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Using Naive Bayes Algorithm in the Internet of Things secure Drone-Cyber

Using Naive Bayes Algorithm in the Internet of Things secure...

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2023 international conference on New Frontiers in Communication, automation, Management and Security, ICCAMS 2023

作者： Ramesh, Janjhyam Venkata Naga Harshitha, Kapuganti Shanmitha, Vetsa Mani Saipavani, Ventrapati Chandrika NagaVenkata Chaitanya, Seepani Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Andhra Pradesh Vaddeswaram India

ISBN: (纸本)9798350317060

Recent developments in drone technologies enable new options and uses, particularly in miniature drones, which may be used in a wide range of settings. But these innovations also introduce new safety, flexibility, and uniformity difficulties. The commercial and military sectors may both benefit from small drones. There are problems with the design of the tiny drones and the way that security and safety are defined for them. Drone technology may now be used in previously unimaginable contexts, but the proliferation of IoT also introduces new risks. The data security and privacy risks the tiny flying intelligent gadgets pose are significant. Small drone design has yet to reach a point of maturity where it can meet the sector's demands. Data conversions, privacy safeguards, and security measures are all necessary additions to the fundamental design problems. Security and flexibility issues with these modern drones may be mitigated by learning more about infiltration and interception in the Internet of Drones (IoD) space. In this study, presents the development of an Internet of Things-enabled drone using a machine-learning technique. An intelligent cyber security system may identify malicious Blockchain transactions using this strategy. © 2023 IEEE.

关键词： Drones

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CNN-LSTM for Secure Distributed Demand Response in Smart Grid 7

CNN-LSTM for Secure Distributed Demand Response in Smart Gri...

引用

7th international conference on Image Information Processing, ICIIP 2023

作者： Tirulo, Aschalew Chauhan, Siddhartha National Institute of Technology Departemet of Computer Science And Engineering HP Hamirpur India

ISBN: (纸本)9798350371406

Integrating cyber and physical elements in smart grids amplifies susceptibility to false data injection attacks (FDIAs), jeopardizing home automation and energy infrastructure. Traditional security strategies often underperform in FDIA detection due to varied data origins. We propose an advanced anomaly detection framework using CNN-LSTM, tailored to detect FDIAs in the grid's demand response. Our model employs supervised learning for improved precision when enriched with label information. Empirical tests with genuine energy data from Austin, Texas, demonstrate our model's superiority over existing methods, with metrics like accuracy, precision, recall, F1 score, and false positive rate consistently affirming its robustness and real-world applicability. © 2023 IEEE.

关键词： Anomaly detection

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Advanced Algorithm for Cardiac Arrhythmia Classification and Efficient Detection of Abnormalities in ECG Signals 4

Advanced Algorithm for Cardiac Arrhythmia Classification and...

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4th IEEE international conference on ICT in Business Industry and Government, ICTBIG 2024

作者： Amhia, Hemant Sarkar, Vikas Pandey, L.N. Shakunthala, M. Anudeep, Sadula Singh, Shreya Jabalpur Engineering College Electrical Engineering Department MP Jabalpur India Ies Institute of Technology and Management Ies University Department of Computer Science & Engineering M.P Bhopal462044 India Gyan Ganga Institute of Technology & Sciences Dept of Cse MP Jabalpur India R.M.D. Engineering College Department of Ece Kavaraipettai TN Thiruvallur India Mlr Institute of Technology Department of Cse Dundigal Hyderabad India Ggits MP India

ISBN: (纸本)9798331518981

High-Performance Algorithm for Cardiac Arrhythmias Classification and ECG Abnormality Detection Aspecific heart rhythms that are not found upon initial examinations, often lead to heart failure and stroke. Arrhythmias have to be diagnosed early through precise classification of ECG signals. Conventional methods employ ineffective and tedious manual intervention in interpreting the time series and the filtering of noise. To boot, we apply intricate deep learning and the Superlet Transform (SLT) for detection's automation and enhancement. A superior time-frequency analysis tool, the SLT, transforms 1D ECG signals into 2D spectrograms to identify transient oscillation phenomena such as arrhythmias with higher resolution than STFT and CWT. SLT spectrograms are classified by deep Convolutional Neural Networks. This duty is accomplished by some advanced CNN models such as AlexNet, GoogLeNet, *** to the flexibility of information flow and gradient propagation in DenseNet-201, it achieves a high accuracy of classification. There are three classes of ECG signals, namely, healthy heart, atrial fibrillation, and ventricular fibrillation. The model is tested and demonstrated in various ECG database such as MIT-BIH arrhythmia and AF termination challenge. This method simplifies the hardware by the removal of various noise reduction processes and enables real-time monitoring of heart rates. The utilization of SLT and deep CNN models for early detection of cardiac arrhythmia is current trends that enhance clinical diagnosis and accurate results. © 2024 IEEE.

