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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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Advanced AI Approaches for Detailed Examination of Individuals Prone to Nightly Respiratory Challenges Using Medical Records 3

Advanced AI Approaches for Detailed Examination of Individua...

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3rd IEEE international conference on ICT in Business Industry and Government, ICTBIG 2023

作者： Srivastava, Dharmesh Kumar Parveen, Suphiya Yadav, Dhananjay Kumar Presidency University Department of Electronics and Communication Engineering Karnataka Bangalore India Department of Genetics Karnataka India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India

ISBN: (纸本)9798350343274

The advancement of Artificial intelligence (AI) has opened new avenues for the detailed examination of individuals prone to nightly respiratory challenges using medical records. This study presents an advanced AI approach that integrates predictive modeling, natural language processing (NLP), continuous monitoring with wearable devices, data integration, risk scoring, and anomaly detection within a Clinical Decision Support system (CDSS). The proposed method offers a holistic and patient-centric solution for early detection and personalized care. In this study, we evaluated the performance of the proposed method against six original traditional methods. Performance metrics, including accuracy, precision, recall, F1 score, AUC-ROC, sensitivity, specificity, positive predictive value, negative predictive value, and heatmaps of multiple metrics, were used for the comparison. The results demonstrate the superiority of the proposed method over traditional approaches. The proposed method consistently outperforms traditional methods across all performance metrics, exhibiting higher accuracy, precision, recall, F1 score, and AUC-ROC. The sensitivity and specificity of the proposed method exceed those of traditional methods. Additionally, the heatmap of multiple metrics reveals the comprehensive and robust nature of the proposed approach, emphasizing its ability to provide a nuanced examination of individuals prone to nightly respiratory challenges. In conclusion, this advanced AI approach, integrated within a CDSS, is poised to revolutionize the management of individuals at risk of nocturnal respiratory challenges. It enables early detection, personalized interventions, and enhanced patient outcomes. The proposed method addresses the limitations of traditional approaches by leveraging the power of AI-driven technologies, offering more accurate, efficient, and patient-focused healthcare solutions. This study contributes to the growing body of research that harnesses AI's pot

关键词： Anomaly detection

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Higgs Boson detection from Monte Carlo Simulation Tensors using Wide and Deep Networks 3

Higgs Boson Detection from Monte Carlo Simulation Tensors us...

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3rd international conference on Artificial intelligence and Signal Processing, AISP 2023

作者： Adhikary, Subhrangshu Mukherjee, Chandrachur Banerjee, Saikat National Institute of Technology Department of Computer Science and Engineering Durgapur713213 India Wingbotics Department of Physics West Bengal 700086 India Wingbotics Department of Remote Sensing West Bengal 700086 India

ISBN: (纸本)9798350320749

Higgs Bosons are the fundamental particles with 0 spin that couple to mass and are produced by quantum excitation of the Higgs Field. They have even parity and have no electric or colour charge. It is very unstable in nature and quickly decays into other particles. Monte Carlo simulation is a probabilistic technique to estimate different outcomes in a process that are difficult to predict because of random variables. The Monte Carlo simulation method can be used to generate a probabilistic dataset containing 211ow-level features and 7 high-level features to detect Higgs Boson. Deep learning, on the other hand, is very useful to create a decision model based on a large amount of data with fast prediction capabilities. But training a deep learning model comes with requiring large computational resources. Other related works which have been conducted on this domain take a large number (50 to several thousand) of iterations to converge to a stable loss. The presented paper aims to reduce the number of iterations and resource consumption to train the dataset by using a Wide and Deep Network based learning model. The presented model converges to an AUC of 0.89 within 12 iterations hitting an early stopping condition. This shows that the presented model is more efficient than other available models. © 2023 IEEE.

关键词： Bosons

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Sound Event detection Using Multi-Scale Dense Convolutional Recurrent Neural Network with Lightweight Attention 3

Sound Event Detection Using Multi-Scale Dense Convolutional ...

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3rd international conference on Electronic Information Engineering and Computer, EIECT 2023

作者： Yang, Hanyue Luo, Liyan Wang, Mei Song, Xiyu Mi, Fukun Guilin University of Electronic Technology School of Information and Communication Guilin China School of Information Science & Engineering Guilin China

ISBN: (纸本)9798350357707

Neural network based sound event detection has attracted considerable attention due to high detection accuracy. Existing sound event detection algorithms often improve performance by constructing deeper and more complex detection models. To address the issue of high computational complexity in existing high performance sound event detection algorithms, this paper proposes a sound event detection algorithm using multiscale dense convolutional recurrent neural network with lightweight attention (MS-AttDenseNet-RNN). It utilizes multiscale dense convolutional neural network with lightweight attention (MS-AttDenseNet) to capture information from different scales of sound signal receptive fields and then generates importance-enhanced multi-scale feature. Finally, recurrent neural network (RNN) is used to model the temporal dependency of the enhanced multi-scale features. Compared to the system model that won the DCASE challenge, this method achieves a 18.9% improvement in F1 score and a 4.0% reduction in error rate, while reducing the number of parameters by 75.8% and the computational complexity by 47.5%. This demonstrates that the proposed method can achieve lower parameter count and computational complexity while maintaining accuracy. © 2023 IEEE.

