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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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Smart IoT based Approach for Accident detection and Prevention 3

Smart IoT based Approach for Accident Detection and Preventi...

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

作者： Chaganti, Pavan Chandra Vishal Sukesh, Koppuravuri Sai Surekha, P. Amrita Vishwa Vidyapeetham Amrita School of Engineering Bengaluru Department of Electrical and Electronics Engineering Bengaluru India Vellore Institute of Technology School of Computer Science and Engineering Tamil Nadu India

ISBN: (纸本)9798350343632

With an exponential rise in population and growth in technology, most people now have access to the luxury of mobility. Road Traffic is increasing day by day and so are accidents. People neglect to take proper precautions and don't follow rules. Also, most of the time these accidents do not get notified to the concerned authority during the golden hour when the victims' lives can be saved. This leads to a rise in the road accidents causing more number of deaths. The main aim of this research study is to build a system to reduce the number of road accidents and deaths or injuries. The proposed IoT device will take care of factors like dizziness, and emotions of the driver, reduction in the speed of the car by automatic application of the brakes, and ensuring notifications are sent to nearby hospitals in a much faster manner even if accidents occur. © 2023 IEEE.

关键词： Accident Prevention Arduino Internet of Things Proteus

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Learning State detection with Multimodal Information in Virtual Reality Learning 3

Learning State Detection with Multimodal Information in Virt...

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3rd IEEE international conference on Digital Twins and Parallel intelligence, DTPI 2023

作者： Zhao, Hongxia Bian, Siyu Liu, Xiwei Jing, Sifeng Chinese Academy of Sciences State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Beijing100190 China Qingdao Academy of Intelligent Industries Institute of Smart Education Systems Qingdao266044 China

ISBN: (纸本)9798350318470

The learning state is a crucial factor that significantly impacts students' learning efficiency and overall learning outcomes. It also serves as a vital indicator for evaluating teachers' effectiveness in their instructional practices. To assess the learning state, various aspects of students' behavior, including their expressions, actions, and language, can effectively reveal valuable insights. Traditionally, studies have focused on investigating the learning state in conventional classroom and online education settings. However, limited research has been conducted in the context of virtual reality (VR) immersive learning environments. Considering the unique challenges posed by VR, such as the occlusion of the upper part of the face by VR devices and the distinct characteristics of VR interaction, we propose a novel method for detecting the learning state while wearing VR during the learning process. This method integrates multimodal information, including video captured by a camera, eye movement sensor data from the VR device, and electroencephalography (EEG) signals from an EEG headband. By comprehensively analyzing these inputs, we can determine the learning state effectively. An experimental evaluation was conducted to validate the effectiveness of the proposed multimodal information infusion learning state detection method in the context of VR immersive learning. The results demonstrate that this approach significantly enhances the accuracy of determining the learning state. Ultimately, this advancement has the potential to improve the overall learning quality and facilitate the widespread adoption of VR in educational settings. © 2023 IEEE.

关键词： Virtual reality

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3rd international conference on Applied Science and Advanced technology, ICASAT 2023

3rd International Conference on Applied Science and Advanced...

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3rd international conference on Applied Science and Advanced technology, ICASAT 2023

ISBN: (纸本)9783031576195

The proceedings contain 15 papers. The special focus in this conference is on Applied Science and Advanced technology. The topics include: Projector-Camera Calibration Method Based on Pattern Geometric Constraints;Evaluation of GAN Network-Based Images for Precision Agriculture;the Pollution of Dams in Mexico: Research Progress Analyzed by a Bibliometric Approach;cadmium Sulfide Bionanoparticles for Methylene Blue Dye Removal;mesoporous Silica-Based Drug Delivery systems Obtained from Sodium Silicate as an Economical Synthesis;synthesis and Characterization of Tri-metallic Nanoparticles (Fe-Cu-Zn) as Potential Nano-catalyzers;tb3+-doped LiCoO2 Produced by the Sol-Gel Method;optimization of Locomotion Secuence to Maximize Speed in a Soft Crawling Robot;development and Evaluation of a Small-Scale Autonomous Aeroponic system with Laser Photostimulation for the Production of Leafy Vegetables in the Mexican Bajío;evaluation of Water Quality of Treated Water with a Composed Material Using Three Types of Starch and Chitosan;Development and Validation of Specific PCR Assays for Phytopathogens Botrytis cinerea and Xylella fastidiosa, detection in Vineyard Soil Samples;from Doping to Mixed Phases of Y2O3/Tb2O3 Powders;Evaluation of UV-Irradiation Degradation of Vegetable Oil Using Optical Absortion Spectra.

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An Extensive Machine Learning Framework Employing Ensemble Classifiers for Heart Disease detection 3

An Extensive Machine Learning Framework Employing Ensemble C...

