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

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Mask detection in Security Cameras Using Deep Learning 3

Mask Detection in Security Cameras Using Deep Learning

引用

3rd international conference on Technological Advancements in Computational Sciences, ICTACS 2023

作者： Kumar, Amodh Hooda, Susheela Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (纸本)9798350342338

Mask detection in security camera surveillance is an important operation for icing public safety and security. The face masks worn by culprits for concealment during criminal acts pose major challenges for traditional surveillance systems. This paper presents a mask detection system that uses deep learning and computer vision ways to analyse live videotape feeds from security cameras for recognizing persons engaged in theft or criminal activity. The proposed system uses a pre-trained convolutional neural network to detect faces in the videotape feed and applies a binary classification model to determine whether the person is wearing a mask or not. The system can detect multiple faces in the videotape feed simultaneously and give real- time cautions when a person is wearing a mask. The proposed system provides a practical and effective result for security personnel to cover the mask-wearing people in the confined areas, thereby helping to reduce the theft rate and enhance public safety. © 2023 IEEE.

关键词： camera deep learning detection machine learning mask security

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Fraud detection Based on Graph Neural Network 3

Fraud Detection Based on Graph Neural Network

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3rd international conference on Artificial intelligence, Big Data and Algorithms, CAIBDA 2023

作者： Zhang, Tao Ye, Haicheng North China University of Technology Beijing China

ISBN: (纸本)9781643684444

With the rapid development of digital and intelligent credit industry, credit fraud detection has become an important task to ensure financial digital intelligence. The traditional credit fraud detection model relies on artificial feature engineering and is built based on supervised learning algorithm, and its performance is greatly affected by data quality, sample distribution and other factors, and it is prone to prediction errors in the face of emerging fraud techniques. With the development of digitalization and fraud methods, these methods are often no longer applicable. There are rich information associations among users such as the users' emergency contacts, home address, and social relationships between themselves, which make a large social network graph formed between users. In this regard, based on the Dgraph-Fin dataset, this paper uses the relationship network formed among users to better learn the weights between different edges through neighbor sampling and attention mechanism. Experimental results show that the accuracy and effectiveness are improved compared with the existing baseline. © 2023 The Authors.

关键词： Convolutional neural networks

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Exploration of Contour Coding and Its Application in Digital Image Processing 3

Exploration of Contour Coding and Its Application in Digital...

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

作者： Little Femilin Jana, D. Kadam, Prema S. Sharma, Neeraj Garg, Avni Acharjya, Kalyan Venkadeshwaran, K. Prince Shri Venkateshwara Padmavathy Engineering College Department of Science and Humanities Chennai127 India Vishwakarma Institute of Information Technology Department of Artificial Intelligence & Data Science Pune India Vivekananda Global University Department of Electrical Engineering Jaipur India Centre of Interdisciplinary Research in Business and Technology Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Faculty of Engineering and Technology Department of Mechanical Engineering Karnataka Ramnagar562112 India

ISBN: (纸本)9798350319125

Photograph processing is a discipline of laptop science that concentrates on manipulating digital photos and using algorithms to recognize and beautify the photo's quality. Contour coding is a powerful approach used in virtual picture processing that allows one to increase, shop, and examine virtual snapshots from a combination of strategies like aspect detection, morphological processing, and segmentation. Contour coding aims to reduce complex virtual pix to a fixed of fundamental symbolic features that may be created, stored, and manipulated without loss in quality or element. Applications of contour coding are specially used for item reputation, segmentation, spatial pattern reputation, and scientific photograph processing. This newsletter offers the theory and fundamentals of contour coding and explores its software in actual-world virtual picture processing scenarios. We speak about the significance of contour coding and present a short evaluation of existing algorithms and improvement techniques used to achieve virtual photograph processing desires. We additionally gift the advances and challenges of contour coding and how it could form the future of digital picture processing. © 2023 IEEE.

关键词： challenges development fundamentals processing segmentation

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Enhancing the detection of Botnet Attacks in the Internet of Things Networks Through the Utilization of Hybrid Feature Selection 3

Enhancing the Detection of Botnet Attacks in the Internet of...