关键词： Convolution

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detection system implementation in streaming video with High standard Accuracy Using Single-Shot Multi-Box Detector system with CNN 3

Detection system implementation in streaming video with High...

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3rd international conference on Advancement in Electronics and Communication Engineering, AECE 2023

作者： Jawarneh, Malik Mohammed, Mohammed Q. Al-Ghanimi, Hayder Diame, Hussein Alaa Lafta, Alaa M. Alchilibi, Haider Oman College of Management and Technology Muscat Oman Al-Esraa University College Forensic Sciences Department Baghdad Iraq Hilla University College Department of Medical Instrumentation Techniques Engineering Babylon Iraq Al-Kunooze University College Computer Engineer Department Basra Iraq National University of Science and Technology Dhi Qar Iraq Al-Farahidi University Medical Laboratory Techniques Department Baghdad10022 Iraq

ISBN: (纸本)9798350330724

The purpose of this study is to compare the Single Shot Multi box Detector with the CNN algorithm approach for detecting face masks (SSD). The study consists of two groups: SSD and CNN algorithm approach, each with a sample size of 20. The pretest power used in the simulation was 0.8. accuracy values are computed to assess how well the SSD and CNN algorithm's function. The accuracy of the CNN algorithm technique and SSD was 92.65% and 81.76%, respectively. Given that the model's p (2-tailed) value is 0.000, the two groups differ significantly from one another. Inthis study, it was discovered that the CNN algorithm approach outperformed SSD substantially in terms of accuracy. © 2023 IEEE.

关键词： Coronavirus

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Design and implementation of scene visualization system based on dual engine structure 3

Design and implementation of scene visualization system base...

引用

3rd international conference on Electronics, Electrical and Information Engineering, ICEEIE 2023

作者： Liu, Mingwei Luo, Jian Zhu, Jing Kang, Yi China Southern Power Grid Digital Grid Research Institute Co. Ltd. Guangzhou510000 China

ISBN: (纸本)9781510671423

Intrusion detection is an advanced security technology that goes beyond traditional measures like access control, passwords, authentication, auditing, and firewalls. It safeguards against internal and external attacks, as well as misoperation. To establish a user's normal behavior, a set of behavior profiles must be created and maintained, describing the user's typical system usage. This paper presents a visualization algorithm for network intrusion scenes, employing a dual-engine structure that integrates network-based and host-based intrusion detection systems. The algorithm offers combined detection, reporting, and response capabilities. Simulation results demonstrate significant improvements in false alarm rates compared to traditional methods. The network engine implementation combines protocol analysis and pattern matching, narrowing down target matches and enhancing detection speed. The approach effectively meets the efficiency and accuracy requirements of intrusion detection systems in cloud computing environments. © 2023 SPIE.

关键词： Intrusion detection

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Intrusion detection Study of Nuclear Power Plant Electronic systems Based on Machine Learning Optimization Algorithms 3

Intrusion Detection Study of Nuclear Power Plant Electronic ...

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3rd international conference on Electronic Information Engineering and Computer Communication, EIECC 2023

作者： Zhang, Xiaoyan School of Management Shanghai University Shanghai China

ISBN: (纸本)9798350359961

In modern society, the rapid development of information technology makes a variety of electronic devices are widely used, and the electronic systems in these devices are often the key to the control system, and their stability and security are crucial to the operation of the whole system. This paper studies and compares the prediction ability of various machine learning algorithm models in the intrusion detection of electronic systems in nuclear power plants. The results show that the decision tree model and random forest model have the best prediction ability, which is far better than the other models. The linear regression model and KNN model have poor prediction ability, while the BP neural network model has poor prediction effect and is not suitable for intrusion detection of nuclear power plant electronic system. Intrusion detection in electronic systems of nuclear power plants is an important security problem, so it is important to study the prediction ability of different machine learning models for this problem. In this paper, through the experimental comparison of multiple machine learning models, we found that the decision tree model and random forest model have the best prediction ability, which indicates that these two models have high prediction ability and accuracy in nuclear power plant electronic system intrusion detection. The linear regression model and KNN model have poorer predictive ability, which may be due to the inability of these two models to capture the complex relationships in the intrusion detection of nuclear power plant electronic systems. And the BP neural network model has poor predictive effect, which may be due to the fact that the model is prone to overfitting phenomenon, resulting in inaccurate prediction results. The findings of this paper show that these conclusions provide valuable references for intrusion detection in electronic systems of nuclear power plants. © 2023 IEEE.

关键词： Decision Tree Machine learning algorithms Random Forest

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A new traffic signal detection system with high robustness and real-time requirements 3

A new traffic signal detection system with high robustness a...