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

作者： Chandralekha, E. Vinodhini, S. Kandasamy, V. Rama, P. Ravikumar, S. Saravanan, T.R. Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Dept.of CSE Chennai India Velammal Engineering College Dept. of Information Technology Chennai India Panimalar Engineering College Dept. of Information Technology Chennai India College of Engineering and Technology Faculty of Engineering and Technology SRM Institute of Science and Technology Dept. of Computing Technologies Chennai India SRM Institute of Science and Technology Dept. of Computational Intelligence Chennai India

ISBN: (纸本)9798350343632

Heart disease is a significant global health challenge, impacting many lives and often being a leading cause of mortality and illness. Detecting heart disease accurately and promptly is essential for effective medical care. Due to the complex interplay of risk factors and the intricate nature of diagnosis, advanced technologies like machine learning are being explored. This study introduces a comprehensive framework that employs machine learning techniques for heart disease detection. The framework involves critical stages such as data preprocessing, feature selection, and training ensemble classifiers. Experimental results highlight the importance of specific attributes in each classifier, offering insights into predictions. Notably, attributes like oldpeak, thalach, ca, cp, and thal are key features in the Random Forest classifier, while Gradient Boosting underscores ca, cp, thal, oldpeak, and age. AdaBoost and the Extra Trees Classifier reveal similar significant attributes. Evaluation metrics emphasize the models' robust performance, with the Extra Trees Classifier achieving the highest accuracy. This research contributes to a practical heart disease detection framework with potential for improving medical decision-making. © 2023 IEEE.

关键词： AdaBoost Extra Trees Classifier Gradient Boosting Machine Learning Random Forest

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A Novel Optimization Strategies for Detecting and Preventing Distributed Denial of Service Attacks in Wireless Networks 3

A Novel Optimization Strategies for Detecting and Preventing...

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

作者： Kumar, Saurabh Ratna Raja Kumar, Jambi Mondal, Dipannita Hemelatha, S. Geetha, B. Diwakar, Mandar P. Mangalayatan University Department of Commerce Aligarh India Genba Sopanrao Moze College of Engineering Balewadi Department of Computer Engineering Maharashtra Pune India Dr. D. Y.Patil College of Engineering and Innovation Artificial Intelligence and Data Science Department Talegaon India Karpagam Academy of Higher Education Department of Computer Science Engineering Coimbatore641021 India Prince Shri Venkateshwara Padmavathy Engineering College Department of Science and Humanities Chennai India Vishwakarma Institute of Information Technology Department of Artificial Intelligence & Data Science Pune India

ISBN: (纸本)9798350319125

allotted Denial of carrier (DDoS) assaults have end up a prime difficulty in trendy wireless networks. these assaults aim to disrupt the regular operation of networks by means of overwhelming them with excessive traffic from more than one assets. traditional DDoS detection and prevention strategies have established to be useless in wireless networks due to the dynamic and unpredictable nature of the wireless surroundings. To deal with this problem, novel optimization techniques were proposed to enhance the potential of wireless networks to discover and prevent DDoS *** of the key techniques is the use of wise algorithms which includes machine learning and synthetic intelligence. these algorithms can analyze and study from network site visitors patterns to pick out strange behavior this is indicative of a DDoS attack. by way of continuously tracking the network and adapting to converting traffic patterns, these algorithms are capable of as it should be locate and mitigate DDoS assaults in actual-time. any other method is the deployment of robust and dynamic defense mechanisms. those mechanisms include the usage of anomaly detection, traffic filtering, and traffic shaping. Anomaly detection strategies rent statistical techniques to hit upon patterns that deviate from ordinary network conduct. traffic filtering involves figuring out and blockading malicious site visitors, even as site visitors shaping manages and controls the flow of legitimate visitors to prevent congestion and enhance community overall performance. © 2023 IEEE.

关键词： filtering legitimate ordinary robust strategies

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An Artificial intelligence Approach in 5G New Radio Beam Enhancement 3

An Artificial Intelligence Approach in 5G New Radio Beam Enh...

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

作者： Upadhyay, Deepak Mittal, Saksham Jain, Ayushi Sharma, Ravi Bagla, Piyush Tripathi, Neha Tiwari, Pallavi Graphic Era Hill University Dept. of Computer Science & Engineering Uttarakhand Dehradun India National Institute of Technology Computer Science & Engineering Dept. Punjab Jalandhar India Dept. of Computer Science & Engineering Uttarakhand Dehradun India

ISBN: (纸本)9798350300710

The paper presents a novel application of artificial neural networks in the context of 5G new radio beamforming. The research leverages the intricate mechanism of ANNs to demonstrate the efficacy of this approach in optimizing the beamforming process and improving the overall performance of the 5G new radio system. The utilization of ANNs allows for real-time adaptation and decision-making, thereby mitigating the limitations of conventional beamforming techniques. The results of the study are quite promising, indicating a substantial enhancement in the accuracy and efficiency of beamforming in 5G new radio. The proposed method is expected to have a significant impact on the development of advanced wireless communication systems. © 2023 IEEE.

