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检索条件"任意字段=2023 3rd International Conference on Electrical Engineering and Computer Technology, ICEECT 2023"

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Revolutionizing Rice Farming: Automated Identification and Classification of Rice Leaf Blight Disease Using Deep Learning 3

Revolutionizing Rice Farming: Automated Identification and C...

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3rd international conference on Secure Cyber Computing and Communications, ICSCCC 2023

作者： Kukreja, Vinay Sharma, Rishabh Vats, Satvik Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Graphic Era Hill University Computer Science and Engineering Dehradun India

ISBN: (纸本)9798350300710

Rice is considered a fundamental dietary component for a significant portion of the global population and is regarded as a crucial crop for ensuring food security on a global scale. Rice crops are susceptible to diverse diseases, among them rice leaf blight disease, which is instigated by the fungus Helminthosporium oryzae. The rice leaf blight disease has the potential to result in substantial reductions in crop yield and poses a significant risk to rice cultivation across various regions globally. The current study introduces a novel approach for the multi-classification of rice leaf blight disease utilizing a hybrid model that combines Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) techniques. The model is designed to classify the disease based on six distinct levels of severity. A dataset comprising 6000 images of the rice crop was gathered and subjected to pre-processing and augmentation techniques to enhance the model's efficacy. The proposed model attained a total accuracy of 95.25% in detecting and classifying rice leaf blight disease. Ablation studies were conducted to assess the respective contributions of the CNN and LSTM components toward the model's overall performance. Furthermore, a comparison was made among severity levels to determine the optimal severity level for diagnostic purposes. The model under consideration exhibits the capacity to function as a mechanism for the timely identification and mitigation of rice leaf blight disease, thereby leading to a substantial enhancement in agricultural productivity and guaranteeing food sustainability. © 2023 IEEE.

关键词： Crops

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Optimal Compensation Strategy for Demand Side Response to Improve the Renewable Energy Consumption 3

Optimal Compensation Strategy for Demand Side Response to Im...

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3rd international conference on Energy, Power and electrical engineering, EPEE 2023

作者： Zhang, Hanbing Yang, Liaoliao Wang, Shurui Zheng, Hua Liu, Bin Lou, Rui State Grid Lishui Power Supply Lishui China Lishui Power Supply Co. Ltd Lishui China Polytechnic Institute of Zhejiang University Zhejiang Univercity Hangzhou China The Technology Network Branch of the Power Industry Group Lishui China

ISBN: (纸本)9798350318180

Herein, efforts are mainly made to investigate the way to promote new energy to be better absorbed in the multi-energy market where comprehensive demand response participates in competition. Considering the future trend of new energy power generation, a multi-market trading framework for IDR (integrated demand response) resources to get involved in competition is hereby proposed to address the problem of wind and electricity abandonment. The integrated demand response exchange (IDRX) market is introduced. In the case of scarce power resources, IDR users can increase their power demand response ability through gas-electric replacement, sell power load reduction resources in IDRX market and purchase natural gas resources for power replacement. The integrated demand response aggregator (IDRA) obtains power load reduction resources through the IDRX market and satisfies the gas purchase demand of IDR users. Meanwhile, the obtained power load reduction resources are used by IDRA to compete in the power wholesale market, and participate in the natural gas market demand bidding competition. Based on this multi-market trading framework, a compensation mechanism is introduced, a two-layer optimization model is constructed. The optimum compensation coefficient is calculated using the benders decomposition algorithm. Finally, an example is given to verify the validity of the hereby proposed trading framework, and the results indicate that the new energy can be better absorbed under the trading framework. © 2023 IEEE.

关键词： Electricity wholesale market Integrated demand response Integrated energy supplier Multi-energy market Natural gas market Optimal compensation coefficient

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Internet of Things (IOT) Intrusion Detection Utilizing CNN and Fine Tuning It Using Various Optimization Parameters 3

Internet of Things (IOT) Intrusion Detection Utilizing CNN a...