关键词： Electrocardiography

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A Real-time Person Identity Detection System Using Machine Learning 6

A Real-time Person Identity Detection System Using Machine L...

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6th international conference on Inventive Computation Technologies, ICICT 2023

作者： Wagh, Anushka Vilas Ghodke, Priti Prem Patil, Prit Ujjawal Chauhan, Prashant Dinanath Gurav, Prachi Department of Computer Engineering St. John College of Enginnering and Management Maharashtra Mumbai400103 India

ISBN: (纸本)9798350398496

This research study presents an automated real-time background face recognition system for a large dataset of human faces. This is very difficult because background subtraction is still an issue in live images. Addition to this there are huge features in human face image in terms of eye, nose, head, lip, etc. The proposed system simplifies many of the facial recognition features. It utilizes AdaBoost with cascade to detect human faces in real-time. The matched face is then used to Identify a person. The real-time security and automation system is based on human face recognition, and it uses a simple and fast algorithm that achieves high accuracy. We have accuracy of 92% by using Adaboost Algorithm. © 2023 IEEE.

关键词： Support vector machines

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Transfer Learning for Russian Handwriting Recognition 46

Transfer Learning for Russian Handwriting Recognition

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46th international conference on Telecommunications and Signal Processing, TSP 2023

作者： Levkov, Andrey Kaplun, Dmitry Safonova, Anastasiia St. Petersburg Electrotechnical University "leti" Depart. of Computer Engineering St. Petersburg Russia St. Petersburg Electrotechnical University "leti" Depart. of Automation and Control Processes St. Petersburg Russia St. Petersburg Electrotechnical University "leti" Depart. of Information Systems St. Petersburg Russia

ISBN: (纸本)9798350303964

Handwriting recognition is one of the most important tasks in computer vision. Despite the variety of methods and algorithms used for text recognition in various languages, the recognition of handwritten Russian words is still a completely unsolved problem, which keeps the relevance of this topic. This paper proposes the application of a convolutional neural network (CNN) to the task of recognizing the 100 most frequently used Russian words, trained on a new unique dataset. A unique algorithm for extracting Russian handwritten words from forms was applied to obtain a new dataset. The paper also presents the results of a comparison of such powerful models as VGG-16, VGG-19, ResNet-50, and ResNet-101, which were trained using transfer learning technology. The best results were shown by VGG-16 with 98.7% accuracy, and the worst results were shown by ResNet-50 with 80% accuracy. © 2023 IEEE.

关键词： Convolutional neural networks

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Scene understanding pipeline for maintenance oriented tasks automation 16

Scene understanding pipeline for maintenance oriented tasks ...

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16th international conference on Quality Control by Artificial Vision

作者： Zegaoui, Younes Dufour, Sebastien Bortolaso, Christophe Berger-Levrault Labege France

ISBN: (数字)9781510667471

ISBN: (纸本)9781510667464

computerized Maintenance Management System (CMMS) are to assist in organising maintenance, both proactive and reactive, as well as technical operations. It usually works alongside constant surveillance and monitoring of said equipment through repetitive and time-consuming tasks. AI can ease maintenance activities by reducing the time spent on these repetitive task and allocate more time on decision. In this article we present our works on automating part of the intervention request handling in Berger-Levrault s CMMs. We designed a pipeline of computer vision operations to predict the type of intervention needed from a picture of the situation at hand. The pipeline is basically a decision tree which combines different computer vision models and funnel images between them according to their respective outputs. Each of these models are trained separately on a specific task. To validate our approach, we performed a topic modeling analysis on the maintenance request forms to identify the ten most common topics of intervention. We show that our pipeline performs better than direct prediction by scene recognition model with a five points increases in global F1 score (40% / 45%) which is even more true for the classes with fewer training examples (23% / 37%). © 2023 SPIE. All rights reserved.

关键词： Object detection

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Research on Polyurethane Creep Prediction Model Based on BWO-BP 7

Research on Polyurethane Creep Prediction Model Based on BWO...

