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Research on collaborative filtering recommendation algorithm with improved K-means clustering 3

Research on collaborative filtering recommendation algorithm...

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3rd international conference on Computer Vision and pattern Analysis, ICCPA 2023

作者： Wei, XuDong Li, Zhaofei Li, Jing School of Automation and Information Engineering Sichuan University of Science & Engineering Yibin643002 China Artificial Intelligence Key Laboratory of Sichuan Province Yibin643002 China

ISBN: (纸本)9781510667563

In response to the issues of data sparsity, limited scalability, and low recommendation accuracy faced by traditional collaborative filtering algorithms used for personalized user recommendations in the face of increasing data volumes, an optimized collaborative filtering algorithm is proposed that addresses these problems by improving the initial clustering center and incorporating Weighted Slop One filling. This algorithm first uses user similarity as the basis for clustering, and utilizes the Weighted Slope One algorithm to predict the user-rating matrix. Then, the generated matrix is clustered using the AP-MMD-K-means algorithm to obtain a set of similar users. Finally, nearest neighbor search is performed on the target user within their corresponding similar set to generate the recommendation results. Comparative experimental results show that this algorithm improved both recommendation performance and accuracy compared to traditional recommendation algorithms. This algorithm effectively alleviates the problem of data sparsity and improves scalability, significantly improving recommendation accuracy. © 2023 SPIE.

关键词： K-means clustering

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Research on Partial Discharge pattern recognition and Fault Diagnosis of Switchgear Based on Simplified Features in Power Internet of Things 3

Research on Partial Discharge Pattern Recognition and Fault ...

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3rd international conference on Industrial IoT, Big Data and Supply Chain (IIoTBDSC)

作者： Zhang, Cheng Jiang, Peng Chen, Keqing Zhang, Lemeng Ren, Ming Chen, Rongfa Ma, Qinghua Zhang, Hongyuan China Railway Signal & Commun Metro Transportat C Changsha Peoples R China Xi An Jiao Tong Univ State Key Lab Elect Insulat & Power Equipment Xian Peoples R China

ISBN: (纸本)9781665454551

This paper has provided a method for partial discharge pattern recognition and fault diagnosis of switchgear based on simplified features in power internet of things (PIOT). An experimental platform was built, and four typical defect models were designed for simulation of partial discharge in switchgear. Then, transient earth voltage (TEV) was measured and twenty-one features were extracted based on phase and time-series. On this basis, the method of maximum variance analysis was applied for selecting features with high correlation to form the simplified feature set for reducing data transmission pressure in PIOT. Finally, the algorithm of k-nearest neighbor (KNN) was adopted to verify the effectiveness of simplified features for partial discharge pattern recognition. Results show that the average accuracy of pattern recognition based on simplified features is over 90%, and this method can be used for practical application which can reduce the pressure of data transmission and improve the operating efficiency of the monitoring system in PIOT.

关键词： partial discharge pattern recognition fault diagnosis switchgear simplified features transient earth voltage (TEV) power internet of thing (PIOT)

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The Application of Deep Learning in Micro-Expression recognition 3

The Application of Deep Learning in Micro-Expression Recogni...

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3rd international conference on machine Learning, Big Data and Business intelligence (MLBDBI)

作者： Zhao, Xinyuan Sun, Furong Shen, Xunbing Jiangxi Univ Chinese Med Nanchang Jiangxi Peoples R China

ISBN: (纸本)9781665417907

With the advent of the era of big data, the application of artificial intelligence and big data technology has led to widespread interest in facial expression recognition. In addition to the common macro-expressions in daily life, facial expressions also have an imperceptible subtle expression called micro-expressions. Micro-expression is a very fast expression, lasting only 1/25 seconds to 1/5 seconds, expressing the real emotions that people try to suppress and hide. Micro-expressions have important applications in public safety, judicial criminal investigation, clinical medicine, etc. Therefore, the research on micro-expression recognition has been increasing in recent years. In the year of 2006, Hinton's proposal of deep learning in an article in "Science" made it appear in front of the world as a new field of machine learning, which set off a wave of deep learning. Deep learning is currently one of the hottest research directions in artificial intelligence and machine learning, and it is widely used in speech recognition, natural language processing, image recognition and other fields. Because of the characteristics of micro-expressions such as short time and subtle changes, traditional machine learning algorithms have poor robustness. Therefore, this paper summarizes the research on microexpression recognition based on deep learning methods, mainly including some mainstream algorithms such as DBN, CNN, and discusses the development problems and trends of deep learning on micro-expression recognition, hoping to provide reference for the subsequent research in this field.

