WSNs. In this setup, client models located at sink nodes undergo training using sensing data while the server model at the gateway is trained using network traffic data. This two-layer training approach amplifies the efficiency of intrusion detection and ensures comprehensive network coverage. The results derived from our simulation experiments corroborate the effectiveness of the proposed hybrid ML IDS. It generates precise aggregation predictions and leads to a substantial reduction in redundant data transmissions. Furthermore, the system exhibits efficacy in detecting intrusions through a dual validation process.

关键词： Wireless sensor Networks Intrusion Detection System Network Security Hybrid Machine Learning Aggregation Prediction Algorithm Federated Learning Ensemble Learning

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Load Balancing in Cloud computing Environment using Butterfly Optimization Algorithm 3

Load Balancing in Cloud Computing Environment using Butterfl...

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3rd ieee international conference on Mobile Networks and Wireless Communications, ICMNWC 2023

作者： Alzubaidi, Laith H. Arabi, Mohammed Yousif Kumar, Bura Vijay Kumar, M. Hema Almoussawi, Zainab Abed The Islamic University Najaf Iraq National University of Science and Technology Dhi Qar Iraq School of Computer Science and Artificial Intelligence SR University Telangana Warangal India Electronics and Communication Engineering Sona College of Technology Salem India College of Islamic Science Ahl Al Bayt University Karbala Iraq

ISBN: (纸本)9798350317022

Load balancing is the process of distributing the incoming traffic of the network among various resources and servers. When one of the servers fails, the load balancer transfers the network traffic to the other servers across the group. As soon as a server is joined to the group, the load balancer begins delivering traffic to that server as part of its balancing mechanism. It is a challenging effort to use distributed storage systems to manage a big amount of data and the synchronisation issue between many consumers or producers. In order to address this issue, this research proposal offers payload benchmarking with dynamic load balancing in distributed streaming storage systems using Butterfly Optimization Algorithm (BOA). The main objective of the optimization technique is to analyze the benchmarking data and find the failure prediction of Kafka benchmarking. The BOA is used to solve optimization problems, particularly when there is insufficient or faulty data. To achieve the highest throughput from the streaming storage system, the performance of the benchmarking tool for distribution has been enhanced. The performance of the Kafka and DevOps systems is validated by the parameters of network latency as 3800ms, Execution time as 970ms, Memory size as 385kb, and Bandwidth as 380kb. The performance of the benchmarking tool for distributing the streaming storage system has been improved to achieve the maximum possible throughput from the streaming storage system. © 2023 ieee.

关键词： Butterfly Optimization Algorithm Cloud computing distributed storage systems Kafka benchmark Load balancing

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Proceedings - 3rd international conference on Smart Technologies, Communication and Robotics 2023, STCR 2023

Proceedings - 3rd International Conference on Smart Technolo...

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3rd international conference on Smart Technologies, Communication and Robotics, STCR 2023

ISBN: (纸本)9798350370867

The proceedings contain 82 papers. The topics discussed include: advanced malware analysis and prevention;design and analysis of Vivaldi antenna by an iterative method;EGFD-AROMA-based pattern forecasting with crime rate level identification;early detection and prognosis of brain tumor from micro array gene data using machine learning classifiers;handwritten digit recognition using fine-tuned convolutional neural network model;design of high gain 5G millimeter wave micro strip patch antenna for wireless applications;using machine learning regression model to predict the optimum election algorithm for parallel and distributed computing systems;a design thinking approach of metaheuristic empowerment for energy-efficient and optimized routing protocol in IoT-enabled wireless sensor networks;and an overview on IoT architecture application layer and security threats.

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Approximation Algorithm for Connected Submodular Function Maximization Problems 44

Approximation Algorithm for Connected Submodular Function Ma...

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44th ieee international conference on distributed computing systems (ICDCS)

作者： Xu, Wenzheng Xue, He Li, Ling Liang, Weifa Xu, Zichuan Zhou, Pan Jia, Xiaohua Das, Sajal K. Sichuan Univ Coll Comp Sci Chengdu Peoples R China Sichuan Fire Sci & Technol Res Inst MEM Chengdu Peoples R China Hong Kong Polytech Univ Dept Comp Hong Kong Peoples R China City Univ Hong Kong Dept Comp Sci Hong Kong Peoples R China Dalian Univ Technol Sch Software Dalian Peoples R China Huazhong Univ Sci & Technol Sch Cyber Sci & Engn Wuhan Peoples R China Missouri Univ Sci & Technol Dept Comp Sci Rolla MO 65409 USA

ISBN: (纸本)9798350386066;9798350386059

In this paper, we study a connected submodular function maximization problem, which arises from many applications including deploying UAV networks to serve users and placing sensors to cover Points of Interest (PoIs). Specifically, given a budget K, the problem is to find a subset S with K nodes from a graph G so that a given submodular function f(S) on S is maximized while the induced subgraph G[S] by the nodes in S is connected, where the submodular function f can be used to model many practical application problems, such as the number of users within different service areas of the deployed UAVs in S, the sum of data rates of users served by the UAVs, the number of covered PoIs by placed sensors, etc. We then propose a novel 1-1/e/2h+2-approximation algorithm for the problem, improving the best approximation ratio 1-1/e/2h+2 for the problem so far, through estimating a novel upper bound on the problem and designing a smart graph decomposition technique, where e is the base of the natural logarithm, h is a parameter depends on the problem and its typical value is 2. In addition, when h = 2, the algorithm approximation ratio is at least 1-1/e/5 and may be as large as 1 in some special cases when K <= 21, and is no less than 1-1/e/6 when K >= 22, compared with the current best approximation ratio 1-1/e/7 (= 1-1/e/2h+3) for the problem. We finally evaluate the algorithm performance in the application of deploying a UAV network. Experimental results demonstrate the number of users within the service area of the deployed UAV network by the proposed algorithm is up to 7.5% larger than those by existing algorithms, and its empirical approximation ratio is between 0.7 and 0.99, which is close to the theoretical maximum value one.

关键词： UAV deployment sensor placement submodular function maximization approximation algorithms

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2023 ieee 3rd international conference on Intelligent Technology and Embedded systems, ICITES 2023

2023 IEEE 3rd International Conference on Intelligent Techno...

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3rd ieee international conference on Intelligent Technology and Embedded systems, ICITES 2023

ISBN: (纸本)9798350343755

The proceedings contain 23 papers. The topics discussed include: location privacy protection method on mobile social mode;jointly events extraction for database alarm based on dynamic matching strategy and GCN;distributed PV sharing service mechanism for park based on the concept of sharing economy;distributed energy sharing service mechanism for prosumers in the IoT-enable integrated energy park;research on big data intelligent analysis method and key technologies for the management of main equipment in power grid transmission and transformation based on digital twin technology;study on the medium fusion data storage technology for high performance airborne sensors;and lightweight design with variable density honeycomb structures for mission-critical embedded devices.

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