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3rd international conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Kaushiv Gill, Kanwarpartap Singh Chauhan, Rahul Dangi, Sarishma Banerjee, Deepak Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Graphic Era Hill University Computer Science and Engineering Uttarakhand Dehradun India Graphic Era Deemed to Be University Computer Science & Engineering Uttarakhand Dehradun248002 India

ISBN: (纸本)9798350319125

The Internet of Things (IOT) has experienced significant expansion in recent years, resulting in the development of countless gadgets and systems aimed at improving everyday life. This study explores the fundamental aspects of security in the Internet of Things (IOT), including vulnerabilities, potential attack routes, and essential countermeasures. This study presents a novel intrusion detection methodology for the Internet of Things (IOT) by leveraging Convolutional Neural Networks (CNNs). The proposed method aims to improve the performance of the CNN model by fine-tuning it with different optimization parameters. The initial approach involves the building of a convolutional neural network (CNN) model for intrusion detection, capitalizing on its inherent capability to extract information in a hierarchical manner. The objective of the fine-tuning procedure is to optimize the model's accuracy, sensitivity and specificity, hence enhancing its capacity to reliably identify and categorize intrusions within the Internet of Things (IOT) network. We conduct tests on a benchmark IOT intrusion detection dataset, assessing the proposed approach's performance in terms of Accuracy and Loss Graphs and accuracy. The findings confirm the effectiveness of utilizing Convolutional Neural Networks (CNNs) in the context of Intrusion Detection Systems (IDS) for Internet of Things (IOT) environments. Furthermore, the study highlights the need of tweaking the parameters of the CNN model to enhance its detection capabilities and achieve higher performance. Moreover, the present study highlights the ever-evolving nature of security in the Internet of Things (IOT), placing significant emphasis on the need of continuous research, collaborative endeavours across several sectors, and the establishment of regulatory frameworks to foster a safe and reliable IOT environment. © 2023 IEEE.

关键词： Artificial Intelligence Classification Data Science Deep Learning Internet of Things Machine Learning

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Automation of Skin Cancer Detection with Image Processing Using Efficient and Lightweight CNN Models 3

Automation of Skin Cancer Detection with Image Processing Us...

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3rd international conference on Pervasive Computing and Social Networking, ICPCSN 2023

作者： Pal, Ritwika Sagnika, Santwana School of Computer Engineering Kalinga Institute of Industrial Technology Bhubaneswar India

ISBN: (纸本)9798350322842

Skin cancer, if detected and treated at an early stage, is curable. Being one of the most common forms of cancer, the number of patients diagnosed is increasing with time. The conventional method of detecting skin cancer is done by dermatologists with biopsy, which is a gradual process. With the help of Convolutional neural networks(CNNs) which are extensively used for image classification, skin cancer diagnosis can be sped up. However, CNNs require high computation power with a large memory. With the aim of reducing the resources required for this application, the MobileNetV2 model, which is a lightweight CNN, was used to classify skin lesion images taken from the international Skin Imaging Collaboration(ISIC), which is suitable for mobiles with limited memory space, since it takes lesser memory space and reduces computational costs. With the motivation of doing further research, Object detection and Image segmentation algorithms have been explored. The Object detection algorithm of You Only Look Once(YOLO), predicts the bounding box around the skin lesion and labels it as either malignant or benign with a probability score. Different models of YOLOv5 were trained for the purpose of object detection. Finally for image segmentation, which tries to classify every pixel of the image, the U-Net model was trained with two base models, ResNet-50 and MobileNetV2. Its aim is to identify the exact position and shape of the lesion and classify each pixel as either malignant skin lesion, benign skin lesion or normal skin. © 2023 IEEE.

关键词： Image segmentation

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Research on multi-mode human-computer interaction design evaluation system based on virtual reality technology 3

Research on multi-mode human-computer interaction design eva...

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3rd international conference on Machine Learning and Big Data Analytics for IoT Security and Privacy, SPIoT2023

作者： Han, Xue Research Institute for Water Transport Engineering M.O.T. 300456 China

The research of multi-mode human-computer interaction design evaluation system based on virtual reality technology is the application of virtual reality technology in the field of human-computer interaction. The main purpose of this study is to provide a new method to evaluate and improve user interface design, which can be used by designers to evaluate their own works. It also provides users with a new way to explore different interfaces through interactive experience. The purpose of this study is to study the impact of various factors on user performance, as well as the impact of different modes on user performance. Therefore, we designed two experiments: (1) single mode experiment and (2) multi-mode experiment. In these experiments, each factor is vertical to find out its impact on user performance. The results of these experiments are presented in this paper. It also discusses how to apply our findings to future research in HCI. © 2023 Elsevier B.V.. All rights reserved.

