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检索条件"任意字段=2023 International Conference on Computer Engineering and Distance Learning, CEDL 2023"

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Review on Vision Based Human Motion Detection Using Deep learning 4

Review on Vision Based Human Motion Detection Using Deep Lea...

引用

4th international conference on Smart Electronics and Communication, ICOSEC 2023

作者： Zachariah, Lino Maharajan, K. Mar Baselios College of Engineering and Technology Trivandrum India Science and Engineering School of Computing Kalasalingam Academy of Research and Education Department of Computer Krishnankoil626126 India

ISBN: (纸本)9798350300888

The challenging problems addressed by vision-based human motion recognition and its wide range of potential applications have captured the interest of numerous researchers. These applications encompass simple gesture recognition to intricate analysis of behavior in surveillance systems. Since there was a need for practical, automatic violence detection techniques that looked at the visual information obtained from security cameras placed throughout various locations, the violence detection sector became quite popular in the domain of human motion detection systems. As a result, there is a significant advancement in the methods for representing and recognizing human motion. The focus of this study is to discuss the recent advancements made by researchers in these fields. It offers a comprehensive analysis of the methodologies employed and evaluates the credibility of the various submissions. Additionally, the article offers guidelines on approaching future directions for this application. © 2023 IEEE.

关键词： Deep learning

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Hybrid Characteristics are used in an Efficient Framework for the Identification of Gastrointestinal Diseases 4

Hybrid Characteristics are used in an Efficient Framework fo...

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4th international conference on Smart Electronics and Communication, ICOSEC 2023

作者： Ammisetty, Veeraswamy Rao Ande, V.S. Sudhakar Krishna Maddinala, Viswanath Veera Koneru Lakshmaiah Educational Foundation Vaddeswaram Department of Computer Science and Engineering Andhra Pradesh Guntur522302 India Vasireddy Venkatadri Institute of Technology Nambur Department of Information Technology Andhra Pradesh Guntur India Sri Vasavi Engineering College Peddatadepalli Department of Cse Andhra Pradesh Tadepalligudem India

ISBN: (纸本)9798350300888

Medical fields frequently use machine learning (ML) algorithms for disease classification and diagnosis. These algorithms base their judgments on features that have been retrieved from images. To achieve high classification accuracy choosing proper feature extraction methods and Machine learning (ML) algorithms is crucial. This study discovers a dearth of knowledge on computer learning techniques for GI disease classification through the literature review. To address this problem, five classes of GI images are gathered from a freely accessible mdi dataset and extracted textural features like RGB and HSV (Hue, Saturation, and Value) color characteristics GLCM Matrix shape data from HOG. These characteristics are integrated, and the size was decreased by Principal Components Analysis (PCA). Last but not least, the chosen features are classified using the well-known ML classifier OAA-SVM -One Against All-Support Vector Machine, achieved classification precision, recall, FI-Score values, and accuracy of 98.8%, 95.44%, and 98.64% respectively. © 2023 IEEE.

关键词： Support vector machines

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Benchmarking of Deep learning Techniques in FDIA 4

Benchmarking of Deep Learning Techniques in FDIA

引用

4th international conference on Distributed Sensing and Intelligent Systems, ICDSIS 2023

作者： Alsaid, Basma Badway, Youssef Waraga, Omnia Abu Talib, Manar Abu Nasir, Qassim Department of Electrical Engineering University of Sharjah Sharjah United Arab Emirates Department of Computer Science University of Sharjah Sharjah United Arab Emirates Department of Computer Engineering University of Sharjah Sharjah United Arab Emirates

ISBN: (纸本)9781839539954

False Data Injection Attacks (FDIA) are among the most significant security threats to critical systems. In this paper, we present a comprehensive benchmarking study to evaluate the performance of four deep learning models for detecting FDIA. The study covers an analysis of three diverse datasets representative of real-world scenarios. Our evaluation leverages a comprehensive set of metrics, including precision, recall, accuracy, F1 score, ROC analysis, and precision-recall plots. The primary objective is to identify the optimal model that exhibits robust performance across various attack scenarios. Subsequently, the selected model undergoes in-depth examination through the application of Principal Component Analysis (PCA) to ascertain the influence of dimensionality reduction on its efficacy. The results of this study enhance the understanding of deep learning capabilities in FDIA detection and lay the foundation for the development of advanced detection and defense mechanisms. © The Institution of engineering & Technology 2023.

