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检索条件"任意字段=2023 3rd International Conference on Detection Technology and Intelligence System, DTIS 2023"

共 2097 条记录，以下是161-170 订阅

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detection Method Of The Distributed Photovoltaic Aggregation Attack 3

Detection Method Of The Distributed Photovoltaic Aggregation...

引用

3rd IEEE international conference on Information technology, Big Data and Artificial intelligence, ICIBA 2023

作者： Guo, Jing Wang, Ziying Mao, Jiaming Guo, Yajuan Jiang, Haitao State Grid Jiangsu Electric Power Co. Ltd. Research Institute Nanjing China

ISBN: (纸本)9781665490788

With the gradual increase in the number of distributed photovoltaic systems connected to the distribution network, various network attacks and abnormal attacks faced by the communication network will also increase. Therefore, it is very important to study the risk protection against distributed photovoltaic aggregation attacks and potential network anomaly attacks. Numerous distributed photovoltaic branches, circular operation, and flexible load fluctuation characteristics are important factors affecting the stable operation of traditional distribution networks and suffering from abnormal network attacks. This paper presents a mathematical model for distributed photovoltaic aggregation attacks, aiming at minimizing load fluctuations and transmission line power fluctuations in distributed photovoltaic network systems, to achieve detection of distributed photovoltaic aggregation attacks and related control and protection methods. © 2023 IEEE.

关键词： Electric power distribution

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A Real-World Dataset "IDSIoT2024" for Machine Learning/Deep Learning based Cyber Attack detection system for IoT Architecture 3

A Real-World Dataset "IDSIoT2024" for Machine Learning/Deep ...

引用

3rd international conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Koppula, Manasa Leo Joseph, L.M.I. Dept. of ECE SR University Warangal India

ISBN: (纸本)9798331527549

The Internet of Things (IoT) is an emerging technology evaluating its inception in all domains like industries, home automation, healthcare, agriculture, etc. The major challenge in IoT is securing the IoT devices and information associated with the IoT devices from hackers or unauthorized persons. These cyber security attacks can be detected early for timely interventions and reduce major risks. Machine Learning (ML)/Deep Learning (DL) has become an influential technique for automatic Attack or Intrusion detection systems (IDS), putting forward numerous advantages over traditional techniques. A well-structured, real-time IoT security dataset must be used to develop IDS using ML/DL approaches. The article proposes a Real-World dataset IDSIoT2024 consisting of 16,230,955 records to help researchers advance solutions for tackling cyber security in IoT networks. The records presented in the dataset were captured using the Wireshark tool in a real-world IoT architecture. The data collection has taken two months from the middle of June 2023 to August 2023. The proposed dataset can be employed in the field of Artificial intelligence (AI) to develop ML/DL approaches and detect cyber-attacks in IoT networks. A large amount of data needs to be trained to an ML algorithm to get precise detection of attacks. Therefore, the proposed dataset with a vast number of records with different attacks can accommodate the researchers for the development of Intrusion detection systems. © 2025 IEEE.

关键词： Cyber attacks

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Prenatal detection of Ventricular Septal Defects by VGG -16 Model Using Ultrasound Images 3

Prenatal Detection of Ventricular Septal Defects by VGG -16 ...

引用

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

作者： Kavitha, D. Lakshmi, C.R. CMR Institute of Technology Department of Electronics and Communication Engineering Karnataka Bengaluru India Cambridge Institute of Technology Department of Electronics and Communication Engineering Karnataka Bengaluru India

ISBN: (纸本)9798350317022

Congenital heart defect is one of the important anomaly. The deformed heart can be differentiated from the normal heart based on several parameters such as the size of auricles, ventricles, valve and position of heart, area, circumference, and perimeter. One of the methods to detect the anomalies in fetal heart is by applying advanced image pprocessing techniques and artificial intelligence algorithms. This proposed system is the primary framework for diagnosing the prenatal ventricular septal defects (PVSD). The first step is to denoise the US images using enhanced anisotropic diffusion Enhanced Perona Malik Filter (EPMF), followed by K-means clustering segmentation method as the second step, and finally, VGG-16 architecture was implemented with the pre-trained weights from the database. The original image is compared with the reference image in terms of different parameters using a VGG16 deep learning algorithm to predict PVSD anomalies at the early stage of pregnancy. The experimental results shows that PSNR reach to high value of 33.45, Dice coefficient and jacquard index and conformity coefficient as 0.88-0.86. VGG-16 is the first attempt to diagnose prenatal ventricular septal defects to achieve an accuracy of 90%. This proposed system will give a second opinion for the doctors in diagnosing the abnormalities at the early stages. © 2023 IEEE.