关键词： Human computer interaction

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Preliminary Staff Collaboration and Artificial Intelligence 3

Preliminary Staff Collaboration and Artificial Intelligence

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3rd international conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2023

作者： Pandey, Arvind Kumar Mahesh, T.R. Christy, Veena Shaikh, Mohsin Yadav, Preksha Das, Saptorshi Arka Jain University Department of Computer Science Jharkhand Jamshedpur India Department of Computer Science and Engineering Bangalore India Bharat Institute of Higher Education and Research Department of Business Administration India Dr. Vishwanath Karad Mit World Peace University Department of School of Management Pune India Cmr Institute of Technology Master of Business Administration Bangalore India Kiit Deemed to Be University Kiit School of Management India

ISBN: (纸本)9798350399264

Artificial Intelligence is viewed as by a few contemporary associations all over the planet to foster able labor forces, where the ways of behaving and abilities of the workers are assisting those associations with acquiring upper hands. The administration experts of the contemporary associations are recognizing that the representatives can work cooperatively because of AI and prescient information examination. Utilization of Artificial Intelligence in the representative administration exercises is assisting a few contemporary firms with driving positive representative commitment as a result of the improvement of durable workplace. This exploration work is centered around the positive effect of Artificial Intelligence on representative commitment to this contemporary worldwide business *** models and hypothetical structures on worker commitment are applied in this review. Then again, unique significant hypothetical parts of Artificial Intelligence in forming representative commitment likewise have been examined in this exploration work. The tone and heading of the exploration show that utilization of Artificial Intelligence decidedly affects the contemporary representative commitment exercises. An exploratory review configuration is created and considered for the conduction of this examination. A subjective philosophy has been viewed as in this examination relying upon the exploratory idea of the review to meet the exploration objective. A likelihood inspecting approach has been embraced by the scientist to choose a significant example size and gather expected information. Essential data collection process has been taken on in this review to upgrade legitimate examination result. The discoveries and conversations have demonstrated toward the end that Artificial Intelligence is significant for the contemporary associations to shape representative commitment to a positive way. © 2023 IEEE.

关键词： Artificial intelligence

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Hybrid Deep Learning for Thai Cannabis Plant Classification: YOLO+CNN Approach 3

Hybrid Deep Learning for Thai Cannabis Plant Classification:...

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3rd international conference on Innovative Mechanisms for Industry Applications, ICIMIA 2023

作者： Trivedi, Naresh Kumar Maheshwari, Himani Tiwari, Raj Gaurang Gautam, Vinay Agarwal, Ambuj Kumar Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Graphic Era Hill University School of Computing Uttrakhand Dehradun India Sharda University Sharda School of Engineering and Technology Department of Computer Science and Engineering Greater Noida India

ISBN: (纸本)9798350343632

Cannabis is a plant species with various subspecies, and its classification is vital for both medical and regulatory purposes. In the context of Thailand, where cannabis has significant cultural and economic importance, accurate identification of different cannabis subspecies is of utmost importance. This work presents a novel approach for the classification of Thai cannabis plants using a hybrid deep learning model that combines You Only Look Once (YOLO) and Convolutional Neural Networks (CNN). The proposed model is evaluated against other deep learning models commonly used for image classification, and it demonstrates exceptional performance, achieving an impressive accuracy rate of 95.37%. Experimental results show that the YOLO+CNN hybrid model outperforms other state-of-the-art deep learning models, in terms of classification accuracy. The proposed model seems efficient due to the YOLO model's ability to precisely localize cannabis plants in images and the CNN's capability to extract fine-grained features for accurate classification. The high classification accuracy achieved by the hybrid model has significant implications for applications such as monitoring the growth of specific cannabis subspecies, ensuring product quality in the cannabis industry, and aiding law enforcement in identifying illegal cannabis plants. This research paves the way for the development of more efficient and accurate tools for cannabis classification, benefiting both the scientific community and society at large. © 2023 IEEE.