关键词： Contrastive learning

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Automated Crop Growth Monitoring and Optimizing the Yield with Reinforcement learning 3

Automated Crop Growth Monitoring and Optimizing the Yield wi...

引用

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

作者： Gorantla, Snehitha Veluchamy, S. Amrita School of Computing Department of Computer Science and Engineering - Artificial Intelligence Amrita Vishwa Vidyapeetham Chennai601103 India Amrita School of Computing Department of Computer Science and Engineering - Cyber Security Amrita Vishwa Vidyapeetham Chennai601103 India

ISBN: (纸本)9798350317022

A reinforcement learning agent for optimal green-house management through Proximal Policy Optimisation is developed and evaluated in this work. With variable elements like lighting, irrigation, humidity, and temperature, a virtual greenhouse is produced. By interacting with the surroundings, the agent modifies the controls and gets input on things like crop health and growth rate. For policy and value estimation, the neural network design consists of distinct actor and critic networks as well as shared feature layers. Compared to fixed or random policies, the trained agent shows the ability to regulate conditions for increased crop yield and sustainability. As the number of trials increased, the RL agent's average return per trial increased steadily. This suggests that the RL agent has become more adept at controlling the greenhouse environment in subsequent experiments. This demonstrates how well the RL agent can manage crop health and resource use in the greenhouse climate control system. © 2023 IEEE.

关键词： Crop Yield Greenhouse Neural Network PPO algorithm Reinforcement learning

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Sign Language Recognition System using CNN

Sign Language Recognition System using CNN

引用

2023 IEEE international conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, ICIITCEE 2023

作者： Deshpande, Aditi Shriwas, Ansh Deshmukh, Vaishnavi Kale, Shubhangi School of Electrical Engineering MIT Academy of Engineering Maharashtra Pune412105 India School of Computer Engineering and Technology MIT Academy of Engineering Maharashtra Pune412105 India

ISBN: (纸本)9781665462631

The most common way of communication for a speech-impaired person is Sign Language. Generally, people do not learn Sign Language for communication with deaf and dumb people which causes their isolation. Sign language is an ancient language for communication that comes naturally, but since people are unaware of systematic sign language and the person cannot have a translator every time with him, there is a need of a mediator system which can translate sign language. For that purpose, a real-time method is presented in this paper where deep learning is used for ASL translation. The main aim of this project is to design a system that could identify the alphabets of American Sign Language that are being signed. The camera captures the frames of the hand being signed and then passed through a filter and later through a classifier for prediction of the class of the hand gestures. The proposed system is an initial step for creating a translator for sign language for making the communication easier. The result is an HCI system that enables people to communicate with D&M people without sign language being known. © 2023 IEEE.

关键词： Deep learning

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Leaf Detection: Techniques and Applications

Leaf Detection: Techniques and Applications

引用

2023 international conference on Sustainable Emerging Innovations in engineering and Technology, ICSEIET 2023

作者： Kumar, Rahul Pal, Arpit Singh, Tejendra Chauhan, Alok Singh School of Computing Science and Engineering Galgotias University Department of Computer Science and Engineering Greater Noida India School of Computing Science & Engineering Galgotias University Greater Noida India