关键词： Enhanced Perona Malik filter K-means clustering algorithm VGG-16 CNN model

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Single Shot Detector Algorithms for Hardware Design on FPGAs for Fast Object detection 3

Single Shot Detector Algorithms for Hardware Design on FPGAs...

引用

3rd international conference on Artificial intelligence and Smart Energy, ICAIS 2023

作者： Bernatin, T. Nisha, A. Sahaya Anselin Sundari, G. Kishoresonti, V.J.K. Sathyabama Institute of Science and Technology Department of Electronics and Communication Engineering Tamil Nadu Chennai India

ISBN: (纸本)9781665462167

Object identification and analysis take place throughout the process of detecting YOLOv2-based objects. It entails determining the path that an item takes through space and time by identifying that object's location in each object. A YOLOv2-based object detection algorithm is developed by using FPGA in this work. The detection of objects that are in motion is a difficult job. Recent research has shown that convolutional neural networks, often known as CNNs, perform better than traditional methods, and a number of different object detectors that are based on CNNs have been developed. In order to implement the proposed model, the CNN algorithm is employed to distinguish individual items against a backdrop image for reference. MATLAB and an FPGA board are used to carry out the experiment. © 2023 IEEE.

关键词： Field programmable gate arrays (FPGA)

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Real-time detection of Taikyoku Shodan Karate Kata Poses Using Classical Machine Learning and Deep Learning Models 3

Real-time Detection of Taikyoku Shodan Karate Kata Poses Usi...

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3rd international Mobile, Intelligent, and Ubiquitous Computing conference, MIUCC 2023

作者： Walid, Mazen Ameen, Mostafa Atia, Ayman Faculty of Computer Science Cairo Egypt Faculty of Computer Science Giza Egypt Helwan University HCI-LAB Faculty of Computers and Artificial Intelligence Egypt

ISBN: (纸本)9798350306231

The research discusses the necessity for a precise feedback karate training system utilizing machine learning (ML) and deep learning (DL) models to facilitate real-time pose detection by offering instantaneous and accurate feedback. This system has the potential to revolutionize training methodologies, improve performance, and make significant contributions to the field of martial arts *** ML & DL algorithms were explored to achieve optimal results. Among the classical ML methods, the Random Forest, attained an accuracy of 98%. Additionally, DL models were also examined, with the Long-Short term memory model achieving an accuracy of 97%. The research presents an overview of the problem, methods for data collection and preprocessing, model training, and evaluation. The results emphasize the system's potential for real-time pose detection among karate practitioners, ultimately benefiting their training and progress. © 2023 IEEE.

关键词： Learning systems

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Agricultural Intellectualization system Design under Artificial intelligence 3

Agricultural Intellectualization System Design under Artific...

引用

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

作者： Han, Yawei Zeng, Ju College of Marxism Sichuan University Chengdu China College of Marxism Chongqing University of Arts and Sciences Chongqing China

ISBN: (纸本)9798350317022

Agricultural intellectualization is a new integration form of information technology represented by artificial intelligence and agriculture. This paper will examine the role of agricultural information technology in the development of modern agriculture through the following aspects as and will further demonstrate the development prospects of digital agriculture. First, Intelligent agriculture is driven by artificial intelligence technology, which runs through the whole process of agricultural production, exchange, distribution and consumption, and applies artificial intelligence technology to agricultural management and decision-making. Second, Modern agriculture is inseparable from the support of information technology represented by technologies such as Internet of things, cloud computing and big data. It is a major breakthrough for modern agriculture. At present, intelligent agriculture is growing rapidly. Countries the world over have accelerated the cultivation of artificial intelligence industry with significant leading role to promote the deep integration of artificial intelligence and agriculture. Moreover, especially for arid and barren land, how to use agricultural technology to detect and manage, and then improve the soil will be a major issue. © 2023 IEEE.

关键词： agriculture artificial intelligence data analysis information technology intellectualization

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Cordyceps@LT-EDI: Depression detection with Reddit and Self-training 3

Cordyceps@LT-EDI: Depression Detection with Reddit and Self-...