关键词： artificial intelligence micro-expression machine learning deep learning Deep Belief Network(DBN) Convolutional Neural Network(CNN)

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An AI-Centred Adaptive Cognitive Network Traffic Modelling & Measurement Technique for EIS 3

An AI-Centred Adaptive Cognitive Network Traffic Modelling &...

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

作者： Bharathi, L. Vasoya, Nimish H. Kansal, Lavish Gehlot, Anita Swathi, Bajila Khan, Suyeb Ahmed Ramachandra College of Engineering Department of Electronics and Communication Engineering Andhra Pradesh Eluru India Children's University Department of Student Startup Gujarat Gandhinagar382021 India Lovely Professional University Phagwara India Uttaranchal University Uttaranchal Institute of Technology India SR University School of Computer Science and Artificial Intelligence Telangana Warangal India Hoshiar Nagar Amritsar Baba Kuma Singh Ji Engineering College India

ISBN: (纸本)9798350399264

The IoT is the foundation of the Enterprise Information System (EIS), which collects a lot of business information. For EIS, timely, accurate data analysis transfer are crucial. For net management traffic optimization in EIS, Network infrastructure traffic is crucial. Nevertheless, the needs for fine-grain traffic monitoring are in conflict with the monitoring overheads precision. Long-standing promises of artificial intelligence (AI) include the ability to recognise the physical attributes of network activity take appropriate steps in response. In order to decrease network monitoring overheads increase measurement precision, we present an AI centred between light Adapt Measuring System (ALAMM) for SDN in this study. Initially, we simulate forecast the network congestion information using AI. We suggest an adaptive strategy to choose the rate of traffic sample centred on the outcomes of traffic forecasting. Next, we instruct switching devices to evaluate the coarse-grain traffic of flow pattern networks using the measuring primitives. In order to retrieve the Fine grained (FG) assessment optimise the measured values, the matrices refilling optimizing approach is finally suggested. Simulated outcomes demonstrate that our method can accurately and efficiently determine network activity. © 2023 IEEE.

关键词： Software defined networking

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Dense Pedestrian Detection Based on Multiple Link Feature Pyramid Networks 3

Dense Pedestrian Detection Based on Multiple Link Feature Py...

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3rd international conference on Computer Vision and pattern Analysis, ICCPA 2023

作者： Huang, Tao Yang, Siyu Yu, Tingjian Fu, Xiang Key Laboratory of Jiangxi Province for Image Processing and Pattern Recognition Nanchang Hangkong University Nanchang China School of Software Nanchang Hangkong University Nanchang China

ISBN: (纸本)9781510667563

Pedestrian detection is widely employed in a variety of industries, including security, autonomous driving, and pedestrian flow statistics. However, because most pedestrians in real-world scenes are densely distributed with occlusion issues, scale variation and pose variability, majority pedestrian detection algorithms have low detection accuracy and a high miss detection rate. In this paper, we propose a new FPN neck module, Multiple Link Feature Pyramid Networks (MLFPN) for dense pedestrian detection, which innovative information transfer pathways and incorporation of Poly-Scale Convolution (PSconv) capable of long-range interaction into feature extraction tasks to reduce feature information loss and enhanced representation of effective pedestrian features, achieving 0.6MR-2, 0.35AP, and 0.21JI improvements on CrowdHuman dataset, improving detection accuracy and reducing missed detection rates. © 2023 SPIE.

关键词： Feature extraction

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An Attention Neural Network for Radar Intra-pulse Modulation pattern recognition 3

An Attention Neural Network for Radar Intra-pulse Modulation...