关键词： Beamforming

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Instance-based adaptive attention algorithm for human-object interaction detection 3

Instance-based adaptive attention algorithm for human-object...

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3rd international conference on Artificial intelligence and Computer Engineering, ICAICE 2022

作者： Ye, Qing Wang, Xikun School of Information Science and Technology North China University of Technology Beijing100144 China

ISBN: (纸本)9781510663473

Aiming at the problem of unclear human-object interaction behavior objects in complex background, we propose an Instance-Based Adaptive Attention(IBAA) algorithm. The algorithm adaptively generates a series of attention matrices according to the objects and subjects of human-object interaction instances. These attention matrices are then used to update the feature map to reduce the interference of the complex background. In order to enrich the human object interaction information, we use the graphical model to represent the interactions between human and objects, and use the graph convolutional neural network to update it. Experimental results on HICO-DET dataset show that the proposed algorithm has significantly improved accuracy and multi-scale object detection ability compared with other human object interaction algorithms. © 2023 SPIE.

关键词： Convolutional neural networks

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Patrolling Robot with Facial detection 1

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3rd international conference on Innovations in Computational intelligence and Computer Vision, ICICV 2022

作者： Golsangi, Mitrajeet Ghuge, Vivek Godse, Divija Harne, Pravin Thopate, Kaushalya Department of Computer Science Vishwakarma Institute of Technology Pune India

ISBN: (数字)9789819926022

ISBN: (纸本)9789819926015

Security of any facility and the people inside it is a huge responsibility of the management of that facility. This, even though it can be achieved using humans, it leaves them at risk and introduces a factor of human error. Thus, the proposed system aims to improve the surveillance and security of a compound, without putting any human lives at risk and naturally removing the human error factor from the process. The paper discusses the implementation of such a system and all the work related to it. The proposed system is a fully functional robot which can follow the direction of a sound. It also has an on board camera which sends live footage of everything to a server. This feed is then processed for detection of a face and it is displayed to the user. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023.

关键词： Face recognition

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detection of DDoS Attacks using Artificial Gorilla Troops Optimizer based Deep Learning Model 3

Detection of DDoS Attacks using Artificial Gorilla Troops Op...

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

作者： Govindaraju, S. Metia, Rajrupa Girija, P. Baranitharan, K. Indirani, M. Mahaveerakannan, R. Sri Ramakrishna College of Arts and Science Department of Cs Coimbatore India Haldia Institute of Technology Dept. of Cse West Bengal Purba Medinipur Haldia India S.A. Engineering College Department of Cse India Vsb Engineering College Cse Department India Hindusthan College of Engineering and Technology Department of It Coimbatore India Saveetha School of Engineering Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9781665462167

The importance of security has skyrocketed alongside the adoption of Internet of Things (IoT) services. Software-defined networking (S DN) provides a means of securely managing IoT devices, which were exposed in a current distributed denial-of-service (DDoS) attack. Many IoT devices unwittingly contributed to the DDoS attack. DDoS attacks, one of the most common types of cyberattack, are particularly pernicious since they can cripple a network's ability to function and render many of its services inaccessible to users. This research used optimised deep learning-based models to detect DDoS in SDN. At first, the dataset's normal and DDoS attack traffic characteristics were gathered from the SDN. The models are recommended to be simpler, easier to read, and to have a shorter training period when using an NSL-KDD dataset for feature selection approaches. Real-time DDoS attack detection in SDN is proposed in this research using an Long Short-Term Memory (LS TM) models. High accuracy in classification is achieved by utilising an artificial gorilla troop optimizer to pick the features of NSL-KDD. Using less time and material, the proposed IDS was able to achieve a detection accuracy of 97.59%. These findings provide encouraging evidence that DDoS attack detection in SDN could benefit from the use of deep learning and feature selection techniques, which could significantly reduce processing loads and runtimes. © 2023 IEEE.

关键词： Internet of things

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A novel method for cycle slip detection and restoration of BDS carrier phase 3

A novel method for cycle slip detection and restoration of B...

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2023 3rd international conference on Mechanical Automation and Electronic Information Engineering, MAEIE 2023

作者： Yuan, Zhenguo School of Mechanical and Electrical Technology Guangdong Industry Polytechnic Guangdong Guangzhou510300 China

The chief purpose of this present study is to develop a new approach to cycle slip detection and restoration of the BeiDou Navigation Satellite system (BDS) carrier phase. Based on the RAIM algorithm in the application of aviation safety, we extend the application to ensure the precision of the carrier phase. To stand-alone receivers of a low-cost single frequency, carrier phase data is made different between adjacent epochs. Then least-square residuals were to design a RAIM scheme with differential carrier phase data to find out the epoch with cycle slip, and finally repair cycle slip by Chebyshev polynomial. Results indicate that the approach can detect cycle slip effectively and both big cycle slip and small cycle slip could be repaired. © Published under licence by IOP Publishing Ltd.

关键词： Restoration

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