引用

3rd Workshop on Language technology for Equality, Diversity and Inclusion, LTEDI 2023

作者： Ninalga, Dean

ISBN: (纸本)9789544520847

Depression is debilitating, and not uncommon. Indeed, studies of excessive social media users show correlations with depression, ADHD, and other mental health concerns. Given that there is a large number of people with excessive social media usage, then there is a significant population of potentially undiagnosed users and posts that they create. In this paper, we propose a depression severity detection system using a semi-supervised learning technique to predict if a post is from a user who is experiencing severe, moderate, or low (non-diagnostic) levels of depression. Namely, we use a trained model to classify a large number of unlabelled social media posts from Reddit1, then use these generated labels to train a more powerful classifier. We demonstrate our framework on Detecting Signs of Depression from Social Media Text - LT-EDI@RANLP 2023 (Sampath et al., 2023) shared task, where our framework ranks 3rd overall. © 2023 LTEDI 2023 - 3rd Workshop on Language technology for Equality, Diversity and Inclusion, associated with the 14th international conference on Recent Advances in Natural Language Processing, RANLP 2023 - Proceedings. All rights reserved.

关键词： Supervised learning

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Smart clustering attack detection system 3

Smart clustering attack detection system

引用

3rd international conference on Artificial intelligence and Smart Energy, ICAIS 2023

作者： Vinod Kumar, R. Jayasri, Galla Madhusri, M.A. Vidya, Eddla Saveetha Engineering College Department of Ece Tamilnadu Chennai India

ISBN: (纸本)9781665462167

Due to the development of the internet of things various devices are connected over the common network which leads to the impact of denial of services attacks involved in the traffic. The adaptive strategy needs to be focused to attend the clustering synchronization hands so the channels are protected from Clustering attacks. In a distributed framework in which the local systems are connected to the Global network using a common topology. The secure cluster synchronization based clustering attack detection and updating need to be focused. In the existing system, the mobile vehicular network degrades with energy usage, traffic and congestion will be more. The proposed approach completely analyses the suspicious nodes present in the wireless sensor Network and detects The Clustering Attacks that create malicious problems in the network. The proposed system focused on a clustering-based attack detection approach. The method named hybrid optimization paired with the Chaos Electron search optimization (MCA) method. The complete network topology and synchronization of clustering network performance of a network are being validated using the presented approach. Making the network authorized is highly important to keep the user data. The creation of an authorized cloud, with a dynamic clustering scheme and Intrusion detection framework, is discussed here. © 2023 IEEE.

关键词： Intrusion detection

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Research on Intelligent Ammonia detection system Based on NB-IOT Networking technology 3

Research on Intelligent Ammonia Detection System Based on NB...

引用

2023 IEEE 3rd international conference on Data Science and Computer Application, ICDSCA 2023

作者： Xinyu, Sun Liangzhe, Chen Jinxiang, Wang Yaya, Xie Xijia, He Qing, Yuan Jingchu University of Technology School of Electronic Information Engineering Jingmen448000 China

ISBN: (纸本)9798350341546

In order to meet the demand of real-time monitoring of pollutants in the air, an intelligent ammonia detection system based on narrowband Internet of Things (NBIoT) communication technology is designed. The system consists of multiple monitoring nodes, remote monitoring platform and Android mobile terminal. STM32 is used as microcontroller for monitoring node. Collect air quality related parameters at the location. It is sent to the remote monitoring platform through the NB-IoT communication module to realize intelligent monitoring and management of ammonia gas in a large area. Time division multiplexing differential modulation (TDM) technique is proposed to eliminate the background noise caused by the fluctuation of laser light source in a single optical path detection system. The stability of ammonia samples with a concentration of 500g/m3 was observed for 10h, and the fluctuation of the detected data was less than ±5.72%. The good stability of the system is characterized. © 2023 IEEE.

关键词： ammonia detection NB-IOT networking node time division multiplexing differential modulation wireless sensor network

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Design of Intelligent Monitoring and Identification system for Abnormal Behavior 3

Design of Intelligent Monitoring and Identification System f...

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3rd IEEE international conference on Information technology, Big Data and Artificial intelligence, ICIBA 2023

作者： Guo, Feng Nantong Institute of Technology College of Mechanical Engineering Nantong China

ISBN: (纸本)9781665490788

Traditional camera monitoring systems had been unable to meet the growing demand, in this case, intelligent monitoring technology had been rapidly developed. With the development of intelligent monitoring systems, great progress had been made in both the ability to respond to unexpected situations and the efficiency of hindsight viewing. By using artificial intelligence algorithms to detect people and their behavior, a lot of human and financial costs and time had been saved, and many valuable case investigation activities had been won over. © 2023 IEEE.

关键词： Artificial intelligence

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