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2022 3rd international conference on Computer Information and Big Data Applications, CIBDA 2022

作者： Xu, Kongli Zhang, Yue School of Electronics and Communication Engineering Sun-Yat-Sen University Guangdong Shenzhen China

ISBN: (纸本)9783800758760

The most existing Low Probability of Intercept (LPI) radar signal recognition algorithms generally have poor recognition performance due to the extraction of radar intra-pulse modulation features at low signal-to-noise ratio (SNR) is difficult. To solve that issue, this paper proposes a novel attention neural network based on the time-frequency images (TFIs) for intra-pulse modulation pattern recognition of radar pulse signals. First, in order to obtain the TFIs from the radar received signals with low cross-terms and high time-frequency resolution, smoothed pseudo Wigner-Ville distribution (SPWVD) is leveraged for generating the TFIs from the received radar signals. Second, Block-matching and 3D filtering (BM3D) is utilized to preprocess the TFIs to eliminate the impact of the background noise for preserving detailed features of the signal, and then we adopt bilinear interpolation algorithm in terms of obtaining the TFIs with the same size for further network training. Finally, the pre-processed TFIs are fed into the deep neural network embedded with SENet attention module, which is called SE-ResNet, to do the recognition. SENet can help the model extract valid feature of TFIs and achieve effective recognition on different radar signal intra-pulse modulation patterns from LPI radar. The simulation results show that the proposed method can achieve an overall recognition rate of 93.2% for eight LPI radar signals when SNR in a low SNR environment, e.g., -8 dB. © VDE VERLAG GMBH - Berlin - Offenbach.

关键词： Radar

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Face anti-spoofing based on ASFF and LBP self-supervision branch 3

Face anti-spoofing based on ASFF and LBP self-supervision br...

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3rd international conference on Computer Vision and pattern Analysis, ICCPA 2023

作者： Ren, Junjie Zhang, Yi Zhang, Ruisheng Luo, Jianfeng Whale Cloud Technology Co. Ltd Nanjing210000 China School of Information Science and Engineering Lanzhou University Lanzhou730000 China

ISBN: (纸本)9781510667563

Since the tremendous advancement of face recognition technology, how to detect real human faces from images has gradually captured the attention of researchers. To address the problem that most face anti-spoofing approaches ignore context information and local information about faces, this paper proposes a face anti-spoofing algorithm that fuses multi-scale features and texture features of images. On the one hand, we extract multi-scale features using adaptively spatial feature fusion (ASFF) and polarized self-attention (PSA) mechanism. On the other hand, local binary patterns (LBP) is used to form an auxiliary self-supervision branch to extract image texture difference information. As a final step, end-to-end live detection is achieved by balancing the difference between two different loss functions based on homoscedastic uncertainty. The EER of the presented method is 0.81% and 1.26% on CASIA-FASD and MSU-MFSD, respectively, and the ACER is only 1.64% and 2.07%±1.05% on the second and third protocols of Oulu_NPU, which indicates the validity of the method. © 2023 SPIE.

关键词： Local binary pattern

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MLCardiovascularPrediction: machine Learning-Based Cardiovascular Disease Prediction 3

MLCardiovascularPrediction: Machine Learning-Based Cardiovas...

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

作者： Abdelminaam, Diaa Salama Madbouly, Magda M. Zeid, Magdy Abd-Elghany Elatif Gomaa, Ibrahim Abd Abualigah, Laith Misr International University Faculty of Computer Science Cairo Egypt Benha University Faculty of Computers and Artificial Intelligence Benha Egypt Alexandria University Faculty of Computer and Data Science Alexandria Egypt Alobour High Institute for Computer and Informatics Computer Science Department Cairo Egypt Lebanese American University Department of Electrical and Computer Engineering Byblos13-5053 Lebanon Al-Ahliyya Amman University Hourani Center for Applied Scientific Research Amman19328 Jordan Middle East University Meu Research Unit Amman11831 Jordan School of Computer Sciences Universiti Sains Malaysia Pinang Pulau11800 Malaysia