ISBN: (纸本)9798350329186

Leaf detection is a critical task in various fields, including agriculture, ecology, and botany, with applications ranging from plant species identification to disease diagnosis. This research paper explores the techniques used for leaf detection, the applications where it is employed, and the challenges faced in this domain. Traditional image processing techniques, machine learning approaches, deep learning-based methods, and hybrid approaches are examined and compared. The applications of leaf detection include agriculture and crop monitoring, plant species identification, disease diagnosis and management, ecological studies, and remote sensing. The challenges in leaf detection arise from the variability in leaf shapes and textures, occlusion and complex backgrounds, realtime processing requirements, and robustness to environmental factors. Future directions and emerging trends in multi-modal leaf detection, domain adaptation, integration of remote sensing technologies, and advanced image analysis techniques are discussed. The findings of this research have implications for further research and practical applications, highlighting the need for more robust and accurate leaf detection techniques and the utilization of advanced technologies. The research contributes to a deeper understanding of plant ecosystems, optimization of agricultural practices, and improved management of natural resources. © 2023 IEEE.

关键词： Remote sensing

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Automated Remote Sensing Image Classification using Enhanced Fireworks Algorithm with Deep learning Model 2

Automated Remote Sensing Image Classification using Enhanced...

引用

2nd international conference on Trends in Electrical, Electronics and computer engineering, TEECCON 2023

作者： Josephine Anitha, A. Gladis, D. PG and Research Department of Computer Science Chennai600 005 India Bharathi Women's College Chennai600 108 India

ISBN: (纸本)9798350339949

Remote Sensing Image (RSI) classification had a crucial role in different applications, like environmental monitoring, land cover mapping, and disaster management. Conventional classification methods frequently depend on manual parameter tuning and handcrafted features that can be less effective and time-consuming in capturing complicated patterns in RS data. Currently, DL methods have shown incredible performance in image classification tasks. Still, the challenges lie in optimizing the deep learning (DL) method and choosing the optimal hyper parameters for attaining the optimal classification accuracy. In this article, we devise an Automated Remote Sensing Image Classification using Enhanced Fireworks Algorithm with Deep learning (ARSIC-EFWADL) model. In the presented ARSIC-EFWADL method, a four-stage process is followed. Initially, the median filtering (MF) approach is used to preprocess the images. Secondly, the ARSIC-EFWADL technique uses a densely connected networks (Densenet) model for feature extraction. Next, the EFWA is used for the parameter tuning process. At last, Adaptive Neuro Fuzzy Inference System (ANFIS) methodology is implemented for classification purposes. To exhibit the improvised classification results of the ARSIC-EFWADL method, wide-ranging simulations were involved. The outcomes of the experiments inferred the enhanced performance of the ARSIC-EFWADL method over compared approaches under several evaluation metrics. © 2023 IEEE.

关键词： Image classification

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Glaucoma Detection Using Deep learning Approach in Research Detection 2

Glaucoma Detection Using Deep Learning Approach in Research ...

引用

2nd IEEE international conference on Advances in Computing, Communication and Applied Informatics, ACCAI 2023

作者： Pandurang Shinde, Anil Singh, Ramandeep Shantappa Anami, Renuka Mohammed, Noor Selvameena, R. Jotiram Patil, Ashwini Department of Computer Technology Maharashtra Pune India Guru Nanak Khalsa College Daroli Kalan Jalandhar Department of Computer Science Punjab Jalandhar India Graphic Era Deemed to Be University Department of Computer Science Engineering Uttrakhand Dehradun India Dr.M.G.R Educational and Research Institute Department of Computer Science and Engineering TamilNadu Chennai India