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7th international conference on computer Network, Electronic and automation, ICCNEA 2024

作者： Di, Ruohai Song, Nan Lv, Zhigang Li, Jianheng Gao, Hui Xu, Yuntao School of Electronics Information Engineering Xi'an Technological University Xi'an China

Polyurethane foam materials can creep under the influence of temperature, humidity, and pressure during use, which seriously affects their safety and state of use. Therefore, studying the creep behavior of polyurethane foam materials and developing corresponding creep mechanism models have significant scientific value for understanding their damage patterns. As a type of machine learning, Backpropagation (BP) neural network can be used for data analysis. However, there are many issues when determining the network structure. Whale Optimization (BWO) algorithm, as a type of swarm intelligence algorithm, can efficiently determine the framework of the BP model. BP which has been optimized through the BWO algorithm, has been utilized to construct a model for the prediction of polyurethane's creep failure. The optimization process has significantly lowered the mean squared error from 0.30 in the non-optimized BP model to 0.07 in the enhanced BWO-BP model, which validates the effectiveness of the proposed algorithm. © 2024 Institute of Physics Publishing. All rights reserved.

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Skin texture recognition of leg images based on chaotic mapping reorganization features 3

Skin texture recognition of leg images based on chaotic mapp...

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3rd international conference on Intelligent Computing and Human-computer Interaction, ICHCI 2022

作者： Zhang, Tianshu Tang, Chaoying Huang, Qiaoyue Nanjing University of Aeronautics and Astronautics Automation College Nanjing China

ISBN: (纸本)9781510661301

In criminal and victim identification, when information such as fingerprint and facial images cannot be obtained, the development of other new bio-metric recognition has become an important task. It is found from investigations that the lower leg is often exposed in an evidence image. If we can use the information to identify the identity of the suspect, it will be a great break-through for criminal investigation. Unlike faces or fingerprints, the image of a lower leg does not have many discriminative features. Therefore, capturing more information from an image has become the key to the success of recognition. Despite the rapid development of deep learning in recent years and the wide application of convolutional neural networks in biometric recognition, they are very dependent on a large amount of training data. In this paper, we propose a recognition method based on the combination of chaotic mapping and local binary pattern (LBP), which captures the nonlinear features of the image. Experimental results show that the method proposed in this paper has a better recognition rate and an encouraging performance. © 2023 SPIE.

关键词： Local binary pattern

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Hierarchical Decision Problems for Non-Affine Multi-Player Systems

Hierarchical Decision Problems for Non-Affine Multi-Player S...

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2023 IEEE international conference on Real-Time Computing and Robotics, RCAR 2023

作者： Lin, Danyu Xue, Shan Liu, Derong Zhao, Bo School of Automation Guangdong University of Technology Guangzhou510006 China South China University of Technology School of Computer Science and Engineering Guangzhou510006 China Southern University of Science and Technology School of System Design and Intelligent Manufacturing Shenzhen518055 China Beijing Normal University School of Systems Science Beijing100875 China

ISBN: (纸本)9798350327182

Adaptive dynamic programming is used in this paper to solve the hierarchical decision problem for non-affine multi-player systems. A new affine system is formed by adding control dynamics to the original system. To solve the hierarchical decision problem of the original system, we modeled it as a Stackelberg game in the new system. Adaptive dynamic programming is used to approximate the value functions. The stability of the system is analyzed according to Lyapunov theory, which leads to the conclusion that both the system states and the weight errors are uniformly ultimately bounded. Finally, matlab simulation is used to verify the conclusion. © 2023 IEEE.

关键词： Dynamic programming

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automation Intervention for Monitoring Agriculture Farming Scale: A Review 14

Automation Intervention for Monitoring Agriculture Farming S...

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14th international conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Joshi, Kapil Kumar, Santosh Bhatla, Shruti Kaushik, Vishal Anandaram, Harishchander Kumar, Anil Uttaranchal University Department of Cse Uttaranchal Institute of Technology Dehradun India Sharda University Department of Mathematics Sharda School of Basic Sciences and Research UP Greater Noida India Graphic Era Hill University Department of Computer Science and Engineering Uttarakhand Dehradun India Upes School of Computer Science Dehradun India Amrita Vishwa Vidyapeetham Centre for Computational Engineering and Networking Tamil Nadu Coimbatore India Dit University School of Computing Dehradun India

ISBN: (纸本)9798350335095

Agricultural data is only valuable when it has been managed successfully. As data has evolved into the fundamental ingredient in contemporary agriculture to assist farmers with vital decision, latest developments in managing data are causing Smart Agriculture to rise dramatically. With factual information obtained by sensors, significant advantages emerge with the goal of enhancing production and ecology. This type of data-driven farm management is built on information that can boost efficiency by preventing resource waste and contamination of the ecosystem. Data-driven farming, with the assistance of automated solutions using artificial intelligence techniques, lays the groundwork for potential sustainable *** article analyses the current state of sophisticated agricultural systems by rehashing each critical step, from data processing in field crops to variable interest rate implementations, so that farmers can make optimised decision making to save funds while safeguarding the natural environment and converting how meal would be generated to meet the upcoming population explosion. © 2023 IEEE.

关键词： Big data

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