关键词： Convolutional Neural Networks Deep Learning Hybrid Model Image Classification Plant Classification Thai Cannabis You Only Look Once

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A Deep Meta-model for Environmental Sound Recognition 1

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3rd international conference on Data Science, Machine Learning and Applications, ICDSMLA 2021

作者： Arun, K.S. Department of Computer Science and Engineering Amal Jyothi College of Engineering Kottayam India

ISBN: (数字)9789811959363

ISBN: (纸本)9789811959356

Nowadays, sound serves as a crucial factor in all facets of human life. Staring from automating personal security systems to critical surveillance systems, sound is an indispensable component. The practical implementation of the present day automatic sound recognition systems in real-life settings is inadmissible due to their poor detection accuracy. However, deep learning-based systems overcome the incompetence of the traditional machine learning-based models, and it can be used to develop automatic sound classification systems. This work proposes a deep meta-model for categorizing environmental sounds on the basis of the spectrogram images generated from these sounds. In the proposed approach, spectrogram images of environmental sounds are used to train five different deep learning models, and the predictions from these base models are then stacked using the proposed deep meta-model. Experimental results on two benchmark datasets such as ESC-50 and UrbanSound 8K demonstrate the fact that the proposed deep meta-model is a promising alternative to the conventional approaches for environmental sound recognition. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Spectrographs

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Community Detection Algorithms in Social Networks: An Empirical Evaluation 3rd

Community Detection Algorithms in Social Networks: An Empiri...

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3rd international conference on Information technology, InCITe 2023

作者： Kumar, Law Kumar, Rajeev Lab School of Computer & System Sciences Jawaharlal Nehru University New Delhi110 067 India

ISBN: (纸本)9789819959969

The detection of communities is an essential task in social network analysis. Many distinct community detection algorithms can be employed for detecting these communities in a network. But the selection of a good community detection algorithm is challenging. No individual community detection technique available provides the best result every time for any social network. The objective of this paper is to access the effectiveness of different community detection algorithms based on quality measures. We use both real and synthetic networks for this compression and evaluation. The synthetic network is generated by the LFR algorithm, whereas a well-known network of Last FM users from Asian countries generates the real network. After evaluation, we found that the Spin glass and Louvain algorithms perform well in most cases, while the Leading Eigen algorithm performs worst in maximum instances. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Signal detection

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Modelling Of Doubly-Fed Induction Generator With Variable Wind Speed 3

Modelling Of Doubly-Fed Induction Generator With Variable Wi...

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3rd IEEE international conference on Sustainable Energy and Future Electric Transportation, SeFet 2023

作者： Dash, Abhisek Behera, Ashutosh Bagarty, Durgesh Prasad Odisha University of Technology and Research Department of Electrical Engineering Bhubaneswar India

ISBN: (纸本)9798350319972

The integration of renewable energy sources, particularly wind power, into the electrical grid has gained significant attention in current years. The Doubly Fed Induction Generator (DFIG) is a mostly used generator in modern wind turbine systems due to its advantages such as improved efficiency, variable speed operation, and enhanced grid stability. However, the performance of a DFIG is highly influenced by the variable wind speed conditions it operates under. This paper presents a comprehensive modelling approach for a DFIG system considering variable wind speeds. The proposed model incorporates the dynamic characteristics of the wind turbine and DFIG components, such as the mechanical system, electrical circuits, and control strategies. The wind speed profile is taken into account, enabling the analysis of the DFIG system's behaviors under different operating conditions. The model is developed using mathematical equations and simulation techniques to accurately represent the interactions between the wind turbine, DFIG, and the grid. Various control strategies, including the rotor-side and grid-side converters, are implemented to optimize the power extraction, enhance grid stability, and mitigate potential issues such as grid faults and voltage variations. The MATLAB simulated results show the effectiveness of this proposed model is captured by the dynamic behaviors of the DFIG system with variable wind speeds. The model provides valuable insights into the system's behaviors, enabling the evaluation of performance metrics such as power output, rotor speed, and control responses. Moreover, the model can be utilized for analyzing the impact of different wind speed profiles, investigating the effectiveness of control strategies, and optimizing the design and operation of DFIG-based wind turbine systems. Overall, the developed model offers a reliable tool for understanding and analyzing the performance of DFIG systems under variable wind speed conditions. It can aid in

关键词： MATLAB

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