ISBN: (纸本)9798350306231

Like most diseases that affect a human's life, car-diovascular disease is dangerous and unwanted for anyone who has heard of it as it brings a high risk of death. In today's world, most deaths are caused by heart disease as it could come from age, heritage, smoking, and many factors that could seem normal, and it doesn't matter what part of the world you are in. There must be a way to know or predict if this person is at high risk of cardiovascular disease. This paper and project talk about six algorithms: K-Nearest-Neighbour (KNN), Decision Tree (DT), Naive Bayes, Neural Network, Logistic Regression, and Support Vector machine (SVM). The results came between 60% to 70% in one dataset and from 80 % to 90 % in the second. Still, they mostly favor the Neural Network algorithm as it can learn and model around complex relationships. The SVM showed different outputs between the two datasets as one is significantly larger than the other, possibly due to the outliers in both datasets being different and more complex in the larger one. Cardiovascular disease can be very dangerous, but finding and discovering it soon can be a lifesaver. These algorithms also can help find and predict it, as it was shown with not just decent, but good and respectable success rates for each one. © 2023 IEEE.

关键词： Decision trees

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An AI-Based Action Detection UAV System to Improve Firefighter Safety 14th

An AI-Based Action Detection UAV System to Improve Firefight...

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14th international conference on Digital Human Modeling and Applications in Health, Safety, Ergonomics and Risk Management (DHM) Held as Part of the 25th international conference on Human-Computer Interaction (HCII)

作者： Wang, Hong Feng, Yuan Huang, Xu Guo, Wenbin Changan Univ Sch Construct Machinery Xian 710064 Peoples R China Univ Missouri Dept Mech & Aerosp Columbia MO 65203 USA Univ Florida Digital Worlds Inst Gainesville FL 32611 USA

ISBN: (纸本)9783031357404;9783031357411

Human hazardous fires can inflict massive harm to life, property, and the environment. Close contact with fire sources threatens firefighters' lives who are critical first responders to fire suppression and rescue. Unmanned aerial vehicles (UAVs) are recently introduced to improve firefighters' performance by monitoring fire characteristics and inferring trajectory. Some UAVs studies used artificial intelligence (AI) for pattern recognition for wildfire prevention and other relevant tasks. However, how to coordinate firefighters is equally important and needs to be explored. Therefore, this research offers an analysis and comparison of AI-based computer vision methods that can annotate human movement automatically. This study proposes to use UAVs combined with AI-based human motion detection to recognize firefighters' action patterns. Based on existing human motion datasets of firefighting videos, we have successfully trained a supervised machine learning algorithm recognizing firefighters' forward movement and water splashing actions. The trained model was tested in the existing video and reached an 81.55% accuracy rate. Applying this model in the UAV system is able to improve firefighters' functioning and safeguard their lives.

关键词： Artificial intelligence Human motion classification Laban movement analysis

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A Deep Learning based Framework Implementation and Artificial intelligence using Optical Communication 3

A Deep Learning based Framework Implementation and Artificia...

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3rd international conference on Smart Electronics and Communication, ICOSEC 2022

作者： Malini, P. Vijayakumari, G. Ganesh, C. Femina, V. Ramani, U. Basha, S.Irfan Vivekanandha College of Technology for Women Triuchengode India Builders Engineering College Triuppur India Sri Eshwar College of Engineering Coimbatore India Psna College of Engineering & Technology Dindigul India K.Ramakrishnan College of Engineering Trichy India

ISBN: (数字)9781665497640

ISBN: (纸本)9781665497640

Deep learning is one of the variety of machine learning in which the higher level features from the data are extracted through numerous layers of processing. The idea of artificial intelligence (AI) was first proposed in the 1950s with the intention of enabling computers to think in a fashion that is understandable to humans. machine learning (ML) focuses on ways to make computers learn without being being programmed as part of making them fit to suppose. Deep literacy goes beyond machine learning (ML), which is designed to mimic how people learn novel knowledge, by building more complicated hierarchical models. Several significant advancements in AI have been made possible by deep literacy. For recycling massive amounts of data with little to no human intervention, the capacity to recognize patterns and classify a wide range of data. © 2022 IEEE.

关键词： Deep learning

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