引用

3rd international conference on Signal Image Processing and Communication, ICSIPC 2023

作者： Tang, Junjie Li, Yicun State Key Laboratory of Disaster Prevention & Mitigation of Explosion & Impact Army Engineering University of PLA Jiangsu Nanjing210007 China Business School Hangzhou City College Hangzhou Yiyuan Technology Co. Ltd. Hangzhou China

ISBN: (纸本)9781510670945

Automatic traffic signal detection has great significance to the development of autonomous driving technology and vehicle warning of dangerous driving behavior. Detecting traffic lights by analyzing images obtained from mobile device cameras is a feasible solution. In this paper, a new traffic signal detection system with high robustness and real-time requirements. Firstly, the HSV color segmentation method is used to segment the original map. Then, the aspect ratio information and area information of traffic lights are utilized for secondary filtering. The morphological processing method is used to filter the background in the image. Finally, intercept the crucial area to obtain the final significance segmentation image of preprocessing. Combining the salient feature preprocessing based on traditional image processing methods with the target detection model based on deep learning, the algorithm of traffic signal detection and recognition is improved. © 2023 SPIE.

关键词： Signal detection

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For the Processing of Laser-Cut Materials, Seam Tracking Using Texture-Based Image Analysis 3

For the Processing of Laser-Cut Materials, Seam Tracking Usi...

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3rd international conference on Advance Computing and Innovative Technologies in Engineering, ICACITE 2023

作者： Raghuram, Kadambari Raghuram, K.S. Nageswari, P. Joshi, Kapil Selvamuthukumar, T. Reddy, Dasari Veera JNTUK Department of Computer Science and Engineering Andhra Pradesh Kakinada India Institute of Technology Department of Mathematics Mahendra Tamilnadu Namakkal637503 India Department of Mechanical Engineering AP Visakhapatnam India Uttaranchal University Uttaranchal Institute of Technology India Telangana Secunderabad India SR University School of Computer Science and Artificial Intelligence Telangana Warangal India

ISBN: (纸本)9798350399264

A camera-based seams monitoring system for lasers material treatment is the subject of this discussion. The lit working piece area and connection point are both captured by the electronic super-fast cameras. The light beams route incorporates the lens equipment symmetrically. In order to regulate the welding operation in a circular path, it is necessary to watch the contact site & connection distance inside one picture. Specifically, a new picture analysis technique is applied for the union separation observations. Rather than searching for an almost undetectable thin line that is reproduced by the connection space, the basic concept is to identify differences in the rough surfaces of the 2 different parts that will be fused joined. The working item seem images based on its texture is greater reliable least impacted with. © 2023 IEEE.

关键词： Textures

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Optimization research of UAV target detection algorithm based on deep learning 3

Optimization research of UAV target detection algorithm base...

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

作者： Zhang, Tianhang Wang, Yong Beijing University of Technology Beijing China

ISBN: (纸本)9781510661301

With the rapid development of deep learning, UAV target detection technology based on computer vision and artificial intelligence has been widely used in practice. However, due to the instability of UAV movement, limited by load and endurance, the development of UAV target detection is slow, and there are challenges such as significant changes in target scale, occlusion between objects, and changes in target density. This paper builds on the network model structure of YOLOv5 to address these challenges. It adds a detection head generated from low-level feature layers and high-resolution combined feature maps to detect tiny objects. We utilize the Bifpn network structure and a weighted fusion splicing approach to fuse more features and introduce an improved Coordinate Attention to obtain location information for feature enhancement accurately. Extensive experiments on the Visdrone2021 dataset show that the model achieves good results in UAV target detection and is helpful for tiny and occluded target detection. © 2023 SPIE.

关键词： Unmanned aerial vehicles (UAV)

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Deep Fake detection: Unmasking the Illusion using CNN and LSTM 3

Deep Fake Detection: Unmasking the Illusion using CNN and LS...

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

作者： Niranjani, V. Aishwarya, S. Devamitra, T. Jagapreetha, B. Sri Eshwar College of Engineering Department of CSE Coimbatore India

ISBN: (纸本)9798350343632

Concerns regarding the veracity of digital media have arisen due to the development of deepfake technology. In order to effectively combat this new threat, this study offers a novel deepfake detection strategy that combines a number of techniques, including Photoplethysmography (PPG). For the detection and classification of deepfakes, our solution combines PPG with sophisticated deep learning techniques. PPG records physiological signals, enhancing analysis of images and sounds. Convolutional neural networks (CNNs), long short-term memory (LSTM) networks, and audio fingerprinting are used to extract features from a large dataset that was used to train the system. A thorough study showing our multi-algorithm fusion approach's improved performance in deepfake detection over single-algorithm approaches, including measures like accuracy and precision. This study also explores its resilience against adversarial attacks and consider ethical implications. This research work represents a significant advancement in deepfake detection, emphasizing the integration of various algorithms, including PPG, for improved accuracy and resilience. It has applications in media forensics, content verification, and online security, offering a comprehensive solution to the deepfake detection challenge while promoting responsible digital content authentication. © 2023 IEEE.

关键词： Convolutional Neural Networks (CNNs) Deepfake Digital harassing Image and video analysis Long Short-Term Memory (LSTM) networks Machine Learning Photoplethysmography (PPG) Social Media

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