ISBN: (纸本)9798350315905

An persistent eye infection called glaucoma causes irreversible vision loss. A crucial problem in the clinical field is glaucoma identification. There have been very few studies designed to detect glaucoma in its early stages. However, using current techniques to carry out glaucoma infection detection was ineffective. Moreover, the conventional glaucoma infection diagnosis method required additional time. Glaucoma identification in various fundus images is a challenging task that necessitates mastery and extensive training. Glaucoma is an ocular neuropathy characterized by mild retinal ganglion cell degeneration. There are several different types of glaucoma, each having a unique a etiology and set of pathogenic factors, but they all have alterations in the structure and function of the optic nerve that are essentially universal. Convolution organisations, a type of deep learning computation, have swiftly emerged as a method for making decisions while analyzing clinical images. A chronic eye condition called glaucoma causes irreversible vision loss. In this section, we discuss two state-of-the-art glaucoma detection methods in the light of deep learning. The glaucoma dataset is used in this deep learning computation proposed for glaucoma photo analysis. This finding contrasts with convolutional brain network grouping techniques in the context of deep learning. Each rating indicates that the newly proposed worldview is more favorable than the one currently in use. © 2023 IEEE.

关键词： Deep learning

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Predicting Smart Grid Stability with Multi-Directional LSTM

Predicting Smart Grid Stability with Multi-Directional LSTM

引用

2023 international conference on Power Energy, Environment and Intelligent Control, PEEIC 2023

作者： Vishnoi, Manish Srivastav, Anamika Thiruvenkadam, T. Vivekananda Global University Department of Civil Engineering Jaipur India Noida Institute of Engineering & Technology Department of Computer Science and Engineering Uttar Pradesh Greater Noida India Computer Science Engineering Department Bangalore India

ISBN: (纸本)9798350357769

The expression relates to the national electric arrangement, used to deliver electricity via the electrical plants to the customers. It is made up of converters, distributors, monitoring stops and lines of communication. Large electricity generation plants that produce hundreds of thousands of watts of electricity and redistribute it across many demographic areas now make up the electrical system. Efficient management of the electricity delivered to many customer domain names including households, smart cities, companies, and organizations, is vital. To meet the changing needs for electricity, an electricity network with advanced technology is being implemented. Its CPS, which stands for concept, which integrates data centres with mechanical structures, is what the idea of smart grids is based on. The computer Intelligence (also known as module represents the IT component in the smart energy system scenario integrated with CPS, while the energy consumption units represent the physical components. This study proposes a unique Broad Long and Short-Term Memory tackle to forecast the smart energy system reliability. The outcomes are compared with different well-liked artificial intelligence techniques, including neural networks with recurrent neurons (an RNN), gates recurrent units (the Government Relations Unit), and conventional the LSTM algorithm The research results demonstrate how the strategy works better than any other learning methods. © 2023 IEEE.

关键词： Artificial intelligence techniques Conventional LSTM Cyber-Physical Systems (CPS) Electricity distribution Energy system reliability and Machine learning methodologies Gated recurrent units (GRU) Multidirectional LSTM Recurrent neural networks (RNN) Smart grids

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Improved Facial Biometric Authentication Using MobileNetV2

Improved Facial Biometric Authentication Using MobileNetV2

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2023 international conference on Computational Intelligence and Sustainable engineering Solution, CISES 2023

作者： Kaur, Veerpal Kumar, Prashant Kaur, Gagandeep Kaur, Amandeep School of Computer Science and Engineering Lovely Professional University Punjab India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India School of Computing University of the Fraser Valley Abbotsford BC Canada

ISBN: (纸本)9798350323917

One of the successful biometric technologies has been face recognition. Despite its enormous success, face recognition has drawbacks, much like any biometric technology. Face spoofing attacks, which are still possible with facial recognition technologies, have become a common threat to biometric applications. Many algorithms have recently been applied to publicly available datasets. However, efficiency has been a concern. One model cannot be a generalized solution for all attacks, so testing datasets over different models is needed to know all the possibilities. In this paper, a deep-learning method MobileNetV2 is proposed, which uses transfer learning and is a lighter convolutional network that is tested over the NUAA Dataset, which is publicly available. The proposed method shows better results than state-of-the-art methods over the same dataset. © 2023 IEEE.

关键词： Face recognition

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