引用

3rd FORTEI-international conference on Electrical Engineering, FORTEI-ICEE 2024

作者： Kurniawan, Hartanto Samsudiat Nugroho, Cahyono Saputra, Andri Nursyahbani, Aditya Research Center for Artificial Intelligence and Cybersecurity National Research and Innovation Agency Jakarta Indonesia Center of Data and Information National Research and Innovation Agency Jakarta Indonesia

ISBN: (纸本)9798331542207

The proliferation of Internet of Things (IoT) technology is leading to an escalation in cyber risks. The Intrusion detection system (IDS) is crucial in countering cyber threats, with botnets being the most commonly detected attack in IoT networks. Anomaly-based IDS is necessary to enhance their capability in identifying cyber threat attributes through the use of supervised machine learning. This work investigates an enhanced supervised machine learning algorithm technique that combines filter-based feature selection methods, specifically Low Variance and Pearson Correlation Coefficient, to detect a new type of botnet attack. The CICIoT2023 dataset, an updated benchmark intrusion dataset specifically designed for IoT networks, is employed to assess the model’s performance using several algorithms including Decision Tree, Random Forest, Logistic Regression, and Naive Bayes. The results demonstrate that the model effectively enhances the identification of botnet attacks by reducing the number of features from 46 to 14. This optimization leads to improved performance, including increased accuracy level and reduced computation time. © 2024 IEEE.

关键词： Bot (Internet)

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Isolation Forest Based Anomaly detection and Fault Localization for Solar PV system 3

Isolation Forest Based Anomaly Detection and Fault Localizat...

引用

3rd international conference on Robotics, Electrical and Signal Processing Techniques, ICREST 2023

作者： Kabir, Shaharier Shufian, Abu Zishan, Md. Saniat Rahman American International University-Bangladesh Department of Electrical and Electronic Engineering Dhaka1229 Bangladesh Institute of Energy Technology Chittagong University of Engineering & Technology Chattogram4349 Bangladesh

ISBN: (纸本)9798350346435

The decrease in fossil fuel reserves has prompted a global move toward distributed energy resources. For this reason, solar PV power generation has recently gained much attention as a feasible renewable energy source. However, large-scale generation is challenging if there are anomalies in individual solar PV panels. This will reduce the efficiency of the PV system and create a potential fire hazard. In this perspective, the anomaly detection technique discloses system anomalies accurately and effectively. Identified anomalies will localize the event for an improved generation. This paper addresses the performance analysis of using the isolation forest technique to identify anomalies in the PV system and the rule-based fault localization technique to identify defective panel events. In the developed model, the isolation forest technique found around 453 anomalies in 45,740 observations, and approximately six panels indicated a fault in the system. The accuracy score is found to be approximately 0.9886. The proposed fault detection method will help detect the faults in solar power systems. © 2023 IEEE.

关键词： Fault detection

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Diabetic Retinopathy Segmentation and Classification Using U-Net with Convolutional Neural Network 3

Diabetic Retinopathy Segmentation and Classification Using U...

引用

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

作者： Ramadan, Ghazi Mohamad Adnan, Myasar Mundher Naser, Zamen Latef Kumar, Bura Vijay Parthiban, K.G. College of MLT Ahl Al Bayt University Karbala Iraq 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 Dhaanish Ahmed Institute of Technology Department of Biomedical Engineering Coimbatore India

ISBN: (纸本)9798350317022

The early detection of Diabetic Retinopathy (DR) is necessary to aid doctors in treating retina patient to increase their existence rate. Due to the major imbalance dataset issue in classification, Deep Learning (DL) based approach is proposed in this research for segmentation and classification. The MESSIDOR dataset is utilized in this research for classification of Diabetic retinopathy. The min-max normalization, CLAHE and Resize is used in this experiment for data pre-processing and fed into Convolutional Neural Network (CNN) classification. The segmentation is extracted using UNet for the semantic segmentation of red lesions, semantic segmentation is achieved due to the relation of the encode and decode side feature maps. Next, Classification using CNN, they are two classifiers, normal and Mild/ moderation to classifying the object. The obtained result show that the proposed DL using CNN model achieved a better accuracy of 99.84% on MESSIDOR dataset, the existing method such as Capsule method and Moth Search Optimization model (MSO) compared to implemented model are best accuracy. © 2023 IEEE.

关键词： Contrast Limited Adaptive Histogram Convolutional Neural Network Deep Learning Diabetic Retinopathy U-net

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Unsupervised Network Traffic Anomaly detection Method Based on Attribute Graph in Power Internet of Things 3

Unsupervised Network Traffic Anomaly Detection Method Based ...

引用

3rd international conference on Energy Engineering and Power systems, EEPS 2023

作者： Dang, Qian Cui, Ajun Shang, Wenbo Du, Chunhui Wang, Chenyu Gui, Xiaolin State Grid Gansu Electric Power Company Digital Division Lanzhou China Xi'an Jiaotong University Faculty of Electronic and Information Engineering Xi'an China

ISBN: (纸本)9798350313857

With the rapid development of computer and communication technology, the power Internet of Things has become an inevitable trend of intelligent and informatized power grid construction. However, the openness of the power Internet of Things makes it more vulnerable to cyber attacks and affects the normal operation of the power system. Therefore, in order to improve the stability of the power system, it is necessary to detect the abnormality of the traffic data generated by the network attack and give early warning of the attack in time. However, almost all existing network traffic anomaly detection methods are strongly dependent on labeled data, manually selected features, and balanced datasets. These methods are not only expensive, but also difficult to distinguish unknown abnormal types. This paper proposes a network traffic anomaly detection method based on autoencoder and attribute graph. This method is designed to learn generic abstract features by autoencoder and avoid the influence of manual features. Then the network traffic is abstracted into an attribute graph based on abstract features, and an anomaly detection model based on the attribute graph is designed to filter out anomalous traffic depending on topology and similarity. At last, the feasibility and effectiveness of the algorithm proposed is verified, on the two network traffic public datasets (NSL-KDD and CICIDS2017). Experimental result demonstrate that the model proposed in this paper has better detection performance compared with other state-of-the-art network traffic anomaly detection algorithms in unsupervised condition, which can be effectively used for imbalanced dataset. © 2023 IEEE.

关键词： Internet of things

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Research on defect detection system of porcelain pillar insulator based on spectrum analysis principle 3

Research on defect detection system of porcelain pillar insu...

引用

3rd international conference on Electronic Information Engineering, Big Data, and Computer technology, EIBDCT 2024

作者： Li, Jiang Liu, Xiaohang Yang, Chun Shi, Changjun Gu, Zijian State Grid Tieling Electric Power Supply Company Tieling112000 China

ISBN: (纸本)9781510680449

Pillar porcelain insulator is an important component of power plants and substations, a large number of use in the power system, play a role in supporting the conductor, circuit breaker and high-voltage switching, however, porcelain insulators charged with the detection technology research field there are still gaps. For this reason, this paper for the detection technology in the information acquisition circuit system to carry out the design, which is mainly divided into three parts of the information acquisition and processing module, respectively, for the following circuit, subtraction and amplification filter circuit and analogue circuit noise analysis. The role of the signal and processing circuit is mainly for the detector output to provide a suitable load to accurately obtain the detector output of the analogue signal, and the analogue signal modulation so that it can be analogue-to-digital conversion circuit for better quantification. Finally, the correctness and effectiveness of this part of the design is verified through field tests. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Electric circuit breakers

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Using Brightness Distribution in Fundus Images to Automated Glaucoma detection 3

Using Brightness Distribution in Fundus Images to Automated ...

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3rd IEEE international Maghreb Meeting of the conference on Sciences and Techniques of Automatic Control and Computer Engineering, MI-STA 2023

作者： Ahmed, Zakia Lawgali, Ahmed University of Omar Al-Mukhtar Faculty of Sciences Department of Computer Science Albayda Libya University of Benghazi Faculty of Information Technology Department of Computer Science Benghazi Libya

ISBN: (纸本)9798350319897

Glaucoma is a condition of the optic system that can cause vision loss as a result of increased intraocular pressure. The disease is becoming more prevalent, and its diagnosis can be both costly and time-consuming. One prominent area of the retina affected by glaucoma is the optic nerve head, which undergoes structural changes. The goal of the study was to detect glaucoma by analyzing the brightness distribution in optic nerve head fundus images, with statistical parameters serving as the primary means of measurement. Specifically, the mean and standard deviation were extracted as features from the fundus images, representing the central tendency and variation of data within the images. The proposed approach demonstrated highly accurate results, outperforming existing literature in the field. © 2023 IEEE.

关键词： Diagnosis

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Kidney Disease detection and Identification Using Artificial intelligence 13

Kidney Disease Detection and Identification Using Artificial...

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13th international conference on Cloud Computing, Data Science and Engineering, Confluence 2023

作者： Saraswat, Tanya Pathak, Savita Sachdeva, Sanya Sahu, Krishna Sawhney, Rahul Amity University Amity School of Engineering and Technology Uttar Pradesh India Benevis Atlanta United States

ISBN: (纸本)9781665462631

Kidney Disease is the 3rd most substantial cause as mentioned and has the highest rate of morbidity and mortality rated all over the world. Chronic Kidney Disease is detected at a very later stage therefore making it one of the most harmful diseases. According to the Electronic Medical Records it is very hard for the detection of Chronic Kidney Disease. Severe medical treatment like hemodialysis is required for the procedures conducted for the curing of Renal Disease. Chronic Kidney disease has affected nearly about 10% of the present day population which itself claims how contagious this disease is. Blood Pressure, Heart Ailment, Glucose Level, Sodium, Potassium are all the causes for the identification of CKD. © 2023 IEEE.

关键